Megan Elizabeth Rollo. Bachelor of Applied Science Bachelor of Health Science (Nutrition and Dietetics) (Hons)

Transcription

1 An innovative approach to the assessment of nutrient intake in adults with type 2 diabetes: the development, trial and evaluation of a mobile phone photo/voice dietary record. Megan Elizabeth Rollo Bachelor of Applied Science Bachelor of Health Science (Nutrition and Dietetics) (Hons) Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy School of Exercise and Nutrition Sciences Faculty of Health Institute of Health and Biomedical Innovation Queensland University of Technology 2012

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3 Keywords Dietary assessment methods, doubly labelled water, food record, mobile phone, nutrient intake, photographic record, portion size estimation, type 2 diabetes mellitus. 1

4 Abstract Nutrition interventions in the form of both self-management education and individualised diet therapy are considered essential for the long-term management of type 2 diabetes mellitus (T2DM). The measurement of diet is essential to inform, support and evaluate nutrition interventions in the management of T2DM. Barriers inherent within health care settings and systems limit ongoing access to personnel and resources, while traditional prospective methods of assessing diet are burdensome for the individual and often result in changes in typical intake to facilitate recording. This thesis investigated the inclusion of information and communication technologies (ICT) to overcome limitations to current approaches in the nutritional management of T2DM, in particular the development, trial and evaluation of the Nutricam dietary assessment method (NuDAM) consisting of a mobile phone photo/voice application to assess nutrient intake in a free-living environment with older adults with T2DM. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM The effectiveness of an automated telephone system, Telephone-Linked Care (TLC) Diabetes, designed to deliver self-management education was evaluated in terms of promoting dietary change in adults with T2DM and sub-optimal glycaemic control. In this secondary data analysis independent of the larger randomised controlled trial, complete data was available for 95 adults (59 male; mean age(±sd)=56.8±8.1 years; mean(±sd)bmi=34.2±7.0kg/m 2 ). The treatment effect showed a reduction in total fat of 1.4% and saturated fat of 0.9% energy intake, body weight of 0.7 kg and waist circumference of 2.0 cm. In addition, a significant increase in the nutrition selfefficacy score of 1.3 (p<0.05) was observed in the TLC group compared to the control group. The modest trends observed in this study indicate that the TLC Diabetes system does support the adoption of positive nutrition behaviours as a result of diabetes self-management education, however caution must be applied in the interpretation of results due to the inherent limitations of the dietary assessment method used. The decision to use a close-list FFQ with known bias may have influenced the accuracy of reporting dietary intake in this instance. This study provided an example of the methodological challenges experienced with measuring changes in absolute diet using a FFQ, and reaffirmed the need for novel prospective assessment methods capable of capturing natural variance in usual intakes. 2

5 Study 2: The development and trial of NuDAM recording protocol The feasibility of the Nutricam mobile phone photo/voice dietary record was evaluated in 10 adults with T2DM (6 Male; age=64.7±3.8 years; BMI=33.9±7.0 kg/m 2 ). Intake was recorded over a 3-day period using both Nutricam and a written estimated food record (EFR). Compared to the EFR, the Nutricam device was found to be acceptable among subjects, however, energy intake was under-recorded using Nutricam (-0.6±0.8 MJ/day; p<0.05). Beverages and snacks were the items most frequently not recorded using Nutricam; however forgotten meals contributed to the greatest difference in energy intake between records. In addition, the quality of dietary data recorded using Nutricam was unacceptable for just under one-third of entries. It was concluded that an additional mechanism was necessary to complement dietary information collected via Nutricam. Modifications to the method were made to allow for clarification of Nutricam entries and probing forgotten foods during a brief phone call to the subject the following morning. The revised recording protocol was evaluated in Study 4. Study 3: The development and trial of the NuDAM analysis protocol Part A explored the effect of the type of portion size estimation aid (PSEA) on the error associated with quantifying four portions of 15 single foods items contained in photographs. Seventeen dietetic students (1 male; age=24.7±9.1 years; BMI=21.1±1.9 kg/m 2 ) estimated all food portions on two occasions: without aids and with aids (food models or reference food photographs). Overall, the use of a PSEA significantly reduced mean (±SD) group error between estimates compared to no aid (-2.5±11.5% vs. 19.0±28.8%; p<0.05). The type of PSEA (i.e. food models vs. reference food photograph) did not have a notable effect on the group estimation error (-6.7±14.9% vs. 1.4±5.9%, respectively; p=0.321). This exploratory study provided evidence that the use of aids in general, rather than the type, was more effective in reducing estimation error. Findings guided the development of the Dietary Estimation and Assessment Tool (DEAT) for use in the analysis of the Nutricam dietary record. Part B evaluated the effect of the DEAT on the error associated with the quantification of two 3-day Nutricam dietary records in a sample of 29 dietetic students (2 males; age=23.3±5.1 years; BMI=20.6±1.9 kg/m 2 ). Subjects were randomised into two groups: Group A and Group B. For Record 1, the use of the DEAT (Group A) resulted in a smaller error compared to estimations made without the tool (Group B) (17.7±15.8%/day vs. 34.0±22.6%/day, p=0.331; respectively). In 3

6 comparison, all subjects used the DEAT to estimate Record 2, with resultant error similar between Group A and B (21.2±19.2%/day vs. 25.8±13.6%/day; p=0.377 respectively). In general, the moderate estimation error associated with quantifying food items did not translate into clinically significant differences in the nutrient profile of the Nutricam dietary records, only amorphous foods were notably over-estimated in energy content without the use of the DEAT (57kJ/day vs. 274kJ/day; p<0.001). A large proportion (89.6%) of the group found the DEAT helpful when quantifying food items contained in the Nutricam dietary records. The use of the DEAT reduced quantification error, minimising any potential effect on the estimation of energy and macronutrient intake. Study 4: Evaluation of the NuDAM The accuracy and inter-rater reliability of the NuDAM to assess energy and macronutrient intake was evaluated in a sample of 10 adults (6 males; age=61.2±6.9 years; BMI=31.0±4.5 kg/m 2 ). Intake recorded using both the NuDAM and a weighed food record (WFR) was coded by three dietitians and compared with an objective measure of total energy expenditure (TEE) obtained using the doubly labelled water technique. At the group level, energy intake (EI) was under-reported to a similar extent using both methods, with the ratio of EI:TEE was 0.76±0.20 for the NuDAM and 0.76±0.17 for the WFR. At the individual level, four subjects reported implausible levels of energy intake using the WFR method, compared to three using the NuDAM. Overall, moderate to high correlation coefficients (r= ) were found across energy and macronutrients except fat (r=0.24) between the two dietary measures. High agreement was observed between dietitians for estimates of energy and macronutrient derived for both the NuDAM (ICC= ; p<0.001) and WFR (ICC= ; p<0.001). All subjects preferred using the NuDAM over the WFR to record intake and were willing to use the novel method again over longer recording periods. This research program explored two novel approaches which utilised distinct technologies to aid in the nutritional management of adults with T2DM. In particular, this thesis makes a significant contribution to the evidence base surrounding the use of PhRs through the development, trial and evaluation of a novel mobile phone photo/voice dietary record. The NuDAM is an extremely promising advancement in the nutritional management of individuals with diabetes and other chronic conditions. Future applications lie in integrating the NuDAM with other technologies to facilitate practice across the remaining stages of the nutrition care process. 4

7 Publications and Conference Presentations Publications: Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A. Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future use. Journal of Telemedicine and Telecare, 2011, 17: Conference oral presentations: Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A. Diet Bytes: Can mobile phones be used to accurately measure intake in adults with type 2 diabetes? Oral presentation ( Best of the Best PhD Students), 29 th Dietitians Association of Australia National Conference, Adelaide Rollo, M.E., Ash, S., Bird, D., Oldenburg, B., Friedman, R. Effectiveness of an automated telephone system in promoting dietary change in adults with type 2 diabetes. Oral presentation, 2011 Annual Meeting of the International Society for Behavioral Nutrition and Physical Activity, Melbourne Conference poster presentations: Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A. The use of a mobile phone for the recording of dietary intake: conclusions and implications for future use. Poster presentation, 28 th Dietitians Association of Australia National Conference, Melbourne Kelly, A., Rollo, M. E., Ash, S. The PEAs study: Comparison of reference food photograph and food models in assisting portion size estimation from photographs. The 7 th International Conference of Diet and Activity Methods, Washington DC Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A. The useability and acceptability of a mobile phone device to record dietary intake. The 7 th International Conference of Diet and Activity Methods, Washington DC

8 Table of Contents Keywords... 1 Abstract... 2 Publications and Conference Presentations... 5 Table of Contents... 6 List of Figures List of Tables List of Abbreviations Statement of Original Authorship Acknowledgments Chapter 1: Introduction Background Purpose and context of research program Significance of research program Content of the thesis Chapter 2: Literature Review Definition and prevalence of T2DM Nutritional management of T2DM Diabetes self-management education Medical nutrition therapy Barriers to the provision of support in the nutritional management of T2DM Novel strategies for the provision of support in the management of T2DM Assessment of dietary intake Uses of dietary intake information Measuring usual dietary intake The process of assessing dietary intake Specific factors relating to the individual for whom diet is being measured Specific factors relating to the dietitian or investigator involved in the measurement of diet

9 2.4 Methods for the assessment of dietary intake hour dietary recall Diet History Food Frequency Questionnaire Food Record Photographic records Portion size estimation in the assessment of dietary intake The cognitive process involved in estimation of portion size The effect of photographic PSEAs on portion estimation error Quantification of photographic records performed by individuals trained in the nutrition and dietetics Validity and reproducibility of dietary intake assessments Validity Reproducibility Conclusions Summary of previous research Aims, research questions and hypotheses of research program Chapter 3: Research Program Rationale for research program Study design and methods Subjects Key analytical techniques Ethical Considerations Chapter 4: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM (Study 1) Introduction Methods Study design and procedure: overview of the TLC Diabetes Study Measures Data analysis Results

10 4.3.1 Subject characteristics Pre-intervention Intervention effect on dietary intake and nutrition status Discussion Conclusions Overview Relevance to research program Implications for future practice Chapter 5: Development and trial of the NuDAM recording protocol (Study 2) Introduction Methods Development of the Nutricam application and recording protocol Study design and procedure Data analysis Results Subject characteristics Evaluation of Nutricam for recording dietary intake Useability and Acceptability of Nutricam Discussion Conclusions Overview Recommendations for the refinement of the Nutricam recording protocol Relevance to the research program Implications for future practice Chapter 6: Development and trial of the NuDAM analysis protocol (Study 3) Introduction The type of portion size estimation aid (PSEA) and estimation error associated with quantifying food items contained in photographs (Study 3 Part A) Background Methods

11 6.2.3 Results Discussion Conclusions The effect of the Dietary Estimation and Assessment Tool (DEAT) on estimation errors relating to portion size and nutrient composition of the Nutricam dietary record (Study 3 - Part B) Background Methods Results Discussion Conclusions Chapter 7: Evaluation of the NuDAM (Study 4) Introduction Methods Subjects Study design and procedure Measurement of dietary intake Data analysis Results Subject characteristics Comparison of estimated nutrient intakes between methods Validation of self-reported EI against TEE Inter-rater reliability for estimates of nutrient intake between methods Useability and acceptability of NuDAM Discussion Conclusions Conclusions and Recommendations Introduction Key findings Strengths and Limitations Strengths

12 8.3.2 Limitations Implications for future practice and research References Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Appendix G Appendix H Appendix I Appendix J

13 List of Figures Figure 2-1: Concepts in the nutritional management of T2DM Figure 2-2: Overview of the core stages of the nutrition care process Figure 2-3: The process of assessing dietary intake Figure 2-4: Data collection in weighed or estimated food records Figure 2-5: Data collection in photographic records Figure 2-6: Comparison of the cognitive skills used in portion size estimation between traditional and novel dietary assessment methods Figure 2-7: Conceptual framework illustrating barriers and limitations to current practice in the nutritional management of T2DM Figure 2-8: Conceptual framework underpinning the research program of this thesis Figure 3-1: Overview of Research Program Figure 4-1: Overview of the TLC Diabetes Nutrition module Figure 4-2: Overview of subjects in the TLC Diabetes study Figure 5-1: Schematic overview of the Nutricam application 124 Figure 5-2: Recording intake using Nutricam Figure 5-3: Screenshot of Nutricam dietary record displayed on website Figure 5-4: Difference in energy intake as measured by Nutricam and estimated food record (Bland- Altman Plot) Figure 5-5: Screenshot of the NuDAM call database Figure 6-1: Example test food photographs Figure 6-2: Cumulative percentage plotted against the mean estimation error for estimates made without PSEAs Figure 6-3: Cumulative percentage plotted against the mean estimation error for estimates made with PSEAs Figure 6-4: Example of a reference food photograph contained in the DEAT. 159 Figure 6-5: Example of a serving vessel photograph contained in the DEAT Figure 6-6: Example of an amorphous mound contained in the DEAT. 160 Figure 6-7: Example of a generic graphic contained in the DEAT Figure 6-8: Design of Study 3 Part B 163 Figure 6-9: Example of the quantification task 165 Figure 6-10: Cumulative percentage of subjects plotted against mean estimation error for Record Figure 6-11: Cumulative percentage of subjects plotted against mean estimation error for Record Figure 7-1: Study 4 timeline 184 Figure 7-2: Components of the NuDAM Figure 7-3: Comparison of energy intake as measured using both methods by each dietitian (Bland- Altman Plots) Figure 7-4: Average ratio of EI:TEE for both NuDAM and weighed food record Figure 8-1: Conceptual framework incorporating recommendations from the research program for future research and practice

14 List of Tables Table 2-1: Overview of current methods used in the assessment of dietary intake Table 2-2: Characteristics of photographic record methods used to assess nutrient intake 52 Table 2-3: Studies investigating error associated with estimating the portion size of foods in real time (i.e. perception) Table 2-4: Studies investigating error associated with estimating the portion size of recalled foods (i.e. conceptualisation & memory) Table 2-5: Studies investigating portion size estimation of photographic records by individuals trained in nutrition and dietetics 65 Table 2-6: Studies investigating the relative validity (energy and macronutrient intake) of three FFQs developed for use in Australian adults Table 2-7: Studies investigating the relative validity of photographic records to quantify nutrient intake Table 2-8: Studies investigating the criterion validity using TEE (DLW technique) of self-reported EI from food records in free-living adults Table 2-9: Studies investigation the reproducibility of photographic records to quantify nutrient intake Table 2-10: Aims, research questions and hypotheses of research program Table 4-1: Summary of dietary intake and nutrition status of subjects at baseline Table 4-2: Summary of dietary intake and nutrition status of subjects at baseline by group Table 4-3: Change in dietary intake and nutritional status following intervention Table 4-4: Change in dietary intake and nutritional status for males by group Table 4-5: Change in dietary intake and nutritional status for females by group Table 5-1: Summary of subject characteristics and estimated energy intake (Study 2) Table 5-2: Subject responses following use of Nutricam to record dietary intake Table 6-1: Overview of weight/volume of test food portions and food models Table 6-2: Summary of subject characteristics of study (Study 3 Part A) Table 6-3: Effect of the use of aids on mean estimation error for each food Table 6-4: Effect of the type of aid on the between group difference in estimation error Table 6-5: Effect of the type of aid on the within group difference in estimation error. 150 Table 6-6: Overall effect of the use of aids on mean estimation error for each food type. 150 Table 6-7: Effect of the type of aid on estimation error between groups for food type 151 Table 6-8: Effect of the type of aid on estimation error within groups for food type 151 Table 6-9: Overview of the food types and nutrient profile of the test Nutricam dietary records. 162 Table 6-10: Summary of subject characteristics (Study 3 Part B). 166 Table 6-11: Between group comparison of estimation error for each day of record Table 6-12: Between group comparison of estimation error for food type Table 6-13: Between group weight difference for each day of record Table 6-14: Between group energy difference for each day of record Table 6-15: Between group protein difference for each day of record Table 6-16: Between group fat difference for each day of record Table 6-17: Between group carbohydrate difference for each day of record Table 6-18: Responses from dietetic students following the use of the DEAT Table 6-19: DEAT aid used to quantify selected food items in Record Table 7-1: Subject characteristics at baseline (Study 4) Table 7-2: Subject body weight status during the study period Table 7-3: Group energy and macronutrient intakes estimated by each dietitian for both methods Table 7-4: Correlation between energy and macronutrient intakes estimated by each dietitian for both methods Table 7-5: Estimated EI and TEE for each subject Table 7-6: Group mean EI, TEE, and ratio of EI: TEE Table 7-7: Agreement between dietitian estimates of nutrient intake for both methods. 196 Table 7-8: Examples of discrepancies in coding of dietary records observed between dietitians for subject ID# Table 7-9: Subject responses regarding useability and acceptability of the methods 198 Table 7-10: Changes in eating behaviours during each recording period Table 7-11: Simple thematic analysis of subjects attitudes towards each method Table 7-12: Maximum time period subjects were willing to use each method again to record intake

15 List of Abbreviations 24R BMI DEAT DH DLW DSME EI EFR FFQ HbA 1c ICT MNT NCP NuDAM PhR PSEA T2DM TEE TLC WFR 24-hour Recall Body Mass Index Dietary Estimation and Assessment Tool Diet History Doubly Labeled Water Diabetes Self-Management Education Energy Intake Estimated Food Record Food Frequency Questionnaire Glycosylated Haemoglobin A1c Information and Communication Technologies Medical Nutrition Therapy Nutrition Care Process Nutricam Dietary Assessment Method Photographic Record Portion Size Estimation Aid Type 2 Diabetes Mellitus Total Energy Expenditure Telephone-Linked Care Weighed Food Record 13

16 Statement of Original Authorship The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Signature: Date: 14

17 Acknowledgments I would like to acknowledge the following people who have contributed in various ways to my candidature: Professor Susan Ash, thank you for your guidance and belief in this project. You have been a fantastic mentor and a source of great encouragement throughout my PhD journey. Associate Professor Philippa Lyons-Wall and Associate Professor Anthony Russell, I am extremely grateful for your dedication to my project and for your timely advice. Dr Dominique Bird, thank you for your insight into the TLC Diabetes project and your continued support over my candidature. Professor Brian Oldenburg, thank you for the opportunity to incorporate TLC Diabetes into my thesis. Ms Alicia Kelly and Mr Junior Lai for their assistance in the data collection, and Ms Jamie Sheard and Ms Chloe McKenna for their assistance in the data coding and entry. Ms Connie Wishart for sharing her expertise in the DLW technique, Dr Rachel Wood and Ms Ainsley Groves for their assistance in the administration of the DLW, and Professor Nuala Byrne for facilitating this process. Mr Kim Barnett and Mr Bruce Satchwell for their enthusiasm towards the concept of Nutricam and their expertise in developing the application. All the individuals who participated in the research studies comprising this thesis. The support provided by the Queensland University of Technology Postgraduate Research Award and Australian Postgraduate Award scholarships that I received throughout my candidature. The Novo Nordisk Regional Diabetes Support Scheme Grant for providing funding to conduct Study 4. My friends and colleagues, thank you for your support over these past three years and making this experience extremely enjoyable. And finally, I am eternally grateful to my family for encouraging me to pursue my interests and their unwavering support throughout this time. Thank you Mum, Catherine, Geoffrey, Sally, and Jake. 15

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19 Chapter 1: Introduction This chapter describes the rationale underpinning this thesis by outlining the background relating to the importance of the measurement of diet in the nutritional management of type 2 diabetes mellitus (T2DM) (Section 1.1). The aims and context of the research program are introduced (Section 1.2), and the significance of the contribution of this investigation is highlighted (Section 1.3). Finally, the content of this thesis is outlined through a description of the proceeding chapters (Section 1.4). 1.1 Background T2DM is a significant chronic disease of global importance (Shaw et al. 2010). Nutrition plays a critical role in the management of T2DM, with the effect of diabetes self-management education (DSME) and medical nutrition therapy (MNT) on dietary behaviour change and health outcomes well documented (American Diabetes Association 2010). Despite the importance of these elements of nutrition care, two key factors currently influence the provision of these services and resources to individuals with T2DM. Firstly, limitations within the health system, in addition to geographical and economical barriers, adversely affect regular access to nutrition support (Williamson et al. 2000; Spikmans et al. 2003; Gucciardi et al. 2008). As a result long-term support and reinforcement of nutrition behaviours is adversely affected (Norris et al. 2002; Franz et al. 2010). Information and communication technologies (ICT) offer a potential solution to overcome these barriers by promoting avenues for regular access and the delivery of comprehensive self-management information, support and resources in adults with T2DM. Automated telephone systems are one example which has demonstrated effectiveness in a number of chronic conditions such as hypertension (Friedman et al. 1996), chronic obstructive pulmonary disease (Young et al. 2001), and asthma (Adams et al. 2003) in the United States, however their application among Australian adults with T2DM has not been investigated. Secondly, the measurement of diet is central to both DSME and MNT in the nutritional management in adults with T2DM. For example, self-monitoring of diet is used to assist in the reinforcement of behaviours leading to individual autonomy for effective diabetes self-management (Tsang et al. 2001). In addition, the assessment of dietary intake is necessary to inform and support the intervention strategies, and Chapter 1: Introduction 17

20 the monitoring and evaluation of outcomes relating to DSME and MNT (Lacey & Pritchett 2003). Traditional prospective methods of recording intake, such as written weighed or estimated food records (WFR and EFR; respectively), are ideal in this setting as they allow for the natural day-to-day variation in dietary intake to be captured (Nelson & Bingham 1997). These methods require the measurement of quantities of food prior to consumption, and as a result are associated with high burden often leading to changes in usual intake to facilitate the recording process (Mela & Aaron 1997; Rebro et al. 1998; Vuckovic et al. 2000). In an effort to simplify the collection of dietary information, other dietary assessment methods, such as the food frequency questionnaire (FFQ), are often used. Countless variations of the FFQ exist, however due to the closed questioning approach of this method, the questionnaire used must be appropriate for the study aim and population, and include a comprehensive listing of foods for the nutrient(s) of interest (Nelson & Bingham 1997). Therefore, concern over the suitability of this particular method to assess absolute nutrient intake in certain situations remains. Photographic records (PhRs) are an innovative method for the assessment of nutrient intake and have shown promise in alleviating the issues associated with the traditional prospective methods. It is anticipated that as these methods simplify the data collection process resulting in a significant reduction in subject burden, dietary intake will be more likely to be reported accurately, however the following fundamental gaps still remain in the evidence base: Previous use of PhRs has been limited to children and young adults, with no exploration in older populations with chronic diseases; A lack of practical, sustainable and standardised approaches to current data collection and analysis techniques of PhRs may restrict use in among some population groups and settings; Exploration of the types and sources of measurement errors associated with PhRs has been limited and not conducted in a systematic approach across the entire dietary assessment process; and Evaluation of the performance of PhRs for assessing nutrient intake has not been undertaken using objective measures which are independent of diet. Chapter 1: Introduction 18

21 1.2 Purpose and context of research program This thesis aimed to address these gaps in the current evidence base of PhRs via the development, trial and evaluation of a novel mobile phone photo/voice dietary record for the assessment of nutrient intake in adults with T2DM. Through a series of four independent but inter-related studies, this research program explored two novel approaches which utilised distinct technologies to aid in the nutritional management of adults with T2DM. The measurement of diet is the central theme throughout this thesis, while the use of technology to facilitate delivery is a secondary shared element. Study 1 evaluated the effectiveness of an automated telephone system to promote dietary change in adults with T2DM. Telephone-Linked Care (TLC) Diabetes was designed to support, counsel and monitor users on four key self-management behaviours, including healthy eating, and addressed the need for regular and ongoing support Use of the TLC Diabetes system resulted in trends towards small improvements in key areas of diet, along with an increase in nutrition self-efficacy. In addition, this study provided an example of the methodological issues encountered when using a FFQ to measure dietary change in this setting, and further supported the need for a stronger evidence base surrounding the use of novel prospective assessment methods such as PhRs. Studies 2, 3 and 4 explored in detail the development, trial and evaluation of an innovative dietary assessment method consisting of a mobile phone photo/voice dietary record. The Nutricam Dietary Assessment Method (NuDAM) used a mobile phone application specifically designed for the recording of dietary intake information. This application, called Nutricam, allowing users to capture both an image of the food items for consumption and a voice record clarifying the details of the image. This information was then sent to a secure website where a dietitian, following a standardised protocol and with the assistance of the Dietary Estimation and Assessment Tool (DEAT), analysed the Nutricam dietary record to calculate an estimate of nutrient intake. The NuDAM is unique, in that the dietitian becomes responsible for both the identification and quantification of intake. Two dedicated studies examined the type, source and effect of measurement errors associated with the data collection and analysis components of the NuDAM. The final study evaluated the performance of this novel method using objective measures. Through this systematic process potential sources of error were identified and the method modified to address these issues. Implementation of the complete NuDAM found similar accuracy and inter-rater reliability as the weighed record method, however a strong user preference existed for the novel method. Chapter 1: Introduction 19

22 1.3 Significance of research program This thesis contributes to the existing body of research involving the use ICT in the nutritional management of T2DM, in particular by addressing gaps in the literature with regard to PhRs (Section 1.1). The nature of diet therapy and certain components of the nutrition care process (NCP) lend themselves to adaptation and integration with other technologies to reduce access barriers relating to services and personnel. The success of ICT applications such as the NuDAM has relevance for applications in dietetic interventions involving MNT. In addition, with the barriers and limitations of traditional primary care modes of delivery, the integration of ICT for the delivery of the dietetic education and counselling component of NCP in this manner offers an extremely promising advancement in the nutritional management of individuals with T2DM, however in-depth exploration of the potential of this concept remains relatively untapped. In particular, given the importance placed on dietary self-monitoring in diabetes self-management and the assessment of intake for the provision of MNT, opportunity exists to integrate ICT to complement and extend the measurement of diet. It is anticipated that the NuDAM and its components will be integrated with existing telemedicine applications to address current inequalities in healthcare. This innovative dietary assessment method will enable those currently limited by geographical constraints and other logistical issues access to regular and comprehensive dietetic services. In particular, the potential exists for the integration of the Nutricam dietary record into the nutrition intervention strategies as part of client education and counselling, and for dietary self-monitoring. 1.4 Content of the thesis The literature surrounding the assessment of dietary intake in the context of the nutritional management of T2DM is firstly reviewed, and existing gaps in the current evidence-base indentified (Chapter 2). A summary of the research program then provides an outline of the aims, study design, and analysis procedures undertaken to inform this thesis (Chapter 3). The use of an automated telephone system, TLC Diabetes, to deliver self-management education to adults with T2DM is then explored in relation to changes in nutrition behaviours and intake (Chapter 4). The limitations of using a FFQ to measure absolute changes in nutrient intake is highlighted, supporting the exploration of a new prospective method capable of capturing the variety present in dietary intake. As a result the concept of the NuDAM consisting of a mobile phone photo/voice dietary record was devised. The Chapter 1: Introduction 20

23 subsequent two chapters detail the design and trial of the data collection and analysis components of the NuDAM. Firstly, the feasibility of the recording protocol and the quality of the data is assessed and necessary modifications undertaken to improve the collection of dietary data using Nutricam (Chapter 5). Secondly, an indepth assessment of the measurement error associated with the quantification of food portions contained in PhRs is undertaken, resulting in the development and evaluation of the DEAT to assist in the quantification of Nutricam dietary records (Chapter 6). Using objective measures, the accuracy and inter-rater reliability of the NuDAM to measure the energy and macronutrient intakes of adults with T2DM is evaluated (Chapter 7). Finally the findings from this research program are interpreted in the context of the strengths and weaknesses of each study, with recommendations regarding implications for future practice and research suggested (Chapter 8). Chapter 1: Introduction 21

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25 Chapter 2: Literature Review Using the context of the nutritional management of type 2 diabetes mellitus (T2DM), this chapter reviews existing evidence relating to the measurement of nutrient intake to inform, support and evaluate dietary intervention strategies in this population group. The first section defines T2DM and provides a general discussion of the broader impact of this condition (Section 2.1). The concept of nutrition in the management of T2DM is introduced, followed by an overview of the importance of dietary measures for self-monitoring by the individual with T2DM and to assess nutrient intake to inform and evaluate nutrition interventions for this group (Section 2.2). The key concepts relating to the assessment of diet are overviewed with a detailed discussion of the unique challenges and potential sources of error associated with this process (Section 2.3). The methods used in the assessment of dietary intake (both traditional and novel) are reviewed in terms of their contemporary design, use, strengths and limitations (Section 2.4). The task of portion size estimation is reviewed with regard to traditional assessment dietary assessment methods, while its relationship to photographic records is also discussed (Section 2.5). The concepts relating to the validity and reproducibility of measures of nutrient intake are then described and the literature involving the performance of the dietary assessment methods used in this thesis is examined (Section 2.6). Finally, conclusions based on this review are summarised and the gaps in the current evidence base highlighted, resulting in the establishment of the rationale for the research program and the aims and hypotheses underpinning this thesis (Section 2.7). 2.1 Definition and prevalence of T2DM Diabetes currently affects 285 million adults or 6.4% of the population worldwide, with the prevalence predicted to rise to 439 million by 2030 (Shaw et al. 2010). The Australian Diabetes, Obesity and Lifestyle (AusDiab) Study, reported that in 2000, 7.4% of Australians aged 25 years and older had diabetes, a figure that had trebled since 1981 (Dunstan et al. 2001). The 5-year follow-up to the AusDiab Study reported that the incidence of diabetes continues to increase, estimating that approximately 275 adults in Australia develop diabetes each day (Barr et al. 2006). Chapter 2: Literature Review 23

26 Of those diagnosed with diabetes, 90-95% have type 2 diabetes (T2DM), which is characterised by the body s inability to sufficiently produce insulin and/or a resistance to the action of insulin (American Diabetes Association 2011). The development of T2DM is strongly associated with a number of modifiable risk factors such as poor nutrition and excess body weight (Stratton et al. 2000; Dunstan et al. 2001; Barr et al. 2006). Diabetes and its associated complications contribute considerably to ill health, disability, poor quality of life and premature death (Stratton et al. 2000). The global economic burden of diabetes is significant, with healthcare expenditure predicted to total USD 376 billion (Zhang et al. 2010). In 2010, diabetes accounted for 6.6% of the total disease burden, with 94% attributed to T2DM (Australian Institute of Health and Welfare 2010). Furthermore, the total direct health costs associated with the treatment of diabetes was estimated at $907 million in , with approximately $733 million allocated to the care of individuals with T2DM (Australian Institute of Health and Welfare 2008). 2.2 Nutritional management of T2DM Strategies aimed at modifying nutrition risk factors, such as weight status and diet, play a vital role in the successful management of T2DM and the prevention of complications associated with the disease. Diabetes self-management education (DSME) and medical nutrition therapy (MNT) are two distinct but inter-related concepts which are essential for effective nutritional management of T2DM (American Diabetes Association 2010). Central to the success of both strategies and the long-term maintenance of outcomes is access to regular and ongoing nutrition support. The measurement of diet is another factor often necessary to inform, guide and evaluate the nutritional management of T2DM. In particular, it has been suggested that the recording of intake has two functions: 1) to self-monitor food intake and consumption behaviours as part of self-management; and 2) to analyse nutrient intakes for outcome assessment at the individual, group, and population levels (Burke et al. 2005). Therefore the basis of each function differs: the former is designed to provide feedback to the individual on the application of selfmanagement behaviours, while the latter is used to guide nutrition diagnosis and intervention provided as part of MNT. Chapter 2: Literature Review 24

27 Figure 2-1 outlines these key factors and their relationship to a) DSME, and b) MNT in the nutritional management of T2DM. The relevance of the measurement of diet to both components will be discussed in detail in the following sections Diabetes self-management education Health care for chronic diseases, such as T2DM, involves a complex interplay of multiple issues and diverse stakeholders. At the core of chronic disease care models lie similar themes, in particular the promotion of patient-centred care and the facilitation of self-management knowledge and skills (Lawn & Schoo 2010) to ensure that these individuals are informed, empowered and motivated to independently manage their condition (Wagner 2000). Lorig & Holman (2003) outline five core skills needed for effective chronic disease self-management: problem solving, decision making, resource utilisation, forming of patient/health care practitioner partnership, and taking action. The application or self-tailoring of these knowledge and skills to one s situation and needs is also a fundamental and defining characteristic of self-management (Lorig & Holman 2003). Figure 2-1a combines the key self-management knowledge and skills illustrating the dynamic relationship between each element. Funnel et al (2011) define diabetes self-management education (DSME) as a sustained process of providing individuals with knowledge and skills for self-care. In particular, education and support relating to goal setting and problem solving skills primarily target the behaviours of nutrition, physical activity, medication adherence, and blood glucose monitoring (Mensing et al. 2007). The effect of DSME on shortterm glycaemic control, measured by glycosylated haemoglobin A1c or HbA1c, has been extensively summarised by a number of reviews. A meta-analysis published by Norris et al (2002) reported on 31 randomised controlled trials which investigated the efficacy of DSME on glycaemic control published between 1980 and After adjusting for differences in sample size, study design and intervention duration, the authors found that the provision of DSME decreased HbA1c by 0.76% (95% CI 0.34%-1.18%; p<0.05) following the intervention (Norris et al. 2002). Another key review focused on the effect of group-based DSME programs on the glycaemic control of adults with T2DM. In this paper, Deakin and colleagues (2005) analysed three studies published between 1988 and 2002 concluding that DSME delivered in a group setting resulted in a decrease of HbA1c of 1.4% (95% CI 0.8%- 1.9%; p<0.0001). Chapter 2: Literature Review 25

28 Nutritional Management of T2DM Regular and ongoing support: access to personnel and services a) Self-Management Education (Lorig & Holman 2003) Problem solving Decision making Individual b) Medical Nutrition Therapy Individualised dietary counselling provided by a dietitian using the Nutrition Care Process (Lacey & Pritchett 2003): Taking action Selftailoring Resource utilisation Measurement of diet 1. Assessment 2. Diagnosis Patientpractitioner partnership 4. Evaluation & Monitoring 3. Intervention Adoption and Maintenance Changes to nutrition-related behaviours Achievement of goals and outcomes Figure 2-1: Concepts in the nutritional management of T2DM. Diabetes self-management education (DSME) and medical nutrition therapy (MNT) in the nutritional management of T2DM are dependent on regular and ongoing support in combination with suitable methods to assess, monitor and evaluate diet for the achievement of key nutrition behaviours and outcomes. Chapter 2: Literature Review 26

29 More recently, Duke et al (2009) reviewed the effect of individualised DSME delivered one-on-one compared to routine care or DSME provided in a group setting on the glycaemic control of individuals with T2DM. In this meta-analysis of nine studies published between 1996 and 2007, the authors found a small non-significant reduction in HbA1c of 0.1% (95% CI -0.3%-0.1%; p=0.33) across all subjects. When individuals with a HbA1c >8% were examined independently a significant reduction of 0.3% (95% CI 0.1%-0.5%; p<0.01) was observed, highlighting the benefit of tailored DSME in those with sub-optimal glycaemic control (Duke et al. 2009). DSME is an effective strategy in the management of T2DM, however regular support and contact is necessary for sustained behaviour change and the maintenance of outcomes, as the reductions in HbA1c observed immediately following the intervention are not maintained long-term. For example, Norris and colleagues (2002) found at follow-up periods of 4 months a decrease in HbA1c of 0.26% (95% CI 0.05%-0.48%) between treatment groups (Norris et al. 2002). Benefits of DSME appear to be proportional to the amount of support received, with a 1% decrease in HbA1c noted for every additional 23.6 hr (95% CL hr) of contact (Norris et al. 2002) Self-monitoring of diet As illustrated in Figure 2-1a, self-tailoring or self-monitoring are key elements of effective self-management. Kanfer (1970) defines self-monitoring as an essential process for the adoption and maintenance of new behaviours by providing feedback for the continued modification of these behaviours. The benefits of monitoring blood glucose levels for those treated with insulin were illustrated through improvements in glycaemic control and reductions in vascular complication in the early work of the Diabetes Control and Complications Trial (The Diabetes Control and Complications Trial Research Group 1993) and the UK Prospective Diabetes Study (UK Prospective Diabetes Study (UKPDS) Group 1998). The use of blood glucose monitoring is also encouraged in those with T2DM not on insulin treatment (St John et al. 2010), however its effectiveness remains less conclusive (Boutati & Raptis 2009). Dietary self-monitoring is essential in the nutritional management of diabetes. In particular, balancing the amount of carbohydrate with administration of insulin and some oral hypoglycaemic medications is necessary to achieve optimal glycaemic Chapter 2: Literature Review 27

30 control (Bantle et al. 2008), and is suggested to assist the individual in the application of knowledge and skills for self-management decision making (Funnell et al. 2011). In a study by Tsang et al (2001), 19 subjects with sub-optimal glycaemic control recorded food intake and blood glucose information into a portable electronic device and were provided with instantaneous feedback on the nutritional content of the meal (Tsang et al. 2001). Use of the device resulted in a significant decrease in mean HbA1c of 0.83% (95%CI %; p<0.05) (Tsang et al. 2001). More recently others have investigated the feasibility of similar mobile devices for dietary monitoring in adults with T2DM (Arsand et al. 2008; Sevick et al. 2008; Fukuo et al. 2009; Sevick et al. 2010). For example, Sevick et al (2010) found that sociodemographic factors such as age, income and education did not adversely impact on the likelihood that adults with T2DM would use technology to record dietary intake, suggesting that these tools are considered an acceptable mechanism to support regular self-monitoring among a groups with diverse characteristics Medical nutrition therapy In the nutritional management of T2DM, individualised dietary counselling or medical nutrition therapy (MNT) with a dietitian is independent, but complementary to DSME (Daly et al. 2009; Funnell et al. 2011). Although dietitians, as part of a multidisciplinary team, play an active role in the provision of DSME, the difference between the two components lies in the delivery and content of the information and resources provided. MNT is defined as the nutritional diagnostic, therapy, and counselling services for the purpose of disease management, which are furnished by a registered dietitian or nutrition professional (Lacey & Pritchett 2003). The prescription of MNT is a cornerstone of successful diabetes management (American Diabetes Association 2010). The aim of MNT in the individual with diabetes is twofold: 1) to achieve and maintain metabolic control to prevent complications, and 2) to prevent and delay mortality and co-morbidity through the management of complications (Bantle et al. 2008). Among adults with T2DM, the effect of one-on-one tailored nutrition education and counselling provided by a dietitian for improving glycaemic control is well supported in the literature, most recently summarised by Franz and colleagues (Franz et al. 2008). These studies, in combination with other findings, show beneficial effects of MNT (both with or without additional complementary DSME) with improvements in HbA1c of % following randomised controlled trial conditions in adults with Chapter 2: Literature Review 28

31 T2DM (UK Prospective Diabetes Study (UKPDS) Group 1990; Glasgow et al. 1992; Franz et al. 1995; Sadur et al. 1999; Miller et al. 2002; Rickheim et al. 2002; Ash et al. 2003; Goldhaber-Fiebert et al. 2003; Ziemer et al. 2003; The Look AHEAD Research Group 2007; Huang et al. 2010). Observational studies undertaken in less controlled settings have reported slightly greater reductions in HbA1c of % (Christensen et al. 2000; Graber et al. 2002; Banister et al. 2004; Lemon et al. 2004; Chima et al. 2005; Gaetke et al. 2006). Similarly to DSME, some evidence suggests that improvements in glycaemic control during or following MNT are proportional to the amount of contact that an individual receives with a dietitian with improvements not often maintained following the completion of the intervention (UK Prospective Diabetes Study (UKPDS) Group 1990; Franz et al. 1995; Paisey et al. 1998; Ash et al. 2003; The Look AHEAD Research Group 2010). For example, Ash et al (2003) reported significant improvements in HbA1c (1.0±1.4%; p<0.001), weight (6.4±4.6 kg; p<0.001), and waist circumference (8.1±4.6cm; p<0.001) following a three month program consisting intensive (weekly) MNT, however treatment effects were not maintained 18 months post-intervention. In addition, results following the first year of the Look AHEAD (Action for Health in Diabetes) intervention showed significant improvements in those receiving intensive lifestyle intervention comprised of monthly individual counselling sessions compared to the control group who received diabetes self-management education delivered in a group setting for weight loss (8.6±6.9% vs. 0.7±4.8% of initial weight, respectively; p<0.001) and glycaemic control (0.64±0.02% vs. 0.14±0.02%, respectively; p<0.001) (The Look AHEAD Research Group 2007). During years 2 to 4 of this longitudinal study, contact with a dietitian reduced to monthly, with optional additional support available via phone, or mail. As a result, the proportion of individuals in this group achieving glycaemic control (i.e. HbA1c 7%) at year 4 decreased to 57.4% compared to 62.7% at the end of year 2. Despite clinical outcomes regressing in both treatment groups at year 4, the number of subjects in the intervention group achieving recommended level of HbA1c was significantly greater compared to the control group (57.4% vs 51.1%; p<0.001) (The Look AHEAD Research Group 2010). Such findings highlight the need for regular nutrition education and counselling to support positive health behaviour change in adults with T2DM. It is therefore suggested that frequent and ongoing support with a dietitian is essential for longterm maintenance of health outcomes, with current evidence-based practice Chapter 2: Literature Review 29

32 guidelines recommending an initial series of 3-4 dietetic consultations over a 3-6 month period, with a minimum of one follow-up visit every 12 months (Franz et al. 2010). These recommendations highlight the importance of regular monitoring and evaluation in the nutrition care process The Nutrition Care Process The nutrition care process (NCP) is at the core of MNT, comprising a standardised framework for the delivery of consistent care for individuals, groups, and communities by nutrition and dietetics professionals (Lacey & Pritchett 2003). MNT is an application of the NCP, as standardised nutrition care for individuals with T2DM can also be provided outside an individualised clinical setting. The NCP comprises four components: assessment, diagnosis, intervention, and monitoring and evaluation (Lacey & Pritchett 2003). Figure 2-2 defines each stage of the NCP. In nutrition assessment the collection of appropriate information is considered essential. The measurement of food and nutrient intake along with the collection of other information relating to health and disease condition (e.g. anthropometry, biochemistry, clinical history), behavioural, functional and psychosocial factors, and knowledge and beliefs are often collected (Lacey & Pritchett 2003). It is important to acknowledge that as part of MNT, the significance placed on obtaining an accurate and reliable quantitative measure of nutrient intake may vary across different practice settings. In the context of the NCP, assessment informs the diagnosis, guides the selection of appropriate strategies for the intervention, and the inclusion of suitable objectives for monitoring and evaluation. 1. Nutrition Assessment: A systematic approach to the collection, analysis and interpretation of nutrient intake and other information relating to nutritional status and dietary behaviours. 2. Nutrition Diagnosis: The identification and description of the nutritional problem cause, and signs and symptoms to inform the nutrition intervention. 4. Nutrition Monitoring & Evaluation: Scheduled review and measurement of status to evaluate progress. 3. Nutrition Intervention: A set of specific strategies designed to address the nutritional problem, including a plan for nutrition evaluation and monitoring of outcomes. Figure 2-2: Overview of the core stages of the nutrition care process (adapted from Lacey & Pritchett, 2003). The nutrition care process (NCP) is a standardised framework for the delivery of nutrition care. Central to the NCP are four stages: assessment, diagnosis, intervention, and monitoring and evaluation. Chapter 2: Literature Review 30

33 2.2.3 Barriers to the provision of support in the nutritional management of T2DM Although a need exists for sustained contact in relation to DSME and MNT in the context of the nutritional management of T2DM, a number of factors could limit access to health services for regular and ongoing support. Firstly, recent inclusions to national health policy in Australia have seen the introduction of governmentsubsidised allied health services for individuals with one or more chronic diseases (Department of Health and Ageing 2011). This scheme has increased access to dietetic services for those with diabetes (Mitchell et al. 2009; Cant 2010a), and promoted the profile of allied health professionals as an integral component of chronic disease care. Despite greater recognition of the importance of allied health services, negative perception among practitioners exists with regard to equity in service access as a result of inherent restrictions on patient consultation duration and frequency (Foster et al. 2008; Haines et al. 2010). A recent survey of Australian dietitians concluded that most believed current policy arrangements provide insufficient access to services, reporting that the number of available visits did not align with the recommendations of standard practice guidelines (Cant 2010b; Cant 2010c). Secondly, certain logistical issues relating to the structure and delivery mode of the intervention have been reported as major factors affecting access and contributing to drop-out within DSME program (Gucciardi et al. 2008). Barriers to dietary compliance identified by individuals with T2DM include time constraints, lack of symptoms, poor understanding of the diet-disease relationship, lack of self-efficacy, and misinformation factors (Williamson et al. 2000). Similar factors have been reported by individuals with diabetes as reasons for non-attendance at dietetic appointments (Spikmans et al. 2003). The provision of services which are offered in flexible models to cater to the needs of the individual and are delivered using accessible and relevant resources and tools have been proposed as approaches that may improve utilisation (Gucciardi et al. 2009). Acknowledgement of the barriers and limitations inherent within current modes of health care delivery has led to the exploration of alternative strategies Novel strategies for the provision of support in the management of T2DM Information and communication technologies (ICTs) can overcome some of the challenges associated with traditional models of service delivery by facilitating greater access, interactive resources for education and self-monitoring, and Chapter 2: Literature Review 31

34 mechanisms for regular support. The use of ICTs to support and enhance activities relating to health care delivery, treatment, and management is becoming widespread. Research into novel interventions, which utilise various interactive technologies to improve self-management of chronic diseases, has been successful in showing improvements in knowledge and a range of behavioural and clinical variables (Murray et al. 2005). In particular, the use of a variety of ICTs, such as computers and mobile phones, has been found to be effective in assisting the individual to apply diabetes self-management skills through the adoption of behaviours resulting in improved glycaemic control (Krishna & Boren 2008; Ramadas et al. 2011). The IDEATel (Informatics for Diabetes Education and Telemedicine) project is one example of large-scale randomised controlled trials which have examined the effect of the integration of various telemedicine applications to assist in the selfmanagement of T2DM. This study investigated the effect of education and support on self-management behaviours provided via video conferencing with a trained diabetes nurse in combination with electronic monitoring and a dedicated education and tracking website on self-care behaviours and health outcomes in 1665 American adults (mean age=71 years). After one year, a reduction in HbA1c was observed for both groups, however the difference was significantly lower in the intervention compared to the control group (-0.18%, p<0.01). A 5-year follow-up of the IDEATel study cohort revealed significant differences between the group receiving care via telemedicine compared to standard care treatment in glycaemic control (HbA1c = -0.29%; p<0.01) (Shea et al. 2009). The sustained changes in this cohort suggest that ICTs may have the potential to play a crucial role in the delivery of care and supplementary services to adults with T2DM. Automated ICT interventions allow advice provided to the user to be semiindividualised based on programmed algorithms and are one example of innovative strategies used to deliver health behaviour change. Overall, these innovative delivery strategies have been shown to be effective for improving general selfmanagement behaviours (Krebs et al. 2010). In relation to diet, the use of various ICT strategies to deliver nutrition interventions to promote and support dietary change has also been associated with positive outcomes in the management of chronic diseases including T2DM (Neville et al. 2009). Glasgow and colleagues (1995) initiated the use of such technologies for the delivery of dietary selfmanagement education to individuals with diabetes during regular visits with their Chapter 2: Literature Review 32

35 primary care physician. In this study, 95 adults with diabetes aged 61.4±10.6 years used a touch-screen computer to generate feedback on key self-management behaviours which were then used to assist the subject set individual dietary goals and problem solving strategies. Significant differences were noted between intervention and control groups for total fat intake (-1.5% of EI, p<0.05) and saturated fat (-1.0% of EI, p<0.01) following completion of the program (Glasgow et al. 1997). Building upon this concept, this group reported on a subsequent evaluation of an internet-based intervention for the delivery of specific T2DM self-management education and support. In the final sample of 320 adults with T2DM aged 59.0±9.2 years, changes in dietary fat intake (-13.6g, p<0.001) and practices relating to fat were reported 10 months following the intervention, highlighting the potential of longterm maintenance using this strategy (Glasgow et al. 2003). This internet-based approach was well received by subjects regardless of previous technology use, gender or disease severity (Feil et al. 2000). The use of telephone-based interventions for the management of health behaviours has been found to be an effective alternative to traditional modes of service delivery by overcoming system and logistical barriers associated with in-person strategies (McBride & Rimer 1999). In general interventions which have used the telephone as the sole delivery modality for dietary behaviour change are limited, with the majority using this medium to supplement in-person, print-based or web-based initiatives and provide ongoing support (Eakin et al. 2007). This mode of intervention delivery has been effective for improving general dietary behaviours relating to intakes of fruit, vegetable and fat (VanWormer et al. 2006; Newman et al. 2008; Djuric et al. 2010). More specifically in relation to T2DM, only one study has reported on the effectiveness of nutrition education delivered by counsellors using the telephone as part of general diabetes self-management skills in relation to changes in dietary intake. In this study, Eakin and colleagues (2009) reported on the effectiveness of a 12-month telephone counselling intervention on the dietary intake of 434 obese adults (age 58.2±11.8 years) with T2DM. Significant intervention effects were found for intakes of total fat (-1.2% of EI, p<0.01), saturated fat (-1.0% of EI, p<0.01), fibre (+2.2g, p<0.001), fruit (+0.3 serves, p<0.001) and vegetables (+0.7 serves, p<0.05). Despite the benefits associated with T2DM dietary interventions that have incorporated either telephone delivery or automated computer applications, research Chapter 2: Literature Review 33

36 into the use of combinations of both technologies in the form of automated telecommunication systems to support nutrition education either alone or in combination with key self-management behaviours is scarce. Two studies have used this type of technology exclusively to support T2DM self-management. Piette and colleagues (2001) reported on the effect of an automated telephone system on the self-care behaviours of 272 older American adults. Following 12 months access to the system, increased blood glucose self-monitoring, foot care and attendance at follow-up outpatient clinics were found compared to usual care, however significant improvements in glycaemic control of -0.5% (p<0.01) were only noted for individuals with HbA1c of 8% at baseline (Piette et al. 2001). Similarly Boren et al (2006) demonstrated the effectiveness of a 12-week DSME program delivered by an automated telephone system in 18 adults with diabetes, with significant improvements observed in the frequency of blood glucose monitoring and general diabetes knowledge. In these studies only the effects on clinical and behavioural outcomes were reported, with the effect on nutrient intake not discussed. To-date, only one study has reported on the effectiveness of an automated telephone system in relation to promoting the adoption and maintenance of dietary change among the general population. In this study Delichatsios et al (2001) found that the use of a TLC system designed to provide general healthy eating advice improved overall diet quality and increased intakes of fruit by 1.1 servings/day (95% CI 0.4 to 1.7; p<0.05) and fibre by 4.0 g/day (95% CI 0.1 to 7.8), and decreased saturated fat by 1.7% (of EI intake) (95% CI -2.7to -0.7). Despite use independently for the self-management of T2DM and dietary change, automated telephone systems have not been applied to support nutrition education either alone or in combination with other key self-care behaviours in an Australian setting. Relevance to research program Section 2.2 provided an insight into the two fundamental aspects influencing the nutritional management of T2DM and their relationship with the measurement of diet. Although highly effective in the presence of frequent support and contact, both DSME and MNT are constrained by a preference for traditional face-to-face delivery modes limiting regular and ongoing support and access to services and resources. Evidence supports the use of ICT interventions to offer the delivery of flexible nutrition interventions to individuals with T2DM, either alone or in combination with other self-care behaviours. Automated telephone interventions offer a flexible and effective alternative to other modes of delivery, however their impact on modifying Chapter 2: Literature Review 34

37 dietary behaviour change among Australian adults with T2DM has not been investigated and will form the first study of this thesis. The importance of the measurement of diet in the nutritional management of T2DM was also introduced with regards to DSME and MNT. 2.3 Assessment of dietary intake The measurement of diet is often required for the self-monitoring of intake and to inform assessment, evaluate nutrition interventions and monitor outcomes associated with a variety of dietary strategies, including both nutrition selfmanagement and dietetic therapy in the care of individuals with T2DM. As the focus of this thesis involves the development, trial and evaluation of a mobile phone photo/voice dietary record method, the following sections will focus on the general uses, process involved, and potential sources of error associated with measuring usual dietary intake Uses of dietary intake information The main objectives for the measurement of dietary intake are to: 1) to compare the mean intake of groups; 2) to rank individuals within a group; and 3) to determine the usual intake of an individual (Nelson et al. 1989). A fourth objective relating to establishing the proportion of a population at risk of inadequate nutrient intake has also been suggested (Gibson 2005). At the population level the assessment of intake is essential for understanding the aetiology of certain diseases and the ongoing diet-disease relationship; the monitoring and surveillance of the diets of the population and the formulation of recommendations for general health. In contrast, at the individual level, the assessment of intake has implications both for the client themselves in the prescription, monitoring and evaluation of medical nutrition therapy interventions for the management of disease-specific conditions Measuring usual dietary intake Usual intake is a theoretical concept which aims to describe the habitual diet of an individual and is obtained via an average of a certain number of days intake recorded over a specified period of time (Livingstone & Black 2003). A one-off measure of an individual s dietary intake for a given day will not account for the natural day-to-day variation in the dietary intake observed within the same individual (Beaton et al. 1979; Todd et al. 1983). The minimum recording period needed to Chapter 2: Literature Review 35

38 establish usual intake is dependent on the objective of the study and/or the nutrient of interest (Marr & Heady 1986). Research carried out in the 1980s attempted to identify the number of days required to capture usual dietary intake. Acheson et al (1980) and Marr & Heady (1986) both concluded that recorded intake over one week would be sufficient to obtain usual energy intake at an individual level, however the level of accuracy assumed for this classification was later disputed by Nelson et al (1989). At the group level, Basiotis and colleagues (1987) found three days adequate for energy intake following the analysis of dietary intake data of 29 adults collected over 365 consecutive days. Using the same data set as Basiotis et al. (1987), Tarasuk and Beaton (1991) examined the within-subject variability reporting that although a large proportion of the day-to-day variation was random in nature, approximately 12.5% was explained by systematic weekly and long-term intake patterns. This pivotal study quantified that in the assessing diet within-subject variation is present, with the extent dependent on the individual. A subsequent analysis showed that this variation is accounted for by selecting non-consecutive recording days and including weekend days when measuring usual nutrient intake (Tarasuk & Beaton 1992). These findings underpin the design and analysis of prospective assessments of nutrient intake, with applications to the intake of foods and food groups (Palaniappan et al. 2003). Given the dynamic nature of an individual s dietary intake, it is essential that any measure accounts for both within-subject and between-subject variability The process of assessing dietary intake This section aims to provide an evaluation of the process of assessing dietary intake and the factors which affect the error of the measure. The source and magnitude of the error is dependent on the method used and characteristics of the subjects studied (Beaton 1994). In particular, the self-reported nature of dietary intake ensures that many inherent factors have the potential to introduce error throughout the process. Error in the measurement of dietary intake can be either random or systematic. Random errors affect the reliability of the measure, whereas systematic errors affect the accuracy of the measure (Bingham 1987). Errors which are random can be caused by mistakes in documenting intake or discrepancies in coding of data, and weaken the measure of diet. In contrast, systematic errors maybe related to the mode of data collection and/or or certain behavioural and psychological characteristics of the subjects under study and introduce bias into Chapter 2: Literature Review 36

39 estimate of intake, affecting the integrity of the analysis and interpretation of outcomes. Slimani and colleagues (2000) detail the errors present throughout the process of gathering and manipulation of dietary data in a 24-hour recall. The sources of potential error and the process involved in the assessment of dietary intake described by this group are not exclusive to this method, rather these are inherent in all dietary assessment methods. The process used to derive an estimate of nutrient intake consists of two stages: 1) the collection of dietary data; and 2) analysis (coding and input) of data and calculation of nutrient intake. Three potential sources of error exist during the assessment: 1) the individual for whom diet is being measured; 2) the dietitian/investigator; and 3) the dietary assessment method used. Figure 2-3 builds upon this early example of Slimani et al. (2000) by illustrating a conceptual model outlining the potential sources of error and the factors present within each stage of the dietary assessment process which have the potential to introduce error into the measure. Many of the factors associated with the individual for whom diet is being measured and the dietitian or other investigator are similar across all dietary assessments. Therefore this section will provide an overview of these issues, followed by a discussion of the various dietary assessment methods currently used within practice and research in Section Specific factors relating to the individual for whom diet is being measured Certain issues relating to the individual for whom diet is being assessed may either introduce systematic bias or random error into the measure, and as a result can affect the accuracy and reproducibility of the estimated intake. This section discusses factors specific to the individual which have been identified in the literature as likely to influence self-reported intake. The process of recording intake information is an observation of eating behaviour, susceptible to changes in either the collection of dietary information and/or deviation from the type or amount of food typically consumed (Macdiarmid & Blundell 1998). This factor, known as the Hawthorne Effect (Roethlisberger 1939), cannot be controlled and is likely to be present in all forms of dietary assessment. Chapter 2: Literature Review 37

40 Factors influencing the assessment of intake Assessment of dietary intake Dietary assessment method Collection of dietary data Observation of behaviour Cognitive skills (literacy, numeracy, memory) Quantifying consumption: o Portion size estimation o Measuring/ weighing Burden/ compliance with recording protocol Analysis of dietary data Coding of records Food composition database Measure of nutrient intake Evaluation of the validity & reproducibility of the measure Demographic, anthropometric, psychological and/or behavioural characteristics Individual (who s diet is measured) Dietitian/Investigator Figure 2-3: The process of assessing dietary intake. The method used to assess intake will influence the relationship between the potential sources of error within each stage and the factors which affect the measurement of diet. An evaluation of the validity and reproducibility of the measure of nutrient intake is necessary to determine the performance of the method. Cognitive skills, such as literacy and numeracy, are essential for the recording of complete dietary information across most assessment methods. The ability of the individual to provide an accurate description of the items consumed is critical. Poor literacy skills can result in the incorrect or incomplete description of items and is commonly considered a potential source of random error in methods such as the written records. For this reason, a lower level of education has been associated with under-reporting of intake (Briefel et al. 1997; Dwyer et al. 2003; Mattisson et al. 2005; Huang et al. 2005; Abbot et al. 2008). Reporting the frequency in which certain foods are consumed is highly dependent on the cognitive ability and numeracy skills of the individual. The collection of dietary information via this approach requires the Chapter 2: Literature Review 38

41 individual to deconstruct the total diet into component foods and/or ingredients, rather than meals eaten, which is generally considered the reference (Kohlmeier 1994). This task becomes more difficult when the individuals is required to report on food items (e.g. eggs) which may be eaten either alone (e.g. poached eggs) or as a component of mixed dishes (e.g. quiche), particularly if these items are prepared by others (Kohlmeier 1994). The reference timeframe for reporting the frequency of consumption can range from weeks or months to years (Willett 1998), which can further compound the difficulty of this task. Finally, the estimation of food quantities consumed or present is also influenced by the cognitive ability of the individual. Section 2.5 discusses the factors influencing portion size estimation in detail. The burden of recording diet can also present a barrier to the collection of complete dietary data. Methods which require the individual to document and measure the quantities of food at the time of consumption, such as weighed or estimated food records, are typically considered the most burdensome. The amount of burden perceived by the individual is likely to influence the motivation to collect intake information and compliance with the recording protocol. As a result, changes to usual intake and eating behaviours, including a reduction in the total intake, the omission of difficult to measure foods or meals, and/or the inclusion of non-typical foods, may occur in order to simplify the recording process. (Mela & Aaron 1997; Rebro et al. 1998; Vuckovic et al. 2000) Additional reasons for deviations from usual intake involve the perceived social acceptability of certain foods (Macdiarmid & Blundell 1997; Mela & Aaron 1997; Vuckovic et al. 2000; Scagliusi et al. 2003). These variations can introduce a systematic bias in the measure of intake. Certain demographic and anthropometric characteristics, including age, gender and body mass index (BMI), have been proposed as influencing the reporting accuracy during dietary assessment. Some studies have reported a relationship between age and the accuracy of self-reported energy intake, with under-reporting more common among older adults (Briefel et al. 1997; Lafay et al. 1997; Johansson et al. 1998; Huang et al. 2005; Tooze et al. 2007). Examination into the effect of gender on reporting accuracy has revealed that women under-report intake compared to men (Briefel et al. 1997; Price et al. 1997; Pryer et al. 1997; Johansson et al. 1998; Dwyer et al. 2003; Novotny et al. 2003; Huang et al. 2005). For example, Johansson et al (1998) found more women underreported energy intake than men (45% and 38% respectively) (Johansson et al. 1998). In addition, if a measurement of intake is repeated, a large proportion (i.e. 55% males, and 58% of females) of Chapter 2: Literature Review 39

42 these subjects, tend to also under-report on subsequent occasions (Briefel et al. 1997). A number of studies have found high levels of under-reporting among overweight and obese adults (Briefel et al. 1997; Lafay et al. 1997; Price et al. 1997; Pryer et al. 1997; Johansson et al. 1998; Kretsch et al. 1999; Johnson et al. 2005; Mattisson et al. 2005; Scagliusi et al. 2009). One study reported that 43% of obese men and 52% of obese women reported implausibly low energy intakes, compared to 11% of lean subjects (Price et al. 1997), while another concluded that the probability of inaccurate reporting of intake increased with BMI (Johansson et al. 1998). However, the association between increasing weight status and inaccuracies in selfreported intake is not absolute, as severe under-reporting has also been observed in normal-weight subjects (Asbeck et al. 2002), however other factors such as behavioural and psychosocial characteristics of the individual were identified as contributing to this outcome. Psychological and behavioural characteristics, such as dietary restraint and social desirability, have also been identified as factors influencing the accuracy of self-reported dietary intake. Dietary restraint refers to a conscious effort to reduce intake in order to promote weight loss or prevent weight gain (van Strien 1999), and has been linked to mis-reporting of dietary intake (Mela & Aaron 1997; Taren et al. 1999; Bathalon et al. 2000; Asbeck et al. 2002; Tooze et al. 2004; Rennie et al. 2006). For example, Rennie et al (2006) found that high levels of dietary restraint were a significant predictor of under-reporting of energy intake among lean women, while inaccurate reporting occurred among overweight women regardless of the restraint score. In contrast, Lafay et al (1997) found that restrained eating and under-reporting of diet was independent of weight status. With regard to the beliefs of individuals in keeping with social norms or social desirability, certain types of foods also tend to be mis-reported. Poppitt and colleagues (1998) reported that snack foods consumed between meals failed to be reported by both obese and non-obese women. Johansson et al (1998) also found that snack foods rich in sugar and/or fat such as soft drinks, chocolate, cake and potato chips were consumed less frequently among under-reporters. Similarly, healthy dietary patterns have also been reported as characteristic of individuals who typically under-report intake (Scagliusi et al. 2008b; Lutomski et al. 2011), while Chapter 2: Literature Review 40

43 others have found among those under-reporting intakes, differences occur across all food groups (Pryer et al. 1997; Krebs-Smith et al. 2000; Millen et al. 2009). As these demographic, anthropometric, behavioural and psychosocial characteristics are inherent within individual, it is often difficult to minimise their effect during the collection of dietary data. However, these factors have the potential to introduce systematic bias into estimates of intake (Section 2.6), and thus accounting for these factors can assist in the analysis and interpretation of the resultant dietary data Specific factors relating to the dietitian or investigator involved in the measurement of diet The dietitian or investigator is another potential source of bias in the assessment process. This source of error may occur in the collection of dietary data and/or in the analysis and calculation of nutrient intake (Margetts 1997). Compliance with collection protocol is considered a major source of error in the assessment of intake. Methods which rely on the investigator to interview the subject are particularly prone to the introduction of bias if non-adherence to data collection procedures is present (Gibson 2005). This aspect of dietary assessment becomes particularly important in large national or multi-centre studies. The need to ensure consistency in the collection of data has lead to the development of standardised protocols for large scale national (e.g. National Health and Nutrition Examination Survey) and cross-country assessment using the 24-hour recall method (e.g. European Perspective on Cancer and Nutrition Study) (Slimani et al. 1999). These computerised interviewer protocols use pre-programmed questions for the identification, quantification, and probing for thousands of food items and numerous quality control procedures. Use of such a system reduced interviewer bias among 90 interviewers in 10 European countries, with the difference between interviewer and country mean energy intake less than 10% in most cases (Slimani et al. 2000). The need for protocols to limit the introduction of bias is not limited to the collection of data, and similar rigor is needed for the entry and calculation of intake. Coding of recorded dietary data using food composition tables is a fundamental task in the calculation of nutrient intake. As this data entry task is dependent on the coder s interpretation of the data, the quality and completeness of the collected data must be high, as error can be introduced through the inappropriate identification and quantification of food items (Bingham 1987). Conway et al (2004b) categorised error Chapter 2: Literature Review 41

44 associated with the coding of dietary records collected as part of the INTERMAP Study, with common errors identified including those relating to data entry, identification of the appropriate or equivalent item (i.e. brand name, type), and interpretation of missing information. Few studies have examined the effect of coding on the calculation of intake. Braakhuis et al. (2003) reported that the coding of food diaries by experienced dietitians introduced large variation into the estimation of nutrient intake, however, this improved as the number of days coded increased. For example, the withinathlete coefficient of variation when different dietitians coded the same athlete s record ranged from 20% for 1-day, 12% for 3-days and 10% for 7-days (Braakhuis et al. 2003). The estimation of micronutrient content is most susceptible to variations in coding (Reid et al. 1999; Braakhuis et al. 2003). In comparison, Adelman et al (1983) found that differences in coding did not influence the calculation of nutrient intake when experienced nutritionists performed the task, although the small variation observed in this study attributed to similarities in background and training between the coders (Adelman et al. 1983). The coding of complex foods and mixed dishes, in particular the separation into component ingredients, also presents an opportunity for the introduction of error (Gibson 2005). Fitt et al (2009) examined the effect of two different approaches to the coding of mixed dishes. The first method consisted of classifying the item based on fish or meat content alone, while the second used the main food component to classify. The group concluded that neither method was devoid of error, and that an individualised approach based on the name of the dish and the proportion of ingredients was recommended as the most appropriate option (Fitt et al. 2009). Therefore, given the potential for this element to introduce bias into the measurement of intake, it is recommended that to minimise inter-coder bias, a standardised protocol is adopted for the coding of dietary records (Conway et al. 2004b). The food composition database used may also introduce error into the calculation of intake due to inherent limitations of sampling and currency of food sources, and the completeness of the nutrient information (Pennington 2008). For example, Braakhuis et al (2003) suggest that a systematic bias exists in relation to the items excluded from food composition databases with the range of mixed dishes, ethnic foods, and pre-packaged or commercial foods is often small (Braakhuis et al. 2003). Chapter 2: Literature Review 42

45 Therefore the choice of food composition database must be relevant to the group being studied, however it is acknowledged that this is a difficult task to achieve given the fluidity of the food supply, especially for commercial products (Pennington 2008). Relevance to research program The literature reviewed in Section 2.3 discussed the unique challenges associated with measuring dietary intake. The evidence highlighted that within-subject variation is a natural phenomenon in the assessment of diet, and therefore obtaining a valid and reproducible measure of usual intake becomes a difficult task to achieve. In addition, this section introduced the process of assessing intake and explored the potential sources of error arising from the individual (who s diet is been measured) and the dietitian or other trained investigator. The factors within these sources must need considered in the collection and analysis of dietary data in order to minimise the introduction of both random error and systematic bias into the measure of nutrient intake. As the main component of this thesis involved the development, trial and evaluation of a novel dietary assessment method, an understanding of the complexity associated with measuring intake was necessary. 2.4 Methods for the assessment of dietary intake This thesis involved a series of studies focused on the methodological process and sources of error associated with the assessment of nutrient intake using a new mobile phone photo/voice dietary record method. Therefore, an examination of current dietary assessment methods was needed to highlight the strengths and limitations of current techniques used to measure diet. Methods for the assessment of dietary intake in both practice and research have been used since the early 1940s. Four methods used extensively include the 24- hour recall, diet history, food frequency questionnaire, and food record. For the purpose of this thesis these techniques will be referred to as traditional dietary assessment methods. In contrast, photographic dietary records are a new method of measuring intake, and although similarities exist in relation to the traditional weighed or estimated food record, sufficient difference exists in the source and type of potential errors that this method is classed as novel in the context of this thesis. In addition, dietary assessment methods can be categorised based on the time point in which the data collection occurs. Retrospective methods, such as diet histories, Chapter 2: Literature Review 43

46 24-hour recalls, and food frequency questionnaires, investigate intake over a period of time in the past with the subject recalling the types and amounts of food consumed. In comparison, prospective methods require the subject to record intake information at the time of consumption and generally take the form of written and photographic records. Prospective methods are preferred for self-monitoring and assessment of intake of the individual for MNT as they account for account for dayto-day variation in intake and provide a direct observation of current diet (Nelson & Bingham 1997). The time point of data collection results in the potential introduction of significant, yet different sources of systematic bias into measure of nutrient intake assessed by methods in both retrospective and prospective methods. This section will provide an overview of each of the dietary assessment methods used within current nutritional research and dietetic practice to determine the nutrient intakes of populations, sub-groups and individuals (Table 2-1). Descriptions outlining the early use of the traditional methods are provided in the seminal paper by Bingham (1987), therefore the current review will focus on their contemporary design and use. As this thesis focused on the development of a novel prospective method for assessing intake, both the written food record and photographic record are discussed in greater detail. The review of the novel photographic method will highlight existing methodological issues and identify current gaps in the evidence base. Table 2-1: Overview of current methods used in the assessment of dietary intake. Method Description 24-hour Recall (24R) Diet History (DH) Food Frequency Questionnaire (FFQ) Food Record (FR) Photographic Record (PhR) Recall of all food consumed over the previous 24-hour period, using portion size aids to assist in the estimation of amounts consumed. Recall of eating patterns and foods consumed over a usual day, with items quantified in household measures. A cross-check process is then used to clarify recorded information. Recall of the consumption frequency of selected food items listed over a specified time period. Real-time recording of food intake over a given time period. Quantities are measured either using household measures or weighed prior to consumption. Real-time recording of food intake over a given time period. Quantities of food are photographed and analysed at a later stage, additional information may or may not be provided to assist in the identification of the photographed foods. Chapter 2: Literature Review 44

47 hour dietary recall The 24-hour recall (24R) is an interview-based method typically used to estimate the average intake of a group or population. During the interview, the subject is systematically asked to list and quantify all the food and beverage items consumed in the previous 24-hour period (Gibson 2005). The relatively quick and easy administration of this method results in low subject burden, leading to the inclusion of the 24R in many large national population based surveys such as the United States Department of Agriculture s (USDA) the What We Eat in America component of the National Health and Nutrition Examination Survey (NHANES) (Briefel 1994; Briefel et al. 1997), and the Australian National Nutrition Survey (Australian Bureau of Statistics 1995a). This method was also used to calibrate the European Perspective on Cancer and Nutrition (EPIC) Study FFQ across participating countries (Slimani et al. 1999). Recent effort has focused on standardising and automating the protocol used during a 24R through the introduction of a multiple-pass method. The Dietary Effects of Lipoprotein and Thrombogenic Activity (DELTA) Study used a triple-pass method (Jonnalagadda et al. 2000), where as further refinement to the 24R protocol has resulted in a five-step multiple-pass method (Moshfegh 2001). This latter approach builds on the earlier triple-pass method through the inclusion of time and meal occasion information and an opportunity to probe for specific categories of foods commonly forgotten (Conway et al. 2003). The automation of this process aims to improve engagement with the subject during the interview and contains a variety of cues to assist in the recall of the previous day s intake (Moshfegh 2001). In turn, through a standardised question and answer tree, the method guides the interviewer through the process to ensure comprehensive collection of dietary intake information for each item and to limit the potential for interviewer bias. This advancement in the delivery of the 24-hr recall has been validated using independent biomarkers of intake (i.e. doubly labelled water technique) (Conway et al. 2003; Conway et al. 2004a; Moshfegh et al. 2008). The 24R can be delivered successfully via the telephone (Posner et al. 1982; Tran et al. 2000; Brustad et al. 2003; Blanton et al. 2006), with the incorporation of automated and self-administered systems becoming more common (Zoellner et al. 2005; Arab et al. 2010a). Due to the large within-subject variability in dietary intake observed on a daily basis, a single 24R is not considered sufficient to measure the usual intake of an individual (Tarasuk & Beaton 1991; Tarasuk & Beaton 1992), however, this technique can also Chapter 2: Literature Review 45

48 be applied to evaluate the habitual intake of individuals through the use of multiple replicate 24Rs (Blanton et al. 2006), with the inclusion of non-consecutive, unannounced days recommended to minimise recall bias (Buzzard et al. 1996). The high reliance on the subject s ability to recall accurately, and the cognitive skills required for the accurate estimation of portion sizes or amounts of food and beverages consumed, mean that this method is susceptible to greater error than a food record (Bingham et al. 1994; Black et al. 1995; Black et al. 1997) Diet History The original diet history (DH) protocol was developed for use in a research setting and consisted of three components: an interview to elicit usual daily eating patterns (with types and amounts of foods), a clarification of intake using a cross-check list of foods, and a food diary consisting of three consecutive days (Burke 1947). This process was designed to gather simply an estimate of nutrient intake and to provide information on eating behaviours, including preparation and cooking methods. Due to the concentrated nature of the data collection, the interview components of the DH were performed by trained nutritionists, however compared to other approaches, this is labour intensive and of high subject burden due to the length of time required for the interview (Gibson 2005). Modifications to the original methods have been used in a variety of settings to elicit relative estimates of nutrient intake (Tapsell et al. 1999; Tapsell et al. 2000). A condensed version of the diet history is often used in a clinical dietetic setting to elicit the usual intake of an individual over a long period of time, allowing the dietitian to subjectively review patterns and behaviours relating to intake and provide information directly to the patient within the consultation (Martin et al. 2003). As a result, a number of versions of automated and/or self-administered versions have been trialled with similar validity and reproducibility to traditional DH methods (Kohlmeier et al. 1997; Mensink et al. 2001; Probst et al. 2008; Slattery et al. 2008) Food Frequency Questionnaire Food frequency questionnaires (FFQs) were designed to gather qualitative data by assessing the consumption frequency of specific nutrients, food items or food groups and eating patterns over a defined period of time to determine links between intake and the aetiology of disease (Bingham 1987). Therefore, this method aims to measure exposure in terms of weeks, months and/or years of the individual to a Chapter 2: Literature Review 46

49 nutrient(s) and/or particular foods (Willett 1998). The method evolved to include aids (i.e. photographs or food models) to assist in the estimation of portions consumed and has been used to determine absolute levels of typical intake (Willett et al. 1985; Block et al. 1986). The majority of FFQs are adaptations of the instruments developed by either Willet et al (1985) or Block et al (1986) (Cade et al. 2002). Semi-quantitative FFQs are also used to evaluate the amount of certain nutrients within the diet of various groups and populations. Examples of semi-quantitative FFQs used to assess intake include the EPIC Study (Bingham & Day 1997; Ocke et al. 1997) and the Cancer Council Victoria Melbourne Collaborative Cohort Study (Hodge et al. 2000). Similarly to 24Rs, the use of FFQs is associated with low subject burden, and data are generally easy to collect and analyse (Gibson 2005). In comparison to other dietary assessment methods, a fundamental difference exists with the FFQ, as this method uses a closed question approach via a finite list of foods. Therefore the inclusion of appropriate foods is essential, with FFQs designed to assess usual intake typically comprised of >100 items and additional questions relating the portion size of some or all items (Thompson 2008). The reference timeframe used can vary from months to years, which can also impact of the ability of an individual to recall frequency accurately. As such this method is not suitable to obtain accurate assessments of past diet or change in individual absolute nutrient intakes (Sempos 1992; Bingham et al. 1994; Schaefer et al. 2000; Cade et al. 2002) Food Record The food record (FR) is a prospective method of recording intake with the subject required to document the description and quantity of the food item eaten at the time of consumption. The weighed food record (WFR) requires the individual to weigh all food items using household scales, while the estimated food record (EFR) require the weight of food items to be estimated indirectly either using models, pictures or without any aid, or measured using household utensils (Thompson 2008). Compared to the three methods described earlier, which follow a retrospective protocol, the prospective nature of the record removes reliance on the highly variable characteristic of subject recall. Chapter 2: Literature Review 47

50 The requirement to weigh all items prior to consumption results in a more precise measure for estimating usual food and nutrient intakes of individuals (Gibson 2005), however it is regarded as the most burdensome for subjects due to the need to measure quantities consumed (Gibson 2005; Thompson 2008). Gersovitz (1978) was one of the first to caution about the decline in accuracy in estimates of intake when food records are used over periods greater than four days (Gersovitz et al. 1978). In comparison, the task of keeping an EFR is considered less onerous on the subject (Bingham 1987). Due to high potential for the subject to change intake to simplify the recording process, the number and sequencing of recording days is of particular importance and must be considered when using this method to ensure that sufficient data are collected to account for both between- and within-person variation observed in intake. Failure to sufficiently sample both week and weekend days is likely to result in an inaccurate estimate of usual intake (Tarasuk & Beaton 1992). At the group level the number of recording days needed when using the prospective methods differs significantly depending on the nutrient of interest, with those nutrients present in a fewer number of foods sources, which may be consumed less frequently, typically requiring more days of collection, for example protein requires at least 4 days whereas vitamin A required 41 days (Basiotis et al. 1989). Even with these limitations the FR method is considered to be the most precise technique available for estimating usual food and nutrient intakes of individuals (Gibson 2005). For example, the British National Diet and Nutrition Surveys used 7- day WFRs until 2001 (Henderson 2002; Henderson 2003; Hoare 2004), where 4- day EFRs were adopted to reduce subject burden (Bates 2010). Recent work by Fife (2010), found that 3-day WFRs produce comparable measures of habitual 7- day intake (Fyfe et al. 2010), suggesting that shorter recording periods can produce accurate estimates of intake. Traditionally this method requires strong literacy and numeracy skills as subjects are required to identify and quantify items in detail. In an attempt to lessen the reliance on cognitive skills, methods incorporating video recorders (Brown et al. 1990), tape recorders (Bingham 1987; Lindquist et al. 2000), electronic scales with inbuilt food database (Kretsch & Fong 1990), smart card technology (Lambert et al. 2005), and personal digital assistants (PDA) (Beasley et al. 2005; Yon et al. 2006; Fukuo et al. 2009; McClung et al. 2009) have been trialled. In addition, portable devices Chapter 2: Literature Review 48

51 such as mobile phones and PDAs are favoured over traditional paper-based diaries commonly used to self-monitor intake with recent application for the management of weight loss (Yon et al. 2007; Burke et al. 2011) and renal disease (Welch et al. 2010), and the support of general healthy eating behaviours (Glanz et al. 2006; Atienza et al. 2008). The advantages of the prospective method for measuring diet ensure that the food record is often preferred for self-monitoring and assessment of usual intake at an individual level (Nelson & Bingham 1997) Photographic records PhRs have been broadly categorised as an extension of the traditional prospective written record method (Thompson 2008), or similar to direct observation methods (Ngo et al. 2009). The PhR method still requires the documentation of food intake at the time of eating in a prospective manner where the dietitian is now responsible for quantifying intake via the observation of the content of the PhR. However, the two methods differ in both the source and type of error associated with the quantification of intake. In the context of a PhR, the source of this error has now shifted from the individual measuring their diet to the dietitian and the type of error is now related exclusively to portion size estimation, and as such PhRs are a novel approach to the measurement of nutrient intake. Figures 2-4 and 2-5 illustrate the factors and tasks involved in the collection of dietary data using either WFRs or EFRs and PhRs, and the shift in the responsibility as result of changes in methodology of the PhR. The validity and reproducibility of the PhR and other dietary assessment methods relevant to this thesis are discussed in Section 2.6. The use of a photographic record (PhR) to document absolute nutrient intake was first examined in the early 1980s by two Welsh researchers who developed and trialled a protocol for the estimation of individual intake via photographic slides (Elwood & Bird 1983). Subjects were instructed to record all foods consumed using a film camera, with descriptions of the items written in a designated notebook. To obtain an estimate of nutrient intake the PhR of each subject was then coded and quantified by a trained nutritionist using a collection of reference photographic slides of foods of known weight (Elwood & Bird 1983). Chapter 2: Literature Review 49

52 Data collection Observation of behaviour Cognitive skills (literacy, numeracy) Quantifying consumption: o Estimating/ Measuring (e.g. weighing) Burden Motivation/ compliance with recording protocol Demographic, anthropometric, psychological and/or behavioural characteristics. Data collection Clarification of record Individual (who s diet is measured) Factors in the assessment of intake Dietitian Measure of nutrient intake Under-reporting Over-reporting Figure 2-4: Data collection in weighed or estimated food records. Factors associated with the collection of dietary data using these methods are listed for both the individual and dietitian. These factors are greater in number for the individual compared to the dietitian, and as a result, this increase in burden associated with quantifying consumption is proposed as a cause for mis-reporting of nutrient intake. The coding of dietary records and the food composition database used in the analysis will also affect the measure of nutrient intake obtained (Figure 2-3). Data collection Observation of behaviour Burden Motivation/ compliance with recording protocol Demographic, anthropometric, psychological and/or behavioural characteristics. Data collection Clarification of record Burden Quantifying consumption: o Portion size estimation Individual (who s diet is measured) Factors in the assessment of intake? Measure of nutrient intake Dietitian Under-reporting Over-reporting Figure 2-5: Data collection in photographic records. Transfer of the quantifying consumption task from the individual to the dietitian results in a shift in the error source, with the type of error now exclusively related to portion size estimation. The burden of collecting dietary data is also reduced for the individual, while some burden is shifted to the dietitian. It is proposed that this change in methodology will result in a more accurate measure of nutrient intake, however this is yet to be determined using objective methods. Furthermore, it is likely that the task of quantifying consumption by the dietitian will occur as part of the data analysis stage during the coding of dietary records and input into a food composition database (not shown here). Chapter 2: Literature Review 50

53 Despite this earlier investigation it is only more recently, due to advances in the capabilities of portable camera technologies, that research into this novel dietary method has gained momentum. These studies have used various devices to capture PhRs, including disposable or digital cameras, and personal digital assistants (PDAs) and mobile phones with camera function. Table 2-2 provides an overview of PhR methods used to exclusively assess individual nutrient intake. Some have used dietitians or similar trained investigators to manually quantify portion sizes of food items present in the image and then convert this data into an independent estimate of nutrient intake (Wang et al. 2002; Williamson et al. 2003; Williamson et al. 2004; Wang et al. 2006; Kikunaga et al. 2007; Martin et al. 2007; Swanson 2008; Higgins et al. 2009; Martin et al. 2009; Dahl Lassen et al. 2010). In comparison, others have investigated devices and techniques for the automated analysis of PhRs (Sevenhuysen et al. 1990; Zhu et al. 2008; Boushey et al. 2009; Mariappan et al. 2009; Six et al. 2010; Sun et al. 2010; Weiss et al. 2010; Woo 2010; Kong & Tan 2012). Evidence from the published studies using PhRs to exclusively assess nutrient intake raises a number of issues relating to the application of these techniques in a dietetic practice setting. Firstly, reporting on the useability and acceptability of this novel method has been restricted to studies involving predominately adolescents and young adults. For example, Wang et al (2006) investigated the use of a PDA method with a WFR and 24R in college students, finding that 57.1% of subjects reported the PDA method to be the least burdensome. Similarly, Higgins et al (2009) reported a strong preference among adolescents and their parents. In this study of 28 healthy aged adolescents aged years, 96% (95% CI %) of subjects and 100% (95% CI %) of parents reported that the PhR was quicker to complete than the written food record. In addition, for future use of the PhR, preference was for the novel method compared to the traditional method in 86% (95% CI 67-96%) of adolescents and 70% (95% CI 62-97%) (Higgins et al. 2009). Two studies have also found this method (when used alone) to be well received among healthy younger adults (mean age years) (Martin et al. 2009; Dahl Lassen et al. 2010). Although the preference for this method to assess diet in these age groups of healthy individuals is encouraging, no studies have used PhRs in older adults with a chronic disease, such as T2DM. Chapter 2: Literature Review 51

54 Table 2-2: Characteristics of photographic record methods used to assess nutrient intake. Name of PhR method (Studies which have used the PhR Method of collection of PhR method or have described its development) Photographic method of diet evaluation (Bird & Elwood 1983; Elwood & Bird 1983) Wellnavi (Wang et al. 2002; Wang et al. 2006; Kikunaga et al. 2007) Digital photography method (Williamson et al. 2002; Williamson et al. 2003; Williamson et al. 2004; Martin et al. 2007; Swanson 2008) Aoki e t al. (2006) Higgins et al (2009) Remote food photography method (Martin et al. 2009) Digital method (Dahl Lassen et al. 2010) Technology Assisted Dietary Assessment (Zhu et al. 2008; Boushey et al. 2009; Mariappan et al. 2009; Six et al. 2010; Woo 2010) Food Intake Visual and voice Recognizer (Weiss et al. 2010), Sun et al. (2010) DietCam (Kong & Tan 2012) Stand-alone camera (film) + written record book (for description). distance string attached to camera to standardise distance photograph captured. Free-living subjects collected PhR of own intake. PDA with camera function (digital), description written using stylus onto photo food displayed on screen. Reference object: stylus was placed next to food items. Free-living subjects collected PhR of own intake; PhR transferred automatically for analysis. Stand-alone camera (digital). Investigator collected PhR of subject s intake (self-served). No reference object described. Multi recording devices may have been used (protocol not standardised). Reference object: eating utensils (i.e. chopsticks) for test phase + no reference object/standardized recording protocol described for other phases. Free-living subjects collected PhR of own intake; subject uploaded PhRs for analysis. Stand-alone camera (disposable, film). Subject (child) and/or their parents collected PhR of child s intake (pre-prepared food items provided and eaten in free-living environment). No reference object described. Mobile phone with camera function (digital) + prompts to remind subject to record + foods consumed but not captured using PhR collected using written record or voice record. Telescopic pen used to standardize distance photograph captured. Subject collected PhR of own intake (pre-prepared food items provided and eaten in controlled/freeliving environments); PhR transferred automatically for analysis. Stand-alone camera (digital) + written record book (for recipes only). Reference object: ruler was placed next to food items Free-living subjects collected PhR of own intake (evening meal only). Mobile phone with camera function (prototypes tested). Early prototypes tested required the subject to identify food items in PhR by writing on screen or confirm food items identified. Reference object: fiduciary marker. Intended for free-living subjects to collect PhR of own intake; PhR transferred automatically for analysis. Mobile phone with camera (digital) function + voice recorded on phone used to clarify items unable to be automatically identified in PhR (prototype tested). Reference object: fiduciary marker. Intended for free-living subjects to collect PhR of own intake; PhR transferred automatically for analysis. Wearable camera (digital video) (prototype tested). Reference objects: measured serving vessels + checkered tablecloth (at home) and lights projected into the field of view (outside home). Automated PhR collection of free-living subject s intake; PhR transferred automatically for analysis. Mobile phone with camera function (prototype tested). Reference object: credit card (camera calibrated prior to use). Intended for free-living subjects to collect PhR of own intake; PhR transferred automatically for analysis. Abbreviations: PhR=photographic record, PDA=personal digital assistant; name of method not stated; method currently still in development Chapter 2: Literature Review 52 Method of analysis of PhR Manual nutritionist reference photographs (of 650 foods). Manual dietitian only. Manual dietitian/trained investigator + reference photographs (cafeteria foods only). Manual dietitian & student dietitian only + quality assurance function (food photographs of known weight randomly estimated). Manual dietitian only. Manual dietitian reference photographs. Manual trained investigator + 61 reference photographs. Automatic image analysis algorithms to identify and quantify food items contained in PhR. Automatic image analysis algorithms to identify and quantify food items contained in PhR. Manual identification of food items by dietitian/investigator only + Automatic for the quantification of food items (using algorithms). Automatic image analysis algorithms to identify and quantify food items contained in PhR.

55 A second limitation of some PhRs tested is that the user is required to identify the photographed items by writing the name of the food (Elwood & Bird 1983) or recipe information (Dahl Lassen et al. 2010) in a paper-based record or on the display of the device (Wang et al. 2002; Wang et al. 2006; Kikunaga et al. 2007; Boushey et al. 2009). The practicality of these approaches may be restricted in certain groups who may have limited dexterity, technical knowledge and/or literacy skills. Voice recorders have been used in the past to reduce subject burden associated with maintaining a written record in adults (Black et al. 1995; Black et al. 1997; Black et al. 2000d) and children (Lindquist et al. 2000). Two recent methods have utilised a voice record as a mode of secondary data collection to clarify some food items contained within a mobile phone PhR which were unable to be identified using automated techniques (Weiss et al. 2010) and to collect information on food items consumed but not captured with the PhR (Martin et al. 2009). Additional approaches are warranted that further simplify the recording process, by combining the camera function of the mobile phone with the in-built voice recorder to collect a photo/voice record of all foods consumed. Furthermore, mobile phone devices can provide additional functions not available on stand-alone cameras, such as automatic data transfer, which could also reduce subject burden as the PhR can be transferred immediately after collection. Finally, of the studies to-date involving the manual quantification of PhR, the protocol used by the dietitian or other trained investigator to estimate the portion size of food items present in the PhR is either not described or is unclear, or has not been evaluated in a free-living situation. For example, the methods adopted by Wang et al (2002; 2006) and Kikunaga et al (2007), relied on the expertise of the dietitians to quantify items contained within records. Aoki et al. (2006) used a number of photographs containing items in known quantities as a quality control mechanism for the dietitians estimation of quantities contained in the PhR. The remaining studies utilised a database of reference photographs in known amounts to quantify intake from PhRs under-controlled conditions, such as school (Martin et al. 2007; Swanson 2008), university (Williamson et al. 2003; Williamson et al. 2004) or military cafeterias (Williamson et al. 2002), pre-prepared foods eaten in the natural environment (Higgins et al. 2009; Martin et al. 2009), or only investigating selected meal occasions (Dahl Lassen et al. 2010). Only the study by Bird and Elwood (1983) has tested the combination of a standardised analysis protocol and reference photograph database in the analysis of the PhR to derive an estimate of average Chapter 2: Literature Review 53

56 nutrient intake in subjects living independently (Bird & Elwood 1983). Further examination of the PhR method using standardised analysis protocols and tools is needed in free-living environments. Approaches involving the automatic quantification of portion size of food items contained in a PhR have also been assessed. Sevenhuysen et al (1990) were the first to examine the feasibility of image analysis techniques for the quantification of portion size of dietary items contained in a photograph obtained in a free-living situation. The group reported moderate success of this technique, with 23 of 49 food items estimated within 10% of actual weight (Sevenhuysen et al. 1990). More recently this concept has been extended through employing more sophisticated equipment and algorithms to estimate the weight of selected food models and single food items (Zhu et al. 2008; Mariappan et al. 2009; Sun et al. 2010; Weiss et al. 2010; Woo 2010; Kong & Tan 2012). Image analysis techniques automatically identify and quantify items based on texture, colour and shape and may not account for differences in preparation, cooking and presentation methods often observed in the dietary records of individuals. It has been suggested that the quality of an artificial intelligence approach to the analysis of a PhR would be inferior to the analysis performed a dietitian (Aoki et al. 2006), however this has not been confirmed. Therefore, while the development of automated techniques for the analysis of PhDRs continues to progress, standardised and sustainable approaches and tools which are validated for the manual analysis of these records by the dietitian are also needed. The lack of a standardised analysis protocol for the PhRs is exacerbated by a deficit in evidence examining the error surrounding the manual quantification of items contained in the record. While dietitians are experts in food and nutrition, potential exists for the introduction of error associated with the cognitive task of portion size estimation, similar to that which is well documented when individuals are asked to quantify food portions present in reality or recall intake (Nelson et al. 1996; Nelson et al. 1994). Examination into the level of error associated with the portion size estimation of food items contained in a PhR is necessary to identify whether this task may be a potential source of error in the assessment of nutrient intake derived from this method. Chapter 2: Literature Review 54

57 Relevance to research program Section 2.4 detailed the distinctive features of both traditional and novel methods currently used to elicit estimates of dietary intake. This section highlighted that the choice of method is dependent on a number of factors such as study objectives, subjects and resources. By detailing the relative strengths and weaknesses of each method, this section identifies a need for further exploration of novel approaches which aim to reduce the limitations of traditional methods. Prospective assessment methods are preferred when quantifying individual nutrient intakes allowing for information to be recorded at the time of consumption and accounting for daily variation. When written WFRs or EFRs are used, this approach is often difficult to maintain long-term and results in deviation from usual intakes. In contrast, less burdensome real-time approaches, such as PhRs offer a promising alternative to traditional prospective methods. The use of this novel approach in the measurement of nutrient intake is an emerging dietary methodology. The useability and acceptability of PhRs in older adults with a chronic disease has not been established, and a lack of standardised analysis techniques for measurement in a free-living environment is common. Exploration into the error associated with the estimation of food portions contained in the photographic record is necessary to establish the effect of this error on overall nutrient intake. 2.5 Portion size estimation in the assessment of dietary intake As illustrated in Figures 2-4 and 2-5, the use of a photographic record (PhR) differs to traditional prospective dietary assessment methods as the dietitian/investigator is now responsible for the quantification of nutrient intake through an estimation of the food portions presented in the images of the record. Therefore, this section explores the concept of portion size estimation and its relationship to both traditional and novel dietary assessment methods The cognitive process involved in estimation of portion size The ability to accurately estimate the portion size of food items consumed has been identified as a major contributor to error in the assessment of dietary intake involving both prospective and retrospective traditional methods (Bingham 1987; Gibson 2005). Portion size estimation aids (PSEAs) attempt to minimise errors associated with the recall of portions of foods consumed or the quantification of food items present in reality. PSEAs are classified as either two-dimensional (e.g. food photographs, graphics and drawings of generic shapes, foods and measuring Chapter 2: Literature Review 55

58 utensils) or three-dimensional (e.g. food samples, real and replica food models, measuring utensils) (Cypel et al. 1997). Early work found that the absence of a PSEA at the time of estimation greatly affected the error, with foods over-estimated by more than 51% of the actual weight served (Guthrie 1984), while the use of PSEAs in the form of photographs and food models minimised between subject variation in error compared to household measured (CV 16-53% and 10-27%; respectively) (Rutishauser 1982). Nelson and colleagues (1994; 1996) first characterised the complex cognitive process that occurs when a PSEA in the form of a photograph is used to estimate portion size through a series of elegant experiments. This process consists of three components: perception, conceptualisation and memory. Perception requires an individual to relate an amount of food present in reality to an amount in a photograph (or other PSEA) in order to estimate. Conceptualisation requires the individual to form a mental image of an amount of food not present in reality, and then relate this construct to a photograph of food (or other PSEA). Memory influences the precision of conceptualisation (Nelson et al. 1994). Past use in traditional dietary assessment methods have shown that the level of error present in the estimation of portion size is dependent on the cognitive skills employed, with quantification tasks involving conceptualisation and memory often more inaccurate than when perception is used alone (Nelson et al. 1996; Nelson et al. 1994). Traditional retrospective methods are associated with the skills of conceptualisation and memory. The cognitive process associated with portion size estimation is not exclusive to traditional dietary assessment methods, as these skills are also relevant to the task of quantifying foods contained in PhRs of dietary intake. In Figure 2-6 generic shapes are used to illustrate the cognitive skills used in the estimation food portion sizes in both settings. An important similarity exists with the EFR and the PhR as both the food item to be estimated and the PSEA are present, with the quantification task is dependent on perception alone (Figure 2-6a). A key difference exists between methods during conceptualisation depending with either the food item to be estimated or the PSEA the absent during the quantification task. In the context of traditional retrospective methods (i.e. 24R, DH, FFQ), where quantities of foods are recalled, the PSEA is present while the item to be estimated (either viewed or consumed in the past) must first be visualised in order to be estimated. In contrast, for prospective methods, such as the PhR (and EFR), the item to be estimated is Chapter 2: Literature Review 56

59 present at all times, and therefore in the absence of PSEAs, both conceptualisation and memory must be used to first visualise a suitable reference object in order to estimate the portion size of the item in the photograph (Figure 2-6b). Reliance on the skills of conceptualisation and memory during the quantification of PhRs can be removed by the use of PSEAs. Despite key similarities and differences with portion size estimation used in traditional dietary assessment methods, exploration of the cognitive processes employed and the type of PSEAs needed to quantify items contained in PhRs has been limited. a) Key Similarity Perception Presence of item to be estimated + PSEA Item to be estimated + PSEA =? Estimation PhR and EFR b) Key Difference Conceptualisation and Memory Absence of item to be estimated Absence of PSEA PSEA + Visualisation = of item to be estimated? Estimation Item to be estimated Visualisation + = of reference item? Estimation DH, 24R or FFQ PhR or EFR Figure 2-6: Comparison of the cognitive skills used in portion size estimation between traditional and novel dietary assessment methods: a) for prospective methods of estimated food records (EFRs) and photographic records (PhRs) the skill of perception is similar in that both the food item to be estimated and the portion size estimation aid (PSEA) are present; b) conceptualisation and memory are used in all methods if either the item to be estimated or the PSEA are absent. A key difference exists between retrospective and prospective methods. In the former (i.e. diet history (DH), 24-hour recall (24R), or food frequency questionnaire (FFQ)) the item to estimate is not present, therefore it must be visualised in order to be quantified. In contrast for the latter situation (i.e. photographic records (PhR) or estimated food records (EFRs)), the PSEA is absent, thus a suitable reference or standard must first be visualised before the portion size of the item can be estimated. Chapter 2: Literature Review 57

60 2.5.2 The effect of photographic PSEAs on portion estimation error In order to gain an understanding of the level of error associated with the cognitive task of portion size estimation, a review of the effect of the use of photographic PSEAs in traditional dietary assessment methods was undertaken. Investigations involving the use of photographs to assist in portion size estimation are often difficult to compare due to heterogeneity in the design of the studies, number and characteristics of test foods, the type of photographic PSEAs used, and the statistical methods used to report the results. Hernández et al (2006) has suggested that when estimation error is expressed as an average the true effect of the variability present may be masked. However, use of this descriptor is still the most common approach to the reporting of error in this setting. Further there is no consensus on an acceptable level of accuracy in the estimation of portion size. Venter et al (2000) suggested that an accurate estimation of portion size conducted in real-time was within ±10% of the known weight of the item, while Godwin and colleagues (2004) used a benchmark of ±20% to evaluate the ability of subjects to accurately recall the portion size of amounts of foods eaten during the previous day. Tables 2-3 and 2-4 summarise previous studies in adults involving the quantification of a minimum of six food items using PSEAs in the form of photographs. Where data is available, results are expressed as either gram/volume or percentage difference error. As the cognitive skills associated with portion size estimation can be isolated and studied independently, investigations examining perception and conceptualisation (including memory) are presented separately. Error associated with estimating the portion size of foods in real time (i.e. perception) This review identified five studies which have investigated the skill of perception using food-specific photographic PSEAs to estimate quantities of between 6 to 45 food items in real-time (Table 2-3). Nelson et al (1994) studied the errors associated with the perception of portion size by estimating the quantities of foods with the assistance of a series of reference photographs in real time. Error for foods estimated with the assistance of 8 photographs of graduated portion size resulted in a greater accuracy compared to a single photograph (-4% to +5% vs. -23% to +9%; respectively), indicating that the presence of a series of images may assist in the perceptual task of quantifying foods present in reality (Nelson et al. 1994). Hernández and colleagues (2006) also used a single reference photograph as the PSEA, however the overall error was less for the 12 foods tested (4.8±1.8%) Chapter 2: Literature Review 58

61 compared to the work be Nelson and colleagues (1994). Other studies have also examined the ability of adults to estimate portion size of a larger number of food items in real-time with the aid of reference photographs. Lucas et al (1995) found moderate error in a sample of French women with half of portion size estimates within ±10% of actual weight. Similarly, Ovaskainen et al (2008) reported ~50% of both males and females residing in Finland correctly estimated the quantities of the test foods. In contrast, Venter et al (2000) reported ~70% of estimates were within ±10% of actual weight among African women and men. The larger number of test items and sample sizes, in addition to a greater diversity in the characteristics of the food tested may have contributed to the larger error observed in the first two studies compared to the third. Error associated with estimating the portion size of recalled foods (i.e. conceptualisation and memory) Five studies investigated the error associated with the cognitive skills of conceptualisation and memory for 9-27 food items (Table 2-4). Nelson et al (1996) reported large variations in estimations across the 22 foods examined, with error ranging from -28.4±22.6% (baked beans) to 242±322% (butter/margarine on cracker). The overall error was 32.0±111%, or when fat-spreads were excluded 10.8±56.2%. Smaller variations in error were reported by Faggiano et al (1992) (- 50% to +89%) for 17 food items, and Frobisher and Maxwell (2003) (-11% to +73%) for 9 foods. Subar et al (2010) also found low accuracy in the estimation of foods with <15% of all estimates within ±10%. The type, presentation, size and number of the photos did not significantly affect the overall error, although there were trends towards improved accuracy with particular formats within different types of food (Subar et al. 2010). Despite large variability in estimates across all studies this error did not translate to error in the nutrient profile of the foods with the majority of nutrients estimated within 7-10% at the group level (Nelson et al. 1996; Robson & Livingstone 2000). Three studies have compared the error across all cognitive skills employed in the estimation of portion size, with error greater when conceptualisation and memory are used compared to perception alone. Nelson and colleagues (1996) compared the level of error observed for conceptualisation to their earlier perception study for six identical food items and reported a smaller level of error for estimates made with the assistance of PSEAs. Chapter 2: Literature Review 59

62 Table 2-3: Studies investigating error associated with estimating the portion size of foods in real time (i.e. perception). First author (year) Sample / study setting Foods Tested PSEAs Methods Results/Conclusions Nelson (1994) 27F: 24M, aged 18-90yrs. UK Lucas (1995) 270F, aged yrs. France Venter (2000) 130F, 39M; (36±16.5yrs) South Africa Hernández (2006) Ovaskainen (2008) 61F, 40M; mean ~37 yrs. USA 101F, 45M; aged 25-64yrs. Finland Six portion sizes (2 sml, 2 med, 2 lge) of 6 foods (mashed potato, boiled potato, quiche, cornflakes, spaghetti, & sliced meat). No liquids 45 foods common to the French diet. Three portions (sml, med, lge) of each food. Only one liquid item (soup) 20 foods common to diets, 3-4 portions of each food foods shown to each group; Real-time: 12 foods (5 solid, 4 amorphous, 3 liquid) Recall: 3 foods from real-time study(one from each food type) 52 food items, 1 randomly selected portion size (a) reference photographs series of 8 portions of test foods, or (b) average (median) portion size photograph. Both sets of photos in colour, and black & white versions. colour photographs of all test food portions colour photographs of 3-4 portions each food + serving vessels reference photographs either (a) computerbased or (b) lifesized printed copies. colour photographs of each food, multiple portions. 2x2 sessions Estimation of portions of real food Estimations made using both sets of PSEAs (both versions); order randomised Estimations made on visual analogue scale with the PSEA representing points and converted to weights. 1 session Estimation of portions of real food Estimations made on 7- category scale and converted to estimated weight, then % error 1 session Estimations of portion size of real foods Estimations made in relation to PSEA: subjects indicated if portion was <, =, or > than photo 1 session Estimations in real-time and short-term recall, where food was eaten and then estimated. Estimations made in relation to PSEA: fraction, percentage or proportion Estimations converted to weight 2 sessions 85% of served foods had identical weight to PSEA Estimations made in relation to PSEA: fraction, percentage or proportion Estimations converted to 7284 assessments of portion size in relation to food photographs. For all foods error = the average photo (-34 to -1 g; -23 to + 9%) vs. series of 8 photos (-8 to + 6 g; -4 to +5%). 71% of assessments using the series of 8 photos were within 50 g of the actual weight compared to 49% of assessments using the average photo. Under-estimated rather than over-estimated, particularly when using the average photo; portion sizes estimated via the series of 8 photos were more accurately estimated. Greater error for lge portions vs.sml portions Gender, age, BMI potential confounders. ~50% (of portions) ±10%, 83% within ±25% 22 foods over- & under-estimated: Sml=+0-60%; Lge = -1-37%.; med portion. 11 foods under-estimated: med= %; lge=-10-35%; sml=<-26%. 9 foods over-estimated: med=+14-58%; sml=+19-74%; lge=<+10% 68% (of 2959 portions) within ±10% of actual wt; 15% over-estimated; 16% under-estimated. Solid foods more accurately estimated than amorphous foods. Sml over-estimated; lge under-estimated Gender, age education had no effect Foods more frequently eaten had less error compared to foods eaten less often. Real-time: Real-time error (mean± SE): overall=4.8±1.8%; solid=8.3± 2.3%; liquid=19±5%; amorphous =-10±2.7%. Portion estimates using computer-based anchors had less mean (±SD) error (5.2±45.5%) vs. life-sized photos (67.1±132.2%) Less error in estimates made in real-time vs. recall for solid (cookie) & liquid (beverage), but not amorphous (potato chips). Foods within food type (i.e. solid, liquid, amorphous) were both over- and underestimated. Type of PSEA used had no effect on error. Sml portions over-estimated, lge portions under-estimated estimations Overall error: M=-10±62g; F=+1±64g Correct estimations 51% men, 49% women. Under-estimation for bread, spread and cold cuts, mixed dishes both genders. Over-estimation in cereals for both genders, and snacks, vegetables and fruit in women. Sml, med portions less error than lge sml, med most over-estimated error weight Abbreviations: M=male; F=female; Sml=small; med=medium; lge=large; PSEAs=portion size estimation aids; weight or volume error= (estimated quantity actual quantity); % error= ([estimated quantity actual quantity]/actual quantity*100) Chapter 2: Literature Review 60

63 Table 2-4: Studies investigating error associated with estimating the portion size of recalled foods (i.e. conceptualisation & memory). First author (year) Sample / study setting Foods Tested PSEAs Methods Results/Conclusions Faggiano (1992) 52F, 51M; aged yrs Italy Nelson (1996) 62F, 74M; aged 18-90years. UK Robson & Livingstone (2000) ^Frobisher & Maxwell (2003) 15F, 15M; aged yrs. UK N=47 (proportions of F & M not specified); age 42 yrs. UK 17 foods 22 commonly eaten foods; excluded foods which could be estimated in household measures 2 menus (b fast, lunch, dinner) B fast meal same on both days 16 foods (Day 1), 17 food (Day 2) 9 food items, no liquids photographs of 23 foods in 7 portions a series of colour photographs of 8 portions of test foods colour photographs, single, average portion 78 foods/meals in 8 portion sizes 1 session Self-served portions at dinner Weighed before and after eating The following estimated portions consumed at dinner previous day Estimations made in relation to PSEA: Self-served portions of 4-6 foods at one meal (b fast, lunch or dinner) Served portions weighed Within 5mins subject estimated using PSEA Estimations made on visual analogue scale (VAS) with the PSEA representing points and converted to weights. Effect of error on nutrient profile examined Comparison of error with 6 foods in Nelson (1994) 2 non-consecutive days Self-served b fast, lunch, dinner Food served weighed before and after eating Day after portions eaten estimated using fractions/multiples of PSEA Above repeated on Day 2 Self-served portions of foods, which were weighed prior to eating Estimated portions served twice: immediately after serving foods and 3-4 days later. Estimations based on selecting portion from PSEA, converted to weight Overall error ranged from -50% to +89% Over-estimation (n=9): +3% to +89% Under-estimations (n=8):-0.6% to -50% 6 foods estimated within ±10% Sml portions over-est.; lge portions under-est. 602 estimates Large variation in error ranging from -28% (baked beans) to +242% for fat-spreads. Overall error: 32.0±111.3%; 10.8± 56.2% with fat-spreads removed Men over-estimated compared to women Energy content was within ±7.0±27.8% (all foods), 1.6±24.8% fatspreads removed. Error: perception vs. conceptualization = 0.8±28.1% vs. 4.0±36.1% Sml portions over-estimated and lge under-estimated. Gender, age, BMI potential confounders Overall error ranged from -29% to +26% Day 1: 10 foods over-est. (+0.4% to +37.4%); 6 under-est. (-64% to -23.3%) Day 2: 7 food over-est. (+2.5% to +25.8%); 10 food under-est. (- 2.7% to -28.6%) Most nutrients estimated to within ±10% on both days. Large variation within food s at the group level and for overall error within and between subjects Median error immediately after serving ranged -11 to 73%, and -13 to 77% 3-4 days later. Across foods 0-44% of adults estimated within ±10% of actual weight, 42-93% within ±50% of actual weight. Trend towards over-estimation Greater error for some amorphous foods Subar (2010) N=49; 50%M; aged yrs; Study 1 n=29. USA Study 1: 27 foods Study 2: 22 foods (from Study 1) Computer-based photographs of foods served in different portion sizes. Screen layout and type of photos differed 2 studies, both over 2 con days Self-served portions, then weighed Following day subjects estimated foods consumed the previous day. Estimations made in relation to PSEA: subjects indicated if portion was <, =, or > than photo. Screen layouts of PSEA were randomised across both studies. In general, type and presentation of photos, and size and number of photos did not significantly affect error Collectively low accuracy across all food types across in both studies with 10-23% of all estimates were within ±10% of actual weight. Preference for few numbers of simultaneous and larger photos. Abbreviations: M=male; F=female; Sml=small; med=medium; lge=large; PSEAs=portion size estimation aids; weight or volume error= (estimated quantity actual quantity); % error= ([estimated quantity actual quantity]/actual quantity*100); ^children also included in study, but findings for adults reported independently. Chapter 2: Literature Review 61

64 Another study examined the estimation error of two foods in 100 adults, aged years and found small improvements for estimates of cornflakes in real-time compared to recall (-2.1% vs. -3.3%), with larger improvements noted for mashed potato (7.7% vs. -17%) (Robinson et al. 1997). In contrast, Hernández et al (2006) found discrepancies in both conditions depending on the food item estimated, with reporting error greater for conceptualisation compared to perception for one food (potato chips, +57% vs. -7%), smaller for another (cookie, -14% vs. -30%), and unchanged for the third food (liquid in a foam cup, +33% vs. +35%). This review suggests that the use of photographic PSEAs is effective for improving error associated with the task of portion size estimation. The high degree of variability observed for portion size estimation error suggests that a large random error exists at the individual level. This finding is not unexpected given the diversity in the subject characteristics and food preferences, factors which have been proposed as possible explanations to the inter-individual differences observed (Nelson et al. 1996; Nelson et al. 1994; Venter et al. 2000). Nelson & Bingham (1997) suggest that variation for portion size estimation in the context of dietary assessment is common, with differences close to ±50% for individuals and foods, and ±20% for nutrients to be expected. Food type and serving size Based on physical characteristics, foods can be categorised as amorphous, liquid or solid. Amorphous foods are semi-solid and tend to mound if served on a flat surface or take the form of the vessel in which they are served (Slawson & Eck 1997). Examples of amorphous foods include diced vegetables, baked beans and pasta. Compared to solid and liquid foods, amorphous items are more difficult to estimate due to difficulties in depth perception (Robinson et al. 1997). Variability in the accuracy of foods within these categories is also commonly reported (Tables 2-3 and 2-4). Some food items do not easily conform to the above food type categories. Spreads (i.e. butter, margarine) have been identified as a food source particularly prone to large variation in estimates of portion size, with errors of +85.8% to +242% of actual weight (Nelson et al. 1996; De Keyzer et al. 2011). Although this translated to a relatively small difference in absolute weight of +4.6 g to g, this error could results in over-estimations in the energy content in the range of ~170kJ to 625kJ. Chapter 2: Literature Review 62

65 Previous research involving portion size estimation suggests a flat-slope syndrome occurs where small portions tend to be over-estimated and large portions underestimated (Young & Nestle 1995), and can introduce systematic bias into the measure (Flegal 1999). All studies in this current review involving perception and conceptualisation reported this phenomenon, except for one investigation (Robson & Livingstone 2000). Other studies examining portion size estimation without the assistance of PSEAs (Harnack et al. 2004) or when using a variety of different types of aids (Chambers et al. 2000) have reported similar findings. Type of PSEA The evidence presented thus far has focused on the use of PSEAs in the form of reference food photographs to assist in the quantification of food portions present both in reality or consumed in the past. Although evidence supports the use of photographic PSEAs to assist in the quantification of food quantities, three investigations have compared the effect of the type of aid (i.e. two-dimensional vs. three-dimensional) on estimation error. In a sample of 204 older adults (aged yrs), Posner et al (1982) reported no significant difference in estimates of energy intake (i.e. -63 kj for men and 370 kj for women) derived from the 24R collected over the phone using the two-dimensional aid, compared to an in-person 24R with three-dimensional PSEAs (on age-matched controls from a large-scale national nutrition survey). A non-significant effect between types of PSEAs used in a 24R was later also supported by a study in a similar cohort (Godwin 2003). The third study (among 120 adults, aged years) investigated the effect of PSEA type on the accuracy of estimating quantities of 13 selected amorphous, liquid, and solid food items consumed hours prior and concluded that the type of PSEA did not significantly affect accuracy, with overall error across all foods and aids within ±20% (Godwin et al. 2004). Others have investigated the effect on estimation error for foods present and recalled using different types of two-dimensional aids. Hernández et al (2006) found no significant difference between estimates using two types of reference photographs in the form of computer-based anchors and life-sized print copies. Subar et al (2010) reported that various characteristics, such as image type and size, and method of presentation, of the photographic PSEAs used in the recall of foods eaten the previous day did not affect error. No study has investigated the effect of the type of PSEA on error associated with the quantification of PhR. Given the increased utilisation of this novel dietary assessment method exploration into Chapter 2: Literature Review 63

66 this area is necessary to ensure that the most suitable and practical aid is being used Quantification of photographic records performed by individuals trained in the nutrition and dietetics Similar to the estimation of food portions present or consumed in the past, when PhRs are used to quantify nutrient intake, an opportunity exists for the introduction of error into the measurement. Despite the increasing use of PhRs to measure intake, relatively little evidence exists on the error associated with performing this task among this group. Five studies have reported on findings investigating the accuracy associated with quantifying food items of known weight or volume contained in photographs (Table 2-5). Morgan et al (1982) reported high estimation (93% of items estimated correctly) for food items present in photographs, with the difference between estimated and actual weights ranging between g (boiled potato) to g (porridge). Similarly, Williamson et al (2003) found high agreement between real and photographed foods with the overall difference small but statistically significant (3.9±0.8g vs. 5.2±1.0g; p<0.05). In contrast, Porter et al (2006) reported high variability, with average estimation error ranged from 33.6 g to 462 g and mixed dishes exhibiting the greatest error. Both the studies by Morgan et al (1982) and Porter et al (2006) did not use PSEAs to assist in the estimation of portion size. The clinical implications of estimation error on the nutrient profile of PhR were reported in two studies. In the absence of PSEAs, Aoki et al (2006) found a high proportion of correct estimations for both energy (86.6±9.7%) and protein content (80.4±15.6%). The use of PSEAs significantly improved estimation accuracy with regard to energy content for food portions estimates made compared to when no PSEAs were used (-25 kj vs. -46 kj, respectively; p<0.05) (Martin et al. 2009). Such small differences in energy intake would be considered clinically insignificant, however given the lack of evidence in this area this conclusion should not be generalised. Chapter 2: Literature Review 64

67 Table 2-5: Studies investigating portion size estimation of photographic records by individuals trained in nutrition and dietetics. First author (year) Morgan (1982) Williamson et al (2003) Aoki et al (2006) Porter et al (2006) Martin et al (2009) Sample/ study setting 13 senior dietitians, Australia 6 dietitians, trained (3 DO vs. 3 PhR) USA, controlled setting (university cafeteria) 13 dietitians, Japan 28 undergraduate dietetic students, Australia 2 dietitians, USA Foods Tested PSEAs Methods Results/Conclusions 100 food items None Test food photographed and weighed. Displayed to dietitians who quantified 60 meals consisting of 10 portion sizes (453 food items). 44 food items contained in photographs 27 single food items and mixed dishes contained in photographs 31foods grouped into 16 meals (3.88 items/meal) DO-reference real food portions (standard serve) PhR- photos of reference food portions None None Database of reference photographs (n=2100) Weights of test and reference food portions measured and identical for DO and PhR. DO dietitians presented with real food items for both test and reference. PhR dietitians presented with photographed food items for both test and reference. Both groups estimated all foods in units of 10% (i.e. 90%, 100%, and 110%) of the reference portion. Food intake (g)= food selections (g) plate waste (g) Dietitians estimated the energy and protein content of test foods. Students estimated the weight of food portions in photographs. Dietitians estimated the weight of food portions in photographs using PSEA. 93% of food items estimated correctly. Error for 12 selected foods presented, ranging from g (boiled potato) to g (porridge). High inter-individual variation in estimates due to differences between dietitians and difficulties estimating certain foods. Mean(±SE) difference between estimated and actual weight food intake (direct visual vs. photo): overall (*3.9±0.8g vs. *5.2±1.0g); entree (*8.7±2.9g vs. *17.5±4.3g); starch (*4.5±1.1g vs. -1.2±1.1g); fruit/veg (*4.2±1.3g vs. *4.8±1.8g); dessert (*12.5±2.1g vs. 4.2±2.6g); beverage (*-12.0±2.8g vs. *7.6±3.1g); condiments (1.1±1.4g vs. *4.9±1.6g). Small differences in actual vs. estimated weights between direct visualisation and photographic records. Application to free-living situation unknown. Proportion of accurate estimates (energy vs. protein): 86.6±9.7% vs. 80.4±15.6%*** Energy intake easier to estimate compared to protein content. Absolute difference kj or g not presented. Lowest error for biscuits (33.6g), sliced turkey (37.9g) & pita bread (45.2g) Highest error for spaghetti with side salad & slice of bread (426.9g), pasta carbonara (352.9g), shrimp chips (326.9g). Group mean estimation error varied suggesting that perception difficulties may exist for some photographs. Overall Mean(±SE) energy intake (kj) (estimated vs. actual) No PSEA used = 360±36 vs. 406±50* PSEA used = 372± 46 vs. 406±50 Abbreviations: M=male; F=female; DO=direct observation; PhR=photographic record; PSEAs=portion size estimation aids; weight or volume error= (estimated quantity actual quantity); % error= ([estimated quantity actual quantity]/actual quantity*100) Chapter 2: Literature Review 65

68 Relevance to research program Section 2.5 explored the concept of portion size estimation, its use in traditional assessment methods and the key elements transferable to PhR for the assessment of nutrient intake. This review highlighted the variability present in error associated with the task of quantifying both food items present in reality and those in viewed or consumed in the past. Evidence on the effect of estimation error associated with foods contained within PhRs is limited. The ability to quantify accurately the portions of food items contained in PhRs is essential to both the reproducibility and validity of the method. Therefore, further investigation to determine the magnitude and direction of this error and its relationship to estimated nutrient intake is warranted. 2.6 Validity and reproducibility of dietary intake assessments Following the collection of dietary data and the calculation of nutrient intake, it is necessary to determine both the validity and reproducibility of the measure. Establishing the performance of a dietary assessment method in relation to these two parameters is critical for the interpretation of nutrient output and the conclusions formed. In addition, it provides an opportunity to identify and understand the relevant strengths and weaknesses of a method when applied in a given context. Therefore, an awareness of the potential sources of errors present throughout the process of assessing dietary intake (outlined in Sections ) is important for the interpretation of findings in relation to the performance of the measure. This section outlines the concepts of validity and reproducibility and reviews the literature with regard to the dietary assessment methods used in this thesis. Therefore, the performance of Australian semi-quantitative FFQs and dietary PhRs is summarised. Given the novelty of photographic records for measuring intake, in the absence of information regarding performance, the literature on written WFRs and EFRs is provided allowing for this new method to be placed in the context of other prospective dietary assessment methods Validity The validity of a dietary assessment method relates to the degree in which it measures what it intends to measure, and is therefore a crucial component when assessing the performance of a method (Nelson 1997). It is widely acknowledged in the assessment of usual intake that a gold standard measure does not exist, as each method contains error (Beaton 1994), therefore only the relative and criterion validity of a method can be established. Chapter 2: Literature Review 66

69 Relative validity Determining the relative validity of a method involves evaluating the level of agreement between a measure of intake obtained by the test or new method and an established method or reference method on the same subjects (Gibson 2005). Relative validity is useful for comparing the level of agreement between two dietary assessment methods, however correlation can be biased if error within the methods is in the same direction (Kaaks 1997). FFQs developed for use with Australian adults The closed nature of the FFQ ensures that the particular questionnaire used must be suitable for the purpose of the investigation and include the main food sources of the nutrient(s) of interest (Nelson & Bingham 1997). As a result, the validity of the FFQ will be dependent, in part, on the number of food items listed and the relevance of these items to the typical intakes of the population sampled (Cade et al. 2004). The application of the FFQ within this thesis was among a group of Australian adults, therefore this section compares the relative validity of the main semiquantitative questionnaires currently used in this setting to derive estimates of nutrient intake. Three FFQs are commonly used throughout Australia to measure nutrient intake, the Cancer Council of Victoria (CCV) FFQ, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) FFQ, and the Nambour FFQ. Table 2-6 summarises the studies which have validated these questionnaires through comparison to traditional dietary assessment methods. The relative validity of the CCV FFQ was initially determined in comparison with 7- day weighed food records in 63 women with the intakes of 19 of 27 nutrients within ±10% of both methods (Hodge et al. 2000). Originally designed to assess intake over the previous 12 months, the FFQ has since been evaluated over both shorter (4 weeks) (Xinying et al. 2004) and longer (10 years) time periods (Ambrosini et al. 2003a). Across the three studies, correlations between methods varied within nutrients with weak to strong relationships reported for energy (r= ), protein (r= ), fat (r= ), carbohydrate (r= ), and alcohol (r= ). The performance of the CSIRO FFQ in relation to prospective dietary assessment methods was examined in two studies (Ambrosini et al. 2003b; Lassale et al. 2009). Chapter 2: Literature Review 67

70 Table 2-6: Studies investigating the relative validity (energy and macronutrient intake) of three FFQs developed for use in Australian adults. First author (year) Sample characteristics Test FFQ Reference method Order & period between administration Mean Diff (test ref) Correlation between methods (Pearson s r or Spearman s rho) Hodge (2000) Ambrosini (2003a) Xinying (2004) 63F: 33.3±9.5yrs; weight/bmi=n/a 56F: 49±10.5 yrs; BMI=28±5.0kg/m2 37M: 53±8.3yrs; BMI=27±4.8 kg/m2 53M, 65F; 58±9 yrs; BMI=26.1±3.3 kg/m 2 CCV: 74 items/10 frequency categories/portions of 4 items used to derive portion factor score to estimate nutrient intake. Past 12m CCV: As for Hodge et al (2000) Past 10 yrs CCV: As for Hodge et al (2000) Past 4 wks 7-d WFR Ref Test Concurrent 28-d EFR (4x7-d over 12 mths) 2x 3-d WFR (2 wks apart) Ref Test 10 years Ref Test 4 weeks EI (MJ)=-0.4; P(g)= -1.1; F(g)=1.4; CHO(g)=-32.9; Alc= 2.1(g) EI (MJ)=0.1;P(g)= 2; F(g)= -2; CHO(g)=5; Alc= 2g) EI (MJ)= -0.3; P(g)= -0.1; F(g)=4.3; CHO(g)=-31.0***; Alc= -0.4(g) EI=0.25; P= 0.47; F=0.55; CHO= 0.61; Alc= 0.34 Statistical significance not reported. EI=0.59***; P= 0.48***; F=0.45***; CHO= 0.57***; Alc= 0.66*** EI=0.39***; P= 0.27***; F=0.32***; CHO= 0.48***; Alc= 0.78*** Ambrosini (2003b) 48M: 55±10yrs; BMI=27±3kg/m 2 24F: 49±11yrs; BMI=28±5kg/m 2 CSIRO: 200 items Standard serve size for each item Open ended frequency scale Qualitative information on food prep/cooking methods Opportunity to add items to list by subject Past 12m 7-d EFR Ref Test 12 months EI (MJ)=-0.9; P(g)=-14 ; F(g)= -12; CHO(g)= -2; Alc=- 5 (g) EI (MJ)=0.6; P(g)= 5; F(g)=2; CHO(g)=27; Alc= - 1(g) EI=0.43*; P= 0.57***; F= 0.64***; CHO=0.58***; Alc =0.86*** EI=0.53**; P= 0.26; F= 0.64***; CHO=0.51*; Alc =0.72*** Lassale (2009) 62F: 49.2±8.2; BMI=27.6±6.1 kg/m 2 CSIRO: As for Ambrosini et al (2003) 2x 4-d WFR (4wks apart) Test Ref 4 weeks EI (MJ)= -0.6; P(g)= -4.0; F(g)=-6.1; CHO(g)= -5.0; Alc= -3.2(g) EI=0.45***; P= 0.54***; F= 0.45***; CHO=0.51* ; Alc=0.41** Keogh (2010) 159M: 55±0.5yrs^; BMI= 27.4±0.3 kg/m 2 CSIRO: As for Ambrosini et al (2003) CCV: As for Hodge et al (2000) Completed at same time EI (MJ)= 0.7; P(g)= -1.3; F(g)=2.2; CHO(g)= 45.5; Alc= -8.0(g) EI=0.70***; P=0.69***; F=0.75***; CHO=0.63***; Alc=0.90*** Marks (2006b) 37M 59F All = yrs Nambour: 129 items Standard serve size listed next to item Nine response for frequency: never to +4 times per day Past 6 months 12-d WFR 6x 2-d every 2 months over 1 yr Ref Test ~ 12 months EI (MJ)= -0.5; P(g)= -1.2; F(g)=-10.1; CHO(g)= -5.5; Alc=0.6(g) EI (MJ)= 1.7*; P(g)= 25.9*; F(g)=11.4*; CHO(g)= 47.7*; Alc=1.3*(g) EI=0.42; P=0.37; F=0.54; CHO=0.27; Alc=0.88 EI=0.45; P=0.50; F=0.46; CHO=0.52; Alc=0.74 Abbreviations: EI=energy intake; P=protein; F=fat; CHO=carbohydrate; Alc=Alcohol; ^mean±se; *p<0.05; **p<0.01; ***p<0.001 Chapter 2: Literature Review 68

71 Differences between the FFQ and reference methods varied across the studies, with energy intake under-reported in males (-0.9MJ) and over-reported for females (0.6MJ) in one study (Ambrosini et al. 2003b). The other found under-reporting of energy intake (-0.6MJ) in the female sample (Lassale et al. 2009). A comparison between the CCV and CSIRO FFQs revealed notable differences in intakes for energy (0.7MJ) and carbohydrate (45.5g) in the CSIRO FFQ compared to the CCV FFQ (Keogh et al. 2010). Previous studies using the CCV FFQ have raised concerns in regard to this tool measuring usual dietary intake. In particular, due to the truncation of the upper frequency categories, the design of the questionnaire limits the precise reporting of foods consumed greater than three times a day (Xinying et al. 2004). The under-reporting of carbohydrate intake derived by this FFQ is common among studies investigating relative validity (Hodge et al. 2000; Xinying et al. 2004; Keogh et al. 2010), and has been attributed to an incomplete listing of common carbohydrate-rich foods (Xinying et al. 2004). The Nambour FFQ was validated using a 12 day WFR collected in 2 day periods at 2 months intervals over 1 year (Marks et al. 2006). In women, energy intakes calculated from the Nambour FFQ were significantly greater (+1.7 MJ) than for estimates compared to the WFR (Table 2-6). Intakes for men were under-recorded energy using the FFQ relative to the WFR (-0.5 MJ). Marks et al (2006) attributed the gender difference in estimates of nutrient intake between the two methods to the use of an identical standard serve size for both groups, which did not reflect usual portions consumed. This finding is supported in a review by Cade et al (2004) who suggest that self-defined portions are consistent with greater relative validity between genders. Photographic records (PhRs) Seven studies examining the use of PhRs have reported on the relative validity of this novel method in comparison with traditional assessment methods (Table 2-7). Bird and Elwood (1983) reported small, non-significant differences in energy (- 0.2MJ) and macronutrient (-6.8g to 0.8g) intakes derived from the PhR compared to the WFR, and strong correlations (r= ) between the two methods were observed (Bird & Elwood 1983). More recently, studies by Wang et al (2002, 2006) showed moderate to high correlations between estimates of energy and macronutrient intake using a PDA-based PhR captured among female college students studying nutrition. Chapter 2: Literature Review 69

72 Author (year) Bird & Elwood (1983) Wang et al (2002) Wang et al (2006) KIkunaga et al (2007) Higgins et al (2009) Martin et al (2009) (main study) Lassen et al (2010) Table 2-7: Studies investigating the relative validity of photographic records to quantify nutrient intake. Sample / country/ Test Reference Study design Mean (±SD) difference study setting method method (test-ref) 4 M, 13F. 4-d PhR 4-d WFR Methods used concurrently Diff Mean(± S.E.) Intake: EI=- Adults (film Both records coded independently by one investigator 155±185 kj; P=-1.2±1.6 g; F=- Office workers camera) Descriptions in WFR used to identify items in PhR. 0.8±2.2; CHO=-6.8±7.0 g. N/S Wales, free-living. PSEA: Reference photographs 20F. College food and nutrition students Japan, free-living. 28F; 19.3±0.5yrs; 21.4±2.9 kg/m2). College food and nutrition students Japan, free-living. Non-obese:17M (42.1±8.23yrs, 22.3±1.7 kg/m 2 ); 26F (50.6±9.7yrs; 22.6±1.7 kg/m 2 ); Obese:10M(48.2±10. 6yrs; 27.4±1.3kg/m 2 ) 22F (52.3±10.2yrs; 26.3±1.4 kg/m 2 ). Japan, free-living. Children (n=28; 14M; 12.6±2.0yrs; 98±17 %IBW. US, free-living under controlled conditions. Adults (n=50; 23M; (±SE) 32.4±1.5yrs; 26.5±0.5kg/m 2. US, controlled + freeliving under controlled conditions. Adults (n=23; 11M; 37±16yrs; 24±3kg/m 2 ). Denmark, free-living. 1-d PhR (PDA 1-d PhR (PDA) 5-d PR (PDA) 3-d PhR (film camera) + diet diary 3-d PhR (mobile phone) 5-d PhR (digital camera) 1-d WFR 1-d WFR 1x 24R 5-d WFR 3-d Est. EE (activity diary) (n=37) 3-d WFR (weighed and prepared meals/ snacks) 3-d WFR (prepared meals/snac ks) Methods used concurrently Dietitians quantified photos with descriptions on image. No PSEAs used as reference to quantify Short questionnaire to supplement PhR. Methods used concurrently 24R collected the following day. PhR quantified as per Wang (2002). Methods used concurrently PhR quantified as per Wang (2002. Sub-group (n=37) EE estimated using average of 3-d activity diary (activity recorded at 5-min intervals over 24- hours). EI from WFR compared to self-reported EE. WFR provided to subjects (125% of estimated EE) for 3d/ PhR collected over the 3d period, Two dietitians quantified PhR No PSEAs used as reference to quantify The written diet diary (DD) used to identify items in PhR. Accurate reports of EI were classified as within ±5%. Methods used concurrently, non-consecutive All subject provided with b fast, eaten in lab; dine-in group lunch and dinner, eaten in lab; take-out group lunch, eaten in the lab; dinner eaten in free-living PhR to record lunches and dinners. Three dietitians estimated intake from PhR. No PSEAs used as reference to quantify. 5-d WFR PhR and WFR used concurrently to capture evening meals only (excluding beverages) over consecutive days Two analysts quantified PhR PSEA - database of reference food photographs. Chapter 2: Literature Review 70 Diff Median Intake: EI=340 kj; P=- 2 g; F=-2 g; CHO=5 g. N/S Diff Median Intake (PhR-WFR): EI=250kJ; P=-4.7g; F=-2g; CHO=11g. N/S Diff Median Intake (24R-WFR): EI=250kJ; P=2.1g; F=-2.7g; CHO=-9.4g. N/S Non-ob M: EI=-1150 kj; P=-8.8g; F=-6.1g; CHO=-34g. Ob M: EI=**-2120 kj; P=*-13.3g; F=-9.2g; CHO=-36g. Non-ob F: EI=*-991 kj; P=*-10.6g; F=-5.5g; CHO=**-39g. Ob F: EI=-680 kj; P=*-9.7g; F=- 1.7g; CHO=-26g. Accuracy of reporting EI (PhR vs.wfr): 29% under-reported (<5%), = -1890±920 kj/d; 36% accurate (±5%); 35% overreported (>5%), =+2780±430 kj/d. dine-in group s lunch and dinner EI (±SE): -368±174 kj*. laboratory conditions of take-out group s lunch EI: -151±81 kj; N/S. free-living conditions of take-out group s dinner EI: -406±159 kj*. Mean# difference (PhR-WFR) (n=88): EI=-250kJ, p<0.001; P=0.0% of EI; F=0.4% of EI; CHO=1.0% of EI. Correlation between methods (Pearson s r) EI=0.86; P=0.91; F=0.90; CHO=0.84. Statistical significance not reported. EI=0.79**; P=0.88**; F=0.57**; CHO=0.78**. WFR vs. PhR: EI=0.58**; P=0.56**; F=0.64**; CHO=0.59**. WFR vs. 24R: EI=0.88**; P=0.89**; F=0.79**; CHO=0.80**. Non-ob M: EI=0.75***; P=0.79***; F=0.85***; CHO=0.71**/Ob M: EI=0.42; P=0.39; F=0.49; CHO=0.61 Non-ob F: EI=0.34; P=0.52**; F=0.62***; CHO=0.20/Ob F: EI=0.71***; P=0.53*; F=0.55**; CHO=0.74*** EI vs. EE: Non-ob M :0.97; Ob M: 0.96; Non-ob F: 1.05; Ob F: N/A Abbreviations: PhR=photographic food record; WFR=weighed food record; EI=energy intake; P=protein; F=fat; CHO=carbohydrate; Non-ob= non-obese; Ob=obese; EE=energy expenditure; Mean±SD unless indicated; #SD or SE not available; *p<0.05; **p<0.01; ***p<0.0001; N/S=non-significant for energy and macronutrients; N/A=not available. N/A N/A

73 Lower associations for estimates of fat intake (r= 0.57) between the novel and traditional methods compared to the other nutrients (r= ) were noted in one study (Wang et al. 2002). The authors concluded that this finding may have been due to an inability to accurately estimate certain fat-rich items, such as nuts and oils (e.g. salad dressing) which were difficult to identify in the photograph or were typically considered as additional items (i.e. spreads, dressings) which may have been added to other foods prior to serving (Wang et al. 2002). In contrast to the earlier two studies, Kikunaga and colleagues (2007) reported that energy intake was significantly (p<0.01) under-estimated using the same PhR method as Wang et al (2002, 2006) compared to the WFR for obese males (-2.1 MJ) and non-obese females (-1.0 MJ). Noticeable differences were also found for males of a normal weight and obese females, with both sub-groups also underreported energy intake using the PhR by -1.2 MJ and -0.7MJ, respectively. It was concluded that the discrepancy between measures of intake were due to poor quality photographs and an inability to separate components of complex meals and mixed dishes (Kikunaga et al. 2007). Of the three remaining investigations, two simulated WFRs by providing subjects with pre-prepared meals and snacks to consume during the study or while in the third study subjects were required to record only one selected meal occasion. In these studies energy intake was considerably under- and over-estimated in twothirds of children (-1.9MJ to +2.8MJ) (Higgins et al. 2009), while modest underestimations were found for adults in a free-living situation (Martin et al. 2009). In the study by Higgins et al (2009), approximately 50% of subjects did not capture all food eaten with the PhR, with Dahl Lassen et al (2010) also reporting food items consumed under-recorded using the PhR compared to the WFR. Kikunaga et al (2007) also included an additional reference method independent of diet to validate reported energy intake (EI) reporting high agreement with estimated energy expenditure (EE), with EI:EE ranging between ) (Kikunaga et al. 2007). Despite this, concern over the true accuracy still remains as, although independent to measured intake, the reference method used to estimate energy expenditure relied on self-reported data. Chapter 2: Literature Review 71

74 The review of the literature examining the relative validity of PhRs revealed good agreement with traditional dietary assessment methods (Table 2-7), however two key limitations arise with these studies. Firstly, for five studies both the test and reference methods were used to record intake at the same time which may have introduced bias into the measure (Bird & Elwood 1983; Wang et al. 2002; Wang et al. 2006; Kikunaga et al. 2007; Dahl Lassen et al. 2010). It is recommended that the administration of both the test and reference methods be separate, with the test method used first, to limit the introduction of this error (Nelson 1997). Secondly, for the two other studies, where prepared foods were provided to subjects to simulate a WFR (Higgins et al. 2009; Martin et al. 2009), this situation is not indicative of a freeliving setting, and therefore the external validity of the PhR remains unknown. Validation with an objective reference method, independent to a measure of dietary intake, is necessary to determine the true accuracy of this method Criterion Validity The criterion validity of a method is evaluated through determining the plausibility of the self-reported intake of a particular nutrient with its relevant biomarker (if one exists) (Nelson 1997; Gibson 2005). The benefit of the inclusion of a biomarker to validate nutrient intake, is that the criterion of independence between errors associated with the test and reference method is fulfilled (Kaaks 1997). Components contained within various body fluids (e.g. urine, blood, saliva) and tissues (e.g. adipose tissue, hair, nail clippings) can be used to determine the concentration of a select number of macro- and micronutrients within the body. The doubly labelled water (DLW) technique is an objective method used to assess total energy expenditure and validate self-reported dietary energy intake. First examined in humans by Schoeller & van Santen (1982) the DLW technique involves the administration of a sample of water containing a higher concentration of two stable isotopes; oxygen-18 ( 18 O) and deuterium ( 2 H). Both isotopes are eliminated from the body at different rates: 2 H in the form of water, and 18 O in the form of water and carbon dioxide (Schoeller & van Santen 1982). The difference in the rates of elimination between the two isotopes is indicative of carbon dioxide production. As such carbon dioxide production can be measured and indirect calorimetry principles then used to calculate total energy expenditure. The DLW technique has been validated as an objective measure of total energy expenditure in number of Chapter 2: Literature Review 72

75 population sub groups including adults (Schoeller & van Santen 1982), obese individuals (Prentice et al. 1986); and athletes (Westerterp et al. 1986). The use of this technique to determine the plausibility of self-reported intake is based upon the fundamental principle of energy balance in that if body weight and composition are stable, energy intake and expenditure must be equal (Black et al. 1993). The use of measured total energy expenditure (TEE) to validate self-reported energy intake (EI) is based on the fundamental principle of EI=TEE±body stores. Therefore the expected ratio of EI:TEE is 1.00 in weight stable individuals (Livingstone & Black 2003). Biomarkers are independent of the recorded food intake and, therefore are considered the most rigorous process to validate measures of diet. However, the use of such techniques is often limited due to cost of the necessary materials and access to appropriate facilities. Techniques which compare energy intake with measured or estimated basal metabolic rate (BMR), such as the Goldberg cut-off technique, may also be used to establish reporting bias at a group level (Goldberg et al. 1991). In circumstances when energy expenditure cannot be measured directly, this method has been validated as a practical alternative (Black 2000a). FFQs developed for use with Australian adults Of the three FFQs commonly used in Australian adults, both the CCV FFQ and the Nambour FFQ have been validated for intakes of polyunsaturated fatty acids using an independent biomarker. Woods et al (2002) investigated the ability of the CCV FFQ to assess usual dietary fish intake in comparison with plasma percentage fatty acids in 174 adults (55%F; aged years). Fish intake was derived from questions on the FFQ regarding the frequency of consumption of steamed, baked, or grilled fish, fried fish, and tinned fish. Excluding individuals who were taking n-3 fatty acid supplements (n=21), intake of all types of fish, except fried fish, was significantly positively correlated with plasma n-3 (r= , p<0.01) and DHA fatty acids ( , p<0.01) (Woods et al. 2002). McNaughton et al (2007) examined the accuracy of the Nambour FFQ for the assessment of polyunsaturated fatty acids. In this study, 43 adults (25F; aged years) recorded intake using both the FFQ and a 12-day WFR collected in 2-day intervals every 2 months for 1 year and fatty acid intakes from both dietary Chapter 2: Literature Review 73

76 assessment methods were compared to plasma phospholipid fatty acid concentrations. Higher median fatty acid intakes were noted for estimates derived from the FFQ compared to the WFR, with correlations for all fatty acids ranging between r=0.32 and 0.59 (p<0.05). Similarly to Woods et al (2002), McNaughton and colleagues (2007) also concluded that their FFQ could adequately estimate polyunsaturated fatty acid intakes, but not trans fat. In contrast to the CCV FFQ, the Nambour FFQ was not a significant predictor of saturated and monounsaturated fatty acid intakes. Although the respective authors concluded that both the Nambour and CCV FFQs were a valid measure of intake of some fatty acids, additional objective evidence to support the use of this method for assessing energy and macronutrient intakes is not available. Photographic records (PhRs) No research has been published using objective measures to validate quantitative estimates of nutrient intake derived from PhRs. To-date, studies have only examined the relative validity of this novel method through comparison to selfreported intake from other traditional assessment methods or to estimated energy expenditure via an activity diary (Table 2-7). Therefore, recommendations on the accuracy of this novel method remain inconclusive. In order to establish a level of expected accuracy for self-reported energy intake using a PhR, the literature relating DLW studies involving weighed and estimated food records were reviewed. Weighed and estimated food records (WFRs and EFRs) From the review of the literature 28 studies were identified which evaluated selfreported EI measured using either EFRs or WFRs against TEE measured using DLW among free-living adults. These studies are summarised in Table 2-8 and are grouped according to the number of recording days. In the context of the heterogeneity between sample size and subject characteristics of these studies a number of observations can be formed. Firstly, across all studies reviewed, reporting bias of EI compared to measured TEE varied considerably ranging from to 2.5%. The effects of longer recording periods on reporting accuracy remain inconclusive. Chapter 2: Literature Review 74

77 Table 2-8: Studies investigating the criterion validity using TEE (DLW technique) of self-reported EI from food records in free-living adults. First author (year) Sample characteristics Country Dietary Assessment Method Mean (±SD) TEE (±SD) EI:TEE % Diff EI (MJ/d) (MJ/d) Goran (1992) 6M: 68±6 yrs; 77.1±7.4kg 9.9± ± USA 3-d EFR (2xwkd, 1xwed) 7F:64±5 yrs; 65.2±kg 6.0± ± Reilly (1993) 10F: 73.0±3.0 yrs; 60.0±7.2kg UK 3-d WFR (2xwkd, 1xwed) 6.7± ± Johnson (1994) 81M: 66.0±6.0yrs; 25.3±3.3kg/m 2 56F: 66±6yrs; 24.3±3.2kgm/ 2 USA 3-d EFR (2xwkd, 1xwed) 9.8± ± ± ± Taren (1999) 37F: 43.6±9.3yrs; 28.7±8.5kg/m 2 USA 3-d EFR (2xwkd, 1xwed). 8.3± ± Tomoyasu (1999) 39M: 70±1yrs^; 25.1±0.6kg/m2^ 8.7±0.3^ 11.3±0.4^ USA 3-d EFR (2xwkd, 1xwed). 43F: 68±1yrs^; 24.8±0.5kg/m2^ 6.9±0.3^ 8.4±0.3^ Tomoyasu (2000) 28M; 65.1±7.0 yrs; 27.6±4.2kg/m 2 36F; 64.6±8.1yrs; 32.1±6.4 kg/m 2 USA 3-d EFR (2xwkd, 1xwed). 9.8± ± ± ± Rafamantanantsoa 44M: 51±14yrs; 23.3±2.6kg/m 2 3-d EFR (2xwkd, 1xwed) with photographs recorded to Japan (2003) cross-check in analysis (not quantification) 10.4± ± Scagliusi (2008a) 65F; 33.7±10.8yrs; BMI=27.9±6.7 kg/m 2 Brazil 3-d EFR (2xwkd, 1xwed). 8.6 ± ± Kaczkowski (2000) 53F: 64.9±11.3yrs; BMI: 24.4±4.0kg/m 2 Canada 4-d EFR (3xwkd, 1xwed; con) via a voice record + photographs used to cross-check in analysis (not 7.5± ± quantification) Clark (1994) 6F(large eaters): 37.3±3.6 yrs; 50.89±2.35kg 10.5± ± Australia 5-d WFR 6F (small eaters): 39.7±2.0 yrs; 59.88±3.25 kg 5.9± ± Prentice (1986) 13F(lean): 29±5yrs; 22.1±2.4 kg/m 2 9F (obese): 35±5 yrs; 32.9±4.6 kg/m 2 USA 7-d WFR 8.2± ± x7-d WFR 6.7± ± Livingstone (1990) 16M: 31.5±7.2yrs; 25.8±3.3kg/m 2 15F: 35.5±11.4yrs; 24.3±3.3 kg/m 2 UK 7-d WFR 11.2± ± ± ± Martin (1996) 29F: 48.7±5yrs; 23.3±2.5 kg/m2 USA 7-d WFR 7.0± ± Sawaya (1996) 10F: 25.2±1.1yrs; 20.9±1.9 kg/m 2 10F: 74.0±1.4 yrs; 24.1±2.8 kg/m 2 USA 7-d WFR 8.0± ± ± ± Seale (1997) 8M: 49.5±7.2 yrs; 25.7±1.3 kg/m 2 11F:51.9±4.9; 22.6±2.5 kg/m 2 USA 7-d EFR 9.0± ± ± ± Jones (1997) 29F: 48.7±0.9; 23.3±0.5kg/m 2 Canada 7-d WFR 7.1± ± Bathalon (2000) 26F (unrest):60.3±0.6 yrs; 23.6±0.6 kg/m 2 34F (restrained):59.4±0.6 yrs; 24.8±0.5 kg/m 2 USA 7-d WFR 8.3± ± ± ± Goris (2000) 30M: 44±7 yrs; 34.1±3.9 kg/m 2 The Netherlands 7-d EFR 10.4± ± Barnard (2002) 7F; 37.1±9.6 yrs; 23.8±5.3 kg/m 2 8M; 35.4±13.1yrs; 25.9± kg/m 2 Australia 7-d WFR 8.2± ± ± ± Scagliusi (2003) 11F (normal wt):33±9yrs: 22.3±1.9 kg/m 2 13F (overwt): 39±11yrs; 27.2±1.5 kg/m 2 11F (obese): 38±8yrs; 32.8kg/m2 Brazil 7-d EFR 7.9±1.3 8,9± ± ± ± ± < Abbreviations: M=male; F=female; EFR=estimated food record; USA=United States of America; UK=United Kingdom; WFR=weighed food record; wkd=week day; wed= weekend day; EI=energy intake; TEE=total energy expenditure; Diff =% difference = ([self-reported EI (MJ/d) TEE]/TEE*100) Chapter 2: Literature Review 75

78 Table 2-8 (continued). First author (year) Sample characteristics Country Dietary Assessment Method Mean (±SD) TEE (±SD) EI:TEE % Diff EI (MJ/d) (MJ/d) Mahabir (2006) 65F, 59.9±7.5 yrs; 27.7±5.6kg/m 2 USA 7-d EFR 6.5± ± Schultz (1989) 4M; 2F: 20-30yrs; wt=70.4±10.3kg The Netherlands 14-d WFR 13.3± ± <1.0 Tuschl (1990) 11F(unrestrained,): 24.5±4.2yrs; 20.0±1.3kg/m2 9.7± ± Germany 14-d EFR 12F(restrained): 22.3±1.4yrs; 21.1±1.3kg/m2 8.6± ± Lichtman (1992) 9F, 1M(restrained): 48±12 yrs; 33.8±4.1 kg/m 2 4.3± ± USA 14-day EFR 6M/F (unrestrained): obese 7.1± ± F: 18-49yrs; kg/m 2 8.1± ± d EFR Howat (1994) 26F (control) USA 7.8± ± F (PSE training) 8.5± ± F (unrestrained; controls): 24.5±4.2 yrs; Platte (1995) 20.1±1.3 kg/m 2 Germany 14-d EFR 11F (obese): 32.6±7.7 yrs; 37.4±8.1 kg/m 2 27M: 67.5±5.03yrs; 25.4±3.6 kg/m 2 Black (1997) 18F:57.9±4.6yrs; 25.0±3.9 kg/m 2 UK 16-d WFR (4(seasons)x4d) using PETRA (combined voice record & scales) Black (2000d) 16F: 57.5±4.6 yrs; 25.1±4.2 kg/m 2 16-d (4x 4-day) WFR using PETRA UK Black(1995) 10F, 1M: 35±9 yrs; 23.5±2.5 kg/m 2. Post-obese UK 21d WFR via a cross-over design of two methods. Alternate subjects:10d traditional written record & scales + 11d PETRA (combined voice record & scales); others reversed procedure 9.7± ± ± ± ± ± ± ± ± ± ± ± Abbreviations: M=male; F=female; USA=United States of America; UK=United Kingdom; EFR=estimated food record; WFR=weighed food record; wkd=week day; wed= weekend day; EI=energy intake; TEE=total energy expenditure; Diff =% difference = ([EI TEE]/TEE*100) Chapter 2: Literature Review 76

79 Seven studies validating the use of 3-day food records revealed consistency in under-reporting of EI (-31.8% to -1.9%), whereas eleven investigations into the accuracy of 7-day records resulted in slightly greater bias (-46.1% to +2.5%). For food records of 14 days (n=5), discrepancy in EI ranged from -58.7% to <+1.0%, compared to -13.7% for 16-day records (n=2), and -25.8% for records over 21 days (n=1). An early study by Schulz et al (1989) found highly accurate reporting (<1% bias) of intake over a 14-day period at the group level in a sample of 6 adults, however the authors noted that large variations (± 30%) in EI compared to TEE should be expected at the individual level (Schulz et al. 1989). Secondly, it is likely that other confounding factors, such as those identified in Section relating to the characteristics of the subjects, may have contributed to the variability observed across the different recording periods. A large proportion of the evidence summarised in Table 2-8 involved female subjects, therefore it was not surprising that greater variation in reporting bias was observed in this group (-58.7% to +4.9%), compared to males (-37.7% to -11.7%). Similarly, a larger number of studies have investigated the validity of self-reported EI using food records among younger (<60 years) compared to older adults with the difference compared to TEE ranging from -58.7% to +4.9% and -31.8% to -9.2%, respectively. Furthermore, a higher BMI has been suggested as a predictor of under-reporting. Of the studies reviewed, with overweight and/or obese subjects (i.e. BMI 25.0 kg/m 2 ), the bias in reported EI ranged from -58.7% to <+1.0%. The majority of individuals studied in this review were lean (i.e. BMI <24.9 kg/m 2 ), with mis-reporting of -52.0% to +20.7%. This range of mis-reporting occurred in one study, with the small eaters significantly under-reporting intake compared to the large eaters (Clark et al. 1994). Similarly, others reported greater discrepancies between intake and expenditure for individuals classified as exhibiting dietary restraint. For example, in one sub-group of adults exhibiting restrained eating under-reporting intake by %, compared to -27.6% in the unstrained group (Lichtman et al. 1992). It is also important to note that of the investigations exploring the effect of modifications to traditional methods, reductions in subject burden did not conclusively transfer to improvements in reporting accuracy. Two studies employed either a written estimated (Rafamantanantsoa 2003) or weighed food record (Kaczkowski & Bayley 2000) supplemented with a photographic record, reporting differences in intake to expenditure of -1.9% and -27.9%, respectively. Black and Chapter 2: Literature Review 77

80 colleagues (1995, 1997, 2000d) found differences between -25.8% to -11.7% when examining the use of electronic food scales with an in-built voice recorded (Black et al. 1995; Black et al. 1997; Black et al. 2000d) Reproducibility The reproducibility of a dietary assessment method relates to its ability to produce equivalent results when repeated in the similar situation (Gibson 2005). Repeatability and reliability are two distinct but related elements of reproducibility: the former is a component required for establishing reproducibility, while the latter is a trait of a method that displays reproducibility (Nelson, 1997b, p. 242). Such a distinction is important to highlight, as various aspects influence the reproducibility of dietary assessment method. Both random error and the true day-to-day variability of dietary intake (discussed in Section 2.3) can affect the reproducibility of a measure of nutrient intake (Beaton 1994). Therefore, the reproducibility of a method can only be estimated as the identical data collection circumstances cannot be produced over multiple measures (Nelson 1997). Inter-rater reliability is important to determine in situations when more than one individual is responsible for deriving an estimate of nutrient intake (Gleason et al. 2010), and is of particular relevance for the analysis of PhR. This measure provides an evaluation of the extent of the agreement between estimates (Nelson 1997), and can assist to identify sources of error within the data collection and calculation of intake stage of the dietary assessment process. FFQs developed for use with Australian adults Of the three FFQs reviewed to-date, only the reproducibility of the Nambour FFQ has been evaluated. In this study by Ibiebele et al (2009), two questionnaires were administered approximately 12 months apart to 100 adults (50M) aged years. Estimates of energy and macronutrient intake were significantly higher (p<0.05) in the first FFQ compared to the second for energy (506 kj), carbohydrate (16 g), and fat (4.5g). Changes in diet between the administration of the first and second FFQ were acknowledged in 11% of the sample which may assist explaining the difference in intake between the two time points (Ibiebele et al. 2009). Photographic records (PhRs) Table 2-9 summarises the research involving the reproducibility of PhRs, with the majority of studies reporting on the inter-rater reliability of this method. Collectively these investigations have reported strong agreement for estimates of nutrient intake Chapter 2: Literature Review 78

81 between different dietitians or other trained investigators. For example, Martin et al (2009) found a high level of agreement (r=0.92) for energy intake derived by two dietitians estimating the portion size of 31 food items with the assistance of an archive of reference food photographs. Similarly, the use of a 3-day PhR among children and adolescents provided with pre-prepared food items produced high agreement (r= ) when coded by two dietitians (Higgins et al. 2009). In comparison, only one study has reported on the repeatability of PhRs. Six months following their first analysis, Wang and colleagues (2006) repeated the measurements on diet using identical methods 6 months later. The relationship between methods on this occasion was slightly stronger for energy, protein, and carbohydrate, however a weaker association for fat was also observed (Wang et al. 2006). The authors reported that the nutrient intake estimated using the PDA PhR tended to be over-estimated, which may in part explain the weaker association reported in the second study compared to the first. Relevance to research program Section 2.6 reviewed previous studies evaluating the validity and reproducibility of the two key methods used throughout this thesis. Of the three FFQs reviewed moderate evidence exists supporting the use of this method to measure absolute nutrient intake, however the limitations common with all FFQs must be addressed when conclusions are drawn regarding findings derived from this tool. To-date, confirmation of the accuracy of the PhR as a method for assess diet is lacking due to small numbers of studies which have only examined relative validity. No previous investigations establishing the criterion validity of this method using objective methods independent of intake. A review of the evidence surrounding the use of EFRs and WFRs to report EI compared to measured TEE, offer some insight into the level of expected accuracy of the PhR. These findings revealed under-reporting of EI using both the EFR and WFR, regardless of the duration of recording with variations across age, gender, BMI and dietary restraint. Inter-rater reliability has been shown to be high among dietitians quantifying these records, however without an in-depth investigation into the random error associated with the task of portion size estimation, conclusions with regards to this aspect of reproducibility of the method remain inconclusive. Chapter 2: Literature Review 79

82 Table 2-9: Studies investigation the reproducibility of photographic records to quantify nutrient intake. First author (year) Wang (2002) Sample / country/ study setting 20F. College students majoring in food and nutrition. Japan, free-living. Test method 1-d PhR (PDA with camera function) Type of reproducibility Inter-rater reliability Study design Mean Diff (test ref) Correlation between estimates As for Wang (2002)# Photos of the same meal recorded using 2 PDAs to create dual PhR.. Diff Median Intake (PhR1- PhR2): EI=25kJ; P=-1 g; F=6 g; CHO=1 g. N/S diff between methods for E and macro ^ρ: EI=**0.83; P=**0.79; F=**0.59; CHO=**0.86. Wang (2006) 28F; 19.3±0.5yrs; BMI=21.4±2.9 kg/m 2 ). College students majoring in food and nutrition Japan, free-living. 1-d PhR (PDA with camera function) Repeatability As Wang et al (2006)# Relative validity study repeated +6 months. Diff Median Intake (PhR- WFR): EI=147kJ; P=1.1g; F=0.5g; CHO=16.7g. Diff Median Intake (24R- WFR): EI=-143 kj; P=- 4.0g; F=-2.7g; CHO=5.2g. ^ρ(wfr vs. PhR): EI=**0.60; P=**0.61; F=**0.50; CHO=**0.68. ^ρ(wfr vs. 24R): EI=**0.79; P=**0.73; F=**0.70; CHO=**0.79. Martin (2007) Higgins (2009) Martin (2009) (main study) Dahl Lassen (2010) 43 children (23M): 11.7 (±SE) 0.08) yrs US, controlled setting 28, 14M; 12.6±2.0yrs; 98±17 %IBW. USA, free-living under controlled conditions. Adults 23M, 27F; age (±SE)=32.4±1.5yrs; BMI(±SE)=26.5±0.5k g/m 2. USA, controlled conditions + freeliving under controlled conditions. Adults 11M, 12F; 37±16yrs; BMI=24±3kg/m 2. Denmark, free-living. PhR (digital camera) 3-d PhR (film camera) + diet diary 3-d PhR (mobile phone with camera function) PhR (digital camera) Inter-rater reliability Inter-rater reliability Inter-rater reliability Inter-rater reliability School cafeteria meals weighed and photographed before after eating Two dietitians quantified PhR As for Higgins et al (2009)# Two dietitians quantified PhR As for Martin et al (2009)# Three dietitians quantified PhR As for Lassen et al (2010)# Two trained analysts quantified PhR N/A ICC: EI=0.93, P=0.89, F=0.93, CHO=0.94. N/A ^ρ for PhR for the two dietitians: EI= ; P= ; F= ; CHO= ICC= for all nutrients except vit A (0.25). N/A ICC: EI=***0.99 (95%CI 0.99, 0.99), P=***0.85 (0.79, 0.90), F=***0.92 (95%CI 0.88, 0.94), CHO=***0.85 (95%CI 0.77, 0.89). N/A ^ρ: EI=0.89; P=0.95; F=0.97; CHO=0.96. Abbreviations: M=male; F=female; PhR=photographic record; PDA=personal digital assistant; ^Spearman s rank correlation coefficient (ρ); EI=energy intake; P=protein; F=fat; CHO=carbohydrate; Alc=Alcohol; #study design described in Table 2-7; N/A: not available; ICC=inter-rater correlation coefficient. Chapter 2: Literature Review 80

83 2.7 Conclusions Summary of previous research From this review of the literature, two areas emerged relating to the nutritional management of individuals with T2DM that require further investigation. These factors and their effect on the nutritional management of T2DM are summarised in Figure 2-7. Firstly, effective nutritional management of T2DM consists of two distinct but complimentary elements: dietary self-management and MNT. Inherent barriers within traditional primary care settings restrict regular and ongoing support and access to these essential resources and services. The inclusion of ICTs, such as telephone counselling, within the delivery of DSME offers a promising solution to this issue and has been successful in facilitating behaviour change within a variety of settings and population groups. In particular, the use of automated telephone systems has shown promise, although the effectiveness of such approaches for the promotion of self-management behaviours, including nutrition, has not been evaluated in Australian adults with T2DM. With the increasing prevalence of T2DM coupled with the expanding health system burden relating to the provision of care, the examination of novel delivery modes for the education, motivation and counselling of individuals within this group is a priority. Secondly, the assessment of intake is necessary to inform individualised dietary education and counselling relating to T2DM, and to evaluate the effectiveness of nutrition intervention strategies provided as part of dedicated self-management education and/or MNT. Although prospective methods are preferred for the assessment of individual nutrient intakes due to their ability to capture real time intake and daily variation, traditional methods such as WFRs and EFR are burdensome for the individual and can result in changes in typical intake to facilitate recording. In addition, a number of unique challenges and potential sources of error present throughout the dietary assessment process ensure that a valid and reproducible measure of intake becomes a complex and difficult task to achieve. Novel methods which utilise technologies, such as mobile phones to capture PhRs aim to simplify the recording process; however a number of gaps remain in the existing literature base encompassing this dietary assessment method: 1. Current approaches using a mobile device to capture photographs often require the user to manually input the description of items contained within the image. Such a task may not be practical among those with limited dexterity, technical aptitude and/or literacy skills. As the majority of mobile devices possess a voice Chapter 2: Literature Review 81

84 recording function, exploration of the combination with the camera function warrants further exploration. In addition, evidence suggests that these methods are acceptable for younger persons; however the feasibility of this technology among older adults is not known and warrants investigation. 2. Compared to traditional prospective dietary assessment methods, both the source and type of error associated with the quantification of intake has changed in the context of PhRs. In this novel method the responsibility for quantifying intake has now shifted from the individual measuring their diet to the dietitian/investigator and the type of error now involves the estimation of portion size. Despite this critical shift in methodology, the subsequent effect of this portion size estimation on the nutrient intake of PhRs has not been examined in-detail. In particular, a dedicated evaluation of the effect of the choice of PSEA on estimation error associated with PhRs has not been undertaken. Further contributing to this poorly explored aspect of this innovative method is a definite lack of a documented standardised approach to the analysis of PhRs, while the feasibility of other techniques remains uncertain in a dietetic practice setting. 3. Evaluations into the performance of PhRs have been limited to establishing the relative validity in comparison with traditional assessment methods. As the use of this method increases, it is imperative that objective evaluations of the accuracy and reproducibility of these methods in free-living environments are conducted to determine performance in comparison with existing methods and the suitability of potential use within future dietetic practice Aims, research questions and hypotheses of research program Table 2-10 outlines the aims, research questions and hypotheses for each study. Figure 2-8 illustrates the conceptual framework underpinning this thesis. The research program underpinning this thesis consists of four independent, but inter-related studies. Chapter 2: Literature Review 82

85 Nutritional Management of T2DM Barriers within face-to face/primary care Health system: policy & workforce shortages + logistical Regular and ongoing support: access to personnel, services and resources Taking action 1. Self-Management Education (Lorig & Holman 2003) Problem solving Selfmonitoring Decision making Resource utilisation Individual Limitations of written weighed/estimated food records. Inconvenient and impractical tools to assess individual nutrient intake. 2. Medical Nutrition Therapy Individualised dietary counselling provided by a dietitian using the Nutrition Care Process (Lacey & Pritchett 2003): 1. Assessment 2. Diagnosis Patientpractitioner partnership Measurement of diet 4. Evaluation & Monitoring 3. Intervention Adoption and Maintenance Changes to nutrition-related behaviours Achievement of goals and outcomes Figure 2-7: Conceptual framework illustrating barriers and limitations to current practice in the nutritional management of T2DM. Various logistical and policy issues inherent to the health system restrict access to ongoing support, while traditional dietary methods are burdensome for the individual affecting self-monitoring and assessment of intake and interventions. As a result, the adoption and maintenance of key behaviours and achievement of outcomes may be adversely affected. Chapter 2: Literature Review 83

86 Nutritional Management of T2DM Novel approaches to nutrition support and methods to measure diet Face-to face/primary care Mobile phone photo/voice food record Blood Glucose Monitoring TLC Diabetes Essential Self-Management Behaviours Medication Adherence Physical Activity Nutrition Regular and ongoing support Measurement of diet Assess Dietary Intake NuDAM Problem solving Decision making Information & Communication Technologies 1. Assessment 2. Diagnosis Taking action Selftailoring Resource utilisation Individual 4. Evaluation & Monitoring 3. Intervention Patientpractitioner partnership Self-Management Education (Lorig & Holman, 2003) Nutrition Care Process MNT (Lacey & Pritchett 2003) Adoption and Maintenance Changes to nutrition-related behaviours Achievement of goals and outcomes Figure 2-8: Conceptual framework underpinning the research program of this thesis. Information and communication technologies offer a potential solution to the limitations associated with traditional modes of service delivery and dietary assessment methods. In particular, strategies, such as automated telephone systems (i.e. TLC Diabetes) and mobile phone photo/voice dietary records (i.e. NuDAM), may assist in overcoming these barriers. Chapter 2: Literature Review 84

87 Study 3 Study 2 Study 1 Table 2-10: Aims, research questions and hypotheses of research program. Aim Research Question Hypothesis To determine the effect of an automated telephone system on promoting dietary change among adults with T2DM. 1. Is the TLC Diabetes system effective in promoting change in dietary intake and nutrition status among adults with T2DM and sub-optimal glycaemic control? Changes in dietary intake and nutrition status will be greater in the intervention group compared to the control group. To develop and trial the Nutricam mobile phone device and associated recording protocol for the recording of dietary intake. 2. Does the Nutricam recording protocol result in a dietary record suitable for the estimation of nutrient intake? 3. Compared to an estimated food record, do individuals perceive the Nutricam dietary record as an acceptable and useable method for recording dietary intake? N/A # N/A # 4. Does the use of portion size estimation aids result in a difference in the error associated with estimating the quantities of single food items contained in photographs? The use of a portion size estimation aid will reduce error in the quantification of single food items contained in photographs. To develop a standardised approach to the analysis of the Nutricam dietary record through the design and trial of a dietary estimation and assessment tool (DEAT). 5. Does the estimation error associated with quantifying single food items contained in photographs differ when using a twodimensional aid (i.e. reference photograph) compared to a three-dimensional aid (i.e. food model)? 6. Does the use of the DEAT effect the error associated with estimating the portion size of food items contained in the photographic component of Nutricam dietary records? The error will be equal or smaller for items estimated using two-dimensional aids compared to those estimated using threedimensional aids. The use of the DEAT will reduce error in the estimates of portion size of items contained in the Nutricam dietary records. Chapter 2: Literature Review 85

88 Study 4 ) Study 3 (continued) Table 2-10 (continued). Aim Research Question Hypothesis To develop a standardised approach to the analysis of the Nutricam dietary record through the design and trial of a dietary estimation and assessment tool (DEAT). 7. Of the food types (i.e. solid, liquid, amorphous, spreads) contained in the photographs, which are most affected by estimation error and does this influence the estimated energy and macronutrient content of the Nutricam dietary records? 8. Do student dietitians perceive the DEAT as an acceptable and useable resource to assist in the quantification of Nutricam dietary records? Amorphous food items and spreads will have higher levels of error compared to liquids and solid items. Estimation error associated with food type will be reflected in error in energy and/or macronutrient composition of the Nutricam dietary records. N/A # To establish the accuracy and interrater reliability of the Nutricam dietary assessment method (NuDAM) for the assessment of usual dietary intake. 9. When evaluated against a measure of total energy expenditure (DLW technique), does the NuDAM produce an accurate measure of selfreported usual dietary energy intake compared to a weighed food record? 10. Between dietitians, is there a difference in the reported usual energy and macronutrient intake as estimated via the NuDAM compared to a weighed food record? The NuDAM will produce a measure of reported usual dietary intake of similar or greater accuracy to a weighed dietary record. The NuDAM will produce a measure of reported usual dietary intake of similar or greater inter-rater reliability to a weighed dietary record. 11. Compared to a weighed food record, do individuals perceive the NuDAM as an acceptable and useable method for recording dietary intake? N/A # # Research question is qualitative, and therefore a hypothesis is not applicable (N/A). Chapter 2: Literature Review 86

89 Chapter 3: Research Program This chapter introduces the research program underpinning this thesis and outlines the methodological framework of the four studies designed to answer the research questions defined in Table Firstly, Section 3.1 provides an overview of the key issues arising from the review of the literature presented in Chapter 2 and then outlines the structure of the research program. Section 3.2 explains the design and selection of methods for each study, highlighting the importance of each in the context of this thesis and contribution to the broader evidence base. In addition, due to the heterogeneity in design and methods across the studies in this thesis, this section is supplemented by additional detail in the corresponding methods sections of Chapters 4-7, in particular the development of the data collection and analysis components of the Nutricam dietary assessment method (NuDAM). The two groups of subjects used throughout this thesis are introduced in Section 3.3. An in-depth discussion of the key analytical techniques employed throughout the studies contained in this thesis is provided in Section 3.4. Finally, the ethical considerations relevant to the conduct of the four studies are described in the Section Rationale for research program The review of diabetes self-management education (DSME) and medical nutrition therapy (MNT) in the management of type 2 diabetes mellitus (T2DM) in Section 2.2 of this thesis exposed barriers within the traditional delivery modes of care in this cohort (Section 2.2.3). Evidence in the use of various innovative technologies to supplement existing care models to promote ongoing support was provided in Section The importance of the relationship between the measurement of diet and DSME and MNT in the nutritional management of T2DM was reinforced by discussion of dietary self-monitoring (Section ) and the assessment of intake to guide individualised dietetic intervention and to monitor and evaluate outcomes (Section ) reinforced a need for simple and practical methods, which are both accurate and reproducible, to measure the diets of individuals in this group. The concept of measuring usual dietary intake, in particular the process involved and the potential sources of error revealed factors which must be addressed in the measurement of nutrient intake (Section 2.3). Examination of current dietary assessment methods revealed that novel photographic records (PhRs) have shown promise for minimising the limitations associated with traditional prospective methods, however several gaps in the existing evidence base of this novel method remain (Section 2.4). In particular, investigations into the task of quantifying food Chapter 3: Research Program 87

90 items contained in the PhRs and the associated error remain limited, while the effect of the type of portion size estimation aid (PSEA) on error has not been established (Section 2.5). Previous evaluations into the performance of PhRs have reported strong inter-rater reliability and high agreement with other traditional methods, yet confirmation of the level of validity is necessary through investigation with objective measures independent of diet had not been determined (Section 2.6). Study 1 addressed the need for regular and ongoing support for diabetes selfmanagement behaviours by evaluating the use of an automated telephone system, TLC Diabetes, to promote dietary change in adults with T2DM. In addition, this study provided an example of the methodological issues and challenges experienced with measuring changes in absolute diet using a FFQ, and reaffirmed the need for novel prospective assessment methods capable of capturing natural variance in usual intakes. Studies 2-4 detail the development, trial and evaluation of a novel mobile phone photo/voice dietary record method, the NuDAM, aimed at minimising the burden associated with traditional prospective methods. Two dedicated studies address the data collection (Study 2) and analysis (Study 3) stages of the process of assessing dietary intake using the NuDAM, with the final study evaluates the performance of this novel method (Study 4). The measurement of diet is the central theme throughout this thesis, while the use of technology to facilitate delivery is a secondary element shared between Study 1 and Studies 2-4. This thesis primarily aimed to address gaps in the current evidence base regarding PhRs through the development, trial and evaluation of a novel mobile phone photo/voice dietary record for the assessment of nutrient intake in adults with T2DM. Therefore, the study designs, methods and statistical techniques comprising this thesis were selected to accentuate the primary link of the measurement of diet in a systematic manner and provide insight into the use of this novel dietary assessment method. Figure 3-1 illustrates the relationship between the four studies comprising this thesis. Chapter 3: Research Program 88

91 An innovative approach to the assessment of nutrient intake in adults with T2DM: the development, trial and evaluation of a mobile phone photo/voice dietary record. TLC Diabetes Nutricam Dietary Assessment Method (NuDAM) Use of a FFQ to assess absolute nutrient intakes: an example of a traditional method. Study 1: Effectiveness of the TLC Diabetes system on promoting dietary change in adults with T2DM. Collection of dietary data. Study 2: The development and trial of the NuDAM recording protocol. Analysis of dietary data. Study 3: Development and trial of the NuDAM analysis protocol Part A: The type of PSEA and estimation error associated with quantifying food items contained in photographs. Part B: The effect of the DEAT on estimation error relating to portion size and nutrient composition of Nutricam dietary records. Evaluation of the measure of nutrient intake. Study 4: Validity and inter-rater reliability of the NuDAM. Conclusions regarding the acceptability, validity and reproducibility of a mobile phone photo/voice dietary record to measure dietary intake, and effectiveness of technology applications in the nutritional management of adults with T2DM. Implications and recommendations for research and practice at the individual and group level. Figure 3-1: Overview of PhD research program. Chapter 3: Research Program 89

92 3.2 Study design and methods This section provides an overview of the design and methods used in each of the four studies comprising this thesis, with more detail provided in Chapters 4-7. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM. This study was undertaken to determine the effect of the TLC (telephone-linked care) Diabetes system on improving dietary intake and nutrition self-efficacy among a group of adults with T2DM and sub-optimal glycaemic control (HbA1c 7.5%). TLC Diabetes was an automated telephone system designed to promote diabetes self-care behaviours, including nutrition, in the form of semi-individualised dietary advice. This study was a secondary analysis of data collected as part of a randomised controlled trial titled The TLC Diabetes study ( Using the Telephone to Improve Diabetes Management ; Trial Registration Number: ACTRN ). As reviewed in Sections 2.3 and 2.4, the choice of method to assess intake is dictated by the aim of the study, the characteristics of the sample under investigation and the resources available. Decisions regarding the data collection instruments used in this study were the responsibility of the Chief Investigator of the TLC Diabetes study, and were based on these factors. In addition, it was necessary to select an established method which would allow for comparison with other similar studies. As a result the Cancer Council of Victoria food frequency questionnaire (CCV FFQ) (version 2) was selected to assess dietary intake in this study (Hodge et al. 2000). Exploration into the use of a traditional dietary assessment method in an intervention study offered evidence on the effectiveness of an automated telephone system to promote dietary change among adults with T2DM. More importantly with regards to this thesis, this study also provided an opportunity to illustrate the limitations associated with the use of the FFQ method for assessing absolute nutrient intakes, and the theoretical elements needing consideration in the development, trial and evaluation a novel dietary assessment method, the NuDAM. Chapter 3: Research Program 90

93 Studies 2, 3 and 4 Following an illustration of the key methodological issues relating to the use of FFQs to assess dietary change in a group of adults with T2DM (Study 1), the three remaining studies of this thesis investigated the components of the NuDAM. The process underpinning the assessment of dietary intake involving the collection and analysis of dietary data (Section 2.3) provided the structure for the design of these components of the NuDAM (Studies 2 and 3), while an evaluation of the performance of the novel method was undertaken to determine validity and reproducibility (Study 4). It is widely acknowledged that the majority of factors present within these stages have the potential to introduce error into a measure of diet (Section 2.3). Therefore, an understanding of these factors in the context of this new method was critical for the success of the NuDAM. Previous investigations into the use of PhRs have not explored in-detail key issues in relation to the error associated with the quantification of records (Sections 2.4 and 2.5) and the criterion validity and reproducibility of this method in a free-living situation using independent methods (Section 2.6). Studies 2-4 aimed to address gaps in the current literature base relating to portion size estimation, standardisation of the analysis protocol, validity and inter-rater reliability of PhRs. The use of a three phase structure for the development, trial and evaluation of the NuDAM allowed for the exploration of the dietary assessment process in detail, a comprehensive and systematic strategy that had not been previously undertaken. The design and development of the NuDAM followed a clear and structured plan (Figure 3-1), however the unique nature of this project ensured that this was a dynamic process. The recording and analysis protocols of the NuDAM were initially piloted independently (Chapters 5 and 6; respectively) with the findings from these studies leading to the refinement of the method prior to implementation and evaluation (Chapter 7). Thus due to the fluidity of this process, Chapters 5 and 6 contain descriptions of the original components in the context for which they were initially explored, with the subsequent modifications explained in detail in the relevant conclusion sections of these chapters. An overview of the final elements comprising the NuDAM is then presented in Chapter 7. The following section explains the study design and methods used to address the aims and research questions associated with Studies 2-4 of this thesis. Chapter 3: Research Program 91

94 Study 2: The development and trial of NuDAM recording protocol This study involved the development and trial of the Nutricam mobile phone software application. Nutricam allowed both an image of the food item and a voice record clarifying the contents of this image, to be packaged into a single file for data transfer to a central server for analysis by a dietitian. In addition to the development of this application, a standardised protocol for the recording of dietary intake using the Nutricam application was designed and trialled. Study 2 involved the recruitment of a convenience sample of 10 adults with T2DM to pilot the Nutricam dietary record and the associated recording protocol. In addition, to evaluating the protocol and the quality of the resultant Nutricam dietary record, process evaluation of the subjects attitudes towards the acceptability and useability of the mobile phone device and the recording protocol were determined (Chapter 5). This systematic approach to the evaluation of the recording protocol, instrument and resultant data allowed for any potential sources of error present within the photographic and/or voice components of the Nutricam record to be identified and subsequent modifications to the method prior to implementation of the NuDAM in Study 4. In the context of Study 2, it was also necessary to determine the nutrient intake of the group and to allow for a qualitative comparison of data recorded via two methods during the same time period. Therefore the use of an established prospective dietary assessment method was important. The estimated food record (EFR) method was selected to achieve this task as it involves less subject burden compared to a weighed food record (WFR), which was important at this stage of the method s development. At the time of this study the resources and protocols for the analysis of the Nutricam dietary records had not been finalised, therefore a semiquantitative analysis was undertaken using the amounts recorded in the EFR to examine the differences in EI recorded via each method. Furthermore, the use of the written EFR allowed for an evaluation of the useability and acceptability in comparison with the NuDAM. The conclusions formed from this study guided the refinement of the data collection elements of the NuDAM, prior to the evaluation of the validity and inter-rater reliability of the method in Study 4. Study 3: The development and trial of the NuDAM analysis protocol As discussed in Section 2.4.5, the use of a PhR differs from other prospective dietary assessment methods as the responsibility for the quantification of amounts consumed has shifted from the subject to the dietitian (or other trained investigator). This modification to the assessment process also results in a change in both the Chapter 3: Research Program 92

95 type of error, with the task of quantifying consumption now becomes one of portion size estimation (see Figures 2-4 and 2-5). Despite this key difference in the methodology compared to written records, little evidence exists on the effect of this error on the accuracy and/or reproducibility of estimated nutrient intake derived from PhRs. In the NuDAM, it was intended that this component of the dietary assessment process would occur as part of the analysis protocol during the coding of dietary records and input into a food composition database. Therefore in this thesis, Study 3 consisted of two sub-studies (i.e. Part A and Part B) which isolated and evaluated the task of portion size estimation through a comprehensive examination of the error involved in the quantification of food items contained in PhRs (Section 2.5). Two final year nutrition and dietetics students assisted with the data collection and entry for both Parts A and B of Study 3. Study 3 - Part A: The type of portion size estimation aid (PSEA) and estimation error with quantifying food items contained in photographs. This exploratory study investigated the effect of the type of PSEA on the estimation error associated with quantifying 15 single food items contained in photographs. This study was conducted in a convenience sample of primarily undergraduate dietetic students, with subjects randomised into receiving either food models or reference photographs to assist in the estimation (Chapter 6). Both food models and reference food photographs are two common PSEAs which can be used to facilitate the estimation of quantities of foods consumed in a research setting. In contrast, food models are typically more common in a clinical dietetic counselling setting. Therefore, this study was designed to compare the effect of two-dimensional (i.e. reference photographs) and three-dimensional (i.e. food models) aids on the estimation error associated with quantifying food items contained in a PhR. A secondary objective of this sub-study was to develop an analysis protocol including a tool containing a variety of PSEAs which was practical and sustainable, and could be applied in a free-living situation to assist in the analysis of Nutricam records. The conclusions drawn from Study 3 Part A provided insight into the most appropriate type of PSEA required for the quantification of food items contained in a PhR and guided the design and production of the Dietary Estimation and Assessment Tool (DEAT) to assist in the quantification of dietary items contained in the Nutricam PhR. Chapter 3: Research Program 93

96 Study 3 - Part B: The effect of the dietary estimation and assessment tool (DEAT) on estimation errors relating to portion size and nutrient composition of Nutricam dietary records. This study evaluated the effect of the DEAT on the error associated with the quantification of food items contained in two 3-day Nutricam PhR among undergraduate dietetic students. Furthermore, this experimental study was designed to establish the effect of this error on the estimated energy and macronutrient composition of the records. Data on the type of aid selected and reasons for choice of aid were also collected to determine the suitability and use of the collection of PSEAs comprising the DEAT. In addition, subjects were asked to comment on the useability and acceptability of the various components of the DEAT as an aid to quantify items contained in a PhR (Chapter 6). The unique nature of the Nutricam PhR ensured that an analysis protocol needed to be developed specifically for use with this method. This protocol comprised elements from existing assessment methods and the production of novel materials. The DEAT overcame some of the issues relating to the need for an analysis approach that was convenient and feasible for regular use within dietetic practice. The DEAT was a multifaceted PSEA containing two-dimensional aids in the form of photographs and graphics in four categories: reference foods, serving vessels, amorphous mounds, and generic shapes. Of these PSEA categories within the DEAT of only reference food photographs had been used previously to analyse PhRs (Section 2.5). In comparison, the generic PSEAs consisting of the serving vessel, amorphous mounds and geometric shapes had not been implemented in the quantification of the novel method. The DEAT is described in detail in Section 6.3. Study 3 - Part B was designed to assess the difference in the estimation error associated with quantifying two Nutricam records between two groups of dietetic students. The two test Nutricam PhRs used in this study included a mix of single food items and mixed dishes to reflect the variety of foods typically available for consumption. A number of foods selected in this study contained characteristics which have been associated with greater estimation error, such as amorphous foods and spreads, and foods served in vessels which obscure full view (e.g. bowls, mugs). The inclusion of a wide range of food items across all food types and with distinct characteristics provided an opportunity to assess the performance of all components of the DEAT, in particular the generic PSEAs. Some foods items were similar across the two Nutricam records, however all items were served and Chapter 3: Research Program 94

97 photographed exclusively for their respective record, essentially reflecting two independent dietary records. Estimates made for Record 1 isolate the effect of the use of the DEAT on the error as one group had access to the DEAT while the other group did not use any PSEAs. This first set of estimations was designed to compare the task of perception with conceptualisation and memory. In contrast, the quantification of items contained within Record 2 involved primarily a perceptual task as both groups used the DEAT. This second set of estimations provided insight into the level of agreement between the two groups. A within-group analysis of error was not undertaken due to the differing food item composition of the two records. Study 3 Part B allowed for an in-depth investigation into the level of error associated with the quantification of items present in Nutricam PhRs. In addition, findings from this study assisted with the refinement of the NuDAM analysis protocol (Section ) prior to use in Study 4. Study 4: Validity and inter-rater reliability of the NuDAM This study established the initial level of accuracy and inter-rater reliability of the NuDAM, a concept fundamental to the interpretation of measures of nutrient intake (Section 2.6). Factors inherent within a dietary assessment method have the potential to introduce error into estimates of intake, therefore Study 4 evaluated the performance of this novel method (Chapter 7). In addition, as some changes to the data collection and analysis components of the NuDAM had been made following Studies 2 and 3, it was considered essential to evaluate the experience and attitudes of subjects towards the novel dietary assessment method. As summarised in Section 2.6.1, previous studies have only established the relative validity of PhRs in comparison with traditional methods, however, as dietary assessment methods rely on self-reporting, a true measure of intake may not be possible and the validity of these methods remains subjective. The doubly labelled water (DLW) technique was used to establish the criterion validity of the NuDAM with regard to self-reported dietary energy intake (EI) against measured total energy expenditure (TEE). In addition, the relative validity of the NuDAM for estimates of energy and macronutrient intake was also determined through comparison with an established assessment method, the WFR method. As food consumed is weighed, the WFR is considered the most precise measure of usual nutrient intake and also allows for day-to-day variation to be captured. A requirement when evaluating the Chapter 3: Research Program 95

98 relative validity of a new method is independence in the source of errors in comparison to the reference method. Although the NuDAM is also a prospective measure of intake, the source and type of error resulting from the task of the quantification of amounts of food consumed is significantly different to constitute independence between the two assessment methods. The inter-rater reliability of the NuDAM was determined through an examination of the agreement between estimates of nutrient intake derived by different dietitians. As discussed in relation to Study 3 Part A of this thesis, the reliance on the dietitian to quantify items contained in photographic component of the Nutricam dietary record presents an opportunity for the introduction of error. As discussed in Section 2.6.2, a small number of similar studies have examined the variability between different observers estimations of intake derived from a PhR. In general, high levels of agreement between observers (i.e. dietitians or other trained investigators) were reported, however these studies were set in a school cafeteria or provided pre-prepared food items to subjects. As the potential for greater food variety is increased in a free-living environment, it is critical that the inter-rater reliability of the NuDAM be established in this situation. Therefore, in Study 4, three dietitians each coded both sets of dietary records, with the reproducibility of the two methods examined through a comparison of the estimated energy and macronutrient composition for the NuDAM and WFRs. 3.3 Subjects Section 2.3 of this thesis introduced the process associated with the assessment of dietary intake. Within this model two broad groups of individuals were identified: the individual for whom diet is being measured, and the dietitian (or other trained investigator). As illustrated in Figure 2-3, both groups are responsible for various tasks which underpin each stage of the dietary assessment process, and as a result each has the potential to introduce error into the measure of diet. Therefore, to be representative of this process this thesis comprised two independent population sub-groups: 1) adults with T2DM (Studies 1, 2, and 4) and 2) student dietitians (Study 3). In addition, three dietitians were used in Study 4 to code the dietary records. As the subject samples differ between studies in this thesis, a detailed description of the sample for each study is included in the corresponding chapter. Chapter 3: Research Program 96

99 3.4 Key analytical techniques In general, data were initially collected via designated forms for each study. Data were then entered directly into electronic databases, and subsequently checked to ensure consistency prior to analysis. All continuous data was tested for normality using the Kolomogorov-Smirnov test in SPSS. Statistical analysis was performed using SPSS Versions 16 and 17 (SPSS Inc, Chicago, IL, USA) using a combination of quantitative and qualitative techniques. For variables with a normal distribution, summary statistics are presented as mean (±SD) and parametric tests used. Variables not normally distributed are presented as median (range), except for variables relating to portion size estimation error which are presented as mean (±SD) to allow for comparison with previous research. Non-parametric tests were performed for all variables which were not normally distributed. Statistical significance was set at the conventional 95% level (two-tailed), however emphasis is also placed on clinical significance in the interpretation and discussion of the results. Subject characteristics (i.e. demographics, anthropometry) for each study sample are presented using descriptive statistics. Data analysis was specific to each of the four studies of the research program and is detailed below. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM This study determined the effectiveness of an automated telephone system for promoting dietary change among adults with T2DM. Data for this study were collected as part of a randomised controlled trial assessing the effect of the TLC Diabetes system on improving diabetes self-management. Dietary intake was measured using a validated 74-item food frequency questionnaire CCV FFQ (Hodge et al. 2000). Dietary Change Subjects with self-reported EI outside established criteria were excluded (Willett 1998), with the remaining subjects with complete records for baseline and 6 months included in the analysis. Outcome measures included intake (i.e. energy, protein, total fat, saturated fat, carbohydrate, fibre, and alcohol), fruit and vegetable serves, nutritional status (i.e. weight, waist circumference), and physical activity. Differences in outcome variables within the intervention and control groups at baseline and 6 months were assessed via paired t-tests (parametric variables) or Wilcoxon signed ranks tests (non-parametric variables). Change scores (post-intervention minus pre- Chapter 3: Research Program 97

100 intervention) were calculated for outcome measures and the differences between intervention and control groups were assessed using independent t-tests (parametric variables) or Mann-Whitney U tests (non-parametric variables). Plausibility of self-reported EI When evaluating self-reported dietary intake it is necessary to determine the accuracy of this measure. In the absence of a biomarker, the plausibility of selfreported EI may be assessed using the Goldberg cut-off technique. This technique compares energy intake with an estimate of basal metabolic rate (BMR) to establish reporting bias at a group level (Goldberg et al. 1991). Energy expenditure (EE) can be expressed in terms of multiples of BMR (i.e. EE:BMR) or physical activity level (PAL) (FAO/WHO/UNU 1985). Thus reported energy intake (EI rep ) validated using this technique is based on the fundamental equation, EI:BMR=PAL (FAO/WHO/UNU 1985). Since publication this method has been widely adopted in many studies, but often not correctly, resulting in a recent review by Black (Black 2000a). The cut-offs calculate the lower and upper confidence limits to determine the likelihood that the mean reported EI is a valid measure (Goldberg et al. 1991; Black 2000a). The equations for the lower and upper confidence limits are given below: EI rep :BMR EI rep :BMR PA x exp s.d. min PA x exp s.d. max ( /100) n ( /100) n Where PAL is the mean group PAL, s.d. min and s.d. max are the -2 and +2 (respectively) for the 95% confidence interval, n is the number of subjects and S is equal to: S 2 wei + 2 wb + 2 tp where CV 2 wei is the within subject coefficient of variation in EI, d is the number of recording days, CV 2 wb is the coefficient of variation in repeated BMR measurements, and CV 2 tp is the total variation in PAL (Black 2000a). To maximise the identification of implausible levels of EI, the calculation of the cut-off values should use PAL and S that are specific for the group under study (Black 2000). When applying this Chapter 3: Research Program 98

101 technique, evidence has highlighted the need to collect information on the physical activity level of individuals to improve the sensitivity and specificity of identifying likely under-reporters of energy intake (Black 2000c). Therefore, where possible, values for these variables have been obtained from the data collected as part of Study 1. Study 2: The development and trial of NuDAM recording protocol The analysis of data obtained in this study focused on qualitatively evaluating the photo and voice components of the Nutricam dietary records and determining the perceived useability and acceptability of the Nutricam dietary record. The proportion of Nutricam entries suitable to be used for nutrient analysis were calculated as a percentage of the total number of entries received, with the reasons for exclusion documented and summarised. The difference in self-reported EI between the Nutricam record and an estimated food record were assessed statistically using a paired t test. This assessment provided some indication as to possible sources of variance in the recording of intake using the Nutricam. In addition, Bland-Altman plots were compiled to establish the level of agreement between the test method and reference methods (Bland & Altman 1986). Subjects attitudes towards the use of the Nutricam dietary record are presented using frequency distributions, with written comments summarised and grouped according to themes. Study 3: The development and trial of the NuDAM analysis protocol Study 3 centred on sourcing the most appropriate PSEA to assist in the quantification of items contained in photographs (Study 3 Part A), and then followed with the trial of the DEAT to determine the level of error associated with the quantification of food items contained in the Nutricam PhRs (Study 3 Part B). Portion Size Estimation Error In both studies, error in the estimation of portion size for food items and types was calculated in terms of both absolute error and relative error based on the following recommendations by Nelson & Haraldsdottir (1998a): Absolute error (g. ml or nutrient) = estimated value (g, ml or nutrient) actual value (g, ml or nutrient) Relative error % estimated value (g, m or nutrient) actual value (g, m or nutrient) actual value (g, m or nutrient) 100 Chapter 3: Research Program 99

102 Although these variables reflected not normal distributions, summary statistics for these variables are presented as mean(±sd) based on the majority of published literature. In Study 3 Part A, Mann-Whitney U tests are used to determine significance between groups and Wilcoxon signed rank tests to assess the effect of the use of an aid within groups. In Study 3 Part B, to determine the effect of the use of the DEAT difference between groups in estimation error and the subsequent effect on the energy and macronutrient content of the two dietary records was assessed using Mann-Whitney U tests. Furthermore, graphs illustrating the cumulative percentage error for all subjects are provided in both studies. These plots are effective for comparing differences between foods and subgroups (Nelson & Haraldsdottir 1998a). Study 4: Evaluation of the NuDAM Following the development, trial and refinement of the NuDAM data collection and analysis protocols undertaken in Studies 2 and 3, the validity and inter-rater reliability of method was established. Validity of the NuDAM In this study the criterion validity of the NuDAM with regard to self-reported EI was determined through comparison with an objective measure of TEE. At the group level and under conditions of stable body weight, it is assumed that a single dietary assessment (independent of method used) provides a valid measure of intake and a single 14-day measure (using DLW) provides an accurate measure of TEE (Black et al. 1997). Therefore, the expected ratio of EI:TEE is 1.00, and the 95% confidence limits (CL) are calculated using the formula (Black & Cole 2001): ±2 x [(CV EI 2 /d)+ CV TEE 2 2r x (CV EI / d) x TEE ] In this equation, CV EI is the within-subject coefficient of variation for EI specific to the dietary assessment method used, d is the number of days to measure intake, CVT EE is the coefficient of variation for repeat measures of TEE (using DLW), and r is the correlation between EI and TEE (Black & Cole 2001). Values of EI:TEE within the 95% CL account for the day-to-day variation in intake as measured by the respective dietary assessment method in this present study, therefore values outside these limits assist to identify under- and over-reporting of intake (Black & Cole 2001). In addition, paired t-tests were used to determine differences in Chapter 3: Research Program 100

103 estimates of nutrient intake between the NuDAM, WFR and TEE, while correlations assessed the strength of the relationship between these variables. The relative validity of the NuDAM compared to the weighed record was assessed via Pearson s (for Normal distributed data) or Spearman s (for non-parametric data) correlation coefficients, and provides information on the strength of the relationship between the two methods. In addition, paired t-tests (normally distributed variables) and Wilcoxon signed-rank tests (non-normally distributed variables) were used to determine differences between dietitians for estimates of nutrient intake derived from the NuDAM and WFR. Bland-Altman plots were compiled to establish the level of agreement between the test method and reference methods for EI. This approach incorporates the mean and standard deviation of the difference between the two measures and will highlight any bias in the test method (Bland & Altman 1986). Inter-rater reliability of the NuDAM As this was the first evaluation of the performance of the NuDAM and the responsibility for quantification on intake placed on the dietitian, emphasis was placed on establishing the inter-rater reliability of this new dietary assessment method. This component of reliability evaluates the level of agreement in estimates of nutrient intake between different raters (observers) for the same subjects (Gleason et al. 2010). In the case of the NuDAM, three dietitians were used to establish the inter-rater reliability of energy and macronutrient intake. This evaluation in performance provides useful information on the dietitians contribution to the variation in intake observed in the novel method, and implications for its future applications to dietary assessment. Reproducibility will be assessed by intra-class correlation coefficient, with >0.4 regarded as good agreement (Nelson 1997). Repeat-measures ANO A and Friedman s ANO A (for normally and non-normally distributed variables; respectively) were used to determine differences for estimates of energy, macronutrient, and weight between the three dietitians. Post hoc analysis was completed with the Bonferroni method to identify which dietitians estimates differed. 3.5 Ethical Considerations The data analysed as part of Study 1 was collected as part of a large multi-centre randomised controlled trial, and received approval from the appropriate human research ethics committees associated with Monash University, University of Chapter 3: Research Program 101

104 Queensland, Queensland University of Technology, Princess Alexandra Hospital, and Royal Brisbane and Women s Hospital. In contrast, Studies 2-4 in this thesis were received approval from the Queensland University of Technology Human Research Ethics Committee (HREC) alone. Upon expression of interest, potential subjects were provided with information relating to the study detailing the objectives, requirements, expected benefits and potential risks of participation. Prior to consenting, an opportunity was provided for each individual for further clarification regarding involvement in the study. Written consent was obtained from each subject across all five studies prior to enrolment. Participation in all studies was entirely voluntary, with subjects free to withdraw at any time without consequence. In addition, all data collected was de-identified to maintain confidentiality and subject anomity. Across also studies, subjects were coded with an ID number, with a list cross referencing identifiable information stored in either a locked cabinet or secure server. All data collected during this research project is only accesible by the candiditate and supervisory team (Studies 2, 3 Parts A and Part B, and 4) or members of the TLC Diabetes research team (Study 1). The information provided in this chapter provides an overview of the research plan undertaken for this thesis. Due to the heterogeneity in the design, methods and subjects used between the four studies, the proceeding chapters outline each of the corresponding studies in greater detail. Chapter 3: Research Program 102

105 Chapter 4: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM (Study 1) 4.1 Introduction This chapter evaluates the effectiveness of an automated telephone system, TLC Diabetes, for promoting dietary change in the presence of other self-care behaviours in a group of adults with type 2 diabetes mellitus (T2DM) and sub-optimal glycaemic control (Study 1). This investigation is a secondary analysis of the data collected during the main intervention trial, and as a result, a number of issues relating to the choice of dietary assessment method present within this study were outside the control of the candidate. This study not only provides insight into the impact of TLC Diabetes for promoting nutrition self-care behaviours, but further illustrates the theoretical challenges associated with the use of a food frequency questionnaire (FFQ) to measure absolute nutrient intake in this setting. Research Question Is the TLC Diabetes system effective in promoting change in dietary intake and nutrition status among adults with T2DM and sub-optimal glycaemic control? 4.2 Methods Study design and procedure: overview of the TLC Diabetes Study Telephone-linked care (TLC) systems engage in a conversation with the user on various key health behaviours. In general, this type of technology aims to complement the care received by the user s health professional and provide supplementary support in-between standard face-to-face consultations (Friedman et al. 1997). TLC systems have been successfully implemented in a variety of settings and condition, ranging from screening for alcohol dependency (Rubin et al. 2006) or mental health issues (Farzanfar et al. 2007; Farzanfar et al. 2011) to general health promotion activities for single behaviours such as physical activity (Jarvis et al. 1997; Pinto et al. 2002), and smoking cessation (Ramelson et al. 1999). Other TLC initiatives have focused on multiple behaviours in the context of the management of chronic diseases such as hypertension (Friedman et al. 1996), chronic obstructive pulmonary disease (Young et al. 2001), asthma (Adams et al. 2003). Although effective, such systems had not been used in Australia to assist in the self- Chapter 4: Study 1 103

106 management of T2DM. The TLC Diabetes study ( Using the Telephone to Improve Diabetes Management ; Trial Registration Number: ACTRN ) evaluated an interactive automated telephone program to improve T2DM selfmanagement among Australian adults with sub-optimal glycaemic control (Bird et al. 2010). The project was jointly funded by the National Health and Medical Research Council, HCF Foundation and Queensland Health. The TLC Diabetes aimed to complement the support currently provided to people with T2DM by their health care team. The system was designed to educate, monitor and coach patients to improve diabetes self-care behaviours. TLC Diabetes consisted of four modules focusing on the key self-management areas of blood glucose monitoring, medication adherence, physical activity, and nutrition. The content and structure of each module was based on behaviour change theories (transtheoretical model (Prochaska et al. 2008), social cognitive theory (Bandura 1986), chronic disease self-management models (Fisher et al. 2005), and clinician experience. TLC Diabetes was an automated telephone system in which users are engaged in a conversation relating to these topics with semi-individualised advice and tailored feedback provided to support and maintain behaviour change. A randomised controlled trial consisting of a 6 month intervention phase, with measures taken at pre-intervention (baseline), post-intervention (6 months) and at 6 months post-intervention (12 months) evaluated the effectiveness of the TLC Diabetes system (Bird et al. 2010). Eligibility criteria included: aged years; HbA1c 7.5%; a diagnosis of T2DM of 3 months; stable pharmacotherapy type ( 3 months and dose ( 4 weeks); residing in the greater Brisbane area, access to a telephone and ability to clearly speak and understand English. Subjects were primarily recruited using a combination of print materials (local newspapers and flyers) to both community and health professionals and patient lists from three tertiary hospital outpatient diabetes clinics. Subjects were randomised into two treatment arms: the usual care (UC) group and the intervention (TLC) group. The UC group were advised to continue with the standard care provided from their health care team. In addition to being advised to continue routine medical care, subjects in the TLC group also received the TLC Diabetes program. This program consisted of access to the TLC Diabetes system, a blood glucose monitoring kit to remotely upload readings to the system, and a handbook. Subjects were required to call (at no cost) the system weekly over the 6 month intervention phase, with calls lasting 5 to 20 minutes. A total of 120 adults with T2DM were enrolled in the study. Chapter 4: Study 1 104

107 Prior to use in the current setting, the content of the nutrition component of the TLC Diabetes was reviewed by an expert panel. The module was modified to ensure that the content was suitable for adults with T2DM and relevant to the Australian food supply. The core content of the module was based on population-based dietary guidelines (National Health and Medical Research Council 2003), with advice further tailored for individuals with diabetes. In accordance with these guidelines, the information encouraged the adoption and maintenance of general healthy eating behaviours, and was not designed to promote weight loss in this cohort. The TLC Diabetes nutrition module consisted of eight calls, with the content of each relating to a specific food group (i.e. fruit) or eating situation (i.e. take-away) (Figure 4-1). The content of the nutrition module was based on social cognitive theory (Bandura 1986) with the aim to promote behaviour capability, including self-efficacy (Friedman 1998). Following an introduction to the call topic, intake over the past 3- day period was assessed in relation to topic-specific foods with feedback provided on the current intake of these foods. Subjects not meeting guidelines for the recommended number of serves for a particular food group were required to set goals to modify the dietary behaviour. During the next call, these subjects were asked to report on their behaviour in relation to this goal. In addition, for some modules following the delivery of the content, short quizzes were administered to assess impact evaluation. In addition, each subject in the intervention group received a handbook containing information on topics covered in the nutrition module, and the other modules comprising TLC Diabetes Measures A number of anthropometric, biomedical, psychosocial and behavioural measures were recorded using standardised tools and protocols at baseline (pre-intervention), 6 months (post-intervention), and 12 months (Bird et al. 2010). Those relevant to the analysis of dietary behaviour change will only be described in this thesis. Height was measured (to the nearest 0.1 cm) without shoes using a fixed stadiometer. Weight was measured (to the nearest 0.1 kg) in a fasted state, without shoes and in light clothing using electronic body weight scales. Waist circumference was measured following a standardised protocol. Perceived self-efficacy relating to nutrition was measured using a validated 5-item scale (Schwarzer & Renner 2000). Physical activity was self-reported over the past 7 days using the Active Australia questionnaire (Australian Institute of Health and Welfare 2003). Chapter 4: Study 1 105

108 Figure 4-1: Overview of the TLC Diabetes Nutrition module (Source: The TLC Diabetes Study, Monash University and Boston University). The Nutrition module for TLC Diabetes consisted of eight calls. The content of each call qualitatively assessed intake of a specific food group (e.g. fruit) or eating occasion (e.g. take-away foods) and provided feedback based on dietary guidelines for the general population. Some calls also evaluated subjects knowledge on various topics through quizzes, and also follow-up on goals set in the previous call. Dietary intake over the previous 6 month period was measured using the semiquantitative Cancer Council of Victoria food frequency questionnaire (CCV FFQ) (version 2) validated in Australian adults (Hodge et al. 2000). The questionnaire consists of 74 food items grouped into four categories: cereal foods, sweets and snacks; dairy products, meat and fish; fruit and vegetables. Subjects are asked to report the frequency of consumption using 10 options ranging from never to 3 or more times per day. Consumption of alcoholic beverages is also assessed. Questions on the usual consumption of serves of fruit and different types of vegetables; types of bread, milk, cheese, and fat spread; and amounts of milk, bread, eggs, and added sugar are also included. In addition, subjects are asked to indicate the usual portion size consumed of four selected food items (i.e. potato, vegetables, steak, and meat/vegetable casserole) with this information used to calculate a portion size factor which is applied to all items listed and then to derive an estimate of nutrient intake. Chapter 4: Study 1 106

109 The completed FFQs were analysed by the external publishers with an estimate of nutrient intake calculated using an Australian food composition database (Lewis 1995). For each subject, the macronutrient (total fat, saturated fat, protein, and carbohydrate) composition of the diet (i.e. % of EI) was derived using standard conversion factors (FAO 2003). Serves of fruit and vegetable intake were calculated by converting the food intake data (g/day) for the 13 fruit and 25 vegetable items using standard serve sizes based on published national guidelines (Department of Health and Ageing 1998). Basal metabolic rate (BMR) was calculated for age and gender using published equations (Schofield 1985) Data analysis Data collected on variables at baseline and post-intervention (6 months) were cleaned, entered and, where necessary transformed using identical methods for both time points. Nutrition self-efficacy score was calculated by summing subject responses (1=very uncertain; 2=rather uncertain; 3=rather certain; 4=very certain) to the 5-items (possible scores ranging between 5 to 20 (Schwarzer & Renner 2000), with only the scores of those who had answered all five items on the scale included in the analysis. Self-reported physical activity was converted to minutes of activity per week and grouped into three categories based on analysis guidelines (Australian Institute of Health and Welfare 2003). Subjects with incomplete FFQs or self-reported EI outside established criteria considered implausible (i.e. >16.8 MJ/d and <3.4MJ/d for males, and >14.7 MJ/d and <2.1 MJ/d for females) (Willett 1998) were excluded from the analysis. Total fat, saturated fat, protein, and carbohydrate (% of EI), fibre (g/day), alcohol intake (g/day) were compared to the national dietary guidelines for the reduction in risk of chronic disease, the Acceptable Macronutrient Distribution Ranges (AMDR), Suggested Dietary Targets (SDT) (National Health and Medical Research Council 2006), and alcohol recommendations (National Health and Medical Research Council 2009), and fruit and vegetable serves (Department of Health and Ageing 1998). In addition, changes in total fat, saturated fat, protein, and carbohydrate intakes were also assessed in absolute terms (i.e. g/day) in order to assist in the interpretation of statistically and clinically significant results. The Goldberg cut-off was calculated for an individual and the group, and applied to identify the level of mis-reporting of EI in this cohort (Goldberg et al. 1991) using the formula outlined in Section 3.4. Chapter 4: Study 1 107

110 Continuous variables were tested for normality using the Kolomogorov-Smirnov test in SPSS. Change scores (post-intervention minus pre-intervention) were calculated for each variable. To allow for comparison to previous studies, means(±sd) were used to describe all variables, however differences between groups was performed for intervention and usual care groups and for males and females using independent t-tests or Mann-Whitney U tests. Paired t-tests or Wilcoxon sign-rank tests were used to compare effects within treatment groups and gender. Associations between change in dietary intake and clinical outcomes using more sophisticated statistics were not investigated in this instance, but will underpin the more extensive analysis to be undertaken by the larger research group. 4.3 Results Subject characteristics Figure 4-2 summarises the flow of subjects through the study. At baseline 10 subjects were excluded from the analysis as no nutrient data was available due to incomplete FFQs (n=5) and self-reported EI outside plausible ranges (n=5). For the purpose of this study, these 10 subjects were excluded from the analysis at this point, as any subsequent data would have no point of reference. At the end of the intervention nine subjects had been lost to follow-up, while four subjects had incomplete FFQs with no nutrient data available, and a further two subjects had reported implausible EI. Data was not able to be collected for one male in the intervention group for self-efficacy at baseline, and for one female in the intervention group for variables relating to weight status (i.e. weight, BMI, and waist circumference) post-intervention, however both subjects were included in all other analyses. Compared to subjects included in the analysis (n=95) (Table 4-1), those excluded (n=25) reported a mean(±sd) weight of 89.5±19.1 kg, age of 59.5±8.7 years, BMI of 31.6±4.9 kg/m 2 and waist circumference of 108.2±14.1 cm. Differences between those included and those excluded (Mann-Whitney U test) were significant for weight only (U=862.5, z=-2.1, p<0.05). HbA1c was similar between those included (8.9±1.4%) and those excluded (8.9±1.3%) with the differences between groups were not significant. Chapter 4: Study 1 108

111 Baseline All subjects (n=120) Excluded from analysis TLC Group (n=60) UC Group (n=60) Excluded from analysis Incomplete FFQ1 (n=3); Implausible EI FFQ1 (n=2) Incomplete FFQ1 (n=2); Implausible EI FFQ1 (n=3) Complete FFQ1 (n=55) Complete FFQ1 (n=55) Excluded from analysis Lost to follow-up (n=4); Incomplete FFQ2 (n=1); Implausible EI FFQ2 (n=2) 6 months Excluded from analysis Lost to follow-up (n=5); Incomplete FFQ2 (n=3); Complete FFQ2 (n=48) Complete FFQ2 (n=47) Used in analysis FFQ1 and FFQ2 (n=95) Figure 4-2: Overview of subjects in the TLC Diabetes study. Starting at baseline with all enrolled subjects (n=120), the cohort is randomised into either the intervention group (TLC=telephone-linked care) or control group (UC=usual care). FFQ1 dietary intake data collected at pre-intervention (baseline); FFQ2 dietary intake data collected postintervention (+6 months); Incomplete FFQ all questions were not completed resulting in the food frequency questionnaire (FFQ) unable to be analysed to determine intake; Implausible EI estimated energy intake (EI) derived from the FFQ was outside plausible range as defined by Willet (1998) Pre-intervention Table 4-1 summarises the dietary intake and nutrition status at baseline for the 95 subjects for whom complete data was available at both time points. On average, subjects were middle- to older-aged adults with a mean (±SD) age of 56.8±8.1 years, and predominately (62.1%) male. BMI ranged from 21.8 kg/m 2 to 56.1 kg/m 2, with high levels of obesity present among the majority of the sample. A small proportion (3.2%; n=3) of the group were sedentary, while most (67.0%; n=63) Chapter 4: Study 1 109

112 subjects achieved physical activity levels sufficient to maintain health (>150 mins/week). Table 4-1: Summary of dietary intake and nutrition status of subjects at baseline. Mean (±SD) All (n=95) Male (n=59) Female (n=36) Weight (kg) # 99.3± ± ±20.3 BMI (kg/m 2 ) # 34.2± ± ±6.9** Waist circumference (cm) # 112.2± ± ±14.0 Energy Intake (MJ/day) 8.0± ± ±2.5** Protein (% of EI) 21.3± ± ±3.7 Protein (g/day) 99.4± ± ±33.9* Total Fat (% of EI) 36.2± ± ±4.5 Total Fat (g/day) # 78.5± ± ±29.6* Saturated Fat (% of EI) 13.7± ± ±3.2 Saturated Fat (g/day) 30.2± ± ±13.3 Carbohydrate (%of EI) 39.6± ± ±5.6 Carbohydrate (g/day) # 186.2± ± ±63.1 Fibre (g/day) 23.4± ± ±6.7*** Alcohol (g/day) # 10.1± ± ±3.7*** Fruit (serves/day) # 2.2± ± ±1.1 Vegetable (serves/day) 2.5± ± ±0.8*** Nutrition self-efficacy score^ 15.0± ± ±3.2 Abbreviations: ^n=58. Difference between genders significant (independent t-test or #Mann-Whitney U test): *p<0.05; **p<0.01; ***p< Overall, intake of protein was within the acceptable range (AMDR: 15-25% of EI), with 82.1% (n=78) subjects meeting recommendations. In comparison, only 18.9% (n=18) of subjects reached the AMDR for carbohydrate (45-65% of EI), and all others below the lower cut-off. Total fat and saturated fat intakes were within range (AMDR: 20-35% of energy intake, 10% of energy intake; respectively) for 42.1% (n=40), and 11.6% (n=11) of individuals, respectively. All other intakes for these two nutrients exceeded upper recommended levels. On average, daily alcohol intake was equivalent to less than one standard drink (i.e. 10 g alcohol/day). Fruit intakes of 2.2 serves/day met guidelines, however vegetable consumption (i.e. 2.5 serves/day) was less than the national recommendations of 5 serves/day. In addition, notable differences between genders were also present for weight, alcohol, and vegetable intake. A high proportion (63.2%; n=60) of subjects were classified as under-reporting energy intake at baseline, with the remainder (36.8%; n=35) reporting intakes at acceptable levels. No subjects over-reported energy intake at this time point. Chapter 4: Study 1 110

113 Between treatment groups, mean subject characteristics were similar for the majority of variables with only small, non-significant differences found, however, the indicators of weight status (i.e. weight, BMI, and waist circumference) were higher in the UC group, with weight significantly different (Table 4-2). Table 4-2: Summary of dietary intake and nutrition status at baseline by group. Mean (±SD) UC (n=47; 28M) TLC (n=48; 31M) Weight (kg) # 103.4± ±18.6* BMI (kg/m 2 ) # 35.4± ±5.6 Waist circumference (cm) # 114.6± ±14.1 Energy Intake (MJ/day) 8.2± ±2.5 Protein (% of EI) 21.5± ±4.3 Protein (g/day) 102.9± ±30.7 Total Fat (% of EI) 36.5± ±5.0 Total Fat (g/day) # 80.5± ±28.8 Saturated Fat (% of EI) 13.8± ±3.2 Saturated Fat (g/day) 30.6± ±13.1 Carbohydrate (%of EI) 39.2± ±6.5 Carbohydrate (g/day) # 187.7± ±67.8 Fibre (g/day) 23.6± ±7.1 Alcohol (g/day) # 10.2± ±18.9 Fruit (serves/day) # 2.1± ±1.4 Vegetable (serves/day) 2.5± ±1.0 Nutrition self-efficacy score^ 15.1± ±2.9 Abbreviations: UC=usual care; TLC=telephone-linked care; ^n=47 in TLC group. Difference between UC and TLC groups (independent t-test or #Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p< Intervention effect on dietary intake and nutrition status Physical activity levels among the group reduced slightly post-intervention, with 62.8% of subjects achieving sufficient levels of physical activity remained similar to baseline (n=59), while 25.5% (n=24) were insufficiently active, and 11.7% (n=11) sedentary. Similar to baseline, a large proportion (64.2%; n=61) of subjects were identified as under-reporting energy intake at the second time point, with the remainder reporting intake at acceptable levels. Exposure to the nutrition module was measured as the number of completed calls to the module. The median number of completed calls was 4 calls (range 0-8), with 45% of the group reaching this point in the module. Twelve (25%) subjects (9 males) completed all calls of the nutrition module. Compared to those that did complete the eight calls to the system, the difference between mean (95% confidence interval (CI)) change scores (independent t-test) were significantly different for those that did not complete the entire module (n=36, 22 male) for energy intake (-1.0MJ, 95% CI -2.0 MJ 0.0MJ; Chapter 4: Study 1 111

114 t(46)=-0.709, p<0.05) and saturated fat intake (-4.5 g/day, 95% CI -9.0 g/day 0.0g/day; t(46)=-2.026, p<0.05). Table 4-3 summarises the mean change scores for the UC group and TLC group. Compared to baseline, the UC group trends in the results revealed small decreases in intakes of energy, fibre and alcohol, and fruit and vegetables, while intakes of total fat, saturated fat, and carbohydrate increased within the group. A significant decrease in protein intake (g/day) was also observed. Measures of weight status all increased, with the change in waist circumference statistically significant. Similar to the control condition, reductions in the intake of energy, fibre, alcohol, and fruit serves, in addition to an increase in carbohydrate (as % of EI) were also observed within the TLC group. Within this group, small reductions in energy, total fat (g/day), saturated fat (g/day), carbohydrate (g/day) intakes, and an increase in protein (% of EI) were all statistically significant. Table 4-3: Change in dietary intake and nutritional status following the intervention. Mean (±SD) change score (n=95) UC (n=47; 28M) TLC (n=48; 31M) Mean difference in change scores TLC-UC (95% CI) Weight (kg)^,#, 0.5± ± ( ) BMI (kg/m 2 )^,#, 0.1± ± ( ) Waist circumference (cm)^,#, 1.7±5.4* -0.2± ( ) Energy Intake (MJ/day) -0.5± ±1.6** -0.1 ( ) Protein (% of EI) -0.3± ±3.5* 1.3 ( ) Protein (g/day) -7.3±24.8* -3.3± ( ) Total Fat (% of EI) 0.6± ± ( ) Total Fat (g/day) #, -3.1± ±18.5* -3.9 ( ) Saturated Fat (% of EI) 0.4± ± ( ) Saturated Fat (g/day) -1.4± ±6.9** -1.9 ( ) Carbohydrate (%of EI) 0.7± ± ( ) Carbohydrate (g/day) #, -9.1± ±40.2* -6.4 ( ) Fibre (g/day) -0.1± ± ( ) Alcohol (g/day) #, -4.0± ± ( ) Fruit (serves/day) #, -0.1± ± ( ) Vegetable (serves/day) -0.1± ± ( ) Nutrition self-efficacy score^ -0.7± ±3.4* 1.3 ( )* Abbreviations: UC=usual care; TLC=telephone-linked care; ^n=47 in TLC group. difference within UC and TLC groups (post-intervention vs. pre-intervention) (paired t-test or #Wilcoxon signed rank test): *p<0.05. difference between group change scores (TLC minus UC) (independent t-test or Mann-Whitney U test): *p<0.05, **p<0.01. Between treatment groups the effect of the intervention is outlined in Table 4-3. A significant difference in saturated fat intake (% of EI) was found for the TLC group Chapter 4: Study 1 112

115 compared to the UC group. Although the proportion of saturated fat increased in the UC group, absolute intakes of the nutrient reduced in both groups, with a difference in 1.9 g/day. Other trends showed improvements in indicators of total fat intake and vegetable serves. Furthermore, measures of weight status (i.e. weight, BMI, and waist circumference) all decreased in the TLC group, compared to an increase in these variables for the UC group. Under-reporting at both time points was moderate with 52.6% (n=50) of subjects categorised as under-reporting energy intake both at baseline and post-intervention. Furthermore, 29 subjects in the UC group were identified as under-reporting at baseline compared to 31 in the TLC group. Postintervention the number of subjects who were classified as under-reporting intake was identical between groups (i.e. 30 subjects). Trends towards improvements were found in similar nutrients for males in the TLC group compared to UC group, however changes between groups were not significant (Table 4-4). In comparison, notable differences in change scores were found between groups for females in total and saturated fat (% of EI) (Table 4-5). Table 4-4: Change in dietary intake and nutritional status for males by group. Mean (±SD) change score (n=59) UC TLC (n=28) (n=31) Mean difference in change scores TLC-UC (95% CI) Weight (kg)^,#, 0.7± ± ( ) BMI (kg/m 2 )^,#, 0.2± ± ( ) Waist circumference (cm)^,#, 1.1± ± ( ) Energy Intake (MJ/day) -0.4± ±1.6* -0.3 ( ) Protein (% of EI) -0.8± ± ( ) Protein (g/day) -7.7± ± ( ) Total Fat (% of EI) 1.3± ± ( ) Total Fat (g/day) #, -0.1± ± ( ) Saturated Fat (% of EI) 0.4± ± ( ) Saturated Fat (g/day) -0.4± ± ( ) Carbohydrate (%of EI) 1.4± ± ( ) Carbohydrate (g/day) #, -5.4± ±37.7* ( ) Fibre (g/day) -0.7± ± ( ) Alcohol (g/day) #, -7.0± ± ( ) Fruit (serves/day) #, -0.1± ± ( ) Vegetable (serves/day) -0.1± ± ( ) Nutrition self-efficacy score^ -0.9± ± ( ) Abbreviations: UC=usual care; TLC=telephone-linked care; ^ n=30 in TLC group. difference within UC and TLC groups (post-intervention vs. pre-intervention) (paired t-test or #Wilcoxon signed rank test): *p<0.05. difference between group change scores (TLC minus UC) (independent t-test or Mann-Whitney U test): not significant. Chapter 4: Study 1 113

116 Table 4-5: Change in dietary intake and nutritional status for females by group. Mean (±SD) change score (n=36) UC TLC Mean difference in (n=19) (n=17) change scores TLC-UC (95% CI) Weight (kg)^,#, 0.1± ±2.8^ 0.3 ( ) BMI (kg/m 2 )^,#, 0.0± ±1.1^ 0.1 ( ) Waist circumference (cm)^,#, 2.6±4.6* 0.2±4.4^ -2.4 ( ) Energy Intake (MJ/day) -0.6± ± ( ) Protein (% of EI) 0.4± ± ( ) Protein (g/day) -6.7± ± ( ) Total Fat (% of EI) -0.3± ±3.5*** -2.9 ( )* Total Fat (g/day) #, -7.6± ±14.5** -2.6 ( ) Saturated Fat (% of EI) 0.3± ±1.6** -1.5 ( )* Saturated Fat (g/day) -3.0± ±6.5* -0.8 ( ) Carbohydrate (%of EI) -0.4± ± ( ) Carbohydrate (g/day) #, -14.6±49.6* -6.3± ( ) Fibre (g/day) 0.7± ± ( ) Alcohol (g/day) #, 0.6± ± ( ) Fruit (serves/day) #, 0.0± ± ( ) Vegetable (serves/day) 0.1± ± ( ) Nutrition self-efficacy score^ - 0.5± ± ( ) Abbreviations: UC=usual care; TLC=telephone-linked care; ^n=16 in TLC group. difference within UC and TLC groups (post-intervention vs. pre-intervention) (paired t-test or #Wilcoxon signed rank test): *p<0.05, **p<0.01; ***p< difference between group change scores (TLC minus UC) (independent t-test or Mann-Whitney U test): *p< Discussion In this study of 95 obese adults with T2DM and sub-optimal glycaemic control, modest trends towards improvements in nutritional status, dietary intake and nutrition self-efficacy were observed following the use of TLC Diabetes, however in general, changes were small and of little clinical significance. It is recommended that the diet of an individual with diabetes comply with macronutrient composition guidelines for the general population (Bantle et al. 2008). At baseline, the collective intake of the group failed to meet dietary guidelines for the majority of the nutrients and food groups examined. Of the macronutrients investigated, only mean protein and alcohol intakes were at levels considered acceptable for the reduction of chronic disease risk (National Health and Medical Research Council 2006), while fruit intake also complied with the recommended 2 serves per day (Department of Health and Ageing 1998). Compared to a national sample of the general Australian population, protein, total fat, saturated fat, and alcohol intakes among adults with T2DM in this current study were slightly higher prior to the intervention (Australian Bureau of Statistics 1995b), while carbohydrate and fibre intakes were lower. Chapter 4: Study 1 114

117 The large proportion of subjects not meeting recommendations for carbohydrate, total fat and saturated fat intake was similar to other recent studies investigating the nutrient intake of adults with T2DM (Helmer et al. 2008; Oza-Frank et al. 2009; Vitolins et al. 2009). For example, Oza-Frank et al (2009) reported on the intakes of American adults with T2DM across five national nutrition surveys from 1988 to 2004, observing similar trends in fat and saturated fat intake. The relatively low carbohydrate intake in this current group may have been due to a conscious effort to restrict consumption of carbohydrate-rich foods in an effort to assist in glycaemic control and/or promote weight loss. Raynor et al (2008) reported that obese subjects with T2DM frequently restricted the consumption of high-carbohydrate foods and sweets in an effort to lose weight. With regard to total fat, and in particular saturated fat, the high proportion of this group consuming intakes outside recommendations is of concern. Excessive fat intakes are linked to increased body fatness and obesity (George et al. 1990; Tucker & Kano 1992; Doucet et al. 1998), with the presence of increased body fat, particularly visceral adiposity, strongly associated with insulin resistance in those with T2DM (Gautier et al. 1998; Miyazaki et al. 2002; Gallagher et al. 2009; Miyazaki & DeFronzo 2009). In addition, large intakes of saturated fat are strongly associated insulin resistance (Maron et al. 1991; Parker et al. 1993; Vessby et al. 2001). Consumption of other key nutrients and food groups did not meet current dietary guidelines. For example, differences between gender, revealed that fibre intake did not meet levels considered adequate for digestive health (i.e. AI: 30 g/day for men and 25 g/day for women) (Table 4-1), and was markedly less than the specific recommendations to reduce chronic disease risk (i.e. 38 g/day for males and 30 g/day for females) (National Health and Medical Research Council 2006). It has been suggested that individuals with diabetes are no more likely to adhere to dietary recommendations than those without diabetes (Nothlings et al. 2011). Despite reporting diets less than conducive with recommendations at baseline, small trends towards improvement in intake for some nutrients and food groups were observed for subjects following use of the TLC Diabetes system. The treatment effect of a reduction in total fat intake 1.4% is comparable to other studies utilising computer-based technologies to deliver nutrition self-management education in adults with T2DM (Glasgow et al. 1995; Glasgow et al. 1997; Glasgow et al. 2010). Interestingly, the change in fat intake observed with use of the TLC Diabetes system is similar to that resulting from one-on-one telephone education delivered by a Chapter 4: Study 1 115

118 trained counsellor for T2DM self-management (Eakin et al. 2009), highlighting that automated systems can deliver similar effects among this cohort. Furthermore, a reduction in saturated fat intake was found for the TLC group compared to the UC group, supporting findings reported elsewhere involving similar work (Glasgow et al. 1997; Glasgow et al. 2010; Eakin et al. 2009). Delichatsios et al (2001) utilised a similar automated telephone system, TLC-Eat, to deliver a nutrition intervention in sedentary adults with sub-optimal diets. Of the intake in nutrients and food groups which could be compared between these studies, a mean reduction in saturated fat of 1.7% (of EI) was reported following the TLC-Eat treatment, a change which is very close to that achieved following use of the TLC Diabetes system. In contrast to the current study where intakes of fibre and vegetables remained static, the intensive exclusive nutrition intervention of TLC-Eat reported significant improvements in fibre (4.0 g/day) and vegetable (0.8 serves/day) intakes following completion of the intervention phase. Although use of the TLC Diabetes system produced small changes in dietary intake, the clinical impact of these changes in terms of dietary significance could be considered marginal. A diet outside dietary recommendations coupled with both obesity and T2DM increases the risk of other co-morbidities such as cardiovascular disease (Haffner et al. 1998; Dauchet et al. 2006). The proportion of subjects with central obesity among this cohort was high, with the mean waist circumference for both genders outside the recommended guidelines of 80 cm for women and 94 cm for men (Lean et al. 1995). A priority for individuals with T2DM is the achievement and maintenance of a healthy weight status (Bantle et al. 2008). Modest improvements in measures of body weight (and therefore BMI) and waist circumference in the TLC group suggest that the use of the TLC Diabetes system may also support behaviours relating to weight loss. A similar, small change in BMI was also reported in another study using a computer-centred approach to the delivery of nutrition information for T2DM (Glasgow et al. 1997). Decreases in weight, BMI and waist circumference were modest in the TLC group, compared to intensive individualised counselling provided by a dietitian reported in other studies. For example, Ash et al. (2003) reported mean reductions in body weight of 6.4kg and waist circumference of 8.1 cm in a sample of middle aged men with T2DM following weekly contact with a dietitian over a 3 month period (Ash et al. 2003). Chapter 4: Study 1 116

119 In addition to subtle improvements in dietary intake and weight status, nutrition selfefficacy also increased in the TLC group following the intervention compared to a decrease in the UC group. Self-efficacy relates to perceived ability to overcome barriers relating to the maintenance of behaviour change (Schwarzer et al. 2007), and is considered an essential element for successful self-management (Lorig & Holman 2003). Among individuals with T2DM eating patterns are dictated by diabetes management knowledge with self-efficacy a mediator of behaviour change (Savoca & Miller 2001). The TLC Diabetes system is designed to coach, motivate and educate on key diabetes self-care behaviours, including nutrition. The significant improvement in the confidence of subjects in their ability to overcome barriers to healthy eating, further supports the conclusion that the TLC system fulfils its design objectives. A number of additional factors may offer some explanation as to the modest observations in dietary intake observed following the intervention. Firstly, intakes were under-reported by a large proportion of the study sample at both time points. Mean weights of 113kg are not usually sustained on energy intakes of 7-8MJ/day, as reported by both the women and men in this study. In the present study, underreporting of energy intake was found for 52.6% of individuals. Three other studies involving adults with T2DM have used calculated requirements to determine the validity of self-reported intake, with all finding high levels of under-reporting in this cohort. Amend et al (2007) employed presumed energy requirements to validate a combined 2-day 24R and FFQ among obese African American women, finding 47% of the group under-reported energy intake. Samuel-Hodge and colleagues (2004), also examined obese African American women using three 24R and energy expenditure estimated via an accelerometer, with 81% of subjects classified as under-reporters. Finally, Adams (1998) used predicted energy expenditure to examine accuracy of self-reported dietary intake among overweight adults with T2DM, concluding that appropriately half of the sample under-reported. The level of under-reporting in this current student of adults with T2DM is comparable to these studies. The cause of this inaccuracy in self-reported intake is difficult to determine, and is likely due to a combination of factors including certain subject-specific factors, in particular the presence of obesity. Secondly, the mis-reporting of diet present among this group of adults with T2DM may be due to the dietary assessment method used. The low carbohydrate intake observed in this study may be explained, at least in part, by the method used to Chapter 4: Study 1 117

120 measure diet. A study by Xinying and colleagues (2004) found significant differences between the CCV FFQ and a 3-day WFR, with the intake of carbohydrate derived from the CCV FFQ under-estimated resulting in an overestimation of saturated fat intake. The authors suggested that a possible explanation for this result may be due to lack of a comprehensive listing of common carbohydrate-rich key foods, as items such as soft drink and muesli bars are missing from the FFQ. Furthermore, it was also suggested that other factors relating to the design of the questionnaire may have biased subject responses towards under-reporting of some items due to the upper end frequency options condensed into 3 or more times per day (Xinying et al. 2004). A subsequent study examining the relationship between the CCV FFQ and a longer 200-item FFQ in men, found an acceptable level of agreement between methods for intakes of energy, total and saturated fat, but not for protein, carbohydrate or fibre (Keogh et al. 2010). The consistent bias in the measurement of carbohydrate intake has been noted as of potential for concern in the use of the CCV FFQ (Keogh et al. 2010). Another contributing factor to the under-estimation of intake using the CCV FFQ may be a result of the method used to calculate quantities of foods consumed. The CCV FFQ derives estimates of nutrient intake based on the calculation of a portion size factor from the typical serve size consumed of four selected items (i.e. potato, vegetables, steak, and meat/vegetable casserole). In comparison, the other FFQs require the individual to indicate the quantities consumed of the items listed (Keogh et al. 2010). This design aspect of the CCV FFQ could potentially also explain some of the variation in intake particularly for some carbohydrate-rich foods, such as pasta and rice, which can be consumed in a large range of portion sizes. This study further emphasised the limitations associated with the use of a FFQ to estimate nutrient intake in studies evaluating dietary change. Originally, the FFQ was designed to study associations between diet and disease through the collection of descriptive qualitative data about specific food items and nutrients of interest (Willett 1998). The method then evolved to determine absolute levels of typical intake (Willett et al. 1985; Block et al. 1986), however, the ability of a FFQ to assess intake is restricted by both the predetermined number of items listed on the questionnaire and the capacity of the individual to recall foods consumed over vast time periods (up to 1 year). As a result of these inherent weaknesses the accuracy of the FFQ when used to obtain a measure of nutrient intake is lower compared to Chapter 4: Study 1 118

121 other methods (Bingham et al. 1994; Bonifacj et al. 1997; Ambrosini et al. 2003b; Subar et al. 2003). Despite these noted limitations associated with the FFQ, certain strengths such as the relatively low administration costs and minimal subject burden have seen this method used in intervention studies to evaluate the treatment effect on usual dietary intake. In this current study, the FFQ was chosen to assess intake for similar reasons with other assessment methods considered too inconvenient for subjects. In addition, selection of this method allowed for comparison with other similar studies involving telephone-delivered counselling intervention among Australian adults with T2DM (Young et al. 2007; Eakin et al. 2008; Eakin et al. 2010). Thirdly, it is possible that the number of calls completed in relation to the TLC Diabetes nutrition module may have also influenced the effect on dietary behaviour change. Twelve subjects completed the entire nutrition program, while less than half the group reached the mid-point (i.e. up to and including call #4) of the module. During the first half of the module information on fruit, vegetables, meat, snacks and desserts was provided. Therefore, it may be that the specific nutrition education messages relating to these topics along with goal setting techniques employed during these calls may be responsible, in part, for the observed treatment effect to dietary intake. This moderate exposure to the nutrition education materials may be responsible for some of the modest overall dietary changes observed for this group. For example, the fact that the importance of high fibre cereal choices in the diet was not discussed until call #5 may explain the lack of change in fibre intake observed following the intervention. In addition, T conversations regarding incorporating more vegetables into the diet and the choice of lower fat cuts and types of meats may correspond to an overall treatment effect of an increase of 0.2 vegetable serves/day and decreases of 1.4% and 0.9% in total and saturated fat intakes, respectively. Significant differences for those that completed all eight calls of the module compared to those that did not, were found for energy and saturated fat intake (g/day) only. Non-completion of the eight call nutrition module was likely due to the end of the intervention period, as the mean(±sd) number of total calls to the TLC system was 18±6. The call schedule for the TLC Diabetes system divided the Nutrition module into two blocks at calls 9-12 and 21-24, with system access provided only during the designated 6 month intervention timeframe (i.e. 24 weeks). Chapter 4: Study 1 119

122 Therefore, given the schedule of the nutrition module in the larger context of the TLC Diabetes system, it is not unexpected that the entire content of nutrition module was not received by all subjects in this group. Finally, a lack of dedicated dietary self-monitoring among users of the TLC Diabetes system may have contributed to the small changes in intake observed. Nutrition selfmanagement has been identified as the most challenging self-care behaviour (Nagelkerk et al. 2006). Section of this thesis emphasised the importance of self-monitoring in the context of diabetes self-management. The TLC Diabetes system consisted of a sophisticated mechanism for the self-monitoring of blood glucose, with users able to upload recent levels and receive feedback weekly during the call to the system. In contrast, subjects in this current study, who were not meeting current dietary recommendations for fruit and vegetable intake were required to set food group specific goals (i.e. to increase fruit intake to 2 serves/ day) during calls #3 and #5 of the module. Goals were evaluated during the following call, however subjects were not encouraged to self-monitor diet. The TLC Diabetes system consists of innovative technology to deliver self-management support to individuals with T2DM, the use of similar technology applications to assist the user in the self-monitoring of diet would complement the nutrition information provided and act as a mechanism to reinforce key behaviours as observed in weight management (Burke et al. 2011). Limitations This study has several limitations which were outside the control of the candidate. The use of an estimated BMR in this group may have exaggerated the actual level of under-reporting in this current study. Black (2000a), proposed that the equations used to calculate BMR (Schofield 1985) are based on individuals with weights <83 kg for men and <76 kg for women. Therefore given the high level of obesity among this cohort, the proportion of under-reporters is potentially inflated. The accuracy of self-reported dietary intake is considered a fundamental concept to establish when applying the findings from nutrition interventions. Therefore, in the absence of independent biomarkers of intake, the use of the Goldberg cut-off technique, although with limitations in this setting, is a justified alternative. In addition, as the use of this particular FFQ had not been validated for use in this population group, the inclusion of a second dietary assessment method may have assisted in the interpretation of the estimates of nutrient intake. Chapter 4: Study 1 120

123 Furthermore, the exclusion of some subjects reduced the sample size and power of this study to detect significant changes in dietary intake. The main study followed an intention to treat protocol, however in this sub-study it was necessary to exclude some subjects with incomplete data at both time points. Self-administration of the FFQ in the setting of this study resulted in missing data due to incomplete or implausible responses. Although care was taken by the study team to ensure that subject responses were complete prior to the conclusion of the visit, this was not always possible. These subjects were excluded from the main analysis as it was deemed inappropriate to correct for this missing data as incomplete or implausible estimates of energy intake were a direct result of the method used, and therefore an inherit limitation. 4.5 Conclusions Overview The majority of changes in nutritional status, dietary intake and nutrition self-efficacy observed following use of the TLC Diabetes system were modest and of little clinical significance. Given the proportion of mis-reporting of intake present, any conclusions formed on the effectiveness of the intervention need to be treated with caution. Furthermore, key limitations inherent in the FFQ used in this study to assess changes in absolute nutrient intake emphasise the practical implications for the interpretation of results due to the choice of assessment method. In particular, these findings reinforced the belief that the use of a FFQ which measures diet via a closed list of foods is not always suitable for assessing absolute nutrient intake, unless a comprehensive list of foods relevant to the group under investigation are included. This statement does not suggest that if a different method had been used in this study that the outcome in relation to dietary change would have been significantly improved. No dietary assessment method is without error. Nor is this conclusion intended to be an indictment on the use of FFQs in general to estimate nutrient intake. Rather it aims to further highlight the importance of selecting the appropriate method for the purpose of the study. It is possible that the TLC Diabetes system could support individuals to adopt and maintain key nutrition-related behaviours in line with self-management goals, however further investigations using different dietary assessment methods to evaluate the effect of such a system would be needed to justify its use in the nutritional management of T2DM. Chapter 4: Study 1 121

124 4.5.2 Relevance to research program Conclusions drawn from this example of the use of a FFQ to measure dietary change provide additional evidence of the need for dietary assessment methods that capture diet variety, ideally allowing for the day-to-day variability in intake to be recorded. Prospective methods meet these criteria by allowing for intake to be captured in real-time, however traditional written record methods, such as WFRs and EFRs, are associated with high subject burden. Novel PhRs offer a potential alternative, however methodological issues and a lack of objective evaluations as to the performance of this method in a free-living situation restrict their application in practice (Section 2.4.5). Furthermore, this study not only highlighted the weakness of this method to measure dietary change in this setting, but also reinforced certain elements in the choice of study design and methods which were of importance to the remaining studies Implications for future practice The modest changes in nutrition parameters observed following exposure to general dietary advice provided by TLC Diabetes further highlights that the tailored advice provided by a dietitian through MNT is still a fundamental factor in the care of T2DM. TLC Diabetes has been designed to support traditional multi-disciplinary management of T2DM and has never been promoted as a replacement. Therefore such systems may be more useful as a means to reinforce and support individualised nutrition education provided by a dietitian, although further evidence of the effectiveness of TLC Diabetes would be needed before this could occur. The interactivity aspect of ICTs for chronic disease self-management could be used to complement the delivery of education through traditional means, by reinforcing skills learnt and promoting self-reliance. In particular, there is a need for other innovative technologies to support dietary self-monitoring so as to complement and sustain the benefits of self-management and individualised MNT. Chapter 4: Study 1 122

125 Chapter 5: Development and trial of the NuDAM recording protocol 1 (Study 2) 5.1 Introduction This chapter explores the data collection components of a novel mobile phone photo/voice dietary record used within the Nutricam dietary assessment method (NuDAM). The development of the tool and protocol to record dietary data are described in detail. An evaluation of the data quality of the Nutricam mobile phone photo/voice dietary records undertaken in adults with type 2 diabetes mellitus (T2DM) (Study 2). Attitudes towards the useability and acceptability of this novel method were also examined. This pilot study provided evidence on the feasibility of the Nutricam tool and associated protocol and guided the refinement of the data collection component of the NuDAM prior to evaluation (Chapter 7). Research Questions Does the Nutricam recording protocol result in a dietary record suitable for the estimation of nutrient intake? Compared to an estimated food record, do individuals perceive the Nutricam dietary record as an acceptable and useable method for recording dietary intake? 5.2 Methods Development of the Nutricam application and recording protocol Nutricam mobile phone application The concept of using a mobile phone photo/voice dietary record was devised by the candidate in conjunction with Professor Susan Ash. This initial concept was then further developed through consultation with Associate Professor Philippa Lyons-Wall and Associate Professor Anthony Russell. Alive Technologies Pty Ltd ( was contracted to develop the mobile phone software application, called Nutricam. 1 A significant proportion of this Chapter has been published in a peer-reviewed journal: Rollo, M.E., Ash, S., Lyons-Wall, P., Russell, A. Trial of a mobile phone method for recording dietary intake in adults with type 2 diabetes: evaluation and implications for future use. Journal of Telemedicine and Telecare, 2011, 17: Chapter 5: Study 2 123

126 The application combined the phone s camera and voice recording functions to allow the user to capture both a photograph of the items for consumption and a voice recording detailing the contents of the photograph. This information is packaged as one entry (i.e. file) and sent to a secure website for analysis by a dietitian (Figure 5-1). The Nutricam program was installed onto Sony Ericsson K800i mobile phones (Sony Ericsson Mobile Communications AB, Sweden) and the camera resolution set to 1 megapixel (1280x960). To facilitate use of Nutricam the flash settings on the mobile phone was set to allow for the automatic deployment in conditions of low light. Nutrition assessment via Nutricam is in the form of a prospective method, with dietary data collected prior to consumption. As opposed to other real-time methods of written WFRs or EFRs which require the individual to measure amounts of food consumed, the responsibility for the quantification of items is placed on the dietitian. This approach is designed to reduce subject burden and minimise bias associated with mis-reporting intake via prospective methods. Figure 5-1: Schematic overview of the Nutricam application (Source: Alive Technologies Pty Ltd). Using the Nutricam mobile phone application, the user is required to collect both a photograph and a voice record description of the food item(s) for consumption. This information was packaged as one file and sent to a website for analysis. Nutricam recording protocol The recording of dietary information using Nutricam consists of two distinct components visual and audio. Using information from similar published studies Chapter 5: Study 2 124

127 involving PhRs, a standardised recording protocol for use with Nutricam was developed by the candidate. Previous work has incorporated a fuducial marker or reference object in the photograph to scale the items contained in the image for analysis. These objects have taken a number of forms including generic items such as a stylus (Wang et al. 2006; Wang et al. 2002), eating utensils (Aoki et al. 2006) or specific objects with geometric patterns such as cards (Six et al. 2010; Weiss et al. 2010). Others have not included a reference object but instead standardised the distance of the camera to the food item (Elwood & Bird 1983; Martin et al. 2009). For the current study a reference object in the form of a prompt card with known dimensions and including a brief summary of the instructions for recording intake using Nutricam was designed. The purpose of the card was two-fold: 1) to act as a reference object during the quantification of items contained in the photograph, and 2) to assist in the consistency of recording using Nutricam entries. The card was attached to the back of the Nutricam phone at all times and was required to be placed next to the items to be photographed. The Nutricam application was active at all times and linked to the phone s camera and voice recording capabilities. Removal of the lens cover initiated the camera function and the process of recording dietary intake using Nutricam. Subjects were instructed to hold the Nutricam mobile phone at an angle of approximately 45 when recording the image to assist in gauging the depth of the items (Wang et al. 2002). After capturing a photograph, a preview of the image appeared on the phone screen, allowing subjects to review for clarity and inclusion of all items (including the reference object). If items within the image were not clearly visible, subjects were required to re-record the image. Subjects are then prompted to make a voice recording describing the contents of the image and are required to list the following for each item (as applicable): Location, Name, Type (i.e. low fat or diet ), Brand/Product name, and Preparation/cooking method of each item. This information was based on a similar level of detail required for EFR or WFR methods (Gibson 2005), and was considered the minimum description necessary to allow for the coding of dietary records into a food composition database. The time available to record a voice description of the items captured in the photograph was Chapter 5: Study 2 125

128 set to 20 seconds. Following the recording of the voice record, closure of the lens cover resulted in the transfer of the photograph and associated voice record (packaged as one entry ) to the Nutricam website (Appendix A). The secure website facilitated the simultaneous collection of Nutricam dietary records from multiple subjects. Entries were automatically date and time-stamped, and stored under a unique identification number. Figure 5-2 summarises the stages associated with recording intake using Nutricam, while Figure 5-3 provides an example of one day s intake recorded using Nutricam as displayed on the website. Figure 5-2: Recording dietary intake using Nutricam. 1) The Nutricam application is installed on to a mobile phone. 2) The user is required to capture a photograph of the food item(s) for consumption, ensuring that all items and the reference object (i.e. prompt card) are clearly visible. 3) Following a standardised protocol, the user is then prompted to collect a voice record description of the food item(s) depicted in the photograph. 4) Both the photograph and voice record are packaged as one file and sent to a dedicated website for analysis. Chapter 5: Study 2 126

129 Figure 5-3: Screenshot of Nutricam dietary record displayed on website. Dietary intake recorded using a Nutricam mobile phone is displayed for one recording day. A photograph (displayed as a thumbnail image) and a link to the voice record is provided for each entry, along with the time that the Nutricam entry was captured. Selecting a photograph will redirect the user to a new screen where a larger photograph is displayed and the voice record can be heard for that entry Study design and procedure The study was approved by the Queensland University of Technology Human Research Ethics Committee. Individuals with T2DM who were ineligible to participate in the TLC Diabetes study (see Section 4.2.1) and had expressed interest in participating in other research projects were contacted and invited to participate. Ten subjects were required to the pilot the Nutricam recording protocol and recruitment ceased once the required number of subjects had been reached. After providing written informed consent, the height and weight of each subject were recorded. Height was measured to the nearest 0.1 cm without shoes using a portable height scale (Model PE087, Mentone Educational, Victoria, Australia). Weight was measured without shoes and in light clothing using body weight scales and weight (measured to the nearest 0.1 kg) (Model , SECA, Germany). Subjects then completed a questionnaire to collect information relating to demographics (i.e. ethnicity, country of birth, occupation), history of recording dietary intake and mobile phone use (Appendix B). Subjects were required to use Nutricam to record all food items prior to the consumption using the protocol outlined in Section (Appendix A). Any food Chapter 5: Study 2 127

130 which was served but not consumed was also recorded at the completion of the eating occasion. At the same time, subjects were required to document all items prior to consumption at each eating occasion using a written estimated food record (EFR), with quantities measured using household utensils such as measuring cups and spoons (Gibson 2005). Recording of intake occurred over three consecutive days (two weekdays and one weekend day). In addition, to receiving comprehensive verbal and written instruction, a demonstration on the use of both dietary recording methods was provided to all subjects. Following completion of the recording period, subjects were required to complete a written questionnaire on the useability and acceptability of the Nutricam device (Appendix B). Nine closed questions regarding the ease of use of Nutricam; the recording of consumed and leftover items; length of time to record using Nutricam compared to written food record; confidence to use Nutricam again; maximum number of days willing to use Nutricam again to record intake; and preferred recording method. Subjects were asked to elaborate if all consumed and/or leftover items were not recorded using Nutricam and to provide a reason for the preferred method. Subjects were also asked to suggest any improvements to the Nutricam method. The feasibility analysis consisted of an evaluation of the quality of the dietary data contained in the Nutricam record. The photographic and voice recording components of each Nutricam entry were assessed independently by the candidate. A photograph was deemed acceptable to be used for the quantification of energy intake if all food items could be seen clearly and the reference card was visible. The voice component of each Nutricam entry was determined to be adequate for the identification of dietary items and coding of these items into a food composition database if the four key descriptors of name, type, Brand/Product name, and preparation/cooking method were listed (where appropriate) for each item. For each subject, energy intake (EI) was estimated from the dietary records of both methods by one dietitian (Candidate). As the NuDAM analysis protocol and resources had not been developed or trialled at the time of this study, an estimate of EI for Nutricam was derived with reference to the EFR. In this instance to quantify energy intake for the Nutricam method the calculation was based on the quantities recorded for the same items in the EFR. For all subjects, EI was estimated for each day of the 3-day EFR using the AUSNUT 1999 food composition database Chapter 5: Study 2 128

131 (Australia New Zealand Food Authority 2001) in the food analysis program FoodWorks (version 5.1, 2007, Xyris Software, Brisbane, Australia) and then averaged to obtain mean daily EI. Food items which were missing in either method, when compared to the other were categorised as either a meal, snack (Drummond et al. 1996), beverage, or other (i.e. additional items added to foods, such as sugar, dressing, etc) Data analysis All statistical analyses were performed using the SPSS for Windows (version 16.0, 2007, SPSS Inc, Chicago, Illinois). Descriptive statistics were used to report demographic and anthropometric characteristics, attitudes towards the useability and acceptability of the device, estimated energy intake from both methods, and the quality of the Nutricam records. A paired t-test was used to compare the difference in estimated energy intake between methods. A Bland-Altman plot was used to assess the level of agreement between self-reported energy intake as recorded by the Nutricam and EFR methods (Bland & Altman 1986). 5.3 Results Subject characteristics Ten adults with T2DM aged years were enrolled in the study. At the time of the study the BMI of the group ranged between kg/m 2, with five subjects reporting a restriction in intake to assist with weight loss. Six subjects had kept a written food record in the past. Nine subjects had used a mobile phone previously, with three using a mobile phone to take photographs Evaluation of Nutricam for recording dietary intake Table 5-1 shows the mean (±SD) daily energy intake for each subject as recorded by both the food record and Nutricam methods. Using the EFR as the reference method, individual differences in energy intake between the two records varied between -0.4 to 2.2MJ/day. On average, energy intake was significantly underrecorded using Nutricam by -0.6±0.8 MJ/day (t(9) = 2.54, p>0.05). The differences between the two measures of energy intake for each subject are shown in Figure 5-4. Discrepancies between records were noted for nine subjects, with half of the group had discrepancies in energy intake that were within ±0.5 MJ/day. Over the three-day period discrepancies between both assessment methods were identified for 58 items. Fifty items consumed and recorded by subjects using the written food record were not subsequently captured via the Nutricam device. Beverages (n=25) Chapter 5: Study 2 129

132 were the items most frequently not recorded using Nutricam, followed by snacks (n=17) and meals (n=8). In comparison, eight dietary items (i.e. beverages (n=3), meals (n=2), snacks (n=2), and other (n=1)) present in the Nutricam records were not recorded in the EFR. Table 5-1: Summary of subject characteristics and estimated energy intake (Study 2). ID Mean (±SD) EI Difference in Mean EI Gender Age BMI (yrs) (kg/m 2 (MJ/day) between methods ) EFR Nutricam MJ/day# 01 M ± ± F ± ± F ± ± M ± ± M ± ± F ± ± M ± ±1.3 < M ± ± M ± ± F ± ± All (Mean±SD) ± ± ± ± ±0.8* Abbreviations: M=male; F=female; BMI=body mass index; EI=energy intake; EFR=estimated food record; #Difference in Mean EI = Mean EI EFR Mean EI Nutricam; difference between methods (paired t-test) significant:*p<0.05. Figure 5-4: Difference in energy intake as measured by Nutricam and estimated food record (Bland-Altman Plot). EFR=estimated food record Chapter 5: Study 2 130

133 Collectively of the items not recorded by either methods, meals contributed the greatest proportion (67.1%) to the difference in estimated energy intake followed by beverages (16.4%), snacks (16.2%) and other (0.2%). Of the 144 Nutricam entries received, 66.0% contained a voice recording suitable to be used to estimate intake, 70.8% included a photograph of adequate quality, and 60.4% contained both a voice recording and photograph of suitable quality Useability and Acceptability of Nutricam Table 5-2 summaries subject responses to the evaluation questionnaire regarding use of Nutricam. All subjects reported that the Nutricam mobile phone was easy to use and the majority found that the time taken to record using Nutricam was shorter compared to recording using the written EFR. All subjects felt confident that they could use Nutricam again to record dietary intake, with five subjects willing to use Nutricam again to record their diet for a maximum period of 30 days. Seven subjects preferred the Nutricam method for recording intake, whilst three favoured the written EFR. Table 5-2: Subject responses following use of Nutricam to record dietary intake. Statements, as presented Responses for all subjects (n=10) 1. Overall, I found the Nutricam mobile phone easy to use: Strongly agree = 6; Agree = 4 2. At home, I found the Nutricam mobile phone easy to use: Strongly agree = 6; Agree = 4 3. Away from home, I found the Nutricam mobile phone easy to use: Strongly agree = 3; Agree = 4; Neutral = 3 4. Did you photograph and record all food and drink items that you consumed during the test Yes = 2; No = 8 period using the Nutricam mobile phone: 5. Did you photograph and record all leftover food and drink items during the test period No = 10 using the Nutricam mobile phone: 6. Overall, compared to the written food record, the time taken to record my food and drinks using the Nutricam mobile phone was: Shorter =6; About the same = 2; Longer =1; Not sure =1 7. I would be willing to use the Nutricam mobile phone again to record all food and drink items I consumed for a maximum time period of: 3 days =1; 5 days = 2; 7 days =1; 14 days =1; 30 days n=5 8. Do you feel confident that you could use the Nutricam mobile phone again to record all food and drink items that you consume over a set time period: 9. Which of the two methods did you prefer: Abbreviations: EFR = estimated food record Yes = 10 Nutricam mobile phone =7; Written EFR =3 Chapter 5: Study 2 131

134 5.4 Discussion This study showed that energy intake was significantly under-reported by -0.65±0.81 kj (p<0.05) with the Nutricam method compared to the EFR (Table 5-1). Discrepancies existed in the dietary records of both methods for nine subjects, with one subject recording identical entries for both records (ID#2). Another subject (ID#7) had a marginal difference of ~33 kj/day, while the remainder of the group reported discrepancies ranging between MJ/day. Although intake was under-recorded using the Nutricam method, the difference between methods demonstrated that no systematic bias existed at levels of low and high energy intake (Figure 5-4). A larger number of dietary items were not recorded using Nutricam (n=50) compared to the EFR (n=8). Missing items were present in the Nutricam records for nine subjects as opposed to four subjects who had incomplete EFR. The omission of these food items, especially meals, contributed to the difference in recorded EI between methods and may have the potential to bias the measure of intake at the individual level, however two factors may offer an explanation for this variability. Firstly, the extent to which the under-recording occurred with Nutricam is likely to have been exacerbated by the use of the written EFR at the same time. When using Nutricam some subjects indicated that it was not important to make additional entries for items which had been captured earlier in the day and were then consumed again throughout the same day, such as beverages. For example, one subject commented in regards to not recording repeat mugs of tea if you have seen it once that is enough. However, these individuals recorded such items on multiple occasions throughout the day using the written EFR. This observation indicates a misunderstanding as to the independence of the two dietary assessment methods tested in this study may have been present among the group, and may assist to explain the level of missing information contained in the Nutricam record in comparison to the written EFR. Secondly, not remembering to record at the time of eating was commonly reported as the reason for a failure to document intake using Nutricam. In the current study one individual remarked, If not an actual meal is consume, e.g. a few nuts or grapes, it is easy to forget to recor. Subjects who forgot to record meals also did not capture snacks and/or beverages, contributing to a greater difference in estimated EI between recording methods for these individuals. Not remembering to record intake has also been reported in similar studies using PhRs (Boushey et al. Chapter 5: Study 2 132

135 2009; Higgins et al. 2009). The two methods used in this study required the individual to record dietary information prospectively. Although items could be documented after eating using either method, the opportunity to document items after consumption using the written EFR method is easier to achieve. To document intake after eating using the EFR would require the individual to simply write a description of the item and estimate the amount consumed into the record. In comparison, using Nutricam would require the individual to source identical items in the same quantities in order to capture a photograph. Furthermore, the automatic allocation of the time and date of the supplemental entry would differ from the actual consumption. The relative ease to add to the EFR after eating compared to Nutricam may offer an explanation to the difference in estimated EI calculated between recording methods in this study. Therefore, it was concluded that an additional procedure is necessary to ensure that dietary data not recorded with Nutricam is captured and a more accurate picture of nutrient intake is established. This modification to the Nutricam recording protocol is discussed in detail in Section A review of the recorded data using Nutricam revealed important issues impacting on the data quality of the resultant photo and voice components of the mobile phone dietary records, and which required further consideration for future use of this novel method. Of the photographs which were deemed not suitable, the majority consisted of food items which were concealed by other items contained within the same serving vessel (e.g. bowl, plate). These entries often consisted of food items which were comprised of multiple ingredients that needed to be quantified separately. For example, in future applications of the method, entries containing photographs of breakfast cereal and milk served in the same bowl were consistently identified as unacceptable due to difficulty in estimating the quantities of the components depicted. The inability to account for items not clearly visible in the photograph had been previously identified as contributing to the under-estimation of the nutrient content in these types of dietary records (Wang et al. 2002; Kikunaga et al. 2007). Findings from this study suggested that to improve the photo quality of the Nutricam record, modifications to the current protocol were necessary, particularly for recording those items comprised of multiple ingredients. However, care needed be taken to ensure that such a change did not alter the behaviour associated with serving foods, as the resulting portions may not be a true representation of usual intake. In light of this, Chapter 5: Study 2 133

136 the Nutricam recording protocol was altered for some items and mixed dishes only. For example, when recording breakfast cereal and milk individuals were required to make two entries; the first entry consisting of the dry cereal only, followed by the second entry depicting the cereal with the milk added. In general, the voice component of the Nutricam record contained descriptions of items which provided insufficient detail for coding and analysis, with the preparation and/or cooking methods often not provided. Mixed dishes without an adequate description of the key component ingredients were also common. Although in this current study training was provided prior to recording intake and a prompt card included to promote consistency in the use of Nutricam, the omission of such information emphasised a trend towards non-compliance with the recording protocol. Such a finding is not unusual when individuals have been asked to record dietary intake, with clarification of the items recorded often necessary (Vuckovic et al. 2000). In a study of 100 women (aged 49±5 years) investigating the adequacy of information contained in a 1-day mailed written food record, Suko et al (2010) found 86% of subjects had descriptions of food items were insufficient for coding to calculate nutrient intake. Therefore, in this current study it was concluded that an additional mechanism would be necessary to clarify the dietary information contained in the Nutricam record to ensure data collected is complete, and assumptions made by the dietitian during the coding of dietary records were limited. One factor that may explain, at least in part, the lack of adherence with the recording protocol was the length of time allocated for the voice record. For this pilot study, the time available to record a description of the items contained in the photograph was set at 20 seconds. Subjects reported that the time period was not sufficient, particularly when recording an eating occasion which consisted of multiple items and/or complex mixed dishes. Following this study, the Nutricam application was modified and the voice recording time extended to 99 seconds to allow the user to include more detailed information. The positive response among subjects towards the acceptability and useability of Nutricam provided further reinforcement of the potential use of this novel dietary assessment method in this population group. The Nutricam method was well received among the group of older individuals with T2DM, with the mobile phone photo/voice device preferred over the pen and paper approach of the EFR. Those who favoured the Nutricam method collectively reported that it was easier and less Chapter 5: Study 2 134

137 time consuming to record. In comparison, preference for the written EFR was based on the belief that the method provided more detailed intake information such as Brands (n=2) or required less preparation to record ( all foo nee e to be assemble first before eating.some foo lost heat as a result ) (n=1). These comments highlight that there may have been some confusion over the Nutricam recording protocol. Subjects were required to include specific descriptions of the items consumed in the voice recording, however sufficient detail was often not included in this component. Similarly, subjects were instructed not to change their eating behaviour to simplify the recording process, such as arranging all food items prior to consumption. Rather subjects were asked to use Nutricam each time any food was eaten with no restriction placed on the number of entries which could be recorded. Other studies evaluating the use of similar devices to collect PhRs of dietary intake have also reported a preference for these novel technologies among college students (Wang et al. 2006; Wang et al. 2002) and adolescents (Boushey et al. 2009; Six et al. 2010). Acceptance of the Nutricam method among this group of older adults with T2DM is encouraging and prompts further exploration of the use of such devices in this population. Limitations A small sample size was selected to pilot the Nutricam recording protocol and to assess the feasibility of the use of the mobile phone photo/voice dietary record for measuring intake. Although the small sample size limited the generalisability of the findings, this study provided valuable information regarding the viability of this novel dietary assessment method including certain factors which need further consideration to improve data quality prior to implementation and evaluation of the NuDAM in Study Conclusions Overview To our knowledge, this pilot study is the first use of a mobile phone application to collect photo/voice dietary records for analysis by a dietitian. Conclusions from this study indicate that the Nutricam mobile phone photo/voice dietary record was considered an acceptable alternative to written records. However, some modifications to the original methodology were needed to ensure that the collection of dietary was as comprehensive as possible in future investigations using the Nutricam to collect dietary data. Chapter 5: Study 2 135

138 5.5.2 Recommendations for the refinement of the Nutricam recording protocol Based on the outcomes of the trial of the Nutricam and the associated recording protocol, the following modifications were undertaken: i. Revision of the Nutricam instructions for the recording of intake to reinforce ii. iii. the importance of ensuring that all items are clearly distinguished in the photograph component. The time available to record a description of the items depicted in the image was increased from 20 seconds to 99 seconds. Inclusion of a mechanism to clarify Nutricam entries and probe for commonly forgotten foods. The first two modifications required small adjustments to existing resources, however the third modification required the development of new tool to assist in the standardised collection of the additional dietary data not captured using the Nutricam recording protocol. The scheduling of this supplemental data collection in the final NuDAM needed to occur so as to minimise the error relating to the effect of memory on the recall of information and maximise the likelihood that accurate information is obtained. Previous methods have revised PhRs with the subject following the completion of the study period (Martin et al. 2009), however, the length of time between consumption and recall can affect the accuracy of the dietary intake information provided (Dwyer & Coleman 1997). Therefore, for the NuDAM, it was proposed that the collection of this information occur in the morning of the day following each Nutricam recording day. Conscious, of the potential for the inclusion of this component to impact on subject burden, it was decided that the most appropriate means of collecting this information would be via a brief telephone call to the subject. To limit the introduction of interviewer bias in this component of the NuDAM, standardised protocols were developed based on the multiple-pass 24-hr dietary recall method. This approach allows for the systematic collection of information through a series of five steps: 1) the quick list, where the subject provides a list of the foods consumed during the previous day; 2) the forgotten food list, which probes the subject on intake of food commonly forgotten; 3) the time and occasion when foods were consumed; 4) the detail cycle, which collects detailed information regarding the descriptions and quantities consumed; and 5) the final probe review (Raper et al. 2004). Of particular relevance for the NuDAM, was the pass involving the probing of forgotten foods. Although some aspects of this existing dietary assessment method were incorporated, given the novelty of the NuDAM and the Chapter 5: Study 2 136

139 objectives of the subject call, a standardised protocol and tool was developed specifically for this purpose. The structure of the post-recording call was divided into two parts: 1) clarification of items contained in the previous day s Nutricam dietary record; and 2) probing for foods consumed during the previous day, but not recorded using Nutricam. Firstly, the protocol developed to determine whether Nutricam entries required clarification was based on the criteria used to evaluate the quality of the photographic and voice description components as described in the pilot study earlier in this chapter. For items requiring clarification, it was decided that these items would be highlighted with a code and the additional information needed listed. During the call, the investigator would then elicit this information from the subject. Secondly, a series of probes were developed to prompt subjects for any items consumed during the previous day but not recorded using Nutricam. Following a general question regarding any remembered which were not recorded, subjects are then probed about specific times from the previous day (i.e. before the first entry; after the last entry; and in-between entries 3 hours apart). Probes regarding forgotten foods grouped into six categories (i.e. non-alcoholic beverages; alcoholic beverages; sweet snack foods; savoury snack foods; fruits, vegetables and cheeses; and breads, rolls, and wraps) are then asked followed by a final probe. Items recalled during this time are entered using a set protocol (i.e. time and occasion, description, quantity, and additions). To ensure consistency during the call, a Microsoft Access database (Microsoft Access 2007) was developed (Figure 5-5). The database allowed for dialogue relating to the call introduction and closing, probing questions, and special instructions on the flow of the interview to be displayed ensuring standardised delivery. In addition, possible answer options for each probing question were also included in a drop-down menu to ensure that the uniform collection of data for these questions. As this additional data collection component of the NuDAM also involved judgement by the dietitian/investigator regarding the quality of the Nutricam record for analysis, it was not finalised until after Study 3. Therefore, the final structure of this component of the NuDAM protocol is outlined in Section and in Appendix H. Chapter 5: Study 2 137

140 Figure 5-5: Screenshot of the NuDAM call database. All calls made to subjects following each NuDAM recording day followed a standardised interviewer protocol detailed in the database. Responses to the probes were recorded into the database and descriptions of food items entered into the food composition database. Chapter 5: Study 2 138

141 5.5.3 Relevance to the research program This trial of the Nutricam software and recording protocol provided valuable information regarding the viability of this novel dietary assessment method. In particular, subject feedback regarding the use of Nutricam reinforced the potential for this technology to be applied in this group to assess intake. A number of factors regarding the recording process were identified that required further consideration to improve the quality and comprehensiveness of the data collected via the NuDAM, and guided the refinement of the method following Study 3. The revised NuDAM recording protocol was used in Study 4 of this thesis (Appendix I) Implications for future practice The recording of intake via Nutricam, offers a simple, interactive and instantaneous mode of collecting dietary data. The positive attitude towards the use of a mobile phone photo/voice dietary record among older adults with a chronic disease is encouraging for practitioners and opens the opportunity for integration with various other technologies to facilitate the nutrition care of these individuals. Chapter 5: Study 2 139

142

143 Chapter 6: Development and trial of the NuDAM analysis protocol (Study 3) 6.1 Introduction This chapter explores elements relating to the development and trial of the data analysis components of the Nutricam dietary assessment method (NuDAM), in particular the error associated with the quantification of food items contained within photographic records (PhRs) across two sub-studies. Firstly, Study 3 Part A investigated the most suitable format and type of portion size estimation aid (PSEA) to assist in the task of estimating the quantity of foods contained in photographs. Findings from this sub-study guided the development of the Dietary Estimation and Assessment Tool (DEAT), a standardised resource to support the analysis of Nutricam PhRs. Secondly, Study 3 Part B evaluated the effect of the DEAT on the estimation error associated with quantifying the portion size of items contained within the photographic component of the Nutricam dietary record. In addition, data on the acceptability of the DEAT was collected from the subjects to determine the suitability and use of the various PSEAs comprising the DEAT. 6.2 The type of portion size estimation aid (PSEA) and estimation error associated with quantifying food items contained in photographs (Study 3 Part A) Background Of the small number of studies which have used PSEAs to assist in the manual quantification of food items contained within PhR, no investigation has been undertaken to inform the need for the use of aids or to confirm the suitability of the type of PSEA used in this task. This study used two different PSEAs, in the form of reference food photographs and food models, to explore the effect of the type of aid on the estimation error associated with quantifying single food items contained within photographs. Findings from this exploratory study were used to guide the development of the DEAT to assist in the quantification of food items contained within the Nutricam dietary record. Research Questions Does the use of PSEAs result in a difference in the accuracy of the estimated quantities of single food items contained in photographs? Chapter 6: Study 3 141

144 Does the accuracy of the estimated portion size of single food items contained in photographs differ when using a two-dimensional aid (i.e. reference photograph) compared to a three-dimensional (i.e. food model) aid? Methods Selection of foods Fifteen (15) single food items known from the literature (Nelson & Haraldsdottir 1998b) to be difficult-to-estimate accurately without the assistance of a PSEA were selected as the test foods for this study (Table 6-1). A range of items with different characteristics were included from the three food type categories: amorphous (n=7), solid (n=6) and liquid (n=2). Four portions of each food item were prepared and weighed using a Philips digital scale, model HR 2385 (Koninklijke Philips Electronics N.V., Amsterdam, The Netherlands) (accurate to 1 g). Three portion sizes ( small, medium, and large ) of each food, as well as a 100 g ( standard ) portion, were prepared and photographed. Apart from the standard portion, the weight of the three other portions were randomly selected to represent serves along a continuum of serve sizes for each food, however due to preparation and serving vessel capacity this was not entirely possible for all food items, as reflected in the similar weight served for two portions for pizza and cornflakes. Table 6-1: Overview of weight/volume of test food portions and food models. Portion size (g or ml^) Food Food Item small medium large type Food Model Mashed Potato A Rice A Pasta A Pizza S Cake S Potato chips A Carrot A Steak S Chicken breast S Ice cream S Cornflakes A Cheese S Baked beans A Coffee^ L Fruit juice^ L Abbreviations: A=amorphous; S-solid; L=liquid; ^Served in ml Chapter 6: Study 3 142

145 Photographs of test food portions All photographs of the test food portions were captured using a Canon IXUS 8 (Canon Corporation Inc, Japan) camera mounted on a tripod at an angel of approximately 45 (to the horizontal). All foods, except ice cream, cornflakes, juice and coffee were photographed on a white dinner plate (24 cm diameter). Portions of ice cream and cornflakes were photographed in a white bowl (16 cm diameter), whilst the juice was depicted in a glass (6 cm diameter) and the coffee in a mug (7 cm diameter). One 15 cm ruler was included in each photograph of all foods; while a second ruler was included for foods served on a plate or in a bowl (Figure 6-1). All food portions were photographed indoors under standard fluorescent lighting conditions on a stainless steel bench. For each food photograph, all four portions were randomly allocated a letter (A to D), and each photograph then labelled with the food item and the letter. All 60 test photographs were printed on A4 paper and laminated. Figure 6-1: Example test food photographs. Study design and procedure The study was approved by the Queensland University of Technology Human Research Ethics Committee. A convenience sample of QUT second, third and final year dietetic undergraduate students and PhD dietetic students approached via internal were recruited and provided written informed consent. Subjects were required to attend two 1-hour testing sessions. At the start of the first session, both the height (to the nearest 0.1 cm) using a stadiometer (Model PE087, Mentone Educational, Victoria, Australia) and weight (to the nearest 0.1 kg) using body weight scales (Model , SECA, Germany) of each subject was measured. Subjects were then taken to a room which had 15 stations; each station contained photographs of the four portions of each food. Subjects were allocated 2 minutes per food to quantify the serve of all four portions and record these estimations on the Chapter 6: Study 3 143

146 form provided. This procedure was repeated until each subject had estimated the portion size of all 60 test food items depicted in the photographs. Following the completion of the portion size estimation task, subjects completed a questionnaire proving details on general demographics (e.g. age, nationality), course and year of study, previous experience in portion size estimation, and consumption frequency of the 15 test foods (Appendix C). One to two days following the first session, subjects returned for the second testing session. This session involved estimating the portion size of the same 60 test food photographs used in the first session, however each subject had access to the use of one of two types of PSEAs to assist in the estimation of portion size. Subjects were randomised into two groups: the RP group and the FM group. Those in the RP group used a reference photograph (RP) which consisted of the 100 g portion of the test food, whilst those in the FM group used replica Nasco food models (FM) ( The serve size depicted by each food model is based on a standard serve of the particular food (Table 6-1). In order to establish the full effect of each aid type and to limit the potential for bias by mixing subjects using different aids, subjects were split into two rooms based on their group allocation for the second estimation session. The protocol for the second session was identical to the first with subjects allocated 2 minutes per food, however in addition to the test food photographs for estimation, the relevant PSEA (either RP or FM) for each food was also present at the station. A final year Nutrition and Dietetic undergraduate student assisted with the collection of data for this study. Data analysis The first calculation produced a value for the error relating to the gram or volume difference (estimated weight (g) or volume (ml) actual weight (g) or volume (ml)) for each estimate. The second calculation, produced a value for the error as a percentage difference ([estimated weight (g) or volume (ml) actual weight (g) or volume (ml)]/actual weight (g) or volume (ml))*100). The mean (±SD) estimation error between actual and estimated weight/volume of the test food portion was calculated from the average of the estimations for each food item per session for the entire sample and each group, food type category and serve size. The percentage difference error was used predominately in the statistical analysis as this calculation incorporated the portion size of the food item estimated. Differences between groups were assessed by independent t-test for variables of a normal distribution and Mann-Whitney U test for non-parametric variables. Differences within groups for Chapter 6: Study 3 144

147 the two sets of estimations were evaluated using a paired t-test for normally distributed variables or a Wilcoxon signed ranks test for non-parametric variables. Therefore, an independent t-test assessed the difference between groups for BMI and a Mann-Whitney U test used for age. All differences in estimation error within and between groups were assessed using non-parametric tests (i.e. Wilcoxon signed ranks test and Mann-Whitney U test, respectively) Results Subject characteristics Seventeen lean individuals (1 Male) aged years consented to participate in this study with the majority undergraduate nutrition and dietetic students (Table 6-2). Overall the groups were well-educated with just over half of the group having completed a tertiary education qualification. Frequency in the use of utensils to measure food during the preparation and cooking of meals varied among subjects, however over half the group used these tools and apparatus at least weekly. In comparison, 76.5% of the group had never used measuring devices when serving food prior to consumption. A large proportion of the group (i.e. 82.4%) reported no previous involvement in portion size estimation training. Differences in age and BMI between the two groups were not statistically significant. Estimation Error The results for the error of all four estimates of each food are summarised in Appendix D. It was intended that estimation error for all 60 food portions tested would be used to calculate the mean error associated with the 15 food items. However, during the analysis it became evident that as one of the test food portions was an identical image to the PSEA, estimates for the RP group using the reference food photograph may have been biased. For this group mean estimation error of this portion size for 13 items was zero. Error for the remaining two items was very small (1.7±5.0g and -3.3±26.0mL). Although differences between type of aids for this portion size were significant for only 6 foods (i.e. cake, potato chips, carrot, steak, ice cream and cornflakes), it was decided that given the vast majority of estimates for the 100g test portion contained no error within the RP group, that this estimate would be excluded from all analyses. The following results presented exclude the estimations of the 100g portion for all subjects. The percentage difference is predominately presented in this section, as this value accounts for the size of the test portion served and is more representative of accuracy in the context of this study. Chapter 6: Study 3 145

148 Table 6-2: Summary of subject characteristics (Study 3 Part A). All(n=17) FM Group (n=8) RP Group (n=9) Age (years) (mean±sd) 24.7± ± ±2.5 BMI^ (kg/m 2 ) (mean±sd) 21.1± ± ±2.2 Country of Birth (n): Australia Other Course of Study: Undergraduate Postgraduate Completed Education Level (n): Secondary Education Tertiary Education* Use of utensils to measure and/or weigh when preparing/ cooking food and/or beverages (n): Never Twice per month or less times per week times per week Once per day More than once per day Use of utensils to measure and/or weigh when serving food and/or beverages (n): Never Twice per month or less times per week Once per day Participation in portion size training in the past (n): Yes No Abbreviations: ^BMI calculated using measured height and weight; *All other education categories ( Certificate, Advanced Diploma/Diploma, Bachelor Degree, Graduate Diploma/Graduate Certificate ) were combined. Difference between groups for age (Mann-Whitney U test) and BMI (independent t-test) were not significant. In general, the use of a PSEA significantly reduced the total estimation error associated with quantifying single food items contained in photographs (19.0±28.8% vs. -2.5±11.5%; p<0.05) (Table 6-3). For twelve of the fifteen food items tested, greater estimation accuracy (i.e. moved closer towards zero) was achieved with the use of an aid. Furthermore, the error for eight of these foods (i.e. mashed potato, pizza, potato ships, steak, cornflakes, cheese, coffee and fruit juice) was significantly different for estimates made without aids compared to those made with an aid. In contrast, estimation error increased (i.e. moved further away from zero) with the use of an aid for rice, cake, and baked beans. Chapter 6: Study 3 146

149 Table 6-3: Effect of the use of aids on mean estimation error for each food. All (n=17) Food item# Mean(±SD) Estimation Error (g or ^ml) Mean (±SD) Estimation Error (%) No Aid Aid No Aid Aid Mashed Potato -29.9± ±22.8* -21.4± ±18.5* Rice -16.5± ± ± ±17.1 Pasta 34.7± ± ± ±15.4 Pizza 59.1± ±18.6** 80.0± ±23.9*** Cake -4.3± ± ± ±14.0 Potato chips 46.2± ±20.7* 35.9± ±15.6* Carrot 13.3± ± ± ±15.6 Steak 70.4± ±34.0*** 44.5± ±21.1*** Chicken breast 40.6± ± ± ±13.1 Ice cream -10.5± ± ± ±16.0 Cornflakes 36.0± ±17.0** 89.8± ±35.4** Cheese 29.8± ±-17.6** 52.8± ±32.8** Baked beans 22.0± ± ± ±16.4 Coffee^ 11.4± ±35.6* 9.9± ±20.3* Fruit juice^ 27.1± ±27.0* 18.4± ±18.2* Total 22.0± ±14.2** 19.0± ±11.5* Abbreviations: Aid=portion size estimation aid; #100g portion not used in analysis; mean(±s.d.) error (g or ml) per food item=mean error ([estimated weight or volume actual weight or volume] of each portion of food item; mean(±s.d.) percentage error per food item=mean percentage error ([estimated weight or volume actual weight or volume]/actual weight or volume x 100) of each portion of food item. Difference between groups (no Aid vs. Aid) (Mann-Whitney U Test) significant: *p<0.05; **p<0.01; ***p< A difference existed in the total estimation error between groups following the first estimation session with a smaller overall error calculated for those in the reference photograph (RP) group compared to the food model (FM) group (10.2±32.7% vs. 28.9±21.5%, p=0.236). Notable differences in errors between groups were observed for estimates of mashed potato and rice made without an aid, with the mean estimation error significantly under-estimated by the RP group. Error between groups for the other foods varied widely, except for pizza and potato chips, which appeared the most closely aligned between groups (Table 6-4). In the second set of estimates, error reduced in both groups when an aid was used to assist in the quantification of items, with greater accuracy in overall error (1.4±5.9 vs ±14.9%, p=0.321; RP group and FM group, respectively), with the difference in estimation error was significant between groups for four food items (i.e. carrot, ice cream, cornflakes, and coffee). The cumulative percentage for estimation error among subjects, both without the use of a PSEA is illustrated in Figure 6-2. For this first set of estimations, a Chapter 6: Study 3 147

150 Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) difference in the accumulation of accurate results occurred between ±20% to ±40% error, with the rate of the FM group noticeably slower compared to the RP group. The variation in error between groups observed at these points is reflected in the difference in the overall mean error (Table 6-4). In contrast, for the second set of estimations with the use of PSEAs, the RP group accumulated accurate results at a more rapid rate compared to the FM group (Figure 6-3). The error for 89% of the RP group is within ±10%, while only half of the FM group had a similar level of accuracy ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±70.0 Mean estimation error (%) All (n=17) Food Model Group (n=8) Reference Photograph Group (n=9) Figure 6-2: Cumulative percentage plotted against the mean estimation error for estimates made without PSEAs. The cumulative percentage of subjects within a given mean percentage estimation error for estimates made without the use of portion size estimation aids (PSEAs) All (n=17) Food Model Group (n=8) Reference Photograph Group (n=9).0 ±10.0 ±20.0 ±30.0 ±40.0 Mean estimation error (%) Figure 6-3: Cumulative percentage plotted against the mean estimation error for estimates made with PSEAs. The cumulative percentage of subjects within a given mean percentage estimation error for estimates made with the use of portion size estimation aids (PSEAs). Chapter 6: Study 3 148

151 The effect of the use of aids on estimation error was more prominent within the FM group, with significant differences observed for eight single food items compared to four items for the RP group (Table 6-5). In addition, the difference in overall error within the FM group was significant (28.9±21.5% vs. -6.7±14.9, p<0.05) compared to the RP group (10.2±32.7 vs. 1.4±5.9, p=0.441). Collectively, the use of a PSEA resulted in notable improvements in error across all food types and portion sizes (Table 6-6). Significant differences were observed between PSEA groups for liquid foods and large portions with the error smaller in the RP group compared to the FM group (Tables 6-7). The level of error within the PSEA differed significantly for the FM group across all food types and serve sizes (Table 6-8). In contrast, the RP group had notable differences between estimations for amorphous foods and small servings only. Table 6-4: Effect of the type of aid on the between group difference in estimation error. Mean (±SD) Estimation Error (%) (n=17) No Aid Aid Food item# FM Group (n=8) RP Group (n=9) FM Group (n=8) RP Group (n=9) Mashed Potato -4.6± ±26.8* 5.8± ±9.7 Rice 5.1± ±33.1* -25.8± ±9.5 Pasta 34.9± ± ± ±16.3 Pizza 91.0± ± ± ±8.9 Cake 9.4± ± ± ±5.7 Potato chips 34.5± ± ± ±13.1 Carrot 31.3± ± ± ±8.2* Steak 54.9± ± ± ±5.9 Chicken breast 50.6± ± ± ±12.0 Ice cream -3.8± ± ± ±11.6** Cornflakes 49.6± ± ± ±16.3*** Cheese 38.1± ± ± ±26.7 Baked beans 24.9± ± ± ±11.9 Coffee 13.8± ± ± ±7.2** Fruit juice 24.9± ± ± ±9.6 Total 28.9± ± ± ±5.9 Abbreviations: Aid=portion size estimation aid; #100g portion not used in analysis FM group = subjects who used the food models for the quantification task, RP group = subjects who used the reference food photographs for the quantification task; mean(±s.d.) percentage error per food item=mean percentage error ([estimated weight or volume actual weight or volume]/actual weight or volume x 100) of each portion of food item. difference between groups (FM group vs. RP group) without an aid (Mann-Whitney U test) significant: *p<0.05. difference between groups (FM group vs. RP group) with an aid (Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p< Chapter 6: Study 3 149

152 Serve size Food type Table 6-5: Effect of the type of aid on the within group difference in estimation error. Mean (±SD) Estimation Error (%) (n=17) FM Group (n=8) RP Group (n=9) Food item# No Aid Aid No Aid Aid Mashed Potato -4.6± ± ± ±9.7* Rice 5.1± ±23.4* -30.1± ±9.5 Pasta 34.9± ±14.4* 4.7± ±16.3 Pizza 91.0± ±34.8* 70.2± ±8.9* Cake 9.4± ±19.7* -8.8± ±5.7 Potato chips 34.5± ±15.9* 37.2± ±13.1 Carrot 31.3± ±22.7* -6.8± ±8.2 Steak 54.9± ±30.9* 35.3± ±5.9* Chicken breast 50.6± ± ± ±12.0 Ice cream -3.8± ± ± ±11.6 Cornflakes 49.6± ±26.2* 125.4± ±16.3 Cheese 38.1± ± ± ±26.7* Baked beans 24.9± ± ± ±11.9 Coffee 13.8± ±25.0* 6.6± ±7.2 Fruit juice 24.9± ±23.9* 12.7± ±9.6 Total 28.9± ±14.9* 10.2± ±5.9 Abbreviations: Aid=portion size estimation aid; #100g portion not used in analysis; FM group = subjects who used the food models for the quantification task, RP group = subjects who used the reference food photographs for the quantification task; mean(±s.d.) percentage error per food item=mean percentage error ([estimated weight or volume actual weight or volume]/actual weight or volume x 100) of each portion of food item. difference within the FM group (No Aid vs. Aid) (Wilcoxon Signed Ranks Test) significant: *p<0.05. difference within the RP group (No Aid vs. Aid) (Wilcoxon Signed Ranks Test) significant: *p<0.05. Table 6-6: Overall effect of the use of aids on mean estimation error for each food type. Mean (±SD) Error (g or ml^) Mean (±SD) Error (%) No Aid Aid No Aid Aid Amorphous (n=7) 15.1± ±15.0* 18.3± ±12.3* Solid (n=6) 30.9± ±13.0** 33.1± ±12.5*** Liquid^ (n=2) 19.3± ±29.9** 14.2± ±18.2** Small (n=15) 11.3± ±7.1** 26.7± ±12.4** Medium (n=15) 21.1± ±16.4** 25.6± ±14.9** Large (n=15) 33.0± ±24.7** 18.9± ±11.9** Abbreviation: Aid=portion size estimation aid; mean(±s.d.) error (g or ml) per food type or serve size =mean error (g or ml) of the food items within the food type category or serve size; mean(±s.d.) percentage error per food type or serve size=mean error (%) of food items within the food type category or serve size. Difference between groups (no Aid vs. Aid) for food type category and serve size (Mann-Whitney U Test) significant: *p<0.05; **p<0.01; ***p< Chapter 6: Study 3 150

153 Serve size Food type Serve size Food type Table 6-7: Effect of the type of aid on estimation error between groups for food type. FM Group (n=8) No Aid Mean (±SD) Error (%) (n=17) RP Group (n=9) FM Group (n=8) Aid RP Group (n=9) Amorphous (n=7) 25.1± ± ± ±5.8* Solid (n=6) 40.0± ± ± ±6.8 Liquid (n=2) 19.3± ± ± ±6.6* Small (n=15) 34.6± ± ± ±6.7 Medium (n=15) 29.6± ± ± ±7.5 Large (n=15) 26.8± ± ± ±6.7** Abbreviations: Aid=portion size estimation aid; FM group = subjects who used the food models for the quantification task, RP group = subjects who used the reference food photographs for the quantification task; mean(±s.d.) percentage error per food type or serve size=mean error (%) of food items within the food type category or serve size. difference between groups (i.e. FM group vs. RP group) without an aid (Mann-Whitney U Test): not significant. difference between groups (i.e. FM group vs. RP group) with an aid (Mann-Whitney U Test) significant: *p<0.05; **p<0.01. Table 6-8: Effect of the type of aid on estimation error within groups for food type. Mean (±SD) Error (%) (n=17) FM Group (n=8) RP Group (n=9) No Aid Aid No Aid Aid Amorphous (n=7) 25.1± ±14.9* 12.3± ±5.8* Solid (n=6) 40.0± ±17.4* 27.0± ±6.8 Liquid (n=2) 19.3± ±23.4* 9.6± ±6.6 Small (n=15) 34.6± ±17.6* 19.6± ±6.7* Medium (n=15) 29.6± ±19.9* 22.0± ±7.5 Large (n=15) 26.8± ±12.8* 12.0± ±6.7 Abbreviations: Aid=portion size estimation aid; FM group = subjects who used the food models for the quantification task, RP group = subjects who used the reference food photographs for the quantification task; mean(±s.d.) percentage error per food type or serve size=mean error (%) of food items within the food type category or serve size. difference within FM group (No Aid vs. Aid) (Wilcoxon Signed Ranks Test) significant: *p<0.05 difference within RP group (No Aid vs. Aid) (Wilcoxon Signed Ranks Test) significant: *p< Discussion Findings from this exploratory study suggest that the use of a PSEA improves the estimation accuracy when quantifying food items contained in photographs, with the level of improvement similar regardless of whether the aid was a reference photograph or food model. Across the entire cohort, mean estimation error reduced significantly from +19.0±28.8% to -2.5±11.5% with the assistance of aids (Table 6-4). As outlined in Section 2.5.1, a complex cognitive process occurs when an individual is asked to estimate food portions present in reality or recalled, which has Chapter 6: Study 3 151

154 implications for the quantification of PhRs. Nelson et al (1994) defined three psychological constructs which are present in this process: perception, conceptualisation and memory. This current study investigated all three components of this process in the context of two difference types of PSEAs used by nutrition and dietetic students to quantify food items present in photographs. Firstly, conceptualisation and memory were tested with the food items estimated without the assistance of PSEAs. The range of error for the individual food items varied widely. Without the use of aids, 11 of the 15 food items were over-estimated, with error ranging from 7.7% to 89.8% of the actual weight served. In contrast the error for the four items which were under-estimated (i.e. mashed potato, rice, cake and ice cream) was smaller ranging between -0.2% to -21.1%. The level of inaccuracy in the estimation of food portions without the assistance of PSEAs is comparable to previous work examining the effect of the cognitive skills of conceptualisation and memory. In the study by Faggiano et al (1992), rice and fresh cheese were identified as food items with the greatest error ranging from -50% to 89%, respectively. Despite variations within and between foods tests, overall error was within ±20% of actual weight, a range considerable acceptable by others investigating the recall of food portions (Chambers et al. 2000). Secondly, the cognitive task of perception was assessed when subjects were required to use PSEAs to estimate the same food portions. In contrast to the first series of estimations, a reverse trend was observed when PSEAs were used to quantify the same food portions. In this situation the majority of items underestimated (-22.9% to -1.5%) except for mashed potato, chicken breast, cornflakes and cheese which were all over-estimated (38.4% to 0.9%). A similar trend towards under-estimation was observed in other studies when photographic PSEA are used to quantify food portions present in reality (Nelson et al. 1994; Lucas et al. 1995; Venter et al. 2000). Examination into the effect of the type of PSEA used demonstrated that the food model (FM) group had a larger, statistically significant improvement in total error between estimates compared to the reference photograph (RP) group (Table 6-4). The use of an aid improved the error for both groups, with the RP group achieving a more accurate estimate (1.4±5.9%) compared to the FM group (-6.7±14.9%), however the difference following the second set of estimates was not significant. Closer investigation of the results revealed a smaller overall error following the first Chapter 6: Study 3 152

155 estimation for the RP group compared to the FM group (10.2±32.7 vs. 28.8±21.5%). The reason for the difference in error between groups after the first estimation is not clear as all subjects completed the task under identical conditions. Characteristics of the subjects were similar between groups, however the RP group contained two postgraduate dietetic research students (Table 6-2). Although not assessed directly, it is possible that as these subjects had already completed their professional training they may have been more experienced with the estimations of food portions compared to the undergraduate students. With regards to individual food items and the type of PSEA, a significant difference in the error between group estimates did not directly correspond to a more accurate estimate (Table 6-5). For example, within the FM group the estimation error for three foods (i.e. rice, cake and coffee) were found to be significantly different between the two sessions, however the actual error increased (i.e. moved further from zero) with the use of food models. Similarly, a significant difference and improvement in error was observed for estimates of four foods in the RP group (i.e. mashed potato, pizza, steak, cheese), however another eight foods also reported an improvement in error with the use of the reference photographs. The range of estimation error in this current study is comparable with previous work investigating the perceptual task of quantifying portion size. Estimates within ±10% of actual weight or volume for real-time studies (Lucas et al. 1995; Venter et al. 2000) have been reported as accurate. Based on these criteria, estimations made by both groups with the assistance of PSEAs would be considered accurate. This current study found that the estimation error relating to perception alone was smaller than the error associated with conceptualisation and memory. In general, similar findings have been reported in other investigations exploring all three cognitive components of portion size estimation (Nelson et al. 1996; Robinson et al. 1997; Hernández et al. 2006). On average, when the cognitive skill of perception alone (with PSEAs) was required 70.6% of subjects estimated within 10% of the actual weight or volume of the food (Figure 6-3). In comparison, ±20% has been suggested as a suitable level for estimations involving conceptualisation and memory. In this current study, while 41.2% were within ±20% of actual weight or volume, with only 11.8% subjects estimating within ±10% without the use of PSEAs (Figure 6-2). The level of accuracy displayed in this investigation aligns favourably with other studies and adds further evidence that the majority of single food items contained in PhRs can be quantified accurately with the assistance of PSEAs. Chapter 6: Study 3 153

156 In general, each food type tended to be over-estimated without the use of aids, with solid food items displaying the greatest error followed by amorphous and then liquid foods (Table 6-6). Estimation error for amorphous, solid and liquid foods revealed a significant difference for estimates made with the assistance of a PSEA for all three food types. Between groups, estimates made without the use of PSEAs were more accurate in the RP group compared to the FM group for the three food types (Table 6-8). The use of PSEAs improved error across food types and was significant for all three food categories within the FM group and for amorphous foods only for the RP group. At the group level, error for amorphous and solid food items was closely aligned within the cohort, a result that is similar to findings reported by Slawson & Eck (1997) in nutrition undergraduate students, however in contrast to others who report amorphous items to be less accurately estimated in the presence of PSEAs or following training among similar cohorts (Yuhas et al. 1989; Howat et al. 1994; Arroyo et al. 2007; Japur & Diez-Garcia 2010). These investigations differed from the current study in that the actual food item was present. The unique characteristics of solid foods such as irregular shapes or discrete pieces of different sizes, ensure that certain judgments relating to the dimensions of these items are required to estimate portion size (Nelson & Haraldsdottir 1998b). Therefore, it may be that in this situation when a three-dimensional version of the food is present, as opposed to the two-dimensional version displayed in the PhR, that the dimensions of solid items are better able to be distinguished resulting in a more accurate estimate. In spite of this, the error for all food types was within the acceptable level of accuracy (i.e. ±10% of actual weight or volume) when PSEAs were used, highlighting their importance in the quantification task. The difference in the error between groups for the type of aid used was significant for both amorphous and liquid foods (Table 6-7), with the error associated with the liquid items for the FM group relatively large compared to the RP group (-17.4±23.4 % vs. -1.4±6.6 %, p<0.05). This difference could potentially be explained by the fact that the serving vessels used for coffee and fruit juice in the test photographs were identical to those used in the reference photographs. This feature may have provided subjects in this group with more specific visual clues to assist in the task of perception and the quantification of these items as opposed to the amorphous items which, except for cornflakes, were all served on a plate. In addition, the estimation error for solid foods was almost identical between groups when an aid was used Chapter 6: Study 3 154

157 indicating that the type of PSEA used for these items may not influence the error to the same extent. These findings support the conclusion by Lucas et al (1995) that visual clues such as the number of pieces, area of spread on the plate, and perception skills influence the error associated with quantifying food items present in reality. The presence of a flat-slope syndrome, where small portions are over-estimated and large portions are under-estimated, has been reported in other studies examining portion size estimation error (Faggiano et al. 1992; Nelson et al. 1994; Lucas et al. 1995; Nelson et al. 1996; Venter et al. 2000; Frobisher & Maxwell 2003; Hernández et al. 2006; Ovaskainen et al. 2008; Subar et al. 2010). In this current study, all portion sizes were over-estimated without the use of PSEAs and underestimated with the assistance of aids, however absolute error was improved with the use of aids (Table 6-6). Similarly to this current study, Robson & Livingstone (2000) also found no difference between the size of the food portion estimated and error. The type of PSEA used in this study did not significantly affect the overall error associated with the quantification of foods contained in photographs. A finding supported by other studies which have examined the effect of two- and three dimensional PSEAs on the recall of foods eaten (Posner et al. 1982; Godwin 2003; Godwin et al. 2004). In addition, Hernández et al (2006) also found that no difference existed in the error of food quantities estimated using two types of lifesize reference photographs in comparison to photographs displayed on a computer. Thus it appears that it is the presence of a PSEA, and not the type which has the most influence on the accuracy of portion size estimation of food items contained in PhRs. Although the average error at the group level were within acceptable levels of accuracy for both settings, the high spread of scores suggests large variation in error at the individual level. This variability in error was similar to the small number of investigations which have also examined the error of portion size estimation (Tables 2-3 and 2-4), and is a finding which is typical, with variation at the individual level to be expected within ±50% of actual weight/volume (Nelson & Bingham 1997). Limitations The small sample size of this pilot study ensures that conclusions drawn may not be indicative of a larger group. Despite this limitation, these findings provided early evidence that the portion size of food items contained in PhRs can be accurately estimated by individuals trained in nutrition and dietetics. This information guided the Chapter 6: Study 3 155

158 next stage in the development a specific PSEA to assist in the quantification of items contained in the Nutricam dietary records. When interpreting these results one must acknowledge the potential for the second set of estimates to be influenced by previous exposure to the test portion photographs and may have also contributed to the smaller estimation error observed in the second set of estimations for the RP group. In particular, the inclusion of a 100g portion as both a test portion and as an aid for the RP group is a limitation of this study. Although the estimates made for these test portions were removed from the analysis, it is likely that there may have been a residual effect at the time of estimation for the other three portions Conclusions Overview The results from this study clearly indicate that the quantification of food items contained in photographs is a task which can be achieved within an acceptable level of error with the assistance of PSEAs by subjects studying nutrition and dietetics. The use of portion size estimation aids in the form of food models and reference photographs resulted in a decrease in the error associated with the quantification of 15 common single foods. The quantification of the amount of a food item depicted in a PhR was highly variable between different food items and portion sizes served. Although single food items are present in the diet, food is often consumed in the form of mixed consisting of multiple food items. Therefore, examination of the estimation error associated with the quantification of more complex meal items contained within a PhR is needed in order to form conclusions with regard to the application of this method in a free-living setting Relevance to research program As the type of PSEA did not appear to offer significant benefit in relation to estimation accuracy, attention was placed on the development of a practical and sustainable tool to assist in the task of quantifying Nutricam dietary records. The use of two-dimensional aids such as reference photographs conformed to the practicality requirements, while the inclusion of additional PSEAs, such as generic aids and graphics would ensure that the tool could be used with minimum maintenance. The findings from this study guided the development of the Dietary Estimation and Assessment Tool (DEAT) for use in the quantification of Nutricam mobile phone photo/voice dietary records. Chapter 6: Study 3 156

159 6.3 The effect of the Dietary Estimation and Assessment Tool (DEAT) on estimation errors relating to portion size and nutrient composition of the Nutricam dietary record (Study 3 - Part B) Background Findings from Study 3 Part A clearly indicated that when quantification of food items contained in photographs was undertaken, the use of PSEAs reduced the portion size estimation error associated with this task. Conclusions from this study indicated that the aids used for the analysis of PhRs be flexible to accommodate the variety of food items and eating experiences, yet still practical and sustainable for use within a dietetic practice setting. As the transfer of the PhR collected via Nutricam occurred electronically with information sent to a website for analysis, the tool developed also needed to be suitable for use under these conditions. In general, the characteristics of two-dimensional aids addressed the above requirements and the Dietary Estimation and Assessment Tool (DEAT) was developed. The DEAT incorporates various PSEAs into one resource and was designed specifically for the task of quantifying items contained within the photographic component of the Nutricam dietary record. This study assessed the effect of the DEAT on the estimation error associated with the quantification of items contained in the photographic component of two 3-day Nutricam dietary records. The impact of the resulting error on the energy and macronutrient content of the records was also established. In addition, student dietitians attitudes towards the acceptability of DEAT were evaluated. Research Questions Does the use of the DEAT effect the error associated with estimating the portion size of food items contained in the photographic component of Nutricam dietary records? Of the food types (i.e. solid, liquid, amorphous, or spreads) contained in the photographs, which are most affected by estimation error and does this influence the estimated energy and macronutrient content of the Nutricam dietary records? Is the DEAT perceived as a useful and acceptable resource to assist in the quantification of food items contained in Nutricam dietary records? Chapter 6: Study 3 157

160 6.3.1 Methods Development of the DEAT The DEAT consisted of a collection of two dimensional PSEAs grouped into four categories: reference food photographs, serving vessel photographs, amorphous mounds, and generic shapes and graphics. Each photograph or graphic in the DEAT contained the Nutricam prompt card, which was also included in each photograph of the Nutricam record (see Section 5.2.1). The card acted as a reference object in the quantification of the food items contained in the record, with dimensions of the card (9 cm x 5 cm) known. Other methods using a PhR have used a variety of objects including cutlery and serving vessels as the reference to assist subjects in the recall of quantities consumed, however given the variability in dimensions of these items it was decided that a standardised reference object would better complement the cognitive process of estimating the portion size of food items contained in photographs. In addition, the characteristics of the food composition database were also a consideration in the development of the DEAT. The selection of aids present is reflective of the specific units available in the database for quantification of common foods. The final version of the DEAT was presented in portable document format (.pdf) with bookmarks enabled. The following section describes the production of the PSEAs comprising the DEAT. Photographs food portions Ten common foods defined as difficult to estimate were included. These foods were selected based on guidelines for the development of food photographic atlases (Nelson & Haraldsdottir 1998b) which recommend that PSEAs should be produced for foods that vary along a continuum of portion sizes, are of irregular shape or are not available in standard amounts. Foods were: pasta (n=2), rice (n=1), salad (n=1), cooked vegetables (n=5), chicken breast (n=1), beef steak (n=1). Three portions of each food were weighed and placed on a white dinner plate (23 cm diameter). The Nutricam prompt card was placed below the plate in the left corner. Each food portion was photographed using Canon IXUS 80 IS (Canon Inc., Japan) camera mounted on a tripod at an angel of approximately 45 (to the horizontal). Adobe Photoshop CS3 Extended software (Adobe Systems Inc., USA) was then used to label each image with a description of the food item and its respective weight. Figure 6-4 provides an example of this aid. Chapter 6: Study 3 158

161 Carrots, cooked 50 g M e g a n R o l l o Figure 6-4: Example of a reference food photograph contained in the DEAT. Photographs serving vessels This category consisted of photographs of serving vessels in four sub-categories: plates (n=3), bowls (n=5), tumblers (n=3), mugs (n=3) and wine glass (n=1), and takeaway containers (n=4). Prior to capturing the photographs, volume increments were marked on all vessels except for the plates. The images were captured using the same protocol described above for the reference food photographs. Adobe Photoshop CS3 Extended software (Adobe Systems Inc., USA) were used to label the incremental volume markers and the diameter of the plates on the image for each vessel. Others have used graphics to illustrate serving vessels (Center for Nutrition Policy and Promotion 2001), however given that this tool would be used to compare directly with items in a PhR (i.e. using the cognitive skill of perception) and not used to recall quantities, it was deemed more appropriate to present this aid in a similar photographic form (Figure 6-5). M e g a n R o l l o Figure 6-5: Example of a serving vessel photograph contained in the DEAT Amorphous mounds Amorphous food items tend to have the greatest error associated with the estimation of portion size. Graphics consisting of generic mounds have been developed (McBride 2001) and shown to improve estimates of these foods by up to one-third Chapter 6: Study 3 159

162 compared to measuring cups (Center for Nutrition Policy and Promotion 2001). Similar to the decision to use photographs in place of graphics for the serving vessels to ensure a more realistic representation and to assist in the comparison with the Nutricam dietary record, the mound aids included in the DEAT were also based on images. Mashed potato was used to model the mounds, and was served in a number of portion sizes on a continuum from 1 teaspoon to 1½ cups. The images were captured using the same protocol described above for the reference food photographs. Images of mashed potato models were modified to represent generic mounds using digital photographic editing software, Adobe Photoshop CS3 Extended software (Adobe Systems Inc., USA). Figure 6-6 contains an example of this aid. M e g a n R o l l o Figure 6-6: Example of an amorphous mound contained in the DEAT Generic shapes and graphics The previous three DEAT categories of aids cater well for amorphous and liquid foods, however these aids are not entirely suitable for the majority of solid items. These foods are often served in blocks, wedges, slices, or as discrete pieces in irregular shapes, as a result of these unique characteristics the dimensions of these foods are used to quantify amounts served (Nelson & Haraldsdottir 1998b). Given the variety within this food type, the PSEA used to estimate portion size must be flexible for use with numerous shapes. Generic graphics appear to fulfil these requirements and offer the most suitable presentation format. A ruler (20 cm length), grid (1 cm x 1 cm) and series of circles (ranging in diameter 2-16 cm) were designed using Adobe Illustrator CS3 software (Adobe Systems Inc., USA). All items were drawn to scale with a Nutricam prompt card overlaid onto each graphic (Figure 6-7). Chapter 6: Study 3 160

163 Figure 6-7: Example of a generic graphic contained in the DEAT Test Nutricam dietary records To evaluate the effect of the use of the DEAT and analysis protocol, two test Nutricam dietary records were compiled (Record 1 and Record 2). Each record was designed to reflect a typical meal pattern of three main meals and three snacks, and consisted of a variety of foods including single items and mixed dishes, and amorphous, liquid and solid food items (Table 6-9). An external food service company ( ite n Easy, Mitchells Foods Pty td, Northgate, Q D, Australia) provided pre-prepared food items and information on the nutrient profile of each item/meal. The nutrient profile was not provided for some food items such as fruit and beverages or for some components of mixed dishes which were weighed separately (e.g. vegetables served with a roast meat). Food composition data from AUSNUT 1999 (Australia New Zealand Food Authority 2001) in the nutrient analysis program FoodWorks Professional 2009 (version ) (Xyris Software, Brisbane, QLD, Australia) was used to provide nutrient information for these items. Appendix E contains a complete list of food items used in each record. Single food items and meals for each Nutricam entry were weighed using a Philips digital scale, model HR 2385 (Koninklijke Philips Electronics N.V., Amsterdam, The Netherlands) (accurate to 1 g). Where possible the components of mixed dishes were separated and weighed. Each meal occasion was then recorded using a Sony Ericsson K800i mobile phone (Sony Ericsson Mobile Communications AB, Sweden) installed with the Nutricam software application. All items for the meal occasion were assembled and the Nutricam prompt card placed in the bottom left-hand corner. The mobile phone was held at an angle of approximately 45 and a photograph captured. A voice record was then made listing the name, type (e.g. low fat or regular milk), Brand/Product name, and preparation/cooking method of each item within the photograph (as applicable). Both the photograph and the voice record were then sent as one entry (i.e. file) to the Nutricam website (see Section 5.2.2), Chapter 6: Study 3 161

164 however only the photographic component of each Nutricam entry for Records 1 and 2 were used in this study. Table 6-9: Overview of the food types and nutrient profile of the test Nutricam dietary records. Number and Type of Items Nutrient Profile (mean±s.d.) Energy Protein Fat (g/d) CHO(g/d) A L S Sd (MJ/d) (g/d) Record 1 (n=65) Record 2 (n=62) ± ± ± ± ± ± ± ±4.5 Abbreviations: A=amorphous; L=liquid; S=solid; Sd=spreads; CHO = carbohydrate Study design and procedure The size of the study sample was calculated based on the findings from Study 3 Part A, where the group mean estimation error made without PSEAs was 23.7%±27.8% compared to an error of -1.7%±11.4% with an aid (i.e. both food models and photographs) (Section 6.2.3). Therefore, to be sure to detect a 30% difference between estimates made with and without the DEAT (standard deviation of 30%) at 90% power and 5% significance, approximately 21 students were needed in each group. This calculation assumed that the estimates were linked in some manner. Third year undergraduate dietetic students enrolled in the Advanced Food Studies (PUB628) unit (n=85) were approached via . Written informed consent was provided by all subjects and the study was approved by the Queensland University of Technology Human Research Ethics Committee. Subjects attended one testing session in a computer laboratory on-campus. All data was collected using three independent questionnaires developed using the Key Survey (WorldAPP Inc., Braintree, MA, USA) online application. At the start of the session, subjects completed the first questionnaire which collected information on general demographics (including age, nationality, height, weight), previous experience in portion size estimation, and frequency in the use of measuring utensils in the preparation and serving of food (Appendix F). Subjects were randomised into one of two groups for the quantification of the two Nutricam dietary records. Group A had access to the DEAT to assist in the quantification of items for both Record 1 and 2; whilst Group B had access to the DEAT for Record 2 only (Figure 6-8). Chapter 6: Study 3 162

165 Student dietitians Group A Group B Estimation 1 Record 1 + DEAT Record 1 + No Aid Estimation 2 Record 2 + DEAT Record 2 + DEAT Figure 6-8: Design of Study 3 Part B. Student dietitians were randomised into either Group A and B, and were required to estimate two different Nutricam photographic records (i.e. Record 1 and Record 2). Group A used the Dietary Estimation and Assessment Tool (DEAT) to assist in the quantification of both records, while Group B used the DEAT for Record 2 only. Estimates of the portion size of items in both records were collected using the second questionnaire. Each question related to a Nutricam entry depicting a meal occasion photograph (resolution 640 x 480 pixels) obtained from the Nutricam website and a list of the food items to be quantified. Subjects were required to input the quantities for each item, with an estimate compulsory for all items. In addition, estimates of portion size could only be made using the set of pre-defined measurement units provided for each item. Units corresponded to weight (e.g. grams), volume (e.g. cup/s, ml, tsp, tbs), and dimensions (cm 3, cm 2, diameter cm). The number of options available were based on listed units for identical or similar items in AUSNUT 1999 (Australia New Zealand Food Authority 2001) food composition data (in the FoodWorks Professional 2009 (version ) (Xyris Software, Brisbane, QLD, Australia), and therefore was specific to the food item to be quantified. This aspect of the quantification task was included to reflect the process which would occur when analysing the contents of a Nutricam record using a food composition database where the quantities of food items are only able to be entered using a selected range of measurement units. Each Nutricam entry for quantification appeared on a new page and subjects were not allowed to view or change previous estimates. All subjects were provided with training on the protocol for the completion of the portion size estimation questionnaire and an electronic copy (viewed on the computer screen) of the DEAT was available for use during the study. Figure 6-9 provides an example of the questionnaire layout and illustrates the quantification task for one Nutricam entry. Chapter 6: Study 3 163

166 Following the quantification of all items contained in the two Nutricam dietary records, subjects were required to complete a third questionnaire with regard the usefulness and acceptability of the DEAT to assist in the estimation of food items contained within the two test PhRs (Appendix F). In addition, this questionnaire provided subjects with an opportunity to comment on the cognitive strategies used and the application of the DEAT to estimate 18 food items contained within Record 2. These selected food items provided a range of foods with different characteristics, and allowed for the comparison of the judgments employed when estimating portion size. A final year Nutrition and Dietetic undergraduate student assisted with the collection of data for this study. Data analysis The difference between actual and estimated weight of each item was calculated for both Nutricam records. The first calculation produced a value for the gram difference (error) (estimated weight (g) actual weight (g)) for each item. The second calculation, produced a value for the error as a percentage ([estimated weight (g) actual weight (g)]/actual weight (g))*100). The percentage difference error was used in the statistical analysis to establish accuracy as this calculation incorporated the portion size of the food item estimated. The gram difference error was used to assess the impact of this error by calculating the effect on the energy and macronutrient composition of each food item. A difference in estimation weight was converted to differences in energy and macronutrient based on the nutrient profile of the food item. The mean estimation error for each food was averaged to give a mean error for each of the three days of the record. The mean error for each day was averaged for the overall mean error per day of the record. A mean error for items within each food type category (i.e. amorphous, liquid, solid, and spreads) for each day of each record was calculated, with an average of these values used for the overall mean error for each food type per record. Differences between groups were assessed by independent t-test for variables of a normal distribution and Mann-Whitney U test for non-parametric variables. Between group differences were assessed using the Mann-Whitney U test for age and an independent t-test for BMI. All differences between groups for estimation error (i.e. weight, percentage and nutrient) were tested using the Mann-Whitney U test. The types and frequencies of the DEAT categories used to estimate selected food items contained in Record 2 were also summarised. Chapter 6: Study 3 164

167 Figure 6-9: Example of the quantification task. Subjects were required to estimate the portion size of each listed food item in the photograph. Both the quantity and unit of estimation was entered for each food item. This task was repeated for all photographic component of each entry within the Nutricam record. Chapter 6: Study 3 165

168 6.3.3 Results Subject characteristics Twenty-nine student dietitians (2 Male) aged years consented to participate in this study and were randomly allocated to two groups. Baseline characteristics were not significantly different between the two groups (Table 6-10). Table 6-10: Summary of subject characteristics (Study 3 Part B). All (n=29) Group A (n=15) Group B (n=14) Age (years) (mean±sd) 23.3± ± ±6.4 BMI^ (kg/m 2 ) (mean±sd) 20.6± ± ±1.4 Country of Birth (n): Australia Other Education Level (n): Secondary Education Tertiary Education* Use of utensils to measure and/or weigh when preparing/ cooking food and/or beverages (n): Never Twice per month or less times per week times per week Once per day Every time I prepare or cook food and/or beverages Use of utensils to measure and/or weigh when serving food and/or beverages (n): Never Twice per month or less times per week times per week Once per day Every time I serve food and/or beverages Confidence in ability to estimate the portion size of food items contained in photographs (n): Agree Neutral Disagree Strongly disagree Participation in portion size training in the past (n): Yes No Abbreviations: ^BMI calculated using self-reported height and weight; *All other education categories ( Certificate, Advanced Diploma/Diploma, Bachelor Degree, Graduate Diploma/Graduate Certificate ) were combined. Difference between groups for age (Mann-Whitney U test) and BMI (independent t- test) were not significant. Percentage estimation error Tables 6-11 and 6-12 summarise the percentage estimation error for both records by day and food type. Due to the large number of foods tested, error for individual items is summarised in the graphs contained in Appendix G (Graphs G1 to G6). Chapter 6: Study 3 166

169 Record 2 Record 1 Record 2 Record 1 On average for Record 1, overall mean estimation error was reduced by 16.3% per day with the use of the DEAT, with a significant difference noted between groups for Day 1. When both groups used the DEAT to assist in the quantification of Record 2, a similar level of accuracy was observed. Difference between groups was noted for the error associated with the estimation of amorphous food items in Record 1, with the use of the DEAT by Group A resulting in significantly less error compared to Group B. In addition, more accurate estimations were observed for Group A across the other food types for Record 1. In comparison, when both groups had access to the DEAT during the quantification of Record 2, the estimation error associated with food type was more closely aligned between groups with only subtle differences observed. The greatest error was observed for spreads in both records. Further information for day and food type is displayed in Appendix G (Tables G1 to G6). Table 6-11: Between group comparison of estimation error for each day of record. Mean(± S.D.) estimation error (%/day) Group A (n=15) Group B (n=14) Overall^ 17.7± ±22.6 Day 1 (n=19) 13.8± ±23.5* Day 2 (n=21) 15.3± ±28.1 Day 3 (n=25) 23.9± ±24.2 Overall^ 21.2± ±13.6 Day 1 (n=21) 24.7± ±27.0 Day 2 (n=18) 10.4± ±12.8 Day 3 (n=22) 28.6± ±16.2 Abbreviations: mean % error for each day = mean % error of each food item ([estimated weight actual weight]/actual weight*100) for day; ^Overall mean (±S.D.) error (%/day) = mean of % error per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05. Table 6-12: Between group comparison estimation error per day for food type. Mean(± S.D.) estimation error (%/day) Group A (n=15) Group B (n=14) Overall^ 17.7± ±22.6 Amorphous Items 21.9± ±31.3 Liquid Items 10.2± ±25.9 Solid Items 22.4± ±27.0 Spreads 19.8± ±118.7 Overall^ 21.2± ±13.6 Amorphous Items 21.2± ±20.1 Liquid Items 1.8± ±5.6 Solid Items 34.5± ±24.8 Spreads 54.5± ±127.3 Abbreviations: ^Overall mean (±S.D.) error (%/day) = mean of % error per day (see Table 6-11) for Day 1, Day 2, and Day 3 of record; mean (±S.D.) error (%/day) for food type = mean % error for food type per day (see Appendix G, Table G1) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test): not significant. Chapter 6: Study 3 167

170 Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Record 1: Overall ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 Mean estimation error (%/day) Record 1: Amorphous Items ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 ±90.0 ±100.0 ±>100.0 Mean estimation error (%/day) Record 1: Liquid Items ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 Mean estimation error (%/day) Record 1: Solid Items ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 Mean estimation error (%/day) Record 1: Spreads ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 Mean estimation error (%/day) Figure 6-10: Cumulative percentage of subjects plotted against mean estimation error for Record 1. The cumulative percentage of subjects within a given mean percentage estimation error per day for Record 1. (Legend: All, n=29, ; Group A, n=15, ; Group B, n=14, ). Chapter 6: Study 3 168

171 Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Cumulative percentage of subjects (%) Record 2: Overall ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 Mean estimation error (%/day) Record 2: Amorphous Items ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 ±90.0 ±100.0 ±>100.0 Mean estimation error (%/day) Record 2: Liquid Items ±10.0 ±20.0 Mean estimation error (%/day) Record 2: Solid Items ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 ±90.0 Mean estimation error (%/day) Record 2: Spreads ±10.0 ±20.0 ±30.0 ±40.0 ±50.0 ±60.0 ±70.0 ±80.0 ±90.0 Mean estimation error (%/day) Figure 6-11: Cumulative percentage of subjects plotted against mean estimation error for Record 2. The cumulative percentage of subjects within a given mean percentage estimation error per day for Record 1. (Legend: All, n=29, ; Group A, n=15, ; Group B, n=14, ). Chapter 6: Study 3 169

172 For Record 1, the accumulation of overall mean estimation error (%/day) occurred at a greater rate for subjects in Group A compared to Group B (Figure 6-10), with the use of the DEAT clearly reducing error among this group, particularly for liquid food items. In contrast, when both groups made estimations under identical conditions with access to the DEAT, the rate of error accumulation occurred at similar rates in Record 2 (Figure 6-11). Effect of error on estimated energy and macronutrient content of Records For Record 1 the overall difference between estimated and actual weights of the food items was significantly lower when the DEAT was used by Group A, compared to Group B (Table 6-13). In addition, the difference was significant for all three days of this record. In contrast when all subjects used the DEAT in Record 2, only a very small difference was observed between groups. For Record 1, the greater difference in estimated weights of the items for Group B translated into larger overall estimates of energy, protein, fat, and carbohydrate intake (Tables 6-13 to 6-17), however no consistent trend in the significance of this difference was evident. Furthermore, the total variation in mean nutrient composition observed between groups for Record 1 was 61 kj/day, 1.1 g/day of protein, 0.4 g/day of fat, and 2.6 g/day of carbohydrate, and was of minimal clinical significance. For Record 2, insignificant differences between groups were observed for 14 kj/day and 0.1 g/day each estimate of protein, fat, and carbohydrate. Tables G3-G6 (Appendix G) summarise the effect of quantification error on estimates of energy and macronutrient content across the four food type categories contained within the two Nutricam PhRs. Significant differences were noted across some nutrients for some food categories, however the clinical importance of these findings are minimal. Of note was the difference in energy content found between groups for amorphous items in Record 1. Both groups over-estimated the portion size of food items within this category, with the difference between estimated and actual energy content significant without the use of DEAT (57kJ/day vs. 274kJ/day, p<0.001). The energy content of solid food items was also over-estimated, however a similar difference was noted between groups for both records. Chapter 6: Study 3 170

173 Record 2 Record 1 Record 2 Record 1 Record 2 Record 1 Table 6-13: Between group weight difference for each day of record Mean(± S.D.) weight difference^ (g/day) Group A (n=15) Group B (n=14) Overall^ 1.5± ±26.7*** Day 1 (n=19) -0.4± ±33.4*** Day 2 (n=21) 1.2± ±26.8** Day 3 (n=25) 3.5± ±25.0*** Overall^ -0.6± ±8.7 Day 1 (n=21) 2.5± ±10.0 Day 2 (n=18) -7.4± ±9.8 Day 3 (n=22) 3.0± ±12.4 Abbreviations: mean weight difference for each day = mean weight difference of each food item ([estimated weight actual weight] for day; ^Overall mean (±S.D.) error (g/day) = mean of weight difference per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: **; p<0.01; ***p< Table 6-14: Between group energy difference for each day of record Mean(± S.D.) difference in energy content (kj/day) Group A (n=15) Group B (n=14) Overall^ 61±67 132±82 Day 1 (n=19) 45±94 143±86* Day 2 (n=21) 53±87 108±110 Day 3 (n=25) 86±69 143±88 Overall^ 42±73 56±34 Day 1 (n=21) 30±76 42 ±56 Day 2 (n=18) 40±125 74±80 Day 3 (n=22) 57±80 53±48 Abbreviations: mean energy difference for each day = mean energy difference of each food item ([estimated weight (g) - actual weight (g)] converted to energy difference for each item) for day; ^Overall mean (±S.D.) energy difference (kj/day) =mean of energy difference per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05. Table 6-15: Between group protein difference for each day of record Mean(± S.D.) Difference in Protein Content (g/day) Group A (n=15) Group B (n=14) Overall^ 0.3± ±0.9*** Day 1 (n=19) -0.2± ±1.0*** Day 2 (n=21) 0.1± ±1.0** Day 3 (n=25) 1.1± ±1.0 Overall^ 0.4± ±0.4 Day 1 (n=21) 0.8± ±0.8 Day 2 (n=18) 0.1± ±0.5 Day 3 (n=22) 0.5± ±0.5 Abbreviations: mean protein difference for each day = mean protein difference of each food item ([estimated weight (g) - actual weight (g)] converted to protein difference for each item) for day; ^Overall mean (±S.D.) protein difference (g/day) = mean of protein difference per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: **p<0.01; ***p< Chapter 6: Study 3 171

174 Record 2 Record 1 Record 2 Record 1 Table 6-16: Between-group fat difference for each day of record Mean(± S.D.) Difference in Fat Content (g/day) Group A (n=15) Group B (n=14) Overall^ 0.4± ±0.4 Day 1 (n=19) 0.4± ±0.5 Day 2 (n=21) 0.4± ±0.5 Day 3 (n=25) 0.5± ±0.5** Overall^ 0.4± ±0.3 Day 1 (n=21) 0.3± ±0.6 Day 2 (n=18) 0.5± ±0.3 Day 3 (n=22) 0.3± ±0.4 Abbreviations: mean fat difference for each day = mean fat difference of each food item ([estimated weight (g) - actual weight (g)] converted to fat difference for each item) for day; ^Overall mean (±S.D.) fat difference (g/day) = mean of fat difference per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: **p<0.01. Table 6-17: Between-group carbohydrate difference for each day of record Mean(± S.D.) Difference in Carbohydrate Content (g/day) Group A (n=15) Group B (n=14) Overall^ 2.1± ±3.0 Day 1 (n=19) 2.0± ±3.2* Day 2 (n=21) 1.8± ±4.4 Day 3 (n=25) 2.7± ±3.2 Overall^ 1.7± ±1.6 Day 1 (n=21) 0.5± ±2.1 Day 2 (n=18) 2.4± ±1.9 Day 3 (n=22) 2.3± ±1.8 Abbreviations: mean carbohydrate difference for each day = mean carbohydrate difference of each food item ([estimated weight (g) - actual weight (g)] converted to carbohydrate difference for each item) for day; ^Overall mean (±S.D.) carbohydrate difference (g/day) = mean of carbohydrate difference per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05. Use and perceived acceptability of the DEAT Table 6-18 outlines the responses from dietetic students regarding the acceptability of the DEAT as a PSEA to assist in the quantification of items contained in Nutricam records. A large proportion (89.6%) of the group found the DEAT helpful when quantifying food items contained in the PhRs, while just under two-thirds (65.5%) of subjects reporting that the Nutricam Prompt Card assisted in this task. In addition, while 58.6% of subjects found the four DEAT categories adequate, just over half of the group believed that the number of aids within these categories was insufficient. Equally high proportions (i.e. 79.3%) of the group reported that PSEAs were necessary when quantifying items contained in PhRs and this method of was an acceptable approach to assessing dietary intake. Chapter 6: Study 3 172

175 Use of the DEAT by subjects to assist in the quantification of selected items contained in Record 2 is summarised in Table The 18 items are separated into food type category, with the overall mean group estimation error for each item also provided. The type of aid selected appeared to be dictated by the food category, with the photographs of reference foods and serving vessels more common for amorphous foods, and serving vessels were predominately used for liquid items. The trend was less clear for solid foods which favoured the use of generic shapes or no aids for some items, while reference photographs for others. Two-thirds of subjects used no DEAT aid in the quantification of margarine, while just over onequarter reported using the mounds. Table 6-18: Responses from dietetic students following the use of the DEAT. n (%) Statements, as presented In general, I found the DEAT useful for estimating the portion size of items contained in the photographs: I found the Nutricam Prompt Card in the photographs useful as a reference object when estimating the portion size of the items: The categories of aids (i.e. photographs of reference foods and serving vessels, amorphous mounds, and generic shapes and graphics) contained in the DEAT were adequate to assist in the estimation of portion size: The number of aids in each category contained in the DEAT were sufficient to assist in the estimation of portion size: I believe that it is essential to use aids, such as those contained in the DEAT, when estimating the portion size of the items contained in a photographic dietary record: I believe that estimating the portion size of items contained in a photographic dietary record using aids, such as those contained in the DEAT, is an acceptable method for measuring dietary intake: Strongly Agree Agree Neither agree or disagree Disagree Strongly Disagree 5 (17.2) 21 (72.4) 3 (10.3) (27.6) 11 (37.9) 6 (20.7) 3 (10.3) 1 (3.4) - 17 (58.6) 4 (13.8) 8 (27.6) - 3 (10.3) 8 (27.6) 3 (10.3) 15 (51.7) - 6 (20.7) 17 (58.6) 5 (17.2) 1 (3.4) - 4 (13.8) 19 (65.5) 5 (17.2) 1 (3.4) - Chapter 6: Study 3 173

176 Spreads Solid Liquid Amorphous Table 6-19: DEAT aid used to quantify selected food items in Record 2. Mean (± S.D.) DEAT Aid Category used; n (%) Food Item and Type estimation error^ (%) RF SV M GS 1+ NoA Cereal -27.4± Chicken curry 7.8± (51.7) 23 (79.3) (34.5) 1 (3.4) - 4 (13.8) 2 (6.9) 1 (3.4) 2 (6.9) Rice -15.0± (75.9) - 5 (17.2) - 1 (3.4) 1 (3.4) Spaghetti bolognaise -34.9±32.4 Two fruits -1.0± (89.7) 1 (3.4) Yoghurt -19.4± Green beans 53.0± (69.0) - 18 (62.1) 21 (72.4) 1 (3.4) 3 (10.3) (10.3) (3.4) 2 (6.9) 1 (3.4) 9 (31.0) 6 (20.7) 4 (13.8) Carrots 19.9± (75.9) 1 (3.4) 3 (10.3) - 1 (3.4) 2 (6.9) Corn 50.6± (72.4) 2 (6.9) 4 (13.8) (6.9) Potato wedges 26.7± (58.6) Water 13.3± Milk 28.5± Tea -6.9± Soup -10.6±22.8 Toast 29.2±115.8 Apple -13.8±19.2 Crumbed fish 19.3± (6.9) 2 (6.9) 1 (3.4) 21 (72.4) (96.6) 24 (82.8) 29 (100.0) 21 (72.4) 1 (3.4) (3.4) 1 (3.4) 1 (3.4) - 11 (37.9) - 4 (13.8) (3.4) 10 (34.5) 18 (62.1) 3 (10.3) 2 (6.9) 3 (10.3) (13.8) 13 (44.8) 10 (34.5) 4 (13.8) Margarine 147.1± (3.4) - 8 (27.6) 1 (3.4) - 19 (65.5) Abbreviations: ^mean(±s.d.) error (%) = ([estimated weight - actual weight]/actual weight)*100; RF=Reference Food Photos; SV=Serving Vessel Photos; M=Mounds; GS=Generic Shapes & Graphics; 1+ = more than one aid category used; NoA=No Aid Discussion This study aimed to define the effect of the DEAT, a multifaceted two-dimensional PSEA, on the magnitude and direction of error associated with the estimation of food items contained in two 3-day Nutricam PhRs. Overall mean estimation error per day suggested a trend towards over-estimation of food items contained in the PhRs, however the use of the DEAT improved accuracy and consistency in portion size estimation. Chapter 6: Study 3 174

177 Effect of the DEAT on portion size estimation error of Nutricam records On average, use of the DEAT reduced overall error for Record 1, with Group A reporting an overall error of 17.7±15.8%/day compared to 34.0±22.6%/day for Group B, however this was not statistically significant. Error was also lower for each of the three days of the record among Group A compared to Group B, although the difference in error was significant for Day 1 only. In addition, using the DEAT estimation error was lower across all four food types of Record 1, however differences between groups were not statistically significant. The overall mean difference between groups for this record approached significance amorphous foods 21.9±30.5 %/day vs. 47.9±31.3 %/day, p=0.51). In addition to improving estimations of amorphous foods (Venter et al. 2000; Byrd-Bredbenner & Schwartz 2004), the use of aids have been shown to be effective in reducing error for liquids (Steyn et al. 2006). Although not significantly different between groups in this study, use of the DEAT also trended (p=0.085) towards a smaller error for liquid items. Findings for Record 2 show a similar error across both groups, with such a result expected given that both groups used the DEAT to assist in the estimation of portion size of food items contained in this record. Although the overall error was similar between groups for this record, a difference existed between food types. Spreads, amorphous and liquid foods were estimated more accurately by Group A, while the estimation error for solid items was slightly lower for Group B. The trend towards more accurate estimations within Group A for the Record 2 may potentially be due to a greater familiarity with the DEAT, as having already performed a similar task for the previous record. Solid items, such as biscuits, muesli bars, and crackers estimated in this study were responsible for a large amount of the overall error in both records. In particular for Record 2, the estimated portion size of these items produced the greatest amount of error compared to amorphous and liquid foods. Solid foods quantified with PSEAs and/or following training are estimated with the least amount of error among cohorts of individuals with training in nutrition and dietetics quantifying foods present using PSEAs and/or following training (Yuhas et al. 1989; Howat et al. 1994; Arroyo et al. 2007; Japur & Diez-Garcia 2010), in line with findings for the general population (Venter et al. 2000). In comparison, only one study found a similar level of error between solid and amorphous foods (Slawson & Eck 1997). Chapter 6: Study 3 175

178 Results from this current study mirror the findings observed in Study 3 Part A of this thesis and further suggests that solid food items present in PhRs are difficult to quantify accurately. As discussed in this earlier study, the physical characteristics of the solid foods presented in a two-dimensional form may be more challenging to estimate compared to portions of real foods (i.e. three-dimensional). An additional factor which may explain the relatively large error for estimations of solid items is likely related to the number of measurement units available for the quantification of these foods. For example, the units available for the majority of solid food items were limited to weight and/or dimensions (i.e. cm 3 or diameter). Solid items are often quantified in count units (i.e. 2 biscuits, 1 slice of bread), however in this study such options were not included in order to evaluate the effect of the generic shapes contained in the DEAT on the error associated with estimating the dimensions of these items. The relatively high estimation error suggests that this category of PSEA may not have been used or were considered unsuitable for use in this situation. The choice of DEAT aid used for the quantification of selected food items from Record 2 is discussed later in this section. The large estimation error associated with spreads (i.e. margarine, jam) was consistent across both records and between groups. Although the total number of these items was less than for the other food categories assessed, each was consistently over-estimated in this current study, a finding also found elsewhere (Lucas et al. 1995; Nelson et al. 1996; De Keyzer et al. 2011). As reported in these other studies the relatively small serve size of these foods may have contributed to the large error. In addition, Nelson et al (1996) attributed the large error to the inclusion of inappropriate reference photographs of these foods. Similarly, in this current study the DEAT did not contain an identical or similar PSEA to assist in the quantification of spreads, a conclusion which is reflected in the decision by the majority of subjects not to use an aid to estimate the amount of margarine present (Table 6-19). Thus, subjects would be reliant on the cognitive skills of conceptualisation and memory, possibly contributing to the large error associated with the estimation of these items. In addition, these items were served on other items (i.e. bread) and may not have been as well distinguished in the Nutricam photograph as the other food types, which may have also contributed to the inaccuracy. Chapter 6: Study 3 176

179 Effect of the DEAT on estimated energy and macronutrient content of Nutricam records Overall, the effect of the small to moderate error relating to the quantification of items within the Nutricam PhR had minimal consequences for the estimation of energy and macronutrient intake at the group level. For Record 1, where only Group A used the DEAT, the mean gram difference between groups was statistically significant, however the effect of this level of error on the nutrient composition varied depending on the nutrient of interest. The overall mean difference in protein content of Record 1 was statistical significant (p=0.001), while for energy the difference was approaching significance (p=0.051), however differences were only 0.9 g/day and 70kJ/day respectively, which is insignificant in a practical sense. In addition, the effect of estimation error on the overall fat and carbohydrate content of this record were neither statistically nor clinically significant, with differences of 0.4 g/day and 2.6 g/day found for fat and carbohydrate intake, respectively (Tables 6-16 and 6-17). Significant differences in nutrient intake between groups were observed for individual recording days for fat (Day 3) and carbohydrate (Day 1), however this was not of clinical importance with discrepancies of 0.5 g/day for fat, and 3.4 g/day for carbohydrate. The effect of error had a more substantial effect on the nutrient profile of certain food types, in particular for energy content. For example, amorphous items in Record 1 translated to a significant difference between groups (57kJ/day vs. 274kJ/day, p<0.001) (Appendix G, Table G3). Therefore, in the quantification of amorphous items, potential for clinical implications relating the estimation of energy intake of these items may exist in the absence of a PSEA. Liquid items for Record 1 also showed a significant difference between groups in estimated weights (Appendix G, Table G2), with a similar effect on protein, fat and carbohydrate content (Appendix G, Tables G4-G6). However, the practical effect on the overall nutrient content of these items in this record is relatively minimal. In Record 2 the effect of estimation error on the energy composition was insignificant, with the difference between groups ranging from 42±73 kj/day (Group A) to 56±34 kj/day (Group B). Good agreement was also observed for the overall mean difference in protein, fat and carbohydrate content of this record. Statistical significance was observed between groups for amorphous items in this record for energy, fat and carbohydrate content, however these differences were very small compared to Record 1, and therefore the clinical implications of this inaccuracy are Chapter 6: Study 3 177

180 negligible. In contrast, the effect of the error on the nutrient profile was the greatest for solid items in this record, with a difference in energy content ranging between 111±125 kj/day for Group A and 118±89 kj/day for Group B. On average, the solid items tested in this study were also more energy dense compared to the amorphous and liquid foods which may explain this finding. The large inaccuracy observed in the estimation of the portion size of spreads was not reflected in significant changes to the nutrient composition of the records, possibly due to the large variance observed for Group B in Record 1 and both groups in Record 2. Only a small number of studies have reported on the effect of portion size estimation error on the nutrient profile, all in the context of recall of food quantities in adults (Nelson et al. 1996; Robson & Livingstone 2000; Martin et al. 2009). These studies also found statistically and clinically insignificant differences between actual and estimated nutrients as a result of portion size estimation error. For example, Robson & Livingstone (2000) reported that estimation error resulted in discrepancies of nutrient intake within ±7%. Martin et al (2009) found small differences in the energy content of food items quantified from a PhR (-25 kj vs. -46 kj, p<0.05). Findings from the current study align closely with this work, and reinforce the conclusion that although error was present in the quantification of the portion size of food items contained in photographs, these differences did not transfer to clinically significant errors in the estimates of energy and macronutrient intake. Perceived acceptability and use of the DEAT The use of PhRs to collect dietary data was considered an acceptable method to assess dietary intake among this group of dietetic students. The DEAT was well received and acknowledged as an important tool in the quantification of PhRs. Over half (58.6%) the sample felt that the four PSEA categories contained in the DEAT were suitable to assist with estimating portion size, however 51.7% of the group did not believe that the number of aids within these categories were sufficient (Table 6-18). Exploration of the error and use of the DEAT to estimate selected food items in Record 2 (Table 6-19) revealed potential areas of improvement. For example, examination of the use of the DEAT revealed that the photographs of either reference food portions or serving vessels were the aids most frequently used. In comparison PSEAs were not used to estimate the portion size of a number of items listed, with margarine, toast and fruit the items most commonly estimated in this manner. Selection of aids was based on similarity of serving vessel used or to the food to be quantified. Such findings should not be unexpected given that the strong Chapter 6: Study 3 178

181 resemblance between reference PSEAs and test food items not only facilitates the cognitive skill of perception, but also limits the need for conceptualisation and memory. The large estimation error observed for some foods items was mirrored by the subjects perceived ability to estimate these items accurately. Items identified by subjects as the most difficult to estimate revealed a strong trend towards solid foods, such as breads, muesli bars, and biscuits. Less common examples given included spreads and amorphous foods such as pasta dishes. In comparison, a large majority (73.9%) of subjects listed liquid items as the least difficult to quantify, with vegetables also identified. It appeared that the attitude as to whether the quantification of a particular food item seemed easy or challenging was based on subjects perceived suitability of the DEAT aids. For example, a large proportion of the group commented that the solid items were the most difficult to estimate because there were no photographs of these foods contained within the DEAT. For the purpose of this study the majority of these solid items needed to be estimated in units of weight, where in reality these foods are often quantified by count (i.e. 1 slice of bread, 2 biscuits). As such, this finding may also indicate a lack of knowledge among the student dietitians regarding the standard serve size or weight of these items. This current study differed from the exploratory study described earlier in this Chapter (Study 3 Part A) as reference food photographs were not available for all test food items. In contrast, a number of generic aids in the form of amorphous mounds and graphics were included within the DEAT for use across a variety of different foods to minimise the need for subjects to conceptualise a reference food item. Despite their inclusion in the DEAT, it appeared that the use of these PSEAs were not maximised for solid foods, as evidenced by the relatively high estimation error associated with these items. Other researchers have found generic aids to be a suitable alternative to photographic food aids to assist in the recall of wedges shaped items (Godwin et al. 2006), meat serves (Godwin et al. 2001), and amorphous foods (Byrd-Bredbenner & Schwartz 2004; Subar et al. 2010). Comments relating to the improvement of the DEAT centred on the inclusion of more food reference photographs, further emphasising that subjects were reluctant to use the generic aids to quantify items when it was perceived that a suitable food or serving vessel photograph was not available. Information on the appropriate use Chapter 6: Study 3 179

182 of each DEAT category, including example food items was provided, however more intensive training on the use of the generic aids may have resulted in greater uptake during the study. Portion size training alone has been effective in improving accuracy among student dietitians in the short-term (Bolland et al. 1988; Yuhas et al. 1989; Slawson & Eck 1997; Brown & Oler 2000; Arroyo et al. 2007), however effects were only maintained up to 4 weeks post-training (Bolland et al. 1990). Brown & Oler (2000) found training nutrition students with three-dimensional food models greatly improved error associated with the estimations of real food items compared to two-dimensional food photographs. The authors concluded the food models better reflected the physical characteristics of the test food items as both were three-dimensional (Brown & Oler 2000). These findings would be applicable to Nutricam PhRs and the use of the DEAT, both of which are two-dimensional. Given the previous success of generic PSEAs in a variety of settings and the preference for replica reference food photographs in this current study, it would be worthwhile to investigate the effect on estimation error if only generic aids (i.e. serving vessels, amorphous mounds, and shapes) were available to aid in the quantification of PhRs. The process involving the quantification of items contained in a PhR is a critical step in the estimation of intake, however within current published literature detailed descriptions of this process is scarce. Few studies have reported on the ability of individuals trained in nutrition and dietetic to quantify food items contained in a PhR. The majority of these investigations have involved dietitians or other trained investigators, and have reported a small level of estimation error (Morgan 1982; Williamson et al. 2003; Aoki et al. 2006; Martin et al. 2009). Only one study has reported briefly on the level of error associated with the estimation of food portions contained in PhRs among student dietitians. In this study, Porter and colleagues (2006) found average estimation error was highly variable for 27 items and ranged from 33.6 g to 462 g, with mixed dishes exhibiting the greatest error. Results from this current study provide further insight into the ability of student dietitians to quantify food portions from PhRs, highlighting there is potential for improvement in this skill among this group. Limitations It is possible that the choice of pre-prepared foods as the test food items in this study may not have provided a true indication of the effect of estimation error on the energy and macronutrient composition of the record. It is also likely that the portion sizes of these foods may have been smaller than if similar items were collected using Nutricam in a free-living situation. Chapter 6: Study 3 180

183 The small sample size was less than estimated as necessary to detect a between group difference (%/day) (Section 6.3.1) for Record 1. Retrospective sample size calculation using the mean difference between groups and the within group variance for each group for Record 1 indicated that 30 subjects per group were needed to detect a significant difference. The purpose of this study was to assess the effect of a novel PSEA, the DEAT, on the accuracy associated with the estimation of food portions and nutrient profiles of test Nutricam dietary records. Therefore, although comparatively small compared to other studies, this current investigation facilitated its aim of evaluating the DEAT prior to the implementation of the NuDAM in Study Conclusions Overview Use of the DEAT reduced the estimation error associated with quantifying items contained in the Nutricam PhR. In addition, the effect of this error on the nutrient profile of the record was minimised with the use of the DEAT. Assessing intake via a PhR, such as Nutricam, was considered an acceptable method, with the use of an aid such as the DEAT considered essential. Some modifications to the DEAT, accompanied by more intensive training may assist in better utilisation of this tool among student dietitians. In addition, future examinations should focus on investigating this method of quantifying PhRs among practising dietitians Refinement of the NuDAM protocol Following the development and testing of both the recording and analysis components and protocols of the NuDAM (Studies 2 and 3), the method was refined based on these conclusions (Appendix H) and used to evaluate the performance of the method Study 4. The final NuDAM analysis protocol consisted of the dietitian identifying and quantifying food items both within the Nutricam record and collected during the call and entering this data directly into a food composition database Relevance to the research program As outlined in Section limited evidence exists on the error associated with the quantification of food items contained in a PhR. Potential exists for the introduction of error during the quantification of items contained in the Nutricam dietary record. This study confirmed the DEAT to be a useful and necessary resource to assist in the accurate quantification of food items contained in PhRs, therefore, inclusion of this tool in the analysis protocol of the NuDAM was justified. Chapter 6: Study 3 181

184 Implications for future practice Estimation of portion size is a complex process, with its role well documented in the assessment of dietary intake using traditional methods. Novel methods such as PhRs are also dependent on this process to quantify nutrient intake. Use of PSEAs, such as the DEAT, to assist in the quantification of items contained in PhRs is successful in reducing estimation error. Further improvements in accuracy may be possible through dedicated training in the use of PSEAs for this task among dietetic professionals Chapter 6: Study 3 182

185 Chapter 7: Evaluation of the NuDAM (Study 4) 7.1 Introduction Chapters 5 and 6 of this thesis have described the development and trial of the data collection and analysis protocols and tools of the NuDAM, with the conclusions drawn from Studies 2 and 3 used to refine the method. This Chapter describes the evaluation of the performance of the NuDAM with regards to its ability to measure nutrient intake. Specifically, Study 4 established both the relative and criterion validity of the NuDAM via comparison with a weighed food record (WFR) and total energy expenditure (TEE) measured using the doubly labelled water (DLW) technique in adults with type 2 diabetes mellitus (T2DM). In addition, the inter-rater reliability of the NuDAM was determined through a comparison between three dietitians estimates of energy and macronutrient intake. Attitudes towards the useability and acceptability of the NuDAM were also addressed in this study. Research Questions When evaluated against TEE (DLW technique) does the NuDAM produce an accurate measure of self-reported usual dietary energy intake (EI) compared to a WFR? Between dietitians, is there a difference in the reported usual energy and macronutrient intake as estimated via the NuDAM compared to a WFR? Compared to the WFR, do individuals with T2DM perceive the NuDAM as an acceptable and useable method for recording dietary intake? 7.2 Methods Subjects Adults with T2DM were recruited through a combination of strategies involving both the use of a database of subjects from an aligned study (Bird et al. 2010) who had expressed interest in participating in other research projects and to university staff via internal list serves. Ten subjects were required to evaluate the NuDAM, with recruitment ceasing once the required number of subjects had been reached. Potential subjects needed to meet the following criteria to be eligible to participate in the study: Aged years; A diagnosis of T2DM of >3 months; Not currently receiving treatment for cancer; Chapter 7: Study 4 183

186 No previous diagnosis of liver, kidney or thyroid diseases; Not currently trying to lose weight, with a stable body weight of ±4 kg over the past 6 months Study design and procedure The study was conducted over 15 consecutive days with Figure 7-1 illustrating the time points at which the various measures were collected. Subjects were required to attend the first session (Day 0) at the Institute of Health and Biomedical Innovation (IHBI), whilst the second (Day 8) and third (Day 15) sessions were held at either IHBI or off-site (i.e. subject s residence) depending on subject preference. Measure Height Weight TEE (DLW) Intake (NuDAM) Intake (WFR) Figure 7-1: Study 4 timeline. Abbreviations: TEE=total energy expenditure; DLW=doubly labelled water; NuDAM; Nutricam dietary assessment method; WFR=weighed food record Day On Day 0, the height (to the nearest 0.1cm) and weight (to the nearest 0.1 kg) of each subject were recorded. Height was measured without shoes using a portable height scale (Model PE087, Mentone Educational, Victoria, Australia). Weight was measured without shoes and in light clothing using portable digital body weight scales (Model HD319, Tanita Corporation, Tokyo, Japan). Weight was also measured on Days 8 and 15 using the same set of scales. General demographic information and dietary restraint (van Strien et al. 1986) were collected in the first session (Appendix J). During the two week period TEE was measured using the DLW technique, whilst dietary intake was assessed using two independent methods: the NuDAM and the WFR. At the end of each dietary recording period, subjects were asked to complete a questionnaire on the experience of using each method (Appendix J). To establish the criterion validity of each dietary assessment method, estimated EI derived from both methods were compared to measured TEE. In addition, the relative validity of the two dietary methods was examined through establishing the level of agreement between the Chapter 7: Study 4 184

187 test (i.e. NuDAM) and reference (i.e. WFR) methods. Three dietitians each coded both sets of dietary records independently, with the reproducibility of the two methods examined through a comparison of the estimated energy and macronutrient composition for the NuDAM and WFR. The procedure for measuring TEE and dietary energy and macronutrient intake will be described in the proceeding sections Measurement of TEE using the DLW technique TEE was measured over 14 consecutive days using the DLW technique. Administration of the DLW occurred on Day 0, with subjects in a fasted state. Body weight was measured and subjects required to provide a urine sample prior to dosing. Subjects were orally dosed with 1.25 g 10% 18 O + 0.1g 99% 2 H/kg. A post-dose urine sample was collected 6 hours after drinking the DLW. During Days 1-14 subjects were required to collect one urine sample each day, record the time of collection on the sheet provided and store the sample in the refrigerator. Samples were collected n Days 8 and 15 and stored at -20 C until analysis. The analysis and calculation of TEE was performed by an experienced laboratory technician at the Institute of Health and Biomedical Innovation QUT. A detailed description of the isotopic analysis and calculation of TEE is beyond the scope of this thesis, however using the framework described by Schoeller (2002) the process can be summarised as follows. The level of enrichment of 18 O and 2 H isotopes contained in the urine samples were measured in triplicate by isotope ratio mass spectrometry (Hydra 20/20 CF-IRMS, Sercon Cheshire, UK). The rate of decay of isotope concentration was log-transformed and plotted against time using the 14-day multi-point method (i.e. baseline, 6hr, Days 1,2,3,7,12,13,14) (Schoeller 1996). Isotope dilution spaces were derived using the equation by Schoeller (1996), and then used to calculate carbon dioxide (CO 2 ) production (Racette et al. 1994). Using the CO 2 production, indirect calorimetry principles were applied and TEE derived via using the modified Weir (1949) equation, with a standard respiratory quotient of 0.85 used for all subjects Measurement of dietary intake Individual dietary intake was measured for each subject using the NuDAM in the first week and the WFR method in the second week. In the validation of a novel dietary assessment method, Nelson (1997) recommends that the administration of the test and reference methods be separate, with the test method used first to limit the introduction of recording bias. The protocol for recording intake using both assessment methods and the analysis of the resultant dietary records will be described in detail in the following sections. Chapter 7: Study 4 185

188 Recording of dietary intake data using the NuDAM Prior to the commencement of this study, the NuDAM was refined based on the conclusions drawn from Studies 2 and 3. Figure 7-2 illustrates the data collection and analysis components of the NuDAM. 1. Data collection 2. Data analysis Mobile phone with Nutricam application Nutricam dietary record Estimate of nutrient intake Call to subject to clarify and probe Nutricam record DEAT + food composition database Figure 7-2: Components of the NuDAM. Collection of dietary data using the NuDAM consisted of a Nutricam mobile phone to capture the photo/voice dietary record and a call (using a standardised interview protocol and database) to the subject the following day to clarify contents within the Nutricam dietary record. Analysis of the dietary data consisted of the dietitian identifying and quantifying food items contained within the Nutricam dietary record. The Dietary Estimation and Assessment Tool (DEAT) was used to assist in the task of portion size estimation. Data is then entered into a food composition database to obtain an estimate of nutrient intake. For this current investigation, subjects were provided with detailed instruction on the use of Nutricam during the first visit and mastery demonstrated by the subject prior to leaving the session. Intake was recorded on the same three non-consecutive days (Days 2, 4, and 6) for each subject and consisted of two week days and one weekend day. As subjects commenced the study at different days of the week, the days were not identical between subjects. During the recording period subjects were required to record all food items prior to consumption using a standardised protocol (Appendix I). Briefly, prior to each eating occasion subjects used the Nutricam mobile phone to capture an entry consisting of a photograph and voice record detailing the food items for consumption. When recording the image, individuals were instructed to place the prompt card next to the items, hold the Chapter 7: Study 4 186

189 Nutricam device at an angle of approximately 45 and ensure that all items were clearly visible on the screen of the device. After an image had been captured, subjects were then prompted to make a voice recording listing the location and meal occasion (i.e. breakfast), and describe each food item contained in the image (i.e. name, type, Brand/Product name, and preparation/cooking method). Food items recorded in a previous entry but not consumed entirely, subjects were required to record another entry detailing the leftover items. On the morning following each Nutricam recording day (i.e. Days 3, 5, and 7), dietitian #1 (D1) (Candidate) reviewed the previous day s record for each subject. Following the standardised protocol (Appendix H, Figure H1), D1 entered the information into the food analysis program FoodWorks (version 5.1, 2007, Xyris Software, Brisbane, Australia) using the AUSNUT 1999 food composition database (Australia New Zealand Food Authority 2001). Food items contained within the Nutricam record that needed clarification were noted and the additional information required was listed for the corresponding item in the program. Following this review of the record, the subject then received a brief phone call from D1. Subjects were briefed that they would receive a call from D1 (Candidate) the following morning, with the time of the call pre-determined with subject on Day 0. The structure of the interview followed the standardised analysis protocol (Appendix H, Figure H2) and used the Access database (Microsoft Access 2007) specifically developed for this task (see Section 5.5.2). Briefly, the purpose of the call was to ensure that the dietary data collected was complete. The interview contained a series of standardised questions to clarify items contained in the Nutricam record and probe for any items consumed the previous day but not recorded using Nutricam. The calls were recorded to allow for review of the content at a later time by the dietitians undertaking the coding and entry of the dietary records. Recording of dietary intake using the WFR method In the second week, each subject used the WFR method to document intake over three nonconsecutive days (Days 9, 11, and 13) (two week days and one weekend day). Subjects were provided with a set of digital food scales Philips model HR 2385 (Koninklijke Philips Electronics N.V., Amsterdam, The Netherlands) (accurate to 1 g) and received comprehensive training during the second session (Day 8). Subjects were required to weigh all food items prior to consumption over the 3-day period and record all information including a detailed description of the items into the paper record supplied. Any food served, but not eaten, was weighed and documented in the record. In addition, subjects were asked to provide recipe details on any composite items or mixed dishes consumed including ingredient descriptions and weights. Following completion of the study period (Day 15), the Chapter 7: Study 4 187

190 record was reviewed by D1 in the presence of the subject to ensure that the information was complete with any clarifications/addition information noted. Calculation of dietary energy and macronutrient intake Three dietitians (i.e. D1, D2, D3) independently performed the data coding and entry for both sets of dietary records for each subject into the food analysis program FoodWorks (version 5.1, 2007, Xyris Software, Brisbane, Australia) using the AUSNUT 1999 food composition database (Australia New Zealand Food Authority 2001). Both D2 and D3 were within 2 years of graduation and had completed an earlier pilot study trialling the NuDAM analysis protocol (findings not reported in this thesis). At the time of their involvement in this current study, D2 and D3 had not received feedback on their estimation accuracy associated with participation in the earlier pilot study. The dietary records for the NuDAM were coded first. Dietitians were provided with access to the Nutricam website and a copy of the DEAT and analysis protocol. Using the voice description and photographs, each dietitian was required to identify and quantify food items contained in the 3-day Nutricam records of all subjects. This data was entered directly into the FoodWorks database provided. Following the completion of this step, dietitians were provided with a recording of the call to the subject. The information obtained during the call was used to adjust the coding of the Nutricam entries and to add details relating to any items which were consumed but not recorded. Following completion of this 2-step coding process for the NuDAM records, the WFRs were coded and entered into the FoodWorks database. For both assessment methods, dietitians were required to enter all information contained in the records as completely as possible using the options available within the food composition database. During coding, each dietitian had the opportunity to supplement the database information by creating new foods for items where an appropriate alternative was not available. Nutrient composition of these items was derived through the use of manufacturers information for packaged items or through the inclusion of recipe information for mixed dishes prepared from raw ingredients. Separate FoodWorks databases were used by each dietitan to code each stage of the NuDAM (i.e. pre-call and post-call) and the WFRs. Dietitians were required to keep a record of the time taken to code and enter both sets of dietary records (including listening to the recorded call and making additional modifications to the database for the NuDAM). Chapter 7: Study 4 188

191 7.2.5 Data analysis All statistical analyses were performed using the SPSS for Windows (version 17.0, 2008, SPSS Inc., Chicago, Illinois). Responses to the 10 questions relating to dietary restraint were measured on a 5-point scale (1=never; 2=seldom; 3=sometimes; 4=often; 5=very often) (van Strien et al. 1986). The restrained eating score was calculated using the mean of the responses with scores ranging from 1 to 5, with the higher the score the greater the dietary restrain (Rennie et al. 2006). Subject comments regarding the acceptability and useability of the both dietary assessment methods were examined for common themes and grouped accordingly. For each dietitian, mean energy and macronutrient intake per day was calculated from an average of the three recording days. These three estimates of nutrient intake for each subject were then averaged to obtain an overall estimate of energy and macronutrient intake for each subject for both methods. Descriptive statistics were used to report demographic and anthropometric characteristics, measured TEE, estimated energy and macronutrient intake from both methods, and attitudes towards the useability and acceptability of the device. Repeated measures ANOVA was used to assess differences in body weight between baseline, Day 8 and Day 15. Comparison of measures of self-reported nutrient intake between NuDAM and WFR Paired t-tests (normally distributed variables) and Wilcoxon signed-rank test (non-normally distributed variables) were used to explore differences in estimated energy and macronutrient intake between methods for each dietitian. Bland-Altman plot (Bland & Altman 1986) was used to assess the level of agreement between self-reported EI as recorded by the NuDAM and WFR to determine if bias was present across various levels of intake. Pearson s (parametric data) and Spearman s rank (non-parametric data) correlation coefficients showed the strength of the relationship between estimates of the same dietary variable measured using both the NuDAM and WFR. Validation of self-reported EI using TEE The use of measured TEE to validate self-reported EI was based on the fundamental principle of EI=TEE ± body stores, where in the absence of non-significant weight change (stable weight) at the group level, the expected ratio of EI:TEE is 1.00 (Livingstone & Black 2003), and the 95% confidence limits (CL) were calculated using the formula by Black & Cole (2001), outlined in Section 3.4. In addition, paired t-tests were used to determine differences in estimates of EI (NuDAM and WFR) and TEE, while correlations assessed the strength of the relationship between these variables. Chapter 7: Study 4 189

192 Inter-rater reliability of estimates of energy and macronutrient intake Intra-class correlation coefficients (ICC) of absolute agreement evaluated the relationship between the three dietitians estimates of energy and macronutrient intake. The I accounts for both the degree of correlation and the difference between the measures (Nelson 1997). Repeat-measures ANO A (normally distributed variables) and Friedman s ANOVA (non-normally distributed variables) was used to determine differences for estimates of energy, macronutrients, and weight between the three dietitians. Post hoc analysis was completed with the Bonferroni method to identify which dietitians estimates differed. 7.3 Results Subject characteristics Details of the subjects are provided in Table 7-1. Six men and four women ranging in age between years with T2DM participated in the study. Five were classified as obese (BMI 30.0 kg/m 2 ), four as overweight (BMI kg/m 2 ), and one was within the normal BMI range ( kg/m 2 ). All had reported stable body weight for a minimum of 6 months prior to commencing the study. The group showed a low level of dietary restraint, with mean individual scores ranging between 1.3 to 3.2 (out of 5). For some subjects, body weight fluctuated throughout the study period (Table 7-2). On average, the group lost weight (- 0.7±1.2 kg) during week 1 and gained weight (0.4±0.9 kg) in week 2. Overall, a loss of weight (-0.3±1.2 kg) was observed during the two week study period, however, at the group level, differences in mean body weight were not significant at each of the three time points. Table 7-1: Subject characteristics at baseline (Study 4). Subject ID# Gender Age (years) BMI (kg/m 2 ) Dietary restraint Mean(±SD) score 11 male ± male ± female ± male ± female ± male ± female ± male ± male ± female ±1.2 Mean(±SD) 61.2± ± ±1.0 Abbreviations: BMI=body mass index Chapter 7: Study 4 190

193 Table 7-2: Subject body weight status during the study period. Subject ID# Body Weight (Wt) (kg) Baseline (Wt 0 ) Day 8 (Wt 1 ) Day 15 (Wt 2 ) Mean(±SD) 93.4± ± ±18.4 Abbreviations: between weights at baseline, Day 8 and Day 15 (repeated measures ANOVA): not significant Comparison of estimated nutrient intakes between methods Table 7-3 summarises the mean estimates for energy and macronutrient intake from both the NuDAM and WFR. For each dietitian, differences in estimates of energy, protein, fat, carbohydrate, and alcohol between methods were not statistically significant. In addition, averages of the three dietitians estimates of nutrient intake were also not significantly different. Figure 7-3 illustrates the differences between estimates of EI derived from the NuDAM and WFR method for each dietitian. Although D1 (Candidate) under-estimated the mean EI in the NuDAM (Figure 7-3a), the variability was smaller compared to the estimates of D2 (Figure 7-3b) and D3 (Figure 7-3c). In addition, no overall trend in the error was evident at estimates of low or high EI (Figure 7-3d). Correlation coefficients for estimates of nutrient intake between methods, revealed that the relationship between measures obtained using the NuDAM and WFR varied depending on the nutrient, with strong correlations between protein and alcohol, moderate associations between estimates of energy and carbohydrate intake, and a weak relationship for fat (Table 7-4). In general, stronger relationships between methods for nutrient intake estimates derived by D1 (Candidate) were present compared to either D2 or D3. Chapter 7: Study 4 191

194 a) Mean difference: 0.4±1.3 MJ/day b) Mean difference: -0.2±2.3 MJ/day c) Mean difference: -0.2±1.8 MJ/day d) Mean difference: 0.0±1.7 MJ/day Figure 7-3: Comparison of energy intake as measured using both methods for each dietitian (Bland-Altman Plots). a) D1=Dietitian No.1, b) D2=Dietitian No.2, c) D3=Dietitian No.3, and d) All dietitians (average). Chapter 7: Study 4 192

195 Table 7-3: Group energy and macronutrient intakes estimated by each dietitian for both methods. Mean(±SD) intake per day D1 D2 D3 Overall Energy (MJ/day)^ Protein (g/day)^ Fat (g/day)^ CHO (g/day)^ NuDAM 8.2± ± ± ±2.0 WFR 8.5± ± ± ±1.8 NuDAM 89.3± ± ± ±23.7 WFR 89.1± ± ± ±26.4 NuDAM 75.6± ± ± ±20.3 WFR 79.5± ± ± ±21.8 NuDAM 194.9± ± ± ±54.4 WFR 206.3± ± ± ±53.9 Alcohol NuDAM 15.0± ± ± ±28.9 (g/day) # WFR 16.1± ± ± ±28.3 Abbreviations: mean (±SD) intake per dietitian = mean intake per day (from three recording days) for all subjects; D1=dietitian No. 1; D2=dietitian No. 2; D3=dietitian No.3; NuDAM=Nutricam dietary assessment method; WFR=weighed food record; CHO=carbohydrate; Overall mean (±SD) intake = mean (D1, D2, and D3 intake per day); difference within each dietitian s mean estimate of nutrient intake NuDAM vs. WFR (^paired t-test or #Wilcoxon Signed Ranked test): not significant. Table 7-4: Correlation between energy and macronutrient intakes estimated by each dietitian for both methods. Correlation Coefficient^ (NuDAM vs. WFR) D1 D2 D3 Overall Energy 0.67* Protein 0.90* * 0.78** Fat Carbohydrate 0.65* * Alcohol# 0.84** 0.91** 0.88** 0.85** Abbreviations: D1=dietitian No. 1; D2=dietitian No. 2; D3=dietitian No.3; Overall correlation coefficient calculated using mean overall intake per day (i.e. mean [D1, D2, and D3 intake per day]); ^Pearson s r; #Spearman s rho: *p<0.05,** p< Validation of self-reported EI against TEE Mean EI estimated by each dietitian for both the NuDAM and WFR methods was significantly less (p<0.01) than TEE. Differences in EI between dietitians for both methods were less than 10% (see Section 7.3.4, Table 7-7), and therefore the estimates for EI of the three dietitians were averaged to obtain an overall mean EI to facilitate comparison with TEE. For each subject the TEE and estimated EI for both the NuDAM and WFR are given in Table 7-5. TEE ranged between 7.8 MJ/day to 13.5 MJ/day. The average of the mean group TEE and EI estimates for both methods are summarised in Table 7-6. Differences between TEE and EI were Chapter 7: Study 4 193

196 significant (p<0.01) for both the NuDAM and WFR, however the difference between estimates of EI between methods was not significant (p=0.979). As reported in Section 7.3.1, a small, but non-significant change in weight status (-0.3±1.2 kg) was found over the two week period. Therefore in the presence of stable body weight at the group level, the ratio of EI:TEE expected is At the group level mean EI:TEE ratio of the NuDAM and WFR method are identical, although slightly greater variation is present for the NuDAM (Table 7-6). Table 7-5: Estimated EI and TEE for each subject. Mean (±SD) EI (MJ/day) Subject ID# NuDAM WFR TEE ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± Mean(±SD) 8.8± ± ±2.3 Abbreviations: NuDAM=Nutricam dietary assessment method; WFR=weighed food record; EI=energy intake; TEE=total energy expenditure; Overall mean (±SD) EI intake = mean (D1, D2, and D3 intake per day). Table 7-6: Group mean EI, TEE, and ratio of EI:TEE Mean (±SD) (n=10) Energy (MJ/day) Difference# (95% EI: TEE %Diff CI) TEE 11.8± EI(NuDAM) 8.8±2.0** -3.1±2.7 ( ) 0.76± ±20.2 EI (WFR) 8.8±1.8** -3.0±2.4 ( ) 0.76± ±17.0 Abbreviations: TEE=total energy expenditure, EI=energy intake, NuDAM=Nutricam dietary assessment method, WFR= weighed food record; #difference = TEE minus EI (NuDAM or WFR); %Difference = ([EI- TEE]/TEE*100); Difference between TEE and EI (paired t-test): **p<0.01 Chapter 7: Study 4 194

197 EI:TEE NuDAM WR WFR Subject ID# Figure 7-4: Average ratio of EI:TEE for both NuDAM and weighed food record. Overall mean energy intake (EI) per day (from 3 dietitians estimates of EI/day) for each method, the Nutricam dietary assessment method (NuDAM) and the weighed food record (WFR). Compared to the total energy expenditure (TEE) (measured using the doubly labelled water technique). The ratio of EI:TEE for both methods for each subject is graphed in Figure 7-4. The 95% confidence limits (95% CL) of the ratio of EI:TEE for both methods were calculated using a within-subject coefficient of variation from an average of the three dietitians estimates for measures of mean EI. In this study, the within-subject coefficient of variation for EI was calculated at 22.8% for the NuDAM and at 20.1% for the WFR, while the within-subject coefficient of variation for repeated measures of TEE using DLW was 8.0% (laboratory data; unpublished). Correlation between EI and TEE was for the NuDAM and for the WFR. Therefore, the 95% CL for the NuDAM were <0.72 and >1.28, with three subjects (Subject ID# 12, 14, 16) below the lower cut-off. In comparison, 95% CL for the WFR were <0.76 and >1.24, with four subjects (Subject ID# 11, 12, 14, 16) below the lower limit. Three subjects classified as under-reporting intake using the NuDAM were also found to be underreporting intake with the WFR method. In addition, only male subjects were identified as under-reporting across both dietary assessment methods. Ratios above the 95% CL were not observed for either method, indicating that over-reporting of EI was not present among this group Inter-rater reliability for estimates of nutrient intake between methods Table 7-7 summarises the group mean energy and macronutrient intake for both methods as estimated by the three dietitians. Repeated-measures ANOVA of nutrient estimates differed significantly between dietitians for the NuDAM in energy (F(2, 18) = 9.14, p<0.01), fat (F(2, 18) = 2.17, p<0.05), and carbohydrate (F(2, 18) = 6.52, p<0.05). Bonferonni post-hoc analysis showed the estimate by D1 to be Chapter 7: Study 4 195

198 significantly different (Mean (95% CI)) compared to both D2 (-0.9 MJ/d ( ), p<0.05) and D3 (-0.9 MJ/d ( ), p<0.05). Furthermore, the estimates of D1 also varied significantly for three macronutrients with differences in protein intake compared to D3 (-8.8 g/day ( ), p<0.05, and compared to D2 in fat (-11.4 g/day ( ), p<0.05) and carbohydrate intake (-17.1 ( ), p<0.05). Estimations of alcohol intake did not differ between dietitians for the NuDAM. No statistically significant difference between dietitians in the estimated energy and macronutrient content of the WFR was observed. Intra-class coefficients (ICC) showed very high agreement between dietitians for estimated energy, carbohydrate and alcohol intake within both methods. Strong correlations were also observed for fat intake in both methods, whereas agreement between dietitians for protein intake was higher (ICC=0.97) for the WFR compared to the NuDAM (ICC=0.79) (Table 7-7). All ICC for energy and macronutrient intake across both methods were statistically significant (p<0.001). Table 7-7: Agreement between dietitian estimates of nutrient intake for both methods. Energy (MJ/day) Protein (g/day) Fat (g/day) CHO (g/day) Mean(±SD) Intake, per dietitian D1 D2 D3 ICC^ (95% CI) NuDAM 8.2± ±2.3* 9.1±2.0* 0.88 ( ) WFR 8.5± ± ± ( ) NuDAM 89.3± ± ±23.1* 0.79 ( ) WFR 89.1± ± ± ( ) NuDAM 75.6± ±25.4* 86.6± ( ) WFR 79.5± ± ± ( ) NuDAM 194.9± ±52.7* 215.3± ( ) WFR 206.3± ± ± ( ) Alcohol NuDAM 15.0± ± ± ( ) (g/day) # WFR 16.1± ± ± ( ) Weight (g/day) NuDAM ± ± ± ( ) WFR ± ± ± ( ) Abbreviations: mean (±SD) intake per day per dietitian = mean (intake for each day)/number of recording days; D1=dietitian No.1; D2=dietitian No.2; D3=dietitian No.3; CHO=carbohydrate; NuDAM=Nutricam dietary assessment method; WFR=weighed food record; Repeated-measures ANOVA (GLM) between dietitians for each dietary method, except for alcohol (#Friedman s ANOVA): *p<0.05, compared to D1; ^ICC = Intra-class Correlation Coefficient significant: all p< Examination of the total estimated weights from both sets of dietary records was undertaken to determine if the quantification of items may have contributed to the difference in energy and macronutrient intake observed between dietitians (Table 7- Chapter 7: Study 4 196

199 7). No significant difference existed in the estimated weights between dietitians for each day or the average weight for each method, although similar to the nutrients, the mean estimated weight of the NuDAM records for D1 was less than both D2 and D3. Agreement for the mean weight of the records was high for both methods, although it was stronger for the WFR (ICC=0.98) compared to the NuDAM (ICC=0.78). The non-significant difference in estimated weights of the NuDAM records between dietitians suggested that exploration of other factors relating to the coding of these records may offer some explanation as to the discrepancies observed in energy and macronutrient intake. In order to explore this assumption further, the records of both methods for the subject (ID #18) with the greatest difference between dietitian estimates of energy intake were examined in detail. Examples of the differences in the coding (identification and/or quantification) of these records resulting in large discrepancies in energy intake are listed in Table 7-8. Table 7-8: Examples of discrepancies in coding of dietary records observed between dietitians for subject ID #18. Method Type of coding discrepancy Description of item entered into food composition database Difference in EI (MJ) Quantification, based on pre-defined category Omission Sausage, pork, grille /BBQ : 3 x thin [D1] vs. 3 x thick [D2 and D3] ) Rice 0.5 cup [D1] NuDAM Quantification, coderbased Identification Peanut butter, NF : -1.5 tbs [D1] vs. 2tbs [D2] or 3 tbs [D3] Pasta, in cheese-based sauce, commercial [D3] or Pasta, with seafood in cream-based sauce, commercial [D1] vs. Pasta, with seafood in cream-based sauce, homema e [D2]. Identical quantities to WFR Identification Identification + Omission Omission Coffee,white w/milk,fr instant,regular,ns str&amt milk [D1 and -0.7 D3] vs. Coffee,ma e w/milk,from instant,reg,n strength [D2] Recipe: Deep ea Mullet 98 g. D1 and D3 vs. D2-0.5 Rice 240 g [D2] -1.3 Abbreviations: NuDAM=Nutricam dietary assessment method; WFR=weighed food record; EI=energy intake; D1=dietitian No.1; D2=dietitian No.2; D3=dietitian No.3 Chapter 7: Study 4 197

200 7.3.5 Useability and acceptability of NuDAM Subject responses to questions regarding certain aspects of the NuDAM recording protocol provided feedback on the inclusion of these components (Table 7-8). In particular, all subjects agreed that collecting both the photograph and the voice record were easy. Six subjects found the Nutricam Prompt Card useful to assist in the recording protocol. All subjects felt confident in using Nutricam again to record intake. In addition, overall the post-recording phone call appeared to have been well received. The duration of each call ranged between 5 to 21 minutes, with a median call time of 11 minutes. Nine subjects agreed that the Nutricam mobile phone was easy to use, in contrast seven subjects were either neutral or disagreed with the statement that weighing foods items was easy (Table 7-9). Table 7-9: Subject responses regarding useability and acceptability of the methods. n Statements, as presented^ Strongly agree Agree Neutral Disagree 1. Overall, I found the Nutricam mobile phone easy to use: 2. Overall, I found weighing my foods and drinks easy: Nutricam only: 3. I found taking photographs of food and drink items easy: 4. I found recording the voice file easy: I found that the Prompt Card was useful for remembering how to use Nutricam: 6. When prompted during the call: a) I found it easy to clarify the details of the food and/or drink items that I had eaten during the previous day: b) I found it easy to remember if there were any food and/or drink items I had not recorded using the Nutricam mobile phone the previous day: c) I found it easy to remember the description of the food and/or drink items I had not recorded using the Nutricam mobile phone the previous day: d) I found it easy to remember the quantities of the food and/or drink items I had not recorded using the Nutricam mobile phone the previous day: e) Overall, I found that the length of the calls I received were appropriate: Abbreviations: ^Questions answered on a 5-point Likert Scale (Strongly agree/agree/neutral/disagree/ Strongly disagree); strongly disagree not included as no responses were recorded in this category. Chapter 7: Study 4 198

201 A slightly greater change in eating behaviours was observed in the group during the period recording with the WFR compared to the NuDAM (Table 7-10). For example, subjects commented on feeling more self-conscious when using the WFR in public as opposed to the NuDAM. Regardless of the method used, a similar number of subjects reported the main reason for not recording all food items was due to not remembering to record prior to eating. Furthermore, there appeared to be a more conscious effort to change their usual diet during the period using the WFR, with justification for altering typical intake in an effort to simplify intake in order to facilitate recording. Table 7-10: Changes in eating behaviours during each recording period. n Statements, as presented No Yes 1. Was there any difference in how you used the Nutricam mobile phone to record your diet when you were alone compared to when you were with other people or in public? 2. Was there any difference in how you recorded your diet using the weighed record method when you were alone, compared to when you were with other people or in public? 3. Did you record all food and drink items that you consumed during the test period using the Nutricam mobile phone? 4. Did you record all food and drink items that you consumed during the test period using weighed record method? 5. Where there any foods and/or drinks that you usually eat, but did not eat during the Nutricam test period? 6. Where there any foods and/or drinks that you usually eat, but did not eat during the weighed record method test period? Similar themes were reflected in the subjects general descriptions of the experience of using each dietary assessment method (Table 7-11). Two themes emerged for each method, with the NuDAM regarded as simple and convenient and the WFR thought of as difficult and burdensome. All subjects preferred to use the NuDAM to record intake compared to the WFR. Subjects explanations for the selection of the novel method over the traditional method were dominated by three main themes: convenience, ease of use, and portability. Furthermore, this trend was also observed in the maximum period of time that subjects would be willing to use each method again to record intake, with a strong shift towards longer recording periods if required to use the NuDAM compared to the WFR (Table 7-12). Chapter 7: Study 4 199

202 Table 7-11: Simple thematic analysis of subjects attitudes towards each method Method Theme Example comment Simple It was quite simple and easy to use the phone NuDAM This was extremely easy to use and no matter where or Convenient when I ate the recording was easy to do. Time consuming. It was awkward having to carry scales with me all the time Burdensome and a little bit embarrassing having to weigh food in front of WFR other people who don't know what you are doing. Somewhat difficult particularly when cooking process was Difficult used and a number of items were part of the meal. Abbreviations: NuDAM=Nutricam dietary assessment method; WFR=weighed food record Table 7-12: Maximum time period subjects were willing to use each method again to record intake Maximum recording period Method 1 day 3 days 5 days 7 days 14 days >30+ days NuDAM WFR Abbreviations: NuDAM=Nutricam dietary assessment method; WFR=weighed food record Documentation of the time taken by the dietitian to analyse the dietary records was not consistently completed prospectively for each subject, and therefore the time spent coding and entering dietary data was estimated retrospectively for each set of records. In general, the time taken to analyse both sets of dietary records was greater for the NuDAM compared to the WFR, with the period of data coding and entry approximately two to three times as long for D3 and D2 (respectively) using the novel method. The estimate for the time taken to analyse the NuDAM records included listening to the clarification/probing call for the NuDAM. On average 2.9±1.4 items per subject contained in Nutricam records required clarification during the call to the subject the following morning. Of the 30 NuDAM recording days, 53.3% (n=16) were collected in full by the subject with no additional items recalled during the call the previous day. For the remaining days, a total of 65 items (including component ingredients of mixed dishes) consumed but not recorded with Nutricam were recalled during the call, with the number of additional food items collected ranging from 1 to 15 items. The majority of these items were recalled either following the first general probe or the probes relating to specific times the previous day. Only 5 items in total recalled following probes relating to the categories of commonly forgotten foods. Clarification of Nutricam entries and probing for missing food items resulted in an increase in estimated energy intake for Chapter 7: Study 4 200

203 all dietitians, ranging from +1.3±1.3 MJ/day for D1, +0.7±1.1 MJ/day for D2, and +0.5±1.0 MJ/day D3, however a significant difference was observed for D1 only (t(9)=3.045; p<0.05). 7.4 Discussion Findings from this study indicate that the novel NuDAM is as accurate as the WFR method when compared to an objective marker of intake, with high inter-rater reliability among dietitians. Furthermore, a strong subject preference for use of the novel method compared to the traditional approach highlight the potential of the NuDAM as an alternative method of assessing dietary intake in this group. Ten older adults with T2DM were involved in evaluating the performance of the NuDAM. All subjects, except for one, were either overweight or obese. High prevalence of obesity observed in this sample is indicative of a large proportion of adults with this condition, with Dauosi et al (2006) reporting that 52% of adults with T2DM were obese. The dietary restraint scale measured both actual restriction of intake and intention to restrict (van Strien 1999), with scores of 3 categorised as low-restraint (Rennie et al. 2006). Overall, low dietary restraint (2.5±1.0) was observed among this group, which is in contrast to others who have shown this characteristic to be common among adults with T2DM, particularly with weight status (Ryan et al. 2008). Comparison of NuDAM with WFR The relative validity of the NuDAM was established through an examination of the relationship with the WFR. Small differences between dietitians estimates of energy and macronutrient intake derived from both methods were not significant. In general, the strength of this relationship varied depending on the nutrient of interest. Overall, moderate to high correlations were found for all nutrient except for fat. The association between methods for estimates of these three nutrients (Table 7-4) were lower in this current study compared to previous published work. For example, Bird and Elwood (1983) reported strong correlations of 0.86, 0.91, and 0.84 for intakes of energy, protein, and carbohydrate respectively, while Wang et al (2000) also found high associations (r=0.79, 0.88, and 0.78). In contrast, the relative validity observed between the NuDAM and WFR for estimated intakes of energy, protein, and carbohydrate were either similar or greater Chapter 7: Study 4 201

204 to the findings reported by Wang et al (2006) (r=0.58, 0.56, and 0.59) and overall by Kikunaga et al (2007) (r=0.60, 0.56, and 0.56). Estimates of fat intake between the two dietary assessment methods showed a weak relationship (r=0.24), compared to other similar studies which have reported correlations of for this macronutrient (Bird & Elwood 1983; Wang et al. 2002; Wang et al. 2006; Kikunaga et al. 2007). The relative validity of PhRs to capture alcohol intake has not previously been reported. Compared to energy and the other macronutrients in the current study, alcohol displayed the strongest agreement between dietitians for both the NuDAM and WFR (ICC=0.99). It is likely, that the smaller number of these items and the standardised vessels (e.g. pint glass) in which these foods are typically served may have contributed to the high level of inter-rater reliability observed for this nutrient. In past studies using PhRs, the recording of dietary intake using the two assessment methods has occurred concurrently, and therefore, given that each method is essentially capturing identical intake information, the high level of agreement between methods in this situation is not unexpected. In comparison, the design of the present study differed in that the recording of intake using the two methods occurred one week apart. Therefore, simply by presence of within-subject variation in intake (Beaton et al. 1979) across the recording period, a discrepancy in the level of agreement for the nutrient content between methods is to be expected. This is particularly evident in the strength of the relationship for estimates of fat intake. In this instance the average estimated fat content of both records was not notably different at the group level (Table 7-3), however the between-subject variation in the intake of this nutrient was high as evidenced by the weak correlation (r=0.24) (Table 7-4). As this finding was not observed for the other dietary variables it is difficult to determine if the source of this variation is random or systematic. One possible explanation may relate to the admissions made by some subjects relating to changes in typical eating behaviours in order to simplify intake and facilitate the recording process during use of the WRF. A number of studies have reported on the changes in dietary intake and behaviours as a result of measuring diet using written food records (Macdiarmid & Blundell 1997; Mela & Aaron 1997; Rebro et al. 1998; Vuckovic et al. 2000; Scagliusi et al. 2003). For example, Vuckovic and colleagues (2003) concluded that a number of factors relating to the task of recording diet such as inconvenience and social acceptability of certain foods influenced the accuracy of reporting usual intakes using a written food record method. Chapter 7: Study 4 202

205 Validation of EI with comparison to TEE Comparison of mean EI derived from the NuDAM and WFR with measured TEE derived from the DLW technique, was used to establish the criterion validity of both methods used in this study. On average, EI measured by the NuDAM accounted for 76±20% of TEE, while mean EI measured using the WFR explained 76±17% of expenditure (Table 7-6). A limited number of studies have validated self-reported intake among adults with T2DM using objective measures. Only one study has examined the accuracy on the self-reported EI in relation to an objective measure of TEE via the DLW technique. In this study, intake measured using a 3-day food recall in 12 obese adults with T2DM was significantly under-reported in this group accounting for 44±13% of TEE (Salle et al. 2006). In this same study, non-diabetic obese individuals also underreported EI (60±18% of TEE), however the level was significantly lower (p<0.05) in the sub-group with diabetes (Salle et al. 2006). The heavy reliance on subject memory to accurately recall three days of intake may have exacerbated the level of under-reporting suggesting that systematic bias may have been present in this study. Two other studies have investigated the validity of self-reported intake among individuals with T2DM using an objective biomarker in the form of urinary nitrogen. Both studies compared intake obtained from 3-day weighed or estimated records against urinary nitrogen excretion in British (Adams 1998) and Brazilian adults with T2DM (Vaz et al. 2008). In these studies under-reporting of protein intake varied between 11% (Adams 1998) to 20% (Vaz et al. 2008) of subjects. Although conducted in a relatively smaller sample and using different methods to assess intake, when evaluated against this past research involving adults with T2DM, the proportion of under-reporting present in this current study was comparable. Thirty percent (n=3) of the group provided implausible levels of energy intake (i.e. below the lower 95% confidence limit) using the NuDAM, while 40% of subjects (n=4) under-reporting intake when using the WFR method. Collectively these studies suggest dietary under-reporting is common among adults with T2DM. The performance of the NuDAM compared favourably with other investigations validating 3-day WFRs or EFRs using the DLW technique (Table 2-8). These studies have shown a difference in self-reported EI of to -1.9% when compared to TEE (Goran & Poehlman 1992; Reilly et al. 1993; Johnson et al. 1994; Taren et al. 1999; Tomoyasu et al. 1999; Tomoyasu et al. 2000; Rafamantanantsoa 2003; Scagliusi et al. 2008a). In this current study, the mean difference in TEE and Chapter 7: Study 4 203

206 EI was -23.7% for the NuDAM and -23.9% for the WFR. The high level of obesity present within this group of subjects may also explain inaccuracies in reporting of dietary intake. A large proportion of previous research has investigated this characteristic using DLW, documenting a strong predisposition within this group toward a higher instance of under-reporting of EI (Prentice et al. 1986; Lichtman et al. 1992; Howat et al. 1994; Platte et al. 1995; Black et al. 1997; Black et al. 2000d; Goris et al. 2000; Tomoyasu et al. 2000; Scagliusi et al. 2003; Mahabir et al. 2006; Scagliusi et al. 2008a). The information presented thus far indicates that the NuDAM is a valid approach for measuring intake compared to traditional assessment methods and for a group of individuals with similar characteristics. To-date no investigations have examined the criterion validity of PhRs. Five studies have validated modified versions of traditional prospective methods which contain some similar methodological components to the Nutricam photo/voice dietary record method using the DLW technique. Two studies used photographs to verify dietary information collected via either tape recorder (Kaczkowski & Bayley 2000) or written record (Rafamantanantsoa 2003), while the remaining three reported on the use of the electronic food scales which recorded both the weight and a verbal description of the items internally on a cassette tape (Black et al. 1997; Black et al. 1995; Black et al. 2000d). Kaczkowski and Bayley (2000) validated the use of a multimedia dietary record consisting of a microcassette tape recorder and film camera to record dietary intake over a 4-day period in lean, older Canadian women. The information contained on the microcassette was in the form of an EFR and was used to assess energy intake. The photographs were used only to confirm the information contained in the taped record and were not used for quantification. Estimated EI from the records explained 76.0±22.9% of TEE (Kaczkowski & Bayley 2000). In comparison, Rafamantanantsoa et al (2003) investigated the accuracy of a written EFR supplemented with photographs recorded using a digital over a 3-day period in lean, middle-aged Japanese men. Energy intake was estimated by trained dietitians from the written EFR, with photographs of the items used to verify amounts consumed. A comparison of energy expenditure obtained via the DLW method, revealed that estimated energy intake accounted for 94±16% of TEE (Rafamantanantsoa 2003). The large difference in reporting accuracy between these two approaches described above is unexpected given the similarly in the methods employed. Black and colleagues (1995, 1997, 2000d) validated another novel prospective method, the Chapter 7: Study 4 204

207 Portable Electronic Tape Recorded Automatic (PETRA) scale, which utilised a set of electronic food scales to internally record the weight of the served items along with a spoken description of the items. Using a cross-over design, eleven post-obese adults recorded intake over 10 days using both the PETRA system and a WFR, with EI accounted for 74% and 73% of TEE, respectively. In the validation of these three novel dietary assessment methods, a high to moderate level of reporting accuracy was observed. These methods attempted to lessen the burden associated with collecting a prospective dietary record, however in reality all still required the subjects measure food quantities either estimated using household measures or weighed with via scales. In contrast to these methods, the NuDAM shifts the responsibility for the quantification of dietary items from the subject to a trained dietitian. It was anticipated that this reduction in subject burden would translate into similar or improved accuracy of self-reported intake. This study found the NuDAM to be as accurate as the established WFR method in this group of adults with T2DM, with the degree of under-reporting of EI present using NuDAM comparable to previous studies in similar cohorts and using both traditional and modified prospective dietary assessment methods. The presence of mis-reporting in this current study provides further evidence that inaccurate reporting of intake is common during dietary assessment regardless of the method used. Therefore, an awareness of the other potential sources of error relating to the individual for whom diet is being measured and the dietitian/investigation (outlined in Section 2.3) may assist in the interpretation of results. All three subjects who were identified as under-reporting intake (i.e. with a ratio of EI:TEE less than the lower bound of 95% CL) for the NuDAM, lost weight during the first week of the study. Subjects #12 and #16 each lost 2.8 kg, while Subject #14 lost 0.3kg. In comparison, among the four subjects who under-reported the WFR, body weight either remained stable or increased during the second week of the study. Subjects #11 and #14 maintained body weight, while an increase in body weight was observed for Subjects #12 and #16 (0.6 kg and 2.4 kg, respectively). In the presence of under-reporting of dietary intake, large variations in weight status may be explained by under-eating, under-recording or both. Under-eating is associated with a loss of weight, whereas in the presence of under-recording body weight tends to remain stable (Goris & Westerterp 1999). It is possible that the larger variations in body weight (i.e. >2.5 kg) observed for some subjects in this current study may be explained by these concepts, and offer some insight into a Chapter 7: Study 4 205

208 possible explanation for the discrepancy between energy intake and expenditure. However, changes in body weight at the group level across the three time points were not statistically significant (Table 7-2). Fluctuations in body weight of ± 0.5 kg/day have been reported due to shifts in fluid balance (Edholm et al. 1974). Therefore, at the group level, it is plausible that the variation in body weight observed in this study of -0.7kg during Week 1 (NuDAM) and +0.4kg in Week 2 (WFR) were likely the result of natural fluctuations in body fluid. Interestingly, subjects reported being more conscious and/or changing usual dietary behaviours as a result of recording intake regardless of the method used. This perception appeared consistently stronger for the WFR compared to the NuDAM, and for different reasons between the two methods. For example, modifications to typical eating patterns for the WFR were based on simplifying the recording of intake by consuming less complex meals with few ingredients and/or less occurrence of eating outside the home or work. Changes in eating patterns and behaviours as a result of recording intake have been noted elsewhere (Mela & Aaron 1997; Vuckovic et al. 2000; Scagliusi et al. 2003). These findings further reinforce the belief that the observation of any behaviour is likely going to have an effect on that behaviour, and the assessment of diet is no exception (Macdiarmid & Blundell 1998). This phenomenon, known as the Hawthorne Effect (Roethlisberger 1939), is a genuine factor affecting the integrity of the data collected in the assessment of dietary intake, even when subject burden is significantly reduced. Furthermore, individuals appear to demonstrate consistent systematic reporting bias across different assessment methods and/or repeat measures (Black & Cole 2001). Consistent with this statement, the three subjects identified as under-reporting using the NuDAM, were also classified as providing an implausible level of intake via the WFR. Inter-rater reliability of NuDAM In methods which utilise multiple individuals to generate assessments of intake, adequate inter-rater reliability is necessary before implementation (Baglio et al. 2004). Overall, absolute agreement between dietitians for estimated energy and macronutrient intake was strong for the NuDAM (ICC= ). This high level of inter-rater reliability compared favourably with the established method of the WFR (ICC= ). In studies which have investigated the use of PhRs to measure diet, with quantification performed by dietitians or other trained observers, only a small number have explored the level of agreement. Wang and colleagues (2002) found strong agreement for estimates of energy, protein, and carbohydrate intakes Chapter 7: Study 4 206

209 (ρ=0.73, 0.79, and 0.86; respectively), where as Martin et al (2009) reported a very high level of agreement (r=0.92) for energy intake between two dietitians estimating the portion size of 31 food items with the assistance of an archive of reference food photographs. Similarly, the quantification of PhR for children and adolescents also revealed strong agreement between dietitian estimates (Martin et al. 2007; Higgins et al. 2009). Apart from absolute nutrient intake, others have reported similar high levels of agreement between different observers for estimates of food selection, plate waste and food intake from photographs using reference photographs (Williamson et al. 2003). An important distinguishing feature between these previous studies and the present investigation was that subjects were in a free-living situation and consumed a variety of food items. As such, dietitians were required to identify and quantify a large variety of food items contained in the Nutricam dietary records. Only one study has reported on the inter-rater reliability of nutrient intakes quantified from PhRs in a situation typical of a free-living environment (Wang et al. 2002), with the majority of other studies investigating only select meal times or pre-prepared food items, or were conducted in a controlled setting with limited food variety (Williamson et al. 2003; Martin et al. 2007; Higgins et al. 2009; Martin et al. 2009; Dahl Lassen et al. 2010). Although inter-rater reliability was high, the differences between dietitian estimates of energy and macronutrients revealed a trend towards under-estimation of intake by D1 compared to the two other dietitians. An examination of the discrepancy in estimated weights between dietitians provided a simple indication of the presence of measurement error relating to the quantification of items contained in the Nutricam dietary records. A small, but non-significant difference was noted between dietitians for the total weight of the NuDAM records. This finding is not unexpected given that the weights of all items, including recipes for mixed dishes were supplied as part of the WFR method s protocol. Further investigation revealed random errors in the identification, quantification and/or omission of items for all three dietitians across both methods. For example, in the coding of the NuDAM record for this subject differences in the quantification of an item based on the selection of a predefined category within the food composition database resulted in a difference of 1.0 MJ, while a discrepancy in the identification of one item translated to a difference of 2.2MJ (Table 7-8). The discrepancies in the energy content for these items may explain the variance observed in the estimates of the nutrient intake. Although the step of quantification was essentially removed Chapter 7: Study 4 207

210 from the WFR (as the weights of the foods were provided), variation in the coding of the records still remained. For example, a difference of 0.5 MJ was noted for an item for which a recipe was provided, while the omission of another item resulted in an under-estimation of 1.3 MJ for a meal occasion (Table 7-8). Coding errors relating to the identification of the item in the food composition database also have potential to affect the reproducibility of the measure, regardless of the method used. The effect of the error relating to the coding of dietary records on estimates of nutrient intake is not a well reported area, with discussion of this component is often reserved for large epidemiology studies where many individuals are involved in the process of data entry (Slimani et al. 2000; Conway et al. 2004b). In these situations, it has been noted that up to 25% of records contain coding errors (Conway et al. 2004b). Omissions, unfamiliar foods, and the identification of appropriate foods and quantification have been identified as factors contributing to the coding error in analysis of dietary records (Reid et al. 1999; Braakhuis et al. 2003). Although these factors were also present in this study, the resultant coding and data entry errors appeared to be random, occurring across both methods and for various items and database entries for each dietitian. The high level of single measure inter-rater reliability observed in the findings from this present study indicate that different dietitians code records from both dietary assessment methods with a similar level of reproducibility. This strong level of absolute agreement suggests that placing the responsibility for quantifying amounts of food present in photographs on to the dietitian does not systematically affect the error associated with the NuDAM, and as such this novel method appears to be robust for use by other dietitians. Useability and acceptability The NuDAM was well received among individuals with T2DM, supporting the findings of the earlier pilot investigation involving the Nutricam photo/voice dietary record (Study 2). Overwhelming evidence exists from past research for the preference of PhRs to assess intake in comparison with traditional methods (Wang et al. 2002; Wang et al. 2006; Kikunaga et al. 2007; Boushey et al. 2009; Higgins et al. 2009), however this is the first project to investigate the use of this novel technology among older adults with a chronic disease. In the context of acceptability, the most promising finding from this study was the willingness of subjects to use the NuDAM again, and for noticeably longer periods of time, to record intake. This provides an opportunity to use this novel method to not Chapter 7: Study 4 208

211 only assess intake, but also to monitor and evaluate the prescribed nutrition intervention. Furthermore, the effects of ongoing self-monitoring of dietary behaviour have been strongly linked to improved and prolonged maintenance in particular for weight loss and maintenance (Streit et al. 1991; Baker & Kirschenbaum 1993; Boutelle & Kirschenbaum 1998; Helsel et al. 2007; Nothwehr et al. 2007). Preference for novel technologies to assist in the process of dietary self-monitoring intake for diabetes management has been recently reported (Arsand et al. 2008; Sevick et al. 2008; Fukuo et al. 2009; Sevick et al. 2010), therefore use of Nutricam to collect dietary information for the process of self-monitoring intake warrants investigation in this group. The use of technology to record intake also introduced potential issues relating to the reliability of equipment. For example, there were two instances where a flat battery and a malfunction of the phone shutter button resulted in some missing dietary intake data. The call to the subject the following day provided an opportunity for this missing data to be captured and improved the accuracy in the assessment of energy intake for the NuDAM. This element of the NuDAM recording protocol also allowed for additional information on food items contained within the Nutricam record to be obtained, with just under three items per subject requiring clarification after each recording day. The omission of important descriptive information relating to cooking and/or preparation methods was the main reason prompting clarification, as well as failure to describe the type of item, in particular for fat or energy reduced products. The need to clarify the content of dietary records is a finding which has been reported by others using written food records (Vuckovic et al. 2000; Sudo et al. 2010) and is similar to the conclusions drawn from Study 2 of this thesis. Although well received by individuals with T2DM, the noticeable increase in the time needed to analyse the NuDAM records is an important issue for consideration. It was expected that, given the shift in responsibility for the quantification of intake, more time would be required to enter data from the NuDAM records into a food composition database. However such an increase in dietitian burden was not intended. It is possible that the extra time needed to code the NuDAM records compared to WFR was due to unfamiliarity with this method, and that continued use of the technique would result in a shorter data entry timeframe. Furthermore, it is possible that some aspects of the NuDAM call component could be refined further to reduce time. For example, the number of missing food components recalled during calls to subjects were not as high as anticipated, with 16 of the 30 recording days Chapter 7: Study 4 209

212 collected in full by the subject and no additional information provided during the majority of calls. Rebro et al (1998) reported an average of 1.6 food components per day were added to the record after probing. In this current study, most missing items were captured following the first probe which asked subjects to recall any food items that were consumed during the previous day but were not recorded using Nutricam. In addition, the probes relating to intake before the first entry and after the last entry, and in-between eating occasions 3 hours apart also resulted in the recall of some items. Conversely, prompts relating to the six categories of commonly forgotten foods were not as effective, with only five food items recalled following these probes. Therefore, although, nine subjects believed that the length of the calls were acceptable, feedback in relation to the repetitive nature of this component of NuDAM suggests that further refinement is necessary to minimise dietitian burden by decreasing analysis times. Limitations The small number of subjects in this study limits the generalisability of these results to the greater population of adults with T2DM. The expense associated with the use of DLW to validate measures of dietary EI is a factor which can restrict use in larger numbers. The sample size of this present study was similar to early research which explored the use of the DLW technique to validate prospective measures of EI in adults. In these studies sample sizes ranged from 7 to 14 individuals (Schoeller 1990; Black et al. 1993). Therefore, initial examination of the NuDAM in a small group was warranted to determine the viability of this method for assessing intake, and to provide evidence to justify further evaluation in a larger sample. The use of the same dietitian to both collect dietary data and then to code the records is another potential limitation of this study. This dietitian (D1) collected the additional dietary data regarding the clarification of Nutricam entries and the probing of forgotten foods, in addition to coding both sets of dietary records. Although, an identical analysis protocol for coding of records was followed for all dietitians, this aspect of the study design may explain the difference in nutrient estimates derived for D1 in comparison with D2 and D Conclusions In comparison to a WFR, the NuDAM was an accurate technique to assess intake in adults with T2DM, with good agreement between methods. Evaluation of the performance of the novel method with an objective measure of TEE, revealed Chapter 7: Study 4 210

213 moderate levels of under-reporting of EI, however the proportion was slightly greater for the WFR method. Furthermore, the NuDAM demonstrated high inter-rater reliability for estimates of energy and macronutrient intake when used by multiple dietitians. Strong subject preference and a willingness to use the NuDAM to record intake for longer periods suggest that this novel method could be used to assess and monitor intake in this group of adults with T2DM, with potential for implementation among other chronic conditions requiring ongoing dietetic support. Certain aspects of the method require further refinement to streamline the recording process for the subject, while some modifications to collection and analysis protocols of the NuDAM is also required to ensure that dietitian burden is reduced Relevance to the research program Following the development and pilot testing of the recording and analysis components of the NuDAM, this study provided evidence on the validity and reproducibility of this method for assessing dietary intake in adults with T2DM. In addition, these findings provide essential information on user perceptions of the NuDAM, and highlighted components of the method which require further refinement Implications for future practice Conclusions from this study offer insights into the performance of a novel mobile phone photo/voice record to measure dietary energy and macronutrient intake. This method displayed accuracy equivalent to a traditional WFR method, with strong agreement between estimates of nutrient intake derived by different dietitians. All subjects preferred the NuDAM for its simplicity and convenience. Further refinement to the NuDAM data collection and analysis protocols are necessary to reduce burden on the dietitian. Future applications lie in integrating the NuDAM with other technologies to promote dietary self-monitoring and facilitate dietetic practice across the remaining stages of the nutrition care process. Chapter 7: Study 4 211

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215 8.0 Conclusions and Recommendations 8.1 Introduction For effective nutritional management of type 2 diabetes mellitus (T2DM), regular and ongoing access to services and personnel can assist individuals in the adoption and maintenance of key dietary behaviours. In addition, the measurement of diet has a fundamental role in informing, supporting and determining the effectiveness of both diabetes self-management education (DSME) and medical nutrition therapy (MNT) interventions. Through a series of four independent, but inter-related studies, this research program explored two novel approaches which utilised distinct technologies to aid in the nutritional management of adults with T2DM. In particular, through the development, trial and evaluation of the Nutricam dietary assessment method (NuDAM) in adults with T2DM, this thesis addressed a number of gaps in the literature regarding the use of photographic records (PhRs) to measure diet. This chapter firstly summarises the key findings from Studies 1-4 in this thesis and then addresses the strengths and limitations of the components of this research program. These conclusions form the basis for the discussion of the potential applications arising from this thesis with regard to the nutritional management of T2DM, including both general implications for dietetic practice and promising areas requiring further exploration. 8.2 Key findings This section reviews the key outcomes arising from the four studies comprising this thesis. The research questions and hypotheses (where relevant) for each study are re-stated and the respective finding then summarised. Study 1: Effectiveness of an automated telephone system in promoting change in dietary intake among adults with T2DM (Chapter 4) Research Question 1: Is the TLC Diabetes system effective in promoting change in dietary intake and nutrition status among adults with T2DM and sub-optimal glycaemic control? Hypothesis: Changes in dietary intake and nutrition status will be greater in the intervention group compared to the control group. Finding: The TLC Diabetes can promote modest changes in dietary intake and nutrition self-efficacy in adults with T2DM, however in general changes were small and of little clinical significance. Chapter 8: Conclusions and Recommendations 213

216 In general, modest trends towards improvements in some parameters of nutritional status and dietary intake were observed in this cohort following use of TLC Diabetes (Study 1). The treatment effect of reduction on total fat (1.4%) and saturated fat (0.9%) intake are comparable with other intervention studies using technology to deliver nutrition education and support. Furthermore, subtle reductions in body weight (0.7 kg) and waist circumference (2.0 cm) in the TLC group indicate that the use of the system may also be supportive of weight loss. In addition, compared to the UC group, a significant increase in the nutrition self-efficacy score (1.3, p<0.05) was observed in the TLC group. Conclusions from this study should be applied with caution given the bias introduced by the large proportion of under-reporting in this sample and the inherent limitations of the FFQ methodology. It is possible that the TLC Diabetes system could be used to improve self-confidence to overcome barriers associated with the adoption of a healthy diet and provide support for the maintenance of nutrition self-management behaviours among adults with T2DM, however further evaluation into the effectiveness of the system using different dietary assessment methods would be needed to justify use in this setting. In addition to providing evidence on the effectiveness of the TLC Diabetes system on promoting dietary change, this study also highlighted the restrictions for the interpretation of results generated from the use of a FFQ to measure changes in absolute nutrient intake in this setting. In particular, the apparent bias towards under-estimations of carbohydrate intake and over-estimation of saturated fat intake has been reported in other studies investigating the performance of this FFQ. The use of a method which measures diet via a closed list of foods is not always suitable for assessing absolute nutrient intake unless a relevant and comprehensive listing of foods is included. This study reinforced the need for convenient prospective dietary assessment methods which allow for the open collection dietary information and account for daily variation in intake. Study 2: Development and trial of the NuDAM recording protocol (Chapter 5) Research Question 2: Does the Nutricam recording protocol result in a dietary record suitable for the estimation of nutrient intake? Finding: The recording of real time dietary intake data using the Nutricam mobile phone photo/voice dietary record has the potential to produce records of insufficient quality. Therefore, additional mechanisms are necessary to Chapter 8: Conclusions and Recommendations 214

217 ensure the comprehensive dietary data is collected to inform the assessment of nutrient intake. Incomplete and missing items were common in the pilot of the Nutricam dietary record compared to the written estimated food record (EFR). Of the Nutricam entries received, 66.0% contained a voice recording which contained sufficient detail to be used to estimate intake, 70.8% included a photograph of adequate quality, while 60.4% contained both a voice recording and photograph of suitable quality. Missing items existed in the records of both dietary methods with energy intake significantly under-recorded by ~650 kj/day using Nutricam compared to the written EFR. Although both assessment methods required the subject to record intake at the time of eating, this did not always happen and the Nutricam record trialled in this study did not allow for missed eating occasions to be recorded retrospectively. In addition, the quality of the data was incomplete and unacceptable for coding in just over twothirds of the Nutricam entries. Therefore in order to ensure complete recording of intake using this novel method an additional mechanism was introduced to clarify the content of Nutricam entries and to probe for commonly forgotten foods. This additional information was obtained from the subject during a brief phone call the next morning. To limit the potential for the introduction of bias, a standardised method and database were developed to facilitate the collection of this information with the effectiveness of this modification on the NuDAM evaluated in Study 4. Research Question 3: Compared to an estimated food record (EFR), do individuals perceive the Nutricam dietary record as an acceptable and useable method for recording dietary intake? Finding: The Nutricam dietary record was well received as an alternative to a written estimated food record in adults with T2DM. Seven of ten subjects preferred to use the mobile phone photo/voice dietary record method to record dietary intake compared to the pen-and-paper approach of the EFR. Preference for the Nutricam dietary record was due to its simple and convenient process of collecting intake information. Willingness amongst older adults to use a mobile phone photo/voice dietary record to collect dietary data is encouraging and highlights the potential for use of this method as alternative to other more burdensome traditional prospective dietary assessment methods. Chapter 8: Conclusions and Recommendations 215

218 Study 3 (Part A and B): Development and trial of the NuDAM analysis protocol (Chapter 6) Study 3 Part A: The type of portion size estimation aid (PSEA) and estimation error with quantifying food items contained in photographs. Research Question 4: Does the use of PSEAs result in a difference in the error associated with estimating the quantities of single food items contained in photographs? Hypothesis: The use of a PSEA will reduce error in the quantification of single food items contained in photographs. Research Question 5: Does the estimation error associated with quantifying single food items contained in photographs differ when using a two-dimensional aid (i.e. reference photograph) compared to a three-dimensional aid (i.e. food model)? Hypothesis: The error will be equal or smaller for items estimated using twodimensional aids compared to those estimated using three-dimensional aids. Finding: The use of PSEAs, regardless of the type (i.e. two-dimensional or three-dimensional), results in a reduction in the error associated with the quantification of single food items contained in photographs. Compared to estimations of food portions without PSEAs, a statistical significant reduction was observed when aids were used to assist in the quantification of food items contained in photographs. Overall, portion size estimation error without an aid was 19.0±28.8% compared -2.5±11.5% (p<0.05) with the assistance of PSEAs. Interestingly the type of PSEA used did not significantly affect the accuracy associated with quantifying single food items displayed in photographs, although estimation error was smaller in the group using the reference photographs. Portion size was under-estimated by the group using the food models (-6.7±14.9%) compared to those using the reference photographs of food items (1.4±5.9%). Findings from this pilot study provided evidence the estimation of food portions items contained in PhRs could be achieved within acceptable level of accuracy (i.e. within ±10% of actual weight or volume) with the assistance of PSEAs. These results highlighted the importance of ensuring that PSEAs are incorporated into the analysis protocol of these novel PhRs in order to minimise error in the measurement of diet. Conclusions from this exploratory study supported the development of the DEAT to assist in the quantification of Nutricam dietary records. Chapter 8: Conclusions and Recommendations 216

219 Study 3 Part B: The effect of the Dietary Estimation and Assessment Tool (DEAT) on estimation errors relating to portion size and nutrient composition of Nutricam dietary records. Research Question 6: Does the use of the DEAT effect the error associated with estimating the portion size of food items contained in the photographic component of Nutricam dietary records? Hypothesis: The use of the DEAT will reduce error in the estimates of portion size of items contained in the Nutricam dietary records. Finding: The use of the DEAT improved the estimation error associated with quantifying food items contained in Nutricam dietary records. When the DEAT was used during the quantification of foods items contained in Record 1, the estimation error was noticeably smaller. For this record, the portion size of items were over-estimated by 34.0±22.6%/day without the DEAT (i.e. Group B), compared to 17.1±15.8%/day when the DEAT was used (i.e. Group A). In contrast, when the DEAT was used by all subjects to quantify Record 2, the estimation error across both groups was similar (21.2±19.2%/day vs. 25.8±13.8%/day; Group A and B, respectively). These conclusions further support the use of aids in the quantification of intake from PhRs to minimise error and ensure consistency in the estimates performed by multiple individuals analysing the record. In the evaluation of the DEAT, a combination of single items and mixed dishes were used to reflect the variety typically present in a free-living environment. A greater level of estimation error was observed in this study compared to the error associated with estimating the single food items assessed in Study 3 Part A, and is likely a reflection of the complexity of a typical food pattern comprising a combination of single food items and mixed dishes. Variation in the estimation of portion size is common across all dietary assessment methods, and the level of error was observed in this study was comparable to previous research involving PhRs. Research Question 7: Of the food types (i.e. solid, liquid, amorphous, spreads) contained in the photographs, which are most affected by estimation error and does this influence the estimated energy and macronutrient content of the Nutricam dietary records? Hypothesis: Amorphous food items and spreads will have higher levels of error compared to liquids and solid items. Chapter 8: Conclusions and Recommendations 217

220 Finding: Error associated with the quantification of items contained within the photographic component of the Nutricam dietary record was greatest for spreads, followed by solid, amorphous and liquid foods. Consistent with the literature spreads and amorphous food items displayed large amounts of estimation error, although variance within all food categories was also present. In Record 1 a large, but statistically insignificant difference (p=0.051) was noted for the error associated with amorphous food items between groups, with mean estimation error for these items was 21.9±30.5%/day for Group A (with the DEAT) compared to 47.9±31.3%/day for Group B (without the DEAT). The difficulty displayed by the dietetic students in estimating solid food items, such as muesli bars and biscuits, in both records was an unexpected result. In Record 1 error for these items was slightly lower when the DEAT was used (22.4±20.9%/day vs. 24.4±27.0%/day); while the error for Record 2 was closely aligned in both groups it was relatively high compared to amorphous and liquid items. Reasons for this outcome may be related to the subjects perceived lack of suitable PSEAs within the DEAT to quantify these food items or a lack of understanding on the application of the DEAT for different food types. In addition, the study design may have inflated this error, as solid foods items are not often quantified in grams or dimensions, but rather in count (e.g. 2 slices, 1 bar). However, given that these items are often served in standardised amounts (e.g. 1 slice of bread, 30g), this finding also suggested that a possible deficit may be present in the practical knowledge of undergraduate dietetic students with regard to the standard serve sizes of common foods. Furthermore, this study highlighted that additional training in the use of the DEAT and quantification of PhRs may be necessary to improve estimation error. Hypothesis: Estimation error associated with food type will be reflected in error in energy and/or macronutrient composition of the Nutricam dietary records. Finding: Errors in the quantification of the items contained within the photographic component of Nutricam dietary records did not translate into clinically significant differences in the estimated energy and macronutrient content of the records. Differences between the actual and estimated weight of the items contained within the two Nutricam records were significantly greater between groups for Record 1, confirming the benefit in using the DEAT for this task. Overall, the error associated Chapter 8: Conclusions and Recommendations 218

221 with the quantification of Record 1 did not translate into clinically significant differences in the overall energy and macronutrient content. However, discrepancies in the estimated weights of amorphous items for this record resulted in an average difference of 274 kj/day for foods in this group not estimated using the DEAT (compared to 57 kj/day with the DEAT, p<0.001), highlighting the importance of using a PSEA to assist in the quantification of these items. The overall estimation error for Record 2 had a marginal effect on the nutrient profile of the record, with energy intake over-estimated by both groups by 42-56kJ/day, protein and fat by g/day, and carbohydrate g/day. These differences in energy and macronutrient content were not considered to be of clinical importance. The estimation of portion size is a potential source of error in the assessment of dietary intake from PhRs. An appreciation of this challenge and the factors which can affect this task are essential. In the context of measuring intake using the Nutricam dietary record, it was necessary to determine the effect of the DEAT on estimation error prior to the evaluation of the NuDAM. Findings from this study confirmed the DEAT to be a useful and necessary resource for the accurate quantification of food items contained in PhRs. Research Question 8: Do student dietitians perceive the DEAT as an acceptable and useable resource to assist in the quantification of Nutricam dietary records? Finding: Student dietitians perceive the analysis of photographic records to be an acceptable method of assessing dietary intake, and the DEAT a useful resource to assist in this process. A high proportion (79.3%) of undergraduate dietetic students found the assessment of dietary intake through the quantification of items contained in a PhR an acceptable method. This finding reinforced the potential of the NuDAM as an alternative to traditional prospective methods of recording intake. In addition, 89.6% of the group found the DEAT to be a useful resource to assist in this process, however the number of PSEAs contained within the DEAT was considered insufficient. Additional aids and more intensive training in the analysis of photographic records are needed to ensure that the use of the DEAT is maximised and estimation error is limited. Chapter 8: Conclusions and Recommendations 219

222 Study 4: Evaluation of the NuDAM (Chapter 7) Research Question 9: When evaluated against a measure of total energy expenditure (TTE) (doubly labelled water (DLW) technique), does the NuDAM produce an accurate measure of self-reported usual dietary energy intake (EI) compared to a weighed food record (WFR)? Hypothesis: The NuDAM will produce a measure of reported usual dietary intake of similar or greater accuracy to a WFR. Finding: In comparison to the WFR method, the NuDAM provides an accurate estimate of energy and macronutrient intake in adults with T2DM. Marginal differences between estimates of energy and macronutrient intake derived by the NuDAM and WFR were not statistically significant. Overall, moderate to high correlation coefficients (r= ) were found across energy and macronutrients except fat (r=0.24), comparing well with past research evaluating the relative validity of PhRs. The relatively weak relationship reported for fat intake across the two recording periods is likely due to between-subject variation as a result of natural day-to-day fluctuations in intake, deviations from usual intake due to the requirement of recording process or a combination of both factors. Finding: In comparison to TEE, adults with T2DM under-report EI at identical levels using both the NuDAM and WFR. This study concluded that EI as measured by the NuDAM and WFR method accounts for very similar levels. Self-reported EI recorded using NuDAM accounted for 76±20% of expenditure, compared to 76±17% for the WFR. Identification of individual under-reporting of EI revealed four subjects reported implausible levels of intake using the WFR method, compared to three using the NuDAM. Underreporting is a common occurrence when dietary measures are performed in obese adults, while a similar finding has been reported among adults with T2DM in a small number of studies. In comparison, similar quality evidence is lacking among individuals with T2DM, while validation of energy intake using an objective measure has not been performed for PhRs. Therefore, compared to cohorts of adults with some similar characteristics, the level of reporting accuracy of EI of the NuDAM comparing favourably with this work, and adds to the evidence base surrounding the use of PhRs. Chapter 8: Conclusions and Recommendations 220

223 Research Question 10: Between dietitians, is there a difference in the reported usual energy and macronutrient intake as estimated via the NuDAM compared to a WFR? Hypothesis: The NuDAM will produce a measure of reported usual dietary intake of similar or greater inter-rater reliability to a WFR. Finding: The NuDAM demonstrates high inter-rater reliability for estimates of energy and macronutrient intake between dietitians. The strong agreement for estimates of nutrient intake derived by different dietitians for the NuDAM, are comparable to the level of reproducibility found for the WFR. Intra-class correlation coefficients for energy and macronutrient ranged between for the NuDAM, and for the WFR. Coding errors identified were random in nature and were present for all three dietitians across both sets of dietary records. This strong level of absolute agreement for estimates of energy and macronutrient intake, suggest that the NuDAM is a method capable of high reproducibly when used by different dietitians. Research Question 11: Compared to a WFR, do individuals perceive the NuDAM as an acceptable and useable method for recording dietary intake? Finding: The NuDAM method was preferred over the WFR method for recording intake among adults with T2DM. All subjects preferred to use the NuDAM method compared to the WFR method, building upon the high acceptability reported in Study 2. In addition, to an overwhelming subject preference for the novel method, the willingness of subjects to use for extended periods of time offers promise for the application of the NuDAM to other areas of nutritional management, such as self-monitoring and for the monitoring and evaluation of nutrition intervention strategies. This finding highlights that subject burden, typically associated with traditional prospective methods can be significantly reduced using the NuDAM. However, the increase in dietitian burden associated with analysing the Nutricam dietary records requires consideration, with modifications to the method necessary to ensure uptake both within research and practice settings. Chapter 8: Conclusions and Recommendations 221

224 8.3 Strengths and Limitations The following strengths and limitations of the research program comprising this thesis should be acknowledged when interpreting the conclusions drawn from these findings Strengths Four major strengths of this research program were: 1. The procedure used to evaluate the performance of the NuDAM consisted of a systematic process. Components of this method relevant to the collection and analysis of dietary data were explored and assessed independently, followed by an evaluation of the performance of the method with regards to validity and reproducibility (Figure 3-1). Such a comprehensive approach to the investigation of the PhR method is unique, and aimed to identify potential sources of error and its effect on the estimation of nutrient intake derived by the NuDAM. 2. The use of the DLW technique to validate EI derived from the NuDAM added significant strength to the evaluation of the performance of this novel PhR method. The DLW technique provides an independent measure of TEE and allowed for the objective evaluation of self-reported EI. The NuDAM is the first to be validated using such a technique. 3. The assessment of the inter-rater reliability of the NuDAM provided evidence as to the reproducibility of this method among other practising dietitians using free-living subjects. To-date the majority of studies into the level of agreement between observers for similar methods have been undertaken in a controlled environment. The evaluation of this form of reproducibility within this thesis, and the subsequent high level of inter-rater reliability, confirms that the NuDAM can be used by different dietitians to assess individual intake in a free-living environment. 4. Other studies involving the quantification of PhRs by dietitians or other trained observers have only reported on the opinions of the individuals using the method to record their own intake. This thesis also reported on the attitudes of this group, in this case older adults with T2DM towards to use of the NuDAM to assess intake. Although well received among those individuals for whom diet was being measured, no investigations have reported on the attitudes of dietitians towards the use of PhRs in practice. Chapter 8: Conclusions and Recommendations 222

225 Examination of the attitudes of student dietitians towards the use of the NuDAM, in particular the analysis protocol and DEAT, provided insight into the acceptability of this novel method and provided evidence to support the development and adoption of new techniques in future practice Limitations Four main limitations of the research program were: 1. The assessment of dietary change in subjects of the TLC Diabetes study was a secondary outcome variable of the main study, and therefore, the sample was not powered to detect dietary change. In addition, the dietary assessment method used to examine the effect of the TLC Diabetes system may have limited the ability to measure changes in intake in this setting. Inherent bias within the FFQ used in Study 1 has been noted by others, with concern raised over its ability to provide an accurate assessment of absolute nutrient intakes. Thus, findings derived from this study although modest need to be interpreted with caution. 2. It is possible that the small sample sizes across Studies 2, 3 and 4 may have impacted on the ability to detect a statistical significance in some situations. In addition, small cohorts of subjects limit the ability to generalise findings to others with the same characteristics. Further investigations in larger samples would be necessary to confirm the external validity of the NuDAM among adults with T2DM. Despite this limitation, exploration of the performance of the NuDAM in a small sample in this thesis provided objective evidence as to feasibility of the use of this concept in a free-living environment and is similar to initiatives in the early validation of traditional dietary assessment methods. 3. In the validation study of the NuDAM, dietary intake was measured for three non-consecutive days. Such a reference period, is considered the minimum time necessary to obtain an accurate measure of usual energy intake at the group level using WFR. Given that the NuDAM is also a prospective method, it is likely that a similar period of time would be necessary to account for dayto-day variation; however this assumption requires confirmation involving the use of the NuDAM over longer recording periods. In addition, the repeatability of the NuDAM was not assessed as part of this research Chapter 8: Conclusions and Recommendations 223

226 program therefore this aspect of reproducibility of this novel method also needs to be established. 4. The use of the same dietitian to collect and code dietary data for Study 4 may have impacted on the estimates of nutrient intake derived for the NuDAM. On average, the estimates of energy and macronutrient intake derived by this dietitian were lower compared to the other two dietitians. Although it was subsequently found that there was no systematic bias present in the coding of records for this dietitian, it is difficult to determine if the task of collecting the additional information influenced these findings. Therefore, in light of this, it would be preferable to ensure that the data collection and coding and entry steps are performed by independent dietitians in future studies. 8.4 Implications for future practice and research Advances in the capabilities and functions of numerous information and communication technologies (ICTs) are expanding rapidly. As such great potential exists for the profession of dietetics to use this novel medium to facilitate practice across the nutrition care process. Given some of the current constraints inherent within the delivery model of traditional dietetic services, practical and sustainable resources are needed to support adults with T2DM. The nature of nutrition care and certain components of the NCP lend themselves to further adaptation using technology to reduce barriers relating to access to services and personnel. The following three recommendations illustrate examples of future research and practice applications for the NuDAM and its components. Figure 8-1 provides a conceptual overview of these recommendations. Recommendation 1 The measurement of diet using the Nutricam mobile phone offers a simple and convenient method for recording intake which could be used in a various contexts including self-monitoring and the assessment of intake. Further modification of the NuDAM protocol is necessary in order to reduce the time currently needed to analyse the Nutricam dietary records. As such, in a dietetic counselling setting where a measure of absolute nutrient intake is not always a priority, the analysis of the Nutricam records could be modified to provide a semi-quantitative assessment of intake (i.e. using a specific ready reckoner) and/or to evaluate diet quality and eating patterns. Chapter 8: Conclusions and Recommendations 224

227 Nutritional Management of T2DM Future applications using information and communication technologies Combination of face-to face and online counselling TLC Diabetes NuDAM Essential Self-Management Behaviours Blood Glucose Monitoring Medication Adherence Physical Activity Nutrition Assess Dietary Intake Educate & Counsel Monitor & Reinforce Problem solving Decision making Regular and ongoing support Measurement of diet Taking action Selftailoring Patientpractitioner partnership Resource utilisation Individual 1. Assessment 2. Diagnosis 4. Evaluation & Monitoring 3. Intervention Self-Management Education (Lorig & Holman, 2003) Nutrition Care Process (Lacey & Pritchett 2003) Adoption and Maintenance Changes to nutrition-related behaviours Achievement of goals and outcomes Figure 8-1: Conceptual framework incorporating recommendations from the research program for future research and practice. The NuDAM offers a promising advancement to the nutritional management of T2DM and other chronic conditions. Future applications lie in integrating the NuDAM with other technologies, such as the TLC Diabetes systems to facilitate practice across the remaining stages of the nutrition care process. Chapter 8: Conclusions and Recommendations 225

228 Such a modification could improve efficiency in the analysis of the record, and allow for greater interaction between patient and dietitian. However, the accuracy and reliability of using such an approach in comparison to the original analysis protocol would need to be determined. Furthermore, automation of some components of the data collection and analysis stages of the NuDAM may also assist in reducing dietitian burden. The high levels of acceptance for the use of the NuDAM to measure intake found among undergraduate dietetic students further reinforce the potential of the integration of components of this novel method into dietetic training and practice. For example, the DEAT could be used by students to improve knowledge of serve sizes and portion size estimation skills. Given the positive reception to the method among the new generation of dietitians, exploration of the attitudes and beliefs of current practising dietitians towards the use of technology in practice, in particular PhRs, is also warranted. It is vital that future methods developed to facilitate the nutrition care process (NCP) are relevant to the needs of not only the client, but also the practitioner. Recommendation 2 The collection of intake information via the Nutricam mobile phone photo/voice dietary record provides a unique opportunity to offer a novel approach for medical nutrition therapy (MNT). In particular, the client s Nutricam record could be not only be used to assess nutrient intake and inform diagnosis, but has the potential for integration into the remaining stages of the NCP. For example, the PhR of intake collected using Nutricam, could be incorporated into the intervention strategies for the client, in particular for counselling and education relating to portion control and eating patterns. The evidence surrounding the effect of visual clues on the amount of food consumed is clear and has a flow on effect in terms of nutritional management, especially for chronic diseases. In the context of T2DM, the amount of food consumed is not only relevant for weight management, but also has implications for glycaemic control. It is recommended that individuals with diabetes monitor the amount of carbohydrate consumed, however this is not easily achieved with traditional prospective methods such as written records. The Nutricam record could facilitate this process, and could also be used to monitor and evaluate client progress towards goals and the effectiveness of intervention strategies by the Chapter 8: Conclusions and Recommendations 226

229 dietitian. Incorporating the photographic component of the Nutricam record into the nutrition intervention in this manner is in contrast to traditional assessment methods used in practice (e.g. written food record or diet history) where the information is only primarily useful to the dietitian to determine intake and inform diagnosis, and due to the format collected (i.e. text), is generally not used again by the dietitian or the client. Recommendation 3 Although the treatment effects of the TLC Diabetes system on changes in dietary intake were small, it may be possible that automated telephone self-management education programs could be more effective on addressing nutrition selfmanagement behaviours if used in conjunction with individualised dietary counselling provided by a dietitian (i.e. MNT). For example, such a program could be used to reinforce tailored nutrition intervention strategies provided by a dietitian in between consultations. In addition, the Nutricam mobile phone provides a simple and convenient alternative to traditional pen and paper records for the tracking of intake which could be utilise for dietary self-monitoring. The integration of this device with an automated system such as TLC Diabetes system could allow for the provision of instantaneous feedback to the individual, allowing for the application of self-management knowledge and skills, and the reinforcement of key nutrition behaviours. Such a development would have valuable applications in regional and rural areas, where rates of chronic diseases are greater compared to metropolitan areas and workforce issues of recruitment and retention of dietitians limit availability to regular and ongoing nutrition care. The costs associated with the implementation of the infrastructure for such a program may potentially be a limiting factor, however it would be worthwhile to investigate whether the combination of these two components is feasible and the effect on nutrition-related goals and health outcomes within this group. It is important to emphasise that Recommendations 1-3 described above are not limited to the use of the Nutricam mobile phone to generate a PhR in order to assess dietary intake. Stand alone digital cameras would also be capable of achieving similar results, however some elements such as the description of the food items and/or the instantaneous transfer of data would not be possible and may, depending on the intended use of the PhR, be a limitation. It is essential to consider the practicalities of the above recommendations, and the use of similar yet less Chapter 8: Conclusions and Recommendations 227

230 sophisticated technologies may assist in promoting greater uptake of PhRs in a broader range of dietetic practice settings and population groups. This thesis explored innovative approaches to assist in the nutritional management of T2DM. In particular, through the development, trial and evaluation of a novel mobile phone photo/voice dietary record this thesis makes a significant contribution to the evidence base surrounding the use of PhRs. The NuDAM is an extremely promising advancement in the assessment of individual nutrient intake, with broad applications for the nutritional management of a variety of chronic conditions. The above recommendations are only some of the possible future uses of this novel method within dietetic practice and research. The true potential of the NuDAM lies in its integration with other technologies to improve access to dietetic services, support interactive self-monitoring and to facilitate practice across the nutrition care process. Chapter 8: Conclusions and Recommendations 228

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251 Appendix A Study 2 Nutricam recording protocol Appendix A 249

252 Study 2 Nutricam recording protocol (cont.) Appendix A 250

253 Appendix B Study 2 Questionnaires Pre-test Questionnaire (Participant Information) Post-test Questionnaire (Evaluation) Appendix B 251

254 Appendix B 252

255 Appendix B 253

256 Appendix B 254

257 Appendix B 255

258 Appendix B 256

259 Appendix B 257

260 Appendix B 258

261 Appendix C Study 3 Part A: Questionnaire Appendix C 259

262 Appendix C 260

263 Appendix C 261

264 Appendix C 262

265 Appendix C 263

266 Appendix D Study 3 Part A Supplementary Results Table D1: Mean estimation error (gram or volume) for all foods portions Food Item Mean(±SD) estimation error # (g or ml^) Mashed Potato Rice Pasta Pizza Cake Potato chips Carrot Steak 100 small medium large Test portion (g) No Aid (n=17) -30.6± ± ± ±71.5 Aid (n=17) -0.9± ± ± ±57.8 Food Model (n=8) -1.9± ± ± ±59.4 Ref Photo (n=9) 0.0± ± ± ±47.9 Test portion (g) No Aid (n=17) 16.2± ± ± ±87.3 Aid (n=17) -3.2± ± ± ±42.0 Food Model (n=8) -8.8± ± ± ±51.6 Ref Photo (n=9) 1.7± ± ± ±33.2 Test portion (g) No Aid (n=17) 35.6± ± ± ±120.5 Aid (n=17) 1.5± ± ± ±59.3 Food Model (n=8) 3.1± ± ± ±45.4 Ref Photo (n=9) 0.0± ± ± ±68.7 Test portion (g) No Aid (n=17) 99.1± ± ± ±94.7 Aid (n=17) 1.8± ± ± ±32.4 Food Model (n=8) 3.8± ± ± ±43.7 Ref Photo (n=9) 0.0± ± ± ±20.2 Test portion (g) No Aid (n=17) -0.9± ± ± ±76.2 Aid (n=17) -10.9± ± ± ±33.4 Food Model (n=8) -23.1± ± ± ±30.1 Ref Photo (n=9) 0.0± ± ± ±21.3 Test portion (g) No Aid (n=17) 57.1± ± ± ±100.9 Aid (n=17) -8.5± ± ± ±39.2 Food Model (n=8) -18.1± ± ± ±28.2 Ref Photo (n=9) 0.0± ± ± ±43.1 Test portion (g) No Aid (n=17) 17.5± ± ± ±63.2 Aid (n=17) -11.2± ± ± ±40.8 Food Model (n=8) -23.8± ± ± ±32.1 Ref Photo (n=9) 0.0± ± ± ±25.0 Test portion (g) No Aid (n=17) 88.5± ± ± ±131.7 Aid (n=17) -3.2± ± ± ±68.8 Food Model (n=8) -6.9± ± ± ±93.3 Ref Photo (n=9) 0.0± ± ± ±32.3 Appendix D 264

267 Table D1: (continued) Food Item Mean(±SD) estimation error # (g or ml^) 100 small medium large Test portion (g) No Aid (n=17) 12.6± ± ± ±134.5 Chicken Aid (n=17) -0.9± ± ± ±63.2 breast Food Model (n=8) -1.9± ± ± ±61.9 Ref Photo (n=9) 0.0± ± ± ±65.1 Test portion (g) No Aid (n=17) 22.6± ± ± ±43.0 Ice cream Aid (n=17) 21.5± ± ± ±20.0 Food Model (n=8) 45.6± ± ± ±15.1 Ref Photo (n=9) 0.0± ± ± ±17.2 Test portion (g) No Aid (n=17) 26.8± ± ± ±70.4 Cornflakes Aid (n=17) -12.9± ± ± ±27.0 Food Model (n=8) -27.5± ± ± ±27.0 Ref Photo (n=9) 0.0± ± ± ±8.3 Test portion (g) No Aid (n=17) 63.2± ± ± ±87.8 Cheese Aid (n=17) 12.1± ± ± ±54.5 Food Model (n=8) 25.6± ± ± ±62.8 Ref Photo (n=9) 0.0± ± ± ±49.8 Test portion (g) No Aid (n=17) 38.1± ± ± ±178.2 Baked Aid (n=17) 8.8± ± ± ±70.5 beans Food Model (n=8) 18.8± ± ± ±63.1 Ref Photo (n=9) 0.0± ± ± ±80.2 Test portion (ml) No Aid (n=17) 8.5± ± ± ±37.0 Coffee^ Aid (n=17) -12.9± ± ± ±21.4 Food Model (n=8) -23.8± ± ± ±28.3 Ref Photo (n=9) -3.3± ± ± ±12.4 Test portion (ml) No Aid (n=17) 8.1± ± ± ±45.9 Fruit Aid (n=17) -4.7± ± ± ±43.5 juice^ Food Model (n=8) -10.0± ± ± ±53.7 Ref Photo (n=9) 0.0± ± ± ±19.4 # Difference (g) = estimated weight (g) - actual weight (g) Appendix D 265

268 Table D2: Mean estimation error (difference) for all four portions of each food without an aid Food Item Mean (±SD) Estimation Error # (g Mean (±SD) Estimation Error^ or ml^) (%) 4 portions 3 portions 4 portions 3 portions Mashed Potato -30.2± ± ± ±30.2 Rice -8.4± ± ± ±31.6 Pasta 35.0± ± ± ±41.9 Pizza 69.1± ± ± ±71.8 Cake -3.4± ± ± ±40.9 Potato chips 48.9± ± ± ±60.2 Carrot 14.3± ± ± ±36.2 Steak 74.9± ± ± ±53.5 Chicken breast 33.6± ± ± ±75.1 Ice cream -2.2± ± ± ±25.0 Cornflakes 33.7± ± ± ±119.4 Cheese 38.1± ± ± ±71.7 Baked beans 26.0± ± ± ±54.8 Coffee^ 10.7± ± ± ±22.5 Fruit juice^ 22.4± ± ± ±30.8 Total 24.2± ± ± ±28.8 # Gram (g) or Volume (ml) Difference = estimated weight (g) or volume (ml) - actual weight (g) or volume (ml); ^Percentage Difference (%) = ([estimated weight (g) or volume (ml) - actual weight (g) or volume (ml)]/actual weight (g) or volume (ml))*100 Appendix D 266

269 Appendix E Study 3 - Part B Nutricam Test Record Content Table E1: Nutricam Test Record 1 - Day 1 Entry # Item Actual Weight (g) Food type Fruit n Bran cereal 56 A Skim milk 130 L Multigrain toast 45 S Apricot jam 10 Sd Tea 230 L Orange 130 S Muesli Bar 35 S Tea 230 L 4 Water 177 L 5 Meatballs & spiral pasta 280 A Water 177 L 6 Fresh fruit salad 156 A 7 8 Spicy fruit biscuits 32 S Cracker & nut mix 36 A Coffee, white 245 L Seafood pasta 390 A Water 214 L Sultana & date cookie 46 S 9 Hot Chocolate 230 L Abbreviations : items served together but quantified separately; A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 267

270 Table E2: Nutricam Test Record 1 - Day 2 Entry # Item Actual Weight (g) Food type 1 Bircher muesli 121 A Multigrain toast 37 S Strawberry jam 14 Sd Coffee, white 245 L Fresh fruit salad 150 A Tropical nut mix 30 A Tea 230 L Sliced turkey 39 S Cranberry sauce 12 A Soy & linseed roll 66 S Water 280 L Red apple 162 S Cracker & nut mix 24 A Water 280 L Mandarin 151 S Pineapple & Date slice 75 S Water 241 L Beef 'n' black bean 230 A Rice 123 A Wine 180 L Apple & sultana pancake 72 S Abbreviations: A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 268

271 Table E3: Nutricam Test Record 1 - Day 3 Entry # Item Actual Weight (g) Food type 1 2 Baked beans 146 A Soy & Linseed Toast 38 S Margarine 6 Sd Tea 230 L Peaches 144 A Zucchini & carrot bread 52 S Coffee, white 245 L 3 Water 280 L Pumpkin soup 247 L 4 Multigrain roll 74 S Penne Pesto Pasta 166 A Water 280 L Cheese 20 S Red apple 161 S Roasted chickpeas 37 A Tea 230 L Roast Lamb 66 S Potato 120 S Pumpkin 48 S Cauliflower 50 S Peas 60 A Gravy 24 L Water 280 L Greek style passionfruit yoghurt 100 A 9 Water 280 L Abbreviations: A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 269

272 Table E4: Nutricam Test Record 2 - Day 1 Entry # Item Actual Weight (g) Food type Fruit n Bran cereal 43 A Reduced fat milk 132 L Multigrain toast 32 S Margarine 4 Sd Vegemite 3 Sd Water 228 L Green apple 161 S Muesli bar 45 S Tea, white 292 L Tuna 54 A Tomato 95 S Mayonnaise 10 A Multigrain roll 67 S Orange 190 S Water 236 L Country beef soup 248 L Multigrain roll 36 S Coffee, white 304 L Thai style chicken curry 196 A Rice 146 A Water 228 L 8 Tea, white 292 L Abbreviations : items served together but quantified separately; A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 270

273 Table E5: Nutricam Test Record 2 - Day 2 Entry # Item Actual Weight (g) Food type 1 2 Vanilla muesli 40 A Reduced fat milk 132 L Multigrain toast 31 S Apricot jam 12 Sd Margarine 6 Sd Coffee, white 304 L Red apple 162 S Yoghurt muesli bar 34 S Tea, white 292 L 3 Water 236 L 4 Chicken satay pizza 195 S 5 Orange 204 S Water 236 L 6 Date & coffee roll 50 S Coffee, white 304 L 7 Coke 161 L Spaghetti Bolognaise 425 A 8 Coke 250 L Abbreviations : items served together but quantified separately; A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 271

274 Table E6: Nutricam Test Record 2 - Day 3 Entry # Item Actual Weight (g) Food type 1 2 Egg 39 S Tomato 31 S Multigrain muffin 67 S Margarine 8 Sd Tea, white 292 L Two fruits 148 A Rice crackers 12 S Coffee, white 304 L 3 Water 226 L Shepherd s pie 349 A 4 Mandarin 136 S Coke 250 L 5 Greek style berry yoghurt 100 A Corn & parmesan muffin 68 S 6 Water 226 L Crumbed fish 100 S 7 Potato wedges 92 A Beans 33 A Carrots 53 A Corn 40 A Coke 250 L 8 Tea, white 292 L Abbreviations: A=amorphous; L=liquid; S=solid; Sd=spreads Appendix E 272

275 Appendix F Study 3 Part B Questionnaires Participant Questionnaire Appendix F 273

276 Appendix F 274

277 Appendix F 275

278 Appendix F 276

279 Appendix F 277

280 Study 3 Part B Questionnaires (cont.) Evaluation Questionnaire Section A: Use of the DEAT to estimate 18 food items from Record 2 Example: fruit n bran cereal Appendix F 278

281 Appendix F 279

282 Appendix F 280

283 Appendix F 281

284 Evaluation Questionnaire (cont.) Section B: Useability and acceptability of the DEAT To what extent do you agree or disagree with the following statements about the Dietary Estimation and Assessment Tool (DEAT): Strongly Agree [1] Agree [2] Neutral [3] Disagree [4] Strongly Disagree [5] In general, I found the DEAT useful for estimating the portion size of items contained in the photographs: I found the Nutricam Prompt Card in the photographs useful as a reference object when estimating the portion size of the items: The categories of aids (i.e. photographs of reference foods and serving vessels, amorphous mounds, and generic shapes and graphics) contained in the DEAT were adequate to assist in the estimation of portion size: The number of aids in each category contained in the DEAT were sufficient to assist in the estimation of portion size: I believe that it is essential to use aids, such as those contained in the DEAT, when estimating the portion size of the items contained in a photographic dietary record: I believe that estimating the portion size of items contained in a photographic dietary record using aids, such as those contained in the DEAT, is an acceptable method for measuring dietary intake: Appendix F 282

285 Section B (cont.) Appendix F 283

286 Section B (cont.) Appendix F 284

287 Appendix F 285

288 Appendix G Study 3 Part B Supplementary Results Figures G1 to G6: mean estimation error (%) per food item Tables G1 to G6: estimation error per food type for each day Appendix G 286

289 Figure G1: Mean(±SD) estimation error (%) Nutricam Test Record 1, Day 1 Appendix G 287

290 Figure G2: Mean(±SD) estimation error (%) Nutricam Test Record 1, Day 2 Appendix G 288

291 Figure G3: Mean(±SD) estimation error (%) Nutricam Test Record 1, Day 3 Appendix G 289

292 Figure G4: Mean(±SD) estimation error (%) Nutricam Test Record 2, Day 1 Appendix G 290

293 Figure G5: Mean(±SD) estimation error (%) Nutricam Test Record 2, Day 2 Appendix G 291

294 Figure G6: Mean(±SD) estimation error (%) Nutricam Test Record 2, Day 3 Appendix G 292

295 Record 2 Record 1 Appendix G (cont.): Study 3 - Part B Supplementary Results Table G1: Between-group comparison of estimation error for food type by each day of record. Mean(± S.D.) estimation error (%) Group A (n=15) Group B (n=14) Amorphous: Overall^ 21.9± ±31.3 Day 1 (n=5) -7.5± ±27.2** Day 2 (n=7) 42.7± ±41.8 Day 3 (n=6) 30.4± ±46.8 Liquid: Overall^ 10.2± ±25.9 Day 1 (n=8) 4.5± ±28.6*** Day 2 (n=6) 4.6± ±25.6 Day 3 (n=9) 21.4± ±30.2 Solid: Overall^ 22.4± ±27.0 Day 1 (n=5) 46.9± ±31.8 Day 2 (n=7) -2.3± ±34.4 Day 3 (n=9) 22.6± ±32.8 Spreads: Overall^ 19.8± ±118.7 Day 1 (n=1) 28.4± ±82.0 Day 2 (n=1) 10.9± ±131.3 Day 3 (n=1) 20.0± ±156.0 Amorphous: Overall^ 21.2± ±20.1 Day 1 (n=5) 20.2± ±24.1 Day 2 (n=2) 24.0± ±35.7* Day 3 (n=7) 19.2± ±21.2 Liquid: Overall^ 1.8± ±5.6 Day 1 (n=8) 3.0± ±7.9 Day 2 (n=8) 3.9± ±6.6 Day 3 (n=7) -1.6± ±6.4 Solid: Overall^ 34.5± ±24.8 Day 1 (n=7) 17.9± ±41.3 Day 2 (n=6) 14.6± ±27.1 Day 3 (n=7) 71.2± ±40.6 Spreads: Overall^ 54.5± ±127.3 Day 1 (n=2) ± ±238.6 Day 2 (n=2) 10.5± ±90.4 Day 3 (n=1) 6.8± ±83.2 Abbreviations: mean % error for food type for each day = mean % error of each food item ([estimated weight actual weight]/actual weight*100) within the food type category for day; ^Overall mean (±S.D.) error (%/day) for food type = mean of % error for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05; ***p<0.001 Appendix G 293

296 Record 2 Record 1 Table G2: Between-group comparison of weight estimation error (g/day) for food type by each day of record. Mean(± S.D.) weight difference (g/day) Group A (n=15) Group B (n=14) Amorphous: Overall^ -3.6± ±43.8*** Day 1 (n=5) -34.4± ±47.9*** Day 2 (n=7) 7.7± ±47.5** Day 3 (n=6) 15.9± ±48.1** Liquid: Overall^ -1.0± ±51.8* Day 1 (n=8) 9.4± ±57.8*** Day 2 (n=6) 3.9± ±59.8 Day 3 (n=9) -16.4± ±45.6** Solid: Overall^ 8.4± ±15.3 Day 1 (n=5) 17.3± ±14.7 Day 2 (n=7) -7.6± ±22.6 Day 3 (n=9) 15.4± ±18.4 Spreads: Overall^ 1.9± ±11.6 Day 1 (n=1) 2.9± ±8.2 Day 2 (n=1) 1.5± ±18.4 Day 3 (n=1) 1.2± ±9.4 Amorphous: Overall^ -15.1± ±22.8 Day 1 (n=5) 10.5± ±17.8 Day 2 (n=2) -60.6± ±41.5 Day 3 (n=7) 4.8± ±25.7 Liquid: Overall^ -1.4± ±13.9 Day 1 (n=8) -0.2± ±18.3 Day 2 (n=8) 2.7± ±14.6 Day 3 (n=7) -6.7± ±17.2 Solid: Overall^ 1.6± ±11.9 Day 1 (n=7) -0.9± ±2.1 Day 2 (n=6) -5.8± ±17.9 Day 3 (n=7) 11.5± ±14.7 Spreads: Overall^ 2.1± ±7.2 Day 1 (n=2) 4.8± ±8.8 Day 2 (n=2) 0.9± ±8.5 Day 3 (n=1) 0.5± ±6.7 Abbreviations: mean weight difference for food type for each day = mean weight difference of each food item ([estimated weight actual weight] within the food type category for day; ^Overall mean (±S.D.) error (g/day) for food type = mean of weight difference for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p< Appendix G 294

297 Record 2 Record 1 Table G3: Between-group comparison of energy estimation error (kj/day) for food type by each day of record. Mean(± S.D.) Difference in Energy Content (kj/day) Group A (n=15) Group B (n=14) Amorphous: Overall^ 57± ±176*** Day 1 (n=5) -118± ±208*** Day 2 (n=7) 107± ±204** Day 3 (n=6) 184± ±229 Liquid: Overall^ 25±14 38±23 Day 1 (n=8) 10±8 49±33*** Day 2 (n=6) 32±25 20±32 Day 3 (n=9) 33±18 46±26 Solid: Overall^ 124± ±129 Day 1 (n=5) 267± ±169 Day 2 (n=7) 23±96 25±171 Day 3 (n=9) 81±94 102±119 Spreads: Overall^ 30±68 103±193 Day 1 (n=1) 35±115 57±100 Day 2 (n=1) 18±86 78±217 Day 3 (n=1) 36±95 174±284 Amorphous: Overall^ 15±290 63±105 Day 1 (n=5) 22±118 63±88 Day 2 (n=2) 1± ±229 Day 3 (n=7) 23±135 13±92 Liquid: Overall^ 0.02±18 1±12 Day 1 (n=8) -2±27-4±24 Day 2 (n=8) 9±27 8±14 Day 3 (n=7) -7±17-0.1±13 Solid: Overall^ 112± ±94 Day 1 (n=7) 69±207 55±151 Day 2 (n=6) 103± ±145 Day 3 (n=7) 103± ±145 Spreads: Overall^ 26±126 74±156 Day 1 (n=2) 44±57 131±214 Day 2 (n=2) 17±116 67±144 Day 3 (n=1) 16±267 24±195 Abbreviations: mean energy difference for food type each day = mean energy difference of each food item ([estimated weight (g) - actual weight (g)] converted to energy difference for each item within the food type category) for day; ^Overall mean (±S.D.) energy difference (kj/day) for food type =mean of energy difference for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only. Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p< Appendix G 295

298 Record 2 Record 1 Table G4: Between-group comparison of protein estimation error (g/day) for food type by each day of record. Mean(± S.D.) Difference in Protein Content (g/day) Group A (n=15) Group B (n=14) Amorphous: Overall^ 0.3± ±2.2*** Day 1 (n=5) -1.8± ±2.5*** Day 2 (n=7) 0.4± ±2.6** Day 3 (n=6) 2.4± ±2.5 Liquid: Overall^ 0.3± ±0.3* Day 1 (n=8) 0.2± ±0.6*** Day 2 (n=6) 0.1± ±0.1 Day 3 (n=9) 0.6± ±0.4 Solid: Overall^ 0.5± ±1.1 Day 1 (n=5) 0.8± ±1.0 Day 2 (n=7) -0.2± ±1.3 Day 3 (n=9) 1.0± ±1.5 Spreads: Overall^ ±0.1 Day 1 (n=1) Day 2 (n=1) 0.0± ±0.1 Day 3 (n=1) Amorphous: Overall^ 0.3± ±1.2 Day 1 (n=5) 2.8± ±2.6 Day 2 (n=2) -2.1± ±2.0 Day 3 (n=7) 0.2± ±1.2 Liquid: Overall^ 0.0± ±0.2 Day 1 (n=8) 0.0± ±0.5 Day 2 (n=8) 0.1± ±0.2 Day 3 (n=7) -0.1± ±0.1 Solid: Overall^ 0.9± ±0.8 Day 1 (n=7) 0.5± ±1.3 Day 2 (n=6) 0.8± ±1.4 Day 3 (n=7) 2.2± ±1.3 Spreads: Overall^ Day 1 (n=2) Day 2 (n=2) Day 3 (n=1) Abbreviations: mean protein difference for food type for each day = mean protein difference of each food item ([estimated weight (g) - actual weight (g)] converted to protein difference for each item within food type category) for day; ^Overall mean (±S.D.) protein difference (g/day) for food type = mean of protein difference for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only; Difference between groups significant (Mann-Whitney U test): *p<0.05; **p<0.01; ***p<0.001 Appendix G 296

299 Record 2 Record 1 Table G5: Between-group comparison of fat estimation error (g/day) for food type by each day of record. Mean(± S.D.) Difference in Fat Content (g/day) Group A (n=15) Group B (n=14) Amorphous: Overall^ 0.7± ±1.0* Day 1 (n=5) 0.2± ±1.4* Day 2 (n=7) 1.0± ±1.1 Day 3 (n=6) 0.9± ±1.0* Liquid: Overall^ 0.1± ±0.1** Day 1 (n=8) ±0.1*** Day 2 (n=6) ±0.1** Day 3 (n=9) 0.2± ±0.2 Solid: Overall^ 0.7± ±0.6 Day 1 (n=5) 1.5± ±0.9 Day 2 (n=7) 0.1± ±0.6 Day 3 (n=9) 0.6± ±0.7 Spreads: Overall^ 0.3± ±2.6 Day 1 (n=1) Day 2 (n=1) 0.01± ±0.1 Day 3 (n=1) 1.0± ±7.7 Amorphous: Overall^ 0.6± ±0.8) Day 1 (n=5) 0.3± ±0.4 Day 2 (n=2) 1.4± ±2.2* Day 3 (n=7) 0.1± ±0.5 Liquid: Overall^ 0.0± ±0.1 Day 1 (n=8) 0.0± ±0.1 Day 2 (n=8) 0.0± ±0.1 Day 3 (n=7) -0.1± Solid: Overall^ 0.6± ±0.5 Day 1 (n=7) 0.2± ±0.5 Day 2 (n=6) 0.9± ±0.9 Day 3 (n=7) 0.8± ±0.7 Spreads: Overall^ 0.6± ±3.7 Day 1 (n=2) 1.2± ±5.8 Day 2 (n=2) 0.3± ±2.0 Day 3 (n=1) 0.4± ±5.2 Abbreviations: mean fat difference for food type for each day = mean fat difference of each food item ([estimated weight (g) - actual weight (g)] converted to fat difference for each item within food type category) for day; ^Overall mean (±S.D.) fat difference (g/day) for food type = mean of fat difference for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only; Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p<0.001 Appendix G 297

300 Record 2 Record 1 Table G6: Between-group comparison of carbohydrate estimation error (g/day) for food type by each day of record. Mean(± S.D.) Difference in Carbohydrate Content (g/day) Group A (n=15) Group B (n=14) Amorphous: Overall^ 1.5± ±6.3*** Day 1 (n=5) -5.2± ±7.3*** Day 2 (n=7) 3.9± ±7.2* Day 3 (n=6) 5.7± ±8.7* Liquid: Overall^ 0.4± ±0.6** Day 1 (n=8) 0.4± ±1.2*** Day 2 (n=6) 0.1± ±0.2 Day 3 (n=9) 0.9± ±0.7 Solid: Overall^ 5.2± ±5.2 Day 1 (n=5) 11.7± ±7.0 Day 2 (n=7) 1.4± ±7.4 Day 3 (n=9) 2.5± ±4.4 Spreads: Overall^ 1.0± ±6.1 Day 1 (n=1) 2.1± ±5.9 Day 2 (n=1) 1.0± ±12.6 Day 3 (n=1) Amorphous: Overall^ -0.7± ±3.5 Day 1 (n=5) -2.1± ±2.3 Day 2 (n=2) -0.7± ±7.5 Day 3 (n=7) 0.7± ±3.2 Liquid: Overall^ 0.2± ±0.4 Day 1 (n=8) 0.1± ±0.6 Day 2 (n=8) 0.6± ±0.5 Day 3 (n=7) -0.1± ±0.6 Solid: Overall^ 4.1± ±3.6 Day 1 (n=7) 3.0± ±6.2 Day 2 (n=6) 3.0± ±5.2 Day 3 (n=7) 6.3± ±4.3 Spreads: Overall^ 0.1± ±1.5 Day 1 (n=2) Day 2 (n=2) 0.4± ±4.6 Day 3 (n=1) Abbreviations: mean carbohydrate difference for food type for each day = mean carbohydrate difference of each food item ([estimated weight (g) - actual weight (g)] converted to carbohydrate difference for each item within food type category) for day; ^Overall mean (±S.D.) carbohydrate difference (g/day) for food type = mean of carbohydrate difference for food type per day ( ) for Day 1, Day 2, and Day 3 of record; Group A = DEAT for both Records 1 & 2; Group B = DEAT for Record 2 only; Difference between groups (Group A vs. Group B) (Mann-Whitney U test) significant: *p<0.05; **p<0.01; ***p<0.001 * Appendix G 298

301 Appendix H Study 4: The dietitian/investigator NuDAM data collection and analysis protocol Figure H1: Stage 1 Figure H2: Stage 2 Appendix H 299

302 1. Enter date of recording day into FW Example Nutricam Entry Orange juice, fresh...baked beans... multigrain toast, unbuttered. 2. Enter time of each NuE (meal occasion) into FW 3. Identify and quantify each food item within the NuE using the following protocol: Abbreviations FW = FoodWorks NFS=not further specified NuE=Nutricam entry i-b NuE= in-between Nutricam entries Unsp serve = unspecified serve Single item OR Mixed/Composite Item Is the description of the Nutricam entry adequate? Item name and type (e.g. low fat); Brand name &/or Product name; Cooking/preparation method. Yes Enter into FW as described No Enter as NFS where possible and highlight Is photo adequate? Prompt card visible All items visible & able to be distinguished for quantification as required Separate into component ingredients where possible Notes : List additional information needed + relevant to coding of description. Yes No Notes : Quantify using DEAT Quantify (for item not clearly visible): Review other NuE for similar; Use unsp serve ; or Ask subject to quantify during call. List additional information needed + relevant to coding of quantity. Code in FW: Requiring clarification: **NuE#...** First: **1 st NuE** Last: **Last NuE** In-between:** i-b NuE #...** Figure H1: The dietitian/investigator NuDAM data collection and analysis protocol (Stage 1). Following the above protocol, the dietitian/investigator identifies and quantifies food items contained in the Nutricam record. Items contained within Nutricam entries which are not able to identified and/or quantified are listed for clarification with the subject during the call (Stage 2 Figure H2) Appendix H 300

303 4. Clarify Nutricam record and probe for forgotten foods using the following protocol: Call Introduction Forgotten Foods Probes Are there Nutricam entries (NuE) requiring clarification? Yes No Probe 1 Probe 2 Probe 3 Quick List (QL) QL item #1 QL item #2 QL item #.. Review each NuE requiring clarification Single item Mixed/composite item Commercial Homemade No Go to next Probe. Probe 4 Probe 5a Probe 5b Probe 5c Probe 5d Probe 5e Yes Collect Info then go to next Probe. QL additional details required: Time and Occasion Description: o Item name and type (e.g. low fat); o Brand name &/or Product name; o Cooking/preparation method; Quantity use unsp serve if unsure Additions. Additional details required: Item name and type (e.g. low fat); Brand name &/or Product name; Cooking/preparation method; Additions. Additional details required: Recipe description: Ingredient list (added fat, sugar, protein kj intake) Additions. Probe 5f Typical Intake? More, Typical (Normal), or Less Call Closing Figure H2: The dietitian/investigator NuDAM data collection and analysis protocol (Stage 2). The subject is probed, firstly for additional information on food items contained within the Nutricam record which require clarification (as listed during Stage 1 Figure H1), followed by probes regarding specific times throughout the recording day and six categories commonly forgotten foods (see Section for further detail). Appendix H 301

304 Appendix I Study 4 Nutricam recording protocol Appendix I 302

305 Appendix I 303

306 Appendix J Study 4 Questionnaires Participant Questionnaire NuDAM Evaluation Questionnaire Weighed Food Record Evaluation Questionnaire Appendix J 304

307 Appendix J 305

308 Appendix J 306

309 Appendix J 307

310 Appendix J 308

311 Appendix J 309

312 Appendix J 310