SMARTPHONE APPLICATION

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1 SMARTPHONE APPLICATION FOR SYNCHRONIZED REAL-TIME DIETARY ASSESSMENT AND PHYSICAL ACTIVITY ANALYSES Sigrid Beer-Borst for the «smartapp» research group, ar&d in Nutrition and Dietetics Funding: Bern University of Applied Sciences, Federal Office of Public Health

the «smartapp» research group, ar&d in Nutrition and Dietetics

2 What to expect Mobile Health services (mhealth) in Public Health Nutrition practice and research Android Smartphone application «smartapp» joint data capture on physical activity & food intake Application test results Next steps: ongoing and projected R&D activities 2

«smartapp» joint data capture on physical activity & food intake

3 mhealth in Public Health Nutrition Mobile health services (mhealth) are trendy In CH, every second person 15 years and older has a Smartphone Smartphones allow synchronizing the collection/measure and use of diet and physical activity data as major lifestyle (risk) factors offer a wide range of applications with increasing requirement as to data accuracy: life style coaching (open access), dietary counseling, research can serve as a motivational tool supporting behavior change Objective: provide a user friendly (different user groups), easy to handle, reliable and valid tool (app) 3

range of applications with increasing requirement as to data accuracy: life style coaching (open access), dietary counseling, research can serve as

4 Android «SmartAPP» Capture joint data on physical activity & food intake Physical activity analysis: from acceleration to energy expenditure Acceleration Variance of acceleration MET value, energy expenditure in metabolic equivalents (1 MET = 3.5 ml O 2 x kg -1 x min -1 or 1 kcal x kg -1 x h -1 ) 4

Acceleration Variance of acceleration MET value, energy expenditure in

5 Android «SmartAPP» Capture joint data on physical activity & food intake 5

6 Android «SmartAPP» Capture joint data on physical activity & food intake - Activity list Limitations of carrying a Smartphone Specific individual activities can be added manually

7 Android «SmartAPP» Capture joint data on physical activity & food intake Food consumption: semi-quantitative food record Other foods can be added manually 7

8 The «SmartAPP» coaching system 8

9 Prototype application test 2010/ Study design Technical Development Android Smartphone App Real-time data assessment Computer-based data analyses Dietary counseling application (smarterb) system Pilot test and App refinement Feature, Functionality General usability Design 5 BUAS dieticians, Sept non Smartphone user laypersons, Oct 2010 Application test in dietetics (prototype) Feasibility Suitability Plausibility 5 RDs: 33-46y; kg/m 2 20 Clients: 25-49y; kg/m 2 partly non Smartphone users 9

Functionality General usability Design 5 BUAS dieticians, Sept 2010 5 non Smartphone user laypersons, Oct 2010 Application test

10 kcal Median energy intakes and expenditures (kcal/d) Reference (RD, n=5) and client group (n=20) over time Non parametric ANOVA Model for repeated measurements, F1_LD_F1-Model by Brunner, Domhof and Langer 2002; p=< energy intake clients energy expenditure clients energy intake dieticians energy expenditure dieticians Fr Sat Sun Fr/Sat Sat/Sun day 1 day 2 day 3 day 4 day 5 day 6 day 1 day 2 day 3 day 4 day 5 day 6 week 1 week 2 Group effect, wk 1 (clients vs. RD) Energy intake p=0.094 Energy expenditure p=0.224 Clients, time effect Energy intake p=0.086 Energy expenditure p=

05 - energy intake clients energy expenditure clients energy intake dieticians energy expenditure dieticians Fr Sat Sun Fr/Sat Sat/Sun day 1 day 2 day 3

11 Plausibility check Underreporting (Wk 1) Goldberg cut-off Technic (Black AE. Int J Obes 2000; Livingston & Black J Nutr 2003) Comparison of the mean energy intake at group level with the estimated energy requirement/expenditure at PAL 1.55 EI rep : BMR < / > PAL 95% lower and upper Conf limits (cut-offs) 20 overweight clients EI rep : BMR = 1.21 < [1.548; 1552] 5 dieticians EI rep : BMR = 1.28 < [1.549; 1551]

with the estimated energy requirement/expenditure at PAL 1.

12 Food intake quantification by group Use of exact measures (ml, g) or predefined units (household measures, portions) by clients vs. RD s in N (%) (GLMM, p=0.0007) Clients RDs OR [95%CI] Predefined units 1437 (61%) 218 (27%) Exact measures 917 (39%) 594 (73%) 5.88 [2.43, 14.25] 12

13 Completeness of food data entry Necessity to enter foods manually (not found in food list) by clients vs. RDs, N (%) (GLMM, p=0.5477) Clients RDs OR [95%CI] Complete data 2219 (96%) 785 (97%) Missing data 100 (4%) 27 (3%) 0.68 [0.20, 2.31] Necessity to enter foods manually (not found in food list) for clients, wk1 vs. wk 2, N(%) (GLMM, p=0.0003) Week 1 Week 2 OR [95%CI] Complete data 2219 (96%) 2226 (98%) Missing data 100 (4%) 57 (2%) 0.56 [0.41, 0.77] 13

5477) Clients RDs OR [95%CI] Complete data 2219 (96%) 785 (97%) Missing data 100 (4%) 27 (3%) 0.68 [0.20, 2.

14 Next steps (1 - ongoing) Implement a web-/server based system concept Test «SmartAPP» image feature for supporting portion size estimation 14

15 Next steps (2 projected, 2013+) «SmartAPP» validation study Step 1 - Physical activity a) mechanical validation power calculation from phone accelerometer vs. ground reaction forces b) energy expenditure by spiroergometry indirect calorimetry Step 2 Food record: against calibrated measure of physical activity Reprogram for iphone Intervention study in dietary counseling / lifestyle coaching 15

ground reaction forces b) energy expenditure by spiroergometry indirect calorimetry Step 2 Food

16 Contacts Health Engineering and Information Technology Sigrid Beer-Borst, MSc Lecturer / senior fellow for ar&d in Nutrition & Dietetics E sigrid.beer@bfh.ch URL Dr. Ing. ETH Marcel Jacomet Head of Institute for Human Centered Engineering E marcel.jacomet@bfh.ch URL 16

Development and testing of a mobile phone application for weight loss

Development and testing of a mobile phone application for weight loss Victoria J Burley Michelle Carter Janet E Cade Funding from: National Prevention Research Initiative (NPRI) grant number G0802108 Background