Development of Validation of the Diabetes Medication Satisfaction Tool (DMSAT)

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1 Research Development of Validation of the Diabetes Medication Satisfaction Tool (DMSAT) Roger T. Anderson, PhD, Penn State University, College of Medicine, A210 Public Health Sciences, Hershey, PA Cynthia J. Girman, Merck Research Laboratories, Epidemiology Department, West Point, PA. Fabian T. Camacho, Penn State University, College of Medicine, A210 Public Health Sciences, Hershey, PA Manjiri D. Pawaskar, MS, Department of Pharmacy Practice and Administration, Ohio State University College of Pharmacy, Columbus, OH Jorge Calles, M.D. Wake Forest University School of Medicine, Winston-Salem, NC William S. Kelly, Kernersville Primary Care, Kernersville, NC. Carla DeMuro, Merck Research Laboratories, Epidemiology Department, West Point, PA. Rajesh Balkrishnan, PhD, Department of Pharmacy Practice and Administration, Ohio State University College of Pharmacy, Columbus, OH, Corresponding author: Roger T. Anderson, PhD, Penn State University, College of Medicine, A210 Public Health Sciences, Hershey, PA Word count: Abstract: 289 Text: 3,533 Number of Tables: 4 1

2 Abstract Background: Patient satisfaction with treatment has an important role in diabetes management for optimum glycemic control. However, few instruments are available that account for the broader context of medication management in diabetic patients. Hence, the purpose of this study was to develop and validate a brief self-reported questionnaire, Diabetes Medication Satisfaction Tool (DMSAT),that can be used by healthcare professionals in clinical settings to identify potential problems that patients experience with diabetes medications. Methods: Item content was generated from focus groups of patients attending community health clinics, pre-tested in a sample of 55 patients with Type I or II diabetes, and examined in two samples of patients of a family practice and diabetes care center who were prescribed medical therapy. Validity and reliability were assessed. Results: In the final step, sixteen items were retained assessing four distinct medication treatment experiences: ease and convenience, lifestyle burdens, well-being, and medical control. Construct validity of the scales and total score were demonstrated by statistically significant (p<0.05) associations with treatment complexity, follow-up visits, self-rated glucose control, health worries, and glycemic control (HbA1c) in the last six months. Internal consistency reliability coefficients for the scales and total score ranged from.89 to.95. Conclusion: The DMSAT offers a comprehensive assessment of patient acceptability and satisfaction with the use of diabetes medication therapy in their daily life. Two independent patient samples showed that DMSAT scores associate with clinically relevant outcomes. Such a measure could be useful in future research to assess strategies to individualize patient therapeutic decision making. 2

3 Background: Diabetes affects 7% of the U.S. population, 1 and as a leading cause of morbidity and mortality, consumes enormous health care resources in the U.S costing around $132 million in An estimated 30% of all primary care office visits made by patients with a principal diagnosis of diabetes mellitus are cited for symptoms and complications related to diabetes (e.g., dizziness, exhaustion, vision and foot complaints), and involve three or more medications. 2,3 Despite that several studies have highlighted the benefits and impact of good glycemic control, long term medication management in diabetes has been always challenging to patients and clinicians alike. 4,5 Hence, it may be essential to take into account patient preferences and satisfaction with treatment in developing a treatment plan and dosing regimen. Routine assessment of patient satisfaction with treatment, and the resultant treatment tailoring, is an important step toward building and maintaining a therapeutic alliance among the patient and family, the physician and the other members of the health care team, 4 shown to improve patient adherence behaviors, and patient outcomes. 3 Because there are potentially multiple aspects of a patient s experience and preferences for treatment to consider, verbally asking the patient questions about their treatment might not elicit an accurate report on the nature or type of difficulties experienced as is possible with the combined use of tested and validated items. 6,7 Despite the growth of diabetes treatment options in recent years, and the growing use of combination therapies, few formal measures of diabetes treatment satisfaction are available in the literature that include a significant focus on treatment complexity, side 3

4 effects, and allowance for multiple medications for diabetes management in ratings of treatment satisfaction. A widely-used diabetes specific measure, the Diabetes Treatment Satisfaction Questionnaire (DTSQ) has performed well in measuring patients' treatment satisfaction for diabetes therapies; 8 providing a brief assessment of qualities of diabetes treatment satisfaction of convenience, well-being and blood glucose control. It does not include an extended range of issues or concerns, such as side effects, dosing schedules which may vary across agents, time spent managing diabetes, and integrating medication regimens into ones lifestyle or routine. The latter qualities may become important sources of dissatisfaction as the treatment regimen grows in complexity either with difficult single agents or with combination therapies designed for intensive glucose management. Another instrument, the Treatment Satisfaction Questionnaire for Medication (TSQM) is a generic measure of satisfaction with medications. It was tested in the diverse populations with different clinical conditions. 9 It too, may not be sensitive to diabetes specific medications, its side effects, complications, different types of therapies (oral vs. insulin therapy), self-management and self-care burden of treatment. On the contrary, other instruments are designed for specific type of medication or therapy in diabetes patients. Instruments such as the Patient Satisfaction with Insulin (PSI) 10, Insulin Treatment Satisfaction Questionnaire (ITSQ) 11 and the Insulin Delivery System Rating Questionnaire (IDSRQ) 12 were used to evaluate patient satisfaction with different types of insulin therapy only. Thus, while well-validated patient assessment tools already have had practical applications in diabetes treatment studies, new emphases on the impact of regimen complexity and intensity may warrant an expanded scope of measurement. The Diabetes Medication Satisfaction Tool (DMSAT) was designed to be a brief 4

5 yet sensitive tool to assess patient s global satisfaction with diabetes medication management, within the context of achieving recommended therapy goals for optimal glucose control and cardiovascular disease risk factor lowering. The balance of satisfaction with diabetes treatment is personal to each patient, and allows the clinician to recognize a given patient s valuation of a treatment regimen, and the researcher to understand the average treatment burden. Therefore, the purpose of this study was to develop a conceptually sound, psychometrically valid and reliable instrument: the Diabetes Medication Satisfaction Tool to measure patient satisfaction with adequate breadth to be applicable across treatment regimens, simple and complex. Methods This study addressed the following objectives: 1) create a pool of candidate items from the literature, expert review, and focus groups interviews, 2) refine and psychometrically appraise the new DMSAT instrument, and 3) confirm the results in an impendent sample and 4) compare of the DMSAT to a leading diabetes treatment satisfaction instrument. Institutional Review Board approval was obtained from the Wake Forest University. Informed consents were obtained from all the study participants for each study phase, and data analysis period. Phase I: Conceptual Development An extensive literature review was conducted for the last 15 years using MEDLINE database and search terms used were: diabetes care, medication use, selfcare, lifestyle burden, and treatment satisfaction. An initial conceptual framework 5

6 was identified including the following constructs: ease and convenience, lifestyle burdens, perceived impact of diabetes on physical and mental wellbeing, medical control over glucose and health risks. We sought to assess patient s satisfaction with all of their medication therapy, on average, rather than focus on each specific medication in isolation, because multiple medications are often taken together, or in coordination, in diabetes management, and patients may have difficulty distinguishing separate effects of the medications they are taking. This is similar to health-related quality of life assessments where the respondent is asked to appraise their wellbeing all things considered. Focus groups We conducted five focus groups of 5 to 8 patients seeking diabetes care from community health clinics in North Carolina, through Project IDEAL, 13 a multi-site diabetes care improvement study of low income patients. Patients were selected to include a range of treatment complexity and successful control (i.e., the well-controlled to uncontrolled with recent change in treatment), and included both adult men and women, with ethnic or race-group diversity (African American, Caucasian, and Hispanic). These focus groups were conducted without participants seeing the item pool or conceptual domains to allow participants to freely express aspects regarding their satisfaction with medications. All group deliberations and comments were transcribed and content coded by the lead investigator using the Q-sort method (RA). Review and coding of the focus group transcripts confirmed four distinct areas of diabetes treatment 6

7 satisfaction envisioned: ease and convenience, lifestyle burdens, perceived impact of diabetes on physical and mental wellbeing, medical control over glucose and health risks. Item Testing: Based on content analysis of the focus group results, a prototype diabetes medication satisfaction instrument was assembled and tested in an exploratory convenience sample of 55 patients with type I and II diabetes in five of thirteen Project IDEAL community care sites. The purpose of this step was to preliminarily assess DMSAT item performance: reliability, means, scale floor and ceilings and construct validity (correlation with most recent HbA1c level and global appraisals). The correlation of DMSAT items with related concepts was examined using the Multidimensional Diabetes Questionnaire (MDQ) 14 lifestyle interference scale, the Medical Outcomes Studies (MOS) Health Worries Scale, 15 and global items from the validated Diabetes Treatment Satisfaction Scale 8 assessing extent that blood sugar has been unacceptably high or low. Patients were invited to complete a mailed questionnaire, and were mailed a survey packet. Final item review was made by the research team to assure clarity of wording and conciseness. Validity and Reliability: In a second clinical population of primary care patients, we identified patients (n=307) with diabetes mellitus (ICD-9 codes: 250.xx) who had an HbA1c result within the last 6 months from a large community family practice In this study, it was expected that levels of diabetes treatment satisfaction would show significant correlation with 7

8 measures of related concepts such as lifestyle burden, treatment complexity, overall health appraisals, more general measures of treatment satisfaction, medication adherence, as well as clinical indicators of diabetes control. We measured or obtained A1c level in the last 3 months (< 8% vs. > 8%); treatment complexity was assessed from the number of daily medications consumed. In this regard a measure of medication complexity was developed to assign an arbitrary weight (0,1) for absence/presence of metformin, sulfonyureas, TZDs, statins, fibrate, angiotensin converting enzyme (ACE) inhibitor and angiotensin II receptor blocker (ARB2), and a weight (0,2) for insulin based on its more demanding regimen. Self-reported adherence was assessed as number of days in the last 10 days in which participant took prescribed diabetes medications at the time and dose suggested by their doctor. Patients were mailed the study questionnaire packet by their physician, inviting study participation. Those who elected to participate returned the completed survey and received a $25 retail gift certificate. A repeat mailing was performed after 10 days of non-response. Clinical data was abstracted by a trained data collector and included most recent HbA1c, microalbumin, blood pressure, lipid and triglyceride values, diabetes medication class (Metformin, Sulfonylureas, Thiazolidinediones) use of insulin, antihypertensive and cholesterol lowering medications, attendance of diabetes education sessions, body mass index (BMI), and number of primary care visits in past (timeframe?). Known groups validity of the DMSAT was assessed by comparing instrument means by levels of A1c (< 8%, > 8%); the number of daily medications consumed; self-reported adherence, assessed as the number of skipped or missed doses in the last 10-day period; and health worries using the MOS scale to assess confidence in future health protection. 8

9 Exploratory factor analysis (EFA) of the DMSAT items was conducted using statistical package SAS Version Kaiser s measure of sampling adequacy was checked to verify whether the common factor model was appropriate for the sample. The EFA method used for factor extraction consisted of principal axes factoring with iteration. 18 The number of factors selected for the model was chosen by a Scree plot test and by choosing the number which explains 100% of the estimated common variance of the items. 18 An oblique rotation of the initial factor solution using the promax method was then performed in order to allow the factor model to have correlated factors. Final Testing Confirmatory factor analysis of the final DMSAT was performed in a new sample of 98 patients from a diabetes clinic and endocrinologist practice at a large academic medical center, fitting the EFA model on the data using maximum likelihood estimation using AMOS. 17 Participants provided informed consent and completed self-report questionnaires on a tablet computer while waiting to see a clinician for their diabetes care. Model fit was assessed using the χ 2 test. Because the χ 2 test of significance is sensitive to sample size 19, model fit was also evaluated in light of four other common fit indices: the normed chi-square (χ2/df), the Comparative Fit Index (CFI), 20 the Normed Fit Index (NFI) 21 and the Root Mean Square Error of Approximation (RMSEA). 22 Values of the normed chi-square less than 3 suggest good fit, 19,22,23 and values of the CFI and NFI greater than 0.90 suggest good fit. Internal consistency reliability of the DMSAT total score and sub-scales was assessed using Cronbach s alpha coefficient. 24 9

10 Estimates of construct validity of the DMSAT from known groups comparisions were repeated in this sample. For this sample, the most recent A1c value was obtained from the medical chart and provided by the clinician. Since theoretically, levels of diabetes treatment satisfaction in the DMSAT should also correlate with measures of related concepts 25 the DTSQ, was administered and examined. Results In the exploratory sample (n=75), 55 patients (73%) returned the completed prototype 32-item DMSAT and additional questionnaires. Item analysis found 9 items with either extreme skewness or redundancy (r > 0.75) with other DMSAT items, and were removed, resulting in a 26-item DMSAT version. The new DMSAT version mailed to the second study sample of 307 primary care patients treated for type II diabetes, yielded 194 (63%) returned surveys, of which n=140 reported medication use to control diabetes and comprised the study population. As shown in Table 1, participants in this validation sample had a mean age of 63 years, most (77%) had completed high school and were aware of having diabetes for at least 5 years (61%). Roughly one-third (29 39%) were taking one, two or three medications for their diabetes, with 16 % taking insulin. Approximately 14% had a recent A1c of > 8.0% and nearly 19% rated their adherence to medication regimen in the last 10 days as less than complete. From item analysis of these data, 10 items in the DMSAT displayed high interitem correlations (>.75) with other same-scale items and were removed. Exploratory factor analysis of the reduced 16-item questionnaire identified a parsimonious 4-factor 10

11 structure shown in Table 2. The factors were consistent with the focus-group identified clusters of life style, medical control, convenience and well-being. Kaiser s measure of sampling adequacy was 0.92, suggesting that a common factor model was appropriate for the sample. The Scree plot criteria also indicated that 4 factors be retained, and explained 80% of the total item variation in the sample. In Table 3, internal consistency reliability estimates 24 of the four DMSAT scales were near (0.89) or above 0.90 for all scales and for the total score. Percentages at the ceiling of the scales was low (1.45% to 6.62%); and each scale score showed high (> 0.80) correlation with the DMSAT total score. Estimates of validity of the DMSAT scales are presented in Table 4. DMSAT total scale score means showed good discrimination at p <.05 between high and low levels of treatment complexity, self-rated glucose control, MOS Health worries score, and categories of recent HbA1c (< 8% vs > 8%). Correlation of the DMSAT scores with mean HbA1c values treated as a continuous value was (p-value = ). In the final confirmatory sample of n=92 patients, the CFA results replicated the 16- item structure found in the exploratory model. In Table 5, DMSAT total scale score means showed close agreement with results in the previous sample (Table 4) by discriminating between high and low levels of treatment complexity, number of follow-up visits, self-rated glucose control, MOS health worries, and most recent A1c value (< 8% vs > 8%). In addition the DMSAT total score was highly correlated with the DFTSQ at r = 0.68 (p <.001). In contrast to the DMSAT, the DTSQ total score did not discriminate between levels of treatment complexity and clinical A1c value, but was sensitive to self-rated glucose and level of MOS health worries. 11

12 Discussion Long term diabetes management requires continuous medication use, and self management, and may meet with poor adherence and deterioration in glycemic control. 26,27 This may stem from the inconvenience and poor patient treatment acceptability, multiple medication, self-care, self-management burden and lifestyle demand as well as secondary failure of currently available medications. 28 Patient psychological issues also play crucial role in effective disease management. The concept of patient satisfaction with treatment enables the researcher and clinician to support a therapeutic alliance, empowering both patient and providers to work to negotiate "preference-driven" decisions that seek to minimize treatment burden with optimal clinical effectiveness. 3,4 The DMSAT was developed in this light, to assess patient satisfaction with their diabetes medication treatment regimen, on average, across agents and drug classes, regardless of? treatment complexity. The DMSAT accomplishes its focus by asking the patient to reflect upon all of the medications they are taking for their diabetes and rate the overall regimen in terms of convenience, lifestyle ease, success at medical control, and effects on wellbeing such as from side effects. to The results of this study support the DMSAT measurement approach, and it was found to discriminate important correlates of patient management; its similarity to the DTSQ in detecting selfrated glucose control and health worries as well as its additional breadth in responding to levels of treatment complexity. Some of measurement precision and specificity of the DMSAT might be gained over the DTSQ total score from the larger number of items: 16 versus 6, respectively. The DMSAT has four conceptually distinct dimensions of 12

13 medication satisfaction: lifestyle impact, convenience, medical control, and well-being. This information was shown to correlate with key issues patients encounter with medication treatment of Type II diabetes such as HbA1c and self-management difficulty. These DMSAT subscales provide patient data on specific aspects of treatment which may be targeted for enhancement or support. The brevity (16 items) of the DMSAT allows its use alongside other diabetes outcome measures relevant to diabetes care and education. While the results for the DMSAT were largely supported by our validation model, there are some limitations. Neither the DMSAT total score nor the subscales were associated with patients attendance of a diabetes patient education program offered by the sample site clinic (data not shown). Hypothetically, learning about effective self-care and gaining competence in overcoming barriers may promote reduced burden of diabetes treatment. The DMSAT does not include an assessment of satisfaction with cost of medication or diabetes management to the patient. This omitted aspect though not pertinent to clinical trials, could be valuable in health services research or clinical practice where patients bear the costs of certain aspects of their care. Finally, we did not have longitudinal data to examine responsiveness to change, or utility of the DMSAT to clinicians. The latter will be the focus of future investigations. Conclusion: The DMSAT is a 16-item instrument that offers a comprehensive assessment of the patient acceptability and satisfaction with the use of diabetes medication therapy in their daily life. Two independent patient samples showed that DMSAT scores associate with clinically relevant outcomes. Such a measure could be useful for clinicians in future 13

14 research to assess strategies to individualize patient therapeutic decision making in diabetes treatment. List of abbreviations: DMSAT: The Diabetes Medication Satisfaction Tool HbA1c: Glycated haemoglobin/ glycemic control DTSQ: Diabetes specific measure, the Diabetes Treatment Satisfaction Questionnaire TSQM: The Treatment Satisfaction Questionnaire for Medication PSI: The Patient Satisfaction with Insulin (PSI) ITSQ: Insulin Treatment Satisfaction Questionnaire IDSRQ: The Insulin Delivery System Rating Questionnaire MDQ: Multidimensional Diabetes Questionnaire MOS: The Medical Outcomes Studies CFI: The Comparative Fit Index NFI: The Normed Fit Index RMSEA: The Root Mean Square Error of Approximation Consent: Written informed consent was obtained from each participant, A copy of the written consent will be available for review by the Editor-in-Chief of this journal. Competing interests: This study was funded by a grant to the Wake Forest University School of Medicine by Merck Inc, West Point, PA

15 References 1. Diabetes Statistics. [ Accessed on May 14, King H, Aubert RE, Herman WH: Global burden of diabetes, : Prevalence, numerical estimates, and projections. Diabetes Care1998, 21: , Anderson RT, Balkrishnan R, Camacho F, Bell R, Duren-Winfield V, Goff D: Patient-centered outcomes of diabetes self-care. Associations with satisfaction and general health in a community clinic setting. N C Med J 2003, 64: Lawson ML, Gerstein HC, Tsui E, Zinman B: Effect of intensive therapy on early macrovascular disease in young individuals with type 1 diabetes: a systematic review and meta-analysis. Diabetes Care 1999, 22:B35 B Stratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR: Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 2000, 321: Sleath B, Roter D, Chewning B, Svarstad B: Asking questions about medication: analysis of physician-patient interactions and physician perceptions. Med Care 1999, 37: Agency for Healthcare Research and Quality: Expanding Patient-Centered Care To Empower Patients and Assist Providers. [ Accessed on October 10,

16 8. Bradley C: The Diabetes Treatment Satisfaction Questionnaire (DTSQ). In Handbook of Psychology and Diabetes: A Guide to Psychological Measurement in Diabetes Rsearch and Practice. Chur, Switzerland, Harwood Academic; 1994: Atkinson MJ, Kumar R, Cappelleri JC, Hass SL: Hierarchical construct validity of the treatment satisfaction questionnaire for medication (TSQM version II) among outpatient pharmacy consumers. Value Health 2005, Sppl 1:S9-S Cappelleri JC, Gerber RA, Kourides IA, Gelfand RA: Development and factor analysis of a questionnaire to measure patient satisfaction with injected and inhaled insulin for type I diabetes. Diabetes Care 2000, 23: Anderson RT, Skovlund SE., Marrero D, Levine DW, Meadows K, Brod M, Balkrishnan R: Development and Validation of the Insulin Treatment Satisfaction Questionnaire. Clin Ther 2004, 26: Peyrot M, Rubin RR. Validity and reliability of an instrument for assessing health-related quality of life and treatment preferences: the Insulin Delivery System Rating Questionnaire. Diabetes Care 2005, 28: Anderson RT, Ory M, Cohen S, McBride JS: Issues of aging and adherence to health interventions. Control Clin Trials 21:171S-183S, Talbot F, Nouwen A, Gingras J, Gosselin M, Audet J: The assessment of diabetes-related cognitive and social factors: the Multidimensional Diabetes Questionnaire. J Behav Med 1997, 20: Stewart A, Hays RD, Ware JE: Health perception, energy/fatigue, and health distress measures. In Measuring functioning and wellbeing: The Medical 16

17 Outcomes Study approach. A.L. Stewart and J.E. Ware, Editors, Duke University Press Statistical Analysis System Version 8. The SAS Institute: Cary, NC. 17. Harman H: Modern Factor Analysis. University of Chicago Press, De Vet HCW, Adèr JH, Terwee CB, Pouwer F: Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Quality of Life Research 2005, 14: Bollen KA: Structural equations with latent variables. New York: Wiley Bentler PM: Comparative fix indexes in structural models. Psychol Bull 1990, 107: Browne MW, Cudeck R: Alternate ways of assessing model fit. In: KA Bollen & JS Long eds. Testing structural equation models. Newbury Park, CA: Sage. 1993: Byrne BM: A Primer of LISREL. New York: Springer-Verlag, Marsh HW, Hocevar D: Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psycho Bull 1985, 97: Cronbach L: Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16: DeVellis RF: Validity. In Scale Development: Theory and Applications. 2 nd edition, Sage Publications, Thousand Oaks, California, 2003:

18 26. Krapek K, King K, Warren SS, George KG: Medication adherence and associated hemoglobin A1c in type 2 diabetes. Ann Pharmacother 2004, 38: Schectman JM, Nadkarni MM, Voss JD: The association between diabetes metabolic control and drug adherence in an indigent population. Diabetes Care 2002, 25: Diabetes Attitudes, Wishes, and Needs (DAWN) Study. Barriers to treatment. [ Accessed on Feburary 10,

19 Table 1. Characteristics of the Validation Sample (n = 140) % or Mean (Standard Deviation) Age (mean, SD) 63.31(10.58) Education (%): Completed high school Less than high school Number of office visits, past year (mean, SD): 3.02 (1.17) Perceived health status a (%) : Excellent 4.29 Very Good Good/Fair/Poor Time with DM (%): < 1 yr yrs yrs yrs Number of DM medicines (%): Take Insulin? Yes No Most recent A1c (%) < > Self-rated adherence last 10 days b (%) Always Less than always a In general, would you say your health is (Ware and Scherbourne 1992). When used as a covariate in analyses, it is dichotomized to contrast those reporting fair or poor health with all others. b Rating of how often respondent took all medications at correct times suggested by doctor. 19

20 Table 2: EFA Factor Analysis Loadings (Standardized Regression Coefficients) and Descriptive Statistics Items in Final Model How Satisfied have you been with... Mean Standard deviation Factor 1 Loading s Factor 2 Loading s Factor 3 Loading s Factor 4 Loading s Lifestyle How easy it is to have the lifestyle you prefer How much you have to plan your social life How much you have to plan your physical activity because of your treatment.how much you may have to plan your meal times because of your treatment.how much time you spend managing your diabetes Medical Control.How well your blood sugar level stays where you think it should How often you can avoid highs in your blood sugar tests during the day How much you feel in control of your diabetes Convenience.How often you can avoid lows in your blood sugar tests during the day Convenience when you are away from home for an extended period The way you take your medications The times of day you have to take your diabetes medications The overall convenience of your diabetes medications Wellbeing.Your diabetes medication in terms of how it makes you feel physically Your diabetes medication in terms of your mood Your diabetes medication in terms of how much energy you have Note: Standardized Regression Coefficients of Oblique Factors are shown. Unrotated factors explain 100% of estimated common variance. 20

21 Table 3. Descriptive statistics of the Diabetes Medication Satisfaction Tool (N = 140) Subscales Meaning No. of Items Mean* (SD) Ceiling % Cronbach α Reliability Correlation with Total Score Lifestyle Difficulty planning and (17.60) living daily life (social, physical, and meal times) around treatment, time spent managing diabetes Medical Success reaching or (19.94) Control maintaining a blood glucose level, lower risk of complications (stroke, heart problems). Being in control of diabetes. Convenience Ease integrating (14.39) medication schedule with usual activities at work, home and leisure. Well-being Physical and mental health (21.29) perceptions, mood and vitality Total (15.18) *Scores range from 0 to 100 (lower score indicates less treatment satisfaction). 21

22 Table 4. Known Groups Validity: DMSAT Subscale and Total Score Means by Levels of Diabetes Treatment Characteristics (N=140). Health Worries 3 DMSAT Treatment Self-Rated Glucose A1C Complexity 1 Control 2 High Low Poor Good High Low > 8% <8% Lifestyle ** ** ** * Convenience ** ** * * Medical ** ** * Control Wellbeing * ** ** Total Score ** ** ** * 1 Score of 0,12 versus 3+ 2 Excellent or very good vs. good, fair or poor 3 Quartile 1 (high) vs. not high (Quartiles 2,3,and 4)* p<

23 Table 5. Known Groups Validity: DMSAT Subscale and Total Score Means by Levels of Diabetes Treatment Characteristics, includes DTSQ ( N=92). DMSAT 4 Treatment Self-Rated Glucose Perceived General A1C Complexity 1 Control 2 Health 3 High Low Poor Good High Low > 8% <8% Lifestyle * ** *** Convenience * ** ** Medical * *** ** ** Control Wellbeing *** *** * Total Score * *** *** * DTSQ ** *** Score of 0,12 versus 3+ 2 Excellent or very good vs. good, fair or poor 3 Excellent or very good vs. good, fair or poor 4 Lower scores indicate less treatment satisfaction * p < 0.05, ** p < 0.01, *** p <