The ARMS-D out performs the SDSCA, but both are reliable, valid, and predict glycemic control
Introduction
Many adults with type 2 diabetes mellitus (T2DM) do not take their medications as prescribed [1], [2]; and suboptimal medication adherence has been associated with poor glycemic control [3], [4], [5], [6], an increased risk of hospitalization [5], [7], [8], [9], early mortality [5], [9], and higher healthcare costs [8]. Improvements in diabetes medication adherence could improve the health outcomes of patients with diabetes [7], [10] and save $661 million to $1.16 billion annually [11]. While identifying nonadherence is a logical first step to intervention [12], more scientifically valuable and clinically feasible measures of diabetes medication adherence are needed for this purpose [13], [14].
More “objective” measures of medication adherence (e.g., pill-counts, pharmacy refills, and electronic monitoring systems) may seem to be the most valid and reliable ways to assess adherence, but there is inconsistent evidence to suggest the superiority of one measure over another [13]. Objective measures do not perform consistently better than self-report measures [15], [16] and are costly and often impractical for research and clinical purposes [13]. Self-report measures are more feasible and, though susceptible to social-desirability bias, have been similar to objective measures in their relationship to clinical outcomes, such as glycemic control [3], [17]. However, agreement between self-report measures varies widely and appears to be influenced by a measure's characteristics [14], [17].
There remains a continued need to identify and evaluate measures of self-reported medication adherence for use with adults with T2DM [14]. Only two self-report measures that are used to assess medication adherence among adults with diabetes have been psychometrically validated against objective measures of refill adherence: the Summary of Diabetes Self-Care Activities medications subscale (SDSCA-MS) [17], [18], [19] and the Morisky scale [20]. The SDSCA-MS is the most widely used measure of diabetes medication adherence. However, many studies use only one item from the 2-item subscale [17], [18], [19], [21]. Gonzalez et al. [17] recently established the predicative validity of a 1-item version of the SDSCA-MS for HbA1C, but, to our knowledge, predictive validity for the 2-item SDSCA-MS has not been established. The Morisky scale is a brief (4-item) self-report measure of adherence to all prescribed medications [20] that has predicted glycemic control among adults with diabetes [3], [6]. Although the SDSCA-MS and the Morisky scale assess adherence to medications quickly, they have limited variability in total scores (0–7 and 0–4, respectively) due to few items and response options. Moreover, they do not provide information on how to intervene once nonadherence is identified.
The identification of specific barriers to adherence may aid in tailoring and targeting adherence promotion interventions and in addressing reasons for nonadherence in clinical practice [12]. The Adherence to Refills and Medications Scale (ARMS) [22] is a longer (12-item) self-report measure of adherence that assesses one's ability to take and refill medications under different circumstances, and, in turn, identifies barriers not assessed by the SDSCA-MS or the Morisky scale. The original ARMS was developed to be a measure of adherence to all prescribed medications and has been associated with objective measures of refill adherence among a predominately African American, inner-city sample with heart disease. While 45% of the sample in which the ARMS was developed and validated had comorbid T2DM [22], the ARMS’ psychometric properties for assessing adherence to diabetes medications, specifically, has not yet been explored.
There is also a lack of standardized and validated measures for patients on insulin for whom adherence may be an even greater problem [2]. Consequently, many studies use the same self-report measures to assess adherence to oral agents and insulin despite the unique challenges associated with insulin adherence. The ARMS may identify barriers to adherence that are more common among insulin users, and may therefore identify insulin nonadherence more effectively, or predict HbA1C differently based on insulin status. In a sample of Japanese patients with T2DM, Mashitani et al. [23] found that self-reported insulin adherence (assessed with one item generated for the study) was only associated with glycemic control for patients younger than 65 years. It remains unclear if this age-related subgroup difference will persist in a different patient population, with validated self-report adherence measures, or if there is an age-related subgroup difference for participants on oral hypoglycemic agents.
To address these gaps and contribute to the literature on self-reported diabetes medication adherence, we modified the ARMS to specify adherence to diabetes medicines (ARMS-D), and then assessed its psychometric properties. First, we examined the ARMS-D's internal consistency reliability. Next, we confirmed a two-factor structure for the ARMS-D and related the ARMS-D to the most widely used measure of diabetes medication adherence (SDSCA-MS) and a measure of diabetes treatment satisfaction (DM-SAT) to establish its construct validity for assessing diabetes medication adherence, specifically. To improve the accuracy of the SDSCA-MS, we administered the instrument for each diabetes medication in the regimen, separately. Then, we compared the predictive validity of the ARMS-D and the SDSCA-MS (2-item and 1-item versions) with HbA1C. Finally, we assessed these measures’ performance by age (<65 vs. ≥65 years) and insulin status.
Section snippets
Sample and recruitment
As part of a larger study testing predictors of medication adherence among adults with T2DM, we recruited outpatients receiving care from a Federally Qualified Health Center (FQHC) in Nashville, TN, USA from July 2010 through November 2012. Eligible patients were English- or Spanish-speaking adults (≥18 years) who were taking prescribed medications for T2DM. Trained research assistants (RAs) worked with clinic personnel each weekday to identify patients with a scheduled clinic appointment and
Results
Of the 833 patients with T2DM who had a clinic appointment during the recruitment period, 245 did not arrive for their appointment. Of the 588 patients with T2DM who arrived for a clinic appointment, 86% were approached by a RA. Of those approached, 11% declined participation without being screened for eligibility and 27% were ineligible (74 did not speak English/Spanish, 52 were not prescribed T2DM medications, and 9 were excluded due to an intellectual, auditory, or speech impairment, or
Discussion
In a sample of diverse, low income adults with T2DM, we examined the internal consistency reliability of the ARMS for measuring diabetes medication adherence (ARMS-D), and its construct validity with the SDSCA-MS and a measure of diabetes treatment satisfaction (DM-SAT). One of the ARMS-D items was identified, empirically and conceptually, as less relevant to adherence to diabetes medications and removed. The 11-item ARMS-D total and subscales were internally consistent and were each associated
Conflict of interest
The authors have no conflicts of interest. An abstract of this work was published as part of the American Diabetes Association's 73rd Annual Scientific Sessions held in Chicago, IL in June, 2013.
Contribution statement
L.S.M. managed data, conducted analyses, interpreted the data, and wrote the manuscript. J.S.G. guided the analyses, interpreted the data, wrote the introduction, and reviewed and edited the manuscript. K.A.W. guided the analyses, wrote the abstract, contributed to writing the conclusions, and reviewed and edited the manuscript. S.K. contributed to the research question, guided the analyses, and reviewed and edited the manuscript. C.Y.O. designed the parent study, supervised all aspects of data
Acknowledgements
This research was funded with support from the Vanderbilt Clinical Translational Scientist Award (UL1TR000445) from the National Center for Advancing Translational Sciences. Dr. Mayberry was supported by a National Research Service Award (F32DK097880) and Dr. Osborn was supported by a Career Development Award (K01DK087894) from the National Institute of Diabetes and Digestive and Kidney Diseases. The contents of this manuscript are solely the responsibility of the authors and do not necessarily
References (42)
- et al.
Predictors of medication adherence and associated health care costs in an older population with type 2 diabetes mellitus: a longitudinal cohort study
Clin Ther
(2003) - et al.
Development and evaluation of the Adherence to Refillls and Medications Scale (ARMS) among low-literacy patients with chronic disease
Value Health
(2009) - et al.
Patient-reported adherence to insulin regimen is associated with glycemic control among Japanese patients with type 2 diabetes:Diabetes Distress and Care Registry at Tenri (DDCRT 3)
Diabetes Res Clin Pract
(2013) A systematic review of adherence with medications for diabetes
Diabetes Care
(2004)Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus
Am J Med
(2005)- et al.
Medication adherence and associated hemoglobin A1C in type 2 diabetes
Ann Pharmacother
(2004) - et al.
Clinical outcomes and adherence to medications measured by claims data in patients with diabetes
Diabetes Care
(2004) - et al.
Effect of medication nonadherence on hospitalization and mortality among patients with diabetes mellitus
Arch Intern Med
(2006) - et al.
Longitudinal association between medication adherence and glycaemic control in type 2 diabetes
Diabetic Med
(2013) - et al.
Oral antihyperglycemic medication nonadherence and subsequent hospitalization among individuals with type 2 diabetes
Diabetes Care
(2004)