Psychometric evaluation of the Adelphi Adherence Questionnaire (ADAQ©) in a type 2 diabetes mellitus population as a new measure of adherence and the complex factors influencing adherence in routine care.

Aims: To evaluate the psychometric properties of the Adelphi Adherence Questionnaire (ADAQ©) as an adherence measure in a diverse type 2 diabetes mellitus (T2DM) population in routine clinical practice.

Materials And Methods: Data were drawn from the Adelphi T2DM Disease Specific Programme™, a survey of physicians and adults with T2DM consulting in the United States, February-May 2021. Participants completed the ADAQ and single questions on medication satisfaction and convenience. Latent variable modelling (exploratory/confirmatory factor analyses, item response theory, Mokken scaling and bifactor analyses) assessed ADAQ dimensionality and composite scoring. Differential item functioning (DIF) analyses were conducted between participants taking injectable and non-injectable therapies, and with <3 or ≥3 long-term conditions. Correlational analyses with physician-reported adherence and compliance, and patient-reported treatment satisfaction, convenience and side-effects assessed construct validity. ADAQ scores by sociodemographic and clinical factors were also described.

Results: Overall, 1287 people with T2DM were included in this analysis (mean age 56.7 years [standard deviation: 12.8], 54.5% [n = 702] male). Latent variable modelling indicated a unidimensional reflective model fit, with a bifactor model confirming an 11-question essentially unidimensional composite score. Negligible DIF was found between groups. Cronbach's alpha and McDonald's omega were both ≥0.90. Moderate correlations with physician-reported adherence and compliance, and patient-reported medication convenience and satisfaction support construct validity.

Conclusions: The ADAQ shows strong construct validity and high internal consistency reliability within a heterogenous T2DM population with negligible DIF between sub-groups. Future work should focus on test-retest reliability and detecting change over time.