Background
Optimal medication adherence is essential for managing type 2 diabetes (T2D), but many individuals find it challenging to adhere to their prescribed treatments. A theory-driven approach provides researchers with a structured framework to explore key factors linked to medication non-adherence. This approach offers valuable insights into behavior change and supports the development of tailored interventions aimed at improving medication adherence.
Objectives
This study used the Information-Motivation-Behavioral Skills (IMB) model to identify pivotal factors of medication adherence and establish connections between the model and A1C levels.
Methods
This cross-sectional study was conducted at five community pharmacies, where participants completed a 46-item questionnaire in Traditional Chinese from September 2023 to May 2024. Eligible participants were adults proficient in Traditional Chinese, diagnosed with T2D, and taking at least one oral diabetes medication. Demographic and clinical data (8 items), eHealth literacy (8 items), belief in medicines (10 items), self-efficacy (8 items), and medication adherence (12 items) were collected. A1C levels and the Medication Regimen Complexity Index were obtained from the National Health Insurance PharmaCloud System. Path analysis was employed to explore potential pathways linking demographic and psychosocial factors to medication-taking and A1C levels, with model fit indices evaluated and modification indices used to refine the models.
Results
Two hundred and seventy-three participants were enrolled in the study. A negative association was found between A1C levels and self-reported adherence to medication-taking behavior. Additionally, older age, lower educational attainment, fewer injectable medications, diminished concerns about medications, and higher self-efficacy were associated with greater adherence to diabetes medications. Improved medication-taking behavior was also linked to higher eHealth literacy, fewer medication concerns, and stronger beliefs in medication necessity through enhanced self-efficacy.
Conclusion
Future interventions for diabetes should prioritize boosting patients’ self-efficacy, improving eHealth literacy, alleviating medication-related concerns, and emphasizing the importance of medication adherence. Additionally, it is vital to integrate cultural adaptations and account for variations in patients’ baseline characteristics when tailoring these interventions.