Objectives Medication adherence remains a major challenge in the management of type 2 diabetes (T2D), especially in middle-income countries such as Morocco. With the rapid development of artificial intelligence, large language models, including ChatGPT, may offer new opportunities for clinical research through simulated patient profiles. This study examined the feasibility of using ChatGPT to generate culturally contextualized virtual profiles of Moroccan patients with T2D and to apply the validated Moroccan Arabic dialect version of the general medication adherence scale (GMAS). Methods: This exploratory pilot simulation study used ChatGPT-4 to generate 11 virtual profiles based on representative sociodemographic and clinical characteristics of Moroccan patients with T2D. Each profile was administered the validated Moroccan Arabic dialect version of the GMAS. Results: Four profiles (36.4%) showed high adherence, 6 (54.5%) displayed moderate adherence, and 1 (9.1%) exhibited low adherence. Expert evaluations were consistent, with all ratings ≥3 on a 4-point scale, supporting the credibility and cultural appropriateness of the profiles. The simulated patterns reflected real-world trends, with higher adherence among profiles treated with oral antidiabetic drugs, covered by health insurance, and having shorter disease duration, and lower adherence among profiles using insulin, lacking insurance, and having longer disease duration. Conclusion: ChatGPT generated credible, culturally adapted patient profiles that enabled application of a validated psychometric tool in a simulated environment. This rapid, low-cost approach may support exploratory research, healthcare training, and intervention design. Comparative studies using real patient data are needed to confirm validity.
Objectives This study was performed to evaluate the prevalence of medication adherence and its determinants among Moroccan patients with type 2 diabetes mellitus. Methods: A multicenter cross-sectional survey was conducted from February to June 2024, involving 584 patients from the Casablanca-Settat and Rabat-Sale-Kenitra regions of Morocco. Medication adherence was assessed using the general medication adherence scale. Associations between independent variables and adherence were analyzed using chi-square tests and multivariate logistic regression. Results: Medication adherence was observed in 96.2% of participants. Multivariate analysis revealed significantly lower medication adherence among patients using 2 or more oral antidiabetic drugs (adjusted odds ratio [aOR], 0.026; 95% confidence interval [CI], 0.001–0.642; p=0.026) and those with a diabetes duration of 11 to 15 years (aOR, 0.037; 95% CI, 0.001–0.956; p=0.047). Conclusion: Despite a high overall adherence rate, patients on dual or polytherapy and those with longer disease duration exhibited lower adherence in multivariate analysis. Targeted interventions are needed to improve adherence in these high-risk groups.
This systematic review and meta-analysis aimed to understand the characteristics of medication adherence interventions for older adults with chronic illnesses, and to investigate the average effect size by combining the individual effects of these interventions. Data from studies meeting the inclusion criteria were systematically collected in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The results showed that the average effect size (Hedges’ g) of the finally selected medication adherence interventions for older adults with chronic illnesses calculated using a random-effects model was 0.500 (95% confidence interval [CI], 0.342−0.659). Of the medication adherence interventions, an implementation intention intervention (using face-to-face meetings and telephone monitoring with personalized behavioral strategies) and a health belief model–based educational program were found to be highly effective. Face-to-face counseling was a significantly effective method of implementing medication adherence interventions for older adults with chronic illnesses (Hedges’ g=0.531, 95% CI, 0.186−0.877), while medication adherence interventions through education and telehealth counseling were not effective. This study verified the effectiveness of personalized behavioral change strategies and cognitive behavioral therapy based on the health belief model, as well as face-to-face meetings, as medication adherence interventions for older adults with chronic illnesses.
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