Artificial Intelligence–Driven Support and Self-Care Competence as Determinants of Medication Adherence in Diabetes Care: A Cross-Sectional Nigerian Study

Vollständiger Abstract

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Medication adherence among patients with diabetes remains suboptimal in low- and middle income countries, including Nigeria. Emerging digital health interventions such as AI-powered virtual support may be associated with improved adherence behaviours. This study examined self-care competence and perceived AI-powered virtual support as predictors of medication adherence among patients with diabetes. A cross-sectional survey was conducted among 450 patients recruited through multistage sampling across hospitals in Benue State, Nigeria. Standardised measures of self-care competence scale, perceived AI support scale, and medication adherence scale were analysed using correlation and regression analyses. Results showed that, self-care competence significantly predicted medication adherence (R² = .161), although some components (glucose management, physical activity, healthcare use) showed negative associations. Perceived AI-powered support demonstrated stronger predictive power (R² = .328), with social presence (β = .311, p < .001) and social interactivity (β = .142, p < .01) emerging as key predictors. The combined model explained 36.3% of variance (R² = .363). In conclusion, perceived AI-powered virtual support, particularly socially interactive features, plays a significant role in enhancing medication adherence and may complement traditional self-care strategies. It is recommended that clinicians should therefore adopt a hybrid care model that integrates traditional patient education with AI-assisted interventions. This approach can help bridge gaps caused by high patient loads and limited consultation time, while also enhancing personalised care.