Assessing User Intentions Towards Artificial Intelligence-Powered Wearable Diabetes Devices — Single Center, Cross-Sectional Study

Objective: This study examines how individuals with diabetes in Greece perceive and accept wearable technologies for diabetes management. It builds upon the Technology Acceptance Model (TAM) by incorporating both external factors (system-related) and internal factors (user-related) to better understand individuals with diabetes attitudes to adopting AI-powered devices. Materials and methods: The data were gathered from 179 individuals with diabetes through structured questionnaires. The study analyzed six factors — information quality, functionality, accessibility, user interface design, system interactivity, and orientation — and their influence on key TAM constructs: perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI). Results: The sample consisted of 179 individuals with diabetes (62% female; 78.2% type 2 diabetes), primarily aged 26–55 years. Regression analysis showed that perceived usefulness explained 31.8% of the variance in behavioral intention, while perceived ease of use explained 22.4%. Among system-related factors, accessibility, user interface design, and information quality showed statistically significant associations with perceived usefulness and behavioral intention, whereas user-related factors, including orientation and system interactivity, were not statistically significant predictors. Conclusions: The findings highlight that well-designed, functional wearables are more likely to be adopted, especially when supported by accessible infrastructure and user-friendly design.