Predicting mHealth use intention among Chinese adults with diabetes: an extended UTAUT2 model integrating personal innovativeness and attitude

Background Diabetes mellitus affects approximately 537 million adults globally, yet mHealth adoption for self-management remains suboptimal. This study aimed to validate an extended UTAUT2 model incorporating personal innovativeness and attitude to predict behavioral intention to use mHealth apps among Chinese adults with diabetes. Methods Data were collected from March to April 2025 via a cross-sectional survey among 445 participants. The data were analyzed using Partial Least Squares Structural Equation Modeling via SmartPLS 4. Results All proposed direct and mediating pathways were statistically significant ( p < 0.05). Personal innovativeness was the primary predictor of attitude ( β = 0.335). Effort expectancy ( β = 0.388, f 2 = 0.260) and attitude ( β = 0.324, f 2 = 0.150) demonstrated medium effect sizes on behavioral intention, while the direct effects of performance expectancy and facilitating conditions exhibited small effect sizes ( f 2 < 0.02). Furthermore, attitude mediated the relationships between these predictor variables and behavioral intention. The model explained 29.8% of the variance in attitude and 51.0% in behavioral intention. Conclusion The extended UTAUT model demonstrates explanatory power for mHealth app adoption in diabetes management by incorporating personal innovativeness and attitude. Highlighting the utility, usability, and social influence of the application, along with encouraging positive attitudes, facilitates adoption. These results provide valuable guidance for healthcare providers and developers seeking to improve mHealth engagement among people with diabetes.