Currently submitted to: Journal of Medical Internet Research
Date Submitted: Feb 19, 2026
Open Peer Review Period: Feb 20, 2026 - Apr 17, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Unpacking the Role of Personal Innovativeness and Attitude in Driving mHealth Adoption for Diabetes Self-Management: Cross-Sectional Survey Study
ABSTRACT
Background:
Diabetes mellitus affects approximately 537 million adults globally, with projections indicating an increase to 643 million by 2030. Mobile health applications (mHealth apps) offer promising support for diabetes self-management, yet adoption rates remain low. Understanding the factors influencing patients' intentions to use mHealth apps is essential for designing effective interventions.
Objective:
To develop and empirically validate an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model incorporating personal innovativeness and attitude to explain behavioral intention to use mHealth apps for diabetes management.
Methods:
A cross-sectional survey was conducted with 485 Chinese adults. The measurement and structural models were assessed using Partial Least Squares Structural Equation Modeling (PLS_SEM).
Results:
Performance expectancy (β = .110, t = 3.401, P < .001), effort expectancy (β = .226, t = 5.942, P < .001), social influence (β = .112, t = 2.953, P =.002), facilitating conditions (β= .095, t = 2.476, P =.007), and personal innovativeness (β = .365, t = 9.280, P < .001) significantly influenced attitudes toward mHealth apps. Performance expectancy (β = .069, t = 2.239, P =.01), effort expectancy (β = .377, t = 8.939, P < .001), social influence (β = .123, t = 3.279, P < .001), and personal innovativeness (β = .116, t = 3.459, P < .001) significantly affected behavioral intention, while facilitating conditions did not (β = .041, t = 1.418, P =.07). Attitude significantly influenced behavioral intention (β = .337, t = 8.010, P < .001). Additionally, attitude significantly and positively mediated the relationships between performance expectancy (β = .037, t = 3.128, P < .001), effort expectancy (β = 0.076, t = 4.568, P < .001), social influence (β = .038, t = 2.775, P =.003), facilitating conditions (β = .032, t = 2.433, P =.007), and personal innovativeness (β = .123, t = 5.787, P < .001) and the behavioral intention to use mHealth apps for diabetes management. The model explained 31.7% of the variance in attitude and 51.5% in behavioral intention.
Conclusions:
The extended UTAUT model effectively explains mHealth app adoption for diabetes management by integrating personal innovativeness and attitude. Emphasizing app utility, usability, social influence, and fostering positive attitudes can enhance adoption. These insights inform healthcare providers and developers aiming to increase mHealth engagement among patients with diabetes.
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