ArticleFrontiers in public health2025
Bridging UTAUT and HBM: determinants of wearable device adoption among chronic disease patients.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Drivers of Continued Fitness Short Video Usage: An Integrated Model of Technology Acceptance and Health Beliefs.Behavioral sciences (Basel, Switzerland) · 2026Article
- The Effectiveness of NIRS-Based Wearable Devices in Estimating Physical Activity Intensity in Patients with Chronic Non-Communicable Diseases: A Structured Narrative Review.Medical sciences (Basel, Switzerland) · 2026Review
- Behavior change pathways by which digital wearable devices support exercise self-management in type 2 diabetes: a scoping review with machine learning-assisted text mining.Frontiers in public health · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Chronic diseases have emerged as a significant global health threat, making the effective management of these conditions crucial for improving patients' quality of life. Wearable devices, a significant innovation in digital healthcare, offer new solutions for managing the health of patients with chronic diseases. This study integrates the UTAUT model with the Health Belief Model (HBM) to analyze key factors influencing the adoption of wearable devices by patients with chronic diseases, aiming to provide a more comprehensive understanding of their behavioral patterns and motivations. Methodology: A cross-sectional survey was conducted among Chinese patients with chronic diseases, yielding 432 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to construct the analytical model and examine the effects of latent variables on patients' behavioral intention and actual use of wearable devices. Findings: The findings reveal that performance expectancy, effort expectancy, social influence, and facilitating conditions have a significant, positive influence on behavioral intention, which, in turn, positively affects actual use behavior. Performance expectancy mediates the relationships between social influence, perceived susceptibility, and perceived severity on behavioral intention. However, physical activity does not moderate the relationship between Performance Expectancy and Behavioral Intention. Conclusion: Performance expectancy, effort expectancy, social influence, and facilitating conditions are identified as key determinants of patients with chronic diseases' adoption intention. Additionally, patients' perceived severity and perceived susceptibility indirectly influence their usage intention through performance expectancy. Implications: These findings provide a theoretical foundation and practical guidance for optimizing the use of wearable devices in the management of chronic diseases. The study suggests that product development should focus on enhancing device performance, simplifying operational procedures, and strengthening social support systems.
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