%0 Journal Article
%@ 2561-7605
%I JMIR Publications
%V 6
%N 
%P e41429
%T Factors Predicting Older People’s Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study
%A Koo,Jun Hyuk
%A Park,You Hyun
%A Kang,Dae Ryong
%+ Department of Precision Medicine, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju, 26426, Republic of Korea, 82 33 741 0391, dr.kang@yonsei.ac.kr
%K environmental risk factor
%K personalized health care service app
%K chronic disease
%K unified theory of acceptance and use of technology
%K structural equation modeling
%K older adult
%K acceptance
%K adoption
%K technology use
%K mHealth
%K mobile health
%K mobile app
%K health app
%K gerontology
%K personalized
%K health care service
%K intention to use
%D 2023
%7 21.6.2023
%9 Original Paper
%J JMIR Aging
%G English
%X Background: Mobile health (mHealth) services enable real-time measurement of information on individuals’ biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. Objective: The study aims to identify the predictors of older people’s intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. Methods: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. Results: Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=–2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. Conclusions: This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users. 
%M 37342076
%R 10.2196/41429
%U https://aging.jmir.org/2023/1/e41429
%U https://doi.org/10.2196/41429
%U http://www.ncbi.nlm.nih.gov/pubmed/37342076