Koo Jun Hyuk, Park You Hyun, Kang Dae Ryong
National Health BigData Clinical Research Institute, Yonsei University Wonju Industry-Academic Cooperation Foundation, Wonju, Republic of Korea.
Department of Biostatics, Yonsei University Graduate School, Wonju, Republic of Korea.
JMIR Aging. 2023 Jun 21;6:e41429. doi: 10.2196/41429.
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.
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.
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.
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.
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.
移动健康（mHealth）服务能够实时测量个人生物信号和环境风险因素的信息；因此，关于使用mHealth进行健康管理的研究正在积极开展。
本研究旨在确定韩国老年人使用mHealth的意愿的预测因素，并验证慢性病是否会调节已确定的预测因素对行为意愿的影响。
对500名年龄在60至75岁之间的参与者进行了横断面问卷调查。使用结构方程模型检验研究假设，并通过自抽样法验证间接效应。自抽样进行了10000次，并通过偏差校正百分位数法确认间接效应的显著性。
在477名参与者中，278名（58.3%）患有至少一种慢性病。绩效期望（β = 0.453；P = 0.003）和社会影响（β = 0.693；P < 0.001）是行为意愿的显著预测因素。自抽样结果显示，便利条件（β = 0.325；P = 0.006；95%CI 0.115 - 0.759）对行为意愿有显著的间接影响。对慢性病存在与否进行检验的多组结构方程模型显示，设备信任对绩效期望的路径存在显著差异（临界比 = -2.165）。自抽样还证实，设备信任（β = 0.122；P = 0.039；95%CI 0.007 - 0.346）对患有慢性病的人的行为意愿有显著的间接影响。
本研究通过对老年人进行基于网络的调查，探索了使用mHealth意愿的预测因素，其结果与其他将技术接受与使用统一理论模型应用于mHealth接受情况的研究相似。绩效期望、社会影响和便利条件被揭示为接受mHealth的预测因素。此外，还研究了对用于测量生物信号的可穿戴设备的信任作为患有慢性病的人的额外预测因素。这表明，根据用户特征需要采取不同的策略。
