A Study on Influencing Factors of Mobile Health Application Use Behavior Among Young-Old Adults in Guangzhou Based on the Health Belief Model——A Partial Least Squares Structural Equation Modeling Analysis

Authors

Chen Xuejiao student
Muiyee cheok

Keywords:

Health Belief Model, Mobile Health Adoption, Young-Old Adults, Population Aging, PLS-SEM

Abstract

Global population aging has become a critical public health issue, while China is confronted with the unique “aging before affluence” dilemma and the world’s largest elderly population. Mobile health applications serve as cost-effective tools for seniors’ chronic disease management and daily health monitoring, yet their adoption rate remains low among urban young-old adults aged 60–74. This study employs the complete six-dimensional Health Belief Model to examine factors affecting mHealth usage behavior among Guangzhou’s young-old adults. A total of 400 valid questionnaires were gathered through stratified mixed sampling and analyzed with PLS-SEM. Outcomes indicate perceived susceptibility, self-efficacy, perceived benefits, action cues and perceived severity significantly boost usage, whereas perceived barriers inhibit adoption. The model presents satisfactory fit with R²=0.443 and SRMR=0.041. This research enriches localized empirical evidence of the Health Belief Model in digital health research for young-old adults and provides targeted optimization references for mHealth developers to improve elderly users’ digital health experience.

Author Biography

Chen Xuejiao, student

Global population aging has become a critical public health issue, while China is confronted with the unique “aging before affluence” dilemma and the world’s largest elderly population. Mobile health applications serve as cost-effective tools for seniors’ chronic disease management and daily health monitoring, yet their adoption rate remains low among urban young-old adults aged 60–74. This study employs the complete six-dimensional Health Belief Model to examine factors affecting mHealth usage behavior among Guangzhou’s young-old adults. A total of 400 valid questionnaires were gathered through stratified mixed sampling and analyzed with PLS-SEM. Outcomes indicate perceived susceptibility, self-efficacy, perceived benefits, action cues and perceived severity significantly boost usage, whereas perceived barriers inhibit adoption. The model presents satisfactory fit with R²=0.443 and SRMR=0.041. This research enriches localized empirical evidence of the Health Belief Model in digital health research for young-old adults and provides targeted optimization references for mHealth developers to improve elderly users’ digital health experience.

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