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Background <p>With the rapid growth of the aging population, older adults in China face significant challenges in health management, and their continuance intention to use mobile health applications remains lower than that of younger users.</p> Methods <p>Based on survey data from older adults, this study employed a hybrid approach combining structural equation modeling (SEM) and artificial neural networks (ANN) to examine both facilitating and hindering factors.</p> Results <p>The results reveal that satisfaction (β = 0.42, <i>p</i> &lt; 0.001) and perceived usefulness (β = 0.31, <i>p</i> &lt; 0.01) exert significant positive effects on continuance intention, while resistance (β = -0.28, <i>p</i> &lt; 0.05) has a significant adverse effect. The integrated model explains 56.6% of the variance in continuance intention. ANN analysis further shows that satisfaction is the most critical predictor (normalized importance = 100%), followed by confirmation (37.7%), perceived usefulness (21.2%), complexity barriers (12.2%), resistance (11.6%), and privacy concerns (11.0%).</p> Conclusions <p>This study confirms the suitability of integrating ECM and IRT to explain older adults’ continuance intention toward mobile health apps. It highlights the multifactorial nature of their continuance behavior and provides theoretical and practical insights for enhancing their continued use of mobile health technologies.</p>

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Modeling older adults’ continuance intention toward mobile health apps: a dual-path SEM–ANN approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Xinxin Wang

摘要

Background

With the rapid growth of the aging population, older adults in China face significant challenges in health management, and their continuance intention to use mobile health applications remains lower than that of younger users.

Methods

Based on survey data from older adults, this study employed a hybrid approach combining structural equation modeling (SEM) and artificial neural networks (ANN) to examine both facilitating and hindering factors.

Results

The results reveal that satisfaction (β = 0.42, p < 0.001) and perceived usefulness (β = 0.31, p < 0.01) exert significant positive effects on continuance intention, while resistance (β = -0.28, p < 0.05) has a significant adverse effect. The integrated model explains 56.6% of the variance in continuance intention. ANN analysis further shows that satisfaction is the most critical predictor (normalized importance = 100%), followed by confirmation (37.7%), perceived usefulness (21.2%), complexity barriers (12.2%), resistance (11.6%), and privacy concerns (11.0%).

Conclusions

This study confirms the suitability of integrating ECM and IRT to explain older adults’ continuance intention toward mobile health apps. It highlights the multifactorial nature of their continuance behavior and provides theoretical and practical insights for enhancing their continued use of mobile health technologies.