ArticleBMC public health2025
Modeling older adults' continuance intention toward mobile health apps: a dual-path SEM-ANN approach.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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1 author.
Funding
Abstract
backgroundWith 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.
methodsBased 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.
resultsThe 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%).
conclusionsThis 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.
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