ArticleDigital health
Impact of short-video health information on older adults' health adoption intentions: A mixed-methods study of a dual-path model.
Article in Digital health. 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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1 citing paper in PubMed.
- Psychological factors associated with Chinese users' adoption of AI-generated health education short videos: a PLS-SEM-ANN-IPMA study.Frontiers in psychology · 2026Article
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3 authors.
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Abstract
Background: With the rapid spread of short health videos, older adults frequently encounter commercialized health appeals. However, there is a lack of research on the mechanisms that drive cognitive and emotional responses and adoption decisions in such situations. Methods: Based on the Health Belief Model (HBM) and the Heuristic-Systematic Model (HSM), this study develops a dual-path model to explain how intrinsic health needs and external persuasion cues jointly shape older adults' emotions and behavioral intentions. Study 1 used a cross-sectional survey of older adults (aged >60 years) to test the structural relationships using the partial least squares structural equation model (PLS-SEM). Study 2 established causal evidence through a 2 × 2 between-subjects randomized experiment among active older short-video users. Results: Study 1's results show a positive correlation between chronic health anxiety and adoption intention through expectation trust (95% CI: [0.100, 0.191]). Conversely, Study 2 reveals that experimentally induced acute anxiety reduces expectation trust, thereby weakening adoption intention (95% CI: [-0.212, -0.071]). The heuristic cues positively affect adoption intention through source trust (95% CI: [0.101, 0.182]). In addition, media literacy attenuates the impact of heuristic cues on source trust (95% CI: [-0.315, -0.179]), and adult children's intervention weakens the relationship between trust and adoption intention (95% CI: [-0.289, -0.149]). Conclusion: Digital health adoption intention among older adults is a complex process driven jointly by internal health vulnerabilities and external heuristic triggers. Safeguarding this population requires a collaborative strategy: integrating strict algorithmic supervision, implementing precision literacy education to build cognitive shields, and actively empowering intergenerational family networks as decision gatekeepers.
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