ArticleJMIR aging2026
Potential Application Value and Needs of Using Generative AI Videos for Health Management for Older Adults: Qualitative Study.
Article in JMIR aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Population aging has emerged as a global concern, older adults' ability to access health knowledge and manage their well-being impacts their health outcomes. In the artificial intelligence era, generative artificial intelligence (GenAI) videos hold promise for enhancing geriatric health management. However, their potential and the needs of older adults in using GenAI videos for health-related purposes deserve a more in-depth investigation. Objective: This study aims to explore the application potential and multifaceted needs of older adults in using GenAI videos for health information acquisition and management, while providing actionable recommendations for future aging-friendly GenAI video tools. Methods: A qualitative approach was adopted. Twenty older adults (aged ≥60 y) with basic digital literacy were recruited from communities for participation in iterative GenAI video workshops. Semistructured interviews were conducted. Thematic analysis, following Braun and Clarke's 6-phase reflexive framework, was used to identify key themes from interview transcripts. Results: Our results have identified a 3-layer hierarchical structure of needs when older adults interact with GenAI videos. The first layer is technical accessibility, which suggests the direct barriers preventing them from operating GenAI tools independently. The second layer is age-friendly design, which depicts the needs of age-friendly design adaptations, which can help older adults to better use GenAI features. The final layer is integration with professional resources, highlighting the need for integrating professional medical information resources with GenAI tools, so that the GenAI videos can be more appropriately used for health management purposes. By triangulating the core needs of older adults, the capabilities of GenAI, and their remaining gaps, we have found that the existing gaps limit the extent to which the health needs of older adults are met, and also inversely restrain the acceptance and use of GenAI videos by older adults. Conclusions: This study explored the potential and gaps of using GenAI videos in older adults' health management. GenAI videos are prominent for self-management of health, but our triangular model reveals coexisting opportunities and challenges. While the core needs drive the development directions of GenAI video technologies, our study suggests that GenAI health videos can benefit from the improvements in technical capabilities, service innovation, localization, and integration of health resources.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.