Potential Application Value and Needs of Using Generative AI Videos for Health Management for Older Adults: Qualitative Study

TL;DR

This study explores how older adults can use AI-generated videos to manage their health, identifying key needs and challenges in making these tools accessible and effective.

Contribution

The study introduces a three-layer model of needs for older adults using GenAI videos in health management, offering actionable design and integration recommendations.

Findings

Older adults require technical accessibility improvements to use GenAI videos independently.

Age-friendly design adaptations are essential for better usability of GenAI tools.

Integration with professional medical resources is needed to enhance the relevance and trustworthiness of GenAI videos for health management.

Abstract

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.

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.

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.

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.

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.

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