Evaluating the Use of Generative AI Videos for Health Self-Management of Older Adults: Mixed Methods Study

Background: Aging is a pressing global issue, and older adults need to build up their knowledge to manage their health. Insufficient self-efficacy and low acceptance of technology hinder their ability to use emerging technologies for self-management. Objective: This study explored the potential of generative artificial intelligence (GenAI) videos in the health management of older adults. Methods: We developed a video-based GenAI prototype, AIHealthV. This study used a mixed methods approach, enrolling 20 older adult participants (aged 60 to 80 years) in 3 rounds of iterative workshops. Data collection included pre- and postquestionnaires, in-depth interviews, prompt text records, and the generated video content. Qualitative data were analyzed using the 6-stage reflexivity thematic analysis method by Braun and Clarke, and quantitative data were analyzed using the Wilcoxon signed-rank test. Results: The findings revealed that GenAI videos can enhance older adults’ self-efficacy and technology acceptance, reduce cognitive load, and simultaneously meet their health management needs. The multimodal content generated by GenAI makes health information more comprehensible and thus improves the accessibility of health knowledge. Following the workshop, the interaction between older adults and AIHealthV exhibits a trend of exploration, adaptation, and verification. Conclusions: Despite ethical, privacy, and usability concerns, AIHealthV has been proven to be beneficial in improving the health management capabilities of older adults. This paper also provides practical insights for developing artificial intelligence health tools tailored to older adults as GenAI tools continue to prevail.