Educating older adults to use technologies for health management in social settings: perspectives of older adults and community workers.

BACKGROUND: With the acceleration of global aging, the potential of digital technologies for self-care management among older adults has surged. Older adults often face numerous difficulties and challenges when seeking health information, including issues related to technology adaptability and the complexity of the social learning environment. In addressing these challenges, Bandura's Social Learning Theory framework offers a robust theoretical foundation for resolving these dilemmas. METHODS: This research employs focus groups and semi-structured interviews with 20 older adults and 6 community workers on four occasions. Qualitative data were analyzed using Braun and Clarke's six-phase reflexive thematic analysis to identify key themes. RESULTS: Older adults learn to utilize technology for health management through peer imitation and community workers. Community workers' lack of systematic training results in a limited range of technologies. The complexity of technology and the absence of family support were found to weaken older adults' self-efficacy, while community workers struggled to provide in-depth guidance due to resource constraints. While encouraging short-term participation, over-reliance on incentives can lead to instrumental motivation. Both older adults and workers called for an age-friendly design, highlighting inadequate community equipment and weak promotional activities as critical issues that need to be addressed. CONCLUSION: The needs of older adults and community workers in utilizing technology for health exhibit a multi-layered interaction. Older adults require simplified technologies and social support, and community workers demand technical training and policy backing. The study's findings support a reframing of immediate recommendations towards age-friendly design, structured training for community workers, and policy support for resources. Developing artificial intelligence-assisted tools is a potential long-term direction inferred from the broader need for scalable and personalized support, thereby narrowing the technology gaps for older adults.