Background: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults’ health care to address growing, complex, and challenging health needs. This study aimed to investigate perspectives on and acceptability of the use of AI-led health technologies among older adults and the potential challenges that they face in adopting them. The findings from this inquiry could inform the designing of more acceptable and user-friendly AI-based health technologies. Objective: The objectives of the study were (1) to investigate the attitudes and perceptions of older adults toward the use of AI-based health technologies; (2) to identify potential facilitators, barriers, and challenges influencing older adults’ preferences toward AI-based health technologies; and (3) to inform strategies that can promote and facilitate the use of AI-based health technologies among older adults. Methods: This study adopted a qualitative descriptive design. A total of 27 community-dwelling older adults were recruited from a local community center. Three sessions of semistructured interviews were conducted, each lasting 1 hour. The sessions covered five key areas: (1) general impressions of AI-based health technologies; (2) previous experiences with AI-based health technologies; (3) perceptions and attitudes toward AI-based health technologies; (4) anticipated difficulties in using AI-based health technologies and underlying reasons; and (5) willingness, preferences, and motivations for accepting AI-based health technologies. Thematic analysis was applied for data analysis. The Theoretical Domains Framework and the Capability, Opportunity, Motivation, and Behavior (COM-B) model behavior change wheel were integrated into the analysis. Identified theoretical domains were mapped directly to the COM-B model to determine corresponding strategies for enhancing the acceptability of AI-based health technologies among older adults. Results: The analysis identified 9 of the 14 Theoretical Domains Framework domains—knowledge, skills, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, intentions, goals, and emotion. These domains were mapped to 6 components of the COM-B model. While most participants acknowledged the potential benefits of AI-based health technologies, they emphasized the irreplaceable role of human expertise and interaction. Participants expressed concerns about the usability of AI technologies, highlighting the need for user-friendly and tailored AI solutions. Privacy concerns and the importance of robust security measures were also emphasized as critical factors affecting their willingness to adopt AI-based health technologies. Conclusions: Integrating AI as a supportive tool alongside health care providers, rather than regarding it as a replacement, was highlighted as a key strategy for promoting acceptance. Government support and clear guidelines are needed to promote ethical AI implementation in health care. These measures can improve health outcomes in the older adult population by encouraging the adoption of AI-driven health technologies.

Objectives Frailty and sleep duration complaints are both prevalent and often coexist among older adults. The purpose of this study was to examine the prospective association between sleep duration and frailty risk in a nationally representative cohort study. Design Prospective cohort study, ten-year follow-up. Setting Community-based setting in 23 provinces of China. Participants A total of 7623 older adults age 65 and over without frailty at baseline were included in the analysis. Measurements The participants were divided into three groups according to self-reported sleep duration: short (≤6 hours per day), middle (>6 but <10 hours per day) and long (≥10 hours per day). Frailty was measured according to the accumulation of health deficits by the construction of a frailty index of 38 items with 0.25 as the cutoff. A Cox proportional hazard model, a competing risk model and a generalized estimating equation (GEE) model with multiple adjustments were performed to evaluate the association between sleep duration and frailty risk. Results During a median follow-up period of 4.4 years (IQR 2.9–9.0), 2531 (33.2%) individuals developed frailty. Compared with participants with middle sleep duration, the risk of frailty was increased among participants with long sleep duration (HR 1.26, 95% CI 1.14–1.38) in the fully adjusted Cox proportional hazard model. However, short sleep duration was insignificantly associated with frailty risk. The competing risk model and the GEE model yielded similar results. Conclusion Long sleep duration is significantly associated with frailty incidence among older adults even after adjustment for confounding factors. This study provides reinforcing longitudinal evidence for the need to design sleep quality improvement interventions in health care programs to prevent frailty among older adults.