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.

To determine the physical activity level and factors influencing physical activity among pregnant urban Chinese women. This prospective cross-sectional study enrolled 1056 pregnant women (18–44 years of age) in Tianjin, China. Their socio-demographic characteristics were recorded, and the Pregnancy Physical Activity Questionnaire was used to assess their physical activity during pregnancy. The data were analyzed by multinomial logistic regression with adjustment for potential confounders. Median total energy expenditure of pregnant women in each of the three trimesters ranged from 18.50 to 21.90 metabolic equivalents of task (METs) h/day. They expended 1.76–1.85 MET h/day on moderate and vigorous activities and 0.11 MET h/day on exercise. Only 117 of the women (11.1%) met the international guideline for physical activity in pregnancy (≥150 min moderate intensity exercise per week). The most frequent reason given for not being more physically active was the fear of miscarriage. Higher education level (OR: 4.11, 95% CI: 1.59–10.62), habitual exercise before pregnancy (OR: 2.14, 95% CI: 1.39–3.28), and husbands who exercised regularly (OR: 2.21, 95% CI: 1.33–3.67) significantly increased the odds of meeting the guideline (p<0.001). A low pre gravid body mass index (OR: 0.42, 95% CI: 0.20–0.87) significantly decreased the odds (p<0.001). Few urban Chinese pregnant women met the recommended physical activity guideline. They also expended little energy exercising. Future interventions should be based on the clinic environment and targeting family members as well as the subjects. All pregnant women should be targeted, not just those in high-risk groups.