Motivations, Barriers, and the Potential of AI in Physical Activity Apps for US Older Adults: Interview Study.

Background: The older adult population is increasing rapidly and experiences a multitude of chronic conditions affecting quality of life. Physical activity and reduced sedentary behavior reduce adverse health conditions and help preserve functional independence and cognitive abilities, but the older adult age group has much lower levels of physical activity than recommended compared with other population demographics. Objective: The study aimed to understand older adults' motivations toward health and physical activity and the role of technology in achieving their health and physical activity goals and to explore the attitudes toward the use of generative AI in health apps. Methods: This study was qualitative and based on hour-long interviews with 20 older adults living in the United States. To elicit specific design preferences, participants were presented with visual probes of AI-generated avatars. The interviews were analyzed using thematic analysis, and the coding process drew on reflexive and grounded theory techniques. Results: Older adults primarily wished to maintain functional independence, both physical and cognitive, prioritizing baseline maintenance rather than continuous optimization. They reported satisfaction with levels of physical activity, adjusting them to chronic conditions and injuries. They were active through everyday functions of living as well as structured exercise programs with many participants using wearable devices and smartphones to help monitor their activity. They reported being motivated by social accountability and gamification, although some found the latter condescending or anxiety-inducing. Accessibility issues were mentioned frequently, including problems with touchscreens, limited viewing, difficulty navigating interfaces with clutter, complex multitouch processes for undertaking actions, and steep learning curves. Computers were preferred for certain tasks over mobile phones due to easier viewability, keyboard access, and greater security features. Personalization was highly desired in health apps. There was a willingness to use more health technology, including generative AI, and they were willing to consider AI in both motivational and informational roles in apps as long as it was personalized. AI embodiment preferences varied widely but with an emphasis on relatability. However, there was marked skepticism toward data privacy, concerns about the loss of independence and self-efficacy, and doubts about AI's accuracy. Conclusions: Older adults are primarily motivated by sustaining functional independence, although accessibility barriers in current health apps remain. Participants were open to AI for physical activity support, but expectations varied. They viewed AI as a passive "tool," a directive "coach," or a relational "companion." Considerable variation in preferences for gamification and visual design suggested that future apps require greater modularity. As skepticism over data privacy and AI accuracy remains high, greater transparency and explainability might be needed to engender trust.