Optimizing Generative Artificial Intelligence Prompts to Engage Urges for Physical Activity in Middle-Aged and Older Adults

Background: Most middle-aged and older adults do not engage in sufficient physical activity. Text messages have proven effective for promoting physical activity, but little is known about how message content can engage motivational mechanisms. Purpose: This study aimed to examine how generative artificial intelligence prompts could be engineered to create messages that engage physical activity urges. Methods: Study 1 involved iterative prompt development and expert evaluations of messages, varying by physical activity context (preparation vs. execution), benefit (short-term vs. long-term), experience (past vs. future), and behavioral target (move more vs. sit less). Study 2 involved a web-based factorial experiment to assess how these factors affected urges among middle-aged and older adults. Results: In Study 1, ratings of confidence that text messages (nMessages=16) would evoke urges were moderate yet heterogenous across the experts (nExperts=15). Themes including physical activity cues and affective appeal emerged as key factors. In Study 2, 640 adults (aged 40–85 years, M = 57; 52% female) rated 80 messages. Men reported stronger urges than women, and participants with higher baseline urges reported higher urges after reading the messages. Age moderated two effects: older adults responded more favorably to execution vs. preparation prompts, and the advantage of past over future experiences diminished with age. Two significant three-way interactions showed that execution-based prompts outperformed preparation prompts, except when prompts targeted (1) long-term future benefits and (2) future physical activity experiences. Conclusions: This study identified how specific prompt features can evoke physical activity urges in middle-aged and older adults, supporting development of personalized interventions.