Related Experiment Video
Updated: Oct 10, 2026

Virtual Agent for Real-Time Motivational Interviewing by Integrating Adaptive Nonverbal Behavior and Language Models
Published on: December 23, 2025
Optimizing generative artificial intelligence prompts to engage urges for physical activity in middle-aged and older
Yingjia Liu1,2, Allyson Tabaczynski2, Saeed Abdullah3
1Department of Kinesiology, The Pennsylvania State University, University Park, PA 16802, United States.
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 (AI) 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 behavioral context (preparation vs. execution), benefit horizon (short-term vs. long-term), temporal focus (recalled past vs. imagined future), and behavioral target (physical activity vs. sedentary behavior). 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, 639 adults (aged 40-85 years, M = 57; 52% female) rated 80 messages. Men reported stronger urges than women, and participants with stronger baseline urges reported stronger urges after reading the messages. Age moderated the effect of context, such that the execution (vs. preparation) prompt advantage increased with age. Two significant 3-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 generative AI prompt features can evoke physical activity urges in middle-aged and older adults, supporting the development of motivationally targeted interventions.
Related Concept Videos
Optimal Arousal Theory
Inverted U-Shaped Performance Curve
The...
Motivational Bias
Drive-Reduction Theory: Push Theory of Motivation
Cognitive Development During Adulthood
Aging
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
Exercise and Cardiovascular Response
Light to moderate physical activity initiates a series of interconnected responses in the body. The heart rate modestly increases in anticipation of the workout, followed by widespread vasodilation as oxygen consumption by skeletal muscles increases. This results in decreased peripheral resistance, increased capillary blood flow, and accelerated...

