Investigating the Relationship Between Physical Activity and Tailored Behavior Change Messaging: Connecting Contextual Bandit with Large Language Models
Journal:
arXiv
Published Date:
Jun 8, 2025
Abstract
Machine learning approaches, such as contextual multi-armed bandit (cMAB)
algorithms, offer a promising strategy to reduce sedentary behavior by
delivering personalized interventions to encourage physical activity. However,
cMAB algorithms typically require large participant samples to learn
effectively and may overlook key psychological factors that are not explicitly
encoded in the model. In this study, we propose a hybrid approach that combines
cMAB for selecting intervention types with large language models (LLMs) to
personalize message content. We evaluate four intervention types: behavioral
self-monitoring, gain-framed, loss-framed, and social comparison, each
delivered as a motivational message aimed at increasing motivation for physical
activity and daily step count. Message content is further personalized using
dynamic contextual factors including daily fluctuations in self-efficacy,
social influence, and regulatory focus. Over a seven-day trial, participants
receive daily messages assigned by one of four models: cMAB alone, LLM alone,
combined cMAB with LLM personalization (cMABxLLM), or equal randomization
(RCT). Outcomes include daily step count and message acceptance, assessed via
ecological momentary assessments (EMAs). We apply a causal inference framework
to evaluate the effects of each model. Our findings offer new insights into the
complementary roles of LLM-based personalization and cMAB adaptation in
promoting physical activity through personalized behavioral messaging.
