[2506.07275] Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models

Computer Science > Machine Learning arXiv:2506.07275 (cs) [Submitted on 8 Jun 2025 (v1), last revised 3 Mar 2026 (this version, v4)] Title:Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models Authors:Haochen Song, Dominik Hofer, Rania Islambouli, Laura Hawkins, Ananya Bhattacharjee, Zahra Hassanzadeh, Jan Smeddinck, Meredith Franklin, Joseph Jay Williams View a PDF of the paper titled Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models, by Haochen Song and 8 other authors View PDF HTML (experimental) Abstract:Contextual multi-armed bandit (cMAB) algorithms offer a promising framework for adapting behavioral interventions to individuals over time. However, cMABs often require large samples to learn effectively and typically rely on a finite pre-set of fixed message templates. In this paper, we present a hybrid cMABxLLM approach in which the cMAB selects an intervention type, and a large language model (LLM) which personalizes the message content within the selected type. We deployed this approach in a 30-day physical-activity intervention, comparing four behavioral change intervention types: behavioral self-monitoring, gain-framing, loss-framing, and social comparison, delivered as daily motivational messages to support motivation and achieve a daily step count. Message content is personalized using dynamic contextual factors, including daily fluctuat...