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This study explores the feasibility of using Large Language Models for delivering structured behavior change interventions, focusing on Brief Action Planning for sedentary lifestyles. We leveraged a zero-shot prompting strategy without fine-tuning or providing specific data to the model. In particular, we used role-play prompting to develop an intelligent agent guided by motivational interviewing principles to support goal-setting and action-planning. The agent’s performance was evaluated through simulations and user studies, assessing its adherence to Brief Action Planning protocols. Results indicate that while role-prompting Large Language Models is a promising approach to scale up time-intensive health interventions, further research is needed to mitigate notable limitations.

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Role-Play Large Language Models for Short Behavior Change Interventions: An Exploratory Study on Brief Action Planning

  • 会议论文

  • 发表日期：

    2025年

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  • Marco Bolpagni,
  • Simone De Carli,
  • Leonardo Sanna,
  • Silvia Gabrielli,
  • Mauro Dragoni

摘要

This study explores the feasibility of using Large Language Models for delivering structured behavior change interventions, focusing on Brief Action Planning for sedentary lifestyles. We leveraged a zero-shot prompting strategy without fine-tuning or providing specific data to the model. In particular, we used role-play prompting to develop an intelligent agent guided by motivational interviewing principles to support goal-setting and action-planning. The agent’s performance was evaluated through simulations and user studies, assessing its adherence to Brief Action Planning protocols. Results indicate that while role-prompting Large Language Models is a promising approach to scale up time-intensive health interventions, further research is needed to mitigate notable limitations.