Abstract
Adopting and maintaining healthy lifestyle behaviors such as regular exercise and balanced nutrition remain challenging despite their well-documented benefits for preventing chronic diseases and promoting overall well-being. Motivational Interviewing (MI) has emerged as a promising technique to address ambivalence and facilitate behavior change. However, traditional face-to-face delivery of MI interventions is limited by scalability and accessibility issues. Leveraging recent advancements in LLMs, this paper proposes an innovative approach to deliver MI-based coaching for lifestyle behavior change digitally. Following a problem-centered DSR approach, we created an initial prototype based on MI theory and qualitative user interviews using ChatGPT (GPT-3.5). We evaluated our prototype in a qualitative study. Our research outcomes include five design principles and thirteen system requirements. This research enhances the design knowledge base in LLM-based health coaching. It marks an essential first step towards designing LLM-based MI interventions, contributing valuable insights for future research in this emerging field.
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Meywirth, S. (2024). Designing a Large Language Model-Based Coaching Intervention for Lifestyle Behavior Change. In: Mandviwalla, M., Söllner, M., Tuunanen, T. (eds) Design Science Research for a Resilient Future. DESRIST 2024. Lecture Notes in Computer Science, vol 14621. Springer, Cham. https://doi.org/10.1007/978-3-031-61175-9_6
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