Tagungsband
Design Science Research for a Resilient Future
Details zur Publikation
Autor(inn)en: | Mandviwalla, M.; Söllner, M.; Tuunanen, T. |
Herausgeber: | Mandviwalla, Munir; Söllner, Matthias; Tuunanen, Tuure |
Verlag: | Springer Cham |
Verlagsort / Veröffentlichungsort: | Basel |
Publikationsjahr: | 2024 |
Seitenbereich: | TBD |
Titel der Buchreihe: | Lecture Notes in Computer Science |
Jahrgang/Band : | 14621 |
ISBN: | 978-3-031-61174-2 |
DOI-Link der Erstveröffentlichung: |
Zusammenfassung, 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 faceto-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 (GPT3.5). 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.
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 faceto-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 (GPT3.5). 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.
