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Promoting a healthy and comprehensive diet through theory-driven large language models-based agents
Bak, Michelle Chaewon
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https://hdl.handle.net/2142/132472
Description
- Title
- Promoting a healthy and comprehensive diet through theory-driven large language models-based agents
- Author(s)
- Bak, Michelle Chaewon
- Issue Date
- 2025-10-23
- Director of Research (if dissertation) or Advisor (if thesis)
- Chin, Jessie
- Doctoral Committee Chair(s)
- Chin, Jessie
- Committee Member(s)
- Wang, Dong
- Brooks, Ian
- Diesner, Jana
- Bhat, Suma
- Department of Study
- Information Sciences
- Discipline
- Information Sciences
- Degree Granting Institution
- University of Illinois Urbana-Champaign
- Degree Name
- Ph.D.
- Degree Level
- Dissertation
- Keyword(s)
- digital health
- health behavior promotion
- Large Language Models
- Abstract
- This dissertation investigates the use of theoretical frameworks in health promotion counseling to improve Large Language Models (LLMs) for recognizing and responding to diverse motivational states. The study identifies the limited capabilities of Large Language Models (LLMs) in providing information tailored to the motivational readiness for change among individuals who are resistant or ambivalent to behavior change. Such information gap can be particularly critical given that individuals in the earlier stages of change require different types of support than those in later stages of behavior change, such as information to encourage self-assessment of one’s cognitive and affective state in relation to health behavior. To address this gap, this study integrates behavior change theories, specifically the Transtheoretical Model (TTM) and Motivational Interviewing (MI), into prompt engineering strategies to address the information needs of these individuals in adopting a healthy and comprehensive diet. The improved LLM demonstrate potential in encouraging cognitive and affective self-image assessment in relations to the health behaviors to reduce ambivalence and strengthen commitment to behavior change through targeted, psychologically grounded interactions. The improved LLM also significantly increased behavioral intention without altering knowledge or risk perception, aligned with the characteristics of individuals in the contemplation stage of the TTM. Through MI-based strategies like reflective listening and affirmations, the improved LLM helped participants reduce ambivalence and considering actionable steps to dietary change. The research lays a foundation for LLM-based digital health solutions that support personalized interventions and support the long-term maintenance of health behaviors.
- Graduation Semester
- 2025-12
- Type of Resource
- Thesis
- Handle URL
- https://hdl.handle.net/2142/132472
- Copyright and License Information
- Copyright 2025 Michelle Bak
Owning Collections
Graduate Dissertations and Theses at Illinois PRIMARY
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