Generating Motivational Messages for Behavior Change: Encouraging Users to Be More Physically Active
Document Type
Honors Thesis
Abstract
High levels of sedentary lifestyles can cause adverse effects in individuals’ health. This has prompted researchers to analyze ways to increase physical activity, including the use of Large Language Models (LLMs) to generate motivational messages. While research has found LLMs to be feasible for this task, the findings are limited in availability and scope given that the research focuses on a conversational, chatbot setting—which is not ideal in the real world. This research assesses OpenAI’s GPT-4o mini’s (one of several models powering ChatGPT) ability to tailor messages towards a user. This is done by passing user health data to the LLM and having the LLM generate motivational messages tailored to this data. To ensure the messages are motivational, two behavioral science models are used: the Behavior Change Techniques Taxonomy and Motivational Interviewing. To mitigate hallucination when using these models, the behavioral science models are encapsulated into a knowledge graph using PathRAG. The results indicate that using knowledge graphs and behavioral science models can help LLMs generate motivational messages. However, there are still improvements that need to be made to ensure that the messages are diverse and that the LLM can make use of the whole knowledge graph.
Disciplines
Artificial Intelligence and Robotics
Publication Date
5-2025
Language
English
Faculty Mentor of Honors Project
Chengkai Li
License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Recommended Citation
Pankaj, Hananeel, "Generating Motivational Messages for Behavior Change: Encouraging Users to Be More Physically Active" (2025). 2025 Spring Honors Capstone Projects. 32.
https://mavmatrix.uta.edu/honors_spring2025/32