{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132472"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132472","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Promoting a healthy and comprehensive diet through theory-driven large language models-based agents","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Bak, Michelle Chaewon"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Information Sciences","degree_department":null,"school":null,"contributors":["Chin, Jessie","Wang, Dong","Brooks, Ian","Diesner, Jana","Bhat, Suma"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["digital health","health behavior promotion","Large Language Models"],"languages":["en"],"rights":["Copyright 2025 Michelle Bak"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132472","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chin, Jessie","Wang, Dong","Brooks, Ian","Diesner, Jana","Bhat, Suma"]},{"key":"dc:creator","label":"Author","values":["Bak, Michelle Chaewon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-10-23"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Information Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["digital health","health behavior promotion","Large Language Models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Michelle Bak"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132472"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Michelle Bak, accepted the attached license on 2025-10-20 at 19:42.","The student, Michelle Bak, submitted this Dissertation for approval on 2025-10-20 at 19:52.","This Dissertation was approved for publication on 2025-10-23 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22828 on 2026-02-19 at 18:24:26"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Promoting a healthy and comprehensive diet through theory-driven large language models-based agents"]}]}],"canonical_facts":{"dc:contributor":["Chin, Jessie","Wang, Dong","Brooks, Ian","Diesner, Jana","Bhat, Suma"],"dc:creator":["Bak, Michelle Chaewon"],"dc:date":["2025-12","2025-10-23"],"dc:description":["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.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2026-02-19 without embargo terms","The student, Michelle Bak, accepted the attached license on 2025-10-20 at 19:42.","The student, Michelle Bak, submitted this Dissertation for approval on 2025-10-20 at 19:52.","This Dissertation was approved for publication on 2025-10-23 at 09:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22828 on 2026-02-19 at 18:24:26"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132472"],"dc:language":["en"],"dc:rights":["Copyright 2025 Michelle Bak"],"dc:subject":["digital health","health behavior promotion","Large Language Models"],"dc:title":["Promoting a healthy and comprehensive diet through theory-driven large language models-based agents"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Information Sciences"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}