Development and evaluation of an large language model-integrated chatbot intervention for physical activity habit formation in adults with prehypertension

Background: Individuals with prehypertension are at risk of developing hypertension, which affects many adults globally. Sustained physical activity (PA) can lower blood pressure, but maintaining long-term behavior change remains difficult. While PA habit formation interventions are promising, they face issues with scalability and accessibility. At the same time, behavior change chatbots have appeared, but their development often lacks systematic methods. Additionally, optimizing large language models (LLMs) to improve chatbot efficiency and reduce costs still needs more research. Objective: This study introduces HabitBot, an LLM-integrated chatbot designed to foster PA habits in prehypertensive adults. HabitBot leverages LLMs for seamless interactions and integrates multidisciplinary insights, theoretical frameworks, and evidence to enhance the behavior change process. Methods: HabitBot was developed through a systematic five-phase process: Phase 1, needs assessment via multidisciplinary discussions; Phase 2, literature review to identify relevant behavior change theories; Phase 3, selection of effective behavior change techniques (BCTs); Phase 4, intervention mapping for prototype design; and Phase 5, usability testing and focus group interviews for refinement. Results: The process led to eight identified user needs and synthesized the Health Action Process Approach with Habit Formation Theory. Twelve effective BCTs were selected. The prototype was developed and refined across six dimensions based on user feedback. Evaluations indicated high usability, with a mean chatbot usability score of 3.84 (SD 0.82). Conclusion: HabitBot integrates behavior change strategies with advanced LLM technology, representing a novel approach in chronic disease prevention. Future research should assess its long-term impact and generalizability.