AI Chatbot-Assisted Aerobic Exercise Guided by the Transtheoretical Model and Self-determination Theory With Machine Learning Algorithms to Provide Real-time, Personalized Feedback in Traumatic Brain Injury

This study aims to develop and evaluate an AI-driven Personalized Exercise Feedback Program (AI-PEF) to enhance health outcomes in mTBI patients. Methods: AI-PEF integrates the transtheoretical model and self-determination theory with machine learning algorithms to provide real-time, personalized feedback. A phased randomized controlled trial will be conducted: Phase I,small-Scale Pilot Study (Early Feedback): A single-arm pilot study with 5 patients will be conducted over four weeks to gather feedback on usability, engagement, and content clarity.Phase II assesses the program's impact on functional capacity, sleep quality and depressive symptoms, with 50 participants in a 2-arm randomized trial in 3 months.Phase III will assess the program's impact on functional capacity, sleep quality and depressive symptoms, with 50 participants of waitlist control evaluation in a two-arm randomized trial.

Inclusion Criteria: * Eligible participants are patients aged over 18 with mild TBI (GCS 13-15) * who can walk independently, * reside in the Greater Taipei area, * and possess sufficient Chinese or Taiwanese language proficiency to understand the trial * complete self-administered questionnaires. Exclusion Criteria: include individuals with severe medical conditions (e.g., respiratory failure, epilepsy, psychiatric disorders), musculoskeletal or neurological impairments * hindering physical activity in the 6-minute walk test, * cognitive impairments (MMSE \< 24), * frontal lobe injuries or penetrating injury causing significant psychological dysfunction. * Patients regularly engaging in moderate-to-high-intensity aerobic exercise or participating in other studies will also be excluded to avoid bias.