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Instructional Course

Technology

Artificial Intelligence

Big Data

Clinical Practice

Machine Learning

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IC20: Future Trends in Mobile Rehabilitation (mRehab): Designing and Implementing Technology-supported Interventions for Remote Rehabilitation

Sunday, October 26, 2025
8:30 AM - 12:30 PM  
Location:  ROOM: Williford C >>> DIRECTIONS: Third Floor >>> From the Main Elevator Bank, proceed left. Williford C is the third room on your right.
This interactive session will address some of the major technological and clinical rehabilitation trends and future directions for mobile rehabilitation. Topics include: exercise adherence, artificial intelligence, conversational agents, large language models, remote therapeutic monitoring (RTM), remote physiologic monitoring (RPM), wearable and environmental sensors, co-design, clinician buy-in to new tools for patient engagement, and implementation of new technology-enabled interventions into clinics and hospital systems. This intermediate-level, half-day instructional course will cover the following cutting-edge topics: - Clinician strategies for home exercise prescription - Understanding and tracking adherence to home exercise prescriptions - Predicting outpatient adherence and success using big data - Engaging rehabilitation clinicians in the development and implementation of outpatient tracking systems - Developing and implementing AI chat solutions to support physical activity and wellness - Wearable technology for activity tracking in the home and community and integrating data into actionable insights - Development and implementation of an AI supported system for integrating and summarizing patient charts and data from wearable devices - Methods and models for translating and sustaining novel technology-enabled interventions in clinical operations PRESENTATIONS Introduction and Overview of Issues, Challenges and Opportunities for Mobile Rehabilitation Empowering Therapist’s Decision Making with Their Patients’ Data: Using Home Exercise Program Data to Inform Clinical Care RehaBot: Artificial Intelligence Enable Chat to Support Behavior Activation by People Living with Brain Injury Smart Coach: Predicting Patient Performance and Engagement Using Large Activity Data Sets Transformative AI Data Capture and Reporting: Informing Clinical Decision Making with Remote Activity and Health Metrics Panel Discussion and Wrap-up: The Future of Mobile Rehabilitation for People with Disabilities

Learning Objectives:

  • Identify socio-economic, policy and technology trends supporting the development and implementation of mobile rehabilitation solutions.
  • Discuss processes for engaging rehabilitation clinicians in the development and adoption of dashboards for patient activity data.
  • Identify the opportunities for supporting patients with brain injury to engage in at-home physical activity using AI-enabled chat routines.
  • Discuss the distinction between open-AI and closed-AI systems. Discuss the comparative performance of major commercial AI solutions in summarizing patient electronic medical records.
  • Discuss the distinction between perseverance and adherence to home exercise programs and identify patient types based on early performance.
  • Define the role of large language models (LLMs) in clinical reporting and list 3 ways they improve upon conventional data processing methods.
  • Explain 2 AI safety guardrails (e.g., transparency, human validation) that support the clinical appropriateness of AI-generated summaries.
  • Identify 3 workflow efficiencies clinicians can gain from using LLM-generated reports in rehabilitation practice.
  • Identify challenges and opportunities for implementing and sustaining innovative mRehab interventions into clinical practice.