AI-Enhanced Telerehabilitation Program Using Automated Video Analysis and Personalized Feedback on Pain, Disability, Mobility, Endurance, for Chronic Non-Specific Low Back Pain in College Students.

Summary

This study tests whether an artificial intelligence (AI)-enhanced telerehabilitation program can effectively treat chronic non-specific low back pain in college students.

Low back pain affects 40-52% of university students due to prolonged sitting during lectures and study sessions, poor posture from laptop use, and lack of physical activity. While exercise therapy is the recommended treatment, many students cannot access traditional physiotherapy due to cost, scheduling conflicts, and location barriers.

This randomized controlled trial compares three treatment approaches: (1) AI-enhanced telerehabilitation with automated video analysis and personalized feedback, (2) standard telerehabilitation with video instructions only, and (3) usual care. The AI system uses computer vision technology (Google MediaPipe Pose) to analyze exercise videos through a standard webcam or smartphone, automatically tracking joint movements, counting repetitions, and providing real-time feedback on exercise form.

College students with chronic low back pain (lasting more than 3 months) will be randomly assigned to one of the three groups. The AI-enhanced group will receive personalized exercise programs delivered remotely, with the AI system monitoring their performance and physiotherapists providing guidance through video consultations.

The study will measure changes in pain levels, disability, physical function, trunk muscle endurance, and quality of life over 8 weeks of treatment and 3 months of follow-up. Researchers will also evaluate how well participants stick to their exercise programs and how easy the technology is to use.

This research aims to determine if AI technology can make remote physiotherapy more effective and accessible for college students, potentially transforming how young adults receive treatment for back pain and improving their long-term health outcomes.

Conditions

• Non Specific Low Back Pain

Interventions

OTHER

AI Based Exercises

This intervention combines structured exercise therapy with artificial intelligence-powered movement analysis using Google MediaPipe Pose technology. Participants perform prescribed exercises (flexibility, core stability, functional strength) while a computer vision system analyzes their movements through standard webcam or smartphone cameras. The AI provides real-time feedback on exercise form, automatically counts repetitions, measures hold times, and flags technique errors. Physiotherapists review AI-generated performance data and provide personalized corrective guidance through scheduled video consultations. The platform delivers 8-week progressive exercise programs specifically designed for chronic non-specific low back pain, integrating objective movement monitoring with human therapeutic oversight to optimize adherence and clinical outcomes.

OTHER

Standard Telerehabilitation

This intervention delivers structured exercise therapy through pre-recorded video instructions without AI monitoring or automated feedback. Participants receive the same exercise components as the AI-enhanced group (flexibility, core stability, functional strength) but rely on video demonstrations and written instructions for proper technique. Physiotherapists conduct periodic consultations via video conferencing to monitor progress, provide guidance, and adjust exercise programs based on visual observation and participant self-reports. The 8-week progressive program represents current standard telerehabilitation practice for chronic non-specific low back pain, serving as an active comparator to isolate the specific effects of AI-enhanced movement analysis and real-time feedback systems.

Sponsors & Collaborators

• Galgotias University

  collaborator UNKNOWN

• Majmaah University

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

TREATMENT

Masking

DOUBLE

Model

FACTORIAL

Eligibility

Min Age

18 Years

Max Age

30 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-10-01

Primary Completion

2026-08-01

Completion

2026-09-01