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.
Age 18-30 years, currently enrolled in undergraduate or postgraduate study.
Diagnosis of non-specific LBP for at least 3 months.
Baseline pain intensity between 3 and 7 (NRS).
Ability and willingness to perform prescribed exercises and participate in video conferencing.
Specific causes of LBP (e.g., fracture, tumor, infection, inflammatory disease).
Recent spinal surgery or confirmed disc herniation (within past year).
Neurological deficits or severe comorbid conditions contraindicating exercise.
Pregnancy or current participation in another structured LBP program.
Factorial
AI Based Exercises
Other interventionThis 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.
Standard Telerehabilitation
Other interventionThis 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.
Numerical pain rating scale
A unidimensional measure of pain intensity in which participants rate their average low back pain over the past week on an 11-point scale from 0 ("no pain") to 10 ("worst imaginable pain"). The NRS is valid, reliable, and sensitive to clinical change in chronic low back pain populations.
Roland-Morris Disability Questionnaire (RMDQ)
A 24-item self-report inventory assessing functional disability due to low back pain. Each affirmative response (e.g., "I stay at home most of the time because of my back") scores one point; total scores range from 0 (no disability) to 24 (maximum disability). The RMDQ demonstrates excellent test-retest reliability and responsiveness in non-specific low back pain.
Timed Up and Go (TUG) Test
An objective measure of basic functional mobility and fall risk. Participants rise from a seated position, walk 3 meters at a comfortable pace, turn, return, and sit down; time to completion is recorded in seconds. The TUG test is reliable, valid, and sensitive to changes in dynamic balance and gait speed in musculoskeletal populations.
Prone Plank Test
An assessment of trunk muscular endurance. Participants assume a prone forearm plank position with elbows under shoulders and body in a straight line from head to heels and hold as long as possible; duration is recorded in seconds. The prone plank test correlates with core stability and functional performance in chronic low back pain.
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Majmaah University
Lead sponsor
Galgotias University
Collaborator
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