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: A Randomized Controlled Trial
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 117
- 试验地点
- 1
- 主要终点
- Numerical pain rating scale
研究概览
简要总结
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.
详细描述
Chronic non-specific low back pain (CNSLBP) has emerged as a significant health concern among college students, with international studies reporting prevalence rates between 40-52%. This high incidence is attributed to the modern academic environment, characterized by prolonged static postures during lectures and study sessions, extensive use of laptops and handheld devices leading to poor trunk alignment, and generally low levels of structured physical activity resulting in deconditioning of core and postural muscles.
The impact of CNSLBP in college students extends beyond physical discomfort, affecting academic performance, causing absenteeism, limiting recreational participation, and potentially leading to persistent pain patterns in adulthood. Current evidence-based management guidelines recommend multidisciplinary approaches emphasizing structured exercise therapy, self-management education, and postural retraining, with particular focus on flexibility, core stability, and functional strength exercises.
However, significant barriers prevent college students from accessing optimal care. These include logistical challenges such as academic scheduling conflicts, economic constraints related to repeated physiotherapy visits, and geographical accessibility issues. Consequently, many students resort to unsupervised home exercise programs that, while cost-effective and flexible, lack real-time monitoring and professional guidance, often resulting in incorrect technique, poor adherence, and suboptimal outcomes.
TECHNOLOGICAL INNOVATION
Recent advances in artificial intelligence (AI) and computer vision technology offer promising solutions to bridge this care gap. Markerless motion capture systems, particularly Google's MediaPipe Pose and OpenPose, can analyze human movement using standard cameras to identify skeletal landmarks, track joint angles, assess posture, and detect movement deviations in real-time. These systems demonstrate approximately 85% accuracy for gross movement tracking and exercise repetition counting, making them suitable for clinical rehabilitation applications.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Double (Participant, Outcomes Assessor)
盲法说明
Outcome assessors evaluating primary endpoints (pain, disability, mobility, trunk endurance) are blinded to group allocation to prevent measurement bias. Independent research assistants conducting assessments remain unaware of participants' intervention assignments.
Participants cannot be blinded due to intervention nature - they know whether receiving AI-enhanced telerehabilitation, standard telerehabilitation, or usual care. Physiotherapists delivering interventions cannot be blinded as they provide group-specific treatments.
Data analysts remain blinded to group codes during statistical analysis until primary analyses complete. Principal investigator maintains randomization knowledge for safety monitoring but doesn't participate in outcome measurements.
入排标准
- 年龄范围
- 18 Years 至 30 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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.
- •Access to suitable device and reliable internet.
- •Informed consent obtained.
排除标准
- •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.
- •BMI ≥ 35 kg/m² (could impair AI pose detection).
- •Inability to understand English instructions or complete measures.
研究组 & 干预措施
AI-Enhanced Telerehabilitation
Participants receive personalized exercise programs delivered through a custom telerehabilitation platform incorporating AI-based movement analysis using Google MediaPipe Pose computer vision technology. The system monitors exercise performance through participants' webcams or smartphones, providing real-time feedback on form, automatically counting repetitions, measuring hold times, and flagging technique errors. AI-generated performance data is reviewed by physiotherapists who provide personalized corrective guidance through scheduled video consultations. Exercises focus on flexibility, core stability, and functional strength targeting chronic non-specific low back pain. The intervention combines objective AI monitoring with human therapeutic guidance to optimize exercise adherence and technique.
干预措施: AI Based Exercises (Other)
Exercise-Only Telerehabilitation
Participants receive structured exercise programs delivered via pre-recorded video instructions without AI monitoring or automated feedback. This represents current standard telerehabilitation practice, with periodic physiotherapist consultations conducted through video conferencing sessions. Exercise programs include the same flexibility, core stability, and functional strength components as the AI-enhanced group, but without objective movement analysis or real-time form correction. Therapists rely on visual observation during video sessions and participant self-reports to monitor progress and provide guidance. This arm serves as an active control to isolate the specific effects of AI-enhanced monitoring and feedback.
干预措施: Standard Telerehabilitation (Other)
Educational Control Group
This control intervention represents standard medical care typically provided to college students with chronic non-specific low back pain. Participants receive general advice on activity modification, recommendations for over-the-counter pain medications (NSAIDs, acetaminophen), basic exercise suggestions, and routine follow-up appointments as clinically indicated. No structured exercise program, telerehabilitation platform, or specialized physiotherapy intervention is provided. Participants may seek additional healthcare services as they normally would, including visits to primary care physicians, specialists, or other healthcare providers. This arm serves as a control group to evaluate the effectiveness of both telerehabilitation interventions against current standard medical management practices.
结局指标
主要结局
Numerical pain rating scale
时间窗: From enrollment to the end of treatment at 6 week and 3 months
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.
次要结局
- 5 times Sit to Stand Test(From enrollment to the end of treatment at 6 weeks and 3 months)
- Roland-Morris Disability Questionnaire (RMDQ)(From enrollment to the end of treatment at 6 weeks and 3 months)
- Timed Up and Go (TUG) Test(From enrollment to the end of treatment at 6 weeks and 3 months)
- Prone Plank Test(From enrollment to the end of treatment at 6 weeks and 3 months)
研究者
faizan kashoo, PT
PhD Scholar
Majmaah University