What type of study is this?

This is a Single-arm Clinical Trial study.

September 5, 2025Open Access

Clinical and Biomechanical Effects of AI-Supervised Exercise Telerehabilitation on Patients with Nonspecific Chronic Low Back Pain: A Single-arm Clinical Trial

PLPengcheng LuFujian Medical University ZZZhuodong Zhang JHJingjing HuMinzu University of China

Key Points

Patients with nonspecific chronic low back pain showed significant pain reduction and improved function after the AI-supervised exercise telerehabilitation program.
The intervention resulted in enhanced muscle performance, as indicated by increased lumbar multifidus and transversus abdominis thickness recorded via ultrasound imaging.
Using OpenSim for musculoskeletal modeling revealed decreased compressive loads on lumbar segments during gait, suggesting improved spinal stability.
Real-time AI feedback during exercises fostered proper technique, highlighting a potential for telehealth to enhance rehabilitation accessibility for those with chronic pain.

Abstract

Abstract Background Nonspecific chronic low back pain (NSCLBP) is a major cause of disability worldwide, and the long-term efficacy of traditional treatments is limited. Although highly recommended, core stabilization exercises are often limited by adherence and access to supervised programs. Recent advances in artificial intelligence (AI) have created new opportunities for remote, real-time supervision of exercise. This study aims to evaluate the clinical and biomechanical effects of a remote AI-supervised virtual exercise program (RAVEP) on patients with NSCLBP. Methods Fourteen patients with NSCLBP underwent a four-week RAVEP intervention, which was delivered via a smartphone application that integrated human key-point recognition and real-time feedback. The program incorporated breathing, core strengthening, and McKenzie exercises. The effects before and after the intervention were assessed using ultrasound imaging of the lumbar multifidus (LM) and transversus abdominis (TrA), gait analysis using 3D motion capture and surface electromyography, and musculoskeletal modeling using a full-body lumbar spine model in OpenSim. The model was scaled using patient-specific anthropometric data, and the muscle properties were calibrated using ultrasound-derived muscle thickness to assess personalized lumbar kinematics, intervertebral loads, and muscle forces and activations. In addition, the evaluation of pain and clinical function was conducted using the Visual Analog Scale (VAS), Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), and Timed Up and Go (TUG) tests. Results Following the RAVEP, patients with NSCLBP showed significant improvements in both pain (VAS) and clinical function (ODI, RMDQ, TUG) (p < 0.01). Ultrasound assessment revealed increased LM and TrA thickness, as well as contraction ratios after the intervention. The results of the OpenSim simulation indicated significant increases in LM force (p < 0.05), and decreased compressive and torsional loads at L4-L5 and L5-S1 during gait. Kinematic results showed more symmetrical lumbar movement. Increased activation of deep core muscles was found; however, the differences in activation did not reach statistical significance. Discussion Our study showed that the RAVEP could significantly improve the pain, functioning, and core muscle performance in NSCLBP patients. AI-supervised real-time feedback in the RAVEP helped patients to perform their home exercises properly, which improved lower back stability and lessened stress on the spine. Personized musculoskeletal modeling and simulation provide a new understanding of how virtual rehabilitation works. This study demonstrates that RAVEP may serve as an effective and accessible strategy for the management of NSCLBP.

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