"The Effectiveness of Virtual Reality and Augmented Reality in the Management of Chronic Musculoskeletal Disorders: A Systematic Review" (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic musculoskeletal disorders (CMDs), such as osteoarthritis, rheumatoid arthritis, and chronic lower back pain, are leading causes of disability and pose significant health and economic burdens worldwide. Traditional rehabilitation methods, including physical therapy and pharmacological treatments, often face challenges related to patient adherence and long-term effectiveness. Emerging technologies like virtual reality (VR) and augmented reality (AR) have been proposed as innovative rehabilitation tools to enhance patient engagement, reduce pain, and improve mobility. OBJECTIVE This systematic review evaluates the effectiveness of VR and AR in managing CMDs, focusing on their impact on pain relief, functional mobility, psychological well-being, and long-term rehabilitation outcomes. METHODS : A systematic search was conducted in PubMed, PEDro, Cochrane, and Scopus for randomized controlled trials (RCTs) investigating VR or AR interventions in CMD rehabilitation. The inclusion criteria were adult patients with CMDs, VR/AR-based interventions, and validated outcome measures assessing pain, kinesiophobia, disability, balance, or depression. Studies were evaluated for quality using the PEDro scale and the Downs and Black checklist RESULTS From 388 identified studies, 8 RCTs met the inclusion criteria, comprising 621 participants aged 18-75 years. The PEDro scale yielded an average study quality score of 6.75/10, with two studies classified as "excellent" (9/10), four as "good" (6-7/10), and one as "fair" (4/10). VR interventions significantly reduced pain intensity in CMD patients, with studies reporting improvements on the Visual Analog Scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). AR-based rehabilitation demonstrated faster recovery times and enhanced functional mobility in post-surgical patients. Psychological benefits, including reduced anxiety and increased motivation, were also observed, particularly in immersive VR environments. However, methodological heterogeneity across studies limited direct comparisons, and no study directly compared AR and VR effectiveness. CONCLUSIONS VR and AR offer promising alternatives to conventional rehabilitation for CMDs, demonstrating benefits in pain management, functional recovery, and psychological well-being. However, long-term effectiveness, cost-efficiency, and ethical considerations regarding data privacy remain underexplored. Future research should focus on conducting meta-analyses, long-term follow-ups, cost-effectiveness evaluations, and ethical framework development to facilitate clinical integration. With continued advancements, VR and AR have the potential to revolutionize musculoskeletal rehabilitation by providing personalized, engaging, and scalable treatment options. CLINICALTRIAL The a priori protocol for the review is published in the International Prospective Register of Systematic Reviews (PROSPERO): CRD42024589007.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".