Efficacy of virtual reality exercise in knee osteoarthritis rehabilitation: a systematic review and meta-analysis
Bibliographic record
Abstract
Background This systematic review and meta-analysis aims to investigate the effects of virtual reality (VR) exercise compared to traditional rehabilitation on pain, function, and muscle strength in patients with knee osteoarthritis (KOA). Additionally, the study explores the mechanisms by which VR exercise contributes to the rehabilitation of KOA patients. Methods We systematically searched PubMed, the Cochrane Library, Embase, Web of Science, Scopus, and PEDro according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our search spanned from the library construction to 24 May 2024, focusing on randomized controlled trials Primary outcomes included pain, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and muscle strength. Meta-analysis was conducted using RevMan (version 5.4) and Stata (version 14.0). The bias risk of included studies was assessed using the Cochrane RoB 2.0 tool, while the quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Results This meta-analysis and systematic review included nine studies involving 456 KOA patients. The results indicated that VR exercise significantly improved pain scores (SMD, −1.53; 95% CI: −2.50 to −0.55; p = 0.002), WOMAC total score (MD, −14.79; 95% CI: −28.26 to −1.33; p = 0.03), WOMAC pain score (MD, −0.93; 95% CI: −1.52 to −0.34; p = 0.002), knee extensor strength (SMD, 0.51; 95% CI: 0.14 to 0.87; p = 0.006), and knee flexor strength (SMD, 0.65; 95% CI: 0.28 to 1.01; p = 0.0005), but not significantly for WOMAC stiffness (MD, −0.01; 95% CI: −1.21 to 1.19; p = 0.99) and physical function (MD, −0.35; 95% CI: −0.79 to −0.09; p = 0.12). Conclusion VR exercise significantly alleviates pain, enhances muscle strength and WOMAC total score in KOA patients, but improvements in joint stiffness and physical function are not significant. However, the current number of studies is limited, necessitating further research to expand on the present findings. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42024540061 , identifier CRD42024540061
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".