Virtual Reality–Based Training in Chronic Low Back Pain: Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background Low back pain is one of the most prevalent pain conditions worldwide. Virtual reality–based training has been used for low back pain as a new treatment strategy. Present evidence indicated that the effectiveness of virtual reality–based training for people with chronic low back pain is inconclusive. Objective This study conducted a meta-analysis to evaluate the immediate- and short-term effects of virtual reality–based training on pain, pain-related fear, and disability in people with chronic low back pain. Methods We searched the PubMed, Embase, Web of Science, PEDro, CENTRAL, and CINAHL databases from inception until January 2024. Only randomized controlled trials assessing the effects of virtual reality–based training on individuals with chronic low back pain were selected. The outcomes were focused on pain, pain-related fear measured by the Tampa Scale of Kinesiophobia, and disability measured by the Oswestry Disability Index. The immediate term was defined as the immediate period after intervention, and the short term was defined as 3 to 6 months after intervention. The Cochrane Risk of Bias tool and the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach were used to evaluate the quality of the methodology and evidence, respectively. Results In total, 20 randomized controlled trials involving 1059 patients were eligible for analysis. Virtual reality–based training showed significant improvements in pain (mean difference [MD] –1.43; 95% CI –1.86 to –1.00; I2=95%; P<.001), pain-related fear using the Tampa Scale of Kinesiophobia (MD –5.46; 95% CI –9.40 to 1.52; I2=90%; P=.007), and disability using the Oswestry Disability Index (MD –11.50; 95% CI –20.00 to –3.01; I2=95%; P=.008) in individuals with chronic low back pain immediately after interventions. However, there were no significant differences observed in pain (P=.16), pain-related fear (P=.10), and disability (P=.43) in the short term. Conclusions These findings indicated that virtual reality–based training can be used effectively for individuals with chronic low back pain in the immediate term, especially to reduce pain, alleviate pain-related fear, and improve disability. However, the short-term benefits need more high-quality trials to be demonstrated. Trial Registration PROSPERO CRD42021292633; http://tinyurl.com/25mydxpz
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".