Benefits of Virtual Reality Balance Training for Patients With Parkinson Disease: Systematic Review, Meta-analysis, and Meta-Regression of a Randomized Controlled Trial
Bibliographic record
Abstract
Background Virtual reality (VR) balance training is increasingly being pursued in biomedical research, specifically with respect to investigating balance ability with VR. However, existing systematic reviews have found inconsistent conclusions about the efficacy of VR in improving balance in Parkinson disease (PD) patients. Objective The goal of the research was to evaluate the impact of VR balance training on the balance ability of patients with PD. Methods All major databases, including Web of Science, PubMed, Scopus, China National Knowledge Infrastructure, and Wanfang, were searched to identify all relevant studies published in English or Chinese since September 15, 2010. Two researchers independently conducted document retrieval, study selection, data extraction, and methodological quality evaluation. Results A total of 16 randomized controlled trials were analyzed (n=583 patients with PD), with the methodological quality evaluation score ranging from 5 to 8 points. A random effects model was selected to combine effect sizes. Meta-analysis showed that the balance ability of PD was significantly improved after VR training compared with the control group (standardized mean difference [SMD] 2.127, 95% CI 1.202 to 3.052, P<.001, I2=95.1, df=15). It is worth noting that the intervention platform may be the main reason for heterogeneity. Meta regression analysis showed that no training program could predict the impact of VR training (P=.57 to .94) on PD balance ability. Subgroup result showed that a single training time of 0 to 20 minutes (SMD 6.446), 4 to 6 times per week (SMD 4.067), training for 3 to 5 weeks (SMD 62.478), training course reached more than 30 times (SMD 4.405), and 201 to 300 minutes per week (SMD 4.059) maybe have more benefit. Conclusions A systematic review and meta-analysis confirmed that VR balance training is a highly effective means to improve balance performance with large effects in PD. In addition, we preliminarily extracted dose-effect relationships for training volume, informing clinicians and practitioners to design effective VR balance training for balance ability. Further research is needed to reveal optimal dose-response relationships following VR balance training.
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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 | 0.025 | 0.057 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.049 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".