Impact of Virtual Reality-Based Therapies on Cognition and Depression of Parkinson's Disease Patients: Systematic Review and Meta-analysis of Randomized Controlled Trials (Preprint)
Bibliographic record
Abstract
Background: As a neurodegenerative disorder, Parkinson disease (PD) demonstrates significant prevalence worldwide. As the population ages, the number of patients with PD increases. Individuals with PD are susceptible to varying degrees of cognitive and psychological impairments. Virtual reality (VR)-based therapy is an emerging technology used for cognitive recovery and mental health treatment, yet controversy remains. Objective: This study aimed to assess the impact of VR-based therapies on cognitive function and depression in patients with PD. Methods: An extensive database search was conducted through PubMed, Web of Science, Embase, and the Cochrane Library to identify randomized controlled trials (RCTs) that investigated the impact of VR on patients with PD. Studies published before March 31, 2026, which met our inclusion and exclusion criteria, were included. A total of 13 RCTs involving 430 patients with PD were included. The Cochrane risk-of-bias tool was used to assess the risk of bias, indicating the included studies generally had a low risk of bias in randomization but a high or unclear risk concerning allocation concealment and blinding. Random-effects meta-analyses were performed using standardized mean differences (SMDs) with 95% CIs. Hartung-Knapp adjustments were applied, and prediction intervals (PIs) were calculated to assess the expected distribution of effects across future settings. The certainty of evidence was assessed using GRADE (Grading of Recommendations, Assessment, Development, and Evaluation). Results: In the meta-analysis, VR-based therapies were associated with statistically significant average improvements in global cognitive function (SMD=0.40, 95% CI 0.11-0.70; 95% PI 0.10-0.70; P=.01; I2=0%) and depressive symptoms (SMD=-0.77, 95% CI -1.42 to -0.12; 95% PI -1.82 to 0.27; P=.03; I2=31%). However, the PI for depression crossed the line of no effect, suggesting that this effect may vary across future settings. No significant average effects were observed for executive function (SMD=0.06, 95% CI -0.31 to 0.44; P=.66), memory (SMD=0.48, 95% CI -0.30 to 1.25; P=.15), attention (SMD=0.01, 95% CI -0.28 to 0.31; P=.94), or quality of life (QoL) outcomes (SMD=0.01, 95% CI -0.46 to 0.47; P=.97). Conclusions: The results suggest that VR-based therapies may be associated with improvements in global cognitive function and depressive symptoms in patients with PD, although evidence for executive function, attention, memory, and QoL remains inconclusive. This review provides an updated synthesis that differs from previous reviews by focusing on both global and domain-specific cognitive outcomes, as well as depressive symptoms and QoL, rather than mainly on motor outcomes. By incorporating recent RCTs and considering PIs, risk of bias, and GRADE certainty, this review offers a more cautious interpretation of the evidence. In practice, VR-based therapies may serve as an engaging adjunct to conventional rehabilitation, but larger and methodologically rigorous trials are needed before clinical recommendations can be made.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".