Effects of Virtual Reality-Based Intervention on Cognition, Motor Function, Mood, and Activities of Daily Living in Patients With Chronic Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: The efficacy of virtual reality (VR)-based intervention for improving cognition in patients with the chronic stage of stroke is controversial. The aims of this meta-analysis were to evaluate the effect of VR-based training combined with traditional rehabilitation on cognition, motor function, mood, and activities of daily living (ADL) after chronic stroke. Methods: The search was performed in the Cochrane Library (CENTRAL), EBSCO, EMBASE, Medline (OVID), Web of Science databases, PubMed, CINAHL Ovid, and Scopus from inception to May 31, 2021. All included studies were randomized controlled trials (RCTs) examining VR-based intervention combined with traditional rehabilitation for chronic stroke. The main outcomes of this study were cognition, including overall cognition (combined with all cognitive measurement results), global cognition (measured by the Montreal Cognitive Assessment, MoCA, and/or Mini-Mental State Examination, MMSE), and attention/execution. The additional outcomes were motor function, mood, and ADL. Subgroup analyses were conducted to verify the potential factors for heterogeneity. Results: Six RCTs including 209 participants were included for systematic review, and five studies of 177 participants were included in meta-analyses. Main outcome analyses showed large and significant effect size (ES) of VR-based training on overall cognition ( g = 0.642; 95% CI = 0.134–1.149; and P = 0.013) and attention/execution ( g = 0.695; 95% CI = 0.052–1.339; and P = 0.034). Non-significant result was found for VR-based intervention on global cognition ( g = 0.553; 95% CI = −0.273–1.379; and P = 0.189). Additional outcome analyses showed no superiority of VR-based intervention over traditional rehabilitation on motor function and ADL. The ES of VR-based intervention on mood ( g = 1.421; 95% CI = 0.448–2.393; and P = 0.004) was large and significant. In the subgroup analysis, large effects for higher daily intensity, higher weekly frequency, or greater dose of VR intervention were found. Conclusion: Our findings indicate that VR-based intervention combined with traditional rehabilitation showed better outcomes for overall cognition, attention/execution, and depressive mood in individuals with chronic stroke. However, VR-based training combined with traditional rehabilitation showed a non-significant effect for global cognition, motor function, and ADL in individuals with chronic stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".