Effects of virtual reality cognitive training in individuals with mild cognitive impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) is used to improve specific health needs by combining multiple technologies; it is increasingly being used in the medical field, showing satisfactory effects, especially in the management of chronic disease. The aim of this study was to assess the effects of VR cognitive training for individuals with mild cognitive impairment (MCI). METHODS: Peer-reviewed articles were searched from the PubMed, Embase, Web of Science, the Cochrane Library, Science Direct, and EBSCOhost databases, as well as CNKI, Sinomed, Vip. and Wan Fang, through 23 May 2021. We only included randomized controlled trials (RCTs) enrolling participants with MCI. RESULTS: Seventeen RCTs were included, with a total of 744 participants. Evidence of moderate quality showed that VR cognitive training significantly enhanced MCI patients' global cognitive function, as measured by the Montreal Cognitive Assessment (standardized mean difference [SMD] = 0.42; 95% confidence interval [CI], 0.04-0.79; p = 0.03) and executive function, as measured by trail making test A (SMD = -0.58; 95% CI, -0.80 to -0.35; p < 0.001). The meta-analysis indicated that the effects of VR cognitive training on delayed memory, immediate memory, attention and instrumental activities of daily living were not statistically significant (p > 0.05). CONCLUSION: The available data showed that VR cognitive training might be beneficial for improving global cognitive function and executive function in individuals with MCI, although the effects were short term.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".