Effects of virtual reality-based interventions on cognitive function, emotional state, and quality of life in patients with mild cognitive impairment: a meta-analysis
Bibliographic record
Abstract
Objectives This meta-analysis aims to systematically evaluate the effects of virtual reality (VR)-based interventions on cognitive function, emotional state, and quality of life in patients with mild cognitive impairment (MCI). Methods A comprehensive literature search was conducted using five databases from their inception to June 2024. The inclusion criteria focused on randomized controlled trials (RCTs) that examined VR-based interventions in adults aged 60 or older diagnosed with MCI. The primary outcome was cognitive function, while secondary outcomes included emotional state, quality of life, and dynamic balance. To investigate potential sources of heterogeneity, subgroup analyses and meta-regression were conducted. Subgroup analyses were stratified by VR parameters (immersion level, duration, session, and frequency) and demographic factors (geographic region, education level, and male proportion). Publication bias was assessed using funnel plots and Egger’s regression test. A “trim and fill” method was employed to adjust for any detected publication bias. The certainty of the evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) framework with the GRADEpro GDT software. Results A total of 30 RCTs involving 1,365 participants from 9 countries across 4 continents were included. The meta-analysis revealed that VR-based interventions significantly improved global cognition, as assessed by the Montreal Cognitive Assessment (MoCA; SMD = 0.82, 95% CI: 0.27 to 1.38, p = 0.003, GRADE: moderate) and the Mini-Mental State Examination (MMSE; SMD = 0.83, 95% CI: 0.40 to 1.26, p = 0.0001, GRADE: low). Additionally, VR interventions enhanced attention, as measured by the Digit Span Backward (DSB; SMD = 0.61, 95% CI: 0.21 to 1.02, p = 0.003, GRADE: low) and Digit Span Forward (DSF; SMD = 0.89, 95% CI: 0.34 to 1.45, p = 0.002, GRADE: low). Improvements were also observed in quality of life, as indicated by scores on the Instrumental Activities of Daily Living (IADL; SMD = 0.22, 95% CI: 0.00 to 0.45, p = 0.049, GRADE: moderate). However, no significant effects were found for executive function, memory, verbal fluency, visual abilities, emotional status, or dynamic balance ( p > 0.05). Subgroup analysis revealed that VR interventions were more effective when using semi-immersive VR, with session durations of ≤60 min and a frequency of more than twice per week. Participants from Asia and Europe demonstrated better outcomes, and a lower proportion of male participants (≤ 40%) was also associated with improvements in targeted cognitive domains. Conclusion The findings indicate that VR interventions can significantly improve global cognition, attention, and quality of life in individuals with MCI. Subgroup analyses further revealed that optimal cognitive outcomes were associated with semi-immersive VR, session durations of ≤60 min, intervention frequencies exceeding twice per week, studies conducted in Asia and Europe, and participant groups with a male proportion of ≤40%. Moreover, the study provides valuable insights into secondary outcomes, suggesting that VR interventions may positively impact emotional state and dynamic balance when appropriately tailored to factors such as immersion level, duration, frequency, and other relevant parameters.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".