Virtual Reality-Based Cognitive and Physical Interventions in Cognitive Impairment: A Network Meta-Analysis of Immersion Level Effects
Bibliographic record
Abstract
Virtual reality (VR) has emerged as an innovative platform for delivering cognitive and physical training to individuals with cognitive impairment. However, the differential effectiveness of fully immersive versus partially immersive VR interventions remains unclear. This network meta-analysis aimed to evaluate how immersion level influences cognitive, motor, and functional outcomes in neurodegenerative populations. A systematic search of PubMed, Embase, Cochrane Library, and Web of Science up to October 2025 identified 20 randomized controlled trials involving 1382 participants with mild cognitive impairment (MCI) or dementia. Interventions were categorized into four groups: (1) fully immersive VR (head-mounted displays), (2) partially immersive VR (screen-based or motion-capture systems), (3) active control (traditional cognitive or physical training), and (4) passive control (usual care or health education). Outcomes included the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Trail Making Test (TMT), Digit Span Test (DST), Timed Up and Go (TUG), and Instrumental Activities of Daily Living (IADL). Standardized mean differences (SMDs) and surface under the cumulative ranking curve (SUCRA) values were calculated using RevMan 5.4 and Stata 18.0. Fully immersive VR significantly improved global cognition compared to passive control (MMSE: SMD = 0.51, 95% CI [0.06, 0.96]), while partially immersive VR showed superior effects on executive function versus active control (TMT-B: SMD = -1.29, 95% CI [-2.62, -0.93]) and on motor function (TUG: SMD = -0.59, 95% CI [-1.11, -0.08]). In MoCA performance, both VR modalities outperformed traditional interventions (SUCRA: fully immersive = 76.0%; partially immersive = 84.8%). SUCRA rankings suggest that fully immersive VR is optimal for memory and foundational cognition (81.7%), whereas partially immersive VR performs best for executive function (98.9%). These findings indicate that the efficacy of VR-based cognitive or physical-cognitive interventions is modulated by immersion level. Tailoring VR modality to specific cognitive domains may optimize rehabilitation outcomes in MCI and dementia care.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".