MétaCan
Menu
Back to cohort
Record W4389080363 · doi:10.1016/j.arr.2023.102146

Virtual reality and cognitive rehabilitation for older adults with mild cognitive impairment: A systematic review

2023· review· en· W4389080363 on OpenAlexaboutno aff
Carla Tortora, Adolfo Di Crosta, Pasquale La Malva, Giulia Prete, Irene Ceccato, Nicola Mammarella, Alberto Di Domenico, Rocco Palumbo

Bibliographic record

VenueAgeing Research Reviews · 2023
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFORehabilitationCognitionScopusCognitive rehabilitation therapyMeta-analysisRandomized controlled trialMEDLINEVirtual realityPsychologyPhysical medicine and rehabilitationSystematic reviewMontreal Cognitive AssessmentClinical psychologyPhysical therapyMedicineCognitive impairmentPsychiatryComputer science

Abstract

fetched live from OpenAlex

Virtual Reality (VR) has been gaining increasing attention as a potential ecological and effective intervention system for treating Mild Cognitive Impairment (MCI). However, it remains unclear the efficacy and effectiveness of VR-based cognitive rehabilitation therapy (VR-CRT) in comparison with cognitive rehabilitation therapy (CRT). Consequently, a systematic review on Pubmed, Scopus, PsycInfo, and Web Of Science was conducted to assess the state of the art of the literature published between 2003 and April 2023. Only articles that adopted CRT as control group and that included some measure of at least one domain among overall cognitive function, executive function and functional status were included. Participants needed to be older adults aged 65 or over with a diagnosis of MCI. The risk of bias and the quality of evidence were assessed using the Version 2 of the Cochrane risk-of-bias tool for randomized trials. Initially, 6503 records were considered and screened after removing duplicates (n = 1321). Subsequently, 81 full texts were assessed for eligibility. Four articles met the inclusion criteria but 2 of them were merged as they were describing different outcomes of the same research project. Consequently, 3 overall studies with a total of 130 participants were included in the final analysis. Due to the high heterogeneity in the methodology and outcome measures employed, it was not possible to conduct a meta-analysis. Included studies used semi-immersive (k = 2) and full-immersive (k = 1) VR systems in their research. Two articles evaluated overall cognitive function through the MoCA together with specific tests for executive functions (n = 69), while one study adopted a comprehensive neuropsychological battery to evaluate both cognitive function and executive function (n = 61). Finally, one study evaluated functional status through instrumental activities of daily living (n = 34). A However, the limited number of studies, the small sample size, and the potential issues with the quality and methodology of these studies that emerged from the risk of bias assessment may raise doubts about the reliability of their results. Nevertheless, although scarce, results of the present review suggest that VR-CRT may be paramount in treating MCI for its additional ecological and adaptive advantages, as all of the studies highlighted that it was at least as effective as conventional CRT for all the outcome measures. Therefore, more rigorous research that compares VR-CRT and CRT is needed to understand the degree to which VR-CRT is effective with older adults with MCI and the potential role of immersion to influence its efficacy. Indeed, these preliminary findings highlight the need for the development of standardized VR protocols, as the integration of such technology into clinical practice may help improve the quality of life and cognitive outcomes for this growing demographic.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.127
GPT teacher head0.461
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations105
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAgeing Research ReviewsSame topicStroke Rehabilitation and RecoveryFrench-language works237,207