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Record W4200387101 · doi:10.2196/27058

The Effect of Video Game–Based Interventions on Performance and Cognitive Function in Older Adults: Bayesian Network Meta-analysis

2021· article· en· W4200387101 on OpenAlexvenueno aff
Chao Yang, Xiaolei Han, Mingxue Jin, Jianhui Xu, Yiren Wang, Yajun Zhang, Chonglong Xu, Yingshi Zhang, Enshi Jin, Chengzhe Piao

Bibliographic record

VenueJMIR Serious Games · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionVideo gameMeta-analysisCognitionStrictly standardized mean differenceEffects of sleep deprivation on cognitive performancePsychologyPhysical medicine and rehabilitationPhysical therapyMedicineComputer scienceMultimediaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The decline in performance of older people includes balance function, physical function, and fear of falling and depression. General cognitive function decline is described in terms of processing speed, working memory, attention, and executive functioning, and video game interventions may be effective. OBJECTIVE: This study evaluates the effect of video game interventions on performance and cognitive function in older participants in terms of 6 indicators: balance function, executive function, general cognitive function, physical function, processing speed, and fear of falling and depression. METHODS: Electronic databases were searched for studies from inception to June 30, 2020. Randomized controlled trials and case-controlled trials comparing video game interventions versus nonvideo game control in terms of performance and cognitive function outcomes were incorporated into a Bayesian network meta-analysis. All data were continuous variables. RESULTS: In total, 47 studies (3244 participants) were included. In pairwise meta-analysis, compared with nonvideo game control, video game interventions improved processing speed, general cognitive function, and depression scores. In the Bayesian network meta-analysis, interventions with video games improved balance function time (standardized mean difference [SMD] -3.34, 95% credible interval [CrI] -5.54 to -2.56), the cognitive function score (SMD 1.23, 95% CrI 0.82-1.86), processing speed time (SMD -0.29, 95% CrI -0.49 to -0.08), and processing speed number (SMD 0.72, 95% CrI 0.36-1.09), similar to the pairwise meta-analysis. Interventions with video games with strong visual senses and good interactivity ranked first, and these might be more beneficial for the elderly. CONCLUSIONS: Our comprehensive Bayesian network meta-analysis provides evidence that video game interventions could be considered for the elderly for improving performance and cognitive function, especially general cognitive scores and processing speed. Games with better interactivity and visual stimulation have better curative effects. Based on the available evidence, we recommend video game interventions for the elderly. TRIAL REGISTRATION: PROSPERO CRD42020197158; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=197158.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.302
Teacher spread0.287 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations20
Published2021
Admission routes1
Has abstractyes

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