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Record W4410453504 · doi:10.3389/fnagi.2025.1553868

Effects of exercise interventions on cognitive function in patients with cognitive dysfunction: an umbrella review of meta-analyses

2025· review· en· W4410453504 on OpenAlexaboutno aff
G. X. Sun, Zhong Zheng, Hongtao Ma

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typereview
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisDementiaCognitionPhysical therapyAerobic exerciseMedicineMontreal Cognitive AssessmentRandomized controlled trialPhysical medicine and rehabilitationPsychological interventionPsychologyDiseaseInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Objective: This umbrella review assessed the quality, potential biases, and effects of exercise interventions on cognitive function in individuals with cognitive impairments. Methods: A comprehensive umbrella review of meta-analyses of randomized controlled trials (RCTs) was performed to evaluate the effects of exercise on cognitive function in individuals with cognitive impairments. Databases including Web of Science, PubMed, Embase, and the Cochrane Database of Systematic Reviews were searched. Outcomes were evaluated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system, classified as "high," "moderate," "low," or "very low" quality. Results: A total of 55 meta-analyses were included, covering dementia, cognitive impairment, MCI, Alzheimer's disease (AD), Parkinson's disease (PD), and stroke. Cognitive outcomes were assessed using scales like MMSE and MoCA. High-quality evidence supports Exergaming (SMD 0.69), Tai Chi (SMD 0.36), and traditional Chinese mind-body exercises (SMD 0.32) for improving MMSE and MoCA Score in MCI patients. For dementia, moderate-quality evidence shows resistance training (SMD 0.60) and Tai Chi (SMD 0.27) have positive effects. Aerobic exercise (MD 2.95) was more effective for AD, while mind-body exercises (MD 1.68) benefitted PD patients. Multi-component exercises (SMD 0.67) improved MMSE and MoCA scores in post-stroke cognitive impairment. For unspecified cognitive impairments, combining exercise with cognitive training and traditional Chinese exercises showed higher effectiveness. Due to small sample sizes, all findings were Class IV evidence, requiring further research. Conclusion: Moderate to high-quality evidence supports Exergaming, Tai Chi, and traditional Chinese exercises in improving cognitive function in MCI. For dementia, resistance training and Tai Chi are effective; for AD, aerobic exercise; for PD, mind-body exercises; and for post-stroke cognitive impairment, multi-component exercises are beneficial. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024587635, identifier [CRD42024587635].

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)0.038
metaresearch head score (Gemma)0.108
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 score0.038
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.108
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0220.033
Bibliometrics0.0150.011
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0030.002
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 head0.102
GPT teacher head0.420
Teacher spread0.318 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

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