Effects Of Exercise On Improving Cognitive Function In MCI: An Umbrella Review Of Systematic Review And Meta-analysis
Bibliographic record
Abstract
PURPOSE: To investigate the effects of exercise intervention on improving cognitive function in patients with Mild Cognitive Impairment (MCI). METHODS: We conducted a general review of existing meta-analyses of randomized controlled trials (RCTs) on the effects of exercise interventions on cognitive function in elderly patients with MCI.The literature was searched via PubMed, Embase, Web of Science, and the Cochrane Database of Systematic Reviews. According to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE), evidence of each outcome was evaluated and graded as “high”, “moderate”, “low” or “very low” quality to draw conclusions. Additionally, we classified evidence of outcomes into 4 categories. RESULTS: We identified 10 RCT meta-analyses from 5634 independent articles. High quality evidence shows that Exergaming( SMD 0.67,95%Cl 0.23, 1.11), Traditional Chinese Exercises (TCEs) (SMD 0.32 95% CI 0.18 to 0.47), and Tai Chi (SMD o.36 95% CI 0.18 to 0.54)can significantly improve cognitive function in patients with MCI.These are moderate-quality evidence. Exercise(SMD 1.25, 95% CI 0.88 to 1.62), multi-component exercises(SMD 1.165, 95% CI 0.741 to 1.589), aerobic exercise(MD 1.23, 95% CI 0.99 to 2.47), the variety of exercises(SMD 0.65, 95% CI 0.39 to 0.91), combining cognitive and physical training(SMD 1.40, 95% CI 0.85 to 1.96), dance(MD 1.24, 95% CI 0.30 to 2.18), Baduanjin(MD 3.37, 95% CI 2.05 to 4.69) can significantly improve MMSE and MoCA scores, indicating exercise's potential to improve cognitive function in MCI patients. CONCLUSION: There is high to moderate evidence that Exergaming, Aerobics, Mind-Body Exercises, Cognitive and Physcial training, and Multiple Exercise are effective in improving cognitive function in people with MCI.Keywords: exercise, MCI, cognitive function, umbrella review, meta-analysis, systematic review
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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 | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.029 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".