A meta‐analysis of randomised controlled trials of physical activity in people with Alzheimer's disease and mild cognitive impairment with a comparison to donepezil
Bibliographic record
Abstract
OBJECTIVES: Physical exercise may benefit people with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, randomised controlled trials (RCTs) of exercise have shown conflicting findings and it is unclear if positive outcomes are comparable to a commonly used cholinesterase inhibitor, donepezil. METHODS: Embase, Medline, PsycINFO, PsycARTICLES, SCOPUS were searched for RCTs of physical activity compared to a control condition, and donepezil compared to placebo in people with AD and MCI. Effect sizes were calculated from pre- and post-MMSE and ADAS-Cog scores and pooled using a random effects meta-analysis. RESULTS: Ninteen RCTs were included in the exercise meta-analysis (AD, N = 524; MCI, N = 1269). Physical exercise improved MMSE scores in AD (Hedges' g = 0.46) and MCI groups (g = 0.63). For the MCI group, exercise appeared to have a stronger effect for those with lower MMSE scores at baseline (p = 0.022). 18 RCTs were included in the donepezil meta-analysis (AD, N = 2984, MCI, N = 1559). In people with AD, donepezil improved cognition (MMSE g = 0.23; ADAS-Cog, g = -0.17) but there was no evidence of improved cognition in MCI. CONCLUSIONS: Physical exercise improved cognition in both AD and MCI groups. Where comparisons were possible, the effect size for physical exercise was generally comparable to donepezil. These results strengthen the evidence base for exercise as an effective intervention in AD and MCI, and future clinical trials should examine exercise type, intensity and frequency, in addition to cholinesterase inhibitors to determine the most effective interventions for AD and MCI.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.058 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".