An empirical comparison of the therapeutic benefits of physical exercise and cognitive training on the executive functions of older adults: A meta-analysis of controlled trials.
Bibliographic record
Abstract
UNLABELLED: A robust body of aging-related research has established benefits of both physical exercise (PE) and cognitive training (CT) on executive functions related to the activities of daily living of older adults; however, no meta-analysis has compared these treatments. OBJECTIVE: The current quantitative review involved a comparison of the overall effect sizes of PE and CT interventions on executive functions (Morris, 2008; pre-post-controlled effect size: d(ppc)), while also exploring contextual moderators of treatment outcomes. METHOD: A systematic review identified 46 studies (23 PE, 21 CT, and 2 both) meeting inclusion criteria (i.e., controlled interventions, executive-related outcomes, mean ages 65+, information to calculate d(ppc)). RESULTS: The weighted mean dppc values came to 0.12 (p < .01) for PE and 0.24 (p < .01) for CT. Treatment effects differed based on executive constructs for CT, with problem solving presenting the highest d(ppc) (0.47, p < .01). Notably, PE produced similar effect sizes across distinct executive functions. Treatment characteristics (e.g., session length/frequency) did not predict effect sizes. CT had a significant benefit on healthy participants (0.26, p < .01), but cognitively impaired samples did not experience a significant effect. CONCLUSIONS: Both treatments improved executive functions, but CT presented a potential advantage at improving executive functions. Improvements in executive functions differed depending on construct for CT, whereas each construct produced similar, modest effect sizes for PE. Publication bias and study quality variability potentially bias these conclusions, as lower quality studies likely produced inflated effect sizes.
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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 | 0.033 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| 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 | 0.002 |
| 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".