EFFECTS OF HOME-BASED COGNITIVE AND PHYSICAL EXERCISE TRAINING ON COGNITION IN OLDER ADULTS: THE COVEPIC TRIAL
Bibliographic record
Abstract
Abstract Background Studies suggest that cognitive training and physical exercise can independently help improve cognition in older adults. This randomized clinical trial (COVEPIC study) aimed to compare the effects of 6 months of home-based physical exercise alone and combined with cognitive training on the cognition of older adults. Methods 127 adults (50 years and older) were randomly assigned to one of the two following intervention arms (1:1): 1/ home-based physical exercise alone or 2/ combined home-based physical exercise and cognitive training. Participants completed videoconference neuropsychological assessments targeting episodic and working memory, processing speed and executive functions, prior to the intervention (T0), mid-intervention (T1) (3 months), and post-intervention (T2) (6 months). Clinicaltrials.gov (NCT04635462). Results Participation in the combined intervention showed a larger improvement than the exercise alone group on the MoCA from T0 to T1, and a change in executive functions which was not observed in the physical exercise only group. Effects of exercise dose were also observed, as participants who went through a higher dose of exercise showed larger improvement in episodic and working memory. Discussion Results suggest that a home-based intervention combining cognitive and physical training can help improve cognition in older adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".