Executive functioning and trait mindfulness in older adults before a remote physical exercise training program
Bibliographic record
Abstract
Background: Physical exercise shows benefits to mood and executive functioning (EF), and there is evidence that trait mindfulness (TM) plays a role. Due to the Covid-19 pandemic, many older adults have reduced their physical activity. Purpose: Using data from the baseline evaluation of a randomized control trial implementing physical exercise training for older adults in Canada, this study examines the relative contributions of physical exercise and TM to EF and mood. Methods: 39 older adults (67% women, 66-78 years-old) were assessed for self-reported frequency of physical exercise; TM; EF that was evaluated by self-report (Executive Function Index -EFI) and computerized tasks (inhibition, working memory, shifting and decision-making); and mood (anxiety and depression). Multiple linear regressions were used to analyze the effects of physical exercise and TM on EF and mood. Results: TM alone significantly predicted self-reported EF, β = .36, t(32) = 2.09, p < .05, and symptoms of depression, β = -.47, t(32) = -2.91, p < .01, and anxiety, β = -.42, t(32) = -2.65, p = .01, but not performance on any EF cognitive tasks. Frequency of physical exercise did not significantly predict any measures of EF or mood. Conclusions: These results suggest that before beginning a remote physical exercise program, trait mindfulness may play a larger role than physical exercise in EF and mood. Funding: Funding for this study was provided by the Natural Sciences and Engineering Research Council (NSERC) and an internal research grant, University of Victoria.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".