THE EFFECTS OF PHYSICAL TRAINING CESSATION ON EXECUTIVE FUNCTIONS IN OLDER ADULTS
Bibliographic record
Abstract
Combined strength and aerobic (S+A) and gross motor skills programs (GMS) have shown promise in selectively improving executive functions (EF) of older adults. However, interruptions in training may occur resulting in losses of training-induced physiological benefits. So far, little is known about the effects of physical training cessation on EF. Therefore, forty older adults (70.5 ± 5.51 years; 67.5% female) who had completed an 8-week S+A or GMS program followed by an 8-week training cessation period were included in this study. Performances in the Random Number Generation (RNG) test (inhibition and working memory) in a single task (ST) and a dual-task (DT, walking at 4 km.h-1) were analyzed. Two-way ANOVAs, with repeated measures for time (pre, post intervention and follow-up), revealed a significant time effect for inhibition scores. For example, Turning Point Index (TPI - occurrence of sequence changes from ascending to descending numbers) improved in ST for all time comparisons (pre to post intervention and post to follow-up) whereas TPI performances in DT improved from pre intervention to follow-up and from post intervention to follow up (p < 0.05). However, participants exhibited worse performances (p < 0.05) from pre intervention to follow-up (ST and DT) and from post intervention to follow-up (ST) for one working memory score (redundancy index). Our study demonstrates training cessation can selectively impact EF. Interestingly, performances for inhibition in a single and a dual-task can be improved after a period of physical training cessation, regardless of the exercise intervention employed.
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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 | 0.001 |
| 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".