The neuroprotective effects of long-term exercise training in older adults: A look at world-ranking elite Masters athletes
Bibliographic record
Abstract
Cognitive impairment has become one of the main threats to health and well-being among the 7% of Canadians who are 75 years and older. Physical activity (PA) has been associated with improved cognitive function or reduced decline in the elderly, yet little is known about the neuroprotective effects of PA in elite older aged athletes (Master’s athletes, MA). This study: (i) examined the association between physical fitness (VO2max) and cognitive function (attention, memory, learning, processing speed) generally and (ii) compared mean cognitive function between world-ranking elite elderly (≥75 years of age) MA (n = 15) and age-sex-matched inactive controls (n = 14). Based on Pearson correlation coefficients, fitness was associated with verbal learning and memory (r = .37 to .51, p < .05) assessed using the Rey Auditory Verbal Learning Test (RAVLT) and Mini Mental State Exam (MMSE). Verbal fluency (assessed using phonemic and semantic tests), attention/processing speed (assessed using Trail Making Tests (TMT)), and executive function (assessed using Digits Forward and Backward tests, and the Modified Wisconsin Card Sorting Test (M-WCST)) were not significantly associated with fitness. MA were significantly better on verbal learning and memory tasks (RAVLT: t(1,28) = 2.99 to 3.62; MMSE: t(1,28) = 3.03) and faster on processing speed tasks (TMT: t(1,28) = -2.09). Based on these findings, MA may demonstrate a profile that is protective of cognitive decline in areas of verbal learning, memory, and attention/processing speed.
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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 | 0.000 | 0.000 |
| 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".