Positive effects of physical activity on facets of quality of life in elderly individuals: Lessons from the Masters athletes
Bibliographic record
Abstract
As the population continues to age rapidly, quality of life (QoL) for elderly individuals has become a growing public health concern. Physical activity participation may be a protective factor against deleterious QoL such as impaired physical, emotional, and cognitive functioning. Elderly Masters Athletes (MA), who train and compete in elite level athletics, may exemplify the positive effects of physical activity on QoL facets. This study compared mean satisfaction with life, emotional well-being, physical functioning, and cognitive function between world-ranking elite elderly (≥75 years of age) MA (n = 15) and age-sex-matched non-athlete controls (NAC; n = 14). MA had significantly better physical health (VO2max (ml?*kg-1?*min-1): t(18) = 5.9; Quadriceps strength (N/m): t(27) = 2.6), emotional well-being (Optimism: t(24) = 3.29; Positive affect: t(20) = 12.17), satisfaction with life (t(15) = 3.53), and cognitive health reflected by better scores on cognitive tests of learning and memory (Rey Auditory Verbal Learning Test: t(27) = 2.99 to 3.62), global cognitive function (Mini Mental State Exam: t(27) = 3.02) and executive function (Trail Making Test- Part A: t(27) = -2.09). These findings demonstrate that physical activity has positive effects on components of QoL, such as physical and cognitive function, emotional well-being and satisfaction with life, in elderly individuals.
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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 | 0.001 |
| 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".