Mastering life: Physical health in older athletes and chess players compared to population norms
Bibliographic record
Abstract
Older adults participating in Masters sport have often been promoted as an ideal model of successful aging. As a result, Masters athletes are a particularly interesting group to investigate issues of aging, due to their continued participation in higher than average levels of physical activity and competitive sport. Researchers have also highlighted the benefits of participating in cognitively engaging activities (e.g., chess, crosswords). A key constraint of involvement in sport and physical activity is presence of chronic conditions or injuries, which is paradoxical since involvement in physical activity is a key preventive strategy for mitigating chronic disease risk. The current study explored the rates and types of physical injuries and chronic conditions experienced in Master Athletes (n=106) and Chess players (n=42), as well as the Canadian normative data for moderately-active adults (n=2647), and sedentary adults (n=5154). All groups included participants aged 50 years and above. Preliminary results suggest Masters athletes experienced significantly higher rates of all injuries (M=1.14, SD=1.2) in conjunction with a decreased prevalence of chronic conditions compared to chess players (M=0.14, SD=0.44), inactive (M=0.12, SD=0.39) and moderately active (M=0.13, SD=0.40). Chess players were significantly less likely to experience chronic conditions compared to the moderately-active and inactive group (OR:8.98 CI:4.38-18.41; OR:10.89 CI:5.33-22.27); however, they were not significantly different from Master athletes. These findings expand our knowledge on the health status of older adults who are Master sport or chess participants, and have implications for promoting both physically- and cognitively-engaging leisure activities for aging cohorts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".