Muscle strength explains the protective effect of physical activity against COVID-19 hospitalization among adults aged 50 years and older
Bibliographic record
Abstract
Physical activity has been proposed as a protective factor for COVID-19 hospitalisation. However, the mechanisms underlying this association are unclear. We examined the association between physical activity and COVID-19 hospitalisation and whether this relationship was explained by risk factors (chronic conditions, weak muscle strength). We used data from adults over 50 years from the Survey of Health, Ageing and Retirement in Europe. The outcome was self-reported hospitalisation due to COVID-19, before August 2020. The main exposure was physical activity, self-reported between 2004 and 2017. Among the 3139 participants included (69.3 ± 8.5 years, 1763 women), 266 were tested positive for COVID-19, 66 were hospitalised. Logistic regression models showed that individuals who engaged in physical activity more than once a week had lower odds of COVID-19 hospitalisation than individuals who hardly ever or never engaged in physical activity (odds ratios = 0.41, 95% confidence interval = 0.22-0.74, p = .004). This association between physical activity and COVID-19 hospitalisation was explained by muscle strength, but not by other risk factors. These findings suggest that, after 50 years, engaging in physical activity is associated with lower odds of COVID-19 hospitalisation. This protective effect of physical activity may be explained by muscle strength.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".