Small steps, strong shield: directly measured, moderate physical activity in 65 361 adults is associated with significant protective effects from severe COVID-19 outcomes
Bibliographic record
Abstract
OBJECTIVE: To determine the association between directly measured physical activity and hospitalisation, intensive care unit (ICU) admission, ventilation and mortality rates in patients with a confirmed diagnosis of COVID-19. METHODS: Directly measured physical activity data from 65 361 adult patients with a COVID-19 diagnosis from 19 March 2020 to 30 June 2021, were grouped by activity level: low (<60 min/week), moderate (60-149 min/week) and high activity (≥150 min/week). The association of physical activity levels and the risk of adverse outcomes was analysed using modified Poisson regression. We accounted for demographics and comorbidities including conditions known to influence COVID-19 outcomes, as well as patient complexity as measured by the Johns Hopkins Adjusted Clinical Group system. The regression approach was further validated with a Bayesian network model built off a directed acyclic graph. RESULTS: High physical activity was associated with lower rates of hospitalisation (risk ratio, RR 0.66, 95% CI 0.63 to 0.70), ICU admission (RR 0.59, 95% CI 0.52 to 0.66), ventilation (RR 0.55, 95% CI 0.47 to 0.64) and death (RR 0.58, 95% CI 0.50 to 0.68) due to COVID-19 than those who engaged in low physical activity. Moderate physical activity also was associated with lower rates of hospitalisation (RR 0.87, 95% CI 0.82 to 0.91), admission to ICU (RR 0.80, 95% CI 0.71 to 0.89), ventilation (RR 0.73, 95% CI 0.62 to 0.84) and death (RR 0.79, 95% CI 0.69 to 0.91). CONCLUSIONS: Adults with high and moderate physical activity levels had significantly better outcomes than those with low activity when contracting COVID-19. The apparent protective effects of regular physical activity extended to those with concomitant chronic medical conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".