High occupational physical activity and its combined effect with leisure-time physical activity on cardiovascular disease and mortality: systematic reviews and meta-analyses
Bibliographic record
Abstract
The objective of our systematic reviews and meta-analyses were to analyse the associations between high occupational physical activity (HOPA) and cardiovascular (CV) disease (CVD) and CV mortality and the role of leisure-time physical activity (LTPA) and fitness capacity on these associations.Two systematic reviews and related meta-analyses were undertaken using several databases to identify prospective cohort studies. Random-effect models were used to provide ORs and 95% CI, index I² to characterise the associations between the effect of exposure to HOPA on CVD and CV mortality in adjusted and unadjusted models. Stratified analyses according to the level of LTPA were provided. The Newcastle-Ottawa Scale was used to assess the quality of studies.From 25 and 28 prospective studies: compared with workers exposed to low OPA, HOPA increased the risk of CVD non-significantly (+12%), while compared with moderate OPA, a significant excess of risk was found (+24%); HOPA did not significantly increase the risk of CV mortality compared with low and moderate OPA groups. Stratified on the practice of high, moderate and low LTPA, the risks of CVD for the HOPA were ORs: 1.27 (0.86 to 1.88), 1.08 (0.61 to 1.92), 1.28 (1.00 to 1.62) versus low OPA group, respectively. A combination of low physical fitness and high OPA seemed to expose individuals to an elevated risk of CVD.Being exposed to HOPA may have the same effect on CVD as being exposed to low OPA and an excess risk compared with moderate OPA exposure, suggesting a curve effect. The combined effects of leisure and OPA must be considered in future research.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".