Association Between Leisure Time Physical Activity, Cardiopulmonary Fitness, Cardiovascular Risk Factors, and Cardiovascular Workload at Work in Firefighters
Bibliographic record
Abstract
BACKGROUND: Overweight, obesity, and cardiovascular disease risk factors are prevalent among firefighters in some developed countries. It is unclear whether physical activity and cardiopulmonary fitness reduce cardiovascular disease risk and the cardiovascular workload at work in firefighters. The present study investigated the relationship between leisure-time physical activity, cardiopulmonary fitness, cardiovascular disease risk factors, and cardiovascular workload at work in firefighters in Hong Kong. METHODS: Male firefighters (n = 387) were randomly selected from serving firefighters in Hong Kong (n = 5,370) for the assessment of cardiovascular disease risk factors (obesity, hypertension, diabetes mellitus, dyslipidemia, smoking, known cardiovascular diseases). One-third (Target Group) were randomly selected for the assessment of off-duty leisure-time physical activity using the short version of the International Physical Activity Questionnaire. Maximal oxygen uptake was assessed, as well as cardiovascular workload using heart rate monitoring for each firefighter for four "normal" 24-hour working shifts and during real-situation simulated scenarios. RESULTS: Overall, 33.9% of the firefighters had at least two cardiovascular disease risk factors. In the Target Group, firefighters who had higher leisure-time physical activity had a lower resting heart rate and a lower average working heart rate, and spent a smaller proportion of time working at a moderate-intensity cardiovascular workload. Firefighters who had moderate aerobic fitness and high leisure-time physical activity had a lower peak working heart rate during the mountain rescue scenario compared with firefighters who had low leisure-time physical activities. CONCLUSION: Leisure-time physical activity conferred significant benefits during job tasks of moderate cardiovascular workload in firefighters in Hong Kong.
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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 | 0.000 | 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 | 0.000 |
| 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".