Workers’ Activity Profiles Associated With Predicted 10‐Year Cardiovascular Disease Risk
Bibliographic record
Abstract
Background There is a need to explore common activity patterns undertaken by workers and the association between these activity profiles and cardiovascular disease (CVD). This study explored the number and type of distinct profiles of activity patterns among workers and the association between these profiles and predicted 10-year risk for a first atherosclerotic CVD event. Methods and Results Distinct activity patterns from a cross-section of workers' accelerometer data were sampled from Canadian Health Measures Survey participants (5 cycles, 2007-2017) and identified using hierarchical cluster analysis techniques. Covariates included accelerometer wear time, work factors, sociodemographic factors, clinical markers, and lifestyle variables. Associations between activity profiles and high atherosclerotic CVD risk >10% were estimated using robust Poisson regression models. Six distinct activity profiles were identified from 8909 workers. Compared with the "lowest activity" profile, individuals in the "highest activity" and "moderate evening activity" profiles were at 42% lower risk (relative risk [RR], 0.58; 95% CI, 0.47, 0.70) and 33% lower risk (RR, 0.67; 95% CI, 0.44, 0.87) of predicted 10-year atherosclerotic CVD risk of >10%, respectively. "Moderate activity" and "fluctuations of moderate activity" profiles were also associated with lower risk estimates, whereas the "high daytime activity" profile was not statistically different to the reference profile. Conclusions Workers accumulating physical activity throughout the day and during recreational hours were found to have optimal CVD risk profiles. Workers accumulating physical activity only during daytime work hours were not associated with reduced CVD risk. Findings can inform alternative strategies to conferring the cardiovascular benefits of physical activity among workers. Large prospective studies are needed to confirm these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".