Occupational physical activity and cardiovascular disease in the United States
Bibliographic record
Abstract
BACKGROUND: Emerging evidence, predominately from European and Asian countries, describes opposing effects of occupational physical activity (OPA) and leisure-time physical activity (LTPA) on cardiovascular health. This analysis examined cardiovascular disease (CVD) prevalence associated with OPA and LTPA. METHODS: This cross-sectional analysis of 2015 National Health Interview Survey data (n=16 974) employed logistic regression to estimate odds (OR) of self-reported CVD (coronary heart disease, heart attack, stroke or angina) with self-reported total occupational activity (TOA), occupational exertion (OE), occupational standing and walking (OSW) and LTPA. OPA was measured using two questions: 'How often does your job involve…' (1) 'repeated lifting, pushing, pulling or bending?' (OE) and (2) 'standing or walking around?' (OSW) with responses on a 5-item Likert scale (0=never, 4=always). TOA was categorised similarly after summing OE and OSW scores. LTPA was defined as 0, 1-149 or ≥150 min/week of moderate-to-vigorous activity. All models adjusted for common socioeconomic variables and additional analyses were stratified by sex, smoking status and LTPA. RESULTS: Odds for CVD were higher when 'always' performing TOA (OR 1.99 95% CI 1.12 to 3.53), OE (OR 2.15, 95% CI 1.45 to 3.19) or OSW (OR 1.84, 95% CI 1.07 to 3.17) compared with 'never'. When restricting to never-smokers, odds for CVD were higher when 'always' performing TOA (OR 3.00, 95% CI 1.38 to 6.51) and OE (OR 3.00, 95% CI 1.80 to 5.02) versus 'never'. CONCLUSION: Associations of high OPA with CVD were equally apparent across sexes, stronger in lower LTPA levels and stronger in never-smokers. While uncontrolled confounding is still possible, even after extensive adjustment, the seemingly paradoxical adverse associations with OPA and CVD should be investigated further.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".