Cross-sectional associations between physical activity and sedentary time with cardiovascular health in children from the ALSPAC study using compositional data analysis
Bibliographic record
Abstract
This study adopted a compositional framework to cross-sectionally examine the associations between physical activity (PA) and sedentary time (ST) with vascular structure and function and clustered cardiovascular disease (CVD) risk factors in 4277 children (2,226 girls), aged 10.6±0.2 years. Cardiovascular outcomes included flow mediated dilation, distensibility coefficient, pulse wave velocity and a clustered CVD risk factor score. Time spent in light PA (LPA) and moderate to vigorous PA (MVPA) and ST were determined using accelerometers. Multiple linear regression analyses were adjusted for key covariates with LPA, MVPA and ST entered as compositional exposure variables. Neither LPA, MVPA or ST were significantly associated with the vascular outcomes. The proportion of time spent in MVPA and ST were inversely (unstandardised b=-0.126; P=0.001) and positively (b=0.136; P=0.016) associated with clustered CVD risk in the whole group analysis, respectively. MVPA was negatively associated with clustered CVD risk in boys (b=-0.144; P=0.011) and girls (b=-0.110; P=0.032). Only girls had a positive association between ST and clustered CVD risk (b=0.199; P=0.005). Although no associations were observed for PA and ST with vascular outcomes, these data provide further support for interventions that promote MVPA and minimise ST for reducing risk factors for CVD in children.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".