Associations of step accelerations and cardiometabolic risk markers in early adulthood
Bibliographic record
Abstract
Physical activity (PA) has a positive effect on risk factors related to cardiometabolic health yet amount of PA and time of onset is unclear. Therefore, we investigated the relationship of PA estimates and cardiometabolic risk factors in a large healthy population of an understudied age group of young adults using a standard gravity-based method on body adiposity and risk markers. In 856 (532 women, 324 men, 32-35 years) subjects we evaluated the association of PA and cardiometabolic risk factors in early adulthood. PA was measured using accelerometers for a period of two weeks. Step counts were divided into light (LPA), moderate (MPA), and vigorous (VPA) intensity classes. Income of the household was 63 446 ± 46 899€ and 57.5% had higher education. Total daily step numbers were 11962.5 ± 5163.2, LPA 5459.6 ± 2986.6, MPA 5932 ± 3404.6, and VPA 572.3 ± 668. Higher total PA volume was associated with lower weight, BMI, % body fat, smaller visceral fat area (VFA) and waist circumference, lower total cholesterol, LDL, and reflection coefficient of the pulse wave. LPA correlated with weight, BMI, waist circumference, total cholesterol, LDL, and central pulse pressure (cPP). Percent body fat (%BF), VFA, total cholesterol, LDL, reflection coefficient, heart minute index, and heart minute volume were significantly associated with MPA and VPA intensity PA volume. Lower PA in early adulthood correlates with increased cardiometabolic risk markers which should be translated into specific recommendations to thrive for a healthier lifestyle to delay and decrease their onset.
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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) | 0.001 | 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".