How Are Physical Activity, Fitness, and Sedentary Behavior Associated With Insulin Sensitivity in Children?
Bibliographic record
Abstract
OBJECTIVE: To describe the associations among moderate-to-vigorous physical activity (MVPA), fitness, sedentary behavior (SB), and insulin sensitivity (IS). RESEARCH DESIGN AND METHODS: Data were drawn from the baseline assessment of the QUALITY cohort, which included 630 white youth (aged 8-10 years at recruitment), with at least one obese biological parent. IS was measured by two fasting indices (insulin, homeostasis model assessment of insulin resistance) and an oral glucose tolerance test (OGTT)-based index (Matsuda IS index [Matsuda-ISI]). Fitness was measured by Vo(2peak); percent fat mass (PFM) was measured by dual-energy X-ray absorptiometry; 7-day MVPA was measured with accelerometry. SB indicators included average hours daily of self-report screen time (SBst), and average minutes daily at <100 counts/min from accelerometry (SBacc). Multivariable linear regression models were adjusted for age, sex, season, and puberty. RESULTS: MVPA and SBacc were independently associated with IS, but this was no longer statistically significant after accounting for PFM. SBst was negatively associated with IS in girls only, even after controlling for physical activity (PA), fitness, and adiposity; for each additional hour of SBst daily, IS decreased by 4.6-5.6% across all IS indices. Fitness was positively associated with IS (measured by Matsuda-ISI) after accounting for PA, SB, and PFM; for every 1 unit increase in Vo(2peak), Matsuda-ISI increased by approximately 1.0% (P < 0.05). CONCLUSIONS: In children with an obese parent, PA and SBacc are associated with IS, but this association is mediated by adiposity. SBst is negatively associated with IS in girls, beyond its known impact on adiposity. Finally, fitness is independently associated with better IS measured by OGTT.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".