Excessive Sitting Time Is Associated With Increased Cardiometabolic Risks Among Korean Adolescents
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the association between sitting-time and cardiometabolic risk factors in a representative sample of Korean youth. METHODS: We analyzed data from the 2013 Korean National Health and Nutrition Examination Survey administered to 721 adolescents aged between 12 and 18 years (49.4% of girls). Excessive sitting time was defined as spending over 14 hours per day sitting (≥90th percentile of total sitting hours per day). Cardiometabolic markers included age and sex specific overweight status (≥85th percentile), waist circumference (WC) (≥90th percentile), systolic blood pressure (SBP) (≥90th percentile), and diastolic blood pressure (DBP) (≥90th percentile), as well as fasting glucose (≥100mg/dL), and triglycerides (TG) (≥110 mg/dL). Sex-stratified logistic regression analyses were performed that controlled for age. RESULTS: Among boys, excessive sitting-time (>14 hours per day) was positively associated with overweight status (odds ratio [OR] = 1.05, 95% confidence interval [CI] = 1.03, 1.06), high SBP (OR = 1.56; 95% CI = 1.54, 1.59), high fasting glucose (OR = 2.15, 95% CI = 2.11, 2.20), and TG (OR = 3.28, 95% CI = 3.23 to 3.33). Among girls, excessive sitting-time was associated with overweight status (OR = 1.03, 95% CI = 1.02, 1.05), high DBP (OR = 1.61, 95% CI = 1.58, 1.64), high SBP (OR = 1.43, 95% CI = 1.40 to 1.45), high TG (OR = 1.73, 95% CI = 1.70, 1.76), and high WC (OR = 1.13, 95% CI = 1.10, 1.15). All analyses were adjusted for age. CONCLUSIONS: Spending more than 14 hours per day sitting was associated with high cardiometabolic risks. Korean youth who engage in excessive sitting should be targeted to promote cardiometabolic health.
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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 | 0.001 | 0.001 |
| 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".