Leisure‐Time Sedentary Behavior and Obesity Among 116,762 Adolescents Aged 12‐15 Years from 41 Low‐ and Middle‐Income Countries
Bibliographic record
Abstract
OBJECTIVE: Rates of adolescent obesity are increasing worldwide, with steeper increases being observed in low- and middle-income countries (LMICs). Sedentary behavior (SB) has been associated with various physical and mental health conditions. Little is known about the association between SB and obesity among adolescents in LMICs. This cross-sectional study explored the associations between SB and obesity in adolescents from 41 LMICs. METHODS: Obesity was measured using BMI; SB was assessed through self-report and was considered for all times except when subjects were at school or doing homework. RESULTS: Data from the Global School-based Student Health Survey were analyzed in 116,762 adolescents (mean [SD] age 13.8 [1.0] years; 48.6% female). The overall prevalence of obesity was 4%, and the prevalence of ≥ 3 h/d of SB was 26%. The prevalence of obesity and SB were lowest in low-income countries and highest in upper-middle-income countries. SB for ≥ 3 h/d was associated with higher odds of obesity in 32 countries. This relationship was strongest among low-income countries. CONCLUSIONS: Being sedentary for ≥ 3 h/d is associated with increased odds of obesity in adolescence. Future longitudinal data are required to confirm these findings and to inform interventions targeting SB among adolescents in LMICs, thereby reducing the prevalence of obesity.
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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 | 0.001 |
| 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 | 0.000 |
| 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".