Association of leisure-time sedentary behavior with fast food and carbonated soft drink consumption among 133,555 adolescents aged 12–15 years in 44 low- and middle-income countries
Bibliographic record
Abstract
Rates of sedentary behavior (SB), fast food and carbonated soft drink consumption are increasing worldwide, with steeper increases being observed in low- and middle-income countries (LMICs) in recent years. Given that these behaviors have been linked to adverse health outcomes among adolescents, this presents a new but rapidly growing challenge to human health in these under-resourced nations. However, very little is known about the associations between SB and fast food or soft drink consumption among adolescents in LMICs. Thus, data from the Global school-based Student Health Survey (GSHS) were cross-sectionally analyzed in 133,555 adolescents aged 12–15 years from 44 LMICs [mean (SD) age 13.8 (1.0) years; 49% females]. The data were collected in the form of self-report questionnaires. Associations were assessed with multivariable logistic regression analysis and meta-analysis. The overall prevalence of fast food consumption (at least once in previous 7 days) and carbonated soft drink consumption (at least once per day during past 30 days) were 49.3 and 43.8%, respectively. The overall pooled estimates based on a meta-analysis with random effects for the association of ≥3 h/day of SB with fast food consumption and soft drink consumption using country-wise estimates were OR = 1.35 (95% CI = 1.27–1.43, I 2 = 62.1%).) and OR = 1.26 (95% CI = 1.19–1.34; I 2 = 54.3%), respectively. Spending > 8 h/day of SB compared to < 1 h/day in females was associated with significantly higher odds for fast food (OR = 1.61, 95% CI = 1.38–1.88) and soft drink consumption (OR = 1.91, 95% CI = 1.60–2.28). Future interventions to address unhealthy behaviors in adolescents should take into account the interrelated nature of SB and unhealthy dietary habits, and seek to further understand the mechanisms linking these behaviors in the LMIC context.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".