A Randomized Trial of Multiple Interventions for Childhood Obesity in China
Bibliographic record
Abstract
INTRODUCTION: Family- and school-based interventions for childhood obesity have been widely applied; however, the prevalence of childhood obesity remains high. The purpose of this RCT is to evaluate the effectiveness of a family-individual-school-based comprehensive intervention model. DESIGN: Cluster RCT. SETTING/PARTICIPANTS: Fourteen primary schools were selected from 26 primary schools in a district of Shanghai, China, and then randomly divided into intervention and control groups with seven schools in each. The trial started with first-grade students. A total of 1,287 students in the intervention group and 1,159 in the control group were studied overall. INTERVENTION: The baseline study was conducted in January 2011, and family-individual-school-based interventions started in March 2011 and ended in December 2013 for intervention group students. Three follow-up studies were conducted in January 2012, January 2013, and January 2014. Data analysis was conducted in March 2014. MAIN OUTCOME MEASURES: Students' weight and height were measured. The prevalence of obesity/overweight and BMI z-scores were calculated and analyzed using a generalized estimating equation approach. RESULTS: The overall prevalence of overweight/obesity declined from 28.92% in 2011 to 24.77% in 2014, with a difference of 4.15% in the intervention group compared with a 0.03% decline (from 30.71% to 30.68%) in the control group. The intervention group had significantly lower odds of developing obesity or overweight and had decreased average BMI z-scores compared with the control group, especially for obese or overweight students. CONCLUSIONS: The family-individual-school-based comprehensive intervention model is effective for controlling childhood obesity and overweight.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".