Nutrition‐related interventions targeting childhood overweight and obesity: A narrative review
Bibliographic record
Abstract
Systematic reviews of nutritional interventions indicate limited efficacy in reducing childhood obesity, but their blanket conclusions could obscure promising components. This narrative review sought more detail on effective components within nutrition-related interventions involving children aged 2 to 11 years. In May 2016, the World Health Organization (WHO) searched the Cochrane Library and PubMed for relevant reviews. From 36 reviews, we screened 182 nutrition-related randomized trials for inclusion. We then reviewed those that reported at least 1 statistically significant (P < 0.05) treatment benefit on body weight and/or composition outcomes at their longest follow-up assessment. Fourteen trials met inclusion criteria (median n = 554; mean intervention duration = 10.8 mo; follow-up = 4.4 mo). "Effective" approaches included environmental changes such as school water fountain installations and cafeteria menu changes and possibly less sustainable strategies such as health education lessons. However, effect sizes even of these selected significant treatment benefits were modest-significant body mass index z-score effects range from -0.1 to -0.2. Each trial was associated with very small improvements in body composition. Because this is a "best-case" scenario (reflecting our design), trialists should rigorously test these strategies alone and possibly together; be open to novel strategies; and ensure that each strategy is culturally relevant and self-sustainable.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".