Effect of Food Environment Interventions on Anthropometric Outcomes in School-Aged Children and Adolescents in Low- and Middle-Income Countries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Food environments may promote access to unhealthy foods, contributing to noncommunicable diseases in low- and middle- income countries (LMICs). This review assessed published evidence on the effects of food environment interventions on anthropometric (BMI and weight status) outcomes in school-aged children (5–9 y) and adolescents (10–19 y) (SACA) in LMICs. We summarized randomized controlled trials (RCTs) and quasi-experimental studies (QES) published since 2000 to August 2019 in the peer-reviewed and gray literature that assessed the effects of food-related behavioral and environmental interventions on diet-related health outcomes in SACA in LMICs. Electronic databases (MEDLINE, Embase, PsycINFO, Cochrane Library) were searched using appropriate keywords, Medical Subject Headings, and free text terms. Eleven RCTs and 6 QES met the inclusion criteria, testing multicomponent behavioral and environmental interventions in schools. Analysis of 6 RCTs (n = 17,054) suggested an overall effect on change in BMI [mean difference (MD): –0.11, 95% CI: –0.19 , –0.03], whereas there was no observed effect in 5 studies using endline BMI (n = 17,371) (MD: 0.05, 95% CI: –0.32, 0.21). There was no significant pooled effect among the 3 QES (n = 5,023) that reported differences in change in BMI or endline (MD: –0.37, 95% CI: –0.95, 0.22). There is limited evidence to support the modification of diet-related health outcomes through school-based food environment interventions in SACA in LMICs. Further studies are needed to understand the impact of school and community-based food environment interventions on nutritional status in this population.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".