Food Environment and Childhood Overweight/Obesity: A Systematic Review
Bibliographic record
Abstract
Childhood overweight/obesity has emerged as a pressing public health concern globally, and the impact of the food environment on children's diets and health outcomes has gained heightened attention. Comprehensive and child-specific monitoring systems are critical for guiding targeted interventions and policies. This review aimed to synthesize recent literature on food environment indicators associated with children with overweight/obesity using the 4A framework, including food availability, accessibility, affordability, and appeal. We conducted a systematic search of peer-reviewed literature (2020-2025). This systematic review is guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses and involved narrative synthesis and framework-based classification (CRD420251116187). A total of 75 observational and 6 intervention studies are included. Indicators related to availability (e.g., home food supply and fast food outlet density) and accessibility (e.g., proximity to healthy food stores) are most commonly studied, whereas affordability and appeal indicators (e.g., food pricing and marketing exposure) are less frequently addressed. Current evidence underscores deficiencies in the measurement and monitoring of the food environment for children, which is important to prevent and manage childhood overweight/obesity using integrated indicators at home, schools, communities, and society. Moreover, there is a necessity to develop a standardized, child-centered food environment monitoring system, facilitating prompt, equity-sensitive policy action to address children with overweight/obesity on a worldwide scale, which also supports global sustainable development. This systematic review paper will be useful for selecting indicators to construct a food environment monitoring system.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".