Neighborhood Environments and Changes in Obesity and in Lifestyle Behaviors Among Children Enrolled in Obesity Management Interventions: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Neighborhood determinants of health have been documented in several populations, yet less is known about their role in pediatric obesity treatment. A systematic review of longitudinal studies examining associations between neighborhood environment features and changes in obesity and in lifestyle behaviors among children participating in obesity management interventions was conducted. METHODS: Searches were conducted in Medline, Embase, CINAHL, and Web of Science for peer-reviewed articles published in English from database inception until April 2025. We included studies of children with overweight/obesity at baseline, participating in multicomponent obesity management interventions, and with at least one pre- and one post-intervention measurement of obesity or lifestyle behaviors. RESULTS: Of the 27,310 records screened, six met inclusion criteria. Studies were conducted in the United States (n = 5) and United Kingdom (n = 1), with participants' age ranging from 6 to 18 years, and a total of 13,364 participants. Studies examined availability of parks (n = 3), supermarkets (n = 2), greenspaces (n = 1), walkability (n = 1), recreational facilities (n = 1), and neighborhood deprivation (n = 1). Residing in neighborhoods with more parks was associated with greater reductions in post-intervention body mass index in two studies. Inconsistent findings relating availability of supermarkets to changes in fruit and vegetable intake were reported. Residing in neighborhoods with more recreational facilities was associated with increases in objectively measured physical activity but not with self-reported screen time. CONCLUSION: Findings among the few studies that examined neighborhood determinants of obesity management outcomes among children were inconsistent. Neighborhood resources that support physical activity (parks, recreational facilities) may be associated with better outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".