Examining the state, quality and strength of the evidence in the research on built environments and physical activity among children and youth: An overview of reviews from high income countries
Bibliographic record
Abstract
BACKGROUND: Built environments have shown to be associated with health, with physical activity (PA) considered one of the critical pathways for achieving benefits. Navigating available evidence on the built environment and PA is challenging given the number of reviews. OBJECTIVE: Examine the current state and quality of research looking at associations between built environments and total PA and domains of PA (i.e., leisure/recreation, transportation, school) among children and youth (1-18 years). METHODS: We systematically searched the grey literature and six bibliographic databases from January 2000 to May 2020. Review quality was assessed using the AMSTAR2. Results by age group were synthesized using narrative syntheses and harvest plots, and certainty of the evidence was assessed using a modified GRADE approach. RESULTS: This overview included 65 reviews. Most reviews were of very low-to-low quality. High certainty was found for positive associations between transportation PA and walking/cycling/active transportation (AT) infrastructure. There was high certainty for positive associations between streets/play streets and total PA, alongside lower certainty for transportation and leisure PA. Very low-to-moderate certainty supports schoolyards designed to promote PA were positively associated with total PA, but mixed for school PA (except children). Less consistent positive associations were found for forests/trees, greenspace/open space, recreation facilities, street lighting, traffic safety, population/residential density, proximity/access to destinations, neighbourhood characteristics, and home environments. There is very low-to-moderate certainty for negative associations between greater distance to school and traffic volume and domains of PA. Generally, null or mixed associations were observed for aesthetics, parks, AT comfort infrastructure, land-use mix, street connectivity, urban/rural status, and public transit. DISCUSSION: There remains a need for high quality systematic reviews and studies to evaluate the effects of environmental changes across the pediatric age spectrum and using a PA domain approach. Given the global physical inactivity crisis the built environment remains and important means to promote PA among children/youth.
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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 | 0.031 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".