Possible Actions in the Built Environment to Enhance Physical Activity: Systematic Review
Bibliographic record
Abstract
As a crucial factor of health, physical activity is widely explored in many empirical studies. The problem of how the built environment may affect physical activity attributes was discussed in previous studies, and the classification of interventions was presented in a limited scope. Therefore, the present study aims to review built environment interventions while classifying them into motivators and barriers of physical activity in residential neighbourhoods worldwide. Firstly, the main dimensions explaining how the built environment affects physical activity are presented. Fifteen papers published between 2009 and 2019 were identified by an extensive search in ScienceDirect, Web of Science, Scopus and PubMed. These works were systematically reviewed based on their main characteristics and then classified based on their relevant operationalisation of variables. Improving motivators and conquering barriers of physical activity on neighbourhoods lead to a healthy and sustainable society. The results of the current work can help policymakers and urban planners use exclusive methods for each part of neighbourhood planning and create an environment that overcomes barriers and promotes public physical activity levels.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| 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".