Built Environment and Physical Activity Evidence Gaps: A Content Analysis of Published Systematic Reviews
Bibliographic record
Abstract
Abstract There has been a rapid proliferation of systematic reviews exploring associations between the built environment (BE) and physical activity (PA). The objective of this study was to conduct a content analysis to synthesize the most commonly reported evidence gaps and limitations. Using text excerpts from systematic reviews, an inductive qualitative content analysis was conducted to identify and synthesize research gaps. Analysis involved three phases: 1) preparation (open coding using a hierarchical structure – grandparent, parent and child codes), 2) organization (codes applied to excerpts), and 3) interpretation (codes synthesized). From the 176 systematic reviews, 713 text excerpts describing gaps and limitations were extracted. A total of 157 codes were produced. Grandparent codes included BE features (n = 123 reviews), measurement (n = 101), PA types or domains (n = 53), populations and countries (n = 98), social environment (n = 49), and study design considerations (n = 155). The most common BE features gaps included BE measures (e.g., barriers, accessibility, quality), walkability, BE features (e.g., size, safety, aesthetics), green/natural spaces, rural, and active transportation infrastructure. BE features and study designs (experimental/longitudinal) was the most common intersection of evidence gaps. Findings identified a need for research using experimental and longitudinal designs. Most frequently cited gaps pertained to BE measures, walkability, green and natural spaces, rural, and active transportation infrastructure. This study serves to identify important gaps and limitations in previous research to help advance our understanding of what BE features promote PA and for whom.
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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 | 0.134 | 0.454 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.074 | 0.058 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".