Abstract P448: Urban Trail Infrastructure and Physical Activity Levels: A Systematic Review and Meta-Analysis of Natural Experiments
Bibliographic record
Abstract
Background: In response to climate change, cities across Canada are investing over $1B in new cycling infrastructure to support more active transportation. Little empirical evidence exists describing the effectiveness of adding protected cycling trails on changes in cycling or physical activity (PA) levels. Hypothesis: We hypothesized that areas with new infrastructure would experience increased PA and trail use by cyclists and pedestrians compared to areas without new infrastructure. Design and Methods: We searched CINAHL, EMBASE (Ovid), MEDLINE (Ovid), SPORTDiscus, TRD/Transportation Research Information Services (TRIS), Web of Science and Google Scholar for articles published from 2010 to 2023. We included studies with an experimental pre-post design that reported a PA outcome or trail counts for an intervention and control area. The interventions were limited to protected and/or separated bike lanes, including cycle tracks, multi-use trails, greenways, and bike lanes with concrete barriers. Our primary outcomes were individual level physical activity (PA) and trail use counts (cyclists and pedestrians). A modified risk of bias tool will be employed to assess the methodological quality of each selected study. We followed PRISMA reporting guidelines and the review was pre-registered with Prospero (CRD42023438891) Results: Three independent reviewers screened abstracts from 3936 articles, of which 58 were included in a full text review. After resolving conflicts, 28 articles describing natural experiments of new cycling infrastructure met eligibility criteria were included for data extraction. We extracted data for population characteristics in both intervention and control areas, such as socioeconomic status, mean age, race, and the percentage of females, as well as outcomes related to physical activity. 1/28 papers used accelerometer data, 12/28 used survey data, 3/28 used eco counter data, 2/28 used manual counters. Of the 15 studies that reported it, sample size ranged from 70 to 21,488. Due to high variance in data reporting style, not all studies could be meta-analyzed. We found a high risk of bias for all natural experiments studies and very few adhered to TREND reporting guidelines for quasi-experimental studies. Conclusions: Changes in built environment preliminarily appear to increase cycling and pedestrian counts. Reporting style varies too greatly across research studies for an effective meta-analysis. As a scientific community, we need to work together to follow reporting standards.
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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 | 0.039 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".