Examining the association between cycling infrastructure exposure and physical activity: a \nnatural experiment study in Victoria, Canada
Bibliographic record
Abstract
Background: Most Canadians are not meeting physical activity guidelines. Physical inactivity is \nan epidemic related to alarming rates of preventable disease and economic burden. Health \nbenefits ensue from modest increases in physical activity (i.e., 5 minutes daily). Despite the \nhealth benefits, there is limited evidence about interventions to increase physical activity at the \npopulation level. Built environment interventions, like bicycle infrastructure, are one \nunderstudied intervention with the potential to increase total population physical activity. The \npurpose of this study was to examine the physical activity impacts of bicycle infrastructure. \nIntervention: The intervention studied is the All Ages and Abilities (AAA) Cycling Network in \nVictoria, BC. This network of bicycle infrastructure is a $7.75M commitment. In 2018, a 5.4km \ngrid of protected cycle tracks was built downtown. \nResearch Design: Adults were recruited in Victoria who ride bicycles at least monthly. Baseline \nactivity data were recorded in 2017 (n = 281) using surveys, Global Positioning System (GPS), \nand accelerometer data, with a follow-up data collection in 2019 (n = 315). The primary outcome \nwas moderate to vigorous physical activity (MVPA), assessed via accelerometer data. \nHypothesis: Exposure to new active transportation infrastructure will be associated with \nincreased MVPA over time. \nAnalysis: I calculated exposure measures to the AAA Cycling Network using GPS and \nGeographic Information System using intersections. I used regression models to examine the \nassociations between exposure to new infrastructure and total location-based physical activity \nlevels, controlling for confounders. \nResults: In the multilevel models analyzing the interaction between exposure and wave with \ncovariates, wave two, compared to wave one, saw a non-statistically significant increase in \nMVPA of 0.93 minutes per week [CI = -5.28, 7.14]. Comparing the models with and without \ncovariates suggests that the wave one and wave two comparisons were highly confounded by \nindividual and weather covariates.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".