Perceived neighborhood environmental attributes associated with adults’ transport-related walking and cycling: Findings from the USA, Australia and Belgium
Bibliographic record
Abstract
BACKGROUND: Active transportation has the potential to contribute considerably to overall physical activity levels in adults and is likely to be influenced by neighborhood-related built environment characteristics. Previous studies that examined the associations between built environment attributes and active transportation, focused mainly on transport-related walking and were conducted within single countries, limiting environmental variability. We investigated the direction and shape of relationships of perceived neighborhood attributes with transport-related cycling and walking in three countries; and examined whether these associations differed by country and gender. METHODS: Data from the USA (Baltimore and Seattle), Australia (Adelaide) and Belgium (Ghent) were pooled. In total, 6,014 adults (20-65 years, 55.7% women) were recruited in high-/low-walkable and high-/low-income neighborhoods. All participants completed the Neighborhood Environmental Walkability Scale and the International Physical Activity Questionnaire. Generalized additive mixed models were used to estimate the strength and shape of the associations. RESULTS: Proximity to destinations, good walking and cycling facilities, perceiving difficulties in parking near local shopping areas, and perceived aesthetics were included in a 'cyclability' index. This index was linearly positively related to transport-related cycling and no gender- or country-differences were observed. The 'walkability' index consisted of perceived residential density, land use mix access, proximity of destinations and aesthetics. A non-linear positive relationship with transport-related walking was found. This association was stronger in women than in men, and country-specific associations were identified: the strongest association was observed in Seattle, the weakest in Adelaide. In Ghent, the association weakened at higher levels of walkability. CONCLUSIONS: For cycling, consistent correlates were found in the three countries, but associations were less straightforward for transport-related walking. Moreover, the identified neighborhood environmental correlates were different for walking compared to cycling. In order to further clarify the shape of these associations and reach more specific international guidelines for developing walkable and bikeable neighborhoods, future studies should include even more countries to maximize environmental variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".