Cross-sectional and longitudinal associations between the built environment and walking: effect modification by socioeconomic status
Bibliographic record
Abstract
BACKGROUND: Although socioeconomic status (SES) has been shown to modify associations between the neighborhood built environment and physical activity, contradictory results exist. Objectives of this cross-sectional and longitudinal analysis were to: 1) examine whether overall neighborhood walkability and specific built characteristics were associated with walking among adults at a single point in time and after they relocate neighborhoods, and 2) test for effect modification of these associations by SES. METHODS: We linked longitudinal data from 703 adults who relocated urban neighborhoods between two waves of Alberta's Tomorrow Project (2008-2015) to neighborhood built environment data. We created a walkability index from measures of population counts, street connectivity, and destination diversity within 400 m of participants' homes. In cross-sectional analyses, we used generalized linear models to estimate associations between built characteristics and minutes walked per week at baseline. For the longitudinal analyses, we used fixed-effects linear regression models to estimate associations between changes in built characteristics and minutes walked per week. We also assessed if indicators of SES (individual education or household income) modified both sets of associations. RESULTS: Most cross-sectional and longitudinal associations were small and statistically non-significant. Neighborhood population count (b = 0.03, 95% CI: 0.01, 0.07) and street connectivity (b = - 1.75, 95% CI: - 3.26, - 0.24) were cross-sectionally associated with walking duration among the overall sample. None of the longitudinal associations were statistically significant among the overall sample. There was limited evidence of effect modification by SES, however, we found negative cross-sectional associations between street connectivity and walking among adults with lower education and income, and a positive association between percent change in walkability and change in walking among lower educated adults. CONCLUSIONS: Despite population count and street connectivity being associated with walking at baseline, changes in these built environment variables were not associated with changes in walking following residential relocation. Our findings also provide evidence, albeit weak, that changes in neighborhood walkability, resulting from residential relocation, might more strongly affect walking among low SES adults. Further longitudinal research is needed to examine built environment characteristics with walking for different purposes and to test for inequitable socioeconomic impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".