Neighborhood walkability, physical activity, and walking for transportation: A cross-sectional study of older adults living on low income
Bibliographic record
Abstract
BACKGROUND: Walking, and in particular, outdoor walking, is the most common form of physical activity for older adults. To date, no study investigated the association between the neighborhood built environment and physical activity habits of older adults of low SES. Thus, our overarching aim was to examine the association between the neighborhood built environment and the spectrum of physical activity and walking for transportation in older adults of low socioeconomic status. METHODS: Cross-sectional data were from the Walk the Talk Study, collected in 2012. Participants (n = 161, mean age = 74 years) were in receipt of a rental subsidy for low income individuals and resided in neighbourhoods across Metro Vancouver, Canada. We used the Street Smart Walk Score to objectively characterize the built environment main effect (walkability), accelerometry for objective physical activity, and the Community Healthy Activities Model Program for Seniors (CHAMPS) questionnaire to measure walking for transportation. We used regression analyses to examine associations of objectively measured physical activity [total volume, light intensity and moderate intensity physical activity (MVPA)] and self-reported walking for transportation (any, frequency, duration) with walkability. We adjusted analyses for person- and environment-level factors associated with older adult physical activity. RESULTS: Neighbourhood walkability was not associated with physical activity volume or intensity and self-reported walking for transportation, with one exception. Each 10-point increase in Street Smart Walk Score was associated with a 45% greater odds of any walking for transportation (compared with none; OR = 1.45, 95% confidence interval = 1.18, 1.78). Sociodemographic, physical function and attitudinal factors were significant predictors of physical activity across our models. CONCLUSIONS: The lack of associations between most of the explored outcomes may be due to the complexity of the relation between the person and environment. Given that this is the first study to explore these associations specifically in older adults living on low income, this study should be replicated in other settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".