"It’s not all about me": interactive effects of psychosocial and built-environment variables on physical-activity
Bibliographic record
Abstract
Background: Neighbourhood environments can support or hinder physical activity. This study examines how individual-level factors interact with environment-factors associated with physical activity in older adults. Methods: The Active Streets, Active People study recruited 193 older adults living in a highly walkable neighbourhood in Vancouver. Participants completed questionnaires on walking attitudes, gait efficacy, social support, and neighbourhood satisfaction. To assess physical activity, participants wore an accelerometer. To test whether neighbourhood satisfaction moderated the association of individual-level variables with physical activity we estimated multiple linear regression models with interaction terms. Findings: In total, 173 had valid accelerometry data and demonstrated high levels of daily moderate-to-vigorous physical activity (M=39.7,SD=34.1 minutes/day). Individual-level factors associated with MVPA were age and attitudes towards walking. Neighbourhood satisfaction moderated the association between gait efficacy and MVPA with stronger associations between gait efficacy and MVPA in individuals with higher levels of neighbourhood satisfaction. Discussion: Our findings suggest that feeling confident about walking ability is not sufficient to encourage physical activity. Only when individuals are satisfied with their neighbourhood will this translate into behavior.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".