Associations between neighbourhood walkability and health-related fitness in adults
Bibliographic record
Abstract
Background: Improving physical fitness lowers the risk of chronic disease. Evidence suggests the neighbourhood built environment is important for physical activity however, few studies have investigated the contribution of the built environment to fitness levels. Purpose: To estimate associations between objectively-determined and self-reported neighbourhood walkability and overall and specific components of perceived health-related fitness (cardiorespiratory, muscular strength, and flexibility). Methods: We recruited a random sample of adults (n=592) from two adjacent southeast neighbourhoods and nine adjacent southwest neighbourhoods in Calgary (Canada). Participants completed an online questionnaire that captured perceived cardiorespiratory fitness (CRF), muscular strength (MSt), flexibility, moderate-to-vigorous physical activity (MVPA), strength training, and sociodemographic characteristics. Participant’s perceptions of neighbourhood walkability (Physical Activity Neighborhood Environment Scale; PANES) and physical activity supportiveness of neighbourhood parks (Park Perceptions Index; PPI) were also captured. Walk Score® was linked to participant’s household addresses. Covariate-adjusted linear regression estimated the associations between Walk Score®, PANES, and PPI and CRF, MSt, flexibility, and overall fitness. Results: The average age of participants was 46.6±14.8 years. Participants, on average, participated in at least 30-minutes of MVPA on 3.4±2.1 days/week and undertook strength training 2.0±1.8 days/week. Walk Score® was not associated with any fitness variables. The PANES was positively associated (p
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".