Walk Score and Physical Activity Among Adults in the Paris Metropolitan Area: A GPS Study
Bibliographic record
Abstract
Background Very few studies have analyzed the relationship between Walk Score and walking assessed with GPS data and have examined the influence of Walk Score with trips. The purpose of the current study is to examine associations at the trip level between Walk Score, transport mode choice, and physical activity among Paris adults—who were tracked with GPS receivers and accelerometers. Methods In the RECORD GPS Study, participants were tracked 7 days with GPS receivers and accelerometers. Participants were surveyed with a GPS-based web mapping application on their activities and modes in each trip over 7 days (227 participants, 4,633 trips ≤2.5 miles of length). Walk Score, which calculates neighborhood walkability, was assessed for origin/destination of each trip. Multilevel logistic and linear regression analysis was conducted to estimate associations between Walk Score and walking in the trip or accelerometry-assessed steps in the trip, after adjustment for individual/neighborhood characteristics and the distance covered in the trip. Results In adjusted trip-level associations between Walk Score and walking in the trip, we found that a walkable neighborhood in the trip origin and trip destination was associated with increased odds of walking in the trip (from the survey). The odds of walking in the trip were 2.49 (95% CI: 1.67 to 3.70) times higher when the Walk Score for the trip origin was “Walker’s Paradise” compared to less walkable neighborhoods (Very/Car-Dependent or Somewhat Walkable). The number of steps per 10 minutes of trips (as assessed with accelerometry) was higher in walkable neighborhoods (i.e. “Very Walkable” neighborhoods) for the trip origin, but not for the trip destination. Conclusion Walkable neighborhoods were associated with increases in walking among adults in Paris, as documented at the trip level. Creating walkable neighborhoods may increase walking and therefore could be a relevant health promotion strategy. Key messages Most studies evaluating association between Walk Score and physical activity outcomes have been conducted in the U.S. This is the first Walk Score study that has been conducted in France Walkable neighborhoods were associated with increases in walking among adults in Paris, as documented at the trip level. Creating walkable neighborhoods may increase walking
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".