Walk score® and Japanese adults' physically-active and sedentary behaviors
Bibliographic record
Abstract
Walk Score® is a free publicly-available tool that evaluates how a particular location is supportive of residents' walking, based on the distance to various local destinations. Several studies have shown associations of Walk Score with walking behaviors. However, these studies have been conducted only in Western countries, such as the U.S.A., Australia, Canada, and France. In addition, the role of Walk Score in sedentary behaviors has not yet been explored. The current study examined associations of Walk Score with physically-active and sedentary behaviors in Japan. This study used cross-sectional survey data from the Healthy Built Environment in Japan (HEBEJ) project. In 2011, adults living in urban and rural areas in Japan (n =1072) reported their walking and sedentary behaviors. Participants reported their walking in the past week for three specific purposes: for commuting; for errands; and for exercise. They also reported two sedentary behaviors in the past week: TV viewing and car driving. Walk Score was obtained manually for each participant's residential address. Logistic regression models (adjusted for covariates) were used to examine the associations of Walk Score with specific walking and sedentary behaviors. There were significant positive associations of Walk Score with two types of walking and car driving. Each 10- point increment in Walk Score (range: 0–97) was associated with a 34% (95%CI: 1.25, 1.42) higher odds of any walking for commuting; a 6% (95%CI: 1.01, 1.11) higher odds of any walking for errands; a 36% (95%CI: 1.23, 1.50) higher odds of sufficient walking for commuting; and, a 10% (95%CI: 0.83, 0.97) lower odds of driving a car for more than one hour per day. This study found for the first time that Walk Score was related to travel behaviors in a non-Western country. Walk Score can be useful to transport and urban designers in identifying local areas that support (or do not support) residents' active travel, and can help to inform broader environmental and urban design policy initiatives to promote active living.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".