Commuting by Public Transit and Physical Activity: Where You Live, Where You Work, and How You Get There
Bibliographic record
Abstract
BACKGROUND: Most public transit users walk to and from transit. We analyzed the relationship between transit commuting and objectively measured physical activity. METHODS: Adults aged 20 to 65 working outside the home (n = 1237) were randomly selected from neighborhoods in Seattle and Baltimore regions. Neighborhoods had high or low median income and high or low mean walkability. Mean daily minutes of accelerometer-measured moderate-intensity physical activity (MPA) were regressed on frequency of commuting by transit and neighborhood walkability, adjusting for demographic factors and enjoyment of physical activity. Interaction terms and stratification were used to assess moderating effect of walkability on the relation between transit commuting and MPA. Associations between transit commuting and self-reported days walked to destinations near home and work were assessed using Chi Square tests. RESULTS: Regardless of neighborhood walkability, those commuting by transit accumulated more MPA (approximately 5 to 10 minutes) and walked more to services and destinations near home and near the workplace than transit nonusers. Enjoyment of physical activity was not associated with more transit commute, nor did it confound the relationships between MPA and commuting. CONCLUSION: Investments in infrastructure and service to promote commuting by transit could contribute to increased physical activity and improved health.
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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) | 0.004 | 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".