School travel and children’s physical activity: a cross-sectional study examining the influence of distance
Bibliographic record
Abstract
BACKGROUND: Walking to school is associated with higher levels of physical activity. The purpose of this study was to examine the relationship between school travel mode and physical activity using a sampling frame that purposefully locates schools in varying neighbourhoods. METHODS: Cross-sectional survey of 785 children (10.57 ± 0.7 years) in Toronto, Canada. Physical activity was measured by accelerometry and travel mode was self-reported by parents. Linear regression models accounting for school clustering effects examined the associations between mode choice, BMI, and physical activity and were estimated adjusting for age, types of neighbourhoods and travel distance to school. RESULTS: Significant associations between walking to school and moderate activity during weekdays were found. Interactions between walking to school and travel distance to school were found only in boys with significant associations between walking to school and higher physical activity levels in those living within 1000-1600 meters from school. Boys walking to school and living in this range accumulated 7.6 more minutes of daily MVPA than boys who were driven. CONCLUSIONS: Walking to school can make a modest but significant contribution to overall physical activity. This contribution was modified by travel distance and not school neighbourhood socioeconomic status or the built environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".