Relationships Among Children’s Independent Mobility, Active Transportation, and Physical Activity: A Multisite Cross-Sectional Study
Bibliographic record
Abstract
PURPOSE: Children's independent mobility (IM) may facilitate both active transportation (AT) and physical activity (PA), but previous studies examining these associations were conducted in single regions that provided limited geographical variability. METHOD: We recruited 1699 children (55.0% girls) in 37 schools stratified by level of urbanization and socioeconomic status in 3 regions of Canada: Ottawa, Trois-Rivières, and Vancouver. Participants wore a SC-StepRx pedometer for 7 days and completed a validated questionnaire from which we derived a 6-point IM index, the number of AT trips over a week, and the volume of AT to/from school (in kilometer per week). We investigated relationships among measures of IM, AT, and PA employing linear mixed models or generalized linear mixed models adjusted for site, urbanization, and socioeconomic status. RESULTS: Each unit increase in IM was associated with 9% more AT trips, 19% higher AT volume, and 147 more steps per day, with consistent results across genders. Both measures of AT were associated with marginally higher PA when pooling boys' and girls' data. Children in Vancouver engaged in more AT. PA did not vary across site, urbanization, or socioeconomic status. CONCLUSION: IM was associated with more AT and PA regardless of where children lived, underscoring a need for IM interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".