Does activity space size influence physical activity levels of adolescents?—A GPS study of an urban environment
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is closely linked with child and youth health, and active travel may be a solution to enhancing PA levels. Activity spaces depict the geographic coverage of one's travel. Little is known about activity spaces and PA in adolescents. OBJECTIVE: To explore the relation between adolescent travel (using a spatial measure of activity space size) and daily moderate-to-vigorous PA (MVPA), with a focus on school days. METHODS: In Fall 2012, we used Global Positioning Systems to manually identify trips and generate activity spaces for each person-day; quantified by area for 39 students (13.8±0.6 years, 38% female) attending high school in urban Downtown Vancouver, Canada. We assessed the association between activity space area and MVPA using multi-level regression. We calculated total, school-day and trip-based MVPA for each valid person-day (accelerometry; ≥ 600 min wear time). RESULTS: (95% CI 1.3-3.0). There was no association between activity space size and school-day MVPA. Students accrued 21.8 min/day (95% CI 19.2-24.4) of MVPA during school hours, 19.4 min/day (95% CI 15.1-23.7) during travel, and 28.3 min/day (95% CI 22.3-34.3) elsewhere. CONCLUSION: School and school travel are important sources of PA in Vancouver adolescents, irrespective of activity space area covered.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".