THE PUBLIC HEALTH IMPLICATION OF ???ACTIVE COMMUTING??? SCHOOL TRIPS ON LIFESTYLE PHYSICAL ACTIVITY
Bibliographic record
Abstract
Increasing lifestyle physical activity (PA) among children is important to prevent obesity and to establish early healthy habits. Active commuting to schools has been identified as a potential source of regular moderate activity and an area for intervention, yet its measurement has been ignored in surveys of children PA. PURPOSE To assess the level of PA associated with active commuting trips and its public health implication. METHODS A telephone interview survey of randomly sampled children aged 5–12 years was conducted as part of the evaluation of the ‘Walk to School Day’. Parents were asked to describe the modes of commuting on each school trip for the whole week and the travel time on each mode for every trip. RESULTS Overall, 811 parents described the travel modes for 10 school trips representing 8,110 students-trips. Of these, 20.3% were ‘walk-only trips’, 1.5% were ‘cycling-only trips’ and 11.5% were a combination of ‘part-way walking’ and motorized mode. The population mean of active commuting was 3.3 sessions per week (95%CI:3.09–3.66) the median was 0. One quarter (26%) commuted actively on most school trips (8–10 trips) and 11% reported active commuting on 5–7 trips. The proportion of those who walked on most sessions was highest (45%) among those living < = 0.75km to school but dropped to 23% if the distance was between 0.76–1.5km. The population mean weekly minutes of active commuting was 28.9 minutes(95%CI:25.3–32.5) the upper quartile range was 45–200 minutes. The mean duration of one ‘all the way’ walking session was 10 minutes (95%CI:9.4–11.7) while for ‘part way’ session it was 5.2 minutes (95% CI:4.3–6.2). About 14% of primary school children walk continuously for duration of 10 minutes or more. CONCLUSION This analysis broaden the PA measures, particularly in the measurement of walking for transport to and from schools. For greater population impact, interventions should focus on increasing the number of active commuting sessions especially for those living within 0.76–1.5km from school. Supported by NSW Health Department.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".