Interval pedometry to quantify physical activity patterns in rural children
Bibliographic record
Abstract
Rural children are less physically active than urban children. Recording step accumulation at specific times (interval pedometry) may identify different patterns of physical activity (PA) participation. This study examined PA patterns between those living in a rural town and those living out of town examined the utility of interval pedometry. Steps were recorded at 6 intervals/d for 7d (n=56, 8–10 years), body composition and aerobic performance were assessed. Children residing in town had higher mean daily steps compared to children living out of town (11,506 vs. 9665; p<0.05); accounted for by weekday differences. Interval pedometry revealed that children living out of town obtained fewer steps (1846 vs. 2806) in the after school interval (15:20–18:00; r=−0.427, p<0.01); with no significant difference in the remaining 5 intervals. The 12:45–15:20 interval represented 26% of daily steps, while the highest step intensity was achieved during lunch recess (30.3 steps/min). The 18:00‐bedtime interval best predicted mean daily steps (r 2 =0.536, p<0.01); the after school intervals (step count and rate) were related to aerobic performance (p<0.05). This study demonstrates that children living outside of a rural town are especially at increased risk for physical inactivity. Interval pedometry is an effective method to detect differences in daily PA patterns. Supported by Manitoba Institute of Child 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 | 0.001 | 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) | 0.002 | 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".