Descriptive Epidemiology of Youth Pedometer-Determined Physical Activity
Bibliographic record
Abstract
PURPOSE: Objective measurement with body worn instrumentation is a preferred and increasingly common way to gather information about young people's physical activity. Measured samples have been typically small and recruited through schools. The purpose of this article was to present the descriptive epidemiology of children and youth pedometer-determined physical activity on the basis of a large national sample. METHODS: Children and youth (19,789) were recruited through random digit dialing. Participants were asked to wear the pedometer for seven consecutive days and to log daily steps. Of the 58% of participants who returned pedometer data, 95% wore the pedometer for at least 5 d. Daily step counts below 1000 or above 30,000 steps were truncated accordingly, and all values were included in the descriptive analysis. RESULTS: Boys and girls aged 5-19 yr took 12,259 and 10,906 steps per day, respectively. Daily steps were higher among boys than girls and declined by age group in a pattern consistent with that predicted by other smaller samples internationally. Weekday steps per day were generally higher than weekend day steps per day and varied by season. CONCLUSIONS: This study demonstrates the viability of using relatively inexpensive pedometers and methods for the surveillance of young people's physical activity. The resulting descriptive data provide key information regarding the population distribution of pedometer-determined physical activity that may be useful for identifying target groups for population strategies and other interventions.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".