Process and Treatment of Pedometer Data Collection for Youth
Bibliographic record
Abstract
BACKGROUND: Pedometry methods for collecting data in young populations are advancing, but it is unclear how many days of data are enough for population monitoring. METHODS: Using random-digit dialing, 11,669 5- to 19-yr-olds were recruited into the Canadian Physical Activity Levels among Youth study and mailed a data collection package. Pedometers were worn for 7 d, and steps counts were logged daily. Reactivity was assessed by examining estimates from the pattern of pedometer data across days (arranged from first day of collection to last) using a repeated-measures ANOVA. Intraclass correlations (ICC) were computed for the first day and consecutive additional days (compared with the criterion estimate based on the whole week) to determine the minimal number of days required to achieve a reliability ICC of 0.70, 0.80, and 0.90. RESULTS: Most children (990%) wore the pedometer for 7 d. Mean steps per day differed across consecutive days (F = 52.7, P = 0.000); however, no difference occurred between the first and the second day of monitoring. Furthermore, no difference was observed between the first and either the third or the fourth day when monitoring commenced on a Monday or a Tuesday. Therefore, there was no clear evidence of reactivity. The first day provided a good representation of steps per day relative to the whole week in terms of both reliability (ICC = 0.79) and validity (relative absolute percent error [APE] =2.5%), and these improved with additional days (2 d, ICC > 0.85; > or = 3 d, ICC > 0.90; and > or = 3 d, APE < 1%). CONCLUSIONS: The Canadian Physical Activity Levels among Youth demonstrates the feasibility of national surveillance of physical activity using pedometers.Two days are sufficient to determine steps per day, and a single day appears defensible in terms of population monitoring if minimal standards for reliability are acceptable.
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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 | 0.114 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".