The Influence of Epoch Length on Physical Activity Patterns Varies by Child's Activity Level
Bibliographic record
Abstract
PURPOSE: Patterns of physical activity (PA) and sedentary time, including volume of bouted activity, are important health indicators. However, the effect of accelerometer epoch length on measurement of these patterns and associations with health outcomes in children remain unknown. METHOD: We measured activity patterns in 308 children (52% girls, age range = 8-11 years) using ActiGraph GT1M accelerometers with 15-s epochs and reintegrated to 60-s epochs. We calculated the volume (minutes per day) of moderate-to-vigorous PA (MVPA), sedentary time, light, moderate, and vigorous PA, as well as bouted MVPA and sedentary time (0-5 min, 5-10 min, 10-20 min, and > 20 min). RESULTS: The difference between 15-s and 60-s epochs was statistically significant for all outcomes; however, effect sizes were small or negligible in 30% of comparisons. Bias ranged from 1.9 min/day (total MVPA) to 102.7 min/day (0-5 min sedentary bouts). Regression-based estimates of bias and 95% limits of agreement illustrated that the magnitude, and in some cases, the direction, of between-epoch differences varied with activity level. Correlations with body mass index and cardiovascular fitness were similar for 15-s (r = -.19 to .20) and 60-s (r = -.16 to .29) epochs. Estimated 15-s data (predicted from 60-s) were similar to measured data and had similar relationships with health outcomes. CONCLUSION: Epoch length influences measurement of PA and sedentary patterns and the effect is modified by activity level. However, associations with health outcomes were similar and epoch differences can be adjusted. Future research should clarify the accuracy of different epoch lengths for measuring bouted activity and evaluate whether epoch length alters relationships with additional health outcomes.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".