ActiGraph Cutpoints Impact Physical Activity and Sedentary Behavior Outcomes in Young Children
Bibliographic record
Abstract
Purpose: Examine the effect of cutpoint selection on physical activity (PA) metrics calculated from young children’s accelerometer data and on the proportion of children meeting PA guidelines. Methods: A total of 262 children (3.6 ± 1.4 years, 126 males) wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days, 24 hr/day. Ten cutpoint sets were applied to the sample categorized by age, based on populations of the original cutpoint calibration studies using ActiLife software. Resulting sedentary behavior, light PA, moderate to vigorous PA, and total PA were compared using repeated-measures analysis of variance. Proportion of children meeting age-appropriate PA guidelines based on each cutpoint set was assessed using Cochran’s q tests. Results: Children wore the accelerometer for an average of 7.6 ± 1.2 days for an average of 11.9 ± 1.2 hr/day. Significant differences in time spent in each intensity were found across all cutpoints except for sedentary, and total PA for three comparisons (Trost vs. Butte Vertical Axis [VA], Pate vs. Puyau, and Costa VA vs. Evenson) and moderate to vigorous PA for four comparisons (Trost vs. Pate, Trost vs. Pate and Pfeiffer, Pate vs. Pate and Pfeiffer, and van Cauwenberghe vs. Evenson). When examined within age-appropriate groups, all sets of cutpoints resulted in significant differences across all intensities and in the number of children meeting PA guidelines. Conclusion: Choice of cutpoints applied to data from young children significantly affects times calculated for different movement intensities, which in turn impacts the proportion of children meeting guidelines. Thus, comparisons of movement intensities should not be made across studies using different sets of cutpoints.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".