Comparing the Actical and ActiGraph Approach to Measuring Young Children’s Physical Activity Levels and Sedentary Time
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Young children's activity and sedentary time were simultaneously measured via the Actical method (i.e., Actical accelerometer and specific cut-points) and the ActiGraph method (i.e., ActiGraph accelerometer and specific cut-points) at both 15-s and 60-s epochs to explore possible differences between these 2 measurement approaches. For 7 consecutive days, participants (n = 23) wore both the Actical and ActiGraph side-by-side on an elastic neoprene belt. Device-specific cut-points were applied. Paired sample t tests were conducted to determine the differences in participants' daily average activity levels and sedentary time (min/h) measured by the 2 devices at 15-s and 60-s time sampling intervals. Bland-Altman plots were used to examine agreement between Actical and ActiGraph accelerometers. Regardless of epoch length, Actical accelerometers reported significantly higher rates of sedentary time (15 s: 42.7 min/h vs 33.5 min/h; 60 s: 39.4 min/h vs 27.1 min/h). ActiGraph accelerometers captured significantly higher rates of moderate-to-vigorous physical activity (15 s: 9.2 min/h vs 2.6 min/h; 60 s: 8.0 min/h vs 1.27 min/h) and total physical activity (15 s: 31.7 min/h vs 22.3 min/h; 60 s: = 39.4 min/h vs 25.2 min/h) in comparison with Actical accelerometers. These results highlight the present accelerometry-related issues with interpretation of datasets derived from different monitors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it