Identifying Children’s Nocturnal Sleep Using 24-h Waist Accelerometry
Bibliographic record
Abstract
PURPOSE: The purposes of this study were 1) to add layers and features to a previously published fully automated algorithm designed to identify children's nocturnal sleep and to exclude episodes of nighttime nonwear/wakefulness and potentially misclassified daytime sleep episodes and 2) to validate this refined sleep algorithm (RSA) against sleep logs. METHODS: Forty-five fourth-grade school children (51% female) participants were asked to log evening bedtime and morning wake time and wear an ActiGraph GT3X+ (ActiGraph LLC, Pensacola, FL) accelerometer at their waist for seven consecutive days. Accelerometers were distributed through a single school participating in the Baton Rouge, USA, site of the International Study of Childhood Obesity, Lifestyle, and the Environment. We compared log-based variables of sleep period time (SPT), bedtime, and wake time to corresponding accelerometer-determined variables of total sleep episode time, sleep onset, and sleep offset estimated with the RSA. In addition, SPT and sleep onset estimated using standard procedures combining sleep logs and accelerometry (Log + Accel) were compared to the RSA-derived values. RESULTS: RSA total sleep episode time (540 ± 36 min) was significantly different from Log SPT (560 ± 24 min), P = 0.003, but not different from Log + Accel SPT (549 ± 24 min), P = 0.15. Significant and moderately high correlations were apparent between RSA-determined variables and those using the other methods (r = 0.61 to 0.74). There were no differences between RSA and Log + Accel estimates of sleep onset (P = 0.15) or RSA sleep offset and log wake time (P = 0.16). CONCLUSIONS: The RSA is a refinement of our previous algorithm, allowing researchers who use a 24-h waist-worn accelerometry protocol to distinguish children's nocturnal sleep (including night time wake episodes) from daytime activities.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".