Validation of Actigraphy in Middle Childhood
Bibliographic record
Abstract
Few studies have examined the validity of actigraphy in school-aged children. The objective of this study was to examine the validity of a commonly used actigraph compared to polysomnography (PSG) in a sample of children age 5 to 12 y born prematurely, sleeping in their natural home environment. 148 children born preterm (85 boys and 63 girls), ages 5–12 y (mean = 9.3 y, standard deviation = 2.0) wore the Philips Respironics Actiwatch-2 for 1 night concurrently with comprehensive, ambulatory PSG in the child's home. Sleep outcome variables were sleep onset latency, total sleep time (TST), and sleep efficiency. Epoch-by-epoch comparisons were used to determine sensitivity, specificity, and accuracy. Secondary analyses examined differences between children with no sleep issues, obstructive sleep apnea syndrome, and periodic limb movements in sleep (PLMS). Actigraphy significantly underestimated TST (30 min) and sleep efficiency (5%). Actigraphy underestimated or overestimated sleep onset latency by at least 10 min for a third of the children. Sensitivity and accuracy were good at 0.88 and 0.84, respectively, whereas specificity was lower at 0.46. Differences between actigraphy and PSG for TST and sleep efficiency were greatest for children with PLMS. This study adds to the small existing literature demonstrating the validity of actigraphy in middle childhood. Although actigraphy shows good sensitivity (ability to detect sleep), specificity (ability to detect wake) is poor in this age group. Further, the results highlight the importance of considering whether a child has PLMS when interpreting actigraphic data, as well as the difficulties in accurately capturing sleep onset latency with actigraphy. The use of actigraphy to measure pediatric sleep-wake patterns is increasing, yet there remains little data about the validity of these devices in middle childhood. This study provides additional support for the use of actigraphy in pediatric research, yet also highlights the special considerations when interpreting the data provided by actigraphy. Future research is needed to not only further validate actigraphic devices, but also to improve the algorithms to improve the ability to detect wake after sleep onset in pediatric populations.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".