Establishing normal values for pediatric nighttime sleep measured by actigraphy: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Despite the widespread use of actigraphy in pediatric sleep studies, there are currently no age-related normative data. Objectives: To systematically review the literature, calculate pooled mean estimates of actigraphy-derived pediatric nighttime sleep variables and to examine the magnitude of change with age. Methods: A systematic search was performed across eight databases of studies that included at least one actigraphy sleep variable from healthy children aged 0-18 years. Data suitable for meta-analysis were confined to ages 3-18 years with seven actigraphy variables analyzed using random effects meta-analysis and meta-regression performed using age as a covariate. Results: In total, 1334 articles did not meet inclusion criteria; 87 had data suitable for review and 79 were suitable for meta-analysis. Pooled mean estimates for overnight sleep duration declined from 9.68 hours (3-5 years age band) to 8.98, 8.85, 8.05, and 7.4 for age bands 6-8, 9-11, 12-14, and 15-18 years, respectively. For continuous data, the best-fit (R2 = 0.74) equation for hours over the 0-18 years age range was 9.02 - 1.04 × [(age/10)^2 - 0.83]. There was a significant curvilinear association between both sleep onset and offset with age (p < .001). Sleep latency was stable at 19.4 min per night. There were significant differences among the older age groups between weekday and weekend/nonschool days (18 studies). Total sleep time in 15-18 years old was 56 min longer, and sleep onset and offset almost 1 and 2 hours later, respectively, on weekend or nonschool days. Conclusion: These normative values have potential application to assist the interpretation of actigraphy measures from nighttime recordings across the pediatric age range, and aid future research.
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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 | 0.026 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".