Genetic and Environmental Influences on Daytime and Nighttime Sleep Duration in Early Childhood
Bibliographic record
Abstract
OBJECTIVES: To determine the relative contributions of genetic and environmental factors on daytime and nighttime continuous sleep duration at 6, 18, 30, and 48 months of age, and to identify different subgroups of children who followed different daytime and nighttime sleep duration trajectories and to investigate their etiology. METHODS: The current study included 995 twins (405 monozygotic and 586 dizygotic) of the Quebec Newborn Twin Study recruited from the birth records of the Quebec Statistics Institute. Daytime and nighttime sleep was assessed through maternal reports at 6, 18, 30, and 48 months of age. A semiparametric modeling strategy was used to estimate daytime and nighttime sleep duration trajectories. Quantitative genetic models were used to examine to what extent genetic and environmental factors influenced daytime and nighttime continuous sleep duration. RESULTS: Genetic modeling analyses revealed environmental influences for all daytime sleep duration trajectories. In contrast, strong genetic influences were found for consolidated nighttime sleep duration (except at 18 months and for the short-increasing sleep duration trajectory). CONCLUSIONS: This is the first indication that early childhood daytime sleep duration may be driven by environmental settings, whereas the variance in consolidated nighttime sleep duration is largely influenced by genetic factors with a critical environmental time-window influence at ∼18 months.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".