The Interplay Between Sleep and Executive Functioning in Children with Autism
Bibliographic record
Abstract
Study Objectives: Up to 80% of children with autism spectrum disorder (ASD) experience sleep disturbance. Poor sleep impairs executive functioning (EF), a lifelong difficulty in ASD. Evidence suggests EF impairments in ASD is exacerbated by poor sleep. We examine whether early childhood sleep disturbances are associated with worsening EF trajectories in school-aged children with ASD. Methods: A subsample (n = 217) from the Pathways in ASD longitudinal study was analyzed. The Children’s Sleep Habits Questionnaire captured sleep duration, onset, and night awakenings before age 5 (Mean = 3.5 years). Metacognition (MI) and Behavioral Regulation (BRI) indices on the Teacher Behavior Rating Inventory of Executive Functioning measured EF difficulties at four time-points (7-11 years). We applied latent growth curve models to examine associations between sleep and EF, accounting for relevant covariates, including school-age sleep (Mean = 6.7 years). Results: Longer sleep onset at 3.5 years predicted a worsening BRI difficulties slope (b = 2.07, p < 0.04), but conversely predicted lower BRI difficulties at age 7.7 (b = -4.14, p = 0.04). A longer sleep onset at age 6.7 predicted higher BRI difficulties at age 7.7 (b = 7.78, p < 0.01). Longer sleep duration at age 6.7 predicted higher BRI difficulties at age 7.7 (b = 3.15, p = 0.01), but subscale analyses revealed shorter sleep duration at age 6.7 predicted a worsening inhibition slope (b = -0.597, p = 0.01). Conclusions: Different sleep phenotypes have different age-related impacts on selective behavioral regulation components, but not metacognition. Delayed sleep onset is a robust early predictor, whereas shorter sleep duration is a later predictor of worsening behavior regulation in school-aged children with ASD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".