Investigating longitudinal associations between parent reported sleep in early childhood and teacher reported executive functioning in school-aged children with autism
Bibliographic record
Abstract
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 difficulties in ASD are exacerbated by poor sleep. We examine whether early childhood sleep disturbances are associated with worsening EF trajectories in school-aged children with ASD. 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, were used to measure cognitive and affective components of EF respectively at four time-points (7.8-11.8 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). Sleep traits had different age-related impacts on behavioral regulation, but not metacognition. Longer sleep onset at 3.5 years was associated with a worsening BRI difficulties slope (b = 2.07, p < 0.04), but conversely associated with lower BRI difficulties at 7.7 years (b = -4.14, p = 0.04). A longer sleep onset at 6.7 years was related to higher BRI difficulties at 7.7 years (b = 7.78, p < 0.01). Longer sleep duration at 6.7 years was associated with higher BRI difficulties at age 7.7 (b = 3.15, p = 0.01), but subscale analyses revealed shorter sleep duration at age 6.7 was linked to a worsening inhibition slope (b = -0.60, p = 0.01). Sleep onset is a robust early correlate of behavior regulation in children with ASD, whereas sleep duration is a later childhood correlate.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".