55 Profiles of Sleep Problems among Young Children with Autism Spectrum Disorders
Bibliographic record
Abstract
Abstract Background Sleep problems are more common and severe among children with autism spectrum disorder (ASD) compared to their typically developing peers. The goal of this study was to characterize sleep problems profiles and their clinical correlates, based on a five-factor model of the Children’s Sleep Habits Questionnaire (CSHQ) among preschool children with ASD. Objectives (1) To describe empirically-derived patterns (i.e., latent profiles) of sleep problems among young children with ASD; and (2) To examine relations between family cumulative risk and emotional-behavioral dysregulation symptoms and sleep profile membership. Design/Methods The study included 318 three-to-five year old children (M= 49.45 months; SD = 5.77). Latent profile analysis was used to identify and describe profiles of sleep problems. Sleep problems were assessed using a previously established CSHQ five-factor model: (1) Bedtime Routine; (2) Sleep Onset & Duration; (3) Night Waking; (4) Morning Waking; and (5) Sleep Disordered Breathing, with higher scores indicating greater problems. We assess whether profile membership was associated with dysregulation difficulties (CBCL 1.5-5) and family cumulative risk index (CRI; constructed based on socioeconomic status, maternal distress, family functioning, and other related factors) using a three-step method (Vermunt & Magidson, 2013). Results A five-profile model of children’s sleep problems showed the best fit (Figure 1). Profile 1, Nighttime Sleep Problems (28%), consisted of children with scores around the sample mean, except relatively lower scores on Morning Waking. Profile 2, Severe Sleep Problems (25%), consisted of children with relatively high scores across all sleep problems. Profile 3, Low Sleep Problems (18%), included children with the lowest levels of all sleep problems. Profile 4, Moderate Sleep Problems (17%), included children with all sleep problem levels near the sample mean. Profile 5, Morning Waking Problems (12%), consisted of children with low scores on Bedtime Routine problems but pronounced Morning Waking problems. Dysregulation difficulties (Wald = 13.90; p = .001) and family CRI (Wald = 13.27; p =.001) emerged as significant predictors of profile membership. Higher CRI was associated with higher odds of membership in Profile 2 (Severe Sleep Problems), and lower scores for dysregulation difficulties were associated with higher odds of membership in Profile 3 (Low Sleep Problems). Conclusion Children with ASD present distinct profiles of sleep problems that differ, not only by overall severity, but also by relative severity across types of sleep problems. Children’s dysregulation and family risk should be considered in examining children’s sleep.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".