ArticlePediatric discovery2026
Latent Profile Analysis of Sleep Patterns in Children With Autism Spectrum Disorder.
Article in Pediatric discovery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
Funding
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Abstract
Sleep disturbances significantly impact children with autism spectrum disorder (ASD), yet their heterogeneous manifestations remain poorly understood. This multicenter prospective cohort study employed latent profile analysis to identify distinct sleep phenotypes among 631 children with ASD (aged 3-6 years) and 768 typically developing (TD) controls across three Chinese cities representing Northern, Central, and Western regions. Analysis of Children's Sleep Habits Questionnaire data revealed three distinct sleep phenotypes based on optimal model fit determined by Bayesian information criterion. Compared to TD children who showed generally better sleep patterns with lower sleep onset delay and fewer disturbances overall, the ASD groups exhibited distinctive profiles: Cluster 1 (9.2%) exhibited severe disturbances across multiple domains (sleep anxiety, parasomnias, night wakings and sleep-disordered breathing) and demonstrated the most severe autism symptoms; Cluster 2 (36.0%) presented a mixed profile with comparable bedtime resistance, sleep duration, and daytime sleepiness to TD children but elevated sleep-disordered breathing; and Cluster 3 (54.8%) showed reduced sleep-disordered breathing but elevated night waking and bedtime resistance. One-year follow-up data indicated that Cluster 3, characterized by mild sleep-disordered breathing, showed significant improvements in core symptoms particularly in social cognition, communication, and motivation domains, whereas Clusters 1 and 2 demonstrated modest changes. These findings suggest that early identification of sleep phenotypes may predict treatment response and inform personalized intervention strategies. Our results underscore the importance of incorporating comprehensive sleep assessment and management into ASD care protocols.
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Registered trials
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