55 Profiles of Sleep Problems among Young Children with Autism Spectrum Disorders

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.