Predicting future sleep problems in young autistic children

Abstract

Sleep problems are common in autistic children and adversely impact daytime functioning. Knowledge of predictive factors could help with treatment and prevention of these problems. This study aimed to determine predictors of sleep problems among young autistic children. Study cohorts consisted of autistic children aged 2–5 years who did not have sleep problems at a first visit and had sleep measure data available at a subsequent visit. Sleep problems for five study cohorts were defined by different methods: Children’s Sleep Habits Questionnaire with two cutoff scores (41 and 48; cohort n = 101 and 204, respectively), Children’s Sleep Habits Questionnaire–autism derived cutoff score (35; n = 188), and parent- (n = 205) or clinician- (n = 252) report. Separate predictive models of sleep problems were developed in each cohort using multivariable logistic regression with final model predictors determined by backward elimination selection. Model performance was evaluated. Two of the five models had both adequate discrimination and calibration performance predicting (1) Children’s Sleep Habits Questionnaire score ⩾ 48 and (2) Children’s Sleep Habits Questionnaire–autism score ⩾ 35. In both models, self-injurious behavior, sensory issues, dental problems, and lower primary caregiver education level were significant risk factors of future sleep problems. These predictors may be useful in helping clinicians provide prevention strategies or early intervention for children who are at a higher risk of developing sleep problems. Lay abstract Sleep problems are common in autistic children and negatively impact daytime functioning. A method for predicting sleep problems could help with treatment and prevention of such problems. This study aimed to determine predictors of sleep problems among young autistic children. Study participants consisted of autistic children aged 2–5 years who did not have sleep problems at a first visit (Autism Treatment Network Registry) and had sleep data available at a subsequent visit (Registry Call-Back Assessment study). Sleep problems for five study cohorts of children were defined by different methods, including parent questionnaires and parent- or clinician-report of sleep problems. We found that self-injurious behavior, sensory issues, dental problems, and lower primary caregiver education level were significant risk factors of future sleep problems. These predictors may help clinicians provide prevention or earlier treatment for children who are at risk of developing sleep problems.