The effects of child complexity on responses to behavioural interventions for sleep problems in children with autism spectrum disorders.

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Theses / Dissertations
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Thesis discipline
Health Sciences
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Master of Science
University of Canterbury
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Ng, I Pin

Sleep problems are one of the most commonly reported concerns among parents of children with Autism Spectrum Disorder (ASD). Unless treated effectively, sleep problems are highly persistent and sometimes continue into adulthood. Given the prevalence of sleep problems and long-term effects associated with sleep problems (i.e., impaired daytime and adaptive functioning, social interaction and communication difficulties), it is crucial that effective treatments are identified. Behavioural treatments such as scheduled awakenings and extinction meet the criteria to be considered possibly efficacious intervention and more future replications are needed in order to be considered well-established interventions. Similarly, in the extant literature, there is an emerging body of evidence demonstrating the effectiveness of behaviourally-based, parent-implemented sleep treatments that are informed by functional behavioural assessment (FBA). However, further replication and expansion of this research is also required. As a starting point, factors that may affect response to behaviourally-based, parent-implemented sleep treatments need to be investigated and identified.

The purpose of this study was, therefore, to investigate and identify the complex array of factors that may affect responses to FBA-informed and parent-implemented behavioural sleep interventions. Following Kazdin and Whitley (2006), various factors associated with the scope and severity of the child’s problem were used to define the dimensions of clinical complexity. These factors included impaired daytime functioning and behaviour, sleep problems, presence of comorbid psychopathology and medications use. Impaired daytime functioning and behaviour were measured using the Child Behaviour Checklist (CBCL), the Gilliam Autism Rating Scale, Second Edition (GARS-2), and the Vineland Adaptive Behaviour Scale, Second Edition (VABS II). Children’s sleep problems were measured using the Children’s Sleep Habits Questionnaire (CSHQ). Presence of comorbid psychopathology was measured using a categorical variable of comorbidity and medications use was measured using a categorical variable of medications. A Sleep Problem Severity (SPS) score was calculated for each child in order to objectively measure the changes before and after treatment. A pretest-posttest research design was used to analyse existing clinical data obtained from 31 children who had parent reported sleep disturbance, and who had received an FBA-informed intervention. Results from paired sample t test showed that there was a statistically significant reduction in sleep problem severity between pre- and post-treatment. Based on evaluation of modified Brinley plots, 27 out of the of 31 children showed improvement posttreatment. Factor analysis identified three latent variables underlying behaviour problems, sleep problems, autism severity, communications, medications and comorbidity. The latent variable Behaviour and Sleep Problems was identified as a statistically significant predictor of changes in SPS score post-treatment. The second latent variable Medication-Communication was not a statistically significant predictor of changes in SPS score post-treatment. The third latent variable Psychopathology Severity was also not a statistically significant predictor of changes in SPS score post-treatment. The present findings add to the limited literature investigating the child characteristics that may impact upon response to behavioural sleep interventions in children with ASD. In terms of treatment, additional components to address behavioural problems will need to be formulated and included as part of the sleep intervention treatment package so that both behaviour and sleep problems can be addressed together to enhance treatment responses. Further research is still needed to develop a deeper understanding of the complex array of factors that may affect treatment responses.

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