BEHAVIORAL CORRELATES OF SLEEP DISTURBANCE IN CHILDREN WITH AUTISM SPECTRUM DISORDER

Transcription

1 BEHAVIORAL CORRELATES OF SLEEP DISTURBANCE IN CHILDREN WITH AUTISM SPECTRUM DISORDER By MAKEDA W. MOORE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2017

2 2017 Makeda W. Moore

3 To the families who participated in Autism Treatment Network

4 ACKNOWLEDGMENTS This thesis would not have been possible without the support and guidance of Dr. Cynthia R. Johnson, Ms. Jaylyn Clark, Mrs. Victoria Evans, and all members of the Clinical Health Autism Program (CHAP). I would also like to thank all of my friends and family who have continuously provided me support and encouragement throughout the duration of my graduate studies. Lastly, I would like to thank the Autism Treatment Network and all those who participated in the study. 4

5 TABLE OF CONTENTS ACKNOWLEDGMENTS...4 LIST OF TABLES...7 LIST OF FIGURES...8 LIST OF ABBREVIATIONS...9 ABSTRACT...10 CHAPTER 1 INTRODUCTION...12 page ASD as a Major Health Problem...12 Prevalence of Sleep Disturbance in Individuals with ASD...13 Contributing Factors of Sleep Disturbances in Children with ASD...14 Consequences of Sleep Disturbance...15 Sleep Disturbances and Challenging Behavior...16 Sleep Disturbance Across Functional Levels...17 Significance of the Current Study...18 Study Aims METHODS...21 Sample Characteristics...21 Procedure...22 Measures...22 Statistical Analyses...24 Preliminary Analysis RESULTS...28 Preliminary Correlations...28 Aim 1. Associations among Specific Sleep Disturbance and Challenging Behavior...29 Aim 2. Challenging Behaviors Predicted by Sleep Problem Severity and Level of Functioning DISCUSSION...36 Sleep Problems and Challenging Behaviors...36 Sleep Problem Severity and Functioning Level predict Challenging Behaviors...37 Study Limitations and Strengths...38 Study Implications and Future Directions

6 LIST OF REFERENCES...41 BIOGRAPHICAL SKETCH

7 LIST OF TABLES Table page 2-1 Sample demographics (n=129) and breakdowns by sleep disturbance severity Descriptive statistics of study variables of interest Preliminary correlations between demographics and sleep variables of interest Regression models for sleep problems predicting challenging behavior MANOVA: Mean scores of challenging behaviors by group

8 LIST OF FIGURES Figure page 3-1 (MANOVA) Effect of sleep problem severity and level of functioning on irritability (MANOVA) Effect of sleep problem severity and level of functioning on social withdrawal (MANOVA) Effect of sleep problem severity and level of functioning on stereotypic behavior (MANOVA) Effect of sleep problem severity and level of functioning on hyperactivity

9 LIST OF ABBREVIATIONS ABC ADOS-2 ASD ATN CSHQ DSM-IV-TR HRSA MANOVA SD VABS Aberrant Behavior Checklist Autism Diagnostic Observation Schedule, 2 nd Edition Autism Spectrum Disorder Autism Treatment Network Child Sleep Habits Questionnaire Diagnostic and Statistical Manual of Mental Disorders, 4 th Edition, text revision Health Resources and Services Administration Multivariate Analysis of Variance Standard Deviation Vineland Adaptive Behavior Scales 9

10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science BEHAVIORAL CORRELATES OF SLEEP DISTURBANCE IN CHILDREN WITH AUTISM SPECTRUM DISORDER Chair: Cynthia R. Johnson Major: Psychology By Makeda W. Moore May 2017 Sleep disturbances occur in up to 80% of children with Autism Spectrum Disorder (ASD). There has been a growing effort to understand the relationship between sleep and daytime functioning in these children, but our current knowledge is limited. Specifically, associations between specific sleep problems and daytime challenging behavior across all age groups and level of functioning has been understudied. Thus, the current study aims to explore the association between sleep disturbance and challenging behavior in children with ASD. Participants included 129 children with ASD, ages 2-12 (M=5.38 years) and a parent who completed questionnaires as part of the Autism Treatment Network. Results indicated that sleep problems were highly correlated with all challenging behaviors, particularly Irritability and Social Withdrawal. Sleep Duration Problems and Parasomnias explained the most variance in challenging behaviors. Children with the most severe levels of sleep disturbances experienced significantly higher levels of challenging behaviors compared to children with no/minimal and mild/moderate sleep disturbances, regardless of level of functioning. The current study also found significant correlations for level of functioning with Stereotypic Behavior and Social Withdrawal. Given that level of functioning was found to explain significant variance in Sleep 10

11 Duration Problems, in combination with the finding that Sleep Duration Problems were one of the strongest predictors of challenging behavior, there may potentially exist underlying associations among level of functioning and challenging behavior that were not revealed in the present analyses. The interrelationships between sleep, daytime behaviors problems, and adaptive functioning are likely complex and warrant further exploration. 11

12 CHAPTER 1 INTRODUCTION ASD as a Major Health Problem Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder marked by impairments in social interaction and communication, in addition to restricted, repetitive, interest and behaviors (APA, 2013). Core symptoms of ASD fall along a continuum of severity and can lead to lifelong impairments in various aspects of daily functioning (APA, 2013; Brereton, Tonge, Einfeld, 2006). Individuals with ASD are at high risk for comorbid emotional, behavioral, and physical conditions that further intensify functional impairments (Simonoff et al., 2008). In fact, nearly three quarters of children with ASD have at least one comorbid medical or psychiatric condition severe enough to necessitate intervention (Brereton, Tonge, & Einfeld, 2006; Hartley, Sikora, & McCoy, 2008; Holloway, Aman, & Butter, 2013; Simonoff et al., 2008). Current estimates indicate that as many as 1 in 68 children in the United States are diagnosed with ASD; which represents an approximate 119.4% increase in prevalence since 2000 (Baio, 2014; CDC, 2014; Christensen et al., 2012). Given the high prevalence of ASD and the broad spectrum of impairments associated with both core symptoms and comorbid conditions, it is no wonder that the current annual cost for providing appropriate treatment in the United States is estimated to be $126 billion per year (Kornack et al., 2014). This raises the question of how to better determine the most effective and efficient approaches for treating ASD. One way to address this issue is to improve our understanding of ASD and the complex interaction between core symptoms, comorbid conditions, and behavioral outcomes that often complicate treatment implementation. Sleep disturbances are one of the most common comorbid conditions in ASD and likely play a critical role in these interactions ( Goel, Rao, Durmer, & Dinges, 2009; Holloway et al., 2013; Lavigne et al., 1999; Sadeh, Lavie, & Scher, 1994). 12

13 Prevalence of Sleep Disturbance in Individuals with ASD Sleep disturbances are commonly reported during childhood, particularly for younger children and those with developmental delays (Campbell, 1995; Sadeh et al., 2002). For children with ASD, sleep disturbances are reported at a prevalence of 40-80% (Adams et al., 2014; Harrman, 2016; Polimeni, Richdale, & Francis, 2005), compared to approximately 25%- 40% of children in the general population (Goldman, Richdale, Clemons, & Malow, 2012; Owens, 2007; May et al., 2015; Meltzer & Mindell, 2008). In contrast, to sleep problems in typical developing children which often remit with age, sleep problems in children with ASD are persistent; continuing even into adulthood (Jones et al., 2015; Matson, Ancona, & Wilkins, 2008; Mazurek & Sohl, 2016; Richdale & Schreck, 2009; Simonoff et al., 2008; Sivertsen et al., 2012). One study also found that children with ASD developed more sleep problems over time at an incidence rate four times higher than that of non-asd controls (Sivertsen et al., 2012). However, other studies have found a decline in sleep problems with age (Gregory & O Connor, 2002; Giannotti et al., 2008; Krakowiak et al., 2008; Liu, Hubbard, Fabes, & Adams, 2006), and other studies do not support associations between age and sleep problems (Adams, Matson & Jang, 2014; Mayes & Calhoun, 2009; Wiggs & Stores, 2004; Williams, Sears, & Allard, 2004). Given the heterogeneity of symptom presentation in ASD, it is not surprising that sleep disturbances also take on a variety of presentations (Krakowiak et al., 2008; Mayes & Calhoun, 2009). The most commonly reported sleep problems for children with ASD relate to difficulties in sleep onset (e.g. delayed sleep latency and sleep onset association problems), maintenance (e.g., night wakings), and disturbed episodes during sleep (e.g., nightmares, night terrors, sleeprelated movement disorders), (Adams et al., 2014; Deliens et al., 2015; Harrman, 2016; Wiggs & Stores, 2004). Bedtime resistance and restricted bedtime routines are also very common among children with ASD (Cortesi, Giannotti, Ivanenko, & Johnson, 2010; Kotagal & Broomall, 2012). 13

14 Whether in combination or alone, these sleep disturbances, can result in diminished sleep duration and sleep quality, increasing the risk for daytime sleepiness and other side effects of poor sleep (Henderson, Barry, Bader, & Jordan, 2011). Contributing Factors of Sleep Disturbances in Children with ASD Sleep disturbances in children with ASD have been differentially related to various biological (e.g., abnormal melatonin, chronic pain, gastrointestinal problems), psychological (e.g., hyperactivity, anxiety, depression, and emotion dysregulation), and environmental factors (Deliens et al., 2015; Holloway, Aman, & Butter, 2013; Mazurek, Engelhardt, Hilgard, & Sohl, 2016; Simonoff et al., 2008; Park et al., 2012). Some studies also suggest that sleep problems may be related to ASD symptom severity; fewer hours of sleep per night was associated with more ASD characteristics such as communication problems, stereotypic behavior, and hypersensitivity to environment (Adams, Matson, Cervantes, & Goldin, 2014; Holloway et al., 2013; Malow et al., 2006; Mayes et al., 2009; Schreck, Mulick, & Smith, 2004). Additionally, children with ASD may be less likely to respond to social-environmental cues to initiate sleep, engage in co-sleeping, exhibit sensory hypersensitivities to their environment, or require television, night lights, or other electronic devices at bedtime, which have been shown to be counterproductive to sleep onset (Hare, Jones, Evershed, 2006; Henderson et al., 2011; Johnson, 1996; Mazurek & Petroski, 2015). Parenting practices at bedtime (e.g., parent s ability to implement a bedtime routine and respond appropriately to bedtime resistance) are also believed to influence sleep in children with ASD, however few studies have explored this, in particular (Weiskop, Richdale, & Matthews, 2005). Taken together, evidence suggests that there may be many factors influencing sleep, however much remains unknown about the about the specific nature of the relationship between these factors and sleep problems for children with ASD (Richdale & Schreck, 2009). 14

15 Consequences of Sleep Disturbance Sleep is an important biological process, particularly for young children in regards to brain development cognition, memory, and energy conservation (Dahl, 1996, Kurth et al., 2010; Sadeh, 2007). A growing body of evidence suggest that chronically disturbed sleep in young children and adolescents can lead to a host of negative consequences including: impaired attention, perception, processing speed, concentration, memory, and learning (Chen, Lemonnier, Lazartigues, & Planche, 2006; Gregory & Sadeh, 2012). Additional reports suggest that chronic sleep disturbance increases the risk of physical health problems including changes in cardiovascular, immune, endocrine, nervous system function, and, for children with ASD in particular, increased weight status and poorer overall health-related quality of life (Broder- Fingert et al., 2014; Delahaye et al., 2014; Haney & Kott, 2014; Gillette et al., 2015). Sleep disturbances have also been closely linked to internalizing and externalizing behaviors (Lavigne et al., 1999; Sikora, Johnson, Clemons, & Katz, 2012), including hyperactivity (Owens, 2008; Touchette et al., 2009), irritability, impulsivity (Touchette et al., 2007), aggression, and oppositional behavior (Shanahan et al., 2014). Given the importance of adequate sleep in the daily functioning, sleep disturbances in children with ASD are likely detrimental (Cohen et al., 2014). Challenging behaviors represent a set of potential consequences of sleep disturbance, that continue to gain more attention due to their impact on, not only children with ASD, but also parents/caregivers and health care providers. Yet, limited empirical research has investigated the associations among sleep problems and challenging behaviors, in particular. Unfortunately, of the research available, inconsistent findings have further hindered the conceptualization of sleep disturbance in children with ASD, including its association with challenging behaviors (Johnson, 1996). This may be due, in part, to discrepancies in defining sleep problems (Adams, et al., 2014; 15

16 Richdale & Schreck, 2009) and differences in methods for measuring sleep (actigraph, polysomnography, video-recording, parent report/self-report), on top of the heterogeneity of symptom presentation in ASD. Despite these limitations, various studies consistently suggest that sleep disturbances are associated with various cognitive, emotional, and behavioral problems, that severely impact daily functioning for children with ASD (Baio, 2014; Bates et al., 2002, Brereton et al., 2006; Kozlowski, Matson, Belva, & Rieske, 2012; May et al., 2015; Mayes & Calhoun, 2009). Sleep Disturbances and Challenging Behavior Challenging behaviors (e.g., hyperactivity, aggression, impulsivity, non-compliance, tantrums, etc.), are a common problem for children with ASD, and have been shown to impede learning, access to normal activities, and most importantly, interfere with treatment of core symptoms of ASD (Matson & Nebel-Schwalm, 2007). Given what we know about the association between sleep problems and behavioral functioning in non-asd children, the presence of sleep problems may likely amplify challenging behaviors, in particular (Malow et al., 2006). The most common types of challenging behaviors for children with ASD include aggression, disruptions/tantrums, self-injury, and stereotypic behavior (Adams et al., 2014; Malow et al., 2006). Studies have found an association between challenging behaviors and sleep disturbance severity. Adams, Matson, and Jang (2014) found that the children with severe sleep problems exhibit significantly more challenging behaviors than children with mild or no sleep problems, and that sleep problems more drastically impact externalizing (challenging) behaviors compared to internalizing behaviors (e.g., depression and anxiety). Other studies report that sleep problems are associated with higher levels of aggression, somatization problems, and both internalizing and externalizing difficulties in children with ASD (Sikora et al., 2012; Park et al., 2012). In a 16

17 review of the relationship between sleep problems and behavior, Cohen and colleagues (2014) concluded that sleep problems not only intensify core symptoms of ASD, but also exacerbate challenging behaviors. Some studies have investigated the association between specific types of sleep problems and behavior in children with ASD. Current findings suggest: sleep onset delay and fragmented sleep (Sadeh, Gruber, & Raviv, 2002) are associated with attention and communication problems (Deliens et al., 2015;Goldman et al., 2009); bedtime resistance and nightmares are associated with internalizing disorders (Mazurek & Sohl, 2016; Sadeh et al., 2002; Gregory & Sadeh, 2012); disorders of excessive somnolence (difficulty waking up, waking up tired, sleep paralysis, and daytime sleepiness) are associated with severe thought and general behavioral problems (Fadini et al., 2015). Evidence also suggests that, regardless of age, night waking is associated with physical aggression, hostility, inattention, and hyperactivity; sleep anxiety is a strong predictor of irritability; and parasomnias are a strong predictor of hyperactivity (Mazurek & Sohl, 2016). Other cross-sectional studies have found that bedtime resistance, shorter sleep duration, and night terrors predict stereotypic behavior (Deliens et al., 2015; Goldman et al., 2009; Park et al., 2012; Schreck et al., 2004), and that the frequency and duration of night waking predicts social communication (Taylor, Schreck, & Mulick, 2012). Overall, there is a consensus that sleep disturbances adversely impact daytime functioning for children with ASD (Anders et al., 2012; Mazurek & Sohl, 2016). Sleep Disturbance Across Functional Levels Evidence regarding the relationship between sleep disturbances and functioning level of children with ASD has not been closely examined. (Richdale, Baker, Short, & Gradisar, 2014). For the small sample of studies that have explored sleep in children with ASD, difficulties in the ability to communicate sleep experiences or comply with actigraphy or other forms of sleep 17

18 measurement, has excluded many low-functioning individuals with ASD from more objective explorations of sleep (Allik, Larsson, & Smedje, 2006; Richdale & Prior, 1995). This has, in turn, limited our understanding of the potentially unique influence of sleep disturbance in low functioning individuals with ASD, particularly children. Many low-functioning children with ASD are known to exhibit severe levels of tantrums, physical aggression, socially inappropriate behavior, and self-injury (Kane et al., 2011). Given the likely association between challenging behaviors, like these, and sleep disturbances, lowfunctioning children with ASD may experience sleep disturbances differentially compared to higher-functioning children with ASD. Within the limited research exploring sleep disturbance across functional levels, findings have been equivocal. While some studies suggest that lower functioning levels are associated with more severe sleep disturbances (Sikora et al., 2012; Taylor et al., 2012), other findings do not (Krakowiak et al., 2008). Given the inconsistencies in this already limited evidence, there is a necessity for exploration of sleep disturbance across functioning levels in individuals with ASD. Significance of the Current Study Because sleep disturbances are so highly prevalent among individuals with ASD, across functioning levels, and have been shown to further intensify symptoms associated with ASD, the National Sleep Foundation has identified children with ASD as one of the highest priority populations for sleep research (Mindell, Meltzer, Carskadon, & Chervin, 2009). Despite the growing body of evidence exploring the associations between sleep and ASD, much remains unknown. As part of advancing the understanding of the impact of sleep disturbance, it s important to explore interrelationships among ASD symptoms, sleep characteristics, and emotional-behavioral difficulties across levels of functioning (May et al., 2015; Mayes & Calhoun, 2009; Richdale & Schreck, 2009). Taking the nature of sleep disturbances into 18

19 consideration may also drastically improve our understanding of the heterogeneity of ASD and daytime behavioral functioning. With this greater understanding, it is hoped that we can improve our ability to screen for and successfully treat sleep disturbances, which are often overlooked in an effort to treat seemingly more problematic challenging behaviors that may be attributable to the untreated sleep disturbances (Haney & Kott, 2014; May et al., 2015; Malow et al., 2012). Similarly, investigation of specific sleep disturbances and behavioral correlates can yield more efficient methods of treating sleeping and thus possibly indirectly reducing challenging behaviors; further optimizing treatment outcomes for core symptoms given that these common treatment barriers (challenging behaviors) have been addressed. To date, few studies have looked at the associations between specific types of sleep problems and specific types of challenging behaviors in children with ASD, across levels of functioning (Mazurek & Sohl, 2016). Therefore, the aim of the current study was to explore associations between specific sleep disturbances and challenging behaviors among low and high-functioning children with ASD. The specific challenging behaviors that were examined in this study include irritability, social withdrawal, hyperactivity, and stereotypic behavior. Study Aims Aim 1. To evaluate the association between sleep problems and daytime challenging behaviors. Hypothesis 1: There will be significant positive correlations between sleep problems and daytime challenging behaviors in children with ASD; As the severity of sleep problems increase, there will be an associated increase in challenging behaviors. Aim 2. To explore daytime challenging behaviors among children with no/minimal, mild/moderate, and severe sleep disturbance across functioning levels in ASD. 19

20 Hypothesis 2a: Children with more severe sleep disturbances will exhibit significantly greater levels of challenging behaviors compared to children with no/minimal and mild/moderate sleep disturbances. Hypothesis 2b: There will be a significant effect of the interaction between sleep disturbance severity and level of functioning on challenging behavior: Low-functioning children with severe sleep disturbances will exhibit significantly greater levels of challenging behaviors compared to high-functioning children with no/minimal and mild/moderate sleep disturbances. 20

21 CHAPTER 2 METHODS Sample Characteristics The sample included archival data from 129 children with ASD and their parents enrolled in the Autism Treatment Network (ATN) between 2010 and The ATN is a network of 17 hospitals and physicians in the United States and Canada with the common goal of improving treatment of individuals with ASD (Murray, Fedele, Shui, & Coury, 2016). The ATN is funded through Autism Speaks and the Health Resources and Services Administration (HRSA). Although the ATN collects data on children age 2 to 17, this study examined data from children up to age 12 years (Goodlin-Jones et al., 2008; Owens, Spirito, & McGuinn, 2000). Inclusionary criteria were: (1) children between 2 12 years of age, (2) a primary diagnosis of ASD that had been determined by a licensed psychologist based on DSM-IV-TR criteria and the Autism Diagnostic Observation Schedule, Second Edition, Modules 1, 2, or 3. The larger ATN study was conducted before publication of DSM-V, thus children may have been classified as meeting criteria for Autistic Disorder, Pervasive Developmental Disorder-not otherwise specified, or Asperger s disorder, at the time of assessment. Exclusionary criteria were: (1) children age 13 years and older and (2) missing data for selected measures of interest.192 cases were selected from an exported sample before exclusionary criteria were applied. Of the remaining sample, 106 were male (82%) and 23 were female (18%); 100 identified as Caucasian (77%), 8 identified as Black/African American (6%), 1 identified as Asian (1%), 19 identified as mixed or other race (15 %), and 1 did not provide a response (1%). Additionally, 118 identified as non-hispanic/non- Latino (91%), 6 identified as Hispanic or Latino (5%), and 5 did not provide a response (4%). The mean age of participants was 5.38 years old (SD = 2.64). Participant demographics are presented in Table

22 Procedure All data was collected as part of each participant s enrollment in the ATN. Participants were assigned a study identification number and all data was entered into a single registry. Data for the current study was then extracted from the registry for analysis, including age, gender, race, ethnicity, and scores from measures of interest. Measures Demographics: Demographic data, including age and gender of participants, was collected at the time of enrollment in the ATN via a caregiver-report measures. Although these demographic forms were not identical across sites, common core variables were collected for this study. Sleep Problems: The Children s Sleep Habits Questionnaire (CSHQ) is a 33-item (two repeated items on different subscales for a total of 35 items), parent questionnaire designed to screen for sleep problems in children aged 4 10 years (Owens et al., 2000), as well as children as young as 2 years (Johnson et al., 2016; Goodlin-Jones et al., 2008). This measure includes items that relate to various sleep domains that have been shown to be most relevant in the clinical presentation of sleep problems among this age group. The CSHQ includes eight subscales: 1) Bedtime Resistance, 2) Sleep Onset Delay, 3) Sleep Duration, 4) Sleep Anxiety, 5) Night Wakings, 6) Parasomnias 7) Sleep-Disordered Breathing, and 8) Daytime Sleepiness. Items are rated on a 1 to 3 Likert-like scale, in which 3 = Usually (5-7 nights per week), 2 = Sometimes (2 4 nights per week), 1 = Rarely (0 1 nights per week). Yes/no responses are also provided to indicate whether the particular sleep item is a problem or not. Ratings are summed from all subscales (33 items) resulting in a Total Sleep Disturbance score, with higher scores indicative of more severe sleep disturbances. A cut-off total score of 41 has shown appropriate sensitivity (0.80) and specificity (0.72) in typically developing school age children (Owens, 22

23 Spirito, & Mcguinn, 2000), and has been used in previous studies to discriminate good and poor sleepers (Goodlin-Jones et al., 2008; Holloway et al., 2013; Johnson, et al., 2012; Owens et al., 2000). The CSHQ exhibits adequate internal consistency across multiple studies in both clinical (0.78) and community ( ) samples (Johnson et al., 2016; Li et al., 2007; Owens et al., 2000; Waumans et al., 2010). CSHQ is the most widely used standardized subjective measure of sleep problems and overall sleep quality in children with ASD (Hodge et al., 2012). Challenging Behavior: The Aberrant Behavior Checklist (ABC) is a 58-item behavior rating scale designed to assess challenging behavior across five subscales: 1) Irritability, Agitation, Crying, 2) Lethargy, Social Withdrawal, 3) Stereotypy, 4) Hyperactivity/Non- Compliance, 5) Inappropriate Speech (Mian & Singh, 1985). Caregiver s rate items on a 4-point Likert scale ranging from 0 (Not at all a problem) to 3 (The problem is severe in degree), in which higher scores indicate more severe behavioral problems. Scores falling within the 85th percentile are classified as clinically significant. The five subscales of the ABC have been shown to have acceptable to excellent internal consistency for all factors (Irritability α =.92,.92; Social Withdrawal α =.88,.89; Stereotypic Behavior α =.87,.85; Hyperactivity α =.94,.93; Inappropriate Speech α =.77,.77) and good convergent and divergent validity in a sample of children with ASD (Kaat, Lecavalier, & Aman, 2014; Rojahn et al., 2011). Level of Functioning: The Vineland Adaptive Behavior Scales (VABS) is a semistructured caregiver interview, delivered by a trained clinician, designed to assess functional skills in four developmental domains: Communication, Socialization, Daily Living Skills, and Motor Skills (Sparrow et al., 1984). The VABS provides an Adaptive Behavior Composite Score based on a summation of these domains for children from birth up to 5 years and 11 months, or a 23

24 summation of all but the Motor Skills domain for individuals age 6 to 90 years. This composite score, with a mean of 100 and standard deviation (SD) of 15, provides an estimate of the child s overall adaptive behavior, in which lower functioning is determined by a score < 70 (Krakowiak et al., 2008). The present study used this composite score cutoff to distinguish between low and high functioning. The VABS has been shown to demonstrate good reliability and construct validity (Pearson s r ; Subscale Cronbach s α = Communication.98, Daily Living Skills.98, Socialization.97 and Vineland total.99) (de Bildt, Kraijer, Sytema, & Minderra, 2005) and has been used widely for the assessment of adaptive skills in individuals with and chronic health conditions and developmental delays (Sikora et al., 2012), including ASD (Scahill et al., 2016). Statistical Analyses Normality of distribution was examined using Shapiro-Wilk test of normality and appropriate transformations were performed. Post-hoc power calculations were conducted for each of the following statistical tests described below. Preliminary Analysis Preliminary analysis included Spearman correlations among demographic variables including age, gender, race, and ethnicity, in addition to all variables of interest (total sleep problem severity (CSHQ Total), specific sleep problem severity (CSHQ subscales), level of functioning (VABS), and challenging behaviors (ABC). The Spearman correlation was chosen due to the non-normal distribution of all variables of interest. Aim 1. Associations among Specific Sleep Disturbances and Challenging Behaviors Hypothesis 1: There will be significant positive correlations between specific sleep problems and daytime challenging behaviors in children with ASD. To evaluate the bivariate association between sleep problems and daytime challenging behaviors, Spearman correlation coefficients were computed for all CSHQ subscales and the four 24

25 ABC subscales of interest. Next, to further assess which specific types of sleep problems were most closely associated with challenging behaviors, a series of four linear regressions were conducted with the challenging behaviors (Irritability, Hyperactivity, Stereotypic Behavior, and Social Withdrawal) as the dependent variables. Aim 2. Predicting Challenging Behaviors with Sleep Problem Severity and Level of Functioning Hypothesis 2a: Children with more severe sleep disturbances will exhibit significantly greater levels of challenging behaviors compared to children with no/minimal and mild/moderate sleep disturbances. Hypothesis 2b: There will be a significant effect of the interaction between sleep disturbance severity and level of functioning on challenging behavior: Low-functioning children with severe sleep disturbances will exhibit significantly greater levels of challenging behaviors compared to high-functioning children with no/minimal and mild/moderate sleep disturbances. To explore daytime challenging behaviors among children with no/minimal, mild/moderate, and severe sleep disturbance across functioning levels in ASD, multivariate analysis of variance (MANOVA) was conducted. Children were categorized into three groups based on their Total Sleep Disturbance score: no/minimal, mild/moderate, and severe. Based on previous literature score of 41, adequately distinguishes good sleepers (no/minimal sleep disturbances) from poor sleepers (severe sleep disturbances) (Holloway et al., 2013; Johnson et al., 2012; Owens et al., 2000). To maintain consistency with previous studies (Krakowiak et al., 2008; Sikora et al., 2012), the no/minimal group were children with a CSHQ total score < 41 (25th Quartile), mild/moderate were children with a total score from 41 to 57, and severe were children with a total score of 58 (75th Quartile), based on quartile estimates of the current 25

26 sample. Characteristics of these three groups are included in Table 2-1. For the MANOVA, sleep problem severity group and level of functioning (categorical) were entered into a single model as the IVs, while covarying for age (continuous), and the challenging behaviors were entered as the DVs. To test the potential influence of the interaction between sleep problems and level of functioning, the interaction term was also included in the model. The MANOVA analysis included individual univariate analyses to further evaluate whether sleep problem severity and level of functioning influence variance in the presentation of challenging behaviors. 26

27 .Table 2-1. Sample demographics (n=129) and breakdowns by sleep disturbance severity Total n=129 No/Minimal Problems (CSHQ < 41) n=31 Mild/Moderate Problems CSHQ (41-57) n=64 Severe Problems CSHQ (>57) n=34 Age in years M (SD) 5.38 (2.6) 5.45 (2.7) 4.92 (2.5) 6.17 (2.8) Gender N (%) Male Female Race N (%) White/Caucasian Black/African American Asian 106 (82) 23 (18) 100 (77.5) 8 (6.2) 1 (0.8) 27 (87.1) 4 (12.9) 26 (83.9) 2 (6.5) 52 (81.3) 12 (18.8) 49 (76.6) 4 (6.3) 1 (1.6) 27 (79.4) 7 (20.6) 25 (73.5) 2 (5.9) Mixed or Other Race 19 (14.7) 3 (9.7) 9 (14.1) 7 (20.6) No Response 1 (0.8) 1 (1.6) Ethnicity Hispanic or Latino/a 6 (4.7) 1 (3.2) 2 (3.1) 3 (8.8) Non-Hispanic or 118 (91.5) 29 (93.5) 60 (93.8) 29 (85.3) Latino/a No Response 5 (3.9) 1 (3.2) 2 (3.1) 2 (5.9) 27

28 CHAPTER 3 RESULTS The Shapiro-Wilk test of normality indicated that CSHQ Total (sleep problem severity), Social Withdrawal, Hyperactivity, Stereotypic Behavior, Bedtime Resistance, Sleep Onset Delay, Sleep Duration, Sleep Anxiety, Night Wakings, Parasomnias, Sleep-Disordered Breathing, and Daytime Sleepiness were significantly non-normal. Given the heterogeneity of ASD presentations, this finding was not expected. Because of the (positive) skewed distribution of the DVs (Irritability, Social Withdrawal, Hyperactivity, Stereotypic Behavior), values were Blom-transformed for the linear regression and MANOVA analyses. Blom-transformation is considered an optimal approach to handling this problem count in multivariate analyses (Finn et al., 2009; van den Oord et al., 2000). This transformation approach improved the normality of distribution for Irritability, Social Withdrawal, and Hyperactivity, as indicated by Shapiro-Wilk that allowed for the rejection of the null hypothesis of non-normality. Shapiro-Wilk for Stereotypy was significant (p<.05), however the data were not extremely non-normal, thus Blom-transformed data were utilized in in the present analyses. Post-hoc power analyses were performed to determine an adequate level of power to detect effects. Given the sample size of 129, a post-hoc power analysis for the linear multiple regressions performed, revealed that for each challenging behavior, standardized coefficients of.13 could be detected at p <.05, onetailed at a power of.98. Post-hoc power analysis for the MANOVA indicated that for a sample size of 129, a medium-sized effect could be detected at.86 power. Descriptive statistics for variables of interests were calculated and can be found in Table 3-1. Preliminary Correlations Preliminary Spearman correlations among demographic variables (age, gender, race, and ethnicity), composite scores for level of functioning (VABS), and sleep problems scores (CSHQ 28

29 subscales and total CHSQ score), revealed strong correlations among many of these variables. Demographic variables, such as, gender, race, and ethnicity, did not correlate with many, if any, other variables. Age was significantly correlated with Hyperactivity (r =.191, p <.05), but did not appear to be associated with any of the sleep disturbance types. Level of functioning was significantly correlated with Sleep Onset Delay (r = -.178, p <.05) and Sleep Duration Problems (r = -.229, p <.01), in addition to challenging behaviors including Social Withdrawal (r = -.390, p <.01), and Stereotypic Behavior (r = -.297, p <.01). Total sleep problem severity was correlated with all challenging behaviors and, as expected, all sleep disturbance types. Additionally, as expected many of the sleep disturbance subscales were significantly correlated with each other. Results of these preliminary correlations can be found in Table 3-2. Aim 1. Associations among Specific Sleep Disturbance and Challenging Behavior Various types of sleep problems were significantly correlated with the challenging behaviors of interest (Irritability, Hyperactivity, Stereotypic Behavior, and Social Withdrawal). Specifically, all 8 types of sleep problems were significantly correlated to Irritability, 6 were correlated to Social Withdrawal, 5 were correlated to Hyperactivity, and 3 were correlated to Stereotypic Behavior (p <.05). Comparing the 8 different types of sleep disturbances explored, Sleep Duration Problems and Parasomnias appeared to be most consistently correlated to the challenging behaviors. See Table 3-2 for a list of the specific sleep problems associated with these challenging behaviors. Next, four separate linear regressions were run with the eight types of sleep problems predicting the challenging behaviors. In the model predicting Irritability, the combination of sleep disturbances accounted for 26% variance (R2 =.26, F (8, 111) = 4.40, p <.001), and Sleep Anxiety (β =.32, t(111) = 2.861, p <.01) and Parasomnias (β =.223, t(111) = 1.979, p <.05) were the most salient predictors (p <.05), with small to moderate effect sizes, respectively. In the model predicting Social Withdrawal, sleep disturbances accounted for 13% 29

30 variance, but this was not statistically significant (R2 =.13, F (8, 111) = 1.930, p =.063). In the model predicting Hyperactivity, sleep disturbances accounted for 17% variance (R2 =.17, F (8, 111) = 2.679, p =.010), and Sleep Anxiety was the most salient predictor (β =.269, t(111) = 2.270, p <.05), with a moderate effect size. For the last model predicting Stereotypic Behavior, the combination of sleep problems predicted 7% variance, however this was not statistically significant (R2 =.07, F (8, 111) =.942, p =.486). (Table 3-3) Aim 2. Challenging Behaviors Predicted by Sleep Problem Severity and Level of Functioning Table 3-4 lists the group means for challenging behaviors. Results of MANOVA revealed an overall significant main effects for sleep problem severity (Wilks λ = 0.85, F (2, 128) = 2.47, p =.014, partial eta squared =.077) and level of functioning (Wilks λ = 0.89, F (1, 128) = 3.78, p =.006, partial eta squared =.113) on challenging behavior; with small effect sizes. There was also no significant effect of the interaction between sleep problem severity and level of functioning (Wilks λ = 0.96, F (2, 128) =.596, p =.781). As expected, there was also no significant main effect of age (Wilks λ = 0.97, F (1, 128) = 1.07, p =.375) on challenging behavior. Levels of Irritability, Hyperactivity, and Stereotypic Behavior differed significantly across sleep problem severity groups: Univariate ANOVAs indicate that children with severe sleep disturbances exhibited significantly higher levels of Irritability (p =.001) (Figure 3-1) and Hyperactivity (p =.001) (Figure 3-4) compared to children with no/minimal and mild/moderate sleep disturbances. No significant differences in Irritability and Hyperactivity were found between the no/minimal and mild/moderate groups. In regards to Stereotypic Behavior, children with severe sleep disturbances exhibited significantly higher levels of stereotypy than the mild/moderate group (p <.01) (Figure 3-3). Interestingly, children with severe sleep disturbances did not differ significantly from children with no/minimal sleep disturbances, who also did not 30

31 differ significantly from the children with mild/moderate sleep disturbances. Lastly, there was no significant main effect of sleep severity group on Social Withdrawal (p =.064), even though children with severe sleep problems exhibited significantly higher levels of Social Withdrawal compared to children with no/minimal (p =.043) and mild/moderate (p =.038) sleep problems. (Table 3-4) Univariate ANOVAs also revealed that, compared to sleep problem severity, level of functioning was not as strong of a predictor of challenging behavior. Still, results indicated that low functioning children exhibited significantly higher levels of Social Withdrawal and Stereotypic Behavior compared to high functioning children (p <.01). (Table 3-4) 31

32 Table 3-1. Descriptive statistics of study variables of interest Measure or Subscale Mean (SD) Median Mode CSHQ Total Score (10.74) Bedtime Resistance 7.81 (2.85) 7 5 Sleep Onset Delay 1.69 (.77) 2 1 Sleep Duration Problems 4.70 (1.90) 4 3 Sleep Anxiety 6.43 (2.33) 6 4 Night Waking 4.62 (1.68) 4 3 Parasomnias 9.61 (2.38) 9 9 Sleep Disordered Breathing 3.37 (.91) 3 3 Daytime Sleepiness (2.91) Irritability (10.26) Social Withdrawal (8.12) Hyperactivity 23.4 (12.89) 23 9 Stereotypic Behavior 5.82 (5.05) 5 0 VABS Composite Score (11.99) *p<.05; **p <.01 Table 3-2. Preliminary correlations between demographics and sleep variables of interest Spearman Correlation Coefficients (1) Gender (2) Age (3) Ethnicity (4) Race (5) VABS Total (6) CSHQ Total (7) Bedtime Resistance ** (8) Sleep Onset Delay *.398 **.293 ** (9) Sleep Duration Problems **.590 **.379 **.423 ** (10) Sleep Anxiety * **.573 ** * (11) Night Waking **.396 **.209 *.453 **.400 ** (12) Parasomnias **.305 ** **.487 **.414 ** (13) Sleep Disordered Breathing **.233 **.203 * *.235 **.435 ** (14) Daytime Sleepiness **.309 **.297 **.279 ** *.295 ** (15) Irritability **.265 **.194 *.322 **.358 **.232 **.371 **.220 *.188 * *p<.05; **p <.01 32

33 Table 3-2. Continued Spearman Correlation Coefficients (16) Social Withdrawal **.322 ** **.245 ** *.279 **.245 **.414 ** (17) Stereotypic Behavior **.295 ** **.199 * *.208 * **.590 ** (18) Hyperactivity * **.211 * **.336 **.195 *.354 **.206 * **.468 **.525 ** *p<.05; **p <.01 Table 3-3. Regression models for sleep problems predicting challenging behavior Social Irritability Hyperactivity R 2 Withdrawal =.255 R 2 R 2 =.172 =.130 p <.001 p =.010 p =.063 Stereotypic Behavior R 2 =.068 p =.486 β p β p β p β p Bedtime Resistance Sleep Onset Delay Sleep Duration Problems Sleep Anxiety.322** Night Waking Parasomnias.223* Sleep Disordered Breathing Daytime Sleepiness Note. Values for Irritability, Social Withdrawal, Hyperactivity, and Stereotypic Behavior are represented using Blom s Formula. *p<.05; **p <.01 Table 3-4. MANOVA: Mean scores of challenging behaviors by group Sleep Problem Severity LoF M (SD) M (SD) F (6, 128) No/ Mild/ Sleep LoF Sleep x Severe Low High Minimal Moderate Group Group LoF Age Irritability -43 (.83) -12 (.90) ** (.99) (1.05) (.93) Social -.31 (.88) -.05 (.93) ** Withdrawal (1.07) (.90) (.95) Hyperactivity (.93) ** (1.01) (.82) (.92) (1.01) Stereotypic Behavior -.24 (.94) -.08 (.83).42 (1.07).29 (.99) -.17 (.88) 3.31* 9.26** Note. Values for Irritability, Social Withdrawal, Hyperactivity, and Stereotypic Behavior are represented using Blom s Formula; Level of Functioning (LoF); Sleep Group corresponds to sleep problem severity groups; LoF Group corresponds to low and high functioning groups; Sleep x LoF corresponds to the interaction. MANOVA = multivariate analysis of variance. *p<.05; **p <.01 33

34 Figure 3-1. (MANOVA) Effect of sleep problem severity and level of functioning on irritability Figure 3-2. (MANOVA) Effect of sleep problem severity and level of functioning on social withdrawal 34

35 Figure 3-3. (MANOVA) Effect of sleep problem severity and level of functioning on stereotypic behavior Figure 3-4. (MANOVA) Effect of sleep problem severity and level of functioning on hyperactivity 35

36 CHAPTER 4 DISCUSSION The current study sought to explore the relationship and contributions of several factors on sleep disturbances. The primary aim was to explore the associations between specific sleep disturbances and challenging behaviors in children with ASD. Existing literature on the associations between sleep disturbances, age, and level of functioning, are inconclusive and warranted further exploration. Approximately 73% of our sample reported elevated sleep problem concerns, and this was consistent in other previous studies (Polimeni et al., 2005; Rzepecka et al., 2011). Sleep Problems and Challenging Behaviors The primary aim of this study was to explore the association between sleep problems and challenging behaviors; results indicated that more severe levels of sleep problems were associated with higher levels of challenging behavior. This finding adds to the growing body of evidence suggesting that higher levels of sleep problems in children with ASD increase the chance of challenging behaviors occurring as well (Lavigne et al., 1999; Owens, 2008; Rzepecka et al., 2011; Shanahan et al., 2014; Sikora et al., 2012; Touchette et al., 2007; Wiggs et al., 1996). Among all sleep problems explored in the present study, Sleep Duration Problems and Parasomnias appeared to be most related to the challenging behaviors, and all sleep disturbances were most strongly related to Irritability and Social Withdrawal. Other studies have also demonstrated strong associations between sleep disturbances and these particular challenging behaviors (Mazurek et al., 2016; Park et al., 2012). Current results also revealed significant effects of specific sleep variables on specific challenging behaviors: Sleep Anxiety was the strongest predictor of Irritability and Hyperactivity; Parasomnias were also a very strong 36

37 predictor of Irritability. Given that parasomnias such as nightmares and night terrors can often lead to adverse emotional responses at bedtime (e.g., sleep anxiety) that often delay the onset of sleep, it is not surprising to find overlap between these associations among sleep disturbances and these specific challenging behaviors (Allik et al., 2006; Rzepecka et al., 2011). In addition, Sleep Duration Problems, Sleep Anxiety, and Parasomnias were significantly correlated to Stereotypic Behavior, yet none of these sleep problems, or any others, were significant predictors of Stereotypic Behavior. This suggests a potential suppressor effect among predictor variables which is not unsurprising given the high correlation among sleep variables. Sleep Problem Severity and Functioning Level predict Challenging Behaviors The second aim of this study was to investigate the potential contributions of sleep problem severity and level of functioning in predicting challenging behaviors. Overall, results suggest that after accounting for age, sleep problem severity is possibly a stronger predictor of challenging behaviors than level of functioning. As expected, children with severe sleep disturbances exhibited significantly higher levels of Irritability, Stereotypic Behavior, and Hyperactivity compared to children with minimal to moderate sleep disturbance. These findings were consistent with other studies which also found more severe behavioral problems in children with a high frequency of sleep problems (Sikora et al., 2012; Wiggs & Stores, 1996). However, significant differences in challenging behaviors were not found between children with no/minimal and mild/moderate sleep disturbances. Although children with severe sleep disturbances exhibited significantly higher levels of Stereotypic Behavior compared to children with moderate sleep disturbance, they did not differ significantly from the no/minimal group. This finding may be indicative of the weak association between specific sleep problems and Stereotypic Behavior evident in earlier correlational and regression analyses. In regards to Social Withdrawal, despite the presence of significantly higher levels of this behavior among children 37

38 with severe sleep disturbances relative to children with no/minimal and mild/moderate sleep disturbances, there was no significant main effect of sleep severity group on challenging behavior. It is possible that these findings were impacted my low power as indicative of p-values close to.05 (p =.043 and p =.038). Results also suggest that lower functioning children with severe sleep disturbances may be particularly vulnerable to elevated symptoms of Social Withdrawal and Stereotypic Behavior. However, when the effect of this interaction (sleep problem severity x functioning level) was tested amongst the four challenging behaviors, results did not support our hypotheses. Given the findings from earlier aims, it is likely that the interaction effect is not significant because the effect of sleep disturbance severity on challenging behaviors does not depend on level of functioning. This further supports indication of the possible superiority of sleep disturbances as a reliable predictor of challenging behaviors relative to other variables explored in the current study. These results also suggest that the interrelationship between sleep problem severity, level of functioning, and challenging behaviors is likely more complex and nuanced than current study methods were able to explore. Study Limitations and Strengths This last point brings attention to the limitations of the current study. As previously noted, this data was extracted from a database as part of an ongoing ATN. As a result, investigators of the present study were unable to select for study measures, thus limiting the potential variables for exploration. Additionally, data regarding children s sleep habits and daytime challenging behaviors were obtained via parent-report measures. Given that parents of children with ASD, especially older children with ASD, may over-report symptoms, symptom severity scores may potentially be inflated; therefore, influencing the associations among study variables (Hodge et al., 2012; Schreck & Mulick, 2000). Additionally, because data was 38