Sleep correlates of pervasive developmental disorders: A review of the literature

Research in Developmental Disabilities 32 (2011) 1399–1421

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Research in Developmental Disabilities

Review

Sleep correlates of pervasive developmental disorders: A review of the literature Jill A. Hollway *, Michael G. Aman The Nisonger Center, The Ohio State University, Columbus, OH 43210, United States

A R T I C L E I N F O

A B S T R A C T

Article history: Received 7 March 2011 Received in revised form 1 April 2011 Accepted 2 April 2011 Available online 14 May 2011

Sleep disturbance is a significant problem in the general pediatric population, and it occurs even more frequently in children with pervasive developmental disorders (PDDs). Much time and energy have been spent examining the characteristics that predispose children to insomnia and it is likely that equivalent factors influence sleep in PDDs. Though similarly affected, it is the unique set of characteristics incumbent in a diagnosis of PDD that has additive effects and increases the likelihood for developing other predisposing factors and subsequent sleep loss. This review summarized research that has explored the behavioral, cognitive, and emotional correlates of sleep disturbance in children with PDDs. The literature provided 38 sleep studies that used either subjective or objective sleep measures. Of these, 17 met criteria for inclusion. Studies were evaluated for their attempts at matching their study samples and adjusting for possible confounding variables. The results revealed that the combined effects of autism symptom severity, internalizing behavior, and externalizing behavior, were the main predisposing factors for the development of insomnia. Other factors included medical conditions, epilepsy, and medication use (likely a proxy for behavior difficulty and even sleep disorder). A bidirectional theoretical framework for sleep disturbance in children with PDDs has been posited as a conceptual guide for future study. Recommendations for future study designs are included. ß 2011 Elsevier Ltd. All rights reserved.

Keywords: PDDs Sleep disturbance Correlates Autism symptom severity Internalizing behaviors Externalizing behaviors Cognition Medical conditions

Contents 1.

2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1. Prevalence of sleep disturbance in Pervasive Developmental Disorders (PDDs) . . . . . . . . . . . 1.2. Correlates of sleep disturbance in pediatric populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3. Correlates of sleep disturbance in PDDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4. Study objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Review criteria for study quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Study descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Intelligence, receptive language, autism spectrum disorders (ASDs), and sleep disturbance . 3.2.1. Intelligence and sleep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2. Receptive language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Adaptive behavior and developmental assessment ASDs and sleep disturbance . . . . . . . . . . 3.3.1. Adaptive behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2. Developmental assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

* Corresponding author. Tel.: +1 624 247 6402; fax: +1 614 247 6402. E-mail address: [email protected] (J.A. Hollway). 0891-4222/$ – see front matter ß 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ridd.2011.04.001

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3.4.

4.

5.

ASDs, core symptom severity, and sleep disturbance. . . . . . . . . . . . . . . . . 3.4.1. Repetitive behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Problem behavior, ASDs, and sleep disturbance . . . . . . . . . . . . . . . . . . . . . 3.6. Internalizing behavior, affective symptoms, ASDs and sleep disturbance . 3.6.1. Internalizing behavior and REM sleep . . . . . . . . . . . . . . . . . . . . . 3.7. Externalizing behavior ASDs and sleep disturbance . . . . . . . . . . . . . . . . . . 3.7.1. Externalizing behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7.2. Attention deficit and hyperactivity . . . . . . . . . . . . . . . . . . . . . . . . 3.8. Other correlates of sleep disturbance in ASDs . . . . . . . . . . . . . . . . . . . . . . 3.8.1. Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.2. Medical conditions and epilepsy. . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.3. Medication use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.8.4. Miscellaneous correlates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Methods for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Sample characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1. Samples of convenience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2. Sample size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3. Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.4. ASDs, intellectual functioning, and sleep . . . . . . . . . . . . . . . . . . . 4.2.5. Operationalizing sleep disturbance in ASDs . . . . . . . . . . . . . . . . . 4.2.6. ASD subtypes and problem behavior . . . . . . . . . . . . . . . . . . . . . . 4.3. Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1. Autism symptom severity measurement and sleep . . . . . . . . . . . 4.3.2. Internalizing behavior, measurement, and sleep . . . . . . . . . . . . . 4.4. Psychotropic medications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5. Objective measures of sleep and psychopathology. . . . . . . . . . . . . . . . . . . 4.6. Directions for future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1. Sleep architecture, learning and memory in ASDs . . . . . . . . . . . . 4.6.2. Perception and motor development . . . . . . . . . . . . . . . . . . . . . . . 4.6.3. Conceptualizing insomnia in children with ASDs . . . . . . . . . . . . 4.7. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviation key Terminology used in sleep literature

Instrument abbreviations

AD: Asperger’s Disorder ASD: autism spectrum disorder DS: Down’s syndrome DD: developmental disability FXS: Fragile-X Syndrome NAW: number of awakenings per night NNW: number of nights awakened PDD: pervasive developmental disorder PSG: polysomnography REM: rapid eye movement REM-L: rapid eye movement latency S1, S2, S3, S4: sleep stages 1 through 4, respectively SEI: sleep efficiency index SOL: sleep onset latency SPT: sleep period time (from sleep onset to sleep end, includes WASO) Ss/h: number of stage shifts per hour SWS: slow wave sleep TD: typically developing TIB: time in bed TST: total sleep time WASO: time spent awake after sleep onset

ADI-R: Autism Diagnostic Interview-Revised ADOS: Autism Diagnostic Observation Schedule AYSR: Achenbach Youth Self Report BDI: Beck Depression Inventory BSID: Bayley Scales of Infant and Toddler Development CARS: Children’s Autism Rating Scale CASD: Checklist for Autism Spectrum Disorder CBCL: Child Behavior Checklist DAS: Differential Abilities Scale DBC: Developmental Behavior Checklist GARS Gilliam’s Autism Rating Scale GMDS: Griffiths Mental Development Scale HFASQ: High Functioning Autism Screening Questionnaire Leiter-R: Leiter International Performance Scale- Revised MSEL: Mullen Scales of Early Learning PCQ: Parent Concerns Questionnaire PEP-R: Psychoeducational Profile-Revised PPVT-III: Peabody Picture Vocabulary Test-Third Edition RBS-R: Repetitive Behavior Scale-Revised SDQ: Strengths and Difficulties Questionnaire STAI: State-Trait Anxiety Inventory VABS: Vineland Adaptive Behavior Scale WISC-III & IV: Wechsler Intelligence Scales for Children-Third and Fourth Editions WPPSI-III: Wechsler Preschool and Primary Scale of Intelligence-Third Edition

Note: As the sleep literature contains a large and complex set of terminology, this listing is intended as an aid to the discussions and summaries that follow.

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1. Introduction 1.1. Prevalence of sleep disturbance in Pervasive Developmental Disorders (PDDs) Sleep problems are quite common in typical children, and yet there are those who believe they may be under diagnosed (Meltzer, Johnson, Crosette, Ramos, & Mindell, 2010). Estimates suggest that 1–6% of the general pediatric population experience some sleep disturbance and, when sleep resistance and disruptive nighttime awakenings are included there is an increase in prevalence rates approaching 25–40% in children of preschool age (Ivanenko & Gururaj, 2009). Prevalence estimates for insomnia in children with Pervasive Developmental Disorders (PDDs) range from 40% to 80% (Johnson, Giannotti, & Cortesi, 2009) and parents often seek relief. Currently, researchers and clinicians are showing interest in the environmental events, behavioral characteristics, and underlying emotional constructs that predispose children with PDDs to insomnia. 1.2. Correlates of sleep disturbance in pediatric populations There are a number of reports suggesting that children with neurodevelopmental disorders (including epilepsy) are at risk for disrupted sleep (Kotagal, 2007; Mayes, Calhoun, Bixler, & Vgontzas, 2009; Touchette, Petit, Tremblay, & Montplaisir, 2009). Sleep disruption in children may also be caused by medical conditions (Reid, Huntley, & Lewin, 2009). Prevalent conditions associated with insomnia include gastrointestinal problems (such as acid reflux), colic, and milk allergies (Owens & Witmans, 2004; Touchette et al.). Other conditions contributing to a sleep-deprived state include respiratory problems, such as upper respiratory infections (URIs) (Camhi, Morgan, Pernisco, & Quan, 2000), asthma (Owens & Witmans, 2004; Reid et al., 2009), and headaches (Owens & Witmans, 2004). Certain forms of child temperament have been associated with insomnia. Children who perceive their lives as more stressful than others, and who experience higher levels of physiological arousal, may be more attentive to even mildly threatening environmental stimuli, and may become vigilant, resistant, and hyperactive, with increased rates of insomnia (Carey, 1974; de Saint Hilaire, Straub, & Pelissolo, 2005; Gregory, Willis, Wiggs, Harvey, & the STEPS team, 2008; Morin, Rodrigue, & Ivers, 2003; Reid et al., 2009; Touchette et al., 2009). For other children stressful environmental events may cause insomnia by increasing vigilance (i.e., harm/avoidance), worry, and changes in routine (Johnson et al., 2009; Reid et al., 2009). Sleep problems often present concurrently with child psychopathology (Reid et al., 2009), and emotional disorders may be exacerbated by insomnia in a bidirectional relationship (Dahl & Harvey, 2007). Strong associations have been found between internalizing behavior (i.e., anxiety and/or depression) and insomnia in children (Dahl & Harvey, 2007; Ivanenko, Crabtree, & Gozal, 2004; Mayes et al., 2009). Likewise, externalizing behavior (i.e., disruptive behavior and hyperactivity) has been linked with sleep disruption (Dahl & Harvey, 2007; Mayes et al., 2009; Meltzer & Mindell, 2008; Mindell, 1993; Owens & Witmans, 2004). In association with this, medications used for the treatment of psychopathology, or medical conditions, may predispose one to insomnia (Johnson et al., 2009). The relationship between intellectual level and insomnia has not been definitively established (Didden & Sigafoos, 2001). However, there is evidence to suggest that EEG sleep patterns are associated with cognitive disability and that individuals with intellectual disabilities experience less REM sleep than individuals with average intelligence (Feinberg, Braun, & Shulman, 1969). 1.3. Correlates of sleep disturbance in PDDs The correlates found to influence sleep in typically developing children probably have an effect on the sleep of children with PDDs (i.e., medical conditions, emotional problems, overarousal, anxiety, depression, externalizing behavior, hyperactivity, and medication consumption). However, it remains unclear which of these factors plays a major role in sleep disturbance. Researchers are only just beginning to sort out the answer to this question and the added effects of the autism symptom domains [i.e., communication deficits, social deficits, and restricted/repetitive behaviors (RRBs)], increase the complexity, while compounding the problem. Further research is needed to reveal the predisposing factors to sleep disturbance in this population. 1.4. Study objectives An empirical understanding of the behavioral, cognitive, and emotional correlates found in children with PDDs and insomnia, may be useful for the prediction of sleep disturbance and its subsequent intervention. To that end, this review summarizes the sleep research carried out over the last decade in children, adolescents, and adults with PDDs. The individual objectives were to: (1) summarize the research and draw general conclusions about the behavioral, cognitive, and emotional correlates of insomnia in children with PDDs; (2) evaluate critically the research methods used and offer recommendations for future research in this area; and (3) develop a theoretical framework that describes the additive function of predisposing factors to sleep disturbance in children with PDDs. 2. Method 2.1. Review criteria for study quality For the purposes of this review, both the umbrella term ‘‘PDD’’ and the classification ‘‘autism spectrum disorder (ASD),’’ will be used interchangeably and will encompass three of the DSM-IV PDD diagnoses, namely autism, Asperger’s disorder (AD), and PDD-not otherwise specified.

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A search of the literature was conducted to locate all sleep studies designed to characterize the emotional, cognitive, and behavioral features of children and adolescents with pervasive developmental disorders (PDDs) and co-morbid sleep disturbance. The following terms were entered into the subject lines of both PsycInfo and Medline: (a) sleep or sleep disturbance and either (b) pervasive developmental disorders, or (c) autism, or (d) Asperger’s disorder or (e) developmental disabilities. The search was limited to articles written in English, and dates of publication were not restricted. In order to obtain all sleep studies conducted in children with PDDs up to this point, and in order not to miss any relevant articles, reference lists were reviewed for additional information. Thirty-seven articles were located and, of these, 17 investigations assessed psychopathology, cognition, and behavior. We did not endeavor to report on the specific details of these sleep profiles. To be included, studies had to use either subjective or objective sleep measures in addition to behavioral, cognitive, or emotional outcome measures in children, adolescents, and adults with a PDD. The studies were evaluated by their attempts at balancing their study populations and adjusting for possible confounding variables. 3. Results Rather than organizing the results of this review by listing each study and the instruments implemented, it was more practical to focus our comments on the specific characteristics endorsed by parents and teachers as being associated with sleep disturbance in this population. To facilitate organization, the studies are arranged in Table 1, ordered by date of publication, and summarized by their methods and findings. Two studies of adults were also located and, to distinguish them from the child/adolescent literature, they are shown in italics within the table. 3.1. Study descriptions Most studies were cross-sectional in design and the first study was as recent as 1998. Most of the designs were developed to describe group differences regarding characteristics of interest. But some were also instrumental in determining correlates and predictors of sleep disturbance in children with ASDs. As for the method of assessment, most studies used either parent or teacher-report measures or a combination of parent and self-report measures in addition to clinician-rated interviews and observations. To do this, rating scales, intelligence scales, adaptive behavior scales, developmental assessment instruments, structured interviews, and semi-structured assessment instruments were used. Eight of 17 studies included group comparisons of sleep variables (Allik, Larsson, & Smedje, 2006; Autism Treatment Network, 2010; Goldman et al., 2009; Limoges, Mottron, Bolduc, Berthiaume, & Godbout, 2005; Malow et al., 2006; Patzold, Richdale, & Tonge, 1998; Tani et al., 2003; Williams, Sears, & Allard, 2004). Twelve of 17 studies included correlational research within the ASD groups (Bruni et al., 2007; Diomedi et al., 1999; Elia et al., 2000; Gabriels, Cuccaro, Hill, Ivers, & Goldson, 2005; Giannotti et al., 2008; Goldman et al., 2009; Krakowiak, Goodlin-Jones, Hertz-Picciotto, Croen, & Hansen, 2008; Liu, Hubbard, Fabes, & Adam, 2006; Malow et al., 2006; Mayes & Calhoun, 2009; Patzold et al., 1998; Schreck, Mulick, & Smith, 2004). Five of the studies reviewed did not use a typically developing control group (Bruni et al., 2007; Elia et al., 2000; Giannotti et al., 2008; Krakowiak et al., 2008; Mayes & Calhoun, 2009). Of the seven studies that took PSG recordings to evaluate sleep variables and structure, three explored possible associations between sleep variables and behavioral, cognitive, and emotional measures (Bruni et al., 2007; Diomedi et al., 1999; Elia et al., 2000). Participants and controls were selected according to current characteristics such as diagnosis of ASD, another developmental disability (DD), and type of insomnia. Most, but not all, control groups were typically developing and matched for age and gender. Additional study samples were matched for certain other characteristics such as regressive and non-regressive autism (i.e., typical language development followed by a period of regression), good sleepers and poor sleepers, participants with and without epilepsy, parent education level, and SES. 3.2. Intelligence, receptive language, autism spectrum disorders (ASDs), and sleep disturbance 3.2.1. Intelligence and sleep Ten studies were located that explored the association between sleep disruption and intelligence level; and the results were mixed. Two studies reported significant associations between level of intelligence and sleep disturbance (Bruni et al., 2007; Giannotti et al., 2008). One study found that parents of children with autism and developmental regression (DR) reported more sleep problems than the parents of children without developmental regression (No-DR) (Giannotti et al., 2008). The investigators used the Leiter International Performance Scale-Revised (Leiter R) (Roid & Miller, 1997) and Griffith’s Mental Development Scale (GMDS) (Griffiths, 1970; Luiz et al., 2006a, 2006b) to measure intelligence and found that level of intellectual functioning predicted sleep disturbance in children with autism. The results showed a significant inverse association between IQ and total score on the Children’s Sleep Habits Questionnaire (CSHQ) (Owens, Spirito, & McGuinn, 2000), meaning that children with lower levels of intelligence had more sleep problems. In another study, investigators found that parents of the children with AD reported problems of sleep initiation, maintenance, and daytime sleepiness (Bruni et al., 2007). Sleep architecture was analyzed comparatively between children with AD, autism, and typically developing children (TD). The data showed only a minor difference between groups in PSG recorded sleep variables (i.e., stage shifts per hr). The results of the WISC-IV were analyzed and they found significant positive associations between REM-Latency (L), Full Scale-IQ, and Performance-IQ, in children with AD. A positive correlation was also found between SWS and VIQ. The results of this study suggested that children with higher levels of intellectual functioning experience longer periods of SWS and longer latencies to REM sleep.

Table 1 Studies of sleep disturbance in pervasive developmental disorders, behavioral, cognitive, emotional correlates. Adult studies in italics. Author/year

Samples

Validation of sleep classification

Matching groups

Findings

Patzold et al. (1998)

Pervasive Developmental Disorder (PDD) with insomnia n = 38 and typically developing (TD) n = 36; 60 males, 14 females; ages 3.6–14.25 yrs (mean, 8.11)

Parent Report, Rating Scale

Matched for intelligence level (IQ), low 55 and high >55, age 7 yrs and >7 yrs and gender

IQ (test not described): No significant difference in sleep initiation and maintenance variables between groups based on IQ. No correlation between IQ and sleep in either group.

Autism with intellectual disability (ID), n = 10 and Down Syndrome (DS) with ID, n = 8 and TD n = 8; 19 males, 7 females; ages 12–31 (mean 20.4).

Polysomnography

Matched for age and gender

Autism Symptom Severity: No correlation found between Childhood Autism Rating Scale (CARS) total score and REM sleep % in AUT/ID. Elia et al. (2000)

Autism n = 17, Fragile X syndrome (FX/ID) n = 7, TD n = 5; 29 males; ages 5.7–16.8 yrs (mean, 10.36)

Polysomnography

Matched for age and gender

Developmental Profile: Significant inverse correlations found between the perception passing items of the Psychoeducational Profile-Revised Test (PEP-R) and sleep onset latency (SOL), number of stage shifts per hour (Ss/h), first rapid eye movement latency (FREM-L), percent of time awake after sleep onset (%WASO). Significant positive correlation shown between perception passing items on the PEP-R, time in bed (TIB). Significant inverse correlation shown between eye–hand coordination scores on PEP-R, and Ss/h, first REM-L, %WASO. Significant positive correlations shown between eye–hand coordination passing items on PEP-R and sleep period time (SPT). Autism Symptom Severity: CARS Visual response category correlated inversely with SPT and positively with %WASO. Nonverbal communication category was inversely correlated with TST. Both CARS scores, Relating to people and Activity level were inversely correlated with REM density.

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Diomedi et al. (1999)

Autism Symptom Severity: Higher scores on the Autism related subscale of the Developmental Behavior Checklist (DBC) was inversely correlated with subjective total sleep time (sTST) (r = 0.53). Problem Behavior: Significant group differences found on all subscales of the DBC except the antisocial subscale (PDD > TD). Higher Total Problem Behavior scores on the DBC correlated with past sleep problems (r = 0.37), and current sleep problems (r = 0.48). Significant differences revealed in Total Problem Behavior scores on the Child Behavior Checklist (CBCL) (PDD > TD), and in Internalizing and Externalizing Behavior scores (PDD > TD). Higher Total Problem Behavior scores on the CBCL correlated with past sleep problems (r = 0.39, and current sleep problems (r = 0.43). IQ (test not described): No correlation found between IQ and rapid eye movement sleep percentage (REM sleep %) in AUT/ID. Significant correlation found between REM sleep % and IQ in DS/ID (R = 0.9).

1403

Samples

Validation of sleep classification

Matching groups

Findings

Tani et al. (2003)

Asperger’s disorder (AD) n = 20, control n = 10; 21 males, 9 females; mean age 27.2; comorbid anxiety n = 13, depression n = 5

Sleep diary Basic Nordic Sleep Questionnaire (BNSQ) Free description by essay

Matched for age, gender, education

Autism Symptom Severity: Significant group differences found on the Autism Spectrum Screening Questionnaire (ASSQ) with AD and insomnia group scoring higher in accordance to anamnesis of AD in childhood (AD > controls) Internalizing Behavior: Significant differences found between groups on the Beck Depression Inventory (BDI) with AD and insomnia group showing higher total scores than the control group (AD > controls).

Schreck et al. (2004)

Gilliam’s Autism Rating Scale (GARS) rating 80 and reported Autism Spectrum Disorder (ASD) n = 38,GARS rating 80 and no reported ASD n = 17; gender not reported; ages 5–13 yrs (mean, 8.2)

Rating Scale

No matching

Autism Symptom Severity: Communication domain score on GARS found to be a predictor of Factor 1 (Expressive awakening) and Factor 2 (Sensitivity to environmental stimuli) on the Behavior Evaluation of Disorders of Sleep (BEDS) (R2 = .18). Developmental domain score found to be a predictor of Factor 1 (Sensitivity to environmental stimuli) (R2 = .11). Stereotypic Behavior domain score found to be a predictor of Factor 1 (Sensitivity to environmental stimuli) and hours slept per night (R2 = .19). Social Skills domain scores found to be a predictor of hours slept per night (R2 = .12). Autism Quotient found to be a predictor of hours slept per night (R2 = .11).

Williams et al. (2004)

ASD/ID, n = 127 ASD/No-ID, n = 83; 169 males, 32 females (unexplained gender sample numbers = 201); ages 2–16 yrs (mean, 8.4)

Sleep Survey

Matched for age, (<6), (6–11), (>11) and IQ (<70) and (70)

IQ (Leiter-R, Differential Abilities Scales): Significant differences found between groups on sNAWs (ASD/ID > ASD/No-ID). Age: No significant differences between groups in parent reported sleep problems when data matched for age. Medical Conditions: Chi-Square analysis indicated significant associations between medical conditions and sleep problems. Participants with poor appetites and poor growth experienced prolonged sSOL (x2 = 11.81; x2 = 11.00, respectively). Vision problems, URIs, and poor growth associated with increased sNAWs (x2 = 11.53; x2 = 14.78; x2 = 9.61). Vision problems, URIs, and runny noses associated with reduced sTST (x2 = 8.16; x2 = 18.41; x2 = 8.91).

Gabriels et al. (2005)

ASD = 14, High NVIQ (97) n = 8, Low NVIQ (56) n = 6; 10 males, 4 females; mean age, 10.7 yrs

Rating Scale

Matched for NVIQ level, high NVIQ = >97 and low NVIQ = 56

IQ (Leiter-R): Significant differences found between groups in Children’s Sleep Questionnaire (CSQ) total scores (Low NV IQ > High NV IQ). Children’s Sleep Questionnaire (CSQ). Significant inverse correlation between NVIQ and total scores on the CSQ (r = .75). Controlling for NVIQ, partial correlation non-significant; therefore, unable to infer that NVIQ had no effect.

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Author/year

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Table 1 (Continued )

Autism Symptom Severity: Significant positive correlation between Repetitive Behavior Scale-Revised (RBS-R) total score and CSQ total score (r = .77). Adolescents and Adults with ASD n = 27; 25 males, 2 females; TD 78, gender not reported; ages 16–27 yrs (mean 21.5)

Rating Scale Polysomnography

Matched for age and gender

Autism Symptom Severity: Inverse relationship shown between TST and social interaction (r = 0.52) and communication (r = 0.54) subscales of the Autism Diagnostic Interview (ADI-R). Problem Behavior: Significant group differences in Total Problem Behavior scores on the Achenbach Youth Self Report (YSR) (ASD > TD). Internalizing Behavior: Significant group differences in Internalizing composite scores on the YSR (ASD > TD). Positive correlation found between percentage of REM sleep and the Internalizing composite score on the YSR in ASD (r = 0.54). No significant differences between groups on indices of depression on the Beck Depression Inventory (BDI) (ASD = TD). Significant group differences found in Trait-Anxiety on the State-Trait Anxiety ASD compared to TD (ASD > TD) No significant differences between groups on indices of state anxiety (ASD = TD).

Allik et al. (2006)

AD/HFA-PS n = 10 vs AD/HFA-GS n = 22, TD n = 32; age and gender matched AS/HFA, TD; 56 males, 8 females; ages 8.5–13.4 yrs (mean, 10.85)

Parent Report, Rating Scale, Actigraphy

Pair wise matched control group (AS/HFA and TD), matched for age, gender, residency

Autism Symptom Severity: Significant group differences in severity of autistic symptoms on High Functioning Autism Spectrum Screening Questionnaire (HFASSQ) (AD/HFA-PS > AD/HFA-GS). Problem Behavior: Significant group differences in problem behavior shown on the Strengths and Difficulties Questionnaire (SDQ): (a) pro-social behavior: AD/HFA-PS < AD/HFA-GS, (b) emotional behavior: AD/HFA-PS > AD/HFA-GS, and (c) SDQ total score: AD/FHA-PS > AD/HFA-GS. Teachers reported significant group differences in hyperactivity: AD/FHA-PS > AD/HFA-GS; emotional behavior: AD/FHA-PS > AD/HFA-GS.

Liu et al. (2006)

Autism n = 108, AD n = 27, PDD-NOS n = 32; 144 males, 23 females; ages 2.4–18.2 yrs (mean, 8.2)

Parent Report, Rating Scale

Controlled for age and gender

Medical Correlates: Results of logistic regression and odds ratios showed significant associations between allergies, hypersensitivity, co-sleeping and paternal sleep problems and bedtime resistance endorsed on the CSHQ (OR = 3.9, 1.4, 4.4, and 3.3). Results of logistic regression and odds ratios showed significant associations between asthma, GI problems, and insomnia endorsed on the CSHQ (OR = 3.1 and 1.6). Results of logistic regression and odds ratios showed significant associations between younger age, GI problems, medication use, bedtime rituals and parasomnias endorsed on the CSHQ (OR = 2.9, 1.8, 2.9, and 2.4). Results of logistic regression and odds ratios showed significant associations between epilepsy, insomnia, parasomnias, and daytime sleepiness endorsed on the CSHQ (OR = 10.7, 3.1, 2.3).

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Limoges et al. (2005)

1405

Author/year

Samples

Validation of sleep classification

Matching groups

Findings

Malow et al. (2006)

ASD-PS n = 11, ASD-GS n = 10, TD-GS n = 10; 26 males, 5 females; aged 4–10 yrs (mean, 6.73)

Sleep Diary Rating Scale (RS) Actigraphy (ACT) Polysomnography (PSG)

ASDs matched for sleep efficiency, poor sleepers and good sleepers

Receptive Language: Significant group differences found in standard scores of PPVT-III (ASD-PS < TD-GS). ASD-PS = ASD-GS.

Bruni et al. (2007)

Autism n = 10, Asperger Disorder (AD) n = 8; TD n = 12; 23 males, 7 females; ages 7–15 yrs (mean, 12.4)

Rating Scale, Polysomnography

Matched for age

IQ (WISC-IV): Significant positive correlations found in AD between REM-Latency (L) and Full Scale-IQ (r = .89) and Performance-IQ (r = .86); Significant positive correlation found between SWS and VIQ (r = .95). Problem Behavior: Significant inverse correlations found in AD between percent rapid eye movement (%REM) and CBCL-internalizing (INT) (r = .81); sleep efficiency (SE) and CBCL-externalizing (EXT) (r = .95). Significant positive correlations found in AD between total cyclic alternating pattern percent (CAP%) rate and total CBCL rating (r = .76); CAP sequence duration and total CBCL rating (r = .88).

Giannotti et al. (2008)

Autism-regressed developmentally prior to age 3 yrs (AUT-DR) n = 34, and autism-non-regressed prior to age 3 yrs (AUT-NDR) n = 70, TD n = 162; 189 males, 77 females; ages 2.3–7.11

Rating Scale, Electroencephalography (EEG)

Controlled for age, gender, IQ

IQ (Leiter-R, Griffiths Mental Development Scales): IQ found to be a predictor of sleep disturbance in autism (b = .23). For each unit increase in IQ, there was a decrease in the CSHQ total score of .23.

Autism Symptom Severity: Significant differences found between groups on Childhood Autism Rating Scale (CARS) (AUT-Reg > AUT-NonR); both autism groups showed greater number of symptoms than TD (ASD-Reg = ASD-NonR > TD). CARS. Severity of autism found to be predictor of sleep disturbance (b = .17). For each additional symptom of autism, CSHQ total score increased by .17. AUT-Reg found to be predictor of sleep disturbance (b = .23). For each unit increase in AUT-Reg symptoms, CSHQ total score increased by .23.

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Autism Symptom Severity: ADOS: No significant group differences in diagnostic coding distribution. ASD-PS = ASD-GS on ADOS. TD group comparable to norms. Problem Behavior: Significant group differences found in Affective Problems on the CBCL (ASD-PS > ASD-GS > TD-GS), Attentional Problems subscale scores (ASD-PS and ASD-GS > TD-GS), and Anxiety/depression subscale scores (ASD-PS > TD-GS). No significant differences in dimensions of Anxiety/ depression shown between ASD-GS, ASD-PS and controls (ASD-GS = ASD-PS and TD-GS). Associations found between SOL and CBCL dimensions Anxiety/depression (r = 0.398), Affective (r = 0.631), and Aggressive Behavior (r = 0.466), on PSG recorded night one.

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Table 1 (Continued )

Age: Age was a predictor of sleep disturbance (b = .18). For each year of age, there was a decrease of .18 on the CSHQ total score. Epilepsy: Epilepsy predicted sleep disturbance (b = .10). For each symptom of epilepsy endorsed, there was a .10 increase in CSHQ total score. Krakowiak et al. (2008)

Autism n = 303, DD n = 63, TD n = 163; 347 males, 182 females; ages 2–5 yrs (mean, 3.6)

Childhood Autism Risks from Genetics and the Environment Study (CHARGE) Sleep History (CSH)

Controlled for age and gender

Overall, higher cognitive and adaptive scores were associated with lower sleep onset factors scores.

ASD-PS n = 27; ASD-GS, n = 15; TD-GS n = 16; 41 males, 8 females; ages 4–10 yrs (mean, 6.2)

Rating scale, Actigraphy, Polysomnography

Matched for age, gender, socioeconomic status, and receptive language.

Autism Symptom Severity: Significant group differences in compulsive and ritualistic behavior on the Repetitive Behavior Scale-Revised (RBS-R) (ASD-PS > ASD-GS). Parents reported significant group differences for all subscales, between ASD-PS and TD-GS. Correlations shown between NAWs in ASD groups and Ritualistic (r = 0.69), Compulsive (r = 0.57), Need for Sameness (r = 0.54), Restricted Patterns of Interests (r = 0.51), and Total RBS Scale scores (r = 0.51). Problem Behavior: Significant group differences found on all CBCL subscales (ASD-PS and ASD-GS > TD). No significant group differences found for CBCL subscales between ASD-PS and ASD-GS. Problem Behavior: Parents reported significant group differences in hyperactivity and inattention items on Parent Concern Questionnaire (PCQ) (ASD-PS > ASD-GS and TD-GS). Lower PCQ item scores revealed for TD-GS (TD-GS < ASD-PS and ASD-GS). Correlation revealed between WASO and PCQ total score for both ASD-PS and ASD-GS (r = 0.48).

Mayes and Calhoun (2009)

Autism; n = 477; 415 males, 62 females; ages 1–15 yrs

Retrospective analysis of clinical data; Rating scale

No matching

IQ (WISC-III or IV, WPPSI-III, SB-IV, BSID): No significant differences in sleep variables between groups (ASD <80 vs 80). IQ did not significantly predict sleep problems.

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Goldman et al. (2009)

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IQ (Mullen Scales of Early Learning): Analyses of group data (ASD, DD, TD) revealed cognitive function predicted length of sSOL (b = 0.049), sNAWs (b = 0.055), sTST (b = 0.121). For each unit increase in Early Learning Composite T-scores, there was a decrease in sSOL of 0.05, a decrease in sNAWs of 0.05, and an increase in sTST of 0.21. Cognitive functioning did not predict sleep disturbance in the autism group. Adaptive Behavior: Analyses group data (ASD, DD, TD) revealed that Vineland Adaptive Behavior Composite Scores (VABS) predicted length of sSOL (b = 0.080), sNAWs (b = 0.081), and sTST (b = 0.115). For each unit increase in VABS composite scores, there was a decrease in sSOL of 0.08, a decrease in sNAWs of 0.08, and an increase in sTST of 0.11. Adaptive behavior did not predict sleep disturbance in the autism group.

1408

Author/year

Samples

Validation of sleep classification

Matching groups

Findings Autism Symptom Severity: Pediatric Behavior Scale (PBS) The parent autism severity rating was the strongest predictor of sleep problems (R = .44). Checklist for Autism Spectrum Disorder (CASD). Total CASD score showed symptom severity significantly correlated with sleep problems (r = .33). Other correlates of Insomnia: PBS items of inattentiveness/hyperactivity, oppositional and aggressive behavior, anxiety, depression, mood variability (r = .26–.42).

Autism Treatment Network Collaboration (2010)

ASD-PS n = 502 ASD-GS n = 264; gender not reported; ages 3–18 yrs

Rating Scale

Matched by Gender, age category, ASD, race, IQ, GI problems, ethnicity

GI Problems: Percentage of children with and without sleep problems were significantly different (ASD-PS > ASD-GS). IQ (SB-IV, Leiter-R, MSEL): Percentage of children with and without sleep problems did not differ significantly (ASD-PS = ASD-GS). ASD subtype: Percentage of children with and without sleep problems did not differ by ASD subtype (ASD-PS = ASD-GS).

J.A. Hollway, M.G. Aman / Research in Developmental Disabilities 32 (2011) 1399–1421

Table 1 (Continued )

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Similarly, two studies explored the differences in sleep patterns between children with ASDs who were intellectually disabled (ID) and those who were not (Gabriels et al., 2005; Williams et al., 2004). The Leiter-R was used to assess nonverbal intelligence (NV) in a study sample consisting of children with low nonverbal intelligence (LNV-IQ, 56) and high nonverbal intelligence (HNV-IQ, 97) (Gabriels et al., 2005). Significant differences were found between groups with the low NV-IQ group showing more sleep disturbance on the Children’s Sleep Questionnaire (CSQ) than the high NV-IQ group. Williams et al. (2004) analyzed the results of previously administered intelligence tests, including the WISC-III and the Differential Ability Scales (DAS) (Eliot, 1990). They split their sample by intelligence level (i.e., IQs <70 and 70) and found significant differences in the number of subjective night-time awakenings (sNAWs) reported by parents of participants in each group, with parents of the ID group reporting more sNAWs. The results of four additional studies did not support these findings. Two studies explored the relationship between insomnia and intellectual functioning and found no association (Diomedi et al., 1999; Krakowiak et al., 2008). Diomedi et al. (1999) analyzed sleep structure in adolescents and young adults with autism and ID, Down’s Syndrome (DS) and nonspecific ID, and typically-developing participants. The researchers found that both the individuals with autism and DS had higher NAWs, lower SEIs, and lower REM sleep percentages compared to typically developing controls. Although, they did not describe the instrument used to assess intelligence, the investigators found no relationship between REM sleep percentage and intelligence in the children with autism and ID. There was, however, a significant positive correlation found between REM sleep percentage and IQ in the children with Down’s syndrome and ID. A direct relationship between REM sleep and intellectual disabilities has been established (Feinberg et al., 1969; Castaldo & Krynicki, 1973). Therefore, the differences in REM sleep percentage shown between children with autism and ID and those with DS and ID were surprising. The investigators attributed the inconsistency in results to the attentional and relational deficits associated with autism and suggested that lower scores on IQ tests did not necessarily reflect ID. Krakowiak et al. (2008) found that children with ASDs had significantly higher sNAW factor scores than typically developing controls, but not children with DDs. The investigators used the Mullen Scales of Early Learning (MSEL) (Mullen, 1995) to assess intelligence in all three groups (ASD, DD, TD) and found that there was an inverse association between MSEL composite scores and sNAWs. The results also showed a positive correlation between the MSEL composite score and total sleep time (TST). However, when a separate analysis was conducted within the ASD group alone sNAWs were not associated with intelligence level in their study sample. The investigators did not report a separate analysis of either control group. Two other studies did not detect differences in sleep variables when level of intelligence was analyzed (Mayes & Calhoun, 2009; Patzold et al., 1998). One study conducted a retrospective analysis of existing clinical data on 477 children with autism (Mayes & Calhoun, 2009). The investigators found that a large percentage of their study sample showed sleep initiation and maintenance problems on the Pediatric Behavior Scale (PBS), an instrument designed to assess ‘‘problem behavior’’ (i.e., insomnia, ADHD, CD and anxiety) in hospital patients (Lindgren & Koeppl, 1987). The study sample was separated by intelligence levels (i.e., IQ, <80 and 80) taken from previously administered tests (i.e., SB-IV, WISC-III or IV, WPPSI, BSID) (Bayley, 2006; Thorndike, Hagen, & Sattler, 1986; Wechsler, 1991, 2002). No significant differences between groups in sleep initiation and maintenance variables were found, and regression analyses revealed that IQ did not predict sleep problems. Patzold et al. (1998) found that parents of children with PDDs reported more sleep problems than parents of typically developing children. The investigators also split their study sample of children with PDDs by level of intellectual functioning (IQ, <55 and 55) and found no difference between groups in sleep initiation and maintenance variables. The investigators did not report their method for determining intellectual ability. In the most recent annual report presented by the Autism Treatment Network (2010), investigators summarized the baseline characteristics of participants included in the sleep analyses. The investigators found that the percentage of children with and without sleep problems did not differ significantly by level of intellectual functioning (IQ, <70 and 70). 3.2.2. Receptive language One group of researchers evaluated the receptive vocabulary of children with ASDs using the Peabody Picture Vocabulary test (PPVT) (Dunn & Dunn, 1997; Malow et al., 2006). The researchers attempted to characterize the daytime behavior of children with ASDs who were good sleepers (ASD-GS), and poor sleepers (ASD-PS), in addition to typically developing children who were good sleepers (TD-GS). The investigators found that the ASD-PS group showed significantly more sleep problems when compared to both control groups of good sleepers. However, there were no significant differences between groups in standard scores on the PPVT, indicating that level of receptive language was not associated with sleep in this sample. For the most part, the investigators used reliable and well-validated instruments to measure intelligence in their study samples and, in all but two instances the measures were described. However, several studies reported results from samples consisting of all the ASD subtypes and the use of these heterogeneous groups may have influenced the outcome. The results described above are mixed and inconclusive; it is not clear whether there is a relationship between level of intellectual functioning and sleep disturbance in children with ASDs, more research is needed to determine whether intellectual functioning is associated with sleep quality in this population. 3.3. Adaptive behavior and developmental assessment ASDs and sleep disturbance 3.3.1. Adaptive behavior In addition to cognitive assessment, Krakowiak et al. (2008) evaluated adaptive behavior by parent interview using the Vineland Adaptive Behavior Scale (VABS) (Sparow, Balla, & Cicchetti, 1984). The VABS composite scores predicted length of

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sSOL, sNAWs, and sTST in all groups (i.e., ASDs, DDs, TDs). An inverse association was found between the VABS composite score, sSOL and sNAW; a positive association was found between the VABS composite score and sTST. The results indicated that children with lower VABS composite scores had more sleep initiation and maintenance problems. However, similar to the cognitive assessments taken, when the researchers conducted a separate analysis of the ASD group alone, the VABS composite score was not a predictor of sleep disturbance, indicating that adaptive behavior and sleep were not associated when comparisons were confined to ASD. 3.3.2. Developmental assessment Another group of investigators conducted a study in children with autism, Fragile-X Syndrome (FXS), and typically developing children (TD). The researchers found significant differences between groups in sleep maintenance variables (i.e., TIB, SPT, TST) (Elia et al., 2000). The children with autism had more difficulty maintaining sleep than the control groups. The Psychoeducational Profile-Revised Test (PEP-R) was used to evaluate the behavior and development of children with autism. The PEP-R is an instrument that provides information concerning a child’s developmental level in seven domains and higher scores indicate more advanced development (Schopler, Reichler, Bashford, Lansing, & Marcus, 1990). Significant inverse associations were found between the perception scores on the PEP-R and SOL, number of stage shifts per hour (Ss/h), duration of the first REM-L period, and percent of time spent awake after sleep onset (WASO). Similarly, there was an inverse relationship between eye–hand coordination scores on the PEP-R, and number of Ss/h, duration of first REM-L and percent of time spent WASO. This indicates that lower scores on the perception and hand–eye coordination subscales of the PEP-R were associated with prolonged periods of SOL, more Ss/h, longer time periods to the first REM-L and more time spent awake after sleep onset (WASO). Significant positive associations were also found between the perception scores on the PEP-R and time in bed (TIB) and between eye–hand coordination scores on PEP-R and SPT. This suggests that children who slept longer and spent more time in bed earned higher passing scores on the perceptual and hand–eye coordination subscales of the PEP-R. These findings may have important implications for children with ASDs, who suffer from insomnia, as researchers are now discovering the importance of REM and NREM sleep in the development of visual and auditory perception and hand–eye coordination (Mednick, Nakayama, & Stickgold, 2003; Nishida & Walker, 2007; Tucker & Fishbein, 2009; Walker, Brakefield, Morgan, Hobson, & Stickhold, 2002). To sum up, 10 studies examined the relationship between intellectual functioning or receptive language and sleep, and the results were mixed. Forty-percent of the comparisons supported the notion that there is a relationship between IQ and sleep, and 60% did not. The one study that evaluated the relationship between sleep and adaptive behavior found no relationship. Another study examined the relationship between sleep and certain developmental milestones and the investigators found a relationship in the predicted direction between sleep and perceptual and motor development. 3.4. ASDs, core symptom severity, and sleep disturbance Investigators involved in 11 studies examined the relationship between core symptom severity and sleep problems in children with ASDs. Researchers from one study evaluated autism symptom severity in children with and without developmental regression (DR) (Giannotti et al., 2008). The researchers used the Childhood Autism Rating Scale (CARS) to assess autism symptoms (Schopler, Reichler, & Renner, 1988). The CARS is a clinician-rated observation and screening tool which aides in identifying children with autism; higher scores indicate more severe autism symptom severity. Clinicianrated observations from the CARS revealed that the children with autism who had regressed developmentally and who reportedly had more sleep problems, showed significantly more autism symptom severity than the children with nonregressed autism. Similarly, Elia et al. (2000) found a relationship between sleep variables and four of the categorical ratings on the CARS. The Visual Response category was inversely correlated with sleep period time (SPT), and it was positively correlated with percent of time spent awake after sleep onset (WASO). The Nonverbal Communication category was inversely correlated with TST. Abnormal visual response and deficits in nonverbal communication were associated with sleep maintenance difficulties. In addition to this the CARS categories, Relating to People and Activity Level, were inversely correlated with REM density. This indicated that difficulties relating to others and increased hyperactivity were associated with less REM density. REM sleep is important for memory consolidation (Cartwright, 2004; Maquet, 2000, 2001; Smith, Nixon, & Nader, 2004); continued research could increase our understanding of the mechanisms involved in the consolidation of memory in children with ASDs. Another group of researchers used the Behavior Evaluation of Disorders of Sleep (BEDS) to assess sleep problems in children with autism (Schreck et al., 2004). The investigators measured core autism features, with the Gilliam Autism Rating Scale (GARS), a behavior checklist used for identifying individuals with autism (Gilliam, 1995). Parents were queried in regard to their child’s ability to sleep at night and during the day (i.e., naptime). The results of the analysis showed that fewer number of hours slept per night (i.e., sTST) predicted the GARS domain and total scores, including Stereotyped Behavior, Social Interaction, and the Total Autism Quotient. In addition, the Communication domain scores on the GARS were found to be predictors of subscales one and two on the BEDS (i.e., Sensitivity to the Environment, Expressive Awakening). The Stereotypic domain scores of the GARS were shown to be predictors of subscale 2 of the BEDS (Expressive Awakening), while the Developmental domain (GARS) scores predicted subscale 1 (Sensitivity to the Environment of the BEDS). The results suggest that more severe autism symptoms predict stronger responses to the sleep environment at night and more frequent awakenings. However, autism severity was not significantly associated with daytime napping.

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In a study targeting children with high functioning autism (HFA), the High Functioning Autism Spectrum Screening Questionnaire (HFASSQ) was used to assess the possible association between autism symptom severity and sleep disturbance (Allik et al., 2006; Ehlers, Gillberg, & Wing, 1999). The HFASSQ is a parent-and/or teacher-rated checklist for identifying autism spectrum disorders in high-functioning children. The researchers found significant group differences in total scores on the HFASSQ; parents of children with AD and HFA who were poor sleepers, reported higher autism symptom severity than parents of children who were good sleepers. In a similar study investigators were interested in learning whether comorbid psychiatric disorders or autism symptom severity (or both) had an impact on sleep in young adults with AD (Tani et al., 2003). The investigators analyzed the results of the Autism Spectrum Screening Questionnaire (ASSQ) and found that the participants with higher scores on the ASSQ (i.e., the AD group) had significantly more sleep problems than controls. In a recent study, the Autism item scores of the Parent–Pediatric Behavior Scale were used by two researchers to determine if there was a relationship between autism symptom severity and sleep disturbance (Mayes & Calhoun, 2009). The investigators found that autism symptom severity was the single strongest predictor of the Sleep Disturbance subscale score from the same instrument (P-PBS). In addition, the investigators found a positive association between the total score on clinician rated Checklist for Autism Spectrum Disorder (CASD) and sleep problems. The CASD is a structured interview developed as a screening instrument for ASDs (Mayes & Calhoun, 1999) and higher scores were associated with sleep problems. Lastly, Patzold et al. (1998) found an inverse relationship between the Autism subscale of the Developmental Behavior Checklist (DBC) as higher scores were associated with decreased sTST. The DBC is a parent rated scale designed to assess problem behavior in children with PDDs and consists of 6 problem behavior subscales. 3.4.1. Repetitive behavior Two groups of investigators examined the relationship between restricted and repetitive behaviors (RRBs) and sleep in children with ASDs (Gabriels et al., 2005; Goldman et al., 2009). Gabriels et al. (2005) asked parents to complete the Repetitive Behavior Scale (RBS) and found that participants with low NV-IQs and sleep disturbance scored significantly higher on the Sameness Behavior subscale of the RBS compared to those with high NV-IQs and no sleep disturbance. Furthermore, there was a positive association between the presence of RRBs and the sleep problem total score on the Children’s Sleep Questionnaire (CSQ). The RBS is intended to assess different forms of RRBs (Bodfish, Symons, & Lewis, 1998; Lam & Aman, 2007). Goldman et al. (2009) found that parents of children with ASDs who were poor sleepers (ASD-PS) reported more compulsive and ritualistic behavior on the RBS than parents of children with ASDs who were good sleepers (ASD-GS) and typically developing children who were good sleepers (TD-GS). RRBs in both ASD groups were also associated with number of awakenings per night (NAWs). These studies indicate that RRBs may be associated with sleep problems in this population and that intelligence level may be a moderator between the two. The above-mentioned researchers indicate that autism symptom severity is associated with the sleep disturbance often found in this population. However, not all of the investigators who analyzed the relationship between autism symptom severity and sleep disturbance found a significant relationship between the two. Diomedi et al. (1999) used The Childhood Autism Rating Scale (CARS) to evaluate autism symptom severity and analyzed its relationship to REM sleep percentage. The investigators found that the CARS total score was not associated to REM sleep percentage in their study sample. NAWs and SEIs were not analyzed between these groups. As reported above, Malow et al. (2006) conducted a study to characterize daytime behavior in children with ASDs and typically developing children. After separating the groups into good sleepers and poor sleepers the researchers analyzed the results of the Autism Diagnostic Observation Schedule (ADOS). The analysis showed no significant group differences in diagnostic coding distribution between the children with ASD who were poor sleepers and those who were good sleepers, suggesting that severity of symptoms was not associated with sleep. Mayes and Calhoun (2009) found that teacher ratings on the (T-PBS) were not associated with sleep problems in children with ASDs. In summary, 11 studies explored the possible association between autism symptom severity and sleep disturbance. The results showed that autism symptom severity was significantly associated with sleep disturbance in 77% of the cases (i.e., 10 out of 13 of the comparisons). Three of the investigations did not report significant results. The study samples included children with autism, AD, and more generally grouped ASDs. A number of instruments were used to assess the core features of autism (i.e., ADOS, CARS, GARS, HFASQ, PBS, RBS), some well validated and others less so. Overall, children with more severe cases of autism spectrum disorders were more likely to develop sleep disorders. 3.5. Problem behavior, ASDs, and sleep disturbance Five studies examined the relationship between problem behavior and insomnia in children with PDDs. Patzold et al. (1998) were the first group of investigators to attempt to characterize problem behavior. The investigators asked parents to rate their child’s problem behavior on the Child Behavior Checklist (CBCL) (Achenbach, 1991a). The researchers found that children with PDDs engaged in more problem behavior than controls. A positive association was found between the Total Problem Behavior score on the CBCL and sleep disturbance in children with PDDs. The investigators also evaluated parent responses taken from the DBC (Einfeld & Tonge, 1995). Similar to the CBCL they found that their study sample engaged in more problem behavior than controls and that problem behavior was associated with sleep disturbance in children with PDDs (Patzold et al., 1998). In another study targeting adolescents and adults with ASDs, investigators found that adults with autism presented with prolonged SOL, increased NAWs and lower SEIs than controls (Limoges et al., 2005). The researchers used Achenbach’s Youth

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Self Report (YSR) to characterize problem behavior in their study sample and found that the ASD group endorsed more problem behaviors than the control group (Achenbach, 1991b) In an effort to determine whether an association existed between the sleep microstructure of children with AD and problem behavior endorsed by parents on the CBCL, Bruni et al. (2007) evaluated the microstructure of NREM sleep by observing its cyclic alternating pattern (CAP). The investigators found a positive correlation between the CAP percent rate, the CAP sequence duration, and the Total Problem Behavior Score on the CBCL. The investigators concluded that the instability observed in the NREM sleep CAP could be associated with the problem behavior found in children with AD. A group of investigators targeting sleep disturbance in children with AD, High Functioning Autism (HFA), and typically developing children, separated the children into poor sleepers and good sleepers prior to analyzing the data (Allik et al., 2006). Parents had completed the Strengths and Difficulties Questionnaire (SDQ) (Goodman, 2001). This questionnaire was designed for assessing psychological adjustment in children and youths. The results showed that the children with AD and HFA who were poor sleepers had more problem behavior than either the typically developing children or the AD/HFA good sleepers as shown by their SDQ total scores. More recently, Goldman et al. (2009) used the CBCL to measure problem behavior in a study sample comprised of children with ASDs and controls. The investigators found that significant group differences existed, as parents of the ASD group reported more problem behavior in their children than parents of controls. However, the investigators were also interested in determining whether the group differences found would stand when they divided their study sample into good sleepers and poor sleepers. Consequently, they analyzed the data within each group separately. Contrary to the Allik et al. (2006) study, results of the CBCL analyses showed no significant differences between the ASD groups (i.e., poor sleepers versus good sleepers) on the CBCL Total Problem Score. This suggested that the earlier differences found between groups (i.e., ASDs and controls) on CBCL Total Problem Scores may not be related to sleep problems but to ASDs in general. To review, five studies (including six comparisons) assessed the relationship between problem behavior and sleep disturbance, and 84% of the collective comparisons revealed a significant relationship between problem behavior and sleep disruption in children with ASDs. Two sets of investigators split their study samples into poor sleepers and good sleepers and then compared the groups (Allik et al., 2006; Goldman et al., 2009). In doing so, one found no significant difference between groups in total problem behavior scores (Goldman et al., 2009), and the other did (Allik et al., 2006). Thus, when separating the study sample by sleep efficiency level, the results were inconsistent. Nevertheless, the results of the Goldman et al. (2009) study should influence future exploratory research, and attempts should be made to define sleep efficiency when examining participants within ASD groups. 3.6. Internalizing behavior, affective symptoms, ASDs and sleep disturbance Investigators from six studies explored the relationship between internalizing behavior and sleep disturbance. Patzold et al. (1998) analyzed the results of the Internalizing composite scores on the CBCL, which includes both anxiety and depressive symptoms. The results of the analyses revealed that children with PDD and sleep disturbance showed more internalizing symptoms than the control group. In another study, Allik et al. (2006) used the Emotional Problems subscale of the SDQ to evaluate internalizing behavior. Similar to the CBCL, this subscale consists of items targeting both anxiety and depression. The investigators found that parents of children with AD and HFA who were poor sleepers reported more emotional problems in their children than parents of children with AD and HFA who were good sleepers or those with typically developing children. Teachers also completed the SDQ and reported similar significant group differences in emotional symptoms between the groups. Their ratings indicated that the children with AD and HFA who showed poor sleep efficiency experienced more emotional problems (i.e., anxiety and depressive symptoms) than controls. Anxiety and depression may be involved in the development of insomnia in children with ASDs. However, it is difficult to say which of the constructs may be associated with sleep disturbance, as the instruments used to measure internalizing behavior integrated both anxiety and depressive symptom items in their composite scores. Patzold et al. (1998) reported results from the Anxiety subscale on the DBC. The addition of the DBC Anxiety subscale provided important information in regard to level of anxiety alone. The researchers found that children with PDDs plus insomnia had significantly more anxiety than the control group. A separate measure of depressive symptoms was not analyzed. The Internalizing composite of the YSR also contains items targeting both anxiety and depression. Limoges et al. (2005) analyzed the results of the Internalizing Composite scores on the YSR and found that the adolescents and adults with ASDs plus sleep disturbance experienced more internalizing symptoms than a typically developing control group. As part of their study design and in conjunction with the YSR, Limoges et al. (2005) assessed anxiety and depression using the State-Trait Anxiety Inventory for adults (STAI) (Spielberger, 1983) and the Beck Depression Inventory (BDI) (Beck, Steer, & Brown, 1996). Both instruments have been validated for their use in adults without ASDs. Although no group differences were found for indices of state anxiety, higher trait anxiety was found in the ASD group on the STAI when compared to the control group. Analyses of the BDI showed no significant differences between groups on indices of depression. In another investigation researchers found that compared to controls, adults with AD had frequent insomnia and earned higher scores on the BDI (Tani et al., 2003). Malow et al. (2006) compared the Affective subscale of the CBCL between children with ASDs who were poor sleepers (PS) and good sleepers (GS). The investigators found that the PSs experienced significantly more depressive symptoms. The results of the two investigations appeared to confirm an association between depressive symptoms and sleep disturbance. Malow et al. (2006) showed that some children with ASDs had significant depressive

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symptoms which influenced sleep, but not all such children. The researchers found that the anxiety/depression subscale scores on the CBCL were associated with SOL. 3.6.1. Internalizing behavior and REM sleep Pressure to REM sleep and increased REM sleep density have been established as co-occurring in typically developing adults with depression (Berger & Rieman, 1993; Ivanenko et al., 2004). However, Bruni et al. (2007) found a significant inverse association between percentage of REM sleep and the CBCL Internalizing Composite scores in children with AD. Higher scores on the Internalizing Composite in children with AD predicted a lower percentage of REM sleep. Limoges et al. (2005) reported the opposite. The investigators found a significant positive correlation between the Internalizing Composite score on the YSR and percentage of REM sleep in adults with ASDs. The contradictory results may have less to do with the instruments used to measure problem behavior and more to do with the developmental differences found in sleep architecture (Ohayon, Carskadon, Guilleminault, & Vitiello, 2004). It has been shown that age-related factors affect sleep regulation, physiology, consolidation, and duration (Crabtree & Williams, 2009; Mindell, 1993). The age span of the Bruni et al. (2007) sample was seven to15 years (mean 12.4), while the Limoges et al. (2005) study sample consisted of individuals aged 16–30 years (mean 21.5). Six studies (including 10 comparisons) evaluated the association between internalizing behavior and insomnia in children with ASDs, and 9 out of 10 comparisons (90%) showed that internalizing behaviors were significantly associated with insomnia. One study found that depression was not significantly associated with sleep disturbance. The results of this review suggest that internalizing behaviors are often found in people with ASDs plus sleep disturbance. However, it remains unclear whether anxiety or depression is more problematic with respect to sleep problems. 3.7. Externalizing behavior ASDs and sleep disturbance 3.7.1. Externalizing behaviors Investigators from five studies examined the relationship between insomnia and externalizing behavior. Patzold et al. (1998) found that children with PDDs and insomnia exhibited significantly more externalizing behaviors than the control group on both the CBCL-Externalizing Behavior composite and the DBC Disruptive Behavior Subscale. Goldman et al. (2009) also found significant group differences in the CBCL-Externalizing composite scores, with the ASD group scoring higher on all subscales. However, when the investigators divided the ASD group into poor sleepers and good sleepers, there were no significant differences between groups in the Externalizing Behavior composite scores on the CBCL, raising doubts as to whether externalizing behavior is associated with sleep problems in ASDs. The Bruni et al. (2007) study results appear to make the most intuitive sense, as the investigators found an inverse association between percent sleep efficiency (SE) and the CBCLExternalizing Behavior composite scores. This suggests that higher composite scores are associated with a lower percentage of sleep efficiency. Hence, sleep deprivation may be related to externalizing behavior in children and adolescents with AD. In their retrospective chart review, Mayes and Calhoun (2009) found that sleep disturbance scores on the Pediatric Behavior Scale (PBS) were related to all other subscales scores of the PBS, including Aggressive behavior. In addition to this, Malow et al. (2006) found that Aggressive Behavior was correlated with sleep disturbance on night one of their sleep study. 3.7.2. Attention deficit and hyperactivity Similarly, investigators from four studies explored the association between sleep disturbance and inattention/ hyperactivity. The Parent Concern Questionnaire (PCQ) is an assessment of core developmental deficits and psychopathology in children (McGrew et al., 2007). Goldman et al. (2009) used the PCQ to assess inattentiveness and hyperactivity in their study sample (i.e., ASD-PS, ASD-GS, TD-GS). Parents of children with ASD who were poor sleepers reported more inattention and hyperactivity in their children on the PCQ than parents of children who were good sleepers. However, the Attentional Difficulties subscale of the CBCL did not support these findings. A positive association between the PCQ total score and WASO was found for both ASD groups. Mayes and Calhoun (2009) found that the PBS attention deficit and hyperactivity subscale scores were related to the sleep subscale scores on the PBS. However, teacher ratings of attention and hyperactivity did not appear to support this finding and were not associated with the sleep subscale scores on the PBS. In another study, the researchers used the SDQ to assess inattention and hyperactivity. Teacher ratings showed that children with AD and HFA who were poor sleepers engaged in significantly more hyperactive behavior than children with AD and HFA who were good sleepers, and also typically developing children (Allik et al., 2006). Malow et al. (2006) found that parents of children with ASDs who were poor sleepers reported no more inattention and hyperactivity in their children than those who were good sleepers. Three comparisons showed that attention deficits and hyperactivity may be associated with sleep problems and three others did not. The results are mixed and inconclusive. All told, nine studies of individuals with ASDs revealed that externalizing behavior and sleep problems were associated in seven out of ten comparisons (70%). Teacher ratings for two studies produced conflicting results. Research in this area is needed to determine whether sleep deprivation influences externalizing behavior, whether externalizing behavior influences sleep, or whether the effects are bidirectional. 3.8. Other correlates of sleep disturbance in ASDs Five studies explored other possible correlates of sleep problems in children with ASDs (ATN, 2010; Giannotti et al., 2008; Liu et al., 2006; Mayes & Calhoun, 2009; Williams et al., 2004), as follows.

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3.8.1. Age Three studies assessed age-related dysomnias and one found that age was associated with sleep problems (ATN, 2010; Giannotti et al., 2008; Mayes & Calhoun, 2009). An inverse association was found between age and total score on the CSHQ (Giannotti et al., 2008). 3.8.2. Medical conditions and epilepsy Medical conditions in general were related to prolonged sSOL (Williams et al., 2004). Allergies and asthma were also positively associated with disrupted sleep in ASDs (Liu et al., 2006); gastrointestinal problems (GI) were found to be associated with insomnia (ATN, 2010; Liu et al., 2006; Williams et al., 2004). Upper respiratory infections (URIs) were associated with sNAWs and reduced TST (Williams et al., 2004). Vision problems were related to increased sNAWs and reduced TST (Williams et al., 2004). Liu et al. (2006) found that parents of children with ASDs and epilepsy were more likely to endorse daytime sleepiness in their children than children without epilepsy, and Giannotti et al. (2008) found a positive association between the amount of epileptic seizure activity and the total score on the CSHQ. 3.8.3. Medication use Two studies found that medication use was also related to sleep problems (Liu et al., 2006; Mayes & Calhoun, 2009). Of course, use of medication was probably a proxy for a variety of other factors, such as behavioral and/or psychiatric conditions. 3.8.4. Miscellaneous correlates One study explored a variety of other possible correlates of sleep disturbance in children with ASDs (i.e., hypersensitivity, co-sleeping, parental sleep problems). The investigators found significant associations between each factor and sleep problems (Liu et al., 2006). 4. Discussion Within the past 10 years, researchers have attempted to investigate the correlates of sleep disturbance in children with ASDs and the relationships are not fully understood. A number of possibilities were discussed in this manuscript, and continued research is needed for further clarification. Based on existing data, there appear to be several interesting areas of study to be followed. The most compelling evidence for an association with sleep disorders concerns presence of internalizing behavior. Further investigation into this area makes intuitive sense, as there is a large body of research linking internalizing behavior to sleep disturbance in typically developing individuals (Lofthouse, Gilcrist, & Splaingard, 2009), and internalizing behaviors are also common in children with ASDs (Simonoff et al., 2008; Morin et al., 2003; White, Oswald, Ollendick, & Scahill, 2009). Although some traits are consistent with those reported in the general population, sleep disturbance in children with ASDs may also be driven by the unique set of characteristics related to diagnostic criteria. Other variables associated with insomnia include autism symptom severity, and externalizing behavior, the severity of which may be influenced by level of intellectual functioning. 4.1. Methods for future research The studies reviewed in this manuscript covered a variety of research questions and, in view of the fact that this is a relatively new area of study, much of the research was exploratory in nature. Therefore, methodological issues that limit the findings are highlighted here. 4.2. Sample characteristics 4.2.1. Samples of convenience Ten out of 17 studies (59%) used clinic-based samples. Six out of 17 studies (36%) used samples recruited specifically for the investigation; and in one study it was unclear where the study sample originated. The large number of clinic-based samples makes it difficult to generalize the results, as clinic-based samples may be biased and unrepresentative of the ASD population at large. Community-based epidemiological samples are to be much preferred. 4.2.2. Sample size Six out of 17 studies (36%) recruited sample sizes consisting of less than or equal to 20 participants. Two studies reported no significant results. It is possible that the results of these tests reflect Type II error, as the probability of committing a Type II error increases as the sample size N decreases. Thus the power of a test increases with the size of N and an adequate sample size will ensure power enough to minimize the probability of committing Type II error (Siegel & Castellan, 1988). 4.2.3. Matching Eleven out of 17 studies matched for age (65%), 8 out of 17 matched for gender (47%), and four out of 17 (24%) matched for intelligence level. Five studies did not match their study samples (30%), but four attempted to control for confounding

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variables in the data analyses. This is encouraging, as the best way to ensure equivalence in groups is to match them on dimensions that could affect the outcome (Kazdin, 2003). Controlling for possible covariates in the analyses is another way to try and avoid confounding effects. 4.2.4. ASDs, intellectual functioning, and sleep It has been established that children with intellectual disabilities experience more sleep problems than typically developing children. Although children with ASDs are often intellectually disabled, level of cognitive functioning can be enormously variable. Studies evaluating the role of intellectual function and its effect on sleep in ASDs were shown to be inconclusive. Some of the inconsistencies may be explained by differences in intellectual functioning associated with ASD subtypes (Witwer & Lecavalier, 2008). For example, Williams et al. (2004) and Gabriels et al. (2005) examined sleep issues in children with all forms of ASDs but circumvented the problem of varying intelligence levels by analyzing insomnia in children with and without ID. They found that children with ID had more sleep disruptions than the children without ID. Giannotti et al. (2008) and Bruni et al. (2007) found that sleep problems were associated with intellectual functioning in their study samples. Each of these studies focused on sleep and behavior in subtypes of the ASDs (i.e., autism and AD, respectively) possibly eliminating considerable variability in intellectual functioning. Diomedi et al. (1999) also examined the relationship between intellectual functioning and percent REM sleep in a homogenous sample of children with autism and ID. Unlike Giannotti et al. (2008), they reported no such association. Varying levels of intellectual function explains some, but not all, inconsistencies. Patzold et al. (1998), Krakowiak et al. (2008), Mayes and Calhoun (2009) and the Autism Treatment Network (2010) addressed this design issue and separated their samples into groups by level of intellectual functioning. The investigators found that the relationship between cognitive functioning and sleep was not significant. Intelligence level may be an important predictor of sleep problems in ASDs, but the results discussed above are perplexing and lead one to believe that other factors, such as variables associated with an individual ASD diagnosis, are involved. Future researchers should separate the effects of intellectual functioning and diagnosis prior to analyzing their data. For instance sampling strategies that will ensure homogeneous study samples and/or the adjustment for covariates in regression analyses may increase the internal validity of such investigations. 4.2.5. Operationalizing sleep disturbance in ASDs Three out of 17 studies (18%) operationally defined sleep disturbance in their study samples prior to analyzing the data (Allik et al., 2006; Goldman et al., 2009; Malow et al., 2006). After separating their study samples into poor sleepers and good sleepers using some designated criterion, Allik et al. (2006) and Malow et al. (2006) found that anxiety and depression were not associated with sleep in individuals with ASDs who did not have sleep problems. This suggested that only children who had difficulty sleeping experienced significant internalizing behaviors. Six of 17 studies (36%) reported results from assessments of externalizing behavior. Three of the studies concluded that there was an association between sleep and externalizing behavior in ASDs (Bruni et al., 2007; Malow et al., 2006; Mayes & Calhoun, 2009). Three others separated their samples into good sleepers and poor sleepers and found no differences between groups, indicating that externalizing behavior may not be associated with sleep disturbance in this population (Allik et al., 2006; Goldman et al., 2009; Patzold et al., 1998). The results are inconclusive and difficult to interpret. Future researchers should define sleep disturbance in their study samples in order to determine whether individuals with ASDs who are poor sleepers present with a unique set of problem behaviors compared to children who are good sleepers. Investigators who defined sleep disturbance in their study samples prior to data analyses were able more accurately to characterize the psychopathology experienced by children with ASDs designated as poor sleepers and allowed comparisons with children who were good sleepers. This is a practice that investigators would do well to consider when conducting future research. 4.2.6. ASD subtypes and problem behavior As stated above Allik et al. (2006) and Goldman et al. (2009) separated their study samples into poor sleepers and good sleepers. Following this, they analyzed the Total Problem Behavior scores on the SDQ and the CBCL. Goldman et al. (2009) found no significant differences between ASD poor sleepers and good sleepers in Total Problem Behavior scores on the CBCL, indicating that problem behavior may not be associated with sleep. However, Allik et al. (2006) found that parents of children with AD and HFA, who were poor sleepers, reported more problem behavior on the SDQ than parents of children who were good sleepers. The results are mixed and are indicative of the complexities inherent to the study of the personal characteristics associated with sleep problems in children with ASDs. The dissimilarities found in the results may reflect variation in the behavioral characteristics found across ASD subtypes. In order to gain a clearer understanding of the behavioral, cognitive, and emotional, characteristics found in children with ASDs and sleep problems, study samples should consist of either one ASD subtype and a control group (which would enhance internal validity at the expense of generalization), or a large study sample including all ASD subtypes with power enough to facilitate secondary analyses for each subtype separately. 4.3. Measurement 4.3.1. Autism symptom severity measurement and sleep It has been shown that autism symptom severity may be moderated by intelligence level. For example, two fairly recent studies reported that individuals with ID presented with more severe symptoms of autism and problem behavior than those

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without ID (Shattuck et al., 2007; Sukholdosky et al., 2008); and one study found this to be stable over time (Shattuck et al., 2007). In this review, autism symptom severity was also found to be related to insomnia in individuals with ASDs. When measuring autism symptoms, however, little consistency was shown in the instruments used across studies. Eleven studies collected data with several instruments. The ADI-R and the ADOS are well-validated instruments and have become the gold standard for use in the diagnosis of ASDs both clinically and for research purposes (Gray, Tonge, & Sweeney, 2008; Lecavalier et al., 2006; Lord, Rutter, DiLavore, & Risi, 2007; Rutter, Le Couteur, & Lord, 2004). Some combination of these measures was used along with a DSM-IV-based clinical interview as inclusion criteria for at least seven of the 17 (41%) studies (Gabriels et al., 2005; Giannotti et al., 2008; Goldman et al., 2009; Krakowiak et al., 2008; Limoges et al., 2005; Malow et al., 2006). However, only one of the studies mentioned above examined the relationship between autism severity and sleep disruption using the ADOS algorithm items (Malow et al., 2006). All other studies used either an autism screening measure (i.e., CARS, CASD, GARS, ASSQ) or a measure focused on one of the three criteria necessary for a diagnosis of an ASD (e.g., RBS). Mayes and Calhoun (2009) examined whether there was a correlation between the autism items on the PBS and the items of sleep on the same instrument. Ten of the 13 comparisons (77%) revealed a relationship between symptom severity and sleep disturbance. However, three (23%) others did not. Autism symptom severity appears to be associated with insomnia in children with ASDs. Given this, further examination of the psychometric properties of currently used measures and the possible development of new instruments is merited. 4.3.2. Internalizing behavior, measurement, and sleep The present review revealed that internalizing behavior is often a correlate of sleep disturbance in individuals with ASDs. This result is not surprising considering that a link has been established between anxious and depressed typically developing individuals with insomnia (Bertocci et al., 2005; Chorney, Detweiler, Morris, & Kuhn, 2008; Gregory, Rijsdijk, Lau, Dahl, & Eley, 2009). Notwithstanding, it remains unclear whether anxiety, depression, or both, are associated with sleep disturbance in this population. Analyses of the CBCL, YSR, and SDQ, Internalizing and Emotional Composite scores provided general information but no further explanation as to whether anxiety or depression was related to insomnia in ASDs (Allik et al., 2006; Limoges et al., 2005; Patzold et al., 1998). Two studies also examined anxiety-related items on the DBC and the STAI, and the results indicated that anxiety was correlated with sleep disturbance in ASDs (Limoges et al., 2005; Patzold et al., 1998). Three studies evaluated depressive symptoms. Malow et al. (2006) examined the Affective subscale scores on the CBCL and found that poor sleepers experienced more depressive symptoms than good sleepers. Two additional studies of adults used the BDI, and the results were mixed (Limoges et al., 2005; Tani et al., 2003). The BDI is a well-validated instrument for use in typically developing adults. However, it may not be the best measure of depressive symptoms in individuals with ASDs and insomnia. There is some concern about the instrument’s ability to discriminate between individuals with depression and insomnia and those without depression and insomnia (Carney, Ulmer, Edinger, Krystal, & Knauss, 2009). Similarly, the CBCL, SDQ, and the YSR, have all been validated in groups of typically developing children, and for this reason, may not be the best choice for assessing individuals with ASDs. In contrast, the DBC which has been validated for use in children with ASDs may be a better option for predicting problem behavior in this population. Future research should ensure the adequate assessment of the relationship between sleep, anxiety, and depression, in order to separate anxious individuals from those with depression. 4.4. Psychotropic medications Eight of 17 studies (47%) excluded children who were taking a psychotropic medication. Two studies (12%) allowed the use of medication and in six studies (36%) medication status was not reported. To state the obvious, medication treatment may influence findings by either improving or exacerbating sleep profiles. At the same time, exclusion of individuals taking psychotropic (or other) medications would likely bias findings, as nearly 50% of individuals with ASDs are taking such treatment at any given time (Aman, Lam, & Van Bourgondien, 2005). In this instance, attempting to ‘‘control’’ for medication effects seems tantamount to ‘‘over-control.’’ A partial resolution appears to be to ‘‘take all comers,’’ but to keep very careful track of medication status within all subgroups. 4.5. Objective measures of sleep and psychopathology Malow et al. (2006) found associations between anxiety, depression, aggressive behavior, and PSG recorded SOL on night one in the sleep laboratory. However, further examination on night two did not reveal an association between SOL and measures of psychopathology. The researchers determined this result a product of design flaws and recommended that three nights of PSG recordings would be optimal for future research. Investigators using PSG as a measure to investigate the correlates of sleep disturbance in children with ASDs, would reduce adjustment night bias and sleep debt effects by adding a third night to their study designs as part of a standard operating procedure. 4.6. Directions for future research 4.6.1. Sleep architecture, learning and memory in ASDs Seven studies (42%) reported the results of PSG or EEG recordings. Three of these included correlational analyses between physiological data and cognitive, developmental, and emotional characteristics associated with ASDs. Diomedi et al. (1999)

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found no relationship between level of intellectual functioning and percent REM sleep in children with autism and ID. This finding was unanticipated considering that there is an established relationship between REM sleep and learning/intellectual abilities (Castaldo & Krynicki, 1973; Feinberg et al., 1969). Further analyses showed that percent REM sleep was related to level of intellectual functioning in children with DS and ID. To clarify, the results of intelligence tests showed that both the autism group and DS group were intellectually disabled, yet the children with autism experienced significantly more REM sleep than the children with DS (but less than the TD group). Recent studies have reported that REM sleep is important for efficient memory consolidation (Cartwright, 2004; Smith et al., 2004). The differences found between groups may provide important clues to understanding the learning and memory potential of children with ASDs. Replication of this research is necessary and similar study designs are defensible. 4.6.2. Perception and motor development Elia et al. (2000) reported associations between the Visual Response (VR) category on the CARS and both SPT, and percent of time spent WASO. Accurate VR to the environment is necessary for perceptual development and eye–hand coordination (Bushnell & Boudreau, 1993). The investigators also found that the Eye–Hand Coordination classification scores on the PEP-R were associated with the number of Ss/h, the first period of latency to REM sleep (FREM-L) and SPT. In addition, the investigators found associations between the Perception classification scores on the PEP-R and SOL, number of Ss/h, the FREM-L period and TIB. Recent research has demonstrated the importance of both NREM sleep and REM sleep in the consolidation of perceptual-motor development (Mednick et al., 2003; Nishida & Walker, 2007; Smith et al., 2004; Walker et al., 2002). Future studies of Motor skill development and NREM sleep are desirable in this population, as time spent in NREM sleep has direct effects on perceptual and motor skill development (Walker et al., 2002; Mednick et al., 2003). Furthermore, a subset of children with ASDs, have experienced delays in this area. 4.6.3. Conceptualizing insomnia in children with ASDs A bidirectional theoretical framework illustrating how sleep-interfering processes may work together to disturb sleep is seen in Fig. 1. The model depicts how autism symptom severity is moderated by level of Intellectual Functioning (Kenworthy, Case, Harms, Martin, & Wallace, 2010; Shattuck et al., 2007; Witwer & Lecavalier, 2008) as higher IQ holds protective properties for prognosis (Dawson & Osterling, 1997). In addition, Autism symptom severity and any combination of the three autism symptom domains (i.e., communication deficits, social deficits, and RRBs) serve as Vulnerability Factors and predispose children with ASDs to insomnia when presented with Environmental Stressors. An additive effect is observed as each symptom domain loads together to increase the likelihood of an acute anxiety-related response to environmental stimuli. Symptom severity acts as a catalyst for triggering maladaptive Coping Strategies (i.e., internalizing and externalizing behavior) and insomnia. Environmental stressors for children with ASDs may involve social interactions and unpredictability in the environment. For example, a child with communication deficits who experiences poor peer relations and isolation, may become depressed and sleepless (Groden et al., 2001; Hallet, Angelica, Rijsdijk, & Happe´, 2010). A child who insists on sameness and who becomes anxious at any sign of a change in his routine may have tantrums when his eating ritual is disturbed (Groden et al., 2001; Richler, Huerta, Bishop, & Lord, 2010). Equally problematic is ‘‘laundry day’’ for the child who is tactilely defensive and who has a very limited supply of clothing acceptable for wear (Groden et al., 2001; Hicks & Picchioni, 2003). Externalizing behavior is often associated with internalizing behavior in children with ASDs (de Bruin, Ferdinand, Meester, de Nijs, & Verheij, 2007; Pugliese & White, 2009; Richdale, Francis, Gavidia-Payne, & Cotton, 2000) and is considered to be a maladaptive coping mechanism used to avoid anxiety-evoking stimuli and to gain access to preferred items or activities (Bauminger, Solomon, & Rogers, 2010; Morin et al., 2003). Engaging in externalizing behavior often generates conflict within the parent–child relationship (Bauminger et al., 2010) and may result in overarousal and a lack of sleep. The model also shares a bidirectional component illustrating the effects of sleep deprivation on problem behavior. Evidence suggests that disturbed sleep impacts daytime behavior (Meltzer & Mindell, 2006) and that the same insomniainducing vulnerabilities experienced by children with ASDs become further exacerbated following a night of fragmented sleep (Schreck et al., 2004). The bidirectional component of this model posits that sleep deprivation leads to an increase in internalizing (Aronen et al., 2009; Banks & Finges, 2007; Meltzer & Mindell, 2008; Neckelmann, Mykletun, & Dahl, 2007) and externalizing behavior (Bruni et al., 2007; Dahl, 1996; Touchette et al., 2007, 2009), which then intensifies the core symptom domains of ASD (Meltzer & Mindell, 2008; Schreck et al., 2004). Additional sleep-interfering correlates include medical conditions (Bandla & Splaingard, 2004), epilepsy (Malow, 2004; Stores, Wiggs, & Campling, 1998) and medication consumption (Liu et al., 2006; Mayes & Calhoun, 2009). The study of sleep in ASD is young, and unanswered questions abound. Nevertheless, the availability of a theoretical framework based on existing research may be a useful jumping off point for future researchers. The framework seems to clarify how the combined effects of sleep-interfering variables may predispose a child to develop insomnia, and conversely, insomnia may exacerbate sleep-interfering behavior. The conceptualization of this framework was inspired by an excellent review of vulnerability-stress models (Ingram & Luxton, 2005), two papers describing theoretical models of insomnia in adults and children (Lundh & Broman, 1999; Richdale & Schreck, 2009), and a chapter written by Asarnow and Asarnow (2003) describing the development of childhood schizophrenia. One term was borrowed to help explain the model; it was taken from the Lundh and Broman manuscript and

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[()TD$FIG] <=============Vulnerability Factors============> <===Environmental===> <===Coping Strategies====> Stressors

Autism Symptom Domains Communication Deficits

Intellectual Functioning

Restricted Repetitive Behaviors (RRBs)

Stimulus Changes and/or Fear Evoking Stimuli

Internalizing Behavior Anxious/ Depressed

Social Skills Deficits

Fig. 1. Bidirectional theoretical framework for Insomnia in children with ASDs.

Overarousal and/or Insomnia

Comorbid Medical Conditions (epilepsy medication use)

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has been used periodically throughout the narrative (i.e., sleep-interfering). The model design was based on the results of this review, and the data were taken from cross-sectional research; therefore, causation cannot be inferred. 4.7. Limitations This review was limited to articles written in English and may contain English language bias (Egger, 1991). Although, an extensive search of the literature was performed, other relevant articles may have been missed. The results of this review can not be generalized to children with ASDs who experience parasomnias, as the focus of research was dysomnias. 5. Conclusion This review of the literature revealed that autism symptom severity and internalizing behavior were the two strongest predictors of sleep disturbance in ASDs. Other possible correlates include externalizing behavior, comorbid medical conditions, medication use, level of intellectual functioning, and hypersensitivity to the environment. 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