Sleep Patterns in Preschool-Age Children With Autism, Developmental Delay, and Typical Development

Sleep Patterns in Preschool-Age Children With Autism, Developmental Delay, and Typical Development BETH L. GOODLIN-JONES, PH.D., KAREN TANG, B.S., JINGYI LIU, M.S., AND THOMAS F. ANDERS, M.D.

ABSTRACT Objective: A prominent noncore symptom of autistic disorder is disturbed sleep, but relatively few studies have investigated this symptom. Method: A multimethod approach assessed the quantity and quality of sleep in 194 children (68 with autism [AUT], 57 with developmental delay without autism [DD], 69 with typical development) recorded over 1 week. Parent perceptions, structured questionnaires, and actigraphy were compared. In addition, problem sleep as defined by parents was compared with research diagnostic criteria for behavioral insomnia obtained from actigraph recordings. Results: On actigraphy, children in the DD group, after sleep onset, exhibited more and longer awakenings than the other two groups. In contrast, children in the AUT group exhibited less total sleep time in 24 hours than the other two groups. Parent reports of sleep problems were higher in the AUT and DD groups than the typical development group, but parent reports did not concur with more objective RDC for behavioral insomnia. Parent reports of sleep problems in all of the groups were significantly associated with increased self-reports of stress. Total 24-hour sleep durations for all of the groups were shorter than recommended for preschool-age children. Conclusions: Our study provides objective evidence that sleep patterns are different in preschool children across the categories of AUT, DD, or typical development. J. Am. Acad. Child Adolesc. Psychiatry, 2008;47(8):930Y938. Key Words: sleep, preschool children, autism, developmental disability, actigraphy, neurodevelopment.

The prevalence of autistic disorder has increased dramatically during the past decade. Current prevalence rates approximate 1 in 160 live births.1,2 Autism (AUT) is characterized by a triad of well-studied, core behavioral symptoms: impaired social interaction, communication deficits, and restricted, repetitive behaviors.3 Far fewer studies have been directed at the associated symptoms of AUT such as sleep disorders.4Y6 Accepted March 12, 2008. Drs. Goodlin-Jones and Anders and Ms. Tang are with the Department of Psychiatry and Behavioral Sciences, University of California, Davis M.I.N.D. Institute; and Mr. Liu is with the Department of Statistics, University of California, Davis. This work was supported in part by a grant to Dr. Anders from the National Institute of Mental Health (RO-1-MH068232). The authors are grateful to Stephanie Sitnick, Sara Waters, and Anny Wu for their assistance and to the parents and children who participated. Correspondence to Dr. Thomas F. Anders, 2825 50th Street, Sacramento, CA 95817; e-mail: [email protected]. 0890-8567/08/4708-0930Ó2008 by the American Academy of Child and Adolescent Psychiatry. DOI: 10.1097/CHI.0b013e3181799f7c

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The Treatment and Education of Autistic and Communicatively Handicapped Children program lists sleep problems as a major behavioral management issue.7 Konstantareas and Homatidis8 found sleep difficulties to be the most frequently reported management problem of parents of children with AUT. Schreck and Mulick9 similarly reported that parents of children with autism described more sleep problems in their children than parents of typically developing children (TYP), or children diagnosed with another disability. More than half of all children with AUT have reported sleep problems.10Y14 However, in almost all of these reports, sleep problems were defined generically. A recent monograph15 documents the significant prevalence and understudied nature of sleep disorders in children with neurodevelopmental disorders and notes that autism and pervasive developmental disorders especially have received little research attention. Moreover, the authors suggest that specific sleep disorders are characteristic of certain diagnoses. For example, children

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with Prader-Willi syndrome experience excessive daytime somnolence, prolonged nighttime sleep, and rapid eye movement sleep abnormalities; girls with Rett syndrome have problems with night waking, early morning arousal, short nighttime sleep, and increased daytime napping; and children with Down syndrome experience sleep apnea with about two thirds of children exhibiting sleeponset problems and one fifth exhibiting more severe night-waking problems. Children with AUT, they suggest, may present with more circadian sleep disorders, possibly related to their impairment in decoding the social cues that serve as important zeitgebers for setting the biological clock. In addition to the common concerns of middle-of-the-night awakenings and difficulties in falling asleep, parents of children with AUT also express fears of their child wandering unsupervised at night.16 Recently, AUT has been associated with rapid eye movement sleep behavior disorder.17 To better understand mechanisms and appropriate treatments, it is important to know how the sleep problems of young children with AUT may differ from the sleep problems of TYP children. Most studies characteristically have used small samples, without control groups, and subjective methods of measuring sleep (e.g., sleep diaries, questionnaires).13 Only a few studies have used more objective methods of recording sleep (e.g., EEG, actigraphy) because of the difficulty in eliciting cooperation from children with autism. Recently, a polysomnography study of 21 school-age children (4Y10 years) with autistic spectrum disorders reported that children with poor sleep by parent report exhibited decreased sleep efficiency (sleep as percentage of time in bed) and prolonged sleep-onset latency.18 Many studies have included mixed diagnostic groups without controlling for IQ, age, and other potential confounding variables.19Y21 When studies have relied on parent report only, they have been suspect of inaccurate reporting. Hering et al.,22 using actigraphic recordings, have suggested that parents of children with AUT may Boverreport[ sleep problems when compared to parents of TYP children. As with TYP children, sleep problems in children with AUT often disrupt the entire family_s sleep, leading to added daytime stress and irritability for all.23 Although clinicians who treat children with AUT and their parents recognize that sleep problems are prominent, the literature regarding the nature, cause, and treatment of the sleep problem remains confused.

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METHOD This study examined sleep and waking behavior in children with AUT and compared these patterns to children with developmental delay without autism (DD) and TYP children. The primary objective of the study was to better understand the sleep patterns and problems of children with AUT. The DD and TYP children served as comparison populations. Because sleep patterns change with age, children in all three groups were restricted to the toddlerpreschool age range. The narrow age range avoided the pitfalls of previous studies that included children from preschool age to adolescence, making it difficult to disentangle age-related effects on sleep from diagnosis-related effects. In addition, children in the toddler-preschool age range are less likely to be taking medications that may confound the interpretation of diagnosis versus medication effects on sleep. The children in the AUT group were matched on maturational level and adaptive capacity to children in the DD group so that differences in sleep-wake patterns could be attributed to diagnostic group differences rather than developmental differences. In an attempt to further control for possible age effects, children in the TYP group were specifically recruited at the younger end of the toddler-preschool chronological age spectrum to better match their maturational levels with the neurodevelopmentally delayed groups. That is, because children in the AUT and DD groups, by design, were matched for delay in maturation, TYP children were enrolled at slightly younger chronological ages to minimize this difference in them. A second objective was to better study problem sleep in these groups. Parent reports of a sleep problem were compared to objective research diagnostic criteria (RDC) derived from actigraphic parameters. Classifying sleep disorders using quantitative RDC is more likely to lead to better neurobiological understanding of their origins, as well as to more appropriate, targeted interventions. For example, children with sleep-disordered breathing, night terrors, or circadian regulation deficits differ in respect to etiologies and treatments. Before clinically meaningful intervention trials are planned for children with AUT, a better understanding of the nature of their sleep disorders needs to be acquired. It is important to untangle the relative contributions of diagnosis, IQ, chronological age, and context factors such as parental concerns and family stress. Sample Recruitment Two hundred eighteen children were originally recruited for the project from several sources: the UC Davis M.I.N.D. Institute_s research recruitment registry, advertisements in local newspapers, and word of mouth. Families were recruited for a study Bto learn more about sleep and waking patterns[ and not for a study about sleep problems. Only one child per family was enrolled. TYP children were included only if there were no other siblings with AUT or other neurodevelopmental disorders. Exclusion criteria for all of the children included the presence of a chronic medical illness or current or previous treatment for a sleep disorder. Two children in the DD group who had been diagnosed with a seizure disorder that was well controlled by anticonvulsant medication were included. The protocol was approved by the UC Davis institutional review board, and all of the parents signed informed consents. No adverse events were reported. Of the 218 families who were originally recruited, 194 (88.5%) children completed the 1-week study. The ages of children ranged from 2.0 to 5.5 years (mean 44.4 months, SD 11.1). The 24 children

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and families who failed to complete the study differed significantly from the participants only by the older chronological age of the child (mean 49.3 versus 44.4 months; p < .05). Noncompleters were equally distributed among the three diagnostic groups and did not differ significantly from participants on IQ, sex, family socioeconomic status, or parental perception of sleep problem at intake. Participants As shown in Table 1, the 194 participants (75% male) included 68 AUT, 57 DD, and 69 TYP children. The AUT and DD groups were well matched. They did not differ significantly from each other on baseline IQ (Mullen Scales of Early Learning [MSEL]) and Vineland Adaptive Behavior Composite scores. As expected, both of the neurodevelopmentally disordered groups scored significantly lower than the TYP group on the developmental and adaptive measures. In contrast, family composition and socioeconomic status variables, such as parents_ age, employment status, and household size, did not differ between the three diagnostic groups. The only exception was that there were significantly fewer college graduates and married spouses in the mothers of the DD children. Mothers of the AUT and TYP children were not significantly different on any of the comparisons. Procedures All of the children with a neurodevelopmental disorder underwent an initial diagnostic evaluation consisting of the Autism Diagnostic Observation Scale (ADOS),24 the MSEL25 (a test of cognitive ability), and the Vineland Adaptive Behavior Scales26 (a test of adaptive functioning). The Autism Diagnostic Interview-Revised27 was also completed with mothers of children in the AUT group. Only children who met diagnostic cutoff criteria for autism on the ADOS and on all of the domains of the Autism Diagnostic Interview-Revised were included in the AUT group. Those who did not meet criteria for AUT but were developmentally delayed were included in the DD group. Thus, children in the DD group scored below the AUT cutoff score on the ADOS and <70 on the MSEL score. Typically developing children were not given the ADOS but completed the MSEL with composite scores >75. The Psychoeducational Profile-Revised (PEP-R)28 was administered to each child

during the week of actigraphic measurement. The overall composite score from the PEP-R, called the developmental score, provides a developmental equivalence in age months. Finally, parents completed the Parenting Stress Inventory.29 Sleep Measures. Each child_s sleep patterns were measured with actigraphy, parent diary, and parent questionnaire. Parents received instruction in the use of the actigraph and were given a predated sleep diary containing a separate page for each of the 7 days. The actigraph, a Mini Mitter Actiwatch (AW64; Mini Mitter, Bend, OR) weighing approximately 2 oz, was embedded in a foam pad secured by a Velcro strap and worn for seven consecutive days and nights on the nondominant ankle. Actigraphic data were scored using the manufacturer_s algorithm set at medium sensitivity (Mini Mitter, Inc.) A secondary laboratory Bsmoothing[ filter required that consecutive awake epochs, after sleep onset, last Q2 minutes. Isolated, single minute waking epochs were recoded to sleep. The validity of this laboratory smoothing method is reported elsewhere.30 A daily sleep diary was completed by the parent the first thing each morning for the previous 24 hours to cross-validate the actigraph sleep start and sleep end times. The research assistant maintained daily telephone contact with parents to maximize compliance. Each mother also completed the Children_s Sleep Habits Questionnaire (CSHQ)31 during the actigraphy recording week. Sleep Variables. Summary actigraph and diary sleep variables included bedtime (recorded clock time from diary), sleep start time (the first minute of 10 consecutive minutes of sleep from the actigraph after bedtime), sleep end (last minute of three consecutive minutes of actigraph sleep before a period of activity consistent with the diary rise time), time in bed (minutes from bedtime to sleep end), nap duration and nap number from the actigraph, and 24-hour sleep (sum of nighttime sleep duration plus nap duration). The quality of the sleep derived from the actigraph was indicated by sleep efficiency (time in bed asleep divided by the total time in bed), sleep-onset latency (number of minutes from bedtime to sleep start time), sleep percentage (percentage of minutes in sleep from sleep start to sleep end), wake after sleep-onset (WASO) duration (total minutes awake after sleep onset), and WASO number (frequency of waking bouts >2 minutes after sleep onset). In addition, the night-to-night variability of the sleep-wake behaviors was examined by analyzing the log transformation of the SDs of the sleep quantity and quality measures. Sleep Problems. The presence of a sleep problem was measured in three ways. First, at intake, the parents were asked whether, in their

TABLE 1 Descriptive Statistics for Overall Sample and Diagnostic Group Characteristics Total Sample AUT DD Sample size Male, no. (%) Age, mo, mean (SD), range Mullen ELC, mean (SD) Vineland ABC, mean SD) % Mothers college graduate % Married

194 145 (74.7) 44.4 (11.3) 24Y69 73.0 (25.5) 74.5 (21.9) 59.7 86.9

68 55 (84.6) 47.2 (10.4) 28Y68 60.3 (18.2) 61.8 (11.0) 63.6 86.4

57 42 (73.7) 45.5 (11.9) 24Y69 55.2 (6.8) 62.2 (12.3) 39.3** 78.6*

TYP 69 48 (69.1) 40.7 (10.6) 24Y62 100.6 (17.1) 97.5 (17.1) 72.5 94.2

Note: AUT = autism, DD = developmental delay without autism, TYP = typically developing; Mullen ELC = Mullen Early Learning Composite; Vineland ABC = Vineland Adaptive Behavior Composite. *p < .05; **p < .01.

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TABLE 2 Research Diagnostic Criteria for Night Waking and Sleep Onset Insomnias Behavioral Insomnia of Childhood

Criteria (for Children Older Than 24 Mo)

Night-waking subtype Q1 awakenings Q20 min for 5Y7 nights/wk 1. 920 min to fall asleep Sleep-onset subtype 2. Parent remains in room for sleep onset (meets 2 of 3 3. 92 reunions (reunions reflect resistances criteria) going to bed such as repeated bids, protests, struggles)

opinion, their child currently had a sleep problem. Their subjective response was coded yes or no. Second, during the week of actigraphy parents completed the CSHQ for the week.31 This questionnaire provides eight subscales and one total score with a clinical cutoff published for children ages 4 to 10 years of age. Third, quantitative and qualitative actigraph and diary variables were used to categorize each child according to previously defined RDC. RDC for diagnosing sleep onset and night-waking behavioral insomnias in preschool children have been proposed32,33 and are listed in Table 2 for this age group. Data Analysis All of the data were entered into SPSS version 15 for analysis. Categorical variables were analyzed using the # 2 statistic; continuous sleep and waking variables, derived from the actigraph, were analyzed using a linear mixed-effect model with diagnostic group, sex, chronological age, the PEP-R developmental score, weekend (Friday, Saturday) versus weekday (Sunday through Thursday), and intake sleep problem rating yes/no as independent variables. The parameters in the covariance matrix, assuming compound symmetry structure, were estimated using the restricted maximum likelihood procedure. A first run used the full mixed model with all of the independent variables. Main effects for each independent variable were examined initially, followed by two-way interaction effects with diagnostic group. A second analysis used a reduced mixed model with only those variables whose main effects were significant. Significant differences among diagnostic groups were further examined with a post hoc test with Tukey-Kramer adjustment to control for family-wise error in the pairwise comparisons. Pearson correlations examined the relationships between sleep-wake variables across measures. RESULTS Overall Sample Sleep Patterns

For 7 days, the average bedtime for this preschool-age sample was 20:57 (SD 1:01) and sleep began approximately 38 minutes later, with younger children showing a longer latency to sleep (F 1,188 = 5.74; p = .02). Once asleep, average sleep efficiency was high across all of the diagnostic groups (mean 91.1%, SD 4.5). The average

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rise time was 7:10 (SD 0:54) and the average time spent in bed was 10 hours, 13 minutes (SD 47 minutes). For those participants who had daytime naps (164/194), the average nap duration was 53 minutes (SD 57 minutes). The overall mean duration for 24-hour sleep was 10:58 (SD 0:52). Sex, Chronological Age, Developmental Age, and Sleep

Sex differences were not significantly different for any comparison; hence, sex was eliminated from the mixedmodel regression analysis. Chronological age and the PEP-R developmental score were significantly correlated with each other, and both predicted an identical number of sleep variables; however, the PEP-R developmental score fit the model better (lower Akaike information criterion score) and was retained. Developmentally less mature children had greater 24-hour sleep durations (F 1,190 = 15.2; p < .0001), longer sleep-onset delays at the beginning of the night (F 1,188 = 5.7; p < .02), and longer WASO durations (F 1,187 = 5.3; p < 05). Developmentally more mature children had higher sleep percentages (F 1,187 = 10.7; p < .001), and greater sleep efficiencies (F 1,189 = 25.3; p < .001) in the mixed-model analysis. Diagnosis and Sleep

As presented in Table 3, several sleep measures differed significantly between the three diagnostic groups. Children with autism had significantly shorter 24hour sleep durations and time in bed compared to both TYP and DD children. In contrast, DD children awakened significantly more times per night and for longer durations than TYP and DD children. Children in the AUT and TYP groups resembled each other in overall WASO duration; however, the TYP children had significantly more awakenings. Thus, TYP children had more but briefer awakenings, and AUT children had fewer but longer awakenings. However, both AUT and TYP children were awake less than DD children during the night. A portion of the preschoolers did not nap during the actigraphy week (22% AUT, 12% DD, 10% TYP). Of those who did nap (n = 164, 84.5%), nap durations differed significantly among the three diagnostic groups. AUT children had the shortest naps. In addition, regardless of diagnostic group, nap patterns were significantly related to developmental age (nap frequency rs = j0.15; p < .0001; nap duration rs = j0.12;

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TABLE 3 Actigraph Sleep-Wake Patterns by Diagnostic Group, Mean (SD) (N = 194) AUT (n = 68) DD (n = 57) TYP (n = 69) Bedtime Sleep start Sleep end Time in bed % Sleep Sleep-onset latency timea WASO durationa WASO no.b Sleep efficiency 24-hour sleep Nappers (n = 164) Nap durationa No. of napsb

21:00 (1:08) 21:36 (1:13) 6:58 (0:57) 9:57 (0:53) 97.1 (2.5) 0:39 (0:28) 0:18 (0:16) 2.5 (1.7) 91.3% (4.6) 10:36 (0:51) AUT (n = 54) 0:47 (0:58) 0.65 (0.76)

21:07 (1:00) 21:46 (1:13) 7:24 (1:02) 10:13 (0:48) 95.4 (3.5) 0:42 (0:31) 0:29 (0:22) 3.7 (2.4) 89.7% (5.1) 11:06 (0:54) DD (n = 48) 0:58 (1:00) 0.65 (0.48)

20:46 (0:51) 21:19 (0:53) 7:11 (0:40) 10:25 (0:35) 97.2 (1.8) 0:35 (0:19) 0:18 (0:12) 3.1 (1.8) 91.8% (3.5) 11:14 (0:43) TYP (n = 62) 0:54 (0:58) 0.66 (0.45)

F

p

F2,185 = 1.4 F2,185 = 1.1 F2,185 = 2.4 F2,190 = 6.9 F2,187 = 7.8 F2,185 = 0.86 F2,187 = 9.7 F2,191 = 6.4 F2,185 = 2.0 F2,187 = 18.4 # 22 8.1 4.5

NS NS NS .001 .001 NS .0001 .002 NS .0001 p .02 NS

Note: AUT = autism; DD = developmental delay without autism; TYP = typically developing; WASO = waking after sleep onset; NS = not significant. a Variables have been transformed logarithmically because of skewed distributions. b Variables have been transformed logarithmically because they are categorical variables.

p < .0001) with younger children napping more often and longer. Among the parents who reported a sleep problem at intake, their children had shorter naps (#22 = 16.9; p < .001). In an attempt to determine whether night-to-night variability in sleep patterns per se were significantly different among groups, the full and reduced mixedmodel analyses were conducted for log-transformed SDs of each sleep variable. Diagnosis did not predict variability of sleep parameters. However, less mature developmental age was significantly associated with greater variability in sleep end (F 1,189 = 5.9; p < 0.02) sleep percentage (F 1,188 = 8.4; p < .004), sleep efficiency F 1,189 = 23.7; p < .001), and nap duration (F 1,159 = 4.8; p < .03). Weekday Versus Weekend Sleep

Using the reduced variable mixed model, day of the week (weekend versus weekday) was examined for significant effects on sleep variables. All of the groups demonstrated significantly later bedtimes (mean 21:05 weekend versus 20:54 weekday) (F 1,193 = 19.9; p < .0001), sleep starts (mean 21:44 weekend versus 21:28 weekday) (F 1,193 = 20.1; p < .0001), and sleep ends (mean 7:21 weekend versus 7:06 weekday; F 1,193 = 27.1; p < .0001) on weekends. There were no significant differences in night-waking or napping patterns between weekdays and weekends.

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Actigraph to Diary

Parent diaries and actigraph recordings were significantly positively correlated but mean values differed (Table 4). Diaries underreported sleep-onset latency times, WASO durations, and the number of WASOs compared to the actigraph. As may be expected, sleep start times, wake up times, and the number and duration of daytime naps were more concordant between the objective and subjective methods. Although statistically significant, the positive correlations, ranging from 0.243 to 0.911, represent associations rather than precise agreements.

TABLE 4 Actigraph and Diary Variables (Mean, SD) and Correlations for All Participants (N = 194) Sleep start Wake up time Sleep duration Sleep-onset latency WASO duration No. of WASO Nap duration No. of naps 24-hour sleep

Actigraph

Diary

r

p

21:32 (1:07) 7:10 (0:54) 10:13 (0:47) 0:38 (0:26) 0:21 (0:17) 3.1 (2.0) 0:53 (1:00) 0.65 (.58) 10:58 (0:52

21:21 (1:13) 7:25 (0:55) 9:59 (0:48) 0:24 (0:42) 0:13 (0:47) 0.48 (0.59) 0:52 (0:45) 0.57 (0.39) 10:48 (1:12)

.812 .911 .580 .425 .243 .274 .902 .506 .470

.001 .001 .001 .001 .001 .001 .001 .001 .001

Note: WASO = waking after sleep onset.

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Prevalence of Sleep Problems

Parental Stress

Parent reports of sleep problems at intake indicated similar rates to what has been previously published.12,23,34 Approximately 23% of parents of children in the TYP group noted that their child had a sleep problem, and 41% parents of children in the AUT and 46% of the parents of DD groups noted that their child had a sleep problem, a statistically significant difference between the TYP and the two neurodevelopmental disorder groups (#2 = 8.0, p = .018). In contrast, the mean Total CSHQ score, another parent-report measure, did not significantly differentiate the groups (Table 5). The RDC determination of behavioral insomnias was made from actigraph and diary measures. The RDC rate for sleep-onset insomnia averaged 15.6% and 25.8% for night-waking insomnia for all of the subjects. There was no difference by diagnostic group for the sleep-onset insomnia subtype. The DD group was significantly more represented in the night-waking subtype (#2 = 8.6; p = .013). Parent report at intake was not associated with RDC ratings for any group. In contrast, the CSHQ Total score was significantly associated with RDC sleep-onset insomnia (# 21 = 10.3; p = .001). Only three children (one in each group) met RDC for both disorders. In contrast, 63.4% of the sample (123 of 194) did not meet RDC for either. Combining RDC sleep onset and night-waking insomnias to produce one RDC behavioral insomnia rating and comparing these ratings with parent report of a sleep problem at intake suggests that the parents of DD children were most accurate in reporting sleep problems (47.3% behavioral insomnia versus 45.6% intake problem). Parents of AUT children slightly overreported sleep problems (31.0% behavioral insomnia versus 41.2% intake problem), whereas parents of TYP children seemed to underreport (46.7% behavioral insomnia versus 23.2% intake problem).

The Parenting Stress Inventory Total Stress score significantly differentiated the diagnostic groups. The highest mean scores were reported by parents of AUT children (mean 258, SD 43), slightly lower for DD children (mean 242, SD 48), and lowest for TYP children (mean 211, SD 40, F 2,180 = 19.5; p < .000). Higher parental stress scores were also associated significantly with shorter naptimes (r = j0.203; p < .006) and higher Total CSHQ scores in all of the diagnostic groups (r = 0.426; p < .001), but not with RDC behavioral insomnia ratings.

DISCUSSION

This study explored sleep-wake patterns and sleep disorders in 68 children with carefully diagnosed AUT. Children with AUT were compared to two other groups of children, one with comparable delays in development but without AUT and a second with typical development. The TYP group was selected to be slightly younger in chronological age in an attempt to control for potential effects on sleep-wake state organization of developmental delay in the two clinical populations. Overall, the narrow age range, the use of a matched neurodevelopmental comparison group and a typically developing group, and the comprehensive diagnostic evaluations of participants were important controls in this study and should be considered in all studies of special needs populations. Moreover, the multimethod data collection protocol provided an opportunity for comparisons of objective, structured, and purely subjective measures. Nighttime sleep and daytime nap patterns in all three groups demonstrated expected age effects.35 Sleep-onset

TABLE 5 Parent-Reported Sleep Problems Versus Research Diagnostic Criteria Behavioral Insomnias AUT (n = 68) DD (n = 57) TYP (n = 69) # 22 p Intake report of sleep problem by parent CSHQ RDC behavioral insomnia, sleep onset RDC behavioral insomnia, night waking

41.2%

45.6%

23.2%

49.5 (7.6)

50.5 (8.2)

47.5 (7.8)

11.9% 19.1%

14.0% 33.3%

20.6% 26.1%

8.0 F2,184 2.3 # 22 2.1 8.6

.018 p NS p NS .013

Note: AUT = autism; DD = developmental delay without autism; TYP = typically developing children; CSHQ = Children_s Sleep Habits Questionnaire; RDC = research diagnostic criteria.

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latency times, 24-hour sleep times, awake minutes after sleep onset, nap durations, and numbers of naps decreased with advancing developmental age; nighttime sleep percentage and sleep efficiency increased. Maturational main effects, however, did not significantly interact with diagnostic group effects. Average bedtimes were late, after 8:30 PM, for all of the groups. Sleep onsets occurred 38 minutes later on average. Twentyfour hour sleep times averaged approximately 11 hours, somewhat less than previously reported for children at these ages,36 suggesting that young children today may not be getting enough sleep.37 The primary hypothesis that children with AUT would have more disrupted sleep than DD children was not confirmed. The nighttime sleep of AUT children closely resembled the sleep of DD children. However, AUT children exhibited significantly less 24hour sleep, shorter naps, and less time in bed at night than their DD controls. Children in the DD group demonstrated the most fragmented sleep with significantly increased numbers and durations of nighttime awakenings and lowest sleep percentages compared to both other groups. Analyzing the data for night-to-night variability, the hypothesis that children with neurodevelopmental disorders would have more variation in their sleep patterns also was not supported, although such variability had been previously reported for older children with ADHD.38 In this study, less mature developmental age was associated with significantly more variability in sleep percentage, sleep efficiency, and morning rise times but not neurodevelopmental status. Although the data from the sleep diaries are a necessary adjunct to actigraph recording and correlated significantly with the actigraph on all sleep and waking variables, correlations do not imply congruence. For example, parents are often not aware of brief awakenings that interrupt their child_s sleep but not their own.39 Thus, they tend to underestimate the number and duration of WASOs as evident by the smaller correlation coefficients in our data. A similar result has been noted previously.40 As may be expected, larger correlations were noted for the number and duration of naps. There was little concordance between the three methods that defined sleep disorders. Others have reported a lack of congruence between objective and subjective descriptions of sleep disorders in children with neurodevelopmental disorders.18,41 By intake

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report, parents of children in the AUT and DD groups endorsed a significantly greater proportion of sleep problems in their children than parents of typically developing children. The levels were similar to those reported in the extant literature.42,43 Contrary to expectations, the smallest percentage of children who met RDC for night-waking insomnia and sleep-onset insomnia were diagnosed with AUT. Only 19.1% met criteria for night-waking insomnia and 11.9% met criteria for sleep-onset insomnia. In contrast, one third of the DD group met the quantitative criteria for night-waking insomnia and 14% for sleep-onset insomnia. Thus, it appears that sleep-onset problems, defined by reunions and/or the need for parental presence while falling asleep, do not seem to be especially problematic for children with neurodevelopmental disorders at this age. In contrast, 26% of the TYP group met RDC for night-waking insomnia, and 20% met criteria for sleep-onset insomnia. Because 23.2% of parents in this group responded positively to the intake sleep questionnaire, it suggests that they may have been referring to the bedtime sleep-onset problem because parents of TYP children at these ages are not always aware of nighttime awakenings. As noted in other studies, sleep disturbances and parent stress are positively associated, although it is not clear which comes first.44 In this study, parents of children with autism endorsed significantly more stress than parents in the other two groups. This also has been reported previously.45 Perhaps the heightened stress is associated with the shorter 24-hour sleep times, and the fewer and shorter naps in these children, which are consequently overreported as sleep problems at intake. Parents of children in the DD group were intermediate in reporting high levels of stress, possibly related to significantly more fragmented sleep and/or more single and less educated mothers in this group. Socioeconomic status and family context variables did not account for the higher stress scores in the AUT group because matching with the TYP group on these variables was satisfactory. Although one partial explanation for overreporting of sleep problems by parents in the AUT group when compared to RDC ratings is the stress of having children sleep less, an equally plausible explanation is that the RDC were too stringent. Sleeping for less time has not been incorporated into the RDC. Clearly more research is necessary to further refine the quantitative behavioral insomnia criteria.

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SLEEP IN PRESCHOOL CHILDREN WITH AUTISM

In summary, the results of this study support previous reports that AUT children and DD children have more sleep problems than TYP children as reported by their parents. These results pertain to children who are in the toddlerYpreschool age range but support previous results in older children. However, these problems are not directly supported by more objective actigraph data. Nevertheless, the actigraph data suggest that the sleep patterns of children with AUT are organized differently from those of DD children and TYP children. Compared to TYP children, AUT children sleep for shorter periods of time during a 24-hour day; DD children present more fragmented nighttime sleep characterized by more frequent and prolonged awakenings. Because this study was designed to study sleep-wake patterns, families were not recruited because their child had a sleep problem. Nevertheless, selection bias may have affected this sample of convenience. Parents of children with a sleep problem may have been selectively attracted to a study of sleep. Thus, our results may not generalize to a more heterogeneous population-based, community sample or to children referred for a clinical sleep disorder. Another limitation stems from reports that the actigraph may not be the best method for substantiating awakenings. Although the actigraph has been shown to be a valid measure for recording sleep compared to polysomnography, the validity of numbers and durations of awakenings is less accurate.30,46,47 Finally, the CSHQ has not been validated for these ages, although data unrelated to this article have suggested that using the CSHQ Total scores as an indicator of a sleep problem may be appropriate in preschool children.48 Despite these limitations, it is clear from these results that preschool children with AUT differ from DD children in their sleep-wake patterns. Moreover, TYP children differ from both groups of children with neurodevelopmental disorders. A number of research questions remain. First and foremost, these results need replication. In particular, the DD group should be further refined by studying specific disorders or genetic pedigrees. Homogeneous populations of children with bona fide sleep disorders as determined by objective criteria should be compared to matched children without sleep disorders, using genetic and/or imaging technologies to better understand possible underlying mechanisms. In addition, research should focus on how

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Politics and the Erosion of Federal Scientific Capacity: Restoring Scientific Integrity to Public Health Science Rest KM, Halpern MH Our nation_s health and prosperity are based on a foundation of independent scientific discovery. Yet in recent years, political interference in federal government science has become widespread, threatening this legacy. We explore the ways science has been misused, the attempts to measure the pervasiveness of this problem, and the effects on our long-term capacity to meet today_s most complex public health challenges. Good government and a functioning democracy require public policy decisions to be informed by independent science. The scientific and public health communities must speak out to defend taxpayer-funded science from political interference. Encouragingly, both the scientific community and Congress are exploring ways to restore scientific integrity to federal policymaking. Am J Public Health 2007;97(11):1939Y44. Epub 2007 Sep 27. Reprinted with permission from the American Public Health Association.

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