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. 2009 Mar 1;32(3):343–350. doi: 10.1093/sleep/32.3.343

Sleep Disturbances in Prepubertal Children with Attention Deficit Hyperactivity Disorder: A Home Polysomnography Study

Reut Gruber 1,✉, Tong Xi 1, Sonia Frenette 2, Manon Robert 3, Phetsamone Vannasinh 4, Julie Carrier 2,3
PMCID: PMC2647788  PMID: 19294954

Abstract

Study Objectives:

To examine sleep architecture and reported sleep problems in children with ADHD and normal controls, while considering the roles of pertinent moderating factors.

Design:

Overnight sleep recordings were conducted in 15 children diagnosed with ADHD (DSM-IV) without comorbid psychiatric problems and in 23 healthy controls aged 7 to 11 years. Children were on no medication, in good health and did not consume products containing caffeine ≥ 7 days prior to the polysomnography (PSG) study. PSG evaluation was performed at each child's home; children slept in their regular beds and went to bed at their habitual bedtimes.

Measurements

Standard overnight multichannel PSG evaluation was performed using a portable polysomnography device. In addition, parents were asked to complete a sleep questionnaire.

Results:

Compared to controls, children in the ADHD group had significantly shorter duration of REM sleep, smaller percentage of total sleep time spent in REM sleep, and shorter sleep duration. In addition, the ADHD group had higher scores on the insufficient sleep and sleep anxiety factors than children in the control group.

Conclusions:

The present findings support the hypothesis that children with ADHD present sleep disturbances.

Citation:

Gruber R; Xi T; Frenette S; Robert M; Vannasinh P; Carrier J. Sleep disturbances in prepubertal children with attention deficit hyperactivity disorder: A home polysomnography study. SLEEP 2009;32(3):343-350.

Keywords: ADHD, Polysomnography, sleep architecture

ATTENTION-DEFICIT/HYPERACTIVITY DISORDER (ADHD), ONE OF THE MOST PREVALENT CONDITIONS IN CHILD PSYCHIATRY, IS CHARACTERIZED BY developmentally inappropriate symptoms of inattention and/or impulsivity/hyperactivity that begin in childhood and lead to functional impairment in various life settings.1 A recent report demonstrated that 50% of children diagnosed with ADHD show clinically significant symptoms and impairment as young adults.2 The Center for Disease Control and Prevention has labeled ADHD “a serious public health problem”2 citing the large estimated prevalence of the disorder, the significant associated impairments in the areas of school performance and socialization, the chronicity of the disorder, the limited ability of current interventions to address all ADHD-associated impairments, and the lack of evidence that intervention provides substantial improvements in long-term outcomes.

Sleep problems have been clinically reported in an estimated 25% to 50% of children and adolescents with ADHD.3 Restless and disturbed sleep was initially included in the DSM diagnostic criteria for ADHD, but was later excluded as being a nonspecific symptom. However, the links between sleep and ADHD have been a topic of ongoing research and clinical interest, because sleep difficulties in children with ADHD present a considerable challenge for parents and for clinicians3 and may increase daytime ADHD symptoms.

Parental reports indicate a 2- to 3-fold higher prevalence of sleep problems in children with ADHD compared to controls; these include difficulty falling asleep, night awakenings, and restless sleep.4–11 However, when objective measures are considered, the results appear to be more complex and inconsistent. Actigraphy studies have suggested that children with ADHD tend to have higher activity during sleep, along with unstable sleep patterns.12–14 Several studies have found an association between sleep disordered breathing and ADHD symptoms15–17 and between ADHD and restless leg syndrome (RLS)/ periodic leg movement disorder (PLMD).18

Polysomnographic (PSG) studies looking at measures of sleep architecture have failed to find consistently significant differences between children with ADHD and controls.3 Some studies have found no differences in PSG measures between children with ADHD and controls,19 whereas other studies have yielded varied and often contradictory findings, such as significant decreases in REM sleep,17,20 significant increases in REM sleep,21 and significant decrease in REM latency21,22 in ADHD children versus controls.

Two recent meta-analyses were conducted to review the evidence regarding sleep disorders in children with ADHD and to examine potential moderating factors that might account for inconsistencies in subjective and PSG-based studies comparing sleep architecture of children diagnosed with ADHD versus controls.23,24 These meta-analyses revealed that age, gender, diagnostic criteria, the use of clinical samples, the need for the child to adapt to the lab environment, the presence of comorbid psychiatric problems, and the use of stimulant medication might moderate the observed associations between sleep characteristics and ADHD. Additional factors that have been associated with the development and disruption of sleep include child's family environment,25 particularly marital conflict between the parents.26,27

To deal with these methodological shortcomings, we sought to compare sleep architecture and reported sleep problems in children with ADHD and controls while considering the roles of multiple identified modulating factors. Portable PSG sleep recorders were used to document the sleep architecture of the children in each child's natural home environment. The use of portable PSG equipment has been validated and has been successfully used in clinical population.28,29 It offers a number of benefits: (1) it decreases the differences between the child's real sleep conditions and those of the study and increases the ecological validity of the study; (2) it limits the problems associated with laboratory recordings, such as signs of stress or adjustment difficulties manifested in distinct sleep characteristics (e.g., longer sleep latency, lower sleep efficiency);23 and (3) it minimizes the demands on participants by allowing significant portions of the study to take place in the child's home environment. To avoid the potential confounding effects of psychiatric or medical comorbidities on the sleep architecture of children with ADHD, only children with ADHD and no comorbid conditions were recruited. Lastly, we have statistically controlled for the impact of family factors on the children's sleep. We hypothesized that children with ADHD would have higher rates of reported sleep problems and alterations in sleep architecture than control children.

METHODS

Subjects

The study population consisted of 15 children with ADHD (age range 7-11 years; mean = 8.45, SD = 1.42) and 23 normal controls (age range 7-11 years; mean = 8.58, SD = 1.3). The diagnosis of ADHD was based on the criteria from the Diagnostic and Statistical Manual-4th edition (DSM-IV). The Diagnostic Interview Schedule for Children (DISC-IV),30 which generates DSM-IV diagnoses, was administered to parents. In addition, in order to achieve consensus regarding ADHD symptoms, the ADHD Conners Global Index Scale was completed by parents (home form) and by teachers (school form).

Of the 15 children who met DSM-IV criteria for ADHD, 2 met the criteria for the inattentive subtype, 1 met the criteria for the hyperactive-impulsive subtype, and 12 fulfilled the criteria for the combined subtype. Subjects were excluded if they scored < 80 on the Wechsler Intelligence Scale-4th edition (WISC-IV),31 or if they had any psychiatric diagnosis. Of the ADHD children, 66% were medication naïve, and 33% had a history of using methylphenidate. None of the children had taken any medication ≥ 7 days prior to the assessment. The children were recruited from regular elementary schools in a district whose residents predominantly belong to the middle socioeconomic classes. The study was approved by the Research Ethics Board of Douglas Mental Health University Institute; informed consent was obtained from parents, and all children assented to participation in the study.

Study Design

Children were not taking medication and were instructed to avoid products containing caffeine ≥ 7 days prior to the polysomnography (PSG) study. Standard overnight multichannel PSG evaluation was performed at each child's home by an experienced sleep technician using a portable PSG device (see below). The sleep recording was supervised by a licensed sleep technician under the auspices of the center for sleep research (Centre d'étude du sommeil) at the Hôpital du Sacré-Coeur which offers comprehensive sleep medicine program with written policies and procedures. Experienced sleep technicians who fulfill the eligibility criteria for the Canadian College of sleep technicians and had extensive experience working with pediatric populations applied the sensors and electrodes directly. Raw data was reviewed by an experienced sleep technician from Centre d'étude du sommeil32 using scoring criteria consistent with current published AASM standards.

METHODS

Polysomnography

Signals recorded using a digital ambulatory sleep recorder (Vitaport-3 System; TEMEC Instruments, Kerkrade, The Netherlands) were used to assess the sleep architecture of the children. Standard measures were recorded, including EEG, bilateral EOG, and bipolar submental EMG. EEG electrodes were placed bilaterally along the anteroposterior axes at locations F3, F4, C3, C4, P3, P4, O1, and O2. A nasal/oral thermistor, a piezosensor respiratory belt (TEMEC, The Netherlands), and EMG leg electrodes were also used to screen for breathing and leg movement sleep disorders. Sleep stages were scored visually on-screen (LUNA, Stellate System, Montreal) from the C3 derivation (referential derivation, linked ears) according to the standard criteria, using 20-sec epochs. The primary variable was the amount of time (minutes and %) spent in each sleep stage. In addition, respiratory disturbance index defined as the sum of obstructive apneas and hypopneas per hour of sleep was recorded. Events with a duration of 1 sec were counted, with a diminution of ≥ 50% in the respiratory signal considered as a hypopnea, and a complete stop in the respiratory signal considered as an apnea.

Reported Sleep Problems

To supplement the PSG data with a general overview of the child's sleep habits, Children's Sleep Habits Questionnaire (CSHQ)33 was used. The CSHQ is a retrospective, 45-item parent questionnaire that has been used in a number of studies to examine sleep behavior in young children. The CSHQ includes items relating to a number of key sleep domains, including sleep anxiety (e.g., whether the child needs a parent in room to sleep or is afraid of sleeping in the dark), night waking, parasomnias, sleep disordered breathing, and daytime sleepiness.

Behavioral and Cognitive Measures

Overall behavioral and cognitive functioning were examined using: (1) the Child Behavioral Checklist (CBCL),34 a 113-item parental questionnaire assessing behavioral and emotional problems grouped into 8 subscales and 3 global scales. At level of global scores, externalizing and internalizing symptoms can be differentiated. Parents provide information for 20 competence items covering their child's activities, social relations, and school performance. Parents rate their child for how true each item is now or within the past 6 months using the following scale: 0 = not true; 1 = somewhat or sometimes true; 2 = very true or often true. The CBCL yields good metric characteristics and excellent representative norms. Reliability and validity of the CBCL has been established repeatedly. (2) the Wechsler Intelligence Scale for Children-III (WISC-III);31 The WISC-III is comprised of Verbal Scale and Performance Scale. Scores are computed for individual subtest scores, the Verbal IQ score, the Performance IQ score and the Full Scale IQ score. The IQ scores have a mean of 100 and a standard deviation of 15. (3) The Conners Global Index-Teacher (CGI-T) and Parent (CGI-P),35,36 widely used instruments designed to assess problem behaviors related to ADHD in the school and in the home settings. These measures evaluate reported problem behavior on 10 items found to be critical in assessing ADHD. They were standardized using a large normative database and offer separate norms for boys and girls in 3-year intervals for ages 3 through 17 years.

Assessment of Puberty

To monitor pubertal development in a nonintrusive way, Petersen and colleagues developed pubertal assessment interview (puberty development scale; PDS) in adolescents are asked to report about auxiliary body hair, growth spurts, and skin changes. For girls, additional items measure menarche and breast changes, whereas for boys, two additional items assessed facial hair and voice change. Development on each characteristic is rated on a 4-point scale ranging from 1 (no development) to 4 (development is completed), with the exception of menarche, which was scored dichotomously (1 = has not occurred or 4 = has occurred). The PDS exhibits adequate reliability (median α = 0.77);37 (see38 for item-total correlations and α reliabilities). The validity of the measure has been evaluated by comparing PDS self-ratings with physicians' ratings (mean correlation between physicians' ratings and total PDS score = 0.71.39 Several years after the development of the PDS, Carskadon and Acebo40 adapted it for use on a written questionnaire. The investigators found the validity of the written adaptation of the PDS to be high, with Spearman correlations ranging from 0.84 and 0.87. The items on the questionnaire were also internally consistent, with Cronbach α coefficients ranging from 0.67 to 0.70. In the present study the validated, reliable adaptation of Petersen's self-administered rating scale for pubertal development40 was used to measure children's pubertal status, without pictorial representations or interviews.

Data Analyses

Different demographics, physical, intellectual, and psychiatric characteristics were considered as dependent variables and were compared across the groups using either one-way analysis of variance (ANOVA) or chi-square analysis, depending on the nature of the data. PSG measures information were considered as dependent variables and were compared across the groups using two-way analyses of covariance (ANCOVA), with the child's age and sex and the parents' marital status as covariates. Principal component analysis was used to reduce the number of variables and aggregate reported sleep problems into reliable indices reflecting the integrity of the reported sleep related dimensions.

Factor analysis is a statistical method used to explain variability among observed variables in terms of fewer unobserved variables called factors. It reduces the number of variables by combining two or more variables into a single factor. The observed variables are modeled as linear combinations of the factors, plus “error” terms. The factor loadings, also called component loadings in PCA, are the correlation coefficients between the variables and factors. Different statistical methods could be used to extract factors from variables, depending on the assumed relationships between the expected factors. In cases where the dimensions that are examined are not independent or orthogonal, it is recommended to use principal components extraction with oblique rotation. This method produces a better estimate of the true factors and a better simple structure in comparison to the alternative orthogonal rotation. In the present study we observed that dimensions of the CSHQ are interrelated and therefore we used principal components extraction with oblique rotation to extract factors from the CSHQ subscales.

Parental reports of sleep problems in ADHD children were examined using multivariate analyses of variance (MANCOVAs); Group (ADHD, Control) was used as the between-subject independent factor, sleep factors (insufficient sleep, sleep anxiety factor, primary sleep disturbances factor) were used as the dependent variables, and the child's age and sex and the parents' marital status were used as the covariates. SPSS 12.0 for Windows (SPSS, Inc., Chicago, IL) was used for all statistical analyses, and P-values < 0.05 were considered to indicate statistical significance.

RESULTS

Study Population

Table 1, we present mean, standard deviations and F values of the demographic and clinical characteristics of the ADHD children and controls examined in this study. A series of analyses of variance (ANOVAs) were conducted to determine whether the groups differed in age, IQ, puberty status or socioeconomic status. No significant between-group differences were observed in any of these measures. However, we did observe significant differences in the clinical characteristics assessed using the CBCL, with children in the ADHD group scoring higher on internalizing, externalizing, and total scores higher than controls. In addition, children in the ADHD group scored higher than controls on the Conners' Parents and Teachers Global Index Scales. Chi-square tests revealed no significant between-group differences in the children's sex and their parents' marital status.

Table 1.

Demographic and Clinical Characteristics of Children with ADHD and Controls

ADHD (n = 15) Controls (n = 23) Total (n = 38)
Age (M/SD) 8.93 (1.39) 8.61 (1.27) 8.74 (1.31) F = 0.55, P = 0.46
Gender M(F) 10 (5) 13 (10) 23 (15) χ2 = 0.39, P = 0.74
SES 49.37 (9.79) 51.1 (11.6) 50.36 (10.74) F = 0.22 P = 0.64
Parent Marital status
    Married 12 18 30 χ2 = 4.73, P = 0.1
    Divorced 0 4 4
    Single 3 1 4
WISC full scale 98.64 (22.47) 108 (18.68) 104.46 (20.42) F = 1.87, P = 0.18
PPS 6.38 (2.26) 6.81 (2.34) 6.65 (2.28) F = 0.27, P = 0.61
BMI 17.19 (3.1) 18.3 (4.85) 17.84 (4.2) F = 0.95, P = 0.34
CBCL T Score 61.40 (9.87) 50.61 (9.04) 54.87 (10.68) F = 12.03**, P = 0.001
CGIT-Teacher Score** 63.00 (18.22) 48.25 (6.82) 54.86 (14.93) F = 9.01**, P < 0.01
CGI-Parents T Score** 63.60 (14.00) 53.26 (8.52) 57.34 (11.98) F = 8.05**, P < 0.01
Diagnosis of PLMD 2 2 4 χ2 = 0, P = 0.70
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M, mean; SD, standard deviation; SES, socioeconomic status; PPS, Petersen's Pubertal Score; CBCL, Child Behavior Checklist; CGIT, Conners' Global Index for Teacher; CRSP, Conners' Global Index for Parents; BMI = body mass index; PLMD = periodic limb movement disorder.

**

P < 0.01

Comparing Measures of Sleep Architecture

In Table 2 we present means and standard deviations of the sleep measures of the ADHD children and controls examined in this study. ANCOVAs were conducted to assess between-group differences in sleep architecture. These analyses revealed that children in the ADHD group had significantly shorter sleep duration (F(1,35) = 4.32, P < 0.05), significantly shorter REM sleep duration (F(1,35) = 4.32, P < 0.05) and a smaller percentage of REM sleep out of total sleep time (F(1,35) = 5.4, P < 0.05), compared to controls. There were no significant differences in the durations, latencies or percentages of sleep stages 1-4, latency of stages 1-4, as well as on the restless PLM index or respiration.

Table 2.

Means and Standard Deviations for Sleep Variables

2.1. Polysomnographic Sleep Measures
ADHD (n = 15) Controls (n = 23)
Sleep latency (min) 29.62 ± 20.29 31.23 ± 24.28
Total sleep time (min) 499.27 ± 72.06* 532.52 ± 47.13
Sleep efficiency (%) 93 ± 5 95 ± 4
Stage 1 (min) 19.42 ±11.30 20.61 ± 9.21
Stage 1 (%) 3.87 ± 2.11 3.93 ± 2.00
Stage 2 (min) 206.24 ± 34.96 223.01 ± 48.11
Stage 2 (%) 41.59 ± 5.43 41.65 ± 7.05
Stage 3 (min) 76.73 ± 29.77 78.01 ± 22.11
Stage 3 (%) 15.16 ± 5.04 14.71 ± 4.32
Stage 4 (min) 112.69 ± 19.83 110.65 ± 24.62
Stage 4 (%) 22.83 ± 3.90 21.03 ± 5.46
REM sleep (min) 84.18 ± 32.73* 100.23 ± 24.99
REM (%) 16.55 ± 5.52* 18.68 ± 3.61
PLMS index 2.77 ± 3.57 3.66 ± 4.20
Arousal index 0.51 ± 0.60 0.72 ± 0.63
Time saturation below 90% 0.00 ± 00 0.05 ± 0.02
2.2 Factor Loading for Reported Sleep Problems
Scale Score ADHD (15)
 Component Loading Controls (23)
ADHD (15) M (SD) Controls (23) M (SD) Primary sleep disturbances Insufficient sleep Sleep anxiety
Bedtime resistance 8.6 (2.8) 7.33 (1.9) 0.12 −0.05 −0.95
Sleep onset delay 1.8 (0.8)* 1.50 (0.7) 0.09 0.82 0.06
Sleep anxiety 6.1 (2)* 4.63 (1.9) 0.55 0.15 −0.87
Night wakings 5.1 (2.4) 3.92 (1.1) 0.72 −0.07 −0.50
Parasomnias 9.2 (1.8) 8.46 (2.1) 0.77 −0.07 −0.04
Sleep disordered breathing 3.5 (1.1) 2.88 (0.9) 0.72 −0.06 −0.25
Daytime sleepiness 13.1 (3.6) 11.92 (3) −0.28 0.66 −0.09
Factor Scores
    Primary sleep disturbances 0.31 (1)* −0.21 (0.93)
    Insufficient sleep 0.33 (1.1)* −0.21 (0.89)
    Sleep anxiety 0.45 (0.94)* −0.29 (0.93)
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PLMS: periodic limb movements of sleep

*

P < 0.05

M = mean; SD = standard deviation;

*

P < 0.05; Bold numbers are variables that are loaded on each one of the factors.

Comparing Measures of Reported Sleep Problems

Principal-components analysis with oblique rotation produced a three-factor solution for the reported sleep problems. These factors accounted for 69% of the variance. Interpretation and labeling of each component was based on component loadings ≥ 0.6 (Table 2). The first factor (eigenvalue 2.56) was weighted by scores from night wakings, parasomnias, sleep disordered breathing; this component appeared to reflect primary sleep disorders, and was therefore labeled “primary sleep disturbances.” The second factor (eigenvalue 1.22) was weighted by scores of sleep onset delay and daytime sleepiness; late sleep onset and increased daytime sleepiness appear to reflect fatigue associated with insufficient sleep and was therefore labeled “insufficient sleep.” The third factor (eigenvalue 1.1) was weighted by scores of bedtime resistance and sleep anxiety; this component appeared to reflect difficulties associated with sleep anxiety, and was therefore labeled “sleep anxiety.” Individuals' scores for each factor were calculated by weighting the items according to the factor loadings presented in Table 2.2.

The MANCOVAs that were conducted to determine between-group differences in reported sleep problems revealed a significant main effect (F(3,33) = 5.35, P < 0.05). Univariate analyses indicated that children in the ADHD group scored higher on the insufficient sleep (F(1,35) = 5.8, P < 0.05) and the sleep anxiety factor (F(1,35) = 4.9, P < 0.05) than controls, and there was a marginal difference between the groups on the primary sleep disturbances factor (F(1,35) = 3, P < 0.06).

DISCUSSION

To our knowledge, this is the first published study comparing in-home measurements of sleep architecture parameters in children diagnosed with ADHD without comorbidity versus controls, while controlling for potential confounders. These results fill a methodological gap in the literature pertaining to sleep recording in children with ADHD and demonstrate differences in REM sleep and in sleep duration between the sleep of children with ADHD and normal controls.

Children with ADHD were found to have decreased REM sleep compared to control children. These findings are in agreement with other studies documenting REM sleep abnormalities in ADHD children.14,41–44 These findings may suggest that ADHD children suffer from an intrinsic sleep problem that could be related to the underlying pathophysiology of the disorder. REM sleep has been associated with increased brain-derived neurotrophic factor (BDNF) levels in the dorsal hippocampus.45,46 BDNF has been suggested to play a role in the pathogenesis of ADHD, and two family-based association studies demonstrated an association of BDNF polymorphisms with ADHD.47–49 The catecholamine systems have also been implicated in both the pathophysiology of ADHD and the regulation of sleep and arousal (for a review see 50,51). For example, a variety of anatomical, animal, and clinical studies have indicated that dopamine signaling acts not only to stimulate arousal and attention, but is also involved in both the regulation of overall sleep50,51 and in mechanisms specifically related to REM sleep.50 Thus, there appears to be a relationship between the mechanisms underlying the pathophysiology of ADHD and the regulation of REM sleep.

Children with ADHD were found to have higher loading on the Insufficient Sleep Factor which was comprised of daytime sleepiness and sleep onset problems. Interestingly, bedtime resistance loaded separately on another factor. This might suggest that the sleep onset problems that loaded on this factor reflect a physiological difficulty to fall asleep as opposed to behavioral sleep onset problems. The findings are in line with reports of clinicians and parents, indicating sleep onset insomnia and long sleep latencies in 25% to 50% of ADHD children.3 The loading of the daytime sleepiness subscale that includes items such as “Takes long time to be alert”; “Seems tired”; “Having hard time getting out of bed” on this factor and the finding showing that children with ADHD scored higher on this factor are also consistent with studies using the multiple sleep latency test, which have shown that children with ADHD exhibited significantly more daytime sleepiness than controls.19,52,53 These symptoms resemble the clinical picture of circadian phase delay which is characterized by a recurrent inability to fall asleep according to a socially acceptable schedule and increased daytime sleepiness. Hence, we suggest that in some cases, the sleep problems that characterize children with ADHD might be related to the circadian system. Consistent with this hypothesis is our finding of decreased time in REM in the ADHD group, raising the possibility that the final REM period, which may occur later when there is a circadian phase delay, may be truncated and the evidence suggesting that ADHD children have a delayed endogenous circadian pacemaker.54 Little research has examined the associations between ADHD and circadian-related sleep parameters. Such studies are currently underway in our lab. Future studies comparing weekday versus weekend sleep time and the timing of the sleep period in children with ADHD and controls are needed to further determine the nature of the sleep problems characterizing children with ADHD.

It is possible that the marginal differences observed between the groups on the primary sleep disturbances are real differences that would be clearly observed in a larger sample size.

Our PSG recordings revealed significant between-group differences in sleep duration, with the sleep duration of ADHD children averaging 33 minutes less than controls when sleep was recorded in-home at the children's habitual bedtimes.

Partial sleep loss on a chronic basis accumulates to become a sleep debt, which can produce significant daytime sleepiness and neurobehavioral impairment. For example, partial sleep restriction under experimental conditions over a 1-week period resulted in performance deficits equivalent to those seen with one to two nights of total sleep deprivation.55 Furthermore, studies have shown that disrupted sleep can affect daytime learning and attention in childhood and can lead to ADHD-like symptoms.56,57 It has been suggested that disrupted sleep architecture can cause executive dysfunction, impaired vigilance, depression, anxiety, and hyperactivity.58–60 These findings collectively suggest that the impact of decreased sleep duration on neuropsychological functioning in children with ADHD should be investigated further.

The differences in sleep duration between ADHD and control children found in this study raise another question: how many minutes of decreased sleep, for how long, have a negative impact on daytime performance? Previous studies showed that restriction or extension of only 1 hour of sleep in normal school-age children for as little as 3 nights in a row could significantly impact performance on neurobehavioral measures such as the Continuous Performance Test (CPT), a vigilance task commonly used for assessing the therapeutic impact of stimulants on children with ADHD.61 Hence, additional studies will be required to examine whether shorter sleep duration in children with ADHD is associated with ADHD-like symptoms, including behavioral dysregulation and poor neurocognitive functioning, especially those functions involving the prefrontal cortex.62 A study examining this link in children with ADHD is currently underway in our lab.

We did not find evidence of an association between ADHD and sleep disordered breathing or periodic limb movements. This is in agreement with several other studies.41–44 However, it is important to note that we did not use nasal cannulas for detection of obstructive hypopneas, so we cannot completely rule out the presence of sleep disordered breathing, which might be common in children with ADHD.14

In terms of clinical implications, if clinical trials and additional research confirm that sleep is functionally altered in individuals with ADHD, it may be possible to develop therapeutic approaches for optimizing and individualizing their sleep regimes. It might also be useful to take sleep related parameters into account in this population, possibly by consulting parents and teachers regarding the implications of chronic sleep deprivation on the child's day and night time behavior.

Limitations and Future Research

Within this study, two limitations should be taken into account: we did not use nasal cannulas to more accurately detect obstructive hypopneas; and 2) Multiple analyses and subsequent potential for type I errors although MANOVA and principal component analyses have been used to decrease risk and number of outcome measures to be compared.

Despite these limitations, we identified an association between specific sleep parameters and ADHD. This suggests the need for future studies exploring the effect of different treatment regiments (long- versus short-acting medications, different regimens, dosage options, etc.) on these sleep parameters of ADHD children and controls. Given that sleep alterations in children with ADHD could reflect the pathophysiology of the disorder, future examination of the impact of stimulant and nonstimulant medication on REM sleep could provide not only clinical information but also increase our understanding of the mechanisms underlying the disorder.

In conclusion, our findings suggest the presence of an intrinsic sleep problem specific to ADHD and support the idea that children with ADHD might be chronically sleep deprived. Our findings further suggest that in-home PSG might represent a practical approach for the evaluation of sleep in ADHD children, and could be useful for evaluating the relationship of sleep and daytime cognitive-behavior problems in this population. These findings add to our understanding of the specific mechanisms underlying sleep disorders and ADHD.

DISCLOSURE STATEMENT

This was not an industry supported study. The authors have indicated no financial conflicts of interest.

ACKNOWLEDGMENTS

The study was Funded by Canadian Institutes of Health Research (CIHR) grant number 153139 and Fonds De La Recherche en sante (FRSQ) grant number 10091 to Dr. Reut Gruber

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