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Sleep in Pediatric ADHD
Sleep Disturbances in Prepubertal Children with Attention Deficit Hyperactivity
Disorder: A Home Polysomnography Study
Reut Gruber, PhD1; Tong Xi, BSc1; Sonia Frenette2; Manon Robert, MSc3; Phetsamone Vannasinh4; Julie Carrier, PhD2,3
Douglas Mental Health University Institute and McGill University, Montreal, Canada; 2Centre du Sommeil et des Rythmes Biologiques, Hôpital
du Sacré-Coeur de Montréal, Québec, Canada; 3Département de Psychologie, Université de Montréal, Province of Québec, Canada; 4Centre de
Recherche de l’Hôpital Sainte-Justine, Montréal, Québec, Canada
1
Study Objective: 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.
Conclusion: The present findings support the hypothesis that children
with ADHD present sleep disturbances.
Keywords: ADHD, Polysomnography, sleep architecture
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.
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
Submitted for publication June, 2008
Submitted in final revised form October, 2008
Accepted for publication October, 2008
Address correspondence to: Reut Gruber, PhD, Department of Psychiatry,
McGill University, Douglas Mental Health University Institute, 6875 LaSalle
Blvd, Verdun (Quebec), Canada H4H 1R3; Tel: (514) 761-6131, Ext: 2110;
Fax: (514) 762-3858; E-mail: reut.gruber@douglas.mcgill.ca
SLEEP, Vol. 32, No. 3, 2009
343
Sleep and ADHD—Gruber et al
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.
somnography (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.
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.
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 113item 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
Study Design
Children were not taking medication and were instructed to
avoid products containing caffeine ≥ 7 days prior to the polySLEEP, Vol. 32, No. 3, 2009
344
Sleep and ADHD—Gruber et al
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.
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.
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.
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. Chisquare tests revealed no significant between-group differences
in the children’s sex and their parents’ marital status.
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
SLEEP, Vol. 32, No. 3, 2009
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
345
Sleep and ADHD—Gruber et al
Table 1—Demographic and Clinical Characteristics of Children with ADHD and Controls
Age (M/SD)
Gender M(F)
SES Parent Marital status
Married
Divorced
Single
WISC full scale
PPS
BMI
CBCL T Score
CGIT-Teacher Score**
CGI-Parents T Score**
Diagnosis of PLMD
ADHD (n = 15)
8.93 (1.39)
10 (5)
49.37 (9.79)
12
0
3
98.64 (22.47)
6.38 (2.26)
17.19 (3.1)
61.40 (9.87)
63.00 (18.22)
63.60 (14.00)
2
Controls (n = 23)
8.61 (1.27)
13 (10)
51.1 (11.6)
18
4
1
108 (18.68)
6.81 (2.34)
18.3 (4.85)
50.61 (9.04)
48.25 (6.82)
53.26 (8.52)
2
Total (n = 38)
8.74 (1.31)
23 (15)
50.36 (10.74)
30
4
4
104.46 (20.42)
6.65 (2.28)
17.84 (4.2)
54.87 (10.68)
54.86 (14.93)
57.34 (11.98)
4
F = 0.55, P = 0.46
χ² = 0.39, P = 0.74
F = 0.22 P = 0.64
χ² = 4.73, P = 0.1
F = 1.87, P = 0.18
F = 0.27, P = 0.61
F = 0.95, P = 0.34
F = 12.03**, P = 0.001
F = 9.01**, P < 0.01
F = 8.05**, P < 0.01
χ² = 0, P = 0.70
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
DISCUSSION
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.
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
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).
SLEEP, Vol. 32, No. 3, 2009
346
Sleep and ADHD—Gruber et al
Table 2—Means and Standard Deviations for Sleep Variables
2.1. Polysomnographic Sleep Measures
Sleep latency (min) Total sleep time (min) Sleep efficiency (%)
Stage 1 (min)
Stage 1 (%)
Stage 2 (min)
Stage 2 (%)
Stage 3 (min)
Stage 3 (%)
Stage 4 (min)
Stage 4 (%)
REM sleep (min)
REM (%) PLMS index
Arousal index
Time saturation below 90%
ADHD (n = 15)
29.62 ± 20.29
499.27 ± 72.06*
93 ± 5
19.42 ±11.30
3.87 ± 2.11
206.24 ± 34.96
41.59 ± 5.43
76.73 ± 29.77
15.16 ± 5.04
112.69 ± 19.83
22.83 ± 3.90
84.18 ± 32.73*
16.55 ± 5.52*
2.77 ± 3.57
0.51 ± 0.60
0.00 ± 00
Controls (n = 23)
31.23 ± 24.28
532.52 ± 47.13
95 ± 4
20.61 ± 9.21
3.93 ± 2.00
223.01 ± 48.11
41.65 ± 7.05
78.01 ± 22.11
14.71 ± 4.32
110.65 ± 24.62
21.03 ± 5.46
100.23 ± 24.99
18.68 ± 3.61
3.66 ± 4.20
0.72 ± 0.63
0.05 ± 0.02
PLMS: periodic limb movements of sleep
*P < 0.05
2.2 Factor Loading for Reported Sleep Problems
Scale Score
ADHD (15)
Controls (23)
M (SD)
M (SD)
Bedtime resistance
8.6 (2.8)
7.33 (1.9)
Sleep onset delay
1.8 (0.8)*
1.50 (0.7)
Sleep anxiety
6.1 (2)*
4.63 (1.9)
Night wakings
5.1 (2.4)
3.92 (1.1)
Parasomnias
9.2 (1.8)
8.46 (2.1)
Sleep disordered breathing
3.5 (1.1)
2.88 (0.9)
Daytime sleepiness
13.1 (3.6)
11.92 (3)
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)
Primary sleep
disturbances
0.12
0.09
0.55
0.72
0.77
0.72
−0.28
Component Loading
Insufficient
sleep
−0.05
0.82
0.15
−0.07
−0.07
−0.06
0.66
Sleep
anxiety
−0.95
0.06
−0.87
−0.50
−0.04
−0.25
−0.09
M = mean; SD = standard deviation; *P < 0.05; Bold numbers are variables that are loaded on each one of the factors.
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 enSLEEP, Vol. 32, No. 3, 2009
dogenous 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
347
Sleep and ADHD—Gruber et al
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.
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.
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
Disclosure Statement
This was not an industry supported study. The authors have
indicated no financial conflicts of interest.
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8.
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 subse­
quent 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
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