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DOI: 10.1542/peds.2009-0690; originally published online January 18, 2010;2010;125;e261Pediatrics
Beebe and Judith OwensKimberly Yolton, Yingying Xu, Jane Khoury, Paul Succop, Bruce Lanphear, Dean W.
ChildrenAssociations Between Secondhand Smoke Exposure and Sleep Patterns in
http://pediatrics.aappublications.org/content/125/2/e261.full.html
located on the World Wide Web at:The online version of this article, along with updated information and services, is
of Pediatrics. All rights reserved. Print ISSN: 0031-4005. Online ISSN: 1098-4275.Boulevard, Elk Grove Village, Illinois, 60007. Copyright 2010 by the American Academypublished, and trademarked by the American Academy of Pediatrics, 141 Northwest Point
publication, it has been published continuously since 1948. PEDIATRICS is owned,PEDIATRICS is the official journal of the American Academy of Pediatrics. A monthly
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Associations Between Secondhand Smoke Exposureand Sleep Patterns in Children
WHATS KNOWN ON THIS SUBJECT:Adult and adolescent
smokers report difficulties with sleep. Young children who are
exposed to tobacco smoke have poorer sleep quality. Children
with asthma report more sleep problems and are more sensitive
to the respiratory effects of tobacco smoke.
WHAT THIS STUDY ADDS: We report significant associations
between SHS exposure, as measured with a biological marker
(serum cotinine levels), and sleep problems in children with
asthma. Reduction in SHS exposure is an area with the potential
for significant impact in the pediatric population.
abstractOBJECTIVES: The objective of this study was to investigate the relation-
ship between exposure to secondhand smoke (SHS) and child sleep
patterns among a group of children with asthma who were exposed
regularly to tobacco smoke at home.
METHODS: We studied 219 children who were enrolled in an asthma
intervention trial and were exposed regularly to SHS. Serum cotinine
levels were used to measure exposure to tobacco smoke, and sleep
patterns were assessed through parent reports using the ChildrensSleep Habits Questionnaire. Covariates in adjusted analyses included
gender, age, race, maternal marital status, education, and income,
prenatal tobacco exposure, maternal depression, Home Observation
for Measurement of the Environment total score, household density,
asthma severity, and use of asthma medications.
RESULTS: Exposure to SHS was associated with sleep problems, in-
cluding longer sleep-onset delay (P .004), sleep-disordered breath-
ing (P .02), parasomnias (P .002), daytime sleepiness (P .022),
and overall sleep disturbance (P .0002).
CONCLUSIONS: We conclude that exposure to SHS is associated with
increased sleep problems among children with asthma. Pediatrics2010;125:e261e268
AUTHORS:Kimberly Yolton, PhD,
a
Yingying Xu, MS, MA,
a
Jane Khoury, PhD,b Paul Succop, PhD,c Bruce Lanphear,
MD, MPH,d Dean W. Beebe, PhD,e and Judith Owens, MDf
Divisions of aGeneral and Community Pediatrics andbBiostatistics and Epidemiology, andeBehavioral Medicine and
Clinical Psychology, Department of Pediatrics, Cincinnati
Childrens Hospital Medical Center, Cincinnati, Ohio;cDepartment of Environmental Health, University of Cincinnati,
Cincinnati, Ohio;dFaculty of Health Sciences, Simon Frasier
University and British Columbia Childrens Hospital, Vancouver,
Canada; andfDepartment of Pediatrics, Brown University,
Providence, Rhode Island
KEY WORDS
passive smoking, environmental exposure, sleep, child, asthma
ABBREVIATIONS
CSHQChildrens Sleep Habits Questionnaire
ORodds ratio
CIconfidence interval
SHSsecondhand smoke
HOMEHome Observation for Measurement of the Environment
www.pediatrics.org/cgi/doi/10.1542/peds.2009-0690
doi:10.1542/peds.2009-0690
Accepted for publication Aug 3, 2009
Address correspondence to Kimberly Yolton, PhD, Cincinnati
Childrens Hospital Medical Center, Division of General and
Community Pediatrics, 3333 Burnet Ave, ML 7035, Cincinnati, OH
45229-3039. E-mail: [email protected]
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright 2010 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE: The authors have indicated they have
no financial relationships relevant to this article to disclose.
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Appropriate sleep quality and quantity
are increasingly being recognized as
critical elements for many aspects
of child health and development. For
children, inadequate sleep has been
linked with poor school performance,1
somatic complaints,1 behavior prob-lems,25 and mental health problems.1,6
In addition, sleep problems during
childhood are associated with in-
creased incidence of anxiety and de-
pression, aggressive behaviors, and
attention problems in adulthood,
which suggests a lasting impact of
sleep problems on mental health.7
Poor childhood sleep also predicts the
development of obesity and its associ-
ated morbidities,8,9 which indicatesan important influence on health
outcomes.
More than 25% of children experience
some type of sleep problem during
childhood.1,1012 Among children with
asthma, the prevalence of sleep
problems is higher, with 40% to 60%
having some difficulty.13,14 Children
with asthma are nearly 4 times more
likely to experience sleep-disordered
breathing,15 which results in sleepdisruption and decreased sleep effi-
ciency,16 reduced sleep quality, in-
creased nighttime activity levels,17 and
more difficulties with daytime sleepi-
ness.16,18 Sleep efficiency has been
shown to improve with effective treat-
ment of asthma symptoms,16 but even
children with clinically stable asthma
have worse sleep quality and more
daytime sleepiness than do children
without asthma.19
Tobacco exposure is a risk factor for
sleep problems in adolescents and
adults. Cigarette smoking is associ-
ated with changes in sleep architec-
ture, with smokers experiencing
longer latency to sleep initiation and
lighter sleep.20 Adult and adolescent
smokers report more sleep problems,
such as trouble initiating sleep, main-
taining sleep, difficulty waking, and
daytime sleepiness.2124 Women who
smoke during pregnancy are more
likely to report insufficient sleep, diffi-
culty initiating sleep, early morning
waking, short sleep duration, snoring,
and excessive daytime sleepiness,
compared with pregnant women whodo not smoke. Interestingly, women
who are nonsmokers but are exposed
to secondhand smoke (SHS) during
pregnancy also report more difficul-
ties with sleep, including insufficient
sleep, difficulty initiating sleep, and
short sleep duration, compared with
those not exposed.25
This effect of SHS on sleep in adult
women raises concerns about the po-
tential impact on children whose fam-ily members smoke. There is little re-
search on the influence of tobacco
smoke exposure on sleep patterns in
childhood. Young children who are ex-
posed to tobacco smoke either prena-
tally or postnatally are reported to
have poorer sleep quality26 and more
symptoms of sleep-disordered breath-
ing,27 compared with those who are
not exposed. Because tobacco smoke
is a known contributor to asthma se-verity,2830 exposure to SHS may have a
particularly marked effect on the sleep
of children with asthma. Although this
possibility has received little empirical
investigation, SHS exposure has been
associated with increased night wak-
ings in children with asthma.31 A major
limitation of these studies linking SHS
and sleep difficulties in children26,27,31
is the fact that they relied on parent
reports of exposure, instead of more-precise and more-objective biological
markers of tobacco exposure.
The objective of this study was to ex-
amine the relationship between SHS
exposure and sleep patterns among a
group of children with asthma. We
used a biomarker of tobacco expo-
sure, serum cotinine levels, to quan-
tify exposure objectively and a vali-
dated pediatric sleep survey32 to
characterize sleep patterns. We hy-
pothesized that children with asthma
who were exposed to higher levels of
SHS would exhibit more sleep prob-
lems, as reported by parents, com-
pared with children with lower levels
of exposure.
METHODS
This study used the Cincinnati Asthma
Prevention Study, an asthma interven-
tion trial based on environmental mod-
ifications to the home in the form of
high-efficiency particulate air clean-
ers, and outcomes focused on asthma
symptoms, health care utilization, and
pulmonary function. For the current
study, SHS exposure, child sleep pat-terns, and potential covariates were
measured before initiation of the
asthma intervention.
Recruitment and enrollment proce-
dures for the 6- to 12-year-old children
are described in detail elsewhere.33
Briefly, all children in the sample had
physician-diagnosed asthma that had
been treated within the previous year
and exposure to SHS from 5 ciga-
rettes per day at home, according toparent report. Children were identified
on the basis of hospital and clinic bill-
ing records, and parents of 1678 chil-
dren were contacted for completion of
a screening survey and request for
participation, if eligible. Children were
excluded if they had other respiratory
diseases, heart disease, mental retar-
dation, or other serious conditions
barring participation in the study. Of
the 348 eligible participants, 232 en-rolled and completed the main study
(67% participation rate), and 219 had
complete data pertinent to the current
study and were retained for the analy-
sis. Protocols were approved by the in-
stitutional review board.
We collected detailed survey data re-
garding the childs daily exposure to
SHS in the home, car, and other loca-
tions, including hours of exposure,
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number of cigarettes per day, whether
the child was in the same room during
smoking, and whether windows were
open during car exposure. SHS expo-
sure also was measured objectively by
using serum cotininelevels detected in
samples collected at the baselinehome visit, which represent our pri-
mary measure of exposure. Cotinine, a
metabolite of nicotine, is a reliable
biomarker of exposure to tobacco
smoke.34 Serum levels provide a short-
term view of exposure over the previ-
ous 48 to 72 hours. However, because
of stability of exposure patterns over
time, a one-time cotinine measure-
ment is considered representative of
typical daily exposure.34 Cotinine levelsin serum were measured by the Cen-
ters for Disease Control and Preven-
tion with published methods involving
high-performance liquid chromatog-
raphy linked to atmospheric-pressure
chemical ionization-tandem mass
spectrometry.35 We applied a logarith-
mic base 2 transformation for analysis
of serum cotinine levels because of
the skewed distribution of the raw
data. This provides for simpler inter-pretation of the coefficients from the
regression analyses, in which there is
an increase in the sleep scale equal to
the coefficient for log cotinine level for
each doubling of the cotinine level.
The Childrens Sleep Habits Question-
naire (CSHQ)3 was used to measure
child sleep patterns within the previ-
ous 2 weeks, as reported by the pri-
mary caregiver. The CSHQ yields a to-
tal sleep disturbance score and scoresfor 8 scales (ie, bedtime resistance,
sleep-onset delay, sleep duration, sleep
anxiety, night wakings, parasomnias,
sleep-disordered breathing, and daytime
sleepiness). This instrument is used in
clinical and research settings to pro-
vide a broad description of child sleep
patterns. Internal consistency mea-
sures for the entire scale are high
( 0.68 for a community sample and
0.78 for a clinical sample), and
test-retest reliabilities among the
scales are high (r 0.620.79). A total
CSHQ score of41 has sensitivity of
0.80 and specificity of 0.72, properly
classifying 80% of a group with clini-
cally relevant sleep disorders. To en-sure uniform thorough completion,
this measure was administered in
an interview in which caregiver re-
sponses were recorded by a trained
research assistant.
Asthma severity was reported as mild,
moderate, severe, or very severe by
the childs caregiver. Severe and very
severe categories were combined be-
cause of small group sizes. Parent
report of asthma symptoms is an ef-fective means of characterizing child
asthmaand is notenhancedby asthma
diaries or pulmonary function test-
ing.36 Parents also reported asthma
medication use by the child, includ-
ing use of short-acting bronchodila-
tors, long-acting inhaled steroids,
and orally administered steroids
prescribed for treatment of acute
exacerbations.
Other measured covariates were ma-ternal depression (Beck Depression In-
ventory II)37 and quality of the home en-
vironment, measured with the Home
Observation for Measurement of the
Environment (HOME) instrument for
elementary school-aged children.38
The HOME instrument is primarily an
observational tool that assesses the
quality of the home environment, in-
cluding physical characteristics, vari-
ety of stimulation, and nurturing be-havior from the parent, and was
completed at a 12-month follow-up
visit. For 14 participants, this assess-
ment was missing and imputed values
generated with SOLAS 3.0 (Statistical
Solutions, Cork, Ireland), on the basis
of age, race, and gender, were used.
Univariate analyses involved inspec-
tion of frequencies and estimation of
means and associated SDs. Because of
nonnormal distributions, serum cotin-
ine levels are reported as geometric
means and 95% confidence intervals
(CIs), and household income is re-
ported as median and 25th and 75th
percentile values. We used linear re-
gression for the daytime sleepinessand total sleep disturbance scales. The
distributions of the other sleep scales
reflected varying degrees of nonnor-
mality; therefore, responses were di-
chotomized, at approximately the 75th
percentile value, and logistic regres-
sion was used. For each outcome mea-
sure, 3 models were developed, to
reflect (1) the simple bivariate associ-
ation between exposure and sleep, (2)
the association after adjustment forall covariates (age, gender, race, ma-
ternal smoking during pregnancy,
marital status, maternal education,
household income, household density,
number of siblings, maternal depres-
sion, HOME score, asthmaseverity, and
asthma medication use), and (3) the
association after adjustment for im-
portant covariates, representing the
most statistically parsimonious final
model. For the final models, age, gen-
der, and asthma severity were re-
tained irrespective of statistical signif-
icance. Other covariates were retained
if they accounted for significant vari-
ance on the given sleep scale (P .05).
Also, if removal of the covariate from
the model was associated with a
10% change in the regression coeffi-
cient for serum cotinine levels, then it
was retained in the model. SAS 9.1
(SAS Institute, Cary, NC) was used for
all analyses.
RESULTS
Descriptive information on the sample
is summarized in Table 1. The mean
age of the subjects at the baseline visit
was 9.4 years. Sixty-one percent of the
children were boys, and 56% were re-
ported to be black. Childrenin thesam-
ple were exposed to a median of 13
cigarettes per day in their homes, as
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reported by their parents, and the geo-
metric mean serum cotinine level for
the sample was 1.16 ng/mL. The corre-
lation between serum cotinine levelsand parent-reported exposure was
0.39 (P .0001) for the full sample.
Mean values for overall sleep distur-
bance and sleep scale scores for
children in our sample fell between
the clinical and control samples re-
ported by Owens et al.32 Internal con-
sistency for the sleep measure also
was comparable to the findings of
Owens et al.32 Surprisingly, 93% of
the children in the sample had a
CSHQ total sleep disturbance score
(41) that would be considered clin-
ically relevant. The mean sleep timereported by parents was 9.6 hours
per night.
In bivariate analyses, the associations
between the logarithm of serum cotin-
ine levels and child sleep patterns
were significant for bedtime resis-
tance, sleep anxiety, parasomnias,
sleep-disordered breathing, daytime
sleepiness, and total sleep distur-
bance but not for sleep-onset delay or
sleep duration (Table 2). There was no
association between total duration of
nighttime sleep and serum cotinine
levels.
In multivariate analyses including all
potential covariates of child sleep pat-
terns, we found that higher levels of
SHS exposure were significantly asso-
ciated with higher scores (ie, more
problems) on the sleep-onset delay,
parasomnias, daytime sleepiness, and
total sleep disturbance scales. Final
models, including only covariates that
had a relationship with the sleep scale
of interest or affected the coefficient
for cotinine levels, revealed significant
associations between SHS exposure
andincreases in sleep-onset delay, para-somnias, sleep-disordered breathing,
daytime sleepiness, and overall sleep
disturbance (Table 2).
Several covariates remained in our
final models because of associations
with sleep scales. Increasing age
was significantly associated with de-
creased bedtime resistance, more
problems with sleep duration, less
sleep anxiety, and lower overall sleep
disturbance. More-severe asthma was
associated with more problems with
sleep duration and more-frequent
night wakings. Maternal smoking
during pregnancy was associated
with decreased sleep-onset delay. A
nonmarried parent was associated
with fewer parasomnias and in-
creased daytime sleepiness. Lower
family income was associated with de-
creased sleep duration and decreased
daytime sleepiness. Higher levels ofmaternal depression were associated
with more-frequent parasomnias,
increased daytime sleepiness, and
greater overall sleep disturbance.
More siblings and increased housing
density were associated with de-
creased sleep-disordered breathing.
Finally, the use of long-term inhaled
asthma medications was associated
with fewer parasomnias.
TABLE 1 Sample Characteristics (N 219)
Age, mean SD, y 9.4 1.8
Male, n(%) 134 (61.2)
Race, n(%)
Black 122 (55.7)
White 92 (42.0)
Other 5 (2.3)
Parent education, n(%)
High school graduate or less 143 (65.3)
Any college 76 (34.7)
Parent marital status, n(%)
Married or living with someone 91 (41.6)
Divorced, separated, or widowed 37 (16.8)
Single, never married 91 (41.6)
Household income, median (interquartile range), $ 25 000 (15 00045 000)
Asthma severity, n(%)
Mild 51 (23.2)
Moderate 105 (48.0)
Severe/very severe 63 (28.8)
Maternal smoking during pregnancy, n(%)
None 73 (33.3)
Until pregnancy recognition 32 (14.6)
Throughout pregnancy 114 (52.1)No. of cigarettes smoked in home daily, median (interquartile range) 13 (920)
Serum cotinine level
Geometric mean (95% CI), ng/mL 1.16 (0.1013.07)
Median (interquartile range), ng/mL 1.45 (0.562.69)
HOME score, mean SD 46.9 8.1
Maternal depression score, mean SD 12.4 9.9
Behavior Assessment System for Children-2 score within clinical range
(70), n(%)
Externalizing symptoms 41 (18.8)
Internalizing symptoms 58 (26.6)
Behavior symptoms 46 (21.1)
Sleep pattern score, mean SD
Bed resistance 8.9 2.7
Sleep-onset delay 1.6 0.8
Sleep duration 4.4 1.7
Sleep anxiety 5.7 1.8
Night waking 4.3 1.3
Parasomnias 9.6 2.1
Sleep-disordered breathing 4.2 1.4
Daytime sleepiness 15.5 3.3
Total sleep disturbance 51.6 8.2
Total sleep disturbance raw score of41, n(%) 203 (92.7)
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We found cotinine level-gender inter-
actions for the sleep-onset delay (odds
ratio [OR]: 0.63; P .011) and sleep
anxiety (OR: 1.47; P .05) scales;
therefore, we performed gender-
stratified regression analyses for
those scales, controllingfor the covari-
ates retained in the final models for
the full sample. For boys, a statistically
significant relationship was found be-
tween serum cotinine levels and
higher scores for sleep anxiety (OR:
1.54; P .003). For girls, a statistically
significant relationship was found be-
tween serum cotinine levels and sleep-
onset delay (OR: 1.54; P .008).
DISCUSSION
For children with asthma, we found
that SHS exposure was associated
with greater parent-reported sleep
problems. Specifically, as SHS expo-
sure increased, parents reported that
their children had longer delays in
sleep onset, more-frequent parasom-
nias and sleep-disordered breathing,
increased daytime sleepiness, and
greater overall sleep disturbance.
Two sleep scales showed significant
gender-cotinine level interactions. In
regression analyses stratified accord-
ing to gender, greater exposure to SHS
was associated with greater sleep anx-
iety in boys and greater sleep-onset
delay in girls.
SHS exposure was associated with in-
creased incidence of parasomnias in
this sample of children. Parasomnias
reflect partial arousal from either non-
rapid eye movement or rapid eye
movement sleep and, although usually
benign, can be highly distressing tochildren and their families. More than
80% of preschoolers experience para-
somnias, but their incidence de-
creases with age.39 Adult men who
smoke report more nightmares and
disturbing dreams than do men who
do not smoke, but there has been no
reported association for women.24
Boys in our study experienced greater
sleep anxiety with increasing SHS ex-
posure. Nighttime fears are reportedby upto 79% of 8- to 16-year-old youths.
In contrast to our findings, however,
they have been reported more fre-
quently among girls (72%) than boys
(55%).40 For girls in our study, greater
SHS exposure was associated with
greater sleep-onset delay, which is con-
sistent with reports that both men and
women who smoke cigarettes have in-
creased difficulty initiating sleep.41 No
other studies have investigated the rela-
tionship between SHS and sleep-onset
delays among children.
The exact mechanism through which
SHS exposure may affect childrens
sleep is not clear. We briefly explore 3
possible explanations, that is, exacer-
bation of respiratory symptoms, nico-
tine arousal mechanisms, and symp-
toms of abstinence. In adults, smokingis known to exacerbate respiratory
disorders such as obstructive sleep
apnea,24,42 and SHS exposure has been
associated with increased snoring in
pregnant women.25 Among children,
parent-reported maternal smoking is
associated with increased snoring,27
and nighttime respiratory symptoms
are exacerbated by exposure to SHS.31
Indeed, higher levels of SHS exposure
were associated with more sleep-disordered breathing among children
in our study. It is likely that SHS
exposure acts as an upper airway
irritant, increasing symptoms of sleep-
disordered breathing and thus contrib-
uting to overall sleep disturbances
among children with asthma.
Although adolescent and adult smokers
report disturbances in sleep,2224,41,43 a
clear causal relationship between to-
TABLE 2 Associations Between Serum Cotinine Levels (Independent Variable) and Childrens Sleep (Dependent Variable) in Logistic and Multiple
Regression Analyses (N 219)
Sleep Scale (Cutoff Point) Bivariate Association Between
Serum Cotinine Levels and Sleep
Outcomes
Full Model of Association
Between Serum Cotinine Levels
and Sleep Outcomes Including
All Covariates
Final Model of Association
Between Serum Cotinine Levels
and Sleep Outcomes Including
Significant Covariates
OR or (95% CI) P OR or (95% CI) P OR or (95% CI) P
Bed resistance (10) 1.29 (1.091.53) .003 1.34 (0.951.88) .093 1.21 (0.9951.47) .056Sleep-onset delay (2) 1.07 (0.921.24) .39 1.50 (1.082.06) .014 1.53 (1.152.03) .004
Sleep duration (5) 1.00 (0.861.17) .98 1.19 (0.871.63) .27 1.12 (0.921.37) .24
Sleep anxiety (7) 1.28 (1.071.53) .006 0.90 (0.651.24) .51 1.06 (0.811.38) .68
Night wakings (5) 1.09 (0.931.28) .28 1.10 (0.811.50) .54 1.09 (0.921.31) .32
Parasomnias (12) 1.62 (1.232.12) .0005 1.91 (1.093.34) .023 1.95 (1.362.79) .0002
Sleep-disordered breathing (5) 1.29 (1.081.54) .005 1.33 (0.941.89) .11 1.26 (1.041.52) .02
Daytime sleepiness 0.40 (0.150.64) .002 0.51 (0.080.94) .021 0.33 (0.050.61) .022
Total sleep disturbance 1.40 (0.811.99) .0001 1.42 (0.372.47) .009 1.14 (0.541.74) .0002
Childage, gender, and asthmaseveritywere included in allanalyses.Other covariateswere included if theywere statisticallyrelevantto the sleepscale of interest and included the following
considerations: maternal race, marital status, education, income, smoking during pregnancy, and depression, HOME inventory total score, household density (house volume per persons),
number of siblings, and use of asthma medication (including short-acting inhaled medicines, long-acting inhaled medicines, and orally administered steroids). Multivariate regression
analyses were used for analysis of daytime sleepiness and total sleep disturbance ( reported); all other analyses used logistic regression analyses of dichotomized sleep scales (OR
reported).
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bacco and sleep disorders has been
difficult to establish.42 Nicotine is a
stimulant that may contribute to in-
creased arousal and attention among
smokers, likely by stimulating neuro-
transmission of acetylcholine and trig-
gering activation of the dopaminergicsystem in the brain.42,44 Nicotine is as-
sociated with altered sleep architec-
ture, resulting in reduced sleep time
and efficiency, longer sleep latency,
lighter sleep, and reduced rapid eye
movement sleep.20,45
Frequent night wakings among smok-
ers often are attributed to withdrawal
during sleep. During the early stages
of smoking abstinence, adult smokers
experience difficulty falling asleep, re-duced sleep efficiency, and longer la-
tency to rapid eye movement sleep.46
Colrain et al47 reported increased
night wakings and sleepiness as the
most-consistent findings of research
on smoking cessation. Insomnia, sleep
disturbance, and anxiety also are
symptoms of nicotine withdrawal
listed in the Diagnostic and Statistical
Manual of Mental Disorders, Fourth
Edition.48 These symptoms in manyways resemble those reported in our
sample. It is possible that children ex-
posed to SHS experience a degree
of nicotine withdrawal during sleep,
which results in disruptions in the
normal sleep process. Although with-
drawal symptoms among children ex-
posed to SHS have, to our knowledge,
not been noted in the literature, sev-
eral studies presented evidence that
newborns who were exposed prena-
tally to tobacco experienced nicotine
withdrawal shortly after birth.4951 This
area requires further study.
SHS exposure results in a much
smaller amount of nicotine intakethan active smoking. Studies of SHS
effects on sleep have included re-
ports of sleep disturbances among
adult men,41 pregnant women,25 and
preschool-aged children,26 which in-
dicates that even small amounts of ex-
posure to nicotine, as would occur in
SHS exposure, are sufficient to affect
sleep adversely. Our study contributes
additional evidence that SHS affects
sleep among school-aged childrenwith asthma.
This study is not without limitations. All
children in the study had asthma, and
the results may not be generalizable
to populations of children without
asthma. However, these results may
be reflective of risks from SHS expo-
sure for the 9% of children in the
United States today who have asthma.52
All children in this study were exposed
to SHS, and we can generalize our find-ings only to exposed children. The de-
gree of exposure varied widely in our
sample, and there was no evidence of
curvilinear or threshold effects that
might suggest a safe level of SHS ex-
posure. In addition, national data sug-
gest that more than one half of chil-
dren are exposed to SHS.53 Our sleep
data were derived from parent reports
only. Additional study in this area
should include use of child-reported
sleep problems and additional mea-
sures of sleep patterns, such as
polysomnography, actigraphy, or de-
tailed sleep diaries. Finally, we had
no information on prematurity in thissample, which could be an important
contributor to sleep problems.54,55
CONCLUSIONS
Among children with asthma, expo-
sure to SHS affects sleep negatively,
as evidenced by greater sleep-onset
delays, more-frequent parasomnias,
more sleep-disordered breathing, in-
creased daytime sleepiness, and
greater overall sleep disturbance. Theconsequences of inadequate sleep in
children are not trivial. Sleep distur-
bances have been linked with in-
creased behavior problems,25 mental
health problems,1,6 and poor school
performance1 in children. In addition,
effects of poor sleep in childhood can
persist into adulthood in the form of
obesity8,9 and behavior and mood dis-
orders.7 Reduction in SHS exposure is
an area with the potential for signifi-cant impact for physical and emotional
health and school performance in the
pediatric population.
ACKNOWLEDGMENTS
This work was supported by grants
from the National Institutes of Health
(grants R21 ES12952-01 and R01
HL65731).
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DOI: 10.1542/peds.2009-0690; originally published online January 18, 2010;2010;125;e261Pediatrics
Beebe and Judith OwensKimberly Yolton, Yingying Xu, Jane Khoury, Paul Succop, Bruce Lanphear, Dean W.
ChildrenAssociations Between Secondhand Smoke Exposure and Sleep Patterns in
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