Poor Parental Sleep and the Reported
Sleep Quality of Their Children
Hanni Rönnlund, MD,a,b Marko Elovainio, PhD,c,d Irina Virtanen, MD, PhD,
e Jaakko Matomäki, MSc,f Helena Lapinleimu, MD, PhDa,g
BACKGROUND: Pediatric sleep disturbances are regularly diagnosed on the basis of parental
abstract
reports. However, the impact of parental sleeping problems on parental perceptions and
reports of their child’s sleep has not yet been studied. We hypothesized that poor parental
sleep decreases the parent-reported child sleep quality.
METHODS: A 1-week actigraph recording was performed in 100 children aged 2 to 6 years
recruited in 16 day care centers. Their biological parents completed a sleep diary and a
Sleep Disturbance Scale for Children (SDSC) on children’s sleep. The parents also completed
the Jenkins’ sleep scale on their own sleep, the 12-item General Health Questionnaire, and
questions on demographic factors. Linear regression analyses were performed to study
the association of the parental Jenkins’ score on their child’s total SDSC score. Analyses
were also performed for 3 of the subscales of the SDSC: disorders of excessive somnolence,
disorders of initiating and maintaining sleep, and sleep-wake transition disorders.
RESULTS: Parental sleeping problems were associated with more frequent reporting of
children’s sleeping problems. This association was unexplained by the actigraph measures
of children’s sleep, such as actual 24-hour sleep time and sleep efficiency, parental mental
health problems, or any other tested potential confounder or mediator. Similar correlations
were seen for the 3 analyzed subscales.
CONCLUSIONS: Parental sleep quality was associated with overreporting of sleep problems
in their children. This finding emphasizes the importance of considering parental sleep
quality in the diagnosis, treatment, and research of pediatric sleeping problems.
aDepartment
of Pediatrics, University of Turku, Turku, Finland; bHealth Care Center of Kaarina, Kaarina, Finland;
of Psychology, University of Helsinki, Helsinki, Finland; dNational Institute for Health and Welfare,
Helsinki, Finland; eClinical Neurophysiology, Public Utility Tyks-Sapa, Medical Care Services, Hospital District
of Southwest Finland, Turku, Finland; fTurku Clinical Research Center and gDepartment of Pediatrics, Turku
University Hospital, Turku, Finland
cDepartment
Dr Rönnlund conceptualized and designed the study, designed the data collection instruments,
coordinated and supervised data collection, and drafted the initial manuscript; Drs Elovainio and
Virtanen conceptualized and designed the study, designed the data collection instruments, and
reviewed and revised the manuscript; Mr Matomäki designed the analysis methods, carried out
the initial analyses, and reviewed and revised the manuscript; Dr Lapinleimu conceptualized and
designed the study, coordinated and supervised data collection, designed the data collection
instruments, and reviewed and revised the manuscript; and all authors approved the final
manuscript as submitted.
DOI: 10.1542/peds.2015-3425
WHAT’S KNOWN ON THIS SUBJECT: Previous studies
have suggested that pediatric sleep problems impair
parental sleep. However, many of these studies used
parental reports instead of objective methods to
analyze the child’s sleep, thus neglecting parental
sleep-related perceptions as a source of bias.
WHAT THIS STUDY ADDS: Parents who slept poorly
overestimated the sleeping problems of their
children when compared with objective sleep
analyses. Parental sleeping problems seem to
decrease parent-perceived child sleep quality, which
verifies parental sleep as an influential factor in
pediatric sleep disturbances.
Accepted for publication Jan 15, 2016
Address correspondence to Helena Lapinleimu, MD, PhD, Department of Pediatrics, University
Hospital of Turku, Kiinamyllynkatu 4-8, 20521 Turku, Finland. E-mail: lehela@utu.fi
PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275).
Copyright © 2016 by the American Academy of Pediatrics
PEDIATRICS Volume 137, number 4, April 2016:e20153425
To cite: Rönnlund H, Elovainio M, Virtanen I, et al. Poor
Parental Sleep and the Reported Sleep Quality of Their
Children. Pediatrics. 2016;137(4):e20153425
ARTICLE
Pediatric medicine is characterized
by the use of second-hand
information when inquiring about
the patient’s medical history
and current symptoms. Parental
reports are a convenient source of
information, but the use of secondhand data allows for the possibility
of bias. For example, in comparison
with the child’s self-report and
the observations of the child’s
teacher, depressive mothers report
more behavioral problems in their
offspring than do nondepressive
mothers.1 This kind of distortion
can become problematic in medical
conditions in which the diagnoses
rely heavily on the description given
by the parents, such as pediatric
sleep disturbances. Many of these
disorders are diagnosed on the
basis of parental reports and the
use of objective methods is rare.2
Nevertheless, the effect of parental
sleep quality and well-being on the
accuracy of their reports on their
child’s sleep has, to our knowledge,
not been studied.
METHODS
Study Design and Sample
The FinAdo 2 study is an ongoing
follow-up study that investigates
the health and well-being of
internationally adopted children <7
years old in Finland. The participants
in this study were parents and their
biological children (aged 2 to 6 years)
who acted as the controls in a study
on adoptee sleep. The study was
approved by the Ethics Committee
of the Hospital District of Southwest
Finland. A total of 1560 information
letters were distributed to the
families in 16 day care centers in the
cities of Turku and Kaarina, Finland.
Parents of 117 children returned
their informed consent to participate.
Seven children who moved away
from the area or could not be
contacted and 2 adopted children
were excluded from the study; thus,
the remaining 108 children were
enrolled. The evaluations were
performed between January 2014
and February 2015.
Procedure
Consequently, the question arises
whether parents who also suffer from
sleep problems tend to overestimate
their child’s sleeping difficulties, thus
distorting the identification of the
causalities involved in the family’s
nightly struggles. The aim of the
current study was to analyze whether
parental sleep quality is associated
with the parent-reported sleep
quality measures in their children
when the effect of the objective sleep
quality of the children was taken
into account. Whether poor parental
sleep correlates with objective child
sleep quality and whether parental
psychiatric symptoms correlate with
parent-reported child sleep quality
were also analyzed. We hypothesized
that parents with poor sleep
quality would report more sleeprelated difficulties in their children
than suggested by an objective
assessment.
2
The enrolled families were contacted
and an appointment was made at
which the parents were provided
with questionnaires and instructed
on the use of an actigraphy bracelet
(GeneActiv Original; Activinsights
Ltd, Kimbolton, United Kingdom),
which was worn on the child’s
nondominant hand. The actigraph
event button was to be pressed
when the child went to sleep and
woke up, and the device was to be
taken off before bathing and contact
sports. Seven days later at the second
appointment the questionnaires
and actigraph were returned. The
parents kept a sleep diary of their
child’s sleep for the duration of the
actigraphy recording. In the diary,
they also recorded times when the
actigraphy bracelet was removed and
reasons for removal.
Both of the parents were also
asked to individually complete
questionnaires on their
socioeconomic status and well-being.
They also completed questionnaires
on the child’s well-being and
illnesses. If the parents completed the
child’s questionnaires together, the
mean scores of the parental scales
were used in the analyses, except for
education, for which the maternal
response was always used. Lower
maternal education has been shown
to be associated with a child’s shorter
time in bed, but this connection
has not been identified for paternal
education.3
Actigraph Recording
An actigraph was used as the means
of objective assessment of the
child’s sleep.4,5 The device is a small
wristwatch-like band attached to
the examinee’s wrist, hip, or ankle.
The method examines the amount
of movement acceleration in time.
It can estimate periods of sleep by
using a threshold value for the lack
of movement, because nonmoving
periods of time represent sleep.
However, it is unable to separate
different stages of sleep, because it
records only physical activity, unlike
electroencephalography.
The movements recorded by the
actigraph enable an estimation of
sleep-onset time, wake-up time,
sleep efficiency, sleep fragmentation,
actual 24-hour sleep duration, and
sleep-onset latency; the sleep-onset
latency depicts a child’s resistance
to falling asleep. The sleep efficiency
index represents the time spent
sleeping compared with the total
time spent in bed and is influenced
by both the resistance to falling
asleep and the restlessness of sleep.
The sleep fragmentation index shows
the amount of physically active time
during the whole sleep phase. It
also includes all of the motionless
sequences of <1 minute that are
situated between 2 physically
active periods. Thus, the sleep
fragmentation index represents the
restlessness of sleep.
RÖNNLUND et al
These actigraphy variables can
provide an estimate of the quality of
the child’s sleep. Due to the restless
nature of children’s sleep, however,
the variables are not as reliable in the
pediatric field as in adult studies6,7;
however, the accuracy can be
enhanced by using an appropriate
algorithm.8
The GeneActiv actigraph has been
proven to be reliable in examining
sleep in adults9 and its validity for
studying movement in children
has also been documented.10 The
device records and sums up gravitysubtracted acceleration vector
magnitudes in 3 spatial directions
by using a microelectromechanical
sensor with a range of ±8 g.11 The
measurement frequency used
was 50 Hz with an epoch length
of 1 minute. The threshold for
interpreting the sum of the signals
as sleep was 8 g per epoch. A
research assistant analyzed the
gathered actigraph data aided by the
sleep diaries. In problematic cases,
both the research assistant Lassi
Sahlberg and an experienced clinical
neurophysiologist (I.V.) analyzed the
data together.
Parent-Reported Sleep Duration and
Sleep Quality of Their Children
The children’s sleep as reported by
the parents was assessed with the
sleep diary and the Sleep Disturbance
Scale for Children (SDSC). The
SDSC is a 30-item questionnaire
that evaluates, along with the total
score, 6 different sleep domains. In
addition to the total score, this study
examined the score for disorders
of excessive somnolence (5 items)
and disorders of initiating and
maintaining sleep (7 items), the
latter considered to depict the bed
resistance of the child. The score
for sleep-wake disorders (6 items)
was also studied because this score
contains the items of bruxism, sleep
talking, and other restless behavior
during sleep and the act of falling
asleep. Such behavior can disturb
PEDIATRICS Volume 137, number 4, April 2016
parental sleep by causing worry
and interruptions. The SDSC was
originally validated for schoolaged children,12 but has since also
been proven reliable in examining
preschool-aged children.13 The
Cronbach’s α for the total scale in the
current sample was 0.76.
Parental Sleeping Problems
Parental sleep quality was evaluated
with the Jenkins’ sleep scale. The
scale consists of 4 questions, which
are answered on a rating scale
scored from 0 to 5. The scale has
been proven reliable for examining
the quality of sleep in adults14 and is
widely used. The Cronbach’s α in the
current sample was 0.78.
Parental Mental Health
The parents also completed
the 12-item General Health
Questionnaire (GHQ-12). The
questionnaire is a valid and
reliable measure to assess parental
psychiatric morbidity, symptoms
of anxiety, and depression.15 The
scale uses a 12-item rating scale
from 1 to 4. The second item on the
questionnaire enquires about lost
sleep due to worry. This item is not
valid when screening for sleeping
difficulties,16 nevertheless,to avoid
overlap, the item was omitted in
the analyses. The Cronbach’s α in
the current sample, both with and
without the sleep-related question,
was 0.87.
Statistical Analysis
The actigraph data were downloaded
to the GeneActiv software, which
converted the information for
analysis by a trained research
assistant to obtain the required
information: (1) actual 24-hour sleep
duration and (2) sleep efficiency. In
step 1, the association between the
total SDSC score and Jenkins’ score
of the respondent was calculated by
using linear regression analyses. In
the subsequent steps, the impact of
the analyzed potential confounders
was investigated, in addition to the
analyzed variables in the previous
steps. Therefore, in step 2, the
influence of the actigraphy measures
(actual 24-hour sleep time and
sleep efficiency) on the association
between the total SDSC score and
the respondent’s Jenkins’ score
was investigated. The third step
consisted of considering the influence
of parental stress and psychiatric
morbidity, represented by the GHQ12 (with the sleep item removed),
on this association. The child’s age
and gender and the existence of longterm illnesses and medication as
confounding factors were adjusted
for in the fourth step. Finally, the
model was adjusted for the time of
year (summer or winter), number
of siblings, parental marital status,
parental age, and maternal education.
The same procedures were then
repeated for the subscale scores of
disorders of excessive somnolence,
disorders of initiating and
maintaining sleep, and sleep-wake
disorders. As additional sensitivity
analyses, all analyses were repeated
without the responses from those
families whose parents completed
the child’s questionnaires together.
The statistical analyses were
performed by using SAS for Windows
version 9.3 (SAS Institute, Cary, NC).
RESULTS
The characteristics of the sample are
shown in Table 1. Of the 108 children
enrolled, 8 either declined to wear
the actigraph or the recording could
not be completed for other reasons.
Of the parents, 18 completed the
child’s questionnaires together.
The mean age of the participating
children was 4 years, and the age
and gender distribution was even.
Thirteen percent (n = 13) of the
children were reportedly taking longterm medication, 1 of whom (8%)
was taking a systemic medication,
thyroxin. However, the number
of children taking medication at
3
the time of the study was slightly
higher, 18% (n = 18); of these
children, 10 (56%) used a systemic
medication, such as antibiotics,
nonsteroidal antiinflammatory
medicines, paracetamol, laxatives, or
antihistamines. One child was using
a first-generation antihistamine at
the time of the study. None of the
children used melatonin during the
study or had been diagnosed with a
sleep disturbance.
High levels of parental sleeping
problems were associated with
more frequently reported children’s
sleeping problems (Table 2). This
association was not attenuated
after the model was adjusted for
the actigraph measures of the
children’s sleep, such as actual
24-hour sleep time or sleep
efficiency. The association was
also robust to adjustment for
parental mental health problems
or any other tested potential
confounder or mediator. The
association was unchanged after
the removal of the 2 children
with only 4 nights of actigraph
data from the analyses (data
not shown).
The parental Jenkins’ score was
not associated with the objective
measures of the child’s sleep. The
association between the parental
Jenkins’ score and the actual 24-hour
sleep time was not statistically
significant (estimate: −0.000601;
95% confidence interval [CI]:
−0.00168 to 0.000479; P = .27). It
was also not significant for sleep
efficiency (estimate: −0.184; 95% CI:
−0.383 to 0.0152; P = .07).
Similar associations as those between
parental sleeping problems and the
total SDSC score were also found for
the 3 subscales (Tables 3, 4, and 5):
disorders of excessive somnolence,
disorders of initiating and maintaining
sleep, and sleep-wake transition
disorders. The only significant
background variable in the final
model for the subscale for disorders
of initiating and maintaining sleep
4
TABLE 1 Characteristics of the Sample
Characteristic
Number of
Responses
Children
Age, n (%)
2 years
3 years
4 years
5 years
6 years
Gender, girls/boys, n (%)
Chronic illness,a number of cases (%)
Long-term medication, number of cases (%)
Current medication, number of cases (%)
Number of siblings, mean (SD)
Actual sleep time by actigraphy, mean (SD), h
Sleep efficiency by actigraphy, mean (SD), %
Winter/summer at time of actigraphy, n (%)
Room-sharing with parents, n (%)
Number of successful actigraph-recorded nights, n (%)
4
5
6
7
Respondent of the child's questionnaires, n (%)
Mother
Father
Parents together
Mothers
Maternal age, mean (SD), y
Maternal education, n (%)
High school
Upper secondary school
Trade school
Postsecondary vocational education
University
Fathers
Paternal age, mean (SD), y
Paternal education, n (%)
High school
Upper secondary school
Trade school
Postsecondary vocational education
University
Parental marital status, n (%)
Married
Cohabitation
Single parent
Sleeping and mental health variables, mean (SD)
Total SDSC score
Parental GHQ-12 scoreb,c
Parental Jenkins' scorec
Value
100
100
99
99
99
91
94
94
87
4 (1.30)
12 (12)
29 (29)
23 (23)
18 (18)
18 (18)
50 (50)/50 (50)
15 (15)
13 (13)
18 (18)
0.75 (0.82)
8.54 (0.53)
78.41 (4.12)
36 (41)/51 (59)
25 (26)
2 (2)
1 (1)
5 (5)
86 (91)
78 (78)
4 (4)
18 (18)
98
36 (4.91)
97
1 (1)
3 (3)
14 (14)
27 (28)
52 (54)
91
38 (4.91)
90
3 (3)
5 (5)
25 (27)
16 (18)
42 (46)
97
58 (59.79)
18 (18.56)
21 (21.63)
99
100
100
39.14 (7.13)
21.58 (4.49)
9.69 (4.16)
a Asthma, allergies, or atopy (n = 13); gastrointestinal reflux (n = 2); ventral septal defect (n = 2); hypothyreosis (n = 1),
autism spectrum disorder (n = 1), and bilateral hearing loss (n = 1).
b Without the question concerning sleep
c Score of the individual parent who completed the child's questionnaire (if the parents completed the child's
questionnaires together, the score is the mean of the parental scores).
was parental age. The number of the
disorders in question seemed to decline
as parental age increased. However, this
finding did not decrease the correlation
between the parental Jenkins’ score
and the SDSC subscale score.
The parental GHQ-12 score, with
the sleep-related item omitted,
correlated with the child’s SDSC
score (estimate: 0.363; 95% CI:
0.533 to 0.673; P = .022). This
association was also identified
RÖNNLUND et al
PEDIATRICS Volume 137, number 4, April 2016
5
R2
Wintertime
Not a single parent versus
single parent
Maternal education
Upper secondary school
versus university degree
Trade school versus
university degree
Postsecondary vocational
education versus
university degree
Number of siblings
Parental age
Current medication
Chronic illness
Child female gender
Child age
Parental GHQ-12 score without
the sleep question
Sleep efficiency by actigraphy
Actual sleep time by actigraphy
Parental Jenkins' score
0.34453
1.01 (0.728 to
1.290)
Estimate (95%
CI)
Step 1
<.001
P
TABLE 2 Effect of Different Variables on the SDSC Total Score
0.36394
.66
−12.6 (−70.0 to
44.7)
−0.0144 (−0.324 to
0.295)
.93
<.001
P
1.02 (0.731 to 1.32)
Estimate (95% CI)
Step 2
0.36548
1.06 (0.730 to
1.38)
−13.5 (−71.3
to 44.2)
−0.0149
(−0.325 to
0.296)
−0.0680
(−0.361 to
0.224)
Estimate (95%
CI)
Step 3
.65
.92
.64
<.001
P
0.37458
.37
−0.524 (−1.69 to
0.643)
0.606 (−2.02 to
3.23)
−3.09 (−11.9 to
5.70)
3.25 (−6.09 to
12.6)
.49
.49
.65
.82
.52
.35
<.001
P
−0.0373 (−0.355
to 0.281)
1.06 (0.708 to
1.41)
−32.5 (−102 to
36.5)
0.124 (−0.260 to
0.508)
Estimate (95% CI)
Step 4
1.20 (−0.90 to
3.30)
−0.813 (−4.19 to
2.57)
0.46064
−5.32 (−15.3 to
4.69)
−3.66 (−8.29 to
0.974)
−2.51 (−6.57 to
1.56)
−0.491 (−1.93 to
0.946)
1.25 (−1.92 to
4.41)
−2.33 (−11.8 to
7.16)
2.70 (−7.64 to
13.0)
−0.334 (−0.690 to
0.0219)
−3.32 (−7.38 to
0.744)
−0.0719 (−0.442
to 0.298)
−40.4 (−122 to
41.2)
0.178 (−0.27 to
0.627)
1.05 (0.633 to 1.47)
Estimate (95% CI)
Step 5
.63
.26
0.22
.12
.31
.29
.11
.065
.60
.62
.43
.50
.70
.43
.33
<.001
P
6
RÖNNLUND et al
R2
Wintertime
Not a single parent versus
single parent
Maternal education
Upper secondary school
versus university
degree
Trade school versus
university degree
Postsecondary vocational
education versus
university degree
Number of siblings
Parental age
Current medication
Chronic illness
Child female gender
Actual sleep time by
actigraphy
Sleep efficiency by
actigraphy
Parental GHQ-12 score
without the sleep
question
Child age
Parental Jenkins' score
0.12387
0.171 (0.0796 to
0.262)
Estimate (95%
CI)
Step 1
<.001
P
0.13717
0.0267 (−0.0763 to
0.130)
0.185 (0.0877 to
0.282)
1.89 (−17.1 to 20.9)
Estimate (95% CI)
Step 2
0.14944
−0.0551 (−0.152 to
0.0416)
0.0262 (−0.0766 to
0.129)
0.61
0.84
0.211 (0.104 to
0.318)
1.26 (−17.8 to 20.3)
Estimate (95% CI)
Step 3
<.001
P
TABLE 3 Effect of Different Variables on the SDSC Subscale Score for Disorders of Excessive Somnolence
.26
.61
.90
<.001
P
0.21690
−0.290 (−0.658 to
0.0784)
0.617 (−0.212 to
1.45)
−1.37 (−4.14 to
1.41)
0.640 (−2.31 to
3.59)
−0.0664 (−0.169 to
0.0340)
0.223 (0.113 to
0.334)
−6.37 (−28.2 to
15.4)
0.0849 (−0.0364 to
0.206)
Estimate (95% CI)
Step 4
.67
.33
.14
.12
.19
.17
.56
<.001
P
.22
−0.764 (−1.99 to
0.462)
.78
.20
.093
−1.19 (−2.59 to
0.204)
0.407 (−0.227 to
1.04)
−0.146 (−1.17 to
0.874)
0.31552
.53
.065
.54
.69
.59
.32
.065
.081
.27
.20
.84
<.001
P
−0.957 (−3.98 to
2.06)
−0.385 (−0.818 to
0.0487)
0.897 (−0.0582 to
1.85)
−1.43 (−4.30 to
1.43)
0.852 (−2.27 to
3.97)
−0.0216 (−0.129 to
0.0858)
−0.376 (−1.60 to
0.850)
−0.0623 (−0.174 to
0.0492)
0.237 (0.112 to
0.363)
−2.42 (−27.0 to
22.2)
0.0883 (−0.0471 to
0.224)
Estimate (95% CI)
Step 5
PEDIATRICS Volume 137, number 4, April 2016
7
R2
Wintertime
Not a single parent versus single
parent
Maternal education
Upper secondary school versus
university degree
Trade school versus university
degree
Postsecondary vocational
education versus university
degree
Number of siblings
Parental age
Current medication
Chronic illness
Child female gender
Child age
Parental GHQ-12 score without the
sleep question
Sleep efficiency by actigraphy
Actual sleep time by actigraphy
Parental Jenkins' score
0.25808
0.408 (0.269
to 0.547)
Estimate
(95% CI)
Step 1
<.001
P
0.29595
0.399 (0.258 to
0.540)
−3.99 (−31.5 to
23.6)
−0.087 (−0.236 to
0.0621)
Estimate (95% CI)
Step 2
.25
.77
<.001
P
Step 3
0.29595
0.399 (0.242 to
0.555)
−3.98 (−31.7 to
23.8)
−0.0871
(−0.237 to
0.0630)
0.000337
(−0.141 to
0.141)
Estimate (95%
CI)
TABLE 4 Effect of Different Variables on the SDSC Subscale Score for Disorders of Initiating and Maintaining Sleep
0.996
.25
.78
<.001
P
0.33983
.049
−0.556
(−1.1 to
−0.00202)
0.231 (−1.02 to
1.48)
−0.675 (−4.85
to 3.50)
0.562 (−3.87 to
5.00)
.80
.75
.71
.87
.74
.15
<.001
P
0.0128 (−0.138
to 0.164)
0.409 (0.242 to
0.576)
−24.0 (−56.8
to 8.77)
0.0302 (−0.152
to 0.213)
Estimate (95%
CI)
Step 4
0.558 (−0.381
to 1.50)
0.0188 (−1.49
to 1.53)
0.46080
−1.11 (−5.58
to 3.37)
−0.0673 (−2.14
to 2.00)
−1.19 (−3.00
to 0.630)
.98
.24
.20
.95
.57
.62
.21
.0085
.98
.89
.38
.059
−0.618 (−1.26
to 0.0247)
0.630 (−0.785
to 2.05)
0.290 (−3.95 to
4.53)
0.0626 (−4.56
to 4.69)
−0.216 (−0.375
to −0.0573)
−1.15 (−2.97
to 0.666)
.78
.66
.16
<.001
P
0.0232 (−0.142
to 0.188)
0.336 (0.149 to
0.522)
−26.1 (−62.5
to 10.4)
0.0447 (−0.156
to 0.245)
Estimate (95%
CI)
Step 5
8
RÖNNLUND et al
R2
Wintertime
Postsecondary vocational education
versus university degree
Number of siblings
Maternal education
Upper secondary school versus
university degree
Trade school versus university degree
Not a single parent versus single parent
Parental age
Current medication
Chronic illness
Child female gender
Child age
Parental GHQ-12 score without the sleep
question
Sleep efficiency by actigraphy
Actual sleep time by actigraphy
Parental Jenkins' score
0.21088
0.254 (0.156
to 0.353)
Estimate
(95% CI)
Step 1
<.001
P
0.21691
0.248 (0.142 to
0.354)
−7.59 (−28.4 to
13.2)
−0.0241
(−0.137 to
0.0886)
Estimate (95%
CI)
Step 2
.67
.47
<.001
P
Estimate (95%
CI)
Step 3
0.23037
0.279 (0.162 to
0.396)
−8.35 (−29.1 to
12.4)
−0.0246
(−0.137 to
0.0877)
−0.0663
(−0.172 to
0.0393)
TABLE 5 Effect of Different Variables on the SDSC Subscale Score for Disorders of Sleep-Wake Transitions
.22
.66
.43
<.001
P
0.24077
0.275 (0.150 to
0.400)
0.460 (−24.1 to
25.0)
−0.0754
(−0.212 to
0.0612)
−0.0698
(−0.183 to
0.0434)
0.291 (−0.124
to 0.706)
−0.0893 (−1.02
to 0.845)
−0.189 (−3.32
to 2.94)
0.0819 (−3.24
to 3.40)
Estimate (95%
CI)
Step 4
.96
.90
.85
.17
.22
.28
.97
<.001
P
−2.98 (−6.49 to
0.523)
−1.34 (−2.96 to
0.284)
−0.330 (−1.75
to 1.09)
0.144 (−0.591
to 0.880)
−0.293 (−1.48
to 0.890)
0.37111
0.311 (0.165 to
0.457)
−4.21 (−32.7 to
24.3)
−0.0588
(−0.216 to
0.0983)
−0.0913
(−0.221 to
0.0382)
0.399 (−0.104
to 0.902)
0.00408 (−1.10
to 1.11)
−0.295 (−3.62
to 3.03)
−0.130 (−3.75
to 3.49)
−0.0648
(−0.189 to
0.0597)
−0.889 (−2.31
to 0.533)
Estimate (95%
CI)
Step 5
.62
.70
.64
.10
.20
.094
.22
.30
.94
.86
.99
.12
.16
.46
.77
<.001
P
for the subscale of disorders of
initiating and maintaining sleep
(estimate: 0.171; 95% CI: 0.0261
to 0.316). The association was
absent for the analysis of the 2
remaining subscales. The estimate
of the subscore for the disorders of
excessive somnolence was 0.0271
(95% CI: −0.0627 to 0.117; P = .55);
and the subscore estimate for the
sleep-wake transition disorder was
0.0511 (95% CI: −0.0511 to 0.153;
P = .32). Nevertheless, the parental
GHQ-12 score, with the sleep-related
item excluded, did not diminish the
association between the parental
Jenkins’ score and the total, or
the subscale, score of the SDSC as
shown in Tables 2 and 4. The GHQ12–related associations disappeared
when the parental Jenkins’ score was
also considered in the analyses.
All of the analyses were also repeated
without the responses from the
families whose parents completed
the child’s questionnaires together.
In these analyses, the associations
between the parental Jenkins’ score
and the total SDSC score and the
parental Jenkins’ score and the
subscale of disorders of initiating
and maintaining sleep were, in fact,
slightly stronger (estimate [95% CI]:
1.173 [0.712 to 1.633] and 0.461
[0.260 to 0.656], respectively; P <
.001 for both) than in the original
analyses. The other associations
remained unchanged.
DISCUSSION
Consistent with the hypothesis, the
current study showed that parents
who reported sleeping difficulties of
their own also reported more sleeprelated problems in their offspring.
They experienced their children as
having more difficulties of initiating
and maintaining sleep, more sleepwake transition difficulties, and
more excessive somnolence than
did parents who slept better. This
association could not be explained by
the children’s sleeping problems as
PEDIATRICS Volume 137, number 4, April 2016
identified with an objective measure,
namely the actigraph. Child’s age,
gender, number of siblings, existence
of chronic illnesses or medication, or
the existence of current medication
also did not attenuate the association.
Furthermore, this finding was not
altered by parental psychiatric
symptoms, investigated with the
GHQ-12, parental education, marital
status, socioeconomic status, or time
of year. Parental sleeping problems
did not correlate with the objective
child sleep variables.
A child’s sleeping difficulties have
been shown to be associated with
parental well-being; Boergers et al17
observed that parents of children
with >1 sleep disorder reported more
daytime tiredness than did parents of
children with only 1 sleep problem.
Other studies have also proposed that
a child’s sleeping problems impair
the sleep of the parents.18–20 On the
other hand, parental depressive
symptoms correlate with actigraphmeasured sleeping problems in the
child,21 and studies also showed that
marital aggression predicts objective
pediatric sleep difficulties.22 Thus,
the connection between parental
well-being and pediatric insomnia
may be reciprocal.
It is worth noting that many of
these studies used parent-response
questionnaires instead of objective
methods to examine the sleep
of the child. Such surveys are a
viable method, especially when
examining large samples, but can
lead to unconscious bias from the
respondent. Therefore, the direction
of the association between the child’s
reported sleeping problems and
the impaired sleep of the parent is
inconclusive.
The current findings are in line
with previous studies, which have
shown that sleep loss causes an
attention bias toward negative events
and enhances memory building
of negative details.23,24 Sleeping
difficulties have also been shown to
cause an attention shift toward sleeprelated stimuli.25 These observations
explain why parents who sleep
poorly notice and remember their
children’s bed resistance and night
waking so well. Hence, in the clinical
field, it is of paramount importance
to aim interventions in a child’s
sleeping difficulties toward the wellbeing of the whole family instead
of only the child. The diagnostic
measures should consider not only
the sleeping habits of the child but
also parental aspects. Tired parents
can unconsciously exaggerate
their child’s sleeping difficulties,
which could lead to misplaced
interventions.
The strengths of this study include
the utilization of both objective and
subjective measures. For 98% of
the children, the actigraphy lasted
5 days or longer, which, according
to previous research, is a reliable
duration of analysis.5 Despite the
recruitment in day care centers and
the low recruitment rate, the parental
employment rate corresponded
with the unemployment rate in
Finland at the time of the study26;
7 mothers and 7 fathers reported
being currently unemployed or
retired. In addition, the families
who participated in the study
well represented the average
population compared with a written
communication from the Official
Statistics of Finland: In 2014, the
mean age of a mother of a 4-year-old
child in Finland was 34 years and
that of a father was 37 years, which
corresponds to the ages in the study
population. On average, a 4-year-old
had 1.5 siblings <18 year old.
However, the limitations of the
study need to be discussed. Although
the families were recruited in a
nonclinical setting, the fact that the
sample included mostly white, highly
educated families can hinder the
generalization of the results.
Sleeping in the parents’ bedroom was
relatively common for the children
in the study population (26%). In
9
a large multicultural study, <10%
of predominantly white (Australia,
Canada, New Zealand, United
Kingdom, and the United States)
children aged 24 to 36 months slept
in the same room as their parents.27
Moreover, the age range of the
participating children was fairly wide
due to the nature of the adoption
study, in which the children of the
current study acted as controls.
In addition, in Finland, children
start school at 7 years old, which
influenced the choice of the inclusion
criteria.
The GeneActiv actigraph has not been
validated for pediatric sleep studies.
However, it has been validated in
a pediatric population10 and its
accelerometer has been shown to be
as reliable as the accelerometer in
the ActiGraph (ActiGraph, Pensacola,
FL),28 which is widely used in studies
on children’s sleep. Furthermore,
the study did not aim to diagnose
sleep disturbances but to compare
individuals within a group.29
confounding factor and to examine
whether poor parental sleep also
affects the sleep of the child.
CONCLUSIONS
Our study shows that parents who
sleep poorly report more sleeping
difficulties in their children than
do parents who sleep well. This
association could not be explained
by an objective measure of the
child’s sleep. It seems that tired
parents present an observation
bias with regard to their child’s
sleep compared with parents not
suffering from sleeping problems.
Therefore, in the field of pediatric
sleep disorders, future diagnostic
methods and treatments should
take into consideration not only
the examined child but also the
whole family, including the parents.
Furthermore, the results presented
highlight the importance for future
pediatric sleep research to take into
account parental sleep quality as a
ACKNOWLEDGMENTS
We thank the research assistants
Anniina Mäkinen, Miia Salokannel,
and Lassi Sahlberg for their work in
executing the actigraph recordings
and the Clinical Neurophysiology
Department at Turku University
Hospital for supplying the facilities
for the actigraph recordings. We also
thank the FinAdo research group for
their collaboration in the study.
ABBREVIATIONS
CI: confidence interval
GHQ-12: 12-item General Health
Questionnaire
SDSC: Sleep Disturbance Scale for
Children
FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.
FUNDING: All phases of this study were supported by the Foundation for Pediatric Research, Finland, the EVO (State funding for university-level health research)
grant from Turku University Hospital, and the Signe and Ane Gyllenberg Foundation. Dr Rönnlund was supported by a grant from the Foundation of Turku
University Central Hospital. Dr Elovainio was funded by the Academy of Finland (265977). Dr Lapinleimu received the EVO grant from Turku University Hospital.
POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.
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