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ORIGINAL ARTICLE
Determinants and impact of sleep duration in
children and adolescents: data of the Kiel Obesity
Prevention Study
B Hitze
1
, A Bosy-Westphal
1
, F Bielfeldt
1
, U Settler
1
, S Plachta-Danielzik
1
, M Pfeuffer
2
,
J Schrezenmeir
2
,HMo
¨nig
3
and MJ Mu
¨ller
1
1
Institut fu
¨r Humanerna¨hrung und Lebensmittelkunde, Christian-Albrechts Universita¨t Kiel, Kiel, Germany;
2
Max Rubner-Institut,
Kiel, Germany and
3
Klinik fu
¨r Allgemeine Innere Medizin, Universita¨tsklinikum Schleswig-Holstein, Kiel, Germany
Background/Objectives: This study investigates determinants of sleep duration and its impact on nutritional status, resting
energy expenditure (REE), cardiometabolic risk factors and hormones in children/adolescents.
Subjects/Methods: In 207 girls and 207 boys (13.0±3.4 (6.1–19.9) years) body mass index standard deviation score (BMI
SDS), waist circumference (WC) z-score, body composition (air-displacement plethysmography), REE (ventilated hood system;
n¼312) and cardiometabolic risk factors/hormones (n¼250) were assessed. Greater than 90th percentile of BMI/WC references
was defined as overweight/overwaist. Sleep duration, media consumption (TV watching/computer use), physical activity,
dietary habits, parental BMI, socio-economic status and early infancy were assessed by questionnaire. Short sleep was defined as
o10 h per day for children o10 years and otherwise o9 h per day.
Results: Total 15.9% participants were overweight, mean sleep duration was 8.9±1.3 h per day. Age explained most variance in
sleep (girls: 57.0%; boys: 41.2%) besides a high nutrition quality score (girls: 0.9%) and a low media consumption (boys:
1.3%). Sleep was inversely associated with BMI SDS/WC z-score (girls: r¼0.17/0.19, Po0.05; boys: r¼0.21/0.20,
Po0.01), which was strengthened after adjusting for confounders. Short vs long sleep was associated with 5.5-/2.3-fold higher
risks for obesity/overwaist (girls). After adjusting for age, REE (adjusted for fat-free mass) was positively associated with sleep in
boys (r¼0.16, Po0.05). Independently of age and WC z-score, short sleep was associated with lower adiponectin levels in boys
(11.7 vs 14.4 mg/ml, Po0.05); leptin levels were inversely related to sleep in girls (r¼0.23, Po0.05). Homoeostasis model
assessment–insulin resistance (r¼0.20, Po0.05) and insulin levels (r¼0.20, Po0.05) were associated with sleep (girls),
which depended on WC z-score.
Conclusions: Age mostly determined sleep. Short sleep was related to a higher BMI SDS/WC z-score (girls/boys), a lower REE
(boys), higher leptin (girls) and lower adiponectin levels (boys).
European Journal of Clinical Nutrition (2009) 63, 739–746; doi:10.1038/ejcn.2008.41; published online 30 July 2008
Keywords: sleep duration; overweight; REE; cardiometabolic risk factors; hormones
Introduction
Childhood overweight is a major public health concern of
complex aetiology and short sleep duration was considered
as a determinant. Already Locard et al. (1992) showed that
short sleep was associated with overweight in children. In a
recent meta-analysis a 58% higher risk for overweight in
children/adolescents with short vs long sleep was described
(Chen et al., 2008). However, determinants of short sleep in
children/adolescents are not well defined. Besides age, which
was inversely associated with sleep duration (Iglowstein
et al., 2003), sleep could be further determined by lifestyle.
Exercise compared to sedentary activities may account for
better/longer sleep, as sleep duration was positively asso-
ciated with physical activity and inversely associated with
television viewing (von Kries et al., 2002). In the same study,
eating snacks while watching television was related to sleep
Received 19 March 2008; revised 4 June 2008; accepted 30 June 2008;
published online 30 July 2008
Correspondence: Professor Dr med MJ Mu
¨ller, Institute of Human Nutrition
and Food Science, Christian-Albrechts University Kiel, Du
¨sternbrooker Weg
17-19, Kiel D-24105, Germany.
E-mail: mmueller@nutrfoodsc.uni-kiel.de
Contributors: Writing of the manuscript, BH and MJM; study design, AB-W and
MJM; data collection, BH, AB-W and FB; data analysis, BH, AB-W, US, MP, JS,
HM; discussion of data, BH, AB-W, SP-D and MJM. All contributors helped
with the revision of the paper.
European Journal of Clinical Nutrition (2009) 63, 739 – 746
&
2009 Macmillan Publishers Limited All rights reserved 0954-3007/09 $
32.00
www.nature.com/ejcn
duration, whereas caloric intake was not. Moreover, a low
socio-economic status (SES) as another potential determi-
nant was related to short sleep (Dollman et al., 2007).
Short sleep influences both sides of energy balance, which
is explained by several determinants such as increased
sympathetic activity, elevated cortisol and ghrelin levels,
decreased leptin levels and insulin resistance (Spiegel et al.,
1999, 2004a). Spiegel et al. (2004b) showed that sleep
deprivation alters feelings of hunger/appetite especially for
high-fat/carbohydrate foods in men. To our knowledge, the
impact of sleep deprivation on resting energy expenditure
(REE) has not been investigated in humans so far. However,
in rats metabolism rose to 166% of baseline during sleep
deprivation (Koban and Swinson, 2005).
When compared to adults, the association between sleep
and cardiometabolic risk factors/hormones is yet not well
defined in children. However, preliminary data obtained in a
small population of children showed that sleep duration was
inversely associated with insulin resistance (Flint et al.,
2007).
This study aims to investigate the determinants of sleep
duration and its impact on nutritional status, REE, cardio-
metabolic risk factors and hormones in children/adolescents.
Subjects and methods
Study design and population
Subjects were recruited by notice-board postings, writing to
families who attended the Kiel Obesity Prevention Study
(KOPS; Danielzik et al., 2004) and propaganda of partici-
pants. After exclusion of two children aged o6 years and
four subjects with incomplete data, 207 girls and 207 boys
(6.1–19.9 (13.0±3.4) years) remained for analysis. In sub-
cohort analyses blood samples were taken (n¼250) and REE
was measured (n¼312). Subjects were healthy and did not
take any medication known to influence body composition,
cardiometabolic risk factors or REE. The ethic committee of
Christian-Albrechts-University Kiel approved the study.
Written informed consent was obtained from each child/
adolescent and their legal guardian.
Anthropometric measurements and body composition analysis
After an overnight fast, height was measured to the nearest
0.5 cm against a stadiometer (Seca, Model 220, Hamburg,
Germany). Body weight was measured to the nearest gram
using the digital scale coupled to the BodPod system (Body
Composition System; Life Measurement Instruments,
Concord, CA, USA). Body mass index (BMI) was calculated
as weight (kg)/height (m
2
). German references were used to
calculate BMI SDS (standard deviation score) and to define
overweight/obesity (Kromeyer-Hauschild et al., 2001). The
upper age limit of 18.5 years for these references resulted in
396 children/adolescents for this analysis. For adults, WHO
(1995) definitions were used.
Waist circumference (WC) was measured to the nearest
0.5 cm midway between the lowest rib and the iliac crest
with subjects dressed in underwear and respiring minimal.
Greater than 90th age-/sex-specific percentile (McCarthy
et al., 2001) was used to define overwaist. Applying these
references, a z-score was calculated: (xm)/s(measured WC
(x), group mean (m), standard deviation (s)). The upper age
limit for these references was 17 years, resulting in 352
children/adolescents for this analysis. Blood samples were
taken from 218 subjects.
Body composition (fat mass, FM and fat-free mass, FFM)
was assessed by air-displacement plethysmography (BodPod)
and child-specific corrections were applied as described
elsewhere (Bosy-Westphal et al., 2005). To define overfat,
490th percentile of FM references (McCarthy et al., 2006)
was used, which had an upper age limit of 18.9 years
resulting in 399 subjects for this analysis.
Assessment of REE
In 312 subjects a valid measure of REE was obtained using a
ventilated hood system (Vmax-model 29n, SensorMedics;
Viasys Healthcare, Bilthoven, the Netherlands), which was
described elsewhere (Bader et al., 2005). REE was adjusted for
FFM (REE
adjFFM
) according to Ravussin and Bogardus (1989).
A total of 298 subjects had measures of thyroid hormones
and were included in our analysis.
Cardiometabolic risk factors and hormones
Blood pressure was measured with a manual sphygmoman-
ometer. Lipid profile and glucose levels were assessed
enzymatically by Konelab 20i Analyzer (Konelab, Espoo,
Finnland). The intra-assay coefficients of variation (CVs)
were o1.2% (total cholesterol), o3.5% (high-density lipo-
protein (HDL) cholesterol), o2.7% (low-density lipoprotein
(LDL) cholesterol), o2.5% (triglycerides) and o2.2% (glu-
cose). Radioimmunoassays (RIAs) were used to assess plasma
insulin (Adaltis, Freiburg, Germany; CVo5.4%), serum
leptin and adiponectin concentrations (Linco Research, St
Charles, MO, USA). Intra-/inter-assay CVs were 3–8 and
4–6% (leptin), 2–6 and 7–9% (adiponectin). Leptin levels were
referred to kg FM. Serum concentrations of thyroid-stimulat-
ing hormone (TSH; Brahms, Henningsdorf, Germany), free
T3 (fT3) and T4 (fT4) (DiaSorin, Dietzenbach, Germany) were
also analysed by RIA (intra-/inter-assay CVs: 2.5/5.7% (TSH),
4.6/6.5% (fT3) and 2.4/6.8% (fT4)).
Insulin resistance was calculated by homoeostasis model
assessment: HOMA-IR ¼(glucose (mmol/l) insulin (mU/
ml))/22.5 (Matthews et al., 1985).
Assessment of sleep duration and confounding factors
Subjects filled out a questionnaire. Children o11 years got
help from their parents, and those above 11 years completed
it by themselves.
Determinants and impact of short sleep
B Hitze et al
740
European Journal of Clinical Nutrition
Sleep duration on weekdays was asked by time bar
reaching half-hourly from o6to412h per day and was
classified into ‘short’/‘long’ according to Chen et al. (2008)
applying the following cutoffs: 10 h per day for children o10
years and otherwise 9 h per day. A stratification of short sleep
into ‘very short’ (o9 h per day for children o10 years and
otherwise o8 h per day; n¼47, girls; n¼37, boys) and ‘short’
(9–10 h per day for children o10 years and otherwise 8–9 h
per day) was conducted. Activity and inactivity (media
consumption) were assessed by information about the
membership in a sports club and time spent daily watching
TV or using computers. Dietary habits were recorded by a
validated Food Frequency Questionnaire. Five healthy items
(whole-meal products, milk products, fruits, vegetables and
potatoes, and fish) and five risk-related items (white bread,
meat products, soft drinks, fast food and sweets) were
analysed according to their consumption frequency (several
times a week and daily vs once a week or less). A nutrition
quality score was calculated with 52 points as the highest
possible score (Mast et al., 1998). Hence, a low score was
characterized by low consumptions of healthy and high
consumptions of risk-related items and vice versa. A mean
nutrition quality score of 31.8±4.4 (range: 17–43) was
obtained.
The highest educational level of parents was used for
classification in three SES groups (low, middle and high).
A subcohort (n¼125) attended the Family Path Study as
part of KOPS (Bosy-Westphal et al., 2006), where parental
BMI was measured. Otherwise parental weight and height
were self-reported, which were shown to be highly correlated
with measured values (McAdams et al., 2007).
Birth weight and weight at the age of 2 were recorded from
children’s examination booklets (measurements took place
in an official routine after birth and between the 20.5th and
29.5th months). Weight SDS at birth and around 2 years was
calculated using German references taking into account
children’s age as exact as possible (Kromeyer-Hauschild et al.,
2001). D-Weight SDS was calculated by subtracting birth
weight SDS from weight SDS at around 2 years. Because of
incomplete data of 23 subjects, this analysis could be
obtained in 391 subjects. For further analyses, birth weight
was adjusted for gestational age (n¼409). Moreover, mothers
were asked about the duration of breastfeeding.
Statistical analysis
Analyses were performed using SPSS 13.0 for Windows
(Chicago, IL, USA). Descriptive statistics were given as
median (interquartile range; IQR) or mean (95% confidence
interval (CI)). Mann–Whitney U-test was used to compare
independent samples. w
2
-Test was applied to analyse differ-
ences in frequency distributions. Pearson’s correlation
was performed to demonstrate the relationship between
two variables. To analyse an association while considering
covariates, partial correlation was adopted. Comparison of
means with regard to covariates was tested by general linear
model (analysis of covariance, ANCOVA; Bonferroni post hoc
test). When calculating odds ratios (OR) for the association
between ‘short’ sleep and overweight/obesity, overwaist
and overfat, ‘long’ sleep was the reference. To explain the
variance in sleep duration, multiple step-wise regression
analyses were performed with the following independent
variables: age, physical activity, media consumption, nutri-
tion quality score, SES and change in weight SDS (birth till
2 years). To explain the variance in BMI SDS/WC z-score
parental BMI, SES, birth weight, change in weight SDS (birth
till 2 years), duration of breastfeeding, sleep duration,
physical activity, media consumption and nutrition quality
score were used as independent variables. Regression analysis
was adopted to adjust for confounders. Normal distribution
was tested by Kolmogorov–Smirnov test. Parameters that
showed no normal distribution were log
10
-transformed for
correlation/regression analysis. A P-value o0.05 (two sided)
was considered to be statistically significant.
Results
Characterization of the study population
Boys had a higher body weight, height and FFM as well as
a lower per cent FM and WC z-score compared to girls
(Table 1).
Determinants of sleep duration
The variance in sleep duration was explained by age (57.0%,
girls; 41.2%, boys) with an additional effect of lifestyle. A
high nutrition quality score (girls) and a low media
consumption (boys) could explain further 0.9/1.3%. ‘Short’
vs ‘long’ sleepers had lower physical activities (girls) and a
higher media consumption (girls/boys; Table 2). However,
adjustment for age weakened this association (sleep duration
vs media consumption in girls: r¼0.08 and boys: r¼0.12;
P40.05).
In brief, 4.9/80.6% of girls with ‘short’ vs 0/93.3% of girls
with ‘long’ sleep ate fast food/sweets several times a weak or
daily (Po0.05/Po0.01) and 30.3% of boys with ‘short’
compared to 13.3% with ‘long’ sleep consumed soft drinks
at a frequency of several times a weak or daily (Po0.01).
Sleep duration and nutritional status
Children/adolescents with ‘short’ compared to those with
‘long’ sleep were older; deductive the differences in weight,
height, BMI, WC, FFM and FM could be explained by age
(Table 3). However, ‘short’ vs ‘long’ sleep was associated with
a higher BMI SDS (girls/boys) and WC z-score (girls).
Moreover, ‘very short’ compared to ‘long’ sleepers had a
higher BMI SDS (girls: 0.55 vs 0.02; boys: 0.42 vs 0.05;
Po0.05) and WC z-score (girls: 1.5 vs 0.7; boys: 0.81 vs 0.42;
Po0.05), whereas children/adolescents with ‘short’ sleep did
not differ from those with ‘very short’ or ‘long’ sleep, which
was probably due to small sample sizes.
Determinants and impact of short sleep
B Hitze et al
741
European Journal of Clinical Nutrition
Short sleep duration explained between 3.6 and 5.2% of
the variance in BMI SDS and WC z-score (Table 4). Moreover,
sleep duration had an inverse relationship with BMI
SDS (girls: r¼0.17, Po0.05; boys: r¼0.21, Po0.01) and
WC z-score (girls: r¼0.19, Po0.05; boys: r¼0.20,
Po0.01), which was strengthened after adjusting for con-
founders (Table 4) for BMI SDS (girls: r¼0.27, Po0.001;
boys: r¼0.25, Po0.01) and WC z-score (girls: r¼0.30,
Po0.001, boys: r¼0.21; Po0.01).
Using ‘long’ sleep as a reference and adjusting for
confounders (Table 4), girls with ‘short’ sleep had increased
risks for being obese (OR (95% CI) ¼5.5 (1.3–23.5)) and
overwaist (2.3 (1.2–4.6)). However, sleep duration did not
influence the risk of being overfat.
Sleep duration and REE
After adjusting for age, REE
adjFFM
was positively associated
with sleep duration in boys (r¼0.16, Po0.05), but not in
girls (r¼0.05, P40.05). In multiple step-wise regression
analyses, variance in REE
adjFFM
was explained by higher fT3
levels in girls (5.6%). In boys lower TSH levels (4.1%) and a
higher sleep duration (3.5%) explained variance in REE
adjFFM
,
whereas age and fT4 levels did not. However, REE
adjFFM
did
not differ between ‘short’ and ‘long’ sleepers (Table 5).
Sleep duration, cardiometabolic risk factors and hormones
After adjusting for age, ‘short’ vs ‘long’ sleep was related to
lower adiponectin levels in boys (Table 5), which was
independent of WC z-score.
The relationship between sleep duration and cardiometa-
bolic risk factors/hormones adjusted for age is shown in
Table 6. Although in boys no association could be found,
sleep duration in girls was inversely associated with leptin
levels. After adjusting for WC z-score, insulin levels and
HOMA-IR were no longer associated with sleep in girls.
Discussion
As to the determinants of sleep duration, sleep was mostly
determined by age with minor but additional effects of
lifestyle. Sleep duration was inversely associated with BMI
SDS/WC z-score in both genders; but ‘short’ sleep was
associated with higher risks for being obese/overwaist in
girls only. In boys, REE
adjFFM
was positively associated with
sleep. ‘Short’ sleep was further related to lower adiponectin
Table 1 Characterization of the study population
Girls (n¼207) Boys (n¼207)
Age (years) 12.7 (10.5–15.6) 13.1 (10.5–15.8)
Weight (kg) 46.9 (36.8–59.7) 51.2 (37.4–67.4)*
Height (m) 1.57 (1.44–1.66) 1.63 (1.45–1.76)***
BMI (kg/m
2
) 19.2 (16.9–22.0) 19.3 (17.0–21.9)
BMI SDS
a
0.18 (0.45–0.86) 0.28 (0.53–0.94)
Prevalence of
overweight (%)
b
4.3 7.7
Prevalence of
obesity (%)
b
10.6 9.2
Waist circumference (cm) 67.1 (61.0–74.8) 70.0 (62.0–76.6)
Waist circumference
z-score
c
0.88 (0.22–1.8) 0.48 (0.01–1.1)**
Body fat (%) 19.0 (13.3–27.3) 12.9 (8.3–19.9)***
Fat-free mass (kg) 38.6 (29.4–45.3) 42.4 (31.5–59.7)***
Sleep duration
(h per day)
9.0 (8.0–10.0) 9.0 (8.0–10.0)
Abbreviations: BMI, body mass index; SDS, standard deviation score.
*Po0.05; **Po0.01; ***Po0.001: difference between girls and boys.
Mann–Whitney U-test; median (IQR) as well as w
2
-test; %.
a
n¼199 for girls and n¼197 for boys.
b
Defined by Kromeyer-Hauschild et al. (2001) and WHO (1995).
c
n¼179 for girls and n¼173 for boys.
Table 2 Lifestyle factors and parameters of parental influence as well as early infancy according to sleep duration
a
Sleep duration in girls
a
Sleep duration in boys
a
‘Short’ (n¼103) ‘Long’ (n¼104) ‘Short’ (n¼109) ‘Long’ (n¼98)
Physically active (% ) 69.9 85.6* 74.8 85.6
Media consumption (min per day) 120.0 (90.0–180.0) 75.0 (51.3–120.0)*** 180.0 (120.0–255.0) 90.0 (60.0–150.0)***
Nutrition quality score 34.0 (30.0–36.0) 32.5 (30.0–35.0) 31.0 (28.0–34.0) 31.0 (28.8–35.0)
Sleep duration (h per day) 8.0 (7.5–8.5) 9.9 (9.0–10.0)*** 8.0 (7.5–8.5) 10.0 (9.0–10.5)***
BMI
mother
(kg/m
2
) 24.1 (20.9–28.6) 24.1 (22.3–26.9) 24.1 (22.3–26.7) 23.5 (22.1–26.9)
BMI
father
(kg/m
2
) 25.4 (23.7–28.1) 25.5 (24.2–28.5) 26.0 (24.3–28.3) 25.3 (23.6–27.5)
Low socio-economic status (%) 8.7 7.8 10.2 10.4
Birth weight
adj
(g)
b
3266 (3046–3466) 3363 (3118–3719) 3580 (3314–3846) 3635 (3399–3966)
D-Weight SDS (birth till 2 years)
c
0.16 (0.60–1.0) 0.05 (0.88–0.68) 0.02 (0.67–0.63) 0.002 (0.67–0.78)
Duration of breastfeeding (week) 24.0 (12.0–40.0) 28.0 (14.5–40.0) 24.0 (12.0–36.0) 24.0 (13.8–36.0)
Abbreviations: BMI, body mass index; SDS, standard deviation score.
*Po0.05; ***Po0.001: difference between ‘short’ and ‘long’ sleep duration.
Mann–Whitney U-test; median (IQR) as well as w
2
-test; %.
a
Cutoffs for sleep duration: 10 h per day for children aged o10 years and 9 h per day for children/adolescents aged X10 years.
b
Adjusted for gestational age; n¼103/102 (girls) and n¼106/98 (boys).
c
n¼98/98 (girls) and n¼103/92 (boys).
Determinants and impact of short sleep
B Hitze et al
742
European Journal of Clinical Nutrition
(boys) and higher leptin levels (girls), whereas the inverse
relationship between sleep and insulin levels/HOMA-IR in
girls depended on WC.
Determinants of sleep duration
Concordant with Iglowstein et al. (2003) sleep duration was
mostly determined by age. Thus, older children suffer from a
greater sleep deprivation, as sleep need does not decrease
(Mercer et al., 1998).
Sleep duration was further determined by a healthier diet
(girls) and low media consumption (boys). Moreover, fast
food (girls) and soft drinks (boys) were more often consumed
by ‘short’ sleepers, whereas for sweets the opposite was true
(girls). Anymore, girls with ‘long’ compared to ‘short’ sleep
were more physically active (Table 2).
Whereas Benefice et al. (2004) could not find an associa-
tion between sleep and physical activity, von Kries et al.
(2002) described that short sleep in children is associated
with increased inactivity and reduced participation in
organized sports, suggesting that physical activity contri-
butes to better/longer sleep. In turn, sleep deprivation affects
physical activity by fatigue (Patel and Hu, 2008).
Short sleep was shown to increase eating (Sivak, 2006) and
to alter feelings of hunger/appetite (Spiegel et al., 2004b).
However, von Kries et al. (2002) found no association
between sleep and caloric intake, whereas eating snacks
while watching television was related to short sleep. Thus,
further studies are needed, which should keep in mind age-
dependent effects, as the association between sleep and
media consumption was mediated through age.
Sleep duration and nutritional status
The inverse relationship between sleep duration and
BMI SDS/WC z-score is concordant with previous studies
Table 3 Age and parameters of nutritional status according to sleep duration
a
Sleep duration in girls
a
Sleep duration in boys
a
‘Short’ (n¼103) ‘Long’ (n¼104) ‘Short’ (n¼109) ‘Long’ (n¼98)
Age (years) 15.3 (12.9–17.1) 10.9 (9.2–12.4)*** 15.0 (13.0–17.1) 11.5 (9.3–13.0)***
Weight (kg) 55.9 (46.9–65.7) 39.1 (30.8–46.6)*** 63.5 (48.9–76.1) 40.2 (31.2–50.4)***
Height (m) 1.65 (1.56–1.69) 1.48 (1.37–1.58)*** 1.73 (1.60–1.80) 1.52 (1.39–1.62)***
BMI (kg/m
2
) 20.8 (18.7–23.0) 17.6 (15.9–19.8)*** 21.0 (18.5–23.7) 17.5 (15.8–19.8)***
BMI-SDS
b
0.3 (0.2–0.9) 0.02 (0.6–0.8)* 0.4 (0.3–1.1) 0.05 (0.7–0.7)**
Prevalence of overweight(%)
c
2.9 5.8 8.3 7.1
Prevalence of obesity (%)
c
14.6 6.7 12.8 5.1
Waist circumference (cm) 72.3 (66.7–78.7) 62.8 (57.6–68.5)*** 72.7 (68.1–79.9) 64.7 (58.4–71.0)***
Waist circumference z-score
d
1.2 (0.5–2.4) 0.7 (0.01–1.6)** 0.6 (0.08–1.7) 0.4 (0.1–1.0)
Body fat (%) 22.4 (16.7–28.8) 15.8 (10.4–25.7)*** 12.9 (8.5–20.2) 12.7 (7.6–19.3)
Fat free mass (kg) 44.7 (39.5–48.3) 32.0 (25.9–38.2)*** 55.2 (40.6–64.4) 34.9 (27.9–41.6)***
Abbreviations: BMI, body mass index; SDS, standard deviation score.
*Po0.05; **Po0.01; ***Po0.001: difference between ‘short’ and ‘long’ sleep duration.
Mann–Whitney U-test; median (IQR) as well as w
2
-test; %.
a
Cutoffs for sleep duration: 10 h per day for children aged o10 years and 9 h per day for children/ado lescents aged X10 years.
b
n¼96/103 for girls and n¼99/98 for boys.
c
Defined by Kromeyer-Hauschild et al. (2001) and WHO (1995).
d
n¼76/103 for girls and n¼81/92 for boys.
Table 4 Results of multiple step-wise regression analyses to explain the variance in BMI SDS and waist circumference z-score
Independent variables BMI SDS Waist circumference z-score
Girls (n¼190) Boys (n¼186) Girls (n¼171) Boys (n¼163)
BMI
mother
(kg/m
2
) 19.1 4.7 7.0
BMI
father
(kg/m
2
) 10.6 14.4 12.9 12.8
Birth weight
adj
(g)
a
3.1 5.2 4.3 4.0
D-Weight SDS (birth till 2 years) 2.1 5.8 6.5 6.6
Duration of breastfeeding (week) 3.9 4.4
Sleep duration (h per day) 4.4 3.6 5.2 3.7
Total explained variance 39.3 37.6 35.9 31.5
Abbreviations: BMI, body mass index; SDS, standard deviation score.
Excluded variables: socio-economic status, physical activity, media consumption and nutrition quality score.
% of explained variance.
a
Adjusted for gestational age.
Determinants and impact of short sleep
B Hitze et al
743
European Journal of Clinical Nutrition
(von Kries et al., 2002; Lumeng et al., 2007; Chaput and
Tremblay, 2007). Contrary to von Kries et al. (2002), we could
not find an association with per cent FM independent of age
(Table 3), which might be due to missing references to create
age-/sex-dependent z-scores.
Contrary to our results, Sekine et al. (2002), Chaput et al.
(2006) and Chen et al. (2008) described that boys compared
to girls were more affected by short sleep. The explanation
for this gender difference remains unclear. Sleep may
influence weight gain differently in girls and boys (Knutson,
2005a) and a greater sleep deprivation may be needed in girls
(Eisenmann et al., 2006). However, ‘short’ compared to ‘long’
sleep was related to increased risks for being obese/overwaist
in girls only, suggesting a greater influence of short sleep in
girls.
Sleep duration and REE
An inverse association between sleep duration and REE was
observed in rats, where sleep deprivation increased energy
expenditure to 66% (Koban and Swinson, 2005). Moreover,
sleep deprivation increased plasma cortisol levels and
sympathetic activity in men suggesting stress-induced
metabolism (Spiegel et al., 2004a). However, it remains
unclear if short sleep increases energy needs to keep the
organism awake and/or if energy expenditure is increased
adaptive to an increased caloric intake. In our study, sleep
duration was positively associated with REE
adjFFM
in boys,
which could result in a positive energy balance.
Sleep duration, cardiometabolic risk factors and hormones
Contrary to studies in adults (Spiegel et al., 1999, 2005;
Gottlieb et al., 2006), short sleep was not associated with
blood pressure, plasma lipids or glucose (Tables 5 and 6),
suggesting that sleep deprivation or overweight as a result of
short sleep may exist longer to affect metabolic risk. In fact,
sleep duration was not related to insulin/HOMA-IR in girls
independently of WC (Table 6), suggesting that insulin
Table 5 Resting energy expenditure adjusted for FFM, cardiometabolic risk factors and hormones according to sleep duration
a
Sleep duration in girls
a
Sleep duration in boys
a
‘Short’ (n¼55) ‘Long’ (n¼67) ‘Short’ (n¼46) ‘Long’ (n¼50)
REE
adjFFM
(kcal per day)
b
1317.0 (1283.4–1350.6) 1362.3 (1326.1–1398.5) 1587.7 (1550.9–1624.5) 1597.4 (1558.7–1636.2)
RRsys (mm Hg) 113.2 (109.7–116.7) 111.2 (108.1–114.2) 114.1 (110.5–117.7) 114.9 (111.6–118.3)
RRdias (mm Hg) 71.4 (68.8–74.0) 69.8 (67.6–72.1) 70.1 (67.9–72.4) 69.8 (67.7–71.9)
Triglycerides (mg/100 ml) 79.3 (68.8–89.8) 80.3 (70.9–89.6) 79.0 (68.5–89.5) 70.2 (60.1–80.3)
Total cholesterol (mg/100 ml) 161.7 (152.4–171.0) 165.7 (157.4–173.9) 155.9 (148.1–163.8) 160.5 (153.0–168.0)
LDL-C (mg/100 ml) 85.3 (77.6–93.0) 90.8 (84.0–97.6) 83.6 (76.6–90.6) 83.9 (77.3–90.5)
HDL-C (mg/100 ml) 60.8 (56.9–64.6) 58.7 (55.2–62.1) 56.5 (52.2–60.8) 61.4 (57.4–65.5)
Glucose (mg/100 ml) 90.2 (88.2–92.2) 89.4 (87.7–91.2) 93.0 (91.1–94.9) 93.6 (91.7–95.4)
Insulin (mU/ml) 12.5 (11.1–13.9) 10.7 (9.4–12.0) 12.5 (10.6–14.4) 10.7 (8.8–12.5)
HOMA-IR ((mmol/l) (mU/ml)) 2.8 (2.4–3.1) 2.4 (2.1–2.7) 2.9 (2.4–3.3) 2.5 (2.1–2.9)
Leptin (ng/ml) 12.8 (9.7–15.9) 10.2 (7.4–13.0) 5.5 (3.6–7.3) 5.1 (3.3–6.8)
Leptin/FM ((ng/ml)/kg) 1.0 (0.9–1.2) 0.9 (0.8–1.0) 0.6 (0.5–0.8) 0.6 (0.5–0.7)
Adiponectin (mg/ml) 14.6 (12.8–16.3) 14.6 (13.0–16.1) 11.7 (9.8–13.5) 14.4 (12.6–16.2)*
TSH (mU/L)
b
2.8 (1.6–4.0) 4.1 (2.7–5.4) 3.0 (2.7–3.4) 2.8 (2.5–3.2)
fT3 (pg/ml)
b
4.9 (4.6–5.1) 4.8 (4.6–5.1) 5.0 (4.8–5.2) 4.9 (4.7–5.1)
fT4 (pg/ml)
b
14.2 (13.6–14.8) 13.9 (13.2–14.5) 14.9 (14.3–15.5) 14.2 (13.6–14.9)
Abbreviations: FM, fat mass; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low-density
lipoprotein cholesterol; REE
adjFFM
, resting energy expenditure adjusted for fat free mass; RRdias, diastolic blood pressure; RRsys, systolic blood pressure.
*Po0.05: difference between ‘short’ and ‘long’ sleep duration.
ANCOVA (adjusted for age); mean (95% CI).
a
Cutoffs for sleep duration: 10 h per day for children aged o10 years and 9 h per day for children/adolescents aged X10 years.
b
n¼79/70 (girls) and n¼78/71 (boys).
Table 6 Relationship between sleep duration and cardiometabolic risk
factors as well as hormones
Sleep duration in
girls (n¼122)
Sleep duration in
boys (n¼96)
log
RRsys (mm Hg) 0.03 0.06
log
RRdias (mm Hg) 0.12 0.06
log
Triglycerides (mg/100 ml) 0.01 0.08
Total cholesterol (mg/100 ml) 0.10 0.10
LDL-C (mg/100 ml) 0.11 0.13
HDL-C (mg/100 ml) 0.01 0.15
Glucose (mg/100 ml) 0.08 0.01
log
Insulin (mU/ml) 0.20* 0.07
log
HOMA-IR ((mmol/l) (mU/ml)) 0.20* 0.06
log
Leptin (ng/ml) 0.20* 0.02
log
Leptin/FM ((ng/ml)/kg) 0.23* 0.00
log
Adiponectin (mg/ml) 0.03 0.08
Abbreviations: FM, fat mass; HDL-C, high-density lipoprotein cholesterol;
HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low-
density lipoprotein cholesterol; RRdias, diastolic blood pressure; RRsys, systolic
blood pressure.
*Po0.05.
Partial correlation adjusted for age.
Determinants and impact of short sleep
B Hitze et al
744
European Journal of Clinical Nutrition
resistance follows overweight. These results are also con-
firmed by Verhulst et al. (2008).
Contrary to studies in adults (Spiegel et al., 2004a; Chaput
et al., 2007) short sleep was associated with higher leptin
levels in girls (Table 6). However, this discrepancy could be
explained by the inverse relationship between sleep duration
and overweight, as leptin levels reflect energy stores.
Moreover, leptin concentrations were higher in children
with respect to FM compared to adults, hypothisizing
that children develop leptin resistance beneficial for their
energy needs (Hassink et al., 1996). Furthermore, high
leptin levels were associated with future weight gain
(Savoye et al., 2002).
Concordant with Kotani et al. (2007), who showed a
positive association between sleep duration and adiponectin
concentrations in men, boys with ‘short’ vs ‘long’ sleep had
lower adiponectin levels (Table 5), which was independent of
age and WC z-score.
Study limitations
Our study has three main limitations. Sleep duration was
self-reported and not measured. However, Taheri et al. (2004)
described, that self-reported sleep duration is highly corre-
lated with polysomnographic measurements and both
measures are stable. Second, age ranged from 6.1 to 19.9
years. As sleep duration decreased with age (Table 3), older
children/adolescents were more sleep deprived. Puberty may
act as a mediator of causal linkage between sleep and
metabolic risk (Knutson, 2005b), as physiological changes
occur during adolescence (Hannon et al., 2006). However,
Flint et al. (2007) found no association between sleep
duration and pubertal status. Third, because of our cross-
sectional study design, we cannot deduce causation. Short
sleep may also be a consequence of overweight. For example,
sleep-disordered breathing leading to impaired/shorter sleep
was associated with overweight (Redline et al., 2007).
However, we feel, that this idea may become true for very
obese only.
In conclusion, short sleep was associated with a higher BMI
SDS and WC z-score. However, when compared to boys
‘short’ vs ‘long’ female sleepers had higher risks for being
obese/overwaist only. The inverse relationship between sleep
and insulin resistance in girls was probably mediated
through the development of overweight, whereas ‘short’
sleep was related to higher leptin (girls) and lower adipo-
nectin levels (boys) independently of WC z-score. REE
adjFFM
was positively associated with sleep in boys. Lifestyle could
add to a positive energy balance, as ‘short’ compared to
‘long’ sleepers were less physically active (girls), had higher
consumptions of soft drinks (boys) and fast food (girls), but
lower consumptions of sweets (girls). Besides age as the
major determinant, variance in sleep duration was explained
by healthier dietary habits (girls) and a low media consump-
tion (boys).
Acknowledgements
This work was supported by Bundesministerium fu
¨r Bildung
und Forschung (project 6.1.2 Network Kiel: Dietary Fat and
Metabolism).
References
Bader N, Bosy-Westphal A, Dilba B, Mu
¨ller MJ (2005). Intra- and
interindividual variability of resting energy expenditure in
healthy male subjects—biological and methodological variability
of resting energy expenditure. Br J Nutr 94, 843–849.
Benefice E, Garnier D, Ndiaye G (2004). Nutritional status, growth
and sleep habits among Senegalese adolescent girls. Eur J Clin Nutr
58, 292–301.
Bosy-Westphal A, Danielzik S, Becker C, Geisler C, Onur S, Korth O
et al. (2005). Need for optimal body composition data analysis
using air-displacement plethysmography in children and adoles-
cents. J Nutr 135, 2257–2262.
Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezen-
meir J et al. (2006). Value of body fat mass vs. anthropometric
obesity indices in the assessment of metabolic risk factors. Int J
Obes 30, 475–483.
Chaput JP, Brunet M, Tremblay A (2006). Relationship between short
sleeping hours and childhood overweight/obesity: results from the
‘Que
´bec en Forme’ Project. Int J Obes 30, 1080–1085.
Chaput JP, Despre
´s JP, Bouchard C, Tremblay A (2007). Short sleep
duration is associated with reduced leptin levels and increased
adiposity: results from the Que
´bec Family Study. Obesity 15,
253–261.
Chaput JP, Tremblay A (2007). Does short sleep duration favor
abdominal adiposity in children? Int J Pediatr Obes 2, 188–191.
Chen X, Beydoun MA, Wang Y (2008). Is sleep duration associated
with childhood obesity? A systematic review and meta-analysis.
Obesity 16, 265–274.
Danielzik S, Czerwinski-Mast M, Langna
¨se K, Dilba B, Mu
¨ller MJ
(2004). Parental overweight, socioeconomic status and high birth
weight are the major determinants of overweight and obesity in
5–7y-old children: baseline data of the Kiel Obesity Prevention
Study. Int J Obes Relat Disord 28, 1494–1502.
Dollman J, Ridley K, Olds T, Lowe E (2007). Trend in the duration of
school-day sleep among 10- to 15-year-old South Australians
between 1985 and 2004. Acta Pediatr 96, 1011–1014.
Eisenmann JC, Ekkekakis P, Holmes M (2006). Sleep duration
and overweight among Australian children and adolescents.
Acta Paediatr 95, 956–963.
Flint J, Kothare SV, Zihlif M, Suarez E, Adams R, Legido A et al. (2007).
Association between inadequate sleep and insulin resistance in
obese children. J Pediatr 150, 364–369.
Gottlieb DJ, Redline S, Nieto FJ, Baldwin CM, Newman AB, Resnick
HE et al. (2006). Association of usual sleep duration with
hypertension: the Sleep Heart Health Study. Sleep 29, 1009–1014.
Hannon TS, Janosky J, Arslanian SA (2006). Longitudinal study of
physiologic insulin resistance and metabolic changes of puberty.
Pediatr Res 60, 759–763.
Hassink SG, Sheslow DV, De Lancey E, Opentanova I, Considine RV,
Caro JF (1996). Serum leptin concentrations in children with
obesity: relationship to gender and development. Pediatrics 98,
201–203.
Iglowstein I, Jenni OG, Molinari L, Largo RH (2003). Sleep duration
from infancy to adolescence: reference values and generational
trends. Pediatrics 111, 302–307.
Knutson KL (2005a). Sex differences in the association between
sleep and body mass index in adolescents. J Pediatr 147,
830–834.
Determinants and impact of short sleep
B Hitze et al
745
European Journal of Clinical Nutrition
Knutson KL (2005b). The association between pubertal status and
sleep duration and quality among a nationally representative
sample of U.S. adolescents. Am J Hum Biol 17, 418–424.
Koban M, Swinson KL (2005). Chronic REM-sleep deprivation of rats
elevates metabolic rate and increases UCP1 gene expression in
brown adipose tissue. Am J Physiol Endocrinol Metab 289, 68–74.
Kotani K, Sakane N, Saiga K, Kato M, Ishida K, Kato Y et al. (2007).
Serum adiponectin levels and lifestyle factors in Japanese men.
Heart Vessels 22, 291–296.
Kromeyer-Hauschild K, Wabitsch M, Kunze D, Geller F, GeiHC, Hesse
Vet al. (2001). Perzentile fu
¨r den Body-mass-Index fu
¨rdasKindes-
und Jugendalter unter Heranziehung verschiedener deutscher
Stichproben. Monatsschrift Kinderheilkunde 149, 807–818.
Lumeng JC, Somashekar D, Appugliese D, Kaciroti N, Corwyn RF,
Bradley RH (2007). Shorter sleep duration is associated with
increased risk for being overweight at ages 9 to 12 years. Pediatrics
120, 1020–1029.
Locard E, Mamelle N, Billete A, Miginiac M, Munoz F, Rey S (1992).
Risk factors of obesity in a five year old population. Parental versus
environmental factors. Int J Obes Relat Metab Disord 16, 721–729.
Mast M, Kortzinger I, Mu
¨ller MJ (1998). Erna
¨hrungsverhalten
und Erna
¨hrungszustand 5-7-ja
¨hriger Kinder in Kiel. Aktuelle
Erna¨hrungsmedizin 23, 282–288.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF,
Turner RC (1985). Homeostasis model assessment: insulin
resistance and beta-cell function from fasting plasma glucose
and insulin concentrations in man. Diabetologia 28, 412–419.
McAdams MA, Van Dam RM, Hu FB (2007). Comparison of self-
reported and measured BMI as correlates of disease markers in
US adults. Obesity 15, 188–196.
McCarthy HD, Cole TJ, Fry T, Jebb SA, Prentice AM (2006). Body fat
reference curves for children. Int J Obes 30, 598–602.
McCarthy HD, Jarrett KV, Crawley HF (2001). The development of
waist circumference percentiles in British children aged 5.0–16.9y.
Eur J Clin Nutr 55, 902–907.
Mercer PW, Merritt SL, Cowell JM (1998). Differences in reported
sleep need among adolescents. J Adolesc Health 23, 259–263.
Patel SR, Hu FB (2008). Short sleep duration and weight gain: a
systematic review. Obesity 16, 643–653.
Ravussin E, Bogardus C (1989). Relationship of genetics, age and
physical fitness to daily energy expenditure and fuel utilization.
Am J Nutr 49, 968–975.
Redline S, Storfer-Isser A, Rosen CL, Johnson NL, Kirchner HL,
Emancipator J et al. (2007). Association between metabolic
syndrome and sleep-disordered breathing in adolescents. Am J
Respir Crit Care Med 176, 401–408.
Savoye M, Dziura J, Castle J, DiPietro L, Tamborlane WV, Caprio S
(2002). Importance of plasma leptin in predicting future weight
gain in obese children: a two-and-a-half-year longitudinal study.
Int J Obes 26, 942–946.
Sekine M, Yamagami T, Handa K, Saito T, Nanri S, Kawaminami K
et al. (2002). A dose–response relationship between short sleeping
hours and childhood obesity: results of the Toyama Birth Cohort
Study. Child Care Health Dev 28, 163–170.
Sivak M (2006). Sleeping more as a way to lose weight. Obesity Rev 7,
295–296.
Spiegel K, Knutson K, Leprould R, Tasali E, Van Cauter E (2005). Sleep
loss: a novel risk factor for insulin resistance and type 2 diabetes.
J Appl Physiol 99, 2008–2019.
Spiegel K, Leproult R, L’hermite-Baleriaux M, Copinschi G, Penev PD,
Van Cauter E (2004a). Leptin levels are dependent on sleep
duration: relationships with sympathovagal balance, carbohydrate
regulation, cortisol, and thyrotropin. J Clin Endocrinol Metab 89,
5762–5771.
Spiegel K, Leproult R, Van Cauter E (1999). Impact of sleep
dept on metabolic and endocrine function. Lancet 354,
1435–1439.
Spiegel K, Tasali E, Penev P, van Cauter E (2004b). Brief communica-
tion: sleep curtailment in healthy young men is associated with
decreased leptin levels, elevated ghrelin levels, and increased
hunger and appetite. Ann Intern Med 141, 846–850.
Taheri S, Lin L, Austin D, Young T, Mignot E (2004). Short sleep
duration is associated with reduced leptin, elevated ghrelin, and
increased body mass index. PLoS Med 1, e62.
Verhulst SL, Schrauwen N, Haentjens D, Rooman RP, Van Gaal L, De
Backer WA et al. (2008). Sleep duration and metabolic dysregula-
tion in overweight children and adolescents. Arch Dis Child 93,
89–90.
von Kries R, Toschke AM, Wurmser H, Sauerwald T, Koletzko B (2002).
Reduced risk for overweight and obesity in 5-and 6-y-old
children by duration of sleep—a cross-sectional study. Int J Obes
26,710–716.
WHO (1995). Physical Status: The Use and Interpretation of Anthro-
pometry Technical Report Series WHO: Geneva.
Determinants and impact of short sleep
B Hitze et al
746
European Journal of Clinical Nutrition
