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Recent epidemiological studies suggest that short sleep duration may be associated with the development of obesity from childhood to adulthood. To assess whether the evidence supports the presence of a relationship between short sleep duration and obesity at different ages, and to obtain an estimate of the risk. We performed a systematic search of publications using MEDLINE (1996-2007 wk 40), EMBASE (from 1988), AMED (from 1985), CINHAL (from 1982) and PsycINFO (from 1985) and manual searches without language restrictions. When necessary, authors were contacted. Criteria for inclusion were: report of duration of sleep as exposure, BMI as continuous outcome and prevalence of obesity as categorical outcome, number of participants, age, and gender. Results were pooled using a random effect model. Sensitivity analysis was performed, heterogeneity and publication bias were also checked. Results are expressed as pooled odds ratios (OR [95% confidence intervals, CIs]) and as pooled regression coefficients (beta; 95% CIs). Of 696 studies identified, 45 met the inclusion criteria (19 in children and 26 in adults) and 30 (12 and 18, respectively) were pooled in the meta-analysis for a total of 36 population samples. They included 634,511 participants (30,002 children and 604,509 adults) from around the world. Age ranged from 2 to 102 years and included boys, girls, men and women. In children the pooled OR for short duration of sleep and obesity was 1.89 (1.46 to 2.43; P < 0.0001). In adults the pooled OR was 1.55 (1.43 to 1.68; P < 0.0001). There was no evidence of publication bias. In adults, the pooled beta for short sleep duration was -0.35 (-0.57 to -0.12) unit change in BMI per hour of sleep change. Cross-sectional studies from around the world show a consistent increased risk of obesity amongst short sleepers in children and adults. Causal inference is difficult due to lack of control for important confounders and inconsistent evidence of temporal sequence in prospective studies.
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SLEEP, Vol. 31, No. 5, 2008
619
IN THE LAST FEW DECADES THERE HAS BEEN A SIG-
NIFICANT INCREASE IN THE PREVALENCE OF OBESI-
TY WORLDWIDE AND THE WORLD HEALTH organiza-
tion has declared it a global epidemic.
1
Obesity in childhood is
a cause of psychosocial problems including low self esteem,
2
and frequently continues into adulthood
3
where it is a cause of
major morbidity and mortality including cardiovascular disease
and type 2 diabetes. At the same time there has been a reduction
in sleep time. National surveys in USA have shown a decline
in self-reported sleep duration over the past 50 years by 1.5 to
2 hours.
4
This sleep curtailment has been attributed to lifestyle
changes.
Several studies have reported associations between duration
of sleep (short as well as long) and ill-health, including relation-
ships with self-reported well-being,
5
morbidity and mortality,
6-12
and with chronic conditions including type 2 diabetes, respiratory
disorders, hypertension, and obesity.
13-18
The associations between
short duration of sleep and obesity, in particular, have stimulated
a debate given the potential implications for children
19,20
as well
as adults.
21,22
However, given the variety of studies and the large
differences in the target populations, it is difcult to draw im-
mediate conclusions on the consistency of the association, the
direction of causality and the likely mechanisms involved. The
aims of this article are to (i) systematically review published
population-based studies, (ii) to carry out a meta-analysis to as-
sess whether the evidence supports the presence of a relationship
between short sleep duration and obesity at different ages, and
(iii) to obtain a quantitative estimate of the risk in order to assess
the consistency and potential public health relevance
METHODS
Literature Search
We performed a systematic search for publications using
Medline (1996-2007 week 40), EMBASE (from 1988), AMED
(from 1985), CINAHL (from 1982) Psychinfo (from 1985).
Search strategies used subject headings and key words and did
not use language restrictions. We (FMT, N-BK, AC, SS, MAM,
and FPC) examined reference lists of the relevant reviews and
all identied studies and reviewed the cited literature. Two re-
viewers (FMT and N-BK) independently extracted the data.
Differences about inclusion of studies and interpretation of data
were resolved by arbitration (FPC), and consensus was reached
after discussion with all authors. Of a total of 696 studies identi-
ed from the search (Figure 1), 12 studies in children met the
inclusion criteria and provided suitable data on 13 population
samples to be included in the pooled analysis
w1-w12
(Table 1).
Seven studies
w13-w19
were excluded, as they did not provide suf-
SLEEP DURATION AND WEIGHT
Meta-Analysis of Short Sleep Duration and Obesity in Children and Adults
Francesco P. Cappuccio, MD, FRCP
1
; Frances M. Taggart, PhD
1
; Ngianga-Bakwin Kandala, PhD
1
; Andrew Currie, MB ChB
1
; Ed Peile, FRCP
2
; Saverio
Stranges, MD, PhD
1
; Michelle A. Miller, PhD
1
1
Clinical Sciences Research Institute and
2
Institute of Education, University of Warwick Medical School, Coventry, UK
Background: Recent epidemiological studies suggest that short sleep
duration may be associated with the development of obesity from child-
hood to adulthood.
Objectives: To assess whether the evidence supports the presence
of a relationship between short sleep duration and obesity at different
ages, and to obtain an estimate of the risk.
Methods: We performed a systematic search of publications using
MEDLINE (1996-2007 wk 40), EMBASE (from 1988), AMED (from
1985), CINHAL (from 1982) and PsycINFO (from 1985) and manual
searches without language restrictions. When necessary, authors were
contacted. Criteria for inclusion were: report of duration of sleep as
exposure, BMI as continuous outcome and prevalence of obesity as
categorical outcome, number of participants, age, and gender. Results
were pooled using a random effect model. Sensitivity analysis was per-
formed, heterogeneity and publication bias were also checked. Results
are expressed as pooled odds ratios (OR [95% condence intervals,
CIs]) and as pooled regression coefcients (β; 95% CIs).
Results: Of 696 studies identied, 45 met the inclusion criteria (19 in
children and 26 in adults) and 30 (12 and 18, respectively) were pooled
in the meta-analysis for a total of 36 population samples. They included
634,511 participants (30,002 children and 604,509 adults) from around
the world. Age ranged from 2 to 102 years and included boys, girls,
men and women. In children the pooled OR for short duration of sleep
and obesity was 1.89 (1.46 to 2.43; P < 0.0001). In adults the pooled
OR was 1.55 (1.43 to 1.68; P < 0.0001). There was no evidence of pub-
lication bias. In adults, the pooled β for short sleep duration was -0.35
(-0.57 to -0.12) unit change in BMI per hour of sleep change.
Conclusions: Cross-sectional studies from around the world show a
consistent increased risk of obesity amongst short sleepers in children
and adults. Causal inference is difcult due to lack of control for impor-
tant confounders and inconsistent evidence of temporal sequence in
prospective studies.
Keywords: Sleep duration; obesity; meta-analysis
Citation: Cappuccio FP; Taggart FM; Kandala NB; Currie A; Peile E;
Stranges S; Miller MA. Meta-analysis of short sleep duration and obe-
sity in children and adults. SLEEP 2008;31(5):619-626.
Disclosure Statement
This was not an industry supported study. Dr. Peile has received research
support from Cephalon. The other authors have indicated no nancial con-
icts of interest.
Submitted for publication April, 2007
Accepted for publication February, 2008
Address correspondence to: Francesco P Cappuccio MD MSc FRCP
FFPH FAHA, Cephalon Chair - Cardiovascular Medicine & Epidemiology,
Clinical Sciences Research Institute, Warwick Medical School, UHCW
Campus, Clifford Bridge Road, Coventry CV2 2DX (UK); Tel: +44 24 7696
8662; Fax: +44 24 7696 8660; E-mail: sleepresearch@warwick.ac.uk
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
620
cient data for inclusion (Table 2). In adults 26 studies met the
inclusion criteria, and 18 provided suitable data on 23 popula-
tion samples to be included in the pooled analysis
w20-w37
(Table
3). Eight studies
w38-w45
were excluded as they did not provide
sufcient data for inclusion (Table 4). Note: All references begin-
ning with a W are available in the website version of this paper on the
SLEEP website at www.journalsleep.org
Inclusion Criteria
The main objective was to assess the relationship between
sleep duration and either obesity or body mass index (BMI).
No restriction was placed on populations included. When data
were not readily available from the published reports, we wrote
to authors to ask for raw data.
Meta-Analysis
Exposure: Sleep Duration
Both the nature and quantity of sleep in children is different
from that of adults. There is a gradual change with age and by
age 10, sleep is similar to that of adults but the total time is
longer (10 h).
23
We analyzed the results of studies in children
separately from those in adults. For children the denition of
“short sleep” was <10 h or <10 h per night unless stated other-
wise in Table 1.
In most of the studies in adults, short sleep was dened as
either <5 h or <5 h per night for either average total sleep time
(TST) in 24 h, nighttime sleep, weekday sleep, or based on
a weighted average TST on weekdays and weekends, unless
stated otherwise in Table 3. For odds ratios, short sleepers were
compared to both middle and long sleepers, although in some
studies they were compared to the reference category of either
7 h or 8 h sleep per night.
Outcome: Obesity
Unless stated otherwise (Table 1), obesity in children was de-
ned either as BMI >95
th
percentile according to local national
growth charts or by international growth charts where the thresh-
olds for obesity is dened as the percentile which passes through
BMI >30 kg/m
2
at age 18 years.
w46
Unless stated otherwise (Table
3), obesity in adults was dened as BMI >30 kg/m
2
.
Confounders
Mean age of the populations, proportion of boys and girls,
men and women, and sample size were collected and used in
stratied analyses of heterogeneity, publication bias and sen-
sitivity.
Statistical Analysis
To estimate the quantitative relation between short sleep du-
ration and obesity, we obtained an estimate from each study of
the unadjusted odds ratio (OR) with 95% condence intervals
(CIs) and the unadjusted regression coefcient β (95% CIs) for
BMI as a continuous outcome. Some studies did not report the
unadjusted OR and β for the relationship between short sleep
duration and obesity. We requested from various authors the
unadjusted OR (95% CIs) for <5 h sleep versus >5 h and obe-
sity dened as BMI >30 kg/m
2
, its standard error (SE), and the
exact sample size (N). We also requested the unadjusted β (95%
CIs) for BMI (as a continuous outcome) on sleep duration, its
standard error (SE), and the exact sample size (N). If the SE
of either the OR or β were not supplied, it was algebraically
computed from the 95% CIs. We used a random effect model
and calculated pooled effects (95% CIs) for both OR and β.
A problem which occurs in observational studies is selection
bias and confounding. Selection bias is a feature of the study
design and the possibility of this can be assessed by examining
the methods of the study. Confounding can be due to known or
unknown factors involved in the etiology and are related to both
exposure and outcome variables. We examined possible sourc-
es of heterogeneity between the studies using a meta-regression
Identified
(n=696)
Retrieved for
evaluation
(n=81)
Selected for
review
(n=45)
Included
(n=30 studies)
(36 samples)
Did not meet inclusion
criteria for exposure
and/or outcome
(n=615)
Inadequate data
(n=36)
Data not suitable
for meta-analysis
(n=15)
Children
(12 studies)
(13 samples)
(n=30,002)
Adults
(18 studies)
(23 samples)
(n=604,509)
Figure 1—Flowchart indicating the results of the systematic re-
view with inclusions and exclusions.
Table 1—Description of the Study Populations of Children In-
cluded in the Meta-Analyses (n=30,002)
Author Year Country Sample Age
size (n) (years)
Locard
w1
1992 France 1,031 5
Ben Slama
w2
* 2002 Tunisia 167 6-10
Sekine
w3
2002 Japan 8,941 2-4
Von Kries
w4
2002 Germany 6,645 5-6
Agras
w5
2004 USA 150 9.5
(sleep at 3-5)
Giugliano
w6
2004 Brazil 165 6-10
Padez
w7
2005 Portugal 4,390 7-9
Reilly
w8
2005 UK 6,426 7
(sleep at 2.5)
Chaput (1)
w9
2006 Canada 422 5-10
Chen
w10
† # 2006 Taiwan 656 13-18
Seicean
w11
§ # 2007 USA 509 14-18
Yu (males)
w12
2007 China 273 10-20
Yu (females)
w12
2007 China 227 10-20
Short sleep: * <8 h per day; † <6 h per night or <3 h weekday per
week; § <5 h on school nights;
¶ average week night sleep in hours
# overweight/obesity dened as >85
th
percentile
Note: All references beginning with a W are available in the
website version of this paper on the SLEEP website at www.
journalsleep.org
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
621
Table 2—Description of the Studies in Children Excluded from the Meta-Analyses
Author Year Country Study design
and Popula-
tion
Sample
size (n)
Age
(yr)
Denition
of sleep and
obesity
Outcome
measures
presented
Summary of nd-
ings
Reason for
exclusion
Gupta
w13
2002 USA Cross-sectional
Heartfelt Study
383 11-16 TST BMI >85th
percentile for
age and sex
and % body fat
>25% male or
30% female
Logistic
regression
Obesity and TST β =
−1.62 (0.28 SE) OR:
0.20 (0.11 to 0.34)
Logistic
regression
for OR and β
only-adoles-
cent study
Hui
w14
2003 Hong
Kong
Selected
groups
Student Health
Service
343 6-7 Usual no. of h
sleep
BMI/overweight
by HK reference
categories
% short
sleepers in
3 categories
of BMI
Association be-
tween short sleep
and obesity (%
obese increased in
short sleepers and
decreased in long
sleepers)
Case-control
analysis
Selected by
BMI group
Knutson
w15
2005 USA National Lon-
gitudinal Study
of Adolescent
Health
4,555 grade
7-12
13-18
BMI and usual
no. h sleep
β for sleep
duration
and BMI
Shorter sleep and
obesity
boys β= − 0.08
(−0.12 to −0.03)
girls β = −0.02
(−0.06 to 0.01)
OR from lo-
gistic regres-
sion only.
Eisen-
mann
w16
2006 Australia Australian
Health and Fit-
ness survey
6,324 7-15 Sleep time in
bed at night.
BMI and Waist
by sleep duration
categories
OR
adj
for
age
Dose response
relationship for short
sleep and overweight
in all age groups
(from 7 to 16 yr)
signicant in boys
but not girls.
β
adj
and OR
adj
for age
Dieu
w17
2007 Vietnam Sample of 20
kindergartens
in Ho Chi
Minh City
670 4-6 Obesity by Cole
IOTF denition.
w45
Night sleep time
Prevalence
ratios with
CI
Prevalence ratio
0.85 in univari-
ate regression for
duration of sleep and
overweight. Children
with longer night-
time sleep had lower
risk of obesity.
No OR or β
available
Knutson
w18
2007 USA Cross-sectional
Child De-
velopment
supplement of
Panel Study
of Income
Dynamics
767
boys
779 girls
10-18 2-d time diary
and self-reported
TST. “Over-
weight” >95th
percentile ac-
cording to CDC
and prevention
growth charts
OR
adj
for
self-report-
ed 0.5 to 7
h sleep vs
9.2 to 19.0
h sleep and
obesity.
Self reported short
sleep duration vs
longest sleep OR
adj
=
0.88 (0.45 to 1.69).
However signi-
cantly higher risk
of overweight with
midrange self-re-
ported sleep duration
compared to longest
sleep.
Reported OR-
adj.
No linear
regression.
Snell
w19
2007 USA Longitudinal
Panel Survey
of Income
Dynamics
1,441 3-17 Average nightly
sleep, BMI and
obesity by Cole
et al.
w45
Linear
regression
BMI, non-
linear (%
categories)
and sleep
and wake
timings.
BMI at time 1 sig
corr <8 h sleep
No OR for
short sleep
vs obesity or
β for cross
sectional
analysis.
Only mean
BMI in sleep
duration
categories
TST = Total Sleep Time; SE = standard error; BMI = Body Mass Index; β = regression coefcient; OR = odds ratio; Adj = adjusted; CI =
condence intervals; sd = standard deviation. Note: All references beginning with a W are available in the website version of this paper on the
SLEEP website at www.journalsleep.org
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
622
articles when available and authors were contacted to request
unavailable data or analyses. Details of the studies are sum-
marized in Table 1.
For the meta-analysis sleep exposure was dichotomized for
all studies. Figure 2 shows the Forest plot of 11 observational
studies of short sleep and obesity involving 29,502 children
studied around the world. Seven of 11 studies reported a sig-
nicant association between short duration of sleep and obesity.
The pooled OR was 1.89 (1.46 to 2.43). Publication bias was
not detected by the Begg’s test (P = 0.12) (Figure 3a). The het-
technique. We performed the Breslow-Day test for homogene-
ity of ORs, Cochran-Mantel-Haenszel test for the null hypoth-
esis of no effect (OR=1), and the Mantel-Haenszel common OR
estimate. We assessed publication bias by using a funnel plot
and Begg’s
w47
test to nd out whether there was a bias towards
publication of studies with positive results among the smaller
studies. In order to avoid bias in selection of papers, we tried to
obtain all population studies which had data on the relationship
between sleep duration and obesity which had been published
worldwide and conducted the searches in an unbiased way us-
ing the main medical databases and reference lists from recent
reviews. We also examined the inuence of individual studies,
in which the meta-analysis estimates are derived omitting one
study at a time to see the extent to which inferences depend on
a particular study or group of studies.
RESULTS
Children
Thirteen population samples from 12 studies were included
in the pooled analysis. They included 30,002 participants from
France, Tunisia, Japan, Germany, USA (n = 2), Brazil, Portugal,
United Kingdom, Canada, Taiwan, and China. Age ranged from
2 to 20 years and included boys and girls. Sample sizes ranged
between 150 and 8,941. Data was extracted from the published
Children
Odds Ratio
0.72
1 1.89 11
Combined
Seicean (2007)
Chen (2006)
Chaput (2006)
Reilly (2005)
Padez (2005)
Giugliano (2004)
Agras (2004)
Von Kries (2002)
Sekine (2002)
BenSlama (2002)
Locard (1992)
OR & 95% CI
2.25 (1.27; 3.98)
11.00 (4.75; 25.49)
1.19 (1.00; 1.42)
2.17 (1.57; 3.00)
2.00 (0.80; 5.02)
5.63 (0.72; 44.06)
1.15 (0.93; 1.43)
1.45 (1.20; 1.76)
2.63 (1.24; 5.58)
1.75 (1.28; 2.39)
2.23 (0.87; 5.73)
1.89 (1.46; 2.43)
Figure 2—Forest plot of the associations between short duration
of sleep and obesity in studies carried out in children. OR and 95
CI indicate odds ratio and 95% condence intervals.
0 0.5 1 1.5
-2
0
2
4
Log Odds ratio
SE of log Odds ratio
0 0.5 1
-2
0
2
SE of log Odds ratio
Log Odds ratio
A. Children
B. Adults
Figure 3—Funnel plot for meta-analysis of studies in children (A:
top) and in adults (B: bottom).
Table 3—Description of the Study Populations of Adults Included
in the Meta-Analyses (n=604,509)
Author Year Country Sample Age
size (n) (years)
Vioque
w20
2000 Spain 1,772 15+
Shigeta
w21
* ¶ 2001 Japan 437 43-63
Kripke
w22
2002 USA 497,037 30-102
Cournot
w23
2004 France 3,127 32-62
Hasler
w24
2004 Switzerland 457 27
Bjorkelund
w25
2005 Sweden 1,460 38-60
Gangwisch (1)
w26
2005 USA 3,682 32-49
Gangwisch (2)
w26
2005 USA 3,324 50-67
Gangwisch (3)
w26
2005 USA 2,582 68-86
Singh
w27
2005 USA 3,158 18-65
Moreno
w28
§ 2006 Brazil 4,878 Mean 40
Vahtera
w29
† ‡ 2006 Finland 26,468 Mean 45
Watari (men)
w30
2006 Japan 19,894 20-54
Watari (women)
w30
2006 Japan 5,418 20-54
Kohatsu
w31
2006 USA 990 Mean 48.3
Bjorvatn
w32
2007 Norway 8,860 40-45
Chaput (men)
w33
* ¶ 2007 Canada 323 21-64
Chaput (women)
w33
* ¶ 2007 Canada 417 21-64
Ko
w34
* # 2007 Hong Kong 4,793 17-83
Tuomilehto
w35
* 2007 Finland 2,770 45-74
Fogelholm (men)
w36
* 2007 Finland 3,377 30+
Fogelholm (women)
w36
* 2007 Finland 4,264 30+
Stranges
w37
2008 UK 5,021 44-69
Short sleep: * <6 h or <6 h per day; † <6.5 h or <6.5 h per night; §
<8 h per night; Obesity: ¶ BMI >25 kg/m
2
; # BMI >25 kg/m
2
and/
or waist >80 cm in women and >90 cm in men; ‡ BMI >27 kg/m
2
or >26.4 kg/m
2
; Note: All references beginning with a W are avail-
able in the website version of this paper on the SLEEP website at
www.journalsleep.org
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
623
between hours of sleep per night and BMI. Unlike studies in
children, all studies in adults showed a consistent and signi-
cant negative association between hours of sleep and BMI. The
pooled β was 0.35 (−0.57 to −0.12) unit of change in BMI per
hour of sleep (P = 0.002; heterogeneity P < 0.001). The sensi-
tivity analysis indicated that the omission of any of the studies
led to changes in estimates between −0.30 (−0.51 to −0.09) and
−0.41 (−0.53 to −0.28) (Appendix 3).
DISCUSSION
This study provides for the rst time a systematic review of
the literature and quantitative estimates of the cross-sectional as-
sociations between duration of sleep and obesity (or measures of
obesity) in population-based studies of children and adults around
erogeneity test was signicant (Q = 46.6, df =10, P < 0.001).
The sensitivity analysis indicated that the omission of any of
the studies led to changes in estimates between 1.61 (1.33 to
1.96) and 2.07 (1.54 to 2.79) (Appendix 1).
Adults
Twenty-two population samples from 17 studies met the in-
clusion criteria and provided suitable data for pooled analyses.
They included 604,509 participants from Spain, Japan (n = 2),
USA (n = 5), France, Switzerland, Sweden, Brazil, Finland
(n = 3), Norway, Canada, Hong Kong, and United Kingdom.
Age ranged from 15 to 102 years and included men and women.
Sample sizes ranged between 437 and 497,037. Data were ex-
tracted from the published articles when available and authors
were contacted to request unavailable data or analyses. Details
of the studies included in the meta-analysis are summarized
in Table 3. For the meta-analysis sleep exposure was used in
two ways: as dichotomized variable and as continuous vari-
able regressed over BMI used as continuous variable. Figure 4
shows the forest plot of 22 population samples from 17 obser-
vational studies of short sleep and obesity involving 603,519
adults studied around the world. Seventeen population samples
showed a signicant association between short duration of sleep
and obesity. The pooled OR was 1.55 (1.43 to 1.68). There was
no evidence of publication bias (Begg’s test P = 0.09) (Figure
3b). The heterogeneity test was signicant (Q = 64.0, df = 21,
P < 0.001). The sensitivity analysis indicated that the omission
of any of the studies led to changes in estimates between 1.50
(1.39 to 1.61) and 1.59 (1.44 to 1.76) (Appendix 2).
Figure 5 shows the Forest plot of 7 studies in adults includ-
ing 16,509 participants and reporting regression coefcients (β)
Odds Ratio
0.67
1
1.55 10
Combined
Stranges (2008)
Fogelholm (Women) (2007)
Fogelholm (men) (2007)
Tuomilehto (2007)
Ko (2007)
Chaput (women) (2007)
Chaput (men) (2007)
Bjorvatn (2007)
Watari (women) (2006)
Watari (men) (2006)
Vahtera (2006)
Moreno (2006)
Singh (2005)
Gangwisch3 (2005)
Gangwisch2 (2005)
Gangwisch1 (2005)
Bjorkelund (2005)
Hasler (2004)
Cournot (2004)
Kripke (2002)
Shigeta (2001)
Vioque (2000)
OR & 95% CI
3.36 (2.24; 5.03)
1.98 (1.03; 3.81)
1.52 (1.46; 1.58)
1.38 (0.98; 1.95)
10.80 (0.99; 117.4)
1.52 (0.68; 3.41)
1.84 (1.40; 2.41)
1.38 (1.06; 1.79)
0.95 (0.67; 1.34)
1.70 (1.26; 2.29)
1.22 (1.07; 1.40)
1.43 (1.34; 1.52)
1.96 (1.19; 3.22)
2.98 (0.77; 11.57)
1.87 (1.22; 2.86)
4.01 (1.72; 9.34)
2.65 (1.27; 5.54)
1.30 (1.14; 1.48)
1.30 (1.06; 1.60)
1.46 (1.13; 1.88)
1.75 (1.36; 2.25)
2.02 (1.57; 2.60)
1.55 (1.43; 1.68)
Adults
Figure 4—Forest plot of the associations between short duration of sleep and obesity in studies carried out in adults. OR and 95 CI indicate
odds ratio and 95% condence intervals.
Regression coefficient: β
(unit of BMI per h sleep per night)
-0.86
-0.57 -0.35 -0.12
0
Combined
Stranges (2008)
Kohatsu (2006)
Gangwisch1 (2005)
Bjorkelund (2005)
Hasler (2004)
Cournot (2004)
Vioque (2000)
β
& 95% CI
-0.60 (-0.75; -0.45)
-0.01 (-0.03; 0.00)
-0.45 (-0.71; -0.19)
-0.18 (-0.36; 0.00)
-0.36 (-0.52; -0.20)
-0.52 (-0.86; -0.18)
-0.39 (-0.51; -0.27)
-0.35 (-0.57; -0.12)
Adults
Figure 5—Forest plot of the associations between duration of sleep
and body mass index in studies carried out in adults. β and 95 CI
indicate regression coefcient and 95% condence intervals.
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
624
Table 4—Description of the Studies in Adults Excluded from the Meta-Analyses
Author Year Country Study design
and Popula-
tion
Sample
size (n)
Age
(yr)
Denition
of sleep and
obesity
Outcome
measures
presented
Summary of
ndings
Reason for
exclusion
Heslop
w38
2002 UK Cross-sec-
tional
Employed
men
6,022 <65 Self reported
TST in 24 h and
BMI
Mean BMI for
sleep duration
categories
Shortest sleepers
had higher BMI.
[25.4 (25.2-
25.6) for <7 h to
25.1(24.7-25.4)
for >8 h; P for
trend = 0.02].
No OR for short
sleep vs obesity
or β
Buraz-
eri
w39
2003 Israel Cross-section-
al analysis in
cohort
1,842 50+ Night sleep
duration >8 h
and TST >8 h
and obesity
Cross sectional
analysis for
both long night
sleep duration
and long total
sleep duration
and obesity
No signicant
association be-
tween long sleep
and obesity - no
analysis with
short sleep
No report of
relation between
short sleep and
obesity–only
looked at 8 h+
vs <8 h
Taheri
w40
2004 USA Cross-sec-
tional
Employ-
ees with
oversampling
of habitual
snorers.
1,024 30-60 Average nightly
sleep from 6-d
diary and BMI
β
adj
for average
nightly sleep
and BMI.
Mean BMI with
se for sleep dura-
tion groups
No OR for short
sleep vs obesity
or β
Tama-
koshi
w41
2004 Japan Japan Collab-
orative Cohort
Study
43,852 men
60,158
women
40-9 Average sleep
duration on
weekdays and
BMI
BMI (SD)
for each of 7
sleep duration
categories from
<4 h to 10 h+
No test for trend No odds ratio
for short sleep
vs obesity or
regression coef-
cient. Only
mean BMI in
sleep duration
categories
Ohayon
w42
2005 France Telephone
survey fol-
lowed by
interviews
1,026 60+ Self-reported
sleep duration
and height and
weight
OR for risk of
short sleep (≤4
h30) among
obese people
(BMI>27)
compared to
people with
normal BMI
Obese people
were more likely
to have the short-
est sleep. OR
for risk of short
sleep (≤4 h 30)
among obese
people compared
to people with
normal BMI.
OR = 3.6 (1.0 to
13.1)
OR not compa-
rable because
analysis does
not include full
range of BMI as
outcome.
Vorona
w43
2005 USA Primary care
population
924 18-91 Self-reported
TST in 24 h for
weekday and w/
end weighted for
number of days.
Self-reported
weight and
height
BMI in 4
groups.
ANOVA
Obese partici-
pants slept less
than individu-
als who were
overweight (P
= 0.04) or had
normal BMI (P =
0.004).
No OR for short
sleep vs obesity
or β
Patel
w44
2006 USA Nurses Health
Study
68,183 30-55 BMI and self-
reported h sleep
in 24 h
BMI and SE
for sleep dura-
tion categories
Short sleep-
ers had higher
BMI; P for trend
<0.000)
No OR for short
sleep vs obesity
or β.
Meis-
inger
w45
2007 Germany MONICA
cohort
3,508 men
3,388
women
45-74 BMI h nighttime
sleep
BMI (SD) by 5
sleep duration
categories
BMI higher for
<5 h sleep and
6 h.
No OR for short
sleep vs obesity
or β
*BMI≥ 25 and/or waist ≥80 cm in women or ≥90 cm in men
Note: All references beginning with a W are available in the website version of this paper on the SLEEP website at www.journalsleep.org
Sleep Duration and Obesity—Cappuccio et al
SLEEP, Vol. 31, No. 5, 2008
625
or by physical illnesses associated with pain—hence disrupted
sleep—and severe limitation in energy expenditure through lim-
ited physical activity. More recent studies have adjusted for these
potential confounders and found no prospective association.
22
Our study does not allow us to study mechanism. Howev-
er, it has been suggested that short sleep may lead to obesity
through the activation of hormonal responses
33
leading to an
increase in appetite and caloric intake. Short sleep is associated
to reciprocal changes in leptin and ghrelin.
33
This in turn would
increase appetite and contribute to the development of obesity.
The evidence in humans comes from short-lived severe sleep
deprivation experiments
35,36
that cannot be extrapolated to long
term effects in the population.
Activation of inammatory pathways by short sleep may
also be implicated in the development of obesity.
37
Finally, it is
not inconceivable that short sleep is just a marker of unfavor-
able health status and of lifestyle characteristics.
38,39
The potential public health implications of a causal relation-
ship between short duration of sleep and obesity have already
been widely disseminated in the media. The ndings of our anal-
ysis suggest that whilst sustained sleep curtailment and ensuing
excessive daytime sleepiness are undoubtedly cause for concern,
the link to obesity is of interest but still to be proven as a causal
link. Many questions still need an answer to determine causal-
ity. Prospective studies in which weight, height, waist measure-
ments. and adiposity are measured at baseline and again at subse-
quent data collection times together with more accurate objective
measurement of sleep duration (including naps) and confounding
factors or mediators such as depression are needed.
Further prospective studies with improved assessment of
long-term exposure (repeated self-reported sleep duration or
repeated actigraphy), more specic outcomes (including mea-
sures of adiposity) and better control for confounders are need-
ed before causality can be determined.
ACKNOWLEDGMENTS
We thank Drs. Agras, Bjorkelund, Chaput, Chen, Cournot,
Gangwisch, Giugliano, Hasler, Kripke, Locard, Sekine and
Vioque for supplying additional data or analyses not available
in the published article. This work is part of the Programme
‘Sleep, Health & Society’ of the University of Warwick.
Authors’ Contribution
FPC conceived the study aims and design, contributed to the
data extraction, planned the analysis, interpreted the results and
drafted the nal version of the paper. FMT carried out the sys-
tematic review and contributed to data extraction and analysis.
N-BK contributed to data extraction and carried out the statistical
analysis. AC, SS and MAM contributed to the systematic review,
analysis and interpretation. EP contributed to interpretation of re-
sults. All authors contributed to the revision of the manuscript.
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To collect relevant literature on the impact of rural residents' sleep quality on the incidence of agricultural injuries, the search time range is 1990~2019. We use RevMan 5.3 software for statistical processing. A total of 7 articles were included. Meta-analysis showed that sleep quality was closely related to agricultural injury. The combined effect was 1.49, 95%CI [1.31, 1.70], Z = 5.93, P < 0.00001. The difference was statistically significant, so poor sleepers had a higher incidence of agricultural injuries than good sleepers. Sleep disturbances and agricultural injuries are two common and significant health problems. Investigations suggest that sleep might increase the risk of agricultural injuries. The aim of the present study was to systematically review and meta-analyze the predictive effect of sleep on agricultural injuries.
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Computerspiele haben sich als integrativer Bestandteil von Kultur und Freizeitverhalten Jugendlicher etabliert. Nach einem Überblick zum konkreten Nutzungsverhalten werden zunächst mögliche Vorteile des Computerspielens für das soziale und psychologische Wohlbefinden Jugendlicher, sowie bezüglich kognitiver Aspekte betrachtet. Hinsichtlich einer gesundheitsschädlichen Nutzung wird plädiert, zwischen exzessivem, problematischen und süchtigen Computerspielens zu unterscheiden und für die Jugendarbeit sinnvolle diagnostische Kriterien zu finden, die eine Loslösung der Fixierung auf reine Nutzungszeiten begünstigen. Nach aktuellem Forschungsstand scheint international etwa einer von 22 Jugendlichen von einer Computerspielsucht betroffen zu sein. Existierende Prävalenzahlen für Luxemburg und seine Anrainerstaaten werden ebenfalls berichtet. Individuelle, soziale, sowie spiel- und nutzungsbezogenen Risikofaktoren erhöhen die Wahrscheinlichkeit einer Suchtentwicklung. Die daran beteiligten psychologischen Mechanismen werden überblicksartig aus einer Kompensations-, einer Bedürfnis- und einer integrativ biopsychosozialen Perspektive beleuchtet. Nach einem kurzen Überblick über den wachsenden Überlappungsbereich zwischen Glücksspiel und Computerspielen mit seinen Herausforderungen für den Jugendschutz werden Implikationen für die Prävention problematischen Spielens bei Jugendlichen diskutiert.
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Background : Childhood obesity (CHO) is a serious global health challenge affecting both developed and developing nations. The feats attained in addressing this global health challenge can be reflected through the top-cited studies. The study's aim was to analyse the features of the 100 top-cited articles concerning CHO. Methods : We used a bibliometric analysis, and searched for relevant articles from the Web of Science (December 1, 2021), using an appropriate keyword search-strategy ((child OR childhood OR children OR adolescent) AND (obese OR obesity OR overweight)). The retrieved documents were analysed based on the citation number, publication year, authorship, institution, journal and country. The analyses were performed mainly by the Bibliometrix application (using R-studio cloud) and HistCite. Results : The 100 top-cited articles were published between 1976 and 2018, their citations ranged from 365 to 10 789, with a mean citation of 1 146.2 and 31 studies had more than 1 000 citations. The articles were published in 31 journals, with the “Pediatrics” journal having the most publications (n = 18). The studies were from 12 countries, with the most-productive being the USA (n = 68), followed by the United Kingdom (n = 12) and France (n = 3). The leading institution was the University of Bristol (n = 8), while Dietz WH (n = 12), and Flegal KM (n = 8) were the most productive authors. The most common research fields covered by these articles were; “General Internal Medicine” (n = 34), “Pediatrics” (n = 29), and “Nutrition Dietetics” (n = 18). The study noted significant correlations between the total article citation and the number of authors (R = 0.203), countries involved (R = 0.407), institutions (R = 0.407), and the publication year (R = 0.847), all with P < 0.001. Conclusions : Through these top-cited articles, this analysis provides discernment into the historical advancements, including the prime roles performed by various stakeholders in addressing the issue of CHO. However, Asian countries’ contribution is not adequately reflected in these articles, and thus more focus and funding for CHO research are needed for effectual prevention and control.
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Although childhood obesity may have detrimental consequences for childhood self-esteem, the prevalence and magnitude of this problem is controversial. In addition, the social and emotional effects of decreased self-esteem in obese children are unknown. A total of 1520 children, 9 to 10 years of age, born to mothers in the National Longitudinal Survey of Youth were studied. Comprehensive demographic data including race and family income were available in 97% of the cohort. Self-esteem was measured using Self-Perception Profile for Children. The 4-year follow-up Self-Perception Profile for Children scores were available in 79% of the children. Obesity was defined as a body mass index greater than the 95th percentile for age and gender. Additional data include a self-administered questionnaire at 13 to 14 years of age concerning emotional well being, smoking, and alcohol consumption. Data were stratified by race and gender. The data were weighted to reflect a nationally representative sample of children born to mothers 17 to 28 years of age. Scholastic and global self-esteem scores were not significantly different among 9- to 10-year-old obese and nonobese children. However, over the 4-year period, obese Hispanic females and obese white females showed significantly decreased levels of global self-esteem compared with nonobese Hispanic females and nonobese white females, respectively. Mild decreases in self-esteem also were observed in obese boys compared with nonobese boys. As a result, by 13 to 14 years of age, significantly lower levels of self-esteem were observed in obese boys, obese Hispanic girls, and obese white girls compared with their nonobese counterparts. Decreasing levels of self-esteem in obese children were associated with significantly increased rates of sadness, loneliness, and nervousness compared with obese children whose self-esteem increased or remained unchanged. In addition, obese children with decreasing levels of self-esteem over the 4-year period were more likely to smoke and drink alcohol compared with obese children whose self-esteem increased or remained unchanged. Obese Hispanic and white females demonstrate significantly lower levels of self-esteem by early adolescence. In addition, obese children with decreasing levels of self-esteem demonstrate significantly higher rates of sadness, loneliness, and nervousness and are more likely to engage in high-risk behaviors such as smoking or consuming alcohol.
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Among Harvard alumni aged 35–74 in 1962 or 1966, incidence rates of physician-diagnosed depression, together with suicide rates, were examined during a 23–27-year follow-up period, by antecedent physical activity habits and other personal characteristics. A total of 387 first attacks of depression developed among 10,201 alumni who survived through 1988; 129 suicides occurred among 21,569 alumni during follow-up through 1988. Depression rates were lower among the physically active and sports players, higher among cigarette smokers, unrelated to alcohol consumption, and higher among alumni reporting such personality traits as insomnia, exhaustion, cyclothymia, and self-consciousness. Suicide rates were largely unrelated to antecedent physical activity and alcohol consumption, higher among smokers, and substantially higher among men reporting the personality traits that predicted increased rates of depression. Report number LIII in a series on chronic disease in former college students.
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The Johns Hopkins Precursors Study, a long-term prospective study, was used to study the relation between self-reported sleep disturbances and subsequent clinical depression and psychiatric distress. A total of 1,053 men provided information on sleep habits during medical school at The Johns Hopkins University (classes of 1948-1964) and have been followed since graduation. During a median follow-up period of 34 years (range 1-45), 101 men developed clinical depression (cumulative incidence at 40 years, 12.2%), including 13 suicides. In Cox proportional hazards analysis adjusted for age at graduation, class year, parental history of clinical depression, coffee drinking, and measures of temperament, the relative risk of clinical depression was greater in those who reported insomnia in medical school (relative risk (RR) 2.0, 95% confidence interval (CI) 1.2-3.3) compared with those who did not and greater in those with difficulty sleeping under stress in medical school (RR 1.8, 95% CI 1.2-2.7) compared with those who did not report difficulty. There were weaker associations for those who reported poor quality of sleep (RR 1.6, 95% CI 0.9-2.9) and sleep duration of 7 hours or less (RR 1.5, 95% CI 0.9-2.3) with development of clinical depression. Similar associations were observed between reports of sleep disturbances in medical school and psychiatric distress assessed in 1988 by the General Health Questionnaire. These findings suggest that insomnia in young men is indicative of a greater risk for subsequent clinical depression and psychiatric distress that persists for at least 30 years.
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To determine the incidence and remission rates of insomnia in older adults according to race and associated risk factors in a three-year longitudinal study. 2,971 men and women, aged 65 years and older, completed questionnaires administered by trained interviewers at baseline and three years later. Data concerning difficulty falling asleep or early morning arousal (insomnia), along with self-reports of physical disability, respiratory symptoms, depressive symptomatology, perceived health status, and use of prescribed sedative medication, were collected and analyzed. Overall, 15% of the participants without symptoms of insomnia at baseline reported chronic difficulty falling asleep or early morning arousal three years later in follow-up interviews. African-American women had a significantly (p < 0.01) higher incidence of insomnia (19%) compared with African-American men (12%) or with white men and women (both 14%). Men were more likely than women to no longer report symptoms at follow-up (64% vs 42%; p < 0.01). For both races, the presence of depressed mood was a risk factor for the incidence of insomnia, and the absence of depressed mood was a predictor of remission. Insomnia occurs more frequently in African-American women than in African-American men or than in white men or women. Regardless of race, women are less likely than men to resolve their insomnia. The high prevalence and incidence of morbidity in elderly African-American women may contribute to their high rate of insomnia.
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Sleep is often assessed in circadian rhythm studies and long-term monitoring is required to detect any changes in sleep over time. The present study aims to investigate the ability of the two most commonly employed methods, actigraphy and sleep logs, to identify circadian sleep/wake disorders and measure changes in sleep patterns over time. In addition, the study assesses whether sleep measured by both methods shows the same relationship with an established circadian phase marker, urinary 6-sulphatoxymelatonin. A total of 49 registered blind subjects with different types of circadian rhythms were studied daily for at least four weeks. Grouped analysis of all study days for all subjects was performed for all sleep parameters (1062-1150 days data per sleep parameter). Good correlations were observed when comparing the measurement of sleep timing and duration (sleep onset, sleep offset, night sleep duration, day-time nap duration). However, the methods were poorly correlated in their assessment of transitions between sleep and wake states (sleep latency, number and duration of night awakenings, number of day-time naps). There were also large and inconsistent differences in the measurement of the absolute sleep parameters. Overall, actigraphs recorded a shorter sleep latency, advanced onset time, increased number and duration of night awakenings, delayed offset, increased night sleep duration and increased number and duration of naps compared with the subjective sleep logs. Despite this, there was good agreement between the methods for measuring changes in sleep patterns over time. In particular, the methods agreed when assessing changes in sleep in relation to a circadian phase marker (the 6-sulphatoxymelatonin (aMT6s) rhythm) in both entrained (n = 30) and free-running (n = 4) subjects.