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Short-term sleep loss decreases physical activity under free-living conditions but does not increase food intake under time-deprived laboratory conditions in healthy men

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Short sleep duration is correlated with an increased risk of developing obesity and cardiovascular disease, but the mechanisms behind this relation are largely unknown. We aimed to test the hypothesis that acute sleep loss decreases physical activity while increasing food intake, thereby shifting 2 crucial behavioral components of energy homeostasis toward weight gain. In 15 healthy, normal-weight men, spontaneous physical activity was registered by accelerometry during the entire experiment, and food intake as well as relevant hormones were assessed during a 15-h daytime period after 2 nights of regular sleep (bed time: 2245-0700) and after 2 nights of restricted sleep (bed time: 0245-0700). Experiments were performed in a crossover design. Sleep restriction significantly decreased physical activity during the daytime spent under free-living conditions after the first night of sleep manipulation (P = 0.008). Also, intensities of physical activity were shifted toward lower levels, with less time spent with intense activities (P = 0.046). Total energy intake, feelings of hunger, and appetite as well as ghrelin and leptin concentrations during day 2 remained unaffected by acute sleep restriction. In contrast to our expectation, short-term sleep loss neither increased food intake nor affected concentrations of the hunger-regulating hormones leptin and ghrelin. However, the observed decrease in daytime physical activity may point to another potentially important behavioral mechanism for the health-impairing influence of sleep loss.
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Short-term sleep loss decreases physical activity under free-living
conditions but does not increase food intake under time-deprived
laboratory conditions in healthy men
1–4
Sebastian M Schmid, Manfred Hallschmid, Kamila Jauch-Chara, Britta Wilms, Christian Benedict, Hendrik Lehnert,
Jan Born, and Bernd Schultes
ABSTRACT
Background: Short sleep duration is correlated with an increased
risk of developing obesity and cardiovascular disease, but the mech-
anisms behind this relation are largely unknown.
Objective: We aimed to test the hypothesis that acute sleep loss
decreases physical activity while increasing food intake, thereby
shifting 2 crucial behavioral components of energy homeostasis
toward weight gain.
Design: In 15 healthy, normal-weight men, spontaneous physical
activity was registered by accelerometry during the entire experi-
ment, and food intake as well as relevant hormones were assessed
during a 15-h daytime period after 2 nights of regular sleep (bed
time: 2245–0700) and after 2 nights of restricted sleep (bed time:
0245–0700). Experiments were performed in a crossover design.
Results: Sleep restriction significantly decreased physical activity
during the daytime spent under free-living conditions after the first
night of sleep manipulation (P= 0.008). Also, intensities of physical
activity were shifted toward lower levels, with less time spent with
intense activities (P= 0.046). Total energy intake, feelings of hun-
ger, and appetite as well as ghrelin and leptin concentrations during
day 2 remained unaffected by acute sleep restriction.
Conclusions: In contrast to our expectation, short-term sleep loss
neither increased food intake nor affected concentrations of the
hunger-regulating hormones leptin and ghrelin. However, the ob-
served decrease in daytime physical activity may point to another
potentially important behavioral mechanism for the health-impairing
influence of sleep loss. Am J Clin Nutr 2009;90:1476–82.
INTRODUCTION
The decrease in average sleep duration over the past century
(1) has been paralleled by an increase in the prevalence of obesity
(2, 3). Epidemiologic studies indicate an inverse relation between
sleep duration and body mass index (BMI) in adults (4, 5) as well
as in children and adolescents (6, 7). Although the relation be-
tween sleep loss and disturbances of energy homeostasis is the
subject of extensive debate (8–10), only a few experimental
studies have addressed the basis of the connection between sleep
loss and risk factors for obesity. In a seminal study, Spiegel et al
(11) showed that 2 consecutive nights of sleep restriction to 4 h
instead of 10 h of sleep induced an 18% reduction in circulating
concentrations of the hunger-suppressing hormone leptin in
conjunction with a 24% elevation in concentrations of the
appetite-stimulating hormone ghrelin. These hormonal changes
were paralleled by markedly increased feelings of hunger and
appetite. However, most recently, subchronic sleep restriction
to 5 h/night for 14 d has been shown to not affect total energy
intake and orexigenic/anorexigenic hormone balance, yielding
only a relative increase in snack intake and raising the question of
whether other critical factors of energy homeostasis may be
sensitive to decreases in sleep duration (12). We investigated the
effects of short-term sleep loss on spontaneous physical activity.
We hypothesized that sleep restriction decreases physical ac-
tivity, thus favoring a positive energy balance and, in the long run,
weight gain. Surprisingly, to our knowledge, this hypothesis,
although very plausible, has not been examined in humans so far.
We also expected an orexigenic effect of sleep restriction on
spontaneous food intake and circulating concentrations of leptin
and ghrelin that were also assessed in our experiments.
SUBJECTS AND METHODS
Subjects
The study was carried out in a crossover design in 15 healthy,
normal-weight men [mean (6SEM) BMI (in kg/m
2
): 22.9 60.3]
aged 20–40 y (27.1 61.3 y) with a regular sleep-wake cycle
during the 4 wk before the experiments. They were recruited via
ads and flyers stating the inclusion criteria. A standardized inter-
view on sleep habits revealed a habitual sleep duration of 459 6
7 min (range: 450–540 min) with bedtime starting between
2200 and 0000 and wake-up time from 0600 to 0800. Exclusion
1
From the Departments of Internal Medicine I (SMS, BW, HL, and BS),
Neuroendocrinology (MH and JB), and Psychiatry (KJ-C), the University of
Luebeck, Luebeck, Germany; the Department of Neuroscience and Func-
tional Pharmacology, Uppsala University, Uppsala, Sweden (CB); and the
Interdisciplinary Obesity Center, St Gallen, Switzerland (BS).
2
The Deutsche Forschungsgemeinschaft had no influence on the design
and conduct of the study; collection, management, analysis, and interpreta-
tion of the data; and preparation, review, or approval of the manuscript.
3
Supported by the Deutsche Forschungsgemeinschaft, KFO 126 (“Selfish
Brain”).
4
Address correspondence to SM Schmid, Department of Internal Medi-
cine I, University of Luebeck, Ratzeburger Allee 160, House 32, 23538
Luebeck, Germany. E-mail: sebastian.schmid@uk-sh.de.
Received April 26, 2009. Accepted for publication September 20, 2009.
First published online October 21, 2009; doi: 10.3945/ajcn.2009.27984.
1476 Am J Clin Nutr 2009;90:1476–82. Printed in USA. Ó2009 American Society for Nutrition
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criteria were chronic or acute illness, current medication of any
kind, smoking, alcohol or drug abuse, obesity, anddiabetes in first-
degree relatives. Also, subjects who displayed a high level of
cognitive control in the Three-Factor Eating Questionnaire (13)
were excluded to ensure unrestrained eating behavior. Subjects
were not informed about the assessment of eating behavior but
were told that the study would focus on the effects of sleep re-
striction on cognitive functions that were repeatedly assessed
throughout the experiments with a battery of cognitive tests (the
results of these tests are not reported here). Thus, subjects were
unaware that their food intake was monitored during the study.
The study protocol was approved by the Ethics Committee on
Research Involving Humans at the University of Luebeck, and all
participants gave written informed consent before participation.
The initial recruitment date was 1 April 2006.
Study design and procedure
After an adaptation night in the laboratory that included
standard polysomnographical recordings, participants were
tested 6 wk apart in a counterbalanced crossover design on 2
conditions. The experiments included 2 consecutive nights of 4
h of sleep (“4-h sleep”) and 2 consecutive nights of 8 h of sleep
(“8-h sleep”). Experimental day 1 followed the first 4-h-sleep/8-
h-sleep night and was spent under free-living conditions outside
the laboratory with physical activity measured by accelerometry
throughout the day. Thereafter, the second night of sleep ma-
nipulation was performed. After the second 4-h-sleep/8-h-sleep
night, the assessment of physical activity, spontaneous food
intake, blood variables, and symptom ratings was performed in
a 15-h time-deprivation laboratory setting (day 2; see below).
Before each experimental night, subjects arrived at the re-
search unit at 2000. They had been instructed to eat a light dinner
before arrival. Thereafter, only water was allowed to be drunk
until the next morning. After the installment of polysomno-
graphic and accelerometric recordings, subjects went to bed and
lights were turned off at 2245 in the 8-h-sleep condition. In the
4-h-sleep condition, subjects remained awake in a sitting posi-
tion until 0245. They were allowed to read and to watch
nonstimulating movies. Brisk physical activities were avoided,
and subjects were constantly monitored by the experimenters.
After each experimental night in both conditions, subjects were
awakened at 0700.
After the first of the 2 consecutive experimental nights (night
1), subjects in both conditions received a standard breakfast and
were allowed to leave the laboratory (day 1). They were
instructed to not deviate from their usual eating habits and to
avoid intense physical activities (eg, working out) and naps
during the day. Subjects reported back to the laboratory at 2000 to
be prepared for experimental night 2, the procedure and setting of
which were identical to night 1.
After waking up the subjects at 0700 at the end of night 2, the
laboratory assessment day (day 2) started. An intravenous
catheter was inserted into a vein of the subject’s nondominant
distal forearm to allow the drawing of blood samples at 0740 and
thereafter at 1-h intervals between 0800 and the end of the ex-
periment at 2300. Immediately before each blood drawing, on
a semi-quantitative questionnaire subjects rated from 0 (none) to
9 (severe) each of the following 6 symptoms: hunger, appetite,
satiety, activity, weakness, and tiredness. During the 15-h day-
time assessment period (0800–2300), subjects were deprived of
all time cues by removing all clocks, radios, and other time
indicators from the sound-attenuated laboratory room (with
adjacent bathroom) that had no natural light. Subjects were
allowed to read and to play video games.
Sleep recordings
Recordings were performed with a Nihon Kohden amplifier
(EEG 4400 series; Nihon Kohden GmbH, Rosbach, Germany)
and were scored offline according to standard criteria (14). The
following sleep variables were determined: total sleep time, time
spent in sleep stage 1, 2, 3, 4, and slow-wave sleep (ie, sleep stage
3 + 4) and in REM sleep (all in min and % of total sleep time),
time spent awake after sleep onset, and movement time (in % of
total sleep time).
Physical activity
Physical activity was assessed by standard accelerometric
recordings of wrist activity (Acti-Watch; Cambridge Neuro-
technology, Cambridge, United Kingdom). Recordings of 3
subjects had to be excluded from analyses for technical reasons,
ie, because of insufficient recording quality in at least one
condition. Analyses of daytime physical activity included the
time interval from 0800 to 2000 during day 1 (ie, under free-
living conditions after night 1) and during day 2 (ie, when
subjects stayed in the laboratory). The sampling interval was 1
min with a subsequent reduction of activity counts (AC) to 5-min
intervals. Total activity counts comprise the sum of activity
counts registered on the respective day. The intensity of physical
activity was grouped into low (below mean: AC/5 min 21 SD),
middle (within mean: AC/5 min 61 SD), and high (above
mean: AC/5 min + 1 SD) activity with reference to the re-
cordings under the 8-h-sleep condition on day 1.
Food intake
At 0800 on day 2, subjects were presented with a large
standardized breakfast buffet (5060 kcal) from which they were
allowed to eat ad libitum. At 1100, the breakfast buffet was
replaced by a snack buffet (5010 kcal at first serving), which
remained in the experimental room until the end of the day (at
2300). Buffet components were refilled whenever necessary. In
addition to the snack buffet, subjects could select main meals
from a menu (1200 kcal at first serving) whenever they wanted to
from 1100 on (for details on buffets and main meals, see Table
1). Food intake was measured outside the experimental room by
weighing buffet components before serving and after clearing
the table. Analyses of food intake were performed for the whole
experimental day and separately for the standardized breakfast
period (0800–1100) as well as for the snack buffet/main meals
period (1100–2300). Nutritional analyses were performed with
the use of a software program for macronutrient analyses (DGE-
PC professional 3.3; Stuttgart, Germany), and buffet compo-
nents and main meals were also analyzed with regard to
different food categories according to recommendations of the
German Nutrition Society (15). On the basis of the power cal-
culations inferred from previous experiments on food intake
(16), the sample size of 15 was sufficient to detect a significant
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difference in food intake, assuming a study power of 0.95 at
a significance level of 0.05.
Assays
Concentrations of serum leptin (Human Leptin RIA kit; Linco
Research, St Charles, MO) and plasma ghrelin [ghrelin (total)
RIA kit; Linco Research] were determined from stored (280°C)
samples by radioimmunoassays.
Statistical analyses
All values are expressed as means 6SEMs. Analyses of sleep
data were based on an analysis of variance (ANOVA) for re-
peated measures, including the factors “condition” (for 4 h
compared with 8 h of sleep) and “night” (for night 1 compared
with night 2). Analyses of physical activity data were based on
ANOVA for repeated measures, including the factors “condi-
tion” and “day” (for day 1 compared with day 2) and “activity
level” (low-, middle-, and high-intensity physical activity).
Analyses of hormonal data as well as symptom ratings were
based on ANOVA for repeated measures, including the factors
condition and time (for repeated measurements during day 2).
Pairwise comparisons of single timepoint values were performed
by using the Student’s ttest. Analyses of food intake were based
on ANOVA for repeated measures, including the factors con-
dition and macronutrients (for the respective macronutrient
composition of ingested food). Pairwise comparisons of single
macronutrients as well as food categories were performed by
using the Student’s ttest. A Pvalue ,0.05 was considered
significant. Analyses were run with SPSS 12.0 for Windows
(SPSS Inc, Chicago, IL).
RESULTS
Sleep
During both nights of the 4-h-sleep condition, subjects slept on
average 229 min less than in the 8-h-sleep condition [236 62
compared with 465 64 min (first night) and 238 61 compared
with 467 63 min (second night) for the 4-h and 8-h conditions,
respectively; P,0.001 for the condition main effect]. The
longer sleep duration in the 8-h-sleep condition was primarily
due to more pronounced shallow sleep—ie, S1 and S2—as well
as REM sleep.
Physical activity and related self-reports
Overall analyses of accelerometric activity data obtained on
days 1 and 2 revealed distinctly lower cumulative daytime AC in
the 4-h-sleep than in the 8-h-sleep condition (P= 0.021 for the
condition main effect). In general, subjects were markedly more
active under the free-living conditions of day 1 than under the
laboratory conditions of day 2 (P,0.001 for the day main
effect) with this difference being independent of the sleep
condition (P= 0.12 for the condition ·day interaction effect;
Figure 1A). Separate analyses of day 1 and day 2 revealed dis-
tinctly lower cumulative AC after 4 h than after 8 h of sleep during
day 1 (43,622 64713 compared with 50,190 64554; P= 0.008),
whereas on day 2, against the background of overall greatly de-
creased activity, this difference was not significant (14,688 6
1814 compared with 16,177 61799; P= 0.52). Including the
factor activity level in the ANOVA models revealed a significant
condition ·activity level interaction on day 1 (P= 0.046). Here,
subjects in the 4-h-sleep condition displayed a significantly higher
proportion of low-intensity activities (57.6 64.5% compared with
52.3 63.5%; P= 0.016) and a lower proportion of high-intensity
TABLE 1
Food components of breakfast buffet, snack buffet, and main meals
1
Postbreakfast
Breakfast buffet Snack buffet Main meals
Whole-wheat bread (1) Fresh fruit (3) Potatoes (1)
Wheaten bread (1) Strawberry yogurt (4) Rice (1)
White biscuit (1) Chocolate bar with waffle (8) Boiled vegetable (2)
Fresh fruit (3) Milk chocolate (8) Tomato salad (2)
10%-Fat condensed milk (4) Chocolate chip biscuits (8) Pork cutlet (5)
Cheese (4) Cereal bar (8) Roast chicken (5)
Cream cheese (4) Wine gum (8) Sauce (6)
Cream cheese with herbs (4) Toffee with chocolate (8) Salad dressing (6)
Curd cheese with fruit (4) Potato chips (9)
Pudding (4) Peanut chips (9)
Poultry sausage (5) Salted peanuts (9)
Salami (pork sausage) (5) Wheat chips (9)
Butter (6) Licorice (9)
Jam (8) Orange juice (7)
Chocolate cream (8)
Honey (8)
Sugar (8)
1
Numbers in parentheses indicate the following food categories according to the German Nutrition Society: (1)
starchy foods; (2) vegetables; (3) fruit; (4) milk and milk products; (5) meat, sausage, eggs, and fish; (6) butter, oil, and
dressing; (7) beverages; (8) sweets; and (9) salty snacks. In addition to the listed nutrients, subjects were served water, tea,
and decaffeinated coffee ad libitum.
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activities (22.6 63.5% compared with 25.4 63.4%; P= 0.044)
than after 8 h of sleep (Figure 1B). Throughout day 2, the self-
reported level of activity was lower after the 2 nights of 4-h sleep
than after the 2 nights of 8-h sleep (P= 0.002 for the condition
main effect; Figure 1C). A roughly corresponding pattern was
revealed for feelings of weakness (P= 0.019) and tiredness (P=
0.001; Figure 1D).
Food intake, ratings of appetite, hunger, and satiety and
related hormones
Total energy intake during the entire experimental day 2 did not
differ between the 4-h-sleep and the 8-h-sleep condition (Table 2).
Although subjects consumed relatively more fat in the 4-h-sleep
than in the 8-h-sleep condition and analyses of food categories
revealed a higher intake of food belonging to the category “fat” in
the 4-h-sleep condition (394 643 compared with 305 646 kcal;
P= 0.029), ANOVA did not reveal a significant condition ·
macronutrient interaction (P= 0.31). Note that consumption of
sweet and salty snacks did not differ between the 4-h-sleep and the
8-h-sleep condition (1169 6151 compared with 1246 6184 kcal
and 461 6101 compared with 520 698 kcal, respectively; P.
0.54). Separate analyses of the breakfast buffet and postbreakfast
energy intake again did not reveal any differences in energy intake
or macronutrient composition (all P.0.64), except for a trend
toward increased fat intake after breakfast in the sleep loss con-
dition (P= 0.06; Table 2).
Ratings before breakfast did not show any differencesin appetite
(5.4 60.5 compared with 4.7 60.5; P= 0.17), hunger (4.7 60.5
compared with 4.4 60.6; P= 0.68), and satiety (1.3 60.3
compared with 1.0 60.5; P= 0.62) between the 4-h-sleep and 8-h-
sleep condition. During breakfast, ratings of appetite (Figure 2A)
and hunger decreased rapidly and remained at rather low levels for
the rest of the experiment (P,0.001 for the time main effects and
P.0.29 for the condition ·time interaction of both variables).
Satiety ratings mirrored those of appetite and hunger and likewise
FIGURE 1. Mean (6SEM) accelerometry-derived and self-reported measures of activity after 1 night of sleep manipulation, assessed under free-living
conditions (day 1), and after 2 nights of sleep manipulation, assessed in the laboratory setting (day 2). (A) Total physical activity counts as derived from
accelerometry on day 1 and day 2. (B) Low, moderate, and high physical activity on day 1 (n= 12). (C) Self-reported level of activity on day 2. (D) Feelings of
tiredness on day 2 (n= 15). During the 2 ·2 nights of sleep manipulation done in a crossover design, healthy men had regular sleep of 8 h (open bars/open
circles) or were allowed to sleep for only 4 h (solid bars/solid circles).
t
P,0.10, *P,0.05, **P,0.01 (all Pvalues derived from Student’s ttests).
TABLE 2
Total food intake during day 2 and food intake separated for standardized breakfast and the postbreakfast period
1
Total Breakfast Snacks and main meals
4-h sleep 8-h sleep P4-h sleep 8-h sleep P4-h sleep 8-h sleep P
Total energy intake (kcal) 3969 6258 4070 6285 0.70 1471 6121 1498 6127 0.83 2496 6220 2572 6237 0.71
Fat (%) 35.7 61.1 34.0 61.3 0.05 42.5 62.0 42.0 633.1 0.68 31.0 61.0 28.3 61.8 0.06
Carbohydrate (%) 50.5 61.1 51.7 61.9 0.45 42.8 62.4 43.0 62.7 0.90 55.8 61.2 57.8 62.6 0.44
Protein (%) 13.8 60.7 14.3 60.8 0.61 14.7 60.6 15.0 60.6 0.58 13.3 61.0 13.9 61.3 0.72
1
All values are means 6SEMs. n= 15. Pvalues are derived from Student’s ttests. ANOVA did not show significant condition ·macronutrient
interactions.
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did not display differences between conditions (P,0.001
for time, P= 0.44 condition ·time, and P.0.11 for pairwise
single-timepoint comparison of respective symptom ratings dur-
ing day 2). As shown in Figure 2, B and C, sleep restriction had
no effect on serum and plasma concentrations, respectively, of
leptin and ghrelin (for statistical comparisons, see also Figure 2,
B and C).
DISCUSSION
Our data show that short-term sleep loss reduces daytime
overall spontaneous physical activity and shifts the intensity of
physical activity toward lower levels under free-living conditions.
In contrast to previous results (9) and to our expectations, short-
term sleep loss affected neither food intake and self-rated hunger
and appetite nor circulating concentrations of leptin and ghrelin.
Collectively, our results significantly add to the still limited
knowledge of the health-impairing influences of sleep loss and
probably point to more diverse underlying mechanisms than
previously suggested.
Although intuition and everyday experience may label this
finding as obvious, the reduction of physical activity and the shift
toward less intense activities as measured by wrist accelerometry
is a novel and highly interesting result. A reduction of time
periods spent at high physical activity levels can be assumed to, in
the long run, reduce physical fitness (17), thereby increasing the
risk of metabolic diseases such as obesity (18) and diabetes (19)
as well as cardiovascular events (20, 21). Although to our
knowledge similar results have not been obtained in humans
before, canines display markedly reduced motor activity after 1
d of total sleep deprivation (22). It should be noted that wrist
accelerometry as measured in our study, although highly cor-
related with whole-body trunk movements (23), does not rep-
resent total-body physical activity and in particular does not
allow any reliable conclusion on daily energy expenditure (24).
Thus, our finding cannot necessarily be taken as an indicator of
a reduction in energy expenditure. Also, the reduction in self-
reported physical activity has to be cautiously interpreted because
sleep-loss-induced fatigue might have biased the subjective
perception of activity. However, these obvious limitations should
not distract from the important and dependable finding of
markedly reduced physical activity that certainly can be expected
to adversely affect health.
Circulating leptin concentrations were previously shown to be
reduced and ghrelin concentrations to be elevated after a roughly
comparable sleep restriction regimen of 4 h in 2 consecutive
nights (11). This apparent contrast to our result of unchanged
circulating concentrations of leptin and ghrelin may be due to
subtle, but probably essential, differences in the designs of both
studies. Although sleep duration in the respective sleep loss
conditions was comparable (237 compared with 233 min;
reference 11), sleep duration in our 8-h-sleep control condition
was on average 80 min shorter than in the previous study in
which subjects were actually tested in an extended sleep con-
dition (466 compared with 543 min). There is some evi-
dence that the effects of sleep loss on leptin and ghrelin
concentrations follow a dose-dependent relation (4, 25, 26). For
example, ghrelin concentrations are distinctly elevated in the
morning after 1 night of total sleep deprivation as compared
with regular sleep, whereas concentrations after 4.5 h of sleep
FIGURE 2. Mean (6SEM) subjective ratings of (A) appetite and
concentrations of (B) serum leptin and (C) plasma ghrelin during an
experimental day after 2 nights of 8 h of sleep/night (open circles) and 2
nights each containing 4 h of sleep (solid circles), respectively. Fasting
prebreakfast concentrations of leptin (4-h sleep, 2.9 60.5 ng/mL, compared
with 8-h sleep, 3.0 60.7 ng/mL; P= 0.79, Student’s ttest) and ghrelin (4-h
sleep, 603 639 pg/mL, compared with 8-h sleep, 615 647 pg/mL; P= 0.57)
were not altered by preceding sleep restriction. ANOVA showed that during
the experimental day leptin concentrations increased (P,0.001 for time) but
did not display differences between conditions (P= 0.21 for condition, and
P= 0.18 for condition ·time). Concentrations of ghrelin rapidly decreased
during breakfast and remained at low concentrations during the rest of the
experimental day (P,0.001 for time). Again, there was no effect of sleep
restriction (P= 0.22 for condition, and P= 0.86 for condition ·time).
n=15.
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are intermediate but not significantly different from regular
sleep (26). Thus, in the present study the difference in sleep
duration between conditions may have been too small to elicit
detectable effects on the concentrations of ghrelin and leptin.
Furthermore, in our study the time point of awakening was
identical in both conditions (0700), whereas in the foregoing
studies, sleep restriction was associated with a somewhat early
awakening at 0500 in contrast to 0800 in the sleep-extension
condition. Therefore, subjects in the sleep loss condition had
been awake 3 h longer than in the control condition before the
first hormone measurements were obtained at 0900. Such an
additional fasting period spent awake may be an important
factor in the sleep-dependent regulation of leptin and ghrelin.
In accordance with unchanged leptin and ghrelin concen-
trations, self-rated hunger and appetite were not affected by sleep
shortage. Moreover, we were unable to detect differences in total
energy intake. The proportion of ingested fat was slightly greater
after sleep loss, but this finding must be interpreted with caution
because it is not buttressed by a significant statistical interaction
between treatment and macronutrient composition. Our subjects
overall ingested a rather high amount of energy that on average
exceeded their estimated daily energy demand by 60% (27). A
recent study has shown that food intake critically depends on the
amount of food provided to a subject (28). Presented with a great
variety of highly palatable foods, our subjects likely displayed
a ceiling effect that might have masked more subtle effects of
sleep loss on spontaneous energy intake. However, sleep re-
striction already failed to affect food intake during breakfast, ie,
at a time when potential ceiling effects were presumably less
pronounced. This suggests that, provided that the time of
awakening is comparable to normal sleep, sleep loss does not
acutely increase food intake in the morning.
In conclusion, our study indicates a profound deteriorating
influence of sleep deprivation on physical activity that in the
long-run possibly adversely affects metabolic and cardiovascular
health. It should be noted that our observations have been
obtained in an acute setting and cannot be directly extrapolated to
the effects of long-term sleep loss. Nevertheless, in conjunction
with a previous series of cogent experimental studies (25, 29, 30)
and a growing number of epidemiologic reports (4, 31, 32), our
results point to reduced physical activity as another potentially
important behavioral mechanism linking sleep loss to the de-
velopment of obesity, type 2 diabetes, and cardiovascular disease.
We are grateful to Mareike Ku
¨ck, Elisa Gustke, Claudia Frenzel, Jutta
Schwanbohm, and Kathleen Kurwahn for their expert and invaluable labora-
tory assistance.
The authors’ responsibilities were as follows—SMS, MH, KJ-C, JB, and
BS: designed the study; SMS, MH, BW, JB, and BS: analyzed the data; SMS,
MH, BW, KJ-C, CB, HL, JB, and BS: contributed to writing the manuscript;
and SMS: collected data and performed experiments for the study. All authors
had full access to all data in the study and take responsibility for the integrity
and accuracy of the data analysis. None of the authors had a conflict of
interest.
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... NHANES data reported that poor sleepers compared to normal sleepers (7-8 h of sleep/night) had a lower consumption of protein, fat, carbohydrate, and fiber [47]. Clinical intervention studies have corroborated these evidences reporting that during sleep restriction fat was also highlighted as a macronutrient of choice in subjects with a normal habitual sleep [48,49]. Although studies reported that there is a relationship between sleep quality and diet these epidemiologic evidence cannot address causality or the direction of the relation among these variables. ...
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... The advanced model further included BMI, hypertension status, hypertension treatment, diabetes status, smoking status, alcohol consumption, leisure time physical activity, history of myocardial infarction or heart failure, and reports of frequent use of hypnotics. Note that our assumptions regarding dependencies between predictor, outcome, and confounder variables were based on data from the published literature [15][16][17][18] . Overall, a two-sided P value of less than 0.05 was regarded as statistically significant. ...
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Chronically blunted nocturnal blood pressure (BP) dipping has been shown to increase the future risk of cardiovascular diseases. In the present cross-sectional study, we investigated whether self-reported insomnia symptoms were associated with an altered 24-h BP profile and blunted nocturnal BP dipping (night-to-day BP ratio > 0.90) in older men. For the analysis, we used 24-h ambulatory blood pressure data and reports of insomnia symptoms (difficulty initiating sleep, DIS; and early morning awakenings, EMA) from 995 Swedish men (mean age: 71 years). Compared to men without DIS, those reporting DIS (10% of the cohort) had a higher odds ratio of diastolic non-dipping (1.85 [1.15, 2.98], P = 0.011). Similarly, men who reported EMA (19% of the cohort) had a higher odds ratio of diastolic non-dipping than those without EMA (1.57 [1.09, 2.26], P = 0.015). Despite a slightly higher nocturnal diastolic BP among men with EMA vs. those without EMA (+ 1.4 mmHg, P = 0.042), no other statistically significant differences in BP and heart rate were found between men with and those without insomnia symptoms. Our findings suggest that older men reporting difficulty initiating sleep or early morning awakenings may have a higher risk of nocturnal diastolic non-dipping. Our findings must be replicated in larger cohorts that also include women.
... NHANES data reported that poor sleepers compared to normal sleepers (7-8 hours of sleep/night) had a lower consumption of protein, fat, carbohydrate, and ber [47]. Clinical intervention studies have corroborated these evidences reporting that during sleep restriction fat was also highlighted as a macronutrient of choice in subjects with a normal habitual sleep [48,49]. Although studies reported that there is a relationship between sleep quality and diet these epidemiologic evidence cannot address causality or the direction of the relation among these variables. ...
Preprint
Full-text available
Background: COVID 19- related quarantine led to a sudden and radical lifestyle changes, in particular in eating habits. Objectives of the study were to investigate the effect of quarantine on sleep quality (SQ) and body mass index (BMI), and if change in SQ was related to working modalities. Materials: We enrolled 121 adults (age 44.9±13.3 years and 35.5% males). Anthropometric parameters, working modalities and physical activity were studied. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index (PSQI) questionnaire. At baseline, the enrolled subjects were assessed in outpatient clinic and after 40 days of quarantine/lockdown by phone interview. Results: Overall, 49.6% of the subjects were good sleepers (PSQI < 5) at the baseline and significantly decreased after quarantine (p<0.001). In detail, sleep onset latency (p<0.001), sleep efficiency (p=0.03), sleep disturbances (p<0.001), and daytime dysfunction (p<0.001) significantly worsened. There was also a significant increase in BMI values in normal weight (p=0.023), in subjects grade I (p=0.027) and II obesity (p=0.020). In all cohort, physical activity was significantly decreased (p=0.004). However, analyzing the data according gender difference, males significantly decreased physical activity as well as females in which there was only a trend without reaching statistical significance (53.5% vs 25.6%; p=0.015 and 50.0% vs 35.9%, p=0.106; in males and females, respectively). In addition, smart working activity resulted in a significant worsening of SQ, particularly in males (p<0.001). Conclusions: Quarantine was associated to a worsening of SQ, particularly in males doing smart working, and to an increase in BMI values.
... NHANES data reported that poor sleepers compared to normal sleepers (7-8 hours of sleep/night) had a lower consumption of protein, fat, carbohydrate, and ber [49]. Clinical intervention studies have corroborated these evidence reporting that during sleep restriction fat was also highlighted as a macronutrient of choice relative to subjects with a normal habitual sleep [50,51]. Although studies reported that there is a relationship between sleep quality and diet these epidemiologic evidence cannot address causality or the direction of the relation among these variables. ...
Preprint
Full-text available
Background: COVID 19- related quarantine led to a sudden and radical lifestyle changes, in particular in eating habits. Objectives of the study were to investigate the effect of quarantine on sleep quality (SQ) and body mass index (BMI), and if change in SQ was related to working modalities. Materials: We enrolled 121 adults (age 44.9±13.3 years and 35.5% males). Anthropometric parameters, working modalities and physical activity were studied. Sleep quality was evaluated by the Pittsburgh Sleep Quality Index (PSQI). At baseline, the enrolled subjects were assessed in outpatient clinic and after 40 days of quarantine/lockdown by phone interview. Results: Overall, 49.6% of the subjects were good sleepers (PSQI < 5) at the baseline and significantly decreased after quarantine (p<0.001). In detail, sleep onset latency (p<0.001), sleep efficiency (p=0.03), sleep disturbances (p<0.001), and daytime dysfunction (p<0.001) significantly worsened. There was also a significant increase in BMI values in normal weight (p=0.023), in subjects grade I (p=0.027) and II obesity (p=0.020). In all cohort, physical activity was significantly increased (p=0.004). However, analyzing the data according gender difference, males significantly increased physical activity compared to females in which there was only a trend without reaching statistical significance (46.5% vs 74.4%; p=0.015 and 50.0% vs 64.1%, p=0.106; in males and females, respectively). Also, smart working activity resulted in a significant worsening of SQ, particularly in males (p<0.001). Conclusions: Quarantine was associated to a worsening of SQ, particularly in males doing smart working, and to an increase in BMI values.
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... Interestingly, when they analyzed the opposite relationship between adiposity in early childhood and sleep duration 24 months later, they also observed an inverse and independent association. Several plausible mechanisms have been proposed for the effect of sleep on adiposity, including appetite dysregulation via ghrelin/leptin, sleepiness-associated reduction in physical activity, and a preference for sedentary activities [44][45][46]. ...
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Overweight (OW) and obesity (OB) during childhood/adolescence are major public health problems in Mexico. Several obesogenic lifestyle (OL) risk factors have been identified, but the burden and consequences of them in Mexican children/adolescents remain unclear. The objective of this study was to estimate the prevalence of OL components and describe their relationships with adiposity, and OW/OB. A population-based cross-sectional study of Mexican children/adolescents with nutritional assessment, data collection on daily habits and adiposity as fat-mass index (FMI) by dual-energy X-ray absorptiometry was performed. Individual OL-components: “inactivity,” “excessive screen time,” “insufficient sleep,” “unhealthy-diet”, were defined according to non-adherence to previously published healthy recommendations. Results: 1449 subjects were assessed between March 2015 to April 2018. Sixteen percent of subjects had all four OL-components, 40% had three, 35% had two, 9% had one, and 0.5% had none. A cumulative OL score showed a significant dose–response effect with FMI. The combination of inactivity, excessive screen time, and insufficient sleep showed the highest risk association to OW/OB and higher values of FMI. Conclusions: The prevalence of OL-components was extremely high and associated with increased adiposity and OW/OB. Several interventions are needed to revert this major public health threat.
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Graphs are indispensable educational tools for a successful dissemination of nutritional knowledge. Pyramids, circles and other geometric forms are frequently used to help consumers in realizing nutritional guidelines. In recent years, the number of graphs published increased tremendously but unfortunately it is not always possible for consumers to follow the rationale behind. Great differences in the key messages also give reason for many uncertainties on the consumers' side. It is time, therefore, to reevaluate traditional concepts (and their suitability for presentation in different media) and to develop new ideas. The present paper deals with the scientific concepts of traditional approaches and discusses the pros and cons with the aim of deriving an optimized model (3D Food Guide Pyramid).
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Over the past several decades, numerous large cohort studies have attempted to quantify the protective effect of physical activity on cardiovascular and all-cause mortality. The aim of the authors' review was to provide an up-to-date overview of the study results. In a systematic MEDLINE search conducted in May 2007, the authors included cohort studies that assessed the primary preventive impact of physical activity on all-cause and cardiovascular mortality. The authors reported risk reductions on the basis of comparison between the least active and the most active population subgroups, with the least active population subgroup as the reference group. Random-effect models were used for meta-analysis. A total of 33 studies with 883,372 participants were included. Follow-up ranged from 4 years to over 20 years. The majority of studies reported significant risk reductions for physically active participants. Concerning cardiovascular mortality, physical activity was associated with a risk reduction of 35% (95% confidence interval, 30-40%). All-cause mortality was reduced by 33% (95% confidence interval, 28-37%). Studies that used patient questionnaires to assess physical activity reported lower risk reductions than studies that used more objective measures of fitness. Physical activity is associated with a marked decrease in cardiovascular and all-cause mortality in both men and women, even after adjusting for other relevant risk factors.