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Effect of shortened sleep on energy expenditure, core body temperature, and appetite: A human randomised crossover trial

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The effects of sleep restriction on energy metabolism and appetite remain controversial. We examined the effects of shortened sleep duration on energy metabolism, core body temperature (CBT), and appetite profiles. Nine healthy men were evaluated in a randomised crossover study under two conditions: a 3.5-h sleep duration and a 7-h sleep duration for three consecutive nights followed by one 7-h recovery sleep night. The subjects’ energy expenditure (EE), substrate utilisation, and CBT were continually measured for 48 h using a whole-room calorimeter. The subjects completed an appetite questionnaire every hour while in the calorimeter. Sleep restriction did not affect total EE or substrate utilisation. The 48-h mean CBT decreased significantly during the 3.5-h sleep condition compared with the 7-h sleep condition (7-h sleep, 36.75 ± 0.11 °C; 3.5-h sleep, 36.68 ± 0.14 °C; p = 0.016). After three consecutive nights of sleep restriction, fasting peptide YY levels and fullness were significantly decreased (p = 0.011), whereas hunger and prospective food consumption were significantly increased, compared to those under the 7-h sleep condition. Shortened sleep increased appetite by decreasing gastric hormone levels, but did not affect EE, suggesting that greater caloric intake during a shortened sleep cycle increases the risk of weight gain.
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Scientific RepoRts | 7:39640 | DOI: 10.1038/srep39640
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Eect of shortened sleep on
energy expenditure, core body
temperature, and appetite: a
human randomised crossover trial
Masanobu Hibi1, Chie Kubota2, Tomohito Mizuno1, Sayaka Aritake3, Yuki Mitsui1,
Mitsuhiro Katashima1 & Sunao Uchida3
The eects of sleep restriction on energy metabolism and appetite remain controversial. We examined
the eects of shortened sleep duration on energy metabolism, core body temperature (CBT), and
appetite proles. Nine healthy men were evaluated in a randomised crossover study under two
conditions: a 3.5-h sleep duration and a 7-h sleep duration for three consecutive nights followed by one
7-h recovery sleep night. The subjects’ energy expenditure (EE), substrate utilisation, and CBT were
continually measured for 48 h using a whole-room calorimeter. The subjects completed an appetite
questionnaire every hour while in the calorimeter. Sleep restriction did not aect total EE or substrate
utilisation. The 48-h mean CBT decreased signicantly during the 3.5-h sleep condition compared
with the 7-h sleep condition (7-h sleep, 36.75 ± 0.11 °C; 3.5-h sleep, 36.68 ± 0.14 °C; p = 0.016). After
three consecutive nights of sleep restriction, fasting peptide YY levels and fullness were signicantly
decreased (p = 0.011), whereas hunger and prospective food consumption were signicantly increased,
compared to those under the 7-h sleep condition. Shortened sleep increased appetite by decreasing
gastric hormone levels, but did not aect EE, suggesting that greater caloric intake during a shortened
sleep cycle increases the risk of weight gain.
Obesity is a serious health problem worldwide1 and a well-known risk factor for cardiovascular disease, hyper-
lipidaemia, hypertension, and type 2 diabetes2–4. Physical inactivity and/or overeating contribute to the devel-
opment of obesity5, in other words, changes in body weight can be explained by an energy imbalance6,7. When
daily energy intake (EI) surpasses energy expenditure (EE), the energy balance in the body becomes positive. e
cumulative eects of even a small daily positive energy balance on body weight regulation are severe8. Several
epidemiological studies have demonstrated a correlation between insucient sleep and increased incidence of
obesity in adults and children9–14. Although sleep disturbances have been recognised to one of the risk factors for
obesity15,16, how sleep curtailment contributes to the physiological and molecular mechanisms by which sleep
restriction aects the daily energy balance remains unclear17,18.
The potential physiological mechanisms related to sleep deprivation and excess energy balance include
changes in appetite and increased time to access food, which may inuence EI, and extended wakeful time at
night, physical inactivity due to fatigue, and decreased thermogenesis, which may aect EE19,20. Recent interven-
tion studies reported an association between sleep deprivation and hyperphagia. Spiegel et al.21 demonstrated
that 2 days of decreased sleep increases the appetite of healthy young subjects to the same extent as did two key
appetite-regulating hormones, the anorexigenic hormone leptin and the orexigenic gut hormone ghrelin. Others
reported that shortened sleep leads to greater EI22–25. Yet, these observations have not been reproduced in similar
intervention studies26,27. Namely, increased EI has not been consistently observed following experimental sleep
restriction.
e eects of sleep deprivation on energy expenditure are also controversial. Nedeltcheva et al.23 and St-Onge
et al.25 used double-labelled water under free-living conditions to demonstrate that restricted sleep does not aect
24-h EE. Brondel et al.22 reported that overnight sleep restriction is followed by a day of increased EI and activity,
1Health Care Food Research Laboratories, Kao Corporation, Tokyo, Japan. 2Graduate School of Sport Sciences,
Waseda University, Saitama, Japan. 3Faculty of Sport Sciences, Waseda University, Saitama, Japan. Correspondence
and requests for materials should be addressed to M.H. (email: hibi.masanobu@kao.co.jp)
Received: 19 August 2016
Accepted: 24 November 2016
Published: 10 January 2017
OPEN
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whereas Bosy-Westphal et al.28 found no eects of sleep restriction on daytime activity. More recently, some
research groups measured 24-h EE and substrate utilisation using whole-room indirect calorimeter26,29–31. Jung et
al.29 reported that 24-h EE is ~7% higher during periods of total sleep deprivation than during periods of habitual
sleep (0-h vs. 8-h sleep time), and Markwald et al.30 demonstrated that 24-h EE increases ~5% during restricted
sleep conditions compared to a normal sleep condition (5-h vs. 9-h sleep time). In the former study, however, EE
decreased in the following recovery sleep day, and in the latter study, the increased EE may have been inuenced
by increased food intake. Moreover, it remains unclear whether the link between shortened sleep time and obesity
is a result of changes in homeostatic consumption behaviour or a decrease in EE.
e aim of the present study was to determine whether shortened sleep (3.5-h/night for 3 nights) aects energy
metabolism, core body temperature (CBT), and appetite, thereby altering the energy balance in normal-weight
healthy young men. We hypothesised that compared to normal sleep duration, a shorter sleep duration would
lead to greater appetite sensations and decreased EE.
Results
Baseline characteristics and sleep variables. Nine subjects completed the trial according to the study
schedule (mean ± SD; age, 23 ± 2 y; body mass index, 22.2 ± 3.0 kg/m2). roughout the 1-week pre-laboratory
period, the mean sleep time, as determined by wrist-actigraphy, was 460 ± 39 min/night. e mean total sleep
time in the calorimeter based on polysomnography was 408.3 ± 7.2 min/night during the 7-h sleep condition and
206.7 ± 2.7 min/night during the 3.5-h sleep condition (day 4 night). During the recovery night, the sleep time
did not dier signicantly between subjects from either sleep condition (7-h sleep, 407.7 ± 7.2 min/night; 3.5-h
sleep, 412.8 ± 4.9 min/night). Although the sleep onset latency was signicantly shorter in the 3.5-h sleep condi-
tion than in the 7-h sleep condition (day 4 night: 7-h sleep; 12.8 ± 6.2 min/night; 3.5-h sleep; 5.4 ± 2.6 min/night,
p = 0.007), the eect of the sleep onset latency during the recovery sleep (day 5 night) was also shorter, but not
signicantly dierent between sleep conditions (7-h sleep, 14.7 ± 5.5 min; 3.5-h sleep, 10.9 ± 6.0 min, p = 0.151).
e duration of slow wave sleep on day 4 night did not dier signicantly between sleep conditions (7-h sleep,
66.7 ± 31.5 min/night; 3.5-h sleep, 79.7 ± 34.1 min/night, p = 0.113), and the duration of slow wave sleep on the
recovery sleep night (day 5) did not dier signicantly between conditions (7-h sleep, 78.4 ± 24.1 min/night;
3.5-h sleep, 89.8 ± 34.1 min/night, p = 0.170). e duration of rapid eye movement (REM) sleep did not dier
signicantly among the shortened sleep night, recovery sleep night, and normal sleep night.
Energy expenditure and substrate oxidation. e 48-h total energy expenditure (TEE), and the 24-h TEE
on day 3/4 and on the recovery night of day 4/5 did not dier signicantly between the 7-h and 3.5-h sleep conditions
(Table1). e hourly EE over 48 h varied with sleep condition and time, as demonstrated by the lack of a signicant
eect of sleep condition, and the presence of a signicant eect of time (p < 0.001) and the condition x time inter-
action (p < 0.001; Fig.1a). Night-time EE on day 3 (00:00 to 07:00) was higher in the 3.5-h sleep condition than in
the 7-h sleep condition (7-h sleep, 409 ± 37 kcal/d; 3.5-h sleep, 464 ± 45 kcal/d, p < 0.001). e 24-h energy balance
measured using the calorimeter on day 3/4 and 4/5 was slightly positive, but did not dier between sleep conditions
(7-h sleep, 114 ± 73 kcal/d and 145 ± 89 kcal/d, respectivly; 3.5-h sleep, 77 ± 72 kcal/d and 157 ± 62 kcal/d, respec-
tively). e 48-h respiratory quotient (RQ) values, and the 24-h RQ on day 3/4 and day 4/5 did not dier between
sleep conditions (Table1). e hourly RQ over 48 h varied with sleep condition and time, as demonstrated by the
lack of a signicant eect of condition, the condition x time interaction, and the presence of a signicant eect of
time (p < 0.001; Fig.1b). e 48-h average activity (%) and the 24-h activity on the recovery night of day 4/5 did not
7-h sleep 3.5-h sleep P value2
EE (kcal/d)
Day 3/431874 ± 145 1911 ± 155 0.183
Day 4/541844 ± 151 1831 ± 149 0.523
48-h53717 ± 288 3741 ± 303 0.508
RQ
Day 3/4 0.881 ± 0.016 0.878 ± 0.021 0.656
Day 4/5 0.884 ± 0.017 0.891 ± 0.012 0.121
48-h 0.883 ± 0.015 0.885 ± 0.015 0.519
CBT (°C)
Day 3/4 36.76 ± 0.15 36.67 ± 0.13 0.122
Day 4/5 36.74 ± 0.13 36.69 ± 0.16 0.116
48-h 36.75 ± 0.12 36.68 ± 0.14 0.016
Activity (%)
Day 3/4 22.8 ± 3.9 27.3 ± 6.6 0.016
Day 4/5 22.3 ± 6.0 23.3 ± 5.2 0.483
48-h 22.6 ± 4.8 25.1 ± 5.5 0.062
Table 1. Twenty-four-hour EE, RQ, CBT, and activity for the 48-h stay in the calorimeter during the 3.5-h
sleep condition and 7-h sleep condition1. 1EE, RQ, and activity data are expressed as mean ± SD; n = 9. CBT
data are expressed as mean ± SD; n = 7. CBT, core body temperature; RQ, respiratory quotient; EE, energy
expenditure. 2P values are calculated by a paired t test. 3Time period on day 3/4 is from 19:00 of day 3 to 19:00 of
day 4. 4Time period on day 4/5 is from 19:00 of day 4 to 19:00 of day 5. 5Time period a total of 48 h is from 19:00
of day 3 to 19:00 of day 5.
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dier signicantly between the 7-h and 3.5-h sleep conditions (Table1). e 24-h average activity on day 3/4 was
signicantly higher in the 3.5-h sleep condition than in the 7-h sleep condition (p = 0.016).
Core body temperature. Due to technical issues, CBT data were acquired for only seven subjects. e
mean CBT over 48 h was signicantly higher in the 7-h sleep condition than in the 3.5-h sleep condition (Table1).
e 24-h mean CBT values for day 3/4 and 4/5 did not dier signicantly between sleep condition (Table1), but
aer 3 days of restricted sleep, mean CBT was signicantly lower in the 3.5-h sleep condition than in the 7-h sleep
Figure 1. Hourly energy expenditure (EE) (a) (n = 9), respiratory quotient (RQ) (b) (n = 9), and core body
temperature (CBT) (c) (n = 7) during 48 h in the whole-room indirect calorimeter. Data are expressed as
the mean ± SD value per hour. e black diamonds with broken lines represent the 7-h sleep condition and
the white circles with black lines represent the 3.5-h sleep condition. A repeated-measures ANOVA revealed
that EE over 48 h varied with the sleep condition and a time, as demonstrated by the non-signicant eect
of sleep condition (p = 0.705), but there was a signicant eect of time (p < 0.001) and a time × condition
interaction (p < 0.001). e hourly RQ across 48 h varied with sleep condition and time as demonstrated by the
nonsignicant eect of condition (p = 0.442) and condition × time interaction (p = 0.317), with a signicant
eect of time (p < 0.001). A repeated-measures ANOVA revealed that CBT varied with sleep condition and
time over 48 h, as demonstrated by a signicant eect of condition (p < 0.001) and time (p < 0.001); there was
no signicant condition x time interaction (p = 0.571). *Signicantly dierent from the 7-h sleep condition
(p < 0.05, aer Bonferroni’s correction).
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condition on day 4 (7-h sleep, 36.72 ± 0.12 °C; 3.5-h sleep, 36.65 ± 0.15 °C, p = 0.015). e CBT proles over 48 h
varied with sleep condition and time, as demonstrated by the signicant eect of condition (p < 0.001) and time
(p < 0.001), but the lack of a signicant condition x time interaction (Fig.1c).
Appetite questionnaire. e area under the curves (AUCs) for the hungry and prospective to food con-
sumption from visual analogue scale (VAS) scores were signicantly increased, and the fullness score was sig-
nicantly decreased over 24 h during the 3.5-h sleep condition on day 3/4 compared to the 7-h sleep condition
(Table2). e AUCs for the 24 h appetite proles, however, did not dier signicantly between the 3.5-h sleep
condition and the 7-h sleep condition over 24 h on day 4/5 during the recovery night. Proles of mean appetite
values (i.e., hunger, fullness, prospective food consumption, and satiety) based on hourly VAS questionnaires are
shown in Fig.2. e condition x time eects for hunger, fullness, prospective food consumption, and satiety were
not signicantly dierent between sleep conditions. e main eects of sleep condition on fullness, prospective
food consumption, and satiety were not signicantly dierent, but the eect of sleep condition on hunger was
signicant (p = 0.004).
Fasting blood and urine analysis. Fasting blood lipid and hormone values aer the 3-night sleep restric-
tion and recovery sleep are shown in Table3. Plasma peptide YY (PYY) concentrations were significantly
lower aer the 3-night sleep restriction in the 3.5-h sleep condition than in the 7-h sleep condition (p = 0.011).
Plasma glucagon-like peptide-1 (GLP-1) levels after the 3-night sleep restriction tended to be lower in the
3.5-h sleep condition than in the 7-h sleep condition (p = 0.055). Tri-iodothyronine and thyroxin, high-density
lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triacylglycerol, non-esteried fatty acids, adiponec-
tin, cortisol, and leptin levels did not dier signicantly between sleep conditions. e mean urinary metabolite
proles (cortisol, c-peptides, epinephrine, and norepinephrine) are shown in Fig.3. Condition x time eects were
not detected for epinephrine, norepinephrine, cortisol, or c-peptide levels. e main eects of sleep condition
on epinephrine, cortisol, and c-peptide levels were not signicant, but there was a signicant main eect of sleep
conditions on norepinephrine (p = 0.006). Aer Bonferronis correction for multiple comparisons, norepineph-
rine levels were signicantly higher in the 3.5-h sleep condition during the hours between 00:00–07:00 on day 3/4
and between 07:00–14:00 on day 5 than in the 7-h sleep condition.
Discussion
e present study investigated the eects of three consecutive nights of reduced sleep duration (3.5-h sleep vs.
7-h sleep) on energy metabolism in healthy young men based on whole-room indirect calorimeter, CBT by rectal
core body temperature thermometry, appetite proles with hourly VAS questionnaires, and blood gut hormone
levels. Our results indicated that shortened sleep for 3 nights did not decrease TEE levels, although the mean CBT
was signicantly decreased during the 3.5-h sleep condition compared to the 7-h sleep condition. Furthermore, in
the 3.5-h sleep condition, hunger and prospective food consumption scores increased, whereas the fullness score
decreased, and fasting levels of the anorexigenic gut hormones PYY and GLP-1 also decreased. ese ndings
indicate that insucient sleep increases food intake, potentially associated with changes in PYY and GLP-1 levels,
which could lead to weight gain. Moreover, reduced CBT and increased urine norepinephrine levels in the 3.5-h
sleep condition might indicate a sleep deprivation-induced disruption of the circadian rhythm of body tempera-
ture regulation in association with whole-body EE rhythm.
We demonstrated that the TEE values during 3.5-h sleep condition did not dier signicantly from those
during the 7-h sleep condition. Nevertheless, we observed a signicant eect of sleep deprivation on hourly EE
patterns over 48 h (p < 0.001), and an approximately 55 kcal increase in the night-time EE on the night of day 3
during the 3.5-h sleep condition compared to the 7-h sleep condition. ese changes in EE were likely due to an
increase in EE during the night hours spent awake. Markwald et al.30, Klingenberg et al.26, and Shechter et al.31
reported that EE changes at night. Whole-room calorimeter, which is the gold standard for measuring energy
metabolism, shows an ~5% increase in 24-h EE during restricted sleep conditions compared to habitual sleep in
healthy adults26,30 and in healthy women31. In our study, shortened sleep led to an ~2% increase in the 24-h TEE,
although this increase was not statistically signicant. Despite the increase in night-time EE, TEE did not change
in the present study, in contrast to the ndings from the other studies. Furthermore, the hourly EE aer one night
7-h sleep 3.5-h sleep P value 2
Hunger (mm·h) Day 3/43774 ± 217 883 ± 177 0.021
Day 4/54779 ± 218 784 ± 176 0.879
Fullness (mm·h) Day 3/4 822 ± 193 716 ± 141 0.020
Day 4/5 734 ± 152 771 ± 180 0.224
Prospective food consumption (mm·h) Day 3/4 896 ± 232 973 ± 203 0.035
Day 4/5 874 ± 228 896 ± 205 0.343
Satiety (mm·h) Day 3/4 864 ± 296 745 ± 187 0.073
Day 4/5 780 ± 271 713 ± 207 0.189
Table 2. Twenty-four hour appetite scores calculated as the area under the curve during day 3/4 and 4/5 of
the intervention with 3.5-h sleep or 7-h sleep1. 1All data are expressed as the mean ± SD; n = 9. 2P values are
calculated by paired t tests. 3Time period on day 3/4 is from 19:00 of day 3 to 19:00 of day 4. 4Time period on
day 4/5 is from 19:00 of day 4 to 19:00 of day 5.
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of recovery sleep did not dier between the sleep conditions. e short-term (~3 days) partial sleep restriction did
not aect EE aer one night of recovery sleep. Jung et al.29 reported that 24-h TEE increases by ~7% during total
sleep deprivation (awake for 40-h) and decreases ~5% during the recovery condition compared to baseline. ey
also reported that the 7% increase in 24-h TEE on the total sleep deprivation day is nearly oset by the energy
saved during the recovery day, resulting in a net cost of 2% across the 48 h examined29. Although the sleep depri-
vation conditions diered between the studies (total sleep deprivation for 1 day vs. shortened sleep for 3 days), our
data are consistent with those from Jung et al.29. us, the lack of an eect on EE aer sleep restriction suggests
that EE does not contribute to the potential weight gain reported in the epidemiological studies9–14.
e 48-h mean CBT and the 24-h mean CBT signicantly decreased by 0.07 °C during the 3.5-h sleep condi-
tion compared to that observed during the 7-h sleep condition. e close relationship between the metabolic rate
Figure 2. Mean appetite ratings of hunger (a), fullness (b), prospective food consumption (c), and satiety
(d) in 3.5-h and 7-h sleep conditions aer 48 h in the whole-room indirect calorimeter. e data are expressed
as the mean ± SD appetite ratings (n = 9). e black diamonds represent the 7-h sleep condition, and the white
circles represent the 3.5-h sleep condition. An ANOVA revealed a signicant eect of condition and time for
hunger (p = 0.004 and p < 0.001), but there was no signicant condition x time interaction (p = 0.857). ere
was a signicant eect of time for fullness (p < 0.001), but there was no signicant eect of condition and
condition x time interaction (p = 0.064 and p = 0.522). ere was a signicant eect of time for prospective
food consumption (p < 0.001), but there was no signicant eect of condition and condition x time interaction
(p = 0.284 and p = 0.833). ere was a signicant eect of time for satiety (p < 0.001), but there was no
signicant eect of condition and condition x time interaction (p = 0.094 and p = 0.758). *Signicantly dierent
from 7-h sleep condition (p < 0.05, aer Bonferroni’s correction).
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and CBT32 may partly explain the risk of future weight gain. A 24-h mean decrease in the CBT of 0.07 °C would
account for a body fat accumulation of 0.5 kg per year33,34, which is a relatively small amount that may change
according to lifestyle. Few studies have directly addressed the relationship between shortened sleep and lower
CBT20. Bach et al.35 reported a decrease in the daytime CBT over 5 nights of 4-h sleep restriction compared to
baseline values. Benedict et al.36 observed higher night-time CBT during 24-h total sleep deprivation (~+ 0.2 °C)
and lower daytime CBT during the following day (~ 0.1 °C). Our ndings are partly consistent with those of the
previous reports, suggesting a disruption in the circadian rhythm of thermoregulation. We also observed higher
norepinephrine levels during the 3.5-h sleep condition and the following recovery night. Previous literature and
the data obtained in the present study might support the idea that lowering the CBT aects norepinephrine
release. Frank et al.37 reported a signicant increase in the norepinephrine concentration when CBT was lowered
(36.0 °C) in younger subjects. Faraut et al.38 observed a 2.5-fold increase in norepinephrine levels during the day
aer a sleep-restricted night, but no change in epinephrine or dopamine levels. Short or restricted sleep times
have also been reported to impair neural brain activity39. e circadian rhythms of CBT and thermoregulation
are regulated by the hypothalamus, and sleep restriction potentially aects hypothalamic function, resulting in
dysfunctional control of body temperature40. Further studies must be conducted to clarify the association among
sleep restriction, circadian rhythms, and related brain function.
Our ndings indicated that the 24-h RQ did not dier signicantly between the 7-h and 3.5-h sleep condi-
tions. Other researchers examined 24-h substrate utilisation measured using whole-room indirect calorimetry
under sedentary conditions26,29–31,41. Shechter et al.31 reported that the 24-h RQ aer 3 nights of short (4-h/night)
versus habitual (8-h/night) sleep duration dose not dier signicantly under xed meal conditions. Our ndings
are consistent with these ndings in that substrate utilisation did not dier aer sleep restriction despite the
greater number of hours spent awake in the 3.5-h sleep condition. Sleep restriction is reported to increase the
insulin response23, but in the present study, the 24-h levels of urinary C-peptide and cortisol were not aected by
shortened sleep time, which is in good agreement with the substrate utilisation values in both sleep conditions.
ese data do not support the concept that sleep restriction alters substrate utilisation in such a way as to favour
future weight gain. Similar to our ndings that sleep restriction signicantly decreased fasting RQ, Shechter et
al.41 and Klingenberg et al.26 reported a lower fasting RQ aer shortened versus habitual sleep. e temporarily
decreased RQ, which might be aected by temporary dierences in energy balance from the prolonged wakeful-
ness in shortened sleep conditions, was not reected by the 24-h RQ.
In the present study, sleep loss led to a signicant increase in the hunger and the prospective food consumption
score, and a signicant decrease in the fullness score during the 3.5-h sleep condition. Moreover, fasting PYY con-
centrations were signicantly lower, and fasting GLP-1 concentrations tended to be lower aer the 3-night sleep
restriction. Spiegel et al.21 and Benedict et al.36 also reported increased hunger feelings aer two nights of short sleep
duration or a total of one night of sleep deprivation relative to habitual sleep. Moreover, sleep fragmentation reduced
daily GLP-1 proles and the fullness score42. PYY received special attention in clinical studies aer Batterham et al.43
demonstrated that infusions of PYY in doses mimicking postprandial increases in plasma PYY levels reduces appe-
tite and food intake for 12- to 24-h in both normal-weight and obese subjects. Our ndings should be taken into
account with these previous ndings that the gut anorexigenic hormone levels are related to feelings of hunger.
In addition, decreased sleep in healthy subjects increased the consumption of snacks, particularly at night23. Our
results identied dierences in appetite proles based on night-time sleep duration. It is not clear why shortened
sleep did not aect glucose or insulin levels. Further investigation is required to elucidate this matter.
Day 43Day 54
7-h sleep 3.5-h sleep P27-h sleep 3.5-h sleep P
Glucose (mg/dL) 88.8 ± 4.6 87.3 ± 3.6 0.311 88.9 ± 3.6 88.9 ± 4.2 1.000
Insulin (μ U/mL) 5.02 ± 1.85 4.52 ± 1.13 0.480 4.89 ± 2.32 5.62 ± 1.63 0.436
TG (mg/dL) 108 ± 28 98 ± 24 0.102 106 ± 32 101 ± 29 0.362
HDL-C (mg/dL) 53 ± 7 56 ± 11 0.122 52 ± 7 55 ± 10 0.145
LDL-C (mg/dL) 109 ± 29 104 ± 17 0.402 110 ± 31 106 ± 19 0.464
NEFA (mEq/L) 0.342 ± 0.100 0.416 ± 0.097 0.109 0.339 ± 0.111 0.304 ± 0.112 0.196
T3 (ng/dL) 102.9 ± 14.9 101.7 ± 11.1 0.723 95.0 ± 8.3 94.4 ± 10.0 0.516
T4 (μ g/dL) 8.1 ± 0.9 8.2 ± 0.8 0.464 8.3 ± 0.7 8.3 ± 0.8 0.946
TSH (μ IU/mL) 1.8 ± 1.1 2.0 ± 1.0 0.071 1.7 ± 0.9 1.5 ± 0.7 0.034
Leptin (ng/mL) 4.7 ± 2.0 4.5 ± 1.8 0.680 4.8 ± 2.1 4.4 ± 2.2 0.172
Adiponectin (μ g/L) 8.9 ± 3.6 8.9 ± 4.0 0.862 8.6 ± 3.7 8.7 ± 4.0 0.667
GLP-1 (pmol/L) 2.1 ± 1.0 1.4 ± 0.6 0.055 1.6 ± 0.8 1.4 ± 0.5 0.260
PYY (ng/mL) 186.2 ± 34.8 163.0 ± 45.5 0.011 185.0 ± 36.7 179.9 ± 29.2 0.541
Cortisol (μ g/dL) 17.8 ± 1.8 18.3 ± 2.1 0.505 19.5 ± 1.7 19.2 ± 1.8 0.604
Table 3. Fasting blood metabolites aer the 3-day sleep restriction or one recovery night1. 1All data
are expressed as the mean ± SD; n = 9. GLP-1, glucagon-like peptide-1; HDL-C, high density lipoprotein
cholesterol; LDL-C, low density lipoprotein cholesterol; NEFA, non-esteried fatty acid; PYY, peptide YY; T3,
tri-iodothyronine; T4, thyroxin; TG, triglyceride; TSH, thyroid stimulating hormone. 2P values are calculated by
paired t tests. 3Time point on Day 4 is 07:30 on day 4 aer the 3- day sleep restriction. 4Time point on Day 5 is
07:30 on day 5 aer the recovery sleep.
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A limitation of the present study was that the ‘normal’ sleep condition of 7 h may be considered a mild sleep
disturbance compared to the studies providing 8 or 9-h sleep opportunities. e Japan Collaborative Cohort
Study reported a U-shaped relationship between sleep duration and total mortality, with a nadir at a 7-h sleep
duration, in a large-scale prospective study of Japanese individuals44. Moreover, the clinical signicance of a
change in mean 48-h CBT of 0.1 degrees or less must be further investigated. Additional potential limitations are
the small sample size, the male-only subject population, and the fact that actual food intake, postprandial blood
samples, and ghrelin levels were not assessed. We did not collect and analyse the subjects’ sleep patterns dur-
ing the washout period or the eating behaviour of the subjects during the pre-intervention period and washout
period, which could aect the second intervention and outcomes. erefore, more research with a larger sample
size including women is necessary to establish general observations. Last, because of the limited space for physi-
cal activity in the calorimeter, and because the subjects were not allowed to sleep during the day, our results only
partially resemble daily life.
In conclusion, the present ndings revealed that sleep restriction reduced gut hormones (PYY and GLP-1) and
increased appetite sensations, but did not alter EE or substrate utilisation during 48-h calorimeter measurements.
Moreover, a 3-night shortened sleep intervention reduced CBT for 48 h in healthy young men. ree nights of
short sleep duration might lead to a positive energy balance. ese ndings suggest that the quantity of sleep-time
leads to changes in individual energy balance and circadian rhythms and may increase the risk of obesity.
Figure 3. Mean urinary metabolite levels of cortisol (a), c-peptides (b), epinephrine (c), and norepinephrine
(d) during the 3.5-h and 7-h sleep conditions aer 48 h in the whole-room indirect calorimeter. e data are
expressed as the mean ± SD values (n = 9). e black diamonds represent the 7-h sleep condition and the white
circle represent the 3.5-h sleep condition. An ANOVA revealed a signicant eect of time for epinephrine,
cortisol, and c-peptides (p < 0.001), but there was no signicant eect of condition or condition x time
interaction. ere was a signicant eect of time and condition for norepinephrine (p < 0.001 and p = 0.006,
respectively), but there was no signicant eect of the condition x time interaction (p = 0.725). *Signicantly
dierent from the 7-h sleep condition (p < 0.05, aer Bonferroni’s correction).
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Methods
Subjects. e study was approved by the Ethics Committee of Waseda University, in accordance with the
approved ethics guidelines. is trial was registered with the University hospital medical information network
(UMIN) clinical trials registry (http://www.umin.ac.jp/) on December 6, 2013 as UMIN000012506. All of the
participants provided written informed consent before study commencement. Nine healthy young men partic-
ipated in the study (mean ± SD, age 23 ± 2 y; body mass index 22.2 ± 3.0 kg/m 2). We calculated the minimum
number of subjects to be enrolled under the empirical, preliminary assumption of a mean dierence of 90 kcal/d
with a standard deviation of 90 kcal/d in 24-h EE between the sleep conditions. Based on the sample size calcula-
tion, 9 subjects would be required to detect a signicant dierence with a two-sided paired t-test with 75% power
and a 5% alpha level. e subjects were recruited through poster advertisements. e inclusion criteria were as
follows: 20 to 40 y of age, body mass index of 18.0 to 29.9 (kg/m2), and a normal sleep pattern. e exclusion crite-
ria were as follows: self-reported sleep problems (Pittsburgh Sleep Quality Index score > 10); shi-work; smoking;
excessive alcohol intake (> 30 g alcohol/day); history of, or currently taking medication for cardiovascular disease,
hypertension, diabetes, hypercholesterolaemia, hyperglycaemia, or hyperlipidaemia; and the use of prescription
medications aecting sleep or metabolism.
Experimental design. is was a randomised crossover study that included one acclimatisation day and
two 5-day intervention periods with either a 7-h sleep condition or a 3.5-h sleep condition (Fig.4). e 7-h
sleep condition involved a 7-h sleep opportunity (from 00:00 to 07:00) for three consecutive nights and a 7-h
sleep opportunity for one recovery night (from 00:00 to 07:00). e 3.5-h sleep condition comprised 3.5-h sleep
opportunities (from 03:30 to 07:00) for 3 consecutive nights and a 7-h sleep opportunity for one recovery night
(from 00:00 to 07:00). A wash-out period of approximately 2 weeks was inserted between interventions. On
the acclimatisation day, the subjects slept from 00:00 to 07:00 in whole-room indirect calorimeters at the Kao
Health Care Food Research Laboratories (Tokyo, Japan) with polysomnographic recordings. e meals provided
in the laboratory were based on energy requirements estimated by the basal metabolic rate (BMR) equation45
with a physical activity level of 1.6. Meal composition was 15 per cent of energy (E%) protein, 25 E% fat, and 60
E% carbohydrate, and both sleep condition groups received meals of identical quantity and composition. e
Figure 4. Illustration of the study scheme. e participants spent awake and sleeping time in the laboratory
from 09:00 on day 1 to 19:00 on day 5. e gure shows the time spent in and out of the respiratory chamber
to remove or install the polysomnography device and to take a shower (removed from 07:15 to 07:45 on days 4
and 5 and installed from 17:15 to 18:15 on day 4). All meals were given at the same time in both conditions. e
7-h sleeping times on days 1 to 3 and the recovery sleep on day 4 and the 3.5-h sleeping time on days 1 to 3 are
shown (black). e VAS questionnaires were provided every hour. Overnight polysomnography was performed
to examine the night-time sleep quality on days 3 and 4. Core body temperatures were continuously measured
from 19:00 on day 3 to 19:00 on day 5, except for when subjects were taking a shower. Key: grey areas – out of
the study protocol, hatched areas – inside the respiratory chamber, black areas – time in bed. B, breakfast at
09:00; L, lunch at 14:00; D, dinner at 19:00; and syringe symbols represent blood drawing at 07:30.
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calories were distributed among meals as follows: 30 E% breakfast, 30 E% lunch, and 40 E% dinner. Dieticians
provided breakfast, lunch, and dinner at 09:00, 14:00, and 19:00, respectively. In the sleep condition, the subjects
were required to go to bed and room lights were turned o at 00:00 for the 7-h bedtime and at 03:30 for the 3.5-h
bedtime. e subjects were awakened at 07:00. e investigators continuously monitored wakefulness and com-
pliance with the protocol. e subjects exited the calorimeter and removed the polysomnographic device at 07:15
and put it back on at 07:45 on days 4 and 5. e subjects exited the calorimeter at 17:15 and re-entered at 18:15
on day 4. Aer exiting the calorimeter, the participants were allowed to shower. We fed the subjects a eucaloric
diet, which was based on their predicted BMR multiplied by a physical activity level of 1.3 to account for their
decreased physical activity due to the limited space in the calorimeter (1.3 × BMR, between chamber stays). Meal
compositions and caloric distributions were consistent with those outside of the calorimeter. On days 4 and 5, we
obtained a fasting blood sample from each subject at 07:30. During the wash-out period, we did not restrict food
intake or exercise and encouraged the participants to maintain their normal lifestyles.
Sleep and core body temperature recording. Sleep was recorded using polysomnography (Polymate
AP216, TEAC Corp., Tokyo, Japan) as four electroencephalograms with two reference electrodes in the earlobes,
two electrooculograms, two submental electromyograms, and two electrocardiograms recordings. e epoch
lengths for visual and computer analyses were 30 s. e recordings of REM and non-REM sleep were analysed
using fast Fourier transformation. We calculated total power as the sum of each frequency band power from all
epochs. CBT was recorded using a portable device (LT-8A, Gram Corporation, Saitama, Japan). Each subject
inserted a probe into their rectum, and the probe remained there except when they took a shower or defecated.
Temperature was continuously recorded every 1 min for 48 h. All recorded data were analysed except for the
excessively low temperature points, 2 h aer a shower, and 45 min aer waking up. e hourly averaged data were
calculated across 60-min periods to obtain a mean CBT for the transient response analysis.
Appetite questionnaire. Appetite proles (hunger, fullness, prospective food consumption, and satiety)
were measured using the 100-mm VAS questionnaire46, which was translated into Japanese from English47. We
measured hourly appetite proles 37 times between the awake time from 19:00 on day 3 to 19:00 on day 5. e
24-h appetite scores were calculated as the AUCs on day 3/4 (from 19:00 on day 3 to 19:00 on day 4) and day 4/5
(from 19:00 on day 4 to 19:00 on day 5).
Whole-room indirect calorimeter measurements. EE and substrate utilisation for each subject were
measured in the respiratory chamber over 48 h in both conditions. Whole-room indirect calorimeter measure-
ments were obtained by the previously described methods48. In brief, room temperature, humidity, and fresh
airow were set to 25 °C, 50%, and 70 L/min, respectively. Oxygen consumption (VO2) and carbon dioxide pro-
duction (VCO2) were calculated using the method reported by Henning et al.49 VO2 and VCO2 were calculated
across 60-min period to obtain EE and RQ values for the transient response analysis. TEE and RQ values were
determined based on the 24-h VO2 and VC O2 values50,51. TEE and RQ were also calculated for three dierent
periods: day3/4 (from 19:00 of day 3 to 19:00 of day 4), day4/5 (from 19:00 of day 4 to 19:00 of day 5), and day
4 (from 07:00 of day 4 to 07:00 of day 5). We calculated energy balance by subtracting TEE from actual EI for
each 24 h periods. We monitored activity levels in the calorimeter using an infrared motion sensor (Matsushita
Automation Controls Co, Ltd, AMP2009B01, Tokyo, Japan). A digital balance, accurate to 0.01 kg (CQ100LW,
Ohaus Corp., Pine Brook, NJ), was used to measure body weight prior to the subject entering the calorimeter
Blood sample analysis. We collected a fasting blood samples from each subject aer he exited the respira-
tory chamber at 07:30 on days 4 and 5. Serum triacylglycerol, non-esteried fatty acid, and glucose levels were
measured using standard enzymatic techniques. Serum low-density lipoprotein-cholesterol and high-density
lipoprotein-cholesterol concentrations were measured using standard direct methods. yroid-stimulating hor-
mone, tri-iodothyronine, and thyroxin were assayed using an electrochemiluminescence immunoassay method.
Serum insulin, adiponectin, and GLP-1 concentrations were assayed using an immunoenzymatic method. Serum
leptin and cortisol concentrations were determined using a radio-immunoassay method. All of the measurements
were performed by LSI Medience Corporation (Tokyo, Japan). Plasma PYY concentrations were determined
using an ELISA kit (Millipore, Billerica, MA).
Urinary analysis. All of the urine samples were collected and weighed while the subjects were in the calo-
rimeter. e subjects were instructed to store urine samples during the following time points: 19:00–24:00, 24:00–
07:00, 07:00–14:00, and 14:00–19:00. Urinary cortisol and c-peptide were determined by radioimmunoassay
aer extraction with dichloromethane. Urinary excretion of norepinephrine and epinephrine were measured by
high-pressure liquid chromatography with a cation exchange column, separated by reversed-phase chromatog-
raphy (model no. 126; Beckman Instruments, San Ramon, CA), and detected with an electrochemical detector
(model LC-4B; BioAnalytical Systems, West Lafayette, IN).
Statistical analysis. e data are presented as the means ± SD unless otherwise indicated. All crossover data
for both treatments were compared using paired t-tests (two-sided α = 0.05). A mixed-model repeated-measures
analysis of variance (ANOVA) was used to assess the signicance of dierences in the proles of EE, RQ, CBT,
and appetite proles for hourly data with the main eects of condition, time, and condition x time interaction as
xed eects. In addition, a mixed-model ANOVA was used to assess signicant changes in the urine metabolite
values for each time period. Bonferonni’s correction for multiple comparisons was used to correct for the number
of planned comparisons. Statistical analyses were performed using SPSS statistical soware (Version 19, IBM
Japan, Ltd., Tokyo, Japan).
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Acknowledgements
We thank the research volunteers for their outstanding dedication. We also thank Ms. Hiroko Yamaguchi and Ms.
Tomomi Yamazaki for their excellent work as the dietary sta of Kao Health Care Food Research Laboratories.
We thank Mr. Hironobu Miyauchi and Mr. Shigeru Nakajima of Fuji Medical Science Co., Ltd., for their technical
expertise in respiratory chamber measurements.
Author Contributions
M.H. and C.K. designed and performed the experiments, analysed the data, and wrote the manuscript. T.M.
and Y.M. performed the experiments and reviewed/edited the manuscript. S.A. and M.K. contributed to the
discussion, reviewed/edited the manuscript. S.U. designed the study, contributed to the discussion, and reviewed/
edited the manuscript. M.H. and C.K. are the guarantors of this work and, as such, had full access to all of the data
in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Additional Information
Competing nancial interests: M.H., T.M., Y.M., and M.K. are employees of the Kao Corporation, a chemical,
cosmetic, and food company headquartered in Japan (Tokyo, Japan). e other authors had no personal or
nancial conicts of interest. Kao Corporation funded the research. e funders had no role in the study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
How to cite this article: Hibi, M. et al. Eect of shortened sleep on energy expenditure, core body temperature,
and appetite: a human randomised crossover trial. Sci. Rep. 7, 39640; doi: 10.1038/srep39640 (2017).
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© e Author(s) 2017
... Overall, both TSD and PSD studies demonstrate either no changes [53][54][55] or minimal increases in TEE. 30,31 Whole-room calorimetry studies have found that 24-h TEE increased by 7% (∼134 kcal) following 40-h TSD 30 and by 5% (∼111 kcal) following 5-h PSD (compared to 9 h of sleep). ...
... Another whole-room calorimetry study found no differences in 24-h TEE or substrate utilization after 3.5-h PSD (compared to 7 h of sleep), but there was a 55 kcal increase in night-time TEE during PSD. 53 In contrast, studies that used the doubly labeled water method found no difference in TEE with PSD. 54,55 Notably, the doubly labeled water method quantifies TEE over a period of days to weeks and may not capture dynamic changes in TEE within a 24-h period. ...
... TA B L E 1 Randomized controlled studies of total sleep deprivation on metabolic outcomes In summary, insufficient sleep results in increased energy intake and adverse eating behaviors, such as late-night eating and snacking, either during or immediately after sleep deprivation.Dysregulated eating: Roles of appetite hormones and hedonic drive for foodIncreased energy intake as a result of insufficient sleep is driven by dysregulated eating. Increased subjective hunger, alterations in appetite hormone levels, and increased activity in brain regions related to hedonic and reward-driven behaviors all contribute to dysregulated eating.Many studies have demonstrated increased subjective hunger and appetite ratings and decreased fullness sensation with TSD 58,71-74 and PSD.53,60,68,69,75 Additionally, functional magnetic resonance imaging (fMRI) studies following TSD showed that sleep deprivation enhanced the activity in brain regions responsible for a hedonic drive for food consumption and reward pathways,71,76 impaired cognitive control in response to food stimuli, ...
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The global epidemic of obesity and type 2 diabetes parallels the rampant state of sleep deprivation in our society. Epidemiological studies consistently show an association between insufficient sleep and metabolic dysfunction. Mechanistically, sleep and circadian rhythm exert considerable influences on hormones involved in appetite regulation and energy metabolism. As such, data from experimental sleep deprivation in humans demonstrate that insufficient sleep induces a positive energy balance with resultant weight gain, due to increased energy intake that far exceeds the additional energy expenditure of nocturnal wakefulness, and adversely impacts glucose metabolism. Conversely, animal models have found that sleep loss–induced energy expenditure exceeds caloric intake resulting in net weight loss. However, animal models have significant limitations, which may diminish the clinical relevance of their metabolic findings. Clinically, insomnia disorder and insomnia symptoms are associated with adverse glucose outcomes, though it remains challenging to isolate the effects of insomnia on metabolic outcomes independent of comorbidities and insufficient sleep durations. Furthermore, both pharmacological and behavioral interventions for insomnia may have direct metabolic effects. The goal of this review is to establish an updated framework for the causal links between insufficient sleep and insomnia and risks for type 2 diabetes and obesity.
... Regarding the desire to eat after short sleep, some studies report no changes in perceived pleasantness of food or the desire to eat after one night with reduced sleep of 4 h and after five consecutive days of insufficient sleep, mimicking a work week, respectively [14,24]. In contrast, others have shown increased hunger and prospective food consumption after three nights of sleep restriction with a sleep duration of 3.5 h per night [25]. Since most studies did not synchronize sleeping and waking periods under experimental conditions, it has been speculated [15] that the waking time is a prerequisite for the orexigenic effect of sleep loss [14,26,27]. ...
... Interestingly, a 3.5 h sleep condition using an early night sleep loss protocol (wake until 03:30 h) for three days in healthy men increased hunger and prospective food consumption, as reported by Hibi and co-workers [25]. These results contrast with previous findings from our group, showing that two nights of sleep loss during the first half of the night (wake until 02:45 h) did not affect feelings of hunger and appetite and energy intake in healthy humans [15]. ...
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... Like physical activity, the effect of sleep deprivation on daily energy expenditure has not been completely elucidated and all modes of increased (66,(136)(137)(138) , decreased (139) and no effect (50,97,(140)(141)(142) have been reported. There are many reasons for the controversies such as the difference in sleep deprivation protocol, very low sample size of some trials, the type of activities that were allowed during waking hours, and age, sex and BMI of participants. ...
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... Besides, sleep and wakefulness are highly fragmented and generally evenly distributed over the 24 h of the day [41]. Sleep deprivation has been demonstrated to be associated with lower temperature levels and may be involved in the circadian disruption of CBT [42]. Food intake was also found to affect CBT [43]. ...
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... [12][13] Esto puede deberse a que las personas que duermen menos horas presentan mayor apetito y reportan mayor consumo calórico. 10,14 Además, también existe mayor riesgo de mortalidad en personas con diabetes por dormir menos horas por noche. 15 No cumplir con las horas correctas de sueño, tiene diversas implicaciones en la salud, sin embargo, no existen estudios en México que asocien las horas de sueño con la diabesidad. ...
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Abstract Data from cross-sectional and longitudinal studies have illustrated a relationship between short sleep duration (SSD) and weight gain. Individuals with SSD are heavier and gain more weight over time than normal-duration sleepers. This sleep-obesity relationship may have consequences for obesity treatments, as it appears that short sleepers have reduced ability to lose weight. Laboratory-based clinical studies found that experimental sleep restriction affects energy expenditure and intake, possibly providing a mechanistic explanation for the weight gain observed in chronic short sleepers. Specifically, compared to normal sleep duration, sleep restriction increases food intake beyond the energetic costs of increased time spent awake. Reasons for this increased energy intake after sleep restriction are unclear but may include disrupted appetite-regulating hormones, altered brain mechanisms involved in the hedonic aspects of appetite, and/or changes in sleep quality and architecture. Obstructive sleep apnea (OSA) is a disorder at the intersection of sleep and obesity, and the characteristics of the disorder illustrate many of the effects of sleep disturbances on body weight and vice versa. Specifically, while obesity is among the main risk factors for OSA, the disorder itself and its associated disturbances in sleep quality and architecture seem to alter energy balance parameters and may induce further weight gain. Several intervention trials have shown that weight loss is associated with reduced OSA severity. Thus, weight loss may improve sleep, and these improvements may promote further weight loss. Future studies should establish whether increasing sleep duration/improving sleep quality can induce weight loss.
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Habitual short sleep duration appears to increase the risk of obesity. The objective of this paper is to investigate the association of habitual sleep duration with objective measures of energy balance. One hundred twelve African-American and 111 non-Hispanic whites aged 21-69 y participated in a cross-sectional study of dietary assessment and biomarkers. Participants reported the mean number of hours per day spent sleeping over the past year. Short sleep duration was defined as ≤6 h/d of sleep. Energy intake (kilocalories) was objectively assessed using the 2-point doubly labeled water technique to determine total energy expenditure, which is approximately equal to energy intake. Physical activity energy expenditure (kilocalories) was estimated as total energy expenditure minus each participant's calculated basal metabolic rate and the thermogenic effect of food. Compared with participants who slept ≤6 h, individuals who slept 8 h were significantly less likely to be obese (OR: 0.33; 95% CI: 0.14, 0.79). However, this association was not linear across 6-9 h of sleep (P-trend = 0.16). There was an inverse association between sleep and energy intake (P-trend = 0.07): compared with ≤6 h/d, adults who reported ≥9 h sleep consumed 178 fewer kcal/d. There was also an inverse association between sleep and physical activity (P-trend = 0.05): compared with ≤6 h/d of sleep, adults who reported 9 h of usual sleep expended 113 fewer kcal/d in physical activity. These data indicate that, compared with longer sleep duration, adults who report habitual short sleep duration have somewhat higher physical activity energy expenditure but considerably higher energy intake. Habitual short sleep duration appears to be 1 of the facets of modern life leading to a mismatch between energy intake and physical activity.
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Background/Objectives The extent to which alterations in energy expenditure (EE) in response to sleep restriction contribute to the short sleep-obesity relationship is not clearly defined. Short sleep may induce changes in resting metabolic rate (RMR), thermic effect of food (TEF), and postprandial substrate oxidation. Subjects/Methods Ten females (age and BMI: 22-43 y and 23.4-28 kg/m2) completed a randomized, crossover study assessing the effects of short (4 h/night) and habitual (8 h/night) sleep duration on fasting and postprandial RMR and respiratory quotient (RQ). Measurements were taken after 3 nights using whole-room indirect calorimetry. The TEF was assessed over a 6-h period following consumption of a high-fat liquid meal. Results Short vs. habitual sleep did not affect RMR (1.01 ± 0.05 and 0.97 ± 0.04 kcal/min; p=0.23). Fasting RQ was significantly lower after short vs. habitual sleep (0.84 ± 0.01 and 0.88 ± 0.01; p=0.028). Postprandial EE (short: 1.13 ± 0.04 and habitual: 1.10 ± 0.04, p=0.09) and RQ (short: 0.88 ± 0.01 and habitual: 0.88 ± 0.01, p=0.50) after the high-fat meal were not different between conditions. TEF was similar between conditions (0.24 ± 0.02 kcal/min in both; p=0.98), as was the ~6-h incremental area under the curve (1.16 ± 0.10 and 1.17 ± 0.09 kcal/min x 356 min after short and habitual sleep, respectively; p=0.92). Conclusions Current findings observed in non-obese healthy premenopausal women do not support the hypothesis that alterations in TEF and postprandial substrate oxidation are major contributors to the higher rate of obesity observed in short sleepers. In exploring a role of sleep duration on EE, research should focus on potential alterations in physical activity to explain the increased obesity risk in short sleepers.
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Epidemiologic evidence has shown a link between short sleep and obesity. Clinical studies suggest a role of increased energy intake in this relation, whereas the contributions of energy expenditure (EE) and substrate utilization are less clearly defined. Our aim was to investigate the effects of sleep curtailment on 24-h EE and respiratory quotient (RQ) by using whole-room indirect calorimetry under fixed-meal conditions. Ten females aged 22-43 y with a BMI (in kg/m(2)) of 23.4-27.5 completed a randomized, crossover study. Participants were studied under short- (4 h/night) and habitual- (8 h/night) sleep conditions for 3 d, with a 4-wk washout period between visits. Standardized weight-maintenance meals were served at 0800, 1200, and 1900 with a snack at 1600. Measures included EE and RQ during the sleep episode on day 2 and continuously over 23 h on day 3. Short compared with habitual sleep resulted in significantly higher (±SEM) 24-h EE (1914.0 ± 62.4 kcal compared with 1822.1 ± 43.8 kcal; P = 0.012). EE during the scheduled sleep episode (0100-0500 and 2300-0700 in short- and habitual-sleep conditions, respectively) and across the waking episode (0800-2300) were unaffected by sleep restriction. RQ was unaffected by sleep restriction. Short compared with habitual sleep is associated with an increased 24-h EE of ∼92 kcal (∼5%)-lower than the increased energy intake observed in prior sleep-curtailment studies. This finding supports the hypothesis that short sleep may predispose to weight gain as a result of an increase in energy intake that is beyond the modest energy costs associated with prolonged nocturnal wakefulness. This trial was registered at clinicaltrials.gov as NCT01751581.