ArticlePDF Available

One night of partial sleep deprivation impairs recovery from a single exercise training session


Abstract and Figures

Purpose The effects of sleep deprivation on physical performance are well documented, but data on the consequence of sleep deprivation on recovery from exercise are limited. The aim was to compare cyclists’ recovery from a single bout of high-intensity interval training (HIIT) after which they were given either a normal night of sleep (CON, 7.56 ± 0.63 h) or half of their usual time in bed (DEP, 3.83 ± 0.33 h). Methods In this randomized cross-over intervention study, 16 trained male cyclists (age 32 ± 7 years), relative peak power output (PPO 4.6 ± 0.7 W kg−1) performed a HIIT session at ±18:00 followed by either the CON or DEP sleep condition. Recovery from the HIIT session was assessed the following day by comparing pre-HIIT variables to those measured 12 and 24 h after the session. Following a 2-week washout, cyclists repeated the trial, but under the alternate sleep condition. Results PPO was reduced more 24 h after the HIIT session in the DEP (ΔPPO −0.22 ± 0.22 W kg−1; range −0.75 to 0.1 W kg−1) compared to the CON condition (ΔPPO −0.05 ± 0.09 W kg−1, range −0.19 to 0.17 W kg−1, p = 0.008, d = −2.16). Cyclists were sleepier (12 h: p = 0.002, d = 1.90; 24 h: p = 0.001, d = 1.41) and felt less motivated to train (12 h, p = 0.012, d = −0.89) during the 24 h recovery phase when the HIIT session was followed by the DEP condition. The exercise-induced 24 h reduction in systolic blood pressure observed in the CON condition was absent in the DEP condition (p = 0.039, d = 0.75). Conclusions One night of partial sleep deprivation impairs recovery from a single HIIT session in cyclists. Further research is needed to understand the mechanisms behind this observation.
This content is subject to copyright. Terms and conditions apply.
1 23
European Journal of Applied
ISSN 1439-6319
Volume 117
Number 4
Eur J Appl Physiol (2017) 117:699-712
DOI 10.1007/s00421-017-3565-5
One night of partial sleep deprivation
impairs recovery from a single exercise
training session
Dale E.Rae, Tayla Chin, Kagiso
Dikgomo, Lee Hill, Andrew J.McKune,
Tertius A.Kohn & Laura C.Roden
1 23
Your article is protected by copyright and
all rights are held exclusively by Springer-
Verlag Berlin Heidelberg. This e-offprint is
for personal use only and shall not be self-
archived in electronic repositories. If you wish
to self-archive your article, please use the
accepted manuscript version for posting on
your own website. You may further deposit
the accepted manuscript version in any
repository, provided it is only made publicly
available 12 months after official publication
or later and provided acknowledgement is
given to the original source of publication
and a link is inserted to the published article
on Springer's website. The link must be
accompanied by the following text: "The final
publication is available at”.
1 3
Eur J Appl Physiol (2017) 117:699–712
DOI 10.1007/s00421-017-3565-5
One night ofpartial sleep deprivation impairs recovery
fromasingle exercise training session
DaleE.Rae1· TaylaChin1· KagisoDikgomo1· LeeHill1· AndrewJ.McKune3,4·
TertiusA.Kohn1· LauraC.Roden2
Received: 11 October 2016 / Accepted: 6 February 2017 / Published online: 28 February 2017
© Springer-Verlag Berlin Heidelberg 2017
variables to those measured 12 and 24h after the session.
Following a 2-week washout, cyclists repeated the trial, but
under the alternate sleep condition.
Results PPO was reduced more 24h after the HIIT ses-
sion in the DEP (ΔPPO 0.22 ± 0.22Wkg1; range
0.75 to 0.1Wkg1) compared to the CON condition
(ΔPPO 0.05 ± 0.09Wkg1, range 0.19 to 0.17Wkg1,
p = 0.008, d = 2.16). Cyclists were sleepier (12h:
p = 0.002, d = 1.90; 24h: p = 0.001, d = 1.41) and felt less
motivated to train (12h, p = 0.012, d = 0.89) during the
24h recovery phase when the HIIT session was followed
by the DEP condition. The exercise-induced 24h reduction
in systolic blood pressure observed in the CON condition
was absent in the DEP condition (p = 0.039, d = 0.75).
Conclusions One night of partial sleep deprivation
impairs recovery from a single HIIT session in cyclists.
Further research is needed to understand the mechanisms
behind this observation.
Keywords Sleep deprivation· Recovery strategies·
Maximal performance· High-intensity interval training·
BP Blood pressure
CK Creatine kinase
CON Control condition: normal night of sleep
DBP Diastolic blood pressure
DEP Partial sleep deprivation condition: 50% of nor-
mal sleep
HIIT High-intensity interval training
HR Heart rate
HRmax Maximum heart rate
IgA Immunogammaglobulin A
PPO Peak power output
Purpose The eects of sleep deprivation on physical
performance are well documented, but data on the conse-
quence of sleep deprivation on recovery from exercise are
limited. The aim was to compare cyclists’ recovery from
a single bout of high-intensity interval training (HIIT)
after which they were given either a normal night of sleep
(CON, 7.56 ± 0.63h) or half of their usual time in bed
(DEP, 3.83 ± 0.33h).
Methods In this randomized cross-over intervention
study, 16 trained male cyclists (age 32 ± 7years), rela-
tive peak power output (PPO 4.6 ± 0.7Wkg1) performed
a HIIT session at ±18:00 followed by either the CON or
DEP sleep condition. Recovery from the HIIT session
was assessed the following day by comparing pre-HIIT
Communicated by Nicolas Place.
Electronic supplementary material The online version of this
article (doi:10.1007/s00421-017-3565-5) contains supplementary
material, which is available to authorized users.
* Dale E. Rae
1 Division ofExercise Science andSports Medicine,
Department ofHuman Biology, Faculty ofHealth Sciences,
University ofCape Town, PO Box115, Newlands,
CapeTown7725, SouthAfrica
2 Department ofMolecular andCell Biology, Faculty
ofScience, University ofCape Town, CapeTown,
3 Research Institute forSport andExercise, University
ofCanberra, Canberra, Australia
4 Discipline ofBiokinetics, Exercise andLeisure Sciences,
School ofHealth, University ofKwaZulu-Natal, Durban,
Author's personal copy
700 Eur J Appl Physiol (2017) 117:699–712
1 3
RER Respiratory exchange ratio
RPE Rating of perceived exertion
SBP Systolic blood pressure
sIgA Salivary immunogammaglobulin A
VO2max Maximum volume of oxygen uptake
In the context of sport and exercise, sleep is recognized as
being critical for an athlete’s well-being and performance.
Much of sleep’s value may lie in its role in recovery, from
both training and competition, an important factor deter-
mining performance. In a recent survey of 890 team sport
athletes, sleep was identified as one of the most impor-
tant recovery strategies (Venter 2014). When elite foot-
ball players had night matches, their sleep duration was
typically reduced on the night of the match and the fol-
lowing day they reported feeling less recovered compared
to day matches (Fullagar etal. 2016). Kölling etal. (2016)
observed 55 junior, national-level rowers during a 4-week
training camp. Following just one night of shortened sleep
at the end of the first week of the camp, the rowers reported
feeling less recovered and in a worsened emotional state
compared to the previous day, which had been preceded by
a normal night of sleep (Kölling etal. 2016). Furthermore,
the fact that more rowers napped on training days compared
to rest days suggests a higher sleep need on training days.
These studies provide some evidence that athletes intui-
tively value sleep in the context of recovery.
Despite this, sleep complaints are prevalent in athletes
(Swinbourne etal. 2015; Gupta etal. 2016). Not only does
competition and travel interfere with their sleep (Fullagar
etal. 2016; Gupta etal. 2016), but also training itself may
reduce sleep quality and quantity. Training periods in which
intensity is increased or early morning sessions are sched-
uled have both been shown to reduce sleep duration in ath-
letes (Sargent etal. 2013; Schaal etal. 2015; Kölling etal.
2016). Thus athletes and coaches are challenged to balance
training programmes, travel and competition schedules,
and sleep routines to facilitate optimal performance. Perti-
nently, a recent consensus statement from the International
Olympic Committee called on coaches to ensure sucient
rest and recovery periods in training programmes for youth
athletes to “encourage positive adaptations and progressive
athletic development” (Bergeron etal. 2015).
A single bout of exercise perturbs the homeostasis of
the skeletal muscle, cardiovascular, nervous, endocrine,
metabolic and immune systems (Reilly and Ekblom
2007; Heinonen etal. 2014; Walsh and Oliver 2016) and
the ensuing recovery of these systems can be viewed as
an adaptation to the specific training stimulus in anticipa-
tion of future exposures. One of the functions of sleep is
to provide an opportunity for both neurological and phys-
iological systems to recover and repair (Akerstedt and
Nilsson 2003). Both complete and partial sleep depriva-
tion are understood to compromise various physiologi-
cal systems (Zhong 2005; Van Cauter etal. 2008; Ruiz
etal. 2012) and reduce physical performance the next
day (Souissi etal. 2003; Oliver etal. 2009; Temesi etal.
2013). Reducing sleep only partially (4h for 6days) has
been shown to decrease glucose tolerance, increase even-
ing cortisol levels, elevate sympathovagal balance, and
disrupt the normal pattern of growth hormone at night
(Akerstedt and Nilsson 2003). Reduced sleep may inter-
fere with muscle recovery due to increased signalling to
initiate proteolysis, essentially creating a more catabolic
state (Dattilo etal. 2011). The fact that resting systolic
blood pressure increases after partial sleep deprivation
(±3.5h), potentially indicates that sympathetic nervous
system activity is altered by reduced sleep (Tochikubo
etal. 1996). Sleep deprivation is also understood to dis-
rupt the immune system (Faraut etal. 2012), with eects
similar to those of intense exercise on both the innate
and acquired immune systems (Walsh and Oliver 2016).
Sleep loss also reduces mood and motivation (Reilly and
Piercy 1994; Scott etal. 2006), both of which may impact
eort produced during a subsequent training session.
Collectively, these data suggest that recovery from exer-
cise may be impaired by sleep deprivation through the
compromised function of any number of systems.
While numerous recovery enhancing modalities such
as nutrition (Beelen etal. 2010), ice (Poppendieck etal.
2013) and compression (MacRae etal. 2011) having been
investigated, studies measuring the eect of sleep depriva-
tion on recovery from exercise are scant (Skein etal. 2013).
As early as 1984 recovery from a 20min training session
was assessed in five males, under both normal sleep and
complete sleep deprivation conditions. While submaximal
performance was not compromised with sleep deprivation,
recovery was hampered, as indicated by altered profiles
of blood glucose, lactate, minute ventilation and oxygen
uptake during submaximal exercise (McMurray and Brown
1984). More recently, recovery of lower limb power in
rugby players who underwent complete sleep deprivation
the night after a match was shown to be impaired compared
to a normal night of sleep (Skein etal. 2013).
Both of these previous studies (McMurray and Brown
1984; Skein etal. 2013) made use of complete sleep depri-
vation models. While they are useful designs to understand
the extreme eects of sleep restriction, athletes may be
more likely to experience poor sleep or reduced sleep dura-
tion following competition or during training phases (Sar-
gent etal. 2013; Schaal etal. 2015; Kölling etal. 2016; Ful-
lagar etal. 2016). Therefore, investigation of the eects of
partial sleep deprivation on recovery from exercise would
Author's personal copy
701Eur J Appl Physiol (2017) 117:699–712
1 3
expand previous research in this area by providing informa-
tion on a sleep scenario many athletes may face.
The aim of this study was to determine whether par-
tial sleep deprivation impairs recovery from training. The
objective was to compare cyclists’ recovery from a single
bout of high-intensity interval training (HIIT) after which
they were given either a normal night of sleep or were par-
tially sleep deprived. Recovery was assessed by monitoring
motivation to train; muscle soreness and tiredness; sleepi-
ness; resting heart rate and blood pressure; maximal power,
heart rate and oxygen uptake; creatine kinase activity; leu-
kocyte cell counts and salivary IgA secretion rate during
the 24h after the HIIT session. The hypothesis was that a
full, normal night of sleep after a HIIT session would be
necessary to facilitate recovery, assessed by the ability to
perform at close to maximum in a peak power output test
24h after the HIIT. Specifically, it was hypothesised that
even one night of partial sleep deprivation would be suf-
ficient to impair recovery.
Sixteen trained male cyclists participated in this study. To
be included they needed to be 20–50years old, usually
sleep >6h per night, train at least three times per week
(past 6months) and have ridden the Cape Town Cycle
Tour, an annual 110km road race in South Africa, within
the last year in a time of <3h 45min. Exclusion criteria
were diagnosed chronic cardiovascular or metabolic dis-
ease, psychiatric condition, sleep disorder, or any other
condition known to aect sleep; current or recent use of
sleep medication (past 2months); recent trans-meridian
travel exceeding three time zones (past 2months); and shift
work. The Human Research Ethics Committee of the Fac-
ulty of Health Sciences, University of Cape Town approved
this study (HREC No: 272/2014) and all participants pro-
vided written informed consent. Descriptive characteristics
of the participants are presented in Table1.
Study overview
An overview of the design of this randomized crossover
intervention study is depicted in Fig.1. Briefly, in session 1
(17:30) participants completed a questionnaire to document
medical and cycling histories, assess chronotype using the
Horne–Östberg morningness–eveningness personality ques-
tionnaire (Horne and Östberg 1976) and measure sleep qual-
ity using the Pittsburgh Sleep Quality Index (Buysse etal.
1989). They also practiced the peak power output (PPO)
test. For the next seven consecutive days participants wore a
wrist actigraph (Actiwatch AW2, Philips Respironics, Bend,
OR, USA) to establish their usual sleep habits. Sleep timing
was verified using a diary. They were asked to refrain from
training 48h before and to avoid caeine and alcohol 24h
before session 2. A dietary log completed in the 24h period
prior to sessions 2 and 5 was used to verify this. There was
94% compliance to the 24h alcohol restriction for both the
CON and DEP conditions, and 100 and 88% compliance to
the caeine restriction prior to the CON and DEP sessions,
respectively. Participants also used the dietary log to match
their food intake times, content and quantity before session 5
to that of session 2.
Participants fasted for 3h before session 2 (17:30) at
which baseline measures (Pre) were taken. These included
weight, sleepiness, motivation to train, muscle soreness and
tiredness scores, resting heart rate and blood pressure, saliva
and blood samples, and a PPO test. This was followed imme-
diately by the HIIT session at 18:00. Participants then slept
in the laboratory under either control sleep (CON) or par-
tial sleep deprivation (DEP) conditions. Follow-up sessions
3 (06:30–07:00) and 4 (18:30) took place 12 and 24h after
session 2, respectively, during which all baselines measures
were repeated, with the exception of the PPO test, which was
only performed at session 4. After a 2-week washout, cyclists
repeated the trial (sessions 5–7) under the alternate sleep con-
dition. The order in which participants underwent the inter-
vention was randomized. While it was not possible to blind
participants to the condition (CON or DEP), they were only
told their condition after the first HIIT session.
Detailed testing procedures
Subjective, heart rate (HR) andblood pressure (BP)
The Epworth Sleepiness Scale was used to measure sleepi-
ness (Johns 1991). Participants rated their “motivation to
Table 1 Participant characteristics (n = 16)
BMI body mass index, VO2max maximum oxygen uptake volume
a 11 of the cyclists were morning-types (69%), 5 were neither-types
(31%), and there were no evening-types
Mean ± SD Range
Age (years) 32.3 ± 7.1 22–47
Height (m) 1.80 ± 0.08 1.62–1.94
Weight (kg) 76.9 ± 9.5 60.6–95.7
BMI (kgm2) 23.8 ± 1.6 21.2–27.4
Chronotype score 63 ± 9 44–76a
Sleep quality 4 ± 1 2–7
VO2max (mlmin1kg1) 57.6 ± 8.3 45.1–79.3
Peak power output (Wkg1) 4.61 ± 0.70 3.69–6.37
Author's personal copy
702 Eur J Appl Physiol (2017) 117:699–712
1 3
train” on a 10-point scale (1: “not into it” through to 10:
“extremely motivated”). Leg muscle soreness and tiredness
were assessed when standing, during a quadriceps stretch
and immediately after climbing two flights of stairs (2 steps
per second), since each scenario may elicit soreness or
tiredness to dierent extents in dierent individuals. These
ratings made use of an 11-point scale (0: “no soreness or
tiredness” through to 10: “extreme soreness or tiredness”),
and the global leg muscle soreness and tiredness scores
summed all three measurements. Only the global values are
presented in the results. Resting HR and BP measurements
represent the average of three values taken after 10min of
seated rest using an automated monitor (Omron HEM-907,
Omron Health Care, Kyoto, Japan).
Immune function andmuscle damage
Saliva samples were collected from participants during ses-
sions 2–7 by unstimulated passive drool to assess immune
function. Salivary immunoglobulin A (sIgA) secretion
rate (µgmin1) was determined using an indirect enzyme
immunoassay kit (Salimetrics, State College, USA). Details
of this procedure have been published previously (McKune
etal. 2006). Venous blood samples were obtained from
participants to measure indices of muscle damage (cre-
atine kinase (CK) activity) and immune function (white
blood cell count, WBC, and dierential). Samples for CK
activity were collected into lithium heparin vacutainer
tubes, placed on ice, centrifuged at 3000×g for 10min at
4 °C, after which the plasma was removed and stored in
microfuge tubes at 80 °C. CK activity was determined
using a commercial enzymatic assay kit (CK-NAC-acti-
vated, Boehringer Mannheim, Meylan, France) and a spec-
trophotometer (Beckman Instruments DU-530, Fullerton,
CA, USA). Samples for WBC analyses were collected into
EDTA vacutainers and immediately sent to a medical diag-
nostic laboratory (Metropolis, Cape Town) that performed
the leukocyte counts.
Peak power output (PPO) test
The cyclists performed PPO tests during sessions 1, 2, 4, 5
and 7. The PPO test in session 1 was for familiarisation and
to establish maximum heart rate. Those performed in ses-
sions 2 and 5 provided baseline values for maximal aerobic
performance and those in sessions 4 and 7 were to assess
24h recovery in maximal performance following the HIIT
under the CON and DEP conditions. Participants used their
own bicycles mounted on a cycle ergometer (Computrainer
Pro 3D, RacerMate, Seattle, USA) and set-up was the
same for each session. To warm up, cyclists completed the
Lamberts and Lambert Submaximal Cycling Test (Lam-
berts etal. 2011). This 15min test comprises three stages:
6min at 60% of maximum heart rate max (HRmax), 6min
at 80% HRmax and 3min at 90% HRmax. Following this, the
ramp format PPO test began at 2.5Wkg1 body weight
Fig. 1 Study overview
Author's personal copy
703Eur J Appl Physiol (2017) 117:699–712
1 3
for 1min. Power then increased by 20Wmin1 until par-
ticipants were unable to maintain a minimum cadence of
70rpm or reached volitional exhaustion. During the test,
HR (bpm), power (W), oxygen uptake and carbon dioxide
production (mlkg1min1) were measured continuously
and rating of perceived exertion (RPE) at the end of every
minute. HR was monitored using a Suunto t6d heart rate
monitor (v2.1.0.3, Suunto, Oy, Vantaa, Finland), RPE using
the 20-point Borg scale (Borg 1998), and respiratory vari-
ables using an online breath-by-breath gas analyser and
pneumotach (Oxycon, Viasis, Hoechberg, Germany).
HIIT session
This 54min session took place 5min after the baseline
PPO test in sessions 2 and 5, and simulated a hard train-
ing bout from which recovery was measured. Cyclists com-
pleted 18 × 1min intervals at PPO, with 2min of active
recovery (50W) between each interval. During the session
they had adlibitum access to water and a 7% carbohydrate
drink (CarboFuel, Cadence Nutrition, Cape Town, South
Africa). The volume of carbohydrate drink consumed
during the HIIT was similar between conditions (CON
360 ± 231ml, DEP 358 ± 187ml, p = 0.983). The same was
true for the volume of water drunk (CON 272 ± 257ml,
DEP 222 ± 165ml, p = 0.523).
Participants ingested a recovery drink (200ml low fat choc-
olate-flavoured milk; 482kJ, 6g protein, 18g carbohydrate
and 4g fat per serving) within 30min of the HIIT session,
and ate dinner (beef lasagne) within 90min. Portion sizes
were standardised so that each cyclist consumed 0.42g car-
bohydrate, 0.62g protein and 0.35g fat per kg body mass.
A standardised breakfast consisting of oats, milk, fruit and
caeine-free tea was provided.
Usual sleep habits andsleep intervention
Participants wore a wrist-worn actigraph (Actiwatch AW2,
Philips Respironics, Bend, OR, USA) and kept a sleep
diary for seven consecutive days between sessions 1 and 2
to establish their usual sleep habits. Data for all 7days were
Table 2 Usual (n = 16), CON
(n = 16) and DEP (n = 16) sleep
Data are presented as mean ± SD or median (interquartile range)
CON control sleep condition, DEP partial sleep deprivation condition, USUAL habitual sleep characteris-
tics, WASO wake after sleep onset time
Significance was determined using either a paired t test or Wilcoxon signed-rank test. p1: USUAL vs.
CON; p2: USUAL vs. DEP; p3: CON vs. DEP
Usual CON DEP p1p2p3
Bed time (h:min) 22:50 ± 0:45 22:42 ± 0:40 00:33 ± 0:19 0.368 <0.001 <0.001
Wake-up time (h:min) 06:24 ± 0:37 6:16 ± 0:25 4:23 ± 0:22 0.109 <0.001 <0.001
Time-in-bed (h) 7.58 ± 0.69 7.56 ± 0.63 3.83 ± 0.33 0.919 <0.001 <0.001
Sleep length (h) 6.81 ± 0.50 6.50 ± 0.56 3.38 ± 0.41 0.126 <0.001 <0.001
Eciency (%) 90.2 ± 3.5 86.4 ± 8.0 88.3 ± 8.1 0.059 0.386 0.246
Latency (min) 6.0 (3.4) 7.5 (0.6) 1.4 (0.4) 0.309 0.522 0.032
WASO (min) 26.9 ± 11.2 32.4 ± 17.7 11.9 ± 7.5 0.156 <0.001 0.002
No. of awakenings 46 ± 14 45 ± 17 20 ± 12 0.609 <0.001 0.002
Table 3 Twelve-hour responses
to the HIIT session under the
CON and DEP conditions
Changes in actual values are presented as mean ± SD or median (IQR), with the range in parentheses
CON control sleep condition, DEP partial sleep deprivation condition, HIIT high-intensity interval training
session, HR heart rate, SBP systolic blood pressure, DBP diastolic blood pressure
The p value indicates significance determined using either a paired t test or Wilcoxon signed-rank test.
Cohen’s d indicates eect size
nCON DEP d p value
ΔSleepiness 16 1 (3) (9 to 1) 4 (6) (3 to 14) 1.88 0.001
ΔMotivation to train 15 0 ± 1 (2 to 2) 1 ± 1 (4 to 0) 0.97 0.018
ΔMuscle soreness 16 3 ± 3 (5 to 8) 3 ± 3 (2 to 8) 0.03 0.882
ΔMuscle tiredness 16 4 ± 4 (4 to 11) 4 ± 3 (0 to 12) 0.02 0.834
ΔResting HR (bpm) 16 1 ± 6 (10 to 12) 1 ± 8 (12 to 12) 0.10 0.734
ΔResting SBP (mmHg) 16 10 ± 6 (19 to 1) 4 ± 8 (19 to 10) 0.96 0.012
ΔResting DPB (mmHg) 16 2 ± 8 (14 to 18) 3 ± 6 (14 to 7) 0.05 0.845
Author's personal copy
704 Eur J Appl Physiol (2017) 117:699–712
1 3
averaged and the following outcome variables obtained:
bed time, wake-up time, time-in-bed (TIB, h), total sleep
time (TST, h), sleep onset latency (SOL, min), sleep
eciency (SE, %), wake-after sleep onset time (WASO,
min) and number of awakenings (count). These data
(USUAL) are presented in Table2.
Participants slept in a sound- and light-proof sleep lab-
oratory following the HIIT sessions. Between the end of
the HIIT session and bed time in both the DEP and CON
conditions, participants showered, ate dinner (±20:00) and
were then allowed to either read, watch TV or make use of
their laptops in the sleep laboratory lounge. They were not
permitted to recline or rest in their beds, and were in the
company of the investigator at all times to ensure they did
not fall asleep. Participants undertook these same activities
in the period between wake-up time and the start of ses-
sions 3 or 6 in the DEP condition.
Bed time and wake-up time during the control night
(CON) replicated their USUAL sleep habits. Participants
wore the wrist actigraph during the CON and DEP nights
to confirm sleep timing and length. There were no dier-
ences in any of the sleep variables between the USUAL and
CON nights (Table2).
For the partial sleep deprivation (DEP) night participants
were allowed only half of their usual time-in-bed. Thus
mean required TIB during the DEP night was calculated to
be 3.77 ± 0.35h and mean TST 3.38 ± 0.23h. The precise
bed time and wake-up time for each participant was deter-
mined individually based on his USUAL TIB, and mid-
point of sleep during the DEP night was matched to that of
USUAL sleep by delaying bed time and advancing wake-
up time. The actigraphy data for the DEP night (Table2)
indicate that actual TIB (p = 0.602) and TST (p = 0.623)
were not dierent to the required times. Furthermore, SE
(p = 0.386) and SOL (p = 0.522) were also similar between
the USUAL and DEP nights, while the cyclists had reduced
WASO time (p < 0.001) and fewer awakenings (p < 0.001)
on the DEP night. Together these data suggest that the par-
ticipants obtained the required sleep durations on the CON
and DEP nights and that the quality was similar to their
usual sleep.
Data andstatistical analyses
Data are expressed as mean ± standard deviation or median
with interquartile range. Time data are represented as
h:min. Normality was confirmed using the Shapiro–Wilks
test and homogeneity of variance using Levene’s test.
Paired t tests or Wilcoxon signed-rank tests were used to
compare single variables between the DEP and CON condi-
tions. A two-way analysis of variance with repeated meas-
ures was used to compare dierences between conditions
in variables measured before and 24h after the HIIT ses-
sion. Eect sizes were calculated using Cohen’s d, where
d < 0.2 was considered trivial, 0.20–0.39 small, 0.40–0.79
moderate and >0.80 large. 12 and 24h measurements
Fig. 2 Individual changes in sleepiness (a), motivation to train (b)
and resting systolic blood pressure (c) from Pre to 12h after the
HIIT session under the CON and DEP conditions. Each data point
represents an individual’s response to the HIIT session (12h minus
Pre), expressed as percentage change. Open and closed circles rep-
resent the normal sleep (CON) and partial sleep deprivation (DEP)
conditions, respectively. The dotted line at y = 0 represents no change
in the variable measured before and 12h after the HIIT session (i.e.
0% change). The slope of the connecting line indicates the extent to
which the change in a variable after HIIT was dierent in the DEP
compared to the CON condition, in one individual. SBP systolic
blood pressure, Pre prior to the HIIT session, 12h 12h after the HIIT
session. Significance was determined using a paired t test. Cohen’s d
indicates eect size
Author's personal copy
705Eur J Appl Physiol (2017) 117:699–712
1 3
were analysed separately to minimise misinterpretation
of data due to circadian variation in physiology. Measure-
ments taken 12h after the HIIT session were obtained in
the morning (±06:30) and therefore were not directly com-
parable to those measured immediately before the HIIT
session (±17:30). Therefore, 12h data are expressed as
changes from baseline (pre HIIT), and these changes are
compared between conditions. Since the pre HIIT and 24h
variables were taken at the same time-of-day (±17:30),
they were compared directly. Correlations were performed
using Pearson’s product moment test and Spearman’s rank
correlation tests. Taking into account that (1) the typi-
cal error of measurement of the PPO test is 1% (Lamberts
2009), (2) the expected relative PPO in trained athletes is
5Wkg1 (Capostagno etal. 2014), and (3) sleep depriva-
tion may reduce aerobic performance by 3% (Oliver etal.
2009), sample size was estimated based on an expected dif-
ference in change of peak power output between the two
conditions of 4% (0.2 ± 0.1Wkg1). For an α level of 0.05
and a power of 95%, 15 participants were required. Data
were analysed using Stata (v12, StataCorp, TX, USA). Sig-
nificance was accepted when p 0.05.
Ecacy oftheHIIT session
Participants reported similar amounts of sleep (CON
7.59 ± 1.10h, DEP 7.88 ± 0.98h, p = 0.244) and had
similar weights (CON 77.4 ± 9.5kg, DEP 78.3 ± 9.4kg,
p = 0.369) prior to the HIIT sessions. Twenty-four-hours
after the HIIT session under the CON condition, cyclists
felt less motivated to train (p < 0.034, d = 2.07), reported
more muscle soreness (p = 0.014, d = 3.07) and tiredness
(p < 0.001, d = 5.09) and had lower resting SBP (p = 0.011,
d = 0.85), absolute PPO (p = 0.012, d = 0.44), and HRmax
(p < 0.001, d = 2.00) values. Collectively these data suggest
that the HIIT session disturbed the cyclists’ physiology,
producing a stimulus from which they needed to recover.
Eects ofpartial sleep deprivation onrecovery
Twelve hours after the HIIT session, participants were
sleepier (p = 0.001), felt less motivated to train (p = 0.018)
and demonstrated a blunted reduction in SBP (p = 0.012)
under the DEP condition compared to the CON condition
(Table3). Figure2 shows the individual 12h responses,
where data are expressed as percentage change (i.e. 12h
value minus Pre value) for the CON (open circles) and DEP
(closed circles) conditions. Compared to before the HIIT
session, all but two participants reported feeling sleepier
12h after the HIIT session in the DEP condition compared
to the CON condition (Fig.2a). Ten participants felt less
motivated to train after the HIIT under the DEP condi-
tion compared to the CON condition; for two cyclists the
change in motivation to train after the HIIT was the same
between conditions; and another two felt more motivated to
train 12h after the HIIT in the DEP condition compared
to the CON condition (Fig.2b). Twelve participants expe-
rienced a smaller reduction or even increase in SBP 12h
after the HIIT under the DEP condition, while four showed
larger reductions in SBP under the DEP condition (Fig.2c).
Twenty-four hour responses to the HIIT session are pre-
sented in Table4 and Figs.3 and 4. Dierences between
the conditions in sleepiness (p = 0.004) and SBP (p = 0.043)
persisted 24h after the HIIT session, accompanied by
Table 4 Twenty-four-hour responses to the HIIT session under the CON and DEP conditions
Data are presented as mean ± SD, (range)
CON control sleep condition, DEP partial sleep deprivation condition, HIIT high-intensity interval training, Pre prior to the HIIT session, 24h
24h after the HIIT session, HR heart rate, SBP systolic blood pressure, DBP diastolic blood pressure, PPO peak power output
The p value represents the time-by-condition interaction eect as determined using a two-way ANOVA with repeated measures. Cohen’s d indi-
cates eect size
nCON DEP d p value
Pre 24h Pre 24h
Sleepiness 16 5 ± 4 (0–14) 7 ± 3 (2–12) 6 ± 3 (2–13) 12 ± 6 (2–23) 1.41 0.004
Motivation to train 15 7 ± 2 (3–10) 6 ± 2 (2–10) 7 ± 1 (6–10) 5 ± 2 (2–8) 0.74 0.128
Muscle soreness 16 4 ± 3 (0–11) 7 ± 5 (0–19) 4 ± 3 (0–11) 7 ± 5 (0–15) 0.15 0.873
Muscle tiredness 16 5 ± 4 (1–13) 10 ± 5 (3–21) 5 ± 4 (0–14) 11 ± 5 (3–21) 0.29 0.344
Resting HR (bpm) 16 59 ± 6 (46–70) 58 ± 8 (46–78) 59 ± 7 (51–70) 60 ± 7 (50–74) 0.27 0.395
Resting SBP (mmHg) 16 128 ± 8 (106–138) 121 ± 8 (112–141) 127 ± 11 (100–146) 127 ± 9 (113–145) 0.75 0.043
Resting DBP (mmHg) 16 72 ± 9 (54–87) 70 ± 8 (59–88) 73 ± 7 (59–83) 71 ± 8 (58–83) 0.04 0.906
Absolute PPO (W) 16 352 ± 44 (291–424) 347 ± 40 (285–413) 355 ± 38 (295–423) 337 ± 36 (280–411) 1.98 0.004
Author's personal copy
706 Eur J Appl Physiol (2017) 117:699–712
1 3
Author's personal copy
707Eur J Appl Physiol (2017) 117:699–712
1 3
a time-by-condition interaction eect for absolute PPO
(p = 0.004, d = 1.98, Table4). Individual responses for
relative PPO, sleepiness, motivation to train and SBP are
plotted in Fig.3. The left hand panel shows the absolute
data for the Pre, 12h and 24h time points, and the right
hand panel shows the 24h percentage change in the vari-
able (i.e. 24h value minus Pre value) for the CON (open
circles) and DEP (closed circles) conditions.
Relative PPO was lower 24h after the HIIT session
under the DEP condition compared to the CON condition
(p = 0.005, d = 1.89, Fig.3a). Relative PPO was lower
24h after the HIIT session under the DEP condition com-
pared to the CON condition (p = 0.005, d = 1.89, Fig.3a).
As indicated by the slope of the connecting line in Fig.3b,
12 cyclists had greater reductions in relative PPO 24h
after the HIIT session under the DEP condition compared
to the CON condition, three had greater reductions under
the CON compared to the DEP condition and for one the
reduction in PPO was not dierent between the conditions
(Fig.3b). As indicated by the slope of the connecting line
in Fig.3d, 15 cyclists were sleepier 24h after the HIIT ses-
sion under the DEP compared to the CON condition, while
one was less sleepy 24h after the DEP compared to the
CON condition (Fig.3d). All but three cyclists had smaller
reductions or even increases in SBP 24h after the HIIT ses-
sion under the DEP condition compared to the CON con-
dition (Fig.3h). Motivation to train was no longer dier-
ent between the groups (d = 0.74, Table4). Sleep duration
was correlated with change in relative PPO, but was not
significant for either condition (CON: r = 0.21, p = 0.457,
n = 16; DEP: r = 0.42, p = 0.119, n = 16).
Individual responses for HRmax, VO2max and RERmax
reached during the PPO are plotted in Fig.4. As for Fig.3,
the left hand panel shows the absolute data and the right
hand panel shows the 24h percentage change in the
variable. The extent to which HRmax was reduced at 24h
was not dierent between conditions (Fig.4a), (d = 0.81).
For all but three of the cyclists HRmax was lower during
the PPO completed 24h after the HIIT compared to before
(Pre) under the DEP condition compared to the CON con-
dition (Fig.4b). Neither VO2max nor RERmax changed
dierently in the DEP compared to the CON condition
(Fig.4c, e, respectively), and the individual responses were
varied (Fig.4d, f, respectively).
The cyclists had similar changes in CK activity, sIgA
secretion rate and leukocyte cell counts at 12 and 24h
after the HIIT session under both sleep conditions (Online
Resource 1, FiguresS1 and S2, Tables S1 and S2, respec-
tively). Time eects indicated that WBC (p = 0.020) and
neutrophil (p < 0.001) counts were lower and basophil
counts (p < 0.001) higher 24h after the HIIT session.
Athletes have identified sleep as an important recovery
strategy (Venter 2014; Kölling etal. 2016; Fullagar etal.
2016). This study provides some support for this concept.
The main finding was that peak power output was reduced
to a larger extent 24h after the HIIT session when cyclists
were partially sleep deprived compared to having had a
normal night of sleep. In addition, the cyclists reported
higher levels of sleepiness and less motivation to train,
and the HIIT-induced reduction in resting systolic blood
pressure the following day was blunted in the partial sleep
deprivation condition. Collectively these data suggest that
recovery from a HIIT session is compromised when fol-
lowed by a single night of partial sleep deprivation, and
that a night of normal sleep facilitates near full recovery in
maximal performance capacity.
The extent to which relative peak power was decreased
following the HIIT session in the DEP condition was 5%,
compared to a reduction of only 1% in the CON condition.
Alternatively, a normal night of sleep following HIIT may
facilitate up to 99% of recovery from the training stimu-
lus, while one night of partial sleep deprivation may limit
this recovery to approximately 95%. Laboratory measure-
ment of peak power output is routinely used to assess per-
formance in cycling since it is well correlated with time-
trial performance (Hawley and Noakes 1992). In terms of
performance amongst elite cyclists, where a 1% alteration
in performance is considered to be important (Currell and
Jeukendrup 2008), the observed 5% reduction appears to be
The variation in the extent to which peak power was
aected in the recovery phase of the HIIT session under the
partial sleep deprivation condition demonstrates individual
sensitivity of physical performance to sleep loss. Twelve of
Fig. 3 Individual peak power output (a, b), sleepiness (c, d), motiva-
tion to train (e, f) and resting systolic blood pressure (g, h) responses
to the HIIT session under the CON and DEP conditions. In the left
panel (a, c, e, g), individual raw data are plotted for the pre HIIT,
12 and 24h time points. The 12h data are for visual purposes, and
are not included in the statistical analysis. The right panel (b, d, f,
h) compares individual responses to each condition, where each data
point represents an individual’s response to the HIIT session (24h
minus Pre), expressed as percentage change. Open and closed circles
represent the normal sleep (CON) and partial sleep deprivation (DEP)
conditions, respectively. The dotted line at y = 0 (right panel) repre-
sents no change in the variable measured before and 24h after the
HIIT session (i.e. 0% change). The slope of the connecting line indi-
cates the extent to which the change in a variable after HIIT was dif-
ferent in the DEP compared to the CON condition, in one individual.
PPO peak power output, DBP systolic blood pressure, Pre prior to
the HIIT session, 12h 12h after the HIIT session, 24h 24h after the
HIIT session. Significance was determined using a two-way ANOVA
with repeated measures (left panel) and a dependent t test (right
panel). Cohen’s d indicates eect size
Author's personal copy
708 Eur J Appl Physiol (2017) 117:699–712
1 3
the 16 cyclists had greater reductions in peak power when
partially sleep deprived, one experienced similar levels
of power reduction between the two conditions, and one
showed a smaller power decrement after partial sleep dep-
rivation and two actually showed improvements in power
after the sleep deprivation intervention.
Possible explanations for dierences in susceptibility
to sleep deprivation and the ensuing influence on maxi-
mal performance may relate to chronotype or habitual bed
times or sleep lengths (Taillard etal. 2003). For example,
evening-types may cope better with sleep deprivation com-
pared to morning-types (Taillard etal. 2011; Barclay and
Myachykov 2016). Individuals with a large sleep debt may
appear to be less susceptible to the eects of one night of
partial sleep deprivation, as their baseline performance
may be impaired by their sleep debt. Change in relative
peak power under the CON and DEP conditions were cor-
related against chronotype score, usual bed time and total
sleep duration, however, no significant relationships were
observed (data not shown). One explanation for this may
be that these cyclists were more morning-oriented than the
general South African population (Kunorozva etal. 2012;
Henst etal. 2015) and lacked any evening-types. Thus the
potentially moderating role of chronotype on sleep depriva-
tion and the ensuing recovery could not be assessed. Future
work would need to be done in this area to test this concept.
The 24h reduction in peak power observed in both con-
ditions occurred in the absence of any change in VO2max.
This suggests a dissociation between VO2max and peak
power, and potentially a lower gross eciency (Noordhof
etal. 2010) 24h after the HIIT session; an eect which
was not altered by recovery sleep length. Neither did sleep
length appear to moderate the reduction in maximum RER
observed 24h after the HIIT session. Together these data
suggest that any perturbances to the metabolic system
induced by the HIIT were able to recover similarly regard-
less of sleep length.
An acute, exhausting bout of exercise, such as HIIT, is
understood to both increase blood volume (Convertino
1991) and alter the sympathovagal balance in favour of
the parasympathetic system (Bellenger etal. 2016). Thus
when physically tired or unrecovered individuals exercise
maximally, they typical reach lower maximum heart rates
(Le Meur etal. 2013), indicative of reduced sympathetic
drive. The reductions of resting SBP and maximum heart
observed in the HIIT recovery phase following normal
sleep in the present study are understood to reflect these
changes, and have been shown previously (Tochikubo etal.
In the partial sleep deprivation condition, however, the
HIIT-induced reduction in resting SBP did not occur. In
contrast to a fatiguing bout of exercise, acute sleep dep-
rivation is thought to increase sympathetic and decrease
parasympathetic contributions to cardiovascular modula-
tion (Zhong 2005). One of the ways in which this could
manifest is via an increase in resting SBP the day after sleep
deprivation (Lusardi etal. 1996). Therefore, one might
hypothesise that the smaller post-HIIT session reduction
in resting SBP pressure observed in this study under the
DEP condition suggests that any exercise-induced increase
in parasympathetic activity may have been marginalised
by the sleep deprivation-induced increase in sympathetic
activity. Future research investigating the potential role of
the autonomic nervous system on physical recovery in the
sleep deprived state is warranted.
Both intense exercise in athletes and sleep deprivation
are understood to decrease innate and acquired immunity
(Faraut etal. 2012; Walsh and Oliver 2016). Although this
study observed some blood leukocyte changes 24h after the
HIIT, likely representative of tracking (Dhabhar 2002),
the extent to which this occurred was not dierent between
the conditions. Therefore perturbation of the immune sys-
tem does not appear to have a role in recovery from a single
bout of HIIT under a condition of partial sleep deprivation.
Although not measured in this study, the longer term
consequences of repeated nights of partial sleep depriva-
tion on recovery should be considered. Not only could
adaptation to training be compromised, but repeated train-
ing sessions with insucient sleep in the recovery phase
may increase the risk for non-functional overreaching and
ultimately overtraining (Lehmann etal. 1997). To improve
performance, athletes finely balance training stimulus with
recovery; since increasing training load elicits a greater
adaptation (functional overreaching), while insucient
recovery results in a maladaptive response (non-functional
overreaching) (Meeusen etal. 2013). Longer term non-
functional overreaching is understood to eventually lead
to a state of overtraining, in which accumulated train-
ing stresses result in long term performance decrements
and fatigue, recovery from which may take several weeks
(Meeusen etal. 2013). The data from this study suggest
that inadequate sleep following a single high load training
session increases the time needed to recover. Therefore,
one might speculate that regular short sleep patterns dur-
ing high load training periods may result in non-functional
overreaching. In contrast, athletes with sucient sleep
during such periods may be more likely to remain in the
functional overreaching zone, maximising adaptation to
A limitation to this study is that neither heart rate vari-
ability nor catecholamine levels were measured to assess
alterations in autonomic nervous system function. Fur-
thermore, given the relatively small sample size, and fairly
large variation in ability of these cyclists, the results need to
be interpreted with caution. Another limitation is that two
of the participants consumed one caeine beverage each in
Author's personal copy
709Eur J Appl Physiol (2017) 117:699–712
1 3
Fig. 4 Individual maximum heart rate (a, b), oxygen uptake (c, d)
and respiratory exchange ratio (e, f) responses to the HIIT session
under the CON and DEP conditions. In the left panel (a, c, e), indi-
vidual raw data are plotted for the pre HIIT and 24h time points.
The right panel (b, d, f) compares individual responses to each con-
dition, where each data point represents an individual’s response to
the HIIT session (24h minus Pre), expressed as percentage change.
Open and closed circles represent the normal sleep (CON) and par-
tial sleep deprivation (DEP) conditions, respectively. The dotted line
at y = 0 (right panel) represents no change in the variable measured
before and 24h after the HIIT session (i.e. 0% change). The slope
of the connecting line indicates the extent to which the change in a
variable after HIIT was dierent in the DEP compared to the CON
condition, in one individual. HRmax maximum heart rate, VO2max max-
imum oxygen uptake volume, RERmax maximum respiratory exchange
ratio, Pre prior to the HIIT session, 24h 24h after the HIIT session.
Significance was determined using a two-way ANOVA with repeated
measures (left panel) and a dependent t-test (right panel). Cohen’s d
indicates eect size
Author's personal copy
710 Eur J Appl Physiol (2017) 117:699–712
1 3
the 24h period prior to their Baseline session, following
which they were partially sleep deprived. Ingested caeine
may enhance physical performance, although the eects are
varied and depend on factors such as dose, form, individual
tolerance to caeine and type of exercise performed (Rog-
ers and Dinges 2005; Spriet 2014). Therefore the baseline
performance of these two athletes may have been artifi-
cially raised. The performance analysis for this study was
repeated excluding these two individuals, but the results
for peak and absolute power remained the same (data not
shown). Although individuals with diagnosed sleep disor-
ders were excluded from this study, the WASO and num-
ber of awakenings measured using the Actiwatch during the
participant’s usual sleep are higher than expected. While
these data cannot be used diagnostically, it is possible that
some of these individuals may have had an undiagnosed
sleep disorder. Alternatively, these variables measured with
the Actiwatch are not directly comparable to those obtained
using polysomnography.
While there is a significant body of work describing the
eects of sleep deprivation on physical performance, few
studies have investigated the consequences of limited sleep
on recovery from exercise. This study confirms a role for
sleep in recovery from training and contributes to our
understanding of the physiological eects of partial sleep
deprivation on recovery. It diers from and is complemen-
tary to previous similar studies (McMurray and Brown
1984; Skein etal. 2013) in that it used a model of partial
sleep deprivation for only one night and tested endurance
sports athletes recovering from a single training session.
From the perspective of athletes and coaches, these
data suggest that recovery of the systems needed to pro-
duce maximal eorts is incomplete when an athlete does
not obtain a full night of sleep following a HIIT session.
The consequence of this may be that if a training session is
scheduled for the following day, the athlete may not be able
to work at a high intensity. In part, this may be reinforced
by the athlete’s lower level of motivation to train follow-
ing sleep restriction. In periods of training where intensity
is more important than duration, an inability to train at the
prescribed intensity may mean that a lower training stimu-
lus is applied, and as a consequence, the level of adaptation
induced might be lower.
• Future research in this area could investigate individual
sensitivity to sleep deprivation on recovery, paying par-
ticular attention to the role of chronotype. Given that
there is some evidence to suggest that a number of ath-
letes suer from poor quality sleep, the eect of frag-
mented sleep on recovery deserves attention. Finally, it
remains to be determined whether or not chronic insu-
cient sleep in the recovery phase of training predisposes
an athlete to worsened performance or non-functional
Acknowledgements Thanks to the cyclists for volunteering; Hendr-
iena Victor, David Leith and Chris Webster for help with data collec-
tion; and Mike Lambert for input into the study design. This study
was funded through a Research Development Grant from the Univer-
sity of Cape Town (DER), South African National Research Founda-
tion Incentive Funding for Rated Researchers (AJM and TAK), and
TAK is a recipient of the Tim and Marilyn Noakes Sports Science
Postdoctoral Fellowship.
Compliance with ethical standards
Conflict of interest The authors have no conflicts of interest to de-
Akerstedt T, Nilsson PM (2003) Sleep as restitution: an introduction.
J Int Med 254:6–12
Barclay NL, Myachykov A (2016) Sustained wakefulness and
visual attention: moderation by chronotype. Exp Brain Res.
Beelen M, Burke LM, Gibala MJ, van Loon L JC (2010) Nutritional
strategies to promote postexercise recovery. Int J Sport Nutr
Exerc Metab 20:515–532
Bellenger CR, Fuller JT, Thomson RL, etal (2016) Monitoring ath-
letic training status through autonomic heart rate regulation: a
systematic review and meta-analysis. Sports Med 46:1461–1486.
Bergeron MF, Mountjoy M, Armstrong N etal (2015) Interna-
tional Olympic Committee consensus statement on youth ath-
letic development. Br J Sports Med 49:843–851. doi:10.1136/
Borg G (1998) Borg’s perceived exertion and pain scales. Human
Kinetics, Champaign
Buysse DJ, Reynolds CF, Monk TH etal (1989) The Pittsburgh Sleep
Quality Index: a new instrument for psychiatric practice and
research. Psychiatry Res 28:193–213
Capostagno B, Lambert MI, Lamberts RP (2014) Standardized ver-
sus customized high-intensity training: eects on cycling per-
formance. Int J Sports Physiol Perform 9:292–301. doi:10.1123/
Convertino VA (1991) Blood volume: its adaptation to endurance
training. Med Sci Sport Exerc 23:1338–1348.
Currell K, Jeukendrup AE (2008) Validity, reliability and sensitivity
of measures of sporting performance. Sports Med 38:297–316
Dattilo M, Antunes HKM, Medeiros A etal (2011) Medical
hypotheses. Med Hypotheses 77:220–222. doi:10.1016/j.
Dhabhar FS (2002) Stress-induced augmentation of immune func-
tion–the role of stress hormones, leukocyte tracking, and
cytokines. Brain Behav Immun 16:785–798
Faraut B, Boudjeltia KZ, Vanhamme L, Kerkhofs M (2012)
Immune, inflammatory and cardiovascular consequences of
Author's personal copy
711Eur J Appl Physiol (2017) 117:699–712
1 3
sleep restriction and recovery. Sleep Med Rev 16:137–149.
Fullagar HHK, Skorski S, Dueld R etal (2016) Impaired sleep and
recovery after night matches in elite football players. J Sport Sci
34:1333–1339. doi:10.1080/02640414.2015.1135249
Gupta L, Morgan K, Gilchrist S (2016) Does elite sport degrade
sleep quality? A systematic review. Sports Med. doi:10.1007/
Hawley JA, Noakes TD (1992) Peak power output predicts maximal
oxygen uptake and performance time in trained cyclists. Eur J
Appl Physiol Occup Physiol 65:79–83.
Heinonen I, Kalliokoski KK, Hannukainen JC etal (2014) Organ-spe-
cific physiological responses to acute physical exercise and long-
term training in humans. Physiology 29:421–436. doi:10.1152/
Henst RHP, Jaspers RT, Roden LC, Rae DE (2015) A chronotype
comparison of South African and Dutch marathon runners: The
role of scheduled race start times and eects on performance.
Chronobiol Int 32:858–868. doi:10.3109/07420528.2015.10488
Horne JA, Östberg O (1976) A self-assessment questionnaire to deter-
mine morningness-eveningness in human circadian rhythms. Int
J Chronobiol 4:97–110
Johns MW (1991) A new method for measuring daytime sleepiness:
the Epworth sleepiness scale. Sleep 14:540–545
Kölling S, Steinacker JM, Endler S etal (2016) The longer the bet-
ter: sleep–wake patterns during preparation of the World Rowing
Junior Championships. Chronobiol Int 33:73–84. doi:10.3109/07
Kunorozva L, Stephenson KJ, Rae DE, Roden LC (2012) Chronotype
and PERIOD3 variable number tandem repeat polymorphism in
individual sports athletes. Chronobiol Int 29:1004–1010. doi:10.
Lamberts R (2009) Measurement error associated with performance
testing in well-trained cyclists: application to the precision of
monitoring changes in training status. Int Sport Med J 10:33–44.
Lamberts RP, Swart J, Noakes TD, Lambert MI (2011) A novel
submaximal cycle test to monitor fatigue and predict cycling
performance. Br J Sports Med 45:797–804. doi:10.1136/
Le Meur Y, Hausswirth C, Natta F etal (2013) A multidisci-
plinary approach to overreaching detection in endurance
trained athletes. J Appl Physiol 114:411–420. doi:10.1152/
Lehmann MJ, Lormes W, Opitz-Gress A etal (1997) Training and
overtraining: an overview and experimental results in endurance
sports. J Sports Med Phys Fitness 37:7–17
Lusardi P, Mugellini A, Preti P etal (1996) Eects of a restricted
sleep regimen on ambulatory blood pressure monitoring in nor-
motensive subjects. Am J Hypertens 9:503–505
MacRae BA, Cotter JD, Laing RM (2011) Compression gar-
ments and exercise: garment considerations, physi-
ology and performance. Sports Med 41:815–843.
McKune AJ, Smith LL, Semple SJ etal (2006) Changes in mucosal
and humoral atopic-related markers and immunoglobulins in
elite cyclists participating in the Vuelta a España. Int J Sports
Med 27:560–566. doi:10.1055/s-2005-865858
McMurray RG, Brown CF (1984) The eect of sleep loss on high
intensity exercise and recovery. Aviat Space Environ Med
Meeusen R, Duclos M, Foster C, etal (2013) Prevention, diagnosis,
and treatment of the overtraining syndrome: joint consensus
statement of the European College of Sport Science and the
American College of Sports Medicine. Med Sci Sport Exerc
45:186–205. doi:10.1249/MSS.0b013e318279a10a
Noordhof DA, de Koning JJ, van Erp T etal (2010) The between
and within day variation in gross eciency. Eur J Appl Physiol
109:1209–1218. doi:10.1007/s00421-010-1497-4
Oliver SJ, Costa RJS, Laing SJ etal (2009) One night of sleep dep-
rivation decreases treadmill endurance performance. Eur J
Appl Physiol 107:155–161. doi:10.1007/s00421-009-1103-9
Poppendieck W, Faude O, Wegmann M, Meyer T (2013) Cooling
and performance recovery of trained athletes: a meta-analyti-
cal review. Int J Sports Physiol Perform 8:227–242
Reilly T, Ekblom B (2007) The use of recovery methods post-exer-
cise. J Sport Sci 23:619–627. doi:10.1080/02640410400021302
Reilly T, Piercy M (1994) The eect of partial sleep deprivation
on weight-lifting performance. Ergonomics 37:107–115.
Rogers NL, Dinges DF (2005) Caeine: implications for alert-
ness in athletes. Clin Sports Med 24:e1–e13. doi:10.1016/j.
Ruiz FS, Andersen ML, Martins RCS etal (2012) Immune altera-
tions after selective rapid eye movement or total sleep depri-
vation in healthy male volunteers. Innate Immun 18:44–54.
Sargent C, Halson S, Roach GD (2013) Sleep or swim? Early-morn-
ing training severelyrestricts the amount of sleep obtained by
elite swimmers. Eur J Sport Sci 14:S310–S315. doi:10.1080/1
Schaal K, Le Meur Y, Louis J, etal (2015) Whole-body cry-
ostimulation limits overreaching in elite synchronized swim-
mers. Med Sci Sport Exerc 47:1416–1425. doi:10.1249/
Scott JPR, McNaughton LR, Polman RCJ (2006) Eects of
sleep deprivation and exercise on cognitive, motor perfor-
mance and mood. Physiol Behav 87:396–408. doi:10.1016/j.
Skein M, Dueld R, Minett GM etal (2013) The eect of over-
night sleep deprivation after competitive rugby league matches
on postmatch physiological and perceptual recovery. Int J
Sports Physiol Perform 8:556–564
Souissi N, Sesboüé B, Gauthier A etal (2003) Eects of one
night’s sleep deprivation on anaerobic performance the fol-
lowing day. Eur J Appl Physiol 89:359–366. doi:10.1007/
Spriet LL (2014) Exercise and sport performance with low
doses of caeine. Sports Med 44:175–184. doi:10.1007/
Swinbourne R, Gill N, Vaile J, Smart D (2015) Prevalence of poor
sleep quality, sleepiness and obstructive sleep apnoea risk factors
in athletes. Eur J Sport Sci 1–9. doi:10.1080/17461391.2015.11
Taillard J, Philip P, Coste O etal (2003) The circadian and homeo-
static modulation of sleep pressure during wakefulness dif-
fers between morning and evening chronotypes. J Sleep Res
Taillard J, Philip P, Claustrat B etal (2011) Time course of neurobe-
havioral alertness during extended wakefulness in morning- and
evening-type healthy sleepers. Chronobiol Int 28:520–527. doi:1
Temesi J, Arnal PJ, Davranche K etal (2013) Does central
fatigue explain reduced cycling after complete sleep depri-
vation? Med Sci Sport Exerc 45:2243–2253. doi:10.1249/
Tochikubo O, Ikeda A, Miyajima E, Ishii M (1996) Eects of insu-
cient sleep on blood pressure monitored by a new multibiomedi-
cal recorder. Hypertension 27:1318–1324
Van Cauter E, Spiegel K, Tasali E, Leproult R (2008) Metabolic con-
sequences of sleep and sleep loss. Sleep Med 9(Suppl 1):S23–
S28. doi:10.1016/S1389-9457(08)70013-3
Author's personal copy
712 Eur J Appl Physiol (2017) 117:699–712
1 3
Venter RE (2014) Perceptions of team athletes on the importance of
recovery modalities. Eur J Sport Sci 14(Suppl 1):S69–S76. doi:1
Walsh NP, Oliver SJ (2016) Exercise, immune function and respira-
tory infection: An update on the influence of training and envi-
ronmental stress. Immunol Cell Biol 94:132–139. doi:10.1038/
Zhong X (2005) Increased sympathetic and decreased parasympa-
thetic cardiovascular modulation in normal humans with acute
sleep deprivation. J Appl Physiol 98:2024–2032. doi:10.1152/
Author's personal copy
... Restricted sleep (also referred to as 'partial sleep deprivation') occurs when an individual has the opportunity to sleep, but this is limited in duration from their normal sleep habit [47] and is often a result of delayed sleep onset (sometimes termed 'early restriction'), earlier than normal waking (sometimes termed 'late restriction'), or fragmented sleep, which is when one or more nocturnal awakenings occur [48] (Fig. 1 depicts the different types of sleep loss). The amount (e.g., deprivation/restriction) and type (e.g., early restriction/late restriction) of sleep loss incurred may have some influence on the magnitude of effect that insufficient sleep has on physical performance [49][50][51][52]. ...
... This resulted in 85 trials, in which 14 measured the same exercise task(s) multiple times (twice, e.g., once at 0600 h, then at 1800 h [43,53,62,64,[79][80][81]90] or more than two times [45,54,55]). Thirty-six trials (derived from 23 studies) reported only one outcome measure [45,49,50,54,55,66,67,110,111,116,[120][121][122][123][124][125][126][127][128][129][130][131][132], with the remaining trials yielding multiple outcome measures. This resulted in 227 separate outcome measures being included in the overall analysis. ...
... A number of investigations have attempted to identify mechanisms explaining the relationship between sleep loss and impaired exercise performance. Studies have explored changes to cardiorespiratory variables (e.g., V O 2peak [49,50,120,126,132], ventilation [41,49,93,110,120,126,132], heart rate [41,49,50,52,91,110,120,124,126,128,132,139], blood pressure [50]); perceived effort (measured via rating of perceived exertion) [41, 43, 44, 51, 52, 56, 57, 75, 86, 89-92, 95, 100, 110, 124, 132, 139]; muscle glycogen [91]; lactate [49,67,77,91,93,95,98,124,128,139]; catecholamines [67,121,126]; hormones (cortisol [43,55,63,67,75,84,127], testosterone [63,75,84,127], growth hormone [67], prolactin [67], melatonin [55], hepcidin [54], insulin [61]); body temperature (oral temperature [43,45,78,79,81,90,94,95,116] and core temperature [53,80,91,110]); immune function [44,50,54,62,127]; and neural drive [60,76,86,92]. However, it was not the intention of the present study to explore these mechanisms; rather our aim was to quantify the magnitude of effects that acute sleep loss has on exercise performance. ...
Full-text available
Background Sleep loss may influence subsequent physical performance. Quantifying the impact of sleep loss on physical performance is critical for individuals involved in athletic pursuits. Design Systematic review and meta-analysis. Search and Inclusion Studies were identified via the Web of Science, Scopus, and PsycINFO online databases. Investigations measuring exercise performance under ‘control’ (i.e., normal sleep, > 6 h in any 24 h period) and ‘intervention’ (i.e., sleep loss, ≤ 6 h sleep in any 24 h period) conditions were included. Performance tasks were classified into different exercise categories (anaerobic power, speed/power endurance, high-intensity interval exercise (HIIE), strength, endurance, strength-endurance, and skill). Multi-level random-effects meta-analyses and meta-regression analyses were conducted, including subgroup analyses to explore the influence of sleep-loss protocol (e.g., deprivation, restriction, early [delayed sleep onset] and late restriction [earlier than normal waking]), time of day the exercise task was performed (AM vs. PM) and body limb strength (upper vs. lower body). Results Overall, 227 outcome measures (anaerobic power: n = 58; speed/power endurance: n = 32; HIIE: n = 27; strength: n = 66; endurance: n = 22; strength-endurance: n = 9; skill: n = 13) derived from 69 publications were included. Results indicated a negative impact of sleep loss on the percentage change (%Δ) in exercise performance (n = 959 [89%] male; mean %Δ = − 7.56%, 95% CI − 11.9 to − 3.13, p = 0.001, I² = 98.1%). Effects were significant for all exercise categories. Subgroup analyses indicated that the pattern of sleep loss (i.e., deprivation, early and late restriction) preceding exercise is an important factor, with consistent negative effects only observed with deprivation and late-restriction protocols. A significant positive relationship was observed between time awake prior to the exercise task and %Δ in performance for both deprivation and late-restriction protocols (~ 0.4% decrease for every hour awake prior to exercise). The negative effects of sleep loss on different exercise tasks performed in the PM were consistent, while tasks performed in the AM were largely unaffected. Conclusions Sleep loss appears to have a negative impact on exercise performance. If sleep loss is anticipated and unavoidable, individuals should avoid situations that lead to experiencing deprivation or late restriction, and prioritise morning exercise in an effort to maintain performance.
... [17][18][19] Athletes' recovery after intense training may also be negatively impacted by acute sleep deprivation as measured by their performance in repeated physical tasks. Cyclists 20,21 and sprinters 22 recorded their baseline performance in a variety of measures (time trials, power output, sprint times) and then performed high-intensity training regimens, followed by normal or acutely restricted sleep. Upon repeated performance of the baseline measures, the acute sleep deprivation groups demonstrated worse performance (e.g., slower time trials, decreased peak power output, slower sprint times) than the control groups. ...
... Upon repeated performance of the baseline measures, the acute sleep deprivation groups demonstrated worse performance (e.g., slower time trials, decreased peak power output, slower sprint times) than the control groups. [20][21][22] These studies provide evidence to support the crucial role that sleep plays in recovery. ...
Full-text available
Young athletes commonly suffer from both acute and chronic sleep deprivation. This has been linked to increased injury rates and decreased athletic and neurocognitive performance. Conversely, sleep optimization in young athletes can lead to improved athletic performance and greater competitive success, with improvement in metrics such as speed, endurance, reaction time, accuracy, alertness, and overall well-being. When aiming to optimize sleep, key elements such as sleep duration, quality, and regularity must be addressed. Clinicians can assess baseline sleep hygiene in young athletes, and educate them on proper methods to optimize sleep. Such methods include limiting screen time before bed, getting exposure to sunlight in the early morning, maintaining an optimal bedroom temperature, avoiding caffeine, and maintaining a consistent sleep schedule throughout the week.
... Appropriate sleep (duration and quality) supports the maintenance of physical and psychological health in athletes (Nedelec et al., 2018;Walsh et al., 2021). Indeed, inadequate sleep quality and quantity are associated with increased injury risk (Milewski et al., 2014), reduced physical and cognitive performance (Romdhani et al., 2019;2021a) and compromised recovery (Rae et al., 2017;Romdhani et al., 2019). Additionally, the lockdown-induced reduction in training load (volume and/or intensity) could lead to detraining (Mujika and Padilla, 2000a, b). ...
Full-text available
Objective: To investigate the effect of 1) lockdown duration and 2) training intensity on sleep quality and insomnia symptoms in elite athletes. Methods: 1,454 elite athletes (24.1 ± 6.7 years; 42% female; 41% individual sports) from 40 countries answered a retrospective, cross-sectional, web-based questionnaire relating to their behavioral habits pre- and during- COVID-19 lockdown, including: 1) Pittsburgh sleep quality index (PSQI); 2) Insomnia severity index (ISI); bespoke questions about 3) napping; and 4) training behaviors. The association between dependent (PSQI and ISI) and independent variables (sleep, napping and training behaviors) was determined with multiple regression and is reported as semi-partial correlation coefficient squared (in percentage). Results: 15% of the sample spent < 1 month, 27% spent 1–2 months and 58% spent > 2 months in lockdown. 29% self-reported maintaining the same training intensity during-lockdown whilst 71% reduced training intensity. PSQI (4.1 ± 2.4 to 5.8 ± 3.1; mean difference (MD): 1.7; 95% confidence interval of the difference (95% CI): 1.6–1.9) and ISI (5.1 ± 4.7 to 7.7 ± 6.4; MD: 2.6; 95% CI: 2.3–2.9) scores were higher during-compared to pre-lockdown, associated (all p < 0.001) with longer sleep onset latency (PSQI: 28%; ISI: 23%), later bedtime (PSQI: 13%; ISI: 14%) and later preferred time of day to train (PSQI: 9%; ISI: 5%) during-lockdown. Those who reduced training intensity during-lockdown showed higher PSQI ( p < 0.001; MD: 1.25; 95% CI: 0.87–1.63) and ISI ( p < 0.001; MD: 2.5; 95% CI: 1.72–3.27) scores compared to those who maintained training intensity. Although PSQI score was not affected by the lockdown duration, ISI score was higher in athletes who spent > 2 months confined compared to those who spent < 1 month ( p < 0.001; MD: 1.28; 95% CI: 0.26–2.3). Conclusion: Reducing training intensity during the COVID-19-induced lockdown was associated with lower sleep quality and higher insomnia severity in elite athletes. Lockdown duration had further disrupting effects on elite athletes’ sleep behavior. These findings could be of relevance in future lockdown or lockdown-like situations (e.g., prolonged illness, injury, and quarantine after international travel).
... Considering the day following a competition is a potential training or competition day, such a significant sleep deprivation would have a great impact on performance. All indications show that sleep deprivation deteriorates athletes' physical performance (Keramidas et al., 2018), affects mood and sleepiness (Romdhani et al., 2019), deteriorates physiological responses , increases muscle damage and impairs the recovery from high-intensity exercise (Mejri et al., 2017;Rae et al., 2017). Furthermore, a recent review highlighted the link between sleep deprivation and injury (Fox et al., 2020), showing that a habitual sleep duration of less than 8 h per night increases musculoskeletal injury risk in young athletes. ...
The effect of a 40-min nap opportunity on physiological responses and specific abilities was investigated. Twelve high-level professional basketball players (26.33±5.2 years; 193.17±7.1 m; 87.48±11.2 kg) undertook randomly 40-min nap opportunity (NAP) and control condition (CON). Wellness (Hooper Index) and Epworth Sleepiness Scale (ESS) were measured before and after both conditions. Defensive (DA) and offensive (OA) agility and upper body power (UBP) were assessed after both conditions. Shooting skill (SST) performance was evaluated prior and after a fatiguing task (FT). Heart rate (HR) and rating of perceived exertion (RPE) were recorded during SST-test, FT and SST-retest. ESS, Hooper's stress and fatigue score were significantly lower after nap compared to those before nap (0.009 ≤ p ≤ 0.03). Better performance was obtained in NAP compared to CON condition for DA, OA and UBP (0.0005 ≤ p ≤ 0.02). SST performance was significantly higher in NAP compared to CON in the retest session (p = 0.003, Δ = 20.2%). The improved performance was associated with significant lower HRpeak (p = 0.01, Δ = 5.25%) and RPE (p = 0.003, Δ = 15.12%). In conclusion, NAP reduced sleepiness and stress and fatigue and enhances physical outcomes of specific skills in elite basketball players.
... Sleep disturbances influence acutely athletic performance. Chase et al. demonstrated that a single night of sleep restriction had a significant negative impact on athletic performance the following morning (68), while Rae et al. showed that even recovery from exercise was diminished after a single night of sleep deprivation (69). Concomitantly, two -night sleep deprivation affects executive function, as it causes central fatigue, signifying fewer high threshold motor units that can be recruited and, therefore, fewer muscle fibers will be activated to produce work (70). ...
Full-text available
Obstructive Sleep Apnea Syndrome (OSAS) is a sleep disorder with high prevalence in general population, but alarmingly low in clinicians' differential diagnosis. We reviewed the literature on PubMed and Scopus from June 1980–2021 in order to describe the altered systematic pathophysiologic mechanisms in OSAS patients as well as to propose an exercise program for these patients. Exercise prevents a dysregulation of both daytime and nighttime cardiovascular autonomic function, reduces body weight, halts the onset and progress of insulin resistance, while it ameliorates excessive daytime sleepiness, cognitive decline, and mood disturbances, contributing to an overall greater sleep quality and quality of life.
... 14 Indeed, important physical (endurance performance, anaerobic capacity and strength) and cognitive aspects of exercise performance have been shown to decrease following sleep loss. [15][16][17] Moreover, reduced basal and post-exercise antioxidant capacity 18 as well as increased muscle damage biomarkers during 19 and post exercise 20 have been observed in athletes following a night of partial sleep deprivation. ...
Full-text available
Mid‐day napping has been recommended as a countermeasure against sleep debt and an effective method for recovery, regardless of nocturnal sleep duration. Herein, we summarize the available evidence regarding the influence of napping on exercise and cognitive performance as well as the effects of napping on athletes’ perceptual responses prior to or during exercise. The existing studies investigating the influence of napping on athletic performance have revealed equivocal results. Prevailing findings indicate that following a normal sleep night or after a night of sleep loss, a mid‐day nap may enhance or restore several exercise and cognitive performance aspects, while concomitantly provide benefits on athletes’ perceptual responses. Most, but not all, findings suggest that compared to short‐term naps (20‐30 min), long‐term ones (>35‐90 min) appear to provide superior benefits to the athletes. The underlying mechanisms behind athletic performance enhancement following a night of normal sleep or the restoration after a night of sleep loss are not clear yet. However, the absence of benefits or even the deterioration of performance following napping in some studies is likely the result of sleep inertia. The present review sheds light on the predisposing factors that influence the post‐nap outcome, such as nocturnal sleep time, mid‐day nap duration and the time elapsed between the end of napping and the subsequent testing, discusses practical solutions and stimulates further research on this area.
... Many studies have identified the severe effect of even partial sleep deprivation on recovery from intense exercise. For instance, a recent study of cyclists (Rae et al. 2017) looked at the effect of a single night of disturbed sleep on recovery from an intense exercise session. All of the participants experienced a significant reduction in performance and reported feeling sleepier and less motivated to train. ...
Full-text available
Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of training for a marathon, combined with the availability of real-time activity data, provide a unique and worthwhile opportunity for machine learning and for recommender systems techniques to support runners as they train, race, and recover. We present a number of case studies—a mix of original research plus some recent results—to highlight what can be achieved using the type of activity data that is routinely collected by the current generation of mobile fitness apps, smart watches, and wearable sensors.
... Indeed, the beneficial effects of optimal sleep on human cognition and motor functioning are well documented (Daviaux et al., 2014;Frey et al., 2004). Disturbances in sleep-wake behaviour, common among sedentary subjects (Yang et al., 2017) and athletes (Rae et al., 2017;Roberts et al., 2019;Romdhani et al., 2019) outside of Ramadan, could be exacerbated during Ramadan (Faris et al., 2019;Trabelsi et al., 2020). In a recent systematic review and meta-analysis conducted in a sample of sedentary (i.e., low level of physical activity) and active individuals (i.e., high level of physical activity), Faris et al. (2019) reported that, compared to before Ramadan, sleep duration decreased by approximately 1 hour during Ramadan. ...
The aim of the present study was to evaluate the impact of Ramadan fasting on sleep quality and daytime sleepiness in team sport referees. Seventy-eight male amateur team sport referees (age: 31.1 ± 10.8 years) participated in this study. Participants responded to the Arabic version of the Pittsburgh Sleep Quality Index (PSQI) and the Epworth sleepiness scale (ESS) questionnaires before (10-days prior) and during (last 7-days) the month of Ramadan. PSQI and ESS scores increased significantly during Ramadan (both p < .001, ES = 0.56 and 0.54, respectively) with 83.3% of participants scoring ≥5 in the PSQI. The percentage of participants suffering from severe excessive daytime sleepiness (ESS score ≥ 16) was 3.8% before vs. 7.7% during Ramadan (p < 0.001). Sleep duration decreased by ~ 1 h during Ramadan (p < .001, ES = 0.61) and was associated with a delay in bedtime of ~ 2 h (p < 0.001, ES = 0.7) and of wake-up time of ~ 1 h (p < 0.001, ES = 0.5). The score for daytime dysfunction and subjective sleep perception, as components of the PSQI, increased (both p < 0.001; ES = 0.79, ES = 0.57, respectively), whereas the score for the use of sleep medication decreased during vs. before Ramadan (p = 0.041, ES = 0.47). Ramadan fasting impaired sleep quality and increased daytime sleepiness in team sport referees. Future studies, using objective assessment tools, are warranted.
Full-text available
Objective: Competitive athletes must undergo fitness testing to monitor athlete progress and to create appropriate, progressive training programs. However, fitness testing adds to training stress; therefore, impacts of testing on wellness and recovery must be considered in test selection. This study investigated the effects of two incremental field tests [VAMEVAL test (T-VAM) and 20-m maximum shuttle test (20-m MST)] on wellness, total quality of recovery (TQR) and physical enjoyment (PE) in competitive soccer players. Subjects and methods: Twenty-two soccer players (20.9±1.5 years) completed two T-VAM and two 20-m MST in a randomized order on separate days with a 1-week interval between tests. TQR and wellness indices (sleep, fatigue, stress and muscle soreness) measures were collected before and 24 hours after each test. Heart rate (HR) was continuously monitored during each test. Rating of perceived exertion (RPE) and PE were assessed after each test. Results: T-VAM resulted in higher PE, TQR and wellness scores than 20-m MST (p<0.05). T-VAM and 20-m MST resulted in similar HR and maximal aerobic speed. For T-VAM, TQR was correlated (p<0.01) with RPE and wellness indices. For 20-m MST, TQR was correlated (p<0.01) with wellness indices. HRmax and RPE were not correlated with wellness indices, TQR or PE. Conclusions: Overall, T-VAM and 20-m MST produced similar aerobic fitness testing results, but athletes responded more favorably to T-VAM. Coaches can use T-VAM for evaluating aerobic fitness while maximizing well-being and physical enjoyment among soccer players.
To date, scientists have listed sleep as a fundamental behavior for the promotion and maintenance of health, while inadequate sleep is attributed to a series of significant organic changes, especially metabolic ones, hormonal, immunological, and cognitive. Collectively these changes can disrupt skeletal muscle homeostasis and compromise its normal functioning include alteration in protein turnover, morphology, and functionality. Skeletal muscle tissue is one of the most dynamic and plastic tissues of the human body, with the main function related to locomotion and support of the skeleton. In addition, currently has been seen as an important modulator of body metabolism, immune system activity, moreover to its endocrine function. Therefore, understanding the bilateral influence between sleep and skeletal muscle seems to be essential to the adoption of intervention strategies that are capable of promotion or maintenance of the population’s quality of life.
Full-text available
Background Information on sleep quality and insomnia symptomatology among elite athletes remains poorly systematised in the sports science and medicine literature. The extent to which performance in elite sport represents a risk for chronic insomnia is unknown. Objectives The purpose of this systematic review was to profile the objective and experienced characteristics of sleep among elite athletes, and to consider relationships between elite sport and insomnia symptomatology. Methods Studies relating to sleep involving participants described on a pre-defined continuum of ‘eliteness’ were located through a systematic search of four research databases: SPORTDiscus, PubMed, Science Direct and Google Scholar, up to April 2016. Once extracted, studies were categorised as (1) those mainly describing sleep structure/patterns, (2) those mainly describing sleep quality and insomnia symptomatology and (3) those exploring associations between aspects of elite sport and sleep outcomes. ResultsThe search returned 1676 records. Following screening against set criteria, a total of 37 studies were identified. The quality of evidence reviewed was generally low. Pooled sleep quality data revealed high levels of sleep complaints in elite athletes. Three risk factors for sleep disturbance were broadly identified: (1) training, (2) travel and (3) competition. Conclusion While acknowledging the limited number of high-quality evidence reviewed, athletes show a high overall prevalence of insomnia symptoms characterised by longer sleep latencies, greater sleep fragmentation, non-restorative sleep, and excessive daytime fatigue. These symptoms show marked inter-sport differences. Two underlying mechanisms are implicated in the mediation of sport-related insomnia symptoms: pre-sleep cognitive arousal and sleep restriction.
Full-text available
IntroductionAttentional networks are sensitive to sleep deprivation and increased time awake. However, existing evidence is inconsistent and may be accounted for by differences in chronotype or time-of-day. We examined the effects of sustained wakefulness over a normal “socially constrained” day (following 18 h of sustained wakefulness), following a night of normal sleep, on visual attention as a function of chronotype. Methods Twenty-six good sleepers (mean age 25.58; SD 4.26; 54 % male) completed the Attention Network Test (ANT) at two time points (baseline at 8 am; following 18-h sustained wakefulness at 2 am). The ANT provided mean reaction times (RTs), error rates, and the efficiency of three attentional networks—alerting, orienting, and executive control/conflict. The Morningness–Eveningness Questionnaire measured chronotype. ResultsMean RTs were longer at time 2 compared to time 1 for those with increasing eveningness; the opposite was true for morningness. However, those with increasing morningness exhibited longer RT and made more errors, on incongruent trials at time 2 relative to those with increasing eveningness. There were no significant main effects of time or chronotype (or interactions) on attentional network scores. Conclusion Sustained wakefulness produced differential effects on visual attention as a function of chronotype. Whilst overall our results point to an asynchrony effect, this effect was moderated by flanker type. Participants with increasing eveningness outperformed those with increasing morningness on incongruent trials at time 2. The preservation of executive control in evening-types following sustained wakefulness is likely driven by differences in circadian phase between chronotypes across the day.
Full-text available
Background Autonomic regulation of heart rate (HR) as an indicator of the body’s ability to adapt to an exercise stimulus has been evaluated in many studies through HR variability (HRV) and post-exercise HR recovery (HRR). Recently, HR acceleration has also been investigated. Objective The aim of this systematic literature review and meta-analysis was to evaluate the effect of negative adaptations to endurance training (i.e., a period of overreaching leading to attenuated performance) and positive adaptations (i.e., training leading to improved performance) on autonomic HR regulation in endurance-trained athletes. Methods We searched Ovid MEDLINE, Embase, CINAHL, SPORTDiscus, PubMed, and Academic Search Premier databases from inception until April 2015. Included articles examined the effects of endurance training leading to increased or decreased exercise performance on four measures of autonomic HR regulation: resting and post-exercise HRV [vagal-related indices of the root-mean-square difference of successive normal R–R intervals (RMSSD), high frequency power (HFP) and the standard deviation of instantaneous beat-to-beat R–R interval variability (SD1) only], and post-exercise HRR and HR acceleration. Results Of the 5377 records retrieved, 27 studies were included in the systematic review and 24 studies were included in the meta-analysis. Studies inducing increases in performance showed small increases in resting RMSSD [standardised mean difference (SMD) = 0.58; P < 0.001], HFP (SMD = 0.55; P < 0.001) and SD1 (SMD = 0.23; P = 0.16), and moderate increases in post-exercise RMSSD (SMD = 0.60; P < 0.001), HFP (SMD = 0.90; P < 0.04), SD1 (SMD = 1.20; P = 0.04), and post-exercise HRR (SMD = 0.63; P = 0.002). A large increase in HR acceleration (SMD = 1.34) was found in the single study assessing this parameter. Studies inducing decreases in performance showed a small increase in resting RMSSD (SMD = 0.26; P = 0.01), but trivial changes in resting HFP (SMD = 0.04; P = 0.77) and SD1 (SMD = 0.04; P = 0.82). Post-exercise RMSSD (SMD = 0.64; P = 0.04) and HFP (SMD = 0.49; P = 0.18) were increased, as was HRR (SMD = 0.46; P < 0.001), while HR acceleration was decreased (SMD = −0.48; P < 0.001). Conclusions Increases in vagal-related indices of resting and post-exercise HRV, post-exercise HRR, and HR acceleration are evident when positive adaptation to training has occurred, allowing for increases in performance. However, increases in post-exercise HRV and HRR also occur in response to overreaching, demonstrating that additional measures of training tolerance may be required to determine whether training-induced changes in these parameters are related to positive or negative adaptations. Resting HRV is largely unaffected by overreaching, although this may be the result of methodological issues that warrant further investigation. HR acceleration appears to decrease in response to overreaching training, and thus may be a potential indicator of training-induced fatigue.
Full-text available
Despite the perceived importance of sleep for elite footballers, descriptions of the duration and quality of sleep, especially following match play, are limited. Moreover, recovery responses following sleep loss remain unclear. Accordingly, the present study examined the subjective sleep and recovery responses of elite footballers across training days (TD) and both day and night matches (DM and NM). Sixteen top division European players from three clubs completed a subjective online questionnaire twice a day for 21 days during the season. Subjective recall of sleep variables (duration, onset latency, time of wake/sleep, wake episode duration), a range of perceptual variables related to recovery, mood, performance and internal training loads and non-exercise stressors were collected. Players reported significantly reduced sleep durations for NM compared to DM (-157 min) and TD (-181 min). In addition, sleep restfulness (SR; arbitrary scale 1 = very restful, 5 = not at all restful) and perceived recovery (PR; acute recovery and stress scale 0 = not recovered at all, 6 = fully recovered) were significantly poorer following NM than both TD (SR: +2.0, PR: -2.6), and DM (SR: +1.5; PR: -1.5). These results suggest that reduced sleep quantity and quality and reduced PR are mainly evident following NM in elite players.
Full-text available
Recovery is essential for high athletic performance, and therefore especially sleep has been identified as a crucial source for physical and psychological well-being. However, due to earlymorning trainings, which are general practice in many sports, athletes are likely to experience sleep restrictions. Therefore, this study investigated the sleep–wake patterns of 55 junior national rowers (17.7 ± 0.6 years) via sleep logs and actigraphy during a four-week training camp. Recovery and stress ratings were obtained every morning with the Short Recovery and Stress Scale on a 7-point Likert-type scale ranging from 0 (does not apply at all) to 6 (fully applies). The first training session was scheduled for 6:30 h every day. With two to four training sessions per day, the training load was considerably increased from athletes’ home training. Objective sleep measures (n = 14) revealed less total sleep time (TST) in the first two weeks (409.6 ± 19.1 and 416.0 ± 16.3 min), while training volume and intensity were higher. In the second half of the camp, less training sessions were implemented, more afternoons were training free and TSTs were longer (436.3 ± 15.8 and 456.9 ± 25.7 min). A single occasion of 1.5-h delayed bedtime and usual early morning training (6:30 h) resulted in reduced ratings of Overall Recovery (OR) (M = 3.3 ± 1.3) and greater Negative Emotional State (NES) (M = 1.3 ± 1.2, p < .05), which returned to baseline on the next day. Following an extended night due to the only training-free day, sleep-offset times were shifted from ~5:30 to ~8:00 h, and each recovery and stress score improved (p < .01). Moreover, subjective ratings of the first six days were summarised as a baseline score to generate reference data as well as to explore the association between sleep and recovery. Intercorrelations of these sleep parameters emphasised the relationship between restful sleep and falling asleep quickly (r = .34, p < .05) as well as few awakenings (r = .35, p < .05). Overall, the findings highlight the impact of sleep on subjective recovery measures in the setting of a training camp. Providing the opportunity of extended sleep (and a day off) seems the most simple and effective strategy to enhance recovery and stress-related ratings.
Full-text available
This review outlines recent advancements in the understanding of athlete immune health. Controversies discussed include whether high levels of athletic training and environmental stress (e.g. heat acclimation, cryotherapy and hypoxic training) compromise immunity and increase upper respiratory tract infection (URTI). Recent findings challenge early exercise immunology doctrine by showing that international athletes performing high-volume training suffer fewer, not greater, URTI episodes than lower level performers and URTI incidence decreases, not increases, around the time of competition compared with heavy training. Herein we raise the possibility of host genetic influences on URTI and modifiable behavioural and training related factors underpinning these recent observations. Continued controversy concerns the proportion of URTI symptoms reported by athletes that are due to infectious pathogens, airway inflammation or as yet unknown causes and indeed whether the proportion differs in athletes and non-athletes. Irrespective of the cause of URTI symptoms (infectious or non-infectious), experts broadly agree that self-reported URTI hinders high-volume athletic training but, somewhat surprisingly, less is known about the influence on athletic performance. In athletes under heavy training both innate and acquired immunity are often observed to decrease, typically 15-25%, but whether relatively modest changes in immunity increase URTI susceptibility remains a major gap in knowledge. With the exception of cell-mediated immunity that tends to be decreased, exercising in environmental extremes does not provide an additional threat to immunity and host defence. Recent evidence suggests that immune health may actually be enhanced by regular intermittent exposures to environmental stress (e.g. intermittent hypoxia training).Immunology and Cell Biology accepted article preview online, 13 November 2015. doi:10.1038/icb.2015.99.
Full-text available
Recently, a high prevalence of morning-types was reported among trained South African endurance athletes. Proposed explanations for this observation were that either the chronotype of these athletes is better suited to coping with the early-morning start times of endurance events in South Africa; or habitual early waking for training or endurance events may have conditioned the athletes to adapt and become morning-types. The South African endurance athletes also had earlier chronotypes compared to a control population of less active individuals, suggesting that individuals who are more physically active may have earlier chronotypes. However, since both the South African athlete and control groups showed an overrepresentation of morning-types compared to European and American populations, the South African climate may in part have explained this bias towards morningness. Given the latitude and climate differences between South Africa and the Netherlands, and that South African marathons typically start at about 06:30 while those in the Netherlands start later (±11:00), comparison of South African and Dutch marathon runners and active controls would allow for simultaneous assessment of the effects of marathon start time, degree of physical activity and climate on chronotype. Therefore, the primary aims of this study were: (i) to assess the effect of marathon start time on chronotype in marathon runners and (ii) to determine the extent to which either degree of physical activity or climate might explain the bias towards morningness observed in South African athletes and controls. A secondary aim was to determine whether any relationships exist between chronotype, PERIOD3 (PER3) variable number tandem repeat (VNTR) polymorphism genotype, habitual training habits and marathon performance. Trained male marathon runners from South Africa (n = 95) and the Netherlands (n = 90), and active but non-competitive male controls from South Africa (n = 97) and the Netherlands (n = 98) completed a questionnaire capturing demographics, training and race history, as well as the Horne-Östberg morningness-eveningness personality questionnaire. All participants donated buccal cell samples from which genomic DNA was extracted and polymerase chain reaction analysis was used to genotype them for the PER3 VNTR polymorphism, which has previously been associated with chronotype. The main finding was that South African runners were significantly more morning-orientated than Dutch runners suggesting that participation in an endurance sport with an earlier start time may influence chronotype. Secondly, both the South African and Dutch runners were significantly more morning-orientated than their respective control groups, indicating that individuals who train for and participate in recreational endurance sport races have an earlier chronotype than physically active but non-competitive males. Thirdly, mean chronotype scores were similar between the South African and Dutch control groups, suggesting that climate does not seem to affect chronotype in these groups. Fourthly, the PER3 VNTR polymorphism distribution was similar between the four groups and was not associated with chronotype, suggesting that the difference in chronotype between the four groups in this study is not explained by the PER3 VNTR genotype. Lastly, in the South African runners group, a higher preference for mornings was associated with a better personal best half-marathon and current marathon performance.
Purpose: Despite the perceived importance of sleep for athletes, little is known regarding athlete sleep quality, their prevalence of daytime sleepiness or risk factors for obstructive sleep apnoea (OSA) such as snoring and witnessed apnoeic episodes. The purpose of the present study was to characterise normative sleep quality among highly trained team sport athletes. Methodology: 175 elite or highly trained rugby sevens, rugby union and cricket athletes completed the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Score (ESS) and Quality of Life questionnaires and an OSA risk factor screen. Results: On average, athletes reported 7.9 ± 1.3 h of sleep per night. The average PSQI score was 5.9 ± 2.6, and 50% of athletes were found to be poor sleepers (PSQI > 5). Daytime sleepiness was prevalent throughout the population (average global score of 8.5) and clinically significant (ESS score of ≥10) in 28% of athletes. OSA may be an important clinical consideration within athletic populations, as a considerable number of athletes (38%) defined themselves as snorers and 8% reported having a witnessed apnoeic episode. The relationship between self-rated sleep quality and actual PSQI score was strong (Pearson correlation of 0.4 ± 0.1, 90% confidence limits). Conclusion: These findings suggest that this cohort of team sport athletes suffer a preponderance of poor sleep quality, with associated high levels of daytime sleepiness. Athletes should receive education about how to improve sleep wake schedules, extend total sleep time and improve sleep quality.