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

Abstract

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.

Full text

1 23
European Journal of Applied
Physiology
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
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Vol.:(0123456789)
1 3
Eur J Appl Physiol (2017) 117:699–712
DOI 10.1007/s00421-017-3565-5
ORIGINAL ARTICLE
One night ofpartial sleep deprivation impairs recovery
fromasingle exercise training session
DaleE.Rae1· TaylaChin1· KagisoDikgomo1· LeeHill1· AndrewJ.McKune3,4·
TertiusA.Kohn1· LauraC.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 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 ses-
sion 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.
Keywords Sleep deprivation· Recovery strategies·
Maximal performance· High-intensity interval training·
Cyclists
Abbreviations
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
Abstract
Purpose The effects 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 ± 0.63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), rela-
tive 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
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
Dale.Rae@uct.ac.za
1 Division ofExercise Science andSports Medicine,
Department ofHuman Biology, Faculty ofHealth Sciences,
University ofCape Town, PO Box115, Newlands,
CapeTown7725, SouthAfrica
2 Department ofMolecular andCell Biology, Faculty
ofScience, University ofCape Town, CapeTown,
SouthAfrica
3 Research Institute forSport andExercise, University
ofCanberra, Canberra, Australia
4 Discipline ofBiokinetics, Exercise andLeisure Sciences,
School ofHealth, University ofKwaZulu-Natal, Durban,
SouthAfrica
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700 Eur J Appl Physiol (2017) 117:699–712
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RER Respiratory exchange ratio
RPE Rating of perceived exertion
SBP Systolic blood pressure
sIgA Salivary immunogammaglobulin A
VO2max Maximum volume of oxygen uptake
Introduction
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 etal. 2016). Kölling etal. (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 etal. 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 etal. 2015; Gupta etal. 2016). Not only does
competition and travel interfere with their sleep (Fullagar
etal. 2016; Gupta etal. 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 etal. 2013; Schaal etal. 2015; Kölling etal.
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 sufficient
rest and recovery periods in training programmes for youth
athletes to “encourage positive adaptations and progressive
athletic development” (Bergeron etal. 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 etal. 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 etal. 2008; Ruiz
etal. 2012) and reduce physical performance the next
day (Souissi etal. 2003; Oliver etal. 2009; Temesi etal.
2013). Reducing sleep only partially (4h for 6days) 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 etal. 2011). The fact that resting systolic
blood pressure increases after partial sleep deprivation
(±3.5h), potentially indicates that sympathetic nervous
system activity is altered by reduced sleep (Tochikubo
etal. 1996). Sleep deprivation is also understood to dis-
rupt the immune system (Faraut etal. 2012), with effects
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 etal. 2006), both of which may impact
effort 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 etal. 2010), ice (Poppendieck etal.
2013) and compression (MacRae etal. 2011) having been
investigated, studies measuring the effect of sleep depriva-
tion on recovery from exercise are scant (Skein etal. 2013).
As early as 1984 recovery from a 20min 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 etal. 2013).
Both of these previous studies (McMurray and Brown
1984; Skein etal. 2013) made use of complete sleep depri-
vation models. While they are useful designs to understand
the extreme effects 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 etal. 2013; Schaal etal. 2015; Kölling etal. 2016; Ful-
lagar etal. 2016). Therefore, investigation of the effects of
partial sleep deprivation on recovery from exercise would
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701Eur J Appl Physiol (2017) 117:699–712
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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 24h 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
24h after the HIIT. Specifically, it was hypothesised that
even one night of partial sleep deprivation would be suf-
ficient to impair recovery.
Methods
Participants
Sixteen trained male cyclists participated in this study. To
be included they needed to be 20–50years old, usually
sleep >6h per night, train at least three times per week
(past 6months) and have ridden the Cape Town Cycle
Tour, an annual 110km road race in South Africa, within
the last year in a time of <3h 45min. Exclusion criteria
were diagnosed chronic cardiovascular or metabolic dis-
ease, psychiatric condition, sleep disorder, or any other
condition known to affect sleep; current or recent use of
sleep medication (past 2months); recent trans-meridian
travel exceeding three time zones (past 2months); 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 Table1.
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 etal.
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 48h before and to avoid caffeine and alcohol 24h
before session 2. A dietary log completed in the 24h period
prior to sessions 2 and 5 was used to verify this. There was
94% compliance to the 24h alcohol restriction for both the
CON and DEP conditions, and 100 and 88% compliance to
the caffeine 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 3h 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 24h 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) andblood pressure (BP)
measurements
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 (kgm−2) 23.8 ± 1.6 21.2–27.4
Chronotype score 63 ± 9 44–76a
Sleep quality 4 ± 1 2–7
VO2max (mlmin−1kg−1) 57.6 ± 8.3 45.1–79.3
Peak power output (Wkg−1) 4.61 ± 0.70 3.69–6.37
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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 different extents in different 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 10min of
seated rest using an automated monitor (Omron HEM-907,
Omron Health Care, Kyoto, Japan).
Immune function andmuscle 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 (µgmin−1) was determined using an indirect enzyme
immunoassay kit (Salimetrics, State College, USA). Details
of this procedure have been published previously (McKune
etal. 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 differential). Samples for CK
activity were collected into lithium heparin vacutainer
tubes, placed on ice, centrifuged at 3000×g for 10min 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
24h 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 etal. 2011). This 15min test comprises three stages:
6min at 60% of maximum heart rate max (HRmax), 6min
at 80% HRmax and 3min at 90% HRmax. Following this, the
ramp format PPO test began at 2.5Wkg−1 body weight
Fig. 1 Study overview
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for 1min. Power then increased by 20Wmin−1 until par-
ticipants were unable to maintain a minimum cadence of
70rpm or reached volitional exhaustion. During the test,
HR (bpm), power (W), oxygen uptake and carbon dioxide
production (mlkg−1min−1) 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 54min session took place 5min 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 × 1min intervals at PPO, with 2min of active
recovery (50W) between each interval. During the session
they had adlibitum 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 ± 231ml, DEP 358 ± 187ml, p = 0.983). The same was
true for the volume of water drunk (CON 272 ± 257ml,
DEP 222 ± 165ml, p = 0.523).
Meals
Participants ingested a recovery drink (200ml low fat choc-
olate-flavoured milk; 482kJ, 6g protein, 18g carbohydrate
and 4g fat per serving) within 30min of the HIIT session,
and ate dinner (beef lasagne) within 90min. Portion sizes
were standardised so that each cyclist consumed 0.42g car-
bohydrate, 0.62g protein and 0.35g fat per kg body mass.
A standardised breakfast consisting of oats, milk, fruit and
caffeine-free tea was provided.
Usual sleep habits andsleep 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 7days were
Table 2 Usual (n = 16), CON
(n = 16) and DEP (n = 16) sleep
characteristics
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
Efficiency (%) 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 effect 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
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704 Eur J Appl Physiol (2017) 117:699–712
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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
efficiency (SE, %), wake-after sleep onset time (WASO,
min) and number of awakenings (count). These data
(USUAL) are presented in Table2.
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 differ-
ences in any of the sleep variables between the USUAL and
CON nights (Table2).
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 ± 0.35h and mean TST 3.38 ± 0.23h. 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 (Table2)
indicate that actual TIB (p = 0.602) and TST (p = 0.623)
were not different 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 andstatistical 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 differences between conditions
in variables measured before and 24h after the HIIT ses-
sion. Effect 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 24h measurements
Fig. 2 Individual changes in sleepiness (a), motivation to train (b)
and resting systolic blood pressure (c) from Pre to 12h after the
HIIT session under the CON and DEP conditions. Each data point
represents an individual’s response to the HIIT session (12h 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 12h 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 different in the DEP
compared to the CON condition, in one individual. SBP systolic
blood pressure, Pre prior to the HIIT session, 12h 12h after the HIIT
session. Significance was determined using a paired t test. Cohen’s d
indicates effect size
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were analysed separately to minimise misinterpretation
of data due to circadian variation in physiology. Measure-
ments taken 12h 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, 12h data are expressed as
changes from baseline (pre HIIT), and these changes are
compared between conditions. Since the pre HIIT and 24h
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
5Wkg−1 (Capostagno etal. 2014), and (3) sleep depriva-
tion may reduce aerobic performance by 3% (Oliver etal.
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 ± 0.1Wkg−1). 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.
Results
Efficacy oftheHIIT session
Participants reported similar amounts of sleep (CON
7.59 ± 1.10h, DEP 7.88 ± 0.98h, p = 0.244) and had
similar weights (CON 77.4 ± 9.5kg, DEP 78.3 ± 9.4kg,
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.
Effects ofpartial sleep deprivation onrecovery
fromexercise
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
(Table3). Figure2 shows the individual 12h responses,
where data are expressed as percentage change (i.e. 12h
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
12h 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 12h 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 12h
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 Table4 and Figs.3 and 4. Differences between
the conditions in sleepiness (p = 0.004) and SBP (p = 0.043)
persisted 24h 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, 24h
24h 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 effect as determined using a two-way ANOVA with repeated measures. Cohen’s d indi-
cates effect size
nCON DEP d p value
Pre 24h Pre 24h
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
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a time-by-condition interaction effect for absolute PPO
(p = 0.004, d = −1.98, Table4). 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, 12h and 24h time points, and the right
hand panel shows the 24h percentage change in the vari-
able (i.e. 24h value minus Pre value) for the CON (open
circles) and DEP (closed circles) conditions.
Relative PPO was lower 24h 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
24h 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 24h
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 different between the conditions
(Fig.3b). As indicated by the slope of the connecting line
in Fig.3d, 15 cyclists were sleepier 24h after the HIIT ses-
sion under the DEP compared to the CON condition, while
one was less sleepy 24h after the DEP compared to the
CON condition (Fig.3d). All but three cyclists had smaller
reductions or even increases in SBP 24h after the HIIT ses-
sion under the DEP condition compared to the CON con-
dition (Fig.3h). Motivation to train was no longer differ-
ent between the groups (d = 0.74, Table4). 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 24h percentage change in the
variable. The extent to which HRmax was reduced at 24h
was not different between conditions (Fig.4a), (d = −0.81).
For all but three of the cyclists HRmax was lower during
the PPO completed 24h after the HIIT compared to before
(Pre) under the DEP condition compared to the CON con-
dition (Fig.4b). Neither VO2max nor RERmax changed
differently 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 24h
after the HIIT session under both sleep conditions (Online
Resource 1, FiguresS1 and S2, Tables S1 and S2, respec-
tively). Time effects indicated that WBC (p = 0.020) and
neutrophil (p < 0.001) counts were lower and basophil
counts (p < 0.001) higher 24h after the HIIT session.
Discussion
Athletes have identified sleep as an important recovery
strategy (Venter 2014; Kölling etal. 2016; Fullagar etal.
2016). This study provides some support for this concept.
The main finding was that peak power output was reduced
to a larger extent 24h 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
meaningful.
The variation in the extent to which peak power was
affected 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 24h time points. The 12h 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 (24h
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 24h 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, 12h 12h after the HIIT session, 24h 24h 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 effect size
◂
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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 differences 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 etal. 2003). For example,
evening-types may cope better with sleep deprivation com-
pared to morning-types (Taillard etal. 2011; Barclay and
Myachykov 2016). Individuals with a large sleep debt may
appear to be less susceptible to the effects 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 etal. 2012;
Henst etal. 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 24h 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 efficiency (Noordhof
etal. 2010) 24h after the HIIT session; an effect which
was not altered by recovery sleep length. Neither did sleep
length appear to moderate the reduction in maximum RER
observed 24h 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 etal. 2016). Thus
when physically tired or unrecovered individuals exercise
maximally, they typical reach lower maximum heart rates
(Le Meur etal. 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 etal.
1996).
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 etal. 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 etal. 2012; Walsh and Oliver 2016). Although this
study observed some blood leukocyte changes 24h after the
HIIT, likely representative of trafficking (Dhabhar 2002),
the extent to which this occurred was not different 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 insufficient sleep in the recovery phase
may increase the risk for non-functional overreaching and
ultimately overtraining (Lehmann etal. 1997). To improve
performance, athletes finely balance training stimulus with
recovery; since increasing training load elicits a greater
adaptation (functional overreaching), while insufficient
recovery results in a maladaptive response (non-functional
overreaching) (Meeusen etal. 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 etal. 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 sufficient sleep
during such periods may be more likely to remain in the
functional overreaching zone, maximising adaptation to
training.
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 caffeine beverage each in
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709Eur J Appl Physiol (2017) 117:699–712
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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 24h 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 (24h 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 24h 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 different 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, 24h 24h 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 effect size
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710 Eur J Appl Physiol (2017) 117:699–712
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the 24h period prior to their Baseline session, following
which they were partially sleep deprived. Ingested caffeine
may enhance physical performance, although the effects are
varied and depend on factors such as dose, form, individual
tolerance to caffeine 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.
Conclusions
While there is a significant body of work describing the
effects 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 effects of partial sleep
deprivation on recovery. It differs from and is complemen-
tary to previous similar studies (McMurray and Brown
1984; Skein etal. 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 efforts 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 suffer from poor quality sleep, the effect of frag-
mented sleep on recovery deserves attention. Finally, it
remains to be determined whether or not chronic insuffi-
cient sleep in the recovery phase of training predisposes
an athlete to worsened performance or non-functional
overreaching.
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-
clare.
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