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Sleep habits and strategies of ultramarathon
Tristan Martin 1 , Pierrick J. Arnal
2 , Martin D. Hoffman
3,4,5 , Guillaume Y. Millet
1 Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada,
2 Rhythm, San Francisco, CA, United States of America, 3 Department of Physical Medicine &
Rehabilitation, Department of Veteran Affairs, Northern California Health Care System, Sacramento, CA,
United States of America, 4 University of California Davis Medical Center, Sacramento, CA, United States of
America, 5 Ultra Sports Science Foundation, Sacramento, CA, United States of America
Among factors impacting performance during an ultramarathon, sleep is an underappreci-
ated factor that has received little attention. The aims of this study were to characterize
habitual sleep behaviors in ultramarathon runners and to examine strategies they use to
manage sleep before and during ultramarathons. Responses from 636 participants to a
questionnaire were considered. This population was found to sleep more on weekends and
holidays (7–8 h to 8–9 h) than during weekdays (6–7 h to 7–8 h; p < 0.001). Work was a mediator of napping habits since 19–25% reported napping on work days and 37–56% on
non-work days. There were 24.5% of the participants reporting sleep disorders, with more
women (38.9%) reporting sleep problems than men (22.0%; p < 0.005). Mean (±SD) sleepi- ness score on the Epworth Sleepiness Scale was 8.9 ± 4.3 with 37.6% of respondents scor- ing higher than 10, reflecting excessive daytime sleepiness. Most of the study participants
(73.9%) had a strategy to manage sleep preceding an ultramarathon, with 54.7% trying to
increase their opportunities for sleep. Only 21% of participants reported that they had a
strategy to manage sleep during ultramarathons, with micronaps being the most common
strategy specified. Sub-analyses from 221 responses indicated that sleep duration during
an ultramarathon was correlated with finish time for races lasting 36–60 h (r = 0.48; p < 0.01) or > 60 h (r = 0.44; p < 0.001). We conclude that sleep duration among ultramarathon run- ners was comparable to the general population and other athletic populations, yet they
reported a lower prevalence of sleep disorders. Daytime sleepiness was among the lowest
rates encountered in athletic populations, which may be related to the high percentage of
nappers in our population. Sleep extension, by increasing sleep time at night and daytime
napping, was the main sleep strategy to prepare for ultramarathons.
Sleep quality and sleep wake cycle characteristics are underappreciated factors that can affect
sport performance [1–3]. Like other body functions, physiological and psychomotor functions
involved in exercise have a circadian rhythmicity, leading to a diurnal variation in sport
PLOS ONE | https://doi.org/10.1371/journal.pone.0194705 May 9, 2018 1 / 18
Citation: Martin T, Arnal PJ, Hoffman MD, Millet
GY (2018) Sleep habits and strategies of
ultramarathon runners. PLoS ONE 13(5):
Editor: Øyvind Sandbakk, Norwegian University of Science and Technology, NORWAY
Received: August 25, 2017
Accepted: March 8, 2018
Published: May 9, 2018
Copyright: © 2018 Martin et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: This material is the result of work
supported with resources and the use of facilities at
the VA Northern California Health Care System.
Rythm Company provided support in the form of
salary for author PJA, but did not play any role in
the study design, data collection and analysis,
decision to publish, or preparation of the
manuscript. The specific roles of this author are
articulated in the ‘author contributions’ section.https://doi.org/10.1371/journal.pone.0194705http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09http://crossmark.crossref.org/dialog/?doi=10.1371/journal.pone.0194705&domain=pdf&date_stamp=2018-05-09https://doi.org/10.1371/journal.pone.0194705https://doi.org/10.1371/journal.pone.0194705http://creativecommons.org/licenses/by/4.0/
performance (for a review, see ). Sleep and sport/exercise have a strong relationship, mutu-
ally influencing each other, positively or negatively. Sleep deprivation is associated with higher
rating of perceived effort (RPE) values, potentially leading to reduced performance, particu-
larly in endurance event (e.g.[4,5]). It has also been suggested that sleep deprivation leads to a
higher rate of injury , reduces muscle glycogen stores  and alters recovery after muscle
damage induced by exercise . Acute sleep deprivation and chronic sleep restriction also
induce alterations in glucose metabolism, with increases in insulin resistance and decreased
insulin sensitivity . Both also impact different aspects of cognitive performance  such as
increased reaction time and lapses during attentional tasks [11,12] and psychomotor functions
. Sleep deprivation and disruption of circadian rhythms also lead to elevated levels of
inflammatory markers such as interleukins (IL-6, IL 10) and tumor necrosis factor (TNF-α), and alter immune status [14,15]. The elevation of inflammatory markers, especially IL-6, has
been associated with increased pain ratings in response to sleep restriction . In contrast,
good sleep, as well as sleep extension strategies, can enhance performance by preventing the
decrease in cognitive performance and reducing the RPE during exercise [11,17].
An ultramarathon is a category of long distance running longer that the traditional mara-
thon of 42.195 km, with races being of specific distances ranging from 50 km to 100 miles (161
km, e.g. Western States Endurance Run, Ultra-trail 1
du Mont Blanc 1
)) or even
longer (e.g., Tor des Geants 1
: 335 km), specific durations (24 h to 6 days) or in stages over
multiple days (e.g. Marathon des Sables). Ultramarathons, particularly those run on trails,
have become more and more popular . Associated with this has been a growing interest in
research related to ultramarathon running . Until now however, research has mostly
focused on factors that impact performance (like pain, gastro-intestinal distress or physiologi-
cal determinants) (e.g. ), health consequences of the elevated training load of ultramara-
thon runners , the medical issues and management of common injuries and illnesses
encountered in ultramarathons [21,22] and more specific issues such as exercise-associated
muscle cramping , exercise-associated hyponatremia  and fatigue/biomarkers changes
(e.g. [25,26]) during ultramarathons.
Ultramarathon participation requires high training loads and long duration training ses-
sions, leading to high recovery needs, particularly relative to sleep. Consistent sleep of 7–9 h
per night is recommended for healthy adults , but some authors suggest that athletes
should sleep between 9 and 10 h per night to allow sufficient recovery . Moreover, some
ultramarathons are long enough that runners are required to maintain their effort for dura-
tions longer than their usual wakefulness period, with nocturnal activity and brief, or some-
times no, opportunities for sleep. Presumably, poor sleep habits and/or inadequate (or lack of)
appropriate sleep strategies before a race can potentially exacerbate fatigue, and increase risk
of injury, hallucinations and failure to finish.
To the best of our knowledge, only one study, conducted by Poussel et al.  on 303 fin-
ishers of the UTMB 1
, has focused on how ultramarathon runners manage sleep before and
sleepiness during an ultramarathon. The study used a questionnaire that was completed after
the 2013 UTMB 1
, a race in which the winning time is about 21 hours and runners are allowed
up to 46 hours to complete, so it involves one or two nights of sleep deprivation. Before the
race, 88% of runners reported that they adopted specific sleep management strategies such as
naps, increased sleep time during the previous nights, or training in sleep deprivation. Most
finishers (72%) reported that they did not sleep at all during the race, and not surprisingly,
these runners finished faster than those who slept. Race time was positively correlated with
drowsiness. Interestingly, runners who increased sleep time before the race as a pre-race strat-
egy also completed the race faster. This observation is in line with studies showing the effects
of prophylactic naps on vigilance (e.g ).
Sleep and ultramarathon
PLOS ONE | https://doi.org/10.1371/journal.pone.0194705 May 9, 2018 2 / 18
Competing interests: We have the following
interests: PJA is an employee of Rythm Company.
There are no patents, products in development or
marketed products to declare. This does not alter
our adherence to all the PLOS ONE policies on
sharing data and materials.https://doi.org/10.1371/journal.pone.0194705
Thus, the first purpose of the present study was to further describe the habitual sleep char-
acteristics and strategies of ultramarathon runners relative to their intensity of training. We
included several components of their habitual lifestyle that might influence sleep behaviors
such as time of training, use of stimulants and napping, as well as the presence of sleep disor-
ders. The second purpose was to examine strategies used by runners to manage sleep before
and during ultramarathons. This descriptive analysis may be of practical importance for ultra-
marathon runners and coaches relative to preparation and performance and can also serve as
baseline information for future intervention studies on sleep and performance.
Ultramarathon runners were invited to complete a questionnaire through electronic mailings,
postings on various ultramarathon-related web sites and forums, and advertisements in maga-
zines related to ultramarathon running in France, Italy and the US. Conditions for participating
were a minimum age of 18 yrs and having completed an ultramarathon at some time in the
past. All participants completed a secure anonymous web-based questionnaire (Google Survey)
that included demographic questions, in addition to questions related to sleep, medical history
and training history. The questionnaire was offered in French, Italian and English. All proce-
dures were approved by the University of Calgary Conjoint Health Research Ethics Board.
The questionnaire inquired about independent variables, corresponding to subject charac-
teristics such as age, weight, height, history of training and competition in ultramarathons. It
also examined various behaviors that might influence sleep (time of day when training was
performed), sleep habits (sleep duration during weekdays, weekends and holidays), use of
naps, and history of sleep disorders (difficulty falling or remaining asleep, use of sleep medica-
tion and medical assistance with sleep problems). The questionnaire also included the Epworth
Sleepiness Scale (ESS) [31,32]. The ESS is a self-administered questionnaire used to investigate
excessive daytime sleepiness that has a high level of internal consistency as measured by Cron-
bach’s alpha (0.88). Scores on the ESS can range from 0 to 24, and a score above 10 is regarded
as an indicator of excessive sleepiness. Additionally, ESS scores of 0–5 indicate low normal
daytime sleepiness, 6–10 indicate high normal daytime sleepiness, 11–12 indicate mild exces-
sive daytime sleepiness, 13–15 indicate moderate excessive daytime sleepiness, and 16–24 indi-
cate severe excessive daytime sleepiness.
Participants were also asked if and how they have been attentive to their sleep in the days
and nights preceding an ultramarathon and if and how they manage sleep during ultramara-
thons. When they indicated they had a strategy, they were invited to describe the type of strat-
egy used. In case of a mismatch in the provided answers (e.g. if the runner answered “yes” to
the question “did you sleep during the race?” then wrote “I did not sleep”), the written
response was considered to be correct. For those with a sleep strategy during an “overnight
ultramarathon” and a “longer than 2 night ultramarathon”, we requested the participant’s
sleep duration (in minutes) and the race finish time (in hours). If respondents provided infor-
mation for several race experiences, we considered each of them. If an answer did not contain
both sleep duration and race finish time, the response was not considered for analysis. We also
considered the number of sleep episodes, the location of the sleep episodes (aid station, build-
ing, etc. vs outside, i.e. on the trail) and the time of day for the sleep episodes.
Descriptive statistics are presented. The data are reported as mean and SD since they passed
normality testing, performed using the Skewness-Kurtosis test. Missing data are noted where
Sleep and ultramarathon
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pertinent. Normality allowed us to use Generalized Estimating Equations (GEE) to compare
sleep durations during weekdays, weekends and holidays [generalized linear model and gener-
alized estimating equations (GENLIN) procedure], due to the correlated nature of observa-
tions from the same participant. Other associations between variables were assessed using a
Chi-square test, with phi (ϕ) value to report the effect size. Correlations between sleep duration during a race and race finish time were determined using Pearson correlation analyses. Statisti-
cal analysis was performed with SPSS (24.0) and statistical significance was set at p < 0.05.
Out of the 636 participants, 393 responded in French, 118 in Italian and 125 in English. They
consisted of 541 (85.1%) men and 95 (14.9%) women. Mean (± SD) height and weight were 177.8 ± 8.5 cm and 72.3 ± 8.4 kg for men and 165.2 ± 24.6 cm and 57.2 ± 8.1 kg for women, respectively. Other subject characteristics are shown in Table 1.
Table 1. Selected characteristics of the subjects.
18–29 66 10.4
30–39 198 31.1
40–49 244 38.4
50–59 104 16.4
> 60 24 3.8
Number of ultra-marathons per year
< 1 72 11.3
1 180 28.3
2 184 28.9
3 112 17.6
4 48 7.5
� 5 40 6.3
Hours of training per week during normal training periods
< 3 23 3.6
3–6 212 33.3
6–9 239 37.6
9–12 118 18.6
12–15 29 4.6
> 15 15 2.4
Hours of training per week during intense training periods
<3 4 0.6
3–6 40 6.3
6–9 149 23.4
9–12 196 30.8
12–15 153 24.1
> 15 94 14.8
Sleep and ultramarathon
Sleep duration. GEE results indicate that there was a significant effect (χ2(2) = 516.683, p < 0.001) of condition (weekdays, weekends, holidays) on sleep duration. Multiple compari-
sons indicate that these three periods were all significantly different from each other
(p < 0.001). During weekdays, sleep durations of 6–7 h and 7–8 h per night were reported
most frequently. During weekends and holidays, sleep durations of 7–8 h and 8–9 h per night
were reported the most (Fig 1).
Napping. Key results related to nap habits are displayed in Fig 2. As expected, the percent-
age of nappers was lower during working days. Indeed, on working days only 19.8%, 19.0%
and 25.2% napped during non-training, normal training and intense training periods, respec-
tively. During non-working days, the percentage of nappers was 37.1%, 42.9% and 55.8% for
the same periods. During non-working days, participants mostly indicated they napped for
10–20 min (33.9%) and 30–60 min (36.9%) during non-training periods, and most of them
indicated they took naps of 30–60 min (37.4%) during normal training periods. During intense
training periods, 39.7% reported napping for 30 to 60 min, 20% between 10 and 20 min and
20% between 20 and 30 min.
The main reasons for not napping in all conditions (working vs non-working and training
vs non-training periods) was either a lack of opportunity (up to 77.0%), or a lack of time (up to
45.0%). One exception was for non-training periods on work days, in which the largest per-
centage of participants indicated they did not need to nap (44.7%).
Sleep disturbances. There were 24.5% (n = 156) of the participants reporting trouble fall-
ing asleep or waking during the night. Of those reporting such sleep disturbances, 22.4%
(n = 35) had sought medical assistance about their sleep problems and 14.1% (n = 22) used
medicine or a medical device to sleep. Medications included zolpidem, benzodiazepines, tricy-
clic antidepressants, zopiclone, homeotherapy and phytotherapy, hypnotics, antiemetics and
Chi square tests did not reveal (p > 0.05) a link between presence of sleep problems and age
category, or time of day when training was performed. However, there was a significant link
between sex and sleep disorders (φ = 0.14; p < 0.005), with more women (38.9%) reporting sleep problems than men (22.0%).
Mean (± SD) ESS score was 8.9 ± 4.3. Over a third (37.6%) of participants had an ESS score higher than 10, reflecting excessive daytime sleepiness, and 7.2% had a score between 16 and
24, reflecting a severe excessive daytime sleepiness (Fig 3).
Fig 1. Habitual sleep patterns for weekdays, weekends and holidays.
Sleep and ultramarathon
Fig 2. Distribution of napping durations and reasons for not napping during the different training periods and
during workdays and non-workdays.
Sleep and ultramarathon
Behaviors that may affect sleep-wake cycle. The time of day when training was per-
formed (allowing multiple answers) was reported as being “at the beginning of the evening (6
PM—8 PM)” by 41.2%, “in the morning (7:00 AM -12:00 PM)” by 38.8%, and between 12 PM
and 2 PM by 25.6% of participants. In the context of sleep, it is interesting to note that 95 run-
ners (14.9%) reported they train “in the evening (after 8:00 PM)”, and 135 (21.2%) reported
training ‘‘before 7:00 AM”.
Participants were also asked about the use of substances susceptible to affecting sleep
(Table 2). First, 96.1% were non-smokers, yet some respondents indicated they smoked more
than 10 cigarettes/day. Concerning consumption of stimulants, 53% reported consuming 1–3
cups (25 cl) of coffee per day, 20% reported consuming 4–6 cups per day and 23.3% indicated
they did not consume coffee. One to 3 cups a day of tea was consumed by 36.6% of respon-
dents, whereas 57% did not consumed tea. Only 17.1% of participants reported drinking soda
containing caffeine. Finally, 64.7% of participants indicated they did not drink alcohol, and
28.4% reported drinking 1–3 glasses of alcoholic beverages per day.
Sleep strategies before ultramarathons. Most of the study participants (73.9%) reported
that they are attentive to their sleep in the days and nights preceding an ultramarathon. Strate-
gies to enhance sleep are shown in Table 3 with sleep extension being the most common
Sleep strategies during ultramarathons. For ultramarathon races lasting through one
night, 75 out of 456 runners (16.4%) reported sleeping during the event. There were 120 out of
216 runners (55.6%) who reported sleeping during a race that lasted through 2 nights, and 88
Fig 3. Percentage of participants with each ESS score range.
Table 2. Use of substances affecting sleep.
(cans or bottles/
n % n % n % n % n %
0 611 96.1 362 56.9 148 23.3 526 82.8 411 64.6
< 1 0 0.0 10 1.6 6 0.9 14 2.2 37 5.8
1 to 3 9 1.4 233 36.6 337 53.0 89 14.0 181 28.5
4 to 6 9 1.4 25 3.9 127 20.0 5 0.8 7 1.1
7 to 10 3 0.5 5 0.8 12 1.9 1 0.2 0 0.0
> 10 4 0.6 1 0.2 6 0.9 0 0.0 0 0.0
Sleep and ultramarathon
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of 93 (94.6%) reported sleeping when competing in races comprising more than 2 nights. Only
21% of participants reported that they have a strategy to manage sleep during ultramarathons.
The frequency of use of various strategies is shown in Table 4, with micronaps being the most
common strategy specified.
We also analyzed sleep duration and number of sleep episodes as a function of race finish
time in the 221 responses provided by the runners. Considering races with finish times � 36h
(mean ± SD finish time = 27.9± 6.2 h), 36–60 h (45.4 ± 6.8 h) and > 60 h (109.0 ± 26.3 h), sleep durations were 0.55 ± 0.70 h, 1.36 ± 1.51 h and 8.24 ± 5.15 h, respectively. Relationships between sleep time and race finish time are shown in Fig 4. The mean (± SD) number of sleep episodes was 1 ± 1, 2 ± 3, 6 ± 3 for races with finish times < 36 h, 36–60 h and > 60 h, respec- tively. Only a small subset of participants (n = 24) indicated the time of day when naps or peri-
ods of sleep took place. Among those, the mean (± SD) time of day was 11:34 ± 5:36 PM. About 75% of the participants slept between 11:00 PM and 7:00 AM and 25% slept between
7:00 AM and 11:00 PM.
Table 3. Sleep strategies used during the days and nights preceding an ultramarathon among subjects indicating
they have pre-race sleep strategies. Most respondents selected multiple responses.
Sleep accumulation: earlier bed time and/or later rise time in the morning (even if no need to sleep) 257 54.7
Good sleep habits: regular sleep time; keeping habitual sleep time; focus on natural need to sleep 119 25.3
Increased napping time 94 20.0
Sleep accumulation at night and increased diurnal napping time 68 14.5
Other: relaxation/avoid stress; isolation; food changes; matching sleep habits with race start time;
decreased training load; “rest”
Adapt work schedule/take days off 17 3.6
Avoid stimulants 14 3.0
Sleep medication 12 2.6
Avoid screens at night 7 1.5
Table 4. Sleep strategies used during ultramarathons among subjects indicating they had slept during an
Micronap < 5 min 6 4.5
Nap 5–10 min 4 3.0
Nap 10–20 min 9 6.7
Nap 20–30 min 10 7.5
Nap 30–60 min 2 1.5
Sleep episode > 1 h 18 13.4
Nap (unspecified duration) 19 14.2
Micronap + sleep episode > 1 h 3 2.2
Sleep when exhausted 12 9.0
Resist pressure to sleep (stimulant use) 17 12.7
Delay to next aid station 5 3.7
Relaxation 4 3.0
Not specified 25 18.7
Micronap was defined as a nap of 5 min or less; nap was defined as a sleep episode of 5 to 60 min. Micronap + sleep
episode was defined as the use of short naps at some points in the race and at least one sleep episode > 1 h.
Sleep and ultramarathon
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The aims of the present study were to (i) characterize sleep habits and behaviors related to
sleep among ultramarathon runners, and (ii) describe sleep management by runners before
and during ultramarathons. Key findings were that this population of ultramarathon runners
Fig 4. Correlations between sleep duration and race time for races shorter than 36 h (panel A), races lasting between
36 and 60 h (panel B) and races longer than 60 h (panel C). No correlation was found between sleep duration and
finish time for races � 36 h (r = -0.05; p = 0.75), yet sleep duration and finish time were correlated for races lasting 36–
60 h (r = 0.48; p < 0.01) or > 60 h (r = 0.44; p < 0.001).
Sleep and ultramarathon
had sleep durations comparable to the general population, with longer sleep durations during
weekends and holidays than weekdays, and a lower prevalence of sleep disorders than the gen-
eral population. We also noticed a high percentage of nappers in our population, especially in
non-working periods. Finally, in preparation for ultramarathon events, runners considered
sleep extension as the main sleep strategy to prepare for ultramarathons.
These results were obtained from 636 participants from different countries, with 14.9% being
women. This is a lower percentage of women compared with previous large surveys. For
instance, 32% of respondents were women in the Ultrarunners Longitudinal TRAcking
(ULTRA) study [20,33], and international ultramarathon race statistic from 2017 indicate that
20.1% of finishers were women . Yet it is a higher percentage than at the UTMB 1
(< 10%). Our population consisted largely of runners who were 30–39 yrs or 40–49 yrs of age
which was in accordance with the mean age of participants in previous ultramarathon studies
of around 40 yrs [18,20,23,33,35], and international ultramarathon race statistics shown that
31% and 33.3% are 30–40 and 40–50 yrs of age, respectively . Thus, our study population
appears to have been an appropriate representation of ultramarathon runners with regards to
sex and age.
Most ultramarathon runners in the present study reported that they sleep 6 to 8 h per night
during weekdays and 7 to 9 h per night during weekends and holidays, which fits with recent
data on healthy adults in which sleep was reported to average 6.85 hours on weekdays and 7.62
hours on non-workdays according to a nationwide survey in the US in 2013 . This also
corresponds to the values found in an European population, i.e. 7–9 h of sleep per night
[37,38]. Leeder et al.  reported an actual mean (±SD) sleep time of 6.9 ± 0.7 h in athletes vs. 7.2 ± 0.4 h for a non-sporting control group, whereas athletes spent 8.6 ± 0.9 h in bed, vs. 8.1 ± 0.3 h for the non-sporting control group. Lastella et al.  reported a total sleep time ranging from 6.1 to 7.1 h in individual sports (cycling, mountain bike, race walking, triathlon
and swimming)  and a total mean (±SD) sleep time of 7.4 ± 0.6 h in endurance cyclists. Unfortunately, these studies did not specify if the data were obtained for weekdays, weekends
or both. As in the general population, ultramarathon runners tend to increase sleep duration
during weekends rather than reducing sleep in order to have more training time.
An interesting result of the present study is that despite taking naps during the day, only
~25% of the respondents reported trouble falling asleep or waking during the night, which
appears less than rates (31% to 46%) reported in the general population of Western European
countries [42,43]. A recent national survey conducted by the American National Sleep Foun-
dation  indicated that exercisers are significantly less likely to have sleep disorders than
non-exercisers. Since longer durations of exercise have been linked with greater benefits from
exercise , it is reasonable to postulate that the type of training performed by ultramarathon
runners would be effective at improving sleep quality [45,46]. It must also be noted that, due to
large differences in the characterization of sleep disorders or problems, comparison across
studies is difficult. For example, Ohayon et al.  examined the incidence of insomnia with-
out symptoms, which was higher (48%) than in the present study and the “dissatisfaction with
sleep quantity or quality”, which was close or even lower than in the present study (from 6.8 to
29.9%). This makes the comparison with our assessment of sleep problems difficult. The pres-
ent finding of sleep disorders being more common in women than men is consistent with pre-
vious studies investigating the epidemiology of sleep disorders [42,47,48]. However, the
Sleep and ultramarathon
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percentage of women with sleep disorders appeared higher in our study (38.9%) compared
with the studies cited above, which is difficult to explain.
The percentage of ultramarathon runners with sleep problems who had sought medical
advice (22.4%) and taken medicine to sleep (14.1%) was lower than what has been observed in
previous surveys in the Western population  indicating that 53% of individuals with sleep
problems have spoken to a physician and 50% have received a drug prescription. Zolpidem
and benzodiazepines were the most cited sleep medications used by the present sample of
ultramarathon runners, which is in accordance with previous data in athletes (see [49–51] for
The present survey revealed that less than half of the ultramarathon runners take naps, but as
anticipated, work was an important mediator of napping habits. This high prevalence may be
related to the higher daytime sleepiness in our sample than in the general population, probably
due to high level of fatigue associated with training load. While 19–25% reported napping on
work days, depending on their level of training, 37–56% reported napping on non-work days.
Consistent with our findings, a previous report of exercisers (23–60 years of age) observed less
napping during workdays (30–34% of respondents) than non-work days (40–45% of respon-
dents) . Yet the percentage of nappers in our study appears to be higher than what was
reported in another study (15%) among participants of individual sports involving heavy train-
ing loads such as cycling, mountain biking, race walking, swimming and triathlon .
The ultramarathon runners examined herewith had a mean ESS score of 8.9, which was higher
than scores observed in general Western populations, i.e. 7.9 in Germany , 6.9 in Norway
, 6.1 to 8.3 in the US . Moreover, 37.6% of our sample had an ESS score higher than
the classical cut off score of 10, reflecting excessive daytime sleepiness. This percentage was
also higher than normative values found in the general population, ranging from 10.8 to 30.4%
[53,55–57]. High sleepiness scores (> 6) in athletes is not a surprising observation since previ-
ous studies in several athletic populations recorded mean ESS scores similar to the present
study (ranging from 8.2 to 8.5) in hockey, cricket, soccer players, cyclists and triathletes .
On the contrary, ESS scores were found to be 12.5 and 9.6 in collegiate tennis players  and
basketball players , respectively.
Athletes who are balancing high training loads and long duration training sessions with
other life demands, coupled with a need for more sleep from the high levels of training, may
receive inadequate sleep, leading to higher sleepiness during the day , despite the high rate
of napping observed in the present study. Daytime sleepiness is a multifactorial phenomenon,
induced by behavioral practices (late night initiation of sleep, light exposure at night, early
waking) or internal disorders such as circadian rhythm desynchronization . Given that
regular exercisers usually display robust circadian rhythms , it can be hypothesized that
daytime sleepiness in this group is largely related to inadequate sleep duration. Another possi-
bility is that some of these athletes are overtrained, since it is known that overtraining can
increase daytime sleepiness score .
Behaviors that may affect the sleep-wake cycle
A relatively high proportion (> 35%) of the study participants reported that they train in the
early morning (before 7:00 AM) or in the evening (after 8:00PM). We can suspect that the
early or late training is generally used to limit disruption of professional, social and personal
Sleep and ultramarathon
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(family) demands, and it likely adversely affects sleep duration. Furthermore, exercise late in the
day can affect sleep quality. Exercise between 8 to 3 h before habitual bedtime has been thought
to be beneficial for sleep, since it could advance the phase of melatonin and promote sleep onset
. Moreover, exercise in the evening can facilitate sleep through increased peripheral skin
blood flow following the initial hyperthermia from exercise, which decreases core temperature
and in turn favors sleep [65,66]. Yet, exercising less than 3 h before bed time may disrupt sleep
onset and phase delay the melatonin excretion . However, several studies have not found
disrupted sleep from training at night or late evening [68–70]. In addition, a recent meta-analy-
sis did not reach a consensus concerning “good timing” of exercise for sleep .
High doses of caffeine (600 mg, i.e. 5–6 cup) [71–73], nicotine consumption  and alco-
hol consumption  are known to have an acute disruptive effect on sleep. For instance, caf-
feine and nicotine increase sleep latency, decrease sleep efficiency and sleep duration, induce
more frequent awakening, and change sleep architecture (decrease slow wave sleep, increase
REM sleep latency). It is not surprising that avoiding stimulants was reported as a strategy to
optimize the duration and quality of sleep before races.
Strategies used to manage sleep before and during ultramarathons
Sleep strategies before an ultramarathon. Seventy-four percent of the runners reported
being attentive to sleep preceding an ultramarathon, a value slightly lower than in the only
study that has examined this question , where 88% of the runners reported adopting a
sleep strategy before the race. In the present study, sleep extension was achieved by 55% of
runners through increasing nighttime sleep duration and by 20% through the use of daytime
napping. Poussel et al.  also have reported the increase of daytime napping as a strategy to
prepare an ultramarathon, but the rate of runners using this strategy was higher (41%) than in
our study. Increased time of napping close to a competition is a way to complement night
sleep and has been previously described by Sargent et al.  in elite athletes. Of note is the
fact that 14.5% of the present subjects reported they increased both time in bed and diurnal
napping time before the race.
In a study of collegiate basketball players , it was suggested that sleep extension could
improve physical performance, though the study was limited by lack of a control group. More-
over, it has been shown that sleep extension can protect against the effects of sleep deprivation,
mainly at the cognitive level [11,77]. For instance, we showed that 6 nights of sleep extension
prevented both reaction time degradation during a psychomotor vigilance test and the number
of microsleep episodes during sleep deprivation . In the same protocol, we also showed
that sleep extension before a night of total sleep deprivation improved time to exhaustion dur-
ing a lower-limb sustained isometric contraction . The beneficial effect on performance
was likely due to the reduced RPE after sleep deprivation when preceded by sleep banking
(increasing sleep opportunity at night) since cortical voluntary activation (assessed by tran-
scranial magnetic stimulation) was not different between the sleep extension and the control
conditions . Although this needs to be confirmed in further studies, it can be speculated
that the longer the exercise, the greater the benefits that can be derived from sleep banking
because the role of perceptual responses is probably more important in prolonged exercise
. Thus, sleep accumulation before an ultramarathon seems to be a good strategy to mini-
mize the cognitive and perceptual effects of sleep deprivation and exercise.
In addition to sleep banking, another highly cited sleep strategy (~25%) among the present
study participants was to utilize good sleep habits (regularity of sleep/wake times or paying
attention to natural need of sleep). Regularity of sleep timing is associated with greater subjec-
tive and objective sleep quality [79,80] and lower daytime sleepiness . As previously
Sleep and ultramarathon
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demonstrated in elite athletes, strategies such as establishing a regular bedtime, avoiding the
consumption of alcohol and caffeine and increasing napping can optimize the duration and
quality of sleep in athletes [40,41].
Sleep strategies during an ultramarathon. As expected, the percentage of ultramarathon
runners sleeping during a race was found to depend on the duration of the race, with less than
20% sleeping for a race lasting through one night and almost all runners sleeping for multiple
days races. In the study of UTMB 1
finishers with a mean (± SD) race finish time of 39.5 ± 5.1 h, Poussel et al.  reported that 28% of the runners did not sleep, and non-sleepers were sig-
nificantly faster than those who slept. From the present 221 runner subsample, we found a
mean (± SD) cumulated sleep duration of 0.55 ± 0.70 h, 1.36 ± 1.51 h and 8.24 ± 5.15 h for races < 36 h, between 36 and 60 h, and longer than 60 h, respectively. Such an amount of
sleep, even if it could decrease the risk of accident due to sleep deprivation, is enough to have
an important effect on finish time, especially among top runners.
The majority of runners who indicated “having a strategy of sleep management” indicated
they take one or several naps. When nap duration was specified, most runners reported that
naps ranged from 10 to 20 min (6.7%) and 20 to 30 min (7.5%). Naps between 10 and 15 min
(5%) and between 15 and 30 min (11%) were also the most cited by Poussel et al. . In the
present study, sleep was taken mainly in aid stations. Some participants also indicated they
would wait until the aid station to sleep, even if sleepiness was elevated. The perspective of
reaching an aid station could be a motivation for participants to continue the race and post-
pone their sleep. A few respondents (n = 24) also provided the time when sleep occurred.
While a small sample, it is interesting to note that about 75% of the runners reported they slept
at night, which is the normal time during the 24-hour day for rest in humans, since sleep pres-
sure is higher and the timing of the circadian system promotes sleepiness .
The present study has some limitations. One limitation relates to recall bias, linking to inaccu-
racy or absence of answers regarding sleep duration and finish time of races. Another issue is
that it is appropriate to emphasize that our study population was a convenience sample that
may not be fully representative of the population of ultramarathon runners. Moreover, results
on sleep duration relative to race time was derived from a subsample of only 221 responses
with some individuals providing multiple responses. We also acknowledge that the percentage
of women participants in our study was slightly lower than in the general population of ultra-
marathon runners. Finally, we acknowledge a potential limitation with our assessment of sleep
disorders, which was done through simply asking if they had trouble falling asleep or waking
during the night, if they had visited a physician for a sleep problem, and if they had taken med-
ication to sleep. While these questions may only provide a limited insight into a sleep disorder,
they focus on the most common complains about sleep disorders (i.e. difficulty falling asleep
and waking up at night) and the management strategy. Those performing future studies should
consider a more elaborate assessment that includes questions beyond insomnia (e.g. related to
other common disorders such as sleep apnea and restless legs syndrome and inclusion of a
scale like the insomnia severity scale.
In conclusion, the ultramarathon runners in this study had sleep durations comparable to the
general population and other athletic populations, yet they reported a lower prevalence of
sleep disorders. Daytime sleepiness, while higher than normative values found in the general
population, was among the lower rates encountered in athletic populations, which may be
Sleep and ultramarathon
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related to the high percentage of nappers in our population. Before ultramarathon races, 55%
of the ultramarathon runners considered sleep extension as the main sleep strategy to prepare
for ultramarathons, largely accomplished through increasing sleep time at night. Finally, sleep-
ing during races was correlated with race duration for races longer than 36 h. Whether short
naps or longer sleep episodes (e.g. one sleep cycle) is the most effective strategy in terms of per-
formance in extremely long ultramarathons remains to be assessed. We suggest that future
studies use actigraphy to provide more precise measurement of sleep before and during races.
We also encourage the scientific community to examine the effects of sleep extension on ultra-
S1 File. Questions and answer provided by all participants.
S2 File. Questionnaire provided to the participant in English, French and Italian Lan-
This material is the result of work supported with resources and the use of facilities at the VA
Northern California Health Care System. The contents reported here do not represent the
views of the Department of Veterans Affairs or the United States Government.
Conceptualization: Pierrick J. Arnal, Guillaume Y. Millet.
Data curation: Tristan Martin, Pierrick J. Arnal, Martin D. Hoffman, Guillaume Y. Millet.
Formal analysis: Tristan Martin, Pierrick J. Arnal, Martin D. Hoffman, Guillaume Y. Millet.
Funding acquisition: Guillaume Y. Millet.
Investigation: Pierrick J. Arnal, Guillaume Y. Millet.
Methodology: Pierrick J. Arnal, Martin D. Hoffman, Guillaume Y. Millet.
Project administration: Guillaume Y. Millet.
Supervision: Pierrick J. Arnal.
Validation: Pierrick J. Arnal, Martin D. Hoffman, Guillaume Y. Millet.
Visualization: Pierrick J. Arnal, Martin D. Hoffman, Guillaume Y. Millet.
Writing – original draft: Tristan Martin.
Writing – review & editing: Tristan Martin, Pierrick J. Arnal, Martin D. Hoffman, Guillaume
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RESEARCH ARTICLE Open Access
Skill execution and sleep deprivation: effects of acute caffeine or creatine supplementation – a randomized placebo-controlled trial Christian J Cook1,3,4*†, Blair T Crewther3†, Liam P Kilduff2†, Scott Drawer1†, Chris M Gaviglio5†
Background: We investigated the effects of sleep deprivation with or without acute supplementation of caffeine or creatine on the execution of a repeated rugby passing skill.
Method: Ten elite rugby players completed 10 trials on a simple rugby passing skill test (20 repeats per trial), following a period of familiarisation. The players had between 7-9 h sleep on 5 of these trials and between 3-5 h sleep (deprivation) on the other 5. At a time of 1.5 h before each trial, they undertook administration of either: placebo tablets, 50 or 100 mg/kg creatine, 1 or 5 mg/kg caffeine. Saliva was collected before each trial and assayed for salivary free cortisol and testosterone.
Results: Sleep deprivation with placebo application resulted in a significant fall in skill performance accuracy on both the dominant and non-dominant passing sides (p < 0.001). No fall in skill performance was seen with caffeine doses of 1 or 5 mg/kg, and the two doses were not significantly different in effect. Similarly, no deficit was seen with creatine administration at 50 or 100 mg/kg and the performance effects were not significantly different. Salivary testosterone was not affected by sleep deprivation, but trended higher with the 100 mg/kg creatine dose, compared to the placebo treatment (p = 0.067). Salivary cortisol was elevated (p = 0.001) with the 5 mg/kg dose of caffeine (vs. placebo).
Conclusion: Acute sleep deprivation affects performance of a simple repeat skill in elite athletes and this was ameliorated by a single dose of either caffeine or creatine. Acute creatine use may help to alleviate decrements in skill performance in situations of sleep deprivation, such as transmeridian travel, and caffeine at low doses appears as efficacious as higher doses, at alleviating sleep deprivation deficits in athletes with a history of low caffeine use. Both options are without the side effects of higher dose caffeine use.
Background Both creatine and caffeine have found common use in sport [1-4] for a variety of training and competitive aims. Popular use of caffeine is often at high concentrations (4-9 mg/kg) on the basis that these are more efficacious, but the proof of this is low with individual variability and consumption habits being the more dominant factors [5,6]. In contrast, popular creatine supplementation dosages appear to have fallen as literature supports the contention that lower doses can be as effective as higher loading schemes, again individual variability and respon- siveness being major determinants .
While the ability of acute caffeine to address cognitive related sleep deficits is reasonably established , it is only recently that creatine has demonstrated similar properties [8,9]. It has been suggested that sleep deprivation is asso- ciated with an acute reduction in high energy phosphates that in turn produces some degree of cognitive processing deficit [8-14]. Creatine supplementation has been shown to improve certain aspects of cognitive performance with sleep deprivation and to have some positive benefits in deficits associated with certain pathophysiologies [13,14]. If sleep deprivation is associated with an energy deficit then errors in performance are perhaps more likely to occur when concentration demands are high and/or for prolonged periods of repeated task execution. Some evi- dence suggests that it is tasks of this nature that are most affected by acute sleep deprivation .
* Correspondence: [email protected] † Contributed equally 1UK Sport Council, 40 Bernard St London, UK Full list of author information is available at the end of the article
Cook et al. Journal of the International Society of Sports Nutrition 2011, 8:2 http://www.jissn.com/content/8/1/2
© 2011 Cook et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.mailto:[email protected]http://creativecommons.org/licenses/by/2.0
Creatine has generally only been used in chronic load- ing protocols. However, if the contention that acute sleep deprivation reduces brain creatine is true, than an acute dose of creatine, as opposed to the classical longer loading periods, may alleviate some of these effects. This would be dependent on creatine uptake not being rate limited, something unknown for the brain. Creatine does however readily cross the blood brain barrier and chronic systemic loading does appear to increase brain stores [13,14]. Acute doses of caffeine appear most ben- eficial at around 30-90 min prior performance  and while the timing of an acute dose of creatine has yet to be determined, it appears to take at least an hour for absorption into the bloodstream [17-19]. Sleep deprivation is not uncommon around competi-
tion in sport particularly with the frequent demands of international travel. Assessing its effects on performance is however difficult, especially in team sports where multiple physical and skill components are involved. While overt physical components such as power don’t appear affected by acute deprivation  a few studies do however suggest acute deprivation can affect certain sport skill and physical performance [21,22]. Given the potential usefulness of safe supplementation
for alleviating cognitive deficits associated with sleep deprivation, this study aimed to investigate if acute admin- istration of creatine or caffeine could offer this advantage. To this end, we tested the effects of acute occurring sleep deprivation on a fundamental rugby skill, passing the ball while running with accuracy, in elite level players. Further to this, we tested if acute administration of creatine or caf- feine would in any way alter this performance.
Method Subjects Ten professional rugby backs (mean ± SD, age; 20 ± 0.5 years) that were in good health and injury-free volun- teered for this trial. Subject bodyweights were 90 ± 4 kg and heights 1.81 ± 0.02 m (mean ± SD). Bodyweights showed no significant changes over the course of this trial. A within-treatment design was used with each sub- ject acting as their own control to improve reliability and the sensitivity of measurements. Subjects all reported a low and infrequent history of both previous caffeine use (in any form) and each had used creatine previously, usually in a classic loading protocol. The ath- letes were all very low and infrequent social consumers of alcohol. A university ethics committee approved the study procedures and each subject signed an informed consent form before participation.
Study design A blinded, repeated measure, placebo-controlled cross- over design was used to examine the effects of acute
supplementation (caffeine or creatine) on the execution of a repeated rugby passing skill during sleep deprivation.
Testing procedures On days of testing the subjects consumed the same breakfast which consisted of a bowl of cereal with fruit, yoghurt and milk in a portion of voluntary choice and two poached eggs on one piece of buttered toast con- sumed between 0700 h and 0800 h. Water was available ad libitum. On the night previous to testing food was not strictly controlled but all subjects reported consum- ing a dinner of at least red meat and 3 vegetables and a latter evening protein milkshake. Initially all 10 players in this study undertook 3 weeks
of familiarisation training on a rugby-specific passing skill (total of 12 sessions). Changes in performance and variability were calculated over these sessions. Familiari- sation was undertaken at 1130 h each time, and required 2 previous nights of greater than 7 h sleep to be performed (i.e. clearly non-sleep deprived). Following familiarisation the players were asked to keep a sleep log to record the number of hours slept per night. This was reported at 0900 h on Monday to Friday. The skill testing procedures were performed on 10
separate occasions across a 10 week period (not less than three days apart) at 1130 h, with between 7-9 h sleep for two nights preceding five of these tests, and with 3- 5 h sleep (sleep deprived) on the night preceding (but more than 7 h on the previous night) on the other 5 trials. At 1000 h on the test days the athletes received one of the following: placebo tablets (sucrose at 5 mg/kg); creatine monohydrate tablets (50 or 100 mg/kg bodyweight); caf- feine tablets (1 or 5 mg/kg bodyweight). Thus, the abso- lute mean dosages of creatine used were 4.5 g and 9 g, respectively, and caffeine dosages of 90 mg and 450 mg were respectively used. The doses were divided into 5 tablets, of same size based upon each individual athlete’s bodyweight at the start of the trial, across all treatments. Maize starch was used where necessary to balance out tablet weights and tablets were hand made using gelatine capsules. Treatment (blinded) was randomised across athletes and the skill execution tests. On all trials subjects refrained from alcohol consump-
tion for at least 48 h prior to testing and from any caf- feine and caffeine containing drinks for at least 24 h (athletes were infrequent caffeine drinkers). The athletes recruited had not used creatine or creatine-based sup- plements within the preceding 3 months of this study.
Rugby passing skill test The repeated rugby passing skill was performed indoors and consisted of: a 20 m sprint in which at the 10 m mark the player had to attempt to pass a rugby ball left or right (alternating) through a hanging hoop (diameter
Cook et al. Journal of the International Society of Sports Nutrition 2011, 8:2 http://www.jissn.com/content/8/1/2
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1.5 m) 10 m away from them. Players were also asked to identify their better passing side (dominant). All 10 players clearly believed they had a better passing side, and this was supported by alternate accuracy. The 20 m protocol had to be completed in less than 20 s (beep timed for the players) and they undertook 20 repeats (10 passes on each side) with a walk back recovery period. Execution success was simply defined as the num- ber of successful attempts on the dominant and non- dominant side. The elite group of athletes were familiar with this common rugby skill and thus, a high level of reliability was expected. To further ensure high test-retest reliability, three weeks of familiarization sessions were also performed before the main testing procedures.
Saliva measures Saliva was collected immediately before each trial as fol- lows: players provided a passive drool of saliva into ster- ile containers (LabServe, NewZealand) approximately 2 ml over a timed collection period (2 min). The saliva samples were aliquoted into two separate sterile con- tainers (LabServe, New Zealand) and stored at – 80°C until assay. Samples were analysed in duplicate using commercial kits (Salimetrics LLC, USA) and the manu- facturers’ guidelines. The minimum detection limit for the testosterone assay was 2 pg/ml with intra- and inter-assay coefficients of variation (CV) of 1.2 -12.7%. The cortisol assay had a detection limit of 0.3 ng/ml with intra- and inter-assay CV of 2.6 – 9.8%.
Statistical Analyses The accuracy of skill execution with sleep deprivation and treatments was examined using a two-way analysis of variance (ANOVA) with repeated measures on both the dominant and non-dominant passing sides. A two-way repeated measures ANOVA was also used to evaluate the effects of sleep state, treatments and any interactions for each hormonal variable. In addition, dominant versus non-dominant side skill performance during familiarisa- tion trials and non-deprived performance versus famil- iarisation performance were examined similarly. The Tukey HSD test was used as the post hoc procedure where appropriate. Significance was set at an alpha level of p ≤ 0.05.
Results Familiarisation training and dominant versus non- dominant passing side A significant main effect for skill performance was identi- fied over time [F(5, 108) = 38.44, p < 0.001]. Skill execution on both sides improved significantly (p < 0.001) across the first 5 sessions (Table 1) and then was unchanged between session 5 and 12. Variability within an individual on non- sleep deprived days was less than 5% and, between
individuals in the group, was less than 15% and no signifi- cant differences were seen. A significant main effect was also identified for passing side [F(1, 108) = 53.85, p < 0.001] with dominant side skill execution found to be superior to the non-dominant side across all trials (p = 0.013). No interactions between passing side and time were found [F(5, 108) = 1.899, p = 0.1].
Placebo non-sleep deprived versus familiarisation Placebo administration on non-sleep deprived days did not produce a significantly different performance result to that seen in the last familiarisation trial [F(1, 36) = 0.00, p = 1.0], but a significant main effect was identified for passing side skill execution, this being consistently higher on the dominant side than the non-dominant side [F(1, 36) = 22.737, p < 0.001]. No significant interactions were identi- fied for these variables [F(1, 36) = 0.00, p = 1.0].
Placebo versus creatine or caffeine on dominant passing side Repeated analyses revealed significant main effects for treatment condition [F(4, 90) = 19.303, p < 0.001], sleep state [F(1, 90) = 19.472, p < 0.001] and their interactions [F(4, 90) = 7.978, p < 0.001] on the dominant passing side (Figure 1). All of the caffeine and creatine doses produce a significant enhancement in skill performance when compared to placebo administration (p < 0.001). In the placebo condition, passing skill performance was found to be superior in the non-sleep deprived than the sleep deprived trial (p < 0.001).
Placebo versus creatine or caffeine on non-dominant passing side On the non-dominant passing side (Figure 2), significant main effects were identified for the treatment conditions [F(4, 90) = 14.871, p < 0.001], sleep state [F(1, 90) = 18.228, p < 0.001], and their interactions [F(4, 90) = 6.026, p < 0.001]. As with the dominant passing side, all of the caffeine and creatine doses produce a significant enhancement in skill performance from the placebo (p < 0.001) and, in the placebo condition, greater perfor- mance accuracy was noted in the non-sleep deprived (vs. sleep deprived) trial (p < 0.001). Figures 1 and 2 summarise this data.
Table 1 Accuracy, out of 10 attempts (20 total per trial), for each of dominant and non-dominant passing sides on the first, fifth and twelve familiarisation trials
1st Trial 5th Trial a 12th Trial a
Dominant 7.3 ± 0.8 9.0 ± 0.7 9.0 ± 0.4
Non-dominant b 5.7 ± 0.8 8.3 ± 0.8 8.2 ± 0.7
Data presented as mean ± SD. a significantly different from the 1st trial (p < 0.001), b significantly different from the dominant side (p = 0.013).
Cook et al. Journal of the International Society of Sports Nutrition 2011, 8:2 http://www.jissn.com/content/8/1/2
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Salivary testosterone and cortisol A significant main treatment effect [F(4, 90) = 4.855, p = 0.001] was identified for salivary testosterone (Figure 3), trending towards higher values after the 100 mg creatine dose (p = 0.067) than the placebo condition. There were no significant effects of sleep state [F(1, 90) = 1.602, p = 0.209], nor any interactions [F(4, 90) = 1.014, p = 0.405], on salivary testosterone. For salivary cortisol (Figure 4), significant results were noted for the main effects of treatment [F(4, 90) = 8.415, p < 0.001] and sleep state [F(1, 90) = 31.31, p < 0.001], but there were no interactions [F(4, 90) = 0.691, p = 0.6]. Cortisol was significantly higher with the 5 mg caffeine dose (p = 0.001) than the placebo treatment. Figures 3 and 4 summarise this data.
Discussion Acute sleep deprivation is a common occurrence in the general population  including elite athletes. Such deprivation has been shown to affect some, but not all, physical and skill executions [15,20-22]. However, quan- tifying an effect in a team sport can be difficult. The repeated passing skill test we described herein is simple
to perform, has sport-specific relevance and appears to be highly reliable across repeat testing. It is not however a one off, high-level performance task, rather a repeat of 20 fairly simple tasks, alternating passing sides. While we don’t claim it to be in any way, yet, a valid perfor- mance measure it did reveal some interesting differences across acute sleep deprivation and across caffeine and creatine treatments. In line with observations in other skill and psychomo-
tor testing acute sleep deprivation reduced the accuracy over repeated trials. There was a general trend to a drop-off in accuracy latter in the repeats (second 10 of the 20 repeats). Whether this is a greater susceptibility to mental fatigue or not remains an interesting question, as does whether single skill repeats separated by more recovery time or by a similar physical activity with no real skill requirement would show a deficit in perfor- mance or not. In non-sport related psychomotor trials there is little evidence that a single episode of sleep deprivation produces significant deficit in a single task ; however across repeat tasks it is perceived that much greater effort is needed to maintain concentration .
Figure 1 Effects of sleep deprivation and acute supplementations on passing accuracy (dominant side). The mean ± SD is displayed for accuracy out of 10 passes on the dominant side (20 passes total per trial) for the 10 subjects under different treatment conditions (placebo; 1 or 5 mg/kg caffeine, 50 or 100 mg/kg creatine) either in non-sleep deprived or sleep deprived states. Dominant was chosen by the subjects as the side they believed showed better passing accuracy. All subjects completed 20 repetitions of the passing skill per trial, alternating passing sides (10 on dominant side). With placebo treatment sleep deprivation was associated with a significant fall in performance (a) (p < 0.001) compared to non-sleep deprivation. The 50 and 100 mg/kg creatine and 1 and 5 mg/kg caffeine doses were all associated with a significantly better performance (b) (p < 0.001) than the placebo conditions.
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Acute sleep deprivation has little effect on weightlift- ing performance , but can influence mood negatively  which may be a driving feature in mental perfor- mance changes. Caffeine, for example, has been shown to improve both mood and mental function following sleep deprivation . It is not known how much mood and other cognitive function, particularly motivation on repeat skill tasks, interact. At the doses and administra- tion time of caffeine use in this study we saw no evi- dence of an effect in non-sleep deprived subjects; however, there was a clear amelioration of skill perfor- mance deficit from the sleep-deprived trials with pla- cebo administration. The psychostimulant effects of caffeine appear to be related to the pre and post synap- tic brakes that adenosine imposes on dopaminergic neu- rotransmission by acting on different adenosine receptor heteromers , although numerous mechanisms are likely to be involved. We did not see a dose related effect with caffeine sup-
plementation, with 1 mg/kg and 5 mg/kg producing similar effects, nor did we see high individual variance (i.e. responders and non-responders). The absorption of caffeine in plasma following consumption has been esti- mated at between 30 and 90 min with half life of several
hours , so the time between consumption and test- ing (90 min) in this study may have been too long to see all effects of differing caffeine dose, or any effect on non-sleep deprived performance. Nonetheless, at 90 min there was still clear evidence of a reduction in the effect of sleep deprivation on the skill measured and no evi- dence this was different between the 1 and 5 mg/kg dose. Subjectively, a number of the subjects reported feeling
slightly nauseous and anxious following the 5, but not 1, mg/kg administration of caffeine suggesting in other ways there were dose differences. Effective doses of caf- feine (and their dose response nature) remain conten- tious in literature [1,5,6,27] possibly reflecting larger inter-subject variability in responses and different sensi- tivities of various physical and behavioural expressions. The subjects in this study were not regular caffeine users so arguably may have been more sensitive to lower doses than would be seen in more regular consu- mers. Certainly in the study herein 1 mg/kg was as effective as 5 mg/kg and from a practical perspective runs less risk of undesirable dose related side effects. Chronic creatine supplementation has been shown
to address certain aspects of sleep deprivation linked
Figure 2 Effects of sleep deprivation and acute supplementations on passing accuracy (non-dominant side). The mean ± SD is displayed for accuracy out of 10 passes on the non-dominant side (20 passes total per trial) for the 10 subjects under different treatment conditions (placebo; 1 or 5 mg/kg caffeine, 50 or 100 mg/kg creatine) either in non-sleep deprived or sleep deprived states. All subjects completed 20 repetitions of the passing skill per trial, alternating passing sides (10 non-dominant side). With placebo treatment sleep deprivation was associated with a significant fall in performance (a) (p < 0.001) compared to non-sleep deprivation. The 50 and 100 mg/kg creatine and 1 and 5 mg/kg caffeine doses were all associated with a significantly better performance (b) (p < 0.001) than the placebo conditions.
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Figure 3 Pre-trial salivary free testosterone (pg/ml) across treatments. The mean ± SD is displayed for salivary testosterone under different treatment conditions (placebo; 1 or 5 mg/kg caffeine, 50 or 100 mg/kg creatine) either in non-sleep deprived or sleep deprived states. The 100 mg/kg creatine dose was associated with a higher concentration of testosterone (a) (p = 0.067) compared to the placebo treatment.
Figure 4 Pre-trial salivary free cortisol (ng/ml) across treatments. The mean ± SD is displayed for salivary cortisol under different treatment conditions (placebo; 1 or 5 mg/kg caffeine, 50 or 100 mg/kg creatine) either in non-sleep deprived or sleep deprived states. The 5 mg/kg caffeine dose was associated with a significantly higher concentration of cortisol (a) (p = 0.001) compared to the placebo treatment.
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and other pathophysiology linked cognitive deficits [8,9,11,13,14,19], although very low dose chronic supple- mentation does not appear to improve function in non- sleep deprived healthy subjects . Sleep deprivation is associated with a reduction in brain stores of phosphocrea- tine  and certainly in some disease states depletion of high energy phosphate stores has been measured, asso- ciated with cognitive deficit, and alleviated to some extent by creatine supplementation [13,14,29]. Interestingly, if there is an energy deficit associated with sleep deprivation then it seems logical to contend that repeat trials would be more susceptible than one off tasks. Our results and indeed other work on sleep deprivation do fit this pattern. If such depletion occurs and is acute, it also stands to reason that acute supplementation (as opposed to longer protocols) would address any associated deficit (given that brain uptake is not a time limiting factor). Little, if any, attention has been given to acute dosing with creatine, mainly because it is assumed that its effects come from a gradual build up of stores over time. We demonstrate here that an acute dose of creatine can ameliorate sleep deprived defi- cits in repeat skill performance trials. Again this possibly reflects the repeat nature of the trials and may not be observable in an acute one off mental skill performance. Further in contrast to caffeine administration, the
creatine dose of 100 mg/kg appeared to elicit a trend towards greater effect in skill performance than 50 mg/kg dosing, thereby suggesting potentially a dose dependent response. As in the case of caffeine we observed no indi- vidual variability suggestive of responders and non- responders or differential dose susceptibility, and no adverse effects were reported to us by the subjects. Clearly at the level of muscle function there does appear to be a division into responders and non-responders to longer term supplementation with different creatine pro- tocols . It is possible that this would be similar with longer term supplementation aimed at skill improvement, or alternatively brain-related creatine stores may operate slightly differently to muscle. Acute sleep deprivation has been demonstrated in
some studies to have small disruptive effects on basal hormonal concentrations [30,31]. Although salivary cor- tisol appeared to be elevated with sleep deprivation, this result did not reach statistical significance. Interestingly the higher dose of caffeine was associated with signifi- cant elevation in pre-trial cortisol, but not testosterone. High doses of caffeine have previously been demon- strated to acutely increase cortisol and, to a lesser extent, testosterone [20,32]. Whether such elevations have any significance in outcome is unknown. Cortisol is associated with arousal but also with anxiety . Unfortunately we did not concurrently measure salivary alpha amylase in this study, which may also be a useful marker with respect to system arousal . Testosterone
was unaffected by sleep deprivation and by all treat- ments except the high dose of creatine, where there was a trend towards higher concentrations. We do not have useful speculation as to why this increase was seen, although it was across all subjects. Still, the increase was relatively small in magnitude and we doubt at this stage that it has any real physical or behavioural consequence. As we used saliva measures we cannot rule out some local oral cavity artefact effect of creatine. Free testoster- one levels have, however, been linked to intra-individual variance in short timeframe muscular power , and long-term creatine supplementation has been reported as influencing testosterone metabolite pathways , so the observation is perhaps worthy of some follow-up. Little has been published on acute creatine use as it
has primarily been regarded as a longer term supple- ment to muscular function gain. In terms of brain and behavioural function it would appear it have some acute effects of value. It is also possible that the observed effects of caffeine and creatine reported in this and other studies are potentially summative and thus, would seem a logical progression for research.
Conclusions We observed a significant effect of acute sleep depri- vation on performance (on both dominant and non- dominant passing sides) of a repeat simple skill test in elite rugby players. The deficit in performance with sleep deprivation was addressed by acute supplementa- tion with either caffeine or creatine. In both cases, the two dosages tested had similar effects on skill perfor- mance. Both may offer practical and viable options prior to training and competition to assist skill performance when sleep loss has occurred.
Acknowledgements We acknowledge with gratitude the professional athletes that contributed to this study. In part this study was supported by grants (ESPRIT) from Engineering and Physical Sciences Research Council UK and by UK Sport Council.
Author details 1UK Sport Council, 40 Bernard St London, UK. 2Sport and Exercise Science Research Centre, Swansea University, Swansea, UK. 3Hamlyn Centre, Institute of Global Health Innovation, Imperial College, London, UK. 4Department for Health, University of Bath, Bath, UK. 5Queensland Academy of Sport and Gold Coast SUNS, AFL Franchise Gold Coast, Brisbane, Australia.
Authors’ contributions CJC participated in protocol design, conduct of the study, data analyses and manuscript preparation. LPK, CMG, SD and BC participated in protocol design, data analyses and manuscript preparation. All authors have read and approved the final manuscript.
Competing interests The authors declare that they have no competing interests.
Received: 31 August 2010 Accepted: 16 February 2011 Published: 16 February 2011
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doi:10.1186/1550-2783-8-2 Cite this article as: Cook et al.: Skill execution and sleep deprivation: effects of acute caffeine or creatine supplementation – a randomized placebo-controlled trial. Journal of the International Society of Sports Nutrition 2011 8:2.
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The Sleep and Recovery Practices of Athletes
Rónán Doherty 1,2,3,* , Sharon M. Madigan 2, Alan Nevill 4 , Giles Warrington 5,6 and Jason G. Ellis 3
Citation: Doherty, R.; Madigan, S.M.;
Nevill, A.; Warrington, G.; Ellis, J.G.
The Sleep and Recovery Practices of
Athletes. Nutrients 2021, 13, 1330.
Academic Editor: Giorgos K. Sakkas
Received: 12 March 2021
Accepted: 15 April 2021
Published: 17 April 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
1 Sports Lab North West, Letterkenny Institute of Technology, Port Road, Letterkenny, F92 FC93 Donegal, Ireland
2 Sport Ireland Institute, National Sport Campus, Abbotstown, D15 PNON Dublin, Ireland; [email protected]
3 Northumbria Centre for Sleep Research, Northumbria University, Newcastle NE7 7XA, UK; [email protected]
4 Faculty of Education, Health and Wellbeing, University of Wolverhampton, Walsall Campus, Walsall WV1 1LY, UK; [email protected]
5 Health Research Institute, Schuman Building, University of Limerick, V94 T9PX Limerick, Ireland; [email protected]
6 Department of Physical Education and Sport Sciences, University of Limerick, V94 T9PX Limerick, Ireland * Correspondence: [email protected]
Abstract: Background: Athletes maintain a balance between stress and recovery and adopt recovery modalities that manage fatigue and enhance recovery and performance. Optimal TST is subject to individual variance. However, 7–9 h sleep is recommended for adults, while elite athletes may require more quality sleep than non-athletes. Methods: A total of 338 (elite n = 115, 74 males and 41 females, aged 23.44 ± 4.91 years; and sub-elite n = 223, 129 males and 94 females aged 25.71 ± 6.27) athletes were recruited from a variety of team and individual sports to complete a battery of previously validated and reliable widely used questionnaires assessing sleep, recovery and nutritional practices. Results: Poor sleep was reported by both the elite and sub-elite athlete groups (i.e., global PSQI score ≥5—elite 64% [n = 74]; sub-elite 65% [n = 146]) and there was a significant difference in sport-specific recovery practices (3.22 ± 0.90 vs. 2.91 ± 0.90; p < 0.001). Relatively high levels of fatigue (2.52 ± 1.32), stress (1.7 ± 1.31) and pain (50%, n = 169) were reported in both groups. A range of supplements were used regularly by athletes in both groups; indeed, whey (elite n = 22 and sub-elite n = 48) was the most commonly used recovery supplement in both groups. Higher alcohol consumption was observed in the sub-elite athletes (12%, n = 26) and they tended to consume more units of alcohol per drinking bout. Conclusion: There is a need for athletes to receive individualised support and education regarding their sleep and recovery practices.
Keywords: sleep; recovery; nutrition; alcohol; athletes
Post-exercise recovery is vital for all athletes and the balance between training stress and physical recovery must be managed to maximise the adaptation from, and perfor- mance in, subsequent training sessions or competitions [1,2]. The repetitive demanding nature of a competitive season can test athletes’ physiological and psychological capac- ity. Athletes must maintain a balance between stress and recovery and adopt recovery modalities that manage fatigue and enhance recovery and performance in subsequent training/competition . The regulation of performance during exercise has increasingly been interpreted as a cohesive, multifaceted process involving both the central nervous system (CNS) and the peripheral nervous system (PNS) [3,4]. While there is debate on whether the regulation of exercise performance is derived primarily from the CNS or PNS  and whether the regulation is conscious  or anticipatory , changing CNS drive and motor unit recruitment is widely considered to be associated with fatigue (i.e., reduced physical and mental capacity) . In contrast, physical fatigue has many potential drivers
Nutrients 2021, 13, 1330. https://doi.org/10.3390/nu13041330 https://www.mdpi.com/journal/nutrientshttps://www.mdpi.com/journal/nutrientshttps://www.mdpi.comhttps://orcid.org/0000-0002-7808-1715https://orcid.org/0000-0003-0506-3652https://orcid.org/0000-0002-8496-520Xhttps://doi.org/10.3390/nu13041330https://doi.org/10.3390/nu13041330https://creativecommons.org/https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.3390/nu13041330https://www.mdpi.com/journal/nutrientshttps://www.mdpi.com/article/10.3390/nu13041330?type=check_update&version=1
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(dehydration, glycogen depletion, muscle damage and mental fatigue), and recovery of muscle function is predominantly a matter of reversing the main causes of fatigue. Sleep deprivation (<7 h) increases circulating stress hormones (e.g., cortisol) ; decreases the regeneration of carbohydrate stores (i.e., glycogen) ; deregulates appetite and impacts on energy expenditure ; increases catabolism and reduces anabolism, impacting the rate of muscle repair (MPS) [11,12]. Therefore, sleep plays a key role in facilitation of post-exercise recovery or the reduction in fatigue and the reversal of the processes that lead to fatigue .
Athletes experience stress for various reasons (e.g., training, competition, travel and lifestyle) including periods of both acute and residual fatigue due to heavy training and competition schedules . For example, field-based team sports are characterised by repeated bouts of intermittent activity (sprinting) with short rest periods, representing high physiological stress , neuromuscular stress [16,17] and high rates of perceived exertion (i.e., how hard exercise seems) . Further, individual endurance athletes experience fatigue due to prolonged activity, resulting in glycogen depletion, thermal stress and/or dehydration . Relative stress is accumulated when successive bouts of training are com- bined with suboptimal recovery (under-recovery) impacting subsequent performance in training and competition . It has been suggested that decreasing the natural timeframe of the bodies’ regenerative processes via recovery strategies is vital for performance . Such recovery strategies can be divided into physiological strategies (e.g., sleep, cold water immersion, cryotherapy, contrast therapy, massage and compression), pharmacological (e.g., non-steroidal anti-inflammatory drugs [NSAIDs]) and nutritional (e.g., nutrient tim- ing, composition and supplementation) . However, it must be noted that some research has suggested that interfering with the body’s natural recovery processes, particularly inflammatory responses and OS, could reduce training adaptations . A recent review addressed these concerns in relation to the application of nutritional strategies to reduce muscle damage .
Sleep has previously been self-reported as the most important recovery modality utilised by both elite and sub-elite athletes [1,25,26]. Furthermore, it has been suggested that sleep was a new frontier in performance enhancement for athletes . Sleep has a restorative effect on the immune system and the endocrine system [28–30], facilitates the recovery of the nervous and metabolic cost of the waking state and has an integral role in cognitive function . The relationship between sleep, nutrition and recovery is an emerging area of interest [26,32–45]. Sleep has two basic states—non-rapid eye movement sleep (NREM) and rapid eye movement (REM) sleep. NREM is subdivided into three stages based on a continuum from light sleep (Stage N1 and N2) to deep sleep (Stage N3). It has been hypothesised that sleep, especially slow-wave sleep (Stage N3), is vital for physical recovery, due to the relationship with growth hormone release [44,46]. The National Sleep Foundation has proposed 12 indicators of sleep quality including 4 sleep continuity variables (sleep latency, awakenings >5 min, wake after sleep onset and sleep efficiency), 5 sleep architecture variables (REM sleep, N1 sleep, N2 sleep, N3 sleep and arousals) and 3 nap-related variables (naps per 24 h, nap duration and days per week with at least one nap) . Sleep can be considered adequate when there is no daytime sleepiness or dysfunction.
For sleep to have a restorative effect on the body, it must be of adequate duration, quality, and appropriately timed [38,48]. The National Sleep Foundation has produced guidelines regarding sleep duration for adolescents (recommended 8–10 h), adults (rec- ommended 7–9 h), and older adults (7–8 h) . It has been argued that elite athletes may require more quality sleep than non-athletes . It has recently been suggested that a one- size-fits-all sleep recommendation (7–9 h) may be inappropriate for athlete performance and health and an individual approach should be adapted including an assessment of perceived sleep needs .
Sleep inadequacy is common in athletes and can be attributed to the lack of an appropriate sleep routine due to changing training schedules, timetables and other sleep-
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incompatible behaviours, e.g., late night blue-light exposure [26,50]. Previous research has reported sleep durations <7 h , long sleep onset latency [26,52], daytime sleepiness , and daytime fatigue . Studies investigating sleep quality in elite athletes have demon- strated that 50–80% experience sleep disturbance and 22–26% experience highly disturbed sleep [37,53,55]. Irregular sleep–wake patterns influence the homeostatic and circadian regulation of sleep, which reduces both sleep quality and quantity . For athletes, post- completion routines and heightened arousal (i.e., medical care, recovery strategies, meals, media commitments and travel) can lead to later bedtimes, which can adversely affect sleep quality and quantity. Reduced sleep is associated with increased catabolic and reduced anabolic hormones, which results in impaired muscle protein synthesis , potentially blunting training adaptations and recovery.
Sleep disorders are identified by a wide range of symptoms that impact health and quality of life , cognitive performance  and physical performance [25,59]. Over 80 sleep disorders are listed in the third edition of the International Classification of Sleep Disorders (ISCD-3) . The ICSD-3 includes seven major categories of sleep disorders: in- somnia, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep wake disorders (CRSWDs), sleep-related movement disorders, parasomnias and other sleep disorders . In the general population, the most common sleep disorders are obstructive sleep apnoea (OSA), insomnia and restless legs syndrome (RLS) . Sleep- related breathing disorders are characterised by breathing issues during sleep . OSA is a frequent condition characterised by repeated episodes of partial or complete reduction in breathing activity during sleep . Insomnia is characterised by difficulty falling asleep, staying asleep, waking too early with daytime symptoms of fatigue, resistance to going to bed and/or difficulty sleeping without intervention occurring at least 3 times per week over a period of one month ([64,65]. Central disorders of hypersomnolence are typified by excessive daytime sleepiness that cannot be attributed to another sleep disorder . CR- SWDs are chronic (≥3 months) patterns of sleep–wake disruption caused by an alteration to the endogenous circadian or desychronisation of the circadian rhythm and the sleep–wake schedule, causing sleep–wake disturbance and distress or impairment . Sleep-related movement disorders may result from an unpleasant crawling, deep-aching sensation in the legs or arms that is relieved through movement . Parasomnias are undesirable movements or behaviours that occur during sleep, e.g., sleep walking, sleep talking, night terrors and REM sleep behaviour disorder . Other sleep disorders include all sleep disorders that do not meet the criteria for another sleep disorder classifications .
Polysomnography (PSG) is the ‘gold-standard’ method of sleep assessment and records sleep continuity, sleep architecture and REM sleep. A common global approach to the assessment of sleep quality is the use of self-report ratings reflecting an individual’s satisfaction with their sleep [47,67]. Sleep continuity is commonly assessed using sleep diaries and measures include time the subject went to bed, time the subject tried to initiate sleep, the length of time from turning off the lights until sleep onset (sleep onset latency), number and duration of awakenings, the degree of sleep maintenance during the night (sleep efficiency or the ratio of wake time to time in bed; awake time after sleep onset) sleep duration (total sleep time), time the subject woke up, time the subject got out of bed and sleep quality (subjective rating of sleep) [68,69].
Actigraphy is also used to assess sleep, regularly in combination with sleep diaries. Actigraphy involves wearing a small monitor (usually on the non-dominant wrist) which records body movement, high levels of activity are used as a measure of wakefulness and low levels of activity are classified as sleep . Activity monitors record movement as a function of time , typically a tri-axial accelerometer is used to determine sleep/wake based on a proprietary algorithm . A limitation of actigraphy is that all activity is recorded as waking unless the sleep diaries show an attempt to sleep (i.e., lying down trying to sleep) and the activity counts are low enough to indicate the subject is stationary . However, actigraphy has been shown to be reliable and valid in relation to PSG for general measures of sleep [72,73].
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Athletes’ schedules can negatively impact their sleep and recovery [51,52], and the repetitive demanding nature of a competitive season can also test athletes’ physiological and psychological capacity, reinforcing the athletes’ need for quality sleep [74–77]. Actigra- phy based sleep assessments reveal suboptimal sleep in athletes, i.e., low TST and high WASO, causing resultant low sleep efficiency [27,30], which improves following a rest day . However, the athletes’ experience of suboptimal sleep remains unclear as sleep need varies between individuals; some may report poor sleep while objective measures indicate sufficient sleep . Therefore, subjective measures of sleep quality, quantity and timing are a valuable addition to objective sleep assessments. Combined subjective markers of sleep (e.g., TST, time in bed, sleep efficiency, sleep quality and sleep onset latency) can highlight the sleep need and recovery status of athletes and identify areas to be addressed in terms of sleep optimisation. Moreover, the use of subjective measures within an athletic population allows the assessment of large cohorts of athletes that are difficult to access, i.e., elite athletes.
Animal models have demonstrated that nutrients such as glucose, amino acids, sodium, ethanol and caffeine, as well as the timing of meals can affect circadian rhythms . Neurotransmitters such as serotonin, gamma-aminobutyric acid (GABA), orexin, dopamine, melanin-concentrating hormone, galanin, noradrenaline and histamine that are involved in the sleep–wake cycle  are affected by nutrition. In terms of recovery, the adaptive response to training is dictated by a number of variables: duration, intensity, frequency and type of exercise, in combination with timing, quality and quantity of nutrition both pre- and post-exercise . Recovery can be maximised by optimal nutrition practices or reduced by suboptimal nutrition practices. Contemporary research has demonstrated the pivotal role of both macronutrient and micronutrient availability in regulating skeletal muscle adaptations to exercise [81–83]. It is important to characterise the sleep quality and quantity of sub-elite and elite athletes and recovery practices. This study aimed to investigate: (i) the quality, quantity and timing of sleep among sub-elite and elite athletes; (ii) the recovery/stress balance of sub-elite and elite athletes; and (iii) the supplement use and alcohol intake of sub-elite and elite athletes. This study also aimed to investigate the difference between elite and sub-elite athletes in terms of their subjective sleep, recov- ery and nutritional practices. It was hypothesised that the sleep, recovery and nutrition practices of elite athletes would be superior to those of sub-elite athletes.
2. Materials and Methods 2.1. Participants
A sample (n = 338) comprising elite (n = 115; male n = 74 and female n = 41) and sub-elite (n = 223; male 129 and female 94) athletes were recruited from both Ireland and the United Kingdom (see Table 1). The elite athletes were recruited directly through Sport Ireland and the national governing bodies (NGBs) of each sport within Ireland and the United Kingdom. The sub-elite athletes were recruited via social media and the researcher’s network within high-performance sport. In line with Swann et al. , elite athletes were defined as: (a) currently receiving support/funding through the international carding scheme and/or (b) members of a national/professional team or a recruitment/academy squad and/or (c) nationally ranked in their sport. Sub-elite athletes were defined as those competing at a regional, university and/or national level of organised sport that trained and/or competed for a combined minimum of 400 min per week. Athletes, at either level, were excluded if they were (i) aged <18 years, (ii) training and competing for <400 min per week or (iii) reported a sleep disorder.
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Table 1. Participant characteristics (mean ± SD).
All (n = 338) Elite (n = 115) Sub-Elite (n = 223) t/x2 Value
Gender Male n = 203;
Female n = 135 Male n = 74;
Female n = 41 Male n = 129; Female n = 94
X 2 = 1.72
Age * 24.94 ± 5.93 23.44 ± 4.91 25.71 ± 6.27 t = 3.384 Body mass (kg) 72.95 ± 13.26 73.95 ± 12.55 72.44 ± 13.61 t = −0.995
Height (cm) 175.60 ± 9.70 176.6 ± 8.78 175.08 ± 10.12 t = −1.361 Training (mins·wk) * 675.12 ± 306.59 801.35 ± 338.81 610.02 ± 266.90 t = −5.682
* Statistically significant difference.
All eligible athletes were invited to take part in an online survey. All procedures were approved by the research ethics committee of the Faculty of Health and Life Sciences, Northumbria University (date of approval 2 July 2019; Submission ID: 17406). After reading the participant information sheet, participants were invited to provide informed consent and then completed an online survey on Qualtricsxm which consisted of a battery of previously validated and reliable widely used questionnaires assessing sleep, recovery and nutritional practices. Following completion of the survey, participants received a debrief sheet with details of how they could contact the researcher if they wished to receive feedback from the survey.
In the initial section of the survey, the participants completed demographic data. Participants recorded their gender, age, body mass (kg), height (cm), sport, athlete type (elite or sub-elite), phase of season (pre-season, competition or off-season), normal training time (before 8 a.m., 8 a.m. to 5 p.m. and after 5 p.m.) and training/competition duration per week (mins).
2.3.1. EuroQoL (EQ-5D-5L)
The EQ-5D-5L is a self-report measure of health status as defined across five dimensions— mobility, self-care, activity, pain and depression/anxiety—with one question per dimension. Each dimension is scored on a 5-point Likert scale (0 = No problem to 5 = Severe prob- lem) . The EQ-5D-5L also includes a visual analogue scale on which respondents are instructed to rate their perceived current health state (0–100). The EQ-5D-5L has capacity to discriminate between slight, moderate and severe issues within each domain compared to previous versions .
2.3.2. Pittsburgh Sleep Quality Index (PSQI)
The PSQI is a self-report measure of sleep quality . The PSQI consists of 19 items grouped into seven component scores (0–3) which are equally weighted. Although overall global scores (GPSQI) are calculated by summing the seven components (range 0–21, with higher scores indicating poorer sleep quality) the component scores provide subscale ratings of: (i) subjective sleep quality, (ii) sleep latency, (iii) TST, (iv) sleep efficiency, (v) sleep disturbances, (vi) use of sleep medication and (vii) daytime dysfunction . Global scores >5 are generally used to indicate poor sleep quality (63). The PSQI has demonstrated a diagnostic sensitivity (89.6%) and specificity (86.5%) in distinguishing between ‘good’ and poor’ sleepers . However, more conservative scores of ≥8 have been used in athletes to indicate poor sleep, potentially due to the increased sleep needs in this population . Although the empirical discussion around the PSQI cut-offs for athletes is ongoing [38,55], given that athletes often strive for marginal gains in their performance, which can be facilitated through optimised sleep, the identification of both ‘poor’ and ‘moderate’ sleep quality is warranted , hence the standard cut-off (≥5) was employed.
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2.3.3. Epworth Sleepiness Scale (ESS)
The ESS is an eight-item self-report measure of general daytime sleepiness . Respondents report their daytime sleepiness in particular situations on a Likert scale (0 = Would never doze to 4 = High chance of dozing). Scores are summed to yield a global ESS score (0–24). The EES global score is indicative of daytime sleepiness . Higher scores indicate greater sleepiness, scores >10 suggest excessive daytime sleepiness . In general ESS scores are interpreted in terms of daytime sleepiness as follows: 0–5 low normal, 6–10 higher normal, 11–12 mild excessive, 13–15 moderate excessive and 16–24 severe .
2.3.4. The Recovery Stress Questionnaire for Athletes (RESTQ Sport)
The RESTQ-Sport is a 52-item self-report measure of general stress and recovery levels of athletes . The RESTQ-Sport consists of seven general stress components with two items per scale (general stress, emotional stress, social stress, conflicts/pressure, fatigue, a lack of energy, and physical complaints), five general recovery components with two items per scale (success, social recovery, physical recovery, general well-being, and sleep quality), three sport-specific stress components with four items per scale (dis- turbed breaks, burnout/emotional exhaustion, and fitness/injury) and four sport-specific recovery components with four items per scale (fitness/being in shape, burnout/personal accomplishments, self-efficacy, and self-regulation) . Sub-scale item mean scores can be combined to give a total score for each of the four major sub-scales (i.e., general stress, general recovery, sport-specific stress and sport-specific recovery). Each item is scored on a Likert scale (0 = Never to 6 = Always) based on how often the respondent engaged in a specified activity over the previous three days/nights, with a response of 0 indicating never having experienced the feeling and 6 indicating always experiencing the associated feeling. High scores on stress scales indicate a high level of stress, while high scores on the recovery scales indicate a high level of recovery .
2.3.5. Athlete Morningness/Eveningness Questionnaire (AMES)
The AMES, which is based on the Horne–Östberg morningness/eveningness ques- tionnaire , is a four-item questionnaire used to classify an athlete’s chronotype in terms of self-identification as being a morning or evening type, preferred sleep/wake phase and preferred competition and training time . The AMES provides a global score which is used to categorise chronotype: extreme evening type (10–12), moderate evening type (13–17), mid-range type (18–23), moderate morning type (24–28) and extreme morning type (29–31) .
2.3.6. Consensus Sleep Diary—Core (CSD-C)
Participants were instructed to complete the CSD-C for two nights (1 ‘training/ competition’ day and 1 ‘rest’ day). The CSD is a standardised sleep diary developed for use in both research and clinical settings . The CSD-C included 8 items, e.g., bed time, time it took to fall asleep, number of awakenings, duration of awakenings, time of final awakening, time the respondent got out of bed, and a Likert scale self-report rating of sleep quality . There was also a comments section where participants could record specific comments about each night’s sleep (i.e., 1 training/competition day and 1 rest day). The data collected were then used to compute indices of sleep continuity such as total time in bed (TIB), total sleep time (TST, sleep onset latency (SOL; time from lights out to N1), wakefulness after sleep onset (WASO; amount of time awake after sleep onset), number of awakenings (NoA) and sleep efficiency (SE; ratio of TST:TIB) .
All participants were instructed to complete questions relating to supplement use (name, dose, frequency and reason for use) on both training/competition days and rest
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days. Athletes also reported their alcohol consumption (number of drinking sessions and unit consumption per session) in the last month prior to completion of the questionnaire.
2.5. Data Analysis
All data were analysed using the Statistical Package for the Social Sciences (SPSS Version 25, IBM Corporation) and Jamovi (Version 1.8.16). Frequency distribution and descriptive statistics were used to present findings . All data were presented as the mean ± standard deviation, and/or frequency. The differences between the groups for athlete type were explored using independent-samples t-tests, chi square tests, Mann–Whitney U and one-way ANOVA .
3. Results 3.1. Participant Characteristics
A total of 338 (elite n = 115 and sub-elite n = 223) athletes were recruited from a variety of team and individual sports (see Tables 1 and 2.). The sample consisted of both male (n = 203; ~60%) and female (n = 135; ~40%) athletes.
Table 2. Participant breakdown.
Sport All Elite n = 115 Sub-Elite n = 223
Athletics 64 10 54 Boxing 12 11 1
Gaelic games 89 26 63 Hockey 10 9 1 Rowing 29 8 21 Rugby 20 8 12 Sailing 4 3 1 Soccer 31 10 21
Swimming 8 4 4 Other 71 26 45
A chi square analysis demonstrated no significant differences between the groups for gender (X2[1, n = 338] = 1.72, p = 0.189). While there were statistically significant differ- ences between the groups for age (elite 23.44 ± 4.91 years and sub-elite 25.71 ± 6.27 years; t(336) = 3.38; p = 0.001) and minutes trained per week (elite 801.35 ± 338.81 and sub-elite 610.02 ± 266.90; t(336) = −5.68; p ≤ 0.001). An independent-samples t-test indicated no significant differences between the groups in terms of height, body mass and normal training time (time of day when training occurred) (see Table 1).
Chi square analyses demonstrated a statistically significant difference between the groups for sport (X2[9, n = 338] = 1.72, p ≤ 0.001). There were no statistically significant differences between the groups for phase of season: pre-season (elite n = 31; sub-elite n = 57), competition (elite n = 65; sub-elite n = 115), off-season (elite n = 19; sub-elite n = 51) (X2[2, n = 338] = 1.88, p = 0.39). There were statistically significant differences between the groups for normal training time: before 8 am (elite n = 8 and sub-elite n = 25), between 8 a.m. and 5 p.m. (elite n = 50; sub-elite n = 58), and after 5 p.m. (elite n = 57; sub-elite n = 140) (X2[2, n = 338] = 10.9, p ≤ 0.001).
There was no statistically significant difference between the groups for their perceived general health rating (0–100) with the elite athlete group reporting slightly higher levels of general health than the sub-elite athlete group (83.05 ± 13.65 vs. 81.05 ± 12.57; t = −1.37; p = 0.172). There were no statistically significant differences between the groups in terms of each of the domains of the quality of life measure (see Table 3). Slight to severe problems with mobility were reported by 19% (n = 65) of participants (elite n = 19 [17%]; sub- elite n = 46 [21%]). Some issues regarding the completion of usual activities (e.g., work,
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study, training, housework, family or leisure activities) were reported by 19% (n = 64) of participants (elite n = 23 [20%]; sub-elite n = 41 [18%]). Issues with self-care were not evident within the athletes as slight to moderate issues were reported by 3% of participants (elite n = 2 [2%]; sub-elite n = 9 [4%]). Pain was reported by 50% (n = 169) of participants (elite n = 53 [46%]; sub-elite n = 116 [52%]). Anxiety/depression was reported by 34% (n = 116) of participants (elite n = 43 [37%]; sub-elite n = 73 [33%]).
Table 3. Athlete responses to the EuroQOL.
None Slight Moderate Severe Extreme
Mobility Elite 96 14 5
Sub-elite 177 42 2 1 1
Self-care Elite 113 1 1
Sub-elite 214 7 2
Usual activities Elite 92 18 3 1
1Sub-elite 182 33 8
Pain Elite 62 47 6
Sub-elite 107 102 14
Anxiety/Depression Elite 72 33 8 2
Sub-elite 150 58 13 2
3.1.2. Pittsburgh Sleep Quality Index
An independent-samples t-test was used to compare PSQI data for the elite and sub- elite athlete groups. A statistically significant difference was observed between the groups for PSQI habitual sleep efficiency % (elite 88.62 ± 8.84 vs. sub-elite 86.55 ± 9.09; t = −2.01; p = 0.046). While no other statistically significant differences were observed, the majority of athletes (64%; n = 220) were classified as poor sleepers (i.e., global PSQI score ≥5—elite 64% [n = 74]; sub-elite 65% [n = 146]). Overall self-reported sleep quality did not reflect this as the athletes rated their sleep quality as very good (elite n = 19 [17%]; sub-elite n = 45 [20%]), fairly good (elite n = 68 [59%]; sub-elite n = 123 [55%]), fairly bad (elite n = 26 [23%]; sub-elite n = 50 [22%]) and poor (elite n = 2 [1%]; sub-elite n = 5 [2%]). Mean total sleep time (hours) varied between the elite athlete (7.58 ± 1.06; range 5–10 h) and the sub-elite athlete groups (7.35 ± 1.05; range 4–10 h) but this was not statistically significant. The athletes reported total sleep time ≤6 h (elite n = 16 [14%]; sub-elite n = 43 [19%]), 7 h (elite n = 38 [33%]; sub-elite n = 80 [36%]), 8 h (elite n = 39 [34%]; sub-elite n = 70 [32%]) and 9 h (elite n = 22 [19%]; sub-elite n = 30 [13%]). The athletes’ responses to the PSQI are summarised in Table 3. The athletes reported total time in bed 8 h (elite n = 53 [46%]; sub-elite n = 109 [49%]), 9–10 h (elite n = 50 [44%]; sub-elite n = 110 [49%]) and 11–12 h (elite n = 12 [10%]; sub-elite n = 4 [2%]).
The reasons reported for poor sleep quality were not getting to sleep within 30 min, waking during the night or early morning, waking to use the bathroom and feeling too hot in bed (see Table 3). The feeling of a lack of enthusiasm for general tasks at least once per week was reported by 44% (n = 51) of the elite group and 41% (n = 92) of the sub-elite group. The use of sleep medication was low in both groups, with 5% (n = 6) of the elite group and 7% (n = 16) of the sub-elite group using medication on a weekly basis (see Table 4).
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Table 4. Athlete responses to the PSQI.
Not during the Last Month
Less than Once per Week
Once or Twice per Week
Three or More Times per Week
Cannot get to sleep within 30 min
Wake up in the middle of the night or early morning
Have to get up to use the bathroom
Cannot breathe comfortably
Cough or snore loudly
Feel too cold Elite
160 27 41
Feel too hot Elite
Sub-elite 54 82
Have bad dreams Elite
114 34 75
Have pain Elite
152 22 48
Other reasons Elite
Sub-elite 101 180
Problems staying awake
Lack of enthusiasm Elite
Sub-elite 35 50
Use of sleep medication
3.2. Epworth Sleepiness Scale
An independent-samples t-test demonstrated no significant differences between the elite and sub-elite athlete groups for ESS scores (p > 0.05). A chi square test highlighted no significant difference between the groups’ ESS classification (X2[20, n = 338] = 21.1, p = 0.391). Approximately 21% (n = 70) of athletes (elite n = 25; 22% and sub-elite n = 45; 20%) reported clinically significant excessive daytime sleepiness (ESS total score ≥10) (see Table 5).
Table 5. ESS classification.
Classification (ESS Score) Elite (n = 115) Sub-Elite (n = 223)
Low Normal (0–5) 53 114 Higher Normal (6–10) 45 70 Mild Excessive (11–12) 6 20
Moderate Excessive (13–15) 8 14 Severe (16–24) 3 5
3.3. Recovery Stress Questionnaire
An independent-samples t-test highlighted significant differences between the elite and sub-elite athlete groups for recovery, i.e., the sport-specific recovery scale (3.22 ± 0.90 vs. 2.91 ± 0.90; t (−2.984); p < 0.001). While no statistically significant differences were ob-
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served for the general stress, general recovery and sport-specific stress subscales. Recovery stress scale scores were similar in both the elite and sub-elite groups with similar scores observed for the general stress scale (1.96 ± 0.91 vs. 2.01 ± 0.86), general recovery scale (2.97 ± 0.79 vs. 2.97 ± 0.77) and sport-specific stress scale (1.97 ± 0.87 vs. 1.99 ± 0.85).
An independent-samples t-test displayed no statistically significant differences be- tween the groups for the majority of the subscales with both groups recording similar scores (see Table 6). However, significant differences between the groups were observed for the following sport-specific recovery subscales: being in shape (3.22 ± 1.08 vs. 2.90 ± 1.04; t = −2.66; p = 0.008), personal accomplishment (2.97 ± 1.04 vs. 2.74 ± 0.98; t = −1.98; p = 0.048), self-efficacy (3.15 ± 1.12 vs. 2.83 ± 1.04; t = −2.58; p = 0.010) and self-regulation (3.55 ± 1.19 vs. 3.18 ± 1.18; t = −2.71; p = 0.007), with higher levels being observed across each domain in the elite athlete group (see Figure 1). While not statistically sig- nificant poor sleep quality was observed (2.77 ± 0.78 vs. 2.83 ± 0.85), concerns related to injury (2.48 ± 1.09 vs. 2.32 ± 1.17) and relatively high levels of fatigue (2.46 ± 1.33 vs. 2.54 ± 1.31).
Table 6. RESTQ scales (Mean ± SD).
All (n = 338)
Elite (n = 115)
Sub-Elite (n = 223)
General Stress 1.7 ± 1.31 1.77 ± 1.39 1.67 ± 1.26 −0.6602 0.51 Emotional Stress 1.95 ± 0.983 1.9 ± 0.98 1.97 ± 0.99 0.6858 0.493
Social Stress 1.85 ± 1.03 1.83 ± 1.04 1.86 ± 1.02 0.2199 0.826 Conflicts/Pressure 2.35 ± 1.24 2.24 ± 1.26 2.41 ± 1.24 1.1382 0.256
Fatigue 2.52 ± 1.32 2.46 ± 1.32 2.55 ± 1.32 0.6125 0.541 Lack of Energy 2 ± 1.06 1.95 ± 1.19 2.02 ± 1 0.5755 0.565
1.61 ± 1.22 1.59 ± 1.34 1.61 ± 1.16 0.1638 0.87
Success 2.85 ± 1 2.92 ± 1.01 2.81 ± 1 −0.9189 0.359 Social Relaxation 3.3 ± 1.28 3.19 ± 1.26 3.36 ± 1.29 1.1573 0.248
2.53 ± 1.06 2.59 ± 1.09 2.49 ± 1.04 −0.8265 0.409
General Well-Being 3.35 ± 1.16 3.37 ± 1.22 3.35 ± 1.13 −0.1497 0.881 Sleep Quality 2.81 ± 0.83 2.77 ± 0.78 2.83 ± 0.85 0.6552 0.513
Disturbed Breaks 1.68 ± 0.92 1.71 ± 0.91 1.67 ± 0.94 −0.4119 0.681 Burnout/Emotional
Exhaustion 1.83 ± 1.13 1.87 ± 1.22 1.81 ± 1.09 −0.4695 0.639
Fitness/Injury 2.43 ± 1.12 2.32 ± 1.17 2.48 ± 1.09 1.2827 0.2 Fitness/Being in
Shape ** 3.01 ± 1.06 3.22 ± 1.08 2.9 ± 1.04 −2.6563 0.008
Burnout/Personal Accomplishment *
2.82 ± 1.01 2.97 ± 1.04 2.74 ± 0.98 −1.9984 0.048
Self-Efficacy ** 2.94 ± 1.07 3.15 ± 1.12 2.83 ± 1.04 −2.5747 0.01 Self-Regulation ** 3.31 ± 1.2 3.55 ± 1.19 3.18 ± 1.18 −2.7121 0.007
Data presented as the mean ± SD * p < 0.05, ** p < 0.01.
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3.3. Recovery Stress Questionnaire An independent-samples t-test highlighted significant differences between the elite
and sub-elite athlete groups for recovery, i.e., the sport-specific recovery scale (3.22 ± 0.90 vs. 2.91 ± 0.90; t (−2.984); p < 0.001). While no statistically significant differences were ob- served for the general stress, general recovery and sport-specific stress subscales. Recov- ery stress scale scores were similar in both the elite and sub-elite groups with similar scores observed for the general stress scale (1.96 ± 0.91 vs. 2.01 ± 0.86), general recovery scale (2.97 ± 0.79 vs. 2.97 ± 0.77) and sport-specific stress scale (1.97 ± 0.87 vs. 1.99 ± 0.85).
An independent-samples t-test displayed no statistically significant differences be- tween the groups for the majority of the subscales with both groups recording similar scores (see Table 6). However, significant differences between the groups were observed for the following sport-specific recovery subscales: being in shape (3.22 ± 1.08 vs. 2.90 ± 1.04; t = −2.66; p = 0.008), personal accomplishment (2.97 ± 1.04 vs. 2.74 ± 0.98; t = −1.98; p = 0.048), self-efficacy (3.15 ± 1.12 vs. 2.83 ± 1.04; t = −2.58; p = 0.010) and self-regulation (3.55 ± 1.19 vs. 3.18 ± 1.18; t = −2.71; p = 0.007), with higher levels being observed across each domain in the elite athlete group (see Figure 1). While not statistically significant poor sleep quality was observed (2.77 ± 0.78 vs. 2.83 ± 0.85), concerns related to injury (2.48 ± 1.09 vs. 2.32 ± 1.17) and relatively high levels of fatigue (2.46 ± 1.33 vs. 2.54 ± 1.31).
Figure 1. Comparison of the sport-specific recovery subscales.
Table 6. RESTQ scales (Mean ± SD).
(n = 338) Elite
(n = 115) Sub-Elite (n = 223)
General Stress 1.7 ± 1.31 1.77 ± 1.39 1.67 ± 1.26 −0.6602 0.51 Emotional Stress 1.95 ± 0.983 1.9 ± 0.98 1.97 ± 0.99 0.6858 0.493
Social Stress 1.85 ± 1.03 1.83 ± 1.04 1.86 ± 1.02 0.2199 0.826 Conflicts/Pressure 2.35 ± 1.24 2.24 ± 1.26 2.41 ± 1.24 1.1382 0.256
Fatigue 2.52 ± 1.32 2.46 ± 1.32 2.55 ± 1.32 0.6125 0.541 Lack of Energy 2 ± 1.06 1.95 ± 1.19 2.02 ± 1 0.5755 0.565
Physical Complaints 1.61 ± 1.22 1.59 ± 1.34 1.61 ± 1.16 0.1638 0.87 Success 2.85 ± 1 2.92 ± 1.01 2.81 ± 1 −0.9189 0.359
Social Relaxation 3.3 ± 1.28 3.19 ± 1.26 3.36 ± 1.29 1.1573 0.248 Physical Relaxation 2.53 ± 1.06 2.59 ± 1.09 2.49 ± 1.04 −0.8265 0.409 General Well-Being 3.35 ± 1.16 3.37 ± 1.22 3.35 ± 1.13 −0.1497 0.881
Figure 1. Comparison of the sport-specific recovery subscales.
An independent-samples t-test demonstrated a statistically significant difference between the groups for preferred competition time (t (336) = −2.45; p = 0.015), with a higher percentage of the elite athlete group (77% [n = 89]) preferring afternoon competition times compared to the sub-elite group (60% [n = 113]) (see Table 7). There was no significant difference between the groups for chronotype, time they usually become tired and preferred training time.
Table 7. Athlete response to the AMES.
Chronotype Elite (n =)
Sub-elite (n =)
Morning type More morning type More evening type Evening type 24 45
Preferred training time
6 a.m.–9 a.m. 9 a.m.–Noon Noon–3 p.m. 3 p.m.–6 p.m. 6 p.m.–9 p.m.
Elite (n =) Sub-elite (n =)
Preferred competition time *
Elite (n =) Sub-elite (n =)
6 a.m.–9 a.m. 5
9 a.m.–Noon 21 78
Noon–3 p.m. 47 62
3 p.m.–6 p.m. 21 47
6 p.m.–9 p.m. 21 24
Time you usually get tired
8 p.m.–9:30 p.m. 9:31 p.m.–10:45 p.m. 10:46 p.m.–12:30 a.m. 12:30 a.m.–1:45 a.m. 1:46 a.m.–3:00 a.m.
Elite (n =) Sub-elite (n =)
* Statistically significant difference (p < 0.05).
3.3.2. Consensus Sleep Diary—Core
All athletes also completed a sleep diary for a training/competition day and a rest day. A one-way ANOVA was conducted to assess the difference between the groups for TIB, TST, SL, NoA and WASO on both the training/competition day and rest day. While there were no statistically significant differences for TIB, SL and WASO, there were statistically significant differences between the groups (elite vs. sub-elite) for TST on the training/competition day (8.01 ± 1.3 vs. 8.2 ± 1.38; F(1, 238) = 3.91; p = 0.049) and NoA on
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the rest day (1.03 ± 1.17 vs. 1.52 ± 2.44; F(1, 334) = 6.34; p = 0.012), with the sub-elite athlete group reporting higher levels of both measures (see Table 8). The majority of athletes in both groups (elite n = 155 [70%]; sub-elite n = 77 [67%]) reported wakening 1–5 times each night. Athletes in both groups reported that it took ≥30 min to fall asleep on the training/competition day (elite n = 33 [29%]; sub-elite n = 72 [32%]) and the rest day (elite n = 35 [30%]; sub-elite n = 70 [31%]). While there was no statistically significant difference between the groups, poor habitual sleep efficiency (<85%) was reported by 20% (n = 23) of the elite athlete group and 25% (n = 55) of the sub-elite athlete group. In the comments section of the sleep diary a subset of athletes (n = 73 [22%]) reported the reasons for waking at night, the most common reasons included injury (n = 15 [4%]), children (n = 11 [3%]), anxiety (n = 19 [6%]), energy restriction (i.e., making weight) (n = 7 [2%]) and waking to use the bathroom (n = 21 [6%]).
Table 8. Sleep diary responses (mean ± SD).
Sleep Measure Training/Competition Day Rest Day
TIB (h) Elite
Sub-elite 9.1 ± 1.18 9.2 ± 1.42
9.53 ± 1.49 9.6 ± 1.5
TST (h) Elite
Sub-elite 8.01 ± 1.30 * 8.2 ± 1.38 *
8.58 ± 1.4 8.59 ± 1.44
SL (Min) Elite
Sub-elite 22.85 ± 20.74 22.65 ± 17.70
21.62 ± 18.7 23.72 ± 22.37
NoA (#) Elite
Sub-elite 1.38 ± 1.43 1.51 ± 1.73
1.03 ± 1.17 * 1.52 ± 2.44 *
WASO (Min) Elite
Sub-elite 11.06 ± 17.06 10.14 ± 16.51
7.31 ± 9.99 9.56 ± 12.60
SE (%) Elite
Sub-elite 88.2 ± 10.18 89.77 ± 7.14
90.21 ± 6.6 89.1 ± 7.05
* Statistically significant difference (p < 0.05).
The athletes also reported their supplement and alcohol consumption in the month prior to completion of the questionnaire. A Mann–Whitney U test indicated no significant differences between the elite and sub-elite athlete groups for supplementation and alcohol consumption (p ≥ 0.05). The most commonly used supplements were whey protein, caffeine, creatine, multivitamins, fish oil, probiotics and vitamin D (see Table 9).
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Table 9. Athlete supplement use, frequency, average dose and reason for use.
Supplement Frequency Dose Reason Elite
(n = 115) Sub-Elite (n = 223)
Caffeine Daily 100 mg Performance 23 37 Creatine Daily Varied Performance 13 20 Fish Oil Daily 1 capsule Health 18 12
Iron Daily Varied Anaemia/Performance 4 10 Multivitamin Daily 1 capsule Health 24 32
Nitrate Daily 1 shot Performance 11 1 Probiotics Daily 1 capsule Health 13 25 Vitamin D Daily 1000–4000 IU Health/Performance 21 5
Whey Daily 25–40 g Recovery 22 48 Other (e.g., BCAA, beta—alanine,
HMB, casein, antioxidants) Daily/weekly Varied Health/Performance 30 19
Spearman’s rank order correlation was used to assess the relationship between sup- plement use and various sleep and recovery variables. There were small significant cor- relations between supplement use and the RESTQ scales: sleep quality, disturbed breaks, emotional exhaustion, being in shape and self-efficacy (see Table 10).
Table 10. Relationship between supplement use and recovery.
Being in Shape
−0.167 ** p = 0.002
0.119 * p = 0.029
0.137 * p = 0.012
−0.114 * p = 0.036
−0.108 * p = 0.048
Statistically significant * p ≤ 0.05; ** p ≤ 0.01.
The athletes reported the number of times that they consumed alcohol in the last month 1–4 times (elite n = 10 [9%]; sub-elite n = 10 [5%]), 5–9 times (elite n = 11 [10%]; sub-elite n = 5 [2%]), and >10 times (elite n = 3 [3%]; sub-elite n = 11 [5%]). The athletes also reported the number of units they usually consumed during each drinking session <4 units (elite n = 11 [10%]; sub-elite n = 6 [3%]), 5–10 (elite n = 9 [8%]; sub-elite n = 8 [4%]) and >10 (elite n = 4 [3%]; sub-elite n = 12 [5%]).
This study recruited a large cohort of elite (n = 115) and sub-elite (n = 223) athletes from a wide variety of sports. Elite athletes were either international athletes, members of a national/professional team, a recruitment/academy squad and/or nationally ranked in their sport . Sub-elite athletes were defined as those competing at a regional, university and/or national level of organised sport that trained and/or competed for a combined minimum of 400 min per week . To the authors’ knowledge, this is one of the largest cohorts of athletes to have been investigated from a sleep and recovery perspective. This study aimed to investigate: the quality, quantity and timing of sleep among sub-elite and elite athletes and characterise their recovery and nutrition practices. It was hypothesised that the sleep, recovery and nutrition practices of elite athletes would be superior to those of sub-elite athletes. Interestingly, similar levels of poor sleep were reported by both the elite and sub-elite athlete groups, whereas there was a significant difference in sport-specific recovery practices.
Poor sleep quality was reported in the PSQI, the REST-Q and it was notable in the sleep diaries that athletes reported improved TIB, TST and WASO on rest days. Excessive daytime sleepiness was also observed in both groups. Similarly, previous research has suggested
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that the quality and quantity of elite athlete’s sleep was inferior to sub-elite athletes and potentially inadequate in relation to optimal recovery and performance [27,30,32,37,95].
4.2. Pittsburg Sleep Quality Index
The PSQI has demonstrated good reliability (Cronbach’s alpha = 0.83, test–retest reliability r = 0.85) . The PSQI having demonstrated acceptable internal consistency and has been shown to be reliable [96,97] and valid [87,96–98] measure of sleep quality. Cronbach’s alpha 0.744 was observed in the current sample. The majority of athletes (~65%; n = 220) were classified as poor sleepers (Global PSQI score ≥5). This is consistent with pre- vious research in elite athletes [53–55,95], and sub-elite athletes [99,100]. A relatively high proportion of athletes (~30%) self-reported their sleep quality as either poor or very poor on the training/competition day compared to rest day (elite 10% [n = 12] and sub-elite 16% [n = 36]). The PSQI data highlighted reasons for poor sleep on both training/competition days and rest days such as feeling too hot in bed and a lack of enthusiasm for general tasks. Poor sleep quality is of particular concern for elite athletes as it can result in a reduction in recovery and/or subsequent athletic performance [29,101–103].
Interestingly the PSQI mean TST (<8 h) was lower than that reported in the CSD-C (>8 h), it has been suggested that athletes tend to overestimate their sleep [104,105]. A recent review suggested that sleep in athletes is limited to 7.2 h per night, with all studies reporting <8 h per night and mean SE was 86.3 ± 6.8% , which is in line with the PSQI and CSD-C data from the current study. The PSQI mean TST for both groups in the current study is adequate according to current sleep recommendations (7–9 h) . However, optimal TST is subject to individual variance and it has been argued that elite athletes may require more quality sleep than non-athletes . It has previously been reported that athletes tend to sleep less (6.5–6.7 h) and that their sleep quality is poor [27,54,107–109]. Optimising sleep gives athletes an advantage when it comes to maximising adaptations from training and performance enhancement .
4.3. Consensus Sleep Diary-Core
There were significant differences between the groups for TST on the training/ competition day and NoA on the rest day. TST was lower in the elite athlete group on both days. However, it did improve on the rest day which was most likely a reflection of their behaviour, e.g., choosing to go to bed earlier. Although not statistically significant there was a trend towards reduced TIB, TST and WASO in both groups on the rest day while the elite athlete group also demonstrated a trend towards reduced SL, NoA and increased SE on the rest day. Similarly, a small study of Australian athletes (n = 6) using objective measures of sleep demonstrated that sleep improved (longer duration) on a rest day (71.6% reported no sleep disturbance following one rest day) . A study involving elite swimmers (n = 7) showed that the athletes went to bed later but slept longer on rest days , where the opportunity for extended sleep provided the athletes with an opportunity to partially recover the sleep debt accumulated during the training week . In the current study, poor sleep was attributed by the athletes in both groups to a number of factors, i.e., injury, children, anxiety, making weight (boxing) and bathroom use. Previous research has high- lighted issues that impair an athlete’s sleep such as stress [32,112], pain/injury [26,32,33] and anxiety [25,29]. The relationship between poor sleep and impaired mood has been reported in non-athletic populations . However, the study involved sleep restriction to 4.98 h per night. Monitoring athletes’ mood (e.g., through wellness monitoring) could identify athletes who require sleep-related intervention.
In the current study, poor habitual SE% previously quantified as <85%  was reported by 20% (n = 23) of the elite athlete group and 25% (n = 55) of the sub-elite athlete group. Previous research has demonstrated that habitual sleep efficiency of elite athletes was 88.47 ± 5.45%  80.6 ± 6.4% , 86.3 ± 6.1%  and 79 ± 9.2% . A recent systematic review reported the pooled average sleep efficiency for athletes (86 ± 5%; range 79–96%)  which straddled and for many athletes overlapped the threshold of 85%,
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below which insomnia symptoms are indicated . While the range of sleep efficiency observed can in part be explained by methodological inconsistencies, the pooled mean nonetheless indicated sleep problems and poor sleep quality. There is a need for clear athlete-friendly interventions that could promote improved sleep and recovery.
4.3.1. Daytime Sleepiness
The ESS score is comparable to objective sleepiness measures such as the multiple sleep latency test (MSLT) and is considered a valid and reliable measure of objective sleepi- ness . The ESS has been widely used in athletic populations such as Australian rules football , collegiate basketball players  and American football players . In the present sample, Cronbach’s alpha was 0.827. Approximately 21% of athletes in the current study reported excessive daytime sleepiness. Similar levels of excessive daytime sleepiness have been reported in rugby players and cricketers , American footballers , Aus- tralian rules footballers  and college athletes . Similarly, previous research reported that 44% (n = 12) Brazilian Paralympians experienced excessive daytime sleepiness . However, it must be noted that these athletes may have had physical impairments (e.g., spinal cord injury) that could impact sleep quantity and quality.
The levels of excessive daytime sleepiness observed in the current study may be due to sleep disorders such as obstructive sleep apnoea (OSA) and periodic limb movement disorder (PLMD). In the general population, the most common sleep disorders are (OSA), insomnia and restless legs syndrome (RLS)/(PLMD) . OSA is a frequent condition characterised by repeated episodes of partial or complete reduction in breathing activity during sleep . PLMD is a condition characterised by repetitive limb movements during sleep that cause sleep disruption . A recent systematic review highlighted the prevalence of insomnia symptoms (longer SOL, increased sleep fragmentation and excessive daytime sleepiness) in elite athletes . Other sleep problems such as OSA are less prevalent but appear to higher in strength and power athletes (e.g., rugby players) most likely due to increased body mass and neck circumference (>42 cm) which are anatomical features related to OSA . A recent study using a combination of PSG and subjective measures demonstrated a high prevalence of sleep disorders in Rugby union players (n = 25), all players displayed insomnia symptoms and 24% (n = 6) had OSA and 12% (n = 3) . In similar study using home-based PSG in rugby league players (n = 22), 45% (n = 10) had OSA . A previous study of NFL players (n = 137) demonstrated that 19% (n = 26) had OSA . Previous research in elite ice hockey players (n = 107) has demonstrated sleep problem, 11% (n = 14) had insomnia, 10% (n = 13) had OSA and 3% (n = 4) had RLS/PLMD . Athletes with poor sleep habits and/or a sleep disorder must be identified and diagnosed and individual interventions (e.g., sleep hygiene, nutrition) must be implemented in order to athlete recovery and performance.
4.3.2. Athlete Morningness/Eveningness
A Cronbach’s alpha of 0.698 was observed in the current sample. Although there was no significant difference between the groups for chronotype, time they usually become tired or preferred training time, a statistically significant difference was evident for preferred competition time, (p = 0.015), with the elite athlete group preferring afternoon competition times, while the sub-elite athlete group preferred morning competition times. The vast majority of the athletes from both groups 58% (n = 197) indicated that their normal training time was after 5 p.m. Training time and chronotype may have an influence on sleep . A study investigating the sleep quality of morning and evening types after a morning (8:00 a.m.) and evening (20:00 p.m.) high intensity interval training session types reported poorer sleep quality (reduced total sleep time, increased sleep disturbance and reduced sleep efficiency) in morning types after the evening session while sleep quality after the morning session was similar for both groups . The late training times reported by the athletes in the current study may have adversely impacted their sleep and recovery. Sleep following training is recognised a being important for recovery , reduced sleep quality
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following evening training sessions (particularly vigorous training) may negatively impact subsequent recovery and performance, the effect may be more pronounced in morning type athletes.
Recovery is a process in time, dependent on the duration of stress and requires a reduction in stress, a change in stress or a break from stress [123,124]. Relatively high levels of fatigue, stress and pain were reported in both groups. A range of supplements were used regularly by athletes in both groups; indeed, whey was the most commonly used recovery supplement in both groups. The results suggest that future research is warranted to further the development of individualised inventions focused on sleep, nutrition and athlete recovery.
The EQ-5D-5L has demonstrated reliability (mean intraclass correlation coefficients 0.69; range 0.43–0.84) and convergent validity (mean Spearman rank coefficients 0.99; range 0.97–0.99) . Cronbach’s alpha of 0.70–0.95 are considered “acceptable” for a scale used in human research [125,126]. Cronbach’s alpha 0.609 was observed in the current sample most likely due to the low number of items (5), as the size if alpha depends on the number of items in a scale . The mean general health rating scores for the elite athlete group (83.1 ± 12.6) and the sub-elite athlete group (81 ± 13.7) were relatively high, which was consistent with current research in athletes . In the current study, the elite athlete group reported higher mean health rating scores. Elite athletes tend to have their training and recovery sessions scheduled for them , hence, they are likely to complete regular if not daily mobility type sessions. Whereas the sub-elite athletes may have had less free time due to work, social and family commitments. A high prevalence of pain was reported by 50% (n = 169) of participants (elite n = 53 sub-elite n = 116). An investigation of ‘mildly sleepy’ (indicative of inadequate TST) but otherwise healthy males (n = 24) showed sleep extension (time in bed 10 h) increased pain tolerance by 20% . While chronic sleep restriction (50% of habitual time for 12 days) is related to increased levels of muscle soreness and increased pain sensitivity . While mobility issues were noted in both groups, there were higher levels mobility issues reported by the sub-elite athlete group coupled with issues completing usual activities. However, it has recently been suggested that elite and high-level athletes have increased pain tolerance (cold pressor test) and that the training time per week has a positive impact on the tolerance .
4.4.2. REST-Q Sport
The RESTQ-Sport has been shown to be valid in athletic populations [131,132]. The scales have displayed good internal consistency (0.67–0.89) and high test–retest reliability (>0.79) [90,124]. A Cronbach’s alpha of 0.784 was observed in the current sample.
Relatively high levels of stress and fatigue were evident from the REST-Q. Stress and fatigue are factors for illness, which must be managed by elite athletes [133,134], during their competitive seasons to avoid missed training/competitions. Significant differences between the elite and sub-elite athletes were observed for four of the REST-Q subscales relating to athletic performance, with higher mean score for each subscale: being in shape, personal accomplishment, self-efficacy and self-regulation reported by the elite athlete group. The injury (2.31 ± 1.17 vs. 2.48 ± 1.09), fatigue (2.45 ± 1.32 vs. 2.54 ± 1.32) subscale scores were relatively high in both the elite athletes and sub-elite athletes, while the sleep quality scores were low (2.76 ± 0.78 vs. 2.83 ± 0.85). The current findings are consistent with previous research which reported that injury risk was significantly positively related to injury subscale scores for disturbed breaks, fatigue, and lower values on the sleep quality subscale score . The relationship between training load and health can be considered on a well-being continuum [123,134,135], with training load and recovery as antagonists. Stress is imposed on athletes, altering their physical and psychological well-being along a
Nutrients 2021, 13, 1330 17 of 25
continuum: homeostasis, acute fatigue, subclinical tissue damage, functional overreaching, non-functional overreaching, clinical symptoms, overtraining syndrome, time-loss injury or illness and, with continued loading in extreme cases, death [134,135]. A recent meta- analysis has linked psychological stress (r = 0.27, 80% CI 0.20–0.37) and history of stressors (r = 0.13, 80% CI 0.11–0.15) to injury rates . Athletes’ injury risks are affected by their responses to multiple stressors that result in not only physical, psychological and attentional changes (e.g., increased reaction time, narrowing of peripheral vision, increased distraction) but also behavioural changes (e.g., poor sleep quality and impaired self-care) .
In the current study, significantly higher levels of sport-specific recovery (3.22 ± 0.91 vs. 2.91 ± 0.90) were reported by the elite athlete group compared to the sub-elite athlete group. This result potentially highlights the fact that elite athletes tend to be under the supervision of a multidisciplinary team, e.g., medical, strength and conditioning, nutrition, physiology and psychology, who are involved in all aspects of the athletes training and recovery. The sub-elite athletes would typically not receive the same access to multidisciplinary support services. It is imperative that athletes have a detailed recovery plan compromising of nutrition, hydration, sleep and psychological recovery . Given the high training and competition load that athletes undertake, it is clear that they must adopt strategies that promote sleep across the domains of quality, quantity and timing. Fatigue can be managed, and recovery enhanced through adequate passive rest and sufficient sleep , it is generally recommended that athletes have at least one ‘rest’ day per week. Rest days can serve to alleviate boredom and stress perception while the absence of a ‘rest day’ during periods of intense training has been related to the onset overreaching and inadequate recovery . It is suggested from the current results that sleep tends to improve on rest days, i.e., increased perceived sleep quality, TIB, TST and reduced WASO in both groups, while SL, NOA and SE also improved in the elite athlete group.
In the current sample, the elite athletes tended to consume more supplements, at higher doses with increased frequency, compared to the sub-elite athletes. Those athletes who used supplements reported high usage of caffeine, whey protein, creatine, multivita- mins, fish oil, probiotics and vitamin D while the use of iron and nitrate was reported to a lesser extent. This is similar to previous research in elite Dutch athletes (n = 778) where the most commonly consumed supplements were multivitamins, caffeine, vitamin D, sports drinks, protein, beta-alanine and sodium bicarbonate . It has also been demonstrated previously that elite athletes tend to take more supplements than sub-elite athletes . Despite the relatively low number of athletes reporting supplement use, the correlations between supplement use and RESTQ scales warrant further investigation. Whey protein was one of the most prevalent supplements used while casein use was also reported. While research is emerging supporting pre-sleep protein ingestion for muscle recovery [140,141], the impact of pre-sleep ingestion of 40 g doses of whey and/or casein warrants further investigation with regards both muscle recovery and sleep improvement.
Daily caffeine use was reported by approximately 20% of the athletes which could neg- atively impact sleep. The low level of caffeine use reported in the current study was most likely due to the fact that athletes were asked to report their supplement use and may have neglected to include habitual caffeine consumption. Caffeine exerts a stimulant effect pro- moting alertness by blocking adenosine receptors . The levels of caffeine consumption reported were lower than previous research which has suggested that 75–90% of athletes consume caffeine before or during competition [143–145]. While, it has been suggested that chronic low dose caffeine ingestion may blunt any potential ergogenic effects , moder- ate doses (~3 mg/kg/d) appear to pose no problems for most athletes . However, in terms of sleep, moderate caffeine doses have been shown to increase SOL and decrease TST, REM sleep and SE . Hence, athletes training/competing in the late afternoon (>5 p.m.) need to consider its potentially detrimental effect on sleep. It has recently been suggested that athletes should adopt a strategic individualised approach to caffeine consumption
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during competition . In the current study, higher alcohol consumption was observed in the sub-elite athletes and they tended to consume more units of alcohol per drinking bout. In line with previous research, the actual amount of alcohol consumed by athletes “in training’ is low . Elite athletes tend to have less opportunity to socialise and their schedules (e.g., early morning training) do not lend themselves to regularly consuming alcohol. Alcohol consumption by athletes often occurs post-competition, where it can be seen as a reward for ‘hard work’ . Alcohol consumption has been associated with poorer sleep quality and quantity, reduced REM sleep and increased sleep disturbance in the second half of the sleep bout .
Due to logistical reasons, the sleep diary was only completed for one training/ competition day and one rest day, and this may have been insufficient in terms of data collection. It has been recommended that sleep diaries should be completed for a duration of 1 week [68,153]. The aim of the 2 day diary was to limit participant burden and recall bias . However, sleep diaries may be more accurate than sleep questionnaires . The intrinsic limitations of self-report measures (i.e., questionnaires and diaries) are mea- surement error and recall bias . Indeed, it has been demonstrated that athletes can overestimate their TST [104,105]. However, self-report measures have their place within ath- letic settings, as they are a relatively simple and inexpensive approach to athlete monitoring affording a more representative overview of the target population . Within elite athlete populations, the use of subjective measures of sleep are often employed, particularly during the competitive season due to the more invasive nature of both PSG and actigraphy . A growing body of research has suggested that self-report measures may be more sensitive and reliable than physiological, biochemical and performance measures [44,137,153–156]. When choosing a particular measure, ultimately the aim is to maintain a balance between the need to obtain meaningful data from an athlete whilst minimising the burden involved in completion of any self-report measure [154–156]. In the current study, it was not fea- sible or practical due to the large sample size to include a subjective assessment of sleep. However, future research should incorporate both objective (e.g., PSG, actigraphy) and subjective measures (e.g., sleep diaries) of sleep to provide a more accurate estimates of sleep and because some individuals may self-report poor sleep quality despite objective measures indicating adequate sleep [155–157]. There was little difference between the elite and sub-elite athlete groups in terms of sleep. The inclusion of a healthy control group would have allowed for comparison and exploration of the differences between the sleep of athletic population and healthy adults.
A specific section in relation to anxiety/depression could have been included in the battery of questionnaires given the potential to impact on sleep and vice versa. The Profile of Mood States (POMS)  is widely used in wellness assessments of athletic populations and has subscales that specifically relate to anxiety and depression. However, as the EuroQoL has a dimension for anxiety/depression, the POMS was omitted to reduce participant burden and survey fatigue which could have negatively impacted the reliability of the data collected.
The demographic difference between the groups was a limitation in that there was a statistically significant difference between the groups with the sub-elite group being significantly older which could have affected the results. This issue was directly related to the sampling method employed where participants are recruited based on their accessibility. However, care was taken to recruit a large cohort (n = 338) and strict inclusion and exclusion criteria were applied .
4.7. Future Research
Future research should replicate this investigation of the sleep and recovery practices of large cohorts of athletes. Such studies should include a combination of subjective and objective measures of sleep and recovery, for a minimum of 1 week [54,153]. The validity
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and reliability of combinations of subjective and objective measures in athletic populations warrants further investigation. While this may not be practical during the competitive season there may be a window of opportunity at the end of the season or in preseason.
As the majority of athletes in the current cohort have reported sleep problems future research is warranted to identify the specific sleep problems that affect athletic populations. It is also necessary in future research to identify if athletes are affected by acute disturbances, e.g., competition anxiety or chronic disorders, e.g., OSA, insomnia and PLMD .
Future research should investigate the effects of specific nutritional recovery strategies (e.g., antioxidants, protein, carbohydrate) on sleep in athletic populations. Such practices may already be an established part of an athlete’s daily routine, but the potential additional benefit of improved sleep must be explored.
4.8. Practical Applications
A strength of this novel study is that it presents ‘real-life’ data from training/competition days and a rest day relating to the sleep and recovery practices of athletes. Poor sleep and inadequate recovery practices were evident in both the elite and sub-elite athlete groups. In a recent study, 95% of swimmers (n = 82) identified their coaches (n = 10) as the primary source of recovery information while the coaches highlighted conferences and workshops as their primary source of recovery information . In order to promote sleep hygiene and adequate recovery practices in athletes, a comprehensive coach and athlete education curriculum may need to be developed and implemented.
The athletes generally reported improved sleep quality and quantity on rest days which has implications for athlete health, well-being and performance. Optimising the sleep and recovery practices of athletes would impact performance. Monitoring of sleep behaviours, nutrition and recovery-stress responses of athletes aids the identification of irregularities (e.g., due to travel or illness) and allows for early interventions with individual athletes as and when necessary . The ongoing collection of data from athletes such as the data collected in the current study could be used by coaches and medical and support staff to implement individual sleep, recovery and nutrition interventions and plans.
Due to the symbiosis between sleep and recovery, it is clear from the current findings that athletes should have a detailed individualised and multifaceted recovery plan in place involving sleep, nutrition, hydration, and other physiological and psychological aspects. At the elite level, athletes and their support teams continually strive for marginal gains over time to improve performance (135). Training and competition load elicit a number of homeostatic responses and adaptations, and the main aim of training is to exploit these in order to elicit an improvement in performance. The training process involves exploitation, manipulation and coordination of numerous variables (e.g., physiology, biomechanics and psychology) to improve performance. Athletes continually strive to improve their perfor- mance, and, as such, variations in training load are necessary, e.g., increased frequency, duration and/or intensity in order to optimise the training response . Depending on the phase of the season (e.g., pre-season, general preparation, and competition), loads must be managed to increase or decrease fatigue, to enhance training adaptations or per- formance . Rest days should also be incorporated into the recovery plan, which could serve to improve sleep quality, alleviate boredom and stress perception.
The majority of athletes were classified as poor sleepers and reported excessive day- time sleepiness even though their TST met current adequate sleep guidelines. The im- portance of a rest day was highlighted by the fact that sleep improved in both groups. Relatively low levels of physical recovery were observed in both groups coupled with relatively high levels of stress. The elite athlete group reported significantly higher levels of sport-specific recovery. A higher prevalence of supplement use was reported by the elite athlete group, while higher levels of alcohol consumption were reported by the sub- elite athlete group. Given the high training and competition load that athletes undertake,
Nutrients 2021, 13, 1330 20 of 25
particularly elite athletes, it is clear that they must adopt strategies that promote sleep and recovery. There is a need for athletes to receive individualised support and education regarding their sleep ad recovery practices.
Author Contributions: Conceptualisation, R.D., S.M.M., G.W. and J.G.E.; methodology, R.D., S.M.M., G.W. and J.G.E.; formal analysis, R.D. and A.N.; investigation, R.D.; data curation, R.D.; writing— original draft preparation, R.D.; writing—review and editing, R.D., S.M.M., G.W. and J.G.E.; super- vision, S.M.M., G.W. and J.G.E.; project administration, R.D. and J.G.E. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Faculty of Life and Health Sciences, Northumbria University (date of approval 2 July 2019; Submission ID: 17406).
Informed Consent Statement: Informed consent was obtained from all subjects involved in this study.
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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- Materials and Methods
- EuroQoL (EQ-5D-5L)
- Pittsburgh Sleep Quality Index (PSQI)
- Epworth Sleepiness Scale (ESS)
- The Recovery Stress Questionnaire for Athletes (RESTQ Sport)
- Athlete Morningness/Eveningness Questionnaire (AMES)
- Consensus Sleep Diary—Core (CSD-C)
- Data Analysis
- Participant Characteristics
- Pittsburgh Sleep Quality Index
- Epworth Sleepiness Scale
- Recovery Stress Questionnaire
- Consensus Sleep Diary—Core
- Participant Characteristics
- Pittsburg Sleep Quality Index
- Consensus Sleep Diary-Core
- Daytime Sleepiness
- Athlete Morningness/Eveningness
- REST-Q Sport
- Future Research
- Practical Applications