biology
RESEARCH ARTICLE
Sleep habits and strategies of ultramarathon
runners
Tristan Martin 1 , Pierrick J. Arnal
2 , Martin D. Hoffman
3,4,5 , Guillaume Y. Millet
1*
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
* gmillet@ucalgary.ca
Abstract
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.
Introduction
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
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OPEN ACCESS
Citation: Martin T, Arnal PJ, Hoffman MD, Millet
GY (2018) Sleep habits and strategies of
ultramarathon runners. PLoS ONE 13(5):
e0194705. https://doi.org/10.1371/journal.
pone.0194705
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
files.
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.
performance (for a review, see [2]). 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 [6], reduces muscle glycogen stores [7] and alters recovery after muscle
damage induced by exercise [8]. Acute sleep deprivation and chronic sleep restriction also
induce alterations in glucose metabolism, with increases in insulin resistance and decreased
insulin sensitivity [9]. Both also impact different aspects of cognitive performance [10] such as
increased reaction time and lapses during attentional tasks [11,12] and psychomotor functions
[13]. 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 [16]. 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
(UTMB 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 [18]. Associated with this has been a growing interest in
research related to ultramarathon running [19]. Until now however, research has mostly
focused on factors that impact performance (like pain, gastro-intestinal distress or physiologi-
cal determinants) (e.g. [18]), health consequences of the elevated training load of ultramara-
thon runners [20], 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 [23], exercise-associated hyponatremia [24] 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 [27], but some authors suggest that athletes
should sleep between 9 and 10 h per night to allow sufficient recovery [28]. 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. [29] 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 [30]).
Sleep and ultramarathon
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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.
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.
Methods
Procedure
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.
Statistical analysis
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.
Results
Population
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.
n %
Age (yrs)
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
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Sleep characteristics
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
melatonin.
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.
https://doi.org/10.1371/journal.pone.0194705.g001
Sleep and ultramarathon
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Fig 2. Distribution of napping durations and reasons for not napping during the different training periods and
during workdays and non-workdays.
https://doi.org/10.1371/journal.pone.0194705.g002
Sleep and ultramarathon
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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
strategy.
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.
https://doi.org/10.1371/journal.pone.0194705.g003
Table 2. Use of substances affecting sleep.
Cigarettes
(number/day)
Tea
(cups/day)
Coffee
(cups/day)
Caffeinated soda
(cans or bottles/
day)
Alcohol
(glasses/day)
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
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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.
n %
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”
39 8.3
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
https://doi.org/10.1371/journal.pone.0194705.t003
Table 4. Sleep strategies used during ultramarathons among subjects indicating they had slept during an
ultramarathon.
n %
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.
https://doi.org/10.1371/journal.pone.0194705.t004
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Discussion
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).
https://doi.org/10.1371/journal.pone.0194705.g004
Sleep and ultramarathon
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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.
Demographic data
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 [34]. 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 [34]. Thus, our study population
appears to have been an appropriate representation of ultramarathon runners with regards to
sex and age.
Sleep characteristics
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 [36]. 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. [39] 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. [40] reported a total sleep time ranging from 6.1 to 7.1 h in individual sports (cycling, mountain bike, race walking, triathlon
and swimming) [41] 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 [36] 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 [44], 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. [47] 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
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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 [42] 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
reviews).
Napping
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) [36]. 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 [41].
Daytime sleepiness
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 [52], 6.9 in Norway
[53], 6.1 to 8.3 in the US [54]. 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 [58].
On the contrary, ESS scores were found to be 12.5 and 9.6 in collegiate tennis players [59] and
basketball players [28], 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 [60], 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 [61]. Given that
regular exercisers usually display robust circadian rhythms [62], 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 [63].
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
[64]. 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 [67]. 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 [44].
High doses of caffeine (600 mg, i.e. 5–6 cup) [71–73], nicotine consumption [74] and alco-
hol consumption [75] 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 [29], 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. [29] 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. [76] 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 [28], 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 [11]. 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 [17]. 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 [4]. 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
[78]. 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 [81]. As previously
Sleep and ultramarathon
PLOS ONE | https://doi.org/10.1371/journal.pone.0194705 May 9, 2018 12 / 18
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. [29] 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. [29]. 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 [82].
Study limitations
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.
Conclusions
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
PLOS ONE | https://doi.org/10.1371/journal.pone.0194705 May 9, 2018 13 / 18
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-
marathon performance.
Supporting information
S1 File. Questions and answer provided by all participants.
(XLSX)
S2 File. Questionnaire provided to the participant in English, French and Italian Lan-
guages.
(DOCX)
Acknowledgments
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.
Author Contributions
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
Y. Millet.
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