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To examine whether i) objective markers of sleep quantity and quality are altered in endurance athletes experiencing overreaching in response to an overload training program and ii) whether potential reduced sleep quality would be accompanied with higher prevalence of upper respiratory tract infections in this population. Twenty seven trained male triathletes were randomly assigned to either overload (n=18) or normal (CTL, n=9) training groups. Respective training programs included a 1-week moderate training phase, followed by a 3-week period of overload or normal training, respectively and then a subsequent 2-week taper. Maximal aerobic power and oxygen uptake (V˙O2max) from incremental cycle ergometry were measured after each phase, whilst mood states and incidences of illness were determined from questionnaires. Sleep was monitored every night of the 6 weeks using wristwatch actigraphy. Nine of the 18 overload training group subjects were diagnosed as functionally overreached (F-OR) after the overload period, as based on declines in performance and V˙O2max with concomitant high perceived fatigue (p<0.05), whilst the nine other overload subjects showed no decline in performance (AF, p>0.05). There was a significant time × group interaction for sleep duration (SD), sleep efficiency (SE) and immobile time (IT). Only the F-OR group demonstrated a decrease in these three parameters (-7.9±6.7%, -1.6±0.7% and -7.6±6.6%, for SD, SE and IT, respectively, p<0.05), which was reversed during the subsequent taper phase. Higher prevalence of upper respiratory tract infections were also reported in F-OR (67%, 22%, 11% incidence rate, for F-OR, AF and CTL, respectively). This study confirms sleep disturbances and increased illness in endurance athletes who present with symptoms of F-OR during periods of high volume training.
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Evidence of Disturbed Sleep and Increased
Illness in Overreached Endurance Athletes
Laboratory of Sport, Expertise and Performance, National Institute of Sport, Expertise and Performance, Paris, FRANCE;
Laboratory of Functional and Cellular Responses to Hypoxia, University Paris 13 North, Sorbonne Paris City, Bobigny,
Sport and Exercise Discipline Group, UTS: Health, University of Technology Sydney, AUSTRALIA
HAUSSWIRTH, C., J. LOUIS, A. AUBRY, G. BONNET, R. DUFFIELD, and Y. LE MEUR. Evidence of Disturbed Sleep and
Increased Illness in Overreached Endurance Athletes. Med. Sci. Sports Exerc., Vol. 46, No. 5, pp. 1036–1045, 2014. Purpose: This study
aimed to examine whether (i) objective markers of sleep quantity and quality are altered in endurance athletes experiencing overreaching
in response to an overload training program and (ii) potential reduced sleep quality would be accompanied with a higher prevalence of
upper respiratory tract infections in this population. Methods: Twenty-seven trained male triathletes were randomly assigned to either
overload (n= 18) or normal (CTL, n= 9) training groups. Respective training programs included a 1-wk moderate training phase
followed by a 3-wk period of overload or normal training, respectively, and then a subsequent 2-wk taper. Maximal aerobic power and
oxygen uptake (V
) from incremental cycle ergometry were measured after each phase, whereas mood states and incidences of
illness were determined from questionnaires. Sleep was monitored every night of the 6 wk using wristwatch actigraphy. Results: Of the
18 overload training group subjects, 9 were diagnosed as functionally overreached (F-OR) after the overload period, as based on declines
in performance and V
with concomitant high perceived fatigue (PG0.05), whereas the other 9 overload subjects showed no
decline in performance (AF, P90.05). There was a significant time–group interaction for sleep duration (SD), sleep efficiency (SE), and
immobile time (IT). Only the F-OR group demonstrated a decrease in these three parameters (j7.9% T6.7%, j1.6% T0.7%, and
j7.6% T6.6% for SD, SE, and IT, respectively, PG0.05), which was reversed during the subsequent taper phase. Higher prevalence of
upper respiratory tract infections were also reported in F-OR (67%, 22%, and 11% incidence rate for F-OR, AF, and CTL, respectively).
Conclusion: This study confirms sleep disturbances and increased illness in endurance athletes who present with symptoms of
F-OR during periods of high volume training. Key Words: FATIGUE, OVERTRAINING, ENDURANCE TRAINING, RECOVERY,
Increases in training intensity or volume are typically
undertaken by athletes in an attempt to enhance physi-
ological adaptation and to improve physical perfor-
mance. However, when the balance between appropriate
training stress and adequate recovery is disrupted, an ab-
normal training response may occur and a state of short-term
‘overreaching’’ (functional OR, F-OR) (21) may develop,
resulting in a decline in performance. Although the F-OR
state is generally reversed when an appropriate period of
recovery is provided (È1–3 wk) (16,21), it can compromise
competition outcomes in the short term, particularly when
insufficient recovery is available before competition. How-
ever, critical reviews of existing scientific literature continue
to conclude that the underlying causes of F-OR in endurance
athletes remain uncertain (10,21,24,36).
One of the most commonly reported methods for man-
aging fatigue and enhancing recovery is obtaining adequate
passive rest and sufficient sleep (23,30). The restorative
qualities of sleep for maintaining optimal bodily function are
well recognized. The recovery of cognitive processes and
metabolic functions, both of which are important contribu-
tors to exercise performance, can be affected by the quality
and quantity of sleep (30). Despite health-based survey re-
search reporting associations between regular moderate
physical activity and better sleep (4), few studies have
reported alterations in sleep quality in response to highly
demanding training programs (17,35). Taylor et al. (35)
measured sleep via polysomnography during the ‘‘onset of
training,’’ ‘‘heavy training,’’ and ‘‘precompetition taper’’ in
elite female swimmers. Sleep onset latency, time awake after
sleep onset, total sleep time, rapid eye movement, and sleep
times were similar at all three training phases, but the number
of movements during sleep was significantly higher (6%)
during higher training volumes, suggesting some alteration
to sleep. Nevertheless, the improvement in performance time
Address for correspondence: Yann Le Meur, Ph.D., Laboratory of Sport,
Expertise and Performance, National Institute of Sport, Expertise and Per-
formance, 11, Avenue du Tremblay, 75012 Paris, France;
Submitted for publication August 2013.
Accepted for publication September 2013.
Copyright Ó2014 by the American College of Sports Medicine
DOI: 10.1249/MSS.0000000000000177
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
and the low levels of tension and anger at peak training
suggest that the swimmers were not F-OR. Recently, Fietze
et al. (6) used wrist actigraphy during a 67-d period of high
physical and mental stress to study sleep patterns in 24
classical ballet dancers before a ballet premiere perfor-
mance. They found small but significant reduction in sleep
duration (j6%), sleep efficiency (j2%), and time in bed
(j3%) and an increase in wakefulness after sleep onset
(+3%). Sleep onset latency did not change. Nevertheless,
these authors did not report changes in physical performance
in response to the prescribed overload program, making
clear conclusions for sleep disruption in OR athletes diffi-
cult. However, studies during which sleep was monitored in
athletes who demonstrated clear signs of OR (i.e., high
perceived fatigue and decreased performance) remain few
and involve self-reporting of reduced perceived subjective
sleep quality (11,12). Given such equivocal findings, a re-
cent joint consensus statement led Meeusen et al. (21) to
recommend additional research to determine the relationship
between F-OR and altered sleep patterns.
Past research showed that aspects of both innate and
adaptive immunity are depressed during sustained periods of
heavy training (for a review, see Walsh et al. [38]). An im-
balance between training loads and recovery has been shown
as a major contributor to illness (38). These abnormalities
share similarities with impairment in immune function ob-
served after moderate sleep deprivation (33). Vgontzas et al.
(37) studied the effects of modest sleep restriction from 8 to
6 h per night for 1 wk in 25 young, healthy, normal sleepers
for 12 consecutive nights in a sleep laboratory. Their results
showed that modest sleep loss is associated with the signif-
icant increased secretion of proinflammatory cytokines,
suggesting a link between the recuperative processes of
sleep and the immune system. Similarly, Cohen et al. (3)
showed that insufficient sleep volume over consecutive
days can impair immune function and increase the risk of
developing upper respiratory tract infections (URTI). These
authors reported that participants with less than 7 h of sleep
were 2.94 times more likely to develop a ‘‘cold’’ than those
with 8 h or more of sleep once administered nasal drops
containing a rhinovirus and monitored for ensuing develop-
ment of a clinical cold. The association with sleep efficiency
was also graded, with participants reporting G92% sleep effi-
ciency 5.5 times more likely to develop a cold than those with
998% efficiency. Taken together, these results have led some
authors to suggest that the potential immunosuppressive ef-
fects of overreaching may act through sleep disturbances (38).
However, no scientific investigation to date provides evi-
dence of this relationship to substantiate this argument.
The aim of the present study was therefore to determine
whether changes in objective sleep parameters were evident
between an experimental group of triathletes developing
F-OR compared with a control group. After 1 wk of low
volume training (baseline), the experimental group com-
pleted a 3-wk overload period followed by a 2-wk taper. By
programming intensified training over a large population of
endurance athletes (n= 28), we hypothesized that some
participants would demonstrated signs of F-OR (i.e., tran-
sient reduced performance). By this way, the present re-
search gave us also the opportunity to determine whether
sleep disturbances would be observed during an overload
period in participants led to F-OR. In the light of past liter-
ature, we hypothesized that the development of F-OR would
be accompanied by a decline in sleep quality and sleep
quantity. Furthermore, we investigated whether the potential
presence of F-OR and reduced sleep quality in F-OR athletes
would be accompanied with a higher prevalence of URTI.
Subjects. Forty well-trained triathletes volunteered to
participate in this study. All subjects had been competing for
3 yr and were training a minimum of 7 times per week.
During the experimental period, seven subjects did not fol-
low the protocol because of injury or personal obligations
and were excluded from subsequent analyses. In addition,
six participants, who worked at night with irregular sched-
ules, were excluded from subsequent analyses. One subject
was also excluded because of technical problems with
equipment. The final sample size included in analysis was
n= 27. The experimental design of the study was approved
by the ethical committee of Saint-Germain-en-Laye (accep-
tance no. 12048) and was conducted in accordance with the
Declaration of Helsinki. Before participation, subjects
underwent medical assessment with a cardiologist to ensure
normal electrocardiograph patterns and to obtain a general
medical clearance. All subjects were free from chronic dis-
eases and were not taking prescribed medication at the
commencement of the study. After comprehensive verbal
and written explanations of the study, all subjects gave their
written informed consent to participate.
Study design. An overview of the study design is
shown in Figure 1. The subjects were randomly assigned to
either the control group (n= 9) or the overload training
group (n= 18) according to a matched group experimental
design based on maximal aerobic power (MAP), habitual
training volume, and years of experience in endurance
sports. All subjects had regularly competed in triathlons for
at least 3 yr and were training a minimum of 10 hIwk
. The
training of each triathlete was monitored for a period of 9 wk
in total, which was divided into a pretesting phase and then
three distinct experimental phases. Both the pretesting phase
and the first phase were the same for all groups. The pre-
testing phase consisted of 3 wk during which the subjects
completed their usual training regime without any study in-
tervention (i.e., normal training load). The first experimental
phase (baseline) consisted of 1 wk of moderate training load
during which the subjects were asked to reduce their habitual
training volume by È50% while maintaining the training in-
tensity. This minitaper was selected according to the guide-
lines for optimal tapering in endurance sports (2). During the
second experimental period (overloading phase), the overload
OVERREACHING, SLEEP, AND ILLNESS Medicine & Science in Sports & Exercise
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
group completed a 3-wk overload program designed to de-
liberately overreach the subjects. The duration of each train-
ing session of the normal (pretesting) training period was
increased by 30% (e.g., a 1-h run including eight repetitions
of 400 m at the maximal aerobic running speed was converted
into an 80-min run including 11 repetitions of 400 m at the
maximal aerobic running speed). As particular subjects were
unable to accommodate such prolonged-duration cycling
sessions (Q5 h) into their routines, these specific sessions were
split (e.g., a 5-h cycling session was converted into two ses-
sions of 2h30). The participants reproduced the same training
program during each week of the overload period so that both
the content and the weekly distribution of the training ses-
sions remained consistent. The control group repeated its
habitual training program during this period. Next, all the
participants completed a 2-wk taper period (third experi-
mental phase, Taper), where their normal training load was
decreased by 50% each week (e.g., a 1-h run including eight
repetitions of 400 m at the maximal aerobic running speed
was converted into a È30 min run including four repetitions
of 400 m at the maximal aerobic running speed). All training
sessions were performed by the triathletes in their own
training structure according to the training program es-
tablished by the same sport scientist. Throughout the entire
study, the same sport scientist was responsible for coaching
and controlling the training loads of all subjects. To avoid
injuries, particular attention was devoted to daily feedback
obtained from the triathletes. Before the beginning of the
experimental period, the subject reported once to the labo-
ratory to become familiarized with the maximal incremental
cycling test (described in the next section) and the daily
testing used during the protocol (sleep monitoring and
questionnaires). Testing was performed on three occasions
(Fig. 1), including Pre (i.e., after baseline phase), Mid (after
overload phase), and Post (after taper phase), respectively. To
ensure that performance variations during the maximal in-
cremental cycling tests were due to the training regimen and
not to the training session(s) performed the day before each
test, the subjects respected a 24-h rest period before each
laboratory session.
Training monitoring. Training volume and intensity
were calculated and controlled on the basis of HR mea-
surement (Polar, Kempele, Finland). For all subjects, HR
was measured every 5 s during each training session over the
entire protocol. The distribution of HR into training zones
was subsequently calculated using three HR zones: 1) eHR
at 2 mmolIL
, 2) between HR at 2 mmolIL
and HR at
lactate threshold, and 3) HR values superior to HR at lactate
threshold (for the description of the lactate threshold deter-
mination method, see Laboratory Testing section). Given
that the relationship between blood lactate accumulation and
HR values at exercise can be influenced by a heavy training
FIGURE 1—Schematic representation of the experimental protocol. Bicycle symbols represent maximal incremental cycling tests. Note that nine
subjects of the overload group developed symptoms of functional overreaching at Mid (decreased performance vs preassociated with high perceived
fatigue, F-OR group). The nine other overloaded subjects were only the AF group.
http://www.acsm-msse.org1038 Official Journal of the American College of Sports Medicine
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
load program (15), these reference HR values were re-
assessed after each maximal incremental cycling test.
Laboratory testing. During the 48 h before each maxi-
mal incremental cycling test, the subjects received specific
nutritional guidelines to ensure muscle glycogen stores were
replenish. Specifically, they were instructed to eat until satiety
was reached during each lunch. Breakfast consisted of a va-
riety of macronutrients from both solid and liquid energy
sources. The selected foods included an assortment of cereals,
bread, fruit, yogurt, milk, juice, ham, and cheese. For lunch
and dinner, the subjects consumed a mixed salad as starter,
then white meat during lunch and fish during dinner. The side
plate consisted of a mixed of 50% carbohydrates (i.e., pasta,
nice, and noodles) and 50% of vegetables (i.e., green beans,
broccoli, and tomatoes). One piece of fruit and tub of yogurt
were added as dessert, at both lunch and dinner. To ensure the
subjects were well hydrated on each testing day, they were
instructed to ensure the maintenance of a well-hydrated state.
POMS. Before exercise testing, subjects were asked to
complete the POMS questionnaire to assess overall mood
disturbance (19). The POMS questionnaire is a 65-item
Likert scale questionnaire, which provides measures of six
specific mood states: vigor, depression, fatigue, anger, anx-
iety, and confusion.
Performance and V
.Maximum oxygen uptake
) was assessed on an electronically braked cycle ergo-
meter (Excalibur Sport; LodeÒ, Groningen, The Netherlands)
equipped with standard 170-mm cranks, and the athletes
used their own shoes. Positions of the handlebars and seat
height were adjusted to the measures used by the athletes on
their own bike and replicated between sessions. The test was
performed until complete exhaustion to estimate V
and MAP. This exercise protocol started with a warm-up of
5 min at a workload of 100 W, followed by 5 min at 150 W
and 5 min at 200 W. Thereafter, further increments of 25 W
were added every 2 min until volitional exhaustion. Subjects
wore a mask covering their mouth and nose for breath col-
lection (Hans Rudolph, Kansas City, MO), and oxygen and
carbon dioxide concentration in the expired gas was con-
tinuously measured and monitored as breath-by-breath
values (Quark; CosmedÒ, Rome, Italy). The gas analyzers
and the flowmeter of the applied spirometer were calibrated
before each test.
After the test, breath-by-breath values were visually
inspected and averaged for 30 s. The highest 30-s average
value was used as V
. MAP was calculated as MAP =
+ 25(t/120), where W
is the last completed
workload and tis the number of seconds in W
. In ad-
dition, the intensities and associated HR at which [La
increased higher than 2 mmolIL
and the lactate threshold
(LT) calculated by the modified D-max method (1) were
subsequently determined.
Sleep monitoring. All subjects were monitored con-
tinuously using an Actiwatch worn on the nondominant
wrist (Cambridge Neurotechnology Ltd., Cambridge, UK),
with the epoch length set to 1 min. Athletes were monitored
in the home environment every day at baseline (7 d), during
overloading (21 d), and during the taper (14 d) (see Fig. 1).
Mean behavioral activity over the entire recording period
was automatically calculated using the Sleepwatch soft-
ware (Actiwatch activity and sleep analysis version 5.28,
Cambridge Neurotechnology Ltd.). Wristwatch actigraphy
is a nonintrusive, cost-effective tool used to estimate sleep
quantity and quality, which has been compared with poly-
somnography, showing an accuracy of up to 80% in sleep
disordered patients for total sleep time and sleep efficiency
(14) and as such is widely used in the sleep literature (22,34).
In a recent review on the role and the validity of actigraphy in
sleep medicine, Sadeh (28) concluded that according to most
studies, actigraphy has reasonable validity and reliability in
normal individuals with relatively good sleep patterns.
Sleep–wake scoring can be reliably obtained only with
additional information provided in manually completed
sleep logs (6). All participants were therefore requested to
complete daily sleep diaries. The subjects were asked to
record the times of going to bed, falling asleep, waking up,
and leaving the bed. In addition, the subjects were asked to
mark the time of switching off the light to sleep and wake-up
time with a push of the button on the face of the Actiwatch.
Individual nights of sleep were analyzed for the following
range of variables: time in bed, bedtime, get-up time, sleep
latency, actual sleep time, percent time sleeping while in bed
(sleep efficiency), and sleep restlessness (fragmentation index)
and immobile minutes. The following dependent variables
were derived from the sleep diary and activity monitor data:
Time in bed (h): the amount of time spent in bed
attempting to sleep between bedtime and get-up time.
Bedtime (hh:mm): the self-reported clock time at which
a participant went to bed to attempt to sleep.
Get-up time (hh:mm): the self-reported clock time
at which a participant got out of bed and stopped
attempting to sleep.
Sleep onset latency (min): the period between bedtime
and sleep start.
Actual sleep time (h:min): the time asleep from sleep
start to sleep end.
Sleep efficiency (%): sleep duration expressed as a
percentage of time in bed.
Fragmentation index: a measure of restlessness during
sleep, using the percentage of epochs where activity is 90.
Immobile time (min): the actual time spent immobile
during time in bed.
The term sleep quality in this investigation is determined
by wrist actigraphy by measures of sleep efficiency and frag-
mentation index; however, this is different from ascertaining
sleep quality from sleep stages measured by polysomnography.
To quantify how the training weeks affected the perceived
sleep quality, the participants reported their perceived feel-
ings on a seven-point scale, going from very, very good to
very, very poor after waking up each morning. In addition,
OVERREACHING, SLEEP, AND ILLNESS Medicine & Science in Sports & Exercise
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the effect of the training regimen on perceived fatigue was
recorded on the morning before each maximal incremental
cycling test via a visual 0–100 analog scale (from no fatigue
to maximum fatigue).
Illness symptoms. During the 6-wk experimental pe-
riod, the subjects were required to complete a health ques-
tionnaire (URTI symptoms and gastrointestinal discomfort
symptoms) on a weekly basis, as performed in previous
studies (7,8). They were not required to abstain from medi-
cation when they were experiencing illness symptoms, but
they were required, on a weekly basis, to report any
unprescribed medication taken, visits to the doctor, and any
prescribed medications. The illness symptoms listed on the
questionnaire were sore throat, inflammation in the throat,
runny nose, cough, repetitive sneezing, fever, joint aches
and pains, and headache. Two usual items of URTI diag-
nosis (i.e., muscle soreness and loss of sleep) were not in-
cluded given that they could be potentially influenced by
training overloading and not necessarily the signs of illness.
The numerical ratings of light, moderate, and severe (L, M,
or S, respectively) were scored as 1, 2, and 3, respectively.
In any given week of total symptom, score Q12 was taken to
indicate that a URTI was present. This score was chosen in
previous studies (7,8) because to achieve it, a subject would
have to record at least three moderate symptoms lasting for
2 d or two moderate symptoms lasting for at least 3 d in a
given week. A single URTI episode was defined as a period
during which the weekly total symptom score was Q12 and
separated by at least 1 wk from another week with a total
symptom score Q12. Subjects were also asked to rate the
impact of illness symptoms on their ability to train (normal
training maintained, training reduced, or training dis-
continued; L, M, or S, respectively). The gastrointestinal
discomfort symptoms listed on the questionnaire were loss
of appetite, stomach upset, vomiting, abdominal pain, and
diarrhea. These symptoms were rated and scored the same
way as the illness symptoms (7,8).
Data analysis. As per the methods of previous research
(15), the subjects in the OR group were distributed into two
subgroups according to their response to the overload period
and during the subsequent taper. The triathletes who dem-
onstrated decreased performance (vs Pre) and high perceived
fatigue (very tired to extremely tired on the POMS scale) at
Mid with subsequent performance restoration or super-
compensation were diagnosed as functionally overreached
(F-OR group). The remaining subjects in the overload group
who maintained or increased their performance after the
overload period, despite increased perceived fatigue, were
considered acutely fatigued (AF) (21). In addition, because
extended monitoring reduces the inherent measurement er-
rors in actigraphy and increases reliability (28), subsequent
analyses were conducted using the mean value of each sleep
parameter over each week of the training protocol.
Statistical analysis. Normality of data was tested using
a Kolmogorov–Smirnov test. Values at baseline for age,
weight, height, experience in endurance sport, MAP, and
were compared between groups (i.e., CTL, AF, and
F-OR) using a one-way ANOVA. Two-way (group time)
ANOVA were used to examine differences in dependent
variables (i.e., V
, RPE, POMS items, perceived sleep
quality and actimetry data during sleep) between group
means at each time point. When the sphericity assumption in
repeated-measures ANOVA was violated (Mauchly_s test), a
Greenhouse–Geisser correction was used. If a significant
main effect was found, pairwise comparisons were conducted
using Duncan’s post hoc analysis. These statistical tests were
conducted using Statistica (Version 7.0; StatSoft, Tulsa, OK),
and the data are presented as means and SD.
Changes in weekly mean training volume, the distribution
of the relative training time spent in the intensity zones and
the number of training sessions per week in the three groups
during each respective training phase are presented in Table 1.
The results demonstrated that the three experimental groups
successfully adhered to the prescribed training program and
that the AF and F-OR groups increased training volume
substantially more than the CTL group (PG0.001). No sig-
nificant difference in any training parameters was reported
between the AF and the F-OR groups at any periods of the
experimental protocol.
TABLE 1. Weekly average training volume (mean TSD), distribution of training intensity, and number of training sessions per week in swimming, cycling, and running during the protocol
in the three experimental groups.
Variables Group Pretesting Phase (3 wk) Baseline (1 wk) Overload (3 wk) Taper (2 wk)
Weekly training volume (h) CTL 12 T26T1
12 T2
AF 13 T27T1
17 T3
F-OR 14 T37T1
19 T3
Distribution of training intensity in zones 1, 2, and 3 (%) CTL 62/30/8 67/26/7 66/26/8 64/28/8
AF 65/26/9 64/26/9 65/27/8 60/30/10
F-OR 64/30/6 68/26/6 68/26/6 65/27/8
Weekly number of swimming, cycling, and running sessions CTL 3/3/3 2/3/3 3/3/3 2/3/3
AF 3/3/3 2/3/3 3/3/3 2/3/3
F-OR 3/5/3 2/4/3 4/5/3 3/4/3
Weekly number of training days CTL 6.1 T0.3 6.0 T0.1 6.2 T0.4 6.0 T0.0
AF 6.2 T0.4 6.0 T0.5 6.4 T0.5 6.0 T0.5
F-OR 6.3 T0.5 6.1 T0.3 6.6 T0.5 6.2 T0.4
Significantly different from baseline at PG0.05.
Significantly different than CTL. The pretesting phase was representative of the subjects’ habitual training plan.
CTL, control; AF, acute fatigue; F-OR, functionally overreached.
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Assessment of the OR syndrome. At Pre, all sub-
jects reported low perceived fatigue at rest (i.e., all subjects
responded ‘‘not at all’’ or ‘a little’’ on the POMS fatigue
item), confirming that they were not already in an OR state.
Of the 18 overloaded subjects, 9 demonstrated a decrease
in performance (j10 T4 W) at Mid followed by a per-
formance restoration or supercompensation effect at Post
(5 T5 W, Table 2). This reduced performance was system-
atically associated with a concomitant high fatigue score at
Mid (i.e., ‘‘quite bit’’ to ‘‘extremely’’ on the POMS fatigue
item; Table 2). On the basis of this analysis, these nine tri-
athletes were considered as ‘‘functionally OR,’’ the short-
term form of OR (F-OR) (21). The other nine subjects in
the overload training group demonstrated higher perceived
fatigued but no reduction in performance. According to the
nomenclature of Meeusen et al. (21), they were not diag-
nosed as OR and instead considered AF. Thus, the subse-
quent results are presented for 9 F-OR subjects (F-OR
group), 9 AF subjects (AF group), and 9 control subjects
(CTL group). Mean TSD age, height, and weight were 37 T
6 yr, 182 T6 cm, and 72 T9 kg for the CTL group; 35 T8 yr,
179 T9 cm, and 73 T8 kg for the AF group; and 35 T5 yr,
180 T5 cm, and 72 T9 kg for the F-OR group. There were
no differences between groups for these descriptive param-
eters (P90.05).
Perceived sleep quality and sleep actigraphy
data. Raw values of sleep parameters for the three experi-
mental groups are presented in Table 3. Changes in sleep
data from baseline are depicted in Figure 2. There was no
significant time–group interaction in perceived sleep quality
time (P= 0.78), sleep latency (P= 0.13), and fragmenta-
tion index (P= 0.07). A significant interaction effect was
observed for actual sleep time (P= 0.02), sleep efficiency
(P=0.002),andimmobiletime(P= 0.006). A progressive
decrease of these three parameters was systematically ob-
served only in the F-OR group during the overload period
compared with baseline (actual sleep time, P= 0.01; sleep
efficiency, P= 0.049; and immobile time, P= 0.005, during
the last week of the overload period). All of these parameters
were progressively restored to baseline values during the en-
suing taper.
Infection–symptom incidence. Analysis of illness
questionnaires indicated that eight subjects reported at least
one episode of URTI during the training overload and/or
tapering periods. The occurrence of URTI symptoms during
the protocol is presented in Table 4. The proportion of
subjects who experienced symptoms of infection was higher
in the F-OR group (n= 6, 67% of total infection cases) than
that in AF (n= 2, 22%) and CTL groups (n= 1, 11%). No
subjects reported symptoms of gastrointestinal discomfort
during any phase of the training program.
In the present study, we studied nocturnal actimetry in a
group of trained triathletes who completed an overload
training program followed by a 2-wk taper period and de-
veloped symptoms of F-OR in comparison with control
counterparts without signs of training intolerance. The most
important finding indicated a progressive decrease in the
indices of sleep quality, alongside small reductions in sleep
quantity, during the overload period in the F-OR athletes,
which was progressively reversed during the subsequent
taper. Furthermore, a higher prevalence of URTI was also
reported in this F-OR group.
Signs of decreased sleep quality in overreached or
overtrained endurance athletes have been reported by pre-
vious researches. Jurimae¨ et al. (11) monitored the recovery-
stress state in competitive male rowers during a 6-d training
camp in response to an average increase in training load by
approximately 100% compared with average weekly loads.
Using the Recovery–Stress Questionnaire for Athletes
(RESTQ-Sport) (13), these authors showed decreased levels
of perceived sleep quality, suggesting that recovery may not
have been adequate during this training camp, leading to
performance impairment and genesis of high perceived
fatigue (i.e., overreaching). However, given the lack of ob-
jective markers of sleep actimetry, the reliance on perception
of sleep quality is problematic to then associate sleep dis-
ruption with overloading. In a similar vein, Matos et al. (20)
recently reported that frequent perceived sleep problems was
one of the most reported physical symptoms by athletes who
had experienced persistent daily fatigue and a significant
decrement in performance that lasted for long periods. Al-
together, these results suggest that heavy load training may
exert a negative effect on sleep quality, but to date, limited
TABLE 2. Mean values TSD at baseline, after the overload period, and after the 2-wk taper in the three experimental groups (control, n= 9; acute fatigue, n= 9; functionally OR, n= 9).
Variables Groups Pre Mid Post
Performance (W) CTL 354 T29 357 T28 357 T30
AF 345 T44 353 T45
360 T42
F-OR 371 T38 361 T37
374 T37
(mL O
) CTL 59.5 T3.6 60.4 T3.8 59.8 T4.6
AF 58.5 T5.9 60.6 T5.8
60.7 T5.8
F-OR 63.0 T4.1 61.0 T5.1
61.8 T4.5
Fatigue (AU) CTL 4.2 T4.5 6.8 T4.6 4.7 T5.3
AF 3.1 T2.3 8.9 T4.9
6.3 T5.6
F-OR 3.9 T3.3 12.7 T5.3
3.7 T4.4
Significantly different from Pre at PG0.05.
Significantly different from Mid at PG0.05.
CTL, control; AF, acute fatigue; F-OR: functional overreaching; V
, maximal oxygen uptake.
OVERREACHING, SLEEP, AND ILLNESS Medicine & Science in Sports & Exercise
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
literature provides objectively measured changes in sleep
characteristics during periods of confirmed F-OR.
The novelty of the present research provides objective
measures of sleep in a group of trained athletes demon-
strating differentiated performance responses (i.e., acute fa-
tigue vs functional overreaching [21]) during a 6-wk training
program involving periodic high training load. Time in bed
and sleep latency were not different at any period of the
training program in the F-OR group; however, these subjects
demonstrated a progressive decrease in actual sleep duration,
sleep efficiency, and immobile time during the overload pe-
riod, suggesting substantial sleep disturbances. This finding
was reported in at least seven of the nine F-OR athletes for
each parameter. During the same period, mean sleep values
remained unchanged in the control and in the AF groups. To
the best of our knowledge, this study is the first to show such
alterations in objective markers of sleep quality during a
period of intensified training that resulted in overreaching.
Although causation is not inferred, it is possible that sleep
disturbance may have been related to mild muscle fatigue or
soreness resulting from the high training loads. Certainly
given neither bed time, sleep latency or time spent in bed
were not significantly altered, and the reduction in sleep
duration may result mainly from the lowered efficiency be-
cause of difficulty in remaining immobile during sleep.
However, despite such measured sleep disruption, subjective
quality of sleep remained unaltered, suggesting some dis-
connect in actual and perceived measures of sleep. Indeed,
despite eight of the F-OR athletes reporting reduced scores
for perceived sleep quality, the magnitude in change was
insufficient to reach statistical significance.
Although the results suggest that F-OR athletes demon-
strated a modest decrease in quality and quantity of sleep
during the overload period, it remained considerably better
than that experienced by sleep disorder patients (29), ex-
treme sleep deprivation (32), or by athletes in response to jet
lag (18) or hypoxic exposure (26). In addition, Halson et al.
(9) reported larger sleep deficiency during leading to
overtraining (G6 h per night) in a talented female sprint cy-
clist who developed signs of overtraining (i.e., persistent
feeling fatigued and underperforming over months). The
actual sleep time in the F-OR subjects during the overload
training period remained higher than the values reported by
Leeder et al. (17) in a cohort of elite athletes under normal
training conditions. Nevertheless, we cannot exclude that the
moderate changes observed in the F-OR during the present
experiment would not unduly affect performance in elite
athletes, where very small differences in performance can
have large impact on the competition issue (25). It remains
also unclear during the present study whether sleep distur-
bance was an etiological mechanism of overreaching or
simply just a symptom. Regardless, it is acknowledged that
the reduction in sleep duration during F-OR was small in the
spectrum of sleep disturbances and may suggest that sleep
monitoring to detect F-OR may require extended periods of
data collection. Further research is required to determine the
TABLE 3. Mean values TSD of weekly sleep actigraphy data during the baseline week, the last week of the overload period, and during the last week of the taper period in the three experimental groups.
CTL (n=9) AF(n= 9) F-OR (n=9)
Variables Baseline
(Third Week)
(Second Week) Baseline
(Third Week)
(Second Week) Baseline
(Third Week)
(Second Week)
Perceived sleep quality (AU) 4.6 T0.5 4.7 T0.6 4.6 T0.5 4.9 T0.5 4.8 T0.5 4.9 T0.8 5.0 T0.6 4.2 T0.6 4.7 T0.9
Time in bed (h:min) 7:21 T0:29 7:34 T0:46 7:37 T0:46 8:11 T0:44 8:01 T0:55 7:54 T0:43 7:58 T0:36 7:25 T1:00 7:59 T0:54
Bedtime (hh:mm) 00:04 T01:06 23:44 T0:41 0:00 T0:16 23:26 T00:50 23:37 T0:42 23:45 T0:45 23:31 T00:52 0:01 T1:08 23:55 T1:15
Get-up time (hh:mm) 7:18 T1:06 7:14 T1:08 7:37 T1:25 7:37 T0:37 7:38 T0:36 7:39 T0:26 7:29 T0:47 7:26 T1:05 7:49 T1:14
Sleep latency (min) 9 T97T77T74T44T46T54T15T24T2
Actual sleep time (h:min) 6:29 T0:31 6:43 T0:47 6:47 T0:41 7:26 T0:39 7:13 T0:44 7:07 T0:30 7:09 T0:30 6:36 T0:51
7:07 T0:49
Sleep efficiency (%) 88.3 T6.3 89.0 T6.5 88.4 T5.5 91.0 T1.9 90.0 T1.9 89.3 T2.8 90.0 T1.3 88.4 T1.7
Fragmentation index 26.3 T9.5 25.6 T10.1 27.7 T8.9 22.1 T2.8 24.8 T3.3 24.7 T5.1 24.3 T5.5 23.0 T6.9 22.9 T8.0
Immobile minutes 377 T31 392 T45 398 T44 431 T37 419 T43 409 T30 417 T32 387 T53
418 T49
Significantly different from baseline at PG0.05.
Significantly different from the third week of the overload period at PG0.05.
CTL, control; AF, acute fatigue; F-OR, functionally overreached.
http://www.acsm-msse.org1042 Official Journal of the American College of Sports Medicine
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
relationship of sleep with training tolerance and adaptation,
especially in athletes developing training maladaptations
(i.e., overreaching and overtraining).
Given the purported relationship between prolonged re-
ductions in sleep quality and quantity with increased risk of
illness, it was interesting to observe a higher infection rate in
FIGURE 2—Change in mean weekly sleep parameters from baseline values during the overload and taper phase in the three experimental groups.
CTL, control; AF, acute fatigue; F-OR, functionally overreached. Gray areas and dashed lines represent 1 CV and 2 CV of the considered parameter
during the 6-wk protocol in the control group. *Significantly different from baseline at PG0.05.
Significantly different from the third week of the
overload period at PG0.05.
OVERREACHING, SLEEP, AND ILLNESS Medicine & Science in Sports & Exercise
Copyright © 2014 by the American College of Sports Medicine. Unauthorized reproduction of this article is prohibited.
the F-OR subjects during the present experiment. Of the nine
F-OR athletes, five reported increased URTI symptoms
during the overload period and concurrent to the noted sleep
disturbances, whereas only two cases were observed in the
AF and CTL groups during the same period. Interestingly,
this illness prevalence was the highest during the last week
of the overload period, which is temporally aligned when
sleep disturbances reached their highest magnitude during
the study, perhaps implying an accumulative effect. The
observed decrease in sleep and increase in URTI during
F-OR is in accordance with several previous studies re-
porting both innate and adaptive immunity are depressed
during sustained periods of heavy training (38). This as-
sociation suggests there is a link between the recuperative
processes of sleep and the immune system (27). The current
study provides supporting evidence that high volume train-
ing periods may result in increased URTI, alongside reduced
sleep quality. However, again whether sleep disturbance was
an etiological mechanism of URTI development as a result
of overreaching or simply coincidental symptoms remains
to be elucidated, particularly given the relatively small re-
ductions in sleep durations reported.
In conclusion, F-OR athletes showed objective signs of
moderate sleep disturbances and higher prevalence of in-
fections in the present study. These results were in con-
rast with control counterparts who did not demonstrate
any symptoms of training intolerance during the protocol.
Whether poor sleep was a consequence of increased training
causing the development of overreaching or whether sleep
disturbances were simply symptoms of OR remain unclear.
Whatever the causative link between F-OR and sleep, we
suggest that endurance athletes should be encouraged to en-
sure ideal sleeping environment (quiet, cool, and dark) (5) and
to avoid early morning schedule (31), when they are exposed
to high training load. Napping for short periods during the
day may also represent a recommended recovery strategy for
athletes to compensate the potential decline in actual sleep
time associated with development of F-OR. Further in-
vestigations are required to confirm this hypothesis and to
investigate the importance of sleep and its relationship
with overreaching.
This study was made possible by a technical support from the French
Federation of Triathlon. The authors are especially grateful to Frank
Bignet and Benjamin Maze for their help and cooperation. The authors
received funding for research on which this article is based from the
French Ministry of Sport and the French National Institute of Sport, Ex-
pertise and Performance (Paris). The authors report no conflict of interest.
The results of the present study do not constitute endorsement by
the American College of Sports Medicine.
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OVERREACHING, SLEEP, AND ILLNESS Medicine & Science in Sports & Exercise
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... Functional overreaching is a common occurrence in endurance sports, as the underperformance following an overload training block is often considered a necessary component of training to induce performance 2 enhancement (1-3). However, functional overreaching has been demonstrated to result in an inferior supercompensation following recovery compared to acute fatigue (3), and is accompanied by additional maladaptive physiological symptoms including: lower exercising cardiac output (4,5), reductions in exercising catecholamines (5,6), increases in resting muscle sympathetic nerve activity (7), a blunting of physiological training adaptations (3,7,8), and an increased incidence of illness (9). It is, therefore, important that athletes and coaches avoid functional and non-functional overreaching in order to prevent unnecessary reductions in training capacity, health, and performance. ...
... Cycling power and HR were recorded continuously, with the first minute of each stage of the LSCT excluded from analysis as per LSCT protocol (22,26). Ratings of perceived exertion (RPE) on a scale of [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] were recorded in the final minute of each stage. Participants were given 10 min of rest following the LSCT prior to a 5 km cycling time-trial on the same Velotron cycle ergometer and Racermate software. ...
... Many factors can affect the sleeping patterns (e.g., travel, unfamiliar environment, pre-competition anxiety, the use of electronic devices, altitude) of athletic populations [2][3][4][5], including intensified training periods [2,3]. For instance, a 3-wk overload training program promoted overreaching in triathletes and this, in turn, promoted a decline in sleep quantity and quality [6]. Similarly, a short period of intensified training affected sleep efficiency among male cyclists [7]. ...
... Still, there is no consensus as to whether impaired sleep is a cause or a consequence of heavy training loads [11,32]. Sleep disturbances are common in sport, particularly among athletes experiencing training maladaptation that elicit overreaching or overtraining [6,31]. ...
... Donzella et al. [49] showed that individuals who had COVID-19 reported lower sleep quality than individuals who did not have COVID-19, although sleep time was higher. Hausswirth et al. [50] demonstrated in their study that functionally overreached athletes had a decline in sleep efficacy and 50% of them had a performance loss after six weeks. The observed decreased sleep efficacy and decrease in performance [50] are similar to long-term symptoms which can occur post COVID-19 [5,51]. ...
... Hausswirth et al. [50] demonstrated in their study that functionally overreached athletes had a decline in sleep efficacy and 50% of them had a performance loss after six weeks. The observed decreased sleep efficacy and decrease in performance [50] are similar to long-term symptoms which can occur post COVID-19 [5,51]. Therefore, we assume that overtraining and COVID-19 may altered the immune system and physical functions in a similar way. ...
Full-text available
Introduction: COVID-19 is a multi-systemic disease which can target the lungs and the cardiovascular system and can also affect parts of the brain for prolonged periods of time. Even healthy athletes without comorbidities can be psychologically affected long-term by COVID-19. Objective: This study aimed to investigate athletes' perceived mental stress and recovery levels in daily life, and their maximal aerobic power, at three different time points, post COVID-19. Methods: In total, 99 athletes (62.6% male), who had been infected by COVID-19, filled out the Recovery Stress Questionnaire for Athletes (REST-Q-Sport) and completed cardiopulmonary exercise testing (endpoint maximal aerobic power output (Pmax)) at the initial screening (t1: 4 months after infection). Follow-up assessments occurred three (t2, n = 37) and seven months after t1 (t3, n = 19). Results: Subgroup means from the Recovery category were significantly below the reference value of four at all three time points, except "General Recovery" (3.76 (± 0.96), p = 0.275, d = 0.968) at t3."Overtiredness" (2.34 (± 1.27), p = 0.020, r = 0.224) was significantly above the reference value of two at t1, while all other Stress subgroups were not significantly different from the reference value or were significantly below the maximum threshold of two at t1, t2 and t3. Spearman's ρ revealed a negative association between Pmax and the subcategories of stress (ρ = -0.54 to ρ = -0.11, p < 0.050), and positive correlations between Pmax and "Somatic Recovery" (ρ = 0.43, p < 0.001) and "General Recovery" (ρ = 0.23, p = 0.040) at t1. Pmax (t1: 3.83 (± 0.99), t2: 3.78 (± 1.14), β = 0.06, p < 0.003) increased significantly from t1 to t2. In addition, REST-Q-Sport indicated a decrease in "Sleep" (t2 = 2.35 (± 0.62), t3 = 2.28(± 0.61), β = -0.18, p < 0.023) at t3, when compared to t2. Conclusion: The perceived recovery seems to be negatively affected in post COVID-19 athletes. Physical performance post COVID-19 correlates with both "Emotional and Somatic Stress" and "Somatic and General Recovery", indicating potential mental and physical benefits of exercise. While it is evident that COVID-19, like other viral infections, may have an influence on physical performance, monitoring stress and recovery perceptions of athletes is critical to facilitate their return-to-sports, while minimizing long-term COVID-19 induced negative effects like the athletic objective and subjective perceived recovery and stress levels.
... Increasing the total duration of training above MMSS was found to lead to better performance outcomes [6]. However, trying to maximize the time spent above MMSS in every training session could lead to delayed fatigue, acute performance impairment, and increased risk of illness [7][8][9]. Therefore, athletes should focus on optimizing, not maximizing the time spent above MMSS both within a single exercise session and across a training cycle. ...
Full-text available
Background To improve sport performance, athletes use training regimens that include exercise below and above the maxi- mal metabolic steady state (MMSS). Objective The objective of this review was to determine the additional effect of training above MMSS on VO2peak, Wpeak and time-trial (TT) performance in endurance-trained athletes. Methods Studies were included in the review if they (i) were published in academic journals, (ii) were in English, (iii) were prospective, (iv) included trained participants, (v) had an intervention group that contained training above and below MMSS, (vi) had a comparator group that only performed training below MMSS, and (vii) reported results for VO2peak, Wpeak, or TT performance. Medline and SPORTDiscus were searched from inception until February 23, 2023. Results Fourteen studies that ranged from 2 to 12 weeks were included in the review. There were 171 recreational and 128 competitive endurance athletes. The mean age and VO2peak of participants ranged from 15 to 43 years and 38 to 68 mL·kg−1·min−1, respectively. The inclusion of training above MMSS led to a 2.5 mL·kg−1·min−1 (95% CI 1.4–3.6; p < 0.01; I2 = 0%) greater improvement in VO2peak. A minimum of 81 participants per group would be required to obtain sufficient power to determine a significant effect (SMD 0.44) for VO2peak. No intensity-specific effect was observed for Wpeak or TT performance, in part due to a smaller sample size. Conclusion A single training meso-cycle that includes training above MMSS can improve VO2peak in endurance-trained athletes more than training only below MMSS. However, we do not have sufficient evidence to conclude that concurrent adaptation occurs for Wpeak or TT performance.
... Therefore, these methods may not be effective and economical screening or diagnostic tools for athletes' sleep behaviour monitoring. Using population-tested questionnaires might be an alternative to help identify athletes who need further sleep assessment (Bender et al., 2018;Fietze et al., 2009;Hausswirth et al., 2014;Leeder et al., 2012;Sargent et al., 2014). Using a screening questionnaire for sleep monitoring compared with the above method is easier to implement. ...
Full-text available
The objectives of this study were to translate the Athlete Sleep Screening Questionnaire (ASSQ) into a simplified Chinese version (ASSQ-CHN) and test its properties. Using a methodological study, a total of 160 elite athletes participated, and 133 athletes completed the experiment. Statistical results show the following: (1) a total of 4 factors were obtained by using exploratory factor analysis conducted on the ASSQ-CHN (sleep difficulty score, travel impact, sleep behaviour and habit, and electronic device use before bedtime). The sleep difficulty score (SDS) part consisted of items 1, 3, 4, 5, and 6. (2) the SDS showed adequate internal consistency (Cronbach’s α = 0.74), as well as test-retest reliability (ICC2,1 = 0.79, 95% CI 0.72–0.85), standard error of measurement = 1.37 points and minimum detectable change = 3.24 points, with no floor or ceiling effects. This suggests that the real change in ASSQ-CHN (SDS) score should be over 3.24 points and the measurement tool can distinguish between subjects with the highest or lowest scores. (3) the SDS was strongly correlated with the PSQI (rs = 0.74, P < 0.01). The correlation between SDS and SF-36 was only moderately correlated with general health (rs = −0.33, P < 0.01), and there was low or no significant correlation with other items. (4) the ASSQ-CHN (SDS) mean score of the sleep disorder group was significantly higher than that of the healthy groups (P < 0.01). The ASSQ-CHN is equivalent to the original version in terms of language and measurement properties. It can be used as an effective and reliable sleep screening tool to evaluate athletes’ sleep status.
Introduction: Sleep is essential for athletes and dancers to optimize recovery. Poor sleep negatively affects cognitive function and injury risk in athletes. Increased athletic participation (hours) is associated with decreased total sleep and quality in athletes. Still, information about how sleep is related with exposure hours and injury in collegiate dancers remains unclear. We examined the relationships among the Athlete Sleep Behavior Questionnaire (ASBQ), dance exposure hours (DEHr), and injuries in collegiate dancers over a 7 -month period (August 2019-February 2020). Methods: Seventy-two dancers (58 female, 14 male; 19.7 ± 1.4 years) completed the 18 question ASBQ at the start of each month (Scale:1 = Never, 5 = Always; Global Scores ≤36 = "good sleep behavior" and ≥42 = "poor sleep behavior"). A DEHr was recorded as 1 hour of dance participation in class, rehearsal, or performance. Injuries were defined as any condition where the dancer sought medical attention, and we calculated an injury rate for total injuries (IR/1000 DEHr). Pearson correlations examined relationships among ASBQ, DEHr, and injuries (P ≤ .05). Results: Dancers participated in 467.8 ± 45.7 DEHr over 7 months, with 14 dancers suffering 18 injuries (IR = 0.5/1000-DEHr; 95% CI:0.3-0.8). Overall, dancers reported poor sleep behaviors (42.6 ± 6.4). ASBQ scores, DEHr, and injuries in August-October, and December-February were not related, except for a weak positive relationship between ASBQ scores and DEHr in November (r = .28, P = .04). Conclusions: Sleep, DEHr, and injuries were inconsistently related in collegiate dancers. Sleep and DEHr were only correlated during the month where dancers had 2 performance weeks. While we did not observe this relationship every month, performance weeks may have negatively affected sleep in November. Despite consistent poor sleep, sleep did not seem to negatively affect injury risk during the 7 -month study period. Future researchers should validate the ASBQ in dancers.
People suffered from insufficient or disrupted sleep due to night shifts, work pressure, and irregular lifestyles. Sleep deprivation caused by inadequate quantity or quality of sleep has been associated with not only increased risk of metabolic diseases, gut dysbiosis, and emotional disorders but also decreased work and exercise performance. In this study, we used the modified multiple platform method (MMPM) to induce pathological and psychological characteristics of sleep deprivation with C57BL/6J male mice, and investigated whether supplementing a prebiotics mixture of short-chain galactooligosaccharides (scGOS) and long-chain fructooligosaccharides (lcFOS) (9:1 ratio) could improve the impacts of sleep deprivation on intestinal physiology, neuropsychological function, inflammation, circadian rhythm, and exercise capacity. Results showed that sleep deprivation caused intestinal inflammation (increased TNFA and IL1B) and decreased intestinal permeability with a significant decrease in the tight junction genes (OCLN, CLDN1, TJP1, and TJP2) of intestine and brain. The prebiotics significantly increased the content of metabolite short-chain fatty acids (acetate and butyrate) while recovering the expression of indicated tight junction genes. In hypothalamus and hippocampus, clock (BMAL1 and CLOCK) and tight junction (OCLN and TJP2) genes were improved by prebiotics, and corticotropin-releasing hormone receptor genes, CRF1 and CRF2, were also significantly regulated for mitigation of depression and anxiety caused by sleep deprivation. Also, prebiotics brought significant benefits on blood sugar homeostasis and improvement of exercise performance. Functional prebiotics could improve physiological modulation, neuropsychological behaviors, and exercise performance caused by sleep deprivation, possibly through regulation of inflammation and circadian rhythm for health maintenance. However, the microbiota affected by prebiotics and sleep deprivation should warrant further investigation.
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Taper is a common training strategy used to reduce fatigue and enhance athletic performance. However, currently, no review has summarised what psychological research has been conducted examining taper, what this research shows and what future research needs to be undertaken to extend the field. Consequently, a scoping review was conducted with three aims: (a) to determine the characteristics of psychological research examining taper, (b) to summarise psychological research collected during taper with adult athletes and coaches, and (c) to identify gaps in psychological research examining taper. Forty-eight articles were identified following an exhaustive search strategy and charted following scoping review guidelines. Results showed most research was quantitative, used a longitudinal design, was conducted in swimming, triathlon, cycling or across multiple sports, and used a university-, regional- or national-level male athlete sample. Eight themes were developed to summarise the research: Mood, Perception of Effort, Perceived Fatigue and Wellness, Recovery-Stress, Taper as a Stressor, Stress Tolerance, Psychological Preparation and Cognitive Functioning. Additionally, four research recommendations were identified: (a) conducting exploratory research that examines the impact taper has on athletes’ and coaches’ competition preparation and stress experience, (b) asking more advanced psychological questions and conducting multi-disciplinary research, (c) including a more diverse participant sample in studies and (d) examining the impact of psychological interventions during taper. Overall, this scoping review has highlighted the limited research examining the psychology of taper and the need for focused research that asks more complex questions across diverse populations. Supplementary Information The online version contains supplementary material available at 10.1007/s40279-022-01798-6.
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PURPOSE. The reliability of competitive performance of athletes in a given sport provides an estimate of the smallest worthwhile change in performance, which is crucial when testing athletes and when assessing factors that affect performance in that sport. We have therefore analyzed the reliability of athletes competing in international Olympic-distance triathlons. METHODS. We obtained official results from websites for triathlons performed before drafting in the cycling stage was permitted. We analyzed times for 103 athletes who entered two or more of nine such races over 19 months. Our measure of reliability was the typical race-to-race variation of an athlete's time, derived as a coefficient of variation by analysis of log-transformed times. RESULTS. (a) Typical race-to-race variations were: swim 1.6%, cycle 2.3%, and run 3.6%. When combined independently or dependently with the durations of each phase (20, 60 and 35 min), these variations yielded predicted variations in total time of 1.6% or 2.6% respectively, whereas the observed variation was 1.8%. (b) Transition times, which were available for three races, averaged 89 s for the swim-cycle and cycle-run transitions combined. Between-athlete variation in these times in each race was 5.2, 5.6 and 7.8 s, or ~0.1% of the mean total time of 115 min. (c) Analysis of reliability between all possible pairs of races showed no substantial effect of time between pairs (14-567 days). (d) Reliability between pairs of races held in normal environmental temperatures was better than when at least one of the pair was held in hot conditions (typical variations of 1.6% and 2.0% respectively). (e) The top 10% of triathletes, who averaged 3.4% faster than the average triathlete, had substantially smaller variations than the other triathletes for total time (1.1%) and for each of the three stages (swim, 1.2%; cycle, 1.3%; run, 2.5%). In triathlons where drafting in the cycle stage is permitted, variation in total time of the top triathletes is probably determined by the run alone and is therefore ~0.8%. CONCLUSIONS. (a) Factors that affect performance of individual elite triathletes act largely independently in the three phases. (b) No worthwhile gains in performance are possible in the transitions. (c) Elite triathletes' performance is remarkably stable over a 19-month period. (d) The outcome of a triathlon staged in a hot environment is somewhat less predictable than normal. (e) The smallest important change in race time for a top triathlete (half the variation in total time) is ~0.5%, which in current triathlons has to be achieved via changes of at least 1.2% in running speed. KEYWORDS: competition, error, race, reproducibility, testing, variability.
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Good sleep is essential for optimal performance, yet few studies have examined the sleep/wake behaviour of elite athletes. The aim of this study was to assess the impact of early-morning training on the amount of sleep obtained by world-class swimmers. A squad of seven swimmers from the Australian Institute of Sport participated in this study during 14 days of high-intensity training in preparation for the 2008 Olympic Games. During these 14 days, participants had 12 training days, each starting with a session at 06:00 h, and 2 rest days. For each day, the amount of sleep obtained by participants was determined using self-report sleep diaries and wrist-worn activity monitors. On nights that preceded training days, participants went to bed at 22:05 h (s�00:52), arose at 05:48 h (s�00:24) and obtained 5.4 h (s�1.3) of sleep. On nights that preceded rest days, participants went to bed at 00:32 h (s�01:29), arose at 09:47 h (s�01:47) and obtained 7.1 h (s�1.2) of sleep. Mixed model analyses revealed that on nights prior to training days, bedtimes and get-up times were significantly earlier (pB0.001), time spent in bed was significantly shorter (pB0.001) and the amount of sleep obtained was significantly less (pB0.001), than on nights prior to rest days. These results indicate that early-morning training sessions severely restrict the amount of sleep obtained by elite athletes. Given that chronic sleep restriction of B6 h per night can impair psychological and physiological functioning, it is possible that early-morning schedules actually limit the effectiveness of training.
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Successful training must involve overload, but also must avoid the combination of excessive overload plus inadequate recovery. Athletes can experience short-term performance decrement, without severe psychological, or lasting other negative symptoms. This Functional Overreaching (FOR) will eventually lead to an improvement in performance after recovery. When athletes do not sufficiently respect the balance between training and recovery, Non-Functional Overreaching (NFOR) can occur. The distinction between NFOR and the Overtraining Syndrome (OTS) is very difficult and will depend on the clinical outcome and exclusion diagnosis. The athlete will often show the same clinical, hormonal and other signs and symptoms. A keyword in the recognition of OTS might be ‘prolonged maladaptation’ not only of the athlete, but also of several biological, neurochemical, and hormonal regulation mechanisms. It is generally thought that symptoms of OTS, such as fatigue, performance decline and mood disturbances, are more severe than those of NFOR. However, there is no scientific evidence to either confirmor refute this suggestion. One approach to understanding the aetiology of OTS involves the exclusion of organic diseases or infections and factors such as dietary caloric restriction (negative energy balance) and insufficient carbohydrate and/or protein intake, iron deficiency, magnesium deficiency, allergies, etc., together with identification of initiating events or triggers. In this paper, we provide the recent status of possible markers for the detection of OTS. Currently several markers (hormones, performance tests, psychological tests, biochemical and immune markers) are used, but none of them meets all criteria to make its use generally accepted.
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We analyzed HR variability (HRV) to detect alterations in autonomic function that may be associated with functional overreaching (F-OR) in endurance athletes. Twenty-one trained male triathletes were randomly assigned to either intensified training (n = 13) or normal training (n = 8) groups during 5 wk. HRV measures were taken daily during a 1-wk moderate training (baseline), a 3-wk overload training, and a 1-wk taper. All the subjects of the intensified training group demonstrated a decrease in maximal incremental running test performance at the end of the overload period (-9.0% ± 2.1% of baseline value) followed by a performance supercompensation after the taper and were therefore diagnosed as F-OR. According to a qualitative statistical analysis method, a likely to very likely negative effect of F-OR on HR was observed at rest in supine and standing positions, using isolated seventh-day values and weekly average values, respectively. When considering the values obtained once per week, no clear effect of F-OR on HRV parameters was found. In contrast, the weekly mean of each HRV parameter showed a larger change in indices of parasympathetic tone in the F-OR group than the control group in supine position (with a 96%/4%/0% chance to demonstrate a positive/trivial/negative effect on Ln RMSSD after the overload period; 77%/22%/1% on LnHF) and standing position [98%/1%/1% on Ln RMSSD; 99%/0%/1% on LnHF; 95%/1%/4% on Ln(LF + HF)]. During the taper, theses responses were reversed. Using daily HRV recordings averaged over each week, this study detected a progressive increase in the parasympathetic modulation of HR in endurance athletes led to F-OR. It also revealed that due to a wide day-to-day variability, isolated, once per week HRV recordings may not detect training-induced autonomic modulations in F-OR athletes.
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Athletes experience minor fatigue and acute reductions in performance as a consequence of the normal training process. When the balance between training stress and recovery is disproportionate, it is thought that overreaching and possibly overtraining may develop. However, the majority of research that has been conducted in this area has investigated overreached and not overtrained athletes. Overreaching occurs as a result of intensified training and is often considered a normal outcome for elite athletes due to the relatively short time needed for recovery (approximately 2 weeks) and the possibility of a supercompensatory effect. As the time needed to recover from the overtraining syndrome is considered to be much longer (months to years), it may not be appropriate to compare the two states. It is presently not possible to discern acute fatigue and decreased performance experienced from isolated training sessions, from the states of overreaching and overtraining. This is partially the result of a lack of diagnostic tools, variability of results of research studies, a lack of well controlled studies and individual responses to training. The general lack of research in the area in combination with very few well controlled investigations means that it is very difficult to gain insight into the incidence, markers and possible causes of overtraining. There is currently no evidence aside from anecdotal information to suggest that overreaching precedes overtraining and that symptoms of overtraining are more severe than overreaching. It is indeed possible that the two states show different defining characteristics and the overtraining continuum may be an oversimplification. Critical analysis of relevant research suggests that overreaching and overtraining investigations should be interpreted with caution before recommendations for markers of overreaching and overtraining can be proposed. Systematically controlled and monitored studies are needed to determine if overtraining is distinguishable from overreaching, what the best indicators of these states are and the underlying mechanisms that cause fatigue and performance decrements. The available scientific and anecdotal evidence supports the existence of the overtraining syndrome; however, more research is required to state with certainty that the syndrome exists.
Overtraining syndrome (OTS) is a major threat for performance and health in athletes. OTS is caused by high levels of (sport-specific) stress in combination with too little regeneration, which causes performance decrements, fatigue an possibly other symptoms. Although there is general consensus about the causes and consequences, many different terminologies have been used interchangeably. The consequences of overreaching and overtraining are divided into three categories: (i) functional overreaching (FO); (ii) non-functional overreaching (NFO); and (iii) OTS. In FO, performance decrements and fatigue are reversed within a pre-planned recovery period. FO has no negative consequences for the athlete in the long term; it might even have positive consequences. When performance does not improve and feelings of fatigue do not disappear after the recovery period, overreaching has not been functional and is thus called NFO. OTS only applies to the most severe cases. NFO and OTS could be prevented using early markers, which should be objective, not manipulable, applicable in training practice, not too demanding, affordable and should be based on a sound theoretical framework. No such markers exist up to today. It is proposed that psychomotor speed might be such a marker. OTS shows similarities with chronic fatigue syndrome and with major depression (MD). Through two meta-analyses, it is shown that psychomotor slowness is consistently present in both syndromes. This leads to the hypothesis that psychomotor speed is also reduced in athletes with OTS. Parallels between commonly used models for NFO and OTS and a threshold theory support the idea that psychomotor speed is impaired in athletes with NFO or OTS and could also be used as an early marker to prevent NFO and/or OTS.
The multitude of publications regarding overtraining syndrome (OTS or ‘staleness’) or the short-term ‘over-reaching’ and the severity of consequences for the athlete are in sharp contrast with the limited availability of valid diagnostic tools. Ergometric tests may reveal a decrement in sport-specific performance if they are maximal tests until exhaustion. Overtrained athletes usually present an impaired anaerobic lactacid performance and a reduced time-to-exhaustion in standardised high-intensity endurance exercise accompanied by a small decrease in the maximum heart rate. Lactate levels are also slightly lowered during submaximal performance and this results in a slightly increased anaerobic threshold. A reduced respiratory exchange ratio during exercise still deserves further investigation. A deterioration of the mood state and typical subjective complaints (‘heavy legs’, sleep disorders) represent sensitive markers, however, they may be manipulated. Although measurements at rest of selected blood markers such as urea, uric acid, ammonia, enzymes (creatine kinase activity) or hormones including the ratio between (free) serum testosterone and cortisol, may serve to reveal circumstances which, for the long term, impair the exercise performance, they are not useful in the diagnosis of established OTS. The nocturnal urinary catecholamine excretion and the decrease in the maximum exercise-induced rise in pituitary hormones, especially adrenocorticotropic hormone and growth hormone, and, to a lesser degree, in cortisol and free plasma catecholamines, often provide interesting diagnostic information, but hormone measurements are less suitable in practical application. From a critical review of the existing overtraining research it must be concluded that there has been little improvement in recent years in the tools available for the diagnosis of OTS.
The current study investigated the effects of combining cold water immersion (CWI), full-body compression garments (CG) and sleep hygiene recommendations on physical, physiological and perceptual recovery following two a day, on-court training and match-play sessions. In a cross-over design, 8 highly-trained tennis players completed two sessions of on-court tennis drill training and match-play, followed by a recovery or control condition. Recovery interventions included a mixture of 15min CWI, 3h of wearing full-body CG and following sleep hygiene recommendations that night; whilst the control condition involved post-session stretching and no regulation of sleeping patterns. Technical performance (stroke and error rates), physical performance (accelerometery, counter-movement jump), physiological (heart rate, blood lactate) and perceptual (mood, exertion and soreness) measures were recorded from each on-court session, along with sleep quantity each night. While stroke and error rates did not differ in the drill session (P>0.05;d<0.20), large effects were evident for increased time in play and stroke rate in match-play following the recovery interventions (P>0.05;d>0.90). Although accelerometry values did not differ between conditions (P>0.05;d<0.20), CMJ tended to be improved before match-play with recovery (P>0.05;d=0.90). Further, CWI and CG resulted in faster post-session reductions in heart rate and lactate and reduced perceived soreness (P>0.05;d>1.00). Further, sleep hygiene recommendations increased sleep quantity (P>0.05;d>2.00) and also maintained lower perceived soreness and fatigue (P<0.05;d>2.00). Mixed-method recovery interventions (CWI and CG) used after tennis sessions increased ensuing time in play, lower-body power and reduced perceived soreness. Further, sleep hygiene recommendations assisted the reduction of perceived soreness.