Fatigue Shifts and Scatters Heart Rate Variability in Elite
Endurance Athletes
Laurent Schmitt1*, Jacques Regnard2, Maxime Desmarets3, Fréderic Mauny3,4, Laurent Mourot5, JeanPierre Fouillot6, Nicolas Coulmy7, Grégoire Millet8
1 National School of Mountain Sports/National Ski-Nordic Centre, Premanon, Les Rousses, France, 2 University of Franche-Comte, Research unit EA3920, ‘‘Prognostic
markers and control factors in cardiovascular pathologies’’ and Physiology Department, University Hospital of Besançon, Besançon, France, 3 Clinical Research Methods
Center, University Hospital of Besançon, Besançon, France, 4 University of Franche-Comte, Research unit UMR 6249 ‘‘Chrono-environment’’, University Hospital of
Besançon, Besançon, France, 5 University of Franche-Comte, Research unit EA4660 ‘‘Culture Sport Health Society and Exercise Performance Health Innovation platform’’,
UPFR des Sports 31 chemin de l’Epitaphe, Besançon, France, 6 University of Paris 13, Research unit EA2363, ARPE, 74 rue Marcel Cachin, Bobigny, France, 7 French Ski
Federation, 50 rue des Marquisats, BP 2451, Annecy, France, 8 Institute of Sport Sciences, Department of Physiology, Faculty of Biology and Medicine, University of
Lausanne, Lausanne, Switzerland
Abstract
Purpose: This longitudinal study aimed at comparing heart rate variability (HRV) in elite athletes identified either in ‘fatigue’
or in ‘no-fatigue’ state in ‘real life’ conditions.
Methods: 57 elite Nordic-skiers were surveyed over 4 years. R-R intervals were recorded supine (SU) and standing (ST). A
fatigue state was quoted with a validated questionnaire. A multilevel linear regression model was used to analyze
relationships between heart rate (HR) and HRV descriptors [total spectral power (TP), power in low (LF) and high frequency
(HF) ranges expressed in ms2 and normalized units (nu)] and the status without and with fatigue. The variables not
distributed normally were transformed by taking their common logarithm (log10).
Results: 172 trials were identified as in a ‘fatigue’ and 891 as in ‘no-fatigue’ state. All supine HR and HRV parameters
(Beta6SE) were significantly different (P,0.0001) between ‘fatigue’ and ‘no-fatigue’: HRSU (+6.2760.61 bpm), logTPSU
(20.3660.04), logLFSU (20.2760.04), logHFSU (20.4660.05), logLF/HFSU (+0.1960.03), HFSU(nu) (29.5561.33). Differences
were also significant (P,0.0001) in standing: HRST (+8.8360.89), logTPST (20.2860.03), logLFST (20.2960.03), logHFST
(20.3260.04). Also, intra-individual variance of HRV parameters was larger (P,0.05) in the ‘fatigue’ state (logTPSU: 0.26 vs.
0.07, logLFSU: 0.28 vs. 0.11, logHFSU: 0.32 vs. 0.08, logTPST: 0.13 vs. 0.07, logLFST: 0.16 vs. 0.07, logHFST: 0.25 vs. 0.14).
Conclusion: HRV was significantly lower in ’fatigue’ vs. ’no-fatigue’ but accompanied with larger intra-individual variance of
HRV parameters in ’fatigue’. The broader intra-individual variance of HRV parameters might encompass different changes
from no-fatigue state, possibly reflecting different fatigue-induced alterations of HRV pattern.
Citation: Schmitt L, Regnard J, Desmarets M, Mauny F, Mourot L, et al. (2013) Fatigue Shifts and Scatters Heart Rate Variability in Elite Endurance Athletes. PLoS
ONE 8(8): e71588. doi:10.1371/journal.pone.0071588
Editor: Andreas Zirlik, University Heart Center Freiburg, Germany
Received February 8, 2013; Accepted July 1, 2013; Published August 12, 2013
Copyright: ß 2013 Schmitt 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.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: laurent.schmitt@ensm.sports.gouv.fr
Such an extreme fatigue can even cause the end of an athlete’s
career.
The difficulty to distinguish FOR and specially NFOR from OT
is well recognized [2,6]. This is partially due to the fact that the
multiple clinical signs of fatigue stem not only from training
overload with inadequate rest but also from various inputs as
psychological stress, nutritional imbalance, mild inflammatory
disorders, etc… Several tools have been proposed to assess the
fatigue level in daily routine. Among them, analysis of heart rate
variability (HRV) provides an indirect evaluation of the heart
control by the autonomic nervous system (ANS), and was
highlighted as a promising tool [2]. Several studies attempted to
unveil links between the training loads, the state of fatigue and the
changes in ANS activity as reflected by HRV [7–14]. Unfortunately, the results of these studies were equivocal. In a case study of
Introduction
In elite sport, athletes training loads and recovery periods are
managed to transitory disturb homeostasis and to subsequently
reap a higher performance level [1]. This management has to
avoid fatigue accumulation, which could abrade performance. As
described by Meeusen et al. [2], the development of fatigue follows
a continuum process ranging from voluntary and controlled
fatigue necessary for performance progression and requesting few
hours or few days of recovery, named functional over-reaching
(FOR) [3,4], until involuntary and uncontrolled fatigue requesting
weeks or even months of recovery, named non functional OR
(NFOR) [2] or overtraining (OT) [5] when it has become a
‘‘prolonged maladaptation’’. In case of NFOR or OT, the
increased recovery time results in a lack of training, a decrease
of the physical capacities and finally an impaired performance [2].
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Fatigue and Heart Rate Variability in Athletes
takes place in May and June and embeds base aerobic and
strength training sessions; the specific training period follows with
base aerobic, maximal aerobic and strength training sessions in
July, August and September; the precompetitive period occurs in
October and November and holds base aerobic, maximal aerobic
and strength training sessions and altitude training camps. The
competition period encompasses December to March, and April is
the recovery period.
one overtrained cross-country skier, Hedelin et al. [11] reported
reduced competition performance and lowered profile of mood
states, along a decrease in absolute low frequency power (LF) while
absolute HF power remained high. Conversely, Uusitalo et al.
[12] showed that OT was associated with HRV descriptors in
which HF power was decreased in nine female endurance athletes
undergoing heavy training over a 6–9 week period. And Hedelin
et al. [14] reported unchanged HRV values in nine overreached
canoeists after increasing training load by 50% over a 6-day
training camp, despite concomitant decreases in maximal blood
lactate concentration, running time to fatigue, maximal and
submaximal heart rate, as well as maximal oxygen uptake
(V_ O2max).
These discrepant results might be related to several conditions.
The former studies were conducted on quite short follow-up
periods (few weeks or months in most cases) and involved a limited
number of athletes (1 to ,10–15). The protocols used were
sometimes designed to artificially overload the athletes, without
substantial evidence that a fatigue state had been induced.
Another point of interest for the monitoring of athletes might be
the assessment of HRV dispersion as shown recently in Plews et al.
[15]. Therefore, if the different fatigue patterns quoted above were
to be recognized according to different shifts in HRV parameters,
then HRV parameters dispersion could reach broader ranges in
‘fatigue’ than in ‘no-fatigue’ states.
The present longitudinal study was conducted with records
taken in ‘real life’ conditions. It aimed firstly at comparing HRV
differences in elite Nordic skiers who were recognized either in a
‘fatigue’ or in a ‘no-fatigue’ state, independently of HRV
recording, and secondly at measuring the fluctuation span of the
HRV parameters in these two states. The hypotheses tested were
firstly that HRV parameters of the athletes would be significantly
different in the ‘fatigue’ and in the ‘no-fatigue’ states and, secondly
that the dispersion of individual HRV parameters would be larger
in the ‘fatigue’ state than in ‘no-fatigue’ condition.
Subjects
The subjects were 57 (27 men, 30 women) members of the
French national teams of the Nordic ski disciplines and included 8
medallists of the Olympics Games, the World championships or
the World cup. Inclusion criteria were as follows: elite athlete
member of the French national teams from 2004–2005 to 2007–
2008 training seasons with at least one declared fatigue state. So,
only athletes with both ‘fatigue’ and ‘no-fatigue’ states were
included retrospectively.
Exclusion criteria were to be injured or sick for longer than 5
consecutive days or to stop the international sporting career.
All these subjects had a maximum oxygen consumption
(V_ O2max) measurement at the entrance of the period of the study,
all measured during an incremental treadmill test at the altitude of
1200 m as described earlier [19].
HRV Tests
The tests were always performed in the same conditions. The
subjects arrived at the national French training centre at 8h30 in
the morning before the first training session of the day. All the
athletes of one team completed together the HRV tests in the
medical laboratory under supervision of the same investigator. No
heavy training session took place during the two days preceding
the HRV tests. During the competition period, the tests were
performed on Wednesday morning, i.e. after two full days of easy
aerobic training following the last competition run which usually
take place Saturday and/or Sunday.
After a 15-min rest, the test began with 8 min supine (SU)
followed by 7 min standing (ST). HRV analyses were performed
on RR intervals recorded between the 3rd and 8th min supine, and
between the 9th and 14th min standing. Measurement of the
interval duration between two R waves of the cardiac electrical
activity was performed with a HR monitor (T6, SuuntoH, Vantaa,
Finland). HRV assessment from these RR intervals has been
validated against ECG measurements [20]. Each data file was
visually inspected for artifacts, which were manually corrected.
Then the spectral power was calculated with Fast Fourier
Transform (FFT) according to the specific software (NevrokardH
HRV, Medistar, Ljubljana, Slovenia). Recording periods of 256 s
were analysed, each yielding 512 data points after re-sampling at
the 2 Hz frequency. The Hanning windowing function was
applied and the Goertz algorithm was used for calculation. The
power spectral density was measured by frequency bands in
ms2.Hz21 and the spectral power was expressed in ms2 [21] : High
frequency (HF) power band (0.15 to 0.40 Hz) reflects the
parasympathetic influence and is related to the respiratory sinus
arrhythmia [22]; Low frequency (LF) power band (0.04 to
0.15 Hz) is also driven by parasympathetic tone, and is presently
considered as carrying vagal resonances to either changes in
vasomotor tone (often sympathetic) or in central modulation of
sympathetic tone [23]; the spectral power in this frequency band
has been found related to arterial blood pressure [22,24] and to
baroreflex activity [25].
During the tests, the investigator surveyed visually the breathing
frequency and timed it with a frequency-meter, because a very low
Methods
Ethic Statement
The study submitted here was part of a larger project designed
as ‘‘Hypoxic training in high level endurance athletes: evaluation
of effects on the performance, research of individual responses,
evaluation of potential risks for the health’’. The whole project was
approved by the Necker Hospital Ethic Committee (Paris, France).
All the subjects provided written, voluntary, informed consent.
Experimental Design
French elite Nordic skiers were involved in a 4-year follow-up.
No ‘artificial’ change was applied to their training program. The
fatigue state of the athletes was assessed by using the questionnaire
designed and validated by the consensus group on overtraining of
the French Society of Sports Medicine (QSFMS) (e.g. Table S1)
[16–18].
The experiments were performed at the French Nordic-ski
centre of Prémanon, France, between 2004 and 2008, by the same
investigator, head physiologist of the Nordic ski teams, in French
national teams of three disciplines: biathlon, Nordic-combined and
cross-country skiing. HRV testing was part of the follow-up of the
athletes during their yearly preparation for World cups and World
championships and also during their Olympic preparation in
2006. During the 4-year study, the recordings of HRV were not
prospectively scheduled, but the athletes were requested to
regularly perform the HRV test, during every period of the
training plan that can be described as: the general training period
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Fatigue and Heart Rate Variability in Athletes
Table 1. Anthropometric characteristics and V_ O2max of the subjects at the time of their inclusion in the study.
Gender (n)
Age (years)
Weight (kg)
Height (cm)
V_ O2max (mL.min21.kg21)
Biathlon
9m
22.664.0
71.865.2
180.264.8
78.065.0
Biathlon
18 w
23.764.1
58.566.1
167.464.7
58.164.4
NC
13 m
22.764.3
63.465.2
176.265.5
67.764.5
XCS
5m
20.460.9
73.262.7
183.063.2
79.863.2
XCS
12 w
23.263.7
58.063.8
166.362.6
56.663.8
All
57
22.9±3.9
62.9±7.7
172.6±7.6
65.1±10.0
NC = Nordic-combined. XCS = Cross country skiing. m = men, w = women.
doi:10.1371/journal.pone.0071588.t001
rate, under 9 cycles per min, could shift frequencies lower than
0.15 Hz and decrease the HF band or increase the LF band [26].
Both in supine (SU) and in standing (ST) positions, LF and HF
were calculated in absolute spectral power units (ms2) and in
normalized units (nu) with LF(nu) = LF/(LF+HF)6100 and
HF(nu) = HF/(HF+LF)6100. The total spectral power (TP) was
calculated by adding LF and HF. The LF/HF ratio was also
calculated as an indicator of sympathetic over parasympathetic
balance [21].
Statistical Analysis
The ‘Fatigue’ variable was dichotomized according to the
threshold of 20 items with a negative answer. In each recording
position (SU and ST) of RR intervals the following variables
describing heart rate variability (HRV) were quantified. HRSU,
HRST, LFSU(nu), HFSU(nu), LFST(nu), HFST(nu) were assumed to
be distributed normally. The remaining variables were transformed by taking their common logarithm (log10).
The relationships between the HRV parameters and the state of
‘fatigue’/‘no-fatigue’ variable were analyzed using a multilevel
linear regression model with a complex variance structure. In this
model, level 1 was the rank of the measure and level 2 the subject.
The rank (for one given subject, each successive rank labelled a
recording date) of the measure was used as an orthogonal
polynomial in order to account for repeated measurements. Each
HRV variable was first analyzed in a univariate model. Gender
and age of the subjects were introduced in the statistical model. A
random parameter was finally introduced at level 1 in order to
control for heteroscedasticity of the HRV parameters. This
random parameter allowed assessing differences in HRV power
dispersion between ‘fatigue’ and ‘no-fatigue’ states in each subject.
Normal distribution of the residuals was checked on final models.
Significance threshold was set to 0.05. Analyses were performed
using MLwiN V2.20 [27]. These models allowed analyzing
unbalanced data, as the number of recordings for each subject.
Definition of the State of Fatigue
The states of fatigue were identified according to the scoring at
the QSFMS (Table S1), which was fulfilled after the RR interval
recording procedure. This questionnaire is used routinely in
French national teams in various sports (rugby, swimming, Nordicski, triathlon…). It has been built up by the consensus group on
overtraining of the French Society of Sports Medicine, a large
panel of scientists and sport physicians. This standardized
description of the psychological/behavioural state takes into
account different contributions to fatigue linked to physical
exercising. A score calculated from the answers to the QSFMS
quantifies the clinical symptoms of overtraining. The state of
fatigue was registered when the score exceeded 20 negative items
out of 54 [16,17]. This score was shown to correlate with
indications of muscular damage (creatine kinase, myosin) and
some hematological variables (blood viscosity, hematocrit, plasma
viscosity, ferritin) [16].
Results
The characteristics of the subjects as they entered the study are
shown in Table 1. The values of V_ O2max were consistent with the
elite level of the subjects.
QSFMS
The distribution of the QSFMS scores is displayed in Figure 1.
The average score of the 1063 tests performed was 8.84 (Standard
deviation, SD = 8.94). 172 tests had a score higher than 20
indicating a ‘fatigue’ state, which amounted to 16.2% of all the
tests performed. Figure 1 also displays the distribution of the
number of tests performed, and for every athlete indicates the
number of ‘fatigue’ and ‘no-fatigue’ states diagnosed with the
QSFMS.
HRV Analysis
Figure 1. Number of HRV tests recorded in ‘No-fatigue’ and
‘Fatigue’ state in each subject, and overall distribution of the
QSFMS scores. In each subject’s column the HRV recordings obtained
when the QSFMS had a score under the 20-item threshold are in white,
and those recorded during a scored state of fatigue are in black.
doi:10.1371/journal.pone.0071588.g001
PLOS ONE | www.plosone.org
During the tests, the breathing frequencies (BF) were recorded
and no breath holding period was observed.
Supine and standing BF data in ‘fatigue’ and ‘no-fatigue’ states
are displayed in Table 2.
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Fatigue and Heart Rate Variability in Athletes
Table 2. QSFMS, Breath Frequency (BF), Heart Rate (HR) and parameters of heart rate variability (HRV): distribution of values across
the ‘no-fatigue’ and ‘fatigue’ states.
Supine
Standing
mean
min
10e
25e
median
75e
90e
max
QSFMS
No F
5.5
0.0
0.0
2.0
5.0
8.0
13.0
19.0
score
F
26.2
20.0
20.0
22.5
25.0
29.5
33.0
37.0
BFSU
No F
12.5
9.0
10.0
11.0
12.0
14.0
15.0
18.0
(bpm)
F
13.9
9.0
11.0
12.0
13.0
16.0
17.0
21.0
HRSU
No F
55.3
33.9
44.7
49.0
55.0
61.0
66.5
92.0
(bpm)
F
63.3
41.0
48.6
55.4
62.8
68.1
78.3
99.0
LFSU
No F
2398
66
458
805
1553
2874
5174
33496
(ms2)
F
1636
12
86
296
846
1864
3602
27133
HFSU
No F
3748
71
790
1411
2687
4563
8089
41526
(ms2)
F
2286
14
66
253
837
2489
5943
29989
TPSU
No F
7779
356
2026
3450
5782
10097
16352
50360
(ms2)
F
4942
67
252
847
2548
5532
12567
57500
LF/HFSU
No F
0.84
0.06
0.20
0.34
0.63
1.07
1.66
5.85
F
1.30
0.08
0.25
0.55
1.02
1.70
2.70
6.92
LFSU
No F
39.38
5.35
16.41
25.45
38.75
51.60
62.36
85.40
(nu)
F
48.98
7.58
19.96
35.69
50.58
62.95
72.95
87.37
HFSU
No F
60.61
14.58
37.64
48.38
61.24
74.55
83.59
94.65
(nu)
F
51.00
12.63
26.97
37.05
49.42
64.31
80.04
92.42
BFST
No F
15.9
10.0
13.0
14.0
15.0
18.0
20.0
23.0
(bpm)
F
17.6
10.0
14.0
15.0
17.0
20.0
22.0
25.0
HRST
No F
77.27
43.38
60.0
68.73
77.0
85.66
93.84
109.0
(bpm)
F
87.93
55.0
68.10
79.78
87.98
97.25
105.08
138.0
LFST
No F
3260
147
627
1108
2286
4133
6910
38881
(ms2)
F
1619
26
187
426
1050
2338
4183
8960
HFST
No F
823
9
86
191
420
925
1908
18167
(ms2)
F
340
1
35
70
165
446
754
2892
TPST
No F
6386
400
1413
2563
4753
8196
13604
53112
(ms2)
F
3223
77
389
1052
2108
4511
7266
21311
LF/HFST
No F
7.23
0.22
1.68
3.03
5.56
9.68
14.70
52.18
F
7.79
0.21
2.09
3.42
5.73
9.75
14.34
44.00
LFST
No F
80.27
17.66
61.00
74.43
84.50
90.61
93.59
98.12
(nu)
F
81.14
4.02
66.65
75.65
84.84
90.39
93.48
97.78
HFST
No F
19.69
1.88
6.36
9.38
15.49
25.43
39.00
82.34
(nu)
F
18.84
2.22
6.52
9.61
15.12
24.35
33.35
95.98
P value*
,0.0001
,0.0001
,0.0001
,0.0001{
,0.0001{
,0.0001{
,0.0001{
,0.0001
,0.0001
,0.0001
,0.0001
,0.0001{
,0.0001{
,0.0001{
0.32
0.43
0.43
No F = No Fatigue state n = 891. F = Fatigue state n = 172.
Min = minimum; 10e, 25e, 75e, 90e = respectively tenth, twenty-fifth, seventy-fifth and ninetieth percentiles; QSFMS score = number of negative items (i.e.); SU = supine;
ST = standing; BF = breath frequency (breaths per minute); HR = heart rate (beats per minute); LF = spectral power in the low frequency band; HF = spectral power in the
high frequency band, TP = total spectral power; nu = normalized units.
*Multilevel analysis.
{
The modeling was conducted on Log transformation.
The table describes pooled data of every athlete of the study. Thus, the differences between ‘fatigue’ and no-fatigue states are not directly comparable to statistical
differences issued from the multilevel analysis in Table 3, which assessed intra-individual changes.
doi:10.1371/journal.pone.0071588.t002
Table 2 and Figure 2 describe the distribution of values of
HRV parameters recorded in all the subjects both the ‘no-fatigue’
and ‘fatigue’ states. Table 3 displays the results of the multilevel
linear regression models with the intra-subject variances.
In supine position, the HR values were on average 6.27 bpm
higher in recordings performed in the ‘fatigue’ state than when
there was ‘no-fatigue’ (P,0.0001). Conversely, the HF power (nu)
was on average 9.55 lower in the ‘fatigue’ than the ‘no-fatigue’
(P,0.0001). Thus, in ‘fatigue’ state, logTPSU, logLFSU, logHFSU,
PLOS ONE | www.plosone.org
and HFSU(nu) were significantly lower than in ‘no-fatigue’ state (all
P,0.0001). Conversely, logLF/HFSU was larger in ‘fatigue’ state
(P,0.0001). In standing position, HRST was higher and logTPST,
logLFST, logHFST were lower with fatigue than without
(P,0.0001).
The intra-subject HR variances were larger in ‘fatigue’ than
‘no-fatigue’ state in the supine position (respectively: 53.09 vs
31.86; P,0.0006) and in the standing position (respectively
115.37 vs 71.44; P,0.001). Intra-subject variance was also larger
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Fatigue and Heart Rate Variability in Athletes
The model did not reveal any difference linked to gender or age
of the subjects.
Figure 3 displays examples of HRV analysis monitoring from
individual measurements in ‘fatigue’ and ‘no-fatigue’ states in
supine and standing positions.
Discussion
This study surveyed elite endurance athletes over a multi-year
period without any ‘artificial’ (e.g. study-related) change of their
training regimen. It led to two main results. Firstly, as
hypothesized, most of the HRV parameters were different in
‘fatigue’ vs. ‘no-fatigue’ states. These differences were observed
both in supine (HR, LF/HF and LFnu increased; LF, HF, TP,
HFnu decreased in ‘fatigue’) and in standing (HR increased; LF,
HF and TP decreased in ‘fatigue’) positions. Secondly, the
intrasubject variance of HR, LF, HF and TP was significantly
larger in ‘fatigue’ than in ‘no-fatigue’ state. This suggests that
different fatigue-shifted HRV patterns coexist in the ‘fatigue’ state.
Most studies investigating the relationships between fatigue (e.g.
during states labelled as OR (FOR or NFOR) or OT [2]) and
HRV embedded methodological features that limit the impact of
their findings. Indeed, most studies enrolled few athletes (1 to 10–
15; e.g.; Hedelin et al. [11,14]; Uusitalo et al. [12]). Considering
the high inter-individual variation in the response to training and
fatigue, the small subject number largely increases the risk of
statistical errors. Also, in some studies the usual training regimen
of the subjects was modified by artificially introducing an overload
period [8,12], which makes it difficult to ascertain that a true state
of fatigue is achieved. Moreover, some of the previously published
studies did not clearly describe the criteria used to separate the
‘fatigue’ and ‘no-fatigue’ states [11,28]. Finally, the fact that
physiological data recorded or investigated in sub-elite [28] or
well-trained subjects cannot be translated to elite level is also of
importance [29].
While HRV recording tests were not performed as many times
in every athlete, we used an appropriate statistical approach. The
statistical multilevel model was not affected by the non balanced
number of recordings in subjects [30]. In addition the model
coped with heteroscedasticity, i.e. the non homogenous variance of
HRV parameters across the two states of the ‘fatigue’ independent
variable. The conditions and procedure of data recording were
also greatly standardized, i.e. same location (medical laboratory);
same timing in the day and the week; same recording device; same
investigator; routinely implemented HRV recordings. This likely
contributed to reduce misleading causes of variance in results.
HRV values were obtained before the subject completed the
QSFMS questionnaire (Table S1). The questionnaire data were
analyzed afterward and blindly from the HR recording. Thus
quoting a ‘fatigue’ state was done independently of HRV analysis.
In this study, quoting a ‘fatigue’ state according to the threshold of
20 negative items out of 54 was the only criterion for statistical
comparison of HRV data in a model that takes into account
potential intra-subject pairing of data between ‘fatigue’ and ‘nofatigue’ states.
In a comprehensive review on prevention, diagnosis and
treatment of the overtraining syndrome, Meeusen et al. [2]
highlighted the importance of the diagnosis and follow-up of the
fatigue changes in elite sport. The authors reported the pros and
cons of different methods currently used; e.g. analysis of the
hormonal status (testosterone/cortisol ratio, ACTH/cortisol ratio,
growth hormone, insulin-like-growth-factor-I), of the performance
level, of the psychological and mood state (Profile of Mood State,
POMS; Recovery-Stress Questionnaire, RestQ-Sport or the Daily
Figure 2. Distribution of values in heart rate and HRV
parameters according to ‘fatigue’ and ‘no-fatigue’ states in
supine (A) and standing (B) positions. The box and whiskers
display values of all recordings for all the subjects. Box is defined by the
first and third quartiles. The thick line stands for the median value. The
tenth and ninetieth of values are marked by the ‘‘wisker’’ bars, and the
circles stand for the lowest and highest values. The highest values are
out of scale and the number is written at the right end of the bar close
to the circle that should stand out of scale. The dotted dark line
symbolizes a discontinued scale. The ‘fatigue’ states are in grey and the
‘no-fatigue’ states are in white. ‘no-Fatigue’ state n = 891, ‘fatigue’ state
n = 172. SU = supine position; ST = standing position; HR = heart rate
(beat per minute); TP = total spectral power (ms2); LF = spectral power in
the low frequency band (ms2); HF = spectral power in the high
frequency band (ms2). Right figure is the highest value of the file. {
Analysis was conducted on Log transformed data. ### for p,0.001 in
differences between ‘fatigue’ and no-fatigue state using multilevel
models. The scaling was chosen for a clear display of figures span of
each parameter. Therefore scales are different between parameters and
between supine and standing positions. It has to be noticed that the
distribution of values displayed in this figure are computed from all the
recordings of every subject cannot be directly compared with the intraindividual variances assessed by the multi-level analysis (Table 3).
doi:10.1371/journal.pone.0071588.g002
in fatigue state for logTPSU, logLFSU, log HFSU, logTPST,
logLFST, logHFST (P,0.0001). These statistically significant
results are thus not reflected in the group description displayed
in Figure 2.
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Fatigue and Heart Rate Variability in Athletes
Table 3. HRV parameters across ‘fatigue’ and ‘no-fatigue’ state: multilevel linear modelling.
Var X = fatigue
Supine
Standing
Intra-subject variance
Var Y
Beta
SE
p value
No-fatigue
Fatigue
p value
HR (bpm)
6.27
0.61
,0.0001
31.86
53.09
,0.0006
logLF
20.27
0.043
,0.0001
0.11
0.28
,0.0001
logHF
20.46
0.05
,0.0001
0.08
0.32
,0.0001
logTP
20.36
0.04
,0.0001
0.07
0.26
,0.0001
log LF/HF
0.19
0.03
,0.0001
0.08
0.09
0.45
LF (nu)
9.55
1.33
,0.0001
201.82
243.89
0.15
HF (nu)
29.55
1.33
,0.0001
201.75
243.86
0.15
HR (bpm)
8.82
0.89
,0.0001
71.44
115.37
,0.001
log LF
20.29
0.03
,0.0001
0.07
0.16
,0.0001
logHF
20.32
0.04
,0.0001
0.14
0.25
,0.0001
logTP
20.28
0.03
,0.0001
0.07
0.13
,0.0001
log LF/HF
0.03
0.03
0.32
0.09
0.10
0.88
LF (nu)
0.79
0.99
0.43
127.41
131.51
0.80
HF (nu)
20.79
0.99
0.43
126.44
131.58
0.75
The relationships between HR or HRV parameters and the fatigue variable (X variable) were analysed separately: one model for each HR or HRV parameters (Y variables)
and each line of the table presents the results of one model. Beta column displays the model parameter and can be seen as the average distance between the values of
the ‘fatigue’ and the ‘no-fatigue’ states, as e.g. in ‘fatigue’ recordings supine HR values were on average 6.27 bpm higher and logTP of HRV were on average 0.36 lower
than when measured with ‘no-fatigue’ QSFMS score. The variance columns display the within–subject variance of values across the ’no-fatigue’ and ‘fatigue’ state
variable (including significance probability).
doi:10.1371/journal.pone.0071588.t003
the power decrease was lesser in LF than HF, leading to a higher
LF/HF ratio and suggesting a less parasympathetic control on
HR. The changes in LFnu and HFnu also displayed this pattern.
The simultaneous decrease of total spectral power and the larger
contribution of LF variability were thus in line with previous
reports in fatigue after competition [31] overreaching [32,33] or
overtraining states [8,10,12]. In the standing position, the ‘fatigue’
state was associated on average with decreased TP, LF and HF
powers and increased HR; but the values of LF/HF ratio and
LF(nu) were not significantly different from the ‘no-fatigue’ state.
The methodology used in this study does not allow searching for
any particular state or cause of fatigue.
The increase in heart rate would be simply explained in a
reciprocal activation pattern of autonomic arms [34]. Indeed the
higher HR in fatigue state leads to the idea of a decreased vagal
influence also possibly associated with an increased sympathetic
activation. As all absolute HRV variables (TP, LF and HF spectral
powers) are mainly under vagal modulation and are lowered, a
lessened vagal modulation of heart activity is most likely when
fatigued. The observation of higher LF/HF at supine rest supports
the idea that a balanced autonomic control has shifted towards
sympathetic predominance. A co-activation of vagal and sympathetic outflows might bring opposite changes in HR and in the
relationship between the dynamics of HR and LF/HF ratio
[34,35]. Such a co-activation of autonomic arms is thus very
unlikely in the present results. A further detailed recognition of
different patterns of changes in spectral powers is difficult, and the
assessment of potential linkages between such successive patterns
requires models of data analysis that are beyond the scope of this
study.
Significant differences in supine LF/HF and normalized LF and
HF between No-Fatigue and Fatigue, but not in standing figures
might be questioning. We believe this discrepancy is due to the
following points. Firstly, the normalized values depend on the total
Analysis of Life demands of Athletes, DALDA), of the immune
function, and finally of the HRV. It is largely recognized that a
single biological marker is only very seldom determinant in
diagnosing a fatigue state. When this occurs, several other
symptoms -and other biological signs- are most often gathered to
highlight a severe fatigue state. Such a recognition happens often
late in the course of the athlete’s follow-up. Obtaining biological
values requires blood sampling and/or urine sampling, is invasive
and costly, and hence cannot be repeated frequently, i.e., as
frequently as requested in monitoring elite athletes in whom
extended impairment of function should be avoided. HRV
recording is just the opposite on several points, and therefore it
could be easily used in daily routine [2]. The idea of fatigue
encompasses more than overtraining. Fatigue likely comprises
different kinds of impairments not only triggered by the imbalance
of training load and recovery, but also by e.g. infectious episode,
impaired sleep (which can be a symptom of fatigue) or emotional/
affective insult… [2]. All these conditions can lead to some
physiological imbalance likely to involve some shift in autonomic
settings, and finally some cardiac HRV change.
Using the 20 items threshold of the QFSMS questionnaire
(Table S1) to separate fatigue and no-fatigue records (Figure 1),
the multilevel linear regression model used in this study led to
significant differences between the ‘fatigue’ and ‘no-fatigue’ states
in most HRV descriptors (Table 3). On average HR was higher
and HRV was lower during fatigue than in no-fatigue state both
supine and standing (Table 2 and Figure 2). Less of autonomic
inputs or resonances to physiological signals imply less accurate
autonomic controls. Hence some loss of efficiency is likely in
metabolic and physiological functions, which are instrumental to
sharp sport performance. The lowering of HR autonomic control
would thus appear as an easily discernible cue of more complex
and widely spread impairments in fatigue states. All the frequency
band descriptors were lowered in fatigue state. In supine position,
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Fatigue and Heart Rate Variability in Athletes
Figure 3. Cases examples of HRV analysis monitoring from individual measurements in ‘fatigue’ and ‘no-fatigue’ states in supine
(A) and standing (B) positions. For three subjects in supine (A) and three other in standing (B) positions, monitoring of total spectral power (TP)
by adding low and high frequencies in absolute units (ms2) and heart rate (HR) (bpm) during the same surveyed year. ‘no-fatigue’ points are in white
and ‘‘fatigue’ points in black.
doi:10.1371/journal.pone.0071588.g003
changes caused by ‘Fatigue’. Relative variables such as LF/HF
ratio and ‘‘normalized’’ power might be useful, but each also
presents a ‘‘constructed’’ view of data, easily confounding if not
considered in the frame of the primary data.
Beside the overall differences in HRV parameters in ‘fatigue’ vs.
‘no-fatigue’ states, another finding of this study was the broader
spanning of intra-subject HRV values in the fatigue state, as
depicted by ‘‘variance’’ figures in the statistical model (Table 3).
To the best of our knowledge, this has not been reported
previously. The dispersion of values of HRV parameters between
subjects (e.g. between-subject variance) is known to depend on age,
gender, heritability, environmental factors such as noise, temperature, light [21], but the statistical analysis did not reveal any
difference linked to gender or age of the subjects in our study. The
dispersion of values of HRV parameters between athletes might
also depend on the level of practice, the amount and type of
physical activity. Such influences have been reported in normal or
power of HRV, which is largely lowered in fatigue states, and
reduces in turn the scaling of normalized indices. Secondly, LF
power holds much of parasympathetic influence. The reduction of
HF power in fatigue states thus influences both terms of the ratio
LF/HF and narrows its changes. Thirdly, since standing reduces
total power and HF power in No-Fatigue state, and this
physiological response occurs also during Fatigue states, the
statistical assessment of significant further change in LF/HF due to
fatigue becomes less likely (as reflected in median and mean
figures). In standing position, the LF power likely embeds a larger
vascular (e.g. pulse pressure) resonance than in supine position,
which accounts for the higher LF/HF ratio in upright position.
LF/HF value is more complexly determined than the quite simple
‘gold-standard’ criterium sometimes considered in HRV fatigue
studies [23,25]. We believe also that, although the comparison of
standing and supine values is more complex in the fatigue than the
‘No-Fatigue’ state, taking it into account adds insight into HRV
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Fatigue and Heart Rate Variability in Athletes
sedentary population [36–38]. Also, while monitoring the usual
training course of such elite athletes, HRV tests are hardly
obtained in exactly similar training conditions, particularly as
regarding training load. In the present study, methodological
precautions were taken to standardize the RR recordings used to
HRV analysis. The subjects were homogenous regarding fitness
and a several years training level. Also, as already stated, the
statistical analysis relied on a model in which each subject was
taken as his own reference, and to avoid potential confusion bias
adjustment on a particular variable was performed when required.
Therefore we believe that the shifts in quantitative repartition of
HRV values are meaningful.
In sedentary subjects, Kleiger et al. [39] have reported the
substantial variation in HRV values in fourteen normal subjects
aged 20 to 55 years. Noritake et al. [40] found a larger diurnal
variation in HRV parameters in nineteen healthy subjects than in
twenty-nine diabetic patients. Similarly, Plews et al. [15] reported
recently a case study of an elite triathlete in whom the day-to-day
variation in the square root of the mean sum of the squares
differences between R-R intervals (RMSSD), a temporal marker of
the parasympathetic influence, was decreased in an OR state. This
narrowed fluctuation of HRV when overreached seems conflicting
with our results. However, we believe this discrepancy is mainly
due to the fact that in the present study the broader span of values
of HRV parameters in the ‘fatigue’ state encompasses different
patterns of fatigue-induced changes in HRV spectra, which can
display different or opposite shifts within the HRV spectrum. The
study was not aimed at analyzing day-to-day variability but to
highlight the complex content of HRV analysis. By the way, we
support the point expressed in the paper by Plews et al. [15] that a
decrease in day-to-day fluctuation of HRV parameters over a
short period of time might help diagnosing the onset of
overreaching. Also, a diversity of types of ‘fatigue’ could explain
the higher intra-individual HRV variance when compared to ‘nofatigue’ state, although each single ‘fatigue’ condition over few
days can be stable or tend towards no response with the ‘law of
initial values’ described in Plews et al. [15].
Our results may suggest that different and sometimes opposite
autonomic regulatory influences could be explained by different
degrees or states of fatigue, such as OR or OT [2]. The point of
transition between these states is not precisely defined in the
literature. This would explain why previous studies have reported
equivocal findings, with increases [11], decreases [12] and no
change [13,14] in HRV power during OR or OT states. The
greater variance of HRV figures in elite athletes in ‘fatigue’ state
could suggest that different fatigue-shifted HRV patterns were
simultaneously considered in this study, which is an entangled
situation. Assessment of different degrees and sub-categories of
fatigue and of the effectiveness of HRV in their recognition in elite
athletes, require other kind of statistical models. Finally, the
QSFMS questionnaire (Table S1) was validated to highlight a
state of fatigue but it is possible that it only allows distinguishing
the ‘fatigue’ state from ‘no-fatigue’ state. As far as we know, this
questionnaire does not allow to distinguish sub-categories of
fatigue.
We thank the athletes of the French national Biathlon, Nordic-combined
and Cross-country skiing teams and their coaches: for biathlon: Patrice
Bailly-Salins, Pascal Etienne, Lionel Laurent; for Nordic-combined:
Etienne Gouy, Jérôme Laheurte; for cross-country skiing: Gilles Berthet,
Olivier Michaud, Philippe Grand-Clément.
Limits and Strengths of the Study
Author Contributions
As most ‘field’ studies, this retrospective longitudinal study has
limits: i) The frequency of records was different between the
athletes due to several factors: variable presence in the National
centre; competition schedule (different between the Nordic
disciplines and between men and women); examinations; illness-
Conceived and designed the experiments: LS JR MD FM LM JPF NC
GM. Performed the experiments: LS JR LM JPF NC GM. Analyzed the
data: LS JR MD FM LM JPF GM. Contributed reagents/materials/
analysis tools: LS JR MD FM LM JPF NC GM. Wrote the paper: LS JR
FM LM JPF GM.
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es… However, the statistical method was chosen to cope with this
non balanced individual number of HRV recordings. ii) The
quantification of objective performance is difficult in the Nordic ski
disciplines since velocity is largely influenced by the environmental
conditions (elevation, quality of snow, weather…). Thus a
performance quotation valid enough to enhance the fatigue
assessment performed with the QSFMS questionnaire would be
difficult to implement. iii) Within the two days preceding HRV
recording, the training content was strictly aerobic although it
might individually fluctuate in volume, and thus somewhat
influence the ensuing HRV values.
However, we believe that this study is unique for several
reasons: i) The sample size is unequalled in a study involving elite
athletes. ii) The very long period of investigation likely counterbalances some seasonal variations. iii) The reproducibility of HRV
recordings and their technical quality were satisfying: same
location (medical laboratory); same timing in the day and the
week; same device; same investigator; routinely implemented
HRV recordings. iv) The assessment of the states of ‘fatigue’ and
‘no-fatigue’ was based on a widely used tool and on an a priori
threshold independent of HRV analysis. v) From a practical point
of view, we consider the present study established a first step in
relevance of frequency-domain HRV analysis for monitoring the
fatigue in elite endurance athletes. It is at this stage difficult to
speculate how various influences combine to enhance or decrease
parasympathetic drive or resonance to sympathetic central or
vasomotor control as carried in vagal efference to the heart.
Conclusion
Values of HRV descriptors were significantly lower in ‘fatigue’
than ‘no-fatigue’ states in elite endurance athletes, both in supine
and standing positions, over a multi-year period and without any
‘artificial’ change in their training regimen. The main trend by far
was a ‘fatigue’-linked lowering of total HRV power as well as in
the LF and HF frequencies bands. In addition, the intra-subject
variance of values of HRV descriptors was larger in ‘fatigue’ than
in ‘no-fatigue’ state. These differences strongly suggest that the
‘fatigue’ state encompassed differently-oriented shifts of HRV
pattern, possibly reflecting differently-organized autonomic responses to ‘‘fatigue’’. Further evaluation is required to assess how a
thorough HRV analysis might help to distinguish between
hypothetical different fatigue patterns.
Supporting Information
Table S1 Questionnaire of the French Society of Sport
Medicine (QSFMS).
(DOC)
Acknowledgments
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Fatigue and Heart Rate Variability in Athletes
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