ArticlePDF AvailableLiterature Review

Training Adaptation and Heart Rate Variability in Elite Endurance Athletes: Opening the Door to Effective Monitoring

Authors:

Abstract and Figures

The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
Content may be subject to copyright.
Author's personal copy
CURRENT OPINION
Training Adaptation and Heart Rate Variability in Elite
Endurance Athletes: Opening the Door to Effective Monitoring
Daniel J. Plews
Paul B. Laursen
Jamie Stanley
Andrew E. Kilding
Martin Buchheit
Published online: 13 July 2013
Ó Springer International Publishing Switzerland 2013
Abstract The measurement of heart rate variability
(HRV) is often considered a convenient non-invasive
assessment tool for monitoring individual adaptation to
training. Decreases and increases in vagal-derived indices
of HRV have been suggested to indicate negative and
positive adaptations, respectively, to endurance training
regimens. However, much of the research in this area has
involved recreational and well-trained athletes, with the
small number of studies conducted in elite athletes
revealing equivocal outcomes. For example, in elite ath-
letes, studies have revealed both increases and decreases in
HRV to be associated with negative adaptation. Addition-
ally, signs of positive adaptation, such as increases in
cardiorespiratory fitness, have been observed with atypical
concomitant decreases in HRV. As such, practical ways by
which HRV can be used to monitor training status in elites
are yet to be established. This article addresses the current
literature that has assessed changes in HRV in response to
training loads and the likely positive and negative adap-
tations shown. We reveal limitations with respect to how
the measurement of HRV has been interpreted to assess
positive and negative adaptation to endurance training
regimens and subsequent physical performance. We offer
solutions to some of the methodological issues associated
with using HRV as a day-to-day monitoring tool. These
include the use of appropriate averaging techniques, and
the use of specific HRV indices to overcome the issue of
HRV saturation in elite athletes (i.e., reductions in HRV
despite decreases in resting heart rate). Finally, we provide
examples in Olympic and World Champion athletes
showing how these indices can be practically applied to
assess training status and readiness to perform in the period
leading up to a pinnacle event. The paper reveals how
longitudinal HRV monitoring in elites is required to
understand their unique individual HRV fingerprint. For
the first time, we demonstrate how increases and decreases
in HRV relate to changes in fitness and freshness, respec-
tively, in elite athletes.
1 Introduction
One of the more promising methods to monitor individual
adaptation to training involves the regular monitoring of
cardiac autonomic nervous system (ANS) status, through
the measurement of resting or post-exercise heart rate
variability (HRV) [35]. Indeed, non-functional over-
reaching (NFOR) and/or negative adaptation to training is
thought to be generally associated with reductions in vagal-
Electronic supplementary material The online version of this
article (doi:10.1007/s40279-013-0071-8) contains supplementary
material, which is available to authorized users.
D. J. Plews (&) P. B. Laursen
High Performance Sport New Zealand, AUT Millennium,
17 Antares Place, Mairangi Bay, 0632 Auckland, New Zealand
e-mail: daniel.plews@hpsnz.org.nz
D. J. Plews P. B. Laursen A. E. Kilding
Sports Performance Research Institute New Zealand (SPRINZ),
Auckland University of Technology, Auckland, New Zealand
J. Stanley
Centre of Excellence for Applied Sport Science, Queensland
Academy of Sport, Brisbane, Australia
J. Stanley
School of Human Movement Studies, The University of
Queensland, Brisbane, Australia
M. Buchheit
Physiology Unit, Football Performance and Science Department,
ASPIRE, Academy of Sports Excellence, Doha, Qatar
Sports Med (2013) 43:773–781
DOI 10.1007/s40279-013-0071-8
Author's personal copy
related indices of HRV [68], whereas increases in fitness
[911] and exercise performance [4, 12, 13] are thought to
be more associated with increases in vagal-related indices
of HRV. While findings from studies involving recreational
and well trained athletes suggest that HRV may be a
valuable tool for assessing individual adaptation to endur-
ance training, data obtained from elite athletes and athletes
with a longer training history remains equivocal [1418].
The purpose of this article is to present a brief summary
of the studies where HRV has been investigated in
response to adaptation and changes in training load. In
doing so, we highlight the methodological issues inherent
in its use and interpretation to date. We advance our current
opinion of how HRV should best be monitored and
assessed with examples from elite endurance athletes. All
references to HRV throughout this manuscript refer to
vagal-related indices of HRV [19]. All HRV data presented
herein were recorded upon waking and measured as the last
5 min of the 6 min supine rest period (for more details on
the methodology, including calculation of the ‘smallest
worthwhile change’’, please refer to [1]).
2 HRV in Response to Different Training Loads
The influence of intensified and reduced training loads on
HRV has been thoroughly studied (see electronic supple-
mentary material [ESM], Table S1). In moderately trained
subjects, moderate training loads increase aerobic fitness,
as well as HRV [15, 2022]. However, when training loads
approach higher levels (100 % of an individual’s maximal
training load), HRV indices are reduced, [15, 21, 23] and
are thought to rebound after periods of reduced training
(i.e., taper) [13, 22, 23]. For example, after 3 weeks of
overload training in swimmers and distance runners, HRV
was reduced by 22 % [13] and 38 % [23], respectively.
Following 2 weeks of reduced training (69 % reduction in
training load compared with overload), HRV rebounded
and increased by 7 % in swimmers [13], and after 1 week
(40 % reduction in training load compared with overload)
increased by 38 % in distance runners [23]. As with
moderately trained athletes, elites and athletes with
extensive training histories also show increases and
decreases in HRV after moderate and high training loads,
respectively [15, 18, 21]). Conversely, however, HRV can
remain depressed in the lead up to competition (e.g.
tapering), despite achievement of an optimal performance
[15, 18]. In the case of these athletes, the reduction in HRV
prior to competition possibly reflects the HRV response to
consecutive days of high intensity training (with a reduc-
tion in training volume in the case of a taper) [24, 25
] and/
or HRV saturation at low heart rate levels [26] (see Sects.
4.1 and 4.2, respectively). With just one study examining
the HRV responses of elites leading into competition, [18]
the optimal HRV response to training overload and pre-
competition tapers (in elites) is yet to be fully understood.
2.1 HRV and Positive Adaptation to Training
The changes in HRV in response to endurance training
have been extensively studied (see ESM, Table S2). In
sedentary and recreationally trained individuals, endurance
training for 2 [9], 6 [27, 28], and 9 [4] weeks has been
shown to induce parallel increases in aerobic fitness and
HRV. For example, Buchheit et al. [4] showed that
improvements in maximal aerobic running speed and
10 km run time had moderate (r = 0.52; confidence
intervals [CI] 0.08 to 0.79) and large (r =-0.73; CI -0.89
to -0.41) correlations with increases in resting HRV,
respectively. While this is the typical response shown in
sedentary and recreationally trained individuals following a
period of endurance training [9, 20, 22, 27, 28], the
response in athletes with extensive training histories (e.g.,
elite athletes) can be markedly different. In these athletes,
the HRV response to training is variable, with longitudinal
studies showing no change in fitness (i.e., maximal oxygen
uptake [
_
VO
2max
]) despite an increase in HRV [17], and
other studies showing decreases in HRV despite increases
in fitness [18]. As such, there is generally a bell-shaped
relationship between vagally-related HRV and fitness.
2.2 HRV and Negative Adaptation to Training
Overtraining (OT) is a verb used to describe the process of
undergoing intensified training to induce possible over-
reaching. Overreaching refers to a short-term stress-
regeneration imbalance that includes negative outcomes
such as increased fatigue and reductions in performance
[29]. While overreaching is typically believed to be an
important component of the elite athlete training cycle,
prolonged overreaching can push an athlete into a state of
NFOR, which is associated with reductions in performance
ability that do not resume for several weeks or months [30].
To date, however, studies that have examined changes in
HRV with NFOR/OT have revealed equivocal findings,
with increases [31], decreases [6, 32] and no changes [31,
33, 34] in HRV reported (ESM, Table S3). In a case study
of an elite cross-country skier that became OT, Hedelin
et al. [31] showed reduced competition performance and
lowered profile of mood states, along with substantially
increased HRV. Conversely, Uusitalo et al. [32] showed
that OT was associated with decreased HRV in endurance
athletes undergoing heavy training over a 6- to 9-week
period. Hedelin et al. [35] also reported unchanged HRV in
elite canoeists, despite decreased run time to fatigue and
774 D. J. Plews et al.
Author's personal copy
reduced
_
VO
2max
. However, the inconsistent findings shown
between HRV and OT/NFOR to date are likely due to the
methodological approach adopted (see Sect. 3) and diffi-
culty with discriminating between the different stages of
the OT process (e.g. overtraining, overreaching, NFOR and
OT syndrome) [30, 36]. This is particularly evident in
studies that have purposely induced overtraining [3335,
37], which unlikely reflects real-life training conditions
[30, 36]. Finally, the possibility that two types of OT may
occur in athletes (parasympathetic vs. sympathetic; [38,
39]) may further contribute to the equivocal research
findings shown.
2.3 Literature Summary
It has been suggested that increases and decreases in HRV
are associated with positive [4, 913] and negative [6, 7,
32] adaptation to endurance training regimens, respec-
tively. However, the bell-shaped relationship typically
apparent between both HRV and training load [15, 18, 21,
22], and HRV and fitness [14, 18, 40], in elites and athletes
with extensive training histories, makes it difficult to
practically use HRV to maximize training in these
populations.
3 Methodological Consideration with the Assessment
of HRV
Indices of HRV display a naturally high day-to-day vari-
ation [41]. We have recently suggested that both environ-
mental factors influencing measurement ‘noise’ and acute
changes in homeostasis may contribute to discrepancies in
interpretation when a single data point is used for analysis.
When HRV is used to assess changes in both negative [1]
and positive adaptation [42], both weekly [1, 42] and 7-day
rolling [1] averages have been shown to provide better
methodological validity compared with values taken on a
single day. For example, when HRV data points were
averaged over 1 week, a meaningful representation of
training status was apparent in a NFOR elite athlete (e.g.,
worthwhile reductions in weekly-averaged HRV were
observed only during the period of NFOR) [1]. Compara-
tively, when single day values were used for analysis, the
HRV data were misleading (i.e., worthwhile reductions in
HRV indicative of NFOR occurred when the athlete was
training and performing effectively). Conversely, when
percentage changes in 10-km running performance were
correlated with percentage changes in HRV, very large
relationships were observed when HRV values were
averaged over 1 week (r =-0.76; CI -0.92 to
-0.36) but
not when using single-day values (r =-0.17; CI -0.66 to
0.42) [42]. This suggests that averaged morning resting
HRV data provide a more consistent representation of
actual changes in an athlete’s autonomic balance with
training compared with a single isolated value. Most
recently, morning resting HRV was shown to deviate little,
irrespective of the prior day/s training, when positive
adaptions to training occurred in well trained individuals
[43].
Another methodological issue apparent within the lit-
erature is the variety of HRV indices that have been used to
assess autonomic balance [19]. It has been shown that time
domain indices of HRV have a lower typical error of
measurement (when expressed as a coefficient variation
[CV]) than other spectral indices of HRV (e.g., the natural
logarithm of the square root of the mean sum of the squared
differences between RR intervals [Ln rMSSD],
CV = 12.3 %; normalized high frequency power [HFnu],
CV = 52.0 %) [41]. We suggest that practitioners and
researchers using HRV measurements to evaluate training
adaptations choose just one vagally-derived HRV variable
for assessment. We prefer Ln rMSSD, as it is the most
practically applicable HRV index for a number of reasons.
First, Ln rMSSD is not significantly influenced by breath-
ing frequency, unlike other spectral indices of HRV, and is
therefore more suited to ambulatory measures [44]. Sec-
ond, Ln rMSSD can capture levels of parasympathetic
activity over a short time frame, which is more convenient
for athletes who have limited time to acquire a reading
[45]. Last, Ln rMSSD values can be easily calculated in
MS Excel using RR intervals [46]. In our opinion, there-
fore, the equivocal findings apparent throughout the HRV
literature are likely due to the large day-to-day variation in
HRV and the variety of HRV indices used for analysis that
are more prone to errors.
4 The Relevance of HRV in Elite Athletes
Training programmes for elite athletes typically consist of
periods of high training loads with limited periods of rest
and recovery [47, 48]. Such athletes are always pushing the
boundary between functional and NFOR in an attempt to
gain the greatest possible fitness level. Despite this, pub-
lished data in elite athletes are rare, with most HRV
research to date involving recreational/well trained subjects
[9, 15, 20, 22, 27, 28]. Due to genetics and training history
[49], elite athletes may respond differently to training
stresses and subsequent recovery [50]. In the following
sections, we describe some of the different HRV profiles of
elite athletes we have observed, and how these fluctuations
may be reflective of training adaptation and the ability to
perform at peak levels.
HRV Monitoring in Elite Endurance Athletes 775
Author's personal copy
4.1 HRV Profiles in Elite Athletes
A common misconception made by sports practitioners
using HRV to assess ANS status is that there is a direct
linear relationship between vagal-related indices of HRV
and the parasympathetic influence on heart rate (HR). In
reality, however, the relationship is quadratic [51, 52] (see
example in Fig. 1). This means that at both low (high HR)
and high (low HR) levels of vagal tone, vagal-related HRV
indices are reduced. For instance, while well trained ath-
letes generally present both a low resting HR and increased
HRV indices, a reduced HRV has also been observed in
many athletes with a low resting HR [53]. This reduction of
HRV at low HR is related to the fact that vagal-related
HRV indexes more reflect the magnitude of modulation in
parasympathetic outflow as opposed to an overall para-
sympathetic tone per se [54]. The underlying mechanism is
likely the saturation of acetylcholine receptors at the
myocyte level: a heightened vagal tone may give rise to
sustained parasympathetic control of the sinus node, which
may eliminate respiratory heart modulation and reduce
HRV [55]. This is an important consideration for practi-
tioners using HRV to assess training status in elites, who
typically have a low resting HR, undergo high training
loads and are therefore prone to saturation [26, 56]. For
example, during different phases/loads of training, reduc-
tions in HRV can occur, ‘theoretically’ indicating ANS
stress/NFOR [8, 57]. However, this trend should only be
interpreted in light of the respective changes in resting HR,
to assess whether this decrease can be the result of the
saturation phenomenon or not. This can be achieved by
using the Ln rMSSD to RR interval ratio [1], which
simultaneously considers changes in both vagal tone (RR
interval) and vagal modulation (HRV) [5].
Figure 2 shows two athletes competing at the same
international rowing world cup event (Lucerne FISA World
Cup 2012), both with suppressed HRV values before the
race. However, in the case of Athlete A who performed
optimally (second place in his event, 0.12 % behind the
winner), the reduction in Ln rMSSD (falling below the
smallest worthwhile change [SWC]; see [1, 2]) in the lead
up to the race was a result of HRV saturation (as demon-
strated by the Ln rMSSD to RR interval ratio falling
below the SWC) and unlikely fatigue [57]. However,
Athlete B (performing poorly; fifth place in her event;
1.92 % behind the leader despite being a 2011 world
championship medalist) incurred reductions in Ln rMSSD
and increases in the Ln rMSSD to RR interval ratio,
suggesting both a loss in vagal tone and modulation. This
was likely due to poor adaptation to her training load
(NFOR) and sympathetic over-activity.
Recently, we have also shown changes in the relation-
ship between Ln rMSSD and the RR interval during
effective training and NFOR in an elite female triathlete
[1]. In this instance, the athlete was saturated when training
effectively and became linear as NFOR manifested. In our
opinion, however, it is unlikely that either occurrence
predicts NFOR; instead each individual has their own
unique cardiac autonomic status and HRV relationship
[52], which is likely related to situational and genetic
factors. Figure 3 reveals the unique morning resting Ln
rMSSD to RR interval ratio profile of four Olympic and
World champions in the 62-day lead up to winning their
2011/2012 event. All athletes show distinctly different
profiles, irrespective of the fact that all athletes executed
gold-medal winning performances.
In summary, reductions in HRV have been associated
with fatigue and/or NFOR in recreationally trained and
well trained subjects [7, 8, 32, 57]. However, conclusions
from past literature reporting isolated HRV values should
be viewed with caution [1, 42]. We suggest the use of both
the Ln rMSSD (weekly average) and Ln rMSSD to RR
interval ratio to correctly interpret fatigue, or a ‘readiness
to perform’ in elite athletes (e.g., worthwhile reductions in
Ln rMSSD with concomitant increases in the Ln rMSSD:
RR interval ratio are more indicative of fatigue, with
decreases in both indicating readiness to perform [Fig. 2]).
Furthermore, the optimal relationship between HRV and
RR interval for training and performance alone is likely to
be individual (Fig. 3; i.e., correlated, non-correlated or
saturated [53]). This implies that longitudinal monitoring
and an understanding of a particular athlete’s response to
training and competition (i.e., recognizing each athlete’s
R-R interval (ms)
800 1000 1200 1400
Ln rMSSD (ms)
3.5
4.0
4.5
5.0
5.5
6.0
Linear Saturated
Fig. 1 Example of the relationship between the RR interval and the
natural logarithm of the square root of the mean sum of the squared
differences between RR intervals (Ln rMSSD) in a subject with
increasing bradycardia. Here, a saturation of heart rate variability is
seen with long RR intervals. Note how at shorter RR intervals there
is a linear relationship between Ln rMSSD (dotted line), which
becomes disassociated as the duration of the RR interval increases,
indicating heart rate variability saturation
776 D. J. Plews et al.
Author's personal copy
optimal Ln rMSSD to RR interval fingerprint) is needed
before this relationship can be useful enough to assist with
training prescription.
4.2 Changes in HRV and Performance in Elites
As mentioned previously (Sect. 2), studies have shown that
during intense training periods, vagal indices of HRV
decrease acutely, and rebound beyond their pre-training
level during subsequent recovery or lighter training periods
[12, 13, 18, 23, 58]. The rebound of HRV has been shown to
be associated with improved performance in recreationally
trained and well trained athletes [12, 13]. However, as
mentioned in Sect. 2.2, the bell-shaped relationship between
fitness and HRV sometimes apparent in both elites and ath-
letes with extensive training histories means that making this
assessment is more challenging. Figure 4 shows Ln rMSSD
values in three elite rowers during their preparation for the
2011 World Rowing Championships and 2012 London
Olympic Games. Each athlete won their event. Over this
62-day period, training was at a high intensity (D.J. Plews,
personal observations), with training volumes reaching 17 h
21 min ± 3 h 51 min/week (2011) and 16 h 44 min ± 5h
05 min/week (2012). In these athletes, HRV generally
increased in the week(s) before each event (going above the
SWC; 4 out of 6 points being ‘clear’ 1–3 weeks prior to the
event), before the values decreased to slightly lower levels
(generally within the SWC) before the race.
*
*
*
*
*
Athlete A
Ln rMSSD (ms)
3.4
3.6
3.8
4.0
4.2
4.4
Day number
0 10203040506070
Ln rMSSD to R-R interval length ratio (ms)
3.4x10
-3
3.6x10
-3
3.8x10
-3
4.0x10
-3
4.2x10
-3
4.4x10
-3
Athlete B
Day number
0 10203040506070
*
*
*
*
*
*
*
*
*
*
*
Fig. 2 Changes in the natural logarithm of the square root of the
mean sum of the squared differences between RR intervals (Ln
rMSSD) and the Ln rMSSD to RR interval ratio with 90 %
confidence intervals (CI) for Athlete A (performing well) and Athlete
B (performing poorly; see text) over a 62-day period in the build-up to
a key rowing world cup event. Black circular symbols indicate the
weekly average values for both Ln rMSSD and Ln rMSSD to R
R interval ratio, respectively; while the black line represents the 7-day
rolling average. The arrows indicate the day of the final (medal) race.
The grey shaded area indicates the individual smallest worthwhile
change (SWC) in both values (see methods in Ref. [1]); the black
dashed line represents the zero line of the SWC to indicate clear/
unclear changes when the 90 % CI overlaps [2]. * indicates a ‘clear’
change in both weekly averaged values above the SWC in the weeks
prior to the race
HRV Monitoring in Elite Endurance Athletes 777
Author's personal copy
As such, it appears that for elite athletes, increases in HRV
in the weeks before their event, during their highest training
loads (Fig. 4), are likely associated with a positive perfor-
mance outcome. This may indicate an athlete is ‘coping’
with the applied training load and is making positive adap-
tations. Conversely, Iellamo et al. [59] reported small, non-
significant decreases in HRV profiles in Olympic rowers
during strenuous training, which is likely due to the very long
history of intensive training and small (undetectable) chan-
ges in fitness. It is possible, however, that the use of ‘indi-
vidual’ SWCs may permit a better representation of
meaningful changes in HRV in elites for the purpose of
monitoring and assessing adaptation.
The fact that HRV values declined as competition
approached is in agreement with other studies, in that lower
levels of HRV prior to competition tend to be associated
with superior performances [15, 18]. As such, it is clear
that in these athletes, who are the best in the world at their
event, a high HRV does not necessarily imply superior
fitness [12, 13, 23, 58] and/or performance [12, 13]. The
reason why HRV reduces to lower values prior to com-
petition, from both a physiological and performance per-
spective, is unknown. However, as mentioned, a lower
HRV does not necessarily imply fatigue (i.e., saturation;
Sect. 4.1), and is therefore unlikely to ‘rebound’ in elites
when training load is reduced and freshness increased.
Furthermore, tapers leading into competition typically
consist of reductions in training volume with the mainte-
nance of intensity [60]. The reduction in training volume
might elicit lowered blood plasma volume, and in turn,
HRV [61, 62]. However, the maintenance of high intensity
exercise during the taper should, in theory, attenuate HRV
reductions [24, 25, 63]. Another possible explanation for
the reduced HRV observed around the time of competition
Ln rMSSD (ms)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
2012 Olympic Champion Athlete 1
r = 0.91 (0.87-0.94)
2012 Olympic Champion Athlete 2
r = -0.03 (-0.18-0.24)
2011 World Champion Athlete 3
r = 0.67 (0.53-0.77)
R-R interval len
g
th (ms)
900 1000 1100 1200 1300 1400 1500
Ln rMSSD (ms)
3.0
3.5
4.0
4.5
5.0
5.5
6.0
2011 World Champion Athlete 4
r = 0.25 (0.04-0.44)
R-R interval length (ms)
800 1000 1200 1400 1600 1800
Fig. 3 Correlation and 90 % confidence intervals (CI) between the
natural logarithm of the square root of the mean sum of the squared
differences between RR intervals (Ln rMSSD) and RR interval
length in two 2012 Olympic Champion rowers and two 2011 World
Champion rowers taken every morning upon waking in their 62-day
build-up to each pinnacle event. Correlation coefficients were almost
perfect (r = 0.91; CI 0.87; 0.94) and trivial (r =-0.03; CI -0.18;
0.24) for Olympic Champion rowers 1 and 2, respectively. Compar-
atively these values were large (r = 0.67; CI 0.53; 0.77) and small
(r = 0.25; CI 0.04; 044) for World Champion rowers 3 and 4
778 D. J. Plews et al.
Author's personal copy
in elites may be due to pre-competition stress. However,
changes in parasympathetic activity have not been shown
to be associated with pre-competition anxiety [64], and the
HRV values reported here (Fig. 4) have all been averaged
over 7-day periods to reduce noise. From a performance
perspective, the higher background of parasympathetic
activity that is associated with intensified training loads
[26] may compromise cardio-acceleration during exercise,
thereby limiting oxygen delivery and performance [65].
Additionally, increases in sympathetic activity have been
linked to improvement in peripheral adaptations such as
faster time to peak torque [66]. Therefore, it is reasonable
to assume that the reduced background of parasympathetic
activity/increases in sympathetic activity [15, 18] that
occurs during the taper may reflect increased ‘freshness’
[67], and readiness to perform. However, more research is
needed to establish why HRV changes in this manner
during the lead up to competition in elites, and what
magnitude of change may predict ‘detrimental’ or ‘opti-
mal’ performance.
5 Conclusion
The measurements of vagal-related indices of HRV remain
promising tools for the monitoring of training status in
endurance sports. However, it is clear that HRV responses
are individual and dependent on fitness level and training
history. As such, and although the data presented in this
paper focused on elite athletes, the HRV response in any
athlete with a long history of training will likely be similar to
that reported here (moderately trained or elite). Accordingly,
it is important to be aware of the different responses of these
variables and the athlete being monitored. In this current
opinion, we suggest that longitudinal monitoring is required
to understand each athlete’s optimal HRV to RR interval
fingerprint (i.e., Fig. 3). The possible indices of HRV that are
practically useful for monitoring training status in elite ath-
letes include weekly and 7-day rolling averaged Ln rMSSD,
and the Ln rMSSD to RR interval ratio, using the individual
SWC to represent a meaningful change in fatigue and/or
fitness [1]. Further, we encourage practitioners to use just one
Athlete B 2012
Athlete A 2011
Ln rMSSD (ms)
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
Athlete C 2012
Day Number
Athlete C 2011
Day Number
0102030405060
0102030405060
Ln rMSSD (ms)
4.4
4.6
4.8
5.0
5.2
5.4
Athlete B 2011
Ln rMSSD (ms)
4.0
4.2
4.4
4.6
4.8
5.0
5.2
5.4
Athlete A 2012
*
*
*
*
*
*
*
*
Fig. 4 The morning resting weekly averaged values of the natural
logarithm of the square root of the mean sum of the squared
differences between RR intervals (Ln rMSSD) with 90 % confidence
intervals (CI) over a 62-day period leading up to the 2011 World
Rowing Championships and 2012 London Olympic Games in three
elite rowers. All athletes won their events and the performance was
perceived to be optimal. The black circles indicate the weekly
averaged Ln rMSSD value, while the black line represents the 7-day
rolling average. The arrows indicate the day of the final (medal) race.
The grey shaded area indicates the individual smallest worthwhile
change (SWC) in Ln rMSSD values (see methods in reference [1]).
The black dashed line represents the zero line of the SWC to indicate
clear/unclear changes when the 90 % CI overlaps [2]. * indicates a
‘clear’ change in weekly averaged Ln rMSSD values above the SWC
in the week/weeks prior to the race
HRV Monitoring in Elite Endurance Athletes 779
Author's personal copy
HRV index for analysis; research suggests Ln rMSSD pro-
vides the most reliable and practically applicable measure for
day-to-day monitoring. In the case of elite athletes,
increasing HRV values (as competition approaches) may be
a sign of positive adaptation and/or coping with training load,
while reductions in HRV in the week/days before pinnacle
events may represent increasing freshness and readiness to
perform. Further research is needed to confirm this initial
finding and gain a clearer understanding of how changes in
HRV relate to training intensity distribution [25], as well as
to describe further the HRV trends for elite athletes leading
into major competition.
Acknowledgments No sources of funding were used to assist in the
preparation of this article. The authors have no conflicts of interest
that are directly relevant to the content of this article.
References
1. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate
variability in elite triathletes, is variation in variability the key to
effective training? A case comparison. Eur J Appl Physiol.
2012;112:3729–41.
2. Hopkins WG, Marshall SW, Batterham AM, Hanin J. Progressive
statistics for studies in sports medicine and exercise science. Med
Sci Sports Exerc. 2009;41(1):3–13.
3. Hautala AJ, Kiviniemi AM, Tulppo MP. Individual responses to
aerobic exercise: the role of the autonomic nervous system.
Neurosci Biobehav Rev. 2009;33(2):107–15.
4. Buchheit M, Simpson MB. Al Haddad H, Bourdon PC, Mendez-
Villanueva A. Monitoring changes in physical performance with
heart rate measures in young soccer players. Eur J Appl Physiol.
2011;112:711–23.
5. Buchheit M, Papelier Y, Laursen PB, Ahmaidi S. Noninvasive
assessment of cardiac parasympathetic function: postexercise
heart rate recovery or heart rate variability? Am J Physiol Heart
Circ Physiol. 2007;293(1):H8–10.
6. Hynynen E, Uusitalo A, Konttinen N, Rusko H. Heart rate vari-
ability during night sleep and after awakening in overtrained
athletes. Med Sci Sports Exerc. 2006;38(2):313–7.
7. Hynynen E, Uusitalo A, Konttinen N, Rusko H. Cardiac auto-
nomic responses to standing up and cognitive task in overtrained
athletes. Int J Sports Med. 2008;29(7):552–8.
8. Bosquet L, Merkari S, Arvisais D, Aubert AE. Is heart rate a
convenient tool to monitor over-reaching? A systematic review of
the literature. Br J Sports Med. 2008;42(9):709–14.
9. Lee CM, Wood RH, Welsch MA. Influence of short-term
endurance exercise training on heart rate variability. Med Sci
Sports Exerc. 2003;35(6):961–9.
10. Mourot L, Bouhaddi M, Tordi N, Rouillon JD, Regnard J. Short-
and long-term effects of a single bout of exercise on heart rate
variability: comparison between constant and interval training
exercises. Eur J Appl Physiol. 2004;92(4–5):508–17.
11. Vesterinen V, Hakkinen K, Hynynen E, Mikkola J, Hokka L,
Nummela A. Heart rate variability in prediction of individual
adaptation to endurance training in recreational endurance run-
ners. Scand J Med Sci Sports. 2013;23:171–80.
12. Atlaoui D, Pichot V, Lacoste L, Barale F, Lacour JR, Chatard JC.
Heart rate variability, training variation and performance in elite
swimmers. Int J Sports Med. 2007;28(5):394–400.
13. Garet M, Tournaire N, Roche F, Laurent R, Lacour JR, Bar-
thelemy JC, et al. Individual Interdependence between nocturnal
ANS activity and performance in swimmers. Med Sci Sports
Exerc. 2004;36(12):2112–8.
14. Buchheit M, Al Haddad H, Mendez-Villanueva A, Quod MJ,
Bourdon PC. Effect of maturation on hemodynamic and auto-
nomic control recovery following maximal running exercise in
highly trained young soccer players. Front Physiol. 2011;2:69.
15. Manzi V, Castagna C, Padua E, Lombardo M, D’Ottavio S,
Massaro M, et al. Dose–response relationship of autonomic ner-
vous system responses to individualized training impulse in
marathon runners. Am J Physiol Heart Circ Physiol. 2009;
296(6):H1733–40.
16. Pagani M, Lucini D. Can autonomic monitoring predict results in
distance runners? Am J Physiol Heart Circ Physiol. 2009;296(6):
H1721–2.
17. Portier H, Louisy F, Laude D, Berthelot M, Guezennec CY.
Intense endurance training on heart rate and blood pressure var-
iability in runners. Med Sci Sports Exerc. 2001;33(7):1120–5.
18. Iellamo F, Legramante JM, Pigozzi F, Spataro A, Norbiato G,
Lucini D, et al. Conversion from vagal to sympathetic predomi-
nance with strenuous training in high-performance world class
athletes. Circulation. 2002;105(23):2719–24.
19. Task Force of the European Society of Cardiology and the North
American Society of Pacing and Electrophysiology. Heart rate
variability: standards of measurement, physiological interpreta-
tion, and clinical use. Eur Heart J. 1996; 17:354–81
20. Buchheit M, Chivot A, Parouty J, Mercier D, Al Haddad H,
Laursen PB, et al. Monitoring endurance running performance
using cardiac parasympathetic function. Eur J Appl Physiol.
2010;108(6):1153–67.
21. Iwasaki K, Zhang R, Zuckerman JH, Levine BD. Dose–response
relationship of the cardiovascular adaptation to endurance train-
ing in healthy adults: how much training for what benefit? J Appl
Physiol. 2003;95(4):1575–83.
22. Pichot V, Busso T, Roche F, Garet M, Costes F, Duverney D,
et al. Autonomic adaptations to intensive and overload training
periods: a laboratory study. Med Sci Sports Exerc. 2002;34(10):
1660–6.
23. Pichot V, Roche F, Gaspoz JM, Enjolras F, Antoniadis A, Minini
P, et al. Relation between heart rate variability and training load
in middle-distance runners. Med Sci Sports Exerc. 2000;32(10):
1729–36.
24. Kaikkonen P, Rusko H, Martinmaki K. Post-exercise heart rate
variability of endurance athletes after different high-intensity
exercise interventions. Scand J Med Sci Sports. 2008;18(4):
511–9.
25. Seiler S, Haugen O, Kuffel E. Autonomic recovery after exercise
in trained athletes: intensity and duration effects. Med Sci Sports
Exerc. 2007;39(8):1366–73.
26. Buchheit M, Simon C, Piquard F, Ehrhart J, Brandenberger G.
Effects of increased training load on vagal- related indexes of
heart rate variability: a novel sleep approach. Am J Physiol Heart
Circ Physiol. 2004;287(6):H2813–8.
27. Mourot L, Bouhaddi M, Perrey S, Rouillon JD, Regnard J.
Quantitative Poincare plot analysis of heart rate variability: effect
of endurance training. Eur J Appl Physiol. 2004;91(1):79–87.
28. Yamamoto K, Miyachi M, Saitoh T, Yoshioka A, Onodera S.
Effects of endurance training on resting and post-exercise cardiac
autonomic control. Med Sci Sports Exerc. 2001;33(9):1496–502.
29. Meeusen R, Duclos M, Gleeson M, Steinacker J, Rietjens G,
Urhausen A. Prevention, diagnosis and treatment of overtraining
syndrome. Eur J Sport Sci. 2006;6:1–14.
30. Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D,
et al. Prevention, diagnosis, and treatment of the overtraining
syndrome: joint consensus statement of the European College of
780 D. J. Plews et al.
Author's personal copy
Sport Science and the American College of Sports Medicine.
Med Sci Sports Exerc. 2013;45(1):186–205.
31. Hedelin R, Wiklund U, Bjerle P, Henriksson-Larsen K. Cardiac
autonomic imbalance in an overtrained athlete. Med Sci Sports
Exerc. 2000;32(9):1531–3.
32. Uusitalo ALT, Uusitalo AJ, Rusko HK. Heart rate and blood
pressure variability during heavy training and overtraining in the
female athlete. Int J Sports Med. 2000;21(1):45–53.
33. Uusitalo AL, Uusitalo AJ, Rusko HK. Endurance training,
overtraining and baroreflex sensitivity in female athletes. Clin
Physiol. 1998;18(6):510–20.
34. Bosquet L, Papelier Y, Leger L, Legros P. Night heart rate var-
iability during overtraining in male endurance athletes. J Sports
Med Phys Fitness. 2003;43(4):506–12.
35. Hedelin R, Kentta G, Wiklund U, Bjerle P, Henriksson-Larsen K.
Short-term overtraining: effects on performance, circulatory
responses, and heart rate variability. Med Sci Sports Exerc.
2000;32(8):1480–4.
36. Halson SL, Jeukendrup AE. Does overtraining exist? An analysis
of overreaching and overtraining research. Sports Med. 2004;34(14):
967–81.
37. Baumert M, Brechtel L, Lock J, Hermsdorf M, Wolff R, Baier V,
et al. Heart rate variability, blood pressure variability, and bar-
oreflex sensitivity in overtrained athletes. Clin J Sport Med.
2006;16(5):412–7.
38. Kuipers H. Training and overtraining: an introduction. Med Sci
Sports Exerc. 1998;30(7):1137–9.
39. Kuipers H, Keizer HA. Overtraining in elite athletes: review and
directions for the future. Sports Med. 1988;6(2):79–92.
40. Bosquet L, Gamelin FX, Berthoin S. Is aerobic endurance a
determinant of cardiac autonomic regulation? Eur J Appl Physiol.
2007;100(3):363–9.
41. Al Haddad H, Laursen PB, Chollet D, Ahmaidi S, Buchheit M.
Reliability of resting and postexercise heart rate measures. Int J
Sports Med. 2011;32(8):598–605.
42. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Evaluating
training adaptation with heart rate measures: a methodological
comparison. Int J Sports Physiol Perform. Epub 8 Mar 2013.
43. Stanley J, Peake JM, Buchheit M. Consecutive days of cold water
immersion: effects on cycling performance and heart rate vari-
ability. Eur J Appl Physiol. 2013;113:371–84.
44. Penttila J, Helminen A, Jartti T, Kuusela T, Huikuri HV, Tulppo
MP, et al. Time domain, geometrical and frequency domain
analysis of cardiac vagal outflow: effects of various respiratory
patterns. Clin Physiol. 2001;21(3):365–76.
45. Hamilton RM, McKechnie PS, Macfarlane PW. Can cardiac
vagal tone be estimated from the 10-second ECG? Int J Cardiol.
2004;95(1):109–15.
46. Aubert AE, Seps B, Beckers F. Heart rate variability in athletes.
Sports Med. 2003;33(12):889–919.
47. Laursen PB. Training for intense exercise performance: high-
intensity or high-volume training? Scand J Med Sci Sports.
2010;20(Suppl. 2):1–10.
48. Fiskerstrand A, Seiler KS. Training and performance character-
istics among Norwegian international rowers 1970–2001. Scand J
Med Sci Sports. 2004;14(5):303–10.
49. Tucker R, Collins M. What makes champions? A review of the
relative contribution of genes and training to sporting success. Br
J Sports Med. 2012;46(8):555–61.
50. Barnett A. Using recovery modalities between training sessions
in elite athletes: does it help? Sports Med. 2006;36(9):781–96.
51. Goldberger JJ, Ahmed MW, Parker MA, Kadish AH. Dissocia-
tion of heart rate variability from parasympathetic tone. Am J
Physiol Heart Circ Physiol. 1994;266:H2152–7.
52. Goldberger JJ, Challapalli S, Tung R, Parker MA, Kadish AH.
Relationship of heart rate variability to parasympathetic effect.
Circulation. 2001;103(15):1977–83.
53. Kiviniemi AM, Hautala AJ, Seppanen T, Makikallio TH, Huikuri
HV, Tulppo MP. Saturation of high- frequency oscillations of R
R intervals in healthy subjects and patients after acute myocardial
infarction during ambulatory conditions. Am J Physiol Heart Circ
Physiol. 2004;287(5):H1921–7.
54. Hedman AE, Hartikainen JE, Tahvanainen KU, Hakumaki MO.
The high frequency component of heart rate variability reflects
cardiac parasympathetic modulation rather than parasympathetic
‘tone’. Acta Physiol Scand. 1995;155(3):267–73.
55. Malik M, Camm AJ, Amaral LA, Goldberger AL, Ivanov P,
Stanley HE, et al. Components of heart rate variability: what they
really mean and what we really measure. Am J Cardiol. 1993;
72:821–2.
56. Sacknoff D, Gleim G, Stachenfeld N, Glace B, Coplan N.
Suppression of high-frequency power spectrum of heart rate
variability in well-trained endurance athletes. Circulation. 1992;
86:I-658.
57. Borresen J, Lambert MI. Autonomic control of heart rate during
and after exercise: measurements and implications for monitoring
training status. Sports Med. 2008;38(8):633–46.
58. Hautala A, Tulppo MP, Makikallio TH, Laukkanen R, Nissila S,
Huikuri HV. Changes in cardiac autonomic regulation after
prolonged maximal exercise. Clin Physiol. 2001;21(2):238–45.
59. Iellamo F, Pigozzi F, Spataro A, Di Salvo V, Fagnani F, Roselli
A, et al. Autonomic and psychological adaptations in Olympic
rowers. J Sports Med Phys Fitness. 2006;46:598–604.
60. Mujika I, Goya A, Padilla S, Grijalba A, Gorostiaga E, Ibanez J.
Physiological responses to a 6-d taper in middle-distance runners:
influence of training intensity and volume. Med Sci Sports Exerc.
2000;32(2):511–7.
61. Buchheit M, Laursen PB, Al Haddad H, Ahmaidi S. Exercise-
induced plasma volume expansion and post exercise parasym-
pathetic reactivation. Eur J Appl Physiol. 2009;105(3):471–81.
62. Convertino VA. Blood volume: its adaptation to endurance
training. Med Sci Sports Exerc. 1991;23(12):1338–48.
63. Kaikkonen P, Nummela A, Rusko H. Heart rate variability
dynamics during early recovery after different endurance exer-
cises. Eur J Appl Physiol. 2007;102(1):79–86.
64. Iellamo F, Pigozzi F, Parisi A, Di Salvo V, Vago T, Norbiato G,
et al. The stress of competition dissociates neural and cortisol
homeostasis in elite athletes. J Sports Med Phys Fitness.
2003;43(4):539–45.
65. Parouty J, Al Haddad H, Quod M, Lepretre PM, Ahmaidi S,
Buchheit M. Effect of cold water immersion on 100-m sprint
performance in well-trained swimmers. Eur J Appl Physiol.
2010;109:483–90.
66. Hedelin R, Bjerle P, Henriksson-Larsen K. Heart rate variability
in athletes: relationship with central and peripheral performance.
Med Sci Sports Exerc. 2001;33(8):1394–8.
67. Bannister EW, editor. Modeling elite athletic performance.
Champaign: Human Kinetics; 1991.
HRV Monitoring in Elite Endurance Athletes 781
... Specifically, the time-domain HRV metric of the root mean square of successive differences between normal heartbeats (RMSSD) [1] has been shown to be stable [5], reliable [6], and accurate [7] during spontaneous breathing patterns [8]. As such, the RMSSD metric of HRV has been utilized by sports scientists to track workload and recovery and to modify the training of athletes [9][10][11][12][13][14], as well as by researchers to prescribe exercise and to track improvements in health and fitness [9,15]. ...
... Examination of the histograms and Q-Q plots indicated that the HRV metrics of RMSSD violated the assumption normality. Therefore, the natural log (ln) of RMSSD (lnRMSSD) was calculated and utilized instead, which normalized the distribution [28] and is consistent with the scientific literature [10]. HR and R-R interval data did not violate normality assumptions. ...
... As such, future research should examine the concurrent validity of the Elite HRV smartphone application when processing R-R interval data collected in different positions and when calculating other relevant HRV metrics. However, it should also be noted that the supine and seated positions were chosen in the current study for ecological reasons, as R-R interval data are most frequently collected in these positions under spontaneous breathing patterns [9][10][11][12][13][14], and the RMSSD metric has shown to be stable [5], reliable [6], and accurate [7] during spontaneous breathing patterns [8]. Finally, although measures of HRV are assessed in nonexercising conditions [27], assessing HRV during postexercise recovery situations is also common [6,43,44]. ...
Article
Full-text available
The purpose of the current study was to determine the concurrent validity of the Elite HRV smartphone application when calculating heart rate variability (HRV) metrics in reference to an independent software criterion. A total of 5 minutes of R–R interval and natural log of root mean square of the successive differences (lnRMSSD) resting HRV data were simultaneously collected using two Polar H10 heart rate monitors (HRMs) in both the seated and supine positions from 22 participants (14 males, 8 females). One H10 HRM was paired with a Polar V800 watch and one with the Elite HRV application. When no artifact correction was applied, significant, but small, differences in the lnRMSSD data were observed between the software in the seated position (p = 0.022), and trivial and nonstatistically significant differences were observed in the supine position (p = 0.087). However, significant differences (p > 0.05) in the lnRMSSD data were no longer identifiable in either the seated or the supine positions when applying Very Low, Low, or Automatic artifact-correction filters. Additionally, excellent agreements (ICC3,1 = 0.938 − 0.998) and very strong to near-perfect (r = 0.889 − 0.997) relationships were observed throughout all correction levels. The Elite HRV smartphone application is a valid tool for calculating resting lnRMSSD HRV metrics.
... A method to monitoring internal load is the heart rate variability (HRV), typically with measurement of resting HRV [14]. HRV has been used in elite athletes to evaluate training adaption [2,14,15] during a season, partly, because HRV is a non-invasive method and easy to use by athletes. ...
... A method to monitoring internal load is the heart rate variability (HRV), typically with measurement of resting HRV [14]. HRV has been used in elite athletes to evaluate training adaption [2,14,15] during a season, partly, because HRV is a non-invasive method and easy to use by athletes. HRV represents the continuous interplay between the sympathetic and parasympathetic branches of the autonomic nervous system in regulating HR [16]. ...
... However, in these two previous studies the HRV was measured during the nights, thus, the protocol was different to our study. On the other hand, Plews and Laursen [14] stated that the bell-shaped relationship between both HRV and training load and HRV and fitness, makes it difficult to use HRV to maximize training in elite athletes. ...
Article
The aim of this study was to determine the effects of different training periods and tapering during a macrocycle on heart rate variability (HRV), overtraining states and performance in young elite swimmers. Method Fifteen swimmers (6 men, 9 women) completed an 8-week training period divided into a basic, specific, competitive and transition blocks. HRV measures were recorded 3 days per week before the morning training session in supine position for 5 minutes. Overtraining state was recorded through the questionnaire of early clinical symptoms of the overtraining syndrome (QSFMS), which one considers different contributions to fatigue linked to physical exercising. The overtraining state was registered when the score exceeded 20 negative items out of 54. Training intensity distribution in five zones and training volume were quantified. The results show that, in these elite young swimmers, no changes in HRV were found during the 8-week training period with an average performance improvement (∼3%) in the competition block. In addition, there was no relationship between the QSFMS score and HRV. To conclude it appears that HRV indices within normal baseline levels could help to develop a well-managed and periodized training program that allows improves the performance in young elite swimmers.
... HRV was found to be higher in athletic and there was no different between the athletes' genders [18]. This result was supported by other researchers [3,[20][21][22][23]. Besides that, it was found that there is difference in HRV between before a motion, during a motion, and after a motion. ...
... Biosignal Analysis and Medical Imaging Group, Department of Physics, University of Kuopio, Kuopio, Finland) [32]. For the HRV analysis, the rMSSD parameter was chosen for each measurement period based on its better suitability and reliability than other indices [33]. rMSSD values were transformed to their natural logarithms (LnrMSSD) to allow parametric statistical comparisons assuming normal distributions [34]. ...
Article
Full-text available
Heart rate variability (HRV) has allowed the implementation of a methodology for daily decision making called day-to-day training, which allows data to be recorded by anyone with a smartphone. The purpose of the present work was to evaluate the validity and reliability of HRV measurements with a new mobile app (Selftraining UMH) in two resting conditions. Twenty healthy people (10 male and 10 female) were measured at rest in supine and seated positions with an electrocardiogram and an application for smartphones at the same time (Selftraining UMH) using recordings obtained through an already validated chest-worn heart rate monitor (Polar H10). The Selftraining UMH app showed no significant differences compared to an electrocardiogram, neither in supine nor in sitting position (p > 0.05) and they presented almost perfect correlation levels (r ≥ 0.99). Furthermore, no significant differences were found between ultra-short (1-min) and short (5-min) length measurements. The intraclass correlation coefficient showed excellent reliability (>0.90) and the standard error of measurement remained below 5%. The Selftraining UMH smartphone app connected via Bluetooth to the Polar H10 chest strap can be used to register daily HRV recordings in healthy sedentary people.
... Heart rate monitoring can reflect the current physical condition and sports status of athletes in real time. 5 Special physical training and heart rate monitoring provide effective support for the technical development and physical data analysis of the project. Moreover, it is conducive to the long-term development of the project. ...
Article
Full-text available
Introduction Chinese boxing is an aggressive, competitive, and combative sport. During its performance, good physical fitness and a stable heart rate can determine the athletes’ sports performance. Objective Study special physical training methods of Chinese boxing athletes by monitoring their heart rates. Methods The implementation period of the experiment totaled eight weeks, performed three times a week. The control group did not get any specific physical training while the experimental group received special physical training, properly protocoled. Their indices were measured before, during, and after the experiment, with classification and data analysis by Excel and SPSS software. Results The fitness data of the experimental group were significantly improved, with the heart rate more stable, corroborating the effectiveness of the special training. Conclusion The use of special physical training can optimize and better adjust the pre-existing protocol according to the athletes’ real competition needs, improving the specialized physical fitness and the athletes’ competitive level. It also helps stabilize the heart rate, helping athletes get better results in combat. Level of evidence II; Therapeutic studies - investigating treatment outcomes. Boxing; Physical Fitness; Heart Rate
... Ultra-endurance sports training elicits severe changes in the autonomous nervous system (ANS) and has the potential to alter the quality of recovery [1]. Researchers have underlined the importance of monitoring athletes' adaptive responses to these high training loads to prevent 1) fatigue; 2) entering a nonfunctional overreaching state; 3) illness and injuries; and to improve physiological adaptations [2][3][4]. ...
Article
To examine the effect of osteopathic manipulative treatment (OMT) on heart rate variability (HRV) indices in an elite open-water swimmer. A female open-water swimmer (age =28 years, height = 172 cm, body mass = 60 kg) participated in this study. The swimmer performed a daily supine HRV test routine 12 days before the 2019 open-water World Championships. OMT was administered when parasympathetic activity (based on HRV indices) was considered below normal values. The swimmer won a bronze medal in the 25 km event and placed fourth in the 10 km event, which qualified her for the 2020 Tokyo Olympics. Parasympathetic falls occurred three times during the taper period. After OMT, we observed a rebound of parasympathetic activity with a moderate to strong increase for High Frequency (HF) values compared with the average baseline from 10 to 150% increase of Ln HF values. OMT appeared to allow a parasympathetic rebound and increase the quality of recovery in an elite open-water swimmer who performed well during the World Championships. This case report illustrates the potential effects of OMT on autonomous nervous system activity, highlighting the possibilities to improve the quality of recovery in world-class athletes. It also shows the necessity to implement individualized training in the context of elite sports.
... r= 0.65]. HRV is an estimate of the cardiovascular mechanism that establishes a reliable approach to noninvasive monitoring of parasympathetic activity, where it is related to the average heart rate, which indicates the successive difference in heartbeats, that is, the study of differences in heart rate HRV, in other words, it is the variance of the time interval between significant R peaks [19]. This study explored the ANS in the supine and standing positions by measuring linear heart rate variability parameters. ...
... Spectral analysis of Heart Rate Variability (HRV) represents today the de facto standard method to assess the features of autonomic cardiac regulation in clinical and, particularly, in sport settings [36][37][38][39], being also simple, economical, and non-invasive. HRV appears particularly useful in sports as a means to follow various training steps [40], also using miniaturized portable instruments [41]. Nevertheless, the complexity of analytical techniques and difficulty of interpretation, in particular, if mechanistically considering one autonomic index at a time, close to the well-known influence of age and gender on CAR, may act as a barrier to more extensive use of HRV in practice, particularly during dynamic conditions, such as exercise [42,43]. ...
Article
Full-text available
Chronic stress may represent one of the most important factors that negatively affects the health and performance of athletes. Finding a way to introduce psychological strategies to manage stress in everyday training routines is challenging, particularly in junior teams. We also must consider that a stress management intervention should be regarded as “efficacious” only if its application results in improvement of the complex underlying pathogenetic substratum, which considers mechanistically interrelated factors, such as immunological, endocrine and autonomic controls further to psychological functioning and behavior. In this study, we investigated the feasibility of implementing, in a standard training routine of the junior team of the Italian major soccer league, a stress management program based on mental relaxation training (MRT). We evaluated its effects on stress perception and cardiac autonomic regulation as assessed by means of ANSI, a single composite percentile-ranked proxy of autonomic balance, which is free of gender and age bias, economical, and simple to apply in a clinical setting. We observed that the simple employed MRT intervention was feasible in a female junior soccer team and was associated with a reduced perception of stress, an improved perception of overall health, and a betterment of cardiac autonomic control. This data may corroborate the scientific literature that indicates psychological intervention based on MRT as an efficacious strategy to improve performance, managing negative stress effects on cardiac autonomic control.
Article
Full-text available
The aim of present study was to investigate the sedentary healthy men’s ultra-short heart rate variability (HRV) during the Wingate Anaerobic Test (WAnT) (30-sec) and parasympathetic reactivation in the first 60-sec after WAnT. The final sample comprised 101 individuals (Mean±SD; Age=28.9±4.8 years, Height=176.5±5.5 cm, Weight=89.8±8.8 kg). Anaerobic powers were measured by WAnT. Heart rate variability (HRV) was then recorded as 60-sec before the test for 30-sec and 60-sec after the test. HRV was measured by Polar V800 GPS Sports Watch with Heart Rate Monitor and Polar H7 band. To compare the testing stages HRV parameters, repeated one-way analysis of variance (ANOVA) was used. Binary comparisons were determined with the Bonferroni test. The relationship between exercise data of heart rate variability and power average watt was assessed by the Pearson correlation test. The Effect Size Cohen's d was calculated. The main finding of this study is that pre-test (60-sec) HRV values continue to drop dramatically during test (30-sec) and post-test (60-sec) measurements (p<0.05). Also, no correlation was observed between performance and HRV data during testing (r=-0.08, p>0.05). In conclusion, the present study was not observed to sign of HRV recovery during 60-sec after the 30-sec WAnT. HRV recorded in the first 60 seconds after maximum anaerobic exercise program in sedentary healthy men may be considered to exhibit an imbalance in the parasympathetic activity of the autonomic nervous system.
Article
Full-text available
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 confirm or 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. We propose a “check list” that might help the physicians and sport scientists to decide on the diagnosis of OTS and to exclude other possible causes of underperformance.
Article
Full-text available
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.
Article
Full-text available
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.
Article
Overtraining is an imbalance between training and recovery. Short term overtraining or ‘over-reaching’ is reversible within days to weeks. Fatigue accompanied by a number of physical and psychological symptoms in the athlete is an indication of ‘stateness’ or ‘overtraining syndrome’. Staleness is a dysfunction of the neuroendocrine system, localised at hypothalamic level. Staleness may occur when physical and emotional stress exceeds the individual coping capacity. However, the precise mechanism has yet to be established. Clinically the syndrome can be divided into the sympathetic and parasympathetic types, based upon the predominance of sympathetic or parasympathetic activity, respectively. The syndrome and its clinical manifestation can be explained as a stress response. At present, no sensitive and specific tests are available to prevent or diagnose overtraining. The diagnosis is based on the medical history and the clinical presentation. Complete recovery may take weeks to months.
Article
CONVERTINO, V. A. Blood volume: its adaptation to endurance training. Med. Sci. Sports Exerc., Vol. 23, No. 12, pp. 1338-1348, 1991. Expansion of blood volume (hypervolemia) has been well documented in both cross-sectional and longitudinal studies as a consequence of endurance exercise training. Plasma volume expansion can account for nearly all of the exercise-induced hypervolemia up to 2-4 wk; after this time expansion may be distributed equally between plasma and red cell volumes. The exercise stimulus for hypervolemia has both thermal and nonthermal components that increase total circulating plasma levels of electrolytes and proteins. Although protein and fluid shifts from the extravascular to intravascular space may provide a mechanism for rapid hypervolemia immediately after exercise, evidence supports the notion that chronic hypervolemia associated with exercise naming represents a net expansion of total body water and solutes. This net increase of body fluids with exercise training is associated with increased water intake and decreased urine volume output. The mechanism of reduced urine output appears to be increased renal tubular reabsorption of sodium through a mate sensitive aldosterone action in man. Exercise training-induced hypervolemia appears to be universal among most animal species, although the mechanisms may be quite different. The hypervolemia may provide advantages of greater body fluid for heat dissipation and thermoregulatory stability as well as larger vascular volume and filling pressure for greater cardiac stroke volume and lower heart rates during exercise. (C)1991The American College of Sports Medicine
Article
Endurance training decreases resting and submaximal heart rate, while maximum heart rate may decrease slightly or remain unchanged after training. The effect of endurance training on various indices of heart rate variability remains inconclusive. This may be due to the use of inconsistent analysis methodologies and different training programmes that make it difficult to compare the results of various studies and thus reach a consensus on the specific training effects on heart rate variability. Heart rate recovery after exercise involves a coordinated interaction of parasympathetic re-activation and sympathetic withdrawal. It has been shown that a delayed heart rate recovery is a strong predictor of mortality. Conversely, endurance-trained athletes have an accelerated heart rate recovery after exercise. Since the autonomic nervous system is interlinked with many other physiological systems, the responsiveness of the autonomic nervous system in maintaining homeostasis may provide useful information about the functional adaptations of the body. This review investigates the potential of using heart rate recovery as a measure of training-induced disturbances in autonomic control, which may provide useful information for training prescription.
Article
Background: Heart rate variability (HRV) recorded over 5 min or 24 h is used increasingly to measure autonomic function and as a prognostic indicator in cardiology. Measuring HRV during a standard 10-s ECG would save time and cut costs. The aim of this study, therefore, was to discover whether indices of HRV calculated over 10 s could predict cardiac vagal tone (CVT) recorded over a 5-min period by the NeuroScope, a new instrument that selectively measures vagal tone. Methods: A total of 50 subjects had ECGs taken at the beginning, middle and end of a 5-min measurement of CVT. Standard deviation of normal-to-normal RR interval (SDNN), root mean square of successive differences in RR intervals (rMSSD), and the average absolute difference (AAD) in RR intervals were calculated from RR intervals derived from the ECGs. Subjects were divided into a training set (n=40) and a test set (n=10). Results: Regression equations derived from the training set predicted 5-min mean CVT in the test set with r2 of 95.8%, 92.9% and 87.9% for AAD, rMSSD and SDNN, respectively. Indices obtained from the third ECG in each set tended to give a closer relationship with CVT than those derived from the first and second ECGs: this could be because of the greater spread of the independent variables in the third set. An underlying linear physiological phenomenon could not be excluded, however, without continuing the measurements over a longer time. Conclusions: These results demonstrate that AAD and rMSSD calculated from a 10-s ECG can accurately predict 5-min mean CVT as measured by the NeuroScope.
Article
We examined heavy training-induced changes in baroreflex sensitivity, plasma volume and resting heart rate and blood pressure variability in female endurance athletes. Nine athletes (experimental training group, ETG) increased intense training (70–90% VO2max) volume by 130% and low-intensity training (<70% VO2max) volume by 100% during 6–9 weeks, whereas the corresponding increases in six control athletes (CG) were 5% and 10% respectively. Maximal oxygen uptake (VO2max) in the ETG and CG did not change, but in five ETG athletes VO2max decreased from 53·0 ± 2·2 (mean ± SEM) (CI 46·8–59·2) ml kg–1 min–1 to 50·2 ± 2·3 (43·8–56·6) ml kg–1 min–1 (P<0·01), indicating overtraining. Baroreflex sensitivity (BRS) measured using the phenylephrine technique and blood pressure variability (BPV) did not change, but the low-frequency power of the R–R interval variability increased in the ETG (P<0·05). The relative change in plasma volume was 7% in the ETG and 3% in the CG. The changes in BRS did not correlate with the changes in plasma volume, heart rate variability and BPV. We conclude that heavy endurance training and overtraining did not change baroreflex sensitivity or BPV but significantly increased the low-frequency power of the R–R interval variability during supine rest in female athletes as a marker of increased cardiac sympathetic modulation.
Article
The aim of this study was to investigate whether nocturnal heart rate variability (HRV) can be used to predict changes in endurance performance during 28 weeks of endurance training. The training was divided into 14 weeks of basic training (BTP) and 14 weeks of intensive training periods (ITP). Endurance performance characteristics, nocturnal HRV, and serum hormone concentrations were measured before and after both training periods in 28 recreational endurance runners. During the study peak treadmill running speed (Vpeak) improved by 7.5±4.5%. No changes were observed in HRV indices after BTP, but after ITP, these indices increased significantly (HFP: 1.9%, P=0.026; TP: 1.7%, P=0.007). Significant correlations were observed between the change of Vpeak and HRV indices (TP: r=0.75, P<0.001; HFP: r=0.71, P<0.001; LFP: r=0.69, P=0.01) at baseline during ITP. In order to lead to significant changes in HRV among recreational endurance runners, it seems that moderate- and high-intensity training are needed. This study showed that recreational endurance runners with a high HRV at baseline improved their endurance running performance after ITP more than runners with low baseline HRV.