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

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 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.

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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) [3–5]. 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 [6–8], whereas increases in fitness
[9–11] 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 [14–18].
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, 20–22]. 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
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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 [33–35,
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, 9–13] 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 R–R 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 R–R 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.
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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 R–R interval ratio [1], which
simultaneously considers changes in both vagal tone (R–R
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 R–R 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 R–R 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 R–R 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 R–R 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 R–R
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:
R–R interval ratio are more indicative of fatigue, with
decreases in both indicating readiness to perform [Fig. 2]).
Furthermore, the optimal relationship between HRV and
R–R 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 R–R interval and the
natural logarithm of the square root of the mean sum of the squared
differences between R–R intervals (Ln rMSSD) in a subject with
increasing bradycardia. Here, a saturation of heart rate variability is
seen with long R–R intervals. Note how at shorter R–R intervals there
is a linear relationship between Ln rMSSD (dotted line), which
becomes disassociated as the duration of the R–R interval increases,
indicating heart rate variability saturation
776 D. J. Plews et al.

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optimal Ln rMSSD to R–R 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 R–R intervals (Ln
rMSSD) and the Ln rMSSD to R–R 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

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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 R–R intervals (Ln rMSSD) and R–R 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.

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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 R–R 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 R–R 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 R–R 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.
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