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Heart Rate Variability Analysis in Exercise
Kuno Hottenrott and Olaf Hoos
11.1 Introduction ........................................................................................................................ 245
11.2 HRV and Endurance Training.......................................................................................... 246
11.2.1 Cross-Sectional Studies ......................................................................................... 246
11.2.2 Longitudinal Studies ............................................................................................. 247
11.3 HRV During Exercise ........................................................................................................248
11.3.1 General Aspects .....................................................................................................248
11.3.2 Effects of Exercise Intensity and Aerobic Fitness on HRV...............................248
11.3.3 Detection of HRV Thresholds ..............................................................................253
11.4 Postexercise HRV ............................................................................................................... 254
11.4.1 General Aspects .....................................................................................................254
11.4.2 HRV Response to a Single Exercise Bout—Linear and Nonlinear
11.5 HRV Analysis as a Tool for Prevention of Overtraining..............................................257
11.5.1 General Aspects .....................................................................................................257
11.5.2 Detection of Overreaching and Overtraining via Resting HRV .....................258
11.5.3 HRV and Orthostatic Stress—A Diagnostic Tool ..............................................261
11.6 Optimization of Endurance Training by HRV Monitoring.......................................... 265
11.7 Summary.............................................................................................................................268
References ....................................................................................................................................269
11.1 Introduction
The autonomous regulation of heart rate (HR) and its acute and chronic adaptation to exer-
section has
been moved
to the end of
the chapter,
cise constitutes a classical and important field of cardiovascular research (Rosenblueth and
Simeone 1934; Robinson et al. 1966). Since the end of the twentieth century the application
of heart rate variability (HRV) analysis in sports has grown in importance because of the
introduction of accurate electrocardiogram (ECG) measurements of beat-to-beat variabil-
ity with portable devices (Laukkanen and Virtanen 1998). HRV-related research in exer-
cise science and sports medicine has mainly focused on the general autonomic response
to exercise training in people of different ages and fitness levels. Additionally, extensive
research has been conducted considering general aspects and mechanisms of autonomic
cardiovascular regulation during exercise and recovery. Over the past years especially, the
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246 ECG Time Series Variability Analysis: Engineering and Medicine
monitoring of training load and recovery as well as the early detection of overreaching and
overtraining via HRV analysis has gained significance. These aspects are essential for an
effective optimization of short-, mid-, and long-term training processes.
11.2 HRV and Endurance Training
11.2.1 Cross-Sectional Studies
Several epidemiological and population-based trials show that physical activity influences
the autonomic nervous system (e.g., Fagard et al. 1999; Horsten et al. 1999; Rennie et al.
2003). In physically active Swedish woman aged 31–68 years, Horsten et al. (1999) found
a significantly higher overall variability (standard deviation of all NN intervals [SDNN])
and low-frequency (LF) power (+30%) in comparison to their inactive peers of the same sex.
Additionally, the British Whitehall-II-Trial confirmed a 20% increase of the high-frequency
(HF) power over the lowest quartile of the cohort in subjects of the highest activity quar-
tile (Rennie et al. 2003). Most cross-sectional studies also confirmed that athletes compared
to untrained subjects are characterized by a higher overall variability (SDNN, TP: total
power of all NN intervals) and increased values in time- and frequency-based parame-
ters (RMSSD: root mean square of successive differences, SD1: standard deviation of the
Poincaré plot data around the horizontal axis, HF: high-frequency power) at rest, which
usually go along with nonpathological bradycardia (De Meersman 1993; Goldsmith et al.
1992; Sztajzel et al. 2008). In 24-hour ECG recordings HF power in athletes was increased
fourfold over untrained peers (Goldsmith et al. 1992). Additionally, the amplitude of respi-
ratory sinus arrhythmia (RSA), which was assessed via short-term recordings (3 minutes)
at a rate of six breaths per minute, increased by 60% and indicated a higher vagal nerve
activity when comparing 72 male runners, aged 15–83 years to 72 age- and weight-matched
sedentary control subjects (De Meersman 1993). Especially endurance athletes seem to
show this favorable shift toward increased overall variability and vagal activity (Sztajzel
et al. 2008).
In contrast, other findings question this simple and straightforward relation between
physical activity, aerobic capacity, and increased HRV (e.g., Sacknoff et al. 1994; Martinelli
et al. 2005; Melanson 2000). For example, Sacknoff et al. (1994) found reduced HF power in
athletes (n=12), although they had a higher overall variability (SDNN) than controls (n=
18)in supine position. A more recent study expressed a similar disparity between time and
frequency analysis as trained cyclists’ SDNN was increased by 50%, while spectral power
was similar to untrained controls (Martinelli et al. 2005). Although time- and frequency-
domain measures of HRV may be greater in active than sedentary individuals, it seems
that HRV does not necessarily increase in a dose-dependent manner with increasing levels
of physical activity (Melanson 2000).
First, these somewhat conflicting results may be at least be partially caused by differ-
ent approaches of physical activity, training status, and aerobic fitness on the one hand
and modulation of HR on the other hand. For example, vagal-related HRV measures ana-
lyzed at rest under controlled breathing were shown to be positively correlated with aer-
obic power in terms of VO2max (r=0.53,p< .001,n=55), but not with weekly training
load (Buchheit and Gindre 2006), which was rather related to heart rate recovery (HRR)
(r=0.55,p< .001,n=55). Moreover, when power at the ventilatory threshold is used as
criteria for aerobic endurance capacity, a relation to cardiovascular autonomic control
may not be detectable (Bosquet et al. 2007). Additionally, interindividual differences
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Heart Rate Variability Analysis in Exercise Physiology 247
of breathing frequency can lead to misinterpretations of the HRV spectrum estimates
(Camann and Michel 2002), especially when breathing is not controlled during HRV mea-
surement at rest.
Second, physiological long-term adaptations to exercise play an important role. A satu-
ration effect of vagal-related HRV measures and a dissociation with resting HR was shown
in trained individuals (Kiviniemi et al. 2004). This can be attributed to the saturation of
acetylcholine receptors at the myocyte level (Malik and Camm 1993). Additionally, as
intrinsic changes in sinus automaticity and AV node conduction changes may be present in
endurance athletes, bradycardia is not necessarily caused by autonomic influences (Stein
et al. 2002).
11.2.2 Longitudinal Studies
The majority of studies with a longitudinal design have investigated effects of endurance
training in the short to medium term (3 weeks up to 1 year). Most of these interventions
confirmed that moderate aerobic exercise (Carter et al. 2003; Melanson and Freedson 2001;
Tulppo et al. 2003) in contrast to resistance training (Forte et al. 2003; Madden et al. 2006)
leads to (higher) increases of overall and vagal nerve-mediated HRV parameters at rest.
This usually goes along with a reduction of resting HR. For example, Carter et al. (2003)
reported increases in overall variability in the frequency domain and a reduction of HR at
rest and during submaximal exercise in male and female recreational endurance runners
in the third (n=12)and fifth decade of their life (n=12). These adaptations to 3 months
of aerobic exercise were independent of sex and age. Similar effects were observed in
untrained subjects after 8 weeks of moderate aerobic training (at 70%–80% of HRmax),
including 6 ×30–60-minute sessions per week (Tulppo et al. 2003). Apart from increased
bradycardia and a shift of spectral power toward the HF band, this study reported a
reduction of the short-term fractal scaling component alpha1, determined by detrended
fluctuation analysis (DFA), after the training period. The exercise-induced alterations of
autonomic regulation of HR toward vagal dominance are supported by a meta-analysis
of trials including training periods of at least 4 weeks (Sandercock et al. 2005). Twelve
studies (298 cases) reported a change in RR intervals with an overall effect size of d=0.75,
although a subanalysis revealed a trend toward smaller responses of RR intervals in older
subjects. A potentially limited HRV adaptation among the elderly was shown by Perini
et al. (2002). The authors found no change of HRV in 70-year-old subjects after 8 weeks
of aerobic training, although increases in physical fitness (maximal power output [Pmax]:
+25%) and aerobic power (VO2max: +18%) were observed. In this respect, either a longer
intervention period up to 6 months (Levy et al. 1998) or modifications in exercise intensity
(Okazaki et al. 2005) and/or modality might be necessary to improve the HRV response in
the elderly. In particular, a combined strength and endurance training seems to elicit higher
benefits on HR dynamics during rest and at moderate exercise in older men than endurance
training alone (Karavirta et al. 2009). This is in line with the observation of increased HRV
during submaximal (absolute) exercise intensities after aerobic training (Leicht et al. 2003;
Martinmäki et al. 2008). However, HRV parameters usually remain unchanged when the
comparisons are based on relative exercise intensity (% Pmax or HRmax). Not all stud-
ies provide evidence for beneficial effects of aerobic training on HRV at rest (Bonaduce
et al. 1998; Boutcher and Stein 1995) and during exercise (Carter et al. 2003). For example,
Bonaduce et al. (1998) were unable to detect changes in time- and frequency-domain mea-
sures of HRV in both waking and sleeping hours (24-hour ECG) after intensive training
(20 hours) in elite cyclists, although aerobic power increased and resting HR decreased.
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248 ECG Time Series Variability Analysis: Engineering and Medicine
Consequently, regular endurance training does not increase HRV per se. On the other hand,
there seems to be a relation between the individual training response of the cardiovascular
system and HRV. A high vagal activity at rest may provide a favorable condition for contin-
uous improvements of maximal oxygen uptake throughout the training process (Hautala
et al. 2009) and may therefore be used as an important variable in training monitoring (see
Chapter 6).
Apart from the influence of different indices used to detect training-induced changes of
HRV, beneficial effects strongly depend on the continuous interaction of variables of train-
ing load (volume, duration, intensity, and frequency) and individual psychophysiological
capacities to cope with exercise stress throughout the training process (Borresen and Lam-
bert 2008; Buchheit 2014; Hottenrott et al. 2006). Adjusting only one variable, for example,
training duration, does not guarantee improvements in vagal modulation of HR (Uusitalo
et al. 2004).
Hence, this specific dose–response relationship between training load and the HRV
response (Iwasaki et al. 2003) implies that training must be tailored toward subjects’ indi-
vidual age, sex, training status, and training goal in order to be efficient (Hottenrott et al.
2006, 2014).
11.3 HRV During Exercise
11.3.1 General Aspects
The underlying mechanism for the autonomic regulation of HR during exercise is the
reduced parasympathetic and increased sympathetic modulation of the sinus node, which
regulates nonpathological tachycardia during exercise. The sympathovagal modulation
during exercise changes as a function of intensity with the central command, circulat-
ing catecholamines, and the exercise pressor reflex being the most relevant physiologi-
cal mechanisms that mediate these changes (Iellamo 2001; Williamson 2010). A significant
withdrawal of vagal activity occurs immediately at the beginning of physical exercise and
continuously decreases from light to moderate intensities. The continuous rise of the HR
at heavy and severe exercise intensity is mainly due to increased sympathetic modulation.
Whether a total withdrawal of vagal activity occurs at exhaustion is not clear, as there is
some evidence for small parasympathetic effects on HR that may persist even during high-
intensity exercise (Kannankeril et al. 2004).
Although some authors investigated HRV during static/isometric contractions (Iellamo
et al. 1999; Taylor et al. 1995; Weippert et al. 2013), the majority of previous laboratory stud-
ies have focused on acute effects of dynamic exercise on HRV parameters. Among these
trials, the test designs were very different, including both single and multiple steady-state
exercises at different intensities and duration as well as incremental and ramp protocols on
different ergometer devices. Additionally, the problem of nonstationarity of RR intervals
during exercise conditions as well as a wide range of HRV processing and analysis further
complicate this issue (Lewis and Short 2010; Sandercock and Brodie 2006).
11.3.2 Effects of Exercise Intensity and Aerobic Fitness on HRV
Although the findings on HRV during dynamic exercise are not consistent due to the
above mentioned differences in exercise protocols and HRV methodologies, changes in
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Heart Rate Variability Analysis in Exercise Physiology 249
absolute values of amplitude-related HRV parameters of time and frequency analysis seem
to provide a functional dependency with exercise intensity and aerobic fitness (Figure 11.1).
As visualized in Figure 11.1, most studies show an almost exponential decrease of abso-
lute values of overall variability (SDNN or TP as the sum of LF and HF power), HF
and LF power and standard time-dependent measures of HRV (RMSSD), standard devia-
tion of the averages of NN intervals in 5-minute segments (SDANN), standard deviation
of differences between adjacent NN intervals (SDSD), mean squared differences (MSD)
from rest to moderate to heavy exercise intensity (Casties et al. 2006; Hautala et al. 2003;
Karapetian et al. 2008; Lewis and Short 2010; Tulppo et al. 1996, 1998). This particular trend
has also been incorporated in HRV decay constants for LF and HF bandwidths (Lewis and
Exercise intensity (W)
150100 125 175 Max50 75
150100 125 175 Max50 75
HF-power (CCV%) SD1n
200 VO2peak =4660 mL/kg/min (n = 25)
VO2peak = 2837 (n = 25)
VO2peak =3845 (n = 36)
100 125 175 Max
50 75
HR (beats/min)
HR (a), 2-D vector analysis of Poincaré plots (SD1n) (b), and HF power (c) in three fitness groups (age matched)
during exercise. Values are means and SD. Kruskal–Wallis H-tests were used at each exercise intensity level
(among all three groups) followed by post hoc analysis (Mann–Whitney U-test) between good fitness group and
poor fitness group. Annotations are as follows: x indicates p.05, xx indicates p.01, xxx indicates p.001, and
ns indicates not significant, for high fitness group compared with poor fitness group. (Reprinted from Tulppo,
M. P. et al., Am J Physiol., 274, H427, 1998. With permission.)
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250 ECG Time Series Variability Analysis: Engineering and Medicine
Short 2007). Although this general trend applies to subjects of different age and fitness lev-
els, athletes with higher aerobic fitness additionally show higher HRV values in time and
frequency domain at light (absolute) exercise intensities (Figure 11.1). Our own data sup-
port the almost exponentially decreasing trend of time- and frequency-dependent HRV
measures of vagal activity with increasing exercise intensity (expressed in % VO2peak,
Figure 11.2). With progressing exercise severity HR increases linearly, while RMSSD and
natural logarithm of high-frequency power (lnHF) decrease in an exponential manner
reaching asymptotic values slightly above 60% VO2peak (Figure 11.2). However, it should
be noted that in contrast to absolute exercise, intensities groups of low and high aero-
bic fitness levels cannot be differentiated from each other, when relative intensity (e.g.,
% VO2peak) is used as a reference.
From a methodological point of view, the reductions of absolute amplitude-based time
and frequency parameters during exercise imply that an unfavorable signal-to-noise ratio
may be reached even at moderate intensities. Although it seems favorable to use relative
spectral power, whereby data can be normalized to total spectral power (LF and HF in %
or normalized units [n.u.], excluding very LF [VLF] bandwidth) or to pre-exercise base-
line values, previous findings on the development of spectral power density distributions
(in % and n.u.) during exercise are inconsistent (Sandercock and Brodie 2006). Older stud-
ies applying classical spectral analysis methods on hardly comparable exercise loads have
shown a variety of changes in relative HF and LF power (in % or n.u.) with increasing inten-
sity: the authors found a decrease in both values, changes in opposite direction (decrease
in HF, increase in LF or vice versa) or no significant change in relative power distribu-
tions (LF/HF ratio) at all (Arai et al. 1989; Bernardi et al. 1990; Casadei et al. 1995; Hager-
man et al. 1996; Perini et al. 1990, 2000). More recent findings confirm a biphasic trend of
the LF/HF ratio, which includes an increase at low intensity and a gradual decrease at
moderate-to-high intensity exercise (Hautala et al. 2003). Additionally, time-variant spec-
tral analysis methods rather consistently indicate that relative HF power tends to be higher
than relative LF power at high exercise intensities (Blain et al. 2005; Cottin et al. 2004). The
underlying mechanisms of these changes in spectral power distribution have already been
mentioned earlier. Bernardi et al. (1989) attributed the HF oscillations during intense exer-
cise primarily to nonneural, respiratory mechanisms, because the central frequency of HF
power is strongly correlated with respiratory frequency. Consequently, for HRV spectral
analysis during exercise it seems favorable to use time-variant spectral methods and an
extended HF bandwidth up to 1 Hz. Alternatively, a time-variant cut-off frequency corre-
sponding to the actual breathing frequency, which is clearly exceeding the Task Force rec-
ommendations (HF: 0.15–0.4 Hz) for resting HRV measurements (Cottin et al. 2006; Lewis
and Short 2010), should be selected for accurate HRV spectral analysis during exercise.
Besides time- and frequency-domain measures, the evolution of nonlinear HRV meth-
ods seems promising for gaining new insights into HR dynamics during exercise. While
the above mentioned nearly exponential decrease of standard amplitude-based HRV mea-
sures during graded exercise hardly allows the differentiation between heavy to severe
exercise intensities, the short-term scaling exponent alpha1 of DFA is not affected by such
limitations. During incremental exercise, alpha1 consistently develops in a biphasic man-
ner: Depending on the resting value (usually between 1.0 and 1.5), stable or slightly rising
values of alpha1 up to 1.5 have been reported at very low to mild intensities indicating a
strongly correlated structure of HR dynamics due to a vagal withdrawal. Conversely, from
moderate-to-high intensity exercise, alpha1 decreases almost linearly (Casties et al. 2006;
Hautala et al. 2003; Platisa et al. 2008).
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Heart Rate Variability Analysis in Exercise Physiology 251
RMSSD (ms)
50 60 70 80 90
% VO2Peak
50 60 70 80 90
InHF (ms2)
50 60 70
80 90
HR (beats/min)
VO2peak: 52, 3+–5, 4 mL/kg/min (n = 15)
VO2peak: 41, 9+–6, 8 mL/kg/min (n = 16)
peak: 58, 7+–5, 4 mL/kg/min (n = 18)
Heart rate (in beats/minute, upper panel), root mean square of successive differences (RMSSD in milliseconds,
middle panel), and natural logarithm of high-frequency power of spectral analysis (lnHF in ms2, lower panel)
during incremental exercise in athletes of three different aerobic fitness levels (high, medium, and low). ns indi-
cates not significant between different fitness levels (two-way mixed ANOVA [aerobic power ×relative intensity]
with Tukey’s honest significant difference (HSD)/Bonferroni used as post hoc test). (Modified from Hoos, O.,
Dynamik und Komplexität der Herzfrequenzregulation im Ausdauersport, Philipps-Universität Marburg, Habilitation-
sschrift, Marburg, 2010.)
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252 ECG Time Series Variability Analysis: Engineering and Medicine
These consistent findings suggest that the correlative structure of RR intervals is, in
principle, first maintained or slightly increased and then decreases gradually from mod-
erate to severe intensities, indicating that the signal character becomes more and more
random (<0.5)until finally an uncorrelated state is reached. These changes in correlation
properties of heart beat dynamics during exercise have been explained by a random walk
model with stochastic feedback (Ivanov et al. 1998; Karasik et al. 2002; Platisa and Gal
Our own data support and extend these findings as there is a gradual decrease of alpha1
during graded exercise denoted by significant changes compared to the pevious inten-
sity level (**), whereby degree and progression of uncorrelated HR dynamics significantly
differ between trained and untrained subjects (Figure 11.3). Additionally, a crossover phe-
nomenon may be present, as with intensities above 70% VO2peak both trained groups
(medium and high level) show a more pronounced reduction in alpha1 compared to the
untrained state. This is similar to findings from a recent study with a longitudinal design
(Karavirta et al. 2009), as the authors reported a more pronounced reduction of alpha1 at
moderate intensities (equal to 60%–70% of maximum power [Pmax]) after a concurrent
strength and endurance training. Although the underlying mechanism is still not known,
the differences in decay rate of alpha1 may be related to lower intrinsic HRs (IHR) in
VO2peak: 58, 7+–5, 4 mL/kg/min (n = 18)
VO2peak: 52, 3+–5, 4 mL/kg/min (n = 15)
VO2peak: 41, 9+–6, 8 mL/kg/min (n = 16)
0 30 40 50 60 70 80 90 100
% VO2Peak
DFA fractal scaling exponent alpha1
Short-term scaling exponent alpha1 of the detrended fluctuation analysis (DFA) during incremental exercise
(% VO2max) in athletes of three different aerobic fitness levels (high, medium, and low). *p.05, **p.01 in
comparison to previous intensity level; (a) p.05 between high and low; (b) p.05 between high and medium;
(c) p.05 between low und medium (two-way ANOVA [aerobic power ×relative intensity] for repeated
measures and Tukey’s honest significant difference (HSD)/Bonferroni used as post hoc test). (Modified from
Hoos, O., Dynamik und Komplexität der Herzfrequenzregulation im Ausdauersport, Philipps-Universität Marburg,
Habilitationsschrift, Marburg, 2010.)
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Heart Rate Variability Analysis in Exercise Physiology 253
trained subjects corresponding to a later occurrence of peak values in alpha1–intensity
curves during incremental tests as these peak values may be used as a surrogate measure
for IHR (Platisa et al. 2008). More work in this area is needed to further corroborate these
findings and to elucidate physiological mechanisms underlying changes in nonlinear HR
dynamics during exercise.
11.3.3 Detection of HRV Thresholds
Performance diagnostics and training prescription by means of ventilatory and metabolic
thresholds constitute an important field of exercise physiology. In order to detect HRV-
related thresholds during exercise and to prove their relation to the transition from aer-
obic to anaerobic energy resources two different approaches were established. The first
one is straightforward and seeks to determine the exercise intensity at which a plateau of
standard time-dependent measures of HRV (SDNN, RMSSD, or modifications) or param-
eters of the Poincaré plot (SD1) occur (Karapetian et al. 2008; Tulppo et al. 1996). The
verification of this approach for both genders as well as groups of different age and fit-
ness levels led to a commercially available HR device, which derives the lower limit of
an exercise intensity zone for effective aerobic training from the above mentioned HRV
plateau (Laukkanen et al. 1998). More recently, Karapetian et al. (2008) found that the
deflection point of standard time-dependent measures of HRV (SDNN and MSD) with
subsequent plateau formation correlates with the first lactate or ventilatory threshold
(0.82 r0.89) during graded exercise. Based on Bland–Altman criteria for comparison
of methods, the HRV threshold provides sufficient agreement with the aerobic thresh-
old from a practical perspective. However, as mentioned earlier, this approach may be
critical in some cases as the HRV plateau develops within a range of low signal-to-noise
The second approach is based on advanced time variant spectral methods (Hilbert
transform, short-term Fourier transform [STFT]) and analyses the changes in instan-
taneous HF oscillations during graded exercise and their correlation with ventilatory
parameters (Anosov et al. 2000; Cottin et al. 2006). This approach takes advantage of
the already mentioned strong association of breathing frequency and depth with the
instantaneous central frequency and power of spectral HF bandwidth of HRV. In healthy
subjects and trained athletes, the estimation of both ventilatory thresholds (first venti-
latory or aerobic threshold: VT1, second ventilatory or anaerobic: VT2) by STFT pro-
vides sufficient accuracy (r>0.9), when the two thresholds of the product of HF peak
(fHF) and spectral power in the extended HF band (0.15 Hz to maximal breathing fre-
quency [bf max]), which occur with increasing exercise intensity, are determined (Cottin
et al. 2006, 2007). This applies to both treadmill testing as well as cycling when VT1
and VT2 are detected by the Wasserman method investigating breakpoints in minute
ventilation over oxygen uptake (VE/VO2)and minute ventilation over carbon dioxide
output (VE/VCO2)–intensity curves (Wasserman et al. 1973). The second increase of spec-
tral power in HF band is associated with mechanical stimulation of the sinus node and
mechanoelectric feedback mechanisms, respectively (Cottin et al. 2006, 2007). The latest
findings in this field suggest that both methodological approaches cannot be applied to
all subject groups without limitations. However, the time-variant spectral method seems
to be more accurate in patients with cardiac disease and diabetes (Mourot et al. 2012;
Quinart et al. 2014).
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254 ECG Time Series Variability Analysis: Engineering and Medicine
11.4 Postexercise HRV
11.4.1 General Aspects
HRR immediately after exercise characterizes the reduction of HR within a defined period
of time. HRR is functionally related to vagal reactivation as well as sympathetic withdrawal
(Coote 2010) and constitutes an important predictor of mortality (Cole et al. 1999). The
reduction in HR and especially cardiac parasympathetic reactivation following a training
session seem to be associated with the recovery process of different organ systems. There-
fore, it might be used to assess changes in autonomic input to different organs and the
blood flow required to restore homeostasis (Stanley et al. 2013).
Previous studies using pharmacological blockade have confirmed that vagal reactiva-
tion dominates within the first few minutes after exercise (Kannankeril and Goldberger
2002; Kannankeril et al. 2004), whereas a coordinated cardiac sympathovagal interaction
in conjunction with the clearance of circulating catecholamines dominates in the following
minutes and hours of recovery (Coote 2010). HRV analysis of immediate, mid-, and long-
term recovery from exercise may therefore help to gain insight into the sympathovagal
background of exercise recovery and the autonomic responses to different training loads.
Although there is a large body of studies, the heterogeneity of subjects’ fitness levels, exer-
cise intensities, durations, and modalities of the preceding exercise as well as the variety
of methods used for HRV analysis and the corresponding measuring intervals complicate
a systematic comparison of previous findings.
11.4.2 HRV Response to a Single Exercise Bout—Linear and Nonlinear Dynamics
The body of evidence suggests that most time- and frequency-domain HRV measures
(especially SDNN, RMSSD, SD1, SD2, TP, and HF) made during the acute recovery phase
are reduced after low-intensity exercise (Gladwell et al. 2010; Martinmäki and Rusko 2008;
Ng et al. 2009; Parekh and Lee 2005; Seiler et al. 2007; Terziotti et al. 2001). This is even more
pronounced after heavy to severe exercise bouts (Casties et al. 2006; Buchheit et al. 2009;
Kaikkonen et al. 2008, 2010) and endurance competitions (Cornolo et al. 2005; Hautala et al.
2001; Murrell et al. 2007).
HRV indices rise back to or even above baseline values during short-, mid- or long-term
recovery (1 minute to 72 hours). Thereby, the temporal structure of the recovery process
is highly individual. Within 1–4 minutes after all-out exercise, there already is a very
prominent impact of vagal reactivation (Kannankeril et al. 2004). While absolute variabil-
ity is strongly reduced at the beginning of recovery, RMSSD and spectral power in HF
and LF bandwidth and LF/HF ratio increase (Arai et al. 1989; Goldberger et al. 2006;
Martinmäki and Rusko 2008; Perini et al. 1990). From 15 minutes to 1–3 hours, vagal-related
HRV indices further increase and LF/HF ratio decreases (Casties et al. 2006; Cornelissen
et al. 2010; Martinmäki and Rusko 2008; Mourot et al. 2004; Parekh and Lee 2005; Seiler
et al. 2007; Terziotti et al. 2001). Even after intense exercise bouts and endurance competi-
tions, these indices are usually restored within 48–72 hours (Al Haddad et al. 2009; Cornolo
et al. 2005; Murrell et al. 2007; Niewiadomski et al. 2007). Some authors reported an over-
compensation of vagal-related HRV indices after very intense or prolonged exercise when
sufficient recovery time was provided (Hautala et al. 2001; James et al. 2002; Mourot et al.
2004; Terziotti et al. 2001). This rebound effect is directly related to plasma volume adapta-
tions (Buchheit et al. 2009).
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Heart Rate Variability Analysis in Exercise Physiology 255
In summary, these findings strongly suggest that HRV recovery is highly influenced by
the interaction of training intensity and duration. Studies investigating both factors clearly
imply that exercise intensity has the highest impact on HRV recovery (Parekh and Lee 2005;
Kaikkonen et al. 2007, 2010; Seiler et al. 2007).
Regarding exercise intensity, the aerobic threshold may denote a threshold for autonomic
nervous system (ANS)/HRV recovery in highly trained athletes (Seiler et al. 2007). Almost
independent from the exercise duration, loads below the aerobic threshold elicit a faster
recovery of the autonomic nervous system than higher exercise intensities (Gladwell et al.
2010; Martinmäki and Rusko 2008). As displayed in Figure 11.4, this notion is further sup-
ported by our own data. In comparison to 20 minutes of light aerobic exercise (E1), 20 min-
utes of exercise at threshold-intensity (E2) significantly extend the suppression of vagal
modulation during acute recovery, while prolonged aerobic exercise (E3) does not. Addi-
tionally, the training method (interval vs. prolonged exercise) may also play an important
role, because in comparison to interval sessions, continuous exercise protocols of similar
intensity and duration (∼ 85%VO2max over 21 minutes) seem to extend the required recov-
ery time (Kaikkonen et al. 2008).
In a recent meta-analysis, Stanley et al. (2013) show generalized vagal recovery kinet-
ics (15 minutes postexercise) after three different exercise intensities (low: below aero-
bic threshold, <70%VO2max; threshold-like: 70%−82%VO2max, high: above anaerobic
threshold >82%VO2max) in relation to pre-exercise baseline levels. Their findings sup-
port the dominant effect of exercise intensity on acute HRV recovery as the authors found
an increase of (A) 116%/hour after low-intensity exercise, (B) 80%/hour after threshold-
intensity exercise, and (C) 40%/hour after high-intensity exercise. In contrast, there was
no clear relationship between exercise duration and cardiac parasympathetic recovery
(Stanley et al. 2013).
Apart from exercise intensity, HRV recovery depends on the individual fitness level. In
this respect, cross-sectional studies found that trained subjects usually show a faster vagal
reactivation after exercise than untrained subjects (Mourot et al. 2004; Seiler et al. 2007). By
using a longitudinal design, Yamamoto et al. (2001) confirmed a faster vagal reactivation
in cyclists after only 1 week of moderate endurance training (at 80% VO2max, 4 ×40 min-
utes/week) and a further enhancement of vagal indices of HR after continuing training for
6 weeks.
Figure 11.5 illustrates the generalized influence of different fitness levels (adjusted for
exercise intensity) as recovery time courses of vagal-related HRV measures are differ-
ent between inactive subjects and moderately as well as highly trained athletes. While
postexercise suppression of cardiac parasympathetic activity is nearly diminished after
15 minutes in highly trained subjects, moderately trained athletes and inactive subjects
require at least 40 minutes and 90 minutes, respectively, for a comparable vagal reactiva-
tion (Stanley et al. 2013).
Furthermore, HRV recovery and vagal reactivation are influenced by postexercise recov-
ery conditions. Especially by using an active cool-down (Takahashi et al. 2002), supine
position (Takahashi et al. 2000) and/or cold water immersion (Al Haddad et al. 2010), the
increase of vagal-related HRV indices after exercise is enhanced.
Until now, a general consensus on the nonlinear dynamics of RR intervals during
recovery has not been reached as only a few studies have focused on nonlinear HRV
indices (Casties et al. 2006; Javorka et al. 2002; Platisa and Gal 2008; Platisa et al. 2008).
However, available findings suggest that the short-term scaling exponent alpha1 increases
independently from training status during recovery (Casties et al. 2006; Platisa and Gal
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256 ECG Time Series Variability Analysis: Engineering and Medicine
E1: 20 min with 50% VO2Peak
E2: 20 min with 70% VO2Peak
E3: 60 min with 50% VO2Peak
36 9 12 15
Recovery time (min)
Baseline E1
Baseline E2
Baseline E3
InHF (ms2)
DFA fractal scaling exponent alpha1
** **
** ** **
** ** ** **
HR (beats/min)
RMSSD (ms)
Time course of heart rate, linear (RMSSD, lnHF) and nonlinear (DFA alpha1) parameters of HRV during
immediate recovery after different exercises protocols in moderately trained athletes (n=17; VO2peak=49.9±
7.3 mL/min/kg). E1: 20 minutes below aerobic threshold; E2: 20-minute anaerobic-threshold intensity; E3:
60 minutes below aerobic threshold; *p.05, **p.01 in comparison to baseline (two-way ANOVA [exercise mode
×recovery time] for repeated measures and Bonferroni post hoc test). (Modified from Hoos, O., Dynamik und
Komplexität der Herzfrequenzregulation im Ausdauersport, Philipps-Universität Marburg, Habilitationsschrift,
Marburg, 2010.)
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Heart Rate Variability Analysis in Exercise Physiology 257
Highly trained
Moderately trained
Vagal-related HRV (% pre-exercise)
0 10 20 30 40 50 60 70 80 90
Time (min)
Influences of athletes fitness/training status, adjusted for exercise intensity, on mean parasympathetic activity
(±standard deviation) during the acute recovery period (up to 90 minutes). (Reprinted from Stanley, J. et al.,
Sports Med., 43, 1259–1277, 2013. With permission.)
2008; Platisa et al. 2008), whereas this does not apply to alpha2 (Platisa and Gal 2008).
Alpha1 has been reported to return to baseline values after an immediate overcompensa-
tion phase of about 30 minutes (Casties et al. 2006). Similarly, in untrained subjects sample
entropy (SampEn) is first reduced and regularity of RR intervals is increased, but rises
back to baseline after 30 minutes of recovery (Javorka et al. 2002; Platisa et al. 2008). Fur-
thermore, values of the largest Lyapunov exponent and minimum embedding dimension
(MED) remain increased even after 50 minutes of recovery (Casties et al. 2006). These pre-
liminary results suggest that more regulating systems are involved in the reorganization
of heart beat dynamics during recovery than during resting conditions. Further research is
needed to verify this assumption.
11.5 HRV Analysis as a Tool for Prevention of Overtraining
11.5.1 General Aspects
Performance improvements in athletes require frequent expositions to intensive and exten-
sive training stimuli. As a consequence, training-induced fatigue may persist until the
next exercise session. This accumulation of stress is not uncommon in elite athletes and
leads to insufficient recovery/functional overreaching (FOR). Whereas performance may
still remain at high level in this state, it is lower than the athletes’ personal best. In the
event that training is continued without recovery, there is a high possibility of develop-
ing nonfunctional overreaching (NFOR), which goes along with significant performance
deficits (Ackel-D’Elia et al. 2010; Lehmann et al. 1997; Meeusen et al. 2013). In this state,
several weeks or even months of highly dynamic and individual recovery are necessary
to successfully return to a performance- enhancing training period. Regeneration pro-
cesses are characterized by heterochronous reactions of different, interacting organ
systems and biological signals of high complexity and dynamics. Often the overtrain-
ing syndrome (OTS) can only be diagnosed retrospectively. Its symptoms comprise
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258 ECG Time Series Variability Analysis: Engineering and Medicine
performance deficits, persistent fatigue, lack of motivation, mood changes (with depres-
sive periods), muscle pain, loss of appetite, concentration difficulties as well as increased
susceptibility to infections (Budgett 1998; Roose et al. 2009; Shephard 2001). Due to the
wide variety of symptoms, exercise scientists seek to identify early indicators and predic-
tors of risk for the OTS. NFOR, a transient state of overload with decreased performance for
weeks to months, is also known to decrease adrenal sensitivity to adrenocorticotropic hor-
mone (ACTH) (cortisol release), which is closely related to the activity of the autonomous
nervous system (Lehmann et al. 1997). Consequently, frequent assessments of vagal activ-
ity provide high potential for the early detection of FOR and NFOR. Figure 11.6 shows the
relation between training and overtraining in the sense of a training–overtraining contin-
uum including states of functional and NFOR.
11.5.2 Detection of Overreaching and Overtraining via Resting HRV
The OTS has a multifactorial etiology characterized by physiological, psychological, bio-
chemical, neuroendocrine and neurovegetative disturbances. A subtle balance between
exercise stress and recovery is necessary to elicit optimal adaptations and performance
improvements in high-performance athletes. The dose–response relationship between
training load and HRV adaptations was confirmed by a laboratory study with six untrained
subjects (Pichot et al. 2002). The authors showed that overload led to a stagnation
of parasympathetic indices associated to a progressive increase in sympathetic activ-
ity, whereas a recovery week induced a significant rebound of parasympathetic activity.
Sufficient recovery periods are of high importance in competitive sport, because one exer-
cise session with overload does not necessarily affect HRV (Bernardi et al. 1997; Cornolo
et al. 2005; Hautala et al. 2001; Sztajzel et al. 2006). In contrast, frequent or chronic overload
Overload training
Acute fatigue
Short-term overtraining Long-term overtraining
Insufficient recovery
Chronic fatigue
Training-overtraining continuum
Performance Recovery
Hours –
Days – weeks Weeks – months Months – year
(↓) – / ↓
Training–overtraining continuum. (Modified from Hottenrott, K. and T. Gronwald, Leistungs Sport., 5, 9–13, 2014.)
9781482243475_C011 2017/4/8 17:34 Page 259 #15
Heart Rate Variability Analysis in Exercise Physiology 259
in training and competition decreases HRV (Earnest et al. 2004; Iellamo et al. 2002). This
offers great potential for the prevention of overtraining, as the use of HRV measure-
ments allow the early detection of functional limitations of the autonomous nervous sys-
tem (Uusitalo 2001). Previous work suggests that changes in vagal activity are related to
altered individual activity levels and training status. In this respect, physical activity corre-
lates inversely with vagal modulation (Hottenrott et al. 2006) and positively with sympa-
thetic activity (Fraga et al. 2007; Mueller 2007). After several weeks of endurance training,
vagal modulation of the HR increases significantly in both untrained subjects and elite
athletes. Similarly, an overload period up to 3 weeks (W1, W2, and W3) can also result in
elevated vagal activity in supine and standing position (Figure 11.7), while the athlete’s
performance is compromised. Recently, Le Meur et al. (2013) showed that vagal activity
in standing position decreased after a recovery week and running performance increased
above initial values in the FOR group, whereas no change was observed in controls.
This state would count as FOR, when performance can be restored to or above baseline
after a short recovery period (1 week). In case of temporarily decreased vagal activity dur-
ing the overload period with subsequent rises in HRV during recovery, the athletes state
has to be determined as FOR, too. This was often observed in highly trained athletes, who
are already characterized by a high vagal activity (Figure 11.8). However, a critical state
is reached, when vagal modulation does not increase despite reductions of training load
AQ2: Le
Meur, Y. et al
(2014) has
changed to
Le Meur, Y.
et al (2013) in
order to
match with
the reference
list. Please
Weekly average values
## #
Heart rate (bpm)
LnRMSSD (ms)
Pre W1 W2 W3 R Pre W1 W2 W3 R
55 ### ### ### ###
Standing position
Supine position CTL
Heart rate (HR) and heart rate variability (HRV) variables (mean T 90% CI) at baseline (PRE) and during the
3 weeks of the experimental training program (W1, W2, and W3) and the 1-week taper (recovery [R]) for the con-
trol group (Ctrl, classic training) and for the functionally overreached group (F-OR, overload training). Results are
presented for supine position and standing position (weekly average values). Gray areas represent the smallest
worthwhile change for each group (light gray: Ctrl; dark gray: F-OR). Dotted and straight circles around symbols
denote likely (i.e., 75%–95% chance that the true value of the statistic is practically meaningful) to very likely or
almost certain (i.e., 95% chance that the true value of the statistic is practically meaningful) within-condition dif-
ference from baseline (PRE), respectively. Between-group difference in change versus Pre: #likely, ##very likely,
and ###almost certain. Within-group changes versus W3: *likely, **very likely, and ***almost certain. Between-
group difference in the change during the taper: †likely, ††very likely, and †††almost certain. ( Reprinted from Le
Meur, Y. et al., Med Sci Sports Exerc., 45, 2061–2071, 2013. With permission.)
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260 ECG Time Series Variability Analysis: Engineering and Medicine
Vagal activity in relation to performance and training load. (Modified from Hottenrott, K. and T. Gronwald,
Leistungs Sport., 5, 9–13, 2014.)
over several days. By retrospective analysis, Plews et al. (2012) showed that this specific
development of HRV indicates NFOR, and competitive performance may be significantly
In case the training is continued without recovery periods, there is a high risk of develop-
ing the OTS. The transition from FOR to NFOR and overtraining is transient and therefore,
the states are difficult to distinguish, especially in elite athletes with high vagal activity
(Buchheit 2014). In order to correctly assess levels of fatigue and regeneration, the mea-
surement of additional variables/parameters is required.
Example: Cyclist (Master athlete)
The following case illustrates changes of vagal modulation associated within one week of
high training volume. The cyclist (aged 57 years) passes a total of 1100 km in 6 days, which
equals 183 km per day. A recovery day was not scheduled during this training period.
Spectral analysis of baseline values (collected before training) indicated sympathovagal
balance as LF/HF ratio was about 1. After 195 km cycling on the first day of training, a
reduction of HF power was observed during measurement of HRV in supine position on
the next morning. Following the third day of training, vagal HRV indices dropped below
baseline values and the LF/HF ratio rose to 2.2. This ratio further increased to 2.7 after the
fifth day of training (Figure 11.9). At the same time, RMSSD was decreased below baseline
values (21.2 ms vs. 49.0 ms). Until the fifth day of training, the cyclist reported a fatigue
level of 1 (no fatigue) to 2 (low fatigue). Only on the last day, his rating of fatigue increased
to 3 (medium fatigue). This indicates a discrepancy between objectively measured vari-
ables (HRV) and subjective ratings of perceived fatigue. The intensified training period
was followed by 1 week of recovery. After 2 days, an increase of vagal activity was con-
firmed and on the fifth day of recovery baseline values were reached. However, the LF/HF
ratio should only be interpreted when HRV was assessed during standardized breathing
patterns, because varying respiration rates affect HR peak, for example, a low respiration
rate may increase LF power of HR peak. In elite athletes, who maintain a high vagal tone
(very low resting HR), NFOR can hardly be detected by using HRV. This may be at least in
part be due to the saturation effect of vagal-related HRV indices (Kiviniemi et al. 2004) and
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Heart Rate Variability Analysis in Exercise Physiology 261
0,8 0.1
Day 1
Frequency (Hz)
PSD (s2/Hz)
2,7 0.1
Day 5
Frequency (Hz)
PSD (s2/Hz)
2,2 0.1
Day 3
Frequency (Hz)
PSD (s2/Hz)
Spectral power density of 6-minute RR measurements in supine position. Displayed are baseline, third and fifth
training day of a cyclist aged 57 years (own data extracted from Hottenrott and Haubold (2006). Colors: dark gray
indicates very low frequency (VLF), blue indicates low frequency (LF), light gray indicates high frequency (HF),
and dotted lines indicate the estimated spectral components from the autoregressive modeling (model order: 20)
of spectral analysis.
long-term monitoring with at least 3 measurements per week are warranted to describe the
athletes’ HRV fingerprint for individual recovery prescriptions (Buchheit 2014).
11.5.3 HRV and Orthostatic Stress—A Diagnostic Tool
First, the orthostatic test requires the athlete to rest for 5–10 minutes in supine position in a
quiet, slightly darkened room. Second, he or she is requested to quickly change into stand-
ing position and to maintain a relaxed posture for 3–5 minutes. Switching from supine
to upright position imposes stress by gravitational pooling of the blood in the splanchnic
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262 ECG Time Series Variability Analysis: Engineering and Medicine
venous reservoir and leg veins (Stewart et al. 2006). Consequently, the autonomic nervous
system is required to maintain the hemodynamics to avoid cerebral hypoperfusion. This
goes along with specific changes of vagal activity, so that the orthostatic stress test provides
a practical method for the detection of overload and overtraining. Figure 11.10 displays
the temporal course of the HR during the orthostatic stress test. Healthy subjects show a
reaction of the sympathetic branch of the ANS, meaning that passive head-up tilt testing
increases HR, while blood pressure decreases. In exercise science, an orthostatic test usu-
ally requires active standing-up, which leads to a higher magnitude of changes in HR and
blood pressure.
In a longitudinal study, Schmitt et al. (2013) recorded RR intervals in standing and supine
position. By using a validated questionnaire, the athlete’s state was either classified as
“fatigue” or “no fatigue.” The authors found that in supine position fatigue was associ-
ated with increased HR, LF/HF and LF in normalized units, while LF, HF, TP and HF in
normalized units decreased. In standing position, HR also increased and LF, HF, and TP
decreased in athletes reporting fatigue. Hence, the orthostatic test can be used to assess the
autonomous nervous system’s response to training.
Based on previous findings (Buchheit 2014; Plews et al. 2012, 2013; Le Meur et al. 2013)
and our own experiences in coaching elite athletes, we show typical training-induced
changes that can be seen in a tachogram during orthostatic testing (Figure 11.11). Panel
A displays the HR during orthostatic testing in healthy athletes with uncompromised
Supine position
Active standing-up
Standing position
0 0.5
FIGURE 11.10
Typical tachogram of the heart rate in supine position, active standing-up and standing position. In healthy sub-
jects, spectral power density depicts a ratio of low frequency (LF) to high frequency (HF) of 50%–50% in supine
position and 75%–25% in standing position, respectively. (Modified from Task Force, Circulation., 93, 1043–1065,
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Heart Rate Variability Analysis in Exercise Physiology 263
Supine (5 min) Standing (3 min)
Effect of intensive training (HIT)
Effect of intensive and volume training
Effect of high volume training
Heart rate (min–1)
Baseline (healthy, best condition)
Heart rate (min–1)Heart rate (min–1)Heart rate (min–1)
FIGURE 11.11
Training-induced changes of vagus-mediated HRV measures (RMSSD) displayed in a tachogram of orthostatic
testing (schematic diagram).
performance. The HR is low in supine position and rises rapidly during active standing-
up. Subsequently, a counter-regulation occurs. When there is high circulation stability, HR
in standing position remains higher than the HR in supine position. Standing also induces
a threefold to fourfold decrease of vagal HRV indices from high baseline values (supine
position). High-intensity training over several days can lead to sympathetic overreach-
ing, which can be identified by increased HR and decreased vagal activity in both supine
and standing position (Figure 11.11, panel B). Furthermore, high-volume training com-
bined with some intensive sessions induces similar changes of baseline values, whereas
there is a lower counter-regulatory response. Consequently, the HR difference between
supine and standing position is reduced (Figure 11.11, panel C). Changes of the tachogram
can also be seen after a week of high-volume training (>100%of baseline training dura-
tion) at low exercise intensity (Figure 11.11, panel D). This kind of training may lead to
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264 ECG Time Series Variability Analysis: Engineering and Medicine
parasympathetic overtraining, which manifests itself by a low HR in both supine and
standing position. Due to a high vagal activity, there is (almost) no HR difference between
both positions. The following example illustrates training-induced alterations of the HR
and vagal activity in an orthostatic test (3 minutes in supine position, 2 minutes in standing
Example: female middle- and long-distance runner (TOP 10, Germany)
The V800 HR monitor (Polar Electro) was used to record HRV daily over 5 minutes
in supine position and over 3 minutes in standing position immediately after wak-
ing up over a period of 3 weeks. Collected data were then analyzed with Polar Flow
( Figure 11.12 displays the runner’s (aged 22 years) tachogram after
an overload training period with 24 hours of mountainbike (MTB) training per week
(day 8), 14 hours of MTB and 2 hours of running per week (day 15) as well as a recov-
ery period including 7 hours of MTB and 3 hours of running (day 22). According to
the training volume, the autonomic nervous system reduces resting HR and increases
vagal activity (supine/standing position: HR =41 per minute/42 per minute, RMSSD =
188 ms/159 ms). The athlete did not develop overtraining but FOR, because the reduction
of training volume within the recovery week also decreased vagal modulation to baseline
HR-supine (1/min)
Day 21
Day 14
Day 7
RMSSD-supine (ms) RMSSD-standing (ms)HR-peak (1/min) HR-standing (1/min)
HR-supine (1/min) RMSSD-supine (ms) RMSSD-standing (ms)HR-peak (1/min) HR-standing (1/min)
HR-supine (1/min)
HR-supine (1/min) RMSSD-supine (ms)
RMSSD-supine (ms) RMSSD-standing (ms)
RMSSD-standing (ms)
HR-peak (1/min)
HR-peak (1/min)
HR-standing (1/min)
HR-standing (1/min)
90 55 70
46 169 88 63 64
8241 42 159
142 94 74 25
week 3
9 h endurance
Wee k 2
17 h
week 1
24 h
Baseline values
00:00:25 00:00:50 00:01:15 00:01:40 00:02:05 00:02:30 00:02:55 00:03:20 00:03:45 00:04:10 00:04:35 00:05:00 00:05:25 00:05:50
00:00:25 00:00:50 00:01:15 00:01:40 00:02:05 00:02:30 00:02:55 00:03:20 00:03:45 00:04:10 00:04:35 00:05:00 00:05:25 00:05:50
00:00:25 00:00:50 00:01:15 00:01:40 00:02:05 00:02:30 00:02:55 00:03:20 00:03:45 00:04:10 00:04:35 00:05:00 00:05:25 00:05:50
FIGURE 11.12
Tachogram of orthostatic stress tests (Polar V800, Polar Flow) after high-volume training (panel A), intensified
training with reduced volume (panel B), and recovery (panel C).
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Heart Rate Variability Analysis in Exercise Physiology 265
11.6 Optimization of Endurance Training by HRV Monitoring
Long-term performance enhancements require an optimization of the training process. The
precondition is a systematic planning of training frequency, intensity, and duration. How-
ever, the organism’s response to training stimuli is very complex and highly individual, so
that training adjustments according to the individual physical and regenerative capacities
of the athlete are necessary. The aim of controlling regeneration processes by parameters
of HRV is to be able to adequately balance training load and recovery periods in order to
elicit optimal adaptations to training and to avoid overreaching. As the autonomic nervous
system is mainly responsible for processing stimuli evoked at rest or during exercise, it
seems reasonable to assess its activity throughout different training periods and to use this
information to control regeneration processes. Therefore, Kiviniemi et al. (2007) used an
algorithm to prescribe training loads (low or high intensity) in accordance with the current
HRV (Figure 11.13). This procedure has proved successful in recreational athletes, but its
applicability in competitive sports remains to be evaluated. The algorithm of HRV is based
on measurements in supine position. In elite athletes, this model may not allow a sequence
of intensive training stimuli, which are necessary for the provocation of favorable exercise
Several studies of small sample size confirmed performance benefits after quantifica-
tion of training loads by individual vagal HRV indices. By using a longitudinal design,
Kiviniemi et al. (2007, 2010) also found that training prescriptions based on daily HRV
assessments allow to individualize and optimize training stimuli in both recreational run-
ners (n=30)and untrained subjects (n=48). Following a baseline period, the authors used
HF power (in lnms²after Kiviniemi et al. 2007) or the SD1 value of the Poincaré plot
(after Kiviniemi et al. 2010) to determine whether training has to be adjusted. Whereas
an increase or no change of HRV resulted in high-intensity training, a decrease or decreas-
ing trend (2 days) of HRV indices led to the prescription of no or low-intensity training.
FIGURE 11.13
Algorithm of HRV-guided training prescription. The boxes are labeled with training load (High, Low, or Rest),
and decisions are based on changes in HRV. (Adapted from Kiviniemi, M. A. et al., Eur J Appl Physiol., 101, 743–
751, 2007.)
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266 ECG Time Series Variability Analysis: Engineering and Medicine
Exercise guided by daily HRV measurements elicited greater improvements of maximal
load (+6%–8%) than predefined training (Kiviniemi et al. 2007). The authors also found that
HRV-guided training allows the achievement of significant performance improvements
with lower training load (Kiviniemi et al. 2010).
Another approach for the control of individual regeneration processes is based on the
assessment of an individual baseline. For this purpose, different methods are discussed
next. First, it is recommended to measure vagal HRV indices (RMMSD, SD1, and HF)
daily over 1–2 weeks of recovery training. Alternatively, session-to-session means of HRV
indices can be calculated. This moving average is analyzed in relation to the current daily
measure of HRV and allows the interpretation of the recovery state. However, it is still
unclear how much the values can deviate from the moving average, before an adjustment
of training loads is necessary. For the assessment of the individual baseline, it is not rec-
ommended to use this method during an overload period. Following this measurement of
baseline values, the athlete can start the scheduled training program. Regular assessments
are used to track HRV changes in relation to baseline values. This allows the coach to inter-
vene and adjust the individual training scheme in order to provoke favorable adaptations.
However, day-to-day changes of HRV have to be interpreted with caution and should not
be used as basis for training corrections.
Plews et al. (2012, 2013) found that HRV values averaged over 1 week provide a supe-
rior representation of training-induced changes than HRV values taken on a single day.
In trained athletes, HRV values averaged over 3 days and 7 days both provide accurate
information on the training state. In contrast, recreationally athletes need at least 5 days
of averaging, because their day-to-day variations in Ln RMSSD values are high (Plews et
al. 2012). However, there is some uncertainty about when the deviation of HRV from base-
line indicates a need for training adjustment. In this respect, Buchheit (2014) recommend
to consider HRV changes greater than the smallest worthwhile change (SWC) to be mean-
ingful. Generally a third of within-athlete variation is determined as SWC, while coeffici-
ents of variation of 0.9, 1.6, and 2.5 count as moderate, large, and very large changes
(Hopkins et al. 2009). This formula cannot be transferred to every athlete, because the
appropriate magnitude of SWC is very complex and highly depends on the training con-
text. For more information on its determination, the reader may be referred to the review
by Buchheit (2014). Another approach for the detection of meaningful HRV changes to
justify training adjustments is proposed by Kiviniemi et al. (2007). The authors used the
difference between the standard deviation of the 10-day HF power and the 10-day average
HF power as daily reference value, which moves day by day over the training period.
When the daily value is lower than the reference value for two successive days, HF
power is defined as decreased. At this point, the coach is expected to intervene and adjust
training load.
The definition and standardization of thresholds, which indicate a need to change the
training load, are challenging and require further research. Nevertheless, changes of HRV
indices allow determining when the athlete should perform low- or high-intensity training
(Figure 11.14). Increases in vagal-related HRV during peak volume-based training loads
may be interpreted as positive adaption to training and reductions as a result of the taper
are potentially a sign of readiness to perform (Plews et al. 2014). When training adaptations
are monitored via HRV, it is necessary to consider the athlete’s individual training phase.
For example, strong fatigue can be tolerated during the preparation period, as there is
sufficient time for recovery. In contrast, HRV changes occurring during the competition
period require the coach to quickly adjust the training load.
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Heart Rate Variability Analysis in Exercise Physiology 267
baseline values
(rMSSD, HF, SD1)
Recovery period
Moderate training
xx x
Decrease training load
Increase training load
Time (days)
8–14 days
FIGURE 11.14
Schematic representation of changes in vagal HRV indices in relation to baseline (8–14 days; regenera-
tive/moderate training) over the training period (daily assessments of RMSSD, HF power, SD1).
Our own studies have shown that not only trained athletes, but also recreational
runners benefit from training guided by HRV measurements. A HRV-based program
included in portable training computers significantly improved maximal oxygen uptake
and velocity at the individual anaerobic threshold (Hottenrott et al. 2014). Despite the
progression of exercise intensity, no mood disturbances occurred over the intervention
period. However, the monitoring of the training status should not be restricted to HRV
measurements as this cannot inform on all aspects of wellness, fatigue, and performance.
Buchheit (2014) therefore recommends the use of HRV assessments in combination with
daily training logs, psychometric questionnaires, and noninvasive, cost-effective perfor-
mance tests.
Moreover, activities of daily living and sleep patterns should be considered when HRV
in orthostatic tests is interpreted. This can easily be realized by 24-hour recordings of HR
and HRV by HR monitors, such as the V800 (Figure 11.15).
In conclusion, RMSSD is a sensitive parameter, which allows the detection of changes
in autonomic regulation elicited by endurance training. Ideally, data for analysis should
be recorded with a standardized measurement at rest (over a period of at least 5 minutes)
shortly after awakening. For the differentiation of FOR from NFOR, we recommend to use
the orthostatic test, because it may be considered as an adequate tool for assessing both
states. During training periods of high-volume or high-intensity daily, HRV measurements
are advised to adjust training loads based on the athlete’s individual functional state. For
training periods of moderate intensity, weekly HRV assessments are suggested to pro-
vide sufficient information on exercise-related stress. In competitive sports, HRV should
be measured regularly throughout the year to control the athlete’s response to differ-
ent training stimuli, allowing the assessment of individual regeneration profiles. Whereas
more frequent measurements are recommended in the transition and competitive phase of
endurance training, a few weekly HRV assessments may be sufficient during the prepara-
tory phase.
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268 ECG Time Series Variability Analysis: Engineering and Medicine
7 h 31 min 6 h 44 min 3 h 35 min 2 h 25 min 3 h 43
FIGURE 11.15
Sleep, rest, and activity of the runner on the seventh day of training (Polar V800).
11.7 Summary
In exercise physiology, HRV analysis is considered a useful noninvasive tool for assessing
autonomic modulation of heart rate (HR) during rest, exercise, and recovery. Addition-
ally, HRV is being investigated as a descriptive and diagnostic tool for monitoring indi-
vidual adaptations to short- and long-term training regimens as well as for the detection of
overreaching and overtraining phenomena. At first, this chapter describes evident baseline
changes in HRV due to endurance training referring to both cross-sectional and longitudi-
nal studies. Afterwards, typical changes in HRV variables during exercise and recovery
are presented, and it is shown how these changes are related to training load, exercise
capacity, and/or training status, respectively. In this context, data on dose–response rela-
tions between exercise training and HRV improvement are reviewed and present analysis
methods for HRV-derived threshold detection are shown. Additionally, potentials and con-
straints of the most frequently used time-domain, frequency-domain, and nonlinear meth-
ods are mentioned. Finally, recent perspectives of HRV indices as a tool for prevention of
overreaching and overtraining as well as for individual day-to-day training monitoring are
critically discussed.
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... In the last decade, analytics conducted from non-linear dynamics of HR time series have been adapted to gain further insights into the complex cardiovascular regulation during endurancetype exercise of different types and modes (Hottenrott and Hoos, 2017;Michael et al., 2017;. Thus, certain measures of HR time series may provide new opportunities for diagnostic and monitoring of cardiac autonomic regulation. ...
... Analysis of these structures were already conducted in the investigation of age and disease regarding qualitative characteristics of the structure (scaling), dynamics of the signal, and interaction of involved subsystems (Mansier et al., 1996;Aubert et al., 2003;Voss et al., 2009). One widely applied approach to analyse such characteristics is the non-linear method of detrended fluctuation analysis (DFA) with the possibility to get more insights into the correlation properties of HR time series caused by physiological processes (Goldberger et al., 2002;Hottenrott and Hoos, 2017). As a modification of the root mean square analysis (RMS) the DFA showed a low dependence on HR and is also suitable for investigating short and non-stationary data of time series (Peng et al., 1995;Sandercock and Brodie, 2006;Silva et al., 2017). ...
... The state of research of DFA-alpha1 as an indicator for correlation properties of HR time series has been reviewed most recently . In the corresponding studies investigating high intensity and incremental cycling exercise until voluntary exhaustion, the study results demonstrate a general loss of complexity and variability of R-R interval fluctuations with increasing exercise intensity Casties et al., 2006;Karavirta et al., 2009;Blasco-Lafarga et al., 2017;Hottenrott and Hoos, 2017;Gronwald et al., 2019a,b). More precisely, from low to high intensity exercise, DFA-alpha1 indicates a biphasic course. ...
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Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g. blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non‐linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behaviour and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport‐specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global” system parameter and proxy for organismic demands.
... Within the last 20 years, analytical methods from non-linear dynamics of heart rate (HR) time series have been adapted to gain further insights into the complex cardiovascular regulation as an integrative sub-domain influenced by the autonomous and central nervous system (Sassi et al., 2015). These methods are usually derived from non-linear heart rate variability (HRV) indices and have been used in different applied settings including exercise physiology enriching the understanding about the physiological regulation of our homeodynamic organism especially during endurance exercise (Aubert et al., 2003;Hottenrott & Hoos, 2017;Michael et al., 2017). The present state of research in this field suggests that cardiac dynamics are controlled by complex interactions between the sympathetic and parasympathetic branches of the autonomous nervous system on the sinus node and other non-neural factors (Billman, 2009;Persson, 1996). ...
... Briefly, these studies show, that DFA-alpha1 develops a biphasic course from low to high exercise intensities, with slightly rising values at very low to moderate exercise intensities and strongly decreasing values from moderate-to high-intensity exercises (Blasco-Lafarga et al., 2017;Casties et al., 2006;Gronwald et al., 2019a;Hautala et al., 2003;Karavirta et al., 2009;. This behaviour was attributed to a qualitative change in the self-organised regulation of cardiac rhythm, which coincides with the gradually increasing HR and vagal withdrawal (Gronwald et al., 2019a;Hottenrott & Hoos, 2017;. Furthermore, the reviewed studies (Gronwald & Hoos, 2019) support the notion that exercise intensity and duration may have an interacting effect on DFA-alpha1 and the indicated qualitative change in the selforganised regulation of cardiac rhythm. ...
... This contrasts the fact that a competitive endurance race performance is a more ecological approach and may provide more insight into the complex organismic regulation during severe to maximum intensity exercise, which could be hardly simulated in a laboratory situation with fixed workloads. Additionally, the influence of different performance levels on non-linear dynamics of HRV during exercise has not been given much attention (Heffernan et al., 2008;Hottenrott & Hoos, 2017;Karavirta et al., 2009;Tulppo et al., 2003;. Therefore, we aimed to analyse the influence of a self-paced 10 km race of runners with different performance levels on standard time-domain measures and non-linear dynamics of HRV. ...
The present study examines the influence of a 10 km race of runners with different performance levels on time-domain measures and non-linear dynamics of HRV. Twenty-two male recreational to elite runners performed a self-paced 10 km race on asphalt with flat profile. The participants were divided into two performance groups based on their 10 km total time with a split at 40 min (fTT: fast total times, sTT: slow total times). During the race (Begin, Mid-Point, End), heart rate and RR-intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA) were calculated. Mean total time from fTT was significant faster compared to sTT (35:14 ± 03:15 min:sec vs. 46:34 ± 05:46 min:sec). While RMSSD and SDNN diminished strongly during the race with no differences between groups, we observed significant lower values in DFA-alpha1 at Begin for fTT. In comparison of Begin vs. Mid-Point as well as Begin vs. End a significant decrease could be determined in DFA-alpha1 for sTT. The earlier loss of correlation properties during Begin in fTT implies a fastened alteration of cardiac autonomic regulation in order to match an all-out performance attractor for maximal endurance performance.
... Keywords: autonomic nervous system, heart rate variability, detrended fluctuation analysis, endurance exercise, voluntary exhaustion, hypoxia INTRODUCTION Over the last 20 years, analytical data on the Non-linear dynamics of a heart rate (HR) time series have been adopted to gain further information of the complex process of cardiovascular regulation Sassi et al. (2015), both at rest and during exercise (Hottenrott and Hoos, 2017;Michael et al., 2017). Thus, measures of complexity of an HR time series, such as heart rate variability (HRV), may aid in monitoring cardiac autonomic activity and in gaining more information on the physiological status of the organismic system during exercise (Aubert et al., 2003). ...
... Consequently, some studies have used DFA to analyze a time series during different types and modes of exercise (Tulppo et al., 2001;Hautala et al., 2003;Casties et al., 2006;Platisa et al., 2008;Hottenrott and Hoos, 2017;Gronwald et al., 2018Gronwald et al., , 2019a. However, further studies are necessary to analyze different modes of exercise, as well as changing environmental factors, such as hypoxic conditions (HC) or heat and cold exposure, to gain new insights for the suitability of DFA-alpha1 as a control or monitoring parameter in endurance exercise training (Gronwald et al., 2019b). ...
... However, the decrease in DFA-alpha1 may verify a demanddependent change from strongly correlated behavior in the warm-up periods, to uncorrelated/stochastic or anti-correlated behavior of the RR-intervals during prolonged exercise in both conditions (Platisa and Gal, 2008). This is consistent with previous studies reporting an almost linear reduction in complexity and correlation properties with a gradual change of the RR data structure towards an anti-correlated and merely random signal for medium-to-high exercise intensity demands (Karasik et al., 2002;Hautala et al., 2003;Casties et al., 2006;Platisa et al., 2008;Hottenrott and Hoos, 2017;Blasco-Lafarga et al., 2017;Gronwald et al., 2018Gronwald et al., , 2019a. Due to increased sympathetic activity and/or decreased parasympathetic activity during endurance exercise, the loss of complexity could be related to the disruption of the equilibrium and interaction between the two branches of the autonomic nervous system (Sandercock and Brodie, 2006;Lewis and Short, 2010). ...
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This study aimed to compare the effect of high-intensity interval training (HIIT) with moderate-intensity continuous training (MCT) on endothelial function, oxidative stress and clinical fitness in patients with type 1 diabetes. Thirty-six type 1 diabetic patients (mean age 23.5 ± 6 years) were randomized into 3 groups: HIIT, MCT, and a non-exercising group (CON). Exercise was performed in a stationary cycle ergometers during 40 min, 3 times/week, for 8 weeks at 50-85% maximal heart rate (HRmax) in HIIT and 50% HRmax in MCT. Endothelial function was measured by flow-mediated dilation (FMD) [endothelium-dependent vasodilation (EDVD)], and smooth-muscle function by nitroglycerin-mediated dilation [endothelium-independent vasodilation (EIVD)]. Peak oxygen consumption (VO2peak) and oxidative stress markers were determined before and after training. Endothelial dysfunction was defined as an increase < 8% in vascular diameter after cuff release. The trial is registered at, identifier: NCT03451201. Twenty-seven patients completed the 8-week protocol, 9 in each group (3 random dropouts per group). Mean baseline EDVD was similar in all groups. After training, mean absolute EDVD response improved from baseline in HIIT: + 5.5 ± 5.4%, (P = 0.0059), but remained unchanged in MCT: 0.2 ± 4.1% (P = 0.8593) and in CON: -2.6 ± 6.4% (P = 0.2635). EDVD increase was greater in HIIT vs. MCT (P = 0.0074) and CON (P = 0.0042) (ANOVA with Bonferroni). Baseline VO2peak was similar in all groups (P = 0.96). VO2peak increased 17.6% from baseline after HIIT (P = 0.0001), but only 3% after MCT (P = 0.055); no change was detected in CON (P = 0.63). EIVD was unchanged in all groups (P = 0.18). Glycemic control was similar in all groups. In patients with type 1 diabetes without microvascular complications, 8-week HIIT produced greater improvement in endothelial function and physical fitness than MCT at a similar glycemic control.
... Furthermore, the model (Figure 3 in Laborde et al., 2018) doesn't explain which changes occur when there is already a saturation effect of vagal activity, e.g., in well-trained endurance athletes (Buchheit et al., 2004). In this case additional training interventions may not cause a further increase in vagal activity in a resting supine or sitting measurement (Plews et al., 2013;Bellenger et al., 2016a,b;Hottenrott and Hoos, 2017). To detect further changes in this saturated state the model shouldn't be limited to the analysis of cardiac vagal activity in just one body position but should consider assessing cardiac vagal activity in two different body positions, to better index adaptation mechanisms (e.g., supine and standing) and thus make the "Vagal Tank Theory" more applicable to endurance sports. ...
... In this regard, the orthostatic test has proved its worth (Le Meur et al., 2013;Bellenger et al., 2016b). When applying the findings of the orthostatic test summarized in the model from Hottenrott and Hoos (2017) to the original "Vagal Tank Theory" from Laborde et al. we developed the following modified model (Figure 1). ...
... Situation 1 shows the response to an aerobic endurance training lasting several days up to 2-3 weeks, which represents a moderate parasympathetic stimuli (Aubert et al., 2003;Le Meur et al., 2013, Plews et al., 2013Stanley et al., 2013;Bellenger et al., 2016a). Situation 2 shows the response to an increase in the aerobic training volume for several days up to 2-3 weeks by 100-200% from the initial training load (at baseline), portraying a very high parasympathetic stimulus (Bellenger et al., 2016a,b;Hottenrott and Hoos, 2017). Situation 3 shows the reaction to several days of high-intensity interval training (HIIT) or a micro-shockcycle, triggering a sympathetic reaction/displaying a sympathetic stimulus (Hottenrott and Hoos, 2017;Schneider et al., 2018Schneider et al., , 2019. ...
... Over the last 20 years, analytical data on the Non-linear dynamics of a heart rate (HR) time series have been adopted to gain further information of the complex process of cardiovascular regulation Sassi et al. (2015), both at rest and during exercise (Hottenrott and Hoos, 2017;Michael et al., 2017). Thus, measures of complexity of an HR time series, such as heart rate variability (HRV), may aid in monitoring cardiac autonomic activity and in gaining more information on the physiological status of the organismic system during exercise (Aubert et al., 2003). ...
... Consequently, some studies have used DFA to analyze a time series during different types and modes of exercise (Tulppo et al., 2001;Hautala et al., 2003;Casties et al., 2006;Hottenrott and Hoos, 2017;Gronwald et al., 2018Gronwald et al., , 2019a. However, further studies are necessary to analyze different modes of exercise, as well as changing environmental factors, such as hypoxic conditions (HC) or heat and cold exposure, to gain new insights for the suitability of DFA-alpha1 as a control or monitoring parameter in endurance exercise training (Gronwald et al., 2019b). ...
... However, the decrease in DFA-alpha1 may verify a demanddependent change from strongly correlated behavior in the warm-up periods, to uncorrelated/stochastic or anti-correlated behavior of the RR-intervals during prolonged exercise in both conditions . This is consistent with previous studies reporting an almost linear reduction in complexity and correlation properties with a gradual change of the RR data structure towards an anti-correlated and merely random signal for medium-to-high exercise intensity demands (Karasik et al., 2002;Hautala et al., 2003;Casties et al., 2006;Hottenrott and Hoos, 2017;Blasco-Lafarga et al., 2017;Gronwald et al., 2018Gronwald et al., , 2019a. Due to increased sympathetic activity and/or decreased parasympathetic activity during endurance exercise, the loss of complexity could be related to the disruption of the equilibrium and interaction between the two branches of the autonomic nervous system (Sandercock and Brodie, 2006;Lewis and Short, 2010). ...
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AimMeasurements of Non-linear dynamics of heart rate variability (HRV) provide new possibilities to monitor cardiac autonomic activity during exercise under different environmental conditions. Using detrended fluctuation analysis (DFA) technique to assess correlation properties of heart rate (HR) dynamics, the present study examines the influence of normobaric hypoxic conditions (HC) in comparison to normoxic conditions (NC) during a constant workload exercise.Materials and Methods Nine well trained cyclists performed a continuous workload exercise on a cycle ergometer with an intensity corresponding to the individual anaerobic threshold until voluntary exhaustion under both NC and HC (15% O2). The individual exercise duration was normalized to 10% sections (10–100%). During exercise HR and RR-intervals were continuously-recorded. Besides HRV time-domain measurements (meanRR, SDNN), fractal correlation properties using short-term scaling exponent alpha1 of DFA were calculated. Additionally, blood lactate (La), oxygen saturation of the blood (SpO2), and rating of perceived exertion (RPE) were recorded in regular time intervals.ResultsWe observed significant changes under NC and HC for all parameters from the beginning to the end of the exercise (10% vs. 100%) except for SpO2 and SDNN during NC: increases for HR, La, and RPE in both conditions; decreases for SpO2 and SDNN during HC, meanRR and DFA-alpha1 during both conditions. Under HC HR (40–70%), La (10–90%), and RPE (50–90%) were significantly-higher, SpO2 (10–100%), meanRR (40–70%), and DFA-alpha1 (20–60%) were significantly-lower than under NC.Conclusion Under both conditions, prolonged exercise until voluntary exhaustion provokes a lower total variability combined with a reduction in the amplitude and correlation properties of RR fluctuations which may be attributed to increased organismic demands. Additionally, HC provoked higher demands and loss of correlation properties at an earlier stage during the exercise regime, implying an accelerated alteration of cardiac autonomic regulation.
... HIIE compared to CMI decreased HRV but there was a significant main effect neitheron exercise condition nor on exercise and time interaction. Findings support the notion that HRV will be decreased after exercise as both CMIE and HIIE presented lower values compared to before exercise (Hottenrott & Hoos, 2017). Both exercise stimuli led to an increased parasympathetic inhibition immediately after exercise with subsequent recovery happening as soon as 1-hr postexercise (Hottenrott & Hoos, 2017). ...
... Findings support the notion that HRV will be decreased after exercise as both CMIE and HIIE presented lower values compared to before exercise (Hottenrott & Hoos, 2017). Both exercise stimuli led to an increased parasympathetic inhibition immediately after exercise with subsequent recovery happening as soon as 1-hr postexercise (Hottenrott & Hoos, 2017). Compared to CMIE, all HIIE HRV-related variables showed the expected behavior due to vagal inhibition in addition to the increase in sympathetic stimulation in response to HIIE. ...
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Purpose: Heart rate variability (HRV) has gained acceptance as a key marker of cardiovascular health. We compared HRV responses after continuous moderate-intensity exercise (CMIE) and high-intensity interval exercise (HIIE) matched for intensity and duration in individuals with midspectrum chronic kidney disease (CKD). Methods: Twenty men and women (age 62.0 ± 10 yrs.) diagnosed with CKD stages G3a and G3b participated in a 2 (condition) x 4 (time point) repeated cross-over measures design study. HRV time-domain indices were based on the standard deviation of all NN intervals (SDNN) and the square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD) and frequency domain. High-frequency (HF), low-frequency (LF), total power (TP) were examined. CMIE consisted of treadmill walking for 30 minutes at a 2% incline and speed corresponding to 60%-65% of reserve volume of oxygen (VO2R). HIIE included five intervals of 3 minutes at 90% of VO2R and 2 minutes at 20% VO2R intervals. Conditions were designed to be of the same average intensity (60% to 65% of VO2R) and caloric expenditure (~144 kcal). Results: Immediately following exercise SDNN, RMSSD, HF, LF, and TP were significantly lower compared to before exercise (p <.05). HRV responses were not different between conditions and conditions X time (p >.05). Conclusions: Thirty minutes of either CMIE or HIIE decreased HRV indices, pointing to an autonomic imbalance favoring vagal mediation. HRV's responses regarding HIIE were no different from CMIE, therefore, from an autonomic function point of view this similarity may be useful for CKD exercise prescription and programming.
... Cardiac interbeat interval variation, commonly referred to as heart rate variability (HRV), has been extensively studied in both resting states (Shaffer and Ginsberg, 2017) as well as during dynamic exercise (Hottenrott and Hoos, 2017;Michael et al., 2017). Certain HRV indexes have been observed to change as exercise intensity rises, potentially providing information regarding an individual's physiologic status (Tulppo et al., 1996;Casties et al., 2006;Sandercock and Brodie, 2006;Karapetian et al., 2008;Michael et al., 2017;Gronwald et al., 2018Gronwald et al., , 2019a. ...
... In a study of young men performing a cycling ramp test, an average DFA a1 of 0.49 was associated with a lactate measurement of 2.49, indicating that LT1 had already been exceeded (Gronwald et al., 2019c). Other cycling ramp studies in men of different fitness levels seemed to indicate that DFA a1 crossed the value of 0.75 at about 73-78% of VO 2MAX (Hautala et al., 2003;Hottenrott and Hoos, 2017), within the approximate realm of VT1 for many individuals (Gaskill et al., 2001;Pallarés et al., 2016). An examination of the DFA a1 response to incremental cycling exercise in teenage males (Blasco-Lafarga et al., 2017) showed an approximate crossing of the 0.75 value at an average intensity near 65% of maximum, also near published ranges of VT1 (50-65% of VO 2MAX ) in that age group (Runacres et al., 2019). ...
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The short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of .75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 40.5 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.8 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson’s r values of .99 (p < .001) and .97 (p < .001) for VO2 and HR comparisons respectively. Intraclass correlation between VT1 GAS and HRVT was .99 for VO2 and .96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of .75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.
... Over the last decades, only a few studies have dealt with the effects of ageing on the players' ability to process exercise stimuli and their recovery kinetics. The recovery of the autonomic nervous system after an exercise stimulus can be detected by the measurement of cardiac vagal control using beat-to-beat heart rate recording in an orthostatic test and the calculation of heart rate variability (HRV) indices [4,5]. An age-related decline of resting HRV has already been established previously [6]. ...
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This study used a single bout of repeated high-intensity sprint exercise as a variable to compare the performance and recovery time of handball players within a typical age range for team sport athletes. Two test groups (U20 players, n = 12, mean age = 18 years; senior players, n = 17, mean age = 27 years) were observed during and after their performance of a high-intensity interval exercise consisting of four sets of 6 × 40 m all-out change-of-direction sprints. U20 players outperformed senior players in all sprint sets. The groups’ physiological responses and perceived exertion and stress levels were measured immediately before and after the exercise and also after 24, 48, and 72 h. Repeated measures ANOVAs revealed no interaction effects between age and measurement time points on jump height, muscle soreness, and perceived stress levels after the high-intensity interval exercise. However, the U20 athletes showed marginally, but not statistically significant lower creatine kinase (CK) values than the seniors 72 h after the exercise. The vagal heart rate variability (HRV) parameter rMSSD indicated a faster recovery for the U20 athletes compared to the senior players 24 h after the sprint intervals. Overall, the results demonstrate that repeated sprint intervals do not differently affect the physical performance ability (i.e., jump height) of U20 and senior players. Single parameters related to the players’ ability to recover, such as CK and HRV values, show some variations as a function of age. Based on this, coaches may want to consider a longer recovery period after a high-intensity exercise for senior players compared to younger ones.
... Studies show that a good recovery status becomes visible through high vagal HRV parameters [22] and must be considered as a prerequisite for intensive training. Based on prospective randomized-controlled training studies, HRV monitoring can positively influence performance development in endurance sports [23]. In particular, the vagal HRV parameters are suitable for detecting overtraining [24]. ...
There is general agreement that exercise training leads to functional, morphological, and metabolic adaptations of different biological systems, thereby increasing overall physical performance and promoting good health. Thus, an active lifestyle is propagated in all age groups. However, not every exercise routine or workout is suitable for everyone. Inappropriate training can also pose risks, and too low or too high training intensity or volume often does not lead to the expected success. To ensure significant benefits, specific principles and strategies need to be considered and accustomed to the individual.
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Мета. Аналіз фізіологічних основ і оцінки ефективності застосування показників варіабельності серцевого ритму у спортсменів. Методи.Системно-функціональний аналіз спеціальної літератури та матеріалів мережі Інтернет ((PubMed, Google Scholar) )за останні десять років. Результати. Науковий доробок щодо вивчення варіабельності серцевого ритму у спортсменів є доволі значним. Регулярні заняття фізичними вправами та спортом сприяють функціональним та структурним змінам центральних та периферичних механізмів роботи серцево-судинної системи. У дослідженнях, що порівнюють варіабельність серцевого ритму між малорухливими та активними суб'єктами або спортсменами різних видів спорту, показано різні профілі варіабельності серцевого ритму, що говорить про можливість моніторингу показників ВСР для покращення фізичних та фізіологічних станів. Варіабельність серцевого ритму відразу після фізичного навантаження відображає характерні реакції, які вказують, чи навантаження відповідає атлетичній підготовленості спортсмена, але у спортсменів високої кваліфікації на витривалість та/або спортсменів з багаторічною історією тренувань, алгоритм змін показників варіабельності серцевого ритму не завжди відповідає загальноприйнятим. Інтенсивність вправ є основним фактором, що впливає на варіабельність серцевого ритму, при цьому більша інтенсивність викликає нижчу варіабельності серцевого ритму під час фізичного навантаження, а об’єм м'язів, які приймають участь при тренуванні / або енерговитрати є визначальними факторами парасимпатичної реактивації після тренування. «Повна автономна дистонія» у більшості спортсменів з перетренуванням відображає більш розвинуту стадію дезадаптації, пов'язану з пригніченою регуляторною функцією автономної нервової системи, як симпатичного, так і вагусного впливу. Висновок. Отже, на сьогодні недостатньо експериментальних робіт, які могли б пояснити механізми змін варіабельності серцевого ритму при інтенсивних і тривалих тренуваннях у спортсменів високої кваліфікації. Про те, це дало б змогу розширити застосування результатів діагностики варіабельності серцевого ритму у спортсменів для планування тривалості, частоти та інтенсивності фізичних навантажень.
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Measures of resting, exercise, and recovery heart rate are receiving increasing interest for monitoring fatigue, fitness and endurance performance responses, which has direct implications for adjusting training load (1) daily during specific training blocks and (2) throughout the competitive season. However, these measures are still not widely implemented to monitor athletes' responses to training load, probably because of apparent contradictory findings in the literature. In this review I contend that most of the contradictory findings are related to methodological inconsistencies and/or misinterpretation of the data rather than to limitations of heart rate measures to accurately inform on training status. I also provide evidence that measures derived from 5-min (almost daily) recordings of resting (indices capturing beat-to-beat changes in heart rate, reflecting cardiac parasympathetic activity) and submaximal exercise (30- to 60-s average) heart rate are likely the most useful monitoring tools. For appropriate interpretation at the individual level, changes in a given measure should be interpreted by taking into account the error of measurement and the smallest important change of the measure, as well as the training context (training phase, load, and intensity distribution). The decision to use a given measure should be based upon the level of information that is required by the athlete, the marker's sensitivity to changes in training status and the practical constrains required for the measurements. However, measures of heart rate cannot inform on all aspects of wellness, fatigue, and performance, so their use in combination with daily training logs, psychometric questionnaires and non-invasive, cost-effective performance tests such as a countermovement jump may offer a complete solution to monitor training status in athletes participating in aerobic-oriented sports.
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Aim was to elucidate autonomic responses to dynamic and static (isometric) exercise of the lower limbs eliciting the same moderate heart rate (HR) response. 23 males performed two kinds of voluntary exercise in a supine position at similar heart rates: static exercise (SE) of the lower limbs (static leg press) and dynamic exercise (DE) of the lower limbs (cycling). Subjective effort, systolic (SBP) and diastolic blood pressure (DBP), mean arterial pressure (MAP), rate pressure product (RPP) and the time between consecutive heart beats (RR-intervals) were measured. Time-domain (SDNN, RMSSD), frequency-domain (power in the low and high frequency band (LFP, HFP)) and geometric measures (SD1, SD2) as well as non-linear measures of regularity (approximate entropy (ApEn), sample entropy (SampEn) and correlation dimension D2) were calculated. Although HR was similar during both exercise conditions (88±10 bpm), subjective effort, SBP, DBP, MAP and RPP were significantly enhanced during SE. HRV indicators representing overall variability (SDNN, SD 2) and vagal modulated variability (RMSSD, HFP, SD 1) were increased. LFP, thought to be modulated by both autonomic branches, tended to be higher during SE. ApEn and SampEn were decreased whereas D2 was enhanced during SE. It can be concluded that autonomic control processes during SE and DE were qualitatively different despite similar heart rate levels. The differences were reflected by blood pressure and HRV indices. HRV-measures indicated a stronger vagal cardiac activity during SE, while blood pressure response indicated a stronger sympathetic efferent activity to the vessels. The elevated vagal cardiac activity during SE might be a response mechanism, compensating a possible co-activation of sympathetic cardiac efferents, as HR and LF/HF was similar and LFP tended to be higher. However, this conclusion must be drawn cautiously as there is no HRV-marker reflecting "pure" sympathetic cardiac activity.
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The aim of this study was to establish the minimum number of days that HRV (i.e., the natural logarithm of square root of the mean sum of the squared differences between R-R intervals, Ln rMSSD) data should be averaged in order to achieve correspondingly equivalent results as data averaged over a 1-week period. Standardised changes in Ln rMSSD between different phases of training (normal training, functional overreaching (FOR), overall training and taper) and the correlation coefficients of percentage changes in performance vs. changes in Ln rMSSD were compared when averaging Ln rMSSD from 1 to 7 days, randomly selected within the week. Standardised Ln rMSSD changes (90% confidence limits, CL) from baseline to overload (FOR) were 0.20 ±0.28; 0.33 ±0.26; 0.49 ±0.33; 0.48 ±0.28; 0.47 ±0.26; 0.45 ±0.26 and 0.43 ±0.29 using from 1 to 7 days, respectively. Correlations (90% confidence limits (CL)) over the same time sequence and training phase were: -0.02 ±0.23; -0.07 ± 0.23; -0.17 ±0.22; -0.25 ±0.22; -0.26 ±0.22; -0.28 ±0.21 and -0.25 ±0.22 from 1 to 7 days, respectively. There were almost perfect quadratic relationships between standardised changes/r values vs. the number of days Ln rMSSD was averaged (r2 = 0.92 and 0.97, respectively) in trained triathletes during FOR. This indicates a plateau in the increase in standardised changes/r values magnitude after 3 and 4 respectively days in trained triathletes. This suggests that practitioners using HRV to monitor training adaptation should use a minimum of 3 (randomly selected) valid data points per week.
The chronic fatigue syndrome is characterised by a fatigue that is disproportionate to the intensity of effort that is undertaken, has persisted for 6 months or longer, and has no obvious cause. Unless there has been a long period of patient- or physician- imposed inactivity, objective data may show little reduction in muscle strength or peak aerobic power, but the affected individual avoids heavy activity. The study of aetiology and treatment has been hampered by the low disease prevalence (probably
Overtraining (OT) as a sports phenomenon can be caused by stressors on various levels (physical, emotional, psychological, and social) and evokes responses on these levels. This study evaluated research and new opportunities in the field of OT by introducing an integrated multidisciplinary approach, based on the single and multistressors approach. The single stressor approach focuses on the training load-recovery imbalance, which results in a stagnating performance, excluding the etiology by nonsport-related factors. The multistressors approach includes all factors as relevant in the etiology of a stagnating performance. In future studies on OT, an integrative approach should not only highlight changes in training regimes and specific responses to training stressors but also focus on the role of training-related recovery, the impact of stressors, and personality factors influencing stress appraisal. This will provide a better insight into the etiology and consequences of OT, necessary for prevention and treatment in sport practice, and enhance the focus on adequate recovery (good sleep, sufficient rest periods) and athletes' stress-related responses.