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There is only very limited data examining cardiovascular responses in real-world endurance training/competition. The present study examines the influence of a marathon race on non-linear dynamics of heart rate (HR) variability (HRV). Eleven male recreational runners performed a self-paced marathon road race on an almost flat profile. During the race, heart rate and beat-to-beat (RR) intervals were recorded continuously. Besides HRV time-domain measurements, fractal correlation properties using short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) were calculated. The mean finishing time was 3:10:22 ± 0:17:56 h:min:s with a blood lactate concentration of 4.04 ± 1.12 mmol/L at the end of the race. Comparing the beginning to the end segment of the marathon race (Begin vs. End) significant increases could be found for km split time (p < .001, d = .934) and for HR (p = .010, d = .804). Significant decreases could be found for meanRR (p = .013, d = .798) and DFA-alpha1 (p = .003, d = 1.132). DFA-alpha1 showed an appropriate dynamic range throughout the race consisting of both uncorrelated and anti-correlated values. Lactate was consistent with sustained high intensity exercise when measured at the end of the event. Despite the runners slowing after halfway, DFA-alpha1 continued to fall to values seen in the highest intensity domain during incremental exercise testing in agreement with lactate assessment. Therefore, the discrepancy between the reduced running pace with that of the decline of DFA-alpha1, demonstrate the benefit of using this dimensionless HRV index as a biomarker of internal load during exercise over the course of a marathon race.
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©Journal of Sports Science and Medicine (2021) 20, 557-563 DOI:
Received: 21 January 2021 / Accepted: 25 June 2021 / Published (online): 10 July 2021
Correlation Properties of Heart Rate Variability during a Marathon Race in
Recreational Runners: Potential Biomarker of Complex Regulation during
Endurance Exercise
Thomas Gronwald 1, Bruce Rogers 2, Laura Hottenrott 3, Olaf Hoos 4 and Kuno Hottenrott 5
1 Department of Performance, Neuroscience, Therapy and Health, Faculty of Health Sciences, MSH Medical School
Hamburg, University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457 Hamburg, Germany; 2 Depart-
ment of Internal Medicine, College of Medicine, University of Central Florida, 6850 Lake Nona Boulevard, Orlando,
Florida, 32827-7408, USA; 3 Institute of performance diagnostics and health promotion, Martin-Luther-University Halle-
Wittenberg, Von-Seckendorff-Platz 2, 06120 Halle (Saale), Germany; 4 Center for Sports and Physical Education, Julius-
Maximilians-University of Wuerzburg, Am Hubland, 97074 Wuerzburg, Germany; 5 Institute of Sports Science, Martin-
Luther-University Halle-Wittenberg, Von-Seckendorff-Platz 2, 06120 Halle (Saale), Germany
There is only very limited data examining cardiovascular re-
sponses in real-world endurance training/competition. The pre-
sent study examines the influence of a marathon race on non-lin-
ear dynamics of heart rate (HR) variability (HRV). Eleven male
recreational runners performed a self-paced marathon road race
on an almost flat profile. During the race, heart rate and beat-to-
beat (RR) intervals were recorded continuously. Besides HRV
time-domain measurements, fractal correlation properties using
short-term scaling exponent alpha1 of Detrended Fluctuation
Analysis (DFA-alpha1) were calculated. The mean finishing time
was 3:10:22 ± 0:17:56 h:min:s with a blood lactate concentration
of 4.04 ± 1.12 mmol/L at the end of the race. Comparing the be-
ginning to the end segment of the marathon race (Begin vs. End)
significant increases could be found for km split time (p < .001, d
= .934) and for HR (p = .010, d = .804). Significant decreases
could be found for meanRR (p = .013, d = .798) and DFA-alpha1
(p = .003, d = 1.132). DFA-alpha1 showed an appropriate dy-
namic range throughout the race consisting of both uncorrelated
and anti-correlated values. Lactate was consistent with sustained
high intensity exercise when measured at the end of the event.
Despite the runners slowing after halfway, DFA-alpha1 contin-
ued to fall to values seen in the highest intensity domain during
incremental exercise testing in agreement with lactate assess-
ment. Therefore, the discrepancy between the reduced running
pace with that of the decline of DFA-alpha1, demonstrate the ben-
efit of using this dimensionless HRV index as a biomarker of in-
ternal load during exercise over the course of a marathon race.
Key words: Autonomic nervous system, Detrended Fluctuation
Analysis, non-linear dynamics, endurance exercise.
Marathon running has been extensively studied in terms of
underlying cardiovascular physiology, substrate metabo-
lism, biomechanical efficiency, and even motivational fac-
tors (Zinner and Sperlich, 2016). However, although there
are data describing behavior of heart rate (HR) variability
(HRV) during the actual event (Franco et al., 2014), the
specific indices studied have been less than optimal for as-
sessment of real-time exercise intensity because of a lack
of appreciable changes during exercise. Although time-
and frequency-domain related linear HRV measures such
as the standard deviation of all normal beat-to-beat (RR)
intervals SDNN, the root mean square of successive differ-
ences (RMSSD) and high-frequency (HF) power have been
shown to change with exercise intensity, they generally fol-
low a trajectory showing a loss of dynamic range around
the first ventilatory threshold (VT1) (Tulppo et al., 1996;
Cottin et al., 2007; Karapetian et al., 2008).
In this context, non-linear HRV analysis is a prom-
ising approach for internal load monitoring and physio-
logic threshold determination (Gronwald et al., 2020a).
Detrended Fluctuation Analysis (DFA; Peng et al., 1995)
with its dimensionless short-term scaling exponent alpha1
(DFA-alpha1) reveals fractal correlation properties of HR
time series within a range of 0 and 2 and shows clear de-
pendency on changes to organismic demands related to ex-
ternal load markers such as intensity, cadence and duration,
leading to insights in complex regulation during endurance
exercise (for review see Gronwald and Hoos, 2020; for ap-
plication see Gronwald et al., 2020a). A recent investiga-
tion has shown that this non-linear index based on fractal
correlation properties of HRV changes with exercise inten-
sity with the midpoint of its range being near the VT1,
thereby demonstrating potential as a physiological marker
for a wide range of workloads (Gronwald et al., 2020a;
Rogers et al., 2021). Therefore, this approach shows great
potential regarding the investigation of Network Physiol-
ogy of Exercise (NPE) recently introduced by Balagué et
al. (2020). However, despite the growing body of literature
from laboratory studies in this field, HRV data from real-
world endurance training/competition is rarely available
for linear HRV indices (Sztajzel et al., 2006; Cottin et al.,
2007; Franco et al., 2014). For non-linear DFA-alpha1,
Gronwald et al. (2020b) conducted a pilot study during pro-
longed severe to maximal endurance exercise during a
10km road race and found a clear reduction in correlation
properties in RR fluctuations that might be attributed to in-
creased organismic demands. Furthermore, they showed
that a higher performance outcome in terms of a faster total
running time implies the necessity of an earlier loss of cor-
relation properties during the beginning of the exercise reg-
imen, indicating an accelerated alteration of cardiac
Research article
Non-linear HRV during a Marathon
autonomic regulation and cardio-pulmonary onset-kinetics
at a higher performance standard. The fact that a self-paced
competitive endurance race performance provides more in-
sights into the complex organismic regulation during se-
vere to maximum intensity exercise, elucidates the need for
further research in this field. In addition, since much of the
current information related to exercise and DFA-alpha1
has been obtained during short-term incremental ramp pro-
tocols (Gronwald and Hoos, 2020), further insights may be
gleaned by observing the behavior over the course of
longer exercise sessions such as a marathon road race.
The purpose of this report is to gain more general
insight into non-linear HRV dynamics during prolonged
endurance exercise competition and to evaluate the poten-
tial of DFA-alpha1 as a marker for internal load and com-
plex regulation during long duration exercise. Most of the
exercise intensity during a marathon race is performed near
aerobic threshold (Zinner and Sperlich, 2016) which is not
considered a strenuous work rate (Jamnick et al., 2020), al-
beit for a very prolonged period of time. However, the
measurement of the running speed at the aerobic threshold
is generally done via short duration constant power or in-
cremental ramp testing (Coen et al., 2001; Faude et al.,
2009). Few studies have examined real time blood lactate
measurements during a long road race, but evidence indi-
cates values between 2 to 3 mmol/L (Santos et al., 2006),
mostly well above typical threshold values for the aerobic
threshold (Faude et al., 2009). Thus, there appears to be a
possible discrepancy between a measure of internal load
intensity (e.g. blood lactate concentration) with that of ex-
ternal load intensity (e.g. running speed at aerobic thresh-
old). Insights into the issue of internal vs. external load
monitoring are possible given the dynamic range of DFA-
alpha1 throughout the spectrum of exercise intensities.
Since direct sampling of blood lactate is not practical dur-
ing a competitive long-distance event, only immediate
post-race values will be obtained. Therefore, non-linear
DFA-alpha1, along with standard linear time-domain
measures of HRV will be examined for potential usefulness
as internal load monitors. We assume that DFA-alpha1 add
complementary information about complex cardiovascular
regulation during marathon racing in addition to the rather
low but steady increases in HR due to cardiovascular drift.
Fifteen participants were recruited by newspaper advertise-
ment or personal approach two hours before the marathon
race in the warm-up area and voluntarily participated in the
study. All runners were free from any medical issues. After
being informed extensively about the procedures and ob-
jectives of the study, they provided informed consent in ac-
cordance to the institutional review board and the guide-
lines of the Helsinki World Medical Association Declara-
tion. After exclusion of RR-interval artefacts eleven male
runners (age: 36.6 ± 9.6 years; height: 1.77 ± 0.07 m; body
mass: 70.9 ± 9.8 kg) with a weekly training volume of 50
to 100km, and a personal best marathon time of 2:45 to
3:30 h:min were included in the study analysis.
Study Design
The data were collected during an official marathon race
on a certified asphalt road course (German Road Races As-
sociation) with an almost flat profile (overall elevation be-
tween start and finish line: 80m; temperature: 22 °C, alti-
tude: 282 m above sea level). During exercise, heart rate
(HR) and RR-intervals were recorded continuously during
the race (beat-to-beat-modus) using a HR-monitor with a
time resolution of 1ms (Polar S810, Polar Electro GmbH,
Germany; Kingsley et al., 2005; Nunan et al., 2009). Addi-
tionally, blood lactate (La) and blood glucose (Glu) con-
centration were assessed from each runner immediately
(within the first minute) after crossing the finish line and
were analyzed using an enzymatic-amperometric method
(Super GL ambulance, Dr. Mueller Geraetebau GmbH,
Germany; Faude and Meyer, 2008).
HRV Analysis
Collected raw data were transferred to a PC via an infrared
interface. The NN intervals were stored as ASCII files for
further data analysis. Using Kubios HRV software (Pre-
mium Version 3.4.1, Biosignal Analysis and Medical Im-
aging Group, Kuopio, Finland; Tarvainen et al., 2014), the
HRV analysis was conducted on data collected from 5 min
every 10% (10-100%) of the race for each runner. Raw data
were visually inspected and artefacts were corrected with
the Kubios proprietary automatic artefact correction
method. Only participants whose data contained artefact
rates of <3% were included for analysis. Besides standard
HRV indices obtained from time-domain analysis includ-
ing the mean of normal RR-interval length (meanRR in
ms), the total variability as standard deviation of all normal
RR-intervals (SDNN in ms) and the root mean square of
successive differences (RMSSD in ms; Ciccone et al.,
2017), the scaling behavior for assessing correlation prop-
erties was calculated using the non-linear short-term scal-
ing exponent DFA-alpha1 (window width: 4 ≤ n ≤ 16
beats). DFA-alpha1 values indicate time series correlation
properties, i.e. type of noise: approx. 1.5 for strongly cor-
related Brownian noise and ≤ 0.5 for uncorrelated white
noise with random signals. Approx. 1 signifies a mix of un-
correlated and maximally correlated signal components
with 1/f noise (represents a balance between the complete
unpredictability (randomness) of white noise and the pre-
dictability (strong correlations) of Brownian noise) (Platisa
and Gal, 2008).
Statistical analysis
The statistical analysis was performed with SPSS 23.0
(IBM Statistics, USA) for Windows (Microsoft, USA).
The Shapiro-Wilk test was applied to verify the Gaussian
distribution of the data. The degree of variance homogene-
ity was verified by the Levene test. Subsequently, to ana-
lyze the effects of the exercise bout on dependent variables
(HR, HRV parameters) and 1 km split times between meas-
uring intervals 10% (Begin) and 100% (End) paired sample
T-tests were applied. For all tests, statistical significance
was accepted for p .05. Cohen’s d was used to denote
effect sizes in comparison of the measurement intervals
(small effect = 0.2, medium effect = 0.5, large effect = 0.8;
Cohen, 1988).
Gronwald et al.
The mean finishing time was 3:10:22 ± 0:17:56 h:min:s
(minimum values: 2:43:18 h:min:s; maximum values:
3:33:14 h:min:s) with a blood lactate concentration of 4.04
± 1.12 mmol/L and a blood glucose concentration of 6.15
± 1.21 mmol/L at the end of the race. Comparing the be-
ginning to the end segment of the marathon race (Begin vs.
End) significant increases with large effect size were found
for km split time (p < .001, d = .934) and for HR (p = .010,
d = .804), showing higher HR with slower km split times.
Significant decreases with large effect size were found for
meanRR (p = .013, d = .798) and DFA-alpha1 (p = .003, d
= 1.132). There were no significant changes in SDNN (p =
.802, d = .072) and RMSSD (p = .188, d = .379). Descrip-
tive values (mean ± standard deviation) of all analyzed pa-
rameters during the race (10-100%) are provided in Table
1. In addition, Figure 1 and Figure 2 show the km split
times, HR and DFA-alpha1 over time. Figure 3 provides an
additional view (km split time, HR, DFA-alpha1, RMSSD
and SDNN) of one subject with the time-varying analysis
function of Kubios HRV Software (Version 3.4.1).
Table 1. Km split times, HR, and HRV measures (mean ± standard deviation) during the marathon race (10%-100%).
Parameters 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Km split times
[ ]
HR: heart rate, meanRR: average of normal RR-intervals, SDNN: standard deviation of all normal RR-intervals, RMSSD: root mean square of succes-
sive differences, DFA-alpha1: short-term scaling exponent of Detrended Fluctuation Analysis; * significant comparing 10% (Begin) vs. 100% (End).
Figure 1. Graphic view of HR and km split times over time (mean ± standard deviation).
The aim of the present study was to gain more insight into
non-linear HRV dynamics during prolonged endurance ex-
ercise competition and to evaluate the potential of the non-
linear index DFA-alpha1 as a marker for internal load and
complex regulation along with standard linear time-do-
main measures of HRV. This study provides unique in-
sights in the behavior of both time-domain measures
(SDNN and RMSSD) and DFA-alpha1 of HRV over the
course of a marathon race. As expected, given the consid-
erable exercise intensity of this event, changes in all three
markers were noted. However, SDNN and RMSSD were
very low (<5ms) during the whole race and did not show
substantial variation during the time course of the race,
which is in agreement with previous work that showed the
lack of discriminative power of linear HRV indices beyond
moderate exercise intensities (Tulppo et al., 1996; Kara-
petian et al., 2008). This is also in line with studies linking
both SDNN and RMSSD as threshold markers signifying
the VT1. To determine a corresponding threshold intensity,
they required an incremental ramp to display a discernible
nadir at the VT1, making them ill-suited to denote internal
load intensity in random, untested subjects. In this context,
Non-linear HRV during a Marathon
Figure 2. Graphic view of DFA-alpha1 and km split times over time (mean ± standard deviation).
DFA-alpha1 as a dimensionless index of fractal correlation
properties and complex regulation has shown the ability to
indicate current intensity loads without prior performance
level information (Gronwald and Hoos, 2020). Based on
previous observations, the VT1 occurs near its midpoint of
range therefore making it suitable for a wide range of ex-
ercise intensities (Gronwald et al., 2020a). Interestingly,
DFA-alpha1 displayed a rather large dynamic range over
the course of the marathon, denoting possible fluctuations
in internal load during the long distance road race.
In regard to the question pertaining to the agreement
of external vs. internal load monitoring several observa-
tions are of note. In accordance with recent findings (San-
tos et al., 2006; Myrkos et al., 2020), most of the runners
in this study ran a slower second half of the race as dis-
played in their reduced running speed (positive pacing pro-
file). Despite a slower pace in the second part of the race,
DFA-alpha1 declined to values well into those associated
with severe intensity domains (Gronwald et al., 2020b).
Fluctuation between values of 0.75 and 0.25 seem to de-
pend upon a narrow band of heart rates in a given individ-
ual (see Fig. 3). In other words, as heart rate fluctuated
around a subject’s particular “threshold” level, DFA-
alpha1 exhibited changes in the range of uncorrelated and
anti-correlated (random) patterns. Although heart rate is a
well-established marker of cardiac strain and internal load-
ing, without a predefined knowledge of each individual’s
physiologic reference values (HR at lactate thresholds, HR
to VO2 relation), defining a particular load level is prob-
lematic. Even though it is well known that marathon exer-
cise intensity, measured by an external non-biological load
parameter (speed), is performed near the aerobic threshold,
it appears that a measure of internal biological load (DFA-
alpha1) indicates intensity near maximal physiologic
bounds. Speculation as to the cause of these disparate
measurements of load (speed vs. HRV derived DFA-
alpha1) may be better understood by reviewing behavior of
another biologic marker such as blood lactate concentra-
tion. Prior studies of lactate measurements taken every
6km in elite marathon runners during a 30km all-out race
have shown values from 2.39 mmol/L at 6km to 3.15
mmol/L at 30 km throughout the race (Santos et al., 2006).
Therefore, even though a marathon may be performed at or
below the conventionally defined intensity of lactate accu-
mulation measured through ramp or stage testing, these
“threshold” intensity levels may not hold for multi hour ex-
ercise. The present findings seem to support a moderate
steady state elevation of lactate but well below the point of
what may be considered a maximal steady state (Perrey et
al., 2003). In a study in cyclists (Gronwald et al., 2019),
DFA-alpha1 measured during a prolonged incremental
ramp averaged 0.5 at lactate values near 2mmol/l. In addi-
tion, despite lactate not being measured during the actual
marathon run in this report, values of blood lactate concen-
tration immediately after the race averaged 4mmol/l, con-
sistent with near maximal steady state values and severe
in tensit y le vels. Al tho ugh fur ther studie s are ne eded to sup-
port the possibility of using the wide dynamic range of
DFA-alpha1 as a biomarker for submaximal metabolic
steady state, both the current study and the previously done
incremental ramp testing have indicated this possibility.
Although the data presented here have been slightly af-
fected by artefacts, very few studies have been conducted
on the influence of artefact presence and correction on
DFA-alpha1 during exercise so far. Further investigations
are needed to determine appropriate cutoffs for artefact and
any resultant bias to the DFA-alpha1 index. In addition,
only male runners were included in this study, diminishing
the generalization to female athletes or participants of var-
ious age and ethnic groups. Ideally, lactate could have been
measured on a regular basis throughout the marathon but
was only obtained immediately post finish. However, do-
ing so would have disrupted the normal procedure of the
race and is not practical in a field race situation. The aver-
age value of 4mmol/l seen here agrees with that of Santos
et al. (2006), and is consistent with severe intensity exer-
cise domains just below the maximal steady state (Faude et
al., 2009; Beneke et al., 2011), avoiding exponential rise in
blood lactate concentration (Farrell et al., 1979). Finally,
the use of a chest belt recording device is often employed
in field studies. However, agreement with medical grade
ECG recording equipment has not yet been proven for
DFA-alpha1 determination during exercise conditions.
Gronwald et al.
Figure 3. Time-varying analysis with Kubios HRV Software (Version 3.4.1; window width: 120 s,
grid interval: 5 s) of one subject during the marathon (Total time: 3:11:57 h:min:s). Top: HR and
DFA-alpha1 plotted over time. Bottom: HR, RMSSD and SDNN plotted over time.
A comparison of non-linear vs. linear time-domain HRV
indices changes over the course of a marathon race
highlight substantial fluctuations in DFA-alpha1 as an
indicator of relative internal exercise load and proxy of or-
ganismic demands. DFA-alpha1 showed a wide dynamic
range throughout the race consisting of both uncorrelated
and anti-correlated values indicating dynamic modulation
processes during the course of the race. Despite that, both
Non-linear HRV during a Marathon
SDNN and RMSSD showed minimal fluctuations and thus
were poorly suitable to discriminate the development of fa-
tigue during prolonged high intensity exercise in the field.
Lactate as a measure of internal metabolic load was con-
sistent with sustained high intensity exercise when meas-
ured at the end of the event. Heart rate was elevated
throughout the marathon and increased over time, but with-
out knowing an individual’s baseline VO2 kinetics (or max-
imum value of HR), it does not provide sufficient infor-
mation on relative intensity. Evaluation of an external load
indicator, running speed showed a gradual slowing espe-
cially past the midpoint (positive pacing profile). Despite a
reduction of this external load indicator, the DFA-alpha1
continued to fall to values seen in the highest intensity do-
main during incremental exercise testing in agreement with
lactate assessment. Therefore, the discrepancy between the
reduced running pace with that of the decline of DFA-
alpha1, may demonstrate that there is a complex modula-
tory dynamics present during the course of the race that
may not be detected by conventional linear HRV measure-
ments (Molkkari et al., 2020). Therefore, the dimensionless
biomarker DFA-alpha1 may demarcate the complex dy-
namics of internal load development over the course of a
marathon race as a descriptor of NPE (Balagué et al.,
2020). Applying these findings to other endurance events
and pacing strategies may provide further information on
complex modulation during exercise training and intensity
distribution for performance enhancement and mitigation
against overtraining from inappropriate internal stress.
The authors wish to thank the athletes for their participation in the present
study. The experiments comply with the current laws of the country in
which they were performed. The authors have no conflict of interest to
declare. The datasets generated during and/or analyzed during the current
study are not publicly available, but are available from the corresponding
author who was an organizer of the study.
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Tulppo, M.P., Makikallio, T.H., Takala, T.E., Seppanen, T. and Huikuri,
H. V. (1996) Quantitative beat-to-beat analysis of heart rate dy-
namics during exercise. American Journal of Physiology 271,
Zinner, C. and Sperlich, B. (2016) Marathon Running: Physiology, Psy-
chology, Nutrition and Training Aspects. Basel: Springer Inter-
national Publishing.
Key points
DFA-alpha1 is defined as an indicator of relative in-
ternal load and proxy of organismic demands
DFA-alpha1 showed a wide dynamic range through-
out the marathon race consisting of both uncorre-
lated and anti-correlated values supporting its ability
to finely differentiate internal physiologic status and
indicating dynamic modulation processes
Lactate as a measure of internal metabolic load was
consistent with sustained high intensity exercise
when measured at the end of the event
The discrepancy between the reduced running pace
with that of the decline of DFA-alpha1 and high lac-
tate measures may demonstrate the utility of DFA-
alpha1 as a biomarker of internal load over the
course of a marathon race showing complex modu-
latory dynamics with increasing fatigue
Kuno Hottenrott, PhD
Institute of Sports Science, Martin-Luther-University Halle-Wit-
tenberg, von-Seckendorff-Platz 2, 06120 Halle (Saale), Germany
Professor for Sports and Exercise Sci-
ence, Dept. Performance, Neuroscience,
Therapy and Health, MSH Medical
School Hamburg
Research interests
Exercise physiology, load monitoring,
diagnostics, injury prevention, HRV
E-mail: thomas.gronwald@medi-
Department of Medicine, University of
Central Florida
Research interests
Exercise physiology, load monitoring,
consumer wearables
Institute of performance diagnostics and
health promotion, Martin-Luther-Uni-
versity Halle-Wittenberg
Master of Science
Research interests
Training adaptations, aging,sex-differ-
ences, endurance sports, cardiac auto-
nomic control
Professor for Sports and Exercise Sci-
ence, Director of the Center for Sports
and Physical Education of the Julius-
Maximilians-University Wuerzburg
Research interests
Exercise physiology, training monitor-
ing, performance diagnostics, adapted
physical activity, heart rate variability
Professor for Sports Science, Martin-Lu-
ther-University Halle-Wittenberg
Research interests
Training adaptation, cardiac autonomic
control, performance diagnostic, exer-
cise sciene, sports nutrion
E-mail: kuno.hottenrott@sport.uni-
... 7 An added benefit of assessing HRV is to provide an additional metric for exercise specialists and certified Army Fitness Trainers over aerobic and strength assessments of participants or cadets. [8][9][10] Certain HRV metrics are used to assess cardiopulmonary health and to predict cardiac complications during exercise. [11][12][13][14] More recently, HRV metrics are now being implemented as metrics related to performance. ...
... [11][12][13][14] More recently, HRV metrics are now being implemented as metrics related to performance. 6,10,15 Time domain variables such as the standard deviation (SD) of NN intervals (SDNN) and the root mean square of successive RR interval differences (RMSSD) are used to monitor the performance and recovery of athletes. 5,16,17 Frequency domain variables, such as high-frequency (HF) power (HF) and low frequency (LF) to HF ratio (LF/HF), are used to assess the recovery and performance of strength and power athletes. ...
... Since the ACFT is a combination of six events that occur consecutively, recovery between each event can have an effect on the cadet's performance. 10,21,23,25 As demonstrated by multiple studies, HRV can be used as a tool to evaluate recovery and is noted by the fact that individuals with lower baseline SI levels appear to recover more efficiently than those with higher SI levels. 18,20 In this case, cadets and active duty soldiers with lower baseline SI levels would be able to recover quicker in-between event sessions and have higher performance outcomes. ...
Background: The Army Combat Fitness Test (ACFT) is a performance assessment used by the U.S. Army to assess a cadet's strength, endurance, and agility with a series of six events to ensure that cadets are combat ready. Heart rate variability (HRV) is an instrument that measures cardiac autonomic modulation and has been incorporated to predict the performance of athletes in daily training and competition since acute bouts of exercise alter HRV variables. Purpose: To assess the applicability of using HRV to predict ACFT score performance outcomes in cadets. Methods: Fifty army cadets (n = 36 male; n = 14 female; age = 20.60 ± 3.61 years; height = 173.34 ± 10.39 cm; body mass = 76.33 ± 14.68 kg; body fat percentage = 17.58 ± 5.26%) completed the ACFT and reported for HRV assessment. HRV assessment had the participant lay supine for 5 minutes, and traditional time and frequency domain variables were assessed. A Pearson's correlation and multiple linear regressions were run. Results: HRV time and frequency domains were not significantly correlated in linear regression models except the stress index (SI) and the 2-mile run (2MR). The standing power throw and sprint drag carry were significantly correlated with traditional HRV variables. Conclusions: HRV was not a predictor of ACFT performance for individual events or overall ACFT. The SI presented predictive properties only for 2MR, with no other significant correlations between HRV variables with standing power throw and sprint drag carry. The SI ability to predict 2MR performance outcome via HRV is a promising tool to assess army cadet performance and recovery.
... Acute fatigue can lead to a drop in the HRV metrics such as root mean square of successive differences between normal heartbeats (RMSSD) and the standard deviation of the heartbeat intervals (SDNN) when measured during exercise (Casties et al., 2006;Gronwald et al., 2020a). HRC variables such as the detrended fluctuation analysis (DFA-α1) coefficient, quantifying the degree of correlation of time series, respond to organismic demands during high intensity exercises (Hautala et al., 2003;Gronwald et al., 2021). Since running biomechanical parameters such as contact time, flight time, trunk flexion angle, vertical stiffness, ground reaction forces (GRF), etc. change in response to acute fatigue (Apte et al., 2021), continuous monitoring of these parameters can assist in understanding the effect of fatigue on neuromuscular function (Paquette et al., 2020). ...
... This approach has an implicit assumption that different participants develop similar levels of fatigue at similar distances during the run, which may not be true for a heterogeneous participant group employing a variety of pacing strategies. Similarly, existing research on the continuous monitoring of heart rate dynamics and cardiac drift (Billat et al., 2019;Gronwald and Hoos, 2020;Gronwald et al., 2021) has generally considered their evolution over the distance of the run. Combined together, these studies investigate the neuromuscular and cardiovascular response to acute fatigue, but not their concurrent evolution and association. ...
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Understanding the influence of running-induced acute fatigue on the homeostasis of the body is essential to mitigate the adverse effects and optimize positive adaptations to training. Fatigue is a multifactorial phenomenon, which influences biomechanical, physiological, and psychological facets. This work aimed to assess the evolution of these three facets with acute fatigue during a half-marathon. 13 recreational runners were equipped with one inertial measurement unit (IMU) on each foot, one combined global navigation satellite system-IMU-electrocardiogram sensor on the chest, and an Android smartphone equipped with an audio recording application. Spatio-temporal parameters for the running gait, along with the heart rate, its variability and complexity were computed using validated algorithms. Perceived fatigability was assessed using the rating-of-fatigue (ROF) scale at every 10 min of the race. The data was split into eight equal segments, corresponding to at least one ROF value per segment, and only level running parts were retained for analysis. During the race, contact time, duty factor, and trunk anteroposterior acceleration increased, and the foot strike angle and vertical stiffness decreased significantly. Heart rate showed a progressive increase, while the metrics for heart rate variability and complexity decreased during the race. The biomechanical parameters showed a significant alteration even with a small change in perceived fatigue, whereas the heart rate dynamics altered at higher changes. When divided into two groups, the slower runners presented a higher change in heart rate dynamics throughout the race than the faster runners; they both showed similar trends for the gait parameters. When tested for linear and non-linear correlations, heart rate had the highest association with biomechanical parameters, while the trunk anteroposterior acceleration had the lowest association with heart rate dynamics. These results indicate the ability of faster runners to better judge their physiological limits and hint toward a higher sensitivity of perceived fatigue to neuromuscular changes in the running gait. This study highlights measurable influences of acute fatigue, which can be studied only through concurrent measurement of biomechanical, physiological, and psychological facets of running in real-world conditions.
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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.
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The basic theoretical assumptions of Exercise Physiology and its research directions, strongly influenced by reductionism, may hamper the full potential of basic science investigations, and various practical applications to sports performance and exercise as medicine. The aim of this perspective and programmatic article is to: (i) revise the current paradigm of Exercise Physiology and related research on the basis of principles and empirical findings in the new emerging field of Network Physiology and complex systems science; (ii) initiate a new area in Exercise and Sport Science, Network Physiology of Exercise (NPE), with focus on basic laws of interactions and principles of coordination and integration among diverse physiological systems across spatio-temporal scales (from the sub-cellular level to the entire organism), to understand how physiological states and functions emerge from integrated network interactions and to improve the efficacy of exercise in health and sport performance; (iii) to create a forum for developing new research methodologies applicable to the new NPE field, to infer and quantify nonlinear dynamic forms of coupling among diverse systems and establish basic principles of coordination and network organization of physiological systems. Here we present a programmatic approach for future research directions and potential practical applications. By focusing on research efforts to improve the knowledge about nested dynamics of vertical network interactions, and particularly the horizontal integration of key organ systems during exercise, NPE may enrich Basic Physiology and diverse fields like Exercise and Sports Physiology, Sports Medicine, Sports Rehabilitation, Sport Science or Training Science, and improve the understanding of diverse exercise-related phenomena such as sports performance, fatigue, overtraining, or sport injuries.
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This study examined physiological and race pace characteristics of medium- (finish time < 240 min) and low-level (finish time > 240 min) recreational runners who participated in a challenging marathon route with rolling hills, the Athens Authentic Marathon. Fifteen athletes (age: 42 ± 7 years) performed an incremental test, three to nine days before the 2018 Athens Marathon, to determine maximal oxygen uptake (VO2 max), maximal aerobic velocity (MAV), energy cost of running (ECr) and lactate threshold velocity (vLTh), and were analyzed for their pacing during the race. Moderate- (n = 8) compared with low-level (n = 7) runners had higher (p < 0.05) VO2 max (55.6 ± 3.6 vs. 48.9 ± 4.8 mL·kg−1·min−1), MAV (16.5 ± 0.7 vs. 14.4 ± 1.2 km·h−1) and vLTh (11.6 ± 0.8 vs. 9.2 ± 0.7 km·h−1) and lower ECr at 10 km/h (1.137 ± 0.096 vs. 1.232 ± 0.068 kcal·kg−1·km−1). Medium-level runners ran the marathon at a higher percentage of vLTh (105.1 ± 4.7 vs. 93.8 ± 6.2%) and VO2 max (79.7 ± 7.7 vs. 68.8 ± 5.7%). Low-level runners ran at a lower percentage (p < 0.05) of their vLTh in the 21.1–30 km (total ascent/decent: 122 m/5 m) and the 30–42.195 km (total ascent/decent: 32 m/155 m) splits. Moderate-level runners are less affected in their pacing than low-level runners during a marathon route with rolling hills. This could be due to superior physiological characteristics such as VO2 max, ECr, vLTh and fractional utilization of VO2 max. A marathon race pace strategy should be selected individually according to each athlete’s level.
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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.
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Fluctuations of the human heart beat constitute a complex system that has been studied mostly under resting conditions using conventional time series analysis methods. During physical exercise, the variability of the fluctuations is reduced, and the time series of beat-to-beat RR intervals (RRIs) become highly non-stationary. Here we develop a dynamical approach to analyze the time evolution of RRI correlations in running across various training and racing events under real-world conditions. In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRIs as functions of the scale and the lag. We relate these changes to the exercise intensity quantified by the heart rate (HR). Beyond subject-specific HR thresholds the RRIs show multiscale anticorrelations with both universal and individual scale-dependent structure that is potentially affected by the stride frequency. These preliminary results are encouraging for future applications of the dynamical statistical analysis in exercise physiology and cardiology, and the presented methodology is also applicable across various disciplines.
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Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of an agreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake (\({\dot{\text{V}}\text{O}}_{{{\text{2max}}}}\)), peak oxygen uptake (\({\dot{\text{V}}\text{O}}_{{{\text{2peak}}}}\)), maximum heart rate (HRmax), and maximum work rate (i.e., power or velocity—\({\dot{\text{W}}}_{{\max}}\) or \({\dot{\text{V}}}_{{\max}}\), respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT1 and LT2), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT1, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.
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Backround: Non-linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi-periodic oscillations, but it also possesses random fluctuations and so-called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the Detrended Fluctuation Analysis (DFA). DFA is a non-linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short-term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in sport-specific settings including higher exercise intensities, non-stationary data segments, and relatively short recording times. Method: Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA-alpha1 as a proxy of correlation properties in the HR time series. Results: The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria. Conclusions: The included studies show that DFA-alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non-linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.
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Within the last years complex models of cardiovascular regulation and exercise fatigue have implemented heart rate variability (HRV) as a measure of autonomic nervous system. Using detrended fluctuation analysis (DFA) to assess heart rate correlation properties , the present study examines the influence of exercise intensity on total variability and complexity in non-linear dynamics of HRV. Sixteen cyclists performed a graded exercise test on a bicycle ergometer. HRV time domain measures and fractal correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Amplitude and complexity of HRV parameters decreased significantly. DFA-alpha1 increased from rest to low exercise intensity and showed an almost linear decrease from higher intensities until exhaustion. These findings support a qualitative change in self-organized heart rate regulation from a complex autonomic control at rest and low intensities towards a breakdown of the interaction in control mechanisms with non-autonomic heart rate control dominating at high intensities.
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.