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Correlation Properties of Heart Rate Variability during a Marathon Race in Recreational Runners: Potential Biomarker of Complex Regulation during Endurance Exercise

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• University of cooperative education sport and health

Abstract and Figures

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
http://www.jssm.org DOI: https://doi.org/10.52082/jssm.2021.557
Received: 21 January 2021 / Accepted: 25 June 2021 / Published (online): 10 July 2021
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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
Abstract
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.
Introduction
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-
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
558
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
regulation during marathon racing in addition to the rather
low but steady increases in HR due to cardiovascular drift.
Methods
Participants
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
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.
559
Results
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
[min:sec]
04:18
±00:24
04:22
±00:23
04:26
±00:24
04:23
±00:22
04:19
±00:22
04:28
±00:25
04:30
±00:27
04:41
±00:31
04:50
±00:38
04:52*
±00:37
HR
[bpm]
163.9
±10.4
165.6
±10.6
166.6
±10.4
165.1
±10.4
167.5
±10.4
168.5
±10.9
168.6
±12.3
170.6
±13.0
170.3
±12.9
173.6*
±13.1
meanRR
[ms]
368
±23
364
±23
362
±22
365
±24
360
±23
358
±24
358
±27
354
±27
354
±27
347*
±27
SDNN
[ms]
2.6
±0.7
2.5
±0.7
2.5
±0.5
2.5
±0.6
2.4
±0.5
2.4
±0.5
2.5
±0.5
2.5
±0.4
2.5
±0.4
2.6
±0.5
RMSSD
[ms]
3.7
±1.1
3.8
±1.1
3.6
±0.8
3.7
±1.0
3.7
±0.8
3.7
±0.9
3.9
±0.8
3.9
±0.6
4.0
±0.6
4.1
±0.7
DFA-alpha1
[ ]
0.54
±0.18
0.48
±0.15
0.47
±0.09
0.48
±0.13
0.41
±0.08
0.41
±0.10
0.40
±0.11
0.42
±0.09
0.42
±0.10
0.37*
±0.08
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).
Discussion
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
560
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 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.
Limitations
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.
561
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.
Conclusion
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
562
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.
Acknowledgements
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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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
AUTHOR BIOGRAPHY
Thomas GRONWALD
Employment
Professor for Sports and Exercise Sci-
ence, Dept. Performance, Neuroscience,
Therapy and Health, MSH Medical
School Hamburg
Degree
PhD, MBA
Research interests
diagnostics, injury prevention, HRV
E-mail: thomas.gronwald@medi-
calschool-hamburg.de
Bruce ROGERS
Employment
Department of Medicine, University of
Central Florida
Degree
MD
Research interests
consumer wearables
E-mail: bjrmd@knights.ucf.edu
Laura HOTTENROTT
Employment
Institute of performance diagnostics and
health promotion, Martin-Luther-Uni-
versity Halle-Wittenberg
Degree
Master of Science
Research interests
ences, endurance sports, cardiac auto-
nomic control
E-mail: hottenrott.laura@gmail.com
Olaf HOOS
Employment
Professor for Sports and Exercise Sci-
ence, Director of the Center for Sports
and Physical Education of the Julius-
Maximilians-University Wuerzburg
Degree
PhD
Research interests
Exercise physiology, training monitor-
physical activity, heart rate variability
E-mail: olaf.hoos@uni-wuerzburg.de
Kuno HOTTENROTT
Employment
Professor for Sports Science, Martin-Lu-
ther-University Halle-Wittenberg
Degree
PhD
Research interests
control, performance diagnostic, exer-
cise sciene, sports nutrion
E-mail: kuno.hottenrott@sport.uni-
halle.de
... 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. ...
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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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