Physiological Reports. 2021;9:e14956.
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The classification of endurance exercise fatigue encom-
passes diverse models and theories (Abbiss & Laursen,
2005), components (Carriker, 2017), and various aspects
of muscular function (Wan et al., 2017), biochemical bal-
ance (Jastrzębski et al., 2015) as well as both the central
and peripheral nervous systems (Davis & Walsh, 2010;
Received: 3 April 2021
Revised: 11 June 2021
Accepted: 17 June 2021
Fractal correlation properties of heart rate variability as a
biomarker of endurance exercise fatigue in ultramarathon
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2021 The Authors. Physiological Reports published by Wiley Periodicals LLC on behalf of The Physiological Society and the American Physiological Society
1College of Medicine, University of
Central Florida, Orlando, FL, USA
2EA3920 Prognostic Factors and
Regulatory Factors of Cardiac and
Vascular Pathologies, Exercise
Performance Health Innovation (EPHI)
platform, University of Bourgogne
Franche- Comté, Besançon, France
3National Research Tomsk Polytechnic
University, Tomsk Oblast, Russia
4Université de Perpignan Via Domitia,
Laboratoire Européen Performance Santé
Altitude (LEPSA), Besançon, France
5Faculty of Health Sciences, Department
of Performance, Neuroscience, Therapy
and Health, MSH Medical School
Hamburg, University of Applied Sciences
and Medical University, Hamburg,
Bruce Rogers, College of Medicine,
University of Central Florida, 6850 Lake
Nona Boulevard, Orlando, FL 32827-
This research received no external
Although heart rate variability (HRV) indexes have been helpful for monitoring the
fatigued state while resting, little data indicate that there is comparable potential dur-
ing exercise. Since an index of HRV based on fractal correlation properties, alpha
1 of detrended fluctuation analysis (DFA a1) displays overall organismic demands,
alteration during exertion may provide insight into physiologic changes accompany-
ing fatigue. Two weeks after collecting baseline demographic and gas exchange data,
11 experienced ultramarathon runners were divided into two groups. Seven runners
performed a simulated ultramarathon for 6h (Fatigue group, FG) and four runners
performed daily activity over a similar period (Control group, CG). Before (Pre) and
after (Post) the ultramarathon or daily activity, DFA a1, heart rate (HR), running
economy (RE) and countermovement jumps (CMJ) were measured while running on
a treadmill at 3m/s. In Pre versus Post comparisons, data showed a decline with large
effect size in DFA a1 post intervention only for FG (Pre: 0.71, Post: 0.32; d=1.34),
with minor differences and small effect sizes in HR (d= 0.02) and RE (d=0.21).
CG showed only minor differences with small effect sizes in DFA a1 (d= 0.19),
HR (d=0.15), and RE (d=0.31). CMJ vertical peak force showed fatigue- induced
decreases with large effect size in FG (d=0.82) compared to CG (d=0.02). At the
completion of an ultramarathon, DFA a1 decreased with large effect size while run-
ning at low intensity compared to pre- race values. DFA a1may offer an opportunity
for real- time tracking of physiologic status in terms of monitoring for fatigue and
possibly as an early warning signal of systemic perturbation.
DFA a1, endurance exercise, fatigue, marathon, running
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ROGERS Et al.
McMorris et al., 2018; Martínez- Navarro et al., 2019; Martin
et al., 2018; for an overview see Ament & Verkerke, 2009).
Objective means to quantify fatigue related to endurance
exercise may include various modalities including salivary
hormone markers (Deneen & Jones, 2017), muscle enzyme
elevation (Martínez- Navarro et al., 2019), blood lactate con-
centration (Jastrzębski et al., 2015), markers of substrate
availability (Schader et al., 2020), cortical activity (Ludyga
et al., 2016), functional testing such as the counter movement
jump (Wu et al., 2019) and measures of running economy
(Scheer et al., 2018). Fatigue can be also measured subjec-
tively through “rating of perceived effort” (RPE, Halperin &
Emanuel, 2020) such as the well- known Borg scale (Borg,
Although well established, none of these tools are eas-
ily implemented for practical usage in the vast majority of
endurance athletes. Since exercise- related fatigue is an in-
evitable consequence of a long duration endurance session,
an easily available objective biomarker using a low- cost con-
sumer wearable device would be ideal. While resting heart
rate (HR) variability (HRV) may provide information on
functional overreaching, and post exercise HRV may indicate
autonomic recovery status (Manresa- Rocamora et al., 2021;
Stanley et al., 2013), neither modality can answer the ques-
tion of whether a specific exercise endeavor is leading to a
fatigued state as the activity occurs.
Recently, a nonlinear index of HRV based on fractal correla-
tion properties termed alpha 1 (short- term scaling exponent) of
detrended fluctuation analysis (DFA a1) has been shown to
change with increasing exercise intensity (Gronwald & Hoos,
2020). This index represents the fractal, self- similar nature of
cardiac beat- to- beat intervals. At low exercise intensity, DFA
a1values usually are near 1 or slightly above, signifying a well
correlated, fractal pattern. As intensity rises, the index will
drop past 0.75 near the aerobic threshold (AT) then approach
uncorrelated, random patterns represented by values near 0.5
at higher work rates (Rogers, Giles, Draper, Hoos et al., 2021).
The underlying mechanism for this behavior is felt to be due
to alterations in autonomic nervous system balance, primarily
withdrawal of the parasympathetic branch and enhancement
of the sympathetic branch as well as other potential factors
(Gronwald et al., 2020). As opposed to other HRV indexes
that reach a nadir value at the aerobic threshold (SDNN: the
total variability as the standard deviation of all normal RR in-
tervals; SD1: standard deviation of the distances of the points
from the minor axis in the Poincaré plot), DFA a1has a wide
dynamic range sufficient to differentiate mild versus moder-
ate versus severe intensity domains. For example, at the AT,
a DFA a1 near 0.75 is usually present (Rogers, Giles, Draper,
Hoos et al., 2021), whereas SDNN and SD1 are already at their
lowest values (Gronwald et al., 2020). One advantageous prop-
erty of DFA a1 revolves around its dimensionless nature, as
values appear to apply to an individual regardless of fitness
status. For example, a value of 0.5 corresponds to an exercise
intensity well above the AT in most individuals without hav-
ing prior knowledge of the current HR or power (Gronwald
et al., 2020). In addition to its recent usage to delineate the AT
during exercise testing, DFA a1has an extensive literature as a
final common pathway of assessing total body “organismic de-
mand” (Gronwald & Hoos, 2020). This concept refers to DFA
a1status as an index of overall systemic internal load rather
than being purely related to isolated single factor measures of
external load such as cycling power, or metrics of subsystem
internal loads such as HR, respiratory rate, or VO2. Therefore,
the dimensionless index DFA a1shows great potential as a
descriptor of the Network Physiology of Exercise (NPE), re-
cently introduced by Balagué et al., (2020). In particular, this
index is well suited for the demarcation of the complex dynam-
ics of internal load development over the course of prolonged
endurance exercise as well as for the assessment of athletes'
fatigued state while still in the process of exercising.
Although various endurance exercise modalities can lead
to fatigue, the ultramarathon represents one of the most ex-
treme examples. As defined by a run distance of over 42km
with a variety of surface/terrain/elevation characteristics
(Scheer et al., 2020), it has been associated with electrolyte
imbalance, severe muscle damage, end organ dysfunction,
altered oxygen cost of running, and hormonal dysregula-
tion (Knechtle & Nikolaidis, 2018; Ramos- Campo et al.,
2016). At the same time, the pace is generally considered
moderate, with only slight lactate elevations above baseline
noted (Jastrzębski et al., 2015; Ramos- Campo et al., 2016).
Therefore, it represents an extreme setting of prolonged but
moderate level exercise intensity that can lead to major sys-
temic perturbation. Since DFA a1has been shown to be a
marker of overall organismic demand, it would be of interest
to explore its behavior after such an endeavor. In addition,
since it has also been noted to be a proxy for the aerobic
threshold, alteration of this relationship may indicate the
need for pace adjustment for the purpose of intensity distri-
bution. Although relatively short durations of exercise below
the AT do not seem to lead to major alterations in DFA a1
behavior (Rogers, 2020), physiologic disruption produced by
an ultramarathon certainly could do so. Hence, the aim of this
report is to evaluate the change in exercise associated DFA a1
dynamics toward the end of a simulated ultramarathon and
compare this to changes in HR and running economy while
still performing dynamic exercise.
MATERIALS AND METHODS
Eleven experienced (nine male, two female) ultramarathon
runners without major past medical history, medications, or
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ROGERS Et al.
recent illness were recruited for the study. All had purpose-
fully trained for an ultramarathon and were experienced in
performing a race of greater than 50km or longer than 6h in
As part of the baseline assessment, participants performed
a familiarization of countermovement jumps (CMJ) prac-
tice with an emphasis on the speed of jump. An incremental
treadmill test to exhaustion was done to determine peak oxy-
gen uptake (VO2MAX), the first and second ventilatory thresh-
olds 2weeks prior to the ultramarathon run. After a warm- up
of about 10min at 3m/s, the initial running speed was set
at 3.6m/s with the first stage lasting 2min. The speed was
then progressively increased by 0.28m/s every 2min until
exhaustion. Breath- by- breath gas exchange was continu-
ously measured via metabolic cart (Metalyzer 3B- R3system;
Cortex Biophysics, Leipzig, Germany). Ventilatory thresh-
olds were determined visually with the first threshold defined
by the V slope method and second threshold by the change
in VCO2/ventilation ratio (Beaver et al., 1986). VO2MAX was
defined as the average VO2 over the last 60s of the test. Peak
effort was confirmed by failure of VO2 and/or HR to increase
with further increases in work rate. Pertinent demographic
data are shown in Table 1 including age, height, weight,
years of training, weekly training volume, and results of the
gas exchange testing. Participants did not consume caffeine,
alcohol, or any stimulant for the 24 h before testing. The
experimental design of the study was approved by the local
Human Research Ethic Committee (2016- A00511- 50), con-
ducted in conformity with the latest version of the Declaration
of Helsinki and written informed consent for all participants
Initially, all participants underwent a CMJ testing sessions
with 3 CMJ trials and 30s rest between to assess fatigue-
induced changes in the neuromuscular function (Claudino
et al., 2017). The maximum jump height and the vertical
peak force normalized per the participants’ body mass(N/
kg) were measured using a portable force platform (Quattro-
Jump, Kistler, Winterthur, Switzerland) at a sampling rate of
500Hz. The average values of the 3 CMJ trials were used in
the subsequent statistical analysis. All participants then per-
formed a treadmill run (Pre) at a fixed velocity of 3m/s for
a duration of 5min the day before the simulated ultramara-
thon for measurements of oxygen uptake (VO2). Breath- by-
breath gas exchange was continuously measured by the same
metabolic cart as in the initial assessment (Metalyzer 3B-
R3system; Cortex Biophysics, Leipzig, Germany). VO2 was
averaged over the last 1min to estimate the running econ-
omy (Bontemps et al., 2020). The following day, seven par-
ticipants ran a simulated ultramarathon for approximately 6h
(Fatigue group, FG, see Table 2), while the remaining four
TABLE 1 Demographic data and data from the baseline assessment of all participants (n=11)
Group Age Sex
[Kg] Ht [cm]
FG 1 20 M 70 190 6 13 80 52 68
FG 2 24 M 65 175 10 12 75 48 65
FG 3 22 M 81 186 10 11 74 47 63
FG 4 44 F 54 162 6 11 63 39 52
FG 5 45 M 64 170 5 5 55 36 45
FG 6 43 M 72 176 30 5 53 35 43
FG 7 49 M 71 170 12 8 52 34 42
Mean±SD 35 (±12) – 68 (±8) 176 (±9) 11 (±8) 9 (±3) 64 (±11) 42 (±7) 54 (±10)
CG 1 24 M 67 162 8 15 75 46 62
CG 2 32 M 68 178 6 9 75 47 65
CG 3 40 M 68 177 20 9 70 45 60
CG 4 42 F 60 168 3 4 49 30 41
Mean±SD 35 (±7) – 66 (±3) 171 (±7) 9 (±6) 9 (±4) 67 (±11) 42 (±7) 57 (±9)
d 0.07 – 0.33 0.48 0.25 0.01 0.22 0.06 0.27
Group: Fatigue group with number of the participant (FG) and Control group with number of the participant (CG), Age, current age, Sex; BW, Body weight; Ht,
Height; Yrs training, total years of marathon training; Hrs/wk training, approximate hours per week of marathon- related training; VO2MAX, peak oxygen uptake
reached on baseline ramp test; VT1, first ventilatory threshold; VT2, second ventilatory threshold. Mean (± standard deviation, SD) and Cohen's d for group
comparisons in last row.
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TABLE 2 Pre and Post intervention data for both groups and all participants (n=11)
Pre Post Ultramarathon
[bpm] DFA a1
[bpm] DFA a1
FG 1 158 1.286 39 – – 75% 170 0.353 41 – – 78% 5:50 42 2.0
FG 2 125 1.192 37 21.7 32.2 77% 133 0.396 36 21.7 30.1 75% 6:35 48 2.0
FG 3 134 0.776 29 18.9 34.6 61% 134 0.356 33 17.1 29.0 70% 6:35 48 2.0
FG 4 132 0.269 36 21.6 21.7 92% 131 0.358 35 20.0 19.9 90% 5:52 44 2.1
FG 5 149 0.706 37 21.9 23.1 102% 141 0.314 37 18.4 20.5 102% 5:54 39 1.8
FG 6 141 0.313 41 20.7 26.9 117% 135 0.124 35 18.8 24.5 100% 6:15 43 1.9
FG 7 148 0.436 35 16.7 15.0 102% 143 0.317 32 16.5 13.8 93% 6:10 45 2.0
Mean±SD 141 (±11) 0.71
89 (±19) 141 (±13) 0.32
87 (±12) 6:10
CG 1 129 1.201 34 25.7 33.7 74% 127 1.301 33 26.1 34.8 72%
CG 2 140 0.853 36 20.2 24.5 76% 136 0.806 35 19.4 24.4 74%
CG 3 110 1.063 32 22.4 23.7 71% 103 1.157 32 22.5 24.0 71%
CG 4 163 0.559 38 17.9 12.3 125% 158 0.598 37 18.5 13.9 122%
Mean±SD 135 (±22) 0.92
87 (±25) 131 (±22) 0.97
d 0.34 0.56 0.38 0.53 0.28 0.12 0.58 3.25 0.49 1.17 0.19 0.12
Group, Fatigue group with number of the participant (FG) and Control group with number of the participant (CG); HR, average heart rate; DFA a1, short- term scaling exponent alpha1 of detrended fluctuation analysis; RE,
running economy via oxygen uptake; CMJ, counter movement jump assessment (please consider that there is one data pair missing in FG due to technical issues) ; VO2 run/VT1, ratio of the oxygen uptake measured during the
Pre or Post 3m/s treadmill run to that of the oxygen uptake of the first ventilatory threshold from baseline assessment; Time, time spent performing the simulated ultramarathon; Distance, distance performed in the simulated
ultramarathon; Speed, calculated average run speed of the ultramarathon based on time and distance. Mean (± standard deviation; SD) and Cohen's d for group comparisons in last row.
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participants (Control group, CG) did normal nonstrenuous
daily activity for 6h. Participants ran on an 11.5- km off road
trail loop at a freely chosen pace (with an elevation change
of 550m) without rest periods and were allowed to ingest
food and water freely. Immediately following the completion
of the 6h run or 6h nonstrenuous activity, an identical CMJ
assessment and treadmill test (Post) was performed on each
individual for the same measurement parameters. No change
in protocol occurred between pre and post intervention test-
ing. Estimated running speed was calculated based on total
covered distance and elapsed time.
RR measurements and calculation of
A Polar H10 (Polar Electro Oy, Kempele, Finland) HR moni-
toring (HRM) device with a sampling rate of 1000Hz was
used to detect RR intervals in all individuals during the Pre
and Post treadmill run over 5min. All RR data were recorded
with a Suunto Memory Belt (Suunto, Vantaa, Finland),
downloaded as text files, and then imported into Kubios
HRV Software Version 3.4.3 (Biosignal Analysis and
Medical Imaging Group, Department of Physics, University
of Kuopio, Kuopio, Finland; Tarvainen et al., 2014). Kubios
preprocessing settings were set to the default values includ-
ing the RR detrending method which was kept at “Smoothn
priors” (Lambda= 500). DFA a1 window width was set to
4≤ n≤16 beats. The RR series was then corrected by the
Kubios “automatic method” (Lipponen & Tarvainen, 2019)
and relevant parameters exported as text files for further anal-
ysis. DFA a1 and average HR were calculated from the RR
data series of the 2min time window consisting of the start
of minute 4 to the end of minute 5 of the treadmill exercise
in both Pre and Post conditions. Two min time windowing
was chosen based on previous calculations as to the mini-
mal required beat count (Chen et al., 2002). Artifact levels
measured by Kubios HRV were below 5%. This limit was
previously shown to have minimal effect on DFA a1 during
exercise (Rogers, Giles, Draper, Mourot et al., 2021).
Statistical analyses of means and standard deviations were
performed for demographic data, Pre and Post treadmill
run DFA a1, average HR and VO2 in Microsoft Excel 365.
Additional statistical analysis was performed using SPSS
23.0 (IBM Statistics, United States) for Windows (Microsoft,
USA). The Shapiro– Wilk test was applied to verify the
Gaussian distribution of the data. The degree of variance
homogeneity was verified by the Levene's test. To account
for the unbalanced and small participant numbers of the elite
ultramarathon runners group comparison of demographic
data, data of baseline assessment, pre intervention data and
to analyze the effects of the intervention (Pre vs. Post) on
dependent variables (DFA a1, HR, RE, and CMJ) were em-
ployed via effect size calculation (Coe, 2002) (the mean
difference between scores divided by the pooled standard de-
viation of group comparison and Pre versus Post comparison
of each variable). The interpretation of effect sizes is based
on Cohen's thresholds for small effects (d< 0.5), moderate
effects (d≥0.5), and large effects (d>0.8) (Cohen, 1988).
Mean and standard deviations for measured parameters are
listed in Table 2 for each group (FG vs. CG). There were only
small effect sizes in group comparison in demographic data
and data from baseline assessment (Table 1). Pre intervention
data showed small to medium effect sizes in comparison of
both groups in dependent variables HR, DFA a1, RE, and
CMJ (Table 2). In Pre versus Post comparisons, data showed
a decline with large effect size in DFA a1 (d= 1.38) and
CMJ vertical peak force (d= 0.82) post intervention only
for FG, with minor differences and small effect sizes in HR
(d=0.02), RE (d=0.21) or CMJ jump height (d=0.43).
CG showed only minor differences with small effect sizes in
DFA a1 (d=0.19), HR (d=0.15), RE (d=0.31) and CMJ
vertical peak force (d=0.02), and jump height (d = 0.09)
The aim of this study was to determine if a simulated ultra-
marathon run- induced changes in a nonlinear HRV index of
fractal correlation properties, DFA a1, during dynamic exer-
cise. Since the ultramarathon has been shown to cause major
perturbation of many metabolic, systemic, and neuromuscu-
lar systems (Knechtle & Nikolaidis, 2018; Ramos- Campo
et al., 2016), it is ideal for investigating whether a HRV
index representing overall organismic demand also exhib-
its analogous alterations while still performing the exercise.
This particular index is especially well suited for the assess-
ment of overall physiologic status during activity by virtue of
its excellent dynamic range over mild, moderate, and severe
exercise intensity domains (Gronwald et al., 2020). A major
finding of this report is that after a 6h ultramarathon, DFA a1
was markedly suppressed while running at a pace close to the
aerobic threshold. Vertical peak force decreases from CMJ
assessment confirmed fatigue- induced changes in the neuro-
muscular function of the lower- limbs. Despite the expected
systemic effects, neither HR nor running economy appeared
to be altered after the ultramarathon. Past analyses have
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shown variable effects on measures of running economy post
ultramarathon with both higher and neutral oxygen usage at
a fixed running speed (Scheer et al., 2018; Vernillo et al.,
2019). In regard to HR over the course of a marathon, it ap-
pears that this metric is not very helpful in monitoring ongo-
ing fatigue. HR can remain stable without much upward drift
over the course of a marathon, at the cost of a slight decrease
in speed (Billat et al., 2012). Therefore, if one were attempt-
ing to track signs of metabolic distress by observing HR, VO2,
or DFA a1 in this particular study, only DFA a1 would have
revealed changes while activity was ongoing. As compared
with Pre measurements, DFA a1 was markedly suppressed in
all athletes during the exercise at a fixed low intensity pace
after the ultramarathon, comprising values well past uncorre-
lated patterns and falling into the anticorrelated range. These
values are generally associated with the highest exercise in-
tensity domain and should not occur during low to moderate
work rates (Gronwald & Hoos, 2020). In accordance with
this observation, prior studies of prolonged cycling exercise
(60min or until voluntary exhaustion) with constant power at
90% to 100% of the second lactate threshold, showed DFA a1
exhibiting a clear decrease comparing the beginning and end
of the exercise bout, potentially showing an effect of fatigue
(Gronwald et al., 2018, 2019). In the present study, all but
one of the FG individuals had suppression of DFA a1 from
their Pre- values. Although the CG did not have similar DFA
a1values compared to the FG before the ultramarathon they
did not have a meaningful decline, when tested again after
normal daily activity. In terms of running pace, the ultramar-
athon speed was well below that of the treadmill test of 3m/s
and below the AT as demonstrated by baseline VO2measure-
ments. Despite this point, it appears that blood lactate does
accumulate above baseline but still remains at a steady state
during an ultramarathon run (Jastrzębski et al., 2015; Ramos-
Campo et al., 2016). Therefore, it seems that blood lactate
could underestimate the severity of this type of long duration
exercise in terms of whole body systemic effects.
The mechanism of DFA a1 decline during both increas-
ing exercise intensity and high organismic demand revolves
around autonomic nervous system balance as well as other
potential factors (Sandercock & Brodie, 2006; Papaioannou
et al., 2013; White & Raven, 2014; Michael et al., 2017). As
overall demand rises there is a withdrawal of the parasympa-
thetic and stimulation of the sympathetic system (White &
Raven, 2014) affecting the sinoatrial node leading to a loss
of fractal correlation properties of the HR times series. This
can also be described in terms of a “networking” process
(Balagué et al., 2020), related to integration of many meta-
bolic, neuromuscular and hormonal inputs. With increasing
exercise intensity and/or fatigue it seems that organismic reg-
ulation starts to disengage subsystems (e.g., dissociation of
cardiac and respiratory systems) in terms of a disintegration,
decoupling, and segregation process (Gronwald et al., 2020).
This behavior could be interpreted as a protective feedback
mechanism where interactions of subsystems fail before the
whole system fails. Interestingly, studies have indicated that
DFA a1 rises in the immediate post ultramarathon recov-
ery period during supine resting conditions, showing highly
correlated patterns with increased correlation properties of
HR time series (Martínez- Navarro et al., 2019). This activ-
ity could be explained as a systematic reorganization of the
organism with increased correlation properties in cardiac au-
tonomic regulation with a predominance of parasympathetic
activity during passive or active recovery with very low ex-
ercise intensity (parasympathetic reactivation) (Casties et al.,
2006; Kannankeril & Goldberger, 2002; Stanley et al., 2013).
FIGURE 1 (a) Mean, 95% confidence interval and individual responses while running on a treadmill at 3m/s for DFA a1 Pre and Post
ultramarathon run (FG) in seven participants, (b) Mean, 95% confidence interval and individual responses while running on a treadmill at 3m/s for
DFA a1 Pre and Post daily activity (CG) in four participants
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ROGERS Et al.
It may also be related to a counter regulation (overcompensa-
tion) of the organism to the prior load (Hautala et al., 2001).
The organism responds with a highly correlated behavior
signifying more order in recovery (Balagué et al., 2020;
Gronwald et al., 2019).
Limitations and future directions
A limitation of this study is a lack of time related de-
tail of speed, HR, and DFA a1 during the ultramarathon.
Additional study looking at a comprehensive analysis of
DFA a1 and related metrics throughout the entire run would
certainly be of interest, especially at what point does its be-
havior begin to deviate from normal. Periodic blood lactate
determinations would also have been of interest, but dif-
ficult on a practical basis. Although a derived running pace
can be inferred from the overall session distance/time, it is
possible that some heterogeneity was present. The over-
all derived pace of 2m/s was consistent with an intensity
below the AT since VO2measurements at 3m/s were usu-
ally slightly above or below the AT. Two female partici-
pants were included but just one was in the FG. Given the
limited data on female participants further evaluation of
DFA a1 behavior during long duration endurance exercise
is needed. An important potential issue in measuring DFA
a1 during running may entail an artifactual suppression of
correlation properties due to device bias, present in some in-
dividuals more than others (Rogers, Giles, Draper, Mourot
et al., 2021). Despite possessing low artifact data, in two
of the FG participants, DFA a1 was already markedly sup-
pressed at a running speed corresponding to their VT1. For
this reason, DFA a1 Pre- values were different (with mod-
erate effect size) in FG versus CG. Further study regarding
the issue of inappropriate DFA a1suppression at moderate
running speed is needed. Sample size was relatively small
but consistent with the difficulty in recruiting appropriate
participants. On a practical note, the required measurement
equipment consists of only a consumer grade HRM device
which most athletes can easily obtain. Although this study
employed a retrospective analysis to determine DFA a1,
as mobile technology improves, it is conceivable that real-
time DFA a1monitoring during endurance exercise could
be used to inform an individual about current physiologic
(fatigue) status and potential metabolic destabilization
(Rogers and Gronwald, 2021; Gronwald et al., 2021). It is
also possible that altered DFA a1kinetics such as a delay
of its decline over a given pace/distance following a train-
ing intervention could signify an improving performance
status. Finally, although during race conditions, pace ad-
justment to mitigate DFA a1 decline is of unclear value, it
certainly merits potential study during training for inten-
sity distribution and as a safety precaution.
At the completion of an ultramarathon, DFA a1 decreased
with large effect size while running at low intensity com-
pared to pre- race values. Despite running at a relatively easy
pace, these values were consistent with those only seen at the
highest levels of internal load and organismic demand. DFA
a1may offer an opportunity for real- time tracking of physi-
ologic status in terms of monitoring for fatigue and possibly
as an early warning signal of systemic perturbation.
This research was supported by the Université of Franche
Comté and TPU development program.
CONFLICTS OF INTEREST
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
B.R. and T.G. conceived the study. G.D. and L.M. performed
the physiologic testing. B.R. wrote the first draft of the arti-
cle. B.R. and T.G. performed the data analysis. All authors
(B.R., G.D., L.M., and T.G.) revised it critically for impor-
tant intellectual content, final approval of the version to be
published, and accountability for all aspects of the work.
INFORMED CONSENT STATEMENT
Informed consent was obtained from all subjects involved in
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Bruce Rogers https://orcid.org/0000-0001-8458-4709
Thomas Gronwald https://orcid.org/0000-0001-5610-6013
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How to cite this article: Rogers, B., Mourot, L.,
Doucende, G., & Gronwald, T. (2021). Fractal
correlation properties of heart rate variability as a
biomarker of endurance exercise fatigue in
ultramarathon runners. Physiological Reports, 9,
e14956. https://doi.org/10.14814/ phy2.14956