Physiological Reports. 2021;9:e14956. 
 
  | 1 of 9
https://doi.org/10.14814/phy2.14956
wileyonlinelibrary.com/journal/phy2
1 | INTRODUCTION
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
DOI: 10.14814/phy2.14956  
O R I G I N A L  A R T I C L E
Fractal correlation properties of heart rate variability as a 
biomarker of endurance exercise fatigue in ultramarathon 
runners
Bruce Rogers1
 |   Laurent Mourot2,3 |   Gregory Doucende4 |   Thomas Gronwald5
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, 
Germany
Correspondence
Bruce Rogers, College of Medicine, 
University of Central Florida, 6850 Lake 
Nona Boulevard, Orlando, FL 32827- 
7408, USA.
Email: bjrmd@knights.ucf.edu
Funding information
This research received no external 
funding.
Abstract
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 6 h (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 3 m/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 a1 may 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.
K E Y W O R D S
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, 
1982).
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 
a1 values 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 a1 has 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 a1 has an extensive literature as a 
final common pathway of assessing total body “organismic de-
mand” (Gronwald & Hoos, 2020). This concept refers to DFA 
a1 status 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 a1 shows 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 42 km 
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 a1 has 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.
2 | MATERIALS AND METHODS
2.1 | Participants
Eleven experienced (nine male, two female) ultramarathon 
runners without major past medical history, medications, or 
   | 3 of 9
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 50 km or longer than 6 h in 
total duration.
2.2 | Baseline assessment
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 2 weeks prior to the ultramarathon run. After a warm- up 
of about 10 min at 3 m/s, the initial running speed was set 
at 3.6 m/s with the first stage lasting 2 min. The speed was 
then progressively increased by 0.28 m/s every 2 min 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 60 s 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 
was obtained.
2.3 | Study protocol
Initially, all participants underwent a CMJ testing sessions 
with 3 CMJ trials and 30 s 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 
500 Hz. 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 3 m/s for 
a duration of 5 min 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- 
R3 system; Cortex Biophysics, Leipzig, Germany). VO2 was 
averaged over the last 1 min to estimate the running econ-
omy (Bontemps et al., 2020). The following day, seven par-
ticipants ran a simulated ultramarathon for approximately 6 h 
(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
BW 
[Kg]
Ht [cm]
Yrs 
training
Hrs/wk 
training
VO2MAX [ml/
kg/min]
VT1 [ml/
kg/min]
VT2 [ml/
kg/min]
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.
4 of 9 |   
ROGERS Et al.
TABLE 2 Pre and Post intervention data for both groups and all participants (n=11)
Group
Pre
Post
Ultramarathon
HR 
[bpm]
DFA a1
RE [ml/
kg/min]
CMJ 
vertical 
peak 
force [N/
kg]
CMJ 
jump 
height 
[cm]
VO2 run/
VT1 [%]
HR 
[bpm]
DFA a1
RE [ml/
kg/min]
CMJ 
vertical 
peak 
force [N/
kg]
CMJ 
jump 
height 
[cm]
VO2 run/
VT1 [%]
Time 
[h:min]
Distance 
[Km]
Speed 
[m/s]
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 
(±0.41)
36
(±4)
20.2 
(±1.9)
25.6 
(±6.6)
89 (±19)
141 (±13)
0.32 
(±0.09)
36
(±3)
18.8 
(±1.7)
23.0 
(±5.6)
87 (±12)
6:10 
(±0:19)
44
(±3)
2.0 (±0.1)
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 
(±0.28)
35
(±3)
21.6 
(±3.3)
23.6 
(±8.8)
87 (±25)
131 (±22)
0.97 
(±0.32)
34
(±2)
21.6 
(±3.4)
24.3 
(±8.5)
85 (±21)
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 3 m/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.
   | 5 of 9
ROGERS Et al.
participants (Control group, CG) did normal nonstrenuous 
daily activity for 6 h. Participants ran on an 11.5- km off road 
trail loop at a freely chosen pace (with an elevation change 
of 550 m) without rest periods and were allowed to ingest 
food and water freely. Immediately following the completion 
of the 6 h run or 6 h 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.
2.4 | RR measurements and calculation of 
DFA a1
A Polar H10 (Polar Electro Oy, Kempele, Finland) HR moni-
toring (HRM) device with a sampling rate of 1000 Hz was 
used to detect RR intervals in all individuals during the Pre 
and Post treadmill run over 5 min. 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 2 min 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).
2.5 | Statistics
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).
3 | RESULTS
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) 
(Figure 1).
4 | DISCUSSION
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 6 h 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 
6 of 9 |   
ROGERS Et al.
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 
(60 min 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 
a1 values 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 3 m/s 
and below the AT as demonstrated by baseline VO2 measure-
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 3 m/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 3 m/s for 
DFA a1 Pre and Post daily activity (CG) in four participants
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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).
4.1 | 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 2 m/s was consistent with an intensity 
below the AT since VO2 measurements at 3 m/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 a1 suppression 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 a1 monitoring 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 a1 kinetics 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.
5 | CONCLUSION
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 
a1 may 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.
ACKNOWLEDGMENTS
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.
AUTHOR CONTRIBUTIONS
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 
the study.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will 
be made available by the authors, without undue reservation.
ORCID
Bruce Rogers 
 https://orcid.org/0000-0001-8458-4709 
Thomas Gronwald 
 https://orcid.org/0000-0001-5610-6013 
REFERENCES
Abbiss, C. R., & Laursen, P. B. (2005). Models to explain fatigue 
during prolonged endurance cycling. Sports Medicine (Auckland, 
N. Z.), 35(10), 865– 898. https://doi.org/10.2165/00007 256- 20053 
5100- 00004.
Ament, W., & Verkerke, G. J. (2009). Exercise and fatigue. Sports 
Medicine (Auckland, N. Z.), 39(5), 389– 422. https://doi.
org/10.2165/00007 256- 20093 9050- 00005.
Balagué, N., Hristovski, R., Almarcha, M., Garcia- Retortillo, S., & 
Ivanov, P. C. (2020). Network physiology of exercise: Vision and 
perspectives. Frontiers in Physiology, 11, 611550. https://doi.
org/10.3389/fphys.2020.611550.
Beaver, W. L., Wasserman, K., & Whipp, B. J. (1986). A new method 
for detecting anaerobic threshold by gas exchange. Journal of 
8 of 9 |   
ROGERS Et al.
Applied Physiology, 60(6), 2020– 2027. https://doi.org/10.1152/
jappl.1986.60.6.2020.
Billat, V. L., Petot, H., Landrain, M., Meilland, R., Koralsztein, J. 
P., & Mille- Hamard, L. (2012). Cardiac output and perfor-
mance during a marathon race in middle- aged recreational 
runners. 
ScientificWorldJournal., 
2012, 
1– 9. 
https://doi.
org/10.1100/2012/810859.
Bontemps, B., Vercruyssen, F., Gruet, M., & Louis, J. (2020). Downhill 
running: What are the effects and how can we adapt? A Narrative 
Review. Sports Medicine, 50(12), 2083– 2110. https://doi.
org/10.1007/s4027 9- 020- 01355 - z.
Borg, G. A. (1982). Psychophysical bases of perceived exertion. 
Medicine and Science in Sports and Exercise, 14(5), 377– 381. 
https://doi.org/10.1249/00005 768- 19820 5000- 00012.
Carriker, C. R. (2017). Components of fatigue: Mind and body. Journal 
of Strength and Conditioning Research, 31(11), 3170– 3176. 
https://doi.org/10.1519/JSC.00000 00000 002088.
Casties, J. F., Mottet, D., & Le Gallais, D. (2006). Non- linear analyses 
of heart rate variability during heavy exercise and recovery in cy-
clists. International Journal of Sports Medicine, 27(10), 780– 785. 
https://doi.org/10.1055/s- 2005- 872968.
Chen, Z., Ivanov, P. C., Hu, K., & Stanley, H. E. (2002). Effect of non-
stationarities on detrended.
Claudino, J. G., Cronin, J., Mezêncio, B., McMaster, D. T., McGuigan, 
M., Tricoli, V., Amadio, A. C., & Serrão, J. C. (2017). The coun-
termovement jump to monitor neuromuscular status: A meta- 
analysis. Journal of Science and Medicine in Sport, 20(4), 397– 
402. https://doi.org/10.1016/j.jsams.2016.08.011.
Coe, R. (2002) It’s the effect size, stupid: what effect size is and why it is 
important. Annual Conference of the British Educational Research 
Association. September 12– 14, 2002. England.University of 
Exeter. fluctuation analysis. Phys. Rev. E 65:041107.
Cohen, J. (1988). Statistical power analysis for the behavioural sci-
ences; Erlbaum.
Davis, M. P., & Walsh, D. (2010). Mechanisms of fatigue. The Journal 
of Supportive Oncology, 8(4), 164– 174.
Deneen, W. P., & Jones, A. B. (2017). Cortisol and alpha- amylase 
changes during an ultra- running event. International Journal of 
Exercise Science, 10(4), 531– 540.
Gronwald, T., Berk, S., Altini, M., Mourot, L., Hoos, O., & Rogers, 
B. (2021). Real- time estimation of aerobic threshold and exercise 
intensity distribution using fractal correlation properties of heart 
rate variability: A single- case field application in a former olympic 
triathlete. Frontiers in Sports and Active Living, 3, 668812.
Gronwald, T., & Hoos, O. (2020). Correlation properties of heart rate 
variability during endurance exercise: A systematic review. Annals 
of Noninvasive Electrocardiology, 25(1), e12697. https://doi.
org/10.1111/anec.12697.
Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of acute nor-
mobaric hypoxia on non- linear dynamics of cardiac autonomic 
activity during constant workload cycling exercise. Frontiers in 
Physiology -  Exercise Physiology, 10, 999. https://doi.org/10.3389/
fphys.2019.00999.
Gronwald, T., Hoos, O., & Hottenrott, K. (2019). Effects of a short- 
term cycling interval session and active recovery on non- linear dy-
namics of cardiac autonomic activity in endurance trained cyclists. 
Journal of Clinical Medicine, 8(2), 194. https://doi.org/10.3390/
jcm80 20194.
Gronwald, T., Hoos, O., Ludyga, S., & Hottenrott, K. (2018). Non- linear 
dynamics of cardiac autonomic activity during cycling cadence 
exercise with varied cadence. Human Movement Science, 60, 
225– 233.
Gronwald, T., Rogers, B., & Hoos, O. (2020). Fractal correlation 
properties of heart rate variability: A new biomarker for inten-
sity distribution in endurance exercise and training prescription? 
Frontiers in Physiology, 11, 550572. https://doi.org/10.3389/
fphys.2020.550572.
Halperin, I., & Emanuel, A. (2020). Rating of perceived effort: 
Methodological concerns and future directions. Sports Medicine 
(Auckland, N. Z.), 50(4), 679– 687. https://doi.org/10.1007/s4027 
9- 019- 01229 - z.
Hautala, A., Tulppo, M. P., Mäkikallio, T. H., Laukkanen, R., Nissilä, 
S., & Huikuri, H. V. (2001). Changes in cardiac autonomic regula-
tion after prolonged maximal exercise. Clinical Physiology, 21(2), 
238– 245. https://doi.org/10.1046/j.1365- 2281.2001.00309.x.
Jastrzębski, Z., Żychowska, M., Konieczna, A., Ratkowski, W., & 
Radzimiński, Ł. (2015). Changes in the acid- base balance and lac-
tate concentration in the blood in amateur ultramarathon runners 
during a 100- km run. Biology of Sport, 32(3), 261– 265. https://doi.
org/10.5604/20831 862.1163372.
Kannankeril, P. J., & Goldberger, J. J. (2002). Parasympathetic effects on 
cardiac electrophysiology during exercise and recovery. American 
Journal of Physiology. Heart and Circulatory Physiology, 282(6), 
H2091– H2098. https://doi.org/10.1152/ajphe art.00825.2001.
Knechtle, B., & Nikolaidis, P. T. (2018). Physiology and pathophysi-
ology in ultra- marathon running. Frontiers in Physiology, 9, 634. 
https://doi.org/10.3389/fphys.2018.00634.
Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for 
heart rate variability time series artefact correction using novel 
beat classification. Journal of Medical Engineering & Technology, 
43(3), 173– 181. https://doi.org/10.1080/03091 902.2019.1640306.
Ludyga, S., Gronwald, T., & Hottenrott, K. (2016). Effects of high vs. 
low cadence training on cyclists’ brain cortical activity during 
exercise. Journal of Science and Medicine in Sport, 19(4), 
342– 347.
Manresa- Rocamora, A., Flatt, A. A., Casanova- Lizón, A., Ballester- 
Ferrer, J. A., Sarabia, J. M., Vera- Garcia, F. J., & Moya- Ramón, 
M. (2021). Heart rate- based indices to detect parasympathetic hy-
peractivity in functionally overreached athletes. A meta- analysis. 
Scandinavian Journal of Medicine and Science in Sports, 31(6), 
1164– 1182. https://doi.org/10.1111/sms.13932.
Martin, K., Meeusen, R., Thompson, K. G., Keegan, R., & Rattray, B. 
(2018). Mental fatigue impairs endurance performance: A phys-
iological explanation. Sports Medicine (Auckland, N. Z.), 48(9), 
2041– 2051. https://doi.org/10.1007/s4027 9- 018- 0946- 9.
Martínez- Navarro, I., Sánchez- Gómez, J. M., Collado- Boira, E. J., 
Hernando, B., Panizo, N., & Hernando, C. (2019). Cardiac damage 
biomarkers and heart rate variability following a 118- Km moun-
tain race: Relationship with performance and recovery. Journal of 
Sports Science and Medicine, 18(4), 615– 622.
McMorris, T., Barwood, M., & Corbett, J. (2018). Central fatigue 
theory and endurance exercise: Toward an interoceptive model. 
Neuroscience and Biobehavioral Reviews, 93, 93– 107. https://doi.
org/10.1016/j.neubi orev.2018.03.024.
Michael, S., Graham, K. S., & Oam, D. G. M. (2017). Cardiac auto-
nomic responses during exercise and post- exercise recovery using 
heart rate variability and systolic time intervals- a review. Frontiers 
in Physiology, 8, 301. https://doi.org/10.3389/fphys.2017.00301.
Papaioannou, V. E., Verkerk, A. O., Amin, A. S., & de Bakker, J. M. 
(2013). Intracardiac origin of heart rate variability, pacemaker 
   | 9 of 9
ROGERS Et al.
funny current and their possible association with critical illness. 
Current Cardiology Reviews, 9(1), 82– 96.
Ramos- Campo, D. J., Ávila- Gandía, V., Alacid, F., Soto- Méndez, F., 
Alcaraz, P. E., López- Román, F. J., & Rubio- Arias, J. Á. (2016). 
Muscle damage, physiological changes, and energy balance in 
ultra- endurance mountain- event athletes. Applied Physiology, 
Nutrition and Metabolism, 41(8), 872– 878. https://doi.org/10.1139/
apnm- 2016- 0093.
Rogers, B. (2020). A personalized low intensity exercise prescription 
based on an index of non linear heart rate variability: a case report. 
The Journal of Athletic Enhancement, 9(1), 327.
Rogers, B., Giles, D., Draper, N., Hoos, O., & Gronwald, T. (2021). A 
new detection method defining the aerobic threshold for endur-
ance exercise and training prescription based on fractal correlation 
properties of heart rate variability. Frontiers in Physiology, 11, 
596567. https://doi.org/10.3389/fphys.2020.596567.
Rogers, B., Giles, D., Draper, N., Mourot, L., & Gronwald, T. (2021). 
Influence of artefact correction and recording device type on the 
practical application of a non- linear heart rate variability biomarker 
for aerobic threshold determination. Sensors (Basel), 21(3), 821. 
https://doi.org/10.3390/s2103 0821.
Rogers, B., & Gronwald, T. (2021) From laboratory to roadside: 
Real- time assessment and monitoring of the aerobic threshold 
in endurance- typed sports. BJSM Blog: https://blogs.bmj.com/
bjsm/2021/02/13/from- labor atory - to- roads ide- real- time- asses 
sment - and- monit oring - of- the- aerob ic- thres hold- in- endur ance- 
typed - sports. Published Feb 13, 2021
Sandercock, G. R., & Brodie, D. A. (2006). The use of heart rate vari-
ability measures to assess autonomic control during exercise. 
Scandinavian Journal of Medicine and Science in Sports, 16(5), 
302– 313. https://doi.org/10.1111/j.1600- 0838.2006.00556.x.
Schader, J. F., Haid, M., Cecil, A., Schoenfeld, J., Halle, M., Pfeufer, 
A., Prehn, C., Adamski, J., Nieman, D. C., & Scherr, J. (2020). 
Metabolite shifts induced by marathon race competition differ be-
tween athletes based on level of fitness and performance: A sub-
study of the Enzy- MagIC Study. Metabolites, 10(3), 87. https://doi.
org/10.3390/metab o1003 0087.
Scheer, V., Basset, P., Giovanelli, N., Vernillo, G., Millet, G. P., & Costa, 
R. J. S. (2020). Defining off- road running: A position statement 
from the ultra sports science foundation. International Journal of 
Sports Medicine, 41(5), 275– 284.
Scheer, V., Vieluf, S., Cramer, L., Jakobsmeyer, R., & Heitkamp, H. C. 
(2018). Changes in running economy during a 65- km ultramar-
athon. Frontiers in Physiology, 9, 1809. https://doi.org/10.3389/
fphys.2018.01809.
Stanley, J., Peake, J. M., & Buchheit, M. (2013). Cardiac parasympa-
thetic reactivation following exercise: Implications for training 
prescription. Sports Medicine (Auckland, N. Z.), 43(12), 1259– 
1277. https://doi.org/10.1007/s4027 9- 013- 0083- 4.
Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta- Aho, P. 
O., & Karjalainen, P. A. (2014). Kubios HRV– heart rate vari-
ability analysis software. Computer Methods and Programs 
in Biomedicine, 113(1), 210– 220. https://doi.org/10.1016/j.
cmpb.2013.07.024.
Vernillo, G., Doucende, G., Cassirame, J., & Mourot, L. (2019). 
Energetically optimal stride frequency is maintained with fa-
tigue in trained ultramarathon runners. Journal of Science and 
Medicine in Sport, 22(9), 1054– 1058. https://doi.org/10.1016/j.
jsams.2019.04.003.
Wan, J. J., Qin, Z., Wang, P. Y., Sun, Y., & Liu, X. (2017). Muscle 
fatigue: general understanding and treatment. Experimental 
& Molecular Medicine, 49(10), e384. https://doi.org/10.1038/
emm.2017.194.
White, D. W., & Raven, P. B. (2014). Autonomic neural control 
of heart rate during dynamic exercise: revisited. Journal of 
Physiology, 592(12), 2491– 2500. https://doi.org/10.1113/jphys 
iol.2014.271858.
Wu, P. P., Sterkenburg, N., Everett, K., Chapman, D. W., White, N., & 
Mengersen, K. (2019). Predicting fatigue using countermovement 
jump force- time signatures: PCA can distinguish neuromuscular 
versus metabolic fatigue. PLoS One, 14(7), e0219295. https://doi.
org/10.1371/journ al.pone.0219295.
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

Fractal correlation properties of heart rate variability as a biomarker of endurance exercise fatigue in ultramarathon runners.
Rogers, Bruce,Mourot, Laurent,Doucende, Gregory,Gronwald, Thomas