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Although heart rate variability (HRV) indexes have been helpful for monitoring the fatigued state while resting, little data indicates there is comparable potential during exercise. Since an index of HRV based on fractal correlation properties, alpha 1of Detrended Fluctuation Analysis (DFA a1) displays overall organismic demands, alteration during exertion may provide insight into physiologic changes accompanying fatigue. Two weeks after collecting baseline demographic and gas exchange data, eleven experienced ultramarathon runners were divided into two groups. Seven runners performed a simulated ultramarathon for 6 hours (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 vs 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 running 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.
Physiological Reports. 2021;9:e14956.
1 of 9
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
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-
7408, USA.
Funding information
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 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.
DFA a1, endurance exercise, fatigue, marathon, running
2 of 9
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
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.
Eleven experienced (nine male, two female) ultramarathon
runners without major past medical history, medications, or
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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.
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.
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
[Kg] Ht [cm]
VO2MAX [ml/
VT1 [ml/
VT2 [ml/
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
RE [ml/
force [N/
VO2 run/
VT1 [%]
[bpm] DFA a1
RE [ml/
force [N/
VO2 run/
VT1 [%]
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
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
87 (±25) 131 (±22) 0.97
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
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.
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).
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)
(Figure 1).
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
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
7 of 9
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 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.
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.
This research was supported by the Université of Franche
Comté and TPU development program.
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 was obtained from all subjects involved in
the study.
The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
Bruce Rogers
Thomas Gronwald
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How to cite this article: Rogers, B., Mourot, L.,
Doucende, G., & Gronwald, T. (2021). Fractal
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... For instance, if the DFA a1 is usually 0.75 at a pace representing the AeT in a wellrested individual, would it be different after lengthy endurance exercise? To help answer this question an examination of running economy, HR, CMJ and DFA a1 in a group of experienced ultramarathon participants was explored before and after a 6-h trail based run (Rogers et al., 2021e). Seven athletes performed a 5-min treadmill test (at or below VT1 intensity) before and after the 6-h session. ...
... In future studies looking at DFA a1 behavior at low to moderate intensities over longer time spans, incorporation of multipoint (rolling) averaging or longer measuring windows may lead to better comparative insights. Additionally, it will be important to examine the day-to-day variation and reproducibility of the DFA a1 FIGURE 2 | (A) Analysis of DFA a1 and HR of a 22-year-old male participant with a VO 2MAX of 74 ml/kg/min during a 5-min treadmill test at 65% of VT1 (VO 2 ) before and immediately following a 6-h continuous trail run (data adapted from Rogers et al., 2021e). (B) Analysis of DFA a1, power and HR of a 41-year-old former Olympic male triathlete with a VO 2MAX of 65 ml/kg/min performing 6-min progressive cycling intervals of 100, 130, 160, 190, 220 W before and immediately after 2 h of continuous cycling exercise at 65% LT1 power (adapted from Gronwald et al., 2021). ...
Full-text available
While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.
... The range of scales considered for the α 1 fitting depends on different authors: 3 ≤ ≤ 10 [32,33], 3 ≤ ≤ 11 [34,35], 4 ≤ ≤ 11 [36,37], 4 ≤ ≤ 12 [38,39] or even larger values (10 ≤ ≤ 30) [40]. More recently [41], it has been shown that the values of α 1 evaluated in the interval 4 ≤ ≤ 16 seem to be a good biomarker of fatigue during extreme exercise. ...
... These overestimations of α 1 could strongly affect the interpretation and implications of the results obtained with physiological time series. For example, Rogers et al. [41] show that the α 1 value obtained from heart rate time series drops to 0.5 when runner's fatigue increases. If we do not take into account these overestimations, we can conclude that fatigue makes the heart rate be random at short scales, whereas, in reality, the heart rate becomes highly anticorrelated at short scales. ...
Full-text available
Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.
... Time-frequency analysis could represent an alternative for the assessment of cardiovagal regulation indexed by respiratory sinus arrhythmia (Mestanik et al., 2019). Finally, alternative techniques (i.e., not based on the debated LF-HF parameters), for the identification of the parasympathetic and sympathetic branches activity are increasingly proposed in the literature (Adjei et al., 2019;Rogers et al., 2021), but remain to be validated in athletes' follow-up. ...
Full-text available
Easy-to-use and accurate heart rate variability (HRV) assessments are essential in athletes’ follow-up, but artifacts may lead to erroneous analysis. Artifact detection and correction are the purpose of extensive literature and implemented in dedicated analysis programs. However, the effects of number and/or magnitude of artifacts on various time- or frequency-domain parameters remain unclear. The purpose of this study was to assess the effects of artifacts on HRV parameters. Root mean square of the successive differences (RMSSD), standard deviation of the normal to normal inter beat intervals (SDNN), power in the low- (LF) and high-frequency band (HF) were computed from two 4-min RR recordings in 178 participants in both supine and standing positions, respectively. RRs were modified by (1) randomly adding or subtracting 10, 30, 50 or 100 ms to the successive RRs; (2) a single artifact was manually inserted; (3) artifacts were automatically corrected from signal naturally containing artifacts. Finally, RR recordings were analyzed before and after automatic detection-correction of artifacts. Modifying each RR by 10, 30, 50 and 100 ms randomly did not significantly change HRV parameters (range -6%, +6%, supine). In contrast, by adding a single artifact, RMSSD increased by 413% and 269%, SDNN by 54% and 47% in supine and standing positions, respectively. LF and HF changed only between -3% and +8% (supine and standing) in the artifact condition. When more than 0.9% of the signal contained artifacts, RMSSD was significantly biased, whilst when more than 1.4% of the signal contained artifacts LF and HF were significantly biased. RMSSD and SDNN were more sensitive to a single artifact than LF and HF. This indicates that, when using RMSSD only, a single artifact may induce erroneous interpretation of HRV. Therefore, we recommend using both time- and frequency-domain parameters to minimize the errors in the diagnoses of health status or fatigue in athletes.
... The analysis in [44] showed that SD1 and SD2 decreased after a table tennis match, indicating activation of the sympathetic system and, simultaneously, deactivation of the parasympathetic system. Another study [45] pointed out that α 1 decreased when running at low intensity. They suggested that α 1 can provide the opportunity to track physiological status in real time to monitor exercise fatigue. ...
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Accurate assessment of physical fatigue is crucial to preventing physical injury caused by excessive exercise, overtraining during daily exercise and professional sports training. However, as a subjective feeling of an individual, physical fatigue is difficult for others to objectively evaluate. Heart rate variability (HRV), which is derived from electrocardiograms (ECG) and controlled by the autonomic nervous system, has been demonstrated to be a promising indicator for physical fatigue estimation. In this paper, we propose a novel method for the automatic and objective classification of physical fatigue based on HRV. First, a total of 24 HRV features were calculated. Then, a feature selection method was proposed to remove useless features that have a low correlation with physical fatigue and redundant features that have a high correlation with the selected features. After feature selection, the best 11 features were selected and were finally used for physical fatigue classifying. Four machine learning algorithms were trained to classify fatigue using the selected features. The experimental results indicate that the model trained using the selected 11 features could classify physical fatigue with high accuracy. More importantly, these selected features could provide important information regarding the identification of physical fatigue.
... Although group sample size was somewhat less than optimal at nine individuals, this is not unexpected given the exclusive nature of high-level athletic training populations. Finally, while participant training status was felt not to be overreached, further investigations into using changes in DFA a1 behavior as a measure of endurance exercise fatigue appear promising based on recent data [30]. ...
Full-text available
A non-linear index of heart rate (HR) variability (HRV) known as alpha1 of Detrended Fluctuation Analysis (DFA a1) has been shown to change with increasing exercise intensity, crossing a value of 0.75 at the aerobic threshold (AT) in recreational runners defining a HRV threshold (HRVT). Since large volumes of low-intensity training below the AT is recommended for many elite endurance athletes, confirmation of this relationship in this specific group would be advantageous for the purposes of training intensity distribution monitoring. Nine elite triathletes (7 male, 2 female) attended a training camp for diagnostic purposes. Lactate testing was performed with an incremental cycling ramp test to exhaustion for the determination of the first lactate threshold based on the log–log calculation method (LT1). Concurrent measurements of cardiac beta-to-beat intervals were performed to determine the HRVT. Mean LT1 HR of all 9 participants was 155.8 bpm (±7.0) vs. HRVT HR of 153.7 bpm (±10.1) (p = 0.52). Mean LT1 cycling power was 252.3 W (±48.1) vs. HRVT power of 247.0 W (±53.6) (p = 0.17). Bland–Altman analysis showed mean differences of −1.7 bpm and −5.3 W with limits of agreement (LOA) 13.3 to −16.7 bpm and 15.1 to −25.6 W for HR and cycling power, respectively. The DFA a1-based HRVT closely agreed with the LT1 in a group of elite triathletes. Since large volumes of low-intensity exercise are recommended for successful endurance performance, the fractal correlation properties of HRV show promise as a low-cost, non-invasive option to that of lactate testing for identification of AT-related training boundaries.
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Background and purpose: Most studies on heart rate variability (HRV) in professional athletes concerned linear, time- and frequency-domain indices and there is lack of studies on nonlinear parameters in this group. The study aimed to determine the inter-day reliability, and group-related and individual changes of short-term symbolic dynamics (SymDyn) measures during sympathetic nervous system activity (SNSa) stimulation among elite modern pentathletes. Methods: Short-term electrocardiographic recordings were performed in stable measurement conditions with a 7-days interval between tests. SNSa stimulation via isometric handgrip strength test was conducted on the second day of study. The occurrence rate of patterns without variations (0V), with one variation (1V), two like (2LV) and two unlike variations (2UV) obtained using three approaches (the Max-min, the σ and the Equal-probability methods) were analyzed. Relative and absolute reliability were evaluated. Results: All SymDyn indices obtained using the Max-min method, 0V and 2UV obtained using the σ method, 2UV obtained using the Equal-probability method presented acceptable inter-day reliability (the intraclass correlation coefficient between 0.91 and 0.99, Cohen’s d between -0.08 and 0.10, the within-subject coefficient of variation between 4% and 22%). 2LV, 2UV and 0V obtained using the Max-min and σ methods significantly decreased and increased, respectively, during SNSa stimulation – such changes were noted for all athletes. There was no significant association between differences in SymDyn parameters and respiratory rate in stable conditions and while comparing stable conditions and SNSa stimulation. Conclusion: SymDyn indices may be used as reliable non-respiratory-associated parameters in laboratory settings to detect ANS activity modulations in elite endurance athletes. These findings provide a potential solution for addressing the confounding influence of respiration frequency on HRV-derived inferences of cardiac-autonomic function. For this reason, SymDyn may prove to be preferable for field-based monitoring where measurements are unsupervised.
Full-text available
A non-linear heart rate variability (HRV) index based on fractal correlation properties called alpha1 of Detrended Fluctuation Analysis (DFA-alpha1), has been shown to change with endurance exercise intensity. Its unique advantage is that it provides information about current absolute exercise intensity without prior lactate or gas exchange testing. Therefore, real-time assessment of this metric during field conditions using a wearable monitoring device could directly provide a valuable exercise intensity distribution without prior laboratory testing for different applied field settings in endurance sports. Until of late no mobile based product could display DFA-alpha1 in real-time using off the shelf consumer products. Recently an app designed for iOS and Android devices, HRV Logger, was updated to assess DFA-alpha1 in real-time. This brief research report illustrates the potential merits of real-time monitoring of this metric for the purposes of aerobic threshold (AT) determination and exercise intensity demarcation between low (zone 1) and moderate (zone 2) in a former Olympic triathlete. In a single-case feasibility study, three practically relevant scenarios were successfully evaluated in cycling, 1) estimation of a HRV threshold (HRVT) as an adequate proxy for AT using Kubios HRV software via a typical cycling stage test, 2) determination of the HRVT during real-time monitoring using a cycling 6 min stage test, 3) a simulated 1 hour training ride with enforcement of low intensity boundaries and real-time HRVT confirmation. This single-case field evaluation illustrates the potential of an easy-to-use and low cost real-time estimation of the aerobic threshold and exercise intensity distribution using fractal correlation properties of HRV. Furthermore, this approach may enhance the translation of science into endurance sports practice for future real-world settings.
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Investigations into the sensitivity of heart rate‐ (HR) derived indices for tracking parasympathetic nervous system (PNS) changes in functionally overreached (F‐OR) endurance‐trained athletes have produced equivocal findings. Lack of clarity may be a result of methodological inconsistencies. Therefore, the aims of this systematic review and meta‐analysis were (a) to determine the sensitivity of resting and post‐exercise vagal‐related HR variability (HRV) and HR recovery (HRR) indices to detect PNS modulation in F‐OR and non‐overreached (non‐OR) athletes, and (b) to investigate the influence of methodological factors on the sensitivity of HR‐based indices to detect PNS hyperactivity in F‐OR athletes. We searched CENTRAL, Scopus, PubMed, Embase and Web of Science up to May 2020 for the following terms: male and female endurance‐trained athletes, controlled and uncontrolled studies that carried out an overload training period, and PNS modulation measured in resting and post‐exercise, pre‐ and post‐ overload training period. A random‐effects model of standardised mean difference (SMD) was estimated for each outcome measure based on the training‐induced fatigue status (F‐OR vs non‐OR athletes) and the influence of methodological issues to detect PNS hyperactivity in F‐OR was assessed by subgroup analyses. Pooled analysis showed that resting vagal‐related HRV indices did not detect PNS hyperactivity in F‐OR athletes (SMD+ = ‐0.01; 95% confidence interval [CI] = ‐0.51, 0.50), and no statistical difference (p = 0.600) was found with non‐OR athletes (SMD+ = 0.15; 95% CI = ‐0.14, 0.45). However, subgroup analysis based on HRV parameter showed a moderate statistical increase in weekly averaged HRV in F‐OR athletes (SMD+ = 0.81; 95% CI = 0.35, 1.26), while isolated HRV values did not reach statistical significance (SMD+ = ‐0.45; 95% CI = ‐0.96, 0.06). We observed a moderate and statistically significant increase in HRR indices among F‐OR athletes (SMD+ = 0.65; 95% CI = 0.44, 0.87), no changes for non‐OR athletes (SMD+ = 0.10; 95% CI = ‐0.15, 0.34), and statistically significant differences between F‐OR and non‐OR athletes (p < 0.001). Insufficient data prevented meta‐analysis for post‐exercise vagal‐related HRV indices. Our findings show that when methodological factors are considered, HR‐based indices are sensitive to increased PNS modulation in F‐OR.
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Recent study points to the value of a non-linear heart rate variability (HRV) biomarker using detrended fluctuation analysis (DFA a1) for aerobic threshold determination (HRVT). Significance of recording artefact, correction methods and device bias on DFA a1 during exercise and HRVT is unclear. Gas exchange and HRV data were obtained from 17 participants during an incremental treadmill run using both ECG and Polar H7 as recording devices. First, artefacts were randomly placed in the ECG time series to equal 1, 3 and 6% missed beats with correction by Kubios software’s automatic and medium threshold method. Based on linear regression, Bland Altman analysis and Wilcoxon paired testing, there was bias present with increasing artefact quantity. Regardless of artefact correction method, 1 to 3% missed beat artefact introduced small but discernible bias in raw DFA a1 measurements. At 6% artefact using medium correction, proportional bias was found (maximum 19%). Despite this bias, the mean HRVT determination was within 1 bpm across all artefact levels and correction modalities. Second, the HRVT ascertained from synchronous ECG vs. Polar H7 recordings did show an average bias of minus 4 bpm. Polar H7 results suggest that device related bias is possible but in the reverse direction as artefact related bias.
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The short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of .75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 40.5 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.8 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson’s r values of .99 (p < .001) and .97 (p < .001) for VO2 and HR comparisons respectively. Intraclass correlation between VT1 GAS and HRVT was .99 for VO2 and .96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of .75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.
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The basic theoretical assumptions of Exercise Physiology and its research directions, strongly influenced by reductionism, may hamper the full potential of basic science investigations, and various practical applications to sports performance and exercise as medicine. The aim of this perspective and programmatic article is to: (i) revise the current paradigm of Exercise Physiology and related research on the basis of principles and empirical findings in the new emerging field of Network Physiology and complex systems science; (ii) initiate a new area in Exercise and Sport Science, Network Physiology of Exercise (NPE), with focus on basic laws of interactions and principles of coordination and integration among diverse physiological systems across spatio-temporal scales (from the sub-cellular level to the entire organism), to understand how physiological states and functions emerge from integrated network interactions and to improve the efficacy of exercise in health and sport performance; (iii) to create a forum for developing new research methodologies applicable to the new NPE field, to infer and quantify nonlinear dynamic forms of coupling among diverse systems and establish basic principles of coordination and network organization of physiological systems. Here we present a programmatic approach for future research directions and potential practical applications. By focusing on research efforts to improve the knowledge about nested dynamics of vertical network interactions, and particularly the horizontal integration of key organ systems during exercise, NPE may enrich Basic Physiology and diverse fields like Exercise and Sports Physiology, Sports Medicine, Sports Rehabilitation, Sport Science or Training Science, and improve the understanding of diverse exercise-related phenomena such as sports performance, fatigue, overtraining, or sport injuries.
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Downhill running (DR) is a whole-body exercise model that is used to investigate the physiological consequences of eccentric muscle actions and/or exercise-induced muscle damage (EIMD). In a sporting context, DR sections can be part of running disciplines (of-road and road running) and can accentuate EIMD, leading to a reduction in performance. The purpose of this narrative review is to: (1) better inform on the acute and delayed physiological effects of DR; (2) identify and discuss, using a comprehensive approach, the DR characteristics that affect the physiological responses to DR and their potential interactions; (3) provide the current state of evidence on preventive and in-situ strategies to better adapt to DR. Key findings of this review show that DR may have an impact on exercise performance by altering muscle structure and function due to EIMD. In the majority of studies, EIMD are assessed through isometric maximal voluntary contraction, blood creatine kinase and delayed onset muscle soreness, with DR characteristics (slope, exercise duration, and running speed) acting as the main influencing factors. In previous studies, the median (25th percentile, Q1; 75th percentile, Q3) slope, exercise duration, and running speed were − 12% (− 15%; − 10%), 40 min (30 min; 45 min) and 11.3 km h−1 (9.8 km h−1; 12.9 km h−1), respectively. Regardless of DR characteristics, people the least accustomed to DR generally experienced the most EIMD. There is growing evidence to suggest that preventive strategies that consist of prior exposure to DR are the most effective to better tolerate DR. The effectiveness of in-situ strategies such as lower limb compression garments and specific footwear remains to be confirmed. Our review finally highlights important discrepancies between studies in the assessment of EIMD, DR protocols and populations, which prevent drawing firm conclusions on factors that most influence the response to DR, and adaptive strategies to DR.
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Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g. blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non‐linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behaviour and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport‐specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global” system parameter and proxy for organismic demands.
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The potential of an index of cardiac interbeat fractal complexity (DFA a1) to demarcate low intensity training was undertaken in a recreational athlete. The influence of absolute heart rate elevation versus work rate as factors responsible for loss of interbeat complexity was also examined via the usage of beta adrenergic blockade. Incremental cycling ramps were performed with and without the beta adrenergic blocking agent Atenolol 25 mg measuring DFA a1 during the last 2 minutes of each stage. No difference was seen between control and Atenolol trials for lactate thresholds, ventilation rates, rectus femoris muscle O2 desaturation and DFA a1 despite a 15 to 20 beat decrease in heart rate across all stages in the Atenolol trial. In both studies, DFA a1 progressively declined with cycling power reaching a value consistent with white noise at 25 Watts above the first ventilatory threshold. In conclusion, DFA a1 transition to an uncorrelated low complexity state occurred just above the VT1. In addition, the complexity index was related to cycling power, ventilation and presumably VO2 rather than the absolute heart rate. Longer constant power intervals near VT1 did not show additional or progressive complexity loss. DFA a1 may be a promising guide for low intensity training zone demarcation.
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This study compared metabolite shifts induced by training for, participation in, and recovery from a marathon race competition among athletes divided into three groups based on fitness (relative maximum oxygen uptake (VO2max)) and performance levels (net running time). Plasma samples from 76 male runners participating in the Munich Marathon were analyzed for metabolite shifts using a targeted metabolomics panel. For the entire cohort of runners, pronounced increases were measured immediately after the race for plasma concentrations of acylcarnitines (AC), the ratio (palmitoylcarnitine + stearoylcarnitine)/free carnitine that is used as a proxy for the activity of the mitochondrial enzyme carnitine palmitoyltransferase, and arginine-related metabolites, with decreases in most amino acids (AA) and phospholipids. Plasma levels of AA and phospholipids were strongly increased 24 and 72 h post-race. Post-race plasma concentrations of AC and arginine-related metabolites were higher in the low compared to top performers, indicating an accumulation of fatty acids and a reliance on protein catabolism to provide energy after the marathon event. This study showed that marathon race competition is associated with an extensive Metabolites 2020, 10, 87 2 of 14 and prolonged perturbation in plasma metabolite concentrations with a strong AC signature that is greater in the slower, less aerobically fit runners. Furthermore, changes in the arginine-related metabolites were observed.
Off-road running continues to grow in popularity, with differing event categories existing, and terminologies are often used interchangeably and without precision. Trail running, mountain running, skyrunning, fell running, orienteering, obstacle course racing and cross-country running all take place predominantly in off-road terrain. Ultramarathon running refers to any running event over marathon distance conducted in any terrain and surface. Although some overlap may exist between these running events, mainly through the common denominator of off-road terrain, distinct features need to be recognised. As scientific interest in these activities grows, it becomes important to clarify these terms and develop a universal language for discussing these events. Similarly, off-road running athletes are generally not properly defined within the scientific literature, which makes intra- and inter-study comparisons difficult. The current position statement of the Ultra Sports Science Foundation highlights the different off-road running events and recommends clear reference to distance, surface, elevation change and altitude, type of event (continuous vs. staged), type of support, name and year of the event, governing body, and guidance on terminology. We further recommend to describe off-road running athletes by basic data, physiological determinants, training and competition characteristics in the scientific literature in order to facilitate and guide further research and practice.