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A New Detection Method Defining the Aerobic Threshold for Endurance Exercise and Training Prescription Based on Fractal Correlation Properties of Heart Rate Variability

Authors:
  • Lattice Training Ltd

Abstract and Figures

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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ORIGINAL RESEARCH
published: 15 January 2021
doi: 10.3389/fphys.2020.596567
Edited by:
Clint Bellenger,
University of South Australia, Australia
Reviewed by:
Daniel Boullosa,
Federal University of Mato Grosso Do
Sul, Brazil
Lars Brechtel,
MSB Medical School Berlin, Germany
*Correspondence:
Bruce Rogers
bjrmd@knights.ucf.edu;
brucerogersmd@gmail.com
Specialty section:
This article was submitted to
Autonomic Neuroscience,
a section of the journal
Frontiers in Physiology
Received: 19 August 2020
Accepted: 18 December 2020
Published: 15 January 2021
Citation:
Rogers B, Giles D, Draper N,
Hoos O and Gronwald T (2021) A
New Detection Method Defining
the Aerobic Threshold for Endurance
Exercise and Training Prescription
Based on Fractal Correlation
Properties of Heart Rate Variability.
Front. Physiol. 11:596567.
doi: 10.3389/fphys.2020.596567
A New Detection Method Defining
the Aerobic Threshold for Endurance
Exercise and Training Prescription
Based on Fractal Correlation
Properties of Heart Rate Variability
Bruce Rogers1*, David Giles2, Nick Draper3, Olaf Hoos4and Thomas Gronwald5
1College of Medicine, University of Central Florida, Orlando, FL, United States, 2Lattice Training Ltd., Chesterfield,
United Kingdom, 3School of Health Sciences, College of Education, Health and Human Development, University
of Canterbury, Christchurch, New Zealand, 4Center for Sports and Physical Education, Julius Maximilians University
of Wuerzburg, Wuerzburg, Germany, 5Department of Performance, Neuroscience, Therapy and Health, Faculty of Health
Sciences, MSH Medical School Hamburg, University of Applied Sciences and Medical University, Hamburg, Germany
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 VO2and 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 0.75 (HRVT). Based on Bland Altman analysis, linear regression,
intraclass correlation (ICC) and ttesting, there was strong agreement between VT1
GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at
39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at
40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between
test modalities, with Pearson’s rvalues of 0.99 (p<0.001) and.97 (p<0.001) for VO2
and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT
was 0.99 for VO2and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT
showed no differences by ttesting, also supporting the method validity. In conclusion, it
appears that reaching a DFA a1 value of 0.75 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.
Keywords: detrended fluctuation analysis, ventilatory threshold, aerobic threshold, intensity distribution, intensity
zones, endurance exercise, endurance training, polarized training
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INTRODUCTION
Training zone identification is part of the foundation for exercise
intensity distribution study and implementation (Stöggl and
Sperlich, 2019. Traditionally, the upper limit of the low intensity
range (zone 1 in a 3 zone model) for intensity distribution
for endurance exercise and training prescription has been
represented by the first ventilatory (VT1) or lactate threshold
(LT1) (Seiler and Kjerland, 2006;Esteve-Lanao et al., 2007;Mann
et al., 2013;Pallarés et al., 2016). Although there may be different
schools of thought on what type of distribution is “optimal”
(polarized vs. pyramidal or threshold) both models are defined
by having the major portion of training in zone 1. In addition,
several training approaches for endurance athletes recommend
spending large amounts of exercise time in a low intensity zone
(Muñoz et al., 2014;Stöggl and Sperlich, 2014, 2019;Bourgois
et al., 2019;Casado et al., 2019). Gold standard methods to obtain
VT1 or LT1 revolve around either formal gas exchange testing
or invasive blood lactate sampling. These procedures can be
costly, require special test equipment, trained operators, ongoing
calibration and verification. Even if these methods are utilized,
there is disagreement on their accuracy as both visual (Yeh
et al., 1983;Meyer et al., 1996) and automated (Ekkekakis et al.,
2008) gas exchange analysis can be subject to substantial error.
In addition, LT1 assessment can vary depending on the chosen
concept of determination (Newell et al., 2007;Faude et al., 2009;
Jamnick et al., 2018). Training guided by erroneous intensity
targets could lead to potential adverse consequences such as
prolonged cardiac parasympathetic recovery (Seiler and Kjerland,
2006;Stanley et al., 2013), central and muscular fatigue (Noakes
et al., 2005;Venhorst et al., 2018), glycogen depletion (Beneke
et al., 2011), and gastrointestinal barrier disruption (van Wijck
et al., 2012). In view of the difficulties involved in gas exchange
analysis, lactate test availability, invasiveness, and accuracy, a
search for alternate methods of identifying the limits of low
intensity exercise seem worthwhile.
Cardiac interbeat interval variation, commonly referred to
as heart rate variability (HRV), has been extensively studied
in both resting states (Shaffer and Ginsberg, 2017) as well as
during dynamic exercise (Hottenrott and Hoos, 2017;Michael
et al., 2017). Certain HRV indexes have been observed to change
as exercise intensity rises, potentially providing information
regarding an individual’s physiologic status (Tulppo et al., 1996;
Casties et al., 2006;Sandercock and Brodie, 2006;Karapetian
et al., 2008;Michael et al., 2017;Gronwald et al., 2018,
2019a,b,c). It has also been shown that several of the examined
HRV indexes also change during lower intensities (Tulppo
et al., 1996;Sandercock and Brodie, 2006;Karapetian et al.,
2008;Botek et al., 2010;Michael et al., 2017) making them
potentially suitable for zone 1 delineation. However, despite some
initial interest, widespread usage for the specific purpose of
low intensity training limitation has not occurred. Frequency-
domain parameters such as high frequency (HF) power have
been noted to be unreliable in a sizable fraction of individuals
with up to 20% of subjects not having identifiable breakpoints
(Cottin et al., 2007). Time domain measures such as SDNN
were found to closely relate with VT1 but little follow-up or
verification has been done (Karapetian et al., 2008). The SD1
is another index that has been examined during exercise. It is
based on a Poincare plot of each RR interval graphed against
the preceding interval and is related to short term trends in
RR patterns often assigned to nonlinear indexes (Shaffer and
Ginsberg, 2017), although it is mathematically equivalent to
another time domain index (Ciccone et al., 2017). While showing
potential as a low intensity marker in some earlier studies
(Tulppo et al., 1996) other evidence indicates that SD1 was
already suppressed in young athletes at the first tested work rate
of 60% VO2MAX making it less useful for zone 1 delineation
(Blasco-Lafarga et al., 2017).
One nonlinear index, the short-term scaling exponent
alpha1 based on Detrended Fluctuation Analysis (DFA a1),
has generated interest as both an indicator of autonomic
nervous system regulation as well as an overall marker of
organismic demands Gronwald and Hoos, 2020). Originally,
Peng et al. (1995) developed this method to measure scale-
invariant behavior; this involved the evaluation of trends of all
sizes in the presence or absence of fractal correlation properties
in a heart rate (HR) time series (Yeh et al., 2010). Thus, the DFA
method allows for the quantification of the degree of correlation
and fractal scale of a HRV signal resulting in dimensionless
measures. The short-term scaling exponent DFA a1 is based
on the fractal dynamics (self-similarity) of the cardiac beat-to-
beat pattern and provides insights into correlation properties
of HR time series caused by physiological processes (Peng
et al., 1995). DFA a1 values indicate time series correlation
properties with approximately 1.5 indicating a strongly correlated
pattern and 0.5 for anti-correlated pattern with random
behavior; approximately 1.0 signifies a mix of uncorrelated
and maximally correlated signal components (represents a
balance between complete unpredictability (randomness) and
predictability (strong correlations), also associated with fractal
(self-similar) behavior (Platisa and Gal, 2008). Larger values
of DFA a1 represent a smoother time series and smaller
values of DFA a1 represent coarser ones (Peng et al., 1995;
Goldberger et al., 2002). Within this framework, DFA a1
has been shown to decline as work rate rises, starting from
strongly correlated patterns (value of 1.5) at rates well below
the first ventilatory threshold (VT1), transitioning (values
of 1.0–0.5) through values representing uncorrelated, less
complex white noise behavior at moderate to high work
rates, then finally showing anti-correlated behavior at the
highest intensities (values of <0.5) (Gronwald et al., 2019c;
Gronwald and Hoos, 2020). Given this relationship, there
may be an opportunity to assist athletes in delineating
intensity training zones by observing the change in DFA
a1 with increasing exercise intensity (Gronwald et al., 2020;
Rogers, 2020).
The purpose of this report is to validate a predefined DFA
a1 value of 0.75 with the exercise intensity at VT1 obtained
during an incremental treadmill run to exhaustion. This is to
be done by a direct comparison of the VT1 intensity based on
both absolute VO2and HR obtained during gas exchange with
the same measures derived from analysis of DFA a1 behavior.
If it can be shown that a predefined “boundary” value in the
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DFA a1 index occurs near the VT1, this could establish a basis
for further research exploiting a non-linear autonomic nervous
system related marker in prospective exercise and training
intensity distribution.
MATERIALS AND METHODS
Participants
Seventeen male volunteers aged 19–52, without previous medical
history, current medications or physical issues were tested.
A background questionnaire regarding medical history was
reviewed along with information of the potential testing risks
then institutionally approved consent was given. Approval
for the study was granted by the University of Derby,
United Kingdom (LSREC_1415_02) and conformed to the
principles of the Declaration of Helsinki. Participants did not
consume caffeine, alcohol or any stimulant for the 24 h before
testing. Background data for each subject included, age, body
weight, and training volume in hours per week (Table 1). All
testing was done in the afternoon and at least 3 h post meal.
No exercise was performed the day prior to the test. Two
participants with a high degree of cardiac ectopy (frequent
atrial premature beats and atrial trigeminy) during testing were
excluded from analysis.
Exercise Protocol
Participants performed an incremental VO2MAX test on a
motorized treadmill (Woodway, Birmingham, United Kingdom).
The treadmill was set for the Bruce protocol with increases
in speed and inclination from 2.7 km/h at ten percent grade,
increasing by 1.3 km/h and two percent grade every 3 min until
volitional exhaustion. A fan was used for cooling.
TABLE 1 | Demographic data of all included participants (n= 15) with
training volume.
Subject number Age (years) BW (Kg) HT (cm) TV (h/wk)
1 19 82 182 3–6
2 19 82 176 3–6
3 20 82 190 3–6
4 23 77 180 >6
5 24 69 171 3–6
6 24 65 165 >6
7 24 76 186 3–6
8 25 78 171 >6
9 26 69 169 >6
10 30 92 189 1–3
11 30 73 175 >6
12 32 65 161 1–3
13 36 75 182 >6
14 50 94 178 3–6
15 52 71 171 1–3
Mean (SD) 29 (±10) 77 (±9) 176 (±9) -
BW, Body weight; TV, Training volume. Mean (±standard deviation, SD) in last row.
Gas Exchange Testing and Calculation of
the First Ventilatory Threshold
Gas exchange kinetics were recorded continuously using a
breath-to-breath metabolic cart (Metalyzer 3B; Cortex Biophysik
GmbH Germany). In addition, a Polar H7 (Polar Electro Oy,
Kempele, Finland) was wirelessly paired to the Metalyzer cart for
the purpose of HR recording concurrent with gas exchange data.
VO2, VCO2, PetO2, PetCO2, Ve/VO2, Ve/VCO2, and HR were
imported into Microsoft Excel 365 for analysis. The native gas
exchange analysis feature of the Metalyzer was not used due to
the unreliability of many automated VT1 calculations (Ekkekakis
et al., 2008). Graphing of the above parameters were done to
derive VT1, VO2MAX, and VO2vs. time. No averaging was done
for either gas exchange parameters or HR. Inspection of the VO2
over time relationship was done to determine any significant
plateau of the VO2curve for estimation of VO2MAX and VO2
linearity. If a significant plateau was found, compensation for
calculating both VO2MAX and the VO2over time equation was
done. To reduce the chance of failure to identify the VT1 by gas
exchange (VT1 GAS) based on a single method, evaluation was
done according to the triple detection method consisting of V
slope, Ve/VO2, and excess CO2from Gaskill et al. (2001) as well
as the PetO2nadir from Binder et al. (2008). Based on the quality
and consistency of the plots, the excess CO2method was chosen
to be used for all participants and reviewed independently by two
investigators (Figure 1A). VO2was plotted over the elapsed time
of the incremental test to produce a linear regression equation.
VO2at the time of VT1 was based on linear regression from the
VO2over time relationship.
RR Measurements and Calculation of
DFA a1 Derived Threshold
A 3-lead ECG (MP36; Biopac Systems Ltd.) with a sampling
rate of 1,000 Hz was used to record the subject’s ECG/RR
times series. Biopac filter settings were set to 0.05 Hz high-
pass filter and 150 Hz low-pass filter. Electrodes were placed
in the CM5 distribution after appropriate skin cleansing and
shaving if necessary. Sample data from the MP36 was saved
as .acq files. ECG files for each subject were imported into
Kubios 3.3.2 (Biosignal Analysis and Medical Imaging Group,
Department of Physics, University of Kuopio, Kuopio, Finland).
Kubios preprocessing settings were at the default values including
the RR detrending method which was kept at “Smoothn priors”
(Lambda = 500, Tarvainen et al., 2014). For DFA a1 estimation,
the root mean square fluctuation of the integrated and detrended
data is measured in observation windows of different sizes. The
data are then plotted against the size of the window on a log-log
scale. The scaling exponent represents the slope of the line, which
relates (log) fluctuation to (log) window size (Mendonca et al.,
2010). DFA a1 window width was set to 4 n16 beats.
For the detection of a HRV derived threshold, a DFA a1 value
of 0.75 was chosen based on this being the midpoint between
a fractal behavior of the HR time series of 1.0 (seen with very
light exercise) and an uncorrelated value of 0.5 which represents
white noise, random behavior (seen with high intensity exercise).
A value of 0.75 has also been used as a cut-off value for survival
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FIGURE 1 | (A) Usage of “Excess CO2” technique to determine VT1 GAS.
The intersection of the baseline and first rise in excess CO2corresponds to
the time when VT1 occurs; (B) DFA a1 plotted over time, the area of linear
drop of DFA a1 from about 1.0 to 0.5 is used to determine the VO2(from the
VO2vs. time relation) at HRVT; (C) DFA a1 plotted against HR, the area of
linear drop of DFA a1 from 1.0 to 0.5 is used to determine the HR at HRVT. All
data taken from Subject #2.
curves and mortality rate assessment during resting conditions
(Huikuri et al., 2000).
The following procedure was used to indicate at what level of
running intensity (as VO2or HR) the DFA a1 would cross a value
of 0.75: DFA a1 was calculated from the incremental exercise test
RR series using 2 min time windows with a recalculation every
5 s throughout the test. Two minute time windowing was chosen
based on the reasoning of Chen et al. (2002). The rolling time
window measurement was used to better delineate rapid changes
in the DFA a1 index over the course of the test. Each DFA a1 value
is based on the RR series 1 min pre and 1 min post the designated
time stamp. For example, at a time of 10 min into the testing, the
DFA a1 is calculated from the 2 min window starting from minute
9 and ending at minute 11 and labeled as the DFA a1 at 10 min.
Based on a rolling time recalculation every 5 s, the next data point
would occur at 10:05 min (start 9:05 min and end 11:05 min).
Plotting of DFA a1 vs. time was then performed. Inspection
of the DFA a1 relationship with time generally showed a reverse
sigmoidal curve with a stable area above 1.0 at low work rates,
a rapid, near linear drop reaching below 0.5 at higher intensity,
then flattening without major change. A linear regression was
done on the subset of data consisting of the rapid near linear
decline from values near 1.0 (correlated) to approximately 0.5
(uncorrelated). The time of DFA a1 reaching 0.75 was calculated
based on the linear regression equation from that straight section
(Figure 1B). The time of DFA a1 reaching 0.75 was then
converted to VO2using the VO2vs. time relation, resulting in the
VO2at which DFA a1 equaled 0.75 (HRVT). A similar analysis
was done for the HR reached at a DFA a1 of 0.75. First, ECG data
from each 2 min rolling window was used to plot the average HR
and DFA a1. The HR at which DFA a1 equaled 0.75 was found
using the same technique as above, a linear regression through
the rapid change section of DFA a1 values of 1.0 to below 0.5,
with a subsequent equation for HR and DFA a1 (Figure 1C).
Using a fixed variable of DFA a1 equals 0.75, the resulting HR was
obtained. The HR at DFA a1 0.75 (based on ECG data) was then
compared to the HR at VT1 GAS obtained from the metabolic
cart data (based on the Polar H7).
Visual inspection of the entire test recording was done to
determine sample quality, noise, arrhythmia, and missing beat
artifact. As mentioned above, two participants with a high degree
of atrial ectopy were excluded from analysis. The RR series
of the included participants was then corrected by the Kubios
“automatic method” and exported as text files for further analysis.
Percent artifact reported refers those occurring during the linear
regression segment (DFA a1 1.0 to near 0.5).
Statistics
Statistical analysis was performed for the main variables, VO2at
VT1 derived from gas exchange testing, VO2at DFA a1 0.75,
HR at VT1 obtained from gas exchange testing and average
HR at DFA a1 0.75. Standard statistical methods were used for
the calculation of means and standard deviations (SD). Normal
distribution of data was checked by Shapiro–Wilks test. The
agreement against the Gold Standard VT1 GAS was assessed
using intraclass correlation coefficient (ICC), linear regression,
Pearson’s rcorrelation coefficient, standard error of estimate
(SEE), coefficient of determination (R2) and Bland Altman plots
with limits of agreement (Bland and Altman, 1999). The size of
Pearson’s rcorrelations evaluated as follows; 0.3 r<0.5 low;
0.6 r<0.8 moderate and r0.8 high (Chan, 2003). The paired
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t-test was used for comparison of VT1 GAS vs. HRVT for both
VO2and HR parameters. For all tests, the statistical significance
was accepted as p0.05. Cohen’s dwas used to denote effect sizes
(small effect = 0.2, medium effect = 0.5, large effect = 0.8; Cohen,
1988). Analysis was performed using Microsoft Excel 365 with
Real Statistics Resource Pack software (Release 6.8).
RESULTS
Gas Exchange Testing
Individual gas exchange results are presented in Table 2. Both the
VO2MAX as well as the percentage of VO2MAX and HR at VT1
GAS varied considerably among participants. VO2MAX ranged
between 41 and 74 ml/kg/min. VT1 GAS was reached between 61
and 86% of the VO2MAX and at HRs between 108 and 183 bpm.
RR Interval Quality
The percentage of artifacts was calculated based on the
Kubios automatic correction method for each subject’s test
data. Since only a portion of the entire treadmill test was
used for the linear interpolation of DFA a1, the artifact
percentage listed refers to that section only. Artifact percentage
for the linear plotted data series was between 0 and 3%,
all consisting of atrial premature complexes (Table 2).
There were no missed beats due to noise interference or
loss of electrode contact. The two participants originally
excluded from analysis had significant ectopy, leading to an
uninterpretable DFA a1 pattern.
Comparison of VT1 GAS vs. HRVT
The average VT1 GAS was 39.8 ml/kg/min (±8.9) compared
to 40.1 ml/kg/min (±8.6) obtained by HRVT. The average HR
at VT1 GAS was 152 bpm (±21) compared to 154 bpm (±20)
obtained by HRVT. Strong linear relationships were seen between
test modalities, with Pearson’s rvalues of 0.99 (p<0.001) and.97
(p<0.001) for VO2and HR comparisons respectively (Figure 2).
Intraclass correlation between VT1 GAS and HRVT was 0.99 for
VO2and 0.96 for HR. The comparison of VT1 GAS and HRVT
showed no differences (VO2:p= 0.347, d= 0.030; HR: p= 0.191,
d= 0.091). Bland Altman analysis for VT1 GAS vs. HRVT for
VO2(Figure 3) showed a mean difference of 0.33 ml/kg/min
(±1.3) with upper and lower limits of 2.2 and 2.9 ml/kg/min.
Bland Altman analysis for VT1 GAS vs. HRVT for HR (Figure 3)
showed a mean difference of 1.9 bpm (±5) with upper and
lower limits of 8 and 12 bpm.
DISCUSSION
This study explored whether values of the nonlinear HRV index,
DFA a1, pass through a defined transitional zone at workloads
near VT1 during an incremental treadmill test. Since many
prior reports have shown DFA a1 to decline during incremental
exercise (Hautala et al., 2003;Casties et al., 2006;Platisa
et al., 2008;Karavirta et al., 2009;Blasco-Lafarga et al., 2017;
Gronwald et al., 2019c), our result showing a similar occurrence
is not unanticipated. However, none have attempted to directly
examine the possibility that the DFA a1 index has a distinct
value at the VT1 work rate. Since many of the prior studies
looking at DFA a1 response to incremental exercise intensity have
used cycling as the exercise modality, it is also reassuring to see
analogous results with treadmill running, adding validity to the
behavior of this index during other endurance exercise types.
The inclusion of a wide range of subject ages, body weights and
fitness abilities, lends strength to the application of our results
TABLE 2 | Comparison of VT1 GAS and HRVT with measures of VO2and HR.
Subject number VO2MAX VT1 GAS VO2VT1 GAS VO2HRVT VO2VT1 GAS HR HRVT HR Artifacts (%)
(ml/kg/min) (%MAX) (ml/kg/min) (ml/kg/min) (bpm) (bpm)
1 58 77 45.2 46.1 167 170 0
2 57 75 42.8 45.1 169 175 0.5
3 47 70 32.7 31.6 178 175 0
4 71 86 61.2 61.2 155 156 0
5 64 68 43.6 43.0 143 137 0
6 54 74 40.1 38.1 165 163 0
7 47 76 35.8 37.2 164 171 2
8 54 70 37.8 37.9 137 135 0
9 72 69 49.3 49.2 183 184 0
10 46 61 27.7 29.2 108 122 0
11 74 65 48.1 46.4 151 148 3
12 49 66 32.0 33.3 154 160 1
13 57 66 37.6 39.4 154 159 1
14 41 70 28.6 28.8 139 136 0
15 54 64 34.5 35.5 118 122 1
Mean (SD) 56 (±10) 70 (±6) 39.8 (±8.9) 40.1 (±8.6) 152 (±21) 154 (±20) 0.6 (±0.9)
VT1 GAS, first ventilatory threshold; HRVT, DFA a1 derived threshold; HR, Heart rate; VO2MAX , VT1 GAS percent of VO2MAX and Artifacts percentage. Mean (±standard
deviation, SD) in last row.
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FIGURE 2 | Regression plots for all subject data. (A) Values of VT1 GAS vs. HRVT for VO2;(B) Values of VT1 GAS vs. HRVT for HR. Bisection lines in light gray. SEE,
standard error of estimate; R2, coefficient of determination.
to the general population and its application in different fields of
physical exercise and training.
In a recent perspective review (Gronwald et al., 2020),
identification of a low intensity exercise zone based on DFA a1
for the purposes of endurance exercise and training prescription
was discussed. The mechanism underlying DFA a1 decline with
exercise is felt to be related to autonomic balance and a complex
interaction of the two main branches, namely parasympathetic
withdrawal, sympathetic intensification as well as other factors
(Gronwald and Hoos, 2020). Since VT1 is usually seen at a point
of significant parasympathetic withdrawal (Tulppo et al., 1996;
Sales et al., 2019), leveraging HRV parameters that reliably reflect
this occurrence can be of use during endurance exercise and
training. Our methodology to determine HRVT utilized the rapid
decline of DFA a1 from 1.0 to below 0.5, seen during progressive
exercise intensity. The results presented here appear to indicate
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FIGURE 3 | Bland Altman Plot of VT1 GAS vs. HRVT for all participants. (A) Values of VT1 GAS vs. HRVT for VO2;(B) Values of VT1 Gas vs. HRVT for HR. Center
line in each plot represents the mean difference between each paired value, the top and bottom lines are 1.96 standard deviations from the mean difference.
that VT1 is reached at a midpoint between a fractal behavior of
DFA a1 and a pattern of uncorrelated white noise with random
behavior, corresponding to a DFA a1 of approximately 0.75.
Bland Altman analysis with limits of agreement showed minimal
difference between VT1 GAS and HRVT looking at either VO2
or HR measurements. Correlation coefficients and ICC were high
for both VO2and HR based comparisons.
Although the DFA a1 value of 0.75 was chosen theoretically, a
brief review of prior investigation is supportive of this figure. In a
study of young men performing a cycling ramp test, an average
DFA a1 of 0.49 was associated with a lactate measurement of
2.49, indicating that LT1 had already been exceeded (Gronwald
et al., 2019c). Other cycling ramp studies in men of different
fitness levels seemed to indicate that DFA a1 crossed the value
of 0.75 at about 73–78% of VO2MAX (Hautala et al., 2003;
Hottenrott and Hoos, 2017), within the approximate realm of
VT1 for many individuals (Gaskill et al., 2001;Pallarés et al.,
2016). An examination of the DFA a1 response to incremental
cycling exercise in teenage males (Blasco-Lafarga et al., 2017)
showed an approximate crossing of the 0.75 value at an average
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Rogers et al. DFA a1 Aerobic Threshold
intensity near 65% of maximum, also near published ranges of
VT1 (50–65% of VO2MAX) in that age group (Runacres et al.,
2019). In the current study, there appeared to be little bias in
the VO2or HR associated with HRVT. Although there was
some variability in VT1 GAS vs. HRVT parameters, for the
most part, associations were similar to that of other comparisons
of threshold approaches such as blood lactate or ventilatory
parameters (Pallarés et al., 2016), or assessment of gas exchange
techniques for VT1 determination (Gaskill et al., 2001). Several
participants had relatively high heart rate at the VT1 (Azevedo
et al., 2011) of which we do not have an explanation. A strength of
this study is that of RR interval quality. Direct ECG visualization
was done and a research grade device with a high sample rate
was used. No missed or lost beats were seen, and the only
artifact type present was APC aberrancy. In view of reports
indicating substantial bias in nonlinear HRV indexes with artifact
presence (Giles and Draper, 2018;Rincon Soler et al., 2018), a
weakening of DFA a1 derived VT1 accuracy could occur with
higher artifact occurrence.
A significant advantage of DFA a1 over other HRV indexes
for the determination of a low intensity threshold revolves
around the nature of testing. Other HRV metrics proposed
to identify VT1 such as SDNN (Karapetian et al., 2008), HF
power (Cottin et al., 2007) or SD1 (Tulppo et al., 1996) require
testing into high intensity zones since they rely on curve
interpretation that displays a demonstrable nadir. With DFA
a1, once the VT1 boundary area is reached, little additional
increase in exercise intensity should be required. The potential
benefit of utilizing a fixed DFA a1 value as the VT1 delineation
marker is especially attractive in populations unable or ill
advised to enter high intensity regions. In addition, for athletes
evaluating low intensity training limits, avoidance of exercise
ramps to volitional failure may help avert undue stress in a
polarized training model.
Limitations and Future Direction
Given the issues with both availability and accuracy of gas
exchange or blood lactate testing in determining VT1 for training
zone purposes, an alternate modality that employs relatively
simple wearable technology seems attractive. However, while
DFA a1 monitoring may be a promising approach, several
questions need to be addressed. Although this study was done
with a wide range of subject age and fitness characteristics, no
female participants were tested. If the DFA a1 index behavior is
to be considered as a zone 1 delimiter for the general population,
further investigation using female subjects is mandatory. Another
area of concern is the transfer of the DFA a1 0.75 breakpoint
obtained during incremental testing to that of one found during
constant load exercise, including moderate length intervals
(5 min). No data is available comparing DFA a1 behavior during
an incremental ramp to constant load exercise (Gronwald and
Hoos, 2020), making automatic transfer of zone boundaries
unclear. Whether the index will remain stable for even longer
exercise intervals (>60 min) performed below VT1 intensity is
another open question as well as day to day repeatability. Another
interesting subject to explore is the impact of athlete overtraining
on DFA a1 behavior and VT1 prediction accuracy during
exercise. Baumert et al. (2006) did show changes in DFA related
scaling behavior after intense training, which may provide both a
potential source of HRVT bias and an opportunity to screen for
overtraining states. Although it seems that ramp protocol slope
has minimal effect on the VT1 gas assessment (Weston et al.,
2002;Boone and Bourgois, 2012), the analogous assumption
needs to be shown in terms of HRVT thresholds. Another area
for investigation is whether DFA a1 cut off values are equivalent
between chest belt and research grade ECG recordings. Although
in this study, the RR intervals were recorded with a research
grade ECG device, it may be possible to reproduce similar
results with chest belt ECG recordings. In that regard, two
major questions need to be addressed. One is that of exercise
associated missed beat artifact with possible faulty interpolation
strategies by interpreting software, creating potential bias in
the calculated DFA a1 values. As mentioned above, several
reports have questioned the degree of bias of nonlinear HRV
indexes if artifacts are present in the RR series (Giles and
Draper, 2018;Rincon Soler et al., 2018;Stapelberg et al., 2018).
Artifacts may be of different types such as missed beats or
aberrancy. In the current report, no missed beats were seen,
and only relatively rare atrial premature complexes were noted.
However, two participants exhibited frequent APC aberrancy,
had uninterpretable DFA a1 curves and were excluded from
group analysis. Given the relative low numbers of participants, no
definitive conclusion can be reached regarding artifact bias, but
further investigation into effects of missed beats and aberrancy
on the use of DFA a1 to delineate zone 1 transition is needed.
Second is the question of DFA a1 value precision obtained by
diverse monitoring devices possessing different sample rates and
prepossessing strategies. Device sample rates have been shown to
variably alter DFA a1 values at rest (Voss et al., 1996;Tapanainen
et al., 1999;Singh et al., 2015) but may have more significant
effects during exercise. Although no recent caffeine use was
noted by history, we have no information on prior long term
intake patterns which could affect autonomic balance on abrupt
discontinuation (La Monica et al., 2018). Finally, it may be
possible to answer many of these questions by “repurposing”
prior work already done. For instance, a study by Boullosa
et al. (2014) assessed the changes in DFA a1 before and after
a typical incremental treadmill ramp to exhaustion. A look
back at previously acquired RR recordings during the active
ramp portion using the methods discussed here may be a way
to rapidly acquire needed information about DFA a1 behavior
during dynamic exercise.
Conclusion
DFA a1, an index of fractal dynamics and correlations properties
of the heart rate time series, was noted to decline during an
incremental treadmill run test to exhaustion. The area of most
rapid change of this index occurred near the first ventilatory
threshold. The point of DFA a1 reaching a value of 0.75 during
the incremental treadmill test was directly associated with the
first ventilatory threshold as measured by heart rate and gas
exchange VO2. As training intensity below the first ventilatory
threshold is felt to have great importance for exercise and
training prescription in endurance sport, utilization of DFA a1
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Rogers et al. DFA a1 Aerobic Threshold
activity may provide guidance for a valid low training zone
boundary without the need for gas exchange or blood lactate
testing. Further study of DFA a1 behavior in female participants,
during constant load intervals, index stability over long periods
of time and across diverse recording devices is recommended.
If investigation into these matters remain consistent with the
results presented here, obtaining a low intensity zone boundary
by automated analysis of a training session via an appropriate
wearable device may be possible.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed
and approved by University of Derby, United Kingdom
(LSREC_1415_02). The patients/participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
BR and TG conceived the study. DG and ND performed the
physiologic testing. BR wrote the first draft of the article. BR and
TG performed the data analysis. All authors revised it critically
for important intellectual content, final approval of the version to
be published, and accountability for all aspects of the work.
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Conflict of Interest: DG was employed by company Lattice Training.
The remaining 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.
Copyright © 2021 Rogers, Giles, Draper, Hoos and Gronwald. This is an open-access
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Frontiers in Physiology | www.frontiersin.org 10 January 2021 | Volume 11 | Article 596567
... Whether the desired program is polarized, pyramidal or threshold in type, identification of the low intensity boundary would be necessary (Seiler and Kjerland, 2006;Esteve-Lanao et al., 2007;Sperlich, 2015, 2019;Bourgois et al., 2019). With this objective in mind, the question of whether a value of DFA a1 between correlated and uncorrelated corresponds to the VT1 was evaluated in a group of male recreational runners (Rogers et al., 2021a). Results indicated that reaching a DFA a1 of 0.75 during an incremental treadmill test was associated with the VT1 and termed the heart rate variability threshold (HRVT). ...
... The 2-min time windowing was chosen based on the calculations by Chen et al. (2002) to achieve a sufficient number of RR data points to achieve DFA a1 validity. By using this method, a nearly straight-lined drop of DFA a1 from values of approximately 1.0 to 0.5 became apparent (Rogers et al., 2021a), providing an opportunity for simple linear interpolation of the corresponding HR or time plotted against DFA a1 of 0.75 (see Figure 1). Although not extensively studied, it also appears that constant power cycling intervals with 2-min measurement windows may also be used for HRVT determination (Gronwald et al., 2021). ...
... Although there appears to be distinct benefits to eccentric training, assessing intensity distribution is problematic. This is due to both the power and HR discrepancy measured from FIGURE 1 | (A) DFA a1 vs. HR of a 26-year-old male runner with a VO 2MAX of 72 ml/kg/min, HR at VT1 of 183 bpm and HR of 192 bpm at VT2 performing an incremental treadmill ramp test, including a qualitative description of the signal pattern; data recorded with an ECG (MP36; Biopac Systems Ltd., Essen, Germany) (data from Rogers et al., 2021a;Rogers et al., 2021c). Data processed in Kubios HRV Premium software (Version 3.5) using automatic correction method (artifact percentage: < 5%). ...
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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.
... These thresholds are determined by the assessment of blood lactate concentration (lactate threshold/s) or gas-exchange parameters (ventilatory threshold/s) while workload progressively increases (Pallarés, Morán-Navarro, Ortega, Fernández-Elías, & Mora-Rodriguez, 2016). Other methods such as the assessment of heart rate variability (HRV) have also been proposed for evaluation of intensity thresholds and training zones in several populations, ranging from patients (Rogers, Mourot, & Gronwald, 2021c) to physically trained individuals (Gronwald et al., 2021;Rogers, Giles, Draper, Hoos, & Gronwald, 2021a;Rogers, Giles, Draper, Mourot, & Gronwald, 2021b) based on the relationship between exercise intensity and autonomous nervous system regulation. Through HRV it is possible to carry out a non-invasive assessment of the autonomous nervous system balance (Heart rate variability, 1996) with the heart rate (HR) response mostly subjected to parasympathetic (vagal) modulation during resting conditions and low-intensity exercise (Karemaker & Lie, 2000) but with a raise in sympathetic drive and a subsequent decrease in vagal activity with increasing workloads. ...
... Despite the promising results for the determination of training intensity domains in different populations with DFA-α1 (Naranjo-Orellana, Nieto-Jimenez, & Ruso-Alvarez, 2020; Rogers et al., 2021b;Rogers et al., 2021a), the usefulness of this method has not been evaluated in elite endurance athletes. This is of importance because this non-invasive methodology would allow a more continuous evaluation throughout the training process. ...
... For DFA-α1 estimation, the root mean square fluctuation of the integrated and detrended data was measured in 2-minute windows (Chen, Ivanov, Hu, & Stanley, 2002). The data were then plotted against the size as reported previously (Rogers et al., 2021a). DFA-α1 window width was set to 4 ≤ N ≤ 16 beats. ...
Article
Background: The evaluation of performance in endurance athletes and the subsequent individualization of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short-term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA-α1) of the Heart Rate Variability (HRV) has been proposed as a non-invasive methodology to detect intensity thresholds. Purpose: The aim of this study is to analyse the validity of DFA-α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds. Methodology: 38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA-α1-0.75, for lactate and HRV, respectively) and second (LT2 and DFA-α1-0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR). Results: There were no significant differences (p > 0.05) between the DFA-α1-0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA-α1-0.5 was different against LT2 in PO (p = 0.04) and HR (p = 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71). Conclusions: The DFA1-a-0.75 can be used to estimate LT1 non-invasively in elite cyclists. Further research should explore the validity of DFA-α1-0.5.
... Therefore, alternate means of determining the AT have been evaluated over the years including modalities related to various heart rate variability (HRV) indexes [5][6][7]. However, despite initial appeal, general use for the purpose of low-intensity training guidance has not occurred for various reasons [8,9]. Recently, HRV monitoring during the exercise session has received a resurgence in attention as a method of measuring the AT [8]. ...
... Recently, HRV monitoring during the exercise session has received a resurgence in attention as a method of measuring the AT [8]. In a group of recreational runners, the AT was found to closely match that of a HRV threshold (HRVT) derived from a non-linear HRV index of fractal correlation properties determined by alpha1 of Detrended Fluctuation Analysis (DFA a1) [9]. As exercise intensity rises, the DFA a1 declines from values near 1, which represent the well-correlated fractal behavior of the cardiac beat-to-beat pattern, passing a value of 0.75 at the AT, then reaching uncorrelated, random behavior at intensities past the AT. ...
... Two-minute time windowing was chosen to achieve a sufficient minimal beat count [14]. For the detection of HRVT, a DFA a1 value of 0.75 was selected based on previous study in recreational athletes [9]. This value is also the midpoint between a fractal, well-correlated behavior of the HR time series of 1.0 (seen with very light exercise) and an uncorrelated value of 0.5 which represents random behavior (seen with high-intensity exercise) [8]. ...
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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.
... Load intensity is a physical quantity that reflects the value of exertion, functional tension, and force in a particular exercise unit and the concentration of training workload in a certain period. Load is an index reflecting a specific practice unit [3]. At present, there are many indicators to measure the size of exercise load, such as maximum oxygen uptake (VO2max), blood lactic acid, heart rate, and Rating of Perceived Exertion (RPE). ...
... e variance contribution rate and the cumulative contribution rate of each principal component are calculated according to (3). e score matrix of the selected main elements is used as the modeling parameter. ...
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Appropriate training load in physical education classes is conducive to improving students’ health. In this study, a training model is proposed for the prediction of the training load of middle school students in physical education based on the backpropagation neural network (BPNN). Ninety students in the seventh, eighth, and ninth grades (30 for each grade) are selected, and the training load is divided into type I, type II, and type III and combined with the average heart rate values of students in each grade during physical training. Next, the principal component analysis is used to select the main components whose cumulative contribution rate is greater than 90%. The corresponding score matrix is used for BPNN model training. Results show that, for most students in all grades, the training load intensity belongs to type II, and the training intensity is moderate. The variance contribution rates of the first, second, third, and fourth principal components of the seventh, eighth, and ninth grades reported are about 60%, 15%, 10%, and 5%, respectively, and the cumulative contribution rate of the first four principal components has reached more than 90%. Comparing the predicted value with the actual value, the proposed model showed the highest prediction performance and can accurately predict the training load in physical education.
... The short-term scaling exponent DFA α1 which is established to quantify the fractal dynamics and self-similar properties of HR and has recently generated interest as an estimate of metabolic exercise intensity (Rogers et al. 2021). This study demonstrates similar reductions of DFA α1 in both women and men with increasing WBGT; although, independent of temperature, women had a lower DFA α1 than their male counterparts. ...
... Interestingly, the reductions of DFA α1 in females observed in the 5 min prior to walking termination are trending toward significance (p=0.079) and appear to reflect the HR. This suggests that the fractal dimension of HRV may be more closely influenced, not by increasing whole body metabolic intensity as suggested by others (Rogers et al. 2021), but rather HR dependency which may result from factors such as sex differences in anatomic cardiac size. ...
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Sex-differences in heart rate (HR) and heart rate variability (HRV), a surrogate of cardiac autonomic modulation, are evident during rest and exercise in young healthy individuals. However, it remains unclear whether sex impacts HRV during prolonged exercise at differing levels of environmental heat stress. Therefore, we completed a secondary analysis upon the effects of sex and wet-bulb globe temperature (WBGT) on HR and HRV during prolonged exercise. To achieve this, HR and HRV were assessed in non-endurance-trained and non-heat-acclimatized healthy men (n=19) and women (n=15) aged 18-45 years during 180-min treadmill walking at a moderate metabolic rate (200 W/m2: equivalent to ~35% peak aerobic power) in 16, 24, 28, and 32°C WBGT. In the final 5 min prior to exercise termination, HR was observed to be higher in women relative to men in all but the 32°C WBGT. Although no sex-differences were observed for the HRV metric of root-mean-square of successive differences, high frequency power was higher in women relative to men across WBGT conditions. These findings indicate that, in healthy non-heat-acclimatized individuals, women respond to prolonged exercise-heat stress with a greater increase in HR despite cardiac vagal autonomic modulation remaining equal or increasing compared to men. Novelty points. • Prior to exercise termination, females respond with a greater increase in heart rate under all wet-bulb globe temperatures except the hottest (32°C). • Sex influenced heart rate variability (HRV) metrics during all wet-bulb globe temperatures, but results were mixed. • Further characterisation of HRV sex differences remains an important area of research.
... LMS, lactate minimum speed. (Wyon and Redding, 2003;Santos et al., 2010;Clemente Suárez and González-Ravé, 2014;von Haaren et al., 2015;Rogers et al., 2021). ...
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There is a great need for objective external training load prescription and performance capacity evaluation in equestrian disciplines. Therefore, reliable standardised exercise tests (SETs) are needed. Classic SETs require maximum intensities with associated risks to deduce training loads from pre-described cut-off values. The lactate minimum speed (LMS) test could be a valuable alternative. Our aim was to compare new performance parameters of a modified LMS-test with those of an incremental SET, to assess the effect of training on LMS-test parameters and curve-shape, and to identify the optimal mathematical approach for LMS-curve parameters. Six untrained standardbred mares (3–4 years) performed a SET and LMS-test at the start and end of the 8-week harness training. The SET-protocol contains 5 increments (4 km/h; 3 min/step). The LMS-test started with a 3-min trot at 36–40 km/h [until blood lactate (BL) > 5 mmol/L] followed by 8 incremental steps (2 km/h; 3 min/step). The maximum lactate steady state estimation (MLSS) entailed >10 km run at the LMS and 110% LMS. The GPS, heartrate (Polar®), and blood lactate (BL) were monitored and plotted. Curve-parameters (R core team, 3.6.0) were (SET) VLa1.5/2/4 and (LMS-test) area under the curve (AUC>/ 0.80), Bland-Altman method, and ordinary least products (OLP) regression analyses were determined for test-correlation and concordance. Training induced a significant increase in VLa1.5/2/4. The width of the AW increased significantly while the AUC>LMS and LMS decreased post-training (flattening U-curve). The LMS BL steady-state is reached earlier and maintained longer after training. BLmax was significantly lower for LMS vs. SET. The 40° angular method is the optimal approach. The correlation between LMS and VMLSS was significantly better compared to the SET. The VLa4 is unreliable for equine aerobic capacity assessment. The LMS-test allows more reliable individual performance capacity assessment at lower speed and BL compared to SETs. The LMS-test protocol can be further adapted, especially post-training; however, inducing modest hyperlactatemia prior to the incremental LMS-stages and omitting inclusion of a per-test recovery contributes to its robustness. This LMS-test is a promising tool for the development of tailored training programmes based on the AW, respecting animal welfare.
... DFA a1 is a non-linear index of HRV representing fractal correlation properties of cardiac interbeat intervals caused by physiological processes [43,44]. This index has been used for analysis of age effects [45,46], prognosis of mortality and cardiovascular risk stratification [24,[47][48][49][50], and assessment of systemic internal load during endurance exercise [32,[51][52][53], and appears as a relevant non-linear parameter for short-term analyses. DFA a1 window width was set to 4 ≤ n ≤ 16 beats [44]. ...
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The value of heart rate variability (HRV) in the fields of health, disease, and exercise science has been established through numerous investigations. The typical mobile-based HRV device simply records interbeat intervals, without differentiation between noise or arrythmia as can be done with an electrocardiogram (ECG). The intent of this report is to validate a new single channel ECG device, the Movesense Medical sensor, against a conventional 12 channel ECG. A heterogeneous group of 21 participants performed an incremental cycling ramp to failure with measurements of HRV, before (PRE), during (EX), and after (POST). Results showed excellent correlations between devices for linear indexes with Pearson’s r between 0.98 to 1.0 for meanRR, SDNN, RMSSD, and 0.95 to 0.97 for the non-linear index DFA a1 during PRE, EX, and POST. There was no significant difference in device specific meanRR during PRE and POST. Bland–Altman analysis showed high agreement between devices (PRE and POST: meanRR bias of 0.0 and 0.4 ms, LOA of 1.9 to −1.8 ms and 2.3 to −1.5; EX: meanRR bias of 11.2 to 6.0 ms; LOA of 29.8 to −7.4 ms during low intensity exercise and 8.5 to 3.5 ms during high intensity exercise). The Movesense Medical device can be used in lieu of a reference ECG for the calculation of HRV with the potential to differentiate noise from atrial fibrillation and represents a significant advance in both a HR and HRV recording device in a chest belt form factor for lab-based or remote field-application.
... A consideration of heart rate measurement techniques is crucial for the future and possible implementation of heart rate sensors with more complex calculations as heart rate variability helps to estimate exercise intensity [95][96][97] for which accuracy is essential [98]. In addition, this potential implementation may lead to gaining an estimation of other different physiological variables such as breathing frequency [99], VO2 [100], and EPOC (Excess Post-Exercise Oxygen Consumption) [101]. ...
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This paper aims to provide a review of the electrically assisted bicycles (also known as e-bikes) used for recovery of the rider’s physical and physiological information, monitoring of their health state, and adjusting the “medical” assistance accordingly. E-bikes have proven to be an excellent way to do physical activity while commuting, thus improving the user’s health and reducing air pollutant emissions. Such devices can also be seen as the first step to help unhealthy sedentary people to start exercising with reduced strain. Based on this analysis, the need to have e-bikes with artificial intelligence (AI) systems that recover and processe a large amount of data is discussed in depth. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used to complete the relevant papers’ search and selection in this systematic review.
... Un índice de las propiedades de correlación de la variabilidad de la frecuencia cardíaca (VFC), el exponente alpha-1 del análisis de fluctuaciones sin tendencia (DFA-a1), ha demostrado potencial para estimar el primer y segundo umbral ventilatorio (VT1 y VT2) (Rogers et al., 2021a;Rogers et al., 2021b). Este estudio pretende comprobar esta correlación en un grupo de corredores amateur con una experiencia mayor de 3 años de entrenamiento. ...
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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.
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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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Backround: Non-linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi-periodic oscillations, but it also possesses random fluctuations and so-called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the Detrended Fluctuation Analysis (DFA). DFA is a non-linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short-term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in sport-specific settings including higher exercise intensities, non-stationary data segments, and relatively short recording times. Method: Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA-alpha1 as a proxy of correlation properties in the HR time series. Results: The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria. Conclusions: The included studies show that DFA-alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non-linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.
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AimMeasurements of Non-linear dynamics of heart rate variability (HRV) provide new possibilities to monitor cardiac autonomic activity during exercise under different environmental conditions. Using detrended fluctuation analysis (DFA) technique to assess correlation properties of heart rate (HR) dynamics, the present study examines the influence of normobaric hypoxic conditions (HC) in comparison to normoxic conditions (NC) during a constant workload exercise.Materials and Methods Nine well trained cyclists performed a continuous workload exercise on a cycle ergometer with an intensity corresponding to the individual anaerobic threshold until voluntary exhaustion under both NC and HC (15% O2). The individual exercise duration was normalized to 10% sections (10–100%). During exercise HR and RR-intervals were continuously-recorded. Besides HRV time-domain measurements (meanRR, SDNN), fractal correlation properties using short-term scaling exponent alpha1 of DFA were calculated. Additionally, blood lactate (La), oxygen saturation of the blood (SpO2), and rating of perceived exertion (RPE) were recorded in regular time intervals.ResultsWe observed significant changes under NC and HC for all parameters from the beginning to the end of the exercise (10% vs. 100%) except for SpO2 and SDNN during NC: increases for HR, La, and RPE in both conditions; decreases for SpO2 and SDNN during HC, meanRR and DFA-alpha1 during both conditions. Under HC HR (40–70%), La (10–90%), and RPE (50–90%) were significantly-higher, SpO2 (10–100%), meanRR (40–70%), and DFA-alpha1 (20–60%) were significantly-lower than under NC.Conclusion Under both conditions, prolonged exercise until voluntary exhaustion provokes a lower total variability combined with a reduction in the amplitude and correlation properties of RR fluctuations which may be attributed to increased organismic demands. Additionally, HC provoked higher demands and loss of correlation properties at an earlier stage during the exercise regime, implying an accelerated alteration of cardiac autonomic regulation.
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Measurement of the non-linear dynamics of physiologic variability in a heart rate time series (HRV) provides new opportunities to monitor cardiac autonomic activity during exercise and recovery periods. Using the Detrended Fluctuation Analysis (DFA) technique to assess correlation properties, the present study examines the influence of exercise intensity and recovery on total variability and complexity in the non-linear dynamics of HRV. Sixteen well-trained cyclists performed interval sessions with active recovery periods. During exercise, heart rate (HR) and beat-to-beat (RR)-intervals were recorded continuously. HRV time domain measurements and fractal correlation properties were analyzed using the short-term scaling exponent alpha1 of DFA. Lactate (La) levels and the rate of perceived exertion (RPE) were also recorded at regular time intervals. HR, La, and RPE showed increased values during the interval blocks (p < 0.05). In contrast, meanRR and DFA-alpha1 showed decreased values during the interval blocks (p < 0.05). Also, DFA-alpha1 increased to the level in the warm-up periods during active recovery (p < 0.05) and remained unchanged until the end of active recovery (p = 1.000). The present data verify a decrease in the overall variability, as well as a reduction in the complexity of the RR-interval-fluctuations, owing to increased organismic demands. The acute increase in DFA-alpha1 following intensity-based training stimuli in active recovery may be interpreted as a systematic reorganization of the organism with increased correlation properties in cardiac autonomic activity in endurance trained cyclists.
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