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An Index of Non-Linear HRV as a Proxy of the Aerobic Threshold Based on Blood Lactate Concentration in Elite Triathletes

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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.
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Citation: Rogers, B.; Berk, S.;
Gronwald, T. An Index of Non-Linear
HRV as a Proxy of the Aerobic
Threshold Based on Blood Lactate
Concentration in Elite Triathletes.
Sports 2022,10, 25. https://doi.org/
10.3390/sports10020025
Academic Editor: Maurizio Bertollo
Received: 6 January 2022
Accepted: 17 February 2022
Published: 18 February 2022
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4.0/).
sports
Article
An Index of Non-Linear HRV as a Proxy of the Aerobic
Threshold Based on Blood Lactate Concentration in
Elite Triathletes
Bruce Rogers 1, * , Sander Berk 2and Thomas Gronwald 3
1
College of Medicine, University of Central Florida, 6850 Lake Nona Boulevard, Orlando, FL 32827-7408, USA
2Dutch Triathlon Federation, Papendallaan 49, 6816 VD Arnhem, The Netherlands;
sander.berk@triathlonbond.nl
3Institute of Interdisciplinary Exercise Science and Sports Medicine, MSH Medical School Hamburg,
University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457 Hamburg, Germany;
thomas.gronwald@medicalschool-hamburg.de
*Correspondence: bjrmd@knights.ucf.edu
Abstract:
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.
Keywords:
heart rate variability; lactate threshold; DFA a1; training intensity distribution;
polarized training
1. Introduction
Knowledge of exercise intensity boundaries is important for guidance in athletic
training and diagnostics. Various models of training intensity distribution have been
proposed (polarized, threshold and pyramidal) but all share one aspect in common, that the
majority of training is to be performed in a low-intensity zone range [
1
]. In the typical three-
zone model, the upper low-intensity limit is felt to be regarded as the aerobic threshold (AT),
represented by either the first lactate (LT1) or ventilatory threshold (VT1) [
2
]. Although an
incremental ramp test with gas exchange monitoring can be performed to determine the
VT1, many athletic training centers utilize progressive constant cycling power intervals
with lactate testing to determine the AT using the LT1. Although, on first glance, this would
seem a simple endeavor, several issues make the concept more complex. Importantly, there
does not appear to be universal agreement on what concept defines the LT1 [
3
]. Some
investigators have used a fixed value of 2 mmol/L, while others use a fixed value above
Sports 2022,10, 25. https://doi.org/10.3390/sports10020025 https://www.mdpi.com/journal/sports
Sports 2022,10, 25 2 of 8
baseline of 0.5 or 1 mmol/L. Other options include logarithmic plotting of lactate against
cycling power or heart rate (HR) [
4
]. This could lead to variable results of a training
boundary obtained by one measure or another.
Besides the confusion surrounding test definition, lactate testing is invasive, relatively
costly and generally requires additional personnel to perform the test while the athlete is
exercising. Therefore, alternate means of determining the AT have been evaluated over
the years including modalities related to various heart rate variability (HRV) indexes
[57]
.
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
]. 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. Potential advantages of this
approach to estimating the AT include cost, easy availability of HR monitoring devices and
its non-invasive nature. However, since initial validation of this approach was performed
by comparing the HRVT to the AT as represented by the VT1 derived from gas exchange
during treadmill running, potential agreement with an alternative method such as the LT1
evaluated during an incremental cycling stage ramp is unknown and should be addressed.
In order to show that the HRVT is a robust, reliable surrogate for the AT, evaluation
in certain population subsets would also be helpful. For instance, this marker may be
applicable in a group possessing average fitness abilities but loses validity with elite
endurance athletes. Since the elite endurance athlete typically performs large amounts of
training in the low-intensity zone, validation of the HRVT principle in these individuals
would be especially beneficial. Recent observational analysis has shown that the volume
of low-intensity training performed by competitive long-distance runners [
10
] as well as
the high training volume performed by recreational half marathon runners [
11
] is related
to their future performance. Therefore, given the need for accurate delineation of the
low-intensity boundary in elite athletes for training and testing purposes, the intent of
this report is to explore the relationship of the DFA a1-based HRVT with that of the LT1,
obtained at an athletic team assessment camp.
2. Methods
2.1. Participants
Nine elite triathletes (7 male, 2 female) of various countries of origin were recruited
from a triathlon assessment training camp. Current competition category ranged between
national and international levels. Average age was 24 years (
±
4), body weight 69 kg (
±
9),
height 175 cm (
±
10), weekly training volume 20 h (
±
3) and
.
V
O
2MAX
67 mL/kg/min (
±
7).
This study was conducted during routine diagnostic procedures in a training camp and
no additional equipment was used for data collection and no further procedures were
performed. Physiologic testing was performed at the beginning of the camp. Athletes
had not performed any recent high-intensity exercise and were deemed well rested by the
coaching staff. All participants were informed about the study procedures and objectives.
They provided written informed consent according to the ethical guidelines in accordance
with the institutional review board and the guidelines of the Helsinki World Medical
Association Declaration.
2.2. Exercise Testing Protocol
An incremental cycling stage test until voluntary exhaustion was performed with a
Cyclus2 ergometer (RBM elektronik-automation GmbH, Leipzig, Germany) with a freely
chosen cadence between 80 and 90 rpm. The test protocol for men consisted of a starting
cycling power of 90 W (watts), with an incremental rise of 30 W every 3 min. For women,
Sports 2022,10, 25 3 of 8
the starting cycling power was 75 W, with an incremental rise every 3 min of 25 W. Machine
calibration was performed in accordance with manufacturer recommendations. Ambient
temperature (14 to 17
C), altitude of 45 m and meal timing were similar for all participants.
Both caffeine and alcohol consumption were avoided for 24 h pretesting. There was no
tobacco usage in any participant.
2.3. Lactate Testing
All lactate samples were measured with the Lactate Pro2 (Arkray KDK, Kyoto, Japan)
between 30 and 40 s before the end of each ramp stage, except the last sample, which was
taken directly after completion of the test (10–15 s after cessation). Determination of the first
lactate threshold was performed using automated testing software [
4
], using logarithmic
plotting of lactate vs. either cycling power or HR.
2.4. RR Measurements and Calculation of DFA a1-Derived HRVT
A Polar H10 (Polar Electro Oy, Kempele, Finland) HR monitoring device (HRM) was
used to detect RR intervals in 7 individuals, a Pioneer HRM (Pioneer Electronics, Torrance,
CA, USA) Inc was used in one individual and a Garmin HRM (Garmin Inc, Olathe, KS,
USA) in another due to individual preferences. All RR data were recorded with a Garmin
530 cycling computer (Garmin Inc., Olathe, KS, USA) 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). Kubios preprocessing settings were set to
the default values including the RR detrending method which was kept at “Smoothness
priors” (Lambda = 500) [
12
]. DFA a1 window width was set to 4
n
16 beats. The RR
series was then corrected by the Kubios “automatic method” and relevant HRV parameters
exported as text files for further analysis. Artifact levels measured by Kubios HRV were
below 5%. This limit was previously shown to have minimal effect on the HRVT [
13
].
DFA a1 was calculated from the RR data series using 2 min time windows with repeat
computation every 5 s throughout the test (time-varying method—window width = 2 min,
grid interval = 5 s). 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
]. Plotting of DFA a1 vs. HR was then performed, generally showing a stable
area above 1.0 at low work rates, a rapid, near linear drop reaching below 0.5 at higher
intensity, then plateauing without major change. The procedure used to indicate at what
level of cycling intensity as HR the DFA a1 would cross a value of 0.75 has been detailed
previously [
9
]. Cycling power at DFA a1 = 0.75 (cycling power at HRVT) was calculated
from the 180 s average cycling power preceding the time DFA a1 reached 0.75.
2.5. Statistics
Statistical analysis was performed for: HR and cycling power at LT1 derived from
lactate testing; HR and cycling power at HRVT derived from DFA a1. Standard statistical
methods were used for the calculation of means and standard deviations (SD). Normal
distribution of data was checked by Shapiro–Wilk’s test. The agreement with LT1 param-
eters was assessed using linear regression, Pearson’s r correlation coefficient, intraclass
correlation coefficient (ICC), Lin’s concordance correlation coefficient (CCC), standard error
of estimate (SEE), and Bland–Altman plots with limits of agreement [
15
]. All Bland–Altman
plots were assessed for proportional bias. The size of Pearson’s r correlations was evaluated
as follows: 0.3
r < 0.5 low; 0.6
r < 0.8 moderate; and r
0.8 high, [
16
] The paired
t-test was used for comparison of LT1 vs. HRVT for both cycling power and HR. For all
tests, the statistical significance was accepted as p
0.05. Analysis was performed using
Microsoft Excel 365 with Real Statistics Resource Pack (Release 6.8) and Analyse-it software
(Version 5.66).
Sports 2022,10, 25 4 of 8
3. Results
Lactate Threshold and HRVT Comparison
Mean LT1 HR for 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 cycling power
of 247.0 W (
±
53.6) (p= 0.17). Regression plots are shown in Figure 1for HR and cycling
power comparisons. Bland–Altman analyses for comparisons of HR and cycling power
are shown in Figure 2. Mean differences were
1.7 bpm and
5.3 W with LOA of 13.3
and
16.7 bpm for HR and of 15.1 and
25.6 W for cycling power; no proportional bias
was found (HR: r = 0.38 p= 0.31; cycling power: r = 0.55 p= 0.11). All data were normally
distributed (All W > 0.90, p> 0.31). ICC (1,1) between LT1 HR and HRVT HR was 0.69
(95% confidence limits 0.14 to 0.92) and was 0.98 (95% confidence limits 0.91 to 0.99) for
cycling power. CCC between LT1 HR and HRVT HR was 0.66 (95% confidence limits 0.11
to 0.90) and was 0.98 (95% confidence limits 0.92 to 0.99) for cycling power.
Figure 1.
Regression plots for all participant data. (
A
): Values of LT1 vs. HRVT for HR in bpm.
(
B
): Values of LT1 vs. HRVT for cycling power in W. Bisection lines in light gray. SEE: standard error
of estimate; r: Pearson’s r. Points symbolized by X represent female participants.
Sports 2022,10, 25 5 of 8
Figure 2.
Bland-Altman Plot of LT1 vs. HRVT for all participants. (
A
): Values of LT1 vs. HRVT for
HR in bpm. (
B
): Values of LT1 vs. HRVT for cycling power in W. 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. Points symbolized by X represent female participants.
4. Discussion
This study aimed to confirm the association of the DFA a1-based HRVT with that of
the LT1, an established marker of the AT [
3
] in a demographic consisting of elite male and
female triathletes using a cycling stage ramp test. The results show clear relationships
between the LT1 calculated by the log–log method and HRVT for both HR and cycling
power. This was supported by linear regression, with Pearson’s r of 0.77 and 0.98 for HR
and cycling power, respectively. There was no difference between mean values of LT1 and
HRVT by paired t testing. Bland–Altman analysis showed minimal mean differences that
were felt to be acceptable for the purpose of exercise and training prescription. Although a
previous report found a good concordance between the HRVT and VT1 assessed by gas
exchange, that study was performed in recreational runners using a treadmill test with
research grade ECG monitoring [
9
]. Given the potential value of an alternate non-invasive
marker for the AT, further investigation into varied user populations, alternate recording
devices and exercise modalities are warranted before widespread usage. The participants
evaluated here did represent an elite class of triathlete including several national team
members. Weekly training volumes and
.
V
O
2MAX
measurements were in agreement with
this and well above typical recreational levels [
17
]. Since elite endurance sport participants
are frequently the subjects of training intensity distribution research, positive confirmation
of the HRVT equivalence to the LT1 could be helpful with non-invasive zone boundary
identification and enforcement.
The underlying mechanism for DFA a1 behavior during exercise appears to be related
to the antagonistic behavior of the sympathetic and parasympathetic branches of the auto-
nomic nervous system on the sinoatrial node [
18
] as well as other potential factors [
19
]. As
work intensity rises there is a withdrawal of the parasympathetic component and enhance-
ment of the sympathetic component with the net result being that of a decline of DFA a1 as
well as other HRV parameters. However, as opposed to conventional HRV indexes that
rely on a nadir to determine the AT, DFA a1 appears to pass a specific dimensionless value
of 0.75. Therefore, asymptotic curve interpretation and calibration to metabolic parameters
such as a lactate or ventilatory threshold is unnecessary. This has obvious advantages
in both retrospective HRV analysis and real-time monitoring of exercise demands. The
reasoning behind choosing a DFA a1 value of 0.75 as a focus for VT1 transition was based
on several factors [
8
,
9
]. Inspection of prior DFA a1 incremental ramp studies appeared
to indicate that the aerobic threshold was situated near this area [
8
]. In addition, this
value represents the midpoint between correlated fractal patterns seen with low intensity
loads (1.0) and uncorrelated random patterns seen at intensity domains past the aerobic
Sports 2022,10, 25 6 of 8
threshold (0.5) [
8
]. Additional studies in other demographic groups also appear to support
the 0.75 HRVT association with the gas exchange-derived VT1 [
20
,
21
]. With respect to DFA
a1 activity in elite athletes, it is certainly plausible that this group could exhibit distinct
differences from inactive or recreational cohorts based on factors such as cardiac remodel-
ing [
22
,
23
] and/or altered vagal tone [
24
]. The concordance between the HRVT to that of
the AT in both elite and non-elite populations is reassuring to the hypothesis that the DFA
a1 threshold concept is widely applicable. The HRVT can also be considered as a separate
concept within the theoretical framework [8] of different organismic regulation patterns.
5. Limitations and Future Directions
Although the derivation of the HRVT is relatively straightforward, both the defini-
tion and calculation of the LT1 are subject to numerous views (i.e., rise of either 0.5 or
1.0 mmol/L, a fixed 2.0 mmol/L threshold, etc.) [
3
,
25
27
]. As one of several established
methods, even logarithmic transformation has interpretive options that could lead to
slightly different results for cycling power or HR [
25
]. Therefore, comparison of the HRVT
to the various LT1 concepts could be performed in future investigations of larger sample
size. Scant data on DFA a1 behavior are available in female athletes and further study is
needed in that population. Fortunately, in this study, two participants were female and
had results similar to the entire group. A possible limitation of this study is the hetero-
geneity of HRM devices. Although they are all share similar form factors, it is certainly
possible that unique device bias could be present. A strength of this study is that artefact
presence in the included data was below 5%, which has been shown to have minimal effect
on the HRVT [
13
]. In the future, devices with a chest belt form factor with single-lead
ECG functionality would be advantageous for both evaluation of artefact type, manual
correction of artefact as well as arrythmia identification [
28
]. It is also feasible that the
DFA a1 measurement be integrated into smartwatch or smartphone monitoring devices
for real-time intensity and zone 1 enforcement. Several new implementations of DFA a1
monitoring via adapted open-source python packages have recently emerged, making
this idea closer to attainment [
29
]. 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].
6. Practical Applications
The intensity region where DFA a1 declines through a value of 0.75 (HRVT) can be
used to delineate the AT in terms of HR and cycling power during an incremental cycling
ramp. The agreement between the HRVT and LT1 was similar to previous VT1 and LT1
comparisons [
24
]. This information can be used for both measurement of current fitness sta-
tus as well as for regulation and monitoring of low-intensity training distribution. Further
extension of the HRVT hypothesis was confirmed in an elite group of endurance athletes.
7. Conclusions
A heart rate variability threshold based on DFA a1, a non-linear measure of fractal
correlation properties, was closely associated with the first lactate threshold in a population
of elite male and female triathletes during a cycling ramp stage test. Given the importance
of identifying the upper boundary for low-intensity zone training for endurance-type sports,
the DFA a1-related threshold demonstrates an exciting low-cost, non-invasive option to
that of lactate testing. In addition, due to its dimensionless nature and agreement with
the aerobic threshold, future use on a retrospective or real-time basis to enforce training
intensity distribution guidelines is possible.
Author Contributions:
Conceptualization, B.R., S.B. and T.G.; investigation, S.B.; writing—original
draft preparation, B.R.; writing—review and editing, B.R. and T.G.; formal analysis, B.R. and T.G. All
authors have read and agreed to the published version of the manuscript.
Sports 2022,10, 25 7 of 8
Funding: This research received no external funding.
Institutional Review Board Statement: This study was conducted during routine diagnostic proce-
dures in a training camp and no additional equipment was used for data collection and no further
procedures were performed. All participants were informed about the study procedures and objec-
tives. They provided written informed consent according to the ethical guidelines in accordance with
the institutional review board and the principles of the Declaration of Helsinki.
Informed Consent Statement:
Written informed consent was obtained from all subjects involved in
this study.
Data Availability Statement:
The raw data supporting the conclusions of this article will be made
available by the authors, without undue reservation.
Conflicts of Interest:
The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential conflict of interest.
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... During the cycling test the HR and VO 2 attained at the VT1 was strongly associated with the HR and VO 2 at the HRVT. Finally, in a contrasting population, the HR and cycling power at the HRVT was associated with the HR and cycling power derived from the LT1 in a group of elite triathletes (7 male, 2 female) performing an incremental cycling stage protocol (Rogers et al., 2022a). Although female data on DFA a1 behavior is sparse, the 2 female participants in this group had typical DFA a1 responses to incremental cycling exercise. ...
... Shading indicates a 3 zone exercise intensity model defined by DFA a1 thresholds. (B) DFA a1 vs. HR of three participants during incremental exercise ramps (data from Rogers et al., 2021f;Rogers et al., 2022a;Rogers et al., 2021a respectively). Data processed in Kubios HRV Premium software (Version 3.5) using automatic correction method (artifact percentage: < 5%). ...
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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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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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