Citation: Neves, L.N.S.; Gasparini
Neto, V.H.; Araujo, I.Z.; Barbieri,
R.A.; Leite, R.D.; Carletti, L. Is There
Agreement and Precision between
Heart Rate Variability, Ventilatory,
and Lactate Thresholds in Healthy
Adults? Int. J. Environ. Res. Public
Health 2022, 19, 14676. https://
doi.org/10.3390/ijerph192214676
Academic Editor: Paulina Hebisz
Received: 9 August 2022
Accepted: 3 September 2022
Published: 9 November 2022
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International  Journal  of
Environmental Research
and Public Health
Article
Is There Agreement and Precision between Heart Rate
Variability, Ventilatory, and Lactate Thresholds in
Healthy Adults?
Letícia Nascimento Santos Neves 1,*
, Victor Hugo Gasparini Neto 1
, Igor Ziviani Araujo 1,
Ricardo Augusto Barbieri 2, Richard Diego Leite 1 and Luciana Carletti 1
1
Laboratory of Exercise Physiology (LAFEX), Physical Education and Sports Center, Federal University of
Espírito Santo (CEFD-UFES), Vitória 29075-910, Brazil
2
Postgraduate Program in Physical Education and Sport, School of Physical Education and Sport of Ribeirão Preto,
University of São Paulo (EEFERP-USP), São Paulo 05360-160, Brazil
*
Correspondence: leticiasantosneves@hotmail.com
Abstract: This study aims to analyze the agreement and precision between heart rate variability
thresholds (HRVT1/2) with ventilatory and lactate thresholds 1 and 2 (VT1/2 and LT1/2) on a
treadmill. Thirty-four male students were recruited. Day 1 consisted of conducting a health sur-
vey, anthropometrics, and Cardiopulmonary Exercise Test (CPx). On Day 2, after 48 h, a second
incremental test was performed, the Cardiopulmonary Stepwise Exercise Test consisting of 3 min
stages (CPxS), to determine VT1/2, LT1/2, and HRVT1/2. One-way repeated-measures ANOVA
and effect size (ηp2) were used, followed by Sidak’s post hoc. The Coefﬁcient of Variation (CV) and
Typical Error (TE) were applied to verify the precision. Bland Altman and the Intraclass Correlation
Coefﬁcient (ICC) were applied to conﬁrm the agreement. HRVT1 showed different values compared
to LT1 (lactate, RER, and R-R interval) and VT1 ( ˙VE, RER, ˙VCO2, and HR). No differences were found
in threshold 2 (T2) between LT2, VT2, and HRVT2. No difference was found in speed and ˙VO2 for
T1 and T2. The precision was low to T1 (CV > 12% and TE > 10%) and good to T2 (CV < 12% and
TE < 10%). The agreement was good to fair in threshold 1 (VT1, LT1, HRVT1) and excellent to good
in T2 (VT1, LT1, HRVT1). HRVT1 is not a valid method (low precision) when using this protocol to
estimate LT1 and VT1. However, HRVT2 is a valid and noninvasive method that can estimate LT2
and VT2, showing good agreement and precision in healthy adults.
Keywords: anaerobic threshold; lactate threshold; ventilatory threshold; exercise test; athletic
performance; prescription
1. Introduction
Thresholds in physiology are essential concepts that establish the boundaries between
different domains of exercise intensity (moderate/heavy/severe) [1,2]. In addition, thresh-
olds can predict athletic performance, monitor training progress, and clinically assess a
patient’s physiological function or dysfunction (e.g., in heart failure) [1]. Furthermore,
some methods for threshold determination are costly, require qualiﬁed professionals, and
need speciﬁc software. Thus, using more economical and less complex tools to determine
thresholds is interesting. However, in this article, threshold 1 (T1) (e.g., ﬁrst lactate, ven-
tilatory or gas exchange, and heart rate variability (HRV) thresholds) lies between the
moderate and heavy domains [2–4] and threshold 2 (T2) (e.g., second lactate threshold,
second ventilatory threshold, or respiratory compensation point) lies between the heavy
and severe domains [1–5]. Thresholds can be identiﬁed invasively by blood analysis (e.g.,
lactate, glucose, catecholamines) and noninvasively (e.g., gas exchange, electromyogra-
phy, heart rate deﬂection point of heart rate, and infrared spectroscopy) [6–8]. Although
different thresholds can be estimated, some still generate confusion about the moment of
Int. J. Environ. Res. Public Health 2022, 19, 14676. https://doi.org/10.3390/ijerph192214676
https://www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2022, 19, 14676
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demarcation by indicating different points on the curve, so they still need to be studied and
analyzed [1].
A potentially cheaper and less invasive method than LT and VT to identify thresholds
can be performed through autonomic balance, determined by heart rate variability (HRV),
which is deﬁned by the oscillation in the interval between consecutive heartbeats as well as
the oscillations between consecutive instantaneous heart rates [9].
The literature suggests using HRV to estimate physiological thresholds, such as the
ventilatory or lactate threshold, in an incremental test. During the test, there was a pro-
gressive increase in exercise intensity over time with a concomitant reduction in HRV and
parasympathetic activity, followed by vagal withdrawal [10,11]. The vagal withdrawal
marks the ﬁrst heart rate variability threshold (HRVT1), which occurs at approximately
50–60% of ˙VO2max [12] and may coincide with the ﬁrst lactate and ventilatory thresholds
(LT1 and VT1) [10]. In contrast, the second lactate threshold (LT2) and second ventila-
tory threshold (VT2) are also investigated to determine whether HRV can estimate them.
However, little evidence has shown comparisons between VT2 and LT2 with the heart rate
variability threshold (HRVT2), and even rarer are treadmill studies [13,14].
Ventilatory thresholds 1 and 2 [3,15] and lactate thresholds 1 and 2 [2,10] are validated
and widely used methods for threshold identiﬁcation. However, although they agree, their
occurrence may not occur at identical work rates, i.e., with insufﬁcient precision to be
determined simultaneously [1,7]. Therefore, comparing both to a potentially cheaper and
recent proposal (HRV) is essential.
As far as we know, no literature has shown the comparison between these six thresh-
olds simultaneously (VT1/VT2, LT1/LT2, and HRVT1/HRVT2) on a treadmill. However,
some authors compared three thresholds but only HRV1 with LV1 and LT1 on a cycle
ergometer [10]. Other authors who used the ﬁrst and second thresholds compared HRV
only with the ventilatory thresholds (HRVT 1 vs. VT1 and HRVT2 vs. VT2) [13,14,16].
Therefore, it is interesting to present and compare the six thresholds in the same individual,
allowing a more signiﬁcant contribution to studies that demonstrate the thresholds in
isolation, such as the comparison between only the ﬁrst thresholds or only the second
ones, and even only the comparison of HRV with the ventilatory threshold or with lactate
threshold alone.
Furthermore, when LT, VT, and HRVT are involved, the studies used a cycle ergometer,
which makes it difﬁcult to transpose HRV behavior into a practical application such as
running. Since ˙VO2, ventilation, ﬁber recruitment patterns, and muscle coordination
according to the speciﬁcity of each test or exercise can inﬂuence threshold identiﬁcation [17],
studies are needed to verify whether both thresholds (VT1/VT2 and LT1/LT2) can be
estimated using HRVT1 and HRVT2 on a treadmill. Therefore, we aimed to test if there is
agreement and precision between HRVT1/2 with LT1/2 and VT1/2 on a treadmill.
2. Materials and Methods
2.1. Participants
The study consisted of 34 healthy male university students who were physically
independent (≥150 min·week−1 of physical exercise), 22 ± 2 years, height: 176.2 ± 6.6 cm,
body mass: 72.89 ± 8.84 kg; body fat (%): 8.77 ± 3.69; BMI: 23.5 ± 2.66; and R-R intervals
(ms): 935.7 ± 151.0). Informed consent was obtained from all subjects involved in the
study. The study followed the ethical guidelines outlined in the Declaration of Helsinki
and was approved by an ethical committee (CAAE: 76607717.5.0000.5542–18/09/2017).
For this sample, the statistical power found was 80% (effect size f: 0.25-equivalent to the
moderate ηp2 found in the present study for ( ˙VO2); alpha: 5%), calculated by G*Power
3.1. University students aged 18 to 30 years were included, and none of the participants
had cardiovascular, metabolic, respiratory, or osteoarticular diseases, and their maximal
oxygen consumption ( ˙VO2max) was considered below 34 mL·kg−1·min−1 according to the
American Heart Association [18]. Alcohol was forbidden for 48 h, and caffeine-containing
products for 24 h prior to the beginning of the study.
Int. J. Environ. Res. Public Health 2022, 19, 14676
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2.2. Study Design
Data collection occurred over two days with a 48-h interval in the morning. On the
ﬁrst day, questionnaires to identify health status were applied, followed by anthropometry,
an electrocardiogram, and a cardiopulmonary exercise test (CPx) applied by a cardiologist
to familiarize and determine the initial intensity of the second day. A Cardiopulmonary
Stepwise Exercise Test (CPxS) was performed on the second day to compare the six thresh-
olds (LT1/2, VT1/2, HRVT1/2). Two evaluators analyzed the criteria of VTs, LTs, and
HRVTs blindly and independently. When necessary, a third evaluator was requested. The
intraclass correlation coefﬁcient between evaluators was used with values varying between
LT1: 0.889, LT2: 0.948; VT1: 0.965, VT2: 0.954; HRVT1: 1.00, and HRVT2: 1.00. HRV, ˙VO2,
and Lactate were collected during the CPxS every 3 min and after the test (10 min recovery).
All participants were instructed to have a light meal at least 2 h before the test and avoid
caffeinated and strenuous exercise 24 h before the tests.
2.3. Anthropometry
Body mass and height were measured using a digital anthropometric scale (Marte
Scientiﬁc, L200, SP), and the Body Mass Index (BMI) was calculated. In addition, seven
skinfolds were measured (subscapular, triceps, mid-axillary, pectoral, abdominal, suprailiac,
mid-thigh) (Mitutoyo Cescorf, Porto Alegre, Brazil) to calculate the body fat percentage
(%BF). To calculate %BF, it is necessary to calculate body density (BD), and Jackson and
Pollock’s seven skinfold equation was used: BD = 1.11200000 − 0.00043499 (X1) + 0.00000055
(X1)2 – 0.00028826 (X4), where X1 = sum of seven skinfolds and X4 = age in Years [19]. The
%BF for the anthropometric technique was estimated using the SIRI (1961) equation %BF =
(495/BD) – 450 [20].
2.4. Cardiopulmonary Exercise Test (CPx)
Before the CPx, the individuals remained in the supine position on a stretcher for
5 min, where a 12-lead resting electrocardiogram was performed (USB Micromed electro-
cardiograph, Brasília, Brazil). In addition, electrocardiographic records were performed
during pre-exertion, standing on the treadmill, and during exertion. Then, the CPx was
performed on a treadmill (Inbra Sports Super ATL, Porto Alegre, Brazil), with a slope of
1% in the ramp protocol, with a duration of 8 to 12 min. The initial speed was 5 km·h−1
with gradual increments of 1 km·h−1 each minute, supervised by a cardiologist (room
temperature: 23 ◦C–25 ◦C). At least four criteria were considered to deﬁne the CPx as the
maximum test: (a) Voluntary exhaustion; (b) ≥ 90% of the age-adjusted estimate of HRmax;
(c) z respiratory exchange ratio (RER) equal to or greater than 1.05; (d) ˙VO2max plateau
or peak with increased exercise intensity; (e) peak blood [lactate] of ≥ 8 mM. During
the CPx, a Metabolic Gas Analyzer (Cortex Metalyzer 3B, Leipzig, Germany) was used,
with breath-by-breath measurement. Before each test, a gas mixture was used to calibrate
(11.97% O2 and 4.95% CO2) the ambient gas. Then, the volume was calibrated with a 3 L
syringe. Ventilation ( ˙VE), oxygen consumption ( ˙VO2), carbon dioxide production ( ˙VCO2),
respiratory exchange ratio (RER), and speed were analyzed for an average of 15 s.
2.5. Cardiopulmonary Stepwise Exercise Test (CPxS)
The CPxS was used to identify the six thresholds (VT1/2, LT1/2, and HRVT1/2) for
comparing and validating the HRVTs. CPxS was applied 48 h after CPx, following the same
precautions and procedures described in CPx. The CPx determined the initial intensity of
this protocol on the ﬁrst day. The CPxS incremental test starts at 4 km·h−1 below the speed
in VT1 determined by CPx with an increment of 1 km·h−1 every 3 min (considering the
ﬁrst two stages as a warm-up, with a slope of 1% until maximum effort), measuring lactate,
gas analysis, and HRV. Before the test, the participants remained at rest for 10 min in the
supine position to measure the HRV.
Int. J. Environ. Res. Public Health 2022, 19, 14676
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2.6. Blood Lactate Concentrations
Samples of 25 µL of capillary blood from the ear lobe were collected, at rest, during
the CPxS test (every 3 min, participants jump off the treadmill and come back quickly in
approximately 15 to 20 s, with no time wasted at each stage), and in passive recovery after 1,
3, 5, 7, and 10 min. The lactate peak in recovery was considered the highest value measured
in this interval. The samples were stored under refrigeration (−4 ◦C) for further analysis
on the equipment YSI 2300 STAT plus (Ohio, USA) [21].
2.7. Heart Rate Variability (HRV)
A Polar H7 Bluetooth heart rate monitor (Polar Electro Oy, Kempele, Finland) was
connected to a smartphone to collect HRV, beat by beat (R-R intervals). The Polar H7 was
validated to measure HRV data [22]. Each step described in CPxS was recorded in the Elite
HRV app (HRV Elite, Asheville-North Carolina) [23] and edited in the Kubios HRV 3.0
Program. Data were collected at rest and during and after CPxS.
2.8. Determination of the Ventilatory Threshold (VT)
VT1 was identified by the increase in the ventilatory equivalent of O2 ( ˙VE/ ˙VO2) without
increasing the ventilatory equivalent of CO2 ( ˙VE/ ˙VCO2) (Figure 1). When necessary, V-
slope and CO2 excess were observed to help identification (MetasoftTM software) [3,15]. VT2
was identified as the moment of the lowest point of ˙VE/ ˙VCO2 with subsequent elevation, in
addition to the moment of the gradual decrease in PetCO2 [3]. The same two evaluators always
determined the VT1 and VT2, and a third opinion was requested in case of disagreement. The
participants were excluded when it was not possible to identify the VT (a success rate of 85.3%
for T1 and 100% for T2). All intervals between steps, such as when participants jumped off the
treadmill to take blood samples, were excluded from the analysis to avoid misinterpretation.
The average for the last 30 s of each stage was analyzed.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
5 of 16 
 
 
Figure 1. Represents an example of threshold identifications 1 and 2 on the CPxS (single participant). 
A represents the lactate threshold 1 and 2 (black line) and heart rate variability thresholds 1 and 2 
(gray line). B represents the ventilatory threshold 1 and 2 (dark gray and black lines) and heart rate 
variability thresholds 1 and 2 (light gray line). Lactate thresholds 1 and 2 (LT1 and LT2), ventilatory 
thresholds 1 and 2 (VT1 and VT2), and heart rate variability thresholds 1 and 2 (HRVT1 and 
HRVT2). 
2.11. Statistical Analysis 
All data were tabulated and double-verified by independent researchers. The analy-
sis was performed using SPSS 20.0 software, and the figures were created by Excel soft-
ware and GraphPad Prism 6. The normality was tested using the Shapiro–Wilk test and 
submitted to evaluate the histogram, kurtosis, and skewness. The results were presented 
as mean ± standard deviation (SD). Student’s t-test was used to compare the maximum 
values of CPx with CPxS. A repeated-measures ANOVA was used to compare the meth-
ods of identifying VT with LT and HRVT, followed by Sidak’s post-hoc (T1, n = 29 and T2, 
n = 34). The Greenhouse-Geisser correction was considered when a lack of sphericity was 
Figure 1. Represents an example of threshold identiﬁcations 1 and 2 on the CPxS (single participant).
(A) represents the lactate threshold 1 and 2 (black line) and heart rate variability thresholds 1 and 2
(gray line). (B) represents the ventilatory threshold 1 and 2 (dark gray and black lines) and heart rate
variability thresholds 1 and 2 (light gray line). Lactate thresholds 1 and 2 (LT1 and LT2), ventilatory
thresholds 1 and 2 (VT1 and VT2), and heart rate variability thresholds 1 and 2 (HRVT1 and HRVT2).
Int. J. Environ. Res. Public Health 2022, 19, 14676
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2.9. Determination of the Lactate Threshold (LT)
The LT was identiﬁed by the visual method. LT1 was determined by the ﬁrst increase
in the blood lactate concentration above resting values [2,10] and LT2 by the second linearity
breakpoint and exponential lactate accumulation [2,5,24] (Figure 1).
2.10. Determination of the Heart Rate Variability Threshold (HRVT)
The R-R intervals were grouped into three-minute sequences for HRV analysis. Data
ﬁltering was performed using the Kubios HRV Standard 3.0 Program (Gronau, Germany),
and it was automatically ﬁltered to remove missing or premature intervals and artifacts not
exceeding 5% [10]. The ﬁrst 90 s of physical effort at each stage were excluded from the
analysis due to the HR and HRV kinetic adjustment. This study chose the Root Mean Square
of the Successive Differences (RMSSD) and Poincaré plot indexes (SD1 and SD2) as the HRV
indexes (time-domain and non-linear HRV parameters). HRVT1 was identiﬁed by the ﬁrst
linearity breakpoint determined by visual inspection for the RMSSD variables, and when
necessary, SD1 was used to conﬁrm the identiﬁcation [25,26]. HRVT2 was determined by
the linearity breakpoint after the lowest value with a subsequent increase in the RMSSD and
conﬁrmed by the linearity break of the SD1/SD2 variables only when necessary (determined
by visual inspection) [26–28]. They were identiﬁed in Excel software (Microsoft Excel®
2022) (Figure 1). Only two evaluators were necessary for HRVT identiﬁcation because there
was no disagreement. The same evaluators were used to determine VTs, LTs, and HRVTs,
and they did not have experience with HRVTs. All thresholds (LT1/2, VT1/2, HRVT1/2)
were represented in Figure 1 (individual example) in CPxS.
2.11. Statistical Analysis
All data were tabulated and double-verified by independent researchers. The
analysis was performed using SPSS 20.0 software, and the figures were created by Excel
software and GraphPad Prism 6. The normality was tested using the Shapiro–Wilk test
and submitted to evaluate the histogram, kurtosis, and skewness. The results were
presented as mean ± standard deviation (SD). Student’s t-test was used to compare the
maximum values of CPx with CPxS. A repeated-measures ANOVA was used to compare
the methods of identifying VT with LT and HRVT, followed by Sidak’s post-hoc (T1,
n = 29 and T2, n = 34). The Greenhouse-Geisser correction was considered when a
lack of sphericity was noted (F statistics, degrees of freedom, degrees of freedom error).
Partial eta-squared (ηp2) was utilized as a measure of effect size in ANOVA, using small
(ηp2 = 0.01), medium (ηp2 = 0.06), and large (ηp2 = 0.14) effects [29]. The typical error
(TE = SDdiff/
√
2; where SDdiff is the standard deviation of difference) was expressed as an
absolute value and as a percentage of the mean value, and the coefficient of variation (CV)
was expressed in a percentage [CV = (SD/X)·100; where SD is the standard deviation
of data and X is the mean], both used to test precision [30,31]. Bland Altman plots
were used to identify agreement between the different methods (one-way ANOVA was
used to compare the bias between the methods). The intraclass correlation coefficient
(ICC) was used to scale the agreement (reliability). The reference values for the ICC
were < 0.5 (poor), 0.5–0.75 (moderate), 0.75–0.90 (good), and ≥0.90 (excellent) [32].
Statistical significance was set at p < 0.05 for all analyses.
3. Results
The maximal values at CPxS are demonstrated in Table 1.
The average initial velocity of participants in the CPxS was 5.3 ± 1.2 km·h−1. HRVT1
showed statistically higher values when compared to LT1 for the variables lactate and RER
(p < 0.05) and lower for the R-R interval in LT1 (Table 2). HRVT, ˙VE, RER, ˙VCO2, and
HR were signiﬁcantly higher than VT1 (p > 0.05). The R-R intervals were lower than VT1
and LT1 (p < 0.05) (Table 2). For the other variables, no statistical differences were found
between LT1 and VT1 (p > 0.05) (Table 2). The percentage demonstrated in Tables 2 and 3
(speed, ˙VO2, RR, and HR) is relative to the maximum value reached during the CPxS.
Int. J. Environ. Res. Public Health 2022, 19, 14676
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Table 1. Maximal physiological characteristics of participants at CPxS.
Variables
Mean ± SD
HRmax (bpm)
194 ± 8
HRmax predicted (%)
97.75 ± 4.07
˙VEmax (L·min−1)
125.42 ± 15.91
˙VO2max (ml·kg−1·min−1)
47.86 ± 4.96
˙VO2max (L·min−1)
3.47 ± 0.42
RERmax
1.02 ± 0.08
[La]peak (mM)
10.47 ± 2.01
Mean ± SD; Stepwise Progressive Test (CPxS), Ventilation ( ˙VE), Respiratory Exchange Ratio (RER), Lactate ([La]).
Table 2. Comparison between the means of the methods for identifying thresholds 1 in CPxS.
LT1
VT1
HRVT1
Within-Participants Effects
Mean ± SD
Mean ± SD
Mean ± SD
F (df, df)
p
ηp2
Speed (km·h−1)
7.2 ± 1.5
6.8 ± 1.4
7.3 ± 1.0
Speed (%)
50.63 ± 8.68
49.26 ± 9.08
52.52 ± 5.01
1.93 (2, 56)
0.155
0.064
Lactate (mM)
1.57 ± 0.89
1.58 ± 0.85
2.01 ± 0.82 *
4.83 (2, 56)
0.012 §
0.147
˙VE (L)
44.44 ± 15.51
39.80 ± 12.57
47.96 ± 9.68 †
3.95 (2, 56)
0.025 §
0.124
˙VO2 (ml·kg−1·min−1)
25.02 ± 6.48
23.75 ± 6.58
27.01 ± 5.22
˙VO2 (%)
51.62 ± 13.43
50.28 ± 13.71
55.68 ± 8.43
2.94 (2, 56)
0.061
0.095
RER
0.84 ± 0.08
0.82 ± 0.07
0.88 ± 0.07 *†
18.06 (2, 56)
0.000 §
0.392
˙VCO2 (L·min−1)
1.56 ± 0.53
1.43 ± 0.48
1.71 ± 0.34 †
4.15 (2, 56)
0.021 §
0.129
˙VO2 (L·min−1)
1.84 ± 0.55
1.72 ± 0.48
1.95 ± 0.32
2.36 (2, 56)
0.103
0.078
RR (ms)
473.8 ± 85.9
498.0 ± 93.0
439.2 ± 52.9 *†
RR (%)
52.04 ± 10.37
53.30 ± 9.33
47.63 ± 7.16 *†
6.56 (2, 56)
0.003 §
0.190
HR (bpm)
131 ± 23
125 ± 25
139 ± 16 †
5.12 (2, 56)
0.009 §
0.155
HR (%)
66.20 ± 7.59
64.53 ± 12.29
71.12 ± 11.14 †
Lactate threshold 1 (LT1), ventilatory threshold 1 (VT1), and heart rate variability threshold 1 (HRVT1). Ventilation
( ˙VE), relative oxygen consumption ( ˙VO2), respiratory exchange ratio (RER), carbon dioxide consumption ( ˙VCO2),
R-R interval (RR), heart rate (HR), variable of heart rate variability (RMSSD), F statistics and degrees of freedom
(F(df, df)), partial eta-squared (ηp2). * (p < 0.05) different from LT1; † (p < 0.05) different from VT1; § (p < 0.05)
ANOVA effect. Percentage values relative to the maximum (for RR it is relative to baseline).
Table 3. Comparison between the means of the methods for identifying thresholds 2 in CPxS.
LT2
VT2
HRVT2
Within-Participants Effects
Mean ± SD
Mean ± SD
Mean ± SD
F (df, df)
p
ηp2
Speed (km·h−1)
10.7 ± 1.1
10.6 ± 1.4
10.9 ± 1.2
Speed (%)
76.59 ± 5.42
76.07 ± 7.22
78.49 ± 6.23
1.45 (2, 66)
0.242
0.042
Lactate (mM)
3.96 ± 1.44
3.95 ± 1.19
4.30 ± 1.36
1.77 (2, 66)
0.178
0.051
˙VE (L)
79.25 ± 13.57
75.83 ± 12.86
80.18 ± 11.08
1.92 (2, 66)
0.155
0.055
˙VO2 (ml·kg−1·min−1)
39.39 ± 3.60
39.19 ± 5.16
39.98 ± 3.86
˙VO2 (%)
82.63 ± 6.32
81.97 ± 7.55
83.85 ± 6.79
0.96 (2, 66)
0.389
0.028
RER
0.92 ± 0.07
0.91 ± 0.06
0.92 ± 0.06
0.79 (2, 66)
0.460
0.023
˙VCO2 (L·min−1)
2.62 ± 0.39
2.59 ± 0.39
2.66 ± 0.31
0.86 (2, 66)
0.429
0.025
˙VO2 (L·min−1)
2.86 ± 0.38
2.84 ± 0.39
2.90 ± 0.31
0.89 (2, 66)
0.417
0.026
RR (ms)
347.2 ± 27.1
348.0 ± 27.0
342.8 ± 21.6
RR (%)
37.85 ± 5.49
37.91 ± 5.28
37.46 ± 5.81
1.26 (2, 66)
0.291
0.037
HR (bpm)
174 ± 13
173 ± 13
176 ± 11
1.18 (2, 66)
0.313
0.035
HR (%)
89.68 ± 4.14
89.49 ± 4.83
90.72 ± 3.71
Lactate threshold 2 (LT2), ventilatory threshold 2 (VT2), and heart rate variability threshold 2 (HRVT2). Ventilation
( ˙VE), relative oxygen consumption ( ˙VO2), respiratory exchange ratio (RER), carbon dioxide consumption ( ˙VCO2),
R-R interval (RR), heart rate (HR), variable of heart rate variability (RMSSD), F statistics and degrees of freedom
(F(df, df), partial eta-squared (ηp2). There were no differences between methods (p > 0.05). Percentage values
relative to the maximum (for RR it is relative to baseline).
No statistical differences were found in the methods’ comparison: HRVT2 vs. LT2,
HRVT2 vs. VT2, and LT2 vs. VT2 (p > 0.05) (Table 3).
Furthermore, repeated-measures ANOVA revealed a signiﬁcant effect mainly for the
variables Lactate (F2, 56 = 4.83, p = 0.012, ηp2 = 0.147, large), RER (F2, 56 = 18.06, p = 0.000,
ηp2 = 0.392, large), RR (F2, 56 = 6.56, p = 0.003, ηp2 = 0.190, large), and HR (F2, 56 = 5,12,
Int. J. Environ. Res. Public Health 2022, 19, 14676
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p = 0.009, ηp2 = 0.155, large) between methods in T1 (Table 2), but no signiﬁcant effects
were found in T2 (Table 3).
At T1 (LT1, VT1, and HRVT1), speed, ˙VO2, and HR demonstrated TE values greater
than 10% (Table 4). At T2 (LT2, VT2, and HRVT2), all variables showed TE lower than
10% (Table 4). In T1, velocity, HR, and ˙VO2 present CV > 12%, while T2 CV < 12% for the
same variables, showing that T2 had less variation than T1. The ICC at T1 was moderate
(p < 0.05), and for T2, the ICC was moderate to good (p < 0.05).
Table 4. Typical Error, Coefﬁcient of Variation, and Intraclass Correlation Coefﬁcient between the
methods for identifying thresholds 1 and 2.
LT1 vs. VT1
LT1 vs. HRVT1
VT1 vs. HRVT1
LT2 vs. VT2
LT2 vs. HRVT2
VT2 vs. HRVT2
Typical Error absolute (%)
Speed (km·h−1)
0.95 (13.8)
0.86 (11.9)
0.81 (12.3)
0.87 (8.1)
0.78 (7.4)
0.85 (8.0)
˙VO2 (ml·kg−1·min−1)
4.51 (18.5)
4.13 (16.4)
4.32 (18.5)
2.61 (6.6)
2.38 (6.0)
2.37 (6.1)
HR (bpm)
15 (12.2)
16 (12.2)
16 (14.1)
7 (3.9)
6 (3.6)
7 (4.1)
Coefﬁcient of Variation
Speed (km·h−1)
19.31
16.82
16.27
11.56
10.28
11.14
˙VO2 (ml·kg−1·min−1)
26.13
22.44
23.97
9.40
8.50
8.47
HR (bpm)
17.10
16.29
17.56
5.52
5.03
5.66
Intraclass Correlation Coefﬁcient
Speed (km·h−1)
0.693 *
0.671 *
0.637 *
0.684 *
0.663 *
0.709 *
˙VO2 (ml·kg−1·min−1)
0.684 *
0.616 *
0.616 *
0.797 *
0.744 *
0.840 *
HR (bpm)
0.748 *
0.559 *
0.504 *
0.842 *
0.840 *
0.783 *
Lactate threshold 1 (LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate
threshold 2 (LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2). Relative oxygen
consumption ( ˙VO2) and heart rate (HR). * p < 0.05.
There were no statistical differences between the means of differences for all methods
(p > 0.05) (Figures 2–4).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
8 of 16 
 
 
Figure 2. Bland Altman plots for speed at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the 
ends represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate thresh-
old 1 (LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate thresh-
old 2 (LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2). 
Figure 2. Bland Altman plots for speed at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the
ends represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate threshold 1
(LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate threshold 2 (LT2),
ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2).
Int. J. Environ. Res. Public Health 2022, 19, 14676
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Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
9 of 16 
 
 
 
Figure 3. Bland Altman plots for V̇ O2 at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the 
ends represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate thresh-
old 1 (LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate thresh-
old 2 (LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2). 
Figure 3. Bland Altman plots for ˙VO2 at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the ends
represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate threshold 1
(LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate threshold 2
(LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2).
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 
10 of 16 
 
 
Figure 4. Bland Altman plots for HR at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the ends 
represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate threshold 1 
(LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate threshold 2 
(LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2). 
4. Discussion 
As far as we know, this is the first study to compare these six thresholds on a tread-
mill (LT1/LT2, VT1/VT2, and HRVT1/HRVT2). The main findings of our study suggest 
that HRVT2 has good agreement and precision to estimate LT2 and VT2 in healthy adults, 
increasing the ecological validity of these noninvasive methods and allowing a real-world 
HRVT1/2 determination. In contrast, HRVT1 did not show enough precision to estimate 
LT1 and VT1. 
4.1. LTs vs. VTs 
L
d
l
h
h ld
d
d
d
l
l
Figure 4. Bland Altman plots for HR at thresholds 1 (A–C) and 2 (D–F). The dashed lines at the ends
represent the limits of agreement (1.96 SD) and the dashed central lines (bias). Lactate threshold 1
(LT1), ventilatory threshold 1 (VT1), heart rate variability threshold 1 (HRVT1), lactate threshold 2
(LT2), ventilatory threshold 2 (VT2), heart rate variability threshold 2 (HRVT2).
Int. J. Environ. Res. Public Health 2022, 19, 14676
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4. Discussion
As far as we know, this is the ﬁrst study to compare these six thresholds on a treadmill
(LT1/LT2, VT1/VT2, and HRVT1/HRVT2). The main ﬁndings of our study suggest that
HRVT2 has good agreement and precision to estimate LT2 and VT2 in healthy adults,
increasing the ecological validity of these noninvasive methods and allowing a real-world
HRVT1/2 determination. In contrast, HRVT1 did not show enough precision to estimate
LT1 and VT1.
4.1. LTs vs. VTs
Lactate and ventilatory thresholds presented a moderate to good intraclass correla-
tion and no statistical difference. However, it is essential to highlight that LT1 vs. VT1
demonstrated less precision (CV > 12% in all variables) and ICC (<0.750) than LT2 vs. VT2.
Some studies also found different results when comparing these methods (LT1 vs. VT1
or LT2 vs. VT2) [8,10,14,16,25,33], which made our ﬁndings important to conﬁrm the use
of both LTs and VTs to estimate the exercise intensity. VT1 is challenging to identify due
to several factors that can inﬂuence the agreement and precision identiﬁcation, such as
the cardiorespiratory ﬁtness level [34], walking-running transition [35], control of food
intake [1], and ergometer used [17]. Still, the present study showed an 85.3% success rate
in identifying VT1, similar to research on a cycle ergometer [10], and a 100% success rate in
second threshold identiﬁcation. The CPx performed before CPxS likely affects the small
loss percentage because it helps deﬁne the initial load of CPxS since the intensity of the
protocol start can inﬂuence the identiﬁcation of the VT1.
4.2. LT1 and VT1 vs. HRVT1
At HRVT1, Lactate, ˙VE, RER, CO2, HR, and RR were statistically different compared
to LT1 and VT1. However, no difference was found in speed, which could contribute to
practical application in training (Table 2). However, more differences were found between
HRVT1 and VT1 than HRVT1 and LT1 when observing the mean values on average (Table 2).
HRVT1 seems closer to LT1 than VT1, especially for the HR parameter, which differed
between HRVT1 and VT1, and it is among the three primary parameters for training
prescription (speed, ˙VO2, and HR). Still, we can infer that HRTV1 tends to overestimate the
values of LT1 and VT1 for some parameters.
Most studies that compared HRVT1 with LT1 or VT1 demonstrated good agreement
in ˙VO2, HR, lactate, and speed [8,10,25,33]. However, one study showed difﬁculty in
identifying VT1 using HRV analyses [16], which is close to our ﬁndings, reinforcing that T1
(HRVT1, VT1, LT1) is less precise to determine using different variables. On the other hand,
some researchers who found good agreement at T1 only presented ˙VO2 and did not present
the parameter of speed or HR (km·h−1) [8,10], not allowing a comparative analysis with
parameters such as speed (km·h−1). Our ﬁndings demonstrate a good mean difference
(Bland Altman) with moderate ICC in ˙VO2, velocity, and HR. However, we found low
precision for all these variables (TE > 10% and CV > 12%) in T1 (LT1, VT1, HRVT1), which
makes it difﬁcult to show good acceptance of the HRVT1 to estimate LT1 and VT1.
Ramos-Campos et al. (2017) did not identify reproducibility between HRVT1 and VT1
for speed [14]. These results differed from the present study because no difference was
found in speed in the present study. However, the precision was low. Besides that, both
studies showed difﬁculties in determining VT1 using HRV analysis. Different methods to
identify heart rate variability thresholds, such as the ﬁxed value of 1 ms between stages,
the visual method, and mathematical analysis, can make it difﬁcult to compare different
thresholds [8].
Despite that, the difﬁculty in determining VT1 is also essential because the occurrence
or not of thresholds can result in a sample loss [10] and may harm the prescription of
training intensity [36,37]. In addition, our study was careful to use an incremental tread-
mill test 48h before the Cardiopulmonary Stepwise Exercise Test to determine the initial
intensity and minimize the loss of the ﬁrst threshold. Different ergometers, treadmills, or
Int. J. Environ. Res. Public Health 2022, 19, 14676
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cycle ergometers can be another problematic factor because they demonstrated different
results. For example, in maximum progressive protocols on the cycle ergometer, many
studies have accurately shown a relationship between vagal withdrawal and thresholds 1
(T1), determined by HRV, concentrations of blood lactate, and gas exchange [10,11,25,33].
Nevertheless, motor differences in exercise speciﬁcity in the different protocols are essential
factors for determining thresholds and must be considered [17].
Consequently, protocols performed on a treadmill can present different behavior than
the cycle ergometer, preferably at T1 [17]. Furthermore, the literature demonstrated that T1
could occur in the moment of transition from walking to running between 6 km·h−1 and
8 km·h−1 [35], which does not happen in the cycle ergometer. These ﬁndings are similar
to our averages, in which they presented means of 7.15, 7.34, and 6.78 km·h−1 for LT1,
HRVT1, and VT1, respectively.
Furthermore, the walk-to-run transition can modify some physiological variables, such
as HR, ˙VE, and ˙VO2, causing interference in autonomic control, HRV responses, and gas
exchange, creating confusion in threshold identiﬁcation [35]. However, further research is
needed to compare different ergometers using HRVT to determine T1 and conﬁrm whether
these transitions inﬂuenced the threshold identiﬁcations. Therefore, the use of HRVT1 to
estimate LT1 and VT1 is not suggested under these conditions because, although they agree,
their occurrence happened with insufﬁcient precision. Another possibility to determine
VT1 using HRV would be through the short-term scaling exponent alpha1 (DFA a1) [38].
However, it is still a recent proposal, in which the authors themselves suggest that the
use of this index still needs to be better studied [38] since it appears to be inﬂuenced by
spontaneous breathing during exercise [39], whereas this is less inﬂuenced when using
RMSSD or the Poincaré plot [40].
4.3. LT2 and VT2 vs. HRVT2
HRVT2 demonstrated excellent agreement for all parameters compared to LT2 and
VT2. Our study used the time domain and the Poincaré plot only when necessary to
identify HRTV2 [26], which is simple to analyze and interpret since a third evaluator
was not required. Furthermore, HRV measurement was performed with lactate and gas
analysis, showing agreement and precision in using HRVT2 to estimate LT2 and VT2, which
is helpful for monitoring and prescribing the intensity zones in exercise. Thus, research that
evaluated VT2 and LT2 with HRVT2 demonstrated good agreement between the methods
even in different sports modalities [14,41,42]. Some authors used variables in the frequency
domain to identify T2 [42]. However, using these variables requires a more complex and
careful analysis because the frequency domain is easily inﬂuenced by breathing, which may
have less inﬂuence when applied to respiratory sinus arrhythmia (RSA) but is used only at
rest and needs to have a ﬁxed breathing rate [40]. However, changes in the breathing rate
do not markedly inﬂuence the RMSSD or Poincaré plot dimension [40], which does not
affect our results. Therefore, it is essential to use simple methods to identify HRVT2, such
as the time domain [9].
These tests to estimate LT1/LT2 and VT1/VT2 by HRVT1/HRVT2 provide new in-
sights into the relationship between the ﬁrst and second thresholds. Some studies empha-
size only the excellent agreement of the ﬁrst thresholds, leaving the second thresholds out
due to their wide range of identiﬁcations, and often do not show the exact timing [1,2].
However, this is not what our study demonstrated. Instead, we demonstrated excellent
agreement on T2 (LT2 and VT2) estimated by HRV.
4.4. Importance to Use Visual Methods to Identify HRVTs
The methodological choices were constrained primarily by visual analysis to deter-
mine thresholds. It is easier to analyze (agreement in 100% of identiﬁcations) and identiﬁes
thresholds in the daily routine of health professionals because the use of the visual method
allows identiﬁcation and does not need expensive software (just need the free Kubios
software or a Microsoft Ofﬁce or Libre Ofﬁce to analyze). Researchers strongly recommend
Int. J. Environ. Res. Public Health 2022, 19, 14676
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determining T1 for health participants but are not highly trained, employing visual, mathe-
matical, or a combination of both methods, as was performed in the present study [1,10].
Furthermore, some authors found that the visual method to determine HRV was vali-
dated in the literature and had better reliability than a mathematical method (Dmax) [27].
Therefore, all the methods used in this study are valid and recommended for all these
reasons.
4.5. Importance of T1 and T2 in Training
Thresholds 1 and 2 are signiﬁcant indices to help establish rigorous exercise inten-
sity domains to individualize training and rehabilitation. They can help predict athletic
performance, monitor training progress, and clinically assess the patient’s physiological
function or dysfunction [1]. Exercise intensity is an important training element for effective
cardiovascular and metabolic adaptations [43], mainly in T2. However, some researchers
have also raised the importance of training volume even at low intensity (below or at
T1), suggesting that duration is crucial to inducing training effects [44]. In this sense,
researchers have demonstrated a polarized model of intensity distribution for training, in
which participants who spend approximately 75% of the training below T1, 5–10% between
T1 and T2, and 15–20% above T2 may have less autonomic stress, better motivation, and
performance [36,37]. Consequently, the present study demonstrates the importance of
determining the intensity in T1 and T2 in favor of better agreement and precision (accuracy)
in practical and prescribing training. Future studies are needed to ﬁnd better indices or
methods to estimate T1. T1 is an important index when it comes to speciﬁc populations,
for example, in patients with cardiovascular disease (ischemia or heart failure), who re-
quire adequate workload control of low-intensity exercises (below T1) [45]. In addition,
the position statement of the European Association for Cardiovascular Prevention and
Rehabilitation suggests the use of threshold-based exercise intensity prescriptions in heart
disease patients [46,47].
4.6. Practical Signiﬁcance
These ﬁndings may be helpful to coaches, conditioning instructors, and laboratories’
daily routines to monitor and prescribe exercise intensity. The HRV presented a simple
analysis and interpretation method, visually using the time domain and Poincaré plot.
Furthermore, HRV measurement using the heart rate sensor via a smartphone reduces the
cost and facilitates its analysis, allowing less complexity to assess and help identify different
intensity domains, which is essential for monitoring exercise intensity and prescriptions.
4.7. Limitations
Although the present study provides meaningful information, some limitations should
be acknowledged. This study is cross-sectional, so future research should be considered
to assess the training effect on HRVTs. In addition, studies are needed to measure HRV
during exercise in different individuals with a wider range of BMI, ages, and ﬁtness levels.
The present study did not strictly control food intake or ﬂuid intake in the pre-test, but
all participants were instructed to eat 2 h before the test, and the same hydration was
offered on the day of the test. Besides, all parameters were measured simultaneously and
with the same conditions, impacting all thresholds equally. Furthermore, to obtain stable
RR intervals, the 3-min stage protocol was used in this study; however, some researchers
recommend the 1-min stage for better VT detection [10].
5. Conclusions
HRVT1 presents low precision using this protocol to estimate LT1 and VT1. However,
HRVT2 is a valid and noninvasive method that can estimate LT2 and VT2, showing good
agreement and precision in healthy adults. Therefore, studying HRVTs using this simple
and visual method on a treadmill must be encouraged to show consistency and increase
ecological validity.
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Author Contributions: Conceptualization, L.N.S.N. and L.C.; methodology, L.N.S.N. and I.Z.A.;
software, L.N.S.N.; validation, L.N.S.N., V.H.G.N., and L.C.; formal analysis, L.N.S.N., V.H.G.N., and
R.A.B.; investigation, L.N.S.N.; resources, L.N.S.N.; data curation, L.N.S.N.; writing—original draft
preparation, L.N.S.N.; writing—review and editing, L.N.S.N., V.H.G.N., I.Z.A., R.D.L., R.A.B., and
L.C.; visualization, L.N.S.N.; supervision, L.C.; project administration, L.N.S.N. and L.C. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of Federal
University of Espírito Santo (CAAE: 76607717.5.0000.5542–18/09/2017).
Informed Consent Statement: Informed consent was obtained from all subjects involved in
the study.
Data Availability Statement: Not applicable.
Acknowledgments: We thank David C. Poole for suggesting important revisions to the publication
of this article, and all the people who helped to obtain this ﬁnal version. We would like to thank
the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES), Finance Code
001–for their ﬁnancial support.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Is There Agreement and Precision between Heart Rate Variability, Ventilatory, and Lactate Thresholds in Healthy Adults?
Neves, Letícia Nascimento Santos,Gasparini Neto, Victor Hugo,Araujo, Igor Ziviani,Barbieri, Ricardo Augusto,Leite, Richard Diego,Carletti, Luciana