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The aims of this study were (1) to establish the best fit between ventilatory and lactate exercise performance parameters in running and (2) to explore novel alternatives to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two trained male athletes ( V ˙ O2max 60.2 ± 4.3 ml·kg·min-1) completed three maximal graded exercise tests (GXT): (1) a preliminary GXT to determine individuals' MAS; (2) two experimental GXT individually adjusted by MAS to record the speed associated to the main aerobic-anaerobic transition events measured by indirect calorimetry and capillary blood lactate (CBL). Athletes also performed several 30 min constant running tests to determine the maximal lactate steady state (MLSS). Reliability analysis revealed low CV (<3.1%), low bias (<0.5 km·h-1), and high correlation (ICC > 0.91) for all determinations except V-Slope (ICC = 0.84). Validity analysis showed that LT, LT+1.0, and LT+3.0 mMol·L-1 were solid predictors of VT1 (-0.3 km·h-1; bias = 1.2; ICC = 0.90; p = 0.57), MLSS (-0.2 km·h-1; bias = 1.2; ICC = 0.84; p = 0.74), and VT2 (<0.1 km·h-1; bias = 1.3; ICC = 0.82; p = 0.9l9), respectively. MLSS was identified as a different physiological event and a midpoint between VT1 (bias = -2.0 km·h-1) and VT2 (bias = 2.3 km·h-1). MAS was accurately estimated (SEM ± 0.3 km·h-1) from peak velocity (Vpeak) attained during GXT with the equation: MASEST (km·h-1) = Vpeak (km·h-1) * 0.8348 + 2.308. Current individualized GXT protocol based on individuals' MAS was solid to determine both maximal and submaximal physiological parameters. Lactate threshold tests can be a valid and reliable alternative to VT and MLSS to identify the workloads at the transition from aerobic to anaerobic metabolism in well-trained runners. In contrast with traditional assumption, the MLSS constituted a midpoint physiological event between VT1 and VT2 in runners. The Vpeak stands out as a powerful predictor of MAS.
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ORIGINAL RESEARCH
published: 25 September 2018
doi: 10.3389/fphys.2018.01320
Frontiers in Physiology | www.frontiersin.org 1September 2018 | Volume 9 | Article 1320
Edited by:
Martin Burtscher,
Universität Innsbruck, Austria
Reviewed by:
Hannes Gatterer,
EURAC Research, Italy
Ricardo Mora-Rodriguez,
Universidad de Castilla-La Mancha,
Spain
*Correspondence:
Jesús G. Pallarés
jgpallares@um.es
Specialty section:
This article was submitted to
Exercise Physiology,
a section of the journal
Frontiers in Physiology
Received: 21 March 2018
Accepted: 31 August 2018
Published: 25 September 2018
Citation:
Cerezuela-Espejo V, Courel-Ibáñez J,
Morán-Navarro R, Martínez-Cava A
and Pallarés JG (2018) The
Relationship Between Lactate and
Ventilatory Thresholds in Runners:
Validity and Reliability of Exercise Test
Performance Parameters.
Front. Physiol. 9:1320.
doi: 10.3389/fphys.2018.01320
The Relationship Between Lactate
and Ventilatory Thresholds in
Runners: Validity and Reliability of
Exercise Test Performance
Parameters
Víctor Cerezuela-Espejo, Javier Courel-Ibáñez, Ricardo Morán-Navarro,
Alejandro Martínez-Cava and Jesús G. Pallarés*
Human Performance and Sports Science Laboratory, Faculty of Sport Sciences, University of Murcia, Murcia, Spain
The aims of this study were (1) to establish the best fit between ventilatory and
lactate exercise performance parameters in running and (2) to explore novel alternatives
to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two
trained male athletes ( ˙
VO2max 60.2 ±4.3 ml·kg·min1) completed three maximal
graded exercise tests (GXT): (1) a preliminary GXT to determine individuals’ MAS; (2)
two experimental GXT individually adjusted by MAS to record the speed associated
to the main aerobic–anaerobic transition events measured by indirect calorimetry
and capillary blood lactate (CBL). Athletes also performed several 30 min constant
running tests to determine the maximal lactate steady state (MLSS). Reliability analysis
revealed low CV (<3.1%), low bias (<0.5 km·h1), and high correlation (ICC >0.91)
for all determinations except V-Slope (ICC =0.84). Validity analysis showed that LT,
LT+1.0, and LT+3.0 mMol·L1were solid predictors of VT1(0.3 km·h1; bias =1.2;
ICC =0.90; p=0.57), MLSS (0.2 km·h1; bias =1.2; ICC =0.84; p=0.74), and VT2
(<0.1 km·h1; bias =1.3; ICC =0.82; p=0.9l9), respectively. MLSS was identified as
a different physiological event and a midpoint between VT1(bias = −2.0 km·h1) and
VT2(bias =2.3 km·h1). MAS was accurately estimated (SEM ±0.3 km·h1) from peak
velocity (Vpeak) attained during GXT with the equation: MASEST (km·h1)=Vpeak (km·h1)
0.8348 +2.308. Current individualized GXT protocol based on individuals’ MAS was
solid to determine both maximal and submaximal physiological parameters. Lactate
threshold tests can be a valid and reliable alternative to VT and MLSS to identify the
workloads at the transition from aerobic to anaerobic metabolism in well-trained runners.
In contrast with traditional assumption, the MLSS constituted a midpoint physiological
event between VT1and VT2in runners. The Vpeak stands out as a powerful predictor of
MAS.
Keywords: blood lactate, ventilation threshold, maximal aerobic speed, VO2max, endurance, maximal lactate
steady state
Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
INTRODUCTION
Numerous studies have embraced the question of how training
programs based on individualized physiological parameters may
increase cardiorespiratory performance in endurance sports
like running or cycling. Evidence suggests establishing exercise
workloads or intensities based on the individual physiological
events (i.e., setting training zones) allows athletes to minimize
injury and fatigue risks, but above all to enhance individual
adaptations and respond to the training plan (Scharhag-
Rosenberger et al., 2012; Mann et al., 2014; Wolpern et al.,
2015). A recent review (Stöggl and Sperlich, 2015) addressed the
fact that similar training intensity distribution shows different
efficacy and adaptations depending on the competitive stage, the
endurance discipline, and the athlete’s performance levels. Thus,
the more individualized and accurate the thresholds and training
zones, the more precise the exercise prescription and the greater
the athletes’ adaptation and performance enhancement (García-
Pallarés et al., 2009; Wolpern et al., 2015). From a competition
point of view, exercise test performance parameters are useful
to track cardiopulmonary and specific adaptations to the entire
season training plan, and to explain performance (Lucía et al.,
2000; Esteve-Lanao et al., 2007; García-Pallarés et al., 2009, 2010).
Physiological variables such as maximal oxygen uptake
(˙
VO2max), submaximal metabolic inflection points like the
pulmonary ventilation thresholds (VT) and lactate thresholds
(LT), the maximal aerobic speed (MAS: the speed associated with
˙
VO2max), or the peak velocity (Vpeak : the highest speed attained
at the end of the test) are regular variables used by coaches
and scientists to estimate and monitor running performance
during training and competition events (Farrell et al., 1979; di
Prampero et al., 1986; Stratton et al., 2009; McLaughlin et al.,
2010). For the evaluation of these parameters in runners, it is
common to use graded exercise tests (GXTs) on the treadmill,
consisting of a series of stages lasting 1–5 min. Differences in
the duration of each stage and the load increments can alter
the cardiorespiratory and metabolic response, and therefore the
measurement (Bentley et al., 2007; Julio et al., 2017). As suggested
by pioneering studies (Buchfuhrer et al., 1983; Lukaski et al.,
1989), recent investigations (Midgley et al., 2007) and reviews
(Julio et al., 2017), traditional longer GXTs (i.e., 20–30 min) to
determine LT including increments each 3–5 min would prevent
the athlete from achieving their MAS due to accumulative fatigue,
dehydration, muscle acidosis, and cardiovascular drift. This is
critical because MAS is a pertinent and widespread criterion
to set training intensities for endurance disciplines (Billat and
Koralsztein, 1996; Jones and Carter, 2000). An interesting
approach carried out with cyclists revealed that shorter protocols
(12–14 min) including 1-min stages are valid both, to estimate
submaximal metabolic inflection points (VT and LT), and to
identify true values for ˙
VO2max and MAS in cyclists (Lucía et al.,
1999, 2000; Gaskill et al., 2001; Midgley et al., 2007; Pallarés et al.,
2016).However, the validity and reliability of GXT with 1-min
stages protocol in runners needs to be fully verified.
Physiological response to exercise in endurance sports is
commonly assessed though measurements based on ventilatory
and lactate methods. However, the relationship between the two
methods is not yet clear (Pallarés et al., 2016). Recent findings
support the idea that a training model based on ventilatory
thresholds (VT1and VT2) could be very effective to set individual
exercise intensity in endurance sports given that it takes into
account individual metabolic responses (Wolpern et al., 2015).
One of the most accurate systems to obtain these ventilatory
responses is on the basis of gas exchange parameters using
indirect calorimetry (Lucía et al., 2000; Gaskill et al., 2001;
Pallarés et al., 2016). In VT1, the ˙
VO2and carbon dioxide
production ( ˙
VCO2) increase proportionally, while HCO3acts
to buffer lactic acid concentration in blood (Wasserman et al.,
1973; Del Coso et al., 2009); this intensity is ideal for high-
volume low-intensity exercise (Stöggl and Sperlich, 2014). In
turn, in VT2, the blood lactate accumulation boosts and rises
considerably and the system collapses due to the homeostatic
compromise and metabolic acidosis (Wasserman et al., 1973;
Jones et al., 2007); this intensity sets a critical limit for high-
intensity interval training (Stöggl and Sperlich, 2014). However,
gas exchange systems require the use of expensive equipment and
laboratory conditions which most teams, coaches, and athletes
are not equipped with or cannot afford.
A further method to set individual exercise intensity is
based on capillary blood lactate (CBL) measurements (Beneke
et al., 2011). A number of authors have defined a list of CBL
parameters associated with specific exercise intensities such as
LT (Wasserman et al., 1973), maximal lactate steady state (MLSS,
Beneke and von Duvillard, 1996), OBLA (onset of blood lactate
accumulation, Sjödin and Jacobs, 1981), or the DMAX (Cheng
et al., 1992).An accurate detection of MLSS is particularly
important due to it being considered the highest intensity in
which glycogen stores are the main exercise limiting factor
(Coyle et al., 1986) and constitutes a prominent part of aerobic
training in world-class athletes (García-Pallarés et al., 2009,
2010). Although CBL methods are commonly used for coaches
to set individual training workloads, the relationship between
lactate-based parameters and VTs load intensities is still an
open debate. In cyclists, it seems clear that workloads at the
VT1are very related to LT (Lucía et al., 1999; Amann et al.,
2006; Pallarés et al., 2016). However, the estimation of VT2
from lactate methods generates some controversy. Traditionally,
VT2intensities have been associated with MLSS (Svedahl and
MacIntosh, 2003). In contrast with this assumption, recent
evidence in cyclists demonstrates that MLSS encompasses a
different metabolic pathway and limiting factor than VT, and
constitutes a midpoint between VT1and VT2(Pallarés et al.,
2016; Peinado et al., 2016). The determination of VT2is essential
due to represents a turn point at which metabolic acidosis cannot
be buffered by ventilation (Lucía et al., 2000) and sets a critical
limit for high-intensity training (Stöggl and Sperlich, 2014). One
previous study conducted with cyclists has attempted to clarify
the relationship between VTs and CBL methods, and reported
a high reliability and validity of the following relationships: (1)
VT1and LT, (2) VT2and LT+2 mMol·L1, and (3) MLSS
and LT+0.5 mMol·L1(Pallarés et al., 2016). To the best of
our knowledge, there are no previous studies examining these
relationships in runners. This is an important gap considering
the existing differences between cycling and running, such as the
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Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
more impaired ventilation in cycling and the higher muscle mass
involved, greater muscle pump efficiency, and the implication
of eccentric muscle actions in running (Bijker et al., 2002;
Millet et al., 2009). Given that these differences may alter the
physiological response to exercise, prescribing training plans for
runners based on cyclists’ reference values could be imprecise.
Thus, the relationships between CBL and VTs intensities in
runners need to be fully clarified.
In addition to the aerobic–anaerobic transition, another
ventilation parameter to predict running performance is the MAS
(McLaughlin et al., 2010), considered as the minimum speed at
which ˙
VO2max is reached (Lacour et al., 1991). As a rule of thumb,
high intensity training in endurance athletes is established at
90–105% of the MAS (Stöggl and Sperlich, 2015). Given its
importance for training plans and workload distribution, coaches
and researchers have invested effort in designing maximal field
tests to estimate the MAS and predict ˙
VO2max in endurance
athletes to establish the aerobic performance limits (Léger and
Boucher, 1980; Berthon et al., 1997). However, these tests have
important limitations: (1) the equations proposed to estimate the
MAS from field tests are not based on accurate measurements
such as gas exchange systems using indirect calorimetry (Lucía
et al., 2000; Gaskill et al., 2001), and (2) maximal efforts criteria
were not tested to ensure reaching values of ˙
VO2max (ACSM,
2013). As stated above, a valid alternative to these field tests is to
determine the MAS through GXT with 1-min increments using
gas exchange systems. These short protocols allow the athletes to
reach their maximal cardiac output, and therefore make possible
obtaining a true Vpeak-value (Pallarés et al., 2016; Julio et al.,
2017). Given that both MAS and Vpeak correspond to very similar
intensities (Lacour et al., 1991) the calculation of an estimated
MAS (MASEST) from the Vpeak, when gas exchange systems are
not available, seems promising. However, this hypothesis is still
to be proven.
Therefore, the aims of this study were (1) to establish the
best fit between ventilatory and lactate exercise performance
parameters in running and (2) to explore novel alternatives to
estimate essential running performance indicators such as the
MAS from similar intensity parameters like the Vpeak when gas
exchange systems are not available.
METHODS
Participants
Twenty-two trained male athletes (runners and triathletes)
volunteered to participate in this study (age 25.9 ±8.0 years,
body mass 68.2 ±6.1 kg, height 174.8 ±5.8 cm, body fat 11.4
±1.9%, ˙
VO2max 60.2 ±4.3 ml·kg·min1, endurance training
experience 7.1 ±4.0 years). All participants were competing at
regional and national level races and following a regular training
load of 4–6 days per week, 1–2 h per day. Measurements were
obtained during the pre-competitive season. All participants
underwent a complete medical examination (including ECG)
that showed all were in good health. No physical limitations or
musculoskeletal injuries that could affect testing procedures were
reported. None of the subjects were taking drugs, medications, or
dietary supplements known to influence physical performance.
The Bioethics Commission of the University of Murcia approved
the study, which was carried out according to the declaration of
Helsinki. Subjects were verbally informed about the experimental
procedures and possible risk and benefits. Written informed
consent was obtained from all subjects.
Experimental Design
Participants visited the lab 5–7 times separated by 2–7 days.
All participants had at least 6 months of familiarization with
the testing procedures used in this investigation. On the first
day, participants completed a preliminary GXT with 1-min
increments (GXTPRE) to determine individuals’ MAS and Vpeak,
including 48–72 h rest before the next session. In the following
two sessions, separated by 48 h, athletes performed two identical
experimental GXT (GXTEXP 1 and GXTEXP 2). For these
two GXTEXP protocols, initial running speed and workload
increments were individually set according to participants’ Vpeak
previously determined in the GXTPRE.
The GXTEXP started with a 5-min warm-up at 13 km·h1less
than each athlete’s Vpeak followed, without a break, by a GXT
1-min (i.e., increments of 1 km·h1·min1) until exhaustion.
Lastly, athletes came back to the lab two to three more times
to perform a 30 min submaximal constant running test to
determine the speed associated with the MLSS (Beneke, 2003). To
maintain physical performance during the investigation period
(2–3 weeks) participants followed an individual training protocol
consisting in: running sessions (runners) or swimming, cycling,
and running sessions (triathletes) of 90 min at individual VT1
intensity interspersed with efforts of 5–7 min at 90–95% of VT2
intensity each 20 min. Training sessions were repeated each 48 h
with 24 h rest before each evaluation to ensure a full recovery.
Individualized Maximal Treadmill GXT
Protocol
All the running trials were performed on the same treadmill
(HP Cosmos Pulsar, HP Cosmos Sports and Medical GMBH,
Nussdorf Traunstein, Germany) with an incline of 1.0%
(Jones and Doust, 1996). Evaluations were performed under
similar environmental conditions (21–24C and 45–55% relative
humidity) at the same time of day (16:00 to 19:00 h) to minimize
the circadian rhythm effects (Mora-Rodríguez et al., 2015). Air
ventilation was controlled with a fan positioned 1.5 m from the
subject’s chest at a wind velocity of 2.55 m·s1.
The GXTPRE under medical supervision to fulfill three
objectives: (1) discard cardiovascular diseases, (2) to minimize
the bias of progressive learning on test reliability, and (3)
to determine the athletes’ MAS and Vpeak subsequently used
to set up the individualized GXTEXP workload (i.e., treadmill
speed). Participants’ HR was monitored by standard 12 lead
ECG (Quark T12, Cosmed, Italy), ventilatory performance ( ˙
VO2,
˙
VO2max, and VE) was recorded on a breath-by-breath basis
using a metabolic cart averaging data every 5 s (MetaLyzer 3B-
R3, Cortex Biophysik GmbH, Leipzig, Germany) and the rate
of perceived exertion (RPE) was assessed using the 6–20 Borg
Scale (Borg, 1998) every 2 min. The MAS was determined from
metabolic cart measurements as the first running velocity where
˙
VO2max was reached (Billat and Koralsztein, 1996). The Vpeak
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Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
was automatically obtained from the treadmill software using the
Kuipers et al.’s formula (Kuipers et al., 2003): Vpeak =Vcomplete +
(Inc t/T), in which Vcomplete is the running velocity of the last
complete stage, Inc is the speed increment (i.e., 1 km·h1), tis the
number of seconds sustained during the incomplete stage and T
is the number of seconds required to complete a stage (i.e., 60 s).
The two GTXEXP were individually set up according to the
MAS previously determined in the preliminary test (GXTPRE),
as follows: starting with a 5-min warm-up at 13 km·h1
less than each athlete’s Vpeak, followed, without a break, by
a GXT 1-min (i.e., increments of 1 km·h1·min1) until
exhaustion. Ventilatory parameters and RPE were assessed as
aforementioned in the GXTPRE. The HR was continuously
monitored (V800, Polar, Finland). Capillary blood lactate
samples from the finger were collected (Lactate Pro, Arkray,
Japan) every 2 min (i.e., each 2 km·h1increments). The design
of this particular protocol and its duration (min–max) were
deliberate, given that: (1) It allows a clear detection of ventilatory
thresholds (VT1and VT2) by indirect calorimetry (Lucía et al.,
1999, 2000; Pallarés et al., 2016); (2) It is effective in determining
a true ˙
VO2max (Midgley et al., 2007); (3) The protocol duration
was short enough (12–14 min) to avoid the local acidosis and
HR rise (cardiac drift) to obtain a true maximum cardiovascular
performance (Dawson et al., 2005); (4) The short duration allows
athletes to achieve a true MAS and Vpeak (Julio et al., 2017); and
(5) By the end of the test, seven to nine capillary blood samples
can be collected from each participant before exhaustion, which
enable the plotting of a complete lactate curve. In particular,
fingerprint blood samples were collected by a specialist placed
beside the treadmill without any pause during the participants’
running test (i.e., in movement) to make the process less invasive
and ensure a constant effort during the GXT protocols.
Maximal effort criteria (ACSM, 2013) were considered to
verify the outcomes, from which participants must reach at
least three from the list: (i) failure of HR to increase with
further increases in exercise intensity; (ii) a plateau in ˙
VO2
(or failure to increase ˙
VO2by 150 mL·min1) with increased
workload; (iii) a respiratory exchange ratio (RER) 1.10; CBL
>8 mmol·L1; (iv) a rating of perceived exertion (RPE) >17
on the 6–20 scale. If verified, physiological parameters were
determined, and the individuals’ treadmill speed at each of the
physiological parameters studied were considered for subsequent
analysis. Blood lactate analyzer and indirect calorimetry devices
were calibrated before each test according to the manufacturer’s
instructions.
Determination of MLSS
Several 30 min constant workloads on a treadmill were
performed to identify the highest workload (km·h1) at which
CBL increased >1 mMol·L1between the 10th and 30th min of
exercise (Beneke, 2003). After 7 days from the second GXTEXP,
all participants performed the first MLSS trial at the individual
workload associated to their 85% of VT2, based on previous
studies (Llodio et al., 2016; Pallarés et al., 2016). Depending on
the results of the first MLSS-test, successive trials with a 48-h rest
between sessions were increased or decreased 0.5 km·h1until
MLSS criteria was fulfilled (Pallarés et al., 2016).
Determination of Ventilation Parameters
VT1was determined using the criteria of an increase in
both ventilatory equivalent of oxygen ( ˙
VE/˙
VO2) and end-tidal
pressure of oxygen (PETO2) with no concomitant increase in
ventilatory equivalent of carbon dioxide ( ˙
VE/˙
VCO2). VT2was
determined using the criteria of an increase in both the ˙
VE/˙
VO2
and ˙
VE/˙
VCO2and a decrease in PETCO2(Lucía et al., 2000;
Figures 1A,B). V-Slope load was identified in that intensity of
exercise which, in a plot of the minute production of CO2over
the minute utilization of oxygen ( ˙
VO2), shows an increase in the
slope above 1.0 (Wasserman et al., 1973; Gaskill et al., 2001).
The ˙
VO2max was defined as the highest plateau (two successive
maximal within 150 mL·min1, averaging the data every 5 s)
reached. MAS was defined as the minimum speed at which
maximum oxygen uptake ˙
VO2max is reached (Lacour et al., 1991).
Vpeak was taken from the highest velocity reached during this
GXT protocol and calculated according to the Kuipers et al.
(2003).
Determination of Lactate Parameters
LT was determined by examining the CBL speed relationship
([Lact]blood/ km·h1) during the GXT as the highest speed
not associated with a rise in CBL above baseline (Weltman
et al., 1990). Baseline CBL was the average during the initial
stages with values 0.8 mMol·L1above rest state. This always
occurred just before the curvilinear increase in blood lactate
observed at subsequent exercise intensities (Coyle et al., 1983;
Lucía et al., 2000). Lactate Threshold +1.0 mMol·L1(LT+1.0)
represents the speed which causes an increase of 1 mMol·L1
above baseline measurements (Coyle et al., 1983). Following this
criterion, five LT-based events were established as previously
described (Pallarés et al., 2016): LT+0.5, LT+1, LT+1.5, LT+2.0,
LT+2.5, and LT+3.0 mMol·L1. DMAX method was determined
by plotting the lactate response to exercise intensity in a third-
order polynomial regression curve. DMAX was defined as the
point on the regression curve that yields the maximal distance
to the straight line formed by the two end points of the
curve (Cheng et al., 1992). Onset of blood lactate accumulation
(OBLA4mM) was defined as the exercise intensity identified
by interpolation across the 4 mMol·L1point in the plot of
[Lact]blood during incremental exercise (Sjödin and Jacobs, 1981).
Two independent observers detected all ventilatory and LT
following the criteria previously described. If they did not agree,
the opinion of a third investigator was sought (Lucía et al., 1999;
Figure 1C).
Statistical Analyses
Standard statistical methods were used for the calculation
of means, standard deviations (SD), and 95% confidence
interval. The reliability of ventilation and lactate parameters
was analyzed comparing the consistence among trials (i.e.,
GXTEXP 1 vs. GXTEXP 2) by calculating the coefficient of
variation (CV), intraclass correlation coefficient (ICC), and
Bland–Altman plots. Linear regression analysis was employed
to estimate a theoretical MAS (MASEST) from the average
of the Vpeak achieved at the end of the two GXTEXP
trials. Validity analysis of the ventilatory thresholds (VT1
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Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
FIGURE 1 | Example of determination of ventilatory thresholds (VT1, A; VT2,
B), and lactate threshold (LT, C) in one test. Each gas-exchange data point
corresponds to a 5-s interval. ˙
VE/ ˙
VO2, ventilatory equivalent for oxygen;
˙
VE/ ˙
VCO2, ventilatory equivalent for carbon dioxide; PETCO2, end-tidal
pressure of oxygen; end-tidal pressure of carbon dioxide (PETCO2).
and VT2), MLSS, and MAS against the other parameters
was conducted over the means obtained in the trials by
ANOVA, ICC, and Bland-Altman bias. Analyses were performed
using GraphPad Prism 6.0 (GraphPad Software, Inc., CA,
USA) and SPSS software version 19.0 (IBM Corp., Armonk,
NY, USA).
FIGURE 2 | Linear regression estimating a theoretical maximal aerobic speed
(MASEST) from the average of the fastest velocity achieved at the end of the
GXTEXP trials (Vpeak).
RESULTS
All participants reached at least two of the criteria for
achievement of maximal efforts during all the GXT-tests,
therefore maximal ventilation and cardiovascular performance
was verified. The initial speed ranged from 6 to 10 km·h1and no
fatigue was detected following the warm-up (i.e., all participants
maintained a RER <0.85 and CBL under the baseline). The Vpeak
reached during the GXTPRE ranged from 18 to 22 km·h1. Linear
regression analysis (Figure 2) revealed a very strong association
between MAS and Vpeak (p<0.01; r =0.954; SEM =0.3 km·h1)
and yielded the equation:
MASEST (km ·h1)=Vpeak (km ·h1)0.8348 +2.308
Intra-subject reliability between GXTEXP trials (Table 1) revealed
low CV (<3.1%), low bias (<0.5 km·h1), and high correlation
(ICC >0.91) for all determinations except V-Slope (ICC =0.84).
Table 2 shows the validity analysis comparing VT1, MLSS,
VT2, and MAS workloads against the rest of the parameters. The
strongest associations (ICC >0.82, p>0.57) were: VT1with LT
(0.3 ±1.2 km·h1), MLSS with LT+1.0 (0.2 ±1.2 km·h1),
VT2with LT+3.0 (<0.1 ±1.3 km·h1), and MAS with MASEST
(<0.1 ±0.4 km·h1).
Table 3 shows the 95% confidence interval for main
physiological parameters under study.
DISCUSSION
The main findings of the current study were that (1) LT obtained
during a 12–14 min, 1 km·h1per minute GXT is a valid
method to determine the main physiological parameters of the
aerobic–anaerobic transition, (2) LT, LT+1 and LT+3.0 are
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Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
TABLE 1 | Reliability of lactate and ventilatory tests.
CV ICC Bland altman
%rBias (SD) LoA 95%
VT12.08 0.98 0.22 (0.47) 0.71; 1.15
VT21.92 0.95 0.13 (0.63) 1.11; 1.37
MAS 2.20 0.91 0.36 (0.64) 0.9; 1.62
LT 1.99 0.98 0.09 (0.43) 0.76; 0.94
LT+0.5 1.23 0.96 0.07 (0.87) 1.64; 1.78
LT+1.0 3.49 0.96 0.07 (0.79) 1.48; 1.62
LT+1.5 3.08 0.96 0.10 (0.76) 1.39; 1.59
LT+2.0 2.99 0.97 0.07 (0.69) 1.29; 1.43
LT+2.5 2.53 0.96 0.11 (0.73) 1.33; 1.55
LT+3.0 2.46 0.96 0.10 (0.77) 1.41; 1.61
V-Slope 2.58 0.84 0.31 (1.11) 1.87; 2.49
DMAX 2.12 0.94 0.27 (1.09) 1.87; 2.41
OBLA4mM 3.08 0.96 0.48 (0.97) 1.43; 2.39
Vpeak 2.79 0.94 0.12 (0.42) 0.71; 0.95
CV,Coefficient of variation; ICC, Intraclass coefficient; and Bland-Altman results. Vpeak , the
fastest velocity achieved at the end of the graded exercise testing protocol; MAS, Maximal
aerobic speed; VT1, First ventilatory threshold; MLSS, Maximal lactate steady state; VT2
Second, ventilatory threshold; LT, Lactate threshold; LT+0.5,+1.0,+1.5,+2.0,+2.5,+3.0,
Concentrations above lactate threshold; DMAX , Maximum distance between the slope
of a polynomial and the line connecting both ends; OBLA4mMol, Onset blood lactate
accumulation 4 mM; LoA, 95% limit of agreement.
solid predictors of VT1, MLSS, and VT2, respectively, (3) the
MLSS was identified as a midpoint between VT1and VT2,
and (4) an estimated maximal aerobic speed (MASEST) can be
accurately obtained (error ±0.3 km·h1) from the fastest speed
achieved during the current GXT (Vpeak). This study adds to
the existing literature by providing a valid alternative test based
on blood lactate to obtain performance workloads without the
need of using indirect calorimetry (less affordable technology).
In addition, we contribute with an accurate method to estimate
the MAS, which is one of the most used indicators to set
training intensities in running. To our knowledge, this is the first
report examining the validity and reliability of such an extensive
battery of tests and parameters to determine critical workloads in
runners.
The high reliability values found in physiological
measurements between the two GXTEXP treadmill trials concurs
with those previously reported in cycle ergometer (Pallarés
et al., 2016). In addition, our results allow us to discourage
using V-Slope when other parameters are available. Although
the causes that might explain these effects are very difficult
to isolate and quantify, it is arguable that an individualized
workload adjustment approach accounted for these increments
(García-Pallarés et al., 2009; Wolpern et al., 2015). In the current
GXT with 1-min increments, athletes started at 13 km·h1below
their Vpeak, previously determined during the GXTPRE session.
By doing this, it is guaranteed that the athlete is running at
the optimal intensity to end up at their maximum workload
after 12–14 min avoiding cardiac drift, local acidosis, and
allowing a clear detection of ventilatory and LT, additionally
getting maximal values of ˙
VO2max Considering this information,
individual GXT protocols based on athletes’ maximal speed
should be developed to enhance the consistency of data during
physiological evaluations.
A number of studies have investigated the relationship
between ventilatory threshold and blood lactate concentration
in endurance athletes. Authors agreed that workloads at the
first ventilatory threshold (i.e., VT1) are strongly related to the
workload at which lactate starts to increase above resting values
(LT; Wasserman et al., 1973; Lucía et al., 2000; Pallarés et al.,
2016). Our findings corroborate this association between VT1
and LT but showing a greater external workload in runners
(VT1=59–65% of MAS) compared to cyclists [VT151.5%
of maximal aerobic power (MAP)]. These findings suggest that
running describes a great relative external workload associated
with the VT1response. Therefore, smaller errors in detecting
ventilatory thresholds may have a greater negative impact
on the running performance compared to other disciplines
like cycling, for instance, misguided training prescription,
undesirable physical adaptations, and a greater probability of the
appearance of the interference phenomenon during concurrent
training (García-Pallarés and Izquierdo, 2011). In turn, there
is no clear agreement about which LT better reflects VT2
intensities. A previous experiment in cyclists (Pallarés et al.,
2016) determined a high correlation between VT2and LT+2
mMol·L1, followed by the OBLA4mM, which established high
intensities at 80% of their maximal aerobic power (MAP).
Interestingly enough, we identified a greater CBL in runners
during the transition phase, locating the VT2at LT+3 mMol·L1
intensities, setting the high intensity limit at 84–87% of the
MAS. The existing physiological differences between running and
cyclists may explain these disparities. Millet et al. (2009) reviewed
the literature and identified a list of potential distinguishing
factors between running and cycling physiological demands.
The authors pointed out differences on ventilatory responses to
exercise in terms of exercise-induced arterial hypoxaemia, O2
diffusion capacity, ventilatory fatigue, and pulmonary mechanics.
Moreover, other factors like running/cycling economy (higher
delta efficiency in running), muscle recruitment patterns (greater
muscle mass involved and eccentric phase activity in running),
and ventilation impairment (higher in cycling) may account for
these differences.
The MLSS constitutes another essential physiological event in
endurance performance, as it is the maximal workload that can
be maintained without elevations in blood lactate concentration
(MLSS). Previous authors have proposed that MLSS workload
coincides with the one for VT2(Smekal et al., 2012). In contrast
to this assumption, recent investigations in cyclists elucidated
that MLSS may correspond to a lower exercise intensity of VT2
and matches better with the midpoint between both ventilatory
thresholds (Pallarés et al., 2016; Peinado et al., 2016). In support
of this theory, our findings revealed that MLSS intensity (72–
74% of MAS) constitutes a transition between VT1(59–65% of
MAS) and VT2(84–87% of MAS). Moreover, MLSS was highly
associated with LT+1 mMol·L1. In cyclists, MLSS has been
associated with LT+0.5 mMol·L1(Pallarés et al., 2016). These
increments on CBL at the same relative intensity might indicate
Frontiers in Physiology | www.frontiersin.org 6September 2018 | Volume 9 | Article 1320
Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
TABLE 2 | Validity results of used methods.
Speed
(km·h1)
Blood lactate
(mMol·L1)
VT1MLSS VT2MAS
Speed: 11.5 ±1.8 km·h1Speed: 13.5 ±1.1 km·h1Speed: 15.8 ±1.4 km·h1Speed: 18.1 ±1.3 km·h1
M±SD M ±SD Student tICC Bland altman Student tICC Bland altman Student tICC Bland altman Student tICC Bland altman
p Bias (SD) LoA 95% p Bias (SD) LoA 95% p Bias (SD) LoA 95% p Bias (SD) LoA 95%
VT111.5 ±1.8 2.2 ±0.7
MLSS 13.5 ±1.1 3.3 ±1.1 <0.01 0.91 2.0 (0.9) 3.8; 0.3
VT215.8 ±1.4 5.7 ±1.9 <0.01 0.92 4.3 (0.9) 6.1; 2.6 <0.01 0.94 2.3 (0.7) 3.6; 1.0
MAS 18.1 ±1.3 10.6 ±3.1 <0.01 0.86 7.6 (2.0) 11.6; 3.7 <0.01 0.95 5.0 (0.6) 6.2; 3.9 <0.01 0.91 3.1 (1.8) 6.7; 0.5
LT 11.8 ±1.8 2.3 ±0.4 0.57 0.90 0.3 (1.2) 2.7; 2.1 <0.01 0.82 1.8 (1.3) 0.9; 4.4 <0.01 0.83 4.1 (1.4) 1.4; 6.9 <0.01 0.75 6.7(1.5) 3.8; 9.7
LT+0.5 12.7 ±2.1 2.8 ±0.4 0.03 0.93 1.3 (1.1) 3.5; 0.9 0.15 0.82 0.7 (1.4) 2.0; 3.5 <0.01 0.84 3.0 (1.4) 0.3; 5.8 <0.01 0.76 5.7 (1.6) 2.7; 8.8
LT+1.0 13.6 ±2.0 3.3 ±0.4 <0.01 0.93 2.2 (1.1) 4.4; 0.1 0.74 0.84 0.2 (1.2) 2.6; 2.3 <0.01 0.84 2.1 (1.4) 0.7; 4.9 <0.01 0.77 4.8 (1.5) 2.0; 7.7
LT+1.5 14.3 ±1.9 3.8 ±0.4 <0.01 0.93 2.8 (1.1) 5.0; 0.7 0.09 0.85 0.8 (1.2) 3.2; 1.6 <0.01 0.84 1.5 (1.3) 1.1; 4.1 <0.01 0.78 4.1 (1.4) 1.4; 7.0
LT+2.0 14.8 ±1.9 4.3 ±0.4 <0.01 0.92 3.4 (1.1) 5.6; 1.3 0.01 0.85 1.3 (1.4) 4.0; 1.4 0.07 0.84 0.9 (1.3) 1.7; 3.5 <0.01 0.78 3.6 (1.4) 0.9; 6.4
LT+2.5 15.3 ±1.9 4.8 ±0.4 <0.01 0.91 3.9 (1.1) 6.1; 1.8 <0.01 0.85 1.8 (1.1) 4.1; 0.5 0.37 0.83 0.4 (1.3) 2.4; 3.2 <0.01 0.78 3.1 (1.4) 0.4; 5.9
LT+3.0 15.8 ±1.8 5.3 ±0.4 <0.01 0.91 4.3 (1.1) 6.5; 0.3 <0.01 0.85 2.3 (1.2) 4.6; 0.1 0.99 0.82 <0.1 (1.3) 2.5; 2.6 <0.01 0.78 2.7 (1.4) 0.1; 5.4
V-Slope 14.6 ±1.4 4.4 ±2.1 <0.01 0.76 3.1 (1.5) 6.1; 0.2 0.01 0.85 1.1 (1.2) 3.6; 1.4 0.01 0.73 1.2 (1.4) 1.6; 4.0 <0.01 0.57 3.9 (1.5) 1.0; 6.8
DMAX 14.1 ±1.6 3.7 ±1.0 <0.01 0.96 2.6 (0.8) 4.2; 1.1 0.19 0.94 0.6 (0.8) 2.1; 0.9 <0.01 0.91 1.7 (1.0) 0.3; 3.7 <0.01 0.92 4.4 (0.9) 2.6; 6.2
OBLA4mM 14.5 ±2.1 4.0 ±<0.1 <0.01 0.93 3.0 (1.1) 5.2; 0.9 0.06 0.84 1.0 (1.3) 3.6; 1.6 0.02 0.86 1.3 (1.4) 0.9; 3.5 <0.01 0.78 3.9 (1.5) 1.0; 7.0
Vpeak 19.3 ±1.3 12.0 ±3.9 <0.01 0.93 8.5 (2.1) 12.7; 4.4 <0.01 0.96 5.8 (0.6) 7.0; 4.7 <0.01 0.93 3.9 (2.0) 7.9; 0.1 0.02 0.97 0.9 (0.5) 1.9; 0.2
MASEST 18.4 ±1.1 – <0.01 0.88 7.6 (2.0) 11.6; 3.7 <0.01 0.96 4.9 (0.5) 6.0; 4.0 <0.01 0.91 3.1 (1.9) 6.9; 0.7 >0.99 0.98 <0.1 (0.4) 0.9; 0.9
Comparison of running speeds at VT1, MLSS, VT2, and MAS against the rest of parameters.VT1, First ventilatory threshold; MLSS, Maximal lactate steady state; VT2Secondary ventilatory threshold; MAS, Maximal aerobic speed;
LT, Lactate threshold; LT+0.5,+1.0,+1.5,+2.0,+2.5,+3.0, Concentrations above lactate threshold; DMAX, Maximum distance between the slope of a polynomial and the line connecting both ends; OBLA4mMol , Onset blood lactate
accumulation 4 mMol·L1; Vpeak, Fastest velocity achieved at the end of the graded exercise testing protocol; MASEST , Theoretical MAS estimated from Vpeak . The most powerful relationships are highlighted in bold. LoA, 95% limit of
agreement.
Frontiers in Physiology | www.frontiersin.org 7September 2018 | Volume 9 | Article 1320
Cerezuela-Espejo et al. Lactate and Ventilatory Thresholds Relationship
TABLE 3 | 95% confidence interval values for main physiological events.
MAS (%) HRMax (%) HRR (%) RPE620
VT159–65 77–81 68–74 10–12
MLSS 72–74 85–87 79–83 12–13
VT284–87 91–93 81–98 15–16
MAS 100 98–100 98–100 18–20
VT1, First ventilatory threshold; MLSS, Maximal lactate steady state; VT2Second,
ventilatory threshold; MAS, Maximal aerobic speed; HRMax, Maximal heart rate; HRR,
Heart rate reserve; RPE, Rate of perceived exertion.
TABLE 4 | Personal author’s approach for exercise prescription (training zones).
Intensity Zone MAS
(%)
Vpeak
(%)
HRMax
(%)
HRR
(%)
RPE620
70–90% VT1or LT R0 43–56 40–52 55–70 50–64 8–10
90–110% VT1or
LT
R1 57–68 53–64 71–83 65–77 11–12
95–105% MLSS
or LT+1.0
R2 69–79 65–75 84–88 78–84 13–14
95–105% VT2or
LT+3.0
R3 80–93 76–89 89–94 85–93 15–16
95–105% ˙
VO2max R3+94–105 90–100 >95 >94 >17
Vpeak, the fastest velocity achieved at the end of the graded exercise testing protocol; VT1,
First ventilatory threshold; MLSS, Maximal lactate steady state; VT2Second, ventilatory
threshold; MAS, Maximal aerobic speed; HRMax, Maximal heart rate; HRR, Heart rate
reserve; RPE, Rate of perceived exertion.
a greater energy cost in running at MLSS workload, which may
imply earlier fatigue and lower performance by accelerating
glycogen depletion (Coyle et al., 1986).
A main contribution of the current study is to provide an
estimated MAS (MASEST) from the maximal speed achieved
(Vpeak) at the end of the GXT protocol with a minimal error
of ±0.3 km·h1. Main physiological events (VT and MLSS)
are related to a given percentage of MAS (Pallarés et al.,
2016), therefore the estimation of MAS would allow coaches to
determine effective working ranges (Table 3) and training zones
(Table 4) with an error of <0.5%. Although there are other track
tests to estimate the MAS (e.g., Léger and Boucher, 1980), the
current protocol adds to the existing methods the possibility to
design individualized training routines based on athletes’ MAS
without the need of indirect calorimetry o CBL records. It is
important to mention that, given the originality of the proposal,
the current outcomes have been shown to be valid only for the
subject group that was tested. Future research should extend
these findings to examine the validity of the MASEST compared
to estimations from existing field tests.
It is noteworthy that, given the high inter-individual response
of training adaptations in endurance exercise (Bouchard et al.,
1986), a similar workload distribution may not have the same
effect among athletes, even if they are from the same discipline
and compete at high level (Stöggl and Sperlich, 2015). In this case,
an individual assessment is required to detect specific workloads.
However, this implies indirect calorimeters methods which are
expensive and out of reach for the majority of coaches and
athletes. In this study, we provide a valid and reliable alternative
to estimate critical workloads (VT1, MLSS, and VT2) using
a cheaper and affordable method such as CBL. Furthermore,
the current GXT individualized protocol (i.e., starting at 13
km·h1below the athlete’s MAS with increments of 1 km·h1/min
until) appears to be a promising method to determine training
zones in well-trained runners. What is now required is to test
the effectiveness of training plans according to the current 5-
zone proposal (Table 4). In addition, future investigations should
examine the validity of this protocol in amateur and female
runners to enhance its applicability within the endurance sport
community.
ETHICS STATEMENT
All procedures performed in this study involving human
participants were in accordance with the ethical standards of
the institutional Human Research Ethics Committee and with
the 1964 Helsinki declaration and its later amendments or
comparable ethical standards. The study was approved by the
Bioethics Commission of the University of Murcia. Written
informed consent was obtained from all subjects prior to
participation.
AUTHOR CONTRIBUTIONS
JP and VC-E: Conception and design of the experiments; JP,
VC-E, RM-N, and AM-C: Pre-testing, experimental preparation,
and data collection; VC-E, JC-I, and JP: data analysis. The first
draft of the manuscript was written by VC-E, JC-I, and JP. All co-
authors edited and proofread the manuscript and approved the
final version.
ACKNOWLEDGMENTS
The authors wish to thank the subjects for their invaluable
contribution to the study.
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Conflict of Interest Statement: 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.
Copyright © 2018 Cerezuela-Espejo, Courel-Ibáñez, Morán-Navarro, Martínez-
Cava and Pallarés. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
Frontiers in Physiology | www.frontiersin.org 10 September 2018 | Volume 9 | Article 1320
... The VT 2 is recognised as the point during incremental exercise at which an exponential increase in minute ventilation (V E ) relative to VO 2 (oxygen uptake) occurs [11]. The steep climb in V E , a product of respiratory rate and tidal volume, is driven by the heightened carbon dioxide (CO 2 ) production as a consequence of hydrogen ion accumulation as exercise intensity progresses [12]. VT 2 signifies the point between easily sustained exercise and exercise that is only able to be sustained for a short period of time. ...
... VT 2 signifies the point between easily sustained exercise and exercise that is only able to be sustained for a short period of time. Ultimately, VT 2 has been established as an important indicator of overall exercise tolerance [1,[12][13][14]. Specifically, it is recognised as a good marker of one's ability to perform higher intensities of exercise for longer durations [13]. ...
... Comparatively, individualised exercise prescription is derived from individual physiological thresholds such as VT 2 and VT 1 (the first ventilatory threshold) [17]. VT1 is defined as the initial increase in VE during incremental exercise, indicating the boundary between low-to moderate-intensity exercise [12]. Indeed, individualised exercise prescription has been suggested to be more effective in eliciting an overall positive training response in comparison to standardised exercise prescription. ...
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The second ventilatory threshold (VT2) is established as an important indicator of exercise intensity tolerance. A higher VT2 allows for greater duration of higher intensity exercise participation and subsequently greater reductions in cardiovascular disease (CVD) risk. This study aimed to compare the efficacy of standardised and individualised exercise prescription on VT2 among physically inactive adults. Forty-nine physically inactive male and female participants (48.6 ± 11.5 years) were recruited and randomised into a 12-week standardised (n = 25) or individualised (n = 24) exercise prescription intervention. The exercise intensity for the standardised and individualised groups was prescribed as a percentage of heart rate reserve (HRR) or relative to the first ventilatory threshold (VT1) and VT2, respectively. Participants were required to complete a maximal graded exercise test at pre-and post-intervention to determine VT1 and VT2. Participants were categorised as responders to the intervention if an absolute VT2 change of at least 1.9% was attained. Thirty-eight participants were included in the analysis. A significant difference in VT2 change was found between individualised (pre vs. post: 70.6% vs. 78.7% maximum oxygen uptake (VO2max)) and standardised (pre vs. post: 72.5% vs. 72.3% VO2max) exercise groups. Individualised exercise prescription was significantly more efficacious (p = 0.04) in eliciting a positive response in VT2 (15/19, 79%) when compared to the standardised exercise group (9/19, 47%). Individualised exercise prescription appears to be more efficacious than standardised exercise prescription in eliciting a positive VT2 change among physically inactive adults. Increasing VT2 allows for greater tolerance to higher exercise intensities and therefore greater cardiovascular health outcomes.
... The algorithm for searching might be more robust and less influenced by unexpected peak noise at the initial or end time range. It has been reported that VT 1 and VT 2 commonly lie at exercise intensities between 50 and 65% of VO 2 and between 75 and 87% of VO 2 , respectively (Beaver et al., 1986;Pallarés et al., 2016;Cerezuela-Espejo et al., 2018;Galán-Rioja et al., 2020). Thus, it is recommended to use a specific range of the input signals only, for example, trimming half of the signal length (i.e., from 30th percentile to 80th percentile for VT 1 , from 50th percentile to 100th percentile for VT 2 ). ...
... It has been reported that maximal aerobic capacity is associated with VT 1 (59 to 65%) and VT 2 (84 to 87%) and that maximal lactate steady state corresponds to VT 2 (Pallarés et al., 2016;Cerezuela-Espejo et al., 2018). We found that when comparing to peak work rate, VT 1 and VT 2 were associated with 50 and 70% of peak W, respectively. ...
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Introduction: The ventilatory threshold (named as VT 1 ) and the respiratory compensation point (named as VT 2 ) describe prominent changes of metabolic demand and exercise intensity domains during an incremental exercise test. Methods: A novel computerized method based on the optimization method was developed for automatically determining VT 1 and VT 2 from expired air during a progressive maximal exercise test. A total of 109 peak cycle tests were performed by members of the US astronaut corps (74 males and 35 females). We compared the automatically determined VT 1 and VT 2 values against the visual subjective and independent analyses of three trained evaluators. We also characterized VT 1 and VT 2 and the respective absolute and relative work rates and distinguished differences between sexes. Results: The automated compared to the visual subjective values were analyzed for differences with t test, for agreement with Bland–Altman plots, and for equivalence with a two one-sided test approach. The results showed that the automated and visual subjective methods were statistically equivalent, and the proposed approach reliably determined VT 1 and VT 2 values. Females had lower absolute O 2 uptake, work rate, and ventilation, and relative O 2 uptake at VT 1 and VT 2 compared to men ( p ≤ 0.04). VT 1 and VT 2 occurred at a greater relative percentage of their peak VO 2 for females (67 and 88%) compared to males (55 and 74%; main effect for sex: p < 0.001). Overall, VT 1 occurred at 58% of peak VO 2, and VT 2 occurred at 79% of peak VO 2 ( p < 0.0001). Conclusion: Improvements in determining of VT 1 and VT 2 by automated analysis are time efficient, valid, and comparable to subjective visual analysis and may provide valuable information in research and clinical practice as well as identifying exercise intensity domains of crewmembers in space.
... VT1 was determined using the criteria of an increase in ventilatory equivalent of oxygen (VE/VO 2 ) with no concomitant increase in ventilatory equivalent of carbon dioxide (VE/VCO 2 ) [22]. VT2 was determined using the criteria of an increase in both the VE/VO 2 and VE/VCO 2 ( Figure 1d) and analysing the behaviour of VCO 2 as a function of VO 2 , during progressive exercise tests when exceeding the muscle metabolism in highly trained athletes, so the objectives of this study are, first, to evaluate SmO 2 during a GXT and establish cut-off points for each training zone based on the scale (0%-100%), ...
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Use of muscle oxygen saturation (SmO2) has been validated as a performance factor during incremental exercise with portable near-infrared stereoscopy (NIRS) technology. However, there is little knowledge about the use of SmO2 to identify training zones. The objective of this study was to evaluate the metabolic zones by SmO2: maximum lipid oxidation zone (Fatmax), ventilatory thresholds (VT1 and VT2) and maximum aerobic power (MAP) during a graded exercise test (GXT). Forty trained cyclists and triathletes performed a GXT. Output power (W), heart rate (HR), oxygen consumption (VO2), energy expenditure (kcal/min) and SmO2 were measured. Data were analysed using the ANOVA test, ROC curves and multiple linear regressions. Significance was established at p ≤ 0.05. SmO2 decreases were observed from baseline (LB) to Fatmax (Δ = -16% p < 0.05), Fatmax to VT1 (Δ = -16% p < 0.05) and VT1 to VT2 (Δ = -45% p < 0.01). Furthermore, SmO2 together with weight, HR and output power have the ability to predict VO2 and energy expenditure by 89% and 90%, respectively. We conclude that VO2 and energy expenditure values can be approximated using SmO2 together with other physiological parameters and SmO2 measurements can be a complementary parameter to discriminate aerobic workload and anaerobic workload in athletes.
... Aunque durante el análisis de datos no se utilizó ningún factor de corrección para pruebas simplificadas como los reportados por estudios previos (Cartón-Llorente et al., 2021;Morgan et al., 2019). El motivo de no utilizar estos factores de corrección es porque existía una correlación casi perfecta entre las dos pruebas simplificas, debido a que los corredores son altamente entrenado, por lo tanto, conocen sus ritmos de carrera máximos y submáximos, además realizan periódicamente test de lactato y de VO2max para determinar sus umbrales de entrenamiento y según estudios previos estos umbrales presentan relación con el FTP (Cerezuela-Espejo et al., 2018;McGRATH et al., 2019;Pallarés et al., 2019), por lo tanto, su FTP/CP se asemeja al calculado a través del PDC. ...
... Therefore, this study aimed to investigate changes in oxygenation in the PFC during various exercise intensities used during regular training sessions for rowing in conjunction with cardiovascular and respiratory variables. To address this issue, we observed a session of rowing exercise consisting of two different intensities of constant-load exercise, that is, 70% (Ex 70% ) and 80% ofVO 2peak (Ex 80% ), which were related to the heavy-intensity domain between the first (VT1) and the second ventilatory threshold (VT2) (Cerezuela-Espejo et al., 2018;Jamnick et al., 2020). Considering the actual rowing training session, supramaximal exercise (ExSp), which surpasses peakVO 2 (VO 2peak ) for a short duration, was also observed. ...
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Purpose: This study aimed to investigate changes in cerebral hemodynamics and oxygenation at moderate, heavy, maximal and supramaximal intensities of rowing exercise. It also examined whether these changes reflect alterations in sensation of effort and mood. We also aimed to examine the effects of peak pulmonary oxygen consumption ( V . O2peak ) on cerebral oxygenation. Methods: Eleven rowers, consisting out of six athletes and five recreational rowers [two female; age, 27 ± 9 years; height, 171 ± 7 cm, body mass, 67 ± 9 kg; V . O2peak , 53.5 ± 6.5 mL min-1 kg-1] rowed a 13-min session separated by 10 and 3 min, at 70 (Ex70%) and 80% of V . O2peak (Ex80%), respectively, on a rowing ergometer, followed by three sessions of 1-min supramaximal exercise (ExSp). After a warm-up at 60% of V . O2peak (ExM), seven male rowers performed a 2,000 m all-out test (Ex2000). Cardiovascular and respiratory variables were measured. Cerebral oxygenation was investigated by near-infrared time-resolved spectroscopy (TRS) to measure cerebral hemoglobin oxygen saturation (ScO2) and total hemoglobin concentration ([HbT]) in the prefrontal cortex (PFC) quantitatively. We estimated the relative changes from rest in cerebral metabolic rate for oxygen (rCMRO2) using TRS at all intensities. During Ex70% and Ex80%, ratings of perceived exertion (RPE) were monitored, and alteration of the subject's mood was evaluated using a questionnaire of Positive-and-Negative-Affect-Schedule after Ex70% and Ex80%. Results: When exercise intensity changed from Ex70% to Ex80%, the sense of effort increased while ScO2 decreased. [HbT] remained unchanged. After Ex70% and Ex80%, a negative mood state was less prominent compared to rest and was accompanied by increases in both ScO2 and [HbT]. At termination of Ex2000, ScO2 decreased by 23% compared to rest. Changes in ScO2 correlated with V . O2peak only during Ex2000 (r = -0.86; p = 0.01). rCMRO2 did not decrease at any intensities. Conclusion: Our results suggest that alterations in the sense of effort are associated with oxygenation in the PFC, while positive changes in mood status are associated with cerebral perfusion and oxygen metabolism estimated by TRS. At exhaustion, the cerebral metabolic rate for oxygen is maintained despite a decrease in ScO2.
... Firstly, subjects performed a self-paced warm-up, and prior to the commencement of the test, subjects were instrumented with a portable metabolic analyzer (Cosmed K5, Cosmed Srl, Rome, Italy) and a heart rate device (Polar H10, Polar Electro, Kempele, Finland). A short-ramp incremental protocol was used (i.e., 13-16 min) as this has been shown to be the most appropriate assessment for identifying individual physiological events in well-trained runners [16][17][18]. The protocol consisted of a 3 min run at 10 km h −1 and a 1% gradient on a treadmill (h/p/cosmos, Nussdorf-Traunstein, Germany), followed by increases of 1 km h −1 min −1 until volitional exhaustion. ...
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The introduction of carbon fiber plate shoes has triggered a plethora of world records in running, which has encouraged shoe industries to produce novel shoe designs to enhance running performance, including shoes containing conductor elements or “grounding shoes” (GS), which could potentially reduce the energy cost of running. The aim of this study was to examine the physiological and perceptual responses of athletes subjected to grounding shoes during running. Ten elite runners were recruited. Firstly, the athletes performed an incremental running test for VO2max and anaerobic threshold (AT) determination, and were familiarized with the two shoe conditions (traditional training shoe (TTS) and GS, the latter containing a conductor element under the insole). One week apart, athletes performed running economy tests (20 min run at 80% of the AT) on a 400 m dirt track, with shoe conditions randomized. VO2, heart rate, lactate, and perceived fatigue were registered throughout the experiment. No differences in any of the physiological or perceptual variables were identified between shoe conditions, with an equal running economy in both TTS and GS (51.1 ± 4.2 vs. 50.9 ± 5.1 mL kg−1 min−1, respectively). Our results suggest that a grounding stimulus does not improve the energy cost of running, or the physiological/perceptual responses of elite athletes.
... In persons with chronic stroke, Boyne et al. [6] suggested that VT1 occurred at 80% of peak heart rate, while the ranges for effectively exercising at VT1 ranged from 55% to 80% of peak heart rate. In another study, Cerezuela-Espejo and colleagues suggested that the 95% confidence interval for VT1 and VT2 heart rate were 77-81% and 91-93% of max heart rate, respectively, and that VT1 and VT2 workload were 59-65% and 84-87% of maximum achieved speed, respectively [22]. Taken together, the ranges for VT1 and VT2 suggested in the aforementioned studies suggest that the underestimation observed in Tables 5 and 6 for S-PRED VT1 may not be physiologically significant, but the underestimation for S-PRED VT2 may be physiologically significant. ...
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The aim of this study was to determine the extent to which the Tyme Wear smart shirt is as reliable and valid in detecting personalized ventilatory thresholds when compared to the Parvo Medics TrueOne 2400. In this validation study, 19 subjects were recruited to conduct two graded exercise test (GXT) trials. Each GXT trial was separated by 7 to 10 days of rest. During the GXT, gas exchange and heart rate data were collected by the TrueOne 2400 (TRUE) in addition to the ventilation data collected by the Tyme Wear smart shirt (S-PRED). Gas exchange data from TRUE were used to detect ventilatory threshold 1 (VT1) and ventilatory threshold 2 (VT2). TRUE and S-PRED VT1 and VT2 were compared to determine the reliability and validity of the smart shirt. Of the 19 subjects, data from 15 subjects were used during analysis. S-PRED exhibited excellent (intraclass correlation coefficient—CC > 0.90) reliability for detection of VT1 and VT2 utilizing time point and workload and moderate (0.90 > ICC > 0.75) reliability utilizing heart rate. TRUE exhibited excellent reliability for detection of VT1 and VT2 utilizing time point, workload, and heart rate. When compared to TRUE, S-PRED appears to underestimate the VT1 workload (p > 0.05) across both trials and heart rate (p < 0.05) for trial 1. However, S-PRED appears to underestimate VT2 workload (p < 0.05) and heart rate (p < 0.05) across both trials. The result from this study suggests that the Tyme Wear smart shirt is less valid but is comparable in reliability when compared to the gold standard. Moreover, despite the underestimation of S-PRED VT1 and VT2, the S-PRED-detected personalized ventilatory thresholds provide an adequate training workload for most individuals. In conclusion, the Tyme Wear smart shirt provides easily accessible testing to establish threshold-guided training zones but does not devalue the long-standing laboratory equivalent.
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Endurance athletes integrate four conditioning concepts in their training programs: high-volume training (HVT), “threshold-training” (THR), high-intensity interval training (HIIT) and a combination of these aforementioned concepts known as polarized training (POL). The purpose of this study was to explore which of these four training concepts provides the greatest response on key components of endurance performance in well-trained endurance athletes. Methods: Forty eight runners, cyclists, triathletes, and cross-country skiers (peak oxygen uptake: (VO2peak): 62.6 ± 7.1 mL·min−1·kg−1) were randomly assigned to one of four groups performing over 9 weeks. An incremental test, work economy and a VO2peak tests were performed. Training intensity was heart rate controlled. Results: POL demonstrated the greatest increase in VO2peak (+6.8 ml·min·kg−1 or 11.7%, P < 0.001), time to exhaustion during the ramp protocol (+17.4%, P < 0.001) and peak velocity/power (+5.1%, P < 0.01). Velocity/power at 4 mmol·L−1 increased after POL (+8.1%, P < 0.01) and HIIT (+5.6%, P < 0.05). No differences in pre- to post-changes of work economy were found between the groups. Body mass was reduced by 3.7% (P < 0.001) following HIIT, with no changes in the other groups. With the exception of slight improvements in work economy in THR, both HVT and THR had no further effects on measured variables of endurance performance (P > 0.05). Conclusion: POL resulted in the greatest improvements in most key variables of endurance performance in well-trained endurance athletes. THR or HVT did not lead to further improvements in performance related variables.
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This study aimed to predict the velocity corresponding to the maximal lactate steady state (MLSSV) from non-invasive variables obtained during a maximal multistage running field test (modified University of Montreal Track Test, UMTT), and to determine whether a single constant velocity test (CVT), performed several days after the UMTT, could estimate the MLSSV. Within 4-5 weeks, 20 male runners performed: 1) a modified UMTT, and 2) several 30 min CVTs to determine MLSSV to a precision of 0.25 km·h(-1). Maximal aerobic velocity (MAV) was the best predictor of MLSSV. A regression equation was obtained: MLSSV=1.425+(0.756·MAV); R(2)=0.63. Running velocity during the CVT (VCVT) and blood lactate at 6 (La6) and 30 (La30) min further improved the MLSSV prediction: MLSSV=VCVT+0.503 - (0.266·ΔLa30-6); R(2)=0.66. MLSSV can be estimated from MAV during a single maximal multistage running field test among a homogeneous group of trained runners. This estimation can be further improved by performing an additional CVT. In terms of accuracy, simplicity and cost-effectiveness, the reported regression equations can be used for the assessment and training prescription of endurance runners. © Georg Thieme Verlag KG Stuttgart · New York.
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The response to an exercise intervention is often described in general terms, with the assumption that the group average represents a typical response for most individuals. In reality, however, it is more common for individuals to show a wide range of responses to an intervention rather than a similar response. This phenomenon of 'high responders' and 'low responders' following a standardized training intervention may provide helpful insights into mechanisms of training adaptation and methods of training prescription. Therefore, the aim of this review was to discuss factors associated with inter-individual variation in response to standardized, endurance-type training. It is well-known that genetic influences make an important contribution to individual variation in certain training responses. The association between genotype and training response has often been supported using heritability estimates; however, recent studies have been able to link variation in some training responses to specific single nucleotide polymorphisms. It would appear that hereditary influences are often expressed through hereditary influences on the pre-training phenotype, with some parameters showing a hereditary influence in the pre-training phenotype but not in the subsequent training response. In most cases, the pre-training phenotype appears to predict only a small amount of variation in the subsequent training response of that phenotype. However, the relationship between pre-training autonomic activity and subsequent maximal oxygen uptake response appears to show relatively stronger predictive potential. Individual variation in response to standardized training that cannot be explained by genetic influences may be related to the characteristics of the training program or lifestyle factors. Although standardized programs usually involve training prescribed by relative intensity and duration, some methods of relative exercise intensity prescription may be more successful in creating an equivalent homeostatic stress between individuals than other methods. Individual variation in the homeostatic stress associated with each training session would result in individuals experiencing a different exercise 'stimulus' and contribute to individual variation in the adaptive responses incurred over the course of the training program. Furthermore, recovery between the sessions of a standardized training program may vary amongst individuals due to factors such as training status, sleep, psychological stress, and habitual physical activity. If there is an imbalance between overall stress and recovery, some individuals may develop fatigue and even maladaptation, contributing to variation in pre-post training responses. There is some evidence that training response can be modulated by the timing and composition of dietary intake, and hence nutritional factors could also potentially contribute to individual variation in training responses. Finally, a certain amount of individual variation in responses may also be attributed to measurement error, a factor that should be accounted for wherever possible in future studies. In conclusion, there are several factors that could contribute to individual variation in response to standardized training. However, more studies are required to help clarify and quantify the role of these factors. Future studies addressing such topics may aid in the early prediction of high or low training responses and provide further insight into the mechanisms of training adaptation.