Article

Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists

Abstract and Figures

Purpose The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold (VT1 and VT2) and with maximal lactate steady state test (MLSS) as a proxy of cycling performance. Methods Fourteen aerobically-trained male cyclists (V˙O2max 62.1±4.6 ml·kg⁻¹·min⁻¹) performed two graded exercise tests (50 W warm-up followed by 25 W·min⁻¹) to exhaustion. Blood lactate, V˙O2 and V˙CO2 data were collected at every stage. Workloads at VT1 (rise in V˙E/V˙O2;) and VT2 (rise in V˙E/V˙CO2) were compared with workloads at lactate thresholds. Several continuous tests were needed to detect the MLSS workload. Agreement and differences among tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was evaluated using ICC, CV and Bland-Altman plots. Results Workloads at lactate threshold (LT) and LT+2.0 mMol·L⁻¹ matched the ones for VT1 and VT2, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respectively). Furthermore, workload at LT+0.5 mMol·L⁻¹ coincided with MLSS workload (p = 0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4–3.7%; r = 0.85–0.89; Bias = -2.1–3.0) except for DMAX method (CV = 10.3%; r = 0.57; Bias = 15.4). Ventilatory thresholds show high reliability (CV = 1.6%–3.5%; r = 0.90–0.96; Bias = -1.8–2.9) except for RER = 1 and V-Slope (CV = 5.0–6.4%; r = 0.79; Bias = -5.6–12.4). Conclusions Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to identify the workloads at the transition from aerobic to anaerobic metabolism.
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RESEARCH ARTICLE
Validity and Reliability of Ventilatory and
Blood Lactate Thresholds in Well-Trained
Cyclists
Jesu
´s G. Pallare
´s
1,2
, Ricardo Mora
´n-Navarro
1,2
, Juan Fernando Ortega
1
, Valentı
´n
Emilio Ferna
´ndez-Elı
´as
1
, Ricardo Mora-Rodriguez
1
*
1University of Castilla-La Mancha, Exercise Physiology Laboratory at Toledo, Toledo, Spain, 2Human
Performance and Sports Science Laboratory, Faculty of Sport Sciences, University of Murcia, Murcia, Spain
*ricardo.mora@uclm.es
Abstract
Purpose
The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-
based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold
(VT
1
and VT
2
) and with maximal lactate steady state test (MLSS) as a proxy of cycling
performance.
Methods
Fourteen aerobically-trained male cyclists ( _
VO2max 62.1±4.6 mlkg
-1
min
-1
) performed two
graded exercise tests (50 W warm-up followed by 25 Wmin
-1
) to exhaustion. Blood lactate,
_
VO2and _
VCO2data were collected at every stage. Workloads at VT
1
(rise in _
VE=_
VO2;) and
VT
2
(rise in _
VE=_
VCO2) were compared with workloads at lactate thresholds. Several contin-
uous tests were needed to detect the MLSS workload. Agreement and differences among
tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was eval-
uated using ICC, CV and Bland-Altman plots.
Results
Workloads at lactate threshold (LT) and LT+2.0 mMolL
-1
matched the ones for VT
1
and
VT
2
, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respec-
tively). Furthermore, workload at LT+0.5 mMolL
-1
coincided with MLSS workload (p =
0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4–3.7%; r =
0.85–0.89; Bias = -2.1–3.0) except for D
MAX
method (CV = 10.3%; r = 0.57; Bias = 15.4).
Ventilatory thresholds show high reliability (CV = 1.6%–3.5%; r = 0.90–0.96; Bias = -1.8–
2.9) except for RER = 1 and V-Slope (CV = 5.0–6.4%; r = 0.79; Bias = -5.6–12.4).
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 1 / 16
a11111
OPEN ACCESS
Citation: Pallare
´s JG, Mora
´n-Navarro R, Ortega JF,
Ferna
´ndez-Elı
´as VE, Mora-Rodriguez R (2016)
Validity and Reliability of Ventilatory and Blood
Lactate Thresholds in Well-Trained Cyclists. PLoS
ONE 11(9): e0163389. doi:10.1371/journal.
pone.0163389
Editor: Øyvind Sandbakk, Norwegian University of
Science and Technology, NORWAY
Received: April 26, 2016
Accepted: September 6, 2016
Published: September 22, 2016
Copyright: ©2016 Pallare
´s et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The authors received no specific funding
for this work.
Competing Interests: The authors have declared
that no competing interests exist.
Conclusions
Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to
identify the workloads at the transition from aerobic to anaerobic metabolism.
Introduction
Maximal oxygen consumption [1], heart rate deflection[2], ventilatory/lactate thresholds [3,4]
and maximum lactate steady state (MLSS) [5] are physiological evaluationsrelated to endur-
ance performance. Although all these tests predict, to some degree, endurance performance its
accuracy, reproducibility and affordability varies. For instance, while maximal oxygen con-
sumption could account for 91% of variability in marathon running performance, the velocity
at lactate threshold explained 98% of the performance variability [6]. In turn, ventilatory
threshold can accurately track the subtle improvements in endurance performance that elite
cyclists obtain during an entire season [4]. Amann and co-workers [7] propose that ventilation,
being under the influence of central and peripheral chemoreflex, is more sensitive and respon-
sive to muscle hydrogen ion accumulation than the measures of blood lactate. Although work-
load at ventilatory threshold seems the more precise predictor of cycling endurance
performance, oftentimes the preferential use of ventilatory or lactate thresholds depends on
equipment availability and ease at data interpretation.
Physiological testing is not only useful to predict performance, but to design successful
training programs. Endurance training geared to enhance performance is more efficient when
workloads are individually prescribed relative to the aerobic-anaerobic transition workload
compared to estimated workload by reference to a maximum (e.g. percentages of maximal
heart rate or about maximal aerobic power) [8]. Identification of anaerobic threshold using
indirect calorimetry (ventilatory threshold) during a graded exercise test is habitual in research
laboratories, professional teams and high performance national centers [4]. However, reliable
metabolic carts are expensive (approx. 30,000 $) and thus are a limited resource for many train-
ers and athletes. Evaluation of anaerobic threshold by capillary blood lactate (CBL) is cheaper
and thus often chosen as an alternative method. However, the validity [7] and reliability [9,10]
of aerobic-anaerobic CBL detection remains controversial.
Thus, performance assessment and training prescription is often based on blood lactate con-
centration changes during a graded exercise test (GXT). Some authors define “lactate thresh-
old” as the workrate beyond which, blood lactate concentration rises above resting level [11].
Other authors sustain that the workloadthat elicits a blood lactate concentration 1 mMolL
-1
above resting levels is better related to endurance performance [12]. To avoid the bias of visual
identification of lactate threshold, curve fitting procedures have been used such as the D
MAX
method [13]. Other investigators have proposed that identification of the workload that elicit a
fixed lactate level of 4 mM can predict endurance performance [14]. Finding the highest work-
load that however does not results in increasing blood lactate concentration is the aim of the
MLSS test [15]. Although blood lactate tests to predict performance proliferate, there has been
few studies comparing the validity of these in comparison to ventilatory thresholds (i.e., VT
1
and VT
2
).
Some investigations have studied the reliability of VT
1
, VT
2
and lactate thresholds (LT)
using a graded exercise testing protocol [7,16]. However, to our knowledge, nobody has inves-
tigated the agreement and differences betweenblood lactate and ventilatory thresholds during
the same incremental test. Thus, the purposeof this study was to assess the validity and
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 2 / 16
reliability of critical workloads found using lactate thresholds or lactate levels in comparison to
the more accepted ventilatory determined thresholds.
Materials and Methods
Subjects
Fourteen trainedmen cyclists volunteered to participate in this study (age 26.7 ± 8.2 yr, body
mass 70.3 ± 4.9 kg, height 173.7 ± 4.2 cm,body fat 12.5 ± 3.0%, _
VO2max 62.1 ± 4.6 mlkgmin
-1
,
endurance training experience10.9 ± 4.9 yr). No physical limitations or musculoskeletal inju-
ries that could affect training were reported. Cyclist underwent a complete medical examina-
tion (including ECG) that showed all were in good health. This study was conducted during
the period from January 2014 to July 2015. The study, which was conducted according to the
declaration of Helsinki, was approved by the Bioethics Commissionof the University of Mur-
cia. Written informed consent was obtained from all subjects prior to participation.
Experimental Design
Following a familiarization GXT, participants rested for 48 hours to ensure adequate recovery.
Participants visited the lab 5–7 times separated by 2–5 days. In the first two sessions, cyclists
performed two identical GXT to establish the average power output (W) associatedto 14 differ-
ent aerobic-anaerobic events based on ventilatory gas exchange and CBL. Thereafter, partici-
pants visited the lab 3–5 more times to determine the workload associatedwith the maximal
lactate steady state (MLSS). All trials were performed between 16:00 h—19:00 h to control the
circadian rhythms effects [17], under similar environmental conditions (21–24°C and 45–55%
relative humidity). In all trials subjects were ventilated at a wind velocity of 2.55 ms
-1
with a
fan positioned 1.5 m from the subject’s chest. A training protocol was established with the
objective of maintaining physical performance individualized to each cyclist for the entire
investigation period (5–6 weeks), always keeping 24 hour of full recovery prior to each assess-
ment session. Thistraining program consisted in cycling sessions of 90 minutes every 48 hours
at the individual intensity of nVT
1
interspersed with efforts of 5–7 min at 90–95% intensity of
VT
2
each 20 min.
Maximal graded exercise tests
Participants performed all the experimental trials on the same cycle ergometer (Ergoselect 200,
Ergoline, Germany). Immediately followinga standardized warm-upof 10 min at 50 W, all
participants performed a ramp protocol with increments of 25 Wmin
-1
until exhaustion. Dur-
ing GXT participants were monitored by standard 12 lead ECG (Quark T12, Cosmed, Italy),
Oxygen consumption ( _
VO2) and carbon dioxide production ( _
VCO2) were recorded using
breath-by-breath indirect calorimetry (Quark B
2
, Cosmed, Italy). Familiarization GXT fulfilled
three objectives:a) discard cardiac defects or diseases in any of the participants, b) to minimize
the bias of progressivelearning on test reliability and c) to discard any participant _
VO2max
lower than 55.0 mlkg
-1
min
-1
.
Both experimental maximal GXT with 15 min warm-up divided in three 5-min steady state
stages at 45%, 55%, and 65% of the peak power output (PPO), being the three intensities below
the second ventilatory threshold (VT
2
). After 10 minutes of passive recovery in which each par-
ticipant ingested 200–250 ml of water to ensure adequate hydration status, a sample of capillary
blood from the finger was obtained to assess CBL (Lactate Pro, Arkray, Japan). Following, par-
ticipants performed the GXT according to a modification of the protocol described by [4].
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 3 / 16
Initial workloadwas set at 50 W, with increments of 25 Wmin
-1
, requiring at all times a
cadence between 80–85 rpm.
Heart rate was continuously monitored (RS400, Polar, Finland), gas exchange was recorded
breath by breath using indirect calorimetryand capillary blood samples were obtained and ana-
lyzed every 2 min (i.e., each 50 W increments). Each participant indicated their rate of per-
ceived exertion every two minutes using the Borg Scale 6–20, where 6 is defined as an effort
"very very light" and one 20 "Maximum, strenuous" effort [18]. Capillary blood lactate analyzer
and indirect calorimetry devices were calibrated before each test. In order to avoid the local aci-
dosis that could impair the attainment of maximum cardiorespiratory performance, and
according to thesubjects’ maximal PPO in the GXT
PRE
(i.e., 375-425W), starting at 50 W, the
workload was progressively increased by 25 Wmin
-1
that ensure that testing duration was not
excessively long (i.e., 13.5–15.0 min). This protocol also allowed collecting between 7 to 9 capil-
lary blood samples before exhaustion to be used in the CBL data analysis.
Maximal lactate steady state test
Several 30 min constant workloads pedaling were performed to identify the highest workload
(i.e. W) which elicited an increment in BLC less than 1 mMol between 10 and 30 min of exer-
cise [5,19]. After 7 days from the second GXT, all participants performed the first MLSS trial at
the individual workload associated to their respective lactate threshold (LT) determined during
the GXT. Depending on the result of the first MLSS test, the workload of the second and fol-
lowing MLSS tests increased or decreased 0.2 WKg
-1
(~ 15 W), until criteria was fulfilled.
Between 3 and 5 tests were necessary to determine theworkload (i.e. W) associatedMLSS for
each cyclist (Fig 1).
VO
2
max and ventilatory thresholds determinations during the GXT
VT
1
was determined using the criteria of an increase in both ventilatory equivalent of oxygen
(_
VE=_
VO2) and end-tidal pressure of oxygen (P
ET
O
2
) with no concomitant increase in
Fig 1. Example of determination of maximal lactate steady state.
doi:10.1371/journal.pone.0163389.g001
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 4 / 16
ventilatory equivalent of carbon dioxide ( _
VE=_
VCO2). VT
2
was determined using the criteria of
an increase in both the _
VE=_
VO2and _
VE=_
VCO2and a decrease in P
ET
CO
2
[4]. Maximal oxygen
uptake (i.e., _
VO2max) was defined as the highest plateau (two successive maximal readings
within 0.15 L/min) reached. V-slope workload was identified in that intensity of exercise
which, in a plot of the minute production of CO
2
over the minute utilization of oxygen ( _
VO2),
shows an increase in the slope above 1.0 [20,21]. The workload associated with a respiratory
exchange ratio equal to unity was defined as RER = 1.00 (Fig 2).
Capillary blood lactate thresholds during the GXT
Lactate Threshold (LT) was determined by examining the lactate concentration-workload rela-
tionship ([Lact]
blood
/W) during the GXT as the highest workload not associated with a rise in
lactate concentration above baseline [9]. Baseline lactate concentration was the average during
the initial stages with values 0.5 mMolL
-1
above rest state. This always occurred just before the
curvilinear increase in blood lactate observed at subsequent exercise intensities [4,22].
Lactate Threshold + 1.0 mML
-1
(LT+1.0) represents the workload (W) which causes an
increase of 1 mML
-1
above baseline measurements [22]. As a novel contribution of this study,
five new lactate thresholds were established following the same criterion as detailed to deter-
mine the LT+1.0 mML
-1
(i.e. concentrations above baseline). Accordingly, the following
thresholds were established:LT+0.5, LT+1.5, LT+2.0, LT+2.5, and LT+3.0 mML
-1
, carrying
out an interpolation results for each of the concentrations proposed (Fig 2).
D
MAX
threshold was determined by plotting the lactate response to exercise intensity in a
third-order polynomial regression curve. The D
MAX
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 [13].
Onset of blood lactate accumulation (OBLA
4mM
) was defined as the exercise intensity (W)
identified by interpolation across the 4 mML
-1
point in the plot of [Lact]
blood
during incre-
mental exercise [14]. Two independent observers detected all ventilatory and lactate thresholds
following the criteria previously described.If they did not agree, the opinion of a third investi-
gator was sought [23].
Body composition
Fat-free mass and fat mass were assessed by X-ray absorptiometry dual energy (DXA) (Hologic
Discovery, Hologic Corp., Waltham, MA, USA). Participant’s height and weight were assessed
in a stadiometer (Seca 202, Seca Ltd., Hamburg, Germany) and body mass index was
calculated.
Statistical analysis
Standard statistical methods were used for the calculation of means, standard deviations (SD)
and 95% confidence interval. The validity against the three Gold Standard methods (i.e., VT
1
,
MLSS and VT
2
) was assessed using one-way repeated measures ANOVA followed by pairwise
comparisons (Bonferroni’s adjustment), intraclass correlation coefficient(ICC) and Bland–Alt-
man plots [24]. The reliability of ventilatory and lactate determinations was assessed using
coefficients of variation (CV), ICC and Bland–Altman plots. The size of thecorrelations was
evaluated as follows; r <0.7 low; 0.7 r<0.9 moderate and r 0.9 high [25]. Analyses were
performed using GraphPad Prism 6.0 (GraphPad Software, Inc., CA, USA) and SPSS software
version 19.0 (SPSS, Chicago, IL).
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 5 / 16
Fig 2. Example of determination of lactate threshold (LT) as well as first (VT
1
) and second ventilatory
thresholds (VT
2
) in one test. Each gas-exchange data point corresponds to a 10-s interval. _
VE=_
VO2,
ventilatory equivalent for oxygen; _
VE=_
VCO2, ventilatory equivalent for carbon dioxide; P
ET
CO
2
, end-tidal
pressure of oxygen; end-tidal pressure of carbon dioxide (P
ET
CO
2
).
doi:10.1371/journal.pone.0163389.g002
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 6 / 16
Results
Validity
VT
1
workload (200 ± 36 W) was different to the workload for the rest of the CBL thresholds
except for the LT threshold (214 ± 33 W, p = 0.147; Table 1). Accordingly, the higher correla-
tion coefficient between VT
1
and CBL was obtained with LT (r = 0.72, p <0.05; Fig 3A). Like-
wise, Bland-Altman analysis (Table 1) showed the highest agreement (i.e., lower bias) for the
LT method (-13.6 ± 34.3; Fig 3B). The workload at MLSS (255 ± 32 W) was different from the
workload obtainedwith the rest of the thresholds except for RER = 1 (259 ± 36 W, p = 0.750),
LT +0.5 (260 ± 36 W, p = 0.449) and D
MAX
(257 ± 40 W, p = 0.830) (Table 1). Meanwhile, LT
+0.5—LT+3.0 and OBLA
4mMol
had the higher coefficientof correlation against MLSS
(r >0.78, p <0.05 in both cases, Fig 3C). Bland-Altman analysis (Table 1 and Fig 3D) revealed
less bias in LT+0.5 (-4.5 ± 23.2), D
MAX
(-1.8 ± 38.1) and RER = 1 (-3.8 ± 45.5).
VT
2
workload (304 ± 39 W) was similar to LT +2.0 (300 ± 37 W, p = 0.539, Fig 3), LT+2.5
(311 ± 38 W, p = 0.250) and OBLA
4mM
(304 ± 40 W, p = 0.965) (Table 1). The highest correla-
tion coefficient between VT
2
and CBL was with LT—LT+3.0 (r >0.79, p <0.05; Table 1,Fig
3E). Lactic determinationswith less bias in the Bland-Altman test were LT +2.0 (2.8 ± 24.0; Fig
3F) and OBLA
4mM
(-1.2 ± 28.5) (S1 File).
Reliability
Intra-subject reliability (GXT I vs. GXT II) of both gold standard thresholds (VT
1
and VT
2
)
revealed low CV (3.6%and 2.1%), high ICC (r = 0.95–0.96)and low Bland-Altman bias
(-2.9 ± 13.3 and -2.7 ± 11.4) suggesting high level of agreement. Similarly, the intra-subject reli-
ability associated to lactate thresholds (LT—LT+3.0) and the OBLA
4mMol
were high
(CV = 3.0%-3.7%; r = 0.85–0.88; p <0.000; Bias = 1.3 ± 18.8–2.9 ± 19.0; Table 2). However,
D
MAX
and RER = 1 had higher CV (10.3–6.4%), lower ICC (r = 0.57–0.79) and higher Bland-
Altman bias (15.4 ± 42.9 and -12.4 ± 24.7) suggesting poorreliability (S1 File).
Discussion
The first aim of this study was to identify during a graded exercise test (25 Wmin
-1
), which
blood lactate concentrationthreshold (LT, LT+0.5, LT+1, LT+1.5, LT+2.0, LT+2.5, LT+3.0
mML
-1
, D
MAX
or OBLA
4mM
) better matched the workload at ventilatory thresholds (VT
1
and
VT
2
). The ultimate goal is to provide coaches and athletes with a valid alternative test to obtain
performance workloads without the need of using indirect calorimetry (less affordable technol-
ogy). In addition, we tested the reliability of each of these thresholds (lactate and ventilatory) to
discard methods with high variability because variability reduces our ability to detect statistical
differences among tests. Finally, we compare all tests to a proxy measurement of performance
(i.e., maximal lactate steady state; MLSS) to study which is a better test to predict endurance
performance (i.e., more reliable and valid).
When the two GXT were compared to test reliability, we found that RER = 1, V-Slope and
D
MAX
were the less reliable determinations with higher CV (5%), lower ICC (<0.80) and
higher Bland-Altman bias (>5) than the rest of the indexes (Table 2). Specifically, D
MAX
was
the least reliable of all used method, returning CV values above 10%, ICC of 0.57 and Bland-
Altman bias above 15. In contrast, VO
2max
, VT
1
and VT
2
were the physiological indexes with
the highest reliability (Table 2). Other authors have also found high VO
2max
(CV = 2% and
ICC = 0.97 [26]; r = 0.92 [16]) and anaerobic threshold (VT
2
r = 0.91 [27] reliability in well
trained cyclist. To our knowledge, no author has compared the reliability of such an extensive
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 7 / 16
Table 1. Validity results of used methods. Comparison of workload at VT
1
, MLSS and VT
2
against workloads.
VT
1
MLSS VT
2
RER = 1 V-SLOPE LT LT+0.5 LT+1.0 LT+1.5 LT+2.0 LT +2.5 LT +3.0 DMAX OBLA
4mM
Workload
(W;
Mean ±SD)
200
±36
255
±32
304
±39
259±36 235±32 214
±33
260
±36
272
±36
288
±37
300
±37
311
±38
320
±39
257
±40
304±40
[Lact] (mmolL
-1
; Mean ±SD) 1.1
±0.4
4.5
±0.9
4.2
±1.0
2.8±1.3 2.1±0.6 1.6
±0.3
2.1
±0.3
2.6
±0.3
3.1
±0.3
3.6
±0.3
4.1
±0.3
4.6
±0.3
2.8
±1.4
4.0±0.0
Differences
(W)
55 104 59 35 14 60 72 88 100 111 120 57 104
First Ventilatory Threshold
(VT
1
)
200±36 W
Mean
Differences
—p value
0.000 0.000 0.000 0.000 0.147 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
ICC r value 0.62 0.73 0.27 0.72 0.72 0.68 0.65 0.66 0.66 0.42 0.44 0.35 0.43
p
value
0.016 0.002 0.346 0.003 0.003 0.008 0.010 0.010 0.01 0.02 0.02 0.215 0.125
Bland
Altman
Bias -55.2 -102.7 -59.0 -34.8 -13.6 -59.7 -72.4 -87.4 -99.9 -110.9 -119.6 -61.6 -104.00
SD of Bias 30.3 28.1 45.8 28.4 34.3 40.0 39.0 39.9 39.7 40.5 40.0 53.1 40.09
Differences
(W)
55 49 4 -20 -41 5 17 33 45 66 65 2 49
Maximal Lactate Steady State
(MLSS)
255±32 W
Mean
Differences
—p value
0.000 0.000 0.750 0.006 0.000 0.449 0.009 0.000 0.000 0.000 0.000 0.830 0.000
ICC r value 0.61 0.85 0.17 0.70 0.76 0.78 0.80 0.81 0.82 0.83 0.84 0.56 0.82
p
value
0.001 0.000 0.397 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.000
Bland
Altman
Bias 55.2 -47.5 -3.8 19.0 41.6 -4.5 -17.1 -32.1 -44.7 -55.7 -64.4 -1.8 -48.93
SD of Bias 30.3 19.9 45.5 25.3 22.4 23.2 21.7 22.0 21.6 21.7 21.0 38.1 21.64
Differences
(W)
-104 -49 -43 -69 -90 -44 -32 -16 -4 7 16 -47 0
Second Ventilatory Threshold
(VT
2
)
304±39 W
Mean
Differences
—p value
0.000 0.000 0.003 0.000 0.000 0.000 0.000 0.015 0.539 0.250 0.018 0.000 0.965
ICC r value 0.85 0.85 0.46 0.82 0.75 0.80 0.80 0.80 0.80 0.79 0.80 0.62 0.74
p
value
0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Bland
Altman
Bias -102.7 47.5 43.7 67.9 89.1 43.0 30.4 15.4 2.8 -8.2 -16.9 41.1 -1.2
SD of Bias 28.1 19.9 38.5 22.2 25.0 23.5 23.2 23.7 24.0 25.2 24.2 39.6 28.5
VT
1
First ventilatory threshold, MLSS Maximal lactate steady state, VT
2
Secondary ventilatory threshold, RER = 1 Respiratory exchange ratio = 1, LT Lactate threshold, LT+0.5,+1.0,
+1.5,+2.0,+2.5,+3.0Concentrations above lactate threshold, D
MAX
Maximum distance between the slope of a polynomial and the line connecting both ends, OBLA
4mMol
Onset blood
lactate accumulation 4 mM
doi:10.1371/journal.pone.0163389.t001
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 8 / 16
battery of tests, and thus our data allow us to discourage the use RER = 1, V-Slope and D
MAX
when other indexes are available.
We found that the workloads at the first ventilatory threshold (i.e., VT
1
) could be deter-
mined by measuring the workload at which lactate start to increase above resting values (i.e.,
Fig 3. CCI and Bland Altman results.
doi:10.1371/journal.pone.0163389.g003
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 9 / 16
Table 2. Reliability of lactate and ventilatory tests. CV, ICC and Bland-Altman results.
Workload
(W;
Mean ±SD)
V
:O2max
388 ±32
VT
1
200 ±36
VT
2
304 ±39
RER = 1
259 ±36
V-Slope
235 ±32
LT
214 ±33
LT+0.5
260 ±36
LT+1.0
272 ±36
LT+1.5
288 ±37
LT+2.0
300 ±37
LT+2.5
311 ±38
LT+3.0
320 ±39
D
MAX
257 ±40
OBLA
4mM
304 ±40
CV (%) 1.6% 3.5% 2.1% 6.4% 5.0% 3.7% 3.7% 3.4% 3.4% 3.4% 3.4% 3.0% 10.3% 3.7%
ICC
r value 0.90 0.95 0.96 0.79 0.79 0.85 0.89 0.88 0.88 0.88 0.87 0.89 0.57 0.85
p value 0.000 0.000 0.000 0.001 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.031 0.000
Bland
Altman
Bias -1.8 -2.9 -2.7 -12.4 -5.6 -2.1 1.9 2.0 1.3 2.9 2.0 3.0 15.4 1.8
SD of Bias 15.4 13.3 11.4 24.9 22.3 18.7 18.1 17.9 18.8 19.0 20.2 19.1 42.7 22.6
_
VO2max Maximal oxygen consumption, VT
1
First ventilatory threshold, MLSS Maximal lactate steady state, VT
2
Secondary ventilatory threshold, RER = 1 Respiratory exchange
ratio = 1, LT Lactate threshold, LT+0.5,+1.0,+1.5,+2.0,+2.5,+3.0Concentrations above lactate threshold, D
MAX
Maximum distance between the slope of a polynomial and the line
connecting both ends, OBLA
4mMol
Onset blood lactate accumulation 4 mM
doi:10.1371/journal.pone.0163389.t002
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 10 / 16
LT) since there was a high level of agreement between these two measurements (Table 1). The
average difference between these two tests (VT
1
vs. LT) was of only 14 watts which is half of
which could be discriminated in each increment of our graded test where workloads increased
25 watts per stage. The coincidence between LT and VT
1
has been known from the seminal
studies of Wasserman and co-workers in the seventies [28]. That agreement between VT
1
and
LT has been confirmed by Lucia and co-workers using elite endurance cyclists [4] and thus our
findings are not novel in this regard but rather confirmatory.
In contrast to the situation with VT
1
, there is no clear agreement as to which is the lactate
threshold that better reflects VT
2
. In our experiment, VT
2
statistically agreed with D
MAX
and
RER = 1 (Table 1). However, as discussed above, reliability is low for these two indexes and
thus there are not fair substitutes of VT
2
. Out of the reliable indexes, VT
2
workload coincided
with LT+2 mMolL
-1
and with the workload that elicits blood lactate concentration of 4
mMolL
-1
(i.e., OBLA 4Mm). Lastly, the workload that elicits LT+0.5 mMolL
-1
nicely agreed
with the maximal workload that can be maintained without elevations in blood lactate concen-
tration (i.e., MLSS; Table 1). Thus, coaches and athletes could, by measuring LT, LT+0.5 and
LT+2 mMolL
-1
detect the workload at VT
1
, MLSS, VT
2
and readily advice optimal perfor-
mance intensity for training or endurance events.
Lactate and ventilatory thresholds are the manifestation of and underlying metabolic events
where homeostasis is lost. For instance, VT
1
(i.e., anaerobic threshold; [28] is the intensity at
which ventilation and VCO
2
increase in parallel. The increase expired CO
2
is generated by the
HCO
3-
buffering of lactic acid that reaches the blood [29]. VT
2
(i.e., RCP, [28] in turn repre-
sents a work intensity at which blood lactate accumulation rises considerable and there is
hyperventilation to buffer acidosis (i.e., ventilatory compensation). Thus, VT
2
represents the
highest metabolic rate at which the system is able to maintain an elevated but stable metabolic
acidosis. Exerciseabove these thresholds results in accumulation of fatigue inducing metabo-
lites [30], rapid increases in intramuscular and arterial lactic acid, hydrogen concentration [31]
and changes in motor unit recruitment [32]. Several authors have reported that long-term
training programs at each of these thresholds or intensity zones will produce particular and dif-
ferent central and peripheral adaptations [3335].
With the objective of applying our findings to training and competition, we developed
Tables 3and 4. In Table 3 the lactate indexes better associated with VT
1
, MLSS and VT
2
just
defined (i.e., proxy for LT, LT+0.5 and LT+2 mMol L
-1
, respectively) are presented with their
correspondent percent of HR
MAX
, heart rate reserve (HRR) and RPE showing the upper and
lower 95% confidence interval. In this way, athletes and coaches that only have access to moni-
toring heart rate and/or RPE could locate the intensities of VT
1
, MLSS and VT
2
. Furthermore,
we proposed several training zones based in a previous publication [35] now locating them
with respect to LT, LT+0.5 and LT+2 mMol L
-1
(Fig 4). We hope that this will allow athletes
and coaches to undergo training at intensities that induce different metabolic adaptations
while only needing measurement of HR
MAX
, HRR or RPE (Table 4).
Table 3. 95% confidence interval for each physiological event.
HR
Max
(%) HRR (%) RPE
VT
1
(LT) 71%—78% 62%—71% 11—12
MLSS (LT+0.5) 81%—87% 76%—83% 13—13
VT
2
(LT+2.0) 87%—92% 83%—89% 14—15
HR
Max
Maximal heart rate, HRR Heart rate reserve, RPE rate of perceived exertion.
doi:10.1371/journal.pone.0163389.t003
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 11 / 16
Some studies in the literature present data on both ventilatory and blood lactate thresholds
during GXT, although their main objective is not to compare them. Regarding VT
1
, Coyle et al.
[22] established LT +1.0 mMolL
-1
as the lactate threshold workload that better matches the
workload at VT
1
. This study was conducted on patients with ischemic heart disease, which
could be behind the difference between our studies. Lucia and co-workers [4] detected a high
agreement between VT
1
and LT (321±8 W vs. 319±10 W) in elite endurance cyclists, and our
data corroborates their findings in well trained cyclist. On the other hand, in an attempt to
locate the VT
2
workload through a CBL test, Smekal et al. [36] found that a value of 4.1±1.0
mMolL
-1
agrees with VT
2
in active and healthy men and women, which coincides with our
findings (Table 1). Nevertheless, Davis and co-workers [37] suggest that anaerobic thresholds
have frequently beendetermined using blood lactate concentrationsof less than 2 mMolL
-1
as
a reference point. Thus, workload at VT
2
could be notably underestimated when following
these previous reports.
Detection of MLSS intensity is particularly important since a substantial portion of aerobic
training in athletes is carried out at MLSS intensities [8,34,35,38,39,40]. Our results indicate
that LT + 0.5 mMolL
-1
during a GXT is a validpredictor of MLSS workload in well trained
cyclist (p = 0.449; r = 0,78; Bias= -4.5). The determinationof LT + 0.5 mML
-1
during an incre-
mental exercise test as a proxy of MLSS will reduce testing time and the fatigue associate with
the several MLSS trials required to achieve the determination. In agreement with Skinner and
McLellan [41], our results showed that MLSS does not correspond to VT
1
(aerobic threshold)
or VT
2
(anaerobic threshold) but represents an intermediate intensity between both physiolog-
ical events. This finding is important since numerous authors have proposed that the workload
at VT
2
coincides with the one for MLSS [36,42], making it difficult for coaches and sport scien-
tists to effectively communicate their findings and the effects produced by different training
intensities. Other authors have tried to estimate the MLSS workload through CBL detected
during a GXT. For example, Beneke [19] found marked differences in the workloads at LT and
OBLA
4mM
in an incremental test with respect to the workload at MLSS in high-level rowers.
Recently, Hauser et al. [43], in male trained subjectsduring a different GXT protocol (40W/
4min), found similar evidences to those described in our work. These authors detected differ-
ences between the results of LT+1.5 mML
-1
and MLSS (i.e., low validity values). However, con-
trary to our results, they found great similarities between the OBLA
4mM
and MLSS values. The
discrepancies between studies may be related to our faster increase in workload during the
GXT protocol (40 W every 4 min for Hauser et al. [43], while 75 W every 4 min, presently).
To predict performance among a group of competitors and to delimit training zones it is
required to assess the workload at the aerobic and anaerobic thresholds. The most accurate way
to measure this metabolic event is with the use of indirect calorimetry. Ventilatory thresholds
have been shown to accurately track the improvements in endurance performance of elite [4]
and well trained endurance cyclist [7]. However, indirect calorimeters are expensive and thus out
Table 4. Personal author’s approach for exercise prescription (training zones).
Percentage Zone HR
Max
(%) HRR (%) RPE
70%—90% VT
1
or LT R0 52%—67% 53%—62% 8—9
90%—110% VT
1
or LT R1 67%—82% 62%—71% 10—11
90%—100% MLSS or LT+0.5 R2 82%—87% 71%—85% 12—14
95%—105% VT
2
or LT+2.0 R3 87%—95% 85%—94% 15—16
95%—105% VO
2max
R3+ 95%—100% 95%—100% 17—19
HR
Max
Maximal heart rate, HRR Heart rate reserve, RPE rate of perceived exertion.
doi:10.1371/journal.pone.0163389.t004
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 12 / 16
of reach of many coaches and athletes. Evaluation of anaerobic threshold by CBL is cheaper and
often chosen as an alternative method. However, the reliability and validity of anaerobic thresh-
old identification by CBL is controversial. Our data support that capillary blood lactate-based
tests are highly reliable and they can be a valid alternative to ventilatory thresholds to identify the
workloads at the transition from aerobic to anaerobic metabolism.Furthermore, LT+0.5 mML
-1
is an alternative test highly correlated with MLSS. These correspondences here presented
between ventilatory and CBL thresholds, as well as the relationship between them and heart rate
and rate of perceive exertion (Tables 3and 4) apply to our GXT protocol (25Wmin
-1
).
Study limitations
Any other graded (e.g., 25 W4 min
-1
) or constant workload protocols, or any other exercise
modes (running, swimming or paddling) may change these relationships, and therefore the
validity values reported in this work could decline.
Supporting Information
S1 File. Graded exercisetest and maximal lactate steady state test results.
(XLSX)
Fig 4. Scheme of physiological events and training zones proposed by the authors.
doi:10.1371/journal.pone.0163389.g004
Ventilatory and Blood Lactate Thresholds
PLOS ONE | DOI:10.1371/journal.pone.0163389 September 22, 2016 13 / 16
Acknowledgments
The authors wish to thank the subjects for their invaluable contribution to the study.
Author Contributions
Conceptualization:JGP RMN RMR.
Formal analysis: JGP RMN.
Funding acquisition: RMR.
Investigation: JGP RMN RMR.
Methodology: JGP RMR.
Resources: JFO VEFE RMR.
Software: RMN VEFE.
Supervision: JGP RMR.
Validation: JFO VEFE RMR.
Writing – original draft: JGP RMN RMR.
Writing – review& editing: JGP RMN RMR.
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... Training prescription is typically based on training zones delimited by previously identified individual physiological "thresholds' during incremental tests (Samuel, Lindsay, & Muniz-Pumares, 2021). These thresholds are determined by the assessment of blood lactate concentration (lactate threshold/s) or gas-exchange parameters (ventilatory threshold/s) while workload progressively increases (Pallarés, Morán-Navarro, Ortega, Fernández-Elías, & Mora-Rodriguez, 2016). Other methods such as the assessment of heart rate variability (HRV) have also been proposed for evaluation of intensity thresholds and training zones in several populations, ranging from patients (Rogers, Mourot, & Gronwald, 2021c) to physically trained individuals (Gronwald et al., 2021;Rogers, Giles, Draper, Hoos, & Gronwald, 2021a;Rogers, Giles, Draper, Mourot, & Gronwald, 2021b) based on the relationship between exercise intensity and autonomous nervous system regulation. ...
... The subjects performed a graded exercise test (GXT) following a standardised protocol (Pallarés et al., 2016). After a 5-min warm-up at 50 W, the workload increased by 25 W·min −1 until volitional exhaustion (or until the cyclists were not able to maintain the set workload). ...
... The peripheral (capillary) blood lactate concentration [La -] was assessed from the participant's right earlobe using a portable analyzer (Lactate Pro 2, Arkray; Kyoto, Japan) (Baldari et al., 2009) at baseline (before the warm-up) and at the end of each 1-minute workload of the GXT. The "lactate thresholds' were estimated from [La -] measures, as explained elsewhere (Pallarés et al., 2016). Thus, the "first lactate threshold" (LT1) was considered as the workload (watts) at which [La -] started to rise above baseline values whereas the LT2 was set at the workload eliciting an increase in [La -] ≥2 mmol·L -1 with regard to baseline values. ...
Article
Background: The evaluation of performance in endurance athletes and the subsequent individualization of training is based on the determination of individual physiological thresholds during incremental tests. Gas exchange or blood lactate analysis are usually implemented for this purpose, but these methodologies are expensive and invasive. The short-term scaling exponent alpha 1 of detrended Fluctuation Analysis (DFA-α1) of the Heart Rate Variability (HRV) has been proposed as a non-invasive methodology to detect intensity thresholds. Purpose: The aim of this study is to analyse the validity of DFA-α1 HRV analysis to determine the individual training thresholds in elite cyclists and to compare them against the lactate thresholds. Methodology: 38 male elite cyclists performed a graded exercise test to determine their individual thresholds. HRV and blood lactate were monitored during the test. The first (LT1 and DFA-α1-0.75, for lactate and HRV, respectively) and second (LT2 and DFA-α1-0.5, for lactate and HRV, respectively) training intensity thresholds were calculated. Then, these points were matched to their respective power output (PO) and heart rate (HR). Results: There were no significant differences (p > 0.05) between the DFA-α1-0.75 and LT1 with significant positive correlations in PO (r = 0.85) and HR (r = 0.66). The DFA-α1-0.5 was different against LT2 in PO (p = 0.04) and HR (p = 0.02), but it showed significant positive correlation in PO (r = 0.93) and HR (r = 0.71). Conclusions: The DFA1-a-0.75 can be used to estimate LT1 non-invasively in elite cyclists. Further research should explore the validity of DFA-α1-0.5.
... On the contrary, Bircher and Knechtle (2004) reported that LT+1.0 and FATmax occurred at ~49 and 65%VO 2peak respectively, without a significant relationship between variables in subjects with obesity (R 2 = 0.10 and 0.19 for men and women). In addition, data from Pallarés et al. (2016) showed that VT1 and LIAB occurred at a similar exercise intensity (Bias = −13.1 W; R 2 = 0.52) in trained cyclist, whilst LT+1.0 was located above VT1 (Bias = +72.4 W; R 2 =0.38). ...
... an ample systematic bias was found between both markers (40 vs. 61%VO 2peak , p < .01) which agrees with data reported by Pallarés et al. (2016) in trained men, and corroborates that LT+1.0 must not be used for FATmax or AeT evaluation. ...
... Otherwise, the HRIP, LIAB, and VT1 were strongly correlated and located at a similar exercise intensity (~41%VO 2peak , Table 2), whereby these could be exchanged for AeT assessment. A good agreement between VT1and LIAB was previously reported by Pallarés et al. (2016) in male cyclist; however, this is the first study demonstrating that HRIP is a valuable marker for defining the aerobic threshold in men with obesity. Moreover, the HRIP (r = 0.85), LIAB (r = 0.88), and VT1 (r = 0.87) showed a strong correlation with FATmax and a similar accuracy for predicting the FATmax HR. ...
Article
Purpose: This work studies the interrelation of the first ventilatory threshold (VT1), the heart rate inflection point (HRIP), and the exercise intensity at which blood lactate started to accumulate (LIAB) or increased 1 mmol∙L-1 above baseline (LT+1.0); and examinee their association with the exercise intensity eliciting maximal fat oxidation (FATmax). Methods: Eighteen young men with obesity performed an incremental-load exercise test on a treadmill after overnight fasting. Gas exchange, heart rate, and blood lactate concentration were recorded. Linear regression analysis was used to determine the association among FATmax and AeT markers. A standard error of estimate (SEE) ≤9 beats∙min-1 and the concordance correlation coefficient (CCC) were used to examine the accuracy of different AeT for predicting FATmax heart rate. Results: The FATmax occurred at 36±7%VO2peak before the HRIP (41±6%VO2peak), LIAB (42±10%VO2peak), LT+1.0 (61±9%VO2peak) and VT1 (40±7%VO2peak). Furthermore, the HRIP (R2= 0.71; SEE= 6 beats∙min-1; CCC=0.77), VT1 (R2= 0.76; SEE= 5 beats∙min-1; CCC=0.84) and LIAB (R2= 0.77; SEE= 5 beats∙min-1; CCC=0.85) were strongly associated to FATmax and showed an acceptable estimation error for predicting FATmax heart rate. Otherwise, LT+1.0 showed a moderate correlation with FATmax, a low accuracy for predicting FATmax HR (R2= 0.57; SEE= 7 beats∙min-1; CCC=0.66) and a poor agreement with the rest of AeT markers (Bias: +20%VO2peak). Conclusion: The HRIP, LIAB and VT1 did not perfectly captured the FATmax, however, these could be exchanged for predicting the FATmax heart rate in men with obesity. Moreover, the LT+1.0 should not be used for AeT or FATmax assessment in men with obesity.
... In those analyses, _ VO 2 responses to exercise greater than 95% of _ VO 2max were considered as peak _ VO 2 responses (i.e., _ VO 2peak ; Black et al., 2017;Jones et al., 2019;Ozkaya et al., 2022). This criterion was used in prior research to calculate "the time spent at _ VO 2max " during a single workout or interval training modalities (de Aguiar et al., 2013;Buchheit & Laursen, 2013a;Dupont et al., 2002;Pallarés et al., 2016;Wakefield & Glaister, 2009). The I HIGH was accepted as the highest workrate that provides a _ VO 2peak value which is still greater than 95% of _ VO 2max (Ozkaya et al., 2022). ...
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Purpose: The highest work-rate that provides maximal oxygen uptake (V˙O2max) may be one of the best exercise stimuli to yield both V˙O2max and lactate accumulation. The aim of this study was to analyze physiological and metabolic acute responses of an exercise modality performed at the upper boundary of the severe exercise domain, and compare those responses with exercise modalities applied within the severe exercise domain. Method: Ten trained male cyclists participated in this study. The V˙O2max, corresponding power output (POVO2max), and the highest work-rate that provides the V˙O2max (IHIGH) were determined by constant work-rate exercises. Cyclists performed three high-intensity interval training (HIIT) strategies as follows; HIIT-1: 4–6 × 3-min at 95% of POVO2max with 1:1 (workout/rest ratio); HIIT-2: 16–18 × 1-min at 105% of POVO2max with 1:1; HIIT-3: 4–7 × 1-2-min at the IHIGH with 1:2. Capillary blood samples were analyzed before, immediately after HIIT sessions, and at the first, third, and fifth minutes of recovery periods. Lactate difference between the highest lactate response and resting status was considered as the peak lactate response for each HIIT modality. Results: Time spent at V˙O2max was greater at HIIT-1 and HIIT-3 (272 ± 127 and 208 ± 111 seconds, respectively; p = 0.155; effect size = 0.43) when compared to the HIIT-2 (~26 seconds; p < 0.001), while there was a greater lactate accumulation at HIIT-3 (~16 mmol·L−1) when compared to HIIT-1 and HIIT-2 (12 and 14 mmol·L−1, respectively; p < 0.001). Conclusions: In conclusion, HIIT-3 performed at IHIGH was successful to provide time spent at V˙O2max with a greater lactate accumulation in a single session.
... A subject's oxygen consumption is directly related to the requirements of different exercise intensities and responds directly to the physiological and metabolic demands of peripheral tissues [8,77]. Thus, the ratio of VO 2 to CO 2 increases responds via the Respiratory Exchange Ratio (RER) to the change in energy substrates in favor of the priority use of carbohydrates and corresponds (like lactate production) to increased exercise intensity [78]. Different changes in VO 2 kinetics related to reaction time, ventilatory capacity, or decreased oxygen debt could lead to improved ventilatory efficiency, increased tissue oxygenation at a given intensity, and therefore increased athletic performance [77]. ...
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Supplementation with Citrulline (Cit) has been shown to have a positive impact on aerobic exercise performance and related outcomes such as lactate, oxygen uptake (VO2) kinetics, and the rate of perceived exertion (RPE), probably due to its relationship to endogenous nitric oxide production. However, current research has shown this to be controversial. The main objective of this systematic review and meta-analysis was to analyze and assess the effects of Cit supplementation on aerobic exercise performance and related outcomes, as well as to show the most suitable doses and timing of ingestion. A structured literature search was carried out by the PRISMA® (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PICOS guidelines in the following databases: Pubmed/Medline, Scopus, and Web of Science (WOS). A total of 10 studies were included in the analysis, all of which exclusively compared the effects of Cit supplementation with those of a placebo group on aerobic performance, lactate, VO2, and the RPE. Those articles that used other supplements and measured other outcomes were excluded. The meta-analysis was carried out using Hedges’ g random effects model and pooled standardized mean differences (SMD). The results showed no positive effects of Cit supplementation on aerobic performance (pooled SMD = 0.15; 95% CI (−0.02 to 0.32); I2, 0%; p = 0.08), the RPE (pooled SMD = −0.03; 95% CI (−0.43 to 0.38); I2, 49%; p = 0.9), VO2 kinetics (pooled SMD = 0.01; 95% CI (−0.16 to 0.17); I2, 0%; p = 0.94), and lactate (pooled SMD = 0.25; 95% CI (−0.10 to 0.59); I2, 0%; p = 0.16). In conclusion, Cit supplementation did not prove to have any benefits for aerobic exercise performance and related outcomes. Where chronic protocols seemed to show a positive tendency, more studies in the field are needed to better understand the effects.
... Validity and reliability were evaluated at two common training intensities. The power, measured in watts (W), developed at aerobic ventilatory thresholds (first ventilatory threshold (VT1) and second ventilatory threshold (VT2)) were calculated by an incremental ramp test [20] 48 h before testing. Then, cyclists performed two bouts of 6 min pedaling at different intensities (VT1 and VT2) in a randomized order, with a 5 min rest between intensity conditions to avoid fatigue interference. ...
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Background: The use of inertial measurement sensors (IMUs), in the search for a more ecological measure, is spreading among sports professionals with the aim of improving the sports performance of cyclists. The kinematic evaluation using the Leomo system (TYPE-R, Leomo, Boulder, CO, USA) has become popular. Purpose: The present study aimed to evaluate the reliability and validity of the Leomo system by measuring the angular kinematics of the lower extremities in the sagittal plane during pedaling at different intensities compared to a gold-standard motion capture camera system (OptiTrack, Natural Point, Inc., Corvallis, OR, USA). Methods: Twenty-four elite cyclists recruited from national and international cycling teams performed two 6-min cycles of cycling on a cycle ergometer at two different intensities (first ventilatory threshold (VT1) and second ventilatory threshold (VT2)) in random order, with a 5 min rest between intensity conditions. The reliability and validity of the Leomo system versus the motion capture system were evaluated. Results: Both systems showed high validity and were consistently excellent in foot angular range Q1 (FAR (Q1)) and foot angular range (FAR) (ICC-VT1 between 0.91 and 0.95 and ICC-VT2 between 0.88 and 0.97), while the variables leg angular range (LAR) and pelvic angle showed a modest validity (ICC-VT1 from 0.52 to 0.71 and ICC-VT2 between 0.61 and 0.67). Compared with Optitrack, Leomo overestimated all the variables, especially the LAR and pelvic angle values, in a range between 12 and 15°. Conclusions: Leomo is a reliable and valid tool for analyzing the ranges of motion of the cyclist's lower limbs in the sagittal plane, especially for the variables FAR (Q1) and FAR. However, its systematic error for FAR and Pelvic Angle values must be considered in sports performance analysis.
... Thus, DFA a1 values of 0.75 and 0.5 may represent a comprehensive solution to accepted physiologic exercise boundaries across a wide spectrum of individuals. In terms of individual participant agreement between HRV and gas exchange/blood lactate derived thresholds, they appear to be of similar magnitude to that of other comparisons of threshold approaches such as blood lactate versus ventilatory parameters (Pallarés et al., 2016), assessment of gas exchange techniques for VT1 determination (Gaskill et al., 2001), comparison of the maximal lactate steady state (MLSS) and functional threshold power (FTP) (Klitzke Borszcz et al., 2019) as well as the muscle oxygen desaturation breakpoint association to the MLSS (Bellotti et al., 2013). Despite the validation with established threshold concepts, it should be kept in mind that the present systemic approach is based on ANS regulation that does not necessarily match perfectly with other concepts based on subsystem parameters. ...
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While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.
... Although the derivation of the HRVT is relatively straightforward, both the definition and calculation of the LT1 are subject to numerous views (i.e., rise of either 0.5 or 1.0 mmol/L, a fixed 2.0 mmol/L threshold, etc.) [3,[25][26][27]. As one of several established methods, even logarithmic transformation has interpretive options that could lead to slightly different results for cycling power or HR [25]. ...
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A non-linear index of heart rate (HR) variability (HRV) known as alpha1 of Detrended Fluctuation Analysis (DFA a1) has been shown to change with increasing exercise intensity, crossing a value of 0.75 at the aerobic threshold (AT) in recreational runners defining a HRV threshold (HRVT). Since large volumes of low-intensity training below the AT is recommended for many elite endurance athletes, confirmation of this relationship in this specific group would be advantageous for the purposes of training intensity distribution monitoring. Nine elite triathletes (7 male, 2 female) attended a training camp for diagnostic purposes. Lactate testing was performed with an incremental cycling ramp test to exhaustion for the determination of the first lactate threshold based on the log–log calculation method (LT1). Concurrent measurements of cardiac beta-to-beat intervals were performed to determine the HRVT. Mean LT1 HR of all 9 participants was 155.8 bpm (±7.0) vs. HRVT HR of 153.7 bpm (±10.1) (p = 0.52). Mean LT1 cycling power was 252.3 W (±48.1) vs. HRVT power of 247.0 W (±53.6) (p = 0.17). Bland–Altman analysis showed mean differences of −1.7 bpm and −5.3 W with limits of agreement (LOA) 13.3 to −16.7 bpm and 15.1 to −25.6 W for HR and cycling power, respectively. The DFA a1-based HRVT closely agreed with the LT1 in a group of elite triathletes. Since large volumes of low-intensity exercise are recommended for successful endurance performance, the fractal correlation properties of HRV show promise as a low-cost, non-invasive option to that of lactate testing for identification of AT-related training boundaries.
Chapter
In diesem Kapitel wird zunächst die Entwicklung der zahlreich existierenden Laktatschwellenkonzepte in chronologischer Reihenfolge tabellarisch dargestellt. Danach werden die einzelnen Schwellenkonzepte – basierend auf der jeweiligen Primärliteratur – grafisch veranschaulicht und die Bestimmungsmethode erläutert. Es folgt eine vergleichende exemplarische Betrachtung ausgewählter Schwellenkonzepte. Als „goldener Standard“, an dem sich zahlreiche Schwellenkonzepte orientieren, wird das maximale Laktat-Steady-State im Zusammenhang mit dem Crossing Point erklärt. Danach werden einzelne Schwellenkonzepte verschiedenen Kategorien zugeordnet, und es wird aufgezeigt, dass basislaktatorientierte Konzepte keine biochemische Grundlage haben. Am Ende des Kapitels wird auf die Frage eingegangen, welches Schwellenkonzept das „richtige“ und welches das „beste“ ist.
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Objectives This study assessed the functional threshold power and the time to exhaustion estimated from the Allen & Coggan test and verify whether performance level has an influence on this parameter. Design Cross-sectional study. Methods Twenty-minute test proposed by Allen & Coggan and cycling test to exhaustion were used to obtain the functional threshold power and a time to exhaustion. Cyclists were divided into performance groups based into 4 categories according to their VO2max. Results The median (interquartile range) time to exhaustion at the functional threshold power was 35 (31–38) minutes for recreationally trained cyclists, 42 (38–51) for trained ones, 47 (41–56) for well-trained ones and 51 (44–59) for professional level cyclists. Time to exhaustion increased with cyclists' experience and performance level (p < 0.001). Conclusions The high time to exhaustion variability observed in this study suggests that functional threshold power and time to exhaustion should be assessed and reported independently for each subject. Also, cyclists' performance level and experience should be factored in when attempting to study the time to exhaustion, as better performing and more experienced cyclists consistently show longer times to exhaustion at the functional threshold power.
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Introduction Prolonged time trials proved capable of precisely estimating anaerobic threshold. However, time trial studies in recreational cyclists are missing. The aim of the present study was to evaluate accuracy and viability of constant power threshold, which is the highest power output constantly maintainable over time, for estimating maximal lactate steady state in recreational athletes. Methods A total of 25 recreational athletes participated in the study of whom 22 (11 female, 11 male) conducted all constant load time trials required for determining constant power threshold 30 min and 45 min, which is the highest power output constantly maintainable over 30 min and 45 min, respectively. Maximal lactate steady state was assessed subsequently from blood samples taken every 5 min during the time trials. Results Constant power threshold over 45 min (175.5 ± 49.6 W) almost matched power output at maximal lactate steady state (176.4 ± 50.5 W), whereas constant power threshold over 30 min (181.4 ± 51.4 W) was marginally higher ( P = 0.007, d = 0.74). Interrelations between maximal lactate steady state and constant power threshold 30 min and constant power threshold 45 min were very close (R ² = 0.99, SEE = 8.9 W, Percentage SEE (%SEE) = 5.1%, P < 0.001 and R ² = 0.99, SEE = 10.0 W, %SEE = 5.7%, P < 0.001, respectively). Conclusions Determination of constant power threshold is a straining but viable and precise alternative for recreational cyclists to estimate power output at maximal lactate steady state and thus maximal sustainable oxidative metabolic rate.
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