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The main aim of this study is to assess the validity of a new cycling protocol to estimate the Maximal Lactate Steady-State workload (MLSS) through a one-day incremental protocol (1day_MLSS). Eleven well-trained male cyclists performed 3 to 4 trials of 30-min constant load test (48-72h in between) to determine their respective MLSS workload. Then, on separate days, each cyclist carried out two identical graded exercise tests, comprised of four 10-minute long stages, with the initial load at 63% of their respective maximal aerobic power, 0.2 W·Kg-1 increments, and blood lactate concentration [La] determinations each 5 min. The results of the 1day_MLSS tests were analysed through three different constructs: i) [La] difference between 5 th and 10 th min of each stage (DIF_5to10), ii) [La] difference between the 10 th min of two consecutive stages (DIF_10to10), and iii) difference in the mean [La] between the 5 th and 10 th min of two consecutive stages (DIF_mean). For all constructs, the physiological steady state was determined as the highest workload that could be maintained with a [La] rise lower than 1mmol·L-1. No significant differences were detected between the MLSS workload (247 ± 22W) and any of the 1day_MLSS data analysis (250 ± 24W, 245 ± 23W and 243 ± 21W, respectively; p>0.05). When compared to the MLSS workload, strong ICCs and low bias values were found for these three constructs, especially for the DIF_10to10 workload (r=0.960; Bias=2.2 W). High within-subject reliability data were found for the DIF10_10 construct (ICC=0.846; CV=0.4%; Bias=2.2 ± 6.4W). The 1day_MLSS test and DIF_10to10 data analysis is a valid assessment to predict the MLSS workload in cycling, that considerably reduces the dedicated time, effort and human resources that requires the original test. The validity and reliability values reported in this project are higher than those achieved by other previous MLSS estimation tests.
J Sci Cycling. Vol. 6(2), 9-16
DOI: 10.28985/180630.jsc.03!
© 2018 Lillo-Bevia licensee JSC. 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 work is properly cited.
Open Access!
A 1-day maximal lactate steady-state
assessment protocol for trained cyclists
Jose Ramon Lillo-Bevia1
!Ricardo Morán-Navarro1,2, Víctor Cerezuela1, Alejandro Martínez-
Cava1 and Jesús G. Pallarés1,2
The main aim of this study is to assess the validity of a new cycling protocol to estimate the Maximal Lactate Steady-
State workload (MLSS) through a one-day incremental protocol (1day_MLSS). Eleven well-trained male cyclists
performed 3 to 4 trials of 30-min constant load test (48-72h in between) to determine their respective MLSS workload.
Then, on separate days, each cyclist carried out two identical graded exercise tests, comprised of four 10-minute long
stages, with the initial load at 63% of their respective maximal aerobic power, 0.2 W·Kg-1 increments, and blood lactate
concentration [La] determinations each 5 min. The results of the 1day_MLSS tests were analysed through three different
constructs: i) [La] difference between 5th and 10th min of each stage (DIF_5to10), ii) [La] difference between the 10th min
of two consecutive stages (DIF_10to10), and iii) difference in the mean [La] between the 5th and 10th min of two
consecutive stages (DIF_mean). For all constructs, the physiological steady state was determined as the highest
workload that could be maintained with a [La] rise lower than 1mmol·L-1. No significant differences were detected
between the MLSS workload (247 ± 22W) and any of the 1day_MLSS data analysis (250 ± 24W, 245 ± 23W and 243 ±
21W, respectively; p>0.05). When compared to the MLSS workload, strong ICCs and low bias values were found for
these three constructs, especially for the DIF_10to10 workload (r=0.960; Bias=2.2 W). High within-subject reliability data
were found for the DIF10_10 construct (ICC=0.846; CV=0.4%; Bias=2.2 ± 6.4W). The 1day_MLSS test and DIF_10to10
data analysis is a valid assessment to predict the MLSS workload in cycling, that considerably reduces the dedicated
time, effort and human resources that requires the original test. The validity and reliability values reported in this project
are higher than those achieved by other previous MLSS estimation tests.
Keywords: Cycling, Habits, Online, Survey, Injury.
Contact email: (JR Lillo)
1 Human Performance and Sports Science Laboratory. University of
Murcia, Murcia, Spain
2 Exercise Physiology Laboratory; University of Castilla-La Mancha,
Toledo, Spain
Received: 14 May 2018. Accepted. 28 May 2018.
The Maximal Lactate Steady State intensity (MLSS) can
be used to detect the highest running speed or cycling
power output at which blood lactate concentration [La]
remains stable during prolonged submaximal constant-
workload exercise (Beneke 2003), which has also been
considered as the upper limit of the heavy intensity
domain (Beneke, Leithaeuser, and Ochentel 2011;
Pringle and Jones 2002). The physiological importance
of the MLSS lies in the fact that it defines the exercise
intensity above which there is a net contribution of
energy associated with lactate accumulation due to an
increased rate of glycolysis that exceeds the rate of
mitochondrial pyruvate utilization (Heck et al. 1985).
The Gold Standard method to determine the MLSS
intensity requires from two to four 30-minute constant
loads, checking the lactate at minute ten and thirty
(Beneke 2003). This methodology has been considered
quite restrictive since it is a highly time-consuming
method, so that, different lactate concepts or even,
original single-day tests, have been proposed aiming to
approximate the real MLSS intensity, looking for a
fewer resources and time determination protocol.
Due to the multiple exercise sessions undergo in
repeated visits to the laboratory, which even implies to
restrict the athletes´ normal training regime,
practitioners and coaches in order to know the MLSS
intensity through the Gold Standard tests, alternative
proposals have been developed to determine the MLSS
speed for running, during a single-session protocol for
determination with fewer resources using heart rate, rate
of perceived exertion, breath frequency and race pace as
predictors (Palmer, Potteiger, Nau, and Tong 1999).
This method was latter validated using the single [La]
during a sub-maximal running field test to predict the
MLSS speed (Kuphal, Potteiger, Frey, and Hise 2004).
Garcia-Tabar et al. (2017), used the single [La] during a
sub-maximal running field test to predict the MLSS
velocity. Billat, Dalmay, Antonini, and Chassain (1994)
compared the [La] from two submaximal intensities of
20 minutes tests, carried out on the same day and
separated by 40 minutes. A similar method was
performed years later by Kilding and Jones (2005). The
MLSS speed was also compared with the [La] during
field testing (Swensen, Harnish, Beitman, and Keller
1999). Figueira, Caputo, Pelarigo, and Denadai (2008),
compared the MLSS with the onset of blood lactate
accumulation (OBLA) at 3.5 mmol·L-1 with cyclist and
runners, and recently, Llodio, Gorostiaga, Garcia-Tabar,
Granados, and Sanchez-Medina (2016) tried to predict
J Sci Cycling. Vol. 7(1), 9-16
the MLSS velocity through a regression equation using
the maximal aerobic speed. Despite the contradictory
findings, these results seem to indicate that it is possible
to calculate the MLSS workload, reducing substantially
the time commitment required by the determination
using the Gold Standard method.
Besides, several studies have also tried to guess a
methodology to specifically predict the MLSS in
cycling. Madrid et al. (2016) estimated the MLSS by
using the rate of perceived exertion, where RPE-13
protocol showed a stronger correlation with MLSS (r =
0.78). Grossl, De Lucas, De Souza, and Antonacci
Guglielmo (2012) found that the minimum equivalent of
the blood lactate-power output relationship plus 1.5
mmol·L-1 (Berg et al. 1990), was the most accurate way
to predict the MLSS workload (r = 0.94). Finally, and
without seeking a specific method to assess the MLSS
intensity, Pallares, Moran-Navarro, Fernando Ortega,
Emilio Fernandez-Elias, and Mora-Rodriguez (2016)
found that the workload at lactate threshold plus 0.5
mml.l-1 coincided with MLSS workload (Bias = -4.5 W).
None of these methods have obtained completely
satisfactory results, due to their questionably validity to
predict the MLSS workload in well trained cyclists, and
besides that, none of them have either studied the
reliability of physiological and/or psychological
With a similar purpose, different studies have previously
associated the exercise intensity corresponding to MLSS
with a value of respiratory exchange ratio (RER) close
to 1.00. Laplaud, Guinot, Favre-Juvin, and Flore (2006)
found a strong relationship between RER = 1.00 and
MLSS in cyclists (R2 = 0.95). Leti, Mendelson, Laplaud,
and Flore (2012), reported a medium correlation
between the speed associated with RER = 1.00 and the
MLSS (r = 0.79; p = 0.0008). Peinado et al. (2016)
suggested that MLSS could be find between both
ventilatory thresholds but they did not find a strong
correlation between MLSS and RER = 1.00 (r = 0.730;
SEM = 8.2). Further, Pallares et al. (2016), neither found
a strong correlation between MLSS and RER = 1.00
(ICC = 0.17). Therefore, and given the different
correlations reported, there is not strong evidence that it
would be a reliably method to predict the MLSS
Therefore, the main purpose of the present study is to
validate a new one-day graded exercise test to determine
the MLSS workload in cycling. This new assessment
must be carry out in a single session, with no need to
self-regulate a pacing, and with the lowest possible
economic and human resources cost.
Eleven trained male cyclists and triathletes volunteered
to participate in this study (age 35.0 ± 9.3 yr, body mass
72.6 ± 10.3 kg, body fat 9.2 ± 1.9 %, 8 skinfold 81.9 ±
25.5 mm, height 174.5 ± 6.7 cm, VO2max 58.2 ± 6.1
ml·kg·min-1), with more than 2 years of endurance
training experience. They were recruited from local
cycling and triathlon clubs. No physical limitations or
musculoskeletal injuries that could affect training were
reported. Cyclist underwent a complete medical
examination (including ECG) that showed all were in
good health. The study, which was conducted according
to the declaration of Helsinki, was approved by the
Bioethics Commission of the University of Murcia.
Written informed consent was obtained from all subjects
prior to participation. Before giving the aforementioned
written consent, all subjects were informed of the aim,
the possible discomforts and the potential benefits of the
Study Design
Participants underwent a graded exercise tests which
works as either a familiarization and a complete medical
examination (including ECG) (GXTpre), fulfilled with
three objectives: a) discard cardiac defects or diseases in
any of the participants, b) to minimize the bias of
progressive learning on test reliability and c) to discard
any participant VO2max lower than 50.0
1. Participants visited the lab 7-8 times, within a 3-week
period and performed only one test on any given day,
separated by at least 48 hours. In the first session,
cyclists performed a preliminary GXT to establish the
average power output (W) associated to maximal
aerobic power, as well as their VO2max (Lucia, Hoyos,
Perez, and Chicharro 2000; Pallares et al. 2016).
Additionally, participants visited the laboratory 2-3
more times to determine the workload associated with
the maximal lactate steady state (MLSS) through 30-
minute constant workload test and an additional 30-
minute test at the specific MLSS intensity. Finally,
subjects performed the one day_MLSS test (1day-
MLSS) twice, with three to four stages of 10-minute
long (Figure 1). The subjects performed the tests on
their own bicycles. The bicycles were attached to the
Cycleops Hammer ergometer (CycleOps, Madisson,
USA) (Lillo-Bevia and Pallares 2017) using a
hyperbolic mode (the work rate was imposed to the
subjects with a constant load independently of the
subjects’ pedalling rates). Subjects were asked to pedal
seated throughout the tests to control the possible
differences in the cycling economy (Arkesteijn, Jobson,
Hopker, and Passfield 2016), as well as, they were
allowed to choose their preferred cadence (Denadai,
Figure 1. Study design
Lillo-Bevia et al. (2018).!A 1-day maximal lactate steady-state assessment protocol for trained cyclists. Journal of Science and Cycling
Ruas, and Figueira. 2006). During each test, PO (W) and
cadence (rev·min-1) of the direct drive ergometer were
transmitted to a unit display fixed on the handlebars,
recording at a frequency of 1Hz using a Garmin 1000
cycling computer (Garmin International Inc., Olathe,
All trials were performed in the same time range of day
(± 3h) to control the circadian rhythms effects (Pallares
et al. 2014), under similar environmental conditions
(22.1 ± 2.5 °C and 39.9 ± 5.4% relative humidity). In all
trials subjects were ventilated at wind velocity of 2.55
m·s-1 with a fan positioned 1.5 meters from the subject’s
chest. To maintain physical performance during the
investigation period (3-4 weeks) participants followed
an individual training protocol consisting in cycling
sessions up to 150 minutes at individual first ventilatory
threshold intensity, interspersed with efforts of 57 min
at 9095% of second ventilatory threshold intensity each
20 min. Training sessions were repeated each 48 h with
24 h rest before each evaluation.
All of them were asked to keep their eating habits
constant following a similar type of high-carbohydrate
diet during the days previous to testing, reaching at least
7 gr·kg-1 during the previous 24 hours (Bussau,
Fairchild, Rao, Steele, and Fournier 2002). The last meal
was ingested 3 h before the beginning of each testing
session. Finally, the intake of any drugs or any other
substance that may affect the results of the study were
prohibited. During the MLSS and 1day_MLSS tests,
subjects were allowed to drink water ad libitum.
Maximal graded exercise tests (GXT)
Participants performed all the experimental trials on
their own bicycles attached to a Cycleops Hammer
ergometer, with a warm-up of 5 min at 50 W, starting
immediately after the ramp protocol with increments of
25 W·min-1 until exhaustion (Pallares et al. 2016).
During GXT oxygen consumption (VO2) and carbon
dioxide production (VCO2) were recorded using breath
to breath indirect calorimetry (Cortex Metalyzer 3B,
Leipzig, Germany). Before the beginning of the test,
each participant ingested 200250 ml of water to ensure
adequate hydration status (1020 usg) (Fernandez-Elias
et al. 2014). Heart rate was continuously monitored
(Polar Bluetooth H7, Finland). Capillary blood samples
were obtained at the beginning (basal values) and three
minutes later of the tests ending (Lactate Pro2, Arkray,
Japan) (bias ranging from 0.32 to -2.16 mmol-1 and
coefficient of variation ranging from 0.0 to 1.0 %
(Bonaventura et al. 2015). Indirect calorimetry device
was calibrated before each test. In order to avoid the
local acidosis that could impair the attainment of
maximum cardiorespiratory performance, and according
to the subjects’ maximal peak power output (PPOpre) in
the GXTpre (i.e., 350-400W), starting at 50 W, the
workload was progressively increased by 25 W·min-1
that ensure that testing duration was not excessively long
(i.e., 13.515.0 min).
Maximal aerobic power (MAP) was determined as the
minimal power output eliciting the maximal oxygen
uptake (VO2max). At least two of the following criteria
were required for the attainment of VO2max: a plateau
in VO2 values (i.e. an increase in VO2 between two or
more consecutive stages of less than 1.5 ml·kg-1·min-1,
a respiratory exchange ratio value 1.10, or the
attainment of a maximal heart rate value (HR max)
above 95% of the age-predicted maximum (207 - 0.7 x
age) (Munoz, Seiler, Alcocer, Carr, and Esteve-Lanao
2015). In case there wasn’t a clear VO2 plateau, or that
the subject couldn’t end the 60 seconds stage, MAP was
computed as follows, “MAP = Wf + [(t/60 x 25)]”,
where “Wf” is the value of the last completed load (in
W), and “t” is the time the last uncompleted workload
was maintained (in seconds) (Padilla, Mujika, Cuesta,
and Goiriena 1999).
Maximal lactate steady state tests
Several 30-min constant workload pedalling were
performed to identify the highest workload (i.e., W)
which elicited an increment in lactate blood
concentration less than 1 mmol·L-1 between 10th and
30th min of exercise. For all tests, the subjects
performed two loads of five minutes at an intensity of
80% and 90% of the VT1 as a warm-up. The first MLSS
trial was performed at the 70% of the individual MAP
(Pallares et al. 2016). Depending on the result of the first
MLSS test, the workload of the second and following
MLSS tests increased or decreased 0.2 W·Kg-1 (~ 15W)
(Beneke 2003), until criteria was fulfilled. MLSS was
identify as the intermediate load between the last two
intensities tested (i.e., interpolation). Between 2 and 3
tests were necessary to determine the workload (i.e., W)
associated with the MLSS for each cyclist. Finally, and
at least 48 hours following the last test, subjects were
asked to perform a 30-min test at the intensity
corresponding to MLSS previously determined. HR,
[La], RPE and cadence were registered every ten
10-Minute-Stages test (1day-MLSS)
On separate days (48-72 h), each cyclist carried out two
identical graded exercise tests, comprised of four 10-
minute long stages, with free cadence. During the test,
the electromagnetically braked cycle ergometer was in
the hyperbolic mode, thus the work rate was independent
of cadence. Warm-up consisted of 5 min at 35% and 5
min at 45% of their respective MAP. The initial
workload was set at the 63% of the individual MAP
previously determined (GXTPRE). The workload of the
second and following stages increased 0.2 W·Kg-1 (~
15W), until either, subjects completed four stages or
until volitional exhaustion. Power output, HR, RPE and
[La] data were registered at minute 5th and 10th of each
stage. To avoid test-retest influence, subjects were only
aware of time, although they did not receive any
information about the physiological values nor the
power output or cadence, what they performed in the
first test.
Three new 1day-MLSS constructs were defined for this
project as follows: i) 1day-MLSS was considered the
workload of the last stage were [La] was 1 mmol·L-1
between minute five and ten of each stage (DIF_5to10);
ii) 1day-MLSS was considered the workload of the last
J Sci Cycling. Vol. 7(1), 9-16
stage were [La] was 1 mmol·L-1 comparing [La] of the
10th-minute of the stage compared with the 10th-minute
of the previous stage (DIF10to10); iii) 1day-MLSS was
considered the workload of the last stage were the mean
[La] of the minutes 5 and 10 of each stage was 1
mmol·L-1 comparing the mean [La] of the minutes 5 and
10 of the previous stage (DIFmean). If some subject was
unable to performed 10 minutes of any stage, 1day-
MLSS was considered the workload of the previous
Body composition
On the first day of testing, baseline measures of height,
body mass, and sum of eight skinfolds were taken
(bicep, tricep, subscapula, supraspinale, suprailiac,
abdomen, front thigh and calf), six perimeters (arm
relaxed and tensed, gluteal, waist, calf and mid-thigh)
and three breadths (biepicondylar humerus,
biepicondylar femur and wrist), always in duplicate by
the same researcher using Harpenden skinfold calipers
(British Indicators, West Sussex, UK). The body lean
mass was calculated for each athlete as described (Lee et
al. 2000).
Statistical analysis
Standard statistical methods were used for the
calculation of means, standard deviations (SD) and 95%
confidence interval. Data were screened for normality of
distribution and homogeneity of variances using a
Shapiro-Wilk normality test and a Levene test
respectively. Some data were deemed in violation of
normality; therefore, a log-transformation was done to
ensure the normal distribution. The validity between the
Gold Standard MLSS and the three constructs (i.e.,
DIF_5to10, DIF_10to10 and DIF_mean) was assessed
using one way repeated measures ANOVA followed by
pairwise comparisons (Bonferroni’s adjustment),
intraclass correlation coefficient (ICC) and Bland
Altman plots (Bland and Altman 1999). The reliability
of these three constructs was assessed using coefficients
of variation (CV), ICC and BlandAltman plots. The
size of the correlations was evaluated as follows; r < 0.7
low; > 0.7 to r <0.9 moderate and r > 0.9 high (Vincent
2005). Effect sizes (d) were also calculated for each
construct as the mean 30-min MLSS test power output
minus the mean 1day_MLSS power output, divided by
the pooled standard deviation (SD). Analyses were
performed using commercially available software
GraphPad Prism 6.0 (GraphPad Software, Inc., CA,
USA) and (IBM SPSS version 21.0, SPSS Inc., Chicago,
IL). Significance was set at an alpha level 0.05.
Validity of the 1day-MLSS test
The mean power output at which the MLSS intensity
was found in these well-trained athletes was 247 ± 22 W,
while the mean power output calculated with the two
trails of the 1day-MLSS were 250 ± 25 W, 245 ± 23 W
and 243 ± 21 W for the DIF_5to10, DIF_10to10 and
DIF_mean constructs, respectively. No significant
differences were detected between de MLSS results
(Gold Standard) and any of the 1day-MLSS constructs
(p > 0.05) (Table 1). Strong correlations coefficients
between MLSS workload and the DIF_10to10 and
DIFmean constructs were found (ICC = 0.960 and 0.925
respectively), while only a moderate correlation was
found with the DIF_5to10 (ICC = 0.850) (Table 1). The
Bland-Altman plots revealed low Bias, SD of Bias and
limits of agreement for the three 1day-MLSS data
analysis or constructs (i.e., DIF_5to10, DIF_10to10 and
DIF_mean) (Figure 1), specifically for the comparison
of the DIF_10to10 approach (Bias = 2.2 ± 6.4 W; Table
1; Figure 1B).
Significantly lower mean values were found between the
rate of perceived exertion reported by participants at the
10th minute of the MLSS (12.7± 1.1) and the three
constructs of the 1day_MLSS analysed (15.5 ± 1.5, 14.3
± 1.3 and 14.1 ± 1.6 for DIF_5to10, DIF_10to10, and
DIF_mean respectively; p < 0.05). Additionally, heart
rate values detected in the three 1day_MLSS constructs
(160 ± 8, 157 ± 8, 156 ± 7 bpm for DIF_5to10,
DIF_10to10, DIF_mean and MLSS respectively) were
significantly higher than the mean heart rate values
achieved at 10th minute of the MLSS tests (154 ± 8 bpm;
p < 0.05).
Within-subject reliability of the 1day-MLSS test
Within-subject reliability (Trial 1 vs. Trial 2) revealed
low CV (ranging from 0.4 ± 7.4 to 2.4 ± 5.0), low Bias
(specifically the DIF_10to10 construct (2.2 ± 6.4W) and
moderate ICC, especially the DIF_10to10 and
DIF_mean constructs (0.846 and 0.841). Table 2
Table 1. Comparison of power output values attained for the MLSS and 1day-MLSS tests.
1day-MLSS (W)
Mean ± SD
247 ± 22
250 ± 25
245 ± 23
243 ± 21
ICC (r value)
Bland A ltman (W)
-3.4 W
2.2 W
3.6 W
SD Bias
12.5 W
6.4 W
8.4 W
-28.4 to 21.6
-10.6 to 15.1
-13.2 to 20.4
Effect Size (d)
ICC = Intraclass correlation coefficient; SD = Standard Deviation; LoA = Limits of Agreement;
Lillo-Bevia et al. (2018).!A 1-day maximal lactate steady-state assessment protocol for trained cyclists. Journal of Science and Cycling
displays the mean results and data analysis of the power
output obtained in both 1day-MLSS trials.
The first aim of this study was to confirm if the 1day-
MLSS test provides a valid and reliable surrogate of the
directly determined MLSS intensity in cycling. The
main finding of this study is that the DIF_10to10
construct of the 1day-MLSS is a valid and reliable
method to estimate the aforementioned MLSS workload,
demanding substantially lower resources and time.
Detection of MLSS intensity is particularly important
since a substantial portion of aerobic training in athletes
is carried out at MLSS intensities (Pallares and Moran-
Navarro 2012; Ronnestad et al. 2014). Different studies
have tried to estimate the MLSS in cycling and running
by using the power output associated with RER = 1.00
with contradictory results. Leti, Mendelson, Laplaud,
and Flore (2012) reported strong correlation in runners
(VO2max 60.8 ± 5.7 ml·kg-1·min-1) between the MLSS
intensity with the speed at RER = 1.00 (r = 0.79; p =
0.0008). Laplaud, Guinot, Favre-Juvin, and Flore (2006)
also reported a stronger relation between RER = 1.00
and MLSS (r2 = 0.95, p < 0.0001) in cyclists (VO2max
62.1 ± 4.6 ml·kg-1·min-1). Finally, Pallares et al. (2016)
show very close values in well trained cyclists (VO2max
62.1 ± 4.6 ml·kg-1·min-1) between RER = 1.00 and
MLSS (259 ± 36 vs 255 ± 32 W), but conversely, the
correlation coefficient between both results was very
low (r = 0.17; p = 0.397). Despite the fact that all these
publications seem to indicate that RER = 1.00 might be
a good predictor of the MLSS workload or speed, even
sometimes better than the ventilatory thresholds
(Pallares et al. 2016), this methodology is very
demanding due to the fact that indirect calorimetry is
required, so becoming inaccessible to most coaches,
practitioners and sport scientists.
Another method to predict in a single day an intensity
similar to MLSS was performed by Billat, Bernard,
Pinoteau, Petit, and Koralsztein (1994). These authors
performed two constant-speed treadmill runs of 20-
minute duration at approximately 65% and 90% of
VO2peak, separated by 40-minute rest. A validation
protocol was developed by Kilding and Jones (2005),
comparing the results previously mentioned, with the
traditional and Gold Standard protocol (3 to 4 stages of
30-minute long), but they founded poor correlations
between each other (r = 0.29, p = 0.49). They stated that
the two-stages of 20-minute long substantially
underestimated the speed, blood lactate concentration
and %VO2max utilized from the actual MLSS.
MLSS was also predicted through the rate of perceived
exertion (RPE), where a value of 13 correlated strongly
with MLSS (r = 0.78) (Madrid et al. 2016). The
validation protocol consisted in three ten-minute stages
corresponding to each RPE identified during and GXT
session as RPE-10, RPE-13 and finally RPE-16. The one
which best fitted was RPE 13 (r = 0.78; p < 0.01), but
high between-subject variability was found (bias = -4.7
W; 95% LoA -27.0 to 17.6 W), whereas no within-
Figure 2. BlandAltman plots results.
Table 2. Test-retest data of the 1day-MLSS test.
1day-MLSS test-retest
Mean ± SD (W)
(r value)
Bias ± SD
95% LoA
Trial 1
Trial 2
252 ± 25
249 ± 27
3.9 ± 18.2 W
-32.4 to 40.3
1.4 ± 7.4%
248 ± 27
242 ± 22
8.7 ± 14.8 W
-23.4 to 35.9
0.4 ± 5.5%
245 ± 23
242 ± 22
6.4 ± 12.6 W
-21.7 to 28.7
2.4 ± 5.0%
ICC = Intraclass correlation coefficient; SD = Standard deviation; CV = Coefficient of variation; LoA = Limits of agreement
J Sci Cycling. Vol. 7(1), 9-16
subject variability was assessed. On the other hand,
Pallares et al. (2016) assess that the RPE associated to
the MLSS workload, and also found a RPE of 13 out of
20 during a GXT with stages of one-minute long. The
results achieved at the present study show that the mean
RPE of the three different constructs of the 1day_MLSS
were significantly higher than those achieved at the
MLSS determination tests. Such a difference may be
explained by the fact that as the time goes by, the
physiological and psychological fatigue accumulated
consequently increases the subjective rate of perception.
A large number of authors have tried to validate other
tests to estimate the MLSS workload using incremental
graded exercises test and [La] analysis. Hauser, Adam,
and Schulz (2014) reported significant correlations
between MLSS and the “onset of blood lactate
accumulation (OBLA4mmol)” (Sjodin and Jacobs 1981),
“the individual anaerobic threshold (IAT)” (Jones and
Doust 1998), and the “+ 1.5 mmol·L-1 lactate model”
(Dickhuth et al. 1999) (r = 0.89; r = 0.83 and r = 0.88,
respectively), but with large individual differences based
on the Bland-Altman model. Pallares et al. (2016) also
found high coefficient of correlation between Lactate
threshold+0.5 and OBLA4mmol and MLSS (r > 0.78, p <
0.05 in all cases). Again, large individual differences
based on the Bland-Altman analysis were found between
MLSS and Lactate Threshold OBLA4mmol, but
surprisingly a low bias was found between MLSS and
Lactate threshold + 0.5 (-4.5 ± 23.2 W). In the present
study, mean values of 4.7 ± 0.7, 3.8 ± 1.0 and 3.7 ± 1.0
mmol·L-1 where found at the different constructs tested.
A key factor to assess the validity of a method is to know
how likely it predicts the true value. Palmer et al. (1999)
reported that their method (which consisted in two stages
of 27-minute runs on a treadmill, collecting blood
samples every 3 min of each 9-min stage), was
successful in predicting the MLSS in 9 out of 12
subjects. Leti et al. (2012) reported that 5 out of 14
subjects showed some disagreement between intensities
of MLSS and RER = 1.00. Paton and Hopkins (2001)
suggested that in elite athletes, a magnitude lower than
2% is required to detect changes in performance from an
ergogenic or training intervention. Applying this very
demanding as an acceptable error of the real MLSS
power output value, this method was successful for 4 out
of 11, 7 out of 11 and 6 out of 11 for the DIF_5to10,
DIF_10to10 and DIF_mean respectively.
In conclusion, the main findings of the present study
were that the DIF_10to0 method of the 1day-MLSS is a
valid and reliable method that could allow to estimate
the MLSS intensity in well trained cyclists with lower
resources and time. The small bias and CV as well as the
consistent correlations and small differences found, lead
to use this protocol for assessing the MLSS workload in
a single testing session.
There are some limitations in the current study which
may be possible to overcome in future studies. Since the
tests were performed at laboratory, additional research
must be done at field, either at flat or hilly conditions, to
confirm these results. Furthermore, it must be mentioned
that our results are limited to male cyclists and triathletes
with a similar performance status and physiological
level (~55 A transfer to other populations
(women, inactive people or even elite cyclists), or
exercise modes (running, swimming or paddling) must
be done carefully.
The authors wish to thank the subjects for their
invaluable contribution to the study.
Conflict of interest
The investigators in the present study have no conflicts
of interest.
1. Arkesteijn M, Jobson S, Hopker J, & Passfield L
(2016) The Effect of Cycling Intensity on Cycling
Economy During Seated and Standing Cycling.
International Journal of Sports Physiology and
Performance 11(7), 907-912. doi:10.1123/ijspp.2015-
2. Beneke R (2003) Maximal lactate steady state
concentration (MLSS): experimental and modelling
approaches. European Journal of Applied Physiology
88(4-5), 361-369. doi:10.1007/s00421-002-0713-2
3. Beneke R., Leithaeuser R M, & Ochentel O (2011)
Blood Lactate Diagnostics in Exercise Testing and
Training. International Journal of Sports Physiology
and Performance 6(1), 8-24. doi:10.1123/ijspp.6.1.8
4. Beneke R, & vonDuvillard S P (1996) Determination
of maximal lactate steady state response in selected
sports events. Medicine and Science in Sports and
Exercise 28(2), 241-246. doi:10.1097/00005768-
5. Berg A, Jakob E, Lehmann M, Dickhuth H H, Huber
G, & Keul J. (1990) Current aspects of modern
ergometry. [Aktuelle Aspekte der modernen
Ergometrie] Pneumologie (Stuttgart, Germany) 44(1),
6. Billat V, Bernard O, Pinoteau J, Petit B, & Koralsztein
J P (1994) Time to exhaustion at VO2max and Lactate
Steady-State Velocity in Sub Elite Long-Distance
Runners.. Archives Internationales De Physiologie De
Biochimie Et De Biophysique 102(3), 215-219.
7. Billat V, Dalmay F, Antonini M T, & Chassain A P
(1994) A Method for Determining the Maximal Steady-
State of Blood Lactate Concentration from 2 Levels of
Submaximal Exercise. European Journal of Applied
Practical applications
This study confirms that the 1day-MLSS test is a
highly valid and reliable test for coaches and
practitioners to estimate the MLSS intensity by
comparing the lactate values achieved at 10th minutes
of each stage of a 10-minute-stages test, providing an
alternative to the Gold Standard determination test in
cycling with lower resources and time.
Lillo-Bevia et al. (2018).!A 1-day maximal lactate steady-state assessment protocol for trained cyclists. Journal of Science and Cycling
Physiology and Occupational Physiology 69(3), 196-
202. doi:10.1007/bf01094788
8. Bland J M, & Altman D G (1999) Measuring
agreement in method comparison studies. Statistical
Methods in Medical Research 8(2), 135-160.
9. Bonaventura J M, Sharpe K, Knight E, Fuller KL,
Tanner RK & Gore CJ (2015) Reliability and Accuracy
of Six Hand-Held Blood Lactate Analysers. Journal of
Sports Science and Medicine 14(1), 203-214.
10. Borg G A V. (1982) Psychophysical Bases of
Perceived Exertion. Medicine and Science in Sports and
Exercise 14(5), 377-381. doi:10.1249/00005768-
11. Bussau V A, Fairchild T J, Rao A, Steele P, &
Fournier P A (2002) Carbohydrate loading in human
muscle: an improved 1 day protocol. European Journal
of Applied Physiology 87(3), 290-295.
12. Denadai B S, Ruas V D A, & Figueira T R (2006)
Maximal lactate steady state concentration independent
of pedal cadence in active individuals. European
Journal of Applied Physiology 96(4), 477-480.
13. Dickhuth H H, Yin L, Niess A, Rocker K, Mayer F,
Heitkamp H C, & Horstmann T (1999) Ventilatory,
lactate-derived and catecholamine thresholds during
incremental treadmill running: Relationship and
reproducibility. International Journal of Sports
Medicine 20(2), 122-127. doi:10.1055/s-2007-971105
14. Fernandez-Elias V E, Martinez-Abellan A, Maria
Lopez-Gullon J, Moran-Navarro R, Pallares J G, De la
Cruz-Sanchez E, & Mora-Rodriguez R (2014) Validity
of Hydration Non-Invasive Indices during the
Weightcutting and Official Weigh-In for Olympic
Combat Sports. Plos One 9(4).
15. Figueira T R, Caputo F, Pelarigo J G, & Denadai B
S (2008) Influence of exercise mode and maximal
lactate-steady-state concentration on the validity of
OBLA to predict maximal lactate-steady-state in active
individuals. Journal of Science and Medicine in Sport
11(3), 280-286. doi:10.1016/j.jsams.2007.02.016
16. European Journal of Applied Physiology 106(4),
629-638. doi:10.1007/s00421-009-1061-2
17. Garcia-Tabar I, Llodio I, Sanchez-Medina L, Asiain
X, Ibanez J, & Gorostiaga E M (2017) Validity of a
single lactate measure to predict fixed lactate thresholds
in athletes. Journal of Sports Sciences 35(4), 385-392.
18. Grossl T, De Lucas R D, De Souza K M, &
Antonacci Guglielmo L G (2012) Maximal lactate
steady-state and anaerobic thresholds from different
methods in cyclists. European Journal of Sport Science
12(2), 161-167. doi:10.1080/17461391.2010.551417
19. Hauser T, Adam J, & Schulz H (2014) Comparison
of Selected Lactate Threshold Parameters with Maximal
Lactate Steady State in Cycling. International Journal
of Sports Medicine 35(6), 517-521. doi:10.1055/s-0033-
20. Heck H, Mader A, Hess G, Mucke S, Muller R, &
Hollmann W (1985) Justification of the 4-mmol/I lactate
threeshold. International Journal of Sports Medicine
6(3), 117-130. doi:10.1055/s-2008-1025824
21. Jones A M, & Doust J H (1998) The validity of the
lactate minimum test for determination of the maximal
lactate steady state. Medicine and Science in Sports and
Exercise 30(8), 1304-1313. doi:10.1097/00005768-
22. Kilding A E, & Jones A M (2005) Validity of a
single-visit protocol to estimate the maximum lactate
steady state. Medicine and Science in Sports and
Exercise 37(10), 1734-1740.
23. Kuphal K. E, Potteiger J A, Frey B B, & Hise M P
(2004) Validation of a single-day maximal lactate steady
state assessment protocol. Journal of Sports Medicine
and Physical Fitness 44(2), 132-140.
24. Laplaud D, Guinot M, Favre-Juvin A, & Flore P
(2006) Maximal lactate steady state determination with
a single incremental test exercise. European Journal of
Applied Physiology 96(4), 446-452.
25. Lee R C, Wang Z M, Heo M S, Ross R, Janssen I, &
Heymsfield S B (2000) Total-body skeletal muscle
mass: development and cross-validation of
anthropometric prediction modelsl American Journal of
Clinical Nutrition 72(3), 796-803.
26. Leti T, Mendelson M, Laplaud D, & Flore P (2012)
Prediction of maximal lactate steady state in runners
with an incremental test on the field. Journal of Sports
Sciences 30(6), 609-616.
27. Lillo-Bevia J R, & Pallares J G (2017) Validity and
Reliability of the Cycleops Hammer Cycle Ergometer.
International journal of sports physiology and
performance 1-19. doi:10.1123/ijspp.2017-0403
28. Llodio I, Gorostiaga E M, Garcia-Tabar I, Granados
C, & Sanchez-Medina L (2016) Estimation of the
Maximal Lactate Steady State in Endurance Runners.
International Journal of Sports Medicine 37(7), 539-
546. doi:10.1055/s-0042-102653
29. Lucia A, Hoyos J, Perez M, & Chicharro J L (2000)
Heart rate and performance parameters in elite cyclists:
a longitudinal study. Medicine and Science in Sports and
Exercise 32(10), 1777-1782.
30. Madrid B., Pires F O, Prestes J, Leite Vieira D C,
Clark T, Tiozzo E, Simoes H G (2016) Estimation of the
Maximal Lactate Steady State Intensity by the Rating of
Perceived Exertion. Perceptual and Motor Skills 122(1),
136-149. doi:10.1177/0031512516631070
31. Munoz I, Seiler S, Alcocer A, Carr N, & Esteve-
Lanao J (2015) Specific Intensity for Peaking: Is Race
Pace the Best Option? Asian journal of sports medicine
6(3), e24900-e24900. doi:10.5812/asjsm.24900
32. Padilla S, Mujika I, Cuesta G, & Goiriena J J (1999)
Level ground and uphill cycling ability in professional
road cycling. Medicine and Science in Sports and
Exercise 31(6), 878-885. doi:10.1097/00005768-
J Sci Cycling. Vol. 7(1), 9-16
33. Pallares J G, Lopez-Samanes A, Moreno J,
Fernandez-Elias V E, Fernando Ortega J, & Mora-
Rodriguez R (2014). Circadian rhythm effects on
neuromuscular and sprint swimming performance.
Biological Rhythm Research 45(1), 51-60.
34. Pallares JG and Moran-Navarro R (2012)
Methodological approach to the cardiorespiratory
endurance training. J Sport Health Res 4, 109-119.
35. Pallares J G, Martinez-Abellan A, Lopez-Gullon J
M, Moran-Navarro R, De la Cruz-Sanchez E, & Mora-
Rodriguez R (2016) Muscle contraction velocity,
strength and power output changes following different
degrees of hypohydration in competitive olympic
combat sports. Journal of the International Society of
Sports Nutrition 13. doi:10.1186/s12970-016-0121-3
36. Pallares J G, Moran-Navarro R, Fernando Ortega J,
Emilio Fernandez-Elias V, & Mora-Rodriguez R (2016)
Validity and Reliability of Ventilatory and Blood
Lactate Thresholds in Well-Trained Cyclists. Plos One
11(9). doi:10.1371/journalpone/e0163389
37. Palmer A S, Potteiger J A, Nau K L, & Tong R J
(1999) A 1-day maximal lactate steady-state assessment
protocol for trained runners. Medicine and Science in
Sports and Exercise 31(9), 1336-1341.
38. Paton C D, & Hopkins W G (2001) Tests of cycling
performance. Sports Medicine 31(7), 489-496.
39. Pringle J S M, & Jones A M (2002) Maximal lactate
steady state, critical power and EMG during cycling.
European Journal of Applied Physiology 88(3), 214-
226. doi:10.1007/s00421-002-0703-4
40. Ronnestad B R, Ellefsen S, Nygaard H, Zacharoff E
E, Vikmoen O, Hansen J, & Hallen J (2014) Effects of
12 weeks of block periodization on performance and
performance indices in well- trained cyclists.
Scandinavian Journal of Medicine & Science in Sports
24(2), 327-335. doi:10.1111/sms.12016
41. Sjodin B, & Jacobs I (1981) Onset of Blood Lactate
Accumulation and Marathon Running Performance.
International Journal of Sports Medicine 2(1), 23-26.
42. Swensen T C, Harnish C R, Beitman L, & Keller B
A (1999) Noninvasive estimation of the maximal lactate
steady state in trained cyclists. Medicine and Science in
Sports and Exercise 31(5), 742-746.
43. Vincent W J (2005) Statistics in Kinesiology. In (3rd
edition ed.). EEUU: Human Kinetics
... However, the downside is that the classical MLSS determination method is not easy to implement in the training schedule of athletes because of the need to perform 4-6 separate exercise sessions at a constant speed, for at least 30 min/session, on consecutive days, to determine the MLSS value (Svedahl and MacIntosh, 2003). Therefore, alternative MLSS protocols have been tested in an attempt to determine the lactate threshold in a different way (shorter 1-day-protocols), likewise alternative SET protocols have been developed in an attempt to approach the MLSS as much as possible in human (Palmer et al., 1999;MacIntosh and Shane, 2002;Lillo-Bevia et al., 2018) and in animal models (Cunha et al., 2009;Rodrigues et al., 2016). ...
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There is a great need for objective external training load prescription and performance capacity evaluation in equestrian disciplines. Therefore, reliable standardised exercise tests (SETs) are needed. Classic SETs require maximum intensities with associated risks to deduce training loads from pre-described cut-off values. The lactate minimum speed (LMS) test could be a valuable alternative. Our aim was to compare new performance parameters of a modified LMS-test with those of an incremental SET, to assess the effect of training on LMS-test parameters and curve-shape, and to identify the optimal mathematical approach for LMS-curve parameters. Six untrained standardbred mares (3–4 years) performed a SET and LMS-test at the start and end of the 8-week harness training. The SET-protocol contains 5 increments (4 km/h; 3 min/step). The LMS-test started with a 3-min trot at 36–40 km/h [until blood lactate (BL) > 5 mmol/L] followed by 8 incremental steps (2 km/h; 3 min/step). The maximum lactate steady state estimation (MLSS) entailed >10 km run at the LMS and 110% LMS. The GPS, heartrate (Polar®), and blood lactate (BL) were monitored and plotted. Curve-parameters (R core team, 3.6.0) were (SET) VLa1.5/2/4 and (LMS-test) area under the curve (AUC>/ 0.80), Bland-Altman method, and ordinary least products (OLP) regression analyses were determined for test-correlation and concordance. Training induced a significant increase in VLa1.5/2/4. The width of the AW increased significantly while the AUC>LMS and LMS decreased post-training (flattening U-curve). The LMS BL steady-state is reached earlier and maintained longer after training. BLmax was significantly lower for LMS vs. SET. The 40° angular method is the optimal approach. The correlation between LMS and VMLSS was significantly better compared to the SET. The VLa4 is unreliable for equine aerobic capacity assessment. The LMS-test allows more reliable individual performance capacity assessment at lower speed and BL compared to SETs. The LMS-test protocol can be further adapted, especially post-training; however, inducing modest hyperlactatemia prior to the incremental LMS-stages and omitting inclusion of a per-test recovery contributes to its robustness. This LMS-test is a promising tool for the development of tailored training programmes based on the AW, respecting animal welfare.
... However, there are also articles which present health disorders, generally related to practising cycling too intensely (Rajasekhar, 2014;Nichols, Racinais, Buchheit, & Girard, 2015) or related to the insufficient level of infrastructure development (Lloyd, Tucker, Archbold, & Eames, 2017). The worldwide unprecedented development of cycling offers a particularly wide range of manifestations, practice and development of road or mountain cycling, as well as of flat or ascending cycling (Berkemeier, Reede, & Alumbaugh, 2017); the scientific approaches target interesting aspects of competitive cycling (Paludo, Cook, Owen., Woodman, Owen, & Crewther 2017;Schneeweiss Haerlen, Ahrend, Niess, & Krauss, 2018;Lillo-Bevia, Morán-Navarro, Cerezuela, Martínez-Cava & Pallarés, 2017). In the scientific analysis of cycling it is frequently used the comparison of different categories of practitioners (Bouillod, Brunet, Soto-Romero, & Grappe, 2017), or the association of cycling with other sports (Freda, et al., 2017). ...
... Para el primer intento se seleccionó la carga que producía el 70% de la PAM individual, obtenida en el promedio de los dos IMA precedentes (de Oliveira Cruz et al., 2015;Lillo-Bevia et al., 2018). Dependiendo del resultado del primer test, la carga del segundo y posteriores intentos fue incrementándose o disminuyéndose en 0,2 W Kg -1 (~ 15W), hasta que el criterio fuese alcanzado (Beneke, 2003 ...
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The doctoral thesis presented in this document is structured in three different parts. The first part of the work is composed of studies I and II, where the validation work of two different workload cycling tools, “drive indoor trainer Cycleops Hammer” and “PowerTap P1 Pedals Power Meter “, is detailed. In both articles, randomized and counterbalanced incremental workload tests (100-500 W) were performed, at 70, 85 and 100 rev·min-1 cadence, with sitting and standing pedalling in 3 different Hammer unit cadences. Then, the results are compared against the values measured by a professional SRM crankset. In general terms, no significant differences were detected between the Hammer devices and the SRM, while strong intraclass correlation coefficients were observed (≥0.996; p=0.001), with low bias (-5,5 a 3,8), and high values of absolute reproducibility (CV<1,2%, SEM<2,1). The PowerTap P1 pedals showed strong correlation coefficients in a seated position (rho ≥ 0.987). They underestimated the power output obtained in a directly proportional way to the cadence, with an average error of 1.2%, 2.7%, 3.5% for 70, 85 and 100 rev∙min-1. However, they showed high absolute reproducibility values (150-500 W, CV = 2.3%, SEM <1.0W). These results prove that both are valid and reproducible devices to measure the power output in cycling, although caution should be exercised in the interpretation of the results due to the slight underestimation. The second part of the thesis is devoted to the study III, where the time to exhaustion (TTE) at the workloads related to the main events of the aerobic and anaerobic pathway in cycling were analysed in duplicate in a randomized and counterbalanced manner (Lactic anaerobic capacity (WAnTmean), the workload that elicit VO2max -MAP-, Second Ventilatory Threshold (VT2) and at Maximal Lactate Steady State (MLSS). TTE values were 00:28±00:07, 03:27±00:40, 11:03±04:45 and 76:35±12:27 mm:ss, respectively. Moderate between-subject reproducibility values were found (CV=22.2%,19.3%;43.1% and 16.3%), although low within-subject variability was found (CV=7.6%,6.9%;7.0% y 5.4%). According to these results, the %MAP where the physiological events were found seems to be a useful covariable to predict each TTE for training or competing purposes. Finally, in the third part of the work, the results of studies IV y V have been included. The validity of two different methods to estimate the cyclists’ workload at MLSS was evaluated. The first method was a 20 min time trial test (20TT), while the second method was a one-day incremental protocol including 4 steps of 10 minutes (1day_MLSS). The 20TT test absolute reproducibility, performed in duplicate, was very high (CV = -0.3±2.2%, ICC = 0.966, bias = 0.7±6.3 W). 95% of the mean 20TT workload overestimated the MLSS (bias 12.3±6.1W). In contrast, 91% of 20TT showed an accurate prediction of MLSS (bias 1.2±6.1 W), although the regression equation "MLSS (W) = 0.7489 * 20TT (W) + 43.203" showed even a better MLSS estimates (bias 0.1±5.0 W). Related to the 1day_MLSS test, the physiological steady state was determined as the highest workload that could be maintained with a [Lact] rise lower than 1mmol·L-1. No significant differences were detected between the MLSS (247±22 W) and the main construct of the test (DIF_10to10) (245±23 W), where the difference of [Lact] between minute 10 of two consecutive steps were considered, with high correlations (ICC = 0.960), low bias (2.2W), as well as high within-subject reliability (ICC = 0.846, CV = 0.4%, Bias = 2.2±6.4W). Both methods were revealed as valid predictors of the MLSS, significantly reducing the requirements needed to individually determine this specific intensity.
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Background: The peaking period for endurance competition is characterized for a relative increase of the intensity of training, after a longer period of training relatively dominated by lower intensity and higher volume. Objectives: The present study was designed to compare physiological and 10 km performance effects of high intensity training (HIT) versus race pace interval training (RP) during peaking for competition in well-trained runners. Patients and methods: 13 athletes took part in the study, they were divided into two groups: HIT and RP. HIT performed short intervals at ~105% of the maximal aerobic velocity (MAV), while RP trained longer intervals at a speed of ~90% of the MAV (a speed approximating 10 km race pace). After 12 weeks of baseline training, the athletes trained for 6 weeks under one of the two peaking regimes. Subjects performed 10 km prior to and after the intervention period. The total load of training was matched between groups during the treatment phase. Subjects completed a graded treadmill running test until volitional exhaustion prior to each 10 km race. MAV was determined as the minimal velocity eliciting maximal oxygen consumption (VO2max). Results: Both groups significantly improved their 10 km time (35 minutes 29 seconds ± 1 minutes 41 seconds vs 34 minutes 53 seconds ± 1 minutes 55 seconds, P < 0.01 for HIT; 35 minutes 27 seconds ± 1 minutes 40 seconds vs 34 minutes 53 seconds ± 1 minutes 18 seconds P < 0.01 for RP). VO2max increased after HIT (69 ± 3.6 vs 71.5 ± 4.2 ml.Kg(-1).min(-1), P < 0.05); while it didn't for RP (68.4 ± 6 vs 69.8 ± 3 ml.Kg(-1).min(-1), p>0.05). In contrast, running economy decreased significantly after HIT (210 ± 6 ml.Kg(-1).km(-1) vs 218 ± 9, P < 0.05). Conclusions: A 6 week period of training at either 105% of MAV or 90% of MAV yielded similar performance gains in a 10km race performed at ~90% MAV. Therefore, the physiological impact of HIT training seems to be positive for VO2max but negative for running economy.
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The maximal lactate steady state is the gold standard for evaluating aerobic capacity; however, it is time-consuming. The lactate minimum protocol is an easier alternative, but is not feasible and still expensive. This study investigated whether the rating of perceived exertion of 13 is an accurate predictor of lactate minimum and maximal lactate steady state intensities. Eleven physically active men performed three tests: (1) incremental exercise with workloads based on rating of perceived exertion of 10, 13, and 16 (experimental protocol - denominated RPE-13 test), (2) lactate minimum, and (3) maximal lactate steady state. No differences were observed among participants' workloads corresponding to rating of perceived exertion 13, lactate minimum, and maximal lactate steady state intensities. Thus, the workload associated with the rating of perceived exertion of 13 was equivalent to the other two protocols investigated.
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This study aimed to validate the use of a single blood lactate concentration measure taken following a 12 km h(-1) running stage (BLC12) to predict and monitor fixed blood lactate concentration (FBLC) thresholds. Three complementary studies were undertaken. Study I: the relationships between BLC12 and the running speeds at FBLC of 3 mmol L(-1) (S3mM) and 4 mmol L(-1) (S4mM) measured during a multistage running field test were examined in 136 elite athletes. Study II: data from 30 athletes tested one year apart were used to test the predictive capacity of the equations obtained in Study I. Study III: 80 athletes were tested before and after an intensified training period to examine whether training-induced changes in FBLC thresholds could be predicted and monitored by BLC12. Study I: BLC12 was significantly (P < 0.001) and inversely related to S3mM (R(2) = 0.89) and S4mM (R(2) = 0.95). Study II: prediction models yielded robust correlations between the estimated and measured FBLC thresholds (r = 0.94-0.99; P < 0.001). Study III: estimated changes predicted actual training-induced changes in FBLC thresholds (r = 0.81-0.91; P < 0.001). This study gives empirical support to use a single lactate measure during a sub-maximal running field test as a simple, low-cost and practical alternative to FBLC thresholds in athletes.
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Background It is habitual for combat sports athletes to lose weight rapidly to get into a lower weight class. Fluid restriction, dehydration by sweating (sauna or exercise) and the use of diuretics are among the most recurrent means of weight cutting. Although it is difficult to dissuade athletes from this practice due to the possible negative effect of severe dehydration on their health, athletes may be receptive to avoid weight cutting if there is evidence that it could affect their muscle performance. Therefore, the purpose of the present study was to investigate if hypohydration, to reach a weight category, affects neuromuscular performance and combat sports competition results. Methods We tested 163 (124 men and 39 woman) combat sports athletes during the 2013 senior Spanish National Championships. Body mass and urine osmolality (UOSM) were measured at the official weigh-in (PRE) and 13–18 h later, right before competing (POST). Athletes were divided according to their USOM at PRE in euhydrated (EUH; UOSM 250–700 mOsm · kgH2O−1), hypohydrated (HYP; UOSM 701–1080 mOsm · kgH2O−1) and severely hypohydrated (S-HYP; UOSM 1081–1500 mOsm · kgH2O−1). Athletes’ muscle strength, power output and contraction velocity were measured in upper (bench press and grip) and lower body (countermovement jump - CMJ) muscle actions at PRE and POST time-points. Results At weigh-in 84 % of the participants were hypohydrated. Before competition (POST) UOSM in S-HYP and HYP decreased but did not reach euhydration levels. However, this partial rehydration increased bench press contraction velocity (2.8-7.3 %; p < 0.05) and CMJ power (2.8 %; p < 0.05) in S-HYP. Sixty-three percent of the participants competed with a body mass above their previous day’s weight category and 70 of them (69 % of that sample) obtained a medal. Conclusions Hypohydration is highly prevalent among combat sports athletes at weigh-in and not fully reversed in the 13–18 h from weigh-in to competition. Nonetheless, partial rehydration recovers upper and lower body neuromuscular performance in the severely hypohydrated participants. Our data suggest that the advantage of competing in a lower weight category could compensate the declines in neuromuscular performance at the onset of competition, since 69 % of medal winners underwent marked hypohydration.
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Background: Previous research has shown that cycling in a standing position reduces cycling economy compared with seated cycling. It is unknown whether the cycling intensity moderates the reduction in cycling economy while standing. Purpose: The aim was to determine whether the negative effect of standing on cycling economy would be decreased at a higher intensity. Methods: Ten cyclists cycled in 8 different conditions. Each condition was either at an intensity of 50% or 70% of maximal aerobic power, at a gradient of 4% or 8% and in the seated or standing cycling position. Cycling economy and muscle activation level of 8 leg muscles were recorded. Results: There was an interaction between cycling intensity and position for cycling economy (P = 0.03), the overall activation of the leg muscles (P = 0.02) and the activation of the lower leg muscles (P = 0.05). The interaction showed decreased cycling economy when standing compared with seated cycling, but the difference was reduced at higher intensity. The overall activation of the leg muscles and the lower leg muscles respectively increased and decreased, but the differences between standing and seated cycling were reduced at higher intensity. Conclusions: Cycling economy was lower during standing cycling than seated cycling, but the difference in economy diminishes when cycling intensity increases. Activation of the lower leg muscles did not explain the lower cycling economy while standing. The increased overall activation therefore suggests that increased activation of the upper leg muscles explains part of the lower cycling economy while standing.
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The aim of this study was to determine the effect of time-of-day on sprint swimming performance and on upper and lower body, maximum strength, and muscle power. Twelve well-trained junior swimmers (six male and six female) were tested for bench press (BP) maximum strength and muscle power, jump height countermovement vertical jump (CMJ), crank-arm peak power (10s Wingate test), and time to complete 25 m freestyle at 10:00 am and at 18:00 pm in a random order. Performance was significantly enhanced in the pm compared to the am in 25 m swimming time (1.7%; p = 0.01), BP maximum strength (3.6%, p = 0.04, ES = 1.87), BP muscle power (5.1%, p = 0.00, ES = 2.10), and CMJ height (5.8%; p = 0.02), but not in crank-arm power (4.1%; p = 0.08). Time-of-day increased swimming performance in a magnitude of one-third of the effects observed on upper and lower neuromuscular power, which suggests that factors beyond peak muscle power (i.e. swimming technique) affect 25 m freestyle performance.
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In Olympic combat sports, weight cutting is a common practice aimed to take advantage of competing in weight divisions below the athlete's normal weight. Fluid and food restriction in combination with dehydration (sauna and/or exercise induced profuse sweating) are common weight cut methods. However, the resultant hypohydration could adversely affect health and performance outcomes. The aim of this study is to determine which of the routinely used non-invasive measures of dehydration best track urine osmolality, the gold standard non-invasive test. Immediately prior to the official weigh-in of three National Championships, the hydration status of 345 athletes of Olympic combat sports (i.e., taekwondo, boxing and wrestling) was determined using five separate techniques: i) urine osmolality (UOSM), ii) urine specific gravity (USG), iii) urine color (UCOL), iv) bioelectrical impedance analysis (BIA), and v) thirst perception scale (TPS). All techniques were correlated with UOSM divided into three groups: euhydrated (G1; UOSM 250-700 mOsm·kg H2O-1), dehydrated (G2; UOSM 701-1080 mOsm·kg H2O-1), and severely dehydrated (G3; UOSM 1081-1500 mOsm·kg H2O-1). We found a positive high correlation between the UOSM and USG (r = 0.89: p = 0.000), although this relationship lost strength as dehydration increased (G1 r = 0.92; G2 r = 0.73; and G3 r = 0.65; p = 0.000). UCOL showed a moderate although significant correlation when considering the whole sample (r = 0.743: p = 0.000) and G1 (r = 0.702: p = 0.000) but low correlation for the two dehydrated groups (r = 0.498-0.398). TPS and BIA showed very low correlation sizes for all groups assessed. In a wide range of pre-competitive hydration status (UOSM 250-1500 mOsm·kg H2O-1), USG is highly associated with UOSM while being a more affordable and easy to use technique. UCOL is a suitable tool when USG is not available. However, BIA or TPS are not sensitive enough to detect hypohydration at official weight-in before an Olympic combat championship.
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.
The reliability and accuracy of five portable blood lactate (BLa) analysers (Lactate Pro, Lactate Pro2, Lactate Scout+, Xpress™, and Edge) and one handheld point-of-care analyser (i-STAT) were compared to a criterion (Radiometer ABL90). Two devices of each brand of analyser were assessed using 22 x 6 mL blood samples taken from five subjects at rest and during exercise who generated lactate ranging ~1-23 mM. Each sample was measured simultaneously ~6 times on each device. Reliability was assessed as the within-sample standard deviation (wsSD) of the six replicates; accuracy as the bias compared with the ABL90; and overall error (the root mean squared error (√MSE)) was calculated as the square root of (wsSD(2) and bias(2)). The √MSE indicated that both the Edge and Xpress had low total error (~0-2 mM) for lactate concentrations <15 mM, whereas the Edge and Lactate Pro2 were the better of the portable analysers for concentrations >15 mM. In all cases, bias (negative) was the major contribution to the √MSE. In conclusion, in a clinical setting where BLa is generally <15 mM the Edge and Xpress devices are relevant, but for athlete testing where peak BLa is important for training prescription the Edge and Lactate Pro2 are preferred. Key pointsThe reliability of five common portable blood lactate analysers were generally <0.5 mM for concentrations in the range of ~1.0-10 mM.For all five portable analysers, the analytical error within a brand was much smaller than the biological variation in blood lactate (BLa).Compared with a criterion blood lactate analyser, there was a tendency for all portable analysers to under-read (i.e. a negative bias), which was particularly evident at the highest concentrations (BLa ~15-23 mM).The practical application of these negative biases would overestimate the ability of the athlete and prescribe a training intensity that would be too high.
The aim of the present investigation was to compare power at “onset of blood lactate accumulation” (OBLA), “individual anaerobic threshold” (IAT) and “+1.5 mmol ∙ l⁻¹ lactate model” with power in maximal lactate steady state (MLSS) in cycling. However, there is a lack of studies concerning the absolute individual differences between different lactate parameters and MLSS. A total of 57 male participants performed several 30-min constant-load tests to determine MLSS by measuring blood lactate concentration (BLC). Depending on BLC, power was increased or decreased by 10 W in the following 30-min test. For detecting power at different threshold parameters, an incremental test was performed that began at 40 W and increased by 40 W every 4 min. Highly significant correlations were found between OBLA and MLSS: r=0.89 (mean difference −7.4 W); IAT and MLSS: r=0.83 (mean difference 12.4W), “+1.5 mmol ∙ l⁻¹ lactate model” and MLSS: r=0.88 (mean difference −37.4W). On average, the parameters of OBLA and IAT approximate MLSS with no significant differences. The “+1.5 mmol ∙ l⁻¹ lactate model” underestimates MLSS significantly. Based on Bland-and-Altman, the comparison of power of all threshold parameters with power in MLSS shows great individual differences despite the high regression coefficients and low mean differences between these methods.