RESEARCH ARTICLE
Repeatability and predictive value of lactate
threshold concepts in endurance sports
Jules A. A. C. HeubergerID1*, Pim Gal1, Frederik E. StuurmanID1, Wouter A. S. de Muinck
KeizerID1,2, Yuri Mejia Miranda1, Adam F. Cohen1,3
1 Centre for Human Drug Research, Leiden, the Netherlands, 2 Free University of Amsterdam, Amsterdam,
the Netherlands, 3 Department of Internal Medicine, Leiden University Medical Centre, Leiden, the
Netherlands
* jheuberger@chdr.nl
Abstract
Introduction
Blood lactate concentration rises exponentially during graded exercise when muscles pro-
duce more lactate than the body can remove, and the blood lactate-related thresholds are
parameters based on this curve used to evaluate performance level and help athletes opti-
mize training. Many different concepts of describing such a threshold have been published.
This study aims to compare concepts for their repeatability and predictive properties of
endurance performance.
Methods
Forty-eight well-trained male cyclists aged 18–50 performed 5 maximal graded exercise
tests each separated by two weeks. Blood lactate-related thresholds were calculated using
eight different representative concepts. Repeatability of each concept was assessed using
Cronbach’s alpha and intra-subject CV and predictive value with 45 minute time trial tests
and a road race to the top of Mont Ventoux was evaluated using Pearson correlations.
Results
Repeatability of all concepts was good to excellent (Cronbach’s alpha of 0.89–0.96), intra-
subject CVs were low with 3.4–8.1%. Predictive value for performance in the time trial tests
and road race showed significant correlations ranging from 0.65–0.94 and 0.53–0.76,
respectively.
Conclusion
All evaluated concepts performed adequate, but there were differences between concepts.
One concept had both the highest repeatability and the highest predictability of cycling per-
formance, and is therefore recommended to be used: the Dmax modified method. As an
easier to apply alternative, the lactate threshold with a fixed value of 4 mmol/L could be used
as it performed almost as well.
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
1 / 16
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Heuberger JAAC, Gal P, Stuurman FE, de
Muinck Keizer WAS, Mejia Miranda Y, Cohen AF
(2018) Repeatability and predictive value of lactate
threshold concepts in endurance sports. PLoS ONE
13(11): e0206846. https://doi.org/10.1371/journal.
pone.0206846
Editor: Laurent Mourot, University of Bourgogne
France Comte´, FRANCE
Received: August 18, 2018
Accepted: October 19, 2018
Published: November 14, 2018
Copyright: © 2018 Heuberger 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 data files are
available from the figshare database (DOI: 10.
6084/m9.figshare.7240571).
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
Trial registration
Dutch Trial Registry NTR5643
Introduction
The measurement of blood lactate is extensively used in sports medicine, although there is
debate on how lactate affects fatigue in endurance athletes. [1] Nevertheless, the concentration
of lactate in the blood relative to the exercise intensity is a relevant marker of endurance per-
formance. [2–5] This can be visualized in a blood lactate curve (BLC) using a maximal graded
exercise test (GXT): as the workload on the athlete increases over time, blood lactate concen-
trations (bLa) are measured at defined intervals. During high intensity contractions lactate is
formed along with H+ in the muscles, [6] followed by an increased elimination of lactate from
plasma. [7, 8] When elimination becomes saturated, bLa will start to rise when production
exceeds clearance. This (exponential) rise in bLa in the BLC is of importance, as the corre-
sponding exercise intensity is associated with endurance performance since it correlates with
the transition from aerobic to anaerobic workout. [9] Since the 1960’s BLCs have been ana-
lysed trying to accurately determine a point in this curve that predicts endurance performance.
Although many terms have been used for this point, in this work they will be termed lactate
threshold (LT) concepts. BLCs and LT concepts can be used to assess ‘endurance fitness’ in
athletes, [10] and to evaluate the effects of and to prescribe training exercises for individual
athletes. [4, 5] Therefore these measures are relevant in sports medicine, both in amateur
and professional sports. But as LT is based on a maximal exercise test protocol that does not
directly mimic endurance exercise, finding a single point in the resulting BLC that has a strong
relation to endurance performance is challenging. Moreover, determining where this single
point lies in the relatively smooth curve, that is the result of a complex system of factors, can
prove difficult as well. On the other hand, the more accurate method of determining maxi-
mum lactate steady state (MLSS), using several sessions with different workloads takes more
time, which is the reason why an approximation of MLSS using lactate threshold concepts was
developed. [11]
A previous literature review showed that there are many methods used to analyse the BLCs,
with approximately 25 different concepts identified in literature to describe some form of LT.
[9] These different concepts are used interchangeably throughout scientific studies and in
sports and show variable repeatability and predictive value. Moreover, populations that were
included in different studies often differed in training status, age and category of sport. For
these reasons there is debate about these LT concepts. [9] The aim of this study is to evaluate
the repeatability and predictive value of representative concepts using a large dataset of BLCs
from a group of well-trained cyclists who performed multiple GXTs, time trials and an uphill
road race in the setting of a clinical study.
Materials and methods
Study design and participants
Blood lactate curve data in this paper were generated in a previously published study. [12]
Briefly, the study was a double-blind, randomized, placebo controlled, parallel, single centre
trial to evaluate the effects of recombinant human erythropoietin (rHuEPO) in forty-eight
healthy male cyclists aged 18 to 50. Informed consent was obtained from all individual
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
2 / 16
participants included in the study. The study was approved by the Independent Ethics Com-
mittee of the Foundation Evaluation of Ethics in Biomedical Research (Stichting Beoordeling
Ethiek Biomedisch Onderzoek, Assen, Netherlands). The study is registered in the Dutch Trial
Registry (Nederlands Trial Register), number NTR5643. For inclusion, participants had to be
well-trained, as evaluated by a maximum power-to-weight ratio during the GXT at screening
that should exceed 4 W/kg. During the eleven week study duration, twenty-four participants
received weekly rHuEPO injections and twenty-four received placebo injections for eight
weeks. Participants had to maintain their regular training schedule during the study.
Procedures
Maximal exercise tests.
Five GXTs were performed on a Monark LC4r ergometer
(COSMED, Rome, Italy) with approximately 2-week intervals between each test, see Fig 1.
After a two-minute warm-up at 75 Watts, the GXT dictated an increase in pedalling resistance
to 175 Watts, which increased by an additional 25 Watts every five minutes. Between 4:15 and
4:45 into each step and immediately after termination of the exercise test, blood was drawn to
measure bLa. Gas exchange was measured using a Quark CPET system (COSMED, Rome,
Italy) and breath-by-breath sampling technology. During the test cadence had to be main-
tained between 70 and 90 rpm. The test terminated when cadence could not be maintained
above 70 rpm or when a participant stopped the test.
Lactate determination.
During the GXTs blood for lactate determination was drawn via
an IV cannula (Venflon 7 Pro Safety, BD, Switzerland) with a 30 cm extension set between the
cannula and a three way stopcock for blood sampling in the antecubital vein. Before the first
and after every sampling the stopcock and extension set were flushed with 2 mL saline. Before
blood sampling 0.5 mL was withdrawn from the stopcock to remove any remaining saline.
Next, 1 mL of blood was taken from the stopcock. Within ten seconds from withdrawal the
blood was placed on the Lactate Pro 2 (Arkray, Kyoto, Japan) strip which was then inserted in
the Lactate Pro 2 device. The same device was used throughout the whole study and was given
at least 20 minutes to adjust to the room temperature before sampling.
Time trial tests.
The time trial tests were performed twice on the same ergometer used
for the GXT, with the first (TT1) 3–8 days after the first GXT and the second (TT2) one week
after GXT four. Participants were instructed to produce the highest mean power output during
a 45-minute period at a cadence of 70–90 rpm, attempting to mimic competitive cycling time
trials. At the start of the test pedalling resistance was set at 80% of the maximal power reached
during GXT1. Participants could adjust the power by indicating to increase or decrease in
power by steps of 10 Watts. They were informed of the remaining time on a regular basis dur-
ing the test.
Mont Ventoux race.
Approximately one week after the last GXT participants competi-
tively climbed the Mont Ventoux (Vaucluse de´partement, France) via Be´doin, a climb of
approximately 21.5 km with an average gradient of 7.5%. The race was preceded by a stage of
110 km in the French Provence (total elevation gain 1524 m) that was completed collectively.
Fig 1. Study design. Study design showing timing of different tests. Time point 0 weeks indicates start of treatment
(rHuEPO or placebo) for all participants. GXT, graded exercise test; TT, time trial test; RR, road race.
https://doi.org/10.1371/journal.pone.0206846.g001
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
3 / 16
Racing bikes of participants were equipped with a Single Leg Power Meter SGY-PM910H2
(Pioneer Europe, Antwerp, Belgium) with Shimano Ultegra 6800 crank (Shimano, Osaka,
Japan) to log power data on the bicycle during the race. Data were uploaded to the dedicated
database Cyclo-Sphere.
Lactate threshold concepts.
The BLCs from the GXTs were then used to calculate several
representative LT concepts. Concepts were selected as follows: First, published concepts were
retrieved from a review by Faude et al. [9] and by a literature research within the PubMed
database. The database was searched for the search terms ‘lactate threshold’, ‘aerobic thresh-
old’, ‘anaerobic threshold’, ‘endurance performance’ or ‘maximal lactate steady state’ or similar
terms in different combinations. The references of the selected articles were searched for fur-
ther relevant articles. Secondly, retrieved concepts were divided into seven different categories,
see S1 Table. A few retrieved concepts could not be implemented, reasons being lacking lactate
concentrations in the recovery phase after exercise and no availability of the full text article
describing the method of the concept despite various efforts obtaining it. (S1 Table, listed
under “not selected categories”). From each remaining category, concepts that were represen-
tative and were used frequently in other research were selected. If there were multiple concepts
in one category that were commonly used and fundamentally different in methodology, more
than one concept of that category was included in the analysis. Selecting multiple commonly
used, but very similar concepts from one category was not deemed useful for the purpose of
this study. This resulted in a final selection of eight concepts from the five implementable cate-
gories for analysis in our study.
Implementation of lactate threshold concepts.
All selected concepts were implemented
according to the articles that described the concept (S1 Table). When exact reproduction of
the method was not feasible due to the use of different parameters (e.g. running velocity was
used), we approximated the description as close as possible (e.g. we used power output). For
concepts that required data fitting of the blood lactate curve a third-order polynomial was cho-
sen, based on the shape of the blood lactate curve data and given that it is a proven method,
although there is no generally accepted method for data fitting. [9] An example of a blood lac-
tate curve with a depiction of all lactate threshold concepts is shown in Fig 2.
LT1.
Similar to what Tanaka described [13] we plotted bLa (mmol/L) versus power (W).
Three authors (JH, WdMK and PG) were asked to independently select the first point in the
BLC that marks a substantial increase above resting level. LT1 was defined as the power value
corresponding to the point selected by at least two researchers, or in cases without consensus,
the three researchers discussed until consensus was reached.
LT2.
Coyle et al. [14] determined LT as 1 mmol/L above a visually determined baseline in
the BLC. We took the lactate measurement chosen as LT1 and calculated the mean of the mea-
surements preceding this point to create an average baseline value. The power value belonging
to the first measured lactate value after baseline that supersedes the baseline value plus 1
mmol/L was considered LT2.
LT3.
As Dickhuth et al., [15] we determined the minimum lactate equivalent (the lowest
value when bLa is divided by work intensity) using third-order polynomial fitting and added
1.5 mmol/L to the corresponding bLa, termed individual anaerobic threshold in the paper, to
find the power value on the fitted polynomial of the BLC and termed it LT3.
LT4.
As described by Amann et al., [16] we calculated the first rise of 1 mmol/L or more
between two bLa measurements where the next rise was similar or larger than 1 mmol/L. The
measurement that preceded this first increase was considered LT4.
LT5.
Based on the method described by Dickhuth et al., [17] we divided bLa (mmol/L) by
the 30 second average VO2 (mL/min/kg) and plotted it against power. These values were
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
4 / 16
interpolated with a third-order polynomial and the power value at the lowest point in this
curve was considered LT5.
LT-4mmol.
A widely used concept is the LT-4mmol method, as described for example by
Sjodin et al. [18] The power in the interpolated third-order polynomial BLC that corresponds
to a bLa of 4 mmol/L was considered LT-4mmol.
Dmax and Dmax modified.
Similar to the method proposed by Cheng et al., [19] we plot-
ted bLa versus power, interpolated with a third-order polynomial and plotted a line from the
first measurement to the last measurement. The point in the interpolated BLC that has the
maximum perpendicular distance with that line was considered Dmax. A modified version as
Fig 2. Graphical representation of lactate threshold concepts. Example of a blood lactate curve with the location of the different lactate threshold
concepts for this particular curve. Open circles: observed blood lactate values at each exercise intensity; Black curve: third-order polynomial; Grey
dashed line: baseline; Green circle and arrow: LT1, observer-determined first rise in blood lactate; Yellow circle and arrow: LT2, first observed blood
lactate value more than 1 mmol/L above baseline; Pink circle and arrow: LT3, minimum lactate equivalent (blood lactate divided by power) plus 1.5
mmol/L; Purple circle and arrow: LT4, first blood lactate value that shows an increase of at least 1 mmol/L; Orange circle and arrow: LT5, minimum
lactate equivalent (blood lactate divided by VO2); Brown circle and arrow and dashed line: LT-4mmol, value at 4 mmol/L; Red circle and arrow and
dashed line: Dmax, value with the maximum perpendicular distance to the polynomial from the dashed line; Blue circle and arrow and dashed line:
Dmax-mod, value with the maximum perpendicular distance to the polynomial from the dashed line.
https://doi.org/10.1371/journal.pone.0206846.g002
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
5 / 16
described by Bishop et al., [20] uses the measurement that precedes an increase of at least 0.4
mmol/L instead of the first bLa measurement to draw the line to the last measurement, which
is termed Dmax modified (Dmax-mod).
Data management
Data was stored in a validated database system (Promasys, Omnicomm Inc., Fort Lauderdale,
USA) and checked for accuracy and completeness. Blinded data review before code-breaking
and analysis was performed according to a standard procedure at our unit. This included eval-
uating whether the GXT was performed to maximal ability, which was based on power, VO2
and bLa values and report by the subject.
Statistical analysis
We used statistical software R version 3.4.0 [21] to plot measurements, calculate the third-
order polynomial that best fits the data using polynomial regression with the R-function lm
(y~poly(3)), implement the LT concepts and perform the statistical testing. R was used with
the following packages: dplyr 0.5.0, [22] psych 1.7.5, [23] tidyr 0.6.3. [24] Data of all subjects
enrolled in the study were used in the analysis.
Repeatability.
To measure repeatability we determined the weighted intra-subject coeffi-
cient of variation (CV) and the Cronbach’s alpha based on the five GXT results for each LT
concept. Weighted intra-subject CV was calculated correcting for missing values (CV based
on the sum of the variance per subject multiplied by the amount of measurements, divided by
the total amount of measurements). Both the weighted intra-subject CV and Cronbach’s alpha
were calculated only using data from participants receiving placebo, as there might have been
longitudinal effects of rHuEPO treatment on the GXTs.
Predictive properties.
For the predictive properties we calculated the Pearson correlation
between each LT concept and the mean power of the corresponding relevant endurance
parameter. The LT concept from the GXT closest in time to the endurance tests TT1 and TT2
and road race (see Fig 1), namely GXT 1, 4 and 5 respectively, were used for correlations
between the LT concept and corresponding average power output. In addition, the difference
between each measurement pair was calculated and averaged to create the mean difference
between the LT concept and endurance test power. This value indicates how the power at the
LT concept translates to average endurance power in a time trial or race. For these Pearson
correlation and mean difference analyses both subjects receiving rHuEPO and placebo were
included. This was done as LT concepts are designed to be a predictive parameter for endur-
ance exercise, which should be irrespective of a subject being treated with rHuEPO or not. In
addition, given that the measurements of each pair are at most a week apart, no changes in the
LT concept or endurance performance are expected due to rHuEPO. Moreover, GXT1 and
TT1 were performed before starting the treatment period, and no rHuEPO administrations
took place between GXT5 and the race. For these analyses therefore no treatment effect was
expected and pooling was considered appropriate.
Results
In total 49 subjects entered the study, of which 47 were completers (Fig 3); one subject dropped
out after having performed the first GXT and time trial test and was replaced. One other sub-
ject dropped out after completing two GXTs and one time trial test and was not replaced. Sub-
ject characteristics can be found in Table 1. Of the remaining 238 planned GXTs, five were not
performed due to illness or injury. An additional 22 were excluded from analysis, five due to
having less than four bLa samples for the GXT, most others due to the fact that subjects
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
6 / 16
Fig 3. CONSORT flowchart.
https://doi.org/10.1371/journal.pone.0206846.g003
Table 1. Subject disposition.
All subjects
Placebo subjects
N
48
24
Age (years)
33.6 (20.0–50.0)
33.8 (20.0–50.0)
Weight (kg)
76.9 (9.0; 59.2–95.6)
76.9 (8.9; 59.2–95.6)
Height (cm)
186 (7.3; 172–203)
186 (6.7; 174–203)
Maximal Power output per kg (W/kg)
4.36 (4.03–5.18)
4.36 (4.03–4.94)
VO2,max (mL/min/kg)
55.7 (4.6; 45.3–67.5)
56.0 (4.1; 47.0–62.8)
Values are presented as mean (standard deviation (SD) where appropriate; range where). VO2,max: maximal oxygen
consumption.
https://doi.org/10.1371/journal.pone.0206846.t001
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
7 / 16
indicated having physical problems (e.g. illness/injury, sore legs from recent exercise) poten-
tially affecting test results, leaving 211 GXTs (of which 109 from placebo subjects) with analy-
sable lactate threshold data. A total of 96 time trial tests were performed and used in the
analysis, and power data of 37 subjects was available for the road race. Out of the 47 subjects
that completed the study, three could not participate in the road race, four did not reach the
finish line due to exhaustion, and three did not have a power meter on their bike, therefore
lacking power data for the road race.
Lactate threshold concepts and endurance
All eight LT concepts were successfully implemented on the GXT data; for LT1 which was
determined visually by three researchers, a unanimous decision about the lactate threshold
was reached in 56.8% of the tests, in 40.0% of the cases two out of three researchers agreed
and there was originally no consensus in 3.2% of the tests. Several concepts were based on the
third-order polynomial data fitting, mean r-squared values of all individual curves were 0.978
(SD = 0.032, range 0.716–1.000). Mean values for each LT concept of the placebo group can be
found in Table 2. Mean (SD) power output for TT1 was 268 W (28 W) in the placebo group
and 271 W (29 W) in the rHuEPO group, and estimated mean for TT2 was 277 W and 283 W
for the placebo and rHuEPO groups respectively. Estimated mean power during RR were 266
W and 257 W for the two groups, during a mean race time of 1 h 37 min 45 s (SD = 12 min 40
s) and 1 h 38 min 23 s (SD = 14 min 9 s), respectively.
Repeatability
The overall intra-subject CV of each LT concept is indicated in Table 2, and shows some
minor differences between concepts, with LT3, LT-4mmol, Dmax and Dmax-mod having
CVs < 5% and LT5 having the highest intra-subject CV with 8.1%. The Cronbach’s alpha val-
ues for all LT concepts in the placebo group are between 0.89 and 0.97 and although 95% CIs
largely overlap, the same four concepts as observed for intra-subject CVs perform best with
Cronbach’s alpha values >0.95 (Table 3).
Predictive properties
Pearson correlation coefficients and the mean difference between each correlation pair are
listed in Table 4. All correlations are highly significant (p<0.0002), indicating the null
Table 2. Mean lactate threshold concept power output.
GXT
number
LT1 (W)
LT2 (W)
LT3 (W)
LT4 (W)
LT5 (W)
LT-4mmol (W)
Dmax (W)
Dmax-mod (W)
1
283.3 (29.9; 225–
350)
292.9 (37.2; 250–
375)
286.1 (32.9; 219–
352)
275.0 (41.1; 200–
350)
225.0 (31.2; 175–
283)
301.8 (41.0; 222–
381)
275.7 (24.6; 222–
323)
299.5 (35.3; 225–
367)
2
283.0 (22.3; 225–
375)
293.2 (31.0; 250–
375)
288.7 (29.2; 231–
373)
276.1 (34.0; 175–
375)
231.8 (25.7; 175–
300)
305.0 (33.0; 234–
389)
280.0 (23.6; 233–
339)
301.2 (28.7; 237–
369)
3
281.0 (29.5; 225–
400)
290.5 (27.9; 250–
400)
285.7 (26.3; 240–
390)
272.6 (33.5; 225–
375)
224.5 (29.5; 175–
318)
300.8 (28.9; 253–
411)
278.7 (20.9; 250–
343)
297.5 (29.6; 257–
413)
4
283.7 (35.8; 225–
400)
292.4 (38.0; 225–
425)
291.6 (29.1; 240–
392)
272.8 (36.9; 200–
400)
229.8 (29.5; 175–
323)
307.0 (34.2; 249–
415)
284.0 (22.7; 232–
338)
308.7 (35.6; 251–
396)
5
278.3 (28.5; 225–
325)
290.2 (37.5; 225–
350)
285.0 (31.6; 216–
339)
271.7 (37.9; 200–
350)
230.4 (30.5; 175–
274)
297.2 (38.9; 204–
364)
280.3 (20.3; 245–
325)
298.9 (29.2; 253–
365)
Overall
282.1 (5.7%)
292.2 (5.0%)
287.7 (3.6%)
274.1 (5.6%)
228.4 (8.1%)
302.7 (3.8%)
280.0 (3.4%)
301.5 (4.3%)
Weighted mean power output (SD; range) for the placebo group at every exercise test. Overall combined (based on 109 GXTs) for each lactate threshold concept (CV).
CV is weighted intra-subject CV.
https://doi.org/10.1371/journal.pone.0206846.t002
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
8 / 16
hypothesis that the correlation is equal to zero can be rejected. The strength of the relationship
differs for different concepts. Correlation with TT1 was very strong for Dmax-mod and strong
for all other concepts except LT5, which showed a moderate correlation. Correlation with TT2
was strong for all concepts except LT5, which showed a moderate correlation. Correlation
with RR was strong for Dmax and Dmax-mod, and moderate for all other concepts. Dmax-
mod has the highest correlation with time trial test 1 (r = 0.94), LT-4mmol with time trial test
2 (r = 0.85) and Dmax-mod with road race power (r = 0.76). The mean difference with the
endurance parameters differs substantially between concepts, ranging from the lactate thresh-
old on average being up to 45.3 W lower than the related endurance parameter for LT5 to 36.6
W higher for LT-4mmol. Linear regression between each LT concept and average race power,
including accompanying r2 values, is plotted in Fig 4.
Discussion
All LT concepts that were included in this analysis performed good on repeatability and rea-
sonable to good on predicting a lab-based time trial and a real-life road race. Nevertheless, this
Table 3. Cronbach’s alpha for each lactate threshold concept.
Lactate threshold concept
Cronbach’s alpha
Lower 95% CI
Upper 95% CI
LT1
0.91
0.85
0.96
LT2
0.95
0.92
0.98
LT3
0.97
0.94
0.99
LT4
0.94
0.91
0.98
LT5
0.89
0.82
0.96
LT 4_mmol
0.96
0.94
0.99
Dmax
0.96
0.93
0.98
Dmax-mod
0.96
0.94
0.98
Cronbach’s alpha for the placebo group for each lactate threshold concept with 95% confidence interval (CI).
https://doi.org/10.1371/journal.pone.0206846.t003
Table 4. Predictive value of lactate threshold concepts.
Lactate threshold concept
Pearson correlation
Mean difference (SD)
TT1
TT2
RR
TT1
TT2
RR
n = 42
n = 46a
n = 34b
n = 42
n = 46a
n = 34b
LT1
0.78
0.74
0.54
-11.3 (18.3)
-8.7 (22.1)
-9.8 (37.2)
LT2
0.87
0.80
0.53
-23.2 (16.0)
-18.5 (19.3)
-27.4 (39.3)
LT3
0.88
0.84
0.64
-16.2 (14.3)
-16.1 (18.1)
-21.9 (33.8)
LT4
0.78
0.82
0.61
-3.5 (23.7)
-0.6 (26.2)
-13.5 (36.1)
LT5
0.67
0.65
0.58
43.7 (21.7)
45.3 (23.5)
39.1 (35.9)
LT-4mmol
0.88
0.85
0.61
-31.7 (19.4)
-32.3 (23.0)
-36.6 (36.1)
Dmax
0.89
0.82
0.73
-4.4 (12.1)
-3.8 (15.8)
-13.4 (32.4)
Dmax-mod
0.94
0.84
0.76
-27.3 (11.8)
-29.9 (16.6)
-33.7 (29.1)
Pearson correlation between each lactate threshold concept in GXT 1 and time trial test 1 (TT1), GXT 4 and time trial test 2 (TT2) and GXT 5 and average road race
(RR) power for all subjects combined. All correlations are significant (p<0.0002). To determine potential differences in power output between the LT concept and time
trial power or race power, mean difference (SD) between each measurement pair is calculated. Negative values indicate lactate threshold power is higher than exercise
test average power.
a For LT5 n = 44;
b for LT5 n = 32.
https://doi.org/10.1371/journal.pone.0206846.t004
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
9 / 16
study identified several LT concepts that outperformed the others in the setting of this trial.
The best method being Dmax-mod, but Dmax, LT-4mmol and LT3 performed well too.
Methodology
The design of the exercise protocol, for example stage duration, is known to impact blood
lactate curves. [25, 26] We selected an exercise protocol with five minute stages and 25 W
increments because it takes 3–4 minutes for the body to reach steady state and lactate accom-
panying that effort level can be measured accurately. [27] In addition, longer protocols may be
more sensitive to performance changes. [25] As described in more detail elsewhere, [12] GXT
results show our subjects were well-trained with maximal power output and VO2 max values
comparable to elite cyclists and triathletes when using longer exercise protocols. [28, 29] All
evaluated concepts were applied to data from the same exercise tests, with the same sampling
and assay method, and the same fitting procedure was used for those applicable concepts. As a
result such factors could not have affected the comparison between concepts within this study.
The current study was designed in that way to give the most accurate estimate of performance
parameters and its controlled set-up seems to be the most robust and valuable way to deter-
mine differences between concepts. Nevertheless, when any of these factors are changed (e.g.
using a different exercise test protocol) it is possible the outcomes might not translate perfectly.
With regards to data fitting, the third-order polynomial in the applicable concepts performed
well given the high mean r-squared values observed.
Selection of concepts
After inspection of all identified lactate concepts, it became clear that there were similarities
between quite some of them. For this reason, the concepts were grouped into categories, and a
selection was made of concepts to be analysed to have at least one representative per category
Fig 4. Linear regression lactate threshold concept power and average race power. Linear regression of lactate threshold power and average race power per LT
concept for all subjects depicting linear regression line (solid line) and 95% confidence interval (dotted lines). r2: R-squared or coefficient of determination is the
proportion of the variance in the dependent variable that is predictable from the independent variable.
https://doi.org/10.1371/journal.pone.0206846.g004
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
10 / 16
and thereby ensuring that results from this study would be informative for all regularly used
lactate concepts. The selection includes concepts such as a fixed lactate value (usually at 4
mmol/L) and the visually determined LT concepts that have been used since the conception of
the LT, and more recent concepts such as LT3, LT4 and Dmax and Dmax-mod. [9]
Mean threshold
The mean power output (Table 2) is relatively constant over time for each concept. These
results confirm there was no placebo-effect on any of the LT concepts, although such an
impact would already theoretically be improbable. What can also be seen is that not all con-
cepts seem to identify the same point in the blood lactate curve: LT5 gives the lowest estimate
of LT (228.4 W), much lower than other concepts (274.1–302.7 W). LT-4mmol and Dmax-
mod have the highest estimates (302.7 and 301.5 W), indicating these concepts identify differ-
ent intensities of performance and have different physiological meanings. Applying the termi-
nology as described in Faude et al, [9] based on mean threshold and mean difference with TT
and RR (Table 4), some concepts seem to be more related to the aerobic threshold (LT5), oth-
ers to the aerobic/anaerobic transition (e.g. LT1, LT4, Dmax) or the anaerobic threshold (LT-
4mmol, Dmax-mod).
Repeatability
Intra-subject CV’s over all five measurements were low (3.4–8.1%) and Cronbach’s alphas
high (0.89–0.97), indicating repeatability of all concepts over the study period of approximately
8 weeks was good. This corresponds well to previous findings of repeatability for power or
speed at different lactate concepts, both in terms of CV, determined at 1.3–5.9% in a meta-ana-
lytic review, [30] and in terms of Pearson correlations 0.88–0.96 [31, 32] or ICC 0.98–0.99.
[33] One study applied different LT concepts to the same dataset from two exercise tests and
showed that intra-subject CV’s and correlation was good for LT2, LT-4mmol and a concept
similar to LT4 (CV 3–4% and r  0.85), but not for Dmax (10.3% and 0.57). [34] Our data,
based on more subjects (24 versus 14) and more measurements per subject (5 versus 2), dis-
putes this relatively poor repeatability for Dmax. However, our study does show differences
between concepts, with LT3, LT-4mmol, Dmax and Dmax-mod having the lowest intra-sub-
ject CV (<5%) and the highest Cronbach’s alpha (>0.95).
Correlation with performance
As we have established that CV and repeatability for all LT concepts was good, the most rele-
vant question is whether these concepts correlate to actual endurance performance. As previ-
ously indicated, it is highly unlikely that the rHuEPO treatment impacted this particular
correlation analysis. When analysing the groups separately, some differences in correlation
coefficients could be observed between the two groups (data not shown), but these differences
were already present for the correlation between GXT1 and TT1 when treatment had not yet
started, indicating that this was not due to rHuEPO treatment. Because combining all subjects
generates more informative and robust results being based on a bigger population, pooling the
groups was considered justified.
Data in Table 4 show that for all concepts correlations with time trial tests were higher com-
pared to the road race (based on all subjects median of all concepts r = 0.875 for TT1 and 0.82
for TT2, versus 0.61 for RR). This is most likely partly due to additional variability in the road
race due to the circumstances (e.g. weather, uphill racing with changes in steepness over the
course, and differences in race duration (range 72–126 minutes)). Possibly there was also a
minor impact of using different equipment for power measurement during the RR, as it was
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
11 / 16
not measured on the ergometer but on the subjects’ bike. What can also be seen is that correla-
tion of the LT concepts with TT1 in general is slightly higher than with TT2. More importantly
however, correlation for both time trials show that the ranking among different concepts is
very similar, confirming the results are robust. It seems that in general, using a technique of
interpolation for the BLC has superior performance, as LT concepts that were based on the
third-order polynomial derived from the individual lactate concentration measurements (LT3,
LT5, LT-4mmol, Dmax, Dmax-mod) performed better than the ones that used actual mea-
sured bLa values without interpolation (LT1, LT2 and LT4), with the exception of LT5. This
poor performance of LT5 is most likely due to the fact that it is conceptually different from the
other concepts; it is the power at the minimum lactate equivalent, in this case the lowest value
for the lactate-VO2 ratio. In contrast, LT3 also uses a form of the minimum lactate equivalent,
but it adds 1.5 mmol/L to this value. As can be seen in Table 2 and Fig 2, this leads to LT5 on
average determining a point even before the first rise in lactate concentration as determined
by LT1. This concept therefore relates to much lower (aerobic) work intensities than the other
concepts. Additionally it is less repeatable (see Table 3). From all tested concepts LT5 corre-
lates least with 45 min TT performance, but for the longer RR performance relative to the
other concepts it performs somewhat better than for TT. This could mean that is this concept
is more related to long-term exercise efforts.
Many studies previously evaluated correlations of LT concepts with endurance perfor-
mance, of which most used running performance. An overview of these studies by Faude et al
shows a median r = 0.84–0.92 for several different LT concepts for endurance distances
(>5km), [9] comparable to our results. There are fewer studies that have compared LT con-
cepts and their correlation with different types of cycling endurance performance, [16, 20, 26,
35–38] but correlation with endurance performances (30–90 minutes) for each concept seem
to vary between these studies, see Table 5. In addition, the comparison between concepts
within these studies shows varying conclusions about which is the best concept. This could
partially be due to differences between studies, for example study populations differ (mean
VO2max ranges from 48 to 68 mL/kg/min, and some studying female, others male cyclists
and/or triathletes). However, they are more or less as heterogeneous as our population with an
SD of 4–8 mL/kg/min on VO2max. The applied exercise protocols all used long stages similar
to ours (3–5 minutes), although the increases in workload differ (20-50W). Finally, correlation
to endurance exercise was based on time trials that lasted between approximately 30 to 90 min-
utes (our TT of 45 min at the lower end and RR of on average 98 min at the higher end), a dif-
ference that might impact the correlation to different LT concepts. Nevertheless, taking these
differences into account, comparison is possible, albeit with some caution. Moreover, a robust
Table 5. Reported correlations between LT concepts and endurance performance.
Correlation reported in publication
Lactate threshold concept
LT1
LT2
LT4
LT-4mmol
Dmax
Dmax-mod
LTlog
Amann [16]
-
0.72
0.59
0.60
-
-
-
Bentley [37]
-
-
-
0.54
0.77
-
0.91
Bishop [20]
0.81
-
0.61
0.81
0.84
0.83
0.69
Borszcz [35]
-
0.31
-
0.56
0.75
-
-
McNaughton [38]
-
-
-
0.90
0.91
-
0.86
Nichols [36]
-
-
0.88
0.67
-
-
-
Literature data for LT concepts and correlation with 30–90 minute-during performances. LTlog: the power output at which bLa starts to increase when log(bLa) is
plotted against log(power output).
https://doi.org/10.1371/journal.pone.0206846.t005
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
12 / 16
and valid LT concept should perform well in any of these datasets. What can be observed is
that all these concepts except LT1, Dmax and Dmax-mod have shown correlations below 0.75,
and that in all four direct comparisons that evaluated both Dmax and LT-4mmol, Dmax
showed a higher correlation. This latter finding could be due to the fact that LT-4mmol is
less robust to changes in settings such as exercise protocol duration, sampling site and lactate
analyser because of its fixed nature. Our study expands on this information, and compared to
previous studies as reviewed in Table 5, is based on approximately 2–4 times more subjects,
therefore allowing for more robust conclusions. This is especially true since our population is a
heterogeneous well-trained, and therefore relevant, group (range maximal power output at
baseline 256–425 W). Similar to what can be extracted from the literature, our study too shows
that Dmax and Dmax-mod have highest correlations with time trial performance, although
LT-4mmol and LT3 show a similarly high correlation in our study. For the correlation with
RR, there are slightly larger differences between concepts. Correlation is highest for Dmax and
Dmax-mod, mainly because for the other concepts correlation for a few subjects is very poor,
as visualized in Fig 4 (e.g. for LT-4mmol). These findings combined, we conclude that Dmax,
and even more so Dmax-mod, have the best correlation with endurance performance. One
recent study evaluated correlation between MLSS, which could be considered to be the gold
standard for the physiological endurance threshold, and different LT concepts generated from
GXTs with different protocol durations. [26] This study concluded that for a GXT with 4-min-
ute steps (most similar to our GXT), correlation was high for many of the concepts, but validity
was highest for LT-2.5mmol, Dmax-mod, and two modified versions of Dmax-mod. In con-
trast, LT2, LT-4mmol and Dmax showed much higher mean differences with MLSS and there-
fore were designated as invalid estimates of MLSS. Combining these findings with our own
results, Dmax-mod determined in a GXT with approximately 5-minute stages is both a valid
estimate of MLSS and has a high correlation with actual endurance performance.
Absolute power difference
The mean difference of each concept with the endurance parameter gives an indication of how
the absolute power of the LT concept corresponds to the average power produced during TT
and RR. On average, power is higher compared to the endurance test for each concept (except
the poorest performing concept LT5). This difference in power between LT concepts and
endurance test is possibly due to having to sustain the power for a much longer time during
the endurance tests, needing a systematic lower power in order to cope with the effort. Inter-
estingly, Dmax-mod and LT-4mmol, concepts that show among the highest correlations, have
the largest difference in absolute power (approximately 30 W). Given the high correlation with
performance this should not disqualify these concepts, but one should take into account that
there is a systematic difference with endurance performance of approximately 30 W.
Conclusions
LT concepts are correlated with endurance performance, but a review showed that many dif-
ferent concepts are used in literature, which is undesirable. [9] Also for cycling performance,
there is no consensus on which LT concept should be applied and results vary highly. [16, 20,
35–38] In this study we compared eight different representative LT concepts on the same large
cycling performance dataset to evaluate repeatability and predictive properties. All concepts
showed high repeatability, and correlated with endurance performance. However, LT3, LT-
4mmol, Dmax and Dmax-mod showed the best repeatability, and had the highest correlation
with time trial performance. As correlation with performance was consistently high for Dmax
and Dmax-mod, also with the uphill road race, the latter performing slightly better on each
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
13 / 16
criterion, and because Dmax-mod was previously shown to be a valid estimate of MLSS, we
would recommend using Dmax-mod when analyzing the blood lactate curve.
Supporting information
S1 Table. Lactate threshold concept categories.
(DOCX)
S1 Protocol. Study protocol CHDR1514.
(PDF)
Author Contributions
Conceptualization: Jules A. A. C. Heuberger, Adam F. Cohen.
Data curation: Jules A. A. C. Heuberger, Pim Gal, Frederik E. Stuurman.
Formal analysis: Jules A. A. C. Heuberger, Wouter A. S. de Muinck Keizer, Yuri Mejia
Miranda.
Investigation: Jules A. A. C. Heuberger, Pim Gal, Frederik E. Stuurman.
Methodology: Jules A. A. C. Heuberger.
Supervision: Jules A. A. C. Heuberger, Adam F. Cohen.
Writing – original draft: Jules A. A. C. Heuberger.
Writing – review & editing: Pim Gal, Frederik E. Stuurman, Wouter A. S. de Muinck Keizer,
Yuri Mejia Miranda, Adam F. Cohen.
References
1.
Cairns SP. Lactic acid and exercise performance: culprit or friend? Sports Med. 2006; 36(4):279–91.
https://doi.org/10.2165/00007256-200636040-00001 PMID: 16573355
2.
Atkinson G, Davison R, Jeukendrup A, Passfield L. Science and cycling: current knowledge and future
directions for research. J Sports Sci. 2003; 21(9):767–87. https://doi.org/10.1080/
0264041031000102097 PMID: 14579871
3.
Kindermann W, Simon G, Keul J. The significance of the aerobic-anaerobic transition for the determina-
tion of work load intensities during endurance training. Eur J Appl Physiol Occup Physiol. 1979; 42
(1):25–34. PMID: 499194
4.
Londeree BR. Effect of training on lactate/ventilatory thresholds: a meta-analysis. Med Sci Sports
Exerc. 1997; 29(6):837–43. PMID: 9219214
5.
Antonutto G, Di Prampero PE. The concept of lactate threshold. A short review. J Sports Med Phys Fit-
ness. 1995; 35(1):6–12. PMID: 7474995
6.
Robergs RA, Ghiasvand F, Parker D. Biochemistry of exercise-induced metabolic acidosis. Am J Phy-
siol Regul Integr Comp Physiol. 2004; 287(3):R502–16. https://doi.org/10.1152/ajpregu.00114.2004
PMID: 15308499
7.
MacRae HS, Dennis SC, Bosch AN, Noakes TD. Effects of training on lactate production and removal
during progressive exercise in humans. J Appl Physiol (1985). 1992; 72(5):1649–56.
8.
Stanley WC, Gertz EW, Wisneski JA, Neese RA, Morris DL, Brooks GA. Lactate extraction during net
lactate release in legs of humans during exercise. J Appl Physiol (1985). 1986; 60(4):1116–20.
9.
Faude O, Kindermann W, Meyer T. Lactate threshold concepts: how valid are they? Sports Med. 2009;
39(6):469–90. https://doi.org/10.2165/00007256-200939060-00003 PMID: 19453206
10.
Coyle EF, Coggan AR, Hopper MK, Walters TJ. Determinants of endurance in well-trained cyclists. J
Appl Physiol (1985). 1988; 64(6):2622–30.
11.
Billat VL, Sirvent P, Py G, Koralsztein JP, Mercier J. The concept of maximal lactate steady state: a
bridge between biochemistry, physiology and sport science. Sports Med. 2003; 33(6):407–26. https://
doi.org/10.2165/00007256-200333060-00003 PMID: 12744715
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
14 / 16
12.
Heuberger J, Rotmans JI, Gal P, Stuurman FE, van’t Westende J, Post TE, et al. Effects of erythropoie-
tin on cycling performance of well trained cyclists: a double-blind, randomised, placebo-controlled trial.
Lancet Haematol. 2017; 4(8):e374–e86. https://doi.org/10.1016/S2352-3026(17)30105-9 PMID:
28669689
13.
Tanaka H. Predicting running velocity at blood lactate threshold from running performance tests in ado-
lescent boys. Eur J Appl Physiol Occup Physiol. 1986; 55(4):344–8. PMID: 3758032
14.
Coyle EF, Martin WH, Ehsani AA, Hagberg JM, Bloomfield SA, Sinacore DR, et al. Blood lactate thresh-
old in some well-trained ischemic heart disease patients. J Appl Physiol Respir Environ Exerc Physiol.
1983; 54(1):18–23. https://doi.org/10.1152/jappl.1983.54.1.18 PMID: 6826403
15.
Dickhuth H-H, Yin L, Niess A, Rocker K, Mayer F, Heitkamp HC, et al. Ventilatory, lactate-derived and
catecholamine thresholds during incremental treadmill running: relationship and reproducibility. Int J
Sports Med. 1999; 20(2):122–7. https://doi.org/10.1055/s-2007-971105 PMID: 10190774
16.
Amann M, Subudhi AW, Foster C. Predictive validity of ventilatory and lactate thresholds for cycling
time trial performance. Scand J Med Sci Sports. 2006; 16(1):27–34. https://doi.org/10.1111/j.1600-
0838.2004.00424.x PMID: 16430678
17.
Dickhuth H.-H.; Huonker M. MT, Drexler H., Berg A., Keul J. Individual anaerobic threshold for evalua-
tion of competitive athletes and patients with left ventricular dysfunctions. Advances in ergometry. 1991.
18.
Sjodin B, Jacobs I. Onset of blood lactate accumulation and marathon running performance. Int J Sports
Med. 1981; 2(1):23–6. https://doi.org/10.1055/s-2008-1034579 PMID: 7333732
19.
Cheng B, Kuipers H, Snyder AC, Keizer HA, Jeukendrup A, Hesselink M. A new approach for the deter-
mination of ventilatory and lactate thresholds. Int J Sports Med. 1992; 13(7):518–22. https://doi.org/10.
1055/s-2007-1021309 PMID: 1459746
20.
Bishop D, Jenkins DG, Mackinnon LT. The relationship between plasma lactate parameters, Wpeak
and 1-h cycling performance in women. Med Sci Sports Exerc. 1998; 30(8):1270–5. PMID: 9710868
21.
Chambers J. Project R The R Project for Statistical Computing [3.4.0:[https://www.r-project.org/.
22.
Hadley Wickham RF, Lionel Henry, Kirill Mu¨ller. dplyr: A Grammar of Data Manipulation dplyr: A Gram-
mar of Data Manipulation2017 [https://cran.r-project.org/web/packages/dplyr/.
23.
Revelle W. psych: Procedures for Psychological, Psychometric, and Personality Research psych: Pro-
cedures for Psychological, Psychometric, and Personality Research2017 [https://cran.r-project.org/
web/packages/psych/.
24.
Hadley Wickham LH. tidyr: Easily Tidy Data with ’spread()’ and ’gather()’ Functions tidyr: Easily Tidy
Data with ’spread()’ and ’gather()’ Functions2017 [https://cran.r-project.org/web/packages/tidyr/.
25.
Bentley DJ, Newell J, Bishop D. Incremental exercise test design and analysis: implications for perfor-
mance diagnostics in endurance athletes. Sports Med. 2007; 37(7):575–86. https://doi.org/10.2165/
00007256-200737070-00002 PMID: 17595153
26.
Jamnick NA, Botella J, Pyne DB, Bishop DJ. Manipulating graded exercise test variables affects the
validity of the lactate threshold and [Formula: see text]. PLoS One. 2018; 13(7):e0199794. https://doi.
org/10.1371/journal.pone.0199794 PMID: 30059543
27.
Thoden JS. Testing aerobic power. In: MacDougall JD, Wenger HA, Green HJ, editors. Physiological
testing of the high-performance athlete. Champaign: Human Kinetics; 1991. p. 107–74.
28.
San Millan I, Bing K, Brill C, Hill JC, Miller LE. Randomized controlled trial of Micro-Mobile Compression
(R) on lactate clearance and subsequent exercise performance in elite male cyclists. Open Access J
Sports Med. 2013; 4:221–7. https://doi.org/10.2147/OAJSM.S51956 PMID: 24379728
29.
Bentley DJ, McNaughton LR. Comparison of W(peak), VO2(peak) and the ventilation threshold from
two different incremental exercise tests: relationship to endurance performance. J Sci Med Sport. 2003;
6(4):422–35. PMID: 14723392
30.
Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med.
2001; 31(3):211–34. https://doi.org/10.2165/00007256-200131030-00005 PMID: 11286357
31.
Weltman A, Snead D, Stein P, Seip R, Schurrer R, Rutt R, et al. Reliability and validity of a continuous
incremental treadmill protocol for the determination of lactate threshold, fixed blood lactate concentra-
tions, and VO2max. Int J Sports Med. 1990; 11(1):26–32. https://doi.org/10.1055/s-2007-1024757
PMID: 2318561
32.
Grant S, McMillan K, Newell J, Wood L, Keatley S, Simpson D, et al. Reproducibility of the blood lactate
threshold, 4 mmol.l(-1) marker, heart rate and ratings of perceived exertion during incremental treadmill
exercise in humans. Eur J Appl Physiol. 2002; 87(2):159–66. https://doi.org/10.1007/s00421-002-0608-
2 PMID: 12070627
33.
Pfitzinger P, Freedson PS. The reliability of lactate measurements during exercise. Int J Sports Med.
1998; 19(5):349–57. https://doi.org/10.1055/s-2007-971929 PMID: 9721059
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
15 / 16
34.
Pallares JG, Moran-Navarro R, Ortega JF, Fernandez-Elias VE, Mora-Rodriguez R. Validity and Reli-
ability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. PLoS One. 2016; 11(9):
e0163389. https://doi.org/10.1371/journal.pone.0163389 PMID: 27657502
35.
Borszcz FK, Tramontin AF, de Souza KM, Carminatti LJ, Costa VP. Physiological Correlations With
Short, Medium, and Long Cycling Time-Trial Performance. Research quarterly for exercise and sport.
2018; 89(1):120–5. https://doi.org/10.1080/02701367.2017.1411578 PMID: 29334005
36.
Nichols JF, Phares SL, Buono MJ. Relationship between blood lactate response to exercise and endur-
ance performance in competitive female master cyclists. Int J Sports Med. 1997; 18(6):458–63. https://
doi.org/10.1055/s-2007-972664 PMID: 9351693
37.
Bentley DJ, McNaughton LR, Thompson D, Vleck VE, Batterham AM. Peak power output, the lactate
threshold, and time trial performance in cyclists. Med Sci Sports Exerc. 2001; 33(12):2077–81. PMID:
11740302
38.
McNaughton LR, Roberts S, Bentley DJ. The relationship among peak power output, lactate threshold,
and short-distance cycling performance: effects of incremental exercise test design. J Strength Cond
Res. 2006; 20(1):157–61. https://doi.org/10.1519/R-15914.1 PMID: 16506862
Lactate threshold concepts in endurance exercise
PLOS ONE | https://doi.org/10.1371/journal.pone.0206846
November 14, 2018
16 / 16

Repeatability and predictive value of lactate threshold concepts in endurance sports.
Heuberger, Jules A A C,Gal, Pim,Stuurman, Frederik E,de Muinck Keizer, Wouter A S,Mejia Miranda, Yuri,Cohen, Adam F