The Influence of Sex, Stroke and Distance on the Lactate
Characteristics in High Performance Swimming
Benjamin Holfelder*, Niklas Brown, Dieter Bubeck
Department of Sport and Exercise Science, University of Stuttgart, Stuttgart, Germany
Abstract
Background: In order to achieve world-class performances, regular performance diagnostics is required as an essential
prerequisite for guiding high performance sport. In high performance swimming, the lactate performance diagnostic is an
important instrument in testing the sport specific endurance capacity. Although the role of lactate as a signaling molecule,
fuel and a gluconeogenic substrate is accepted, lactate parameters are discussed concerning stability, explanatory power
and interpretability.
Methods: We calculated the individual anaerobic threshold (IAT) of Bunc using the swimming-specific lactate threshold test
by Pansold.
Results: The cross-sectional analysis (ANOVA) of n = 398 high performance swimmers showed significant effects for sex,
stroke and distance on the IAT, the percentage of personal best time on the IAT (% of PB on IAT) and maximal lactate values
(max. bLA). For the freestyle events the IAT decreased, % of PB on IAT and max. bLA increased from 100 to 400 m
significantly in men and women. Women showed significantly higher % of PB on IAT with descriptive lower IAT in 7 of 8
analyzed events. Men showed significantly higher max. bLA in 5 of 8 events. In the second step, the analysis of 1902 data
sets of these 398 athletes with a multi-level analysis (MLA) showed also significant effects for sex, swimming distance and
stroke. For initial status and development over time, the effect sizes for the variables distance and sex were medium to
large, whereas for stroke there were no or small effect sizes.
Discussion: These significant results suggest that lactate tests in swimming specifically have to consider the lactate
affecting factors sex and distance under consideration of the time period between measurements. Anthropometrical factors
and the physiology of women are possible explanations for the relative better performance for lower lactate concentrations
compared to men.
Citation: Holfelder B, Brown N, Bubeck D (2013) The Influence of Sex, Stroke and Distance on the Lactate Characteristics in High Performance Swimming. PLoS
ONE 8(10): e77185. doi:10.1371/journal.pone.0077185
Editor: Jonatan R. Ruiz, University of Granada, Spain
Received March 6, 2013; Accepted September 2, 2013; Published October 22, 2013
Copyright:  2013 Holfelder 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.
Funding: This work was supported by the German Research Foundation (DFG) within the funding programme Open Access Publishing. The funder had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: benjamin.holfelder@inspo.uni-stuttgart.de
Introduction
In order to achieve maximum performance in important
competitions, regular performance diagnostics is required as an
essential prerequisite for guiding high performance sport [1]. It is
used to determine the actual performance and thus enhance the
planning and periodization of the training process [2], to recognize
the athletes actual stress-recovery balance and integrate it into the
training schedule. For analyzing the endurance capacity in
swimming, measuring lactate is common, due to the difficult
conditions for spirometric testing in a pool. However, lactate
parameters are currently discussed concerning their stability,
explanatory power, validity and interpretability, because factors
like the training state, in particular overtraining [3], diet and
nutritional status [4] and the types and sizes of muscle groups and
fibers [5] are affecting the individual lactate kinetics. Although
research on lactate is far away from complete [1,6] the role of
lactate as a signaling molecule, fuel and a gluconeogenic substrate
is accepted [5,7,8]. However, the determination of the individual
anaerobic threshold (IAT) by means of lactate concentration is still
a gold standard [9,10]. Besides, there is currently no adequate
method in swimming to substitute the lactate diagnostic in the
field, thus it seems important to increase the knowledge of lactate
affecting factors before, during and after exercise to further
optimize the interpretation [1]. Thus, this article evaluates the use
of the IAT of Bunc et al. [11] on the lactate threshold test of
Pansold [12], which is used for assessing the sport specific
endurance capacity in the German Swimming Association (DSV),
since the beginning of the 1990 s. The choice of the threshold
concept of Bunc et al. [11] can be explained by the exponential
function as a common functional basis with the Pansold-test and
the calculation of the IAT regarding the characteristics of the
whole lactate curve. The consideration of these seems important
for more reliable statements in performance diagnostics, because
thereby it is easier to differentiate between sprinters, endurance
athletes and untrained people [4,13].
PLOS ONE | www.plosone.org
1
October 2013 | Volume 8 | Issue 10 | e77185
Influence of Sex
Because of anthropometric, hormonal and genetic differences,
sex is a major factor influencing best performances [14].
Specifically for swimming, several studies [15,16] reported that
the technique of women is more economical than the technique of
men. This could be explained by anthropometrical factors like
body density, a lower hydrodynamic torque and the better ability
to adapt to a horizontal body alignment [15,17]. For example, a
higher body fat content, naturally observed in women [18],
increases the prone gliding distance [19]. It can be assumed, that a
more economical technique will cause lower lactate values of
comparable load situations. However, could only identify sex-
specific differences for the freestyle events, with greater post-race
lactate concentrations in men. Crewther et al. [4] reported that
men react to a bout of resistance training with higher lactate
concentrations when compared to women. Thus, men exhibit a
greater lean muscle mass and can train with heavier relative loads
than women [4]. It was shown that performance differences
between men and women decrease with increasing distance, also
explained by physiological and morphological factors [21]. It
seems that in women the aerobic metabolism and in men the
anaerobic metabolism is better developed [22]. The higher
content of muscle tissue and the better-developed anaerobic
metabolism in men, result in higher lactate concentrations
especially for 50 and 100 m events [22]. examined the muscle
fiber type distribution in m. vastus lateralis of 140 healthy
untrained subjects (55 women and 95 men) at the age between
19.0 and 23.9 years. No sex specific differences were found for the
muscle fiber type distribution, but the area occupied by each type
differs (women I.IIA.IIB; men IIA.I.IIB). In addition type
IIA fibers were the largest in men, whereas type I fibers tended to
be the largest in women [23]. From a physiological perspective, in
an active state, glycolytic muscle fibers act as the main producers
of lactate [5,7]. In contrast, oxidative fibers serve as lactate
consumers [6], also enclosing the muscle fibers of the heart within
the scope of the cell-to-cell lactate shuttle [6,7,24].
Influence of Stroke
Most studies about lactate in swimming were conducted in
freestyle; only a few studies analyzed the influence of the other
strokes on lactate. Sawka et al. [25] found similar mean lactate
concentrations after 200 yd races (182,88 m) for all strokes in 23
competitive athletes. Capelli et al. [26] measured the lactate
concentration after maximal swim of 50 yd, 100 yd and 200 yd in
20 male college swimmers. The descriptive data, which bases on
only 3 to 8 subjects per stroke, show different orders depending on
the distance. Issurin et al. [27] reported the highest lactate
concentrations in butterfly, followed by breaststroke, backstroke
and freestyle across three different tests with 22 highly trained
swimmers (14 male, 8 females). The study of Vescovi et al. [20]
with 100 swimmers (50 male and 50 females) showed significantly
lower post-race lactate concentrations for breaststroke compared
to butterfly and backstroke in 50 and 100 m. Regarding the four
different swimming strokes it seems to be clear that freestyle
followed by backstroke show the most economic energy expendi-
ture [15,26]. An explanation could be that freestyle and backstroke
are characterized by a lower intracyclic variation of the swimming
velocity compared to butterfly and breaststroke [16,26,28].
Butterfly and breaststroke are characterized by a gliding phase
after the arm action, resulting in a greater relative loss of speed in
every cycle but also underwater recoveries, especially in breast-
stroke [29]. A classification between butterfly and breaststroke is
unclear at present [15]. Though it could be supposed, that the
economy of butterfly is the slightest on account of the high
technical-coordinative demand. Especially the importance of the
ability to coordinate arm and leg action for a rhythmical body
motion and the high demand of potential energy raising the upper
body out of the water seems to be key factors for an economic
technique [30]. Although, at higher swimming speeds, breaststroke
seems to be less economic [15]. An explanation could be, that
breaststroke is the only stroke in which great body masses are
moved against the swimming direction, which means that a lot of
energy will be utilized to overcome the increased drag with
increasing velocity [31]. Furthermore, breaststroke is character-
ized by different styles of the flat and undulating technique, which
influence the energy expenditure differently but also making it
difficult to classify this stroke clearly [29].
Influence of Distance
With higher swimming distance, the aerobic endurance capacity
becomes more important [32]. Vescovi et al. [20] describe the
post-race lactate concentrations of 50, 100, 200, 400, 800 and
1500 m events as an inverted U-shape pattern with similar
concentrations for 100, 200 and 400 m. They also showed the
highest post-race lactate concentrations after 200 m for backstroke
and breaststroke. Similar results were shown in the study of Capelli
[26], where the highest values were achieved in 200 yd for three of
four strokes. From a physiological perspective, muscular power is
highly determined by the muscle fiber type distribution [33]. The
velocity and strength development of a muscle fiber is associated
with the myosin heavy chain (MyHC) isoforms [34]. A higher
content of type II fibers causes a bigger strength development
[33,35], which is essential for sprinters. Because type IIA fibers act
as main producers of lactate in an active state [5,7], an increased
IAT is to be observed in sprinters with a larger amount of type II
fibers. In contrast, it is supposed that longer distances require a
training
contribution
with
the
trend
towards
achieving
a
maximum aerobic capacity with a greater content of type I fibers.
Thus, higher training extents in low mean intensities are
recommended, promoting a fiber shift towards the slower type I
fibers. Type I fibers influence the lactate clearance positively [6].
This also explains, why with a higher endurance capacity the
lactate curve shifts to the right [35]. Nevertheless, the right shift
alone does not necessarily implicate an improvement in aerobic
metabolism [1]. At a cellular level, the mitochondrial biogenesis
seems to be important concerning the muscle fiber differentiation
[36]. In oxidative fibers, the mitochondria can occupy 20–40% of
the cell volume, whereas in glycolytic fibers only down to 1% of
the cell volume is filled by mitochondria [37]. Within the scope of
the intracellular lactate shuttle hypothesis, mitochondria have an
important function for the lactate metabolism [7,24]. It has to be
added, that the classification of the MyHC does not completely
correlate with the oxidative capacity [38]. The work of supports
this impression with swimming taking a special position. The
overlappings at molecular and cellular level are reflected in
competitions, with some athletes achieving world-class perfor-
mances in several disciplines (e.g. 100–400 m swimming) with
different performance profiles. To current knowledge, specific
training has to be planned for each discipline, avoiding endurance
and strength specific signaling pathways overlapping and thus
reducing or even eliminating training effects [38,40,41]. Summa-
rized, to understand the physiological adaptations as a result of
specific
training
content
seems
to
be
very
important
for
interpreting lactate tests [1].
Aims of the Study
The first aim of this article is to improve the interpretability of
lactate diagnostics in swimming by identifying lactate-affecting
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
2
October 2013 | Volume 8 | Issue 10 | e77185
Table 1. Information about the Pansold test protocol [42] (p. 169).
distance
number of steps
number of
repititions
stroke
recommended intensity for the first step
in % of personal best time
break between
repititions
break between
steps
lactate measurement
men
women
100 m
1
3
Bu
60–65
70–75
1 min
3 min
directly
2
2
Ba
70–75
75–80
1 min
3 min
directly
3
1
Br
70–75
80–85
5 min
after 1 min
4
1
Fr
65–70
70–75
approx. 20 min
after 1–3 min
5
1
increase of 3–4 s/per steplast step = maximum speed
after 4, 7 and 10 min
200 m
1
3
Bu
70–75
75–80
1 min
3 min
directly
2
2
Ba
75–80
80–85
1 min
3 min
directly
3
1
Br
75–80
83–87
5 min
after 1 min
4
1
Fr
75–80
80–85
approx. 20 min
after 1–3. min
5
1
increase of 5–8 s/step. last step = maximum speed
after 4, 7 and 10 min
400 m
1
1
Fr
80–85
85–90
3 min
after 1 min
2
1
increase of 8–12 s/step. last step = maximum speed
5 min
after 3 min
3
1
up to 30 min
after 3 min
4
1
after 4, 7 and 10 min
Abbreviations: Ba = Backstroke, Br = Breaststroke, Bu = Butterfly, Fr = Freestyle.
doi:10.1371/journal.pone.0077185.t001
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
3
October 2013 | Volume 8 | Issue 10 | e77185
variables. Hence, the influence of sex, distance and swimming
stroke on the IAT, percentage of personal best time on IAT (% of
PB on IAT) and maximum lactate (max. bLA) concentration is
evaluated. The second aim of this study is to present the Multi-
Level Analysis (MLA) as a statistical method which is able to
analyze the typical data structure in high performance sports in a
formally correct way.
Materials and Methods
Subjects
This investigation is based on a retrospective analysis of lactate
tests from the data pool of the Olympic Training Centre
Hamburg/Schleswig Holstein (GER), Department of Training
Science. Because the whole data set was collected by the Olympic
Training Centre Hamburg/Schleswig Holstein, a unified data
collection and assessment of qualified personnel is assumed. The
18-year data-collection period itself was not monitored, thus the
exact procedure of data acquisition cannot be described here. In
the analysis, 2063 data sets measured between 1992 and 2010
were examined. 1902 data sets of 398 athletes [female n = 170
(42.7%, age 16.9462.78 years, age range 13–26 years), male
n = 228 (57.3%, age 19.1063.17 years, age range = 15–36 years)
met the inclusion criteria. As an inclusion criterion, only data sets
of athletes with personal bests around 700 points of the LEN point
table (1000 points = world record for the period of validity) were
included. Furthermore only data sets were examined in which the
coefficients of determination were r2$0.92 (5 steps) or r2$0.95 (4
steps) [42]. The data were collected as part of the regular
performance diagnostics of the swimming association. After
consultation of the Ethics Committee of the University Tu¨bingen
Medical School (GER), the retrospective and anonymous analysis
of data of own patients, which where collected as part of
diagnostics, therapy or therapy control need no guidance after
the Professional Code for Physicians in Germany (115 (1)) and no
informed consent of the patients. There are no concerns of the
commission about collecting, processing and publishing such data.
Test Protocols
Lactate threshold test by pansold.
The lactate test by
Pansold [12,43] is a swimming-specific field test for diagnosing the
endurance capacity, accounting for the different structures of
swimming disciplines. This test protocol is used in the DSV since
the beginning of the 1990 s. The test is carried out for 100 and
200 m disciplines in five steps, for 400 m in four steps in a 50 m
pool, usually in the athletes’ main event. The load specification is
determined by a percentage of the individual best, whereby the
rest periods between the steps are fixed [42] (cf. table 1). The step
duration is reduced from step to step, because of the constant
distance and increasing swimming speed. After every step the
lactate concentration of the capillary blood is measured with blood
samples from the ear lobe. The lactate concentration of the last
step (maximum speed) represents the highest value of the
measurements after 4, 7 and 10 min. Therefore this lactate
concentration represents the maximum individual lactate concen-
tration of the test. The analysis of the lactate kinetic is based on the
exponential function y = a*e(b*x) [y = lactate in mmol*L21; a = free
coefficient; b = slope coefficient, x = speed in m/s]. For the
calculation of the regression coefficients ‘‘a’’ and ‘‘b’’ a quasilinear
regression analysis (method of the smallest squares) is carried out.
In addition, the coefficient of determination is calculated, which
provides information about the reliability of the Pansold-test. With
five steps r2$0.92 (100 m and 200 m disciplines) and with four
steps r2$0.95 (400 m disciplines) [42]. The parameters of the
Pansold-function were calculated with MS Access 2007.
The Individual Threshold Concept of Bunc et al. (1985)
The IAT of Bunc et al. [11] is represented by the point in which
the inclination of the lactate-load function changes the most.
Faude et al. [44] report in a recent review, that there is a high
correlation between the IAT of Bunc et al. and MLSS of r = 0.98–
0.99 in 16 healthy male runners and r = 0.89 in n = 22 healthy
cyclists. In both cases, the running speed and the power output in
watt at IAT are higher than at MLSS (+0,14–0,31 m/s/+71,5 W).
For the calculation of the IAT, based on the lactate curve and the
given exponential function, the following steps are recommended
(cf. figure 1):
1. Tangent t1 to the point with the lowest load (y = a*e(b*x
1.step
))
and tangent t2 to the point of 15 mmol/l (15 = a*e(b*x))
2. Calculate intersection (S1) of both tangents
3. Angle bisector by the intersection of the tangents
4. Intersection between angle bisector and lactate curve
(y = a*e(b*x)) represents the IAT of Bunc et al. [11].
The use of this threshold concept on the exponential function of
the Pansold-test y = a*e(b*x) requires the extension of the Pansold-
function with the lactate concentration of the first step (L1. step).
This corresponds to the point of the lowest load in which the
tangent t1 is calculated. The angle bisector is described by the
graphical bisector with an axis relation between x-axis (km/h) to y-
axis (lactate in mmol/l) from 1:2 [11]. The extrapolation of the
IAT was carried out with MATLABH.
Statistic Analysis
All statistics were performed using SPSS (version 19.0 for
Macintosh). For analyzing the influence of sex, swimming stroke
and distance on the IAT, the percentage of the personal best time
on IAT (% of PB on IAT) and on maximum lactate value (max.
bLA), a three-factor analysis of variance (ANOVA) was conducted
for each variable for 398 subjects. Because the correlations
between the three variables were only weak, three ANOVAs were
calculated instead of a MANOVA. The post hoc tests of
Bonferroni (equal variances), Tamhane-T2 (unequal variances)
were carried out for sex, stroke and distance specific analysis to test
which means are significantly different from each other. T-Tests
for two independent samples were used to evaluate differences in
sex in every stroke and every distance. For the athletes with more
than one data set, the first data set was used for the ANOVAs,
representing the initial status for the second part of the analysis.
In the second part of the statistic analysis all 1902 data sets,
which met the inclusion criteria, were involved. The number of
data sets for each event and sex are in table 2. Most athletes had
several data sets with different in-between time intervals, therefore
a Multi-Level Analysis (MLA) for IAT (Model 1), % of PB on IAT
(Model 2) and max. bLA (Model 3) as the dependent variable was
conducted. The advantage of this method is, that information
about the change over time of the dependent variables under
consideration of the in-between time intervals between measure-
ments is given. Also valid data does not have to be excluded.
Therefore, this method is more flexible and formally correct for
such data structures. The comparison of the basic models (without
predictors; df = 1) (A) random intercept and (B) random intercept
& random slope with the help of the information criteria proved a
significantly (p,.001) different change of the dependent variables
of the athletes over time. Random intercept (A) is based on the
assumptions that the subjects have different base values, but the
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
4
October 2013 | Volume 8 | Issue 10 | e77185
same rate of change over time. Random intercept & random slope
(B) assumes different base values and different change rates over
time. Comparing these two basic models by using information
criteria helps to fit the most economical model with the highest
statistical power. ‘‘The smaller the values of these criteria, the
better the fit of the model’’ [45] (p. 218). Values of the information
criteria are difficult to interpret, but differences larger than 10 are
substantial [46]. There is a decrease in the information criteria
from Model (A) to (B) for Log-Likelihood (-2logL), Akaike
information criterion (AIC), and Bayesian information criterion
(BIC) for all dependent variables .24. In addition model (B)
reduces the variance by 5.7% for IAT, by 23.48% for % of PB on
IAT and by 5.6% for max. bLA (level 1-pseudo R2) compared to
model (A), which is why further analysis are conducted with model
(B). At first, the model was calculated in each case individually
with the time invariant predictors (fixed) sex (S), swimming
distance (D) and swimming stroke (St) to check the explained
between-individual variation for each predictor on the initial status
of the IAT (level 2-pseudo R2
C) and the individual variation
( = slopes) of the dependent variables over time (level 2-pseudo
R2
S) [46,47]. According to guideline and suggested in Kwok et al.
[34], statements about the effect size can be made with the help of
R2 changes (.02; .13; .26 representing a small, medium and large
effect). Finally the following model with all three predictors for
each dependent variable separately was calculated.
Level 1 Yi~b0izb1izeij
ð1Þ
Level 2 b0i~b0(S,D,St)izb2(S,D,St)izv0i
ð2Þ
b1i ~ b1(S,D,St)izv1i
ð3Þ
Integrated formula:
Yij~b0(S,D,St)izb2(S,D,St)izv0i
zb1(S,D,St)izv1izeij
ð4Þ
Level 1 represents athletes IAT/% of PB on IAT/max. bLA at
different measuring times. Level 2 represents the different subjects
(n = 398) taking the fixed predictors sex (S), distance (D) and stroke
(St) into account. The alpha level of the tests was set to p,0.05.
Figure 1. Graphical Determination of the IAT by Bunc et al. [11] with the Pansold-function y = a*e(b*x) [42,43].
doi:10.1371/journal.pone.0077185.g001
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
5
October 2013 | Volume 8 | Issue 10 | e77185
Results
ANOVAs
In all three ANOVAs no significant interaction effects were
found. IAT: The ANOVA with IAT [mmol*L21] as dependent
variable showed significant effects for sex F(1, 382) = 7.88,
p = .005, par. g2 = .020, distance F(2, 382) = 10.98, p,.001, par.
g2 = .054 and stroke F(3, 382) = 6.76, p,.001, par. g2 = .050.
Overall, 100 m differed significantly from 200 and 400 m
(p,.001). Butterfly differed significantly from all other strokes
(p,.001). The descriptive data showed lower lactate concentra-
tions on IAT for women in all events, with only 200 m freestyle
being significant (p = .05; cf. table 3). Sex specific analysis for
stroke per distance showed significant differences for butterfly and
breaststroke (p,.001), freestyle (p = .002) and tending to be
significant for backstroke (p = .076) in the males 100 m events.
The highest mean values were achieved in butterfly, followed by
backstroke, freestyle and breaststroke. The sex specific analysis for
stroke showed significant differences (p,.001) between 100 and
400 m, as well as between 200 and 400 m freestyle, each for men
and women.
% of PB on IAT/% of PB.
The ANOVA with % of PB on
IAT as dependent variable showed significant effects for sex F(1,
382) = 40.58, p,.001, par. g2 = .096, distance F(2, 382) = 88.36,
p,.001, par. g2 = .316 and stroke F(3, 382) = 3.60, p = .014, par.
g2 = .028. Overall, 100 m differed significantly from 200 and
400 m, as well as 200 from 400 m (p,.001) with higher values in
longer distances. Significant differences were found between
butterfly
and
backstroke/freestyle
(p,.001),
backstroke
and
breaststroke (p = .006), as well as between freestyle and breast-
stroke (p,.001). Under consideration of the mean values and
confidence intervals (cf. table 3) the lowest values were produced in
butterfly, the highest for the freestyle events. Women showed
significantly higher % of PB on IAT (t-test) with descriptive lower
IAT in 7 of 8 events. There was also a significant difference
(p = .039) for % of PB on IAT between 100 m butterfly and
backstroke in women, with lower mean values in the butterfly
event. For both sexes, the means are significantly higher (p,.001)
with increasing distance for the freestyle events. The same effect
occurred between 100 and 200 m breaststroke (p = .015) in
women. The descriptive data showed (cf. table 4) that women
achieve a higher % of PB with lower bLA compared to men in
Table 2. Attribution of the 1902 data sets of n = 398 athletes for the multi level analysis (MLA) for each event and sex.
100 m Bu
100 m Ba
200 m Ba
100 m Br
200 m Br
100 m Fr
200 m Fr
400 m Fr
men
66
97
120
124
78
194
347
119
women
19
66
67
75
76
145
232
77
Abbreviations: Ba = Backstroke, Br = Breaststroke, Bu = Butterfly, Fr = Freestyle.
doi:10.1371/journal.pone.0077185.t002
Table 3. Differences in IAT of Bunc et al. [11] [mmol*L21] between sexes given as means (M), standard deviations (SD) and
confidence intervals of the lactate step test of Pansold and the percentage of the personal best time on the IAT (% of PB on IAT) for
male (M) and female (F) of n = 398 athletes.
IAT [mmol*L21]
% of PB on IAT
95% Confidence Interval
95% Confidence Interval
Event
sex
n
M ± SD
Lower Bound
Upper Bound
p
M ± SD
Lower Bound
Upper Bound
p
100 m Bu
M
15
7.5261.69
6.70
8.59
.35
86.3964.92
83.88
89.04
.87
F
6
6.7861.35
5.63
7.89
86.0661.92
84.18
87.40
100 m Ba
M
15
6.4361.25
5.82
7.09
.22
87.2662.81
85.72
88.64
.02*
F
13
5.8761.07
5.29
6.52
89.9662.81
88.54
91.57
200 m Ba
M
19
5.6661.12
5.19
6.22
.26
88.2661.69
87.48
89.01
.001**
F
16
5.1661.48
4.39
5.89
91.6063.48
90.19
93.67
100 m Br
M
29
5.9661.09
5.58
6.39
.19
85.6763.40
84.42
86.88
.02*
F
13
5.4461.31
4.76
6.14
88.3663.54
86.40
90.18
200 m Br
M
20
5.6460.87
5.29
6.03
.06
86.9362.94
85.66
88.22
,.001***
F
18
5.1360.74
4.81
5.48
91.2661.74
90.40
92.15
100 m Fr
M
44
6.1960.99
5.92
6.49
.06
84.9362.83
84.14
85.80
,.001***
F
35
5.7661.01
5.43
6.10
88.3262.61
87.46
89.17
200 m Fr
M
58
5.7060.95
5.46
5.96
.05*
87.9662.61
87.32
88.70
,.001***
F
50
5.3361.04
5.05
5.63
91.3962.26
87.46
89.17
400 m Fr
M
28
5.1961.13
4.77
5.63
.96
92.5661.67
91.90
93.20
.001**
F
19
5.2161.16
4.66
5.74
94.6162.41
93,52
95.73
p = .05*, p = .001**, p,.001***.
Abbreviations: Ba = Backstroke, Br = Breaststroke, Bu = Butterfly, Fr = Freestyle.
doi:10.1371/journal.pone.0077185.t003
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
6
October 2013 | Volume 8 | Issue 10 | e77185
nearly all submaximal steps. In the last step, the % of PB values
differ significant between men and women only for 200 m
breaststroke (p = .041).
Max. bLA.
The ANOVA with max. bLA [mmol*L21] as
dependent
variable
offered
significant
effects
for
sex
F(1,
382) = 17.74, p,.001, par. g2 = .044, distance F(2, 382) = 49.43,
p,.001, par. g2 = .206 and stroke F(3, 382) = 8.36, p,.001, par.
g2 = .062. Overall, 100 m differed significantly from 200 and
400 m, as well as 200 from 400 m (p,.001) with lower values in
longer distances (cf. table 4). For the strokes, there was a significant
difference (p = .009) between breaststroke and backstroke, with
higher mean values for the backstroke events. For the 100 m
events, significant differences were found between freestyle and
butterfly (p = .009), as well as between freestyle and breaststroke
(p = .001), but only in men. Thereby the values in freestyle were
the highest. For both sexes, the mean values were significantly
lower (p = ,.001 to.03) with increasing distance for the freestyle
events. The comparison of max. bLA for each stroke and sex
showed significant differences between men and women for 200 m
backstroke (p = .041), 200 m breaststroke (p = .001) and for all
freestyle events (p = .001, .002 &.002), with constant higher values
in men.
Multi-Level Analysis (MLA)
The Multi-Level Analysis (MLA) for the predictors sex, stroke
and distance is significant in each case (p,.001). The calculation
of the Level 2-pseudo-R2
C to check the individual within variation
for each predictor showed a reduction of variance for sex [13.3%
(IAT), 21.1% (% of PB on IAT) and 17.7% (max. bLA)], for
distance [22.5% (IAT), 40.1% (% of PB on IAT) and 22.1% (max.
bLA)] and stroke [6.4% (IAT), 1.1% (% of PB on IAT) and 0%
(max. bLA)]. Consequently, according to Cohen [35], predomi-
nantly medium effect sizes exist for the predictors sex and distance
in initial status, whereas only a small effect exists for stroke on
IAT. The Level 2-pseudo-R2
S showed a reduction of the variance
of the slopes for sex [1.4% (IAT), 13% (% of PB on IAT) and 28%
(max. bLA)], distance [13.1% (IAT), 52.4% (% of PB on IAT) and
32.6% (max. bLA)] and stroke [7.6% (IAT), 10.4% (% of PB on
IAT) and 1% (max. bLA)].
The models with all three predictors each (S, D, St) proves a
reduction of variance of in initial status of 36.6% for IAT, 60.3%
for % of PB on IAT and 39.9% for max. bLA (Level 2-pseudo-
R2
C). A reduction of variance was shown for the slopes of 13.6%
for IAT, 62.5% for % of PB on IAT and 55.8% for max. bLA
(Level 2-pseudo-R2
S).
For IAT (Model 1, cf. table 5) the time intervals between
measurements have a significant influence (p = .005) and the
athletes showed different change rates over time (b = 7.95E28,
p = .005), measuring a slight decrease (b = 1.19E24, p,.009). The
IAT of the women (men set to 0) are substantially lower
(b = 20.47, p,.001), confirming the descriptive results for the
398 data sets (cf. table 3). The factor distance also shows
decreasing IAT with increasing length (b = 20.31, p,.001;
100 m set to 0). There is also a significant effect for IAT for
stroke (b = 20.14, p,.001; butterfly set to 0), with the highest
values for butterfly. The effect of stroke is difficult to interpret,
because this variable is not ordinal scaled and consists of more
than two strokes.
For % of PB on IAT (Model 2, cf. table 5) there is a significant
influence of the time intervals between measurements (p,.001)
and the athletes showed different change rates over time
(b = 1.638, p,.001) with a slight decrease (b = 1.08E-3, p,.001).
The % of PB on IAT are significantly higher for women (b = 3.06,
p,.001; men set to 0). For the factor distance the % of PB on IAT
increases with increasing distance (b = 3.14, p,.001; 100 m set to
0). The effect of stroke is also significant (b = 20.23, p = .025;
butterfly set to 0).
For max. bLA (Model 3) the different time periods between
measurements are not significant (p = .057), but the change rates
Table 4. Blood lactate concentrations (bLA [mmol*L21]) and percentage of individual best time (% of PB) for each step.
step 1
step 2
step 3
step 4
step 5
event
sex
bLA
[mmol*L21] % of PB
bLA
[mmol*L21]
% of PB
bLA
[mmol*L21]
% of PB
bLA
[mmol*L21] % of PB
bLA
[mmol*L21]
% of PB
100 vm Bu M
3.9862.21
72.8164.10
4.9862.61
76.9162.96
6.2562.60
82.0763.41
8.1462.63
86.9063.60
10.2162.11
93.6062.37
F
3.4561.49
73.6564.70
4.6061.52
78.9061.83
6.0361.67
83.8861.09
7.8061.86
88.7661.72
10.3562.23
93.4062.56
100 m Ba
M
2.9961.29
76.6163.63
4.1861.52
80.5563.38
5.5061.75
84.9162.62
7.6661.64
89.3062.69
12.0162.41
95.6762.46
F
2.4660.96
80.6163.55
3.9161.60
84.5864.67
5.5962.51
86.1864.57
7.9863.39
88.2465.23
11.2463.41
95.6462.56
200 m Ba
M
2.2361.08
80.1161.91
3.2161.46
83.3161.88
4.6761.84
86.6061.91
6.9662.10
89.6461.79
11.2462.21
93.7062.21
F
1.9961.14
83.4162.36
2.6961.45
85.9062.30
3.6461.75
88.6262.70
5.5362.40
91.4062.66
9.0763.74
94.5762.16
100 m Br
M
2.5461.01
74.5863.72
3.7061.40
78.4062.60
5.0761.77
82.8562.45
7.3662.30
86.8262.96
10.3062.35
92.9062.72
F
2.1661.06
78.9962.75
2.9461.18
81.7262.58
4.3961.43
85.5962.90
6.6962.01
89.5663.54
9.0862.54
93.2163.82
200 m Br
M
2.2460.77
79.0963.11
3.2161.10
82.1463.35
4.5961.65
85.2163.26
6.7862.07
88.0263.16
10.2061.76
91.7662.84
F
1.8460.57
84.8061.97
2.8660.86
87.3462.04
4.2061.15
89.7962.04
5.7861.34
91.6461.75
7.9962.10
93.8161.73
100 m Fr
M
2.7061.01
72.4663.49
3.8961.34
77.2963.43
5.1561.78
82.6663.70
7.9962.55
87.7863.61
12.6562.58
95.1962.44
F
2.3561.01
75.2464.36
3.5261.29
81.7763.32
4.8461.64
85.9663.24
7.2361.93
90.0763.40
10.8262.26
95.4062.53
200 m Fr
M
2.3060.85
79.3962.90
3.3161.10
82.8062.44
4.6861.36
86.4462.36
6.9362.14
89.5562.68
10.9762.46
94.5062.61
F
2.0360.85
83,.562.78
2.9861.27
86.2862.71
4.1361.63
89.3362.74
6.0162.10
91.6562.49
9.4362.61
95.2862.32
400 m Fr
M
1.9560.93
85.5162.29
2.9361.16
88.5462.29
4.5561.28
91.2662.26
8.2061.92
95,5362,66
F
1.9660.89
87.3661.67
2.7661.06
90.1562.05
4.2061.79
94.9462.52
6.0862.19
94,9562,52
Abbreviations: Ba = Backstroke, Br = Breaststroke, Bu = Butterfly, Fr = Freestyle.
doi:10.1371/journal.pone.0077185.t004
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
7
October 2013 | Volume 8 | Issue 10 | e77185
over time are significant (b = 4.16E24; P,.001). The results
confirm the descriptive results of the 398 data sets. Women (men
set to 0) show significantly lower max. bLA (b = 1.71, p,.001). For
distance (100 m set to 0) there are lower bLA values for longer
distances (b = 1.45, p,.001). The differences of max. bLA for the
variable stroke (butterfly set to 0) are significant (p = .039) but
slight (b = 0.19).
Discussion
The present study examined the influence of sex, stroke and
distance on the IAT, the % of PB on IAT and the max. bLA in
high performance swimming. Furthermore the MLA was used as a
method, which is able to analyze typical data structures in high
performance sports. Compared to other studies [9,49] the
calculated
IAT
in
this
study
(range
of
means:
5.19–
7.52 mmol*L21) seem to be very high. Otherwise, Dekerle and
Pelayo [50] described, that lactate thresholds in swimming occur
at speeds up to 90% of the 200 m pace, which is the case in most
events (cf. table 3) in this study. Furthermore, Faude et al. [44]
reported higher running speeds/power output in watt at IAT than
at MLSS using the threshold concept of Bunc et al. [11]. MLSS
concentrations are reported for swimming up to 3–5 mmol*L21
[50]. Therefore the calculated IATs seem to be realistic with the
used threshold concept. Furthermore, the differences show that the
IAT is strongly dependent on the applied method [1,50].
Irrespective of the mean values of the IAT, it was not the aim of
the study to give recommendations for training intensities on basis
of the IAT, but to identify lactate-affecting factors. According to
current research, the lack of evidence for the effect of threshold
training [51], the positive findings of high intensity training (HIT)
on the endurance capacity [52,53], conceptions like the polarized
training model [54] lead to a critical perspective on the ‘‘classical’’
threshold training. The results for the three independent variables
are discussed in the following paragraphs.
Sex
The descriptive data (cf. table 3) and estimations of the MLA (cf.
table 5) show in average 0.4–0.6 [mmol*L21] (b = 20.47; p,.001)
lower IAT and 1–2 [mmol*L21] lower max. bLA (b = 21.70;
p,.001) for women compared to men. These effects are
significant, both in ANOVA and MLA, with medium effect sizes.
For the IAT these sex specific differences are only significant for
200 m freestyle, whereas the differences for max. bLA are
significant for five events (200 m Ba & Br and the freestyle
events). These findings are similar to the results of Vescovi et al.
[20] where sex specific differences in post-race bLA were only
found for the freestyle events. With women reaching lower lactate
concentrations in swimming, being in agreement with the findings
of for resistance training. Together with muscle mass, the area
occupied by muscle fiber types and the size of the fibers types seem
to be sex specific, described by for the m. vastus lateralis. This
could provide an explanation on a physiological level. With the
metabolic characteristics of the muscle fiber types, the connection
to the lactate kinetic is given [5,7,13]. The overall lower lactate
concentrations on IAT and max. bLA in women support the idea
of a better developed aerobic metabolism in women compared to
men [21,22]. For % of PB on IAT, both statistical methods
showed significant influences for sex (p,.001) with higher values
Table 5. Estimates of Fixed Effects of the multi level analysis (MLA, random intercept & random slope) for IAT [mmol*L21], % of PB
on IAT and max. bLA [mmol*L21].
Model 1: IAT [mmol*L21]
Parameter
Estimate
SE
df
t
p
95% Confidence Interval
Lower Bound
Upper Bound
Intercept
6.35
0.09
644.33
67.96
.000
6.16
6.54
Time*
21.19E-4
4.44E-5
71.62
22.67
.009
22.07E-4
23.02E-5
Sex
20.47
0.08
313.60
26.16
.000
20.62
20.32
Distance
20.31
0.05
841.46
26.25
.000
20.40
20.21
Stroke
20.14
0.04
728.85
23.80
.000
20.21
20.07
Model 2: % of PB on IAT
Intercept
86.00
0.27
893.37
322.80
.000
85,47
86.52
Time*
21.07E-3
1.45E-4
79.86
27.38
.000
21,36E-3
27.83E-4
Sex
3.06
0.24
417.17
12.70
.000
2,58
3.53
Distance
3.14
0.13
1419.17
23.69
.000
2,88
3.40
Stroke
20.23
0.10
1221.69
22.25
.025
20,43
20.03
Model 3: max. bLA [mmol*L21]
Intercept
11.49
0.23
787.76
49.41
.000
11.03
11.94
Time*
4.16E-4
8.60E-5
47.06
4.83
.000
2.42E-4
5.89E-4
Sex
21.70
0.20
368.90
28.60
.000
22.10
21.32
Distance
21.45
0.12
1115.11
212.22
.000
21.69
21.22
Stroke
20.19
0.09
973.45
2.07
.039
9.83E-3
0.37
*Time = number of days between the measuring times of the athlete.
doi:10.1371/journal.pone.0077185.t005
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
8
October 2013 | Volume 8 | Issue 10 | e77185
for women (b = 3.06). These differences are also significant
between men and women in 7 of 8 events (cf. table 3). The
results are supported by, reporting a more economic swimming of
women, explaining the lower lactate concentrations and the higher
% of PB values at submaximal intensities. Other reasons could be
a greater proportion of fatty tissue and different distribution in
women compared to men [18]. This can give women a higher net
buoyancy [55]. Furthermore, Caspersen et al. [18] reported lower
added mass in women as a result of differences of body shape,
which seems to be positive for the drag.
Stroke
For both statistical methods the effects for stroke is significant
for all three dependent variables. Although the influence of the
stroke is significant in each Model of the MLA (cf. table 5), there
are only little reductions of variance for initial status of 0–6,4%
(Level 2-pseudo-R2
C) and for the slopes of 1–11,4% (Level 2-
pseudo-R2
S), meaning no to small effect sizes for stroke. For the
max. bLA, the post-hoc analysis (Bonferroni adjusted) showed only
a significant difference (p = .009) between breaststroke and
backstroke with higher values for backstroke. These findings
support the results of other studies [25,26] which show no stroke
differences/different orders for bLA depending on the distance
after maximal swim. Other studies [20,27] reported the highest
max. bLA for butterfly, but none of these studies analyzed the
influence of stroke on the IAT. Overall the highest IAT were
achieved in 100 m butterfly, which differs significantly from all
other strokes (p,.001, Bonferroni adjusted). The MLA confirms
this results with the highest IATs in butterfly (b = 20.14; p,.001).
There are also the lowest % of PB on IAT for butterfly (b = 20.23;
p,.025), which differs significantly from backstroke and freestyle
(p,.001, Bonferroni adjusted). A possible explanation could be,
that around the IAT swimming speed is not at maximum, the
statement of Barbosa et al. [15] could be confirmed in terms of
economy, with butterfly showing the slightest swimming economy
at lower swimming speeds. However the economy improves with
increasing speed [26]. The highest IAT was found for 100 m
butterfly, presumably connected with the fact that it seems to be
difficult to swim butterfly with low intensities (first steps) [26],
because of the high demand of interaction between strength and
coordination [30,56]. On the other hand, the butterfly technique
entirely makes high demands for strength, thus low intensities (in
%) could be a high individual exposure [30]. Therefore, the
glycolytic muscle fibers could be recruited at an early stage
[57,58], operating as main lactate producers when recruited [5,7],
explained by the size principle of motor unit recruitment [58,59].
However these results have to be interpreted carefully because of
the small data sets for butterfly. Anyhow, the statements of
Barbosa et al. [15] are not confirmed by the descriptive results of
the IAT and the max. bLA for the breaststroke events. The lactate
concentrations of the breaststroke events are the lowest on average
in direct comparison with the same distance for the other strokes.
Regarding the 200 m events, the IATs are in a similar zone for
both sexes in each case, although the energy consumption in
freestyle and backstroke seems to be lower, because of the lower
intracyclic variation of swimming velocity compared to butterfly
and breaststroke [16,28]. Summarized, for the variable stroke the
biggest effects occurred for butterfly on the IAT for the 100 m
events, with the significantly highest values. Overall the stroke
seems not to play a key role in terms of affecting lactate
parameters. The knowledge about the economy [16,28] of the
strokes is reflected only partly in our results.
Distance
For distance all ANOVAs and Models of the MLA were
significant (p,.001) for the three independent variables IAT, % of
PB on IAT and max. bLA. The MLA show reductions of variance
for initial status between 22.1 to 40.1% (Level 2-pseudo-R2
C) and
for the slopes between 13.1 to 52.4% (Level 2-pseudo-R2
S),
meaning mostly large effect sizes. The descriptive data, the
ANOVAs
and
MLA
showed
significant
decreasing
IAT
(b = 20.31; p,.001), increasing % of PB on IAT (b = 3.14;
p,.001) and decreasing max. bLA (b = 21.45; p,.001) with
increasing distance. These general results were confirmed by sex
and stroke specific analysis only for the freestyle events (Bonferroni
adjusted) in men and women. The results are not surprising,
because with increasing distance the aerobic capacity becomes
more important [32]. From a practitioner perspective a certain
versatility of 100 and 200 or 200 and 400 m seems to be attractive
to qualify for the 4*100 m and 4*200 m freestyle relays in
international competitions, because five to six places are awarded.
For the individual events at most two athletes can qualify for each
country, therefore the chances to qualify but also the performance
density are much higher in the 100 m and 200 m freestyle events.
Although Meckel et al. [39] described that swimming is taking a
special position in order to achieve world-class performances in
various events with different performance profiles, but priorities in
the training content are necessary from current point of view [22],
which are reflected in the significant differences in the freestyle
events. According to actual knowledge it is known, that signaling
pathways of endurance training mainly trigger transcriptional
changes, while weight training adaptations are caused by changes
in mRNA-translation [41]. Concerning this, it is unclear whether
signaling pathways initiated by weight training or endurance
training overlap or hinder each other [38,40,41]. Therefore it is
not completely clarified at the moment what this specifically means
for training periodization, thus e.g. to coordinate strength and
endurance training over the season in a synergetic way [59] is
important to achieve the best performance in the individual best
events on the main competition. Therefore it can be supposed that
on the one hand the constitutional conditions and anthropome-
trical and morphological factors [60] influences the ‘‘choice’’ of the
individual special event (sprints or longer distances) and on the
other hand the individual specificity is tried to be maintained or
optimized in the training process. Consequently a sprinter (50–
100 m events) will try to maintain the dominance of muscle fiber
type II and train in terms of an ‘‘optimum’’ endurance capacity
[54]. An ‘‘optimum’’ endurance capacity means that the quality of
training, which implies the training intensity and the exercise
tolerance, becomes the key factor and not the volume in kilometers
[32]. This point of view confirms the positive effects of high
intensity training on the endurance capacity [52,53] and the
concept of the polarized-training model (about 75% of the training
below threshold intensity, only 5–10% in threshold intensity and
15–20% above) versus the ‘‘classical’’ threshold-training model
[54]. This implements avoiding a shift towards type I fibers by
excessive endurance training stimuli, which seems to be only partly
reversible [33,34,61]. Nevertheless, in swimming, current training
regimes seem to be characterized by an aerobic predominance
[39], which is possibly not up to date anymore for sprint and
middle distances.
Methodical Discussion
The large number of data sets of athletes at highest performance
level supports the explanatory power of the results. It is to be
mentioned, that the threshold concept of Bunc et al. [11] is not
swim-specific. However, the Pansold-test is characterized by a
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
9
October 2013 | Volume 8 | Issue 10 | e77185
step-shaped load protocol, which is carried out under field
conditions. Therefore a reliable realization of the Pansold-test
requires a good sense of time of the swimmers with the ability to
swim evenly, particularly in the first steps. Short-term speed
increases or to quick beginning speeds can lead to high first-step
lactate concentrations. This lactate concentration influences the
further course of the test and the lactate curve [44]. However this
is accepted for the examination of the endurance capacity in the
field
accounting
for
the
different
structures
of
swimming
disciplines. A methodical strength of this study is the statistical
analysis with a MLA. This method allows analyzing different
numbers of data sets for an athlete with different time intervals
between measurements [46]. For elite sport, it seems to be
important to consider the time periods between performance
diagnostics to make statements about e.g. the effect of training
input on the lactate kinetics without excluding data. For that
reason it was possible to analyze all 1902 valid data sets in the
second part. Furthermore it is common, that some athletes are
part of the squad for longer time periods, experiencing more
performance diagnostics than other athletes. A disadvantage of this
method is, that detailed information about descriptive data cannot
be provided because of the different numbers of data sets for each
athlete. A limitation of this retrospective analysis of data between
1992–2010 is, that the equipment used for the lactate diagnostics
and the exact process of acquisition is unknown.
Conclusion
In conclusion, we identified the influence, especially of sex and
distance, on lactate parameters in swimming and tried to explain
them with current physiological knowledge. Furthermore we
showed the importance of considering the different time periods
between measurements in a formally correct way by using the
MLA for general statements on basis of large data sets in high
performance sports. The slight but significant influences of the
time periods between measurements show the dynamic and
sensitivity of the lactate molecule. These findings may help
interpreting results of lactate tests in context of e.g. strength
parameters or as a consequence of specific training content
explained by physiological adaptations (e.g. metabolism of fiber
types) or sex specific factors like body density or a better developed
aerobic/anaerobic metabolism. Men overall showed higher IAT
and max. bLA lactate concentrations compared to women.
Whereas in submaximal intensity women achieved higher % of
PB with lower lactate concentrations compared to men, which
confirms a more economical technique [15,16] and a better
developed aerobic metabolism [22] in women. For the variable
stroke, the MLA showed significant results but no or small effect
sizes. Therefore, when comparing inter- and intraindividual results
of lactate tests in swimming, especially sex and distance specific
lactate parameters have to be considered for initial status
measurements and the development over time. In particular,
longitudinal comparisons under steady conditions, which means
applying the same test protocol and threshold concept, could
benefit from this [1,51].
Acknowledgments
We would like to thank the Olympic Centre Hamburg/Schleswig-Holstein
(Department of Training Science) for the data sets, in particular Janina
Gerkens and Ronald Berndt. Special thanks to Jochen Schweikert and
Martin Keh for the support on the mathematical conversion.
Author Contributions
Analyzed the data: BH NB. Contributed reagents/materials/analysis tools:
BH NB DB. Wrote the paper: BH NB DB.
References
1. Olbrecht J (2011) Lactate Production and Metabolism in Swimming. In: Seifert
L, Chollet D, Mujika I, editors. World Book of Swimming. From Science to
Performance. New York: Nova: pp. 255–276.
2. Kiely J (2012) Periodization paradigms in the 21st century: evidence-led or
tradition-driven? Int J Sports Physiol Perform 7(3): 242–250.
3. Bosquet L, Le´ger L, Legros P (2001) Blood lactate response to overtraining in
male endurance athletes. Eur J Appl Physiol 84(1–2): 107–114.
4. Crewther B, Cronin J, Keogh J (2006) Possible stimuli for strength and power
adaptation: acute metabolic responses. Sports Med 36(1): 65–78.
5. Philp A, Macdonald AL, Watt PW (2005) Lactate–a signal coordinating cell and
systemic function. J exp biol 208(Pt 24): 4561–4575.
6. Gladden LB (2008) A lactatic perspective on metabolism. Med Sci Sports Exerc
40(3): 477–485.
7. Gladden LB (2004) Lactate metabolism: a new paradigm for the third
millennium. J Physiol 558(Pt 1): 5–30.
8. van Hall G (2010) Lactate kinetics in human tissues at rest and during exercise.
Acta Physiol (Oxf) 199(4): 499–508.
9. Pyne DB, Lee H, Swanwick KM (2001) Monitoring the lactate threshold in
world-ranked swimmers. Med Sci Sports Exerc 33(2): 291–297.
10. Di Michele R, Gatta G, Leo A Di, Cortesi M, Andina F, et al. (2012) Estimation
of the anaerobic threshold from heart rate variability in an incremental
swimming test. J Strength Cond Res 26(11): 3059–3066.
11. Bunc V, Heller J, Novak J, Leso J (1985) Determination of the individual
anaerobic threshold. Acta Universitatis Carolinae Gymnica 21(1): 73–81.
12. Pansold B, Zinner J (1994) Die Laktat-Leistungs-Kurve - ein Analyse und
Interpretationsmodell der Leistungsdiagnostik im Schwimmen. In: Clasing D,
Weicker H, Bo¨ning D (editors). Stellenwert der Laktatbestimmung in der
Leistungsdiagnostik. Stuttgart: Fischer: p. 47–64.
13. Holfelder B, Bubeck D (2012) Theoretische Betrachtungen u¨ber die Trainings-
steuerung anhand des Laktatstoffwechsels und der Muskelfasertypisierung.
Schweiz Z Sportmed Sporttraumatol 60(1): 32–39.
14. Thibault V, Guillaume M, Berthelot G, El Helou N, Schaal K, et al. (2010)
Women and men in sport performance: The gender gap has not evolved since
1983. J Sport Sci Med 9: 214–223.
15. Barbosa TM, Fernandes R, Keskinen KL, Colac¸o P, Cardoso C, et al. (2006)
Evaluation of the energy expenditure in competitive swimming strokes. Int J Sport
Med 27(11): 894–899.
16. Barbosa TM, Fernandes RJ, Keskinen KL, Vilas-Boas JP (2008) The influence
of stroke mechanics into energy cost of elite swimmers. Eur J Appl Physiol
103(2): 139–149.
17. Onodera S, Miyachi M, Yano H, Yano L (1999) Effects of buoyancy and body
density on energy cost during swimming. In: Keskinen KL, Komi PV, Hollander
AP, editors. Biomechanics and Medicine in Swimming. VIII. Jyvaskyla:
Gummerus Printing: 355–358.
18. Caspersen C, Berthelsen PA, Eik M, Paˆkozdi C, Kjendlie P-L (2010) Added
mass in human swimmers: Age and gender differences. J Biomech 43(12): 2369–
2373.
19. Barbosa TM, Costa MJ, Morais JE, Moreira M, Silva AJ, et al. (2012) How
Informative are the Vertical Buoyancy and the Prone Gliding Tests to Assess
Young Swimmers’ Hydrostatic and Hydrodynamic Profiles? J Hum Kinet 32:
21–32.
20. Vescovi JD, Falenchuk O, Wells GD (2011) Blood lactate concentration and
clearance in elite swimmers during competition. Int J Sports Physiol Perform
6(1): 106–117.
21. Chatterjee S, Laudato M (1995) Gender and performance in athletics. Soc Biol
42(1–2): 124–132.
22. Stanula A, Maszczyk A, Roczniok R, Pietraszewski P, Ostrowski A, et al. (2012)
The development and prediction of athletic performance in freestyle swimming.
J Hum Kinet 32: 97–107.
23. Staron RS, Hagerman FC, Hikida RS, Murray TF, Hostler DP, et al. (2000)
Fiber type composition of the vastus lateralis muscle of young men and women.
J Histochem Cytochem 48(5): 623–629.
24. Brooks GA (2009) Cell-cell and intracellular lactate shuttles. J Physiol 587(Pt 23):
5591–5600.
25. Sawka MN, Knowlton RG, Miles DS, Critz JB (1979) Post-competition blood
lactate concentrations in collegiate swimmers. Eur J Appl Physiol Occup Physiol
41(2): 93–99.
26. Capelli C, Pendergast D, Termin B (1998) Energetics of swimming at maximal
speeds in humans. European Eur J Appl Physiol Occup Physiol 78: 385–393.
27. Issurin VB, Kaufman LE, Tenenbaum G (2001) Modeling of velocity regimens
for anaerobic and aerobic power exercises in high-performance swimmers.
J Sports Med Phys Fitness 41(4): 433–440.
28. Barbosa TM, Bragada JA, Reis VM, Marinho DA, Carvalho C, et al. (2010)
Energetics and biomechanics as determining factors of swimming performance:
updating the state of the art. J Sci Med Sport 13(2): 262–269.
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
10
October 2013 | Volume 8 | Issue 10 | e77185
29. Seifert L, Leblanc H, Chollet D, Sanders RH, Persyn U (2011) In: Seifert L,
Chollet D, Mujika I, editors. World Book of Swimming. From Science to
Performance. New York: Nova: pp. 135–152.
30. Sanders RH (2011) Rhythms in Butterfly Swimming. In: Seifert L, Chollet D,
Mujika I, editors. World Book of Swimming. From Science to Performance.
New York: Nova: pp. 191–202.
31. Rodriguez FA, Mader A (2011) Energy Systems in Swimming. In: Seifert L,
Chollet D, Mujika I, editors. World Book of Swimming. From Science to
Performance. New York: Nova: pp. 225–240.
32. Chatard J-C, Stewart AM (2011) Training Load and Performance in Swimming.
In: Seifert L, Chollet D, Mujika I, editors. World Book of Swimming. From
Science to Performance. New York: Nova: pp. 359–374.
33. Schiaffino S (2010) Fibre types in skeletal muscle: a personal account. Acta
physiol (oxf) 2010: 199(4): 451–463.
34. Koulmann N, Bigard A-X (2006) Interaction between signalling pathways
involved in skeletal muscle responses to endurance exercise. Pflugers Arch -
Eur J Appl Physiol 452(2): 125–139.
35. Cormie P, McGuigan MR, Newton RU (2011a) Developing maximal
neuromuscular power: Part 1–biological basis of maximal power production.
Sports Med 41(1): 17–38.
36. Baar K (2010) Epigenetic control of skeletal muscle fibre type. Acta physiol
199(4): 477–487.
37. Guzun R, Saks V (2010) Application of the principles of systems biology and
Wiener’s cybernetics for analysis of regulation of energy fluxes in muscle cells
in vivo. Int J Mol Sci 11(3): 982–1019.
38. Van Wessel T, De Haan A, Van der Laarse WJ, Jaspers RT (2010) The muscle
fiber type-fiber size paradox: hypertrophy or oxidative metabolism? Eur J Appl
Physiol 110(4): 665–694.
39. Meckel Y, Bishop DJ, Rabinovich M, Kaufman L, Nemet D, et al. (2012) The
relationship between short- and long-distance swimming performance and
repeated sprint ability. J Strength Cond Res 26(12): 3426–3431.
40. Aagaard P, Andersen JL, Bennekou M, Larsson B, Olesen JL, et al. (2011)
Effects of resistance training on endurance capacity and muscle fiber
composition in young top-level cyclists. Scand J Med Sci Sports 21(6): e298–307.
41. Hoppeler H, Baum O, Lurman G, Mueller M (2011) Molecular Mechanisms of
Muscle Plasticity with Exercise. Comprehensive Physiology. Wiley Online
Library.
42. Deutscher Schwimmverband (2000) Komplexe Leistungsdiagnostik Testbes-
chreibung. In: Freitag W, editor. Schwimmen Lernen und Optimieren. Band 17.
p.168.
43. Rudolph K, Berbalk A (2000) Ausdauerdiagnostik im Rahmen der DSV-KLD
von 1992–1997. In: Freitag W, editor. Schwimmen Lernen und Optimieren,
Band 17. pp. 33–55.
44. Faude O, Kindermann W, Meyer T (2009) Lactate threshold concepts: how
valid are they? Sports Med 39(6): 469–490.
45. West BT (2009) Analyzing longitudinal data with the linear mixed models
procedure in SPSS. Eval Health Prof 32(3): 207–228.
46. Kwok O-M, Underhill AT, Berry JW, Luo W, Elliott TR, et al. (2008) Analyzing
Longitudinal Data with Multilevel Models: An Example with Individuals Living
with Lower Extremity Intra-articular Fractures. Rehabil Psychol 53(3): 370–386.
47. Singer J, Willett J (2003) Applied longitudinal data analysis: Modeling change
and event occurrence. Oxford University Press.
48. Cohen J (1988) Statistical Power analysis for the behavioral sciences (2nd ed.).
Mahwah, NJ: Erlbaum.
49. Skorski S, Faude O, Urhausen A, Kindermann W, Meyer T (2012) Intensity
control in swim training by means of the individual anaerobic threshold.
J Strength Cond Res 26(12): 3304–3311.
50. Dekerle J, Pelayo P (2011) Assessing Aerobic Endurance in Swimming In: Seifert
L, Chollet D, Mujika I, editors. World Book of Swimming. From Science to
Performance. New York: Nova: pp. 277–296.
51. Beneke R, Leitha¨user RM, Ochentel O (2011) Blood lactate diagnostics in
exercise testing and training. Int J Sports Physiol Perform 6(1): 8–24.
52. Laursen PB (2010) Training for intense exercise performance: high-intensity or
high-volume training? Scand J Med Sci Sports 20(2): 1–10.
53. Little JP, Safdar A, Bishop D, Tarnopolsky MA, Gibala MJ (2011) An acute bout
of high-intensity interval training increases the nuclear abundance of PGC-1a
and activates mitochondrial biogenesis in human skeletal muscle. Am J Physiol
Regul Integr Comp Physiol 300(6): R1303–1310.
54. Seiler KS, Kjerland GØ (2006) Quantifying training intensity distribution in
elite endurance athletes: is there evidence for an ‘‘optimal’’ distribution?
Scand J Med Sci Sports 16(1): 49–56.
55. Zamparo P, Antonutto G, Capelli C, Francescato MP, Girardis M, et al. (1996)
Effects of body size, body density, gender and growth on underwater torque.
Scand J Med Sci Sports 6: 273–280.
56. Vilas-Boas JP, Fernandes RJ, Barbosa TM (2011) Intra-Cycle Velocity
Variations, Swimming, Economy, Performance, and Training in Swimming.
In: Seifert L, Chollet D, Mujika I, editors. World Book of Swimming. From
Science to Performance. New York: Nova: pp. 119–134.
57. Cormie P, McGuigan MR, Newton RU (2011b) Developing maximal
neuromuscular power: Part 2 - training considerations for improving maximal
power production. Sports Med 41(2): 125–146.
58. Toigo M, Boutellier U (2006) New fundamental resistance exercise determinants
of molecular and cellular muscle adaptations Eur J Appl Physiol 97(6): 643–63.
59. Rønnestad BR, Mujika I (2013) Optimizing strength training for running and
cycling endurance performance: A review. Scand J Med Sci Sports [Epub ahead
of print].
60. Kjendlie PL, Stallman R (2011) Morphology and swimming performance. In:
Seifert L, Chollet D, Mujika I, editors. World Book of Swimming. From Science
to Performance. New York: Nova: pp. 203–222.
61. Harridge SDR (2007) Plasticity of human skeletal muscle: gene expression to
in vivo function. Exp Physiol 92(5): 783–797.
Lactate Characteristics in Swimming
PLOS ONE | www.plosone.org
11
October 2013 | Volume 8 | Issue 10 | e77185

The influence of sex, stroke and distance on the lactate characteristics in high performance swimming.
Holfelder, Benjamin,Brown, Niklas,Bubeck, Dieter