ORIGINAL ARTICLE

Changes in arm coordination and stroke parameters on transitionthrough the lactate threshold

Pedro Figueiredo • Pedro Morais • Joao Paulo Vilas-Boas •

Ricardo J. Fernandes

Received: 10 July 2012 / Accepted: 15 February 2013 / Published online: 21 March 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract The purpose of the present study was to

understand the energetic, biomechanical and coordinative

changes occurring throughout the transition of the lactate

threshold. Twelve high-level swimmers (six males and six

females) performed a paced intermittent incremental pro-

tocol of 7 9 200 m (0.05 m s-1 increments and 30 s

intervals). The stroking parameters (stroke rate and stroke

length) and the index of coordination (IdC) were assessed

by analysis of video recordings from aerial and underwater

side-view cameras. Energy cost (C) was determined by the

ratio energy expenditure/velocity. Energy expenditure was

determined by measuring oxygen uptake _VO2

� �and blood

lactate concentrations ([La-]). The swimming velocity at

the inflection point of stroke rate, stroke length, IdC, _VO2,

and [La-] was determined (m s-1). The results showed that

stroke rate, stroke length, IdC, _VO2, and [La-] all exhibited

inflection point as a function of swimming velocity, and

these velocities were highly correlated with the velocity at

[La-]inflex (1.35 ± 0.07 m s-1; R = 0.99, P \ 0.001).

Furthermore, these values were not significantly different

(P [ 0.05), and Bland–Altman plots estimations were

almost unbiased. These findings seem to confirm that as

swimming velocity increases and lactate threshold is sur-

passed, it induces changes in stroke mechanics and orga-

nization suggesting an important biomechanical,

coordinative and metabolic boundary between moderate

and heavy intensity domains.

Keywords Motor control � Biomechanics � Front crawl �Swimming � Metabolic parameters � Lactate threshold

Introduction

Previously, the physiological factors influencing human

movement coordination have been applied to front crawl

swimming (e.g. Sparrow and Newell 1998), and more recently,

a direct relationship between front crawl inter-arm coordina-

tion and physiological parameters, such as energy cost (C), has

been demonstrated (Fernandes et al. 2010a, b; Komar et al.

2011; Seifert et al. 2010). This suggests that coordinative and

biomechanical parameters are related to physiological factors,

whereby swimmers naturally adopt a movement pattern, in a

process termed self-optimization, to achieve their goal of

swimming faster while becoming fatigued.

An index of coordination (IdC) has been developed by

Chollet et al. (2000) offering the opportunity to study the

relationship between front crawl swimming coordination and

factors such as fatigue. However, although C and energy

expenditure ( _E) have a linear relationship with IdC, changes of

both aerobic (oxygen uptake, _VO2) and anaerobic (blood lac-

tate concentrations, [La-]) pathways with velocity are not

linear.

At moderate intensities, the assessment of an athlete’s

lactate threshold is important in establishing individual

Communicated by David C. Poole.

P. Figueiredo (&) � P. Morais � J. P. Vilas-Boas �R. J. Fernandes

Centre of Research, Education, Innovation and Intervention

in Sport, Faculty of Sport, University of Porto,

Rua Dr. Placido Costa, 91, 4200-450 Porto, Portugal

e-mail: spafg@vofafone.pt

P. Figueiredo

Higher Education Institute of Maia (ISMAI), Av. Carlos Oliveira

Campos, Avioso S. Pedro, 4475-690 Maia, Portugal

J. P. Vilas-Boas � R. J. Fernandes

Porto Biomechanics Laboratory, University of Porto,

Rua Dr. Placido Costa, 91, 4200-450 Porto, Portugal

123

Eur J Appl Physiol (2013) 113:1957–1964

DOI 10.1007/s00421-013-2617-8

training zones and evaluating training interventions. To

determine the lactate threshold, the analysis of the blood

lactate concentration profile, obtained during an incre-

mental protocol, is commonly used (Faude et al. 2009). It

has also been noted that the lactate threshold seems also to

influence the behaviour of some biomechanical variables.

In fact, concomitant changes on some selected biome-

chanical parameters and [La-] during incremental and

constant load tests have been reported (Keskinen and Komi

1993; Psycharakis et al. 2008; Wakayoshi et al. 1995),

supporting the idea that biomechanical changes could be

related to metabolic effects. Indeed, the lactate threshold

seems to represent not only a physiological transition but

also a biomechanical boundary, with several studies

reporting a stroke length (SL) drop and a change in stroke

rate (SR) coincident with that swimming intensity (Dekerle

et al. 2005; Keskinen and Komi 1993).

The purpose of the present study was to explore the way

in which the physiological, biomechanical and swimming

coordination measures change from below to above lactate

threshold and to explore the way in which they inter-relate.

Materials and methods

Subjects

Twelve elite front crawl swimmers (six males and six

females) volunteered to participate and composed two

groups: six males and six females. The male swimmers

characteristics were: 19.5 ± 0.7 years old, 72.8 ± 14.4 kg

body mass, 180.0 ± 17.0 cm height, and 5.0 ± 1.4 % fat

mass. The females characteristics were: 17.0 ± 1.5 years

old, 58.4 ± 3.2 kg body mass, 165.0 ± 3.2 cm height, and

20.0 ± 2.2 % fat mass. The protocol was approved by the

University ethics committee and explained to the swim-

mers, who then gave their written consent to participate.

The study was performed according to the Declaration of

Helsinki.

Swim trials

In a 25-m indoor pool (27.5 �C), swimmers performed an

individualized intermittent incremental protocol, consisting

of seven consecutive 200-m steps, with increments of

0.05 m s-1, and 30-s resting intervals, until exhaustion

(Fernandes et al. 2008, 2011). Initial velocity was estab-

lished according to the individual level of fitness and was

set at the swimmer’s individual performance on the 400-m

freestyle event minus seven increments of velocity.

Swimming velocity was controlled by a visual pacer (TAR.

1.1, GBK-electronics, Aveiro, Portugal), with flashing

lights on the bottom of the pool, helping swimmers to keep

up the predetermined velocity. Swimmers were accus-

tomed during the training practice to use the swimming

snorkel. In-water start and open turns were utilized because

of the use of a swimming snorkel for gas collection.

Swimmers were verbally encouraged to reach their maxi-

mal velocity during the final 200-m step. Each set repre-

sented the same relative intensity for all swimmers.

Video analysis and stroking parameters

Two synchronized video cameras (JVC GR-SX1 SVHS,

50 Hz), both fixed on the lateral wall of the pool at 10-m of

the swimmer’s line of movement, and with the optical axis

perpendicular to the swimmer’s displacement direction,

were used. The cameras were connected to a double entry

audiovisual mixer (Panasonic AG 7355), and edited on a

mixed table (Panasonic digital mixer WJ-AVE55 VHS),

giving a double projection, up and above the water surface

(Barbosa et al. 2008). SR was determined using the Ariel

Performance analysis system software (Ariel Dynamics,

USA), and computed as the inverse of the time (s) to

complete one stroke cycle, which was multiplied by 60 to

yield units of strokes per min. SL was calculated by

dividing mean velocity to SR. Both stroking parameters

were calculated in each step, as the mean value of the data

obtained in each 50-m.

Arm coordination

Two arm strokes were analysed at every 50 m (at the mid-

pool) of each 200-m step, totalizing eight arm strokes. Arm

stroke coordination was obtained through IdC, following

the methodology proposed by Chollet et al. (2000). Each

arm stroke was divided into four phases (entry, pull, push

and recovery), and the duration of each phase was mea-

sured for each arm stroke cycle with a precision of 0.02 s.

The duration of a complete arm stroke was the sum of all

the four phases. The IdC represented the time gap between

the propulsion of the two arms as a percentage of the

duration of the complete front crawl arm stroke cycle. Arm

coordination shifts from catch-up (IdC \ 0 %) to opposi-

tion (IdC = 0 %) and superposition (IdC [ 0 %) modes.

Metabolic parameters

_VO2 was directly measured using a telemetric portable gas

analyzer (K4b2, Cosmed, Rome, Italy) connected to the

swimmer by a respiratory snorkel and valve system (Sousa

et al. 2011). The K4b2 apparatus was calibrated following

the procedures described in the specialized studies, and

expired gas concentrations were measured breath-by-

breath. Capillary blood samples (25 ll) for [La-] analysis

were collected from the earlobe at rest (after previous local

1958 Eur J Appl Physiol (2013) 113:1957–1964

123

hyperemia with Finalgon�), in the 30-s rest interval, at the

end of exercise and during the recovery period (1, 3, 5 and

7 min after the end of the protocol), using YSI1500LSport

auto-analyzer (Yellow Springs Incorporated, Yellow

Springs, OH, USA).

The total energy expenditure ( _E) values for each exer-

cise step were obtained through the addition of the net _VO2

values and the values resultant from the transformation of

the net [La-] into O2 equivalents, using the proportionality

constant of 2.7 mlO2 kg-1 mM-1 (Barbosa et al. 2008;

Fernandes et al. 2006). The aerobic (Aer) and anaerobic

energy expenditure (Anaer) were computed as percentage

of the E, and C was determined as the ratio of _E and the

corresponding swimming velocity (di Prampero 1986).

Inflection point determination

To determine the inflection point of the [La-], _VO2, IdC, SR,

and SL data were plotted vs. velocity, and the curves were

modelled (Fig. 1) according to the mathematical model

proposed by Machado et al. (2006). These authors consid-

ered a set of N distinct data points (xi,yi), that might be fitted

by a straight line or exponential function, and found the

unknown coefficients of the curve-fitting (using the Method

of least squares). They proposed that, at low values of

velocity, there would be a linear relationship and thereafter,

above the velocity corresponding to the inflection point, the

increment would be exponential. As an example, a given

observed set of N velocity-[La-] data points were split into

two groups: (a) points 1 to k and (b) points k ? 1 to N, where

k ranged from 2 to N - 2. Separate least squares fits were

made: a straight line fit to the first group, and an exponential

fit to the second one, for all N - 2 possible values of k. From

the values of k it is found that in some cases the fit was indeed

an interpolation since the number of data points and the

number of coefficients of the fitting function are the same.

This occurs for the straight line when k = 2, while for the

exponential line it occurs when k = N - 2; in both situa-

tions the number of points to be fitted by one of the lines is

two. This method was shown to be valid against a gold

standard test (Fernandes et al. 2011) and to be consistent in

different populations (Fernandes et al. 2010a, b). Other

methods found in the literature, as the one proposed by Pyne

et al. (2001), were not validated, making comparisons dif-

ficult to establish.

Statistical analysis

Mean (± SD) computations for descriptive analysis were

obtained for all variables (normal distribution of the data was

verified with Shapiro–Wilk’s test). A one-way repeated

measures ANOVA was performed to compare the variables

along within the protocol. When a significant F-value was

achieved, Bonferroni post hoc procedure was performed to

locate the pairwise differences. Linear regression was used to

study the relationship between C and velocity, IdC, _E, Aer,

and Anaer. A between-method agreement analysis was

performed on the inflection point values calculated from

[La-], _VO2 net, IdC, SR, and SL measurements. The anal-

ysis included an evaluation of the relationships between

parameters using Pearson’s r correlation coefficients, a

comparison of mean values using paired Student’s t tests, and

an examination of the bias and level of agreement using 95 %

limits of agreement according to Bland and Altman (1986).

All statistical analyses were performed using STATA 10.1

(Stata Corp., USA) and the level of statistical significance

was set at P B 0.05.

Results

Swim step effect

From the first to the last 200-m step, all subjects increased

velocity (F6.60 = 566.79, P \ 0.001, g2p = 0.98) by

Fig. 1 Determination of inflection point of blood lactate concentra-

tions ([La-]inflex), net oxygen uptake (VO2 netinflex), index of

coordination (IdCinflex), stroke rate (SRinflex), and stroke length

(SLinflex), from the relation between these parameters and swimming

velocity

Eur J Appl Physiol (2013) 113:1957–1964 1959

123

increasing the SR (F6.60 = 213.15, P \ 0.001, g2p = 0.95)

and decreasing the SL (F6.60 = 21.44, P \ 0.001, g2p =

0.68). It was also observed an IdC increase

(F6.60 = 174.45, P \ 0.001, g2p = 0.95) over the seven sets

of 200 m, but it remained negative, evidencing an arm

coordination catch-up mode (Table 1). The increase in C

along the protocol (F6.60 = 40.62, P \ 0.001, g2p = 0.80)

was related to a significant increase in _VO2 net

(F6.60 = 45.14, P \ 0.001, g2p = 0.82) and [La-]

(F6.60 = 54.31, P \ 0.001, g2p = 0.84).

In Fig. 2, linear regression models between C and IdC,_E, Aer, Anaer are presented, with the coefficients of

determination ranging from 0.97 \ R2 \ 0.99 (all

p \ 0.01, n = 12). In addition, significant increase in _E

(F6.60 = 106.9, p \ 0.001, g2p = 0.91), as well as an Aer

decrease, whereas, Anaer increase (F6.60 = 13.14,

p \ 0.001, g2p = 0.57) were observed; however, the aero-

bic pathway remained the main contributor to the total

energy expenditure (Fig. 2).

Inflection points

The velocity at [La-]inflex (1.35 ± 0.07 ms-1) was highly

correlated (r = 0.99, p \ 0.001) with the IdCinflex

(1.34 ± 0.07 ms-1), SRinflex (1.35 ± 0.07 ms-1), SLinflex

(1.35 ± 0.07 ms-1), and _VO2 netinflex (1.37 ± 0.06 ms-1),

and their means were not significantly different (p [ 0.05).

Similar results were observed in Bland–Altman plots

reports with the bias and 95 % limits of agreement (Fig. 3).

Estimations between [La-]inflex and the IdCinflex

(0.008 ± 0.011 ms-1; 95 % CI 0.001–0.014), SRinflex

(-0.001 ± 0.007 ms-1; 95 % CI -0.005–0.003), SLinflex

(-0.003 ± 0.007 ms-1; 95 % CI -0.007–0.001), and_VO2 netinflex (-0.023 ± 0.009 ms-1; 95 % CI -0.030–

0.017) were almost unbiased and between the 95 % limits

of agreement (Fig. 3). Reinforcing these results, the Pitman

test of difference in variance showed that the correlation

coefficients of the difference versus average of the two

measurements were non-significant (p \ 0.05) for IdCinflex

(r = 0.25), SRinflex (r = 0.17), SLinflex (r = 0.45), and_VO2 netinflex (r = 0.51).

Discussion

The main findings of this study were that: (1) inflection

points were identified in all biomechanical, physiological

and coordinative parameters, during transition through the

lactate threshold, using incremental swimming; (2) the

inflection points in the IdC, SL, SR, and _VO2 net were

coincident with the [La-] turnpoint, showing the interplay

of the parameters resulting from the increase in velocity. It

is also relevant the fact that the progressive 7 9 200-m

protocol induced an increase in C, probably as a result of

the increase in [La-] and _VO2, accompanied by an increase

Table 1 Mean ± SD swimming velocity (v), index of coordination (IdC), stroke rate (SR), stroke length (SL), blood lactate ([La-]), oxygen

uptake _VO2

� �, and energy cost (C) along the seven sets of 200 m

Step v (m s-1) SR (cycles min-1) SL (m) IdC (%) [La-] (mmol l-1) _VO2 (ml kg-1 min-1) C (kJ kg-1 m-1)

1 1.22 ± 0.09 27.62 ± 1.11 2.68 ± 0.16 -12.48 ± 2.57 1.10 ± 1.09 42.21 ± 8.16 10.24 ± 1.98

2 1.26 ± 0.09 28.48 ± 1.21 2.65 ± 0.17 -11.74 ± 2.67 2.15 ± 1.17 45.25 ± 8.95 10.74 ± 1.95

3 1.31 ± 0.08 29.70 ± 1.42 2.61 ± 0.18 -11.26 ± 2.53 2.45 ± 1.20 48.89 ± 9.28 11.54 ± 1.89

4 1.35 ± 0.09 31.35 ± 1.25 2.56 ± 0.16 -10.48 ± 2.47 2.83 ± 1.48 51.18 ± 9.18 12.11 ± 1.90

5 1.39 ± 0.08 32.64 ± 1.52 2.52 ± 0.16 -9.40 ± 2.41 3.82 ± 1.81 53.35 ± 8.94 12.54 ± 1.77

6 1.44 ± 0.07 34.37 ± 1.43 2.46 ± 0.17 -8.27 ± 2.51 5.84 ± 2.61 56.83 ± 9.90 13.38 ± 1.55

7 1.48 ± 0.08 36.17 ± 1.80 2.40 ± 0.18 -7.14 ± 2.32 7.82 ± 2.64 59.32 ± 10.61 13.94 ± 1.66

Fig. 2 Index of coordination (IdC), energy expenditure (E), aerobic

contribution (Aer), and anaerobic contribution (Anaer) as a function

of energy cost (C)

1960 Eur J Appl Physiol (2013) 113:1957–1964

123

in IdC (concomitant with changes in stroke parameters, SL

and SR).

The observed C values and its increase along the

incremental protocol are in agreement with the literature

(Fernandes et al. 2006; Komar et al. 2011; Seifert et al.

2010; Zamparo et al. 2005). Regarding the inter-arm

coordination, the increase of the IdC with the swimming

velocity is in accordance with previous studies, maintain-

ing a catch-up coordination mode (IdC \ 0 %) during the

whole 200-m sets (Fernandes et al. 2010a, b; Komar et al.

2011; Seifert et al. 2010). The modifications in stroke

parameters (increase of SR and decrease of SL) with

velocity have also been observed previously (Barbosa et al.

2008; Dekerle et al. 2002; Komar et al. 2011; Psycharakis

et al. 2008; Zamparo et al. 2005), suggesting a relationship

between the stroke parameters and the inter-arm coordi-

nation in front crawl (Alberty et al. 2008; Potdevin et al.

2006; Seifert et al. 2007). In addition, it was also observed

that there was an increase in [La-] and _VO2 with

increasing velocity, in line with the findings that report an

increase on _E with velocity (Capelli et al. 1998; di

Prampero 1986; Fernandes et al. 2006; Wakayoshi et al.

1995). In fact, high velocities require a higher contribution

of the anaerobic pathway, leading to an increase in [La-]

(Capelli et al. 1998; Gastin 2001). Thus, to achieve a

certain velocity, swimmers adopt an individualized inter-

limb coordination (Seifert et al. 2004, 2007) with a con-

comitant individual ratio between SR and SL, supported

and/or influenced by bioenergetical variables (Barbosa

et al. 2008, 2010; Seifert et al. 2010; Zamparo et al. 2011).

However, these changes in inter-arm coordination, stroke

parameters (SL and SR), _VO2, and [La-] with the velocity

increase are not strictly linear at the studied swimming

intensities, and a critical turning point was identified for all

these parameters.

The intensity at which the changes occurred could be

interpreted as a threshold between the moderate and heavy

intensity domains. According to Seifert et al. (2004),

skilled swimming is inherently rhythmical and stable,

although flexible modes of coordination between the arms

emerge from the interaction between the mechanical

properties of the water and the intrinsic dynamics of the

body. Therefore, the increase in the IdC values might be

seen as an individual response of the swimmer to the

constraints imposed by a particular task (Seifert et al.

2007), reflecting the intensity that the task is performed.

With the above-referred coordination changes, SR and

SL changed as well, in a non-linearly way, as found by

Dekerle et al. (2002, 2005) and Psycharakis et al. (2008),

which linked this to changes in [La-] kinetics during the

incremental exercise. These results suggest that the energy

requirements seem to influence the biomechanical

Fig. 3 Bland–Altman plots representing the bias (central dashed line) and 95 % limits of agreement (dotted lines) between the velocity at the

[La-]inflex, and the velocity at the IdCinflex, SRinflex, SLinflex, and VO2 netinflex

Eur J Appl Physiol (2013) 113:1957–1964 1961

123

characteristics of swimming at the referred intensities, in

accordance with the proposals of Barbosa et al. (2008,

2010).

The inflection points observed in IdC, SR, SL and_VO2 net were concomitant with an inflection in [La-]

kinetics, frequently used to identify the individual anaer-

obic threshold intensity (Faude et al. 2009; Fernandes et al.

2011). The observed correlation between changes in stroke

parameters and the lactate threshold seem to be in accor-

dance with the studies conducted on this topic (Dekerle

et al. 2005; Keskinen and Komi 1993; Psycharakis et al.

2008), which have shown similar changes in the [La-]

dynamics and the stroking parameters, when the individual

lactate threshold is reached. Swimmers appear to keep a

low SR and a high SL during exercise performed at slow

and predominantly aerobic velocities. However, when the

intensity reaches the individual lactate threshold, the

increase in SR and the reduction in SL becomes progres-

sively greater (Dekerle et al. 2002, 2005; Keskinen and

Komi 1993). Indeed, is commonly accepted that the lactate

threshold intensity represents not only a physiological but

also a biomechanical boundary, where the beginning of an

exponential accumulation in [La-] are linked with changes

in swimming technique (Dekerle et al. 2002, 2005;

Keskinen and Komi 1993; Wakayoshi et al. 1996).

According to Alberty et al. (2009) the modifications in

front crawl inter-arm coordination and stroke parameters at

lactate threshold might be interpreted as a response of the

swimmer’s to achieve a higher velocity, while minimising

the effects of the drag force increase, resulting from the

velocity increase. Moreover, a declining ability seems to

exist to develop the force necessary to overcome the

resistance to forward movement, due to fatigue, manifest-

ing as a decrease in SL and a concomitant increase in SR.

Thus, our results seem to highlight the relation between

arm coordination, stroke, and metabolic parameters as

suggested before (Barbosa et al. 2010). Several biome-

chanical, physiological, and neuromuscular parameters

related assumptions have been proposed to explain move-

ment pattern changes at the lactate threshold intensity, not

only in swimming, but also in other cyclic activities, such

as cycling and running. Indeed, regarding the lactate

threshold, which evidence the transition between moderate

and high intensity domain, with a significant energy con-

tribution from the glycolysis pathway, changes in motor

unit recruitment, muscle perfusion, neuromuscular fatigue

have mainly been suggested (Wakeling and Horn 2009)

that, in a self-optimization perspective, would lead to the

most efficient coordination pattern (Alberty et al. 2011;

Sparrow and Newell 1998).

The mechanics of the limb movement during swimming

depends on muscles that actively develop force to drive

movement, and these are affected by swimming intensity.

This is especially important in intensity transitions such as

the lactate threshold. This emphasizes the influence that

lactate has on the limbs in terms of power output and

efficiency, as well as on mechanics (Wakeling et al. 2006,

2010).

Movement coordination allows the best power produc-

tion in face of the imposed constraints (task). The power

output from a limb should thus be expected to depend on

the coordination pattern. Coordination relates to the

mechanics, as motor control refers to the absolute magni-

tude of the limb or limb segment movement, expressed as a

property of the kinetics or kinematics (Sparrow and Newell

1998). Optimal coordination is required for high power

outputs and high mechanical efficiencies from the limb.

Subsequently, the metabolic cost (i.e. C) of generating a

given power is changed as well.

The _E is the result of the metabolic equivalent of all the

forces generated by the muscles (Minetti 2011). However,

mechanisms behind the effect of various factors (e.g.

velocity, fatigue) on energy consumption, and thereby on

the function of the metabolic processes involved in work

production, are not only directly associated with the work

conversion process (muscle contraction), but also, for

example, associated with ventilation and circulation

(Ettema and Loras 2009). An example in swimming is the

energy expended in moving masses of water by changing

its kinetic energy (Barbosa et al. 2010; Zamparo et al.

2011). Thus, to achieve higher swimming intensities, sen-

sory feedback is integrated and alters the motor output that

controls movement of the body and limbs.

In addition, upon transition through the lactate thresh-

old, front crawl swimmers might change the specific con-

tribution of the leg-kick to overall propulsion and,

consequently, to inter-arm coordination, as upper and

lower limbs are coupled to each other (Figueiredo et al.

2013). Data reported in the literature indicate that the

contribution of the legs to propulsion is about 10–15 %, in

terms of speed and power output (Deschodt et al. 1999;

Hollander et al. 1988). However, through the lactate

threshold transition, front crawl swimmers possibly change

the leg-kick contribution and, consequently, affecting inter-

arm coordination. Below the lactate threshold the leg-kick

is usually de-emphasized, but upon the transition, when the

small muscle groups of the arms and shoulders become

fatigued, the less-efficient larger muscle groups of the legs

begin to work much harder, increasing the C of swimming.

In fact, Zamparo and Swaine (2012) showed an increase of

the contribution of the lower limbs to the total power

output during a front crawl incremental protocol on a

special ergometer. Thus, a greater understanding of the

changes in swimming biomechanics and coordination, at

1962 Eur J Appl Physiol (2013) 113:1957–1964

123

the point which lactate entry into the blood exceeds its

removal (i.e. lactate threshold) would probably be gained

from a more in-depth study of the leg-kick contribution at

these swimming intensities.

Conclusion

The findings of this study provide evidence for the inter-

play between changes in swimmers metabolic, biome-

chanical and coordinative factors on the transition through

the lactate threshold. The inter-play between these

parameters appears to be the result of a response to the

higher swimming intensity, requiring higher rates of energy

disposal, part of which must be supplied anaerobically. On

this basis, coordination as well as stroking kinematics,

could be a useful criterion to control aerobic training loads,

because they seem to reflect the different effects of

swimming bellow versus above the lactate threshold. This

might also be a useful tool for coaches and scientists

wishing to better understand the swimming technique

modifications that occur at the individual lactate threshold.

Acknowledgments This investigation was supported by grant of

Portuguese Science and Technology Foundation (PTDC/DES/

101224/2008 - FCOMP-01-0124-FEDER-009577).

Conflict of interest The authors declare that they have no conflict

of interest.

References

Alberty M, Potdevin F, Dekerle J, Pelayo P, Gorce P, Sidney M

(2008) Changes in swimming technique during time to exhaus-

tion at freely chosen and controlled stroke rates. J Sports Sci

26(11):1191–1200

Alberty M, Sidney M, Pelayo P, Toussaint HM (2009) Stroking

characteristics during time to exhaustion tests. Med Sci Sports

Exerc 41(3):637–644. doi:10.1249/MSS.0b013e31818acfba

Alberty MR, Potdevin FP, Dekerle J, Pelayo PP, Sidney MC (2011)

Effect of stroke rate reduction on swimming technique during

paced exercise. J Strength Cond Res 25(2):392–397. doi:

10.1519/JSC.0b013e3181b94a51

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. doi:

10.1007/s00421-008-0676-z

Barbosa TM, Bragada JA, Reis VM, Marinho DA, Carvalho C, Silva

AJ (2010) Energetics and biomechanics as determining factors of

swimming performance: updating the state of the art. J Sci Med

Sport 13(2):262–269. doi:10.1016/j.jsams.2009.01.003

Bland JM, Altman DG (1986) Statistical methods for assessing

agreement between two methods of clinical measurement.

Lancet 1:307–310

Capelli C, Pendergast DR, Termin B (1998) Energetics of swimming

at maximal speeds in humans. Eur J Appl Physiol Occup Physiol

78(5):385–393

Chollet D, Chalies S, Chatard JC (2000) A new index of coordination

for the crawl: description and usefulness. Int J Sports Med

21:54–59

Dekerle J, Sidney M, Hespel JM, Pelayo P (2002) Validity and

reliability of critical speed, critical stroke rate, and anaerobic

capacity in relation to front crawl swimming performances. Int J

Sports Med 23:93–98

Dekerle J, Nesi X, Lefevre T, Depretz S, Sidney M, Marchand FH,

Pelayo P (2005) Stroking parameters in front crawl swimming

and maximal lactate steady state speed. Int J Sports Med 6:53–58

Deschodt VJ, Arsac LM, Rouard AH (1999) Relative contribution of

arms and legs in humans to propulsion in 25 m sprint front-crawl

swimming. Eur J Appl Physiol 80:192–199

di Prampero PE (1986) The energy cost of human locomotion on land

and in water. Int J Sports Med 7(2):55–72. doi:10.1055/

s-2008-1025736

Ettema G, Loras HW (2009) Efficiency in cycling: a review. Eur J

Appl Physiol 106(1):1–14

Faude O, Kindermann W, Meyer T (2009) Lactate threshold concepts:

how valid are they? Sports Med 39:469–490

Fernandes RJ, Billat VL, Cruz AC, Colaco PJ, Cardoso CS, Vilas-

Boas JP (2006) Does net energy cost of swimming affect time to

exhaustion at the individual’s maximal oxygen consumption

velocity? J Sports Med Phys Fitness 46(3):373–380

Fernandes RJ, Keskinen KL, Colaco P, Querido AJ, Machado LJ,

Morais PA, Novais DQ, Marinho DA, Vilas Boas JP (2008)

Time limit at VO2max velocity in elite crawl swimmers. Int J

Sports Med 29(2):145–150. doi:10.1055/s-2007-965113

Fernandes RJ, Morais P, Keskinen K, Seifert L, Chollet D, Vilas-Boas

JP (2010a) Relationship between arm coordination and energy

cost in front crawl swimming. In: Kjendlie P, Stallman R, Cabri J

(eds) Proceedings of the XIth International symposium for

biomechanics and medicine in swimming. Norwegian School of

Sport Science, Oslo, pp 74–76

Fernandes RJ, Sousa M, Pinheiro A, Vilar S, Colaco P, Vilas-Boas JP

(2010b) Assessment of individual anaerobic threshold and

stroking parameters in 10–11 years-old swimmers. Eur J Sport

Sci 10:311–317

Fernandes RJ, Sousa M, Machado L, Vilas-Boas JP (2011) Step

length and individual anaerobic threshold assessment in swim-

ming. Int J Sports Med 32:940–946

Figueiredo P, Sanders R, Gorski T, Vilas-Boas JP, Fernandes RJ

(2013) Kinematic and electromyographic changes during 200 m

front crawl at race pace. Int J Sports Med 34:49–55. doi:

10.1055/s-0032-1321889

Gastin PB (2001) Energy system interaction and relative contribution

during maximal exercise. Sports Med 31(10):725–741

Hollander AP, de Groot G, van Ingen Schenau GJ, Kahman R,

Toussaint HM (1988) Contribution of the legs to propulsion in

front crawl swimming. In: Ungherects BE, Wilke K, Reischle K

(eds) Swimming Science V. Human Kinetics, Champaign,

pp 39–43

Keskinen KL, Komi PV (1993) Stroking characteristics of front crawl

swimming during exercice. J Appl Biomech 9:219–226

Komar J, Lepretre PM, Alberty M, Vantorre J, Fernandes RJ, Hellard

P, Chollet D, Seifert L (2011) Effect of increasing energy cost on

arm coordination in elite sprint swimmers. Hum Mov Sci. doi:

10.1016/j.humov.2011.07.011

Machado L, Almeida M, Morais P, Fernandes RJ, Vilas-Boas JP

(2006) Assessing the individual anaerobic threshold: the math-

ematical model. Port J Sport Sci 6:142–144

Minetti AE (2011) Bioenergetics and biomechanics of cycling: the

role of ‘internal work’. Eur J Appl Physiol 111(3):323–329

Potdevin F, Bril B, Sidney M, Pelayo P (2006) Stroke frequency and

arm coordination in front crawl swimming. Int J Sports Med

27(3):193–198. doi:10.1055/s-2005-837545

Eur J Appl Physiol (2013) 113:1957–1964 1963

123

Psycharakis SG, Cooke CB, Paradisis GP, O’Hara J, Phillips G (2008)

Analysis of selected kinematic and physiological performance

determinants during incremental testing in elite swimmers.

J Strength Cond Res 22:951–957

Pyne DB, Lee H, Swanwick KM (2001) Monitoring the lactate

threshold in world-ranked swimmers. Med Sci Sports Exerc

33:291–297

Seifert L, Chollet D, Bardy BG (2004) Effect of swimming velocity

on arm coordination in the front crawl: a dynamic analysis.

J Sports Sci 22:651–660

Seifert L, Chollet D, Rouard A (2007) Swimming constraints and arm

coordination. Hum Mov Sci 26(1):68–86. doi:10.1016/j.humov.

2006.09.003

Seifert L, Komar J, Lepretre PM, Lemaitre F, Chavallard F, Alberty

M, Houel N, Hausswirth C, Chollet D, Hellard P (2010) Swim

specialty affects energy cost and motor organization. Int J Sports

Med 31(9):624–630. doi:10.1055/s-0030-1255066

Sousa AC, Figueiredo P, Oliveira NL, Oliveira J, Silva AJ, Keskinen

KL, Rodriguez FA, Machado LJ, Vilas-Boas JP, Fernandes RJ

(2011) VO2 kinetics in 200-m race-pace front crawl swimming.

Int J Sports Med 32:765–770

Sparrow WA, Newell KM (1998) Metabolic energy expenditure and

the regulation of movement economy. Psychon Bull Rev

5:173–196

Wakayoshi K, D’Acquisto LJ, Cappaert JM, Troup JP (1995)

Relationship between oxygen uptake, stroke rate and swimming

velocity in competitive swimming. Int J Sports Med

16(1):19–23. doi:10.1055/s-2007-972957

Wakayoshi K, Acquisto J, Cappaert JM, Troup JP (1996) Relationship

between metabolic parameters and stroking technique charac-

teristics in front crawl. In: Troup JP, Hollander AP, Strasse D,

Trappe SW, Cappaert JM, Trappe TA (eds) Biomechanics and

medicine in swimming VII. E & FN Spon, London, pp 152–158

Wakeling JM, Horn T (2009) Neuromechanics of muscle synergies

during cycling. J Neurophysiol 101:843–854

Wakeling JM, Uehli K, Rozitis AI (2006) Muscle fibre recruitment

can respond to the mechanics of the muscle contraction. J R Soc

Interface 3(9):533–544

Wakeling JM, Blake OM, Chan HK (2010) Muscle coordination is

key to the power output and mechanical efficiency of limb

movements. J Exp Biol 213(3):487–492

Zamparo P, Swaine IL (2012) Mechanical and propelling efficiency in

swimming derived from exercise using a laboratory-based whole-

body swimming ergometer. J Appl Physiol 113:584–594. doi:

10.1152/japplphysiol.00324.2012

Zamparo P, Bonifazi M, Faina M, Milan A, Sardella F, Schena F,

Capelli C (2005) Energy cost of swimming of elite long-distance

swimmers. Eur J Appl Physiol 94(5–6):697–704. doi:10.1007/

s00421-005-1337-0

Zamparo P, Capelli C, Pendergast D (2011) Energetics of swimming:

a historical perspective. Eur J Appl Physiol 111(3):367–378. doi:

10.1007/s00421-010-1433-7

1964 Eur J Appl Physiol (2013) 113:1957–1964

123