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Apr 22, 2008 - On the basis of experiments involving the lactate shuttle hypothesis carried out by Brooks (2000), it is apparent that the lactate ion itself has no ...
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Comparing the lactate and EMG thresholds of recreational cyclists during incremental pedaling exercise Cla´udia Tarragoˆ Candotti, Jefferson Fagundes Loss, Moˆnica de Oliveira Melo, Marcelo La Torre, Maicon Pasini, Lucas Arau´jo Dutra, Jose´ Leandro Nunes de Oliveira, and Lino Pinto de Oliveira Jr.

Abstract: The purpose of this study was to determine the validity of using the electromyography (EMG) signal as a noninvasive method of estimating the lactate threshold (LT) power output in recreational cyclists. Using an electromagnetic bicycle ergometer and constant pedaling cadence of 80 rpm, 24 recreational cyclists performed an incremental exercise protocol that consisted of stepwise increases in power output of 25 W every 3 min until exhaustion. The EMG signal was recorded from the right vastus lateralis (VL) and right rectus femoris (RF) throughout the test. Blood samples were taken from the fingertip every 3 min. The LT was determined by examining the relation between the lactate concentration and the power output using a log–log transformation model. The root mean square (RMS) value from the EMG signal was calculated for every 1-second non-superimposing window. Sets of pairs of straight regression lines were plotted and the corresponding determination coefficients (R2) were calculated. The intersection point of the pair of lines with the highest R2 product was chosen to represent the EMG threshold (EMGT). The results showed that the correlation coefficients (r) between EMGT and LT were significant (p < 0.01) and high for the VL (r = 0.826) and RF (r = 0.872). The RF and VL muscles showed similar behavior during the maximal incremental test and the EMGT and LT power output were equivalent for both muscles. The validity of using EMG to estimate the LT power output in recreational cyclists was confirmed. Key words: EMG, lactate, pedaling exercise. Re´sume´ : La pre´sente e´tude a eu pour but de valider l’emploi du signal EMG pour estimer de fac¸on non effractive le seuil lactique (SL) chez les cyclistes de loisir. Vingt-quatre cyclistes sur cycloergome`tre e´lectromagne´tique ont suivi un protocole d’exercice consistant en des augmentations progressives de puissance de 25 W toutes les 3 min jusqu’a` e´puisement, a` une cadence constante de 80 rpm. On a enregistre´ le signal EMG sur le muscle vaste externe droit (VE) et le muscle droit ante´rieur de la cuisse (FD) pendant toute la dure´e du test. On a pre´leve´ des e´chantillons sanguins au bout des doigts toutes les 3 min. On a de´termine´ le SL en examinant la relation bilogarithmique entre la concentration de lactate et la puissance. On a calcule´ la valeur efficace du signal EMG pour des segments d’une seconde ne se chevauchant pas. On a trace´ les paires de droites de re´gression et calcule´ les coefficients de de´termination (R2) correspondants. On a choisi le point d’intersection des deux droites avec le produit R2 le plus e´leve´ pour repre´senter le seuil EMG (SEMG). Les re´sultats ont montre´ que les coefficients de corre´lation (r) entre le SEMG et le SL e´taient significatifs (p < 0,1) et e´leve´s pour les muscles VE (r = 0,826) et FD (r = 0,872). Les deux muscles ont montre´ un comportement similaire durant le test progressif maximal, ainsi qu’une puissance au SEMG et au SL e´quivalente. On a confirme´ l’utilite´ de l’EMG pour estimer le SL chez les cyclistes de loisir. Mots-cle´s : EMG, lactate, exercice de pe´dalage. [Traduit par la Re´daction]

Introduction The correlation between performance during endurance activities and physiological variables has been widely Received 12 October 2007. Published on the NRC Research Press Web site at cjpp.nrc.ca on 22 April 2008. C.T. Candotti, M.O. Melo, M. La Torre, M. Pasini, L.A. Dutra, J.L.N. Oliveira, and L.P. Oliveira Jr. Universidade do Vale do Rio dos Sinos, Curso de Educac¸a˜o Fı´sica, Sa˜o Leopoldo, Brazil. J.F. Loss.1 Universidade Federal do Rio Grande do Sul, Rua Felizardo, 750, 90690-200 Porto Alegre, RS, Brazil. 1Corresponding

author (e-mail: [email protected]).

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studied. Classically the maximal oxygen uptake (VO2max) has been used as a performance predictor in such activities. Recently, however, some researchers have suggested that VO2max is not the best indicator for high-intensity longduration exercise (Passfield and Doust 2000). It has been noted that among athletes with similar VO2max levels, the capacity to maintain intense exercise at high VO2 levels in combination with low levels of lactate in the blood and active muscles is what determines success (Lucia et al. 2000). Therefore, as an alternative, the lactate threshold (LT) has been proposed as an efficient predictor of performance in such exercises. Although some authors question the relation between the accumulation of lactate in the blood and inadequate oxygen delivery during exercise (Brooks

doi:10.1139/Y08-020

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1985; Gladden 2000), there is consensus concerning its significance in relation to the intensity of the exercise and the nonlinear increase in lactate concentration in the blood and active muscles. Alternative causes of incremental lactate concentration include biochemical regulation that promotes glycolysis (Gladden 2000), increased sympathoadrenal activity (Svedahl and MacIntosh 2003), progressive recruitment of glycolytic motor units (Gladden 2000; Lucia et al. 1999), and an imbalance between lactate removal and production (Brooks 2000). Coaches have used the LT as a reference to assign training because the increase in lactate concentration in the blood during intensive exercise is a performancelimiting factor (Rowlands and Downey 2000). Measurement of the lactate threshold provides a benchmark intensity around which training programs can be designed, that is, exercise performed at an intensity around the lactate threshold may be considered moderate, below this threshold it may be considered mild, and when it substantially exceeds the threshold, it may be considered intense. For these reasons, several methods have been developed to determine the intensity of exercise associated with LT, especially in highperformance athletic populations (Beneke 1995; Billat 1996). In the direct technique, this threshold can be determined only by measurement of blood lactate in blood samples obtained during incremental testing. Unfortunately, this practice is costly and causes discomfort due to its invasive nature. While it is relatively widely used among highperformance athletes, it is not used in recreational athletes, who, in ignorance of their LT, perform exercise without adequate guidance. In addition, given that there are metabolic differences between high-performance and recreational athletes, it is appropriate to investigate alternative methods of determining this threshold. Surface electromyography (EMG) has been suggested as one such indirect method, since variations in patterns of muscular activity during exercise could also be related to the accumulation of metabolic by-products such as lactic acid (Moritani and Yoshitake 1998; Lucia et al. 1999). The accumulated lactate appears to exert a negative influence on the intracellular environment and active muscles, resulting in deficient muscular contraction and the necessity of recruiting additional motor units to compensate. As this recruitment has an impact on the amplitude of the EMG signal (Vaz et al. 1996), we hypothesized that the lactate threshold could be identified from the EMG signal during a progressive load protocol (Lucia et al. 1999). The purpose of this study was to determine the validity of using the EMG signal as a noninvasive method of estimating the lactate threshold power output in recreational cyclists.

Materials and methods Subjects Twenty-four male recreational cyclists participated in the study. They had a mean age of 24.9 ± 3.7 years, body mass 72.4 ± 7.5 kg, height 164 ± 1.8 cm, and regularly performed physical activities, including cycling, between 2 and 3 h per week. This study was approved by the university ethics committee, and all subjects gave signed written consent. Protocol Before testing, subjects were familiarized with the equip-

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ment and procedures used in this investigation. The exercise testing was performed on an electromagnetic bicycle ergometer (model SF, Funbec, Brazil), fitted with competitive clipless pedals, handlebars, and saddle. The incremental exercise protocol consisted of 3 min unloaded pedaling followed by stepwise increases in power output of 25 W every 3 min until exhaustion while the pedaling cadence was kept constant at 80 rpm. The 2 criteria for stopping the test were (i) subjects were no longer able to maintain pedaling cadence at a minimum of 80 rpm, or (ii) subjects voluntarily stopped at exhaustion. Data acquisition EMG data and blood samples were obtained before and during exercise testing. Blood samples (25 mL) for the measurement of blood lactate were taken from the fingertip by using an Accusport lactate analyzer (Boehringer Mannheim, Mannheim, Germany) (Baldari and Guinetti (2000). The test protocol was only performed when subjects presented restlevel lactate concentrations before the start of exercise that were lower than 2 mmol/L. Blood samples were taken throughout the exercise testing at the end of every 3 min before the exercise intensity was increased to the next level. EMG signals were recorded by using a Pentium 200 MHz PC compatible microcomputer, with a converting 12 bit AD board (Lynx Tecnologia Eletroˆnica Ltda, Sa˜o Paulo) at a sampling frequency of 1000 Hz per channel. EMG activity was recorded from the right side vastus lateralis and rectus femoris muscles, in accordance with standard guidelines for reporting EMG data (Journal of Electromyography and Kinesiology 1997). Disposable surface electrodes (Ag/AgCl, 1.0 cm diameter) were placed in a bipolar configuration on the bellies of the muscles, following the supposed alignment of the muscle fibers. The reference electrode was placed on the left wrist. Preparation for surface EMG detection included shaving and alcohol applied to the cleansed skin. Impedance between electrodes was checked and accepted when below 5 kO. Recordings were made with the aid of a 16channel EMG system (Model EMG-800C, EMG System do Brasil Ltda, Sa˜o Jose´ dos Campos) consisting of preamplifiers (fixed gain of 20) located approximately 10 cm from the electrodes. The input impedance of the system was 10 GO; the common mode rejection rate (CMRR) was greater than 100 dB (at 60 Hz); and the signal-to-noise ratio was 3.0 mV root mean square (RMS). Data processing and analysis LT was determined by examining the relation between the lactate concentration and power output during the test by using a log–log transformation model (Beaver et al. 1985). In this method, the log of the blood lactate concentration is plotted on the y-axis as a function of the log of the power output on the x-axis. The resulting graph shows a gradual increase immediately interrupted by a sharp increase in the lactate concentration; this breakpoint clearly defines a transition in the relation of lactate versus power output (Fig. 1). Two independent experts identified and confirmed the LT, that is, the point immediately before the curvilinear increase in blood lactate concentration observed with subsequent exercise intensities. The EMG signal was analyzed by using Matlab software #

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274 Fig. 1. (a) Blood lactate concentration values ([La]) obtained from one subject during incremental exercise and (b) the corresponding lactate threshold (LT) determined by examining the relation between the lactate concentration and power output during the test using a log–log transformation model. The LT breakpoint is located at the intersection of the 2 straight lines.

(version 5.3, Mathworks 1996). Raw EMG data were initially submitted to a band-pass filter (Butterworth, 3rd order, 20–400 Hz), after which the RMS value from the EMG signal was calculated for every 1-second non-superimposing window. Each point on the graph corresponds to 1 s of EMG (Fig. 2). The total points were divided into 2 groups. The first 60 points, representing the first 2 min of the test, were arbitrarily included in the first group, with the remaining points representing the second group. A straight regression line was plotted for each set of points and the corresponding determination coefficients (R2) were calculated. The product of the 2 coefficients resulted in an index representing the linearity of the 2 regions. The initial value of the second set of points was included with the first set, increasing the number of points in the first set of points and decreasing the number of points in the second. New regression lines were plotted and the corresponding regression coefficients calculated, resulting in a new index for the 2 new groups. This procedure was repeated until the second set of points was composed of the final 60 points of the file, corresponding to the last 2 min of the test, and consequently the first set of points corresponded to the remainder of the points. The 2 sets of points that together formed the highest index were chosen to represent the EMG threshold (EMGT), that is, the intersection of the 2 straight lines marked the breakpoint at which the EMGT was established. Figure 2 shows the EMGT procedure. Statistical analysis The data obtained were analyzed with SPSS 10.0 soft-

Can. J. Physiol. Pharmacol. Vol. 86, 2008 Fig. 2. Determination of the electromyography threshold (EMGT) by examining the relation between the root mean square (RMS) of the EMG signal and power output of 24 men during incremental exercise. The EMG breakpoint is located at the intersection between the 2 straight lines.

ware. The normality of the data (Shapiro–Wilk test) and homogeneity of the variance (Levene’s test) were confirmed. One-way ANOVA was applied to identify possible differences between the EMGT and LT for both the rectus femoris and vastus lateralis muscles. The EMG method was further validated by using the procedures suggested by Bland and Altman (1986). The data were presented graphically by plotting the difference between each method versus its average. The mean differences (bias) and standard deviation (SD) of the differences between the values obtained with the 2 methods, expressed in watts and minutes, were calculated. The limits of agreement were set at bias ± 2 SD. This procedure indicated the degree of precision of the EMG method. Significance was set at p < 0.01 for all statistical analyses.

Results The mean lactate concentration at the LT was 4.5 ± 0.7 mmol/L and the mean maximal value of lactate concentration was 9.0 ± 2.2 mmol/L. Of the 24 subjects, the rectus femoris EMGT was not found in 2 subjects, and vastus lateralis EMGT was not found in another 2 subjects. Table 1 shows mean values of power output and time at the EMGT (in both muscles) and LT. There was no significant difference between power output at the EMGT and at the LT (p = 0.96). The mean maximal power was 131.3 ± 29.7 W and the mean duration of the test was 19.8 ± 2.9 min. Figure 3 illustrates the time course of blood lactate concentrations and RMS in the vastus lateralis muscle for one subject. As expected, curvilinear increases in the blood lactate concentration and RMS were observed. The statistical procedure suggested by Bland and Altman (1986) was used to express the degree of agreement between the threshold values obtained from the lactate curve and the EMG data for both muscles, expressed in both watts (Fig. 4) and minutes (Fig. 5). The bias and the limits of agreement of the EMG method were small for both muscles (rectus femoris and vastus lateralis). For power output, expressed in watts, 100% of the values from both muscles were within the limits of agreement (Fig. 4). For the time to reach the threshold, expressed in minutes, only the rectus femoris #

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Table 1. Power output and time to reach the lactate threshold and electromyography threshold for the vastus lateralis and rectus femoris muscles of 24 men performing incremental exercise. Variable Lactate threshold EMGT – VL EMGT – RF

Power output, W 132±30 134±27 134±27

Fig. 3. EMG recording of the vastus lateralis muscle of one subject during incremental exercise. Each EMG point represents a root mean square (RMS) value in a 1-second window (small circles). Large circles are the blood lactate concentration (La).

Time to reach threshold, s 774±222 690±192 690±192

Note: EMGT, electromyography threshold; VL, vastus lateralis; RF, rectus femoris. Values are means ± SD.

muscle in one subject remained outside the limits of agreement, while all the values for the vastus lateralis were 100% within the limits of agreement (Fig. 5).

Discussion The purpose of the study was to investigate the validity of the use of EMG in analyzing the power output corresponding to the LT in recreational cyclists. An important finding in this study was that, in contrast to studies involving athletes in which 2 EMGT are normally found (Lucia et al. 1999), recreational cyclists presented a single EMGT. Furthermore, the power output of the EMGT corresponded to the power output of the LT. Also, although a difference would be expected because of the 2-minute delay (Svedahl and MacIntosh 2003), there was no statistical difference in the time to reach the threshold between lactate production at the muscle level and the increase of lactate in the blood. This result confirms the validity of using the EMG method in analyzing the LT in recreational cyclists during incremental tests. As expected, the blood concentrations of lactate gradually increased in response to the increase in power output until the concentrations became exponential, marking the nonlinear behavior of the curve. The multifactorial explanation of the increase in lactate concentration during incremental exercise until exhaustion takes into consideration a number of processes, including increased lactate production, reduced lactate removal, or both (Svedahl and MacIntosh 2003). Thus, when the exercise reaches the intensity corresponding to LT, theoretically the rate of lactate production and transport into the blood will exceed the rate of removal from the blood. This process could be due to redistribution of blood flow away from lactate-removal sites (nonexercising muscle, liver, kidney, heart), or to transformation of lactate-removal sites into lactate-producing sites as the intensity of exercise increases. The consequence of this imbalance between production and removal is the recruitment of additional motor units within an active muscle, since some lactate is liable to diffuse between active and inactive muscle cells within a muscle (Karlsson and Jacobs 1982). In this situation, an accumulation of lactate may occur because, as the pool units become more active, fewer inactive muscle fibers are available to serve as lactate-removal sites (Svedahl and MacIntosh 2003). The tendency for the muscle fibers to produce lactate therefore increases in relation to the pattern of recruitment. Nonlinear EMG increases similar to those reported in this study have been found in studies assessing neuromuscular

fatigue during sustained and dynamic muscular contraction (Moritani et al. 1982; Takaishi et al. 1994), the lactate threshold (Lucia et al. 1999; Seburn et al. 1992), the ventilatory threshold during incremental exercise (Glass et al. 1998; Hug et al. 2003), and the EMG threshold (Hug et al. 2006). Although the abrupt increase in EMG activity is not fully understood, some authors have attributed it to the local accumulation of metabolic by-products such as lactic acid and hydrogen ions (Moritani and Yoshitake 1998; Masuda et al. 1999; Lucia et al. 1999). These changes would in turn affect the muscle excitation–contraction coupling, including the muscle membrane properties and muscle action potential propagation, leading to a subsequent decrease in the developed force and to deficient contractility (Moritani and Yoshitake 1998). Thus there is a consensus that, to compensate for this situation during an incremental exercise, muscle force output must be increased through the recruitment of additional motor units. A relation between the muscle fiber distribution (percentage of type II fibers) and metabolic changes may be expected, since lactate accumulation occurs during fatigue events such as maximal incremental tests, in which at certain intensities a greater twitch rate in fast muscle fibers occurs due to their functional and metabolic properties (Mannion 1999). In addition, lactate formation or associated pH changes in the sarcolemma of the muscle fibers have been thought to be responsible for lowering the mean conduction velocity, one of factors that can cause an increase in EMG signal amplitude (Lindstrom et al. 1970). On the basis of experiments involving the lactate shuttle hypothesis carried out by Brooks (2000), it is apparent that the lactate ion itself has no major role in the fatigue process (Brooks 2001). Accordingly, a number of studies have sought to show a correlation between the accumulation of lactate and the changes in EMG. Seburn et al. (1992) investigated whether EMG was sensitive to dynamic alterations in the plasma lactate levels during incremental exercise at 90 rpm in 6 trained cyclists. The results showed that lactate and EMG increase in different ways during incremental cycling exercise. The authors speculated that a muscle’s recruitment pattern for incremental exercise would differ between a light load with high cadence and a heavy load with low cadence, despite #

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Fig. 4. Graphic analysis of power output data corresponding to electromyographic threshold (EMGT) and lactate threshold (LT) of the vastus lateralis (VL) and rectus femoris (RF) muscles of 24 men during incremental exercise.

Fig. 5. Graphic analysis of time to reach the electromyographic threshold (EMGT) and lactate threshold (LT) of the vastus lateralis (VL) and rectus femoris (RF) muscles of 24 men during incremental exercise.

equivalent power outputs. Similarly inconsistent results were reported by Jansen et al. (1997), who found no relation between changes in lactate concentration and median power frequency of the EMG in 12 subjects during cycle ergometer with step-wise increases in exercise intensities up to 100% of VO2max. One of the arguments put forward was that the intensity used was insufficient to cause an accumulation of metabolic by-products and to influence the rate of muscle fiber conduction, thus suggesting the existence of some type of metabolic tolerance at a certain level of exercise. Inconsistencies in the results reported in the literature may be partly attributable to differences in methodology, which on the one hand make comparisons difficult, but on the other, demonstrate that lactate cannot be considered the only factor influencing changes in EMG. Studies involving

patients with myophosphorylase deficiency (McArdle’s disease) (Mills and Edwards 1984) have shown that during fatigue consistent alterations in EMG activity may occur without lactate accumulation. This observation suggests that factors within the central and peripheral nervous system are also involved in the neuromuscular fatigue process. Mateika and Duffin (1994a, 1994b), upon submitting subjects to 2 consecutive tests that induced positive and negative variation in lactate concentration, also showed that such variations do not necessarily occur in accordance with EMG variations. During tests where the load variation was exclusively incremental, however, Mateika and Duffin (1994a, 1994b), like other authors (Nagata et al. 1981; Lucia et al. 1999; Hug et al. 2006; Moritani and Yoshitake 1998), reported correlation between EMG and lactate. Thus, even #

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though the mechanism by which each of these variables (EMG and lactate) occurs is not fully understood, in a single progressive load test they appear to be correlated. While some studies have found significant correlation between the changes in lactate and the EMG curves (Lucia et al. 1999), others have not (Seburn et al. 1992; Pringle and Jones 2002; Jansen et al. 1997). Using methodology similar to the present study, Lucia et al. (1999) investigated the validity and reliability of EMG as a new noninvasive determinant of the metabolic response to incremental exercise in 20 elite road cyclists and detected 2 thresholds in EMG that occurred concomitantly with lactate thresholds. The authors reported that these 2 thresholds occurred as a result of a change in the pattern of motor unit recruitment with the possible participation of type IIa and IIb fibers (at the EMGT1 and EMGT2, respectively). This change in recruitment produced larger action potentials, followed by some degree of synchronization as these fibers underwent progressive fatigue. The results of the present study are in overall agreement with Lucia et al. (1999) because the distinct EMGT we observed may have occurred as result of changes in the pattern of motor unit recruitment from a predominantly slow-twitch motor unit to a fast-twitch motor unit. Indeed, the fact that only one EMG threshold was found in recreational cyclists during incremental testing may indicate that only highly trained subjects (such as those selected in the study by Lucia et al. (1999)) are able to effectively recruit a sufficient number of motor units (especially type IIb fibers) at near-maximal intensities during incremental testing (Deschenes and Kraemer 2002) to be able to induce a second EMG breakpoint. In summary, the results of the present study showed an EMGT that suggests a correlation between the percentage of type II fiber recruitment and the lactate accumulation, since the changes in the pattern of neuromuscular activity during maximal incremental testing appear to be associated with lactate accumulation, independently of the subject’s level of training. The initial speculation that the LT could be identified by using surface EMG was thus confirmed in cases where the EMGT and LT power outputs were equivalent. We conclude that the rectus femoris and vastus lateralis muscles show similar behavior during maximal incremental testing and that the EMGT and LT power outputs are equivalent for both muscles. The validity of using EMG to measure the power output corresponding to the LT in recreational cyclists was confirmed. Given this finding, EMGT can be considered one more useful tool for coaches and recreational cyclists when designing training programs.

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