J Appl Physiol 90: 2212–2220, 2001.

˙ O slow component dependent on progressive Is the V 2 recruitment of fast-twitch fibers in trained runners? F. BORRANI,1,2 R. CANDAU,1,2 G. Y. MILLET,3,2 S. PERREY,2 J. FUCHSLOCHER,4,2 AND J. D. ROUILLON2 1 Laboratoire Sport Performance et Sante´, 34090 Montpellier, Faculte´ des Sciences du Sport, Universite´ de Montpellier I, France; 2Laboratoire des Sciences du Sport, 25000 Besanc¸on, Unite´ de Formation et de Recherche en Science et Techniques des Activite´s Physiques et Sportives, Besanc¸on, France; 3Groupe Analyse du Mouvement, Dijon 21000, UFR STAPS Universite´ de Bourgogne, France; and 4Institut des Sciences du Sport et de l’Education Physique, 1015 Lausanne, Faculte´ des Sciences Sociale et Politiques, Switzerland Received 9 March 2000; accepted in final form 6 February 2001

with constant power output performed below the work rate that elicits the onset of ˙O ) blood lactate accumulation (OBLA), O2 uptake (V 2 increases exponentially, after the initial cardiodynamic response, toward a steady state (62). Healthy subjects reach the stable state after ⬃2 to 3 min. During high-intensity, constant-load cycling, the ini˙ O is followed by a slower increase in tial rapid rise in V 2

˙ O exceeding the value extrapolated from the V ˙O V 2, 2 work rate relationship established at sub-OBLA work ˙ O rise continues for several minrates. The slower V 2 ˙ O (V ˙O utes and may reach maximal V 2 2 max) when ex˙ O during haustion occurs (59, 62). The increase in V 2 high-intensity exercise at a constant load is known as ˙ O (19, 49, 51, 62). the slow component of V 2 ˙ O slow component The mechanisms underlying the V 2 are not yet completely understood. Among the physiological factors suspected to cause the slow component, lactate accumulation initially attracted the most attention (5, 7, 47, 56). In fact, it has been shown that the amplitude of the slow component and the rise in blood lactate were strongly correlated during stationary cycling (51). However, it is now generally accepted that ˙ O slow component but lactate is not a cause of the V 2 coincides with its appearance (47). Epinephrine was also proposed as an explanation of the slow component because its infusion increases basal metabolism (53), but because the administration of epinephrine has not ˙ O kinetics during exercise, epishown an effect on V 2 nephrine cannot be regarded as a factor causing the ˙ O slow component (25, 64). A strong correlation beV 2 ˙ O suggested that it tween plasma potassium and V 2 may have an effect on the slow component (26, 66). However, the concentration of potassium increased until the third minute of exercise and then remained stable despite the appearance of the slow component. Thus it seems improbable that potassium plays a significant role in the slow component (48). Altered availability or use of energy substrates has been hypothe˙ O slow component, but their sized to influence the V 2 influence is questionable or weak. Contradictory results of the studies on this topic do not allow a clear interpretation (26). On the basis of data showing that an increase in mitochondria temperature decreased the coupling of oxidative phosphorylation (P/O ratio), it has been suggested that the increase in body temper-

Address for reprint requests and other correspondence: F. Borrani, UPRES-EA “Sport Performance Sante,” Faculte´ des Sciences du Sport, 700 Ave. pic saint loup, 34090 Montpellier, France (E-mail: [email protected]).

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked ‘‘advertisement’’ in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Borrani, F., R. Candau, G. Y. Millet, S. Perrey, J. Fuch˙ O slow component slocher, and J. D. Rouillon. Is the V 2 dependent on progressive recruitment of fast-twitch fibers in trained runners?. J Appl Physiol 90: 2212–2220, 2001.—The goal of this study was to use spectral analysis of EMG data to ˙ O ) slow component is test the hypothesis that the O2 uptake (V 2 due to a recruitment of fast fibers. Thirteen runners carried out a treadmill test with a constant speed, corresponding to 95% of ˙ O response ˙ O . The V the velocity associated with maximal V 2 2 was fit with the classical model including three exponential functions. Electrical activity of six lower limb muscles (vastus lateralis, soleus, and gastrocnemius of both sides) was measured using electromyogram surface electrodes. Mean power frequency (MPF) was used to study the kinetics of the electromyogram discharge frequency. Three main results were observed: 1) a common pattern of the MPF kinetics in the six muscles studied was noted; 2) MPF decreased in the first part of the exercise, followed by an increase for all the muscles studied, but only the vastus lateralis, and gastrocnemius muscles of both sides increased significantly (P ⬍ 0.05); and 3) the beginning of the MPF increase of the four muscles mentioned above corresponded with the beginning of the slow component. Our results suggest a progression in the average frequency of the motor unit discharge toward the high frequencies, which coheres with the hypothesis of the progressive recruitment of fast-twitch fibers ˙ O slow component. However, this interpretation during the V 2 must be taken with caution because MPF is the result of a balance between several phenomena. ˙ O kinetics; mean power frequency; electromyogram; power V 2 spectrum; O2 uptake

AT THE ONSET OF EXERCISE

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ature could explain ⬃25% of the slow component (29, 60, 63). However, this attractive hypothesis was unconfirmed by experiments. In fact, Koga et al. (34) did ˙O not find a significant rise in the slow component of V 2 after having increased muscle temperature by the use of hot water-perfused pants. It has been suggested that ˙ O that corresponds to the V ˙ O -increased work of the V 2 2 the respiratory muscles due to increased breathing ˙ O slow component (19, 64), and it contributes to the V 2 has been found that it could explain up to 25% of the ˙O slow component during exercise of 95% V 2 max (16). However, a hypoxia (12% O2) experiment reported contradictory results. Compared with normoxia, the subjects increased their ventilation as much as 40 l/min ˙ O slow component was not affected (22). although the V 2 ˙ O of the contracting The authors argued that the V 2 muscles (or other tissues such as renal or splanchnic) was reduced under the hypoxic condition, and other metabolic processes must have increased to compen˙ O was unchanged. sate because whole body V 2 It is not surprising then that the origin of the slow component has been attributed to the muscles concerned with exercise (48). It has been suggested that ˙ O slow component is due to the progressive rethe V 2 cruitment of fast-twitch fibers to compensate for the deficiency of slow-twitch fibers (26, 47). In fact, efficiency of fast-twitch fibers is lower than the efficiency of slow-twitch fibers (36, 55, 63). This phenomenon was ˙ O , the also observed in situ in humans. At a given V 2 subjects with a high percentage of slow-twitch fibers produced a higher mechanical power than their counterparts with a lower percentage of slow-twitch fibers (20). On the basis of amplitude changes in an integrated electromyogram (EMG) during the slow compo˙ O , it has been suggested that fast-twitch nent of V 2 fibers are gradually recruited during exhausting exercises (52). It is well known that, for progressive exercises, small motor units (i.e., composed of slow-twitch fibers) are first recruited at a submaximal level of force. At high intensity, metabolic modifications, such as a reduction in muscular pH, inorganic phosphate, and potassium accumulation, are associated with an alteration in the excitation-contraction coupling (for a review, see Ref. 1). As a result, other fibers must be recruited to sustain the needed muscular work. As a result of the recruitment law, these progressively recruited fibers are likely to include fast-twitch fibers (31, 52). A previous investigation (6) has shown that ˙ O slow component was correthe amplitude of the V 2 lated with the percentage of fast-twitch fiber of the vastus lateralis muscle. On the basis of the increase in integrated EMG observed during a constant-load exercise (52), Shinohara and Moritani argued that fasttwitch fibers are progressively recruited. Nevertheless, to the best of our knowledge, no direct relationship ˙ O slow component and the progressive between the V 2 recruitment of fast-twitch fibers has been shown. Consequently, the purpose of this study was to test, through spectral analysis of the EMG signal, the as˙ O is partially sumption that the slow component of V 2

induced by a progressive recruitment of fast-twitch fibers. MATERIAL AND METHODS

Subjects Thirteen regional-level competitive runners agreed to take part in the study. The local ethics committee approved the experiment, and the subjects gave their written consent. Experimental Design Two running tests separated by 72 h were completed. The first test consisted of progressive exercise on a treadmill (Adal race, Tecmachine, Andre´zieux-Bouthe´on, France). A warm-up at a speed of 12 km/h (3.3 m/s) for 5 min was first carried out. The test then started at 14 km/h (3.9 m/s), and the speed was increased by 1 km/h every 2 min according to the method described by Le´ger and Boucher (41). The second test consisted of a run at constant speed cor˙O responding to 95% of V 2 max until exhaustion after a warm-up of 10 min at a velocity of 3.3 m/s. During the test, the breath-by-breath gases were measured on a computerized system (CPX, Medical Graphics, St. Paul, MN). This system uses an infrared sensor and a zirconium oxide electrode for measuring fractional concentration of CO2 and O2. A pneumotachograph was used to measure expired gas volume. Immediately before each exercise, known-composition gases and a 3-liter Rudolph syringe were used for calibration of the gas analyzers and the pneumotachograph. EMG activity was obtained from the vastus lateralis, gastrocnemius lateralis, and soleus muscles of both lower limbs. The bipolar surface electrodes (Biochip, Grenoble, France) had a constant intrapair distance of 12 mm and included a differential amplifier (impedance ⫽ 2 G⍀, filter 6–600 Hz). The motor points were located with an electrostimulator (Compex, Echallens, Switzerland). The surface of the skin was prepared by removing the hair and rubbing it with abrasive paper, then washing it with acetone. The electrodes were fixed longitudinally over the muscle belly. An electrolytic gel was applied between the skin and the surface of the electrodes to improve conductivity. The neutrals of the electrodes were placed on the front and median part of the tibia. An analog-digital board (12 bits, National Instrumentation, LPM16, Paris, France) was used to acquire the data, and a personal computer managed the system. Acquisition was continuous throughout the test at a sample rate of 1,000 Hz. The signal was temporally cut out with respect to each stride. Because the treadmill was equipped with piezoelectric sensors able to measure the force exerted during foot contact in three dimensions, the definition of each stride was performed using the vertical force signal. Data Analysis ˙ O kinetics. Several models have been proposed to deV 2 ˙ O kinetics. For primary analysis, we used the scribe the V 2 exponential model (8) ˙ O2b ˙ O2共t兲 ⫽ V V ⫹ A1兵1 ⫺ e ⫺ 关共t ⫺ td1兲/␶1兴其U1 ⫹ A 2 兵1 ⫺ e

Phase 2 共primary component兲

⫺ 关共t ⫺ td2兲/␶2兴

其U2 Phase 3 共slow component兲

where U1 ⫽ 0 for t ⬍ td1 and U1 ⫽ 1 for t ⱖ td1, and U2 ⫽ 0 for t ⬍ td2 and U2 ⫽ 1 for t ⱖ td2. ˙ O is the V ˙ O at rest; A and A are the asymptotic V 2b 2 1 2 amplitudes for the second and third exponential, respec-

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tively; ␶1 and ␶2 are the time constants of each exponential; and td1 and td2 represent the time delays of each equation. ˙O Because the focus of our study was the slow component of V 2 kinetics, and the primary component phase is not distorted by any early cardiodynamic influence (45, 61), the initial component was not modeled in this study. As a consequence, the first 20 s were removed from analysis to ensure that the early initial component did not influence the result (61). The amplitude of slow component was assigned the value (A2⬘ ) A⬘2 ⫽ A 2 兵1 ⫺ e ⫺ 关共t e ⫺ td2兲/␶2兴其 where te is the time at end of exercise. As pointed out by Linnarsson (42), when the second exponential component has a time constant that is substantially longer than the duration of the data collection, it is indistinguishable from a linear “drift.” If this case appears, the linear model proposed by Paterson and Whipp and colleagues (45, 61) would be used. The parameters of the model were determined with an iterative procedure by minimizing the sum of the mean ˙ O based squares of the differences between the estimated V 2 ˙ O . Values of the measured on the model and the measured V 2 ˙ O that were greater than three standard deviations from V 2 ˙ O of the model were considered outliers and were the V 2 removed. These outlier values were assumed to be due to abnormal breaths during exercise, such as shallow breathing or breath holding. These values represented ⬍1% of the total data collected. Iterations continued until successive repetitions reduced both the sum of residuals by ⬍10⫺8 and the correlation coefficient of the relationship between residuals and time by ⬍10⫺6. The bootstrap method was used to test the confidence interval of the model parameters. This method estimates the potential error in the determination of model parameters using repeated samples from the original data set. EMG. The rough signal was divided into temporal segments corresponding to the strides. For each muscle, the segments were determined between two successive impacts of the opposing foot on the treadmill, detected with the vertical force signal of the treadmill. Each signal was filtered (band-pass filter 20–500 Hz, Butterworth 5th degree). To clear the truncation error, the signal was multiplied by the Hanning function (Hanning’s window). The spectrum was analyzed using the MPF. The MPF was calculated using a fast Fourier transformation of 1,024 points, applying the power spectral density to the results of the fast Fourier transformation, and then by calculating MPF from the power spectral density. To present the MPF kinetics graphically, the MPF was normalized with respect to the value measured at the beginning of the slow component. Statistical Analysis A Fisher test was used to determine the degree of signifi˙ O . The bootstrap method cance of the exponential model of V 2 used in the present study to assess the 95% confidence intervals of the model parameters, has been described in details by Efron and Tibshirani (21). Briefly, the procedure consists to resample the original data set with replacement to create a number of “bootstrap replicate” data sets of the same size as the original data set. A random number generator to determine which data of the original data set will be included in a replicate data set was used. A given data can be used more than once in the replicate data set or not at all. For each replicate data, the model parameters were estimated accord-

ing to the same procedures than the original data. This was repeated 1,000 times, and the parameters estimated were retained. For each parameter, the 1,000 estimates were sorted, and the estimates that fell at the 2.5th and the 97.5th percentiles were used to construct a 95% confidence interval. The coefficient of variation (CV) was used to normalize the range of the confidence interval. A nonparametric ranging test (Spearman) was used to assess the correlation between the rise in MPF at the end of exercise and the amplitude of the slow component (A2). The EMG data collected for the lower right limb were compared with those for the left limb by using an ANOVA for independent samples. When the assumption of normality or the equality of variance was violated, an ANOVA (Kruskal-Wallis) for nonparametric values was used. The effect of time on the EMG variables was tested by using a one-way ANOVA with repeated measures. When the normality or the equality of variance assumptions was violated, a nonparametric test (Friedman) was used. A nonparametric test (Mann-Whitney) was used to compare the concomitance between the onset of ˙ O and the time delay of the lowest point slow component of V 2 of the MPF response. For all tests, significance was declared when P ⬍ 0.05. Dispersion about the mean was expressed as ⫾SD. RESULTS

Table 1 presents individual results of the incremen˙O tal tests. The mean value for V 2 max was 64.6 ⫾ 4.6 ml 䡠 min⫺1 䡠 kg⫺1, and the average speed corresponding ˙O to this V 2 max was 5.36 ⫾ 0.28 m/s, indicating that the subjects were well-trained runners. Table 2 shows kinetic parameters for the exponential ˙ O responses. The pricurve fitting of the individual V 2 mary component had an amplitude (A1) of 48.5 ⫾ 6 ml 䡠 min⫺1 䡠 kg⫺1 (CV ⫽ 1.7%) with a time delay (td1) of 4.9 ⫾ 5.6 s (CV ⫽ 105%) and a time constant (␶1) of 17.2 ⫾ 5.8 s (CV ⫽ 20.4%). For all subjects, a slow component was observed. The average value (A2⬘) was 6.9 ⫾ 2.2 ml 䡠 min⫺1 䡠 kg⫺1 (CV ⫽ 11.4%). The time delay for the slow component (td2) was 119 ⫾ 25 s (CV ⫽ 10.7%), and the time constant (␶2) was 84 ⫾ 46 s (CV ⫽ 15.6%). The time constant ␶2 was substantially shorter Table 1. Individual anthropometric characteristics and results of incremental tests Subject No.

Age, yr

Weight, kg

˙O V 2max, km/h

˙ O2max, V ml 䡠 min⫺1 䡠 kg⫺1

1 2 3 4 5 6 7 8 9 10 11 12 13 Mean ⫾ SD

30 29 18 25 37 25 22 30 31 33 18 25 23 26.6 ⫾ 5.7

85 68.2 62.8 69 71 72 71 64 63.8 62.6 60.8 71.4 54.8 67.4 ⫾ 7.3

20 20 18 20 19 20 20 20 21 18 18 20 18 19.4 ⫾ 1.0

63 71 59 65 68 55 64 66 64 67 66 70 59 64.6 ⫾ 4.6

˙O ˙ V 2max is the maximal oxygen consumption; VO2max is the speed ˙O corresponding to V 2max.

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˙ O response Table 2. Parameters estimated for exponential curve fitting of individual V 2 Subject No.

A1, ml 䡠 min⫺1 䡠 kg⫺1

td1, s

␶1, s

A2, ml 䡠 min⫺1 䡠 kg⫺1

td2, s

␶2, s

te, s

r Model

1 2 3 4 5 6 7 8 9 10 11 12 13 Mean ⫾ SD CV Mean

46 56 49 51 53 39 46 46 44 54 45 59 41 48.5 ⫾ 6.0 1.7%

0 0 9 11 0 17 2 0 11 0 9 6 0 4.9 ⫾ 5.7 105.2%

28 20 14 14 17 10 18 16 12 29 15 12 20 17.2 ⫾ 5.8 20.4%

5 5 5 4 5 6 8 10 8 7 12 6 8 6.8 ⫾ 2.3 11.4%

147 126 174 144 130 117 88 104 108 135 94 78 121 120.5 ⫾ 26.4 10.7%

83 75 84 35 35 51 127 60 63 118 94 64 209 84.3 ⫾ 46.6 15.6%

605 606 485 561 414 530 668 548 573 728 495 428 550 553 ⫾ 88

0.90 0.97 0.93 0.52 0.93 0.73 0.89 0.92 0.92 0.95 0.95 0.95 0.79 0.87 ⫾ 0.12

˙ O ; ␶ and ␶ , time constants of each component; td and td , time delays to onset of each component; t , A1 and A2, amplitude terms for V 2 1 2 1 2 e time to exhaustion; r, coefficient of correlation of the model; CV, coefficient of variation estimated by the bootstrap method.

(⬃2.7 time in the worst case) than the exercise duration, and the sum of residuals was lower than that obtained with the linear model. Thus the double monoexponential model was used for each case (42). The coefficient of determination (R2) obtained be˙ O s and modeled responses was 0.77 ⫾ tween actual V 2 0.19. Figure 1A shows an example of breath-by-breath ˙ O associated with the V ˙ O model. Figure 1B repreV 2 2 sents the distribution of the residual errors. The sum of residuals (sr ⬍ 10⫺8) and the coefficient of correlation (r ⬍ 10⫺6) clearly indicate that the breath-by-breath “noise” was independent of time, distributed randomly around zero, and similar for the whole group. Typical EMG signals of the six muscles studied and the vertical force signal of the treadmill are shown in Fig. 2. Generally, the activation of the vastus lateralis and soleus muscle started before the contact of the foot on the treadmill showing a clear phase of preactivation (see right line in Fig. 2). The beginning of EMG activity for the gastrocnemius muscle and impact of the foot on the treadmill were simultaneous. A common pattern of MPF changes over time was observed among the six muscles studied (Fig. 3). MPF decreased during the primary component. This drop was significant for the right and left gastrocnemius, the left soleus, and the right vastus lateralis muscles (P ⬍ 0.05, P ⬍ 0.001, P ⬍ 0.001, and P ⬍ 0.05, respectively). During the slow component period, MPF increased significantly (P ⬍ 0.05), except for the right and left soleus muscles (P ⫽ 0.47 and P ⫽ 0.19, respectively). The onset of the increase in MPF values for the vastus lateralis and the gastrocnemius lateralis muscles were found to be concurrent with the beginning of the slow component, whereas the minimal MPF values were delayed (⬃13%) for the soleus muscles. For all muscles, the difference in time delay between the onset ˙ O and the lowest point of of the slow component of V 2 the MPF response failed to be significant. No significant difference was found in the evolution of MPF when comparing the left side to the right. The relative amplitude of the MPF was significantly corre-

lated to the relative amplitude of the slow component ˙ O for the left gastrocnemius lateralis muscle and of V 2 the right soleus muscle (R ⫽ 0.70, P ⬍ 0.05 and R ⫽ 0.75, P ⬍ 0.05, respectively). For all other muscles studied, this relationship was never significant.

˙ O ) in the square-wave exercise (A) Fig. 1. Best fit of O2 uptake (V 2 and residual sum of squares (B). Note that the breath-by-breath “noise” of the individual exercise transition was independent of time, indicating that the residuals were distributed randomly around zero. This result suggests that no further improvement of the fit could be obtained without using a more complex model.

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Fig. 2. Typical electromyogram (EMG) signals of the 6 muscles studied and the vertical signal of the treadmill. The right line represents the beginning of contact phase. DISCUSSION

number of measurement points greater than 10 times the number of degrees of freedom of the model plus 10 points. In our case, the minimal number of points is 70 (6 ⫻ 10 ⫹ 10). The number of breaths collected in the present study is clearly greater than the minimal number required. The estimated coefficient of variation, especially those of the two critical parameters (i.e., td2 and A2), were relatively small (⬃10%) suggesting an accurate determination of the critical parameters even if a single transition was performed. Moreover, it is not possible to exclude the fact that the breath-by-breath variability may have biological significance, although Lamarra et al. (38) suggested stochastic properties of the breath-by-breath noise. In the present study, the lack of relationship between the residuals and the time supports the view of these authors. In the present study designed to test the fiber type hypothesis, no measurement from moderate exercise has been achieved. Thus our results could not demon˙ O is excessive with strate that the slow component of V 2 respect to the values predicted from moderate exercise. However, the prediction of energy demand corresponding to high-intensity exercise requires a number of assumptions, and there are inherent limitations. It is necessary to assume that it is possible to predict the energy demand of high-intensity exercise on the basis of linear extrapolation from submaximal work rates. In addition, it was necessary to assume that the energy demands of these high-intensity work rates remained constant throughout the period over which we evaluated the kinetics (32). The energy demand for exercise at work rates above the OBLA cannot be accurately predicted because it is essential to sum to total aerobic plus anaerobic energy contributions (4). Finally, a major obstacle in the extrapolation of energy requirement during heavy exercise is the unknown contribution of slow-twitch muscle fibers. As work rate increases, the fast-twitch fibers are gradually recruited (28). Taking into account these limitations in the assessment of energy demand for high-intensity exercise, it seems

The two most important findings of this study are ˙ O and MPF of the extensor muscles inthat both V 2 creased from the end of the second minute to exhaus˙O tion during running at 95% of V 2 max and that the start of these increases were concurrent. Limits of the Methods One could argue that the number of transitions between rest and exercise is not sufficient to assess accurately the parameters of the model. In the studies ˙ O kinetics, the focusing on the primary phase of V 2 transition was repeated several times (generally 2–4 times) to collect a sufficient number of points during this short phase to decrease the breath-by-breath noise using an averaging procedure. However, recent studies focusing on the slow component (9, 10, 39, 40, 44) used only a single transition, because enough measurements were obtained to fit a monoexponential function (⬎400 points during the slow component phase in our case). The common practice in modeling is to collect a

Fig. 3. Common pattern of mean power frequency (MPF) changes over time, i.e., mean group response. For clarity, the SE was presented only for the highest and lowest curves of the primary and slow ˙ O . R, lower right limb; L, lower left limb. The values component of V 2 are standardized with respect to the value of MPF (y-axis) at the beginning of the primary and the slow component (x-axis) and with respect to the duration of each phase.

SLOW COMPONENT AND EMG

difficult to evaluate accurately the energy demand from moderate exercise. Nevertheless, the fact that the ˙ O seems slow component corresponds to excessive V 2 widely accepted for cycling exercise (5, 8, 26, 29, 60, 61, 64). This could be more problematic with treadmill exercise for two reasons. First, the power output does not necessarily increase linearly with the speed of progression. Second, the quantification of the power output in running is complex and is still the object of controversies. Therefore it seems difficult to verify whether the slow component amplitude represents O2 excess or not. Slow Component: Comparison With the Known Facts Two models are generally used in the literature to ˙ O as describe the phenomenon of the slow increase in V 2 a function of time: the exponential model (8) and the linear model (2, 45). As pointed out by Linnarsson (42), if this second exponential component has a time constant (␶2) that is substantially longer than the duration of the data collection (te), it is indistinguishable from a linear “drift,” and the linear model must be used. Because this was not the case in our study (see Table 2), application of a linear model would be inappropriate. Moreover, the random distribution of residuals about zero suggests that no further improvement of the fit can be obtained. As explained by Casaburi et al. (18), the advantage of this exponential equation is that if the underlying response is truly monoexponential, ˙ O slow component (A ) will the amplitude of the V 2 2 converge to zero (7). Furthermore, if the second component is linear (rather than exponential) over the time observed, the amplitude (A2) and the time constant of the second component (␶2) will converge to unphysiologically high values. However, the derivative of the second term (A2/␶2) would be identical to the best fitting slope of a linear function to the data (7, 18). It seems that the time constant of response (␶2) tends to be longer with increasing work rate (18). The relatively small coefficients of variation found for td2 (10.7% ⫾ 4.9) lend support to the robustness of ˙ O slow component. Morethe estimated onset of the V 2 over, the td2 observed in the present study is in agreement with the values reported previously (6, 7, 45). Few data are available on the amplitude of the slow component for trained runners. For an intensity of exercise similar to the present study, Candau et al. (16) found an amplitude of 216 ml/min. A recent study (17) reported an amplitude of 301 ml/min during a running exercise at similar intensity as in the present study, whereas a higher amplitude (700 ml/min) was reported in experiment by Sloniger et al. (54) in which the ˙O intensity was 99% of V 2 max. However, Billat et al. (12) ˙ O (⫺0.9 ⫾ 2.1 found no significant rise in V 2 ⫺1 ⫺1 ml 䡠 min 䡠 kg ) between the third minute and the end ˙O of a test performed at 90% of V 2 max. In the study of Carter et al. (17), the amplitude of the slow component was slightly lower in running than in cycling exercise. Thus, to the best of our knowledge, no satisfactory explanation has been proposed in the literature which

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would support a lack of slow component in running exercise observed in the study of Billat et al. (12). ˙ O and EMG Signal Kinetics of V 2 ˙ O . As previously described Primary component of V 2 in the literature, a decrease in MPF was noted during the primary phase of O2 kinetics. Three main phenomena can explain this result, namely 1) muscle wisdom, 2) changes in muscle fiber conduction velocity, and 3) synchronization of the slow motor units. Muscle wisdom refers to the decline in the rate at which motor unit action potentials are discharged. This decrease is not thought to be associated with a modification of the action potential generation and propagation but is more likely connected to an adaptation that matches the neural activity to the changing conditions in the muscle. For instance, it has been reported that a declining frequency of electrical stimulation induced a lower decrease in strength over a 60-s interval when compared with a constant frequency of stimulation (14). Fatigue shifts leftward the stimulus frequency-force relationship showed (13). This change is usually interpreted as an increase in time course of the twitch, such that the activation rate decline induces a decrease in MPF without change in force. In fact, because the time of relaxation increases in fatiguing conditions, the discharge rate does not need to be as high as in normal conditions when it induces tetanus. It was hypothesized that peripheral feedback reflex from either type III/IV mechanoreceptors, muscle spindles, or type Ib tendon organs (24) could lead to changes in stimulus frequency such that the decrease in force with fatigue is minimized. Nevertheless, other experiments on both motor unit (57) and whole muscle (13) have found that the stimulus frequency-force relationship does not always shift to the left with fatigue. Powers and Binder (50) have suggested that the type of motor unit can indirectly influence the kinetics of MPF. According to these authors, the frequency-force relationship of fast-twitch fatigue resistant motor units shifted to the left immediately after a stimulation period and to the right after about 30 min of recovery, whereas this relationship shifted only to the right in fast-twitch fatigable motor units. Because the measurement of electrical activity of the entire muscle is influenced by the weighted average of the motor unit response, Thomas et al. (57) suggested that the direction of shift depends on the balance between time course of fatigability and potentiation for each unit. Also, Garland et al. (27) suggest that muscle wisdom is not applicable for submaximal contractions because they find a decrease in discharge rate even in the absence of any slowing of muscle relaxation time. Thus, although muscle wisdom may be an appropriate expression for isometric contractions, it is more questionable for cyclic activity like running. Changes in muscle fiber conduction velocity are a second factor that could potentially influence the shift of MPF toward low frequency with fatigue. The MPF is directly related to the muscle fiber conduction velocity,

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and changes in muscle fiber conduction velocity produced by changes in electrolytes or metabolites have direct implications on the MPF value. Bouissou et al. (15) have found a correlation between muscle lactate concentration and MPF during supramaximal dynamic exercise, suggesting that metabolite accumulation can influence MPF. However, Mills and Edwards (43) have observed that patients with myophosphorylase deficiency also present a power spectral shift to the left with fatigue without any modification of muscle fiber conduction velocity and lactate or H⫹ accumulation. To explain this apparent contradiction, loss of K⫹ in the intracellular space has been suggested because ionic change affects membrane potential, which changes the muscle cell membrane excitability. It can be speculated that the resulting alteration of the action potential propagation has an effect on the decrease of MPF (33). This hypothesis is supported by the study of Poole et al. (48), who found that the potassium rise takes place mainly during the first 3 min of exercise. The third factor that could explain the shift in MPF toward the left is the synchronization of the slow motor units. In fact, Lago and Jones (37), as well as Kranz et al. (35), have shown that a rise in synchronization can alter the EMG spectrum. Synchronization increases the relative power in low frequency, and this could decrease MPF. ˙ O . In this study, the MPF of the Slow component of V 2 muscles studied increased during the period corre˙ O , i.e., with fasponding to the slow component of V 2 tigue. Although it is not statistically significant, it is of interest to note that the time delay for the onset of the MPF increase in the soleus appeared greater than for the other muscles (see Fig. 3). The soleus is known for having a high percentage of slow-twitch fibers. We speculate that this might be due to a higher possibility of turnover in slow-twitch motor units (24) for this muscle, because the pool of this type of fibers is higher. Taking the factors discussed above into consideration, another physiological phenomenon is probably involved. We suggest that this phenomenon could be the progressive recruitment of fast-twitch fibers. It is interesting to note that the beginning of the slow component corresponded to the beginning of the increase in MPF for the vastus lateralis and gastrocnemius lateralis muscles. The two muscles with a major composition of slow-twitch fibers did not display a significant increase. Moreover, the significant correlation observed between the amplitude of the slow component and the rise in MPF for two of the six muscles studied reinforced the link between the two phenomena. Progressive recruitment of new motor units agrees favorably with the study of Vøllestad et al. (58). A sequential depletion of glycogen in type IIA, IIAB, and IIB fibers was observed during constant-load submaximal exercise (58). In a fatigued state, the slow-twitch motor units recruited at the beginning of the exercise seem to be unable to sustain work. This hypothesis of sequential fiber recruitment is also indirectly corroborated by the study of Enoka et al. (23). Some low-threshold motor units active during a

ramp-and-hold task in nonfatigued conditions were not active in fatigued conditions, although the task was identical to the one performed before the fatigue exercise (23). Fatigue-related studies show that accumulation of metabolites or ions (hydrogen ions, inorganic phosphate, magnesium, potassium) may impair 1) the Ca2⫹ release from the sarcoplasmic reticulum, 2) the troponin sensitivity to Ca2⫹, and consequently 3) the contraction force of the cross-bridge attachments. Finally, the increase in activity of the motor units toward higher frequencies could be interpreted as a reflection of fast-twitch fiber recruitment. The strong relationship reported by Wretling et al. (65) between MPF and muscle fiber composition during dynamic exercise, the positive and significant relationship between the changes in integrated EMG and amplitude of the slow component previously reported by Shinohara and Moritani (52) and the pattern of the group response in the present study (Fig. 3) lend some support for the tested hypothesis. Nevertheless, the increase in activity of the motor units toward the high frequencies is not necessarily due to the recruitment of fast-twitch fibers but may be also the consequence of 1) an increase in motor neuron discharge rate of the slow-twitch fibers and/or 2) a rise in temperature. The first suggestion, to the best of our knowledge, has not been described in the literature. Concerning the second proposition, there are some controversies. Holewijn and Heus (30) reported a lack of change in MPF between a reference condition and warming (40°C water) on arm muscle function, whereas Petrofsky and Lind (46) showed a positive correlation between MPF and intramuscular exercising muscle temperature during brief isometric contractions. The temperature increase may affect MPF under specific conditions through a modification of muscle fiber conduction velocity (11). It is well known that thermoregulation processes increase during exercise and that heat muscle production is quite constant during constant-power exercise. Therefore, the increase in muscle temperature is more marked at the beginning of exercise than during the slow component phase (3). The fact that MPF increased mainly in the second part of the exercise suggests that the increase in muscle temperature cannot explain the entire increase observed for the EMG signal. The lack of major effect of temperature and muscle fiber conduction velocity on MPF must be further confirmed by direct measurements. To summarize, the MPF profile is probably the result of a balance between negative and positive effects in action potential shape and the motor unit discharge rate. On the basis of this aspect, one could not exclude the possibility that the fiber type recruitment is initiated before the minimum of the MPF profile. The fiber type recruitment could occur sooner and could be masked by a more prominent declining phase. In conclusion, an interesting concomitance was observed in this study between the beginning of the slow ˙ O and the beginning of the increase in component of V 2 MPF. Even if an increase in motor neuron discharge

SLOW COMPONENT AND EMG

rate of the slow-twitch together with an increase in muscle temperature and a rise in muscle fiber velocity conduction cannot be completely ruled out to explain rise of MPF, the present study gives some support to the hypothesis that low-efficiency type II fibers are progressively recruited during short-term fatigue. This partially clarifies the existence of the slow component ˙O . of V 2 REFERENCES 1. Allen DG, Westerblad H, Lee JA, and La¨nnergen J. Role of excitation-contraction in muscle fatigue. Sports Med 13: 116– 126, 1992. 2. Armon Y, Cooper DM, Flores R, Zanconato S, and Barstow TJ. Oxygen uptake dynamics during high-intensity exercise in children and adults. J Appl Physiol 70: 841–848, 1991. 3. Åstrand P-O and Rodal K. Pre´cis de physiologie de l’exercice musculaire. Paris: Masson, 1980, p. 420. 4. Bangsbø J, Gollnick PD, Graham TE, Juel C, Kiens B, Mizuno M, and Saltin B. Anaerobic energy production and O2 deficit-debt relationship during exhaustive exercise in humans. J Physiol (Lond) 422: 539–559, 1990. ˙ O kinetics during heavy 5. Barstow TJ. Characterization of V 2 exercise. Med Sci Sports Exerc 26: 1327–1334, 1994. 6. Barstow TJ, Andrew MJ, Nguyen PH, and Casaburi R. Influence of muscle fiber type and pedal frequency on oxygen uptake kinetics of heavy exercise. J Appl Physiol 81: 1642–1650, 1996. 7. Barstow TJ, Casaburi R, and Wasserman K. O2 uptake kinetics and O2 deficit as related to exercise intensity and blood lactate. J Appl Physiol 75: 755–762, 1993. 8. Barstow TJ and Mole´ PA. Linear and nonlinear characteristics of oxygen uptake kinetics during heavy exercise. J Appl Physiol 71: 2099–2106, 1991. ˙ O kinetics and the O deficit 9. Bearden SE and Moffatt RJJ. V 2 2 in heavy exercise. J Appl Physiol 88: 1407–1412, 2000. 10. Bernard O, Maddio F, Ouattara S, Jimenez C, Charpenet A, Melin B, and Bittel J. Influence of the oxygen uptake slow component on the energy cost of high-intensity submaximal treadmill running in humans. Eur J Appl Physiol 78: 578–585, 1998. 11. Bigland-Ritchie B, Donovan EF, and Roussos CS. Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J Appl Physiol 51: 1300–1305, 1981. 12. Billat V, Binsse V, Petit B, and Koralsztein JP. High level ˙ O steady-state below V ˙O runners are able to maintain a V 2 2 max in an all-out run over their critical velocity. Arch Physiol Biochem 106: 38–45, 1998. 13. Binder-Macleod SA and McDermond LR. Changes in the force-frequency relationship of the human quadriceps femoris muscle following electrically and voluntarily induced fatigue. Phys Ther 72: 95–104, 1992. 14. Binder-Macleod SA and Guerin T. Preservation of force output through progressive reduction of stimulation frequency in human quadriceps femoris muscle. Phys Ther 70: 619–625, 1990. 15. Bouissou P, Estrade PY, Goubel F, Guezennec CY, and Serrurier B. Surface EMG power spectrum and intramuscular pH in human vastus lateralis muscle during dynamic exercise. J Appl Physiol 67: 1245–1249, 1989. 16. Candau R, Belli A, Millet GY, Georges D, Barbier B, and Rouillon JD. Energy cost and running mechanics during a treadmill run to voluntary exhaustion in humans. Eur J Appl Physiol 77: 479–485, 1998. 17. Carter H, Williams CA, Jones AM, and Doust JH. Oxygen uptake kinetics during treadmill running in children and adults. J Physiol (Lond) 523P: 243P–244P, 2000. 18. Casaburi R, Barstow TJ, Robinson T, and Wasserman K. Influence of work rate on ventilatory and gas exchange kinetics. J Appl Physiol 67: 547–555, 1989. 19. Casaburi R, Storer TW, Bendov L, and Wasserman K. ˙O Effect of endurance training on possible determinants of V 2 during heavy exercise. J Appl Physiol 62: 199–207, 1987.

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