UTILITY OF ELECTROMYOGRAPHIC FATIGUE THRESHOLD DURING TREADMILL RUNNING LUCIANO F. CROZARA, MSc,1 ALEX CASTRO, MSc,1 ANTONIO F. DE ALMEIDA NETO, MSc,1 DAIN P. LAROCHE, PhD,2 ADALGISO C. CARDOZO, PhD,1 and MAURO GONC¸ALVES, PhD1 1 2
Department of Physical Education, S~ao Paulo State University, 1515, 24 A Avenue, Bela Vista, 13506-900 Rio Claro, S~ao Paulo, Brazil Department of Kinesiology, University of New Hampshire, Durham, New Hampshire, USA
Accepted 11 March 2015 ABSTRACT: Introduction: We investigated 2 different methods for determining muscle fatigue threshold by electromyography (EMG). Methods: Thirteen subjects completed an incremental treadmill running protocol for EMG fatigue threshold (EMGFT) determination based on the critical power concept (EMGFT1) and the breakpoint in the linear relationship between EMG amplitude and exercise intensity (EMGFT2). Then, both the EMGFT1 and EMGFT2 were tested in a continuous treadmill running protocol. EMG was recorded from the rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles. Results: For BF, EMGFT2 was higher than EMGFT1, and EMGFT1 for BF was lower than EMGFT1 for LG. EMG of RF was higher at EMGFT2 than at EMGFT1, and LG EMG was lower at EMGFT2. Conclusions: EMGFT can be determined during a single treadmill running test, and EMGFT1 may be the most appropriate method to estimate the muscle fatigue threshold during running. Muscle Nerve 52: 1030–1039, 2015
The accumulation of metabolites (lactate, hydrogen ions, and inorganic phosphate), and the fall in muscle pH due to greater rates of adenosine triphosphate hydrolysis and a greater reliance on anaerobic metabolism during exercise1 affect muscle excitation-contraction coupling, including muscle membrane properties and propagation of action potentials in muscle.2,3 In this sense, surface electromyography (EMG), a technique that records the changes in action potentials originating from muscle, becomes an attractive technique to evaluate localized muscle fatigue. This can be accomplished by observing an increase in EMG amplitude (greater motor unit recruitment or synchronization to maintain the required force level) and a shift to the lower frequencies of the EMG Abbreviations: BF, biceps femoris; BFRMS, EMG amplitude of the biceps femoris; EMG, electromyography; EMGFT, fatigue threshold; EMGFT1, fatigue threshold determination based on the critical power concept; EMGFT1VL, fatigue threshold of the vastus lateralis muscle based on the critical power concept; EMGFT2, fatigue threshold determination based on the breakpoint in the linear relationship between EMG amplitude and exercise intensity; EMGFT2VL, fatigue threshold of the vastus lateralis muscle based on the breakpoint in the linear relationship between EMG amplitude and exercise intensity; ES, effect size; [La], blood lactate concentration; LG, lateral gastrocnemius; LGRMS, EMG amplitude of the lateral gastrocnemius; HR, heart rate; HRpeak, peak heart rate; LT, lactate threshold; RF, rectus femoris; RFRMS, EMG amplitude of the rectus femoris; RMS, root mean square; VL, vastus lateralis; VLRMS, EMG amplitude of the vastus lateralis; VO2, oxygen uptake; VT, ventilatory threshold Key words: electromyographic threshold; electromyography; fatigue threshold; muscle fatigue; treadmill running Correspondence to: L.F. Crozara; e-mail: [email protected] C 2015 Wiley Periodicals, Inc. V
Published online 18 March 2015 in Wiley Online Library (wileyonlinelibrary. com). DOI 10.1002/mus.24658
1030
EMG Fatigue Threshold during Running
frequency spectrum (decrease in action potential conduction velocity over the muscle).4–9 DeVries et al.10 proposed a mathematical model based on the critical power concept of Monod and Scherrer11 to estimate the steady-state workload that can be sustained without evident increase in EMG amplitude. This model is termed the electromyographic fatigue threshold (EMGFT), herein referred to as EMGFT1. On the other hand, Lucia et al.12 proposed a model to estimate the EMGFT by determining the breakpoint in the linear relationship between EMG amplitude and exercise intensity (i.e., identifying the point of nonlinearity in the plot of EMG against work rate), herein referred to as EMGFT2. Several studies have used fatigue-inducing protocols to identify EMGFT and have related it to other well-established markers of intensity, such as the lactate threshold (LT) and ventilatory threshold (VT),12–14 demonstrating moderate to strong correlations (r 5 0.64–0.92) between these variables. In an attempt to test the EMGFT1 hypothesis (in a few subjects, n 5 3), Moritani et al.14 analyzed EMG behavior in the time domain during a continuous cycle ergometry protocol at the EMGFT1 intensity and found evidence that it represents the highest workload that can be sustained for 20 minutes without a significant increase in EMG amplitude. In contrast, Pavlat et al.15 concluded that the EMGFT1 was not a valid representation of the highest attainable non-fatiguing workload, overestimating by 42% and 52% the exercise intensity that could be maintained for 30 and 60 min, respectively. These discrepancies led to a discussion of the possibility that EMG may not be a reliable indicator of the onset of neuromuscular fatigue.15,16 Exercise modality is an additional confounding factor that complicates the evaluation of EMGFT as a marker of exercise intensity. Both methods of EMGFT determination have most often been applied to tests performed during cycle ergometry,10,12,13,17,18 whereas only a few studies have been conducted using treadmill running.8,19 It is believed that the constant pattern of movement during cycle ergometry contributes to a more stable EMG signal, facilitating EMGFT detection. This MUSCLE & NERVE
December 2015
FIGURE 1. Research timeline.
is likely, because the myoelectric activity in concentric muscle actions differs from that seen during the stretch-shortening cycle of eccentric contractions.20 It has been suggested that the ability to determine the EMGFT during treadmill running should be scrutinized carefully. During running, the succession of stretch-shortening cycles and changes in gait biomechanics that occur with increased running velocity may influence the onset of neuromuscular fatigue and, consequently, the behavior of the EMG signal.6,7,21 As different muscle groups of the lower limb play different roles during running and are recruited differently with increased running velocity,21 the exercise intensity that elicits EMGFT may differ depending on the muscle analyzed.7 For example, increased running speed is mainly accomplished by larger hip flexor and extensor actions to increase stride length and rate that require a disproportionate increase in EMG activity of the bi-articular thigh muscles (e.g., rectus femoris and biceps femoris).7 EMG analysis has great potential to characterize the neuromuscular behavior associated with the local fatigue process during running, and EMGFT may have utility as an objective, non-invasive, neuromuscular-based marker of exercise intensity that can complement or be used instead of metabolic markers. EMGFT may be a useful tool for researchers who study fatigue, for sports performance specialists who monitor adaptation to training, and for clinicians who evaluate rehabilitation programs.10,12 However, before the EMGFT can be used in this manner, it is necessary to evaluate the utility of these EMG techniques to determine whether they are feasible and comparable indicators of neuromuscular fatigue. It is also important to determine whether the EMGFT can be measured during running the way it has been in cycling, as running elicits higher amplitude, shorter duration EMG bursts that may be more variable than those seen during cycling exercise.22 Therefore, the main objective of this study was to evaluate the utility of these EMG techniques to determine whether they are feasible and comparable indicators of neuEMG Fatigue Threshold during Running
romuscular fatigue. We hypothesized that: (1) it is possible to identify the EMGFT during incremental treadmill running using the EMGFT1 and EMGFT2 methods; (2) the EMGFT intensity determined by these methods would be sustainable for a long period of time without evidence of neuromuscular fatigue; and (3) different lower extremity muscles would show onset of fatigue at different times, as they have different contractile characteristics and distinct roles during running. METHODS Ethics Approval.
The study was approved by our institutional ethics committee. Participants were informed of the purpose of the study and procedures, and they subsequently signed an informed consent form. A research timeline is shown in Figure 1. Participants. Thirteen men who were amateur indoor soccer players with a mean age of 20.77 6 1.92 years, height of 1.76 6 0.05 m, body mass of 68.61 6 10.12 kg, body mass index of 22.06 6 2.59 kg/m2, and body fat percentage of 12.82 6 5.3%, participated in the study. The participants were healthy and without any history of severe injury in the lower limbs in the 6 months preceding the experiment. They maintained a training frequency of 2 or 3 times per week with at least 2 hours of exercise per session and had participated in at least 2 competitions in the previous year. EMG Instrumentation and Measurement. EMG was recorded using a telemetry system (Telemyo 900; Noraxon, Phoenix, Arizona) with a differential amplifier with a total gain of 2000 (203 in the preamplifier 3 1003 in the data acquisition system), common-mode rejection ratio >100 dB (60 HZ), baseline noise 10 MX, and bandwidth limited between 10 and 500 HZ. A pair of pre-gelled, self-adhesive, disposable surface electrodes (circular, 10 mm diameter silver/silver chloride; Meditrace) were placed in a MUSCLE & NERVE
December 2015
1031
outdoor running.25 To assess exercise intensity, heart rate was measured at rest and at the end of each incremental running stage via a commercial heart rate receiver (MFC RS100; Polar, Kempele, Finland). Data Processing and Analysis. The EMG signals were bandpass filtered between 20 HZ and 500 HZ using a Butterworth digital filter (second order for high pass and fourth order for low pass), notchfiltered (60 HZ) with its harmonics, and the RMS of the signal was obtained using 5-s sliding contiguous windows. FIGURE 2. Individual example of a 5-s raw EMG signal from rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles recorded during the incremental running exercise.
bipolar configuration (20 mm center to center) on the right lower limb over the bellies of the rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles, longitudinally and parallel to the underlying muscle fibers, according to the recommendations of Hermens et al.23 The reference electrode was placed on the lateral malleolus. Before placement of the electrodes, the skin was shaved and cleaned with alcohol. The electrodes and wires were secured well with tape to avoid movement-induced artifacts. The EMG signals were sampled at 1,000 HZ using a 16-bit analog-to-digital converter and stored on a computer hard drive using MyoResearch software (Telemyo Master Edition XP 1.07; Noraxon) for off-line analysis. Figure 2 shows an example of a 5-second raw EMG signal of the muscles analyzed. Incremental Treadmill Running Test. Before the test, subjects were familiarized with the equipment and procedures to be used. In addition, they were instructed to refrain from intense training the day before testing and during the day of testing. After a 5-min warm-up at a treadmill (Super ATL; Inbramed, Gravataı, Brazil) velocity of 5 km/h, they performed an incremental running protocol in a temperate environment (25 –27 C, 50%–60% relative humidity), starting at 8 km/h with increases of 1 km/h every 3 min.8,24 The selection of the initial treadmill running velocity was based on a previous pilot study conducted in our laboratory. During the test, subjects were strongly encouraged to provide a maximal effort. The exercise test was terminated when subjects reached volitional exhaustion and were not able to maintain the running pace. The inclination of the treadmill was fixed at 1%, because this grade has been shown to accurately reflect the energetic cost of 1032
EMG Fatigue Threshold during Running
Estimation of the EMGFT1. First, the slope coefficient of the linear regression equation relating EMG vs. time was obtained at each running velocity of the incremental running test (Fig. 3A). Next, each of these slope coefficients (x-axis) was plotted against its respective running velocity (yaxis), and a new linear regression line was drawn to identify the running velocity at EMGFT1. This was determined by finding the y-intercept of the
FIGURE 3. Illustration of the mathematical determination of EMGFT1. (A) The relationship between RMS and time for the velocities used. (B) The relationship of the velocity-vs.-EMG slope coefficients with the y-intercept defined as the EMGFT1. MUSCLE & NERVE
December 2015
FIGURE 4. Illustration of the mathematical determination of EMGFT2. The relationship between RMS and velocity. The EMG breakpoint is located at the intersection between the 2 straight lines.
regression line, that is, the velocity that elicited a slope coefficient of zero (Fig. 3B). This value corresponds to the velocity that could be sustained without an increase in the EMG amplitude over time. Estimation of the EMGFT2. All RMS data points obtained during the incremental test were divided into 2 sets of points. The first 60 RMS data points were arbitrarily included in a first set, with the remaining points included in a second set. Then a regression line was fit to each set of points, and the coefficient of determination (r2) from each line was calculated. The product of these 2 coefficients (representing the linearity of the 2 regions) was then calculated and recorded for further analysis. This procedure was repeated, and at each time incorporated the next data point of the second set into the first set of points, until the second set was composed of the last 60 RMS data points and the first set with the remaining points. The pair of lines that elicited the largest product of the 2 coefficients was chosen, and the intersection between these lines (the breakpoint) was identified as the velocity corresponding to EMGFT2 (Fig. 4). Digital processing of the raw EMG signals and mathematical determination of both EMGFT1 and EMGFT2 were performed using specific algorithms developed in MATLAB (The Mathworks, Inc., Natick, Massachusetts). Continuous Treadmill Running Test at EMGFT. Fortyeight to 72 h after the incremental treadmill running test, the subjects performed a continuous treadmill running test at either the running velocity corresponding to EMGFT1 or the velocity corresponding to EMGFT2, which were calculated from the VL EMG record. This muscle was chosen because it has been shown elsewhere that fatigue thresholds in the quadriceps and triceps surae muscles are not significantly different in cycling or running,26,27 and to allow comparisons between EMG Fatigue Threshold during Running
our results and the results from several previous studies.10,14,15,28,29 The alternate continuous running protocol was performed on a different day, separated by 36–48 h, in a randomized order. The intent of the continuous running protocols was to determine whether the running velocities (exercise intensities) determined using the EMGFT1 and EMGFT2 methods were sustainable for 30 min as done previously by Pavlat et al.28 After a 5-min warm-up at a treadmill velocity of 5 km/h, the subjects performed a continuous treadmill running test at a running pace corresponding to either EMGFT1 or EMGFT2. During the tests, the subjects received strong verbal encouragement and were blinded to the experimental trial and elapsed time. The test was terminated if a subject either completed 30 min of exercise or reached volitional exhaustion before 30 min and was unable to maintain the prescribed running pace. To test whether EMG remained stable over the duration of the continuous running protocol, EMGs from RF, VL, BF, and LG were recorded and processed following the same procedures described previously. For the EMG-vs.-time analysis, each muscle was normalized to the average of its last 10 EMG burst peaks during the warm-up period. Statistical Procedures. Normality of the data distribution was confirmed by the Shapiro–Wilk test for all dependent variables, except for heart rate and percentage of heart rate peak. A 2-way repeatedmeasures analysis of variance (ANOVA) was performed to compare the EMGFT values between the methodologies (EMGFT1 and EMGFT2) and among the muscles examined (RF, VL, BF, and LG). Analysis of EMG against time was achieved using a 3way repeated-measures ANOVA (muscle [RF, VL, BF and LG]) 3 method [EMGFT1 and EMGFT2] 3 time [time-points]). When appropriate, the Greenhouse–Geisser correction was applied when the Mauchly test of sphericity was violated. Significant differences were followed-up with comparisons employing the Sidak correction. The Bland–Altman30 procedure was used to assess agreement between the EMGFT values obtained by the 2 methods, including plots with 95% limits of agreement. Paired t-tests were used to compare the average RMS and endurance time, and Wilcoxon tests were used to compare the heart rate and percentage of heart rate peak between continuous running at EMGFT1 and EMGFT2. The slopes of the VL RMSvs.-time relationship during the continuous running at EMGFT1 and EMGFT2 were compared with a slope of 0 using one-sample t-tests to determine whether tVL EMG amplitude was stable or increased over time. Pearson correlation coefficients were MUSCLE & NERVE
December 2015
1033
Table 1. Treadmill running velocities (mean 6 SD) obtained by the 2 methods of EMG fatigue threshold (EMGFT) determination. EMGFT1 (km/h) RF VL BF LG
11.1 6 0.8 11.2 6 1.2 10.4 6 1.4 11.5 6 0.9†
N 12 13 13 13
(100%) (100%) (100%) (100%)
EMGFT2 (km/h) 10.9 6 1.5 12.0 6 1.6 12.4 6 1.4* 11.0 6 1.3
N 9 7 9 7
(75%) (54%) (69%) (54%)
N, number of subjects in whom the EMGFT was detected; EMGFT1, EMGFT obtained by method adapted from DeVries et al.10; EMGFT2, EMGFT obtained by method adapted from Lucia et al.12; RF, rectus femoris; VL, vastus lateralis; BF, biceps femoris; LG, lateral gastrocnemius. *Significant difference between EMGFT1 and EMGFT2 (P < 0.01). †
Significant difference between BF and LG within EMGFT1 (P < 0.05).
calculated to determine the degree of association between the slopes of the VL RMS-vs.-time relationship and the endurance times obtained in the continuous running tests. For all statistical procedures, significance was set at P < 0.05. All statistical analyses were performed with PASW 18.0 statistical software (SPSS, Inc., Chicago, Illinois). Each effect size (ES) was calculated using G*Power 3.1.7
software (Franz Faul, Universitat Kiel, Germany), according to the recommendations of Beck.31 RESULTS
The average duration of the incremental test was 21.65 6 3.74 min, reaching an average treadmill velocity of 14.4 6 1.3 km/h and 7.4 6 1.3 incremental stages. The average peak heart rate (HRpeak) reached in the incremental test was 192 6 9 beats/min (96% of age-predicted maximal heart rate), indicating that the participants obtained near-maximal exercise intensities. The mean value for EMGFT1 occurred at 77% of peak treadmill velocity for RF, 78% for VL, 72% for BF, and 80% for LG, whereas EMGFT2 occurred at 74% of peak treadmill velocity for RF, 84% for VL, 85% for BF, and 74% for LG. There were no main effects of the EMGFT method (P 5 0.112; ES 5 0.49) or muscle (P 5 0.145; ES 5 0.40). However, there was a significant method 3 muscle interaction for EMGFT (P 5 0.004; ES 5 0.66). EMGFT2 was 16% higher than EMGFT1 for BF (P 5 0.002; ES 5 1.05), but there were no differences between EMGFT2 and
FIGURE 5. Analysis of agreement between the methods of EMG fatigue threshold determination adapted from DeVries et al.10 and Lucia et al.12 (EMGFT1 and EMGFT2, respectively). The middle solid (black) line represents the mean of the differences between the methods (Bias). The upper and lower dashed lines represent the bias 6 1.96SD (i.e., 95% limits of agreement). *Significant difference between the bias and 0 (P < 0.05). 1034
EMG Fatigue Threshold during Running
MUSCLE & NERVE
December 2015
Table 2. Endurance time, heart rate (HR), and percentage of heart rate peak (%HRPEAK) obtained at the end of continuous running at the EMG fatigue threshold EMGFT1VL Subject 1 2 3 4 5 6 7 Mean 6 SD
EMGFT2VL
Endurance time (min)
HR (beats/min)
%HRpeak (%)
Endurance time (min)
HR (beats/min)
%HRpeak (%)
26 30 23 30 30 30 30 28.4 6 2.8
184 176 210 183 199 186 158 185.1 6 16.5
97 96 100 97 100 91 90 95.9 6 4
30 30 23 9 13 22 30 22.4 6 8.6
164 137 210 188 199 188 153 177 6 26.3
86 75 100 100 100 92 87 91.4 6 9.5
EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12
EMGFT1 for RF (P 5 0.796; ES 5 0.07), VL (P 5 0.183; ES 5 0.39), and LG (P 5 0.253; ES 5 0.33) (Table 1). Also, there were no differences between muscles within EMGFT1 and EMGFT2, except between BF and LG within EMGFT1, where EMGFT1 was 10% higher in LG than in BF (P 5 0.015; ES 5 0.81) (Table 1). The analysis of agreement between methods (Bland–Altman30) (Fig. 5) indicated that the average difference between EMGFT1 and EMGFT2 (bias) was significantly different from zero only for BF (P 5 0.017; ES 5 0.99). Also, there was a high degree of within-subject variability for the 2 methods (large limits of agreement) that was influenced by running velocity, as evidenced by the negative correlations shown in Figure 5. The correlations indicate that EMGFT1 was biased toward predicting a higher velocity than EMGFT2 for individuals running on the slow end of the velocity spectrum, and that EMGFT2 was biased toward predicting a higher velocity for individuals running on the fast end of the velocity spectrum. It was not possible to identify EMGFT2 of VL in 6 subjects; that is, there was not a breakpoint in the linear relationship between EMG amplitude and exercise intensity. For this reason, only data from 7 subjects were available to test EMGFT during continuous running at EMGFT1 and EMGFT2 of VL. There were no statistical differences between continuous running at EMGFT1 and EMGFT2 of VL for endurance time (P 5 0.151; ES 5 0.62), heart rate (P 5 0.375; ES 5 0.51), or percentage of heart rate peak (P 5 0.375; ES 5 0.52) (Table 2). The EMG responses of RF, VL, BF, and LG during continuous running at EMGFT1 and EMGFT2 of VL were analyzed at each 20% increment of the time to exhaustion (or 30-min criterion time) (Table 3). There was a significant main effect for muscle (P 5 0.004; ES 5 1.66). RF EMG was higher than VL (P 5 0.029) and LG (P 5 0.022), but not different from BF (P 5 0.079). BF EMG was higher EMG Fatigue Threshold during Running
than LG (P 5 0.021), but not different from VL (P 5 0.885). There was no difference between VL EMG and LG (P 5 0.131), and there were no main effects for method (P 5 0.431; ES 5 0.34) or time (P 5 0.367; ES 5 0.43). There were no interactions for muscle 3 method (P 5 0.088; ES 5 0.65), muscle 3 time (P 5 0.103; ES 5 0.51), method 3 time (P 5 0.365; ES 5 0.43), or muscle 3 method 3 time (P 5 0.241; ES 5 0.46). However, the RMS averaged across time-points was 14% higher during continuous running at EMGFT2 than EMGFT1 for RF (P 5 0.014; ES 5 1.51), and 9% lower for LG (P 5 0.018; ES 5 1.24); there were no differences in RMS between running at EMGFT2 and EMGFT1 for VL (P 5 0.951; ES 5 0.03) and BF (P 5 0.064; ES 5 0.97). Also, the EMG responses of RF, VL, BF, and LG during continuous running at EMGFT1 and EMGFT2 of VL were analyzed at each 2-min increment (from 1 to 9 min; Table 4), and the results are similar to those of the relative time analysis. Therefore, the statistical results of the absolute time analysis are not presented here. The mean of the slopes of the VL RMS-vs.time relationship obtained during continuous running at EMGFT1 (0.000 6 0.06 mV/s) and EMGFT2 (0.04 6 0.07 mV/s) of VL were not significantly different from zero (P 5 1.0; ES 5 0.00 and P 5 0.2; ES 5 0.54, respectively) indicating VL RMS was stable over time. However, there was a moderate inverse correlation between VL slope and endurance time values (r 5 –0.631, P 5 0.016). DISCUSSION
These findings confirm the hypothesis that the neuromuscular fatigue threshold can be identified by analyzing the behavior of the EMG signal recorded during a single incremental treadmill running test. Although EMGFT1 has been used previously during incremental treadmill running,8 EMGFT2 has not been tested during running. MUSCLE & NERVE
December 2015
1035
Table 3. EMG responses (mean 6 SD) during running at the EMG fatigue threshold (EMGFT) against relative time. Relative time (% end-test) Variable RFRMS (%) EMGFT1VL EMGFT2VL VLRMS (%) EMGFT1VL EMGFT2VL BFRMS (%) EMGFT1VL EMGFT2VL LGRMS (%) EMGFT1VL EMGFT2VL
0 (at 1 min)
20
40
60
80
100
Average
155 6 50 186 6 69
145 6 66 198 6 98
163 6 86 179 6 78
169 6 85 175 6 80
169 6 77 186 6 96
138 6 74 163 6 84
156 6 13 181 6 12*
85 6 28 76 6 20
68 6 21 72 6 17
70 6 20 68 6 15
72 6 29 75 6 23
71 6 22 72 6 23
71 6 21 75 6 25
73 6 6 73 6 3
83 6 11 82 6 25
80 6 16 83 6 34
79 6 18 80 6 38
78 6 18 85 6 37
74 6 19 87 6 42
77 6 19 90 6 45
79 6 3 84 6 3
56 6 20 48 6 10
50 6 15 50 6 15
52 6 13 44 6 11
52 6 14 49 6 17
49 6 19 46 6 13
51 6 17 48 6 13
52 6 2 47 6 2*
RFRMS, EMG amplitude of the rectus femoris; VLRMS, EMG amplitude of the vastus lateralis; BFRMS, EMG amplitude of the biceps femoris; LGRMS, EMG amplitude of the lateral gastrocnemius; EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12 *Significant difference between EMGFT1VL and EMGFT2VL (P < 0.05).
Comparing the 2 EMGFT Methods. Although 2 methods that were theoretically developed to measure the same variable (i.e., EMGFT) should not differ in the mean of scores, they should also agree at an individual level to be used interchangeably in clinical applications.30 The hypothesis that the EMGFT values would be similar between EMGFT1 and EMGFT2 was supported by comparison of mean scores between methods, but it was refuted by the Bland–Altman agreement of methods that showed a high degree of variability between measures. In comparing the mean treadmill velocities at EMGFT1 and EMGFT2, a difference was found only for BF, with no differences for RF, VL, and LG. The difference in EMGFT for BF may have been due to the different mathematical models employed by the 2 methods (critical power vs.
breakpoint concept), but this would not explain why the BF muscle behaved differently from the others. When running velocity increases, the magnitude and duration of BF activation during pushoff increases to aid in forward propulsion of the body.7 BF may therefore be more prone to early to fatigue7 (as seen in EMGFT1), which facilitates detection of EMGFT for this muscle. At the same time, early fatigue of BF may make it difficult to detect EMGFT for other muscles (i.e., RF, LG, and VL), because the running test can be completed to volitional exhaustion before substantial fatigue is seen in those muscles. The behavior of BF EMG amplitude was crucial in differentiating the 2 EMGFT methods, because it demonstrated that identifying the breakpoint in EMG requires a welldefined increase in EMG amplitude vs. time.
Table 4. EMG responses (mean 6 SD) during running at the EMG fatigue threshold (EMGFT) against absolute time. Absolute time (min) Variable RFRMS (%) EMGFT1VL EMGFT2VL VLRMS (%) EMGFT1VL EMGFT2VL BFRMS (%) EMGFT1VL EMGFT2VL LGRMS (%) EMGFT1VL EMGFT2VL
1
3
5
7
9
Average
155 6 50 186 6 69
150 6 66 187 6 83
142 6 68 187 6 96
151 6 65 189 6 91
151 6 77 189 6 83
150 6 5 188 6 1*
85 6 28 76 6 20
77 6 22 69 6 15
70 6 21 73 6 20
74 6 22 67 6 14
71 6 19 72 6 22
75 6 6 71 6 3
83 6 11 82 6 25
82 6 16 79 6 31
85 6 20 83 6 36
79 6 19 83 6 28
82 6 17 90 6 40
82 6 2 83 6 4
56 6 20 48 6 10
56 6 18 47 6 13
53 6 13 50 6 17
52 6 15 49 6 16
51 6 13 47 6 14
53 6 2 48 6 1*
RFRMS, EMG amplitude of the rectus femoris; VLRMS, EMG amplitude of the vastus lateralis; BFRMS, EMG amplitude of the biceps femoris; LGRMS, EMG amplitude of the lateral gastrocnemius; EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12 *Significant difference between EMGFT1VL and EMGFT2VL (P < 0.05).
1036
EMG Fatigue Threshold during Running
MUSCLE & NERVE
December 2015
The analysis of agreement between methods indicates that the differences between EMGFT1 and EMGFT2 showed high within-subject variability that was influenced by velocity for all muscles. This result shows that, in subjects who ran at slow velocities at EMGFT, EMGFT1 predicted a higher velocity than EMGFT2, and, in subjects who ran at higher velocities at EMGFT, EMGFT2 predicted a higher velocity than EMGFT1. This indicates that, although there were no statistical differences between the mean EMGFT1 and EMGFT2 values for RF, VL, and LG, the 2 methods do not agree, particularly for a given individual. In addition, the low agreement between the 2 methods was also evidenced by the endurance times during the continuous running test at the EMGFT of VL. For example, when running at EMGFT2 intensity, subjects 4 and 5 fatigued twice as quickly as they did at EMGFT1 (Table 2). The endurance time for EMGFT2 for these subjects was considerably below the adopted criterion of 30 min.28 Therefore, to a certain extent, these findings suggest that EMGFT2 sometimes underestimated and sometimes overestimated EMGFT. On the other hand, the EMG responses of VL during the continuous running test at EMGFT1 and EMGFT2 remained stable for the duration of the test, suggesting for this muscle that neither method overestimated the running pace that can be sustained without evidence of neuromuscular fatigue. Another finding was that the slope of the VL RMS EMG amplitude during the continuous running protocol was inversely correlated with running endurance time. This suggests that, in those subjects who demonstrated a positive slope of the VL RMS EMG-vs.-time curve, neuromuscular fatigue was occurring and required recruitment of additional motor units to sustain the required muscular effort. With increasing exercise intensity there is an orderly recruitment pattern of muscle fibers from predominantly slow-twitch fibers (type I) at low intensity to inclusion of fast-twitch fibers (type II) at high intensity.32 This neuromuscular response promotes changes in metabolic and EMG parameters as a result of increased muscular work that can be used to predict the onset of fatigue. For example, Lucia et al.12 found 2 breakpoints in the EMGvs.-intensity curve using a sample of highly trained athletes. However, in our study, only 1 breakpoint in the EMG-vs.-intensity curve was observed in a sample of amateur athletes. Therefore, we speculate that highly trained individuals are potentially better able to recruit the type IIb muscle fibers close to exhaustion, leading to a second breakpoint in the EMG-vs.-intensity curve.12 Thus, it was hypothesized that EMGFT2 (based on breakpoint determination) may be more useful for fit EMG Fatigue Threshold during Running
individuals19 than EMGFT1 (based on critical power concept), because they are able to work at exercise intensities high enough to promote a sharp increase in the EMG amplitude-time relation (substantial recruitment of type IIb muscle fibers). However, this concept was not supported in this study due to failure to identify the EMGFT2 breakpoint in 6 subjects. Issues for EMGFT Detection. The increase in EMG amplitude during dynamic and isometric contractions reflects additional recruitment of motor units and increased firing rate and/or synchronization of these motor units.12,33,34 These neuromuscular events have been identified either as a result of increased exercise intensity35,36 or the onset of fatigue when, for example, a breakpoint in the linear relationship between EMG amplitude and exercise intensity occur.7,27 However, in our study it was not always possible to determine the EMGFT, particularly when using the mathematical model of EMGFT2. In EMGFT2 detection, when there was no intersection between the 2 best straight lines, we considered that the EMG increased linearly with velocity, and there was no breakpoint in the relationship between EMG amplitude and running velocity. Unlike static (isometric) contractions, the EMG signal during dynamic contractions does not have a well-defined stationary behavior,5 leading to difficulty in detecting the breakpoint in the EMG amplitude and exercise intensity relationship, as others have observed.12,13,17–19 This fact can be explained by: (1) compensation between synergistic muscles in an attempt to delay fatigue during an activity; (2) possible spatial variability of muscle recruitment during dynamic contractions; and/or (3) inability of individuals to recruit enough highthreshold motor units to enhance the breakpoint in the linear relationship between EMG amplitude and exercise intensity.12,37 It has been suggested that standardization of muscle contractions contributes to successful EMGFT determination,18 such as by controlling cadence.38 In the incremental treadmill running test used in this study, it was not possible or even desirable to control stride parameters, as this condition more closely reflects real running situations. Yet, changing biomechanical strategies (variation in stride length and frequency) in response to the increase in running velocity and the onset of fatigue could have affected the recruitment of specific muscles as well as the behavior of the EMG signal.7,36 The dynamic nature of running may make identification of EMGFT potentially more difficult during this exercise modality, but, we have demonstrated in this study, it is possible to identify a fatigue threshold, particularly with the EMGFT1 method. MUSCLE & NERVE
December 2015
1037
Differences between Muscles During Incremental and
The hypothesis that different muscles would be affected by fatigue in a different manner was confirmed partially for EMGFT1 in BF, which had a lower fatigue threshold than LG. The lower EMGFT in BF may have occurred because, as running speed increases, stride length increases, requiring higher BF activity for proper hip extension. At faster speeds, the increase in LG activity may be less than in BF due to the shorter duration of the stance phase and because of an increased synergy of knee extensors and ankle plantarflexors.21 Thus, EMGFT1 could potentially be used in clinical and athlete populations alike to identify specific muscles with a low fatigue threshold that may be limiting physical performance. The EMGFT was consistent between RF, VL, and LG, which suggests that neuromuscular fatigue occurs similarly in these muscles during a running task. Although studies have shown that biarticular and monoarticular muscles play distinct roles during running,39 and we demonstrated earlier fatigue in BF (biarticular), the results indicate that RF and LG (biarticular) and VL (monoarticular) muscles are affected by fatigue in the same way. Hanon et al.7 showed that some thigh biarticular muscles (BF and RF) fatigued earlier than some thigh and calf monoarticular muscles (VL and TA) during running. Differences between the studies in physical fitness levels of the subjects, the exercise protocol, and methods of EMGFT determination used may explain these discrepancies. Continuous Running.
Physiological Intensity Domains of the EMGFT. The mean EMGFT running speed found here was 11.3 km/h, comparable with the running speeds reported in the literature for prolonged activity of 11.1 km/h and 11.7 km/h.29,40 Also, the mean values of EMGFT1 and EMGFT2 expressed as a percentage of peak treadmill velocity (72%–80% and 74%–85%, respectively) were similar to those obtained in previous studies.17,27 This fact confirms that the EMGFT usually lies between the heavy and severe exercise intensity domains.9,36 The heavy domain refers to the zone of exercise intensity above LT and below the maximum lactate steady state. This is the critical velocity at which the blood lactate concentration [La] and oxygen uptake (VO2) tend to be stable over time at levels above those seen in the moderate-intensity domain. In the severe domain, the [La] and VO2 do not stabilize with time, eliciting maximum values when the exercise is performed to exhaustion.41 Hence, the EMGFT technique used in this study may be a noninvasive alternative to LT and VT for detection of the transition between exercise intensity domains. Future research should therefore address the 1038
EMG Fatigue Threshold during Running
relationship between EMGFT and the more commonly used metabolic measures of LT and VT. In conclusion, EMGFT can be determined during a single treadmill running test as a noninvasive alternative for obtaining an index of localized muscle fatigue. The EMGFT based on the critical power concept (EMGFT1) proved to be the most sensitive method for estimating the running pace that can be sustained over a long period of time without evidence of fatigue, and it could differentiate fatigue thresholds between lower extremity muscles. Although EMGFT2 appears to be a valid indicator of the neuromuscular fatigue threshold, the inability to identify this threshold in nearly 50% of subjects questions its utility. EMGFT can be a useful tool for prescribing and monitoring exercise intensity in training and rehabilitation programs because of its capacity to demarcate the transition from heavy to severe exercise and its ability to identify the onset of localized muscle fatigue REFERENCES 1. Robergs RA, Ghiasvand F, Parker D. Biochemistry of exerciseinduced metabolic acidosis. Am J Physiol Regul Integr Comp Physiol 2004;287:R502–516. 2. Fitts RH. Cellular mechanisms of fatigue muscle. Physiol Rev 1994; 74:49–93. 3. Westerblad H, Allen DE, Lannergren J. Muscle fatigue: lactic acid or inorganic phosphate the major cause? News Physiol Sci 2002;17:17– 21. 4. Hendrix CR, Housh TJ, Johnson GO, Mielke M, Camic CL, Zuniga JM, et al. A new EMG frequency-based fatigue threshold test. J Neurosci Methods 2009;181:45–51. 5. MacIsaac D, Parker PA, Scott RN. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions. J Electromyogr Kinesiol 2001;11:439–449. 6. Silva SRD, Fraga CHW, Gonc¸alves M. Efeito da fadiga muscular na biomec^anica da corrida: uma revis~ao. Motriz 2007;13:225–235. 7. Hanon C, Thepaut-Mathieu C, Vandewalle H. Determination of muscular fatigue in elite runners. Eur J Appl Physiol 2005;94:118–125. 8. Fraga CHW, Silva SRD, Gonc¸alves M. Efeito da velocidade de corrida em variaveis eletromiograficas e metab olicas. Motriz 2009;15:911–918. 9. Camic CL, Housh TJ, Johnson GO, Hendrix CR, Zuniga JM, Mielke M, et al. An EMG frequency-based test for estimating the neuromuscular fatigue threshold during cycle ergometry. Eur J Appl Physiol 2010;108:337–345. 10. DeVries HA, Moritani T, Nagata A, Magnussen K. The relation between critical power and neuromuscular fatigue as estimated from electromyographic data. Ergonomics 1982;25:783–791. 11. Monod H, Scherrer J. The work capacity of a synergic muscular group. Ergonomics 1965;8:329–338. 12. Lucia A, Sanchez O, Carvajal A, Chicharro JL. Analysis of the aerobic-anaerobic transition in elite cyclists during incremental exercise with the use of electromyography. Br J Sports Med 1999;33: 178–185. 13. Candotti CT, Loss JF, Melo MDE, La Torre M, Pasini M, Dutra LA, et al. Comparing the lactate and EMG thresholds of recreational cyclists during incremental pedaling exercise. Can J Physiol Pharmacol 2008;86:272–278. 14. Moritani T, Takaishi T, Matsumoto T. Determination of maximal power output at neuromuscular fatigue threshold. J Appl Physiol 1993;74:1729–1734. 15. Pavlat DJ, Housh TJ, Johnson GO, Eckerson JM. Electromyographic responses at the neuromuscular fatigue threshold. J Sports Med Phys Fitness 1995;35:31–37. 16. Evetovich TK, Housh TJ, Johnson GO, Evans SA, Stout JR, Bull AJ, et al. Effect of workbout duration on the physical working capacity at fatigue threshold (PWCFT) test. Ergonomics 1996;39:314–321. 17. Hug F, Laplaud D, Lucia A, Grelot L. EMG threshold determination in eight lower limb muscles during cycling exercise: a pilot study. Int J Sports Med 2006;27:456–462. 18. Jurimae J, von Duvillard SP, Maestu J, Cicchella A, Purge P, Ruosi S, J€ urim€ ee T, Hamra J. Aerobic-anaerobic transition intensity measured
MUSCLE & NERVE
December 2015
19. 20.
21. 22. 23. 24. 25. 26. 27. 28. 29.
via EMG signals in athletes with different physical activity patterns. Eur J Appl Physiol 2007;101:341–346. Tikkanen O, Hu M, Vilavuo T, Tolvanen P, Cheng S, Finni T. Ventilatory threshold during incremental running can be estimated using EMG shorts. Physiol Meas 2012;33:603–614. Nakazawa K, Kawakami Y, Fukunaga T, Yano H, Miyashita M. Differences in activation patterns in elbow flexor muscles during isometric, concentric and eccentric contractions. Eur J Appl Physiol 1993;66: 214–220. Gazendam MG, Hof AL. Averaged EMG profiles in jogging and running at different speeds. Gait Posture 2007;25:604–614. Bijker KE, De Groot G, Hollander AP. Differences in leg muscle activity during running and cycling in humans. Eur J Appl Physiol 2002;87:556–561. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 2000;10:361–374. Bentley DJ, Newell J, Bishop D. Incremental exercise test design and analysis: implications for performance diagnostics in endurance athletes. Sports Med 2007;37:575–586. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sport Sci 1996;14:321–327. DeVries HA, Housh TJ, Johnson GO, Evans SA, Tharp GD, Housh DJ, et al. Factors affecting the estimation of physical working capacity at the fatigue threshold. Ergonomics 1990;33:25–33. Hanon C, Thepaut-Mathieu C, Hausswirth C, Le Chevalier JM. Electromyogram as an indicator of neuromuscular fatigue during incremental exercise. Eur J Appl Physiol Occup Physiol 1998;78:315–323. Pavlat DJ, Housh TJ, Johnson GO, Schmidt RJ, Eckerson JM. An examination of the electromyographic fatigue threshold test. Eur J Appl Physiol Occup Physiol 1993;67:305–308. Guffey DR, Gervasi BJ, Maes AA, Malek MH. Estimating electromyographic and heart rate fatigue thresholds from a single treadmill test. Muscle Nerve 2012;46:577–581.
EMG Fatigue Threshold during Running
30. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–160. 31. Beck TW. The importance of a priori sample size estimation in strength and conditioning research. J Strength Cond Res 2013;27: 2323–2337. 32. Sasaki K, Tomioka Y, Ishii N. Activation of fast-twitch fibers assessed with twitch potentiation. Muscle Nerve 2012;46:218–227. 33. Holtermann A, Gronlund C, Karlsson JS, Roeleveld K. Motor unit synchronization during fatigue: described with a novel sEMG method based on large motor unit samples. J Electromyogr Kinesiol 2009;19: 232–241. 34. Silva SR, Gonc¸alves M. Dynamic and isometric protocols of knee extension: effect of fatigue on the EMG signal. Electromyogr Clin Neurophysiol 2006;46:35–42. 35. Fraga CHW, Bianco R, Gonc¸alves M. Comparac¸~ao do sinal EMG e das caracterısticas da passada em diferentes protocolos de corrida incremental. Rev Bras Educ Fıs Esporte 2012;26:599–610. 36. Castro A, LaRoche DP, Fraga CHW, Gonc¸alves M. Relationship between running intensity, muscle activation, and stride kinematics during an incremental protocol. Sci Sports 2013;28:e85–e92. 37. Hug F. Is the EMG fatigue threshold a valid tool to assess muscle function? Med Sci Sports Exerc 2010;42:629–630. 38. Mello RG, Oliveira LF, Nadal J. Detection of the anaerobic threshold by surface electromyography. Conf Proc IEEE Eng Med Biol Soc 2006;1:6189–6192. 39. Jacobs R, Bobbert MF, van Ingen Schenau GJ. Function of monoand biarticular muscles in running. Med Sci Sports Exerc 1993;25: 1163–1173. 40. Florence S, Weir JP. Relationship of critical velocity to marathon running performance. Eur J Appl Physiol Occup Physiol 1997;75: 274–278. 41. Carter H, Pringle JSM, Jones AM, Doust JH. Oxygen uptake kinetics during treadmill running across exercise intensity domains. Eur J Appl Physiol 2002;86:347–354.
MUSCLE & NERVE
December 2015
1039
Department of Physical Education, S~ao Paulo State University, 1515, 24 A Avenue, Bela Vista, 13506-900 Rio Claro, S~ao Paulo, Brazil Department of Kinesiology, University of New Hampshire, Durham, New Hampshire, USA
Accepted 11 March 2015 ABSTRACT: Introduction: We investigated 2 different methods for determining muscle fatigue threshold by electromyography (EMG). Methods: Thirteen subjects completed an incremental treadmill running protocol for EMG fatigue threshold (EMGFT) determination based on the critical power concept (EMGFT1) and the breakpoint in the linear relationship between EMG amplitude and exercise intensity (EMGFT2). Then, both the EMGFT1 and EMGFT2 were tested in a continuous treadmill running protocol. EMG was recorded from the rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles. Results: For BF, EMGFT2 was higher than EMGFT1, and EMGFT1 for BF was lower than EMGFT1 for LG. EMG of RF was higher at EMGFT2 than at EMGFT1, and LG EMG was lower at EMGFT2. Conclusions: EMGFT can be determined during a single treadmill running test, and EMGFT1 may be the most appropriate method to estimate the muscle fatigue threshold during running. Muscle Nerve 52: 1030–1039, 2015
The accumulation of metabolites (lactate, hydrogen ions, and inorganic phosphate), and the fall in muscle pH due to greater rates of adenosine triphosphate hydrolysis and a greater reliance on anaerobic metabolism during exercise1 affect muscle excitation-contraction coupling, including muscle membrane properties and propagation of action potentials in muscle.2,3 In this sense, surface electromyography (EMG), a technique that records the changes in action potentials originating from muscle, becomes an attractive technique to evaluate localized muscle fatigue. This can be accomplished by observing an increase in EMG amplitude (greater motor unit recruitment or synchronization to maintain the required force level) and a shift to the lower frequencies of the EMG Abbreviations: BF, biceps femoris; BFRMS, EMG amplitude of the biceps femoris; EMG, electromyography; EMGFT, fatigue threshold; EMGFT1, fatigue threshold determination based on the critical power concept; EMGFT1VL, fatigue threshold of the vastus lateralis muscle based on the critical power concept; EMGFT2, fatigue threshold determination based on the breakpoint in the linear relationship between EMG amplitude and exercise intensity; EMGFT2VL, fatigue threshold of the vastus lateralis muscle based on the breakpoint in the linear relationship between EMG amplitude and exercise intensity; ES, effect size; [La], blood lactate concentration; LG, lateral gastrocnemius; LGRMS, EMG amplitude of the lateral gastrocnemius; HR, heart rate; HRpeak, peak heart rate; LT, lactate threshold; RF, rectus femoris; RFRMS, EMG amplitude of the rectus femoris; RMS, root mean square; VL, vastus lateralis; VLRMS, EMG amplitude of the vastus lateralis; VO2, oxygen uptake; VT, ventilatory threshold Key words: electromyographic threshold; electromyography; fatigue threshold; muscle fatigue; treadmill running Correspondence to: L.F. Crozara; e-mail: [email protected] C 2015 Wiley Periodicals, Inc. V
Published online 18 March 2015 in Wiley Online Library (wileyonlinelibrary. com). DOI 10.1002/mus.24658
1030
EMG Fatigue Threshold during Running
frequency spectrum (decrease in action potential conduction velocity over the muscle).4–9 DeVries et al.10 proposed a mathematical model based on the critical power concept of Monod and Scherrer11 to estimate the steady-state workload that can be sustained without evident increase in EMG amplitude. This model is termed the electromyographic fatigue threshold (EMGFT), herein referred to as EMGFT1. On the other hand, Lucia et al.12 proposed a model to estimate the EMGFT by determining the breakpoint in the linear relationship between EMG amplitude and exercise intensity (i.e., identifying the point of nonlinearity in the plot of EMG against work rate), herein referred to as EMGFT2. Several studies have used fatigue-inducing protocols to identify EMGFT and have related it to other well-established markers of intensity, such as the lactate threshold (LT) and ventilatory threshold (VT),12–14 demonstrating moderate to strong correlations (r 5 0.64–0.92) between these variables. In an attempt to test the EMGFT1 hypothesis (in a few subjects, n 5 3), Moritani et al.14 analyzed EMG behavior in the time domain during a continuous cycle ergometry protocol at the EMGFT1 intensity and found evidence that it represents the highest workload that can be sustained for 20 minutes without a significant increase in EMG amplitude. In contrast, Pavlat et al.15 concluded that the EMGFT1 was not a valid representation of the highest attainable non-fatiguing workload, overestimating by 42% and 52% the exercise intensity that could be maintained for 30 and 60 min, respectively. These discrepancies led to a discussion of the possibility that EMG may not be a reliable indicator of the onset of neuromuscular fatigue.15,16 Exercise modality is an additional confounding factor that complicates the evaluation of EMGFT as a marker of exercise intensity. Both methods of EMGFT determination have most often been applied to tests performed during cycle ergometry,10,12,13,17,18 whereas only a few studies have been conducted using treadmill running.8,19 It is believed that the constant pattern of movement during cycle ergometry contributes to a more stable EMG signal, facilitating EMGFT detection. This MUSCLE & NERVE
December 2015
FIGURE 1. Research timeline.
is likely, because the myoelectric activity in concentric muscle actions differs from that seen during the stretch-shortening cycle of eccentric contractions.20 It has been suggested that the ability to determine the EMGFT during treadmill running should be scrutinized carefully. During running, the succession of stretch-shortening cycles and changes in gait biomechanics that occur with increased running velocity may influence the onset of neuromuscular fatigue and, consequently, the behavior of the EMG signal.6,7,21 As different muscle groups of the lower limb play different roles during running and are recruited differently with increased running velocity,21 the exercise intensity that elicits EMGFT may differ depending on the muscle analyzed.7 For example, increased running speed is mainly accomplished by larger hip flexor and extensor actions to increase stride length and rate that require a disproportionate increase in EMG activity of the bi-articular thigh muscles (e.g., rectus femoris and biceps femoris).7 EMG analysis has great potential to characterize the neuromuscular behavior associated with the local fatigue process during running, and EMGFT may have utility as an objective, non-invasive, neuromuscular-based marker of exercise intensity that can complement or be used instead of metabolic markers. EMGFT may be a useful tool for researchers who study fatigue, for sports performance specialists who monitor adaptation to training, and for clinicians who evaluate rehabilitation programs.10,12 However, before the EMGFT can be used in this manner, it is necessary to evaluate the utility of these EMG techniques to determine whether they are feasible and comparable indicators of neuromuscular fatigue. It is also important to determine whether the EMGFT can be measured during running the way it has been in cycling, as running elicits higher amplitude, shorter duration EMG bursts that may be more variable than those seen during cycling exercise.22 Therefore, the main objective of this study was to evaluate the utility of these EMG techniques to determine whether they are feasible and comparable indicators of neuEMG Fatigue Threshold during Running
romuscular fatigue. We hypothesized that: (1) it is possible to identify the EMGFT during incremental treadmill running using the EMGFT1 and EMGFT2 methods; (2) the EMGFT intensity determined by these methods would be sustainable for a long period of time without evidence of neuromuscular fatigue; and (3) different lower extremity muscles would show onset of fatigue at different times, as they have different contractile characteristics and distinct roles during running. METHODS Ethics Approval.
The study was approved by our institutional ethics committee. Participants were informed of the purpose of the study and procedures, and they subsequently signed an informed consent form. A research timeline is shown in Figure 1. Participants. Thirteen men who were amateur indoor soccer players with a mean age of 20.77 6 1.92 years, height of 1.76 6 0.05 m, body mass of 68.61 6 10.12 kg, body mass index of 22.06 6 2.59 kg/m2, and body fat percentage of 12.82 6 5.3%, participated in the study. The participants were healthy and without any history of severe injury in the lower limbs in the 6 months preceding the experiment. They maintained a training frequency of 2 or 3 times per week with at least 2 hours of exercise per session and had participated in at least 2 competitions in the previous year. EMG Instrumentation and Measurement. EMG was recorded using a telemetry system (Telemyo 900; Noraxon, Phoenix, Arizona) with a differential amplifier with a total gain of 2000 (203 in the preamplifier 3 1003 in the data acquisition system), common-mode rejection ratio >100 dB (60 HZ), baseline noise 10 MX, and bandwidth limited between 10 and 500 HZ. A pair of pre-gelled, self-adhesive, disposable surface electrodes (circular, 10 mm diameter silver/silver chloride; Meditrace) were placed in a MUSCLE & NERVE
December 2015
1031
outdoor running.25 To assess exercise intensity, heart rate was measured at rest and at the end of each incremental running stage via a commercial heart rate receiver (MFC RS100; Polar, Kempele, Finland). Data Processing and Analysis. The EMG signals were bandpass filtered between 20 HZ and 500 HZ using a Butterworth digital filter (second order for high pass and fourth order for low pass), notchfiltered (60 HZ) with its harmonics, and the RMS of the signal was obtained using 5-s sliding contiguous windows. FIGURE 2. Individual example of a 5-s raw EMG signal from rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles recorded during the incremental running exercise.
bipolar configuration (20 mm center to center) on the right lower limb over the bellies of the rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), and lateral gastrocnemius (LG) muscles, longitudinally and parallel to the underlying muscle fibers, according to the recommendations of Hermens et al.23 The reference electrode was placed on the lateral malleolus. Before placement of the electrodes, the skin was shaved and cleaned with alcohol. The electrodes and wires were secured well with tape to avoid movement-induced artifacts. The EMG signals were sampled at 1,000 HZ using a 16-bit analog-to-digital converter and stored on a computer hard drive using MyoResearch software (Telemyo Master Edition XP 1.07; Noraxon) for off-line analysis. Figure 2 shows an example of a 5-second raw EMG signal of the muscles analyzed. Incremental Treadmill Running Test. Before the test, subjects were familiarized with the equipment and procedures to be used. In addition, they were instructed to refrain from intense training the day before testing and during the day of testing. After a 5-min warm-up at a treadmill (Super ATL; Inbramed, Gravataı, Brazil) velocity of 5 km/h, they performed an incremental running protocol in a temperate environment (25 –27 C, 50%–60% relative humidity), starting at 8 km/h with increases of 1 km/h every 3 min.8,24 The selection of the initial treadmill running velocity was based on a previous pilot study conducted in our laboratory. During the test, subjects were strongly encouraged to provide a maximal effort. The exercise test was terminated when subjects reached volitional exhaustion and were not able to maintain the running pace. The inclination of the treadmill was fixed at 1%, because this grade has been shown to accurately reflect the energetic cost of 1032
EMG Fatigue Threshold during Running
Estimation of the EMGFT1. First, the slope coefficient of the linear regression equation relating EMG vs. time was obtained at each running velocity of the incremental running test (Fig. 3A). Next, each of these slope coefficients (x-axis) was plotted against its respective running velocity (yaxis), and a new linear regression line was drawn to identify the running velocity at EMGFT1. This was determined by finding the y-intercept of the
FIGURE 3. Illustration of the mathematical determination of EMGFT1. (A) The relationship between RMS and time for the velocities used. (B) The relationship of the velocity-vs.-EMG slope coefficients with the y-intercept defined as the EMGFT1. MUSCLE & NERVE
December 2015
FIGURE 4. Illustration of the mathematical determination of EMGFT2. The relationship between RMS and velocity. The EMG breakpoint is located at the intersection between the 2 straight lines.
regression line, that is, the velocity that elicited a slope coefficient of zero (Fig. 3B). This value corresponds to the velocity that could be sustained without an increase in the EMG amplitude over time. Estimation of the EMGFT2. All RMS data points obtained during the incremental test were divided into 2 sets of points. The first 60 RMS data points were arbitrarily included in a first set, with the remaining points included in a second set. Then a regression line was fit to each set of points, and the coefficient of determination (r2) from each line was calculated. The product of these 2 coefficients (representing the linearity of the 2 regions) was then calculated and recorded for further analysis. This procedure was repeated, and at each time incorporated the next data point of the second set into the first set of points, until the second set was composed of the last 60 RMS data points and the first set with the remaining points. The pair of lines that elicited the largest product of the 2 coefficients was chosen, and the intersection between these lines (the breakpoint) was identified as the velocity corresponding to EMGFT2 (Fig. 4). Digital processing of the raw EMG signals and mathematical determination of both EMGFT1 and EMGFT2 were performed using specific algorithms developed in MATLAB (The Mathworks, Inc., Natick, Massachusetts). Continuous Treadmill Running Test at EMGFT. Fortyeight to 72 h after the incremental treadmill running test, the subjects performed a continuous treadmill running test at either the running velocity corresponding to EMGFT1 or the velocity corresponding to EMGFT2, which were calculated from the VL EMG record. This muscle was chosen because it has been shown elsewhere that fatigue thresholds in the quadriceps and triceps surae muscles are not significantly different in cycling or running,26,27 and to allow comparisons between EMG Fatigue Threshold during Running
our results and the results from several previous studies.10,14,15,28,29 The alternate continuous running protocol was performed on a different day, separated by 36–48 h, in a randomized order. The intent of the continuous running protocols was to determine whether the running velocities (exercise intensities) determined using the EMGFT1 and EMGFT2 methods were sustainable for 30 min as done previously by Pavlat et al.28 After a 5-min warm-up at a treadmill velocity of 5 km/h, the subjects performed a continuous treadmill running test at a running pace corresponding to either EMGFT1 or EMGFT2. During the tests, the subjects received strong verbal encouragement and were blinded to the experimental trial and elapsed time. The test was terminated if a subject either completed 30 min of exercise or reached volitional exhaustion before 30 min and was unable to maintain the prescribed running pace. To test whether EMG remained stable over the duration of the continuous running protocol, EMGs from RF, VL, BF, and LG were recorded and processed following the same procedures described previously. For the EMG-vs.-time analysis, each muscle was normalized to the average of its last 10 EMG burst peaks during the warm-up period. Statistical Procedures. Normality of the data distribution was confirmed by the Shapiro–Wilk test for all dependent variables, except for heart rate and percentage of heart rate peak. A 2-way repeatedmeasures analysis of variance (ANOVA) was performed to compare the EMGFT values between the methodologies (EMGFT1 and EMGFT2) and among the muscles examined (RF, VL, BF, and LG). Analysis of EMG against time was achieved using a 3way repeated-measures ANOVA (muscle [RF, VL, BF and LG]) 3 method [EMGFT1 and EMGFT2] 3 time [time-points]). When appropriate, the Greenhouse–Geisser correction was applied when the Mauchly test of sphericity was violated. Significant differences were followed-up with comparisons employing the Sidak correction. The Bland–Altman30 procedure was used to assess agreement between the EMGFT values obtained by the 2 methods, including plots with 95% limits of agreement. Paired t-tests were used to compare the average RMS and endurance time, and Wilcoxon tests were used to compare the heart rate and percentage of heart rate peak between continuous running at EMGFT1 and EMGFT2. The slopes of the VL RMSvs.-time relationship during the continuous running at EMGFT1 and EMGFT2 were compared with a slope of 0 using one-sample t-tests to determine whether tVL EMG amplitude was stable or increased over time. Pearson correlation coefficients were MUSCLE & NERVE
December 2015
1033
Table 1. Treadmill running velocities (mean 6 SD) obtained by the 2 methods of EMG fatigue threshold (EMGFT) determination. EMGFT1 (km/h) RF VL BF LG
11.1 6 0.8 11.2 6 1.2 10.4 6 1.4 11.5 6 0.9†
N 12 13 13 13
(100%) (100%) (100%) (100%)
EMGFT2 (km/h) 10.9 6 1.5 12.0 6 1.6 12.4 6 1.4* 11.0 6 1.3
N 9 7 9 7
(75%) (54%) (69%) (54%)
N, number of subjects in whom the EMGFT was detected; EMGFT1, EMGFT obtained by method adapted from DeVries et al.10; EMGFT2, EMGFT obtained by method adapted from Lucia et al.12; RF, rectus femoris; VL, vastus lateralis; BF, biceps femoris; LG, lateral gastrocnemius. *Significant difference between EMGFT1 and EMGFT2 (P < 0.01). †
Significant difference between BF and LG within EMGFT1 (P < 0.05).
calculated to determine the degree of association between the slopes of the VL RMS-vs.-time relationship and the endurance times obtained in the continuous running tests. For all statistical procedures, significance was set at P < 0.05. All statistical analyses were performed with PASW 18.0 statistical software (SPSS, Inc., Chicago, Illinois). Each effect size (ES) was calculated using G*Power 3.1.7
software (Franz Faul, Universitat Kiel, Germany), according to the recommendations of Beck.31 RESULTS
The average duration of the incremental test was 21.65 6 3.74 min, reaching an average treadmill velocity of 14.4 6 1.3 km/h and 7.4 6 1.3 incremental stages. The average peak heart rate (HRpeak) reached in the incremental test was 192 6 9 beats/min (96% of age-predicted maximal heart rate), indicating that the participants obtained near-maximal exercise intensities. The mean value for EMGFT1 occurred at 77% of peak treadmill velocity for RF, 78% for VL, 72% for BF, and 80% for LG, whereas EMGFT2 occurred at 74% of peak treadmill velocity for RF, 84% for VL, 85% for BF, and 74% for LG. There were no main effects of the EMGFT method (P 5 0.112; ES 5 0.49) or muscle (P 5 0.145; ES 5 0.40). However, there was a significant method 3 muscle interaction for EMGFT (P 5 0.004; ES 5 0.66). EMGFT2 was 16% higher than EMGFT1 for BF (P 5 0.002; ES 5 1.05), but there were no differences between EMGFT2 and
FIGURE 5. Analysis of agreement between the methods of EMG fatigue threshold determination adapted from DeVries et al.10 and Lucia et al.12 (EMGFT1 and EMGFT2, respectively). The middle solid (black) line represents the mean of the differences between the methods (Bias). The upper and lower dashed lines represent the bias 6 1.96SD (i.e., 95% limits of agreement). *Significant difference between the bias and 0 (P < 0.05). 1034
EMG Fatigue Threshold during Running
MUSCLE & NERVE
December 2015
Table 2. Endurance time, heart rate (HR), and percentage of heart rate peak (%HRPEAK) obtained at the end of continuous running at the EMG fatigue threshold EMGFT1VL Subject 1 2 3 4 5 6 7 Mean 6 SD
EMGFT2VL
Endurance time (min)
HR (beats/min)
%HRpeak (%)
Endurance time (min)
HR (beats/min)
%HRpeak (%)
26 30 23 30 30 30 30 28.4 6 2.8
184 176 210 183 199 186 158 185.1 6 16.5
97 96 100 97 100 91 90 95.9 6 4
30 30 23 9 13 22 30 22.4 6 8.6
164 137 210 188 199 188 153 177 6 26.3
86 75 100 100 100 92 87 91.4 6 9.5
EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12
EMGFT1 for RF (P 5 0.796; ES 5 0.07), VL (P 5 0.183; ES 5 0.39), and LG (P 5 0.253; ES 5 0.33) (Table 1). Also, there were no differences between muscles within EMGFT1 and EMGFT2, except between BF and LG within EMGFT1, where EMGFT1 was 10% higher in LG than in BF (P 5 0.015; ES 5 0.81) (Table 1). The analysis of agreement between methods (Bland–Altman30) (Fig. 5) indicated that the average difference between EMGFT1 and EMGFT2 (bias) was significantly different from zero only for BF (P 5 0.017; ES 5 0.99). Also, there was a high degree of within-subject variability for the 2 methods (large limits of agreement) that was influenced by running velocity, as evidenced by the negative correlations shown in Figure 5. The correlations indicate that EMGFT1 was biased toward predicting a higher velocity than EMGFT2 for individuals running on the slow end of the velocity spectrum, and that EMGFT2 was biased toward predicting a higher velocity for individuals running on the fast end of the velocity spectrum. It was not possible to identify EMGFT2 of VL in 6 subjects; that is, there was not a breakpoint in the linear relationship between EMG amplitude and exercise intensity. For this reason, only data from 7 subjects were available to test EMGFT during continuous running at EMGFT1 and EMGFT2 of VL. There were no statistical differences between continuous running at EMGFT1 and EMGFT2 of VL for endurance time (P 5 0.151; ES 5 0.62), heart rate (P 5 0.375; ES 5 0.51), or percentage of heart rate peak (P 5 0.375; ES 5 0.52) (Table 2). The EMG responses of RF, VL, BF, and LG during continuous running at EMGFT1 and EMGFT2 of VL were analyzed at each 20% increment of the time to exhaustion (or 30-min criterion time) (Table 3). There was a significant main effect for muscle (P 5 0.004; ES 5 1.66). RF EMG was higher than VL (P 5 0.029) and LG (P 5 0.022), but not different from BF (P 5 0.079). BF EMG was higher EMG Fatigue Threshold during Running
than LG (P 5 0.021), but not different from VL (P 5 0.885). There was no difference between VL EMG and LG (P 5 0.131), and there were no main effects for method (P 5 0.431; ES 5 0.34) or time (P 5 0.367; ES 5 0.43). There were no interactions for muscle 3 method (P 5 0.088; ES 5 0.65), muscle 3 time (P 5 0.103; ES 5 0.51), method 3 time (P 5 0.365; ES 5 0.43), or muscle 3 method 3 time (P 5 0.241; ES 5 0.46). However, the RMS averaged across time-points was 14% higher during continuous running at EMGFT2 than EMGFT1 for RF (P 5 0.014; ES 5 1.51), and 9% lower for LG (P 5 0.018; ES 5 1.24); there were no differences in RMS between running at EMGFT2 and EMGFT1 for VL (P 5 0.951; ES 5 0.03) and BF (P 5 0.064; ES 5 0.97). Also, the EMG responses of RF, VL, BF, and LG during continuous running at EMGFT1 and EMGFT2 of VL were analyzed at each 2-min increment (from 1 to 9 min; Table 4), and the results are similar to those of the relative time analysis. Therefore, the statistical results of the absolute time analysis are not presented here. The mean of the slopes of the VL RMS-vs.time relationship obtained during continuous running at EMGFT1 (0.000 6 0.06 mV/s) and EMGFT2 (0.04 6 0.07 mV/s) of VL were not significantly different from zero (P 5 1.0; ES 5 0.00 and P 5 0.2; ES 5 0.54, respectively) indicating VL RMS was stable over time. However, there was a moderate inverse correlation between VL slope and endurance time values (r 5 –0.631, P 5 0.016). DISCUSSION
These findings confirm the hypothesis that the neuromuscular fatigue threshold can be identified by analyzing the behavior of the EMG signal recorded during a single incremental treadmill running test. Although EMGFT1 has been used previously during incremental treadmill running,8 EMGFT2 has not been tested during running. MUSCLE & NERVE
December 2015
1035
Table 3. EMG responses (mean 6 SD) during running at the EMG fatigue threshold (EMGFT) against relative time. Relative time (% end-test) Variable RFRMS (%) EMGFT1VL EMGFT2VL VLRMS (%) EMGFT1VL EMGFT2VL BFRMS (%) EMGFT1VL EMGFT2VL LGRMS (%) EMGFT1VL EMGFT2VL
0 (at 1 min)
20
40
60
80
100
Average
155 6 50 186 6 69
145 6 66 198 6 98
163 6 86 179 6 78
169 6 85 175 6 80
169 6 77 186 6 96
138 6 74 163 6 84
156 6 13 181 6 12*
85 6 28 76 6 20
68 6 21 72 6 17
70 6 20 68 6 15
72 6 29 75 6 23
71 6 22 72 6 23
71 6 21 75 6 25
73 6 6 73 6 3
83 6 11 82 6 25
80 6 16 83 6 34
79 6 18 80 6 38
78 6 18 85 6 37
74 6 19 87 6 42
77 6 19 90 6 45
79 6 3 84 6 3
56 6 20 48 6 10
50 6 15 50 6 15
52 6 13 44 6 11
52 6 14 49 6 17
49 6 19 46 6 13
51 6 17 48 6 13
52 6 2 47 6 2*
RFRMS, EMG amplitude of the rectus femoris; VLRMS, EMG amplitude of the vastus lateralis; BFRMS, EMG amplitude of the biceps femoris; LGRMS, EMG amplitude of the lateral gastrocnemius; EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12 *Significant difference between EMGFT1VL and EMGFT2VL (P < 0.05).
Comparing the 2 EMGFT Methods. Although 2 methods that were theoretically developed to measure the same variable (i.e., EMGFT) should not differ in the mean of scores, they should also agree at an individual level to be used interchangeably in clinical applications.30 The hypothesis that the EMGFT values would be similar between EMGFT1 and EMGFT2 was supported by comparison of mean scores between methods, but it was refuted by the Bland–Altman agreement of methods that showed a high degree of variability between measures. In comparing the mean treadmill velocities at EMGFT1 and EMGFT2, a difference was found only for BF, with no differences for RF, VL, and LG. The difference in EMGFT for BF may have been due to the different mathematical models employed by the 2 methods (critical power vs.
breakpoint concept), but this would not explain why the BF muscle behaved differently from the others. When running velocity increases, the magnitude and duration of BF activation during pushoff increases to aid in forward propulsion of the body.7 BF may therefore be more prone to early to fatigue7 (as seen in EMGFT1), which facilitates detection of EMGFT for this muscle. At the same time, early fatigue of BF may make it difficult to detect EMGFT for other muscles (i.e., RF, LG, and VL), because the running test can be completed to volitional exhaustion before substantial fatigue is seen in those muscles. The behavior of BF EMG amplitude was crucial in differentiating the 2 EMGFT methods, because it demonstrated that identifying the breakpoint in EMG requires a welldefined increase in EMG amplitude vs. time.
Table 4. EMG responses (mean 6 SD) during running at the EMG fatigue threshold (EMGFT) against absolute time. Absolute time (min) Variable RFRMS (%) EMGFT1VL EMGFT2VL VLRMS (%) EMGFT1VL EMGFT2VL BFRMS (%) EMGFT1VL EMGFT2VL LGRMS (%) EMGFT1VL EMGFT2VL
1
3
5
7
9
Average
155 6 50 186 6 69
150 6 66 187 6 83
142 6 68 187 6 96
151 6 65 189 6 91
151 6 77 189 6 83
150 6 5 188 6 1*
85 6 28 76 6 20
77 6 22 69 6 15
70 6 21 73 6 20
74 6 22 67 6 14
71 6 19 72 6 22
75 6 6 71 6 3
83 6 11 82 6 25
82 6 16 79 6 31
85 6 20 83 6 36
79 6 19 83 6 28
82 6 17 90 6 40
82 6 2 83 6 4
56 6 20 48 6 10
56 6 18 47 6 13
53 6 13 50 6 17
52 6 15 49 6 16
51 6 13 47 6 14
53 6 2 48 6 1*
RFRMS, EMG amplitude of the rectus femoris; VLRMS, EMG amplitude of the vastus lateralis; BFRMS, EMG amplitude of the biceps femoris; LGRMS, EMG amplitude of the lateral gastrocnemius; EMGFT1VL, EMGFT of the vastus lateralis muscle obtained by method adapted from DeVries et al.10; EMGFT2VL, EMGFT of the vastus lateralis muscle obtained by method adapted from Lucia et al.12 *Significant difference between EMGFT1VL and EMGFT2VL (P < 0.05).
1036
EMG Fatigue Threshold during Running
MUSCLE & NERVE
December 2015
The analysis of agreement between methods indicates that the differences between EMGFT1 and EMGFT2 showed high within-subject variability that was influenced by velocity for all muscles. This result shows that, in subjects who ran at slow velocities at EMGFT, EMGFT1 predicted a higher velocity than EMGFT2, and, in subjects who ran at higher velocities at EMGFT, EMGFT2 predicted a higher velocity than EMGFT1. This indicates that, although there were no statistical differences between the mean EMGFT1 and EMGFT2 values for RF, VL, and LG, the 2 methods do not agree, particularly for a given individual. In addition, the low agreement between the 2 methods was also evidenced by the endurance times during the continuous running test at the EMGFT of VL. For example, when running at EMGFT2 intensity, subjects 4 and 5 fatigued twice as quickly as they did at EMGFT1 (Table 2). The endurance time for EMGFT2 for these subjects was considerably below the adopted criterion of 30 min.28 Therefore, to a certain extent, these findings suggest that EMGFT2 sometimes underestimated and sometimes overestimated EMGFT. On the other hand, the EMG responses of VL during the continuous running test at EMGFT1 and EMGFT2 remained stable for the duration of the test, suggesting for this muscle that neither method overestimated the running pace that can be sustained without evidence of neuromuscular fatigue. Another finding was that the slope of the VL RMS EMG amplitude during the continuous running protocol was inversely correlated with running endurance time. This suggests that, in those subjects who demonstrated a positive slope of the VL RMS EMG-vs.-time curve, neuromuscular fatigue was occurring and required recruitment of additional motor units to sustain the required muscular effort. With increasing exercise intensity there is an orderly recruitment pattern of muscle fibers from predominantly slow-twitch fibers (type I) at low intensity to inclusion of fast-twitch fibers (type II) at high intensity.32 This neuromuscular response promotes changes in metabolic and EMG parameters as a result of increased muscular work that can be used to predict the onset of fatigue. For example, Lucia et al.12 found 2 breakpoints in the EMGvs.-intensity curve using a sample of highly trained athletes. However, in our study, only 1 breakpoint in the EMG-vs.-intensity curve was observed in a sample of amateur athletes. Therefore, we speculate that highly trained individuals are potentially better able to recruit the type IIb muscle fibers close to exhaustion, leading to a second breakpoint in the EMG-vs.-intensity curve.12 Thus, it was hypothesized that EMGFT2 (based on breakpoint determination) may be more useful for fit EMG Fatigue Threshold during Running
individuals19 than EMGFT1 (based on critical power concept), because they are able to work at exercise intensities high enough to promote a sharp increase in the EMG amplitude-time relation (substantial recruitment of type IIb muscle fibers). However, this concept was not supported in this study due to failure to identify the EMGFT2 breakpoint in 6 subjects. Issues for EMGFT Detection. The increase in EMG amplitude during dynamic and isometric contractions reflects additional recruitment of motor units and increased firing rate and/or synchronization of these motor units.12,33,34 These neuromuscular events have been identified either as a result of increased exercise intensity35,36 or the onset of fatigue when, for example, a breakpoint in the linear relationship between EMG amplitude and exercise intensity occur.7,27 However, in our study it was not always possible to determine the EMGFT, particularly when using the mathematical model of EMGFT2. In EMGFT2 detection, when there was no intersection between the 2 best straight lines, we considered that the EMG increased linearly with velocity, and there was no breakpoint in the relationship between EMG amplitude and running velocity. Unlike static (isometric) contractions, the EMG signal during dynamic contractions does not have a well-defined stationary behavior,5 leading to difficulty in detecting the breakpoint in the EMG amplitude and exercise intensity relationship, as others have observed.12,13,17–19 This fact can be explained by: (1) compensation between synergistic muscles in an attempt to delay fatigue during an activity; (2) possible spatial variability of muscle recruitment during dynamic contractions; and/or (3) inability of individuals to recruit enough highthreshold motor units to enhance the breakpoint in the linear relationship between EMG amplitude and exercise intensity.12,37 It has been suggested that standardization of muscle contractions contributes to successful EMGFT determination,18 such as by controlling cadence.38 In the incremental treadmill running test used in this study, it was not possible or even desirable to control stride parameters, as this condition more closely reflects real running situations. Yet, changing biomechanical strategies (variation in stride length and frequency) in response to the increase in running velocity and the onset of fatigue could have affected the recruitment of specific muscles as well as the behavior of the EMG signal.7,36 The dynamic nature of running may make identification of EMGFT potentially more difficult during this exercise modality, but, we have demonstrated in this study, it is possible to identify a fatigue threshold, particularly with the EMGFT1 method. MUSCLE & NERVE
December 2015
1037
Differences between Muscles During Incremental and
The hypothesis that different muscles would be affected by fatigue in a different manner was confirmed partially for EMGFT1 in BF, which had a lower fatigue threshold than LG. The lower EMGFT in BF may have occurred because, as running speed increases, stride length increases, requiring higher BF activity for proper hip extension. At faster speeds, the increase in LG activity may be less than in BF due to the shorter duration of the stance phase and because of an increased synergy of knee extensors and ankle plantarflexors.21 Thus, EMGFT1 could potentially be used in clinical and athlete populations alike to identify specific muscles with a low fatigue threshold that may be limiting physical performance. The EMGFT was consistent between RF, VL, and LG, which suggests that neuromuscular fatigue occurs similarly in these muscles during a running task. Although studies have shown that biarticular and monoarticular muscles play distinct roles during running,39 and we demonstrated earlier fatigue in BF (biarticular), the results indicate that RF and LG (biarticular) and VL (monoarticular) muscles are affected by fatigue in the same way. Hanon et al.7 showed that some thigh biarticular muscles (BF and RF) fatigued earlier than some thigh and calf monoarticular muscles (VL and TA) during running. Differences between the studies in physical fitness levels of the subjects, the exercise protocol, and methods of EMGFT determination used may explain these discrepancies. Continuous Running.
Physiological Intensity Domains of the EMGFT. The mean EMGFT running speed found here was 11.3 km/h, comparable with the running speeds reported in the literature for prolonged activity of 11.1 km/h and 11.7 km/h.29,40 Also, the mean values of EMGFT1 and EMGFT2 expressed as a percentage of peak treadmill velocity (72%–80% and 74%–85%, respectively) were similar to those obtained in previous studies.17,27 This fact confirms that the EMGFT usually lies between the heavy and severe exercise intensity domains.9,36 The heavy domain refers to the zone of exercise intensity above LT and below the maximum lactate steady state. This is the critical velocity at which the blood lactate concentration [La] and oxygen uptake (VO2) tend to be stable over time at levels above those seen in the moderate-intensity domain. In the severe domain, the [La] and VO2 do not stabilize with time, eliciting maximum values when the exercise is performed to exhaustion.41 Hence, the EMGFT technique used in this study may be a noninvasive alternative to LT and VT for detection of the transition between exercise intensity domains. Future research should therefore address the 1038
EMG Fatigue Threshold during Running
relationship between EMGFT and the more commonly used metabolic measures of LT and VT. In conclusion, EMGFT can be determined during a single treadmill running test as a noninvasive alternative for obtaining an index of localized muscle fatigue. The EMGFT based on the critical power concept (EMGFT1) proved to be the most sensitive method for estimating the running pace that can be sustained over a long period of time without evidence of fatigue, and it could differentiate fatigue thresholds between lower extremity muscles. Although EMGFT2 appears to be a valid indicator of the neuromuscular fatigue threshold, the inability to identify this threshold in nearly 50% of subjects questions its utility. EMGFT can be a useful tool for prescribing and monitoring exercise intensity in training and rehabilitation programs because of its capacity to demarcate the transition from heavy to severe exercise and its ability to identify the onset of localized muscle fatigue REFERENCES 1. Robergs RA, Ghiasvand F, Parker D. Biochemistry of exerciseinduced metabolic acidosis. Am J Physiol Regul Integr Comp Physiol 2004;287:R502–516. 2. Fitts RH. Cellular mechanisms of fatigue muscle. Physiol Rev 1994; 74:49–93. 3. Westerblad H, Allen DE, Lannergren J. Muscle fatigue: lactic acid or inorganic phosphate the major cause? News Physiol Sci 2002;17:17– 21. 4. Hendrix CR, Housh TJ, Johnson GO, Mielke M, Camic CL, Zuniga JM, et al. A new EMG frequency-based fatigue threshold test. J Neurosci Methods 2009;181:45–51. 5. MacIsaac D, Parker PA, Scott RN. The short-time Fourier transform and muscle fatigue assessment in dynamic contractions. J Electromyogr Kinesiol 2001;11:439–449. 6. Silva SRD, Fraga CHW, Gonc¸alves M. Efeito da fadiga muscular na biomec^anica da corrida: uma revis~ao. Motriz 2007;13:225–235. 7. Hanon C, Thepaut-Mathieu C, Vandewalle H. Determination of muscular fatigue in elite runners. Eur J Appl Physiol 2005;94:118–125. 8. Fraga CHW, Silva SRD, Gonc¸alves M. Efeito da velocidade de corrida em variaveis eletromiograficas e metab olicas. Motriz 2009;15:911–918. 9. Camic CL, Housh TJ, Johnson GO, Hendrix CR, Zuniga JM, Mielke M, et al. An EMG frequency-based test for estimating the neuromuscular fatigue threshold during cycle ergometry. Eur J Appl Physiol 2010;108:337–345. 10. DeVries HA, Moritani T, Nagata A, Magnussen K. The relation between critical power and neuromuscular fatigue as estimated from electromyographic data. Ergonomics 1982;25:783–791. 11. Monod H, Scherrer J. The work capacity of a synergic muscular group. Ergonomics 1965;8:329–338. 12. Lucia A, Sanchez O, Carvajal A, Chicharro JL. Analysis of the aerobic-anaerobic transition in elite cyclists during incremental exercise with the use of electromyography. Br J Sports Med 1999;33: 178–185. 13. Candotti CT, Loss JF, Melo MDE, La Torre M, Pasini M, Dutra LA, et al. Comparing the lactate and EMG thresholds of recreational cyclists during incremental pedaling exercise. Can J Physiol Pharmacol 2008;86:272–278. 14. Moritani T, Takaishi T, Matsumoto T. Determination of maximal power output at neuromuscular fatigue threshold. J Appl Physiol 1993;74:1729–1734. 15. Pavlat DJ, Housh TJ, Johnson GO, Eckerson JM. Electromyographic responses at the neuromuscular fatigue threshold. J Sports Med Phys Fitness 1995;35:31–37. 16. Evetovich TK, Housh TJ, Johnson GO, Evans SA, Stout JR, Bull AJ, et al. Effect of workbout duration on the physical working capacity at fatigue threshold (PWCFT) test. Ergonomics 1996;39:314–321. 17. Hug F, Laplaud D, Lucia A, Grelot L. EMG threshold determination in eight lower limb muscles during cycling exercise: a pilot study. Int J Sports Med 2006;27:456–462. 18. Jurimae J, von Duvillard SP, Maestu J, Cicchella A, Purge P, Ruosi S, J€ urim€ ee T, Hamra J. Aerobic-anaerobic transition intensity measured
MUSCLE & NERVE
December 2015
19. 20.
21. 22. 23. 24. 25. 26. 27. 28. 29.
via EMG signals in athletes with different physical activity patterns. Eur J Appl Physiol 2007;101:341–346. Tikkanen O, Hu M, Vilavuo T, Tolvanen P, Cheng S, Finni T. Ventilatory threshold during incremental running can be estimated using EMG shorts. Physiol Meas 2012;33:603–614. Nakazawa K, Kawakami Y, Fukunaga T, Yano H, Miyashita M. Differences in activation patterns in elbow flexor muscles during isometric, concentric and eccentric contractions. Eur J Appl Physiol 1993;66: 214–220. Gazendam MG, Hof AL. Averaged EMG profiles in jogging and running at different speeds. Gait Posture 2007;25:604–614. Bijker KE, De Groot G, Hollander AP. Differences in leg muscle activity during running and cycling in humans. Eur J Appl Physiol 2002;87:556–561. Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. J Electromyogr Kinesiol 2000;10:361–374. Bentley DJ, Newell J, Bishop D. Incremental exercise test design and analysis: implications for performance diagnostics in endurance athletes. Sports Med 2007;37:575–586. Jones AM, Doust JH. A 1% treadmill grade most accurately reflects the energetic cost of outdoor running. J Sport Sci 1996;14:321–327. DeVries HA, Housh TJ, Johnson GO, Evans SA, Tharp GD, Housh DJ, et al. Factors affecting the estimation of physical working capacity at the fatigue threshold. Ergonomics 1990;33:25–33. Hanon C, Thepaut-Mathieu C, Hausswirth C, Le Chevalier JM. Electromyogram as an indicator of neuromuscular fatigue during incremental exercise. Eur J Appl Physiol Occup Physiol 1998;78:315–323. Pavlat DJ, Housh TJ, Johnson GO, Schmidt RJ, Eckerson JM. An examination of the electromyographic fatigue threshold test. Eur J Appl Physiol Occup Physiol 1993;67:305–308. Guffey DR, Gervasi BJ, Maes AA, Malek MH. Estimating electromyographic and heart rate fatigue thresholds from a single treadmill test. Muscle Nerve 2012;46:577–581.
EMG Fatigue Threshold during Running
30. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res 1999;8:135–160. 31. Beck TW. The importance of a priori sample size estimation in strength and conditioning research. J Strength Cond Res 2013;27: 2323–2337. 32. Sasaki K, Tomioka Y, Ishii N. Activation of fast-twitch fibers assessed with twitch potentiation. Muscle Nerve 2012;46:218–227. 33. Holtermann A, Gronlund C, Karlsson JS, Roeleveld K. Motor unit synchronization during fatigue: described with a novel sEMG method based on large motor unit samples. J Electromyogr Kinesiol 2009;19: 232–241. 34. Silva SR, Gonc¸alves M. Dynamic and isometric protocols of knee extension: effect of fatigue on the EMG signal. Electromyogr Clin Neurophysiol 2006;46:35–42. 35. Fraga CHW, Bianco R, Gonc¸alves M. Comparac¸~ao do sinal EMG e das caracterısticas da passada em diferentes protocolos de corrida incremental. Rev Bras Educ Fıs Esporte 2012;26:599–610. 36. Castro A, LaRoche DP, Fraga CHW, Gonc¸alves M. Relationship between running intensity, muscle activation, and stride kinematics during an incremental protocol. Sci Sports 2013;28:e85–e92. 37. Hug F. Is the EMG fatigue threshold a valid tool to assess muscle function? Med Sci Sports Exerc 2010;42:629–630. 38. Mello RG, Oliveira LF, Nadal J. Detection of the anaerobic threshold by surface electromyography. Conf Proc IEEE Eng Med Biol Soc 2006;1:6189–6192. 39. Jacobs R, Bobbert MF, van Ingen Schenau GJ. Function of monoand biarticular muscles in running. Med Sci Sports Exerc 1993;25: 1163–1173. 40. Florence S, Weir JP. Relationship of critical velocity to marathon running performance. Eur J Appl Physiol Occup Physiol 1997;75: 274–278. 41. Carter H, Pringle JSM, Jones AM, Doust JH. Oxygen uptake kinetics during treadmill running across exercise intensity domains. Eur J Appl Physiol 2002;86:347–354.
MUSCLE & NERVE
December 2015
1039
