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Fatigue-induced dissociation between rate of force development and maximal force across repeated rapid contractions
Human Movement Science 54 (2017) 267–275
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Full Length Article
Fatigue-induced dissociation between rate of force development and maximal force across repeated rapid contractions
MARK
⁎
Gennaro Bocciaa,b, , Davide Dardanelloa, Cantor Tarperic, Luca Festac, Antonio La Torred, Barbara Pellegrinib,c, Federico Schenab,c, Alberto Rainoldia a
NeuroMuscularFunction Research Group, School of Exercise & Sport Sciences, Department of Medical Sciences, University of Turin, 12, P.za Bernini, 10143 Turin, Italy b CeRiSM Research Center “Sport, Mountain, and Health”, via del Ben 5/b, 38068 Rovereto, TN, Italy c School of Sport and Exercise Sciences, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati 43, 37137 Verona, Italy d Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
AR TI CLE I NF O
AB S T R A CT
Keywords: Intermittent contractions Neuromuscular fatigue Multichannel electromyography
We examined whether the presence of fatigue induced by prolonged running influenced the time courses of force generating capacities throughout a series of intermittent rapid contractions. Thirteen male amateur runners performed a set of 15 intermittent isometric rapid contractions of the knee extensor muscles, (3 s/5 s on/off) the day before (PRE) and immediately after (POST) a half marathon. The maximal voluntary contraction force, rate of force development (RFDpeak), and their ratio (relative RFDpeak) were calculated. At POST, considering the first (out of 15) repetition, the maximal force and RFDpeak decreased (p < 0.0001) at the same extent (by 22 ± 6% and 24 ± 22%, respectively), resulting in unchanged relative RFDpeak (p = 0.6). Conversely, the decline of RFDpeak throughout the repetitions was more pronounced at POST (p = 0.02), thus the decline of relative RFDpeak was more pronounced (p = 0.007) at POST (−25 ± 13%) than at PRE (−3 ± 13%). The main finding of this study was that the fatigue induced by a half-marathon caused a more pronounced impairment of rapid compared to maximal force in the subsequent intermittent protocol. Thus, the fatigue-induced impairment in rapid muscle contractions may have a greater effect on repeated, rather than on single, attempts of maximal force production.
1. Introduction Repeated high-force contractions of skeletal muscles cause a decline in force-generating capacity, referred to as muscle fatigue (Bigland-Ritchie & Woods, 1984). During exercise of maximal intensity, fatigue result into a decline of force (BiglandRitchie & Woods, 1984) or power (Cheng & Rice, 2005). Research investigating the influence of an exercise-induced fatigue on the neuromuscular function focused mainly on the decline in maximal voluntary contraction force. However, the effect of fatigue on the ability to produce force rapidly, also referred to as explosive strength (Maffiuletti et al., 2016), has received less attention despite its
Abbreviations: ANOVA, analysis of variance; ARV, average rectified value; CV, muscle fiber conduction velocity; EMG, electromyography; MVC, maximal voluntary contraction; RFD, rate of force development ⁎ Corresponding author at: NeuroMuscularFunction Research Group, School of Exercise & Sport Sciences, Department of Medical Sciences, University of Turin, 12, P.za Bernini, 10143 Turin, Italy. E-mail address: [email protected] (G. Boccia). http://dx.doi.org/10.1016/j.humov.2017.05.016 Received 29 November 2016; Received in revised form 23 May 2017; Accepted 31 May 2017 0167-9457/ © 2017 Elsevier B.V. All rights reserved.
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importance for the production of many movements. Particularly, the rate of force development (RFD) is functionally more informative than maximal force when considering sports in which rapid movements are important, such as running, jumping, or kicking (Aagaard, Simonsen, Andersen, Magnusson, & Dyhre-Poulsen, 2002; de Ruiter, Van Leeuwen, Heijblom, Bobbert, & de Haan, 2006). Muscle fatigue can impair the explosive strength (Buckthorpe, Pain, & Folland, 2014) which in turn can negatively influence explosive sport activities (Krustrup et al., 2006; Mohr, Krustrup, & Bangsbo, 2003; Zoppirolli, Pellegrini, Bortolan, & Schena, 2016). Moreover, maintaining the ability to produce high RFD values is important to limit the risks of injury (Minshull, Gleeson, Walters Edwards, Eston, & Rees, 2007). Therefore, an understanding of how fatigue affects rapid force production would seem important in understanding its influence on athletic performance and injury risk. Maximal force and RFD should not be used as interchangeable indices when assessing high-intensity muscle fatigue. Indeed, it has recently been demonstrated a more pronounced drop in rapid compared with maximal force production during fatiguing protocols constituted by intermittent explosive isometric contraction (Buckthorpe et al., 2014). Similar disproportionate drop in explosive than maximal torque was recently found also using dynamic contractions (Morel et al., 2015). Particularly, these studies, which was constituted by high-force fatiguing protocols, showed that the early phase (0–50 ms) of explosive muscle contraction seemed to be the most susceptible to muscle fatigue (Buckthorpe et al., 2014; Morel et al., 2015). The relationship between maximal force and RFD decrements also varied among muscles when used to assess muscle fatigue induced by prolonged cross-country skiing (Boccia et al., 2016). Given the different influence of muscle fatigue on rapid and maximal force production we aimed to determine the influence of muscle conditions at the beginning of the intermittent protocol on the time course of force generating capacities. We compared the time course of maximal force and RFD across a set of 15 intermittent rapid isometric contractions executed in two conditions: before, i.e. fresh condition, and after a half-marathon run (21.097 m), i.e. fatigued condition. We hypothesized that the ratio between the time course of maximal force and RFD would change in fatigued condition. 2. Methods 2.1. Participants For this specific study 14 amateur male runners were recruited (age 36 ± 8, body weight 74 ± 10 kg, height 173 ± 8 cm) who successfully concluded a half-marathon run (21.097 km). All participants were habitually involved in amateur running with a mean training regimen of 220 min/week. None of them had clinical evidences of cardiovascular, neuromuscular, or joint diseases. Participants were instructed to refrain from performing strenuous physical activity in the 24 h before the first experimental session. All participants provided their written informed consent before participation in the investigation. The study was approved by the local Ethical Committee (Department of Neurological and Movement Sciences, University of Verona) and performed in accordance with the Helsinki Declaration. 2.2. General overview The study was performed during a specific event called “Run For Science”, held in Verona (Italy) in April 2016. Participants were involved in two measurement sessions: the first was performed the day before the race (PRE), and the second immediately after the race (POST). The neuromuscular test consisted in a set of isometric explosive maximal voluntary contractions (MVCs) of the knee extensors. Force and electromyographic (EMG) measurements were obtained from the rapid (rising) and maximal (plateau) phases of the MVCs. During the PRE session, participants were familiarized with the procedures. For that purpose, they repeated a number of trials of the test procedures until they were able to produce consistent results. In the PRE session participants performed 15-min of a standardised warm-up (details are given below) before neuromuscular testing. In the POST session the neuromuscular assessment started within 10 min after the race. A researcher was positioned at the finishing line to conduct the runners to the testing site, which was located about 50 m from the finishing line. The testing session at POST lasted 3–4 min. 2.3. Procedure 2.3.1. Warm-up The warm-up at PRE consisted of 15-min of outdoor running at an incremental intensity from 75% to 90% of the maximal heart rate previously determined by an incremental test. The duration of the warm-up was chosen based on previous studies showing that muscle temperature rises rapidly after 5 min and reaches an equilibrium after 15 min (Bishop, 2003). 2.3.2. Force measurement Participants were seated on a custom-made chair that allowed the assessment of the knee extensors, and straps were fastened across the chest and hips to avoid lateral and frontal displacements. During the testing, participants’ knee and hip were flexed at 90° from full extension and they were instructed to maintain the arms crossed on the chest. The knee extensors mechanical response was recorded with a strain gauge load cell (546QD-220 kg; DSEurope, Milan, Italy), fixed with non-compliant straps at the level of the external malleolus. All measurements were taken from the participants’ right limbs (which was the dominant limb for 13 out of 14 participants). The force signals were sampled at 2048 Hz together with EMG signals and converted to digital data with a 16-bit A/D converter (EMG-USB2, OT Bioelettronica, Turin, Italy). 268
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The intermittent protocol comprised 15 explosive maximal voluntary contractions each lasting 3 s, and interspersed by 5 s of rest. In response to an audio and visual signal, participants were instructed to push as fast and as hard as they can and to maintain this throughout the contraction for 3 s (Aagaard et al., 2002). During the protocol, participants received a visual feedback of exerted torque and strong verbal encouragements were provided by two operators to reach his/her highest level in each contraction. Following each explosive MVC, they were instructed to relax quickly in order to return to the resting baseline force. The total duration of the intermittent protocol was 2 min, with 45 s of maximal voluntary effort.
2.3.3. Electromyographic measurements EMG signals were recorded from the vastus lateralis muscle in a single differential configuration using linear adhesive electrode arrays composed of eight electrodes (silver bars, 5 × 1 mm in size, 5 mm inter-electrode distance, OT Bioelettronica, Turin, Italy). Before the placement of the electrode arrays, the skin was slightly abraded with abrasive paste and cleaned with water in accordance with SENIAM recommendations for skin preparation (Hermens, Freriks, Disselhorst-Klug, & Rau, 2000). The optimal position and orientation of the array were sought for each muscle by visual inspection of the EMG signals and following guidelines previously described (Beretta Piccoli et al., 2014). The sites with clear muscle fiber action potential propagation and the main innervation zones were identified using a linear array of 16 electrodes with 5-mm inter-electrode distance (OT Bioelettronica, Turin, Italy). The adhesive electrode arrays were then placed parallel to the muscle fibres where unidirectional propagation of the motor unit action potentials was detected. To ensure proper electrode-skin contact, the electrode cavities of the arrays were filled with 20–30 μL of conductive paste (Spes-Medica, Battipaglia, Italy). The electrode arrays were fixed with an extensible dressing (Fixomull®, Beiersdorf, Hamburg, Germany). The position of the electrodes was marked on the skin so that they could be positioned in the same place after the race. The EMG signals were amplified, sampled at 2048 Hz, bandpass filtered (3-dB bandwidth, 20–450 Hz) and converted to digital data with a 16-bit A/D converter (EMG-USB2, OT Bioelettronica, Turin, Italy). Signals were visualized during acquisition and then stored on a personal computer using OT BioLab software version 1.8 (OT Bioelettronica, Turin, Italy) for further analysis.
2.4. Signal processing 2.4.1. Force signals Force signals were firstly smoothed using a moving average filter with a window of 51 samples. Maximum force was determined for each contraction and the time instant at which it occurred was used for successive EMG analysis. For the analysis of the explosive phase of contraction, the force signal of all contractions was visually checked. The repetitions were included in the analysis only there was not evident a countermovement, or a pre-tension in the 100 ms preceding the onset of contraction. RFD (change in force divided by the change in time) was quantified for consecutive 50 ms time intervals: 0–50 ms (RFD50), 0–100 ms (RFD100), and 0–150 ms (RFD150). Peak of RFD (RFDpeak) was also determined using a moving 20-ms window throughout the force–time curve as the highest RFD value at any time during the contraction. The RFD were quantified in absolute terms (N/s) and normalized to the maximal force (relative RFD) recorded within the same contraction (%/s).
2.4.2. Electromyographic signals The EMG signals were visually inspected off-line to select the best channels for estimating variables (Merletti, Knaflitz, & De Luca, 1990). The average muscle fiber conduction velocity (CV) was estimated among all the accepted channels and computed as e/d, where e is the inter-electrode distance and d is the delay time between the signals obtained from the two double differential arrays spaced 5 mm apart (Merletti et al., 1990). The correlation coefficient between the two adjacent double differential signals was calculated; if the correlation coefficient was < 0.8, the recorded signals were excluded from the analysis. The amplitude of the EMG signal was assessed as the average rectified value (ARV) during the same overlapped intervals of explosive forces (ARV50, ARV100, and ARV150) and maximal force (500 ms epoch centred in instant of maximal force, ARVmax). ARV values signals were averaged among all the accepted EMG channels to increase the robustness of the estimates. Data were analyzed by custom-written software in MATLAB R2014a (Mathworks, Natick, Massachusetts).
2.5. Statistical analysis The time course of force and EMG parameters was analyzed by applying linear or exponential regression analysis (on the base of best fitting) to the data calculated across the 15 repetitions. The first and last value of each variable was calculated as the intercept of the regression line in the first and the last contraction, respectively. The decline of force and EMG variables across the 15 contractions was calculated as the percent ratio between the last and first contraction (expressed as %). Kolmogorov-Smirnov normality test was used to assess distributions normality. If the data were not normally distributed were log-transformed before statistical analysis and back-transformed to obtain descriptive statistics. Paired Student’s t tests were then used to compare the maximal force, RFD, and EMG values between PRE and POST sessions. Threshold for statistical significance was set to p < 0.05. Statistical analyses were performed with SPSS statistics (version 20.0, IBM Corporation, Somers, NY). Data are all expressed as mean ± SD. The magnitude of the difference was calculated as Cohen’s d effect size. Threshold values for effect size statistics were: < 0.2, trivial; > 0.2, small; > 0.6, moderate; > 1.2 large; > 2.0, very large (Batterham & Hopkins, 2006). 269
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Fig. 1. Representative example of the time course of force (left panel) and rate of force development (right panel) of 15 intermittent explosive contractions lasting 3 s, and separated by 5 s of rest, at PRE session. It is evident the decline of both maximal force exerted in each contraction as well as the decline in the rate of force developed in the raising phase of contraction.
3. Results Out of the 14 initially recruited participants, one did not perform the POST session because he dropped out of the race. Thus, data are reported for 13 participants which completed the 21.097 km race in a time ranged from 1 h 25 min to 1 h 50 min. 3.1. Curve fitting of mechanical data across the intermittent protocol Maximal force and RFDpeak data across the 15 contractions was analyzed with exponential and linear fitting. While at PRE maximal force was better fitted by exponential than linear regression (exponential r2 = 0.79 ± 0.18; linear r2 = 0.75 ± 0.17; p = 0.002), at POST exponential and linear fitting showed similar goodness (exponential r2 = 0.49 ± 0.36; linear r2 = 0.48 ± 0.37; p = 0.196) (Fig. 2). RFDpeak data did not show goodness differences between exponential and linear fitting both at PRE (exponential r2 = 0.47 ± 0.30; linear r2 = 0.46 ± 0.30; p > 0.578) and POST (exponential r2 = 0.42 ± 0.28; linear r2 = 0.42 ± 0.27; p > 0.731) (Fig. 2).
3.2. Effects of half marathon run on muscle function The maximal force decreased by 22 ± 6% (p < 0.0001) and similarly RFDpeak decreased by 24 ± 22% (p < 0.0001) from PRE to POST. RFD calculated in all time intervals showed large to very large decrease (all p values < 0.0001), see Table 1 for details. Small to large decrease in EMG amplitude was recorded in correspondence of maximal force (ARVmax −26%, p = 0.023) and RFD (ranged −21% to −30%), see Table 1 for details. No differences were detected in muscle fiber CV in correspondence of maximal force (CVmax p = 0.188) and during the first 150 ms of contraction (CV150 p = 0.370), see Table 1.
Fig. 2. Time courses (mean ± SD) of peak force (black) and peak of rate of force development (RFD, white) normalized with respect to the first contraction performed at PRE session. PRE session was performed the day before, and POST session was performed after (with 10 min of recovery) a 21-km running time trial. At PRE the decline of maximal force and rate of force development was similar, whereas at POST the decline of rate of force development was more pronounced than maximal force.
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Table 1 Decrements (PRE vs POST) of mechanical and electromyographic values calculated in the first contraction of the intermittent protocol. Mechanical
PRE
POST
Difference
p value
Cohen’s d
Max. force RFDpeak RFD50 RFD100 RFD150
(N) (N/s) (N/s) (N/s) (N/s)
257 ± 51 1522 ± 305 844 ± 356 1292 ± 321 1172 ± 235
193 ± 47 1130 ± 300 539 ± 240 907 ± 292 854 ± 243
−24 ± 15 −392 ± 221 −291 ± 226 −391 ± 276 −315 ± 170
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
−1.30 −1.30 −1.04 −1.21 −1.32
(very large) (very large) (large) (large) (very large)
EMG ARVmax CVmax ARV50 ARV100 ARV150 CV150
(μV) (m/s) (μV) (μV) (μV) (m/s)
259 ± 106 4.3 ± 0.5 224 ± 125 257 ± 118 272 ± 117 3.9 ± 0.5
190 ± 66 4.0 ± 0.5 177 ± 80 184 ± 65 193 ± 63 3.7 ± 0.5
−69 ± 85 −0.3 ± 0.7 −47 ± 87 −73 ± 97 −78 ± 104 0.2 ± 0.7
0.023 0.188 0.071 0.019 0.019 0.370
−0.78 −0.66 −0.44 −0.76 −0.84 −0.39
(medium) (medium) (small) (medium) (large) (small)
3.3. Effects of running induced fatigue on intermittent explosive contractions 3.3.1. Maximal force and RFDpeak The percent decline of maximal force across the 15 contractions was non-statistically different (p = 0.087) at POST (−10 ± 15%) compared to PRE (−19 ± 5%). On the contrary the decline of RFDpeak was more pronounced (p = 0.036) at POST (−33 ± 14%) than at PRE (−20 ± 13%). Thus, the decline of relative RFDpeak resulted more pronounced (p = 0.007) at POST (−25 ± 13%) than at PRE (−3 ± 13%), see Fig. 2 for details. 3.3.2. Overlapped time intervals of RFD The decline of relative RFD was more pronounced (all p values < 0.05) at POST (−27%, −24%) than at PRE (−5%, −2%) in the time intervals 0–100 and 0–150, respectively (see Fig. 3). Conversely, the decline of absolute RFD (all overlapped time intervals) did not reached statistically significant differences, despite RFD150 (p = 0.075) decreased more at POST than at PRE, showing a medium effect size d, see Table 2. 3.3.3. Electromyographic activity No differences in the decline ARV calculated in correspondence of maximal force (ARVmax) was found (p = 0.973) between PRE (−14 ± 22%) and POST (−13 ± 25%). The decline of ARV during the rising phase of the force was more pronounced (p values < 0.05) at POST (−46% and −44%) than at PRE (−24% and −26%) in the time intervals 0–100 and 0–150, respectively (see Table 2). No difference was detected in the decline of muscle fiber CV across repetitions in correspondence of maximal force (CVmax p = 0.340) and in correspondence of the first 150 ms of contraction (CV150 p = 0.405), see Table 2. 4. Discussion We examined whether the fatigue induced by a half-marathon run influenced the capacities to generate force across 15 intermittent explosive contractions. The main findings of this study were that the prolonged run resulted in 1) a decline of maximal force and RFD by similar amounts when measured on single maximal contraction attempt 2) a more pronounced decrement of RFD compared to maximal force when measured across the intermittent protocol composed by 15 repetitions. 4.1. Effects of half marathon run on muscle function After the half-marathon run the maximal force and rapid force production decreased substantially (maximal force: −22 ± 6% and RFDpeak: −24 ± 22%), confirming that the knee extensor muscles were in fatigued condition at the beginning of POST session (Fig. 1). The relative peak of RFD did not change after the run (Table 1), this is an original result demonstrating that rapid and maximal force production were equally affected by fatigue induced by prolonged run. Considering the impairment in maximal force, Ross and colleagues (Ross, Goodall, Stevens, & Harris, 2010) reported a less pronounced decrease of 15% in knee extensors strength (accompanied by a decrease of 20% in EMG amplitude) after 20 km laboratory time trial. Peterson and colleagues (Petersen, Hansen, Aagaard, & Madsen, 2007) found a knee extensors strength loss similar to our study, but they tested a group of athletes after an official marathon race (42.192 km), i.e. a trial twofold the length of the one herein adopted. Thus, our results showed a slightly more pronounced strength loss with respect to the expected results. This is likely to be caused by two reasons. Firstly, we used the same contraction (i.e. the first of the 15 repetitions) to determine the maximal force and RFD. In all repetitions participants was encouraged to contract “as fast and as hard as possible” with the emphasis on the “fast/explosive/rising” phase of contraction (Maffiuletti et al., 2016). Thus, even if participants were encouraged to achieve high peak forces, the maximal force obtained in the first contraction might be possibly lower than that obtained when attempting to achieve only maximal force, without emphasis on explosive phase (Maffiuletti et al., 2016). Secondly, even if the request for maximal force production should not allow for pacing strategies, knowing 271
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Fig. 3. The percent decline (mean ± SD) (last vs first contraction) of PRE (white) and POST (grey) sessions are reported for the relative rate of force development (RFD), that is RFD normalized to maximal voluntary contraction force (MVCF). The values are reported for four overlapping time intervals (0–50 ms, 0–100 ms, and 0–150 ms). The decline of relative RFD was more pronounced at POST than at PRE, highlighting the rate of force development decrease more than maximal force. Differences PRE vs POST are reported as *p < 0.05; ***p < 0.001. Statistically significant difference from 0 (indicating a decline across the 15 contractions) are reported as #p < 0.05; ##p < 0.01.
the number of maximal contractions requested could induce to utilize a pacing strategy (Halperin, Aboodarda, Basset, Byrne, & Behm, 2014). In turn this pacing strategy might produce in the firsts contractions less force and EMG than a couple of maximal contraction usually adopted to test fatigue after endurance events. However, we put great attention to motivate participants to exerted their maximal effort. Indeed, participants received a visual feedback of exerted torque and strong verbal encouragements were provided by 272
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Table 2 Decline (last vs first contraction) of mechanical and electromyographic values (%). Mechanical
PRE
POST
Max. force RFDpeak RFD50 RFD100 RFD150
−19 −20 −10 −23 −19
EMG ARVmax CVmax ARV50 ARV100 ARV150 CV150
−14 ± 22 −9 ± 19 −31 ± 47 −24 ± 38 −26 ± 29 −7 ± 22
± ± ± ± ±
5 13 57 27 18
−10 −33 −18 −34 −33
± ± ± ± ±
15 14 64 37 24
−13 ± 25 −3 ± 11 −41 ± 43 −46 ± 27 −44 ± 27 −1 ± 9
Difference
p value
Cohen’s d
9 ± 14 −13 ± 17 −8 ± 62 −11 ± 29 −14 ± 23
0.087 0.036 0.691 0.252 0.075
0.80 (large) −0.96 (large) −0.13 (trivial) −0.33 (small) −0.65 (medium)
−1 ± 30 −6 ± 21 10 ± 43 22 ± 37 18 ± 30 6 ± 25
0.973 0.340 0.430 0.046 0.044 0.405
0.04 (trivial) 0.38 (small) −0.22 (small) −0.66 (medium) −0.64 (medium) 0.35 (small)
Statistically significant differences between PRE vs POST are highlighted in bold.
two operators to reach their highest maximal and explosive force in each contraction. 4.2. Effects of fatigued state on the subsequent intermittent explosive contractions Our findings showed that the time course of maximal and rapid force capacities was dissociated in the presence of fatigue. Our protocol allowed to detect that rapid force production was more influenced by fatigue than maximal force, when monitored over repeated rapid contractions. During the PRE session, undertaken with quadriceps muscle in fresh condition, the time course of maximal force and RFD showed similar rate of change during the intermittent protocol (maximal force declined by 19 ± 5%, RFDpeak declined by 20 ± 13%, Fig. 2A). This was underlined by the time course of relative RFDpeak (normalized to maximal force) which remained, on average, constant throughout the intermittent protocol at PRE. During the intermittent protocol executed at POST, with the muscle in fatigued condition, the decline of peak of RFD was more pronounced (−35 ± 14%), whereas the decline of maximal force showed a less pronounced trend (−10 ± 15%) than at PRE (Fig. 2B). Thus at POST, the time course of relative peak of RFD (normalized to maximal force) resulted in a substantial decrease (−25 ± 13%) throughout the contractions. Importantly, the fatigue-induced changes in RFD after the prolonged run was not evident in the firsts contractions, but only at the end of the 15 contractions (as stated above) of POST session. Thus, one can argue that the fatigue-induced impairment in rapid force capacity may have a stronger effect on multiple rather than on single attempts of maximal voluntary contraction. The herein finding is in line with the study of Buckthorpe et al. (2014) which reported that fatigue exerted a more rapid reduction of RFD than maximal force during 160 intermittent isometric explosive contractions. However, the present study demonstrate that in fatigued state the dissociation between RFD and maximal force occurs in as few as 15 contractions, which are much less than the 160 adopted in the study of Buckthorpe et al. (2014). Overall, these studies suggested that the effects of fatigue on maximal and explosive phase of muscle contraction is likely to be different. Also the mechanisms underpinning the fatigue-induced changes in maximal and explosive force were potentially different (Andersen & Aagaard, 2006). 4.3. Electromyographic activity The more pronounced decline of RFD at POST, was accompanied by a more pronounced decline of EMG activity during the same time intervals (Table 2). Thus, these findings suggest that the central motor command in the early phase of muscle contraction might have a role in the more pronounced decline of RFD in fatigued condition. This is in accordance with previous study reporting a substantial impairment of explosive neural drive during intermittent explosive contractions (Buckthorpe et al., 2014). Although the amplitude of the surface EMG increases with neural drive, there are several cofounding factors that prevent to directly associate a change in EMG amplitude with a change in neural drive, especially during fatiguing contractions (Farina, Merletti, & Enoka, 2004). Thus, since we did not adopt electrically evoked contraction, which would be essential to distinguish the origins of fatigue, we can only speculate this finding on the base of EMG amplitude. Noteworthy, contractile mechanisms cannot be excluded on the base of EMG results, since changes in muscle excitability might have affected EMG amplitude. 4.4. Limitations and methodological considerations In this study, participants were not requested to exert pure short fast contractions, but rather fast and maximal contractions. This type of precedure is largely adopted in the literature (Aagaard et al., 2002; Buckthorpe et al., 2014) and allows the continuous monitoring of both explosive and maximal strength during a series of contractions. We cannot directly speculate that the results would be the same if we would have adopted short fast contractions. However, it is unlikely that the main results of the study, i.e. the more pronounced decrements of explosive than maximal strength, would differ. The inability to distinguish between central and peripheral fatigue is a major limitation of this study. Due to the ecological nature 273
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of the study, we could not apply the electrically evoked contraction that would be necessary to elucidate the origin of muscle fatigue. Since we collected these data in a real race situation, the timing for measurements of each athletes at the end of the race was dramatically short, about 3–4 min. As stated before, on the base of the EMG results it is possible to suppose that central fatigue might have an important role on the decay of explosive force after the race, muscle contractile impairments cannot however be excluded. In this study, we recruited a group of amateur runners, which training programs in the past years were mainly focused on endurance and aerobic metabolism. However, the sample chosen for the study may have affected the results. Indeed, has been demonstrated that power athletes, i.e. those who undertake sport training to compete in strength and power disciplines, compared to endurance athletes, showed higher rate of mechanical fatigue during intermittent dynamic contractions (Rainoldi, Gazzoni, Merletti, & Minetto, 2008). Thus, one can expect that the relationship between rate of force development and maximal force may be different in strength/power trained participants. Multichannel surface EMG allows the estimation of muscle fiber conduction velocity, which seems to be the most affordable variable to relate the modifications in EMG signals with the recruited motor units pool (Farina et al., 2004). Despite we previously demonstrated that the changes in muscle fiber conduction velocity was correlated to the changes in muscle force after a prolonged run (Boccia et al., 2017), in the present study the vastus lateralis did not show any muscle fiber conduction velocity changes. The use of multichannel EMG to non-invasively characterise muscle fatigue cannot thus be recommended on the base of the herein findings. Indeed, it did not provide sensitive measurements to deepen the understanding of the origin of fatigue. However, beyond estimating muscle fiber conduction velocity, multichannel EMG allowed to analyze EMG signals from the best muscle location (Beretta Piccoli et al., 2014) and to average the estimates of the EMG amplitude across multiple channels, thus providing more consistent results with respect to the bipolar montage (Farina et al., 2004). 5. Conclusions We monitored the time course of maximal and explosive force capacities during the execution of 15 intermittent explosive contractions of the knee extensors, before and after a half-marathon run. Our results revealed that fatigue induced by prolonged run 1) equally affected rapid and maximal force production when measured on single attempt 2) caused a more pronounced decrements of rapid compared to maximal force when measured across repeated muscle contraction. This indicate that fatigue influences differently the maximal and explosive force, and that these two capacities should not be used as interchangeable indices to determine the effects of fatigue. Conflict of interest Authors declare no conflict of interest. Acknowledgments The authors wish to thank all the athletes who participated to the study. We know that it was not easy and comfortable to take part in POST session. We also thank Lorenzo Bortolan for his precious help with technical issues, and Luna Bonatti, Luca Di Nubila, Luca Ferrari, and Giacomo Faraci for their valuable help in data collection. References Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). 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Warm up I: Potential mechanisms and the effects of passive warm up on exercise performance. Sports Medicine (Auckland, N. Z.), 33, 439–454. Boccia, G., Dardanello, D., Tarperi, C., Rosso, V., Festa, L., La Torre, A., ... Rainoldi, A. (2017). Decrease of muscle fiber conduction velocity correlates with strength loss after an endurance run. Physiological Measurement, 38, 233–240. Boccia, G., Dardanello, D., Zoppirolli, C., Bortolan, L., Cescon, C., Schneebeli, A., ... Pellegrini, B. (2016). Central and peripheral fatigue in knee and elbow extensor muscles after a long-distance cross-country ski race. Scandinavian Journal of Medicine and Science in Sports. Buckthorpe, M., Pain, M. T., & Folland, J. P. (2014). Central fatigue contributes to the greater reductions in explosive than maximal strength with high-intensity fatigue. Experimental Physiology, 99, 964–973. Cheng, A. J., & Rice, C. L. (2005). Fatigue and recovery of power and isometric torque following isotonic knee extensions. Journal of Applied Physiology, 1985(99), 1446–1452. de Ruiter, C. J., Van Leeuwen, D., Heijblom, A., Bobbert, M. F., & de Haan, A. (2006). Fast unilateral isometric knee extension torque development and bilateral jump height. Medicine and Science in Sports and Exercise, 38, 1843–1852. Farina, D., Merletti, R., & Enoka, R. M. (2004). The extraction of neural strategies from the surface EMG. Journal of Applied Physiology, 96, 1486–1495. Halperin, I., Aboodarda, S. J., Basset, F. A., Byrne, J. M., & Behm, D. G. (2014). Pacing strategies during repeated maximal voluntary contractions. European Journal of Applied Physiology, 114, 1413–1420. Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000). Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyography and Kinesiology, 10, 361–374. Krustrup, P., Mohr, M., Steensberg, A., Bencke, J., Kjaer, M., & Bangsbo, J. (2006). 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Full Length Article
Fatigue-induced dissociation between rate of force development and maximal force across repeated rapid contractions
MARK
⁎
Gennaro Bocciaa,b, , Davide Dardanelloa, Cantor Tarperic, Luca Festac, Antonio La Torred, Barbara Pellegrinib,c, Federico Schenab,c, Alberto Rainoldia a
NeuroMuscularFunction Research Group, School of Exercise & Sport Sciences, Department of Medical Sciences, University of Turin, 12, P.za Bernini, 10143 Turin, Italy b CeRiSM Research Center “Sport, Mountain, and Health”, via del Ben 5/b, 38068 Rovereto, TN, Italy c School of Sport and Exercise Sciences, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Via Casorati 43, 37137 Verona, Italy d Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
AR TI CLE I NF O
AB S T R A CT
Keywords: Intermittent contractions Neuromuscular fatigue Multichannel electromyography
We examined whether the presence of fatigue induced by prolonged running influenced the time courses of force generating capacities throughout a series of intermittent rapid contractions. Thirteen male amateur runners performed a set of 15 intermittent isometric rapid contractions of the knee extensor muscles, (3 s/5 s on/off) the day before (PRE) and immediately after (POST) a half marathon. The maximal voluntary contraction force, rate of force development (RFDpeak), and their ratio (relative RFDpeak) were calculated. At POST, considering the first (out of 15) repetition, the maximal force and RFDpeak decreased (p < 0.0001) at the same extent (by 22 ± 6% and 24 ± 22%, respectively), resulting in unchanged relative RFDpeak (p = 0.6). Conversely, the decline of RFDpeak throughout the repetitions was more pronounced at POST (p = 0.02), thus the decline of relative RFDpeak was more pronounced (p = 0.007) at POST (−25 ± 13%) than at PRE (−3 ± 13%). The main finding of this study was that the fatigue induced by a half-marathon caused a more pronounced impairment of rapid compared to maximal force in the subsequent intermittent protocol. Thus, the fatigue-induced impairment in rapid muscle contractions may have a greater effect on repeated, rather than on single, attempts of maximal force production.
1. Introduction Repeated high-force contractions of skeletal muscles cause a decline in force-generating capacity, referred to as muscle fatigue (Bigland-Ritchie & Woods, 1984). During exercise of maximal intensity, fatigue result into a decline of force (BiglandRitchie & Woods, 1984) or power (Cheng & Rice, 2005). Research investigating the influence of an exercise-induced fatigue on the neuromuscular function focused mainly on the decline in maximal voluntary contraction force. However, the effect of fatigue on the ability to produce force rapidly, also referred to as explosive strength (Maffiuletti et al., 2016), has received less attention despite its
Abbreviations: ANOVA, analysis of variance; ARV, average rectified value; CV, muscle fiber conduction velocity; EMG, electromyography; MVC, maximal voluntary contraction; RFD, rate of force development ⁎ Corresponding author at: NeuroMuscularFunction Research Group, School of Exercise & Sport Sciences, Department of Medical Sciences, University of Turin, 12, P.za Bernini, 10143 Turin, Italy. E-mail address: [email protected] (G. Boccia). http://dx.doi.org/10.1016/j.humov.2017.05.016 Received 29 November 2016; Received in revised form 23 May 2017; Accepted 31 May 2017 0167-9457/ © 2017 Elsevier B.V. All rights reserved.
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importance for the production of many movements. Particularly, the rate of force development (RFD) is functionally more informative than maximal force when considering sports in which rapid movements are important, such as running, jumping, or kicking (Aagaard, Simonsen, Andersen, Magnusson, & Dyhre-Poulsen, 2002; de Ruiter, Van Leeuwen, Heijblom, Bobbert, & de Haan, 2006). Muscle fatigue can impair the explosive strength (Buckthorpe, Pain, & Folland, 2014) which in turn can negatively influence explosive sport activities (Krustrup et al., 2006; Mohr, Krustrup, & Bangsbo, 2003; Zoppirolli, Pellegrini, Bortolan, & Schena, 2016). Moreover, maintaining the ability to produce high RFD values is important to limit the risks of injury (Minshull, Gleeson, Walters Edwards, Eston, & Rees, 2007). Therefore, an understanding of how fatigue affects rapid force production would seem important in understanding its influence on athletic performance and injury risk. Maximal force and RFD should not be used as interchangeable indices when assessing high-intensity muscle fatigue. Indeed, it has recently been demonstrated a more pronounced drop in rapid compared with maximal force production during fatiguing protocols constituted by intermittent explosive isometric contraction (Buckthorpe et al., 2014). Similar disproportionate drop in explosive than maximal torque was recently found also using dynamic contractions (Morel et al., 2015). Particularly, these studies, which was constituted by high-force fatiguing protocols, showed that the early phase (0–50 ms) of explosive muscle contraction seemed to be the most susceptible to muscle fatigue (Buckthorpe et al., 2014; Morel et al., 2015). The relationship between maximal force and RFD decrements also varied among muscles when used to assess muscle fatigue induced by prolonged cross-country skiing (Boccia et al., 2016). Given the different influence of muscle fatigue on rapid and maximal force production we aimed to determine the influence of muscle conditions at the beginning of the intermittent protocol on the time course of force generating capacities. We compared the time course of maximal force and RFD across a set of 15 intermittent rapid isometric contractions executed in two conditions: before, i.e. fresh condition, and after a half-marathon run (21.097 m), i.e. fatigued condition. We hypothesized that the ratio between the time course of maximal force and RFD would change in fatigued condition. 2. Methods 2.1. Participants For this specific study 14 amateur male runners were recruited (age 36 ± 8, body weight 74 ± 10 kg, height 173 ± 8 cm) who successfully concluded a half-marathon run (21.097 km). All participants were habitually involved in amateur running with a mean training regimen of 220 min/week. None of them had clinical evidences of cardiovascular, neuromuscular, or joint diseases. Participants were instructed to refrain from performing strenuous physical activity in the 24 h before the first experimental session. All participants provided their written informed consent before participation in the investigation. The study was approved by the local Ethical Committee (Department of Neurological and Movement Sciences, University of Verona) and performed in accordance with the Helsinki Declaration. 2.2. General overview The study was performed during a specific event called “Run For Science”, held in Verona (Italy) in April 2016. Participants were involved in two measurement sessions: the first was performed the day before the race (PRE), and the second immediately after the race (POST). The neuromuscular test consisted in a set of isometric explosive maximal voluntary contractions (MVCs) of the knee extensors. Force and electromyographic (EMG) measurements were obtained from the rapid (rising) and maximal (plateau) phases of the MVCs. During the PRE session, participants were familiarized with the procedures. For that purpose, they repeated a number of trials of the test procedures until they were able to produce consistent results. In the PRE session participants performed 15-min of a standardised warm-up (details are given below) before neuromuscular testing. In the POST session the neuromuscular assessment started within 10 min after the race. A researcher was positioned at the finishing line to conduct the runners to the testing site, which was located about 50 m from the finishing line. The testing session at POST lasted 3–4 min. 2.3. Procedure 2.3.1. Warm-up The warm-up at PRE consisted of 15-min of outdoor running at an incremental intensity from 75% to 90% of the maximal heart rate previously determined by an incremental test. The duration of the warm-up was chosen based on previous studies showing that muscle temperature rises rapidly after 5 min and reaches an equilibrium after 15 min (Bishop, 2003). 2.3.2. Force measurement Participants were seated on a custom-made chair that allowed the assessment of the knee extensors, and straps were fastened across the chest and hips to avoid lateral and frontal displacements. During the testing, participants’ knee and hip were flexed at 90° from full extension and they were instructed to maintain the arms crossed on the chest. The knee extensors mechanical response was recorded with a strain gauge load cell (546QD-220 kg; DSEurope, Milan, Italy), fixed with non-compliant straps at the level of the external malleolus. All measurements were taken from the participants’ right limbs (which was the dominant limb for 13 out of 14 participants). The force signals were sampled at 2048 Hz together with EMG signals and converted to digital data with a 16-bit A/D converter (EMG-USB2, OT Bioelettronica, Turin, Italy). 268
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The intermittent protocol comprised 15 explosive maximal voluntary contractions each lasting 3 s, and interspersed by 5 s of rest. In response to an audio and visual signal, participants were instructed to push as fast and as hard as they can and to maintain this throughout the contraction for 3 s (Aagaard et al., 2002). During the protocol, participants received a visual feedback of exerted torque and strong verbal encouragements were provided by two operators to reach his/her highest level in each contraction. Following each explosive MVC, they were instructed to relax quickly in order to return to the resting baseline force. The total duration of the intermittent protocol was 2 min, with 45 s of maximal voluntary effort.
2.3.3. Electromyographic measurements EMG signals were recorded from the vastus lateralis muscle in a single differential configuration using linear adhesive electrode arrays composed of eight electrodes (silver bars, 5 × 1 mm in size, 5 mm inter-electrode distance, OT Bioelettronica, Turin, Italy). Before the placement of the electrode arrays, the skin was slightly abraded with abrasive paste and cleaned with water in accordance with SENIAM recommendations for skin preparation (Hermens, Freriks, Disselhorst-Klug, & Rau, 2000). The optimal position and orientation of the array were sought for each muscle by visual inspection of the EMG signals and following guidelines previously described (Beretta Piccoli et al., 2014). The sites with clear muscle fiber action potential propagation and the main innervation zones were identified using a linear array of 16 electrodes with 5-mm inter-electrode distance (OT Bioelettronica, Turin, Italy). The adhesive electrode arrays were then placed parallel to the muscle fibres where unidirectional propagation of the motor unit action potentials was detected. To ensure proper electrode-skin contact, the electrode cavities of the arrays were filled with 20–30 μL of conductive paste (Spes-Medica, Battipaglia, Italy). The electrode arrays were fixed with an extensible dressing (Fixomull®, Beiersdorf, Hamburg, Germany). The position of the electrodes was marked on the skin so that they could be positioned in the same place after the race. The EMG signals were amplified, sampled at 2048 Hz, bandpass filtered (3-dB bandwidth, 20–450 Hz) and converted to digital data with a 16-bit A/D converter (EMG-USB2, OT Bioelettronica, Turin, Italy). Signals were visualized during acquisition and then stored on a personal computer using OT BioLab software version 1.8 (OT Bioelettronica, Turin, Italy) for further analysis.
2.4. Signal processing 2.4.1. Force signals Force signals were firstly smoothed using a moving average filter with a window of 51 samples. Maximum force was determined for each contraction and the time instant at which it occurred was used for successive EMG analysis. For the analysis of the explosive phase of contraction, the force signal of all contractions was visually checked. The repetitions were included in the analysis only there was not evident a countermovement, or a pre-tension in the 100 ms preceding the onset of contraction. RFD (change in force divided by the change in time) was quantified for consecutive 50 ms time intervals: 0–50 ms (RFD50), 0–100 ms (RFD100), and 0–150 ms (RFD150). Peak of RFD (RFDpeak) was also determined using a moving 20-ms window throughout the force–time curve as the highest RFD value at any time during the contraction. The RFD were quantified in absolute terms (N/s) and normalized to the maximal force (relative RFD) recorded within the same contraction (%/s).
2.4.2. Electromyographic signals The EMG signals were visually inspected off-line to select the best channels for estimating variables (Merletti, Knaflitz, & De Luca, 1990). The average muscle fiber conduction velocity (CV) was estimated among all the accepted channels and computed as e/d, where e is the inter-electrode distance and d is the delay time between the signals obtained from the two double differential arrays spaced 5 mm apart (Merletti et al., 1990). The correlation coefficient between the two adjacent double differential signals was calculated; if the correlation coefficient was < 0.8, the recorded signals were excluded from the analysis. The amplitude of the EMG signal was assessed as the average rectified value (ARV) during the same overlapped intervals of explosive forces (ARV50, ARV100, and ARV150) and maximal force (500 ms epoch centred in instant of maximal force, ARVmax). ARV values signals were averaged among all the accepted EMG channels to increase the robustness of the estimates. Data were analyzed by custom-written software in MATLAB R2014a (Mathworks, Natick, Massachusetts).
2.5. Statistical analysis The time course of force and EMG parameters was analyzed by applying linear or exponential regression analysis (on the base of best fitting) to the data calculated across the 15 repetitions. The first and last value of each variable was calculated as the intercept of the regression line in the first and the last contraction, respectively. The decline of force and EMG variables across the 15 contractions was calculated as the percent ratio between the last and first contraction (expressed as %). Kolmogorov-Smirnov normality test was used to assess distributions normality. If the data were not normally distributed were log-transformed before statistical analysis and back-transformed to obtain descriptive statistics. Paired Student’s t tests were then used to compare the maximal force, RFD, and EMG values between PRE and POST sessions. Threshold for statistical significance was set to p < 0.05. Statistical analyses were performed with SPSS statistics (version 20.0, IBM Corporation, Somers, NY). Data are all expressed as mean ± SD. The magnitude of the difference was calculated as Cohen’s d effect size. Threshold values for effect size statistics were: < 0.2, trivial; > 0.2, small; > 0.6, moderate; > 1.2 large; > 2.0, very large (Batterham & Hopkins, 2006). 269
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Fig. 1. Representative example of the time course of force (left panel) and rate of force development (right panel) of 15 intermittent explosive contractions lasting 3 s, and separated by 5 s of rest, at PRE session. It is evident the decline of both maximal force exerted in each contraction as well as the decline in the rate of force developed in the raising phase of contraction.
3. Results Out of the 14 initially recruited participants, one did not perform the POST session because he dropped out of the race. Thus, data are reported for 13 participants which completed the 21.097 km race in a time ranged from 1 h 25 min to 1 h 50 min. 3.1. Curve fitting of mechanical data across the intermittent protocol Maximal force and RFDpeak data across the 15 contractions was analyzed with exponential and linear fitting. While at PRE maximal force was better fitted by exponential than linear regression (exponential r2 = 0.79 ± 0.18; linear r2 = 0.75 ± 0.17; p = 0.002), at POST exponential and linear fitting showed similar goodness (exponential r2 = 0.49 ± 0.36; linear r2 = 0.48 ± 0.37; p = 0.196) (Fig. 2). RFDpeak data did not show goodness differences between exponential and linear fitting both at PRE (exponential r2 = 0.47 ± 0.30; linear r2 = 0.46 ± 0.30; p > 0.578) and POST (exponential r2 = 0.42 ± 0.28; linear r2 = 0.42 ± 0.27; p > 0.731) (Fig. 2).
3.2. Effects of half marathon run on muscle function The maximal force decreased by 22 ± 6% (p < 0.0001) and similarly RFDpeak decreased by 24 ± 22% (p < 0.0001) from PRE to POST. RFD calculated in all time intervals showed large to very large decrease (all p values < 0.0001), see Table 1 for details. Small to large decrease in EMG amplitude was recorded in correspondence of maximal force (ARVmax −26%, p = 0.023) and RFD (ranged −21% to −30%), see Table 1 for details. No differences were detected in muscle fiber CV in correspondence of maximal force (CVmax p = 0.188) and during the first 150 ms of contraction (CV150 p = 0.370), see Table 1.
Fig. 2. Time courses (mean ± SD) of peak force (black) and peak of rate of force development (RFD, white) normalized with respect to the first contraction performed at PRE session. PRE session was performed the day before, and POST session was performed after (with 10 min of recovery) a 21-km running time trial. At PRE the decline of maximal force and rate of force development was similar, whereas at POST the decline of rate of force development was more pronounced than maximal force.
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Table 1 Decrements (PRE vs POST) of mechanical and electromyographic values calculated in the first contraction of the intermittent protocol. Mechanical
PRE
POST
Difference
p value
Cohen’s d
Max. force RFDpeak RFD50 RFD100 RFD150
(N) (N/s) (N/s) (N/s) (N/s)
257 ± 51 1522 ± 305 844 ± 356 1292 ± 321 1172 ± 235
193 ± 47 1130 ± 300 539 ± 240 907 ± 292 854 ± 243
−24 ± 15 −392 ± 221 −291 ± 226 −391 ± 276 −315 ± 170
< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001
−1.30 −1.30 −1.04 −1.21 −1.32
(very large) (very large) (large) (large) (very large)
EMG ARVmax CVmax ARV50 ARV100 ARV150 CV150
(μV) (m/s) (μV) (μV) (μV) (m/s)
259 ± 106 4.3 ± 0.5 224 ± 125 257 ± 118 272 ± 117 3.9 ± 0.5
190 ± 66 4.0 ± 0.5 177 ± 80 184 ± 65 193 ± 63 3.7 ± 0.5
−69 ± 85 −0.3 ± 0.7 −47 ± 87 −73 ± 97 −78 ± 104 0.2 ± 0.7
0.023 0.188 0.071 0.019 0.019 0.370
−0.78 −0.66 −0.44 −0.76 −0.84 −0.39
(medium) (medium) (small) (medium) (large) (small)
3.3. Effects of running induced fatigue on intermittent explosive contractions 3.3.1. Maximal force and RFDpeak The percent decline of maximal force across the 15 contractions was non-statistically different (p = 0.087) at POST (−10 ± 15%) compared to PRE (−19 ± 5%). On the contrary the decline of RFDpeak was more pronounced (p = 0.036) at POST (−33 ± 14%) than at PRE (−20 ± 13%). Thus, the decline of relative RFDpeak resulted more pronounced (p = 0.007) at POST (−25 ± 13%) than at PRE (−3 ± 13%), see Fig. 2 for details. 3.3.2. Overlapped time intervals of RFD The decline of relative RFD was more pronounced (all p values < 0.05) at POST (−27%, −24%) than at PRE (−5%, −2%) in the time intervals 0–100 and 0–150, respectively (see Fig. 3). Conversely, the decline of absolute RFD (all overlapped time intervals) did not reached statistically significant differences, despite RFD150 (p = 0.075) decreased more at POST than at PRE, showing a medium effect size d, see Table 2. 3.3.3. Electromyographic activity No differences in the decline ARV calculated in correspondence of maximal force (ARVmax) was found (p = 0.973) between PRE (−14 ± 22%) and POST (−13 ± 25%). The decline of ARV during the rising phase of the force was more pronounced (p values < 0.05) at POST (−46% and −44%) than at PRE (−24% and −26%) in the time intervals 0–100 and 0–150, respectively (see Table 2). No difference was detected in the decline of muscle fiber CV across repetitions in correspondence of maximal force (CVmax p = 0.340) and in correspondence of the first 150 ms of contraction (CV150 p = 0.405), see Table 2. 4. Discussion We examined whether the fatigue induced by a half-marathon run influenced the capacities to generate force across 15 intermittent explosive contractions. The main findings of this study were that the prolonged run resulted in 1) a decline of maximal force and RFD by similar amounts when measured on single maximal contraction attempt 2) a more pronounced decrement of RFD compared to maximal force when measured across the intermittent protocol composed by 15 repetitions. 4.1. Effects of half marathon run on muscle function After the half-marathon run the maximal force and rapid force production decreased substantially (maximal force: −22 ± 6% and RFDpeak: −24 ± 22%), confirming that the knee extensor muscles were in fatigued condition at the beginning of POST session (Fig. 1). The relative peak of RFD did not change after the run (Table 1), this is an original result demonstrating that rapid and maximal force production were equally affected by fatigue induced by prolonged run. Considering the impairment in maximal force, Ross and colleagues (Ross, Goodall, Stevens, & Harris, 2010) reported a less pronounced decrease of 15% in knee extensors strength (accompanied by a decrease of 20% in EMG amplitude) after 20 km laboratory time trial. Peterson and colleagues (Petersen, Hansen, Aagaard, & Madsen, 2007) found a knee extensors strength loss similar to our study, but they tested a group of athletes after an official marathon race (42.192 km), i.e. a trial twofold the length of the one herein adopted. Thus, our results showed a slightly more pronounced strength loss with respect to the expected results. This is likely to be caused by two reasons. Firstly, we used the same contraction (i.e. the first of the 15 repetitions) to determine the maximal force and RFD. In all repetitions participants was encouraged to contract “as fast and as hard as possible” with the emphasis on the “fast/explosive/rising” phase of contraction (Maffiuletti et al., 2016). Thus, even if participants were encouraged to achieve high peak forces, the maximal force obtained in the first contraction might be possibly lower than that obtained when attempting to achieve only maximal force, without emphasis on explosive phase (Maffiuletti et al., 2016). Secondly, even if the request for maximal force production should not allow for pacing strategies, knowing 271
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Fig. 3. The percent decline (mean ± SD) (last vs first contraction) of PRE (white) and POST (grey) sessions are reported for the relative rate of force development (RFD), that is RFD normalized to maximal voluntary contraction force (MVCF). The values are reported for four overlapping time intervals (0–50 ms, 0–100 ms, and 0–150 ms). The decline of relative RFD was more pronounced at POST than at PRE, highlighting the rate of force development decrease more than maximal force. Differences PRE vs POST are reported as *p < 0.05; ***p < 0.001. Statistically significant difference from 0 (indicating a decline across the 15 contractions) are reported as #p < 0.05; ##p < 0.01.
the number of maximal contractions requested could induce to utilize a pacing strategy (Halperin, Aboodarda, Basset, Byrne, & Behm, 2014). In turn this pacing strategy might produce in the firsts contractions less force and EMG than a couple of maximal contraction usually adopted to test fatigue after endurance events. However, we put great attention to motivate participants to exerted their maximal effort. Indeed, participants received a visual feedback of exerted torque and strong verbal encouragements were provided by 272
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Table 2 Decline (last vs first contraction) of mechanical and electromyographic values (%). Mechanical
PRE
POST
Max. force RFDpeak RFD50 RFD100 RFD150
−19 −20 −10 −23 −19
EMG ARVmax CVmax ARV50 ARV100 ARV150 CV150
−14 ± 22 −9 ± 19 −31 ± 47 −24 ± 38 −26 ± 29 −7 ± 22
± ± ± ± ±
5 13 57 27 18
−10 −33 −18 −34 −33
± ± ± ± ±
15 14 64 37 24
−13 ± 25 −3 ± 11 −41 ± 43 −46 ± 27 −44 ± 27 −1 ± 9
Difference
p value
Cohen’s d
9 ± 14 −13 ± 17 −8 ± 62 −11 ± 29 −14 ± 23
0.087 0.036 0.691 0.252 0.075
0.80 (large) −0.96 (large) −0.13 (trivial) −0.33 (small) −0.65 (medium)
−1 ± 30 −6 ± 21 10 ± 43 22 ± 37 18 ± 30 6 ± 25
0.973 0.340 0.430 0.046 0.044 0.405
0.04 (trivial) 0.38 (small) −0.22 (small) −0.66 (medium) −0.64 (medium) 0.35 (small)
Statistically significant differences between PRE vs POST are highlighted in bold.
two operators to reach their highest maximal and explosive force in each contraction. 4.2. Effects of fatigued state on the subsequent intermittent explosive contractions Our findings showed that the time course of maximal and rapid force capacities was dissociated in the presence of fatigue. Our protocol allowed to detect that rapid force production was more influenced by fatigue than maximal force, when monitored over repeated rapid contractions. During the PRE session, undertaken with quadriceps muscle in fresh condition, the time course of maximal force and RFD showed similar rate of change during the intermittent protocol (maximal force declined by 19 ± 5%, RFDpeak declined by 20 ± 13%, Fig. 2A). This was underlined by the time course of relative RFDpeak (normalized to maximal force) which remained, on average, constant throughout the intermittent protocol at PRE. During the intermittent protocol executed at POST, with the muscle in fatigued condition, the decline of peak of RFD was more pronounced (−35 ± 14%), whereas the decline of maximal force showed a less pronounced trend (−10 ± 15%) than at PRE (Fig. 2B). Thus at POST, the time course of relative peak of RFD (normalized to maximal force) resulted in a substantial decrease (−25 ± 13%) throughout the contractions. Importantly, the fatigue-induced changes in RFD after the prolonged run was not evident in the firsts contractions, but only at the end of the 15 contractions (as stated above) of POST session. Thus, one can argue that the fatigue-induced impairment in rapid force capacity may have a stronger effect on multiple rather than on single attempts of maximal voluntary contraction. The herein finding is in line with the study of Buckthorpe et al. (2014) which reported that fatigue exerted a more rapid reduction of RFD than maximal force during 160 intermittent isometric explosive contractions. However, the present study demonstrate that in fatigued state the dissociation between RFD and maximal force occurs in as few as 15 contractions, which are much less than the 160 adopted in the study of Buckthorpe et al. (2014). Overall, these studies suggested that the effects of fatigue on maximal and explosive phase of muscle contraction is likely to be different. Also the mechanisms underpinning the fatigue-induced changes in maximal and explosive force were potentially different (Andersen & Aagaard, 2006). 4.3. Electromyographic activity The more pronounced decline of RFD at POST, was accompanied by a more pronounced decline of EMG activity during the same time intervals (Table 2). Thus, these findings suggest that the central motor command in the early phase of muscle contraction might have a role in the more pronounced decline of RFD in fatigued condition. This is in accordance with previous study reporting a substantial impairment of explosive neural drive during intermittent explosive contractions (Buckthorpe et al., 2014). Although the amplitude of the surface EMG increases with neural drive, there are several cofounding factors that prevent to directly associate a change in EMG amplitude with a change in neural drive, especially during fatiguing contractions (Farina, Merletti, & Enoka, 2004). Thus, since we did not adopt electrically evoked contraction, which would be essential to distinguish the origins of fatigue, we can only speculate this finding on the base of EMG amplitude. Noteworthy, contractile mechanisms cannot be excluded on the base of EMG results, since changes in muscle excitability might have affected EMG amplitude. 4.4. Limitations and methodological considerations In this study, participants were not requested to exert pure short fast contractions, but rather fast and maximal contractions. This type of precedure is largely adopted in the literature (Aagaard et al., 2002; Buckthorpe et al., 2014) and allows the continuous monitoring of both explosive and maximal strength during a series of contractions. We cannot directly speculate that the results would be the same if we would have adopted short fast contractions. However, it is unlikely that the main results of the study, i.e. the more pronounced decrements of explosive than maximal strength, would differ. The inability to distinguish between central and peripheral fatigue is a major limitation of this study. Due to the ecological nature 273
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of the study, we could not apply the electrically evoked contraction that would be necessary to elucidate the origin of muscle fatigue. Since we collected these data in a real race situation, the timing for measurements of each athletes at the end of the race was dramatically short, about 3–4 min. As stated before, on the base of the EMG results it is possible to suppose that central fatigue might have an important role on the decay of explosive force after the race, muscle contractile impairments cannot however be excluded. In this study, we recruited a group of amateur runners, which training programs in the past years were mainly focused on endurance and aerobic metabolism. However, the sample chosen for the study may have affected the results. Indeed, has been demonstrated that power athletes, i.e. those who undertake sport training to compete in strength and power disciplines, compared to endurance athletes, showed higher rate of mechanical fatigue during intermittent dynamic contractions (Rainoldi, Gazzoni, Merletti, & Minetto, 2008). Thus, one can expect that the relationship between rate of force development and maximal force may be different in strength/power trained participants. Multichannel surface EMG allows the estimation of muscle fiber conduction velocity, which seems to be the most affordable variable to relate the modifications in EMG signals with the recruited motor units pool (Farina et al., 2004). Despite we previously demonstrated that the changes in muscle fiber conduction velocity was correlated to the changes in muscle force after a prolonged run (Boccia et al., 2017), in the present study the vastus lateralis did not show any muscle fiber conduction velocity changes. The use of multichannel EMG to non-invasively characterise muscle fatigue cannot thus be recommended on the base of the herein findings. Indeed, it did not provide sensitive measurements to deepen the understanding of the origin of fatigue. However, beyond estimating muscle fiber conduction velocity, multichannel EMG allowed to analyze EMG signals from the best muscle location (Beretta Piccoli et al., 2014) and to average the estimates of the EMG amplitude across multiple channels, thus providing more consistent results with respect to the bipolar montage (Farina et al., 2004). 5. Conclusions We monitored the time course of maximal and explosive force capacities during the execution of 15 intermittent explosive contractions of the knee extensors, before and after a half-marathon run. Our results revealed that fatigue induced by prolonged run 1) equally affected rapid and maximal force production when measured on single attempt 2) caused a more pronounced decrements of rapid compared to maximal force when measured across repeated muscle contraction. This indicate that fatigue influences differently the maximal and explosive force, and that these two capacities should not be used as interchangeable indices to determine the effects of fatigue. Conflict of interest Authors declare no conflict of interest. Acknowledgments The authors wish to thank all the athletes who participated to the study. We know that it was not easy and comfortable to take part in POST session. We also thank Lorenzo Bortolan for his precious help with technical issues, and Luna Bonatti, Luca Di Nubila, Luca Ferrari, and Giacomo Faraci for their valuable help in data collection. References Aagaard, P., Simonsen, E. B., Andersen, J. L., Magnusson, P., & Dyhre-Poulsen, P. (2002). 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