Electromyography (EMG) is an experimental technique concerned with the recording and analysis of myoelectric signals. Since the EMG signal detected on the surface of the skin directly reflects the recruitment and firing characteristics of the detected motor units within an area, EMG activity can be used to study the neuromuscular activation of muscles within postural tasks, functional movements, work conditions and treatment/training regimes (Basmajian and De Luca 1985). Furthermore, EMG activity has been correlated on multiple occasions with fatigue-related events occurring within the muscle (Bigland-Ritchie et al. 1986a; Bigland-Ritchie and Woods 1984; Moritani et al. 1986; Nordlund et al. 2004). Other chapters in this book describe the use of EMG for various applications. In the exercise sciences, EMG activity is typically used to explore muscle recruitment strategies (
However, studies have mainly explored constant work-rate tests, along with incremental tests to maximum effort. In these examples, ecological validity has been limited due to work rate being either fully or partly dictated by the protocol; this excludes the individual subjective assessment of the task. Because performance in competitive events depends largely upon pacing strategies (Figure 3) (Billaut et al. 2011; Hettinga et al. 2006; Palmer et al. 1997; Paterson and Marino 2004; St Clair Gibson et al. 2006), it is necessary to investigate the neuromuscular responses to self-paced exercise to further understand the role of the central nervous system (CNS) in the regulation of exercise performance. Thus, a more realistic paradigm for future research in the exercise sciences is one that would permit the individual to use sensory cues to adjust the effort along with the fatigue process. In fact, Marino and colleagues (2011) recently re-emphasised that bringing the brain (and subsequent muscle recruitment strategies) into modern fatigue research represents the next phase in the unravelling of the fatigue process.
Traditionally, endurance exercise has been researched extensively but our understanding of the factors that regulate muscle recruitment during very high-intensity exercise is much poorer. This is surprising since
newly-developed training regimes targeted at improving health include repetitions of sprint exercise (Gibala 2007).
Certainly, a greater understanding of neural recruitment strategies during high-intensity tasks would lead to better training programs to enhance fitness in athletes and patient populations.
Performing a sprint at “all-out” intensity requires very high levels of neural drive (typically +/– 5000 µV in elite sprinters) (Ross et al. 2001); therefore failure to activate fully the contracting musculature can theoretically decrease force and power production and, thereby, impair the ability to sprint. Whilst not a well-studied mode of exercise, several studies have demonstrated that the fatigue that develops during single and repeated sprints is associated with changes in muscle recruitment strategies which ultimately originate within the CNS. The aims of this chapter are
to use most recent data to describe the behaviour of the EMG signal (serving as a surrogate for muscle recruitment) during sprint exercise using traditional and innovative analysis techniques, and
to give some insights into the main mechanisms thought to contribute to the regulation of muscle recruitment and the fatigue process during sprint exercise.
2. A contemporary view of the fatigue phenomenon
For over a century, neuromuscular fatigue has been viewed and researched as a finite quantity of essential (metabolic and/or cardiovascular) resources causing exhaustion, independent of any regulation by the CNS (Allen et al. 1995; Bassett and Howley 2000; Fitts 1994; Hill 1924; Shephard 2009). This view has encouraged the interpretation that exercise results in linear changes in metabolism, in energy provision, and in the cardiovascular, respiratory, thermoregulatory, and hormonal responses, among many others. Ultimately, demand exceeds capacity in one or more systems, which causes them to fail. As a result, this failure to maintain homeostasis in the active muscles causes the termination of exercise. Overall, this has produced a “brainless” physiology (Marino 2004; Marino et al. 2011; Noakes 2011; Noakes et al. 2001) that is still currently taught in most exercise science classes throughout the world. In contrast, increasing evidence has accumulated in the last few years suggesting an anticipatory regulation of exercise intensity (Billaut et al. 2011; Kayser 2003; Marino 2004; Noakes 2011; Noakes et al. 2001; Noakes and St Clair Gibson 2004; St Clair Gibson et al. 2006; St Clair Gibson and Noakes 2004; Tucker et al. 2004). This mechanism allows feedback from varied sources to influence the magnitude of the feed-forward neural drive that determines the quantity of muscle mass recruited (
In this perspective, the results from several recent studies conducted on well-trained athletes and patient populations reveal that during high-intensity, constant-load and self-paced exercises participants terminate the task with a given level of severe locomotor muscle fatigue (assessed via quadriceps twitch force) that appears to be never exceeded under ‘normal’ exercise conditions, despite manipulations of exercise performance (Amann 2011; Amann et al. 2006a; Duffield et al. 2010; Romer et al. 2007; Saey et al. 2003). This end-exercise level of peripheral muscle fatigue has been purported to be task specific and to vary across participants (for review see (Amann 2011)). So, overall, in the early years of the 21st century, several scientists worldwide agree that the CNS constantly monitors and deliberately regulates and limits the development of peripheral fatigue in the exercising limbs (via changes in the extent of muscle recruitment). This may presumably be to avoid overtraining / overexertion and potentially long-lasting harmful consequences to critical organs (Amann 2011; Calbet 2006; Kayser 2003; Marino et al. 2011; Noakes 2011; Nybo and Secher 2004). Several physiological variables (
To the author’s best knowledge, the presence of pacing during short and “all-out” sprints has been examined in one study. The authors (Billaut et al. 2011) deceived the participants to evaluate the degree of pacing depending upon the number of sprints to be performed. Astonishingly, the anticipation of performing fewer sprints (
3. Electromyographic events during single-sprint exercise
Although not extensively studied, changes in skeletal muscle recruitment may contribute to performance decrement during maximal sprint exercise. Vandewalle and colleagues (1991) observed a parallel decline in power output and integrated EMG of the
4. Electromyographic events during repeated-sprint exercise
4.1. Muscle recruitment strategies
The first investigations of the changes of the EMG signal (serving as surrogate for muscle recruitment) provoked by “all-out” repeated sprints have only been conducted recently (Billaut and Basset 2007; Billaut et al. 2006; Giacomoni et al. 2006). These studies have examined the EMG activity of the quadriceps muscles during brief, maximal, isometric voluntary contractions of the knee extensors performed before and immediately after the ten 6-s sprints separated by 30 s of passive rest. In every case, the maximal knee extensors force was reduced after the sprints (average: ~12%; P < 0.05) and this was accompanied by a higher EMG activity (average: ~15%; P < 0.05). The authors also observed a concomitant decrease in frequency components, which suggests a modification in the pattern of muscle fibre recruitment and a decrease in conduction velocity of active fibres (Basmajian and De Luca 1985; De Luca 1997; Gerdle and Fugl-Meyer 1992; Linnamo et al. 2000; Moritani et al. 1986). In fact, during the final sprints, the relative contribution of less-powerful type I muscle fibres involved in the production of power may have increased as a result of the greater fatigability of type II muscle fibres, highly solicited during this type of exercise (Casey et al. 1996; Gerdle and Fugl-Meyer 1992; Komi and Tesch 1979; Ross et al. 2001). However, the conclusions from these studies may have been confounded by methodological factors. Indeed, while investigating EMG activity during isometric contractions may greatly reduce muscle movements underneath EMG electrodes and artefacts due to wire movements, and thus ease signal processing (Farina et al. 2004; Merletti and Lo Conte 1997), it is now well accepted that physiological responses and neuromuscular fatigue are highly task specific (Hunter 2009; Maluf and Enoka 2005). Accordingly, results and conclusions would be highly specific to the task performed, hence, the isometric contraction, with less relevance to the actual fatigue that develops during the repeated sprints.
More recently, muscle recruitment strategies have been investigated during sprints, and authors have reported a concurrent decline in mechanical performances and the amplitude of the EMG signal in primer-mover muscles (Billaut and Smith 2009; Billaut and Smith 2010; Mendez-Villanueva et al. 2007, 2008; Racinais et al. 2007; Smith and Billaut 2010). For example, Billaut and Smith (2010) demonstrated a ~15% decline (P < 0.05) in the quadriceps muscles EMG activity from the first to the twentieth cycle sprint of 5 s separated by 25 s of rest. Importantly, such a reduction in agonist muscle recruitment during repeated cycle sprints has been strongly correlated (r = 0.91 to 0.98, P < 0.05, Figure 4) with the decline in mechanical output (Billaut and Smith 2009; Billaut and Smith 2010; Mendez-Villanueva et al. 2008). That being said, it is interesting to note that when fatigue is moderate (
4.2. Timing of muscle activation
Thus far, the above sections have particularly highlighted the importance of the “quantity” of EMG activity or muscle recruitment in the production of maximal power. During “all-out” actions, the timing of muscle activity also comes into play in determining neuromuscular performance. In fact, during sprint exercises maximal power production requires muscles to be fully activated during shortening and fully relaxed during lengthening phases (Neptune and Kautz 2001). Thus, when studying sprint exercises, the inter-muscle coordination patterns (
As this is a rather innovative approach of using EMG signals to study the brain regulation of exercise capacity, very few data are available during sprint exercise. To the author’s best knowledge, the only study on coordination patterns in sprints has demonstrated that the agonist–antagonist activation strategy is altered during ten 6-s cycle sprints. The
4.3. At least two potential causes for down-regulation of muscle recruitment
While it is suspected that multiple feedback originating from various locations within the body alter the output of the spinal motor neuron pool, and therefore muscle recruitment, research into neuromuscular fatigue during “all-out” sprints has defined at least two potential influences. The mechanisms that lead to a decreased output of the spinal motor neuron pool are still not well understood, especially during repeated sprints. Nevertheless, it is now clear that the CNS receives sensory input from muscle afferents (
dependent, inverse effect on muscle recruitment and power output during a 5-km time trial (Amann and Dempsey 2008). Specifically, the higher the level of pre-existing fatigue, the lower the muscle recruitment and power output during the trial. Furthermore, in a nicely-designed series of studies Amann and colleagues (2008; 2009) presented convincing results that sensory feedback from fatiguing muscles restrict muscle recruitment and, therefore, the exercise-induced development of peripheral fatigue during whole-body endurance exercise. However, whether the central projection of afferent feedbacks from fatigued muscles changes according to the continuous vs. intermittent nature of the exercise task remains unknown. It is intuitive that the dramatic metabolic disturbances that occur within the exercising muscle during “all-out” and repeated sprints (for review see (Billaut and Bishop 2009)) influence the central projection of these feedbacks and thus affect the central neural drive during such intense tasks (Billaut et al. 2005; Billaut and Smith 2010; Mendez-Villanueva et al. 2008; Racinais et al. 2007). That being said, the manipulation of intramuscular pH, for example, by oral administration of sodium bicarbonate does not influence EMG activity and mechanical performance during repeated cycling sprints (Matsuura et al. 2007). Further studies manipulating the metabolic “milieu interieur” during sprint repetitions to examine the effects on centrally-regulated muscle recruitment are needed.
A second potential line of inquiry for fatigue research during “all-out” activity is the influence of oxygenation on motor neuron activity. Recently, studies have been performed to further unravel changes in muscle activation. In this perspective, it is worth noting that the drop in EMG amplitude observed during repeated sprints has been shown to be strongly correlated (r = 0.80 to 0.95, P < 0.05) with a decline in arterial blood oxygen (O2) saturation (Billaut and Smith 2009; Billaut and Smith 2010). This relationship is also very similar in men and women, suggesting that there is no sex dimorphism in this phenomenon (Billaut and Smith 2009) and demonstrating the strong recurrence of this phenomenon. So, is it simply coincidence that muscle recruitment falls concomitantly with arterial saturation? One may interpret existing correlative evidence (Billaut et al. 2010; Billaut and Smith 2009; Billaut and Smith 2010) to mean that the CNS deliberately down-regulates muscle activity to keep peripheral fatigue within “tolerable” limits. We further tested this hypothesis and used a hypoxia paradigm to alter O2 delivery to tissues and evaluate its impact on muscle recruitment. The reduction in prefrontal cortex oxygenation induced by acute hypoxia was correlated (r = 0.89 to 0.92, P < 0.05) with the changes in EMG amplitude of active muscles during ten 10-s cycle sprints (Smith and Billaut 2010). This shows that the larger the brain deoxygenation, the lower the muscle recruitment. At this point however, it is important to acknowledge that the use of surface EMG as a sole determinant of the neural response is questionable and requires caution, even though its rate of change throughout the exercise may be used as an index of the rate of motor unit recruitment (Amann et al. 2008; Amann et al. 2006b; Amann et al. 2007; Romer et al. 2007). Taken as it is (
It is becoming clear that the CNS modulates muscle recruitment (both quantity of muscle mass and coordination strategies) to cope with task constraints during high-intensity exercise. It may do so presumably to counteract the increasing fatigue process and limit the development of peripheral muscle fatigue to a non-harmful level. Exercise physiologists and sport scientists can no longer pretend that conscious or subconscious decision making plays no role in the fatigue process and, ultimately, in the regulation of exercise intensity. Perhaps, as Marino and colleagues wrote