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Article

Recovery and Fatigue Behavior of Forearm Muscles during a Repetitive Power Grip Gesture in Racing Motorcycle Riders

by
Michel Marina
1,*,
Priscila Torrado
1 and
Raul Bescós
2
1
Research Group in Physical Activity and Health (GRAFiS), Institut Nacional d’Educació Física de Catalunya (INEFC), Universitat de Barcelona (UB), 08038 Barcelona, Spain
2
Plymouth Institute of Health and Care Research (PIHR), University of Plymouth, Plymouth PL4 8AA, UK
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(15), 7926; https://doi.org/10.3390/ijerph18157926
Submission received: 3 June 2021 / Revised: 19 July 2021 / Accepted: 21 July 2021 / Published: 27 July 2021
(This article belongs to the Collection Sports Medicine and Physical Fitness)
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Abstract

:
Despite a reduction in the maximal voluntary isometric contraction (MVCisom) observed systematically in intermittent fatigue protocols (IFP), decrements of the median frequency, assessed by surface electromyography (sEMG), has not been consistently verified. This study aimed to determine whether recovery periods of 60 s were too long to induce a reduction in the normalized median frequency (MFEMG) of the flexor digitorum superficialis and carpi radialis muscles. Twenty-one road racing motorcycle riders performed an IFP that simulated the posture and braking gesture on a motorcycle. The MVCisom was reduced by 53% (p < 0.001). A positive and significant relationship (p < 0.005) was found between MFEMG and duration of the fatiguing task when 5 s contractions at 30% MVCisom were interspersed by 5 s recovery in both muscles. In contrast, no relationship was found (p > 0.133) when 10 s contractions at 50% MVC were interspersed by 1 min recovery. Comparative analysis of variance (ANOVA) confirmed a decrement of MFEMG in the IFP at 30% MVCisom including short recovery periods with a duty cycle of 100% (5 s/5 s = 1), whereas no differences were observed in the IFP at 50% MVCisom and longer recovery periods, with a duty cycle of 16%. These findings show that recovery periods during IFP are more relevant than the intensity of MVCisom. Thus, we recommend the use of short recovery periods between 5 and 10 s after submaximal muscle contractions for specific forearm muscle training and testing purposes in motorcycle riders.

1. Introduction

Simulation of highly repetitive intermittent muscle contractions present during motorcycle competitions is currently under investigation because of their relationship with the development of clinically significant conditions, especially in the hand/forearm. These conditions, characterized by pain and loss of the hand or forearm function, are defined as exertional compartment syndrome [1,2,3,4]. They frequently lead to long periods of illness in motorcycle riders, especially in those participating in endurance competitions such as 24 h races, where they must brake more than 4000 times and make 10,000 gear changes [5]. Similar pathological patterns can also occur among workers in the manufacturing industry [6]. The fact that many athletes, manual workers, and musicians must endure their mechanical work over long periods of time, muscle contraction intensities that characterize each activity explains the large number of studies focused on neurophysiological fatigue of the forearm muscles [7,8,9,10,11]. These muscles are involved in a great variety of repetitive grip tasks that can lead to neuromuscular fatigue and functional impairment when these tasks become chronic. Thus, it is important to obtain better knowledge and understanding of the mechanisms involved in these physiological situations to prevent forearm syndrome.
When assessing human muscle fatigue with superficial electromyography (sEMG), the power spectrum displacement towards lower frequencies has been extensively documented in continuous fatiguing protocols (CFP), in which submaximal voluntary contractions are maintained until exhaustion [9,12,13,14,15,16]. Intermittent fatiguing protocols (IFP) have also been extensively studied because intermittent contractions at different intensities are very common in the everyday life of the majority of workers and athletes [17,18]. Consequently, when comparing both types of fatiguing tasks (CFP versus IFP) specifically adapted to motorcycle riders [9], IFP showed a stronger relationship with the level of motorcyclist forearm discomfort compared to CFP [9].
The relative intensity of the contraction with respect to maximal voluntary isometric contraction (MVCisom) registered in a non-fatigued condition (%MVCisom) is a key factor that modulates muscle fatigue. Studies looking at CFP confirmed that the higher the intensity of the effort, the shorter the time to task failure [14,16,19], obviously because of the lack of recovery periods. Moreover, it has been generally observed that %MVCisom and the time to task failure (also called time limit) have a significant effect on the decrement of sEMG frequency (MFEMG) and increment of the sEMG amplitude (RMSEMG) [14,15,20]. A reduction in MFEMG was observed during CFPs at 10% MVC [21,22], 25% MVC [22,23,24], 30% [21], 40% [9,21,24], 60% [25], 55, 70, 80, and 90% of MVC [24]. Nevertheless, some caution is recommended in regard to MFEMG because %MVCisom should not be considered as a definitive factor explaining the absence of a reduction in MFEMG during fatiguing protocols [24,25].
A second factor that is necessary to consider when measuring fatigue is the duration of the effort or exertion time. It is known that the duration of fatiguing tasks at a constant relative submaximal %MVCisom is negatively associated with MVCisom decrements, reaching the maximal point at time to task failure [26]. Duration of the effort induces a linear decrement of MFEMG [14,24,27] whose slope may differ slightly depending on the muscle group and type of movement [15,16,20,21]. With %MVCisom and duration of the effort as the main triggers of fatigue in CFPs, the greatest MFEMG decrements were observed at longer durations due to lower %MVCs [28].
A third factor must be taken into account in IFP: the duration of the recovery interspersed between muscle contractions. Controversial MFEMG results have been observed when applying IFPs, despite the lower MVCisom recorded at the end of such fatigue protocols. For example, some authors, but not all [29,30], reported a reduction in MFEMG during an IFP [31,32]. MFEMG was similar to pre-fatigue values with different work–rest cycles, whatever the intensity used in the IFP, [22]. These results are consistent with the findings of Mundale [28], who also studied the factors that lengthen the endurance time of an IFP. It seems that the duration of the recovery period could be one of the key factors explaining the disparity in MFEMG results, particularly among IFPs. Looking at motorcycle riders, we [5] observed no significant MFEMG decrement throughout a 24-h motorcycle endurance race despite the significant decrease in MVCisom. Following the recommendation of previous studies [33,34,35], we took care not to exceed an interval of 4–5 min between the end of each relay and the handgrip assessment. The lack of MFEMG decrement led to conclude that this interval was too long. According to these findings, we decided to compare an IFP and CFP specifically adapted to motorcycle riders [9]. The lack of a reduction in MFEMG in the IFP suggested that rest cycles were too long, achieving basal values of MFEMG between the work cycles. These findings are in agreement with another study by Krogh-Lund and Jorgensen [23] that compared two pairs of fatiguing sustained isometric contractions at 40% MVCisom separated by different rest intervals. They found that the MFEMG at the start of the second contraction did not recover to pre-fatigued values when the rest interval was less than 1 min, [23]. Other studies reached similar conclusions when they used intermittent contractions [24,25,36], suggesting that a MFEMG shift toward the pre-fatigue state occurs independently of the contraction intensity (25–50%) [36].
Some authors [37] suggest that the validity of the spectral shift of the sEMG signal in assessments of fatigue must be taken with caution because a clear MVC decrement is sometimes weakly reflected in the sEMG signal [38]. This is supported by studies that used IFP to assess muscle fatigue [29,30,39,40]. In contrast, the usefulness of the sEMG signal for studying muscle fatigue in occupational field studies [41] is supported by other studies that reported a reduction in MFEMG with IFP [31,32]. These overall discrepancies between studies suggest that the combination of different, contraction–relaxation periods, effort intensities (%MVCisom), muscle groups, and other non-controlled or non-reported factors, are critical to understanding muscle fatigue in IFPs [18,22,42].
Therefore, this study aimed to verify in road racing motorcycle riders whether the recovery period performing an IFP matching the braking movement was more relevant than the contraction intensity and effort duration in two forearm muscles (flexor digitorum superficialis and carpi radialis). We hypothesized that MFEMG will not decrease during the contractions performed at 50% MVCisom because they are preceded by long recovery periods. On the contrary, MFEMG recorded at 30% MVCisom and during a shorter exertion time (5 s) may decrease due to short recovery periods (5 s).

2. Methods

2.1. Subjects

Twenty-one road racing motorcycle riders aged 29.1 ± 8.0 years (body mass: 72.1 ± 5.5 kg; height: 176.2 ± 4.9 cm) participated in this study. Of these riders, 48% were winners within the Spanish and/or World Championships and 24% were on the podium of the Championship at the end of the season over the previous 6 years. The remaining 28% participated in races at the regional level with at least 5 years of racing experience. The study was approved by the Clinical Research of the Ethics Committee for Clinical Sport Research of Catalonia (Ref. number 15/2018/CEICEGC) and written consent was given by all the participants. The data were analyzed anonymously, and the clinical investigation followed the principles of the Declaration of Helsinki.

2.2. Procedures

Before the assessment, the brake lever to handgrip distance was adjusted to the participant’s hand size to ensure that hand placement in relation to the brake was similar across all subjects. Afterwards, during the familiarization period, the subject practiced six to ten submaximal non-stationary contractions while watching the dynamometric feedback displayed on the PC screen, while the researcher provided feedback about how to interpret the auditory and visual information. A continuous linear feedback and a columnar and numerical display showed the subject the magnitude of the force they exerted against the brake lever. In addition, a different tone was provided depending on the force level. Dynamometric and sEMG signals were recorded and these signals were synchronized with an external trigger. Five minutes before the beginning of the intermittent fatigue protocol (IFP), two MVCisom trials separated by a 1-min rest were performed to provide a baseline value of MVCisom. The 1-min resting period between the two MVCisoms was considered sufficient to avoid fatigue from the previous contraction [43,44]. The higher MVCisom was recorded as the basal value of that day and used to calculate the submaximal efforts (50% and 30% of the maximum). During the IFP, the subject adopted the “rider position” with both hands on the handlebar.

2.3. Sequence and Structure of the IFP

The intermittent protocol comprised a succession of a maximum of 25 rounds. Each round comprised two sections (Figure 1A). Section one consisted of six 5-s voluntary contractions of 30% MVCisom, with a resting period of 5 s between each contraction. Section two comprised a 3-s MVCisom followed by a 1-min resting period and a 50% MVCisom maintained for 10 s. During the 1-min resting period subjects were in the seated position with their hands resting on their thighs.
Intensities ranging from 10% to 40% of MVC have previously been used to carry out a continuous or intermittent fatigue protocol [18,22,45]. A sequence of 30% of MVCisom was finally adopted after consulting with expert riders (exclusively, winners of races at the national and world level) who agreed about the perception of applying approximately this percentage of force during very strong braking in real situations.
Section two was designed to replicate an experimental protocol from one of our previous studies of motorcycle riders [5]. The test stopped when the subject was unable to maintain the established 50% of MVCisom for 10 s, or the concurrent MVCisom was 10% lower than 50% of the MVCisom value. The number of rounds achieved by each subject was used as a performance measure.

2.4. Dynamometric Assessment

To simulate the overall position of a rider on a 600–1000-cc racing motorcycle, a static structure was built to preserve the distances between the seat, stirrups, and particularly the combined system of shanks, forks, handlebar, brake and clutch levers, and gas (Figure 2). As it happens in a road race motorcycle, levers tilt, distances between levers and handle gas, and distance between the handlebar and seat were modified according to the ergonomic requirements of the rider (Figure 2).
The subjects were asked to exert a force against the brake lever (always the right hand) using the second and third finger to hold the lever half way, and the thumb and other fingers grasping the handgrip at the same time, which is the most common way of braking of road racing motorcycle riders (Figure 2). Both arms had a slight elbow flexion (angle 150–160°), forearms half-pronated, wrist in neutral abduction/adduction position and alienated with respect to the forearm, dorsal flexion of the wrist no bigger than 10°, and legs flexed with feet above the footrests; in short, the typical overall position of a rider piloting a motorcycle in a straight line.
Special attention was given to controlling the handgrip position, and the wrist, elbow, and trunk angles to avoid any modification of the initial overall body position during the test. One experimenter supervised the recording of force and sEMG signals, and another continuously checked the maintenance of body position. It has been reported that variations in body posture [46] and wrist angles [47] alter the behavior of the forearm muscles during handgrip force generation.
To measure the force exerted against the brake lever we used a unidirectional gauge connected to the MuscleLabTM system 4000e (Ergotest Innovation AS, Stathelle, Norway). The frequency of measurement was 400 Hz, and the loading range was from 0 to 4000 N. The gauge (Ergotest Innovation AS, Norway), with a linearity and hysteresis of 0.2%, and 0.1 N sensibility, was attached to the free end of the brake lever in such a way that the brake lever system and the gauge system laid over the same plane and formed a 90° angle approximately when the subject was exerting force. The MVC at the end of the IFP was compared to the MVC in the pre-fatigued state. The 30% and 50% MVC contractions were used for sEMG analysis.

2.5. Electromyography

A ME6000 electromyography system (Mega Electronics, Kuopio, Finland) was used to register flexor digitorum superficialis (FS) and carpi radialis (CR) EMG signals. Adhesive surface electrodes (Ambu Blue Sensor, M-00-S, Ballerup, Denmark) were placed 2 cm apart (from center to center) according to the anatomical recommendations of the SENIAM Project [48,49]. The raw signal was recorded at a sampling frequency of 1000 Hz. Data were amplified with a gain of 1000 using an analog differential amplifier and a common-mode rejection ratio of 110 dB. The input impedance was 10 GΩ. A Butterworth bandpass filter of 8–500 Hz (–3 dB points) was used. To compute the median frequency (MFEMG, Hz), Fast Fourier Transform was used with a frame width at 1024, a shift method of 30% of the frame width, and the “flat-topped” windowing function. The power spectrum densities were computed and averaged afterwards to obtain one mean or median for each submaximal contraction of 30% MVCisom (5 s duration) and 50% MVCisom (10 s duration). Afterwards, the median frequency (MFEMG) was normalized with respect to the basal condition during the MVCisom.
In order to obtain the same number of MFEMG values from the IFP of each individual, and for each round and MVCisom intensity, the six 30% MVCisoms of the first section (Figure 1A) were averaged to obtain one MFEMG (MFEMG30). Each MFEMG30 was paired with the only MFEMG of the second section (Figure 1A) obtained from the 50% MVCisom (MFEMG50).

2.6. Statistics

Parametric statistics were used after confirming the normal distribution of the normalized parameters used in this study (MVCisom, MFEMG30, and MFEMG50) with the Shapiro-Wilk test. Descriptive results were reported as the mean and standard deviation. A paired sample t-test was used to compare the MVCisom in the pre-fatigued state and at the end of the IFP. Two methodological approaches were used to verify the study’s hypothesis. First, we used regression analysis for each individual, to study the strength of the relation and detect possible trends between the number of rounds accomplished (independent variable) and the MFEMG30 (dependent variable). Second, we used a 2 (time points: T1 and T2) × 2 (muscles: FS and CR) × 2 (%MVCisom: 30 and 50) ANOVA of repeated measures to compare all MFEMG values at the beginning and the end of the IFP, and to study potential interactions with the two muscle groups analyzed (CR and FS) and the two intensities that were preceded by distinct recovery periods (5 s for 30% MVCisom and 1 min for 50% MVCisom). When necessary, the Greenhouse-Geisser’s correction was used if the sphericity test to study matrix proportionality of the dependent variable was significant (p < 0.05). Then, when a significant effect was found, a post-hoc analysis was carried out conducting multiple comparisons between the normalized rounds with Sidak’s adjustment. Partial Eta squared (η2p) was used to report effect sizes (0.01 ≈ small, 0.06 ≈ medium, >0.14 ≈ large). Statistical analysis was performed using the PASW Statistics for Windows, Version 18.0 (SPSS, Inc., Chicago, IL, USA). The level of significance was set at 0.05.

3. Results

At baseline conditions, MVCisom (276 ± 46.6) was 53% lower than the MVCisom at the end of the IFP (147 ± 46.3; p < 0.001).
Individual regression analysis (Table 1, Figure 3) was conducted to verify possible trends between the NMF of the CR and FS and the number of rounds accomplished by the motorcycle riders during an intermittent fatigue protocol (IFP) at two different intensities (30% and 50% of MVCisom). The overall individual regression analysis showed a significant linear relationship (p < 0.005) between the MFEMG and the number of rounds accomplished by both muscles when they were exercised at 30% MVCisom (CR30 and FS30), with pauses of 5 s between each contraction. In contrast, when both muscles were exerted at 50% MVCisom (CR50 and FS50), after 1 min of recovery, no significant relationship was observed (p > 0.133). The higher correlation observed in CR30 and FS30 (r ≥ −0.71) in comparison to CR50 and FS50 (r ≤ 0.59) supports the hypothesis of a weaker relationship between the MFEMG50 and the number of rounds when both muscles had the opportunity to recover for longer (1 min for CR50 and FS50). Similarly, the overall individual regression analysis showed that the fraction of MFEMG variance, explained by the number of rounds attained during the intermittent protocol, was bigger with CR30 and FS30 (r2 ≥ 0.50) in comparison to CR50 and FS50 (r2 ≤ 0.40) (Table 1).
In addition to the regression analysis performed for each individual, Table 2 reveals that a greater number of riders satisfied better levels of statistical condition in CR30 and FS30 in comparison to CR50 and FS50. Moreover, the higher correlation values (r > 0.70) and higher levels of significance (p < 0.001) were associated with higher frequency values in CR30 and FS30, while lower correlation values (r < 0.39) and lower levels of significance (p > 0.05) were associated with a higher number of riders in CR50 and FS50.
Figure 3 is an example of the regression analysis carried out in one subject showing higher MFEMG values for the CR in comparison to the FS. Moreover, at 50% MVC, the MFEMG of the CR never dropped below the MFEMG level established during the basal assessment (Figure 3B), which is consistent with the comparative results (Table 3).
The second methodological approach was used to determine whether less intense and shorter muscle contractions (30% MVCisom instead of 50%; 5 s instead of 10 s) could induce bigger MFEMG decrements in the CR and FS. The second objective was to determine whether the two muscles (CR and FS) had a similar MFEMG decrement due to fatigue. Thus, we compared two times of measurement (T1 and T2), two muscles (CR and FS) and two contraction intensities (30% and 50% of MVCisom) (Table 3).
A significant three-way interaction was found (p < 0.001) with a large effect size (η2p = 0.5) (Table 3). Paired comparisons found lower values for the FS than the CR at both times and both intensities. Moreover, we observed a higher MFEMG in the CR muscle at 30% MVCisom (CR30) than at 50% MVCisom (CR50) at the beginning of the IFP, but the opposite response was observed at the end. Finally, regarding the CR, while MFEMG was lower at the end than at the beginning of the IFP at the 30% MVCisom (CR30), the opposite was observed at the 50% MVCisom exertion (CR50) (Table 3).
In addition, a significant two-way interaction was found between the time and MVCisom intensity (time per intensity) with a large effect size (η2p = 0.63), but not for the other interactions (time per muscle, and intensity per muscle) with a small and medium effect size, respectively (Table 3). The MFEMG was higher at the beginning than at the end of the IFP when both muscles were exerted at 30% MVCisom, but no significant differences were observed when they were exerted at 50% MVCisom. Finally, we observed a significant main effect for intensity and muscle factor (Table 3).

4. Discussion

The MVCisom decrement observed in our IFP confirmed the occurrence of muscle fatigue as this physiological phenomenon is commonly defined as the “loss of the maximal force-generating capacity” [37,50]. From a functional and neurophysiological point of view, and according to the literature, the decrement of the sEMG power spectrum is related, among other factors, to: (1) a reduction in the conduction velocity of the active fibers [35]; (2) impairment of the excitation–contraction coupling [27] related to metabolic changes that occur during fatigue [51]; (3) the recruitment of new units [52], based on the knowledge that subjects with a high relative number of fast twitch fibers may have higher sEMG frequency values [53], and that during fatigue, they show a greater shift towards lower MFEMG compared to subjects with a low relative number of fast twitch fibers [54]; (4) structural damage to muscle cells when muscle soreness is reported by the subjects [18]; (5) other reactions taking place beyond the muscle cell membrane [55], based on observations that short resting periods between each muscle activation are sufficient to maintain the neuromuscular excitability at normal levels during IFP. It must be highlighted that this study did not intend to explain the changes in MFEMG induced by fatigue from a physiological perspective, we were focused on the relationship between the MFEMG and the two factors controlled in our IFP: the load intensity and the work–rest cycle.
High variability of MFEMG values at low loads has been attributed to the influence of the number of recruited muscle fibers and the synchronism and firing rate [56]. According to this, it could be more difficult to find a significant pattern at 30% MVCisom rather than 50% MVCisom, but we found that the MFEMG of the CR and FS decreased more consistently throughout the IFP when the muscles were exerted at 30% MVCisom in comparison to 50% MVCisom. The regression analysis of each individual revealed systematically stronger correlations, coefficients of determination, and statistical significance with CR30 and FS30 in comparison with CR50 and FS50. Moreover, participants reported a stronger relationship between the number of rounds accomplished and the MFEMG at 30% MVCisom, rather than 50% MVCisom, in both muscles that were assessed. In agreement with this, we found a higher and more significant MFEMG decrement when the participants performed the IFP at 30% MVCisom, which may suggest different neuromuscular fatigue patterns between the CR50 and FS50 during the IFP [9]. If force intensity was the only one factor explaining these differences, it would be difficult to argue that time to exhaustion of any fatigue protocol would be longer when muscles work at higher intensities. As expected, other studies proved the opposite [22,23,57]. Moreover, when studying the magnitude of fatigue in two different IFPs at two different intensities (25 and 50% MVCisom), Seghers and Spaepen [42] observed very similar relative MFEMG decrements in the two muscles analyzed (IFP at 25% MVCisom: 29%, and 30%; IFP at 50% MVCisom: 29%, and 28%), when sustaining an isometric contraction at 75% of prefatigued MVCisom at the end of both protocols [42]. On the other hand, whereas the same authors observed a significant negative slope of the MFEMG during the IFP at 25% MVCisom, during the IFP at 50% MVCisom the slope did not differ significantly from zero. It is possible that the differences in MFEMG changes during the two IFPs could be more related to differences in their work–rest cycles (10 + 10 s in 25% MVCisom and 5 + 15 s in 50% MVCisom) than in the contraction intensity. In rock climbers, the significant reduction in the MFEMG observed during an intense IFP (80% MVCisom) [58], with a work–rest cycle of 5 + 5 s (same cycle as in our IFP for the 30% MVCisom), indicates that the majority of the frequency components of the MFEMG are unaffected by tension [24]. Thus, we believe that the key point for understanding the different MFEMG patterns during our IFP must be the resting period before the two intensities. Only 5 s of recovery were interspersed between braking muscle contractions of the forearm at 30% MVCisom compared to the 60 s (1 min) at 50% MVCisom. This clearly indicates that MFEMG can be explained to a greater extent when the riders have a very short recovery time despite a smaller contraction intensity (30% MVCisom instead of 50% MVCisom) and a shorter contraction time (5 s for 30% MVCisom instead of 10 s for 50% MVCisom). Similar results were reported by Nagata et al. [25].
Nevertheless, it is important to highlight that these authors used a continuous fatigue protocol in which the force was maintained at an intensity of 60% MVCisom until exhaustion, which substantially differs to the IFP in our study.
Before undertaking this study, it was not evident that 1 min of recovery before the 50% MVCisom could be long enough to allow a systematic recovery of the MFEMG towards baseline levels (pre-fatigued). The MFEMG recovery curve towards pre-fatigued values can be characterized by an exponential function [59,60,61], as well as a logarithmic course characterized by large inter-individual variations [61,62]. Therefore, a large proportion of the MFEMG spectrum recovery corresponds to the first 1 min of the exponential recovery curve [21,23,43,44,59,60,61,62,63]. However, depending on the fatigue protocol, this does not mean full restoration comparable to pre-fatigued or basal MFEMG values. Following the completion of ten cycles of work/rest (10 s/10 s) at MVCisom, Mills [59] observed that the mean power frequency of a compound muscle action potential evoked by supramaximal nerve stimulation required 3 min to recover 50% of its initial values. Three to six minutes, depending on age, are sometimes necessary to recover the pre-fatigued MFEMG values of the abductor digiti-minimi muscle after a MVCisom exertion maintained until 50% MVCisom [64]. Other studies [62,65,66] have confirmed that the majority of the MFEMG spectrum is re-established after 1 and 3 min of recovery, but full recovery it may take until the fifth minute [23,62]. Interestingly, Krogh-Lund and Jorgensen [23] observed that the restoration of MFEMG paralleled that of conduction velocity for the last 4 min of recovery. Regarding the first part of the exponential recovery curve, 35 s were sufficient to allow restoration of 50% of the decline in MFEMG during the previous fatigue protocol [61], but a longer interval (1.4 min) was required to reach 50% of pre-fatigued values for the biceps brachii [67]. Faster MFEMG recovery (up to 85% of the pre-fatigued state during the first minute) was found by Krogh-Lund [21] in the brachioradialis and biceps brachii muscles. Nevertheless, the standard error of the measurement (about 60 s) reported by Elfving et al. [61], which was much larger than the average recovery, reflects the large between-subject variability of the MFEMG parameter when studying the recovery phase. The inconclusive results reported in the literature combined with the accepted large variability that characterizes this type of analysis, support the idea that different combinations of IFP (contraction intensities and durations of contraction and relaxation) to assess muscle fatigue can provide different results [42]. Thus, although it is difficult to compare sEMG data from different studies it is even more complicated when the protocol involves voluntary exercise [37]. The fact that the physiological mechanisms causing muscle fatigue are specific to the task [68], should encourage future studies looking at road racing motorcycle riders to focus on the specific conditions of the forearm muscles, in order to understand better pathologies such as exercise-induced compartment syndrome.
The main limitations of this study were that effort duration, contraction intensity, and recovery time were not separated in different IFPs. Ideally, swapping these three factors would mean that riders had to attend the laboratory on at least six occasions to undertake different IFPs and following a randomized protocol. However, this approach was not feasible in the current study due to the busy racing and training schedules and other commitments of the population of this study.

5. Conclusions

This study reproduced, in the most accurate way and under laboratory conditions, the braking action in road racing motorcycle riders to investigate different work–rest cycles during an IFP. For training purposes, we recommend using short recovery periods between 5 and 10 s after submaximal muscle contractions as the most effective way to induce muscle fatigue than intermittent tasks performed at higher intensities and with longer recovery periods. That is, much less than 1 min for the resting time (no more than 30 s) according to the results of previous studies [21,23,43,44,60,61,63]. Furthermore, contraction intensities above 50% MVCisom may not be useful for road racing motorcycle riders since only around 30% MVCisom is required to break in real conditions when they have to slow down at high speed (more than 270 km/h) to connect a straight line with a slow curve [9]. Muscle contraction times longer than 10 s are not useful either to match road racing requirements, so protocols involving this type of contraction are not recommended for these individuals. Finally, accelerations with the right hand promote hand dorsal flexion and the assessment of both movements (braking and acceleration) have not been combined in a single IFP. This must be taken into account in future studies to match the real conditions of road motorcycle racing in laboratory settings. This knowledge is needed to enhance our understanding of the most appropriate stimulus (muscle contraction intensities and recovery periods) to be applied within the training programs of road racing motorcycle riders in order to mimic racing conditions and to reduce the risk of muscle pathologies such as the forearm chronic exertional compartmental syndrome.

Author Contributions

Conceptualization, M.M.; methodology, M.M.; software, P.T.; validation, M.M. and P.T.; formal analysis, P.T.; investigation, M.M., P.T. and R.B.; resources, M.M.; data curation, P.T. and R.B.; writing—original draft preparation, M.M.; writing—review and editing, M.M., P.T. and R.B.; visualization P.T. and R.B.; supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Spanish Ministry of Economy and the European Funds for Regional Development under Grant [DEP2015-70701-P (MINECO/FEDER)].

Institutional Review Board Statement

This study was conducted according the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee for Clinical Research of the Catalan Sports Council (protocol code 15/2018/CEICEGC, date 10 February 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study is not available.

Acknowledgments

This work was supported by the Spanish Ministry of Economy and the European Funds for Regional Development under the Grant [DEP2015-70701-P (MINECO/FEDER)]; the Institut Nacional d’Educació Física de Catalunya (INEFC) de la Generalitat de Catalunya—Universitat de Barcelona (UB); and the Research Group in Physical Activity and Health (GRAFiS, Generalitat de Catalunya 2014SGR/1629). We are grateful to MONLAU Competició and Dani Ribalta Pro-School.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Barrera-Ochoa, S.; Haddad, S.; Correa-Vazquez, E.; Segura, J.F.; Gil, E.; Lluch, A.; Soldado, F.; Mir-Bullo, X. Surgical Decompression of Exertional Compartment Syndrome of the Forearm in Professional Motorcycling Racers: Comparative Long-term Results of Wide-Open Versus Mini-Open Fasciotomy. Clin. J. Sport Med. 2016, 26, 108–114. [Google Scholar] [CrossRef]
  2. Brown, J.S.; Wheeler, P.C.; Boyd, K.T.; Barnes, M.R.; Allen, M.J. Chronic exertional compartment syndrome of the forearm: A case series of 12 patients treated with fasciotomy. J. Hand Surg. Eur. Vol. 2011, 36, 413–419. [Google Scholar] [CrossRef] [Green Version]
  3. Gondolini, G.; Schiavi, P.; Pogliacomi, F.; Ceccarelli, F.; Antonetti, T.; Zasa, M. Long-Term Outcome of Mini-Open Surgical Decompression for Chronic Exertional Compartment Syndrome of the Forearm in Professional Motorcycling Riders. Clin. J. Sport Med. 2019, 29, 476–481. [Google Scholar] [CrossRef]
  4. Goubier, J.N.; Saillant, G. Chronic compartment syndrome of the forearm in competitive motor cyclists: A report of two cases. Br. J. Sports Med. 2003, 37, 452–454. [Google Scholar] [CrossRef] [Green Version]
  5. Marina, M.; Porta, J.; Vallejo, L.; Angulo, R. Monitoring hand flexor fatigue in a 24-h motorcycle endurance race. J. Electromyogr. Kinesiol. 2011, 21, 255–261. [Google Scholar] [CrossRef] [PubMed]
  6. Silverstein, B.A.; Fine, L.J.; Armstrong, T.J. Occupational factors and carpal tunnel syndrome. Am. J. Ind. Med. 1987, 11, 343–358. [Google Scholar] [CrossRef] [PubMed]
  7. Bystrom, S.; Sjogaard, G. Potassium homeostasis during and following exhaustive submaximal static handgrip contractions. Acta Physiol. Scand. 1991, 142, 59–66. [Google Scholar] [CrossRef] [PubMed]
  8. Torrado, P.; Cabib, C.; Morales, M.; Valls-Sole, J.; Marina, M. Neuromuscular Fatigue after Submaximal Intermittent Contractions in Motorcycle Riders. Int. J. Sports Med. 2015, 36, 922–928. [Google Scholar] [CrossRef] [PubMed]
  9. Marina, M.; Torrado, P.; Busquets, A.; Ríos, J.G.; Angulo-Barroso, R. Comparison of an intermittent and continuous forearm muscles fatigue protocol with motorcycle riders and control group. J. Electromyogr. Kinesiol. 2013, 23, 84–93. [Google Scholar] [CrossRef] [PubMed]
  10. Dousset, E.; Jammes, Y. Reliability of burst superimposed technique to assess central activation failure during fatiguing contraction. J. Electromyogr. Kinesiol. 2003, 13, 103–111. [Google Scholar] [CrossRef]
  11. Liu, J.Z.; Shan, Z.Y.; Zhang, L.D.; Sahgal, V.; Brown, R.W.; Yue, G.H. Human brain activation during sustained and intermittent submaximal fatigue muscle contractions: An FMRI study. J. Neurophysiol. 2003, 90, 300–312. [Google Scholar] [CrossRef] [PubMed]
  12. De Luca, C.J. Myoelectrical manifestations of localized muscular fatigue in humans. CRC Crit. Rev. Biomed. Eng. 1984, 11, 251–279. [Google Scholar]
  13. Merletti, R.; LoConte, L.R. Surface EMG signal processing during isometric contractions. J. Electromyogr. Kinesiol. 1997, 7, 241–250. [Google Scholar] [CrossRef]
  14. Mamaghani, N.K.; Shimomura, Y.; Iwanaga, K.; Katsuura, T. Mechanomyogram and electromyogram responses of upper limb during sustained isometric fatigue with varying shoulder and elbow postures. J. Physiol. Anthr. Appl. Hum. Sci. 2002, 21, 29–43. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Fuglevand, A.J.; Zackowski, K.M.; Huey, K.A.; Enoka, R.M. Impairment of neuromuscular propagation during human fatiguing contractions at submaximal forces. J. Physiol. 1993, 460, 549–572. [Google Scholar] [CrossRef] [PubMed]
  16. Gamet, D.; Maton, B. The fatigability of two agonistic muscles in human isometric voluntary submaximal contraction: An EMG study - I. Assessment of muscular fatigue by means of surface EMG. Eur. J. Appl. Physiol. Occup. Physiol. 1989, 58, 361–368. [Google Scholar] [CrossRef]
  17. Mathiassen, S.E.; Winkel, J. Quantifying variation in physical load using exposure-vs-time data. Ergonomics 1991, 34, 1455–1468. [Google Scholar] [CrossRef]
  18. Bystrom, S.E.; Mathiassen, S.E.; Fransson-Hall, C. Physiological effects of micropauses in isometric handgrip exercise. Eur. J. Appl. Physiol. Occup. Physiol. 1991, 63, 405–411. [Google Scholar] [CrossRef]
  19. Hunter, S.K. Sex differences in human fatigability: Mechanisms and insight to physiological responses. Acta Physiol. 2014, 210, 768–789. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Hunter, S.K.; Enoka, R.M. Changes in muscle activation can prolong the endurance time of a submaximal isometric contraction in humans. J. Appl. Physiol. 2003, 94, 108–118. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Krogh-Lund, C. Myo-electric fatigue and force failure from submaximal static elbow flexion sustained to exhaustion. Eur. J. Appl. Physiol. Occup. Physiol. 1993, 67, 389–401. [Google Scholar] [CrossRef] [PubMed]
  22. Bystrom, S.E.; Kilbom, A. Physiological response in the forearm during and after isometric intermittent handgrip. Eur. J. Appl. Physiol. Occup. Physiol. 1990, 60, 457–466. [Google Scholar] [CrossRef] [PubMed]
  23. Krogh-Lund, C.; Jorgensen, K. Changes in conduction velocity, median frequency, and root mean square-amplitude of the electromyogram during 25% maximal voluntary contraction of the triceps brachii muscle, to limit of endurance. Eur. J. Appl. Physiol. Occup. Physiol. 1991, 63, 60–69. [Google Scholar] [CrossRef]
  24. Petrofsky, J.S.; Lind, A.R. Frequency analysis of the surface electromyogram during sustained isometric contractions. Eur. J. Appl. Physiol. Occup. Physiol. 1980, 43, 173–182. [Google Scholar] [CrossRef]
  25. Nagata, S.; Arsenault, A.B.; Gagnon, D.; Smyth, G.; Mathieu, P.A. EMG Power spectrum as a measure of muscular fatigue at different levels of contraction. Med. Biol. Eng. Comput. 1990, 28, 374–378. [Google Scholar] [CrossRef]
  26. Ratkevicius, A.; Skurvydas, A.; Povilonis, E.; Quistorff, B.; Lexell, J. Effects of contraction duration on low-frequency fatigue in voluntary and electrically induced exercise of quadriceps muscle in humans. Eur. J. Appl. Physiol. Occup. Physiol. 1998, 77, 462–468. [Google Scholar] [CrossRef] [PubMed]
  27. Krogh-Lund, C.; Jorgensen, K. Myo-electric fatigue manifestations revisited: Power spectrum, conduction velocity, and amplitude of human elbow flexor muscles during isolated and repetitive endurance contractions at 30 percent maximal voluntary contraction. Eur. J. Appl. Physiol. Occup. Physiol. 1993, 66, 161–173. [Google Scholar] [CrossRef]
  28. Mundale, M.O. The relationship of intermittent isometric exercise to fatigue of hand grip. Arch. Phys. Med. Rehabil. 1970, 51, 532–539. [Google Scholar]
  29. Eksioglu, M. Optimal work-rest cycles for an isometric intermittent gripping task as a function of force, posture and grip span. Ergonomics 2006, 49, 180–201. [Google Scholar] [CrossRef] [PubMed]
  30. Clancy, E.A.; Bertolina, M.V.; Merletti, R.; Farina, D. Time- and frequency-domain monitoring of the myoelectric signal during a long-duration, cyclic, force-varying, fatiguing hand-grip task. J. Electromyogr. Kinesiol. 2008, 18, 789–797. [Google Scholar] [CrossRef] [PubMed]
  31. Lee, C.; Katsuura, T.; Harada, H.; Kikuchi, Y. Localized muscular load to different work patterns and heat loads during handgrip. Ann. Physiol. Anthr. 1994, 13, 253–262. [Google Scholar] [CrossRef] [Green Version]
  32. Quaine, F.; Vigouroux, L.; Martin, L. Finger flexors fatigue in trained rock climbers and untrained sedentary subjects. Int. J. Sports Med. 2003, 24, 424–427. [Google Scholar] [CrossRef]
  33. De Luca, C.J. The use of surface electromyography in biomechanics. J. Appl. Biomech. 1997, 13, 135–163. [Google Scholar] [CrossRef] [Green Version]
  34. Merletti, R.; Sabbahi, M.A.; De Luca, C.J. Median Frequency of the myoelectric signal: Effects of muscle ischemia and cooling. Eur. J. Appl. Physiol. 1984, 52, 258–265. [Google Scholar] [CrossRef]
  35. Stulen, F.B.; De Luca, C.J. Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans. Biomed. Eng. 1981, 28, 515–523. [Google Scholar] [CrossRef] [Green Version]
  36. Oliveira, A.S.; Goncalves, M. Neuromuscular recovery of the biceps brachii muscle after resistance exercise. Res. Sports Med. 2008, 16, 244–256. [Google Scholar] [CrossRef]
  37. Vøllestad, N.K. Measurement of human muscle fatigue. J. Neurosci. Methods 1997, 74, 219–227. [Google Scholar] [CrossRef]
  38. Mathiassen, S.E. The influence of exercise/rest schedule on the physiological and psychophysical response to isometric shoulder-neck exercise. Eur. J. Appl. Physiol. Occup. Physiol. 1993, 67, 528–539. [Google Scholar] [CrossRef] [PubMed]
  39. Christensen, H.; Fuglsangfrederiksen, A. Quantitative surface EMG during sustained and intermittent submaximal contractions. Electroencephalogr. Clin. Neurophysiol. 1988, 70, 239–247. [Google Scholar] [CrossRef]
  40. Hagg, G.M.; Milerad, E. Forearm extensor and flexor muscle exertion during simulated gripping work - An electromyographic study. Clin. Biomech. 1997, 12, 39–43. [Google Scholar] [CrossRef]
  41. Luttmann, A.; Matthias, J.; Laurig, W. Electromyographical indication of muscular fatigue in occupational field studies. Int. J. Ind. Ergon. 2000, 25, 645–660. [Google Scholar] [CrossRef]
  42. Seghers, J.; Spaepen, A. Muscle fatigue of the elbow flexor muscles during two intermittent exercise protocols with equal mean muscle loading. Clin. Biomech. 2004, 19, 24–30. [Google Scholar] [CrossRef]
  43. Kamimura, T.; Ikuta, Y. Evaluation of grip strength with a sustained maximal isometric contraction for 6 and 10 seconds. J. Rehabil. Med. 2001, 33, 225–229. [Google Scholar]
  44. Kleine, B.U.; Schumann, N.P.; Stegeman, D.F.; Scholle, H.C. Surface EMG mapping of the human trapezius muscle: The topography of monopolar and bipolar surface EMG amplitude and spectrum parameters at varied forces and in fatigue. Clin. Neurophysiol. 2000, 111, 686–693. [Google Scholar] [CrossRef]
  45. Green, J.G.; Stannard, S.R. Active recovery strategies and handgrip performance in trained vs. untrained climbers. J. Strength Cond. Res. 2010, 24, 494–501. [Google Scholar] [CrossRef]
  46. Keir, P.J.; Mogk, J.P. The development and validation of equations to predict grip force in the workplace: Contributions of muscle activity and posture. Ergonomics 2005, 48, 1243–1259. [Google Scholar] [CrossRef] [PubMed]
  47. Duque, J.; Masset, D.; Malchaire, J. Evaluation of handgrip force from EMG measurements. Appl. Erg. 1995, 26, 61–66. [Google Scholar] [CrossRef]
  48. Hermens, H.J.; Freriks, B.; Disselhorst-Klug, C.; Rau, G. Development of recommendations for SEMG sensors and sensor placement procedures. J. Electromyogr. Kinesiol. 2000, 10, 361–374. [Google Scholar] [CrossRef]
  49. Hermens, H.J.; Freriks, B.; Merletti, R.; Stegeman, D.; Blok, J.; Rau, G.; Disselhorst-Klug, C.; Hägg, G. SENIAM Projecte: European Recommendations for Surface electroMyoGraphy; Roessingh Research and Development: Enschede, The Netherlands, 1999. [Google Scholar]
  50. Bigland-Ritchie, B.; Cafarelli, E.; Vøllestad, N.K. Fatigue of submaximal static contractions. Acta Physiol. Scand. Suppl. 1986, 556, 137–148. [Google Scholar]
  51. Allen, D.G.; Lamb, G.D.; Westerblad, H. Skeletal muscle fatigue: Cellular mechanisms. Physiol. Rev. 2008, 88, 287–332. [Google Scholar] [CrossRef] [Green Version]
  52. Solomonow, M.; Baten, C.; Smit, J.; Baratta, R.; Hermens, H.; D’Ambrosia, R.; Shoji, H. Electromyogram power spectra frequencies associated with motor unit recruitment strategies. J. Appl. Physiol. 1990, 68, 1177–1185. [Google Scholar] [CrossRef]
  53. Moritani, T.; Gaffney, F.; Carmichael, T.; Hargis, J. Interrelationships among muscle fiber types, electromyogram, and blood pressure during fatiguing isometric contraction. In Biomechanics IX-A; Winter, D.A., Norman, R., Well, R., Hayes, K., Patla, A., Eds.; Human Kinetics: Champaign, IL, USA, 1985; Volume 5A, pp. 287–292. [Google Scholar]
  54. Komi, P.V.; Tesch, P. EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man. Eur. J. Appl. Physiol. Occup. Physiol. 1979, 42, 41–50. [Google Scholar] [CrossRef]
  55. Klass, M.; Guissard, N.; Duchateau, J. Limiting mechanisms of force production after repetitive dynamic contractions in human triceps surae. J. Appl. Physiol. 2004, 96, 1516–1521. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Peixoto, L.R.; da Rocha, A.F.; de Carvalho, J.L.; Goncalves, C.A. Electromyographic evaluation of muscle recovery after isometric fatigue. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2010, 2010, 4922–4925. [Google Scholar] [CrossRef]
  57. West, W.; Hicks, A.; Clements, L.; Dowling, J. The relationship between voluntary electromyogram, endurance time and intensity of effort in isometric handgrip exercise. Eur. J. Appl. Physiol. Occup. Physiol. 1995, 71, 301–305. [Google Scholar] [CrossRef]
  58. Vigouroux, L.; Quaine, F. Fingertip force and electromyography of finger flexor muscles during a prolonged intermittent exercise in elite climbers and sedentary individuals. J. Sports Sci. 2006, 24, 181–186. [Google Scholar] [CrossRef]
  59. Mills, K.R. Power spectral analysis of electromyogram and compound muscle action potential during muscle fatigue and recovery. J. Physiol. 1982, 326, 401–409. [Google Scholar] [CrossRef]
  60. Kroon, G.W.; Naeije, M.; Hansson, T.L. Electromyographic power-spectrum changes during repeated fatiguing contractions of the human masseter muscle. Arch. Oral Biol. 1986, 31, 603–608. [Google Scholar] [CrossRef]
  61. Elfving, B.; Liljequist, D.; Dedering, A.; Németh, G. Recovery of electromyograph median frequency after lumbar muscle fatigue analysed using an exponential time dependence model. Eur. J. Appl. Physiol. 2002, 88, 85–93. [Google Scholar] [CrossRef] [PubMed]
  62. Kuorinka, I. Restitution of EMG spectrum after muscular fatigue. Eur. J. Appl. Physiol. Occup. Physiol. 1988, 57, 311–315. [Google Scholar] [CrossRef]
  63. Broman, H.; Bilotto, G.; De Luca, C.J. Myoelectric signal conduction velocity and spectral parameters: Influence of force and time. J. Appl. Physiol. 1985, 58, 1428–1437. [Google Scholar] [CrossRef] [PubMed]
  64. Hara, Y.; Findley, T.W.; Sugimoto, A.; Hanayama, K. Muscle fiber conduction velocity (MFCV) after fatigue in elderly subjects. Electromyogr. Clin. Neurophysiol. 1998, 38, 427–435. [Google Scholar] [PubMed]
  65. Kadefors, R.; Kaiser, E.; Petersén, I. Dynamic spectrum analysis of myo-potentials and with special reference to muscle fatigue. Electromyography 1968, 8, 39–74. [Google Scholar] [PubMed]
  66. Petrofsky, J.S. Quantification through the surface EMG of muscle fatigue and recovery during successive isometric contractions. Aviat. Space Environ. Med. 1981, 52, 545–550. [Google Scholar]
  67. Van der Hoeven, J.H.; Van Weerden, T.W.; Zwarts, M.J. Long-lasting supernormal conduction velocity after sustained maximal isometric contraction in human muscle. Muscle Nerve 1993, 16, 312–320. [Google Scholar] [CrossRef] [PubMed]
  68. Enoka, R.M.; Duchateau, J. Muscle fatigue: What, why and how it influences muscle function. J. Physiol. 2008, 586, 11–23. [Google Scholar] [CrossRef] [PubMed]
Ijerph 18 07926 g001 550
Figure 1. (A): Description of the sequence and structure of the intermittent protocol. Auditory feedback was provided to ensure the exact duration of each contraction and resting period. (B): Represents an illustration of a subject who performed 20 rounds, which means that each one of the four successive relative rounds is composed of five rounds.
Figure 1. (A): Description of the sequence and structure of the intermittent protocol. Auditory feedback was provided to ensure the exact duration of each contraction and resting period. (B): Represents an illustration of a subject who performed 20 rounds, which means that each one of the four successive relative rounds is composed of five rounds.
Ijerph 18 07926 g001
Ijerph 18 07926 g002 550
Figure 2. Simulation of the overall position of a rider above a motorcycle race from 600 cc to 1000 cc. A static structure was built to preserve the distances between seat, stirrups, and particularly the combined system of shanks, forks, handlebar, brake and clutch levers, and gas.
Figure 2. Simulation of the overall position of a rider above a motorcycle race from 600 cc to 1000 cc. A static structure was built to preserve the distances between seat, stirrups, and particularly the combined system of shanks, forks, handlebar, brake and clutch levers, and gas.
Ijerph 18 07926 g002
Ijerph 18 07926 g003 550
Figure 3. Example of a comparative regression analysis of an individual. Regression of the carpi radialis (CR) and flexor superficialis digitorum (FS) at the two intensities: (A) is 30% of MVCisom, and (B) is 50% of MVCisom; both used in the intermittent protocol.
Figure 3. Example of a comparative regression analysis of an individual. Regression of the carpi radialis (CR) and flexor superficialis digitorum (FS) at the two intensities: (A) is 30% of MVCisom, and (B) is 50% of MVCisom; both used in the intermittent protocol.
Ijerph 18 07926 g003
Table
Table 1. Regression analysis of normalized median frequency (MFEMG, dependent variable), against the number of rounds (independent variable) accomplished by each rider (n = 21). Muscles analyzed are the carpi radialis (CR) and flexor digitorum superficialis (FS) at 30% and 50% of MVC.
Table 1. Regression analysis of normalized median frequency (MFEMG, dependent variable), against the number of rounds (independent variable) accomplished by each rider (n = 21). Muscles analyzed are the carpi radialis (CR) and flexor digitorum superficialis (FS) at 30% and 50% of MVC.
n = 21rr2Error of EstimateFp
CR30Mean
sd		−0.756
± 0.176		0.580
± 0.266		0.026
± 0.012		54.163
± 57.827		0.005
± 0.009
CI sup
CI inf		0.758
0.753		0.583
0.576		0.027
0.026		54.954
53.372		0.006
0.005
CR50Mean
sd		0.594
± 0.284		0.397
± 0.302		0.045
± 0.019		28.046
± 43.913		0.133
± 0.295
CI sup
CI inf		0.598
0.590		0.401
0.393		0.045
0.045		28.647
27.445		0.137
0.129
FS30Mean
sd		−0.711
± 0.152		0.504
± 0.214		0.022
± 0.008		27.659
± 23.267		0.005
± 0.007
CI sup
CI inf		0.713
0.709		0.507
0.501		0.022
0.002		27.977
27.341		0.005
0.004
FS50Mean
sd		−0.542
± 0.283		0.338
± 0.290		0.033
± 0.016		20.524
± 31.906		0.158
± 0.288
CI sup
CI inf		0.546
0.539		0.342
0.334		0.033
0.033		20.960
20.087		0.161
0.154
Pearson coefficient correlation (r), R squared (r2), error of the estimate, F-statistics (F), level of significance (p), degree of freedom (df: 1, 10–23). The minor number of accomplished rounds was 10. Five riders succeeded to perform all 25 rounds of the intermittent protocol.
Table
Table 2. Frequency table. Number of motorcycle riders who match the condition reported in the individual linear regression analysis. Normalized median frequency (MFEMG) was the variable taken for analysis against the number of rounds accomplished during the intermittent fatigue protocol.
Table 2. Frequency table. Number of motorcycle riders who match the condition reported in the individual linear regression analysis. Normalized median frequency (MFEMG) was the variable taken for analysis against the number of rounds accomplished during the intermittent fatigue protocol.
n = 21rp
>0.700.40–0.69<0.39<0.0010.001–0.05ns
CR3013801470
CR5010741074
FS3013801290
FS50777957
Table
Table 3. 2 (Time) × 2 (Muscles) × 2 (% MVCisom) ANOVA of repeated measures between the beginning (T1) and the end (T2) of the intermittent fatiguing protocol (IFP). The parameter of analysis is the normalized median frequency (MFEMG) of the Carpi Radialis (CR) and Flexor Digitorum Superficialis (FS).
Table 3. 2 (Time) × 2 (Muscles) × 2 (% MVCisom) ANOVA of repeated measures between the beginning (T1) and the end (T2) of the intermittent fatiguing protocol (IFP). The parameter of analysis is the normalized median frequency (MFEMG) of the Carpi Radialis (CR) and Flexor Digitorum Superficialis (FS).
EffectFdfpη2pPaired Comparisonsp
T × In × M20.041, 20<0.0010.5T1 & T2: FS30 < CR30; FS50 < CR50<0.001
T1: CR30 > CR50; T2: CR30 < CR50<0.002
CR30: T1 > T2; CR50: T1 < T2<0.001
T × In33.61, 20<0.0010.63In30: T1 > T2<0.001
In50: T1 < T2<0.024
T × M0.741, 20ns0.04
In × M3.021, 20ns0.13
T1.431, 20ns0.07
In28.581, 20<0.0010.59
M42.431, 20<0.0010.68
Time (T), Intensity (In) of 30% MVCisom (In30) and 50% MVCisom (In50), Muscle (M).
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Marina, M.; Torrado, P.; Bescós, R. Recovery and Fatigue Behavior of Forearm Muscles during a Repetitive Power Grip Gesture in Racing Motorcycle Riders. Int. J. Environ. Res. Public Health 2021, 18, 7926. https://doi.org/10.3390/ijerph18157926

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Marina M, Torrado P, Bescós R. Recovery and Fatigue Behavior of Forearm Muscles during a Repetitive Power Grip Gesture in Racing Motorcycle Riders. International Journal of Environmental Research and Public Health. 2021; 18(15):7926. https://doi.org/10.3390/ijerph18157926

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Marina, Michel, Priscila Torrado, and Raul Bescós. 2021. "Recovery and Fatigue Behavior of Forearm Muscles during a Repetitive Power Grip Gesture in Racing Motorcycle Riders" International Journal of Environmental Research and Public Health 18, no. 15: 7926. https://doi.org/10.3390/ijerph18157926

APA Style

Marina, M., Torrado, P., & Bescós, R. (2021). Recovery and Fatigue Behavior of Forearm Muscles during a Repetitive Power Grip Gesture in Racing Motorcycle Riders. International Journal of Environmental Research and Public Health, 18(15), 7926. https://doi.org/10.3390/ijerph18157926

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