*Article* **Anthropometric Characteristics and Vertical Jump Abilities by Player Position and Performance Level of Junior Female Volleyball Players**

**Suncica Pocek 1 , Zoran Milosevic 1 , Nemanja Lakicevic 2 , Kristina Pantelic-Babic 3 , Milka Imbronjev 1 , Ewan Thomas 2, \* , Antonino Bianco <sup>2</sup> and Patrik Drid 1**


**Abstract:** Although absolute jump heights should be considered an important factor in judging the performance requirements of volleyball players, limited data is available on age-appropriate categories. The purpose of this study is to determine the differences in specific anthropometric characteristics and jumping performance variables in under−19 female volleyball players in relation to playing position and performance level. The sample of subjects consisted of 354 players who prepared for the U19 Women's Volleyball European Championship 2020 (17.4 ± 0.8 years, 1.81 ± 0.07 m, 67.5 ± 7.1 kg). Playing positions analyzed were setters (*n* = 55), opposites (*n* = 37), middle blockers (*n* = 82), outside hitters (*n* = 137), and liberos (*n* = 43). The results showed player position differences in every performance level group in variables of body height, spike, and block jump. Observed differences are a consequence of highly specific tasks of different positions in the composition of the team. Players of different performance levels are significantly different, with athletes of higher-ranked teams achieving better results. The acquired data could be useful for the selection and profiling of young volleyball players.

**Keywords:** spike jump; block jump; critical threshold; specialization

#### **1. Introduction**

Volleyball is a team sport with highly specific tasks and responsibilities for each player on the court according to player's position [1–3]. From the beginners' level and composition of 6:0, each player goes through a transition period of composition 3:3 and 4:2 to the advanced level of 5:1 team composition, with the highest level of player specialization. Based on anthropometric characteristics, the skill quality and motor abilities of players can be talent-identified and assigned to one of the player's positions [4–12]. The dominant composition played at the highest level of contemporary volleyball is the 5:1 composition. The first number denotes the number of the spikers/hitters, while the second number denotes the number of players who are in charge of the organization of the game in terms of setting. Accordingly, a contemporary volleyball team is composed of 5 hitters, namely, an opposite hitter, two outside hitters, two middle blockers, and the one and only player in charge of the organization of the game—the setter. The "7th" player on the court is a specialist defensive player—the libero—who replaces the middle blocker in serve reception and court defense responsibilities. All of them have highly specific and precise tasks.

Through the long-term process of training, talent identification, and selection, players should distinguish themselves, besides in skill level, in terms of above-average body height,

**Citation:** Pocek, S.; Milosevic, Z.; Lakicevic, N.; Pantelic-Babic, K.; Imbronjev, M.; Thomas, E.; Bianco, A.; Drid, P. Anthropometric Characteristics and Vertical Jump Abilities by Player Position and Performance Level of Junior Female Volleyball Players. *Int. J. Environ. Res. Public Health* **2021**, *18*, 8377. https:// doi.org/10.3390/ijerph18168377

Academic Editor: Matthew Driller

Received: 22 June 2021 Accepted: 6 August 2021 Published: 7 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

upper and lower muscular power, speed, and agility [13]. Vertical jump is a fundamental part of the spike, block, and serve. At high-level volleyball, jumping is also used while setting because it reduces the flight time of the ball, speeds up the attack, and makes it harder for the first line of defense–block to read through the possibilities of the attacking team. Vertical jump assessment in volleyball is an inevitable part of training and testing procedures [1,14–21]. In a volleyball 5-set match, players in different positions perform a range of jumps that go from 65 to 136 jumps. On average, the highest number of jumps is performed by setters, followed by middle hitters, opposite hitters, and outside hitters [22]. At an elite playing level, a typical match is likely to impose the greatest stress from maximal jumping on middle players, but the setters also perform a very high number of submaximal jumps [1].

The purpose of this study is to determine the differences in anthropometric and jumping performance variables in under-19 women volleyball players in relation to playing position and performance level. This information could provide significant insight and reference values for talent identification and evaluation of the training programs applied.

#### **2. Materials and Methods**

#### *2.1. Sample*

The Confederation European de Volleyball U19 Women's Volleyball European Championship 2020 (CEV U19W ECH 2020) was held in Bosnia & Hercegovina and Croatia from 22–30 August as the first major European competition since the outbreak of the coronavirus (COVID-19) pandemic. Out of the 12 teams initially planned for the championship, Russia, Italy, and Germany withdrew from the competition due to COVID-19 travel restrictions and specific government measures. The subject sample included 354 female volleyball players from the 12 teams planned for the competition and 3 teams that went through the 1st round of the qualification process for the U19 women's category.

#### *2.2. Data Collection*

All data were retrieved from the CEV U19 Women Volleyball webpage (https://wwwold.cev.eu/Competition-Area/CompetitionView.aspx?ID=1201) (accessed on 11 September 2020).

Displayed data on the official CEV cite for the abovementioned players included measures of body height, body mass, spike jump, block jump, year of birth, and player position. Body mass index was calculated based on the values of body height and body mass. Team level criteria were composed as follows: Level 1, classified 1st–4th, added by volleyball players from Russia and Italy (CEV U17W ECH 2018 1st and 2nd); Level 2, classified 5th–8th, added by volleyball players from Germany (CEV U17W ECH 2018 7th); and Level 3, which consisted of the 9th team of the final standings of the U19 competition (CEV ranking 17th), added by volleyball players from the 3 teams that played in the 1st round of qualification (CEV ranking 30th−32nd). Player position criteria were observed through the role of setter, opposite hitter, middle blocker, outside hitter, and libero (Table 1).

#### *2.3. Statistical Analysis*

Descriptive and inferential analyses of the data were done using the software SPSS v.20 (SPSS Inc., Chicago, IL, USA). Multivariate analysis of variance (MANOVA) with an LSD post hoc test was used to determine the differences in jumping tests and anthropometric measures (dependent variables) between different playing positions and performance levels in volleyball (independent variables). Statistical significance was set at *p* < 0.05.


**Table 1.** Number of players in each position and each team, analyzed according to the CEV U19 Volleyball European Championship 2020 data.

\* Teams which did non participate in the competition due to COVID restritions. § Teams which played only the 1st round of qualification.

#### **3. Results**

Table 2 presents mean (SD) anthropometric, physical, and age characteristics of junior female volleyball players regarding position and performance level. The MANOVAs revealed there are statistically significant differences for player position in 1st (F = 5.69, *p* = 0.00, partial eta-squared = 0.19), 2nd (F = 3.62, *p* = 0.00, partial eta-squared = 0.16), and 3rd performance level groups (F = 3.41, *p* = 0.00, partial eta-squared = 0.22) for body height (1st f = 50.12, *p* = 0.00, partial eta-squared = 0.58, 2nd f = 24.77, *p* = 0.00, partial eta-squared = 0.46, and 3rd f = 26.90, *p* = 0.00, partial eta-squared = 0.59), body mass (1st f = 14.68, *p* = 0.00, partial eta-squared = 0.28, and 2nd f = 8.06, *p* = 0.00, partial eta-squared = 0.22), body mass index (2nd f = 2.71, *p* = 0.03, partial eta-squared = 0.09), spike jump (1st f = 23.99, *p* = 0.00, partial eta-squared = 0.39, 2nd f = 7.70, *p* = 0.00, partial eta-squared = 0.21, and 3rd f = 3.70, p = 0.01, partial eta-squared = 0.17), and block jump (1st f = 15.70, *p* = 0.00, partial eta-squared = 0.30, 2nd f = 6.78, *p* = 0.00, partial eta-squared = 0.19, and 3rd f = 3.21, *p* = 0.02, partial eta-squared = 0.15).

Significant position-related differences in the best performance group in terms of body height are evident in all positions except between opposite and middle blocker players (post hoc LSD *p* = 0.54), with greater values of both opposites and middles in comparison with outside hitters, after whom are setters and libero players. For spike and block jumps, there are no significant differences between players in the positions of opposite and middle blocker (post hoc LSD for spike *p* = 0.24 and block 0.06) as well as the middle blocker and outside hitter (post hoc LSD for spike *p* = 0.11 and block 0.42), while there are statistically significant differences between opposites and outside hitters (post hoc LSD for spike *p* = 0.02 and block 0.01), with better results for opposite players. In all other mutual relations, there are statistically significant differences in the following order: from opposites, middles, outside hitters, setters and libero players, from best to worst results in spike and block jump. In the 2nd performance group, there are no significant differences between opposites and outside hitters in body height, spike, and block jump (post hoc LSD for body height *p* = 0.72, spike 0.28, and block 0.32). In the lowest performance group (3rd) of young volleyball players, differences in the abovementioned variables between positions are even less pronounced significant differences are not observed, although it was expected otherwise (for example, between setters and opposites, post hoc LSD for spike *p* = 0.97 and block 0.40).

**Table 2.** Anthropometric, physical, and age characteristics of junior female volleyball players regarding position and performance level.


Significantly different from: <sup>1</sup>—Setter; <sup>2</sup>—Opposite; <sup>3</sup>—Middle blocker; <sup>4</sup>—Outside; <sup>5</sup>—Libero; \*—significantly different by player position; ‡—significantly different by performance level.

> The MANOVAs revealed there are statistically significant differences by performance level in the player position of opposites (F = 2.23, *p* = 0.02, partial eta-squared = 0.31), middle blockers (F = 2.87, *p* = 0.00, partial eta-squared = 0.19), outside hitters (F = 5.77, *p* = 0.00, partial eta-squared = 0.21), and libero players (F = 2.03, *p* = 0.03, partial eta-squared = 0.25). Univariate analysis showed that there are statistically significant differences in the observed variables: for opposites, in body height (f = 7.52, *p* = 0.00, partial eta-squared = 0.31), spike jump (f = 12.04, *p* = 0.00, partial eta-squared = 0.41), and block jump f = 12.22, *p* = 0.00, partial eta-squared = 0.42); for middle blockers, in body height (f = 6.13, *p* = 0.00, partial eta-squared = 0.13), body mass (f = 4.49, *p* = 0.01, partial eta-squared = 0.10), spike jump (f = 17.90, *p* = 0.00, partial eta-squared = 0.31), and block jump (f = 5.39, *p* = 0.01, partial eta-squared = 0.12); for outside hitters, in body height (f = 23.67, *p* = 0.00, partial etasquared = 0.26), body mass (f = 13.19, *p* = 0.00, partial eta-squared = 0.17), body mass index (f = 8.93, *p* = 0.00, partial eta-squared = 0.12), spike jump (f = 10.96, *p* = 0.00, partial eta-squared = 0.14), and block jump (f = 7.65, *p* = 0.00, partial eta-squared = 0.10). Based on the post hoc LSD test, we can observe that the 1st and 2nd group of opposites, middles, outsides, and liberos, in comparison to the 3rd group, have greater values of body height and better results of spike and block jump. In the player position of opposites, there are also statistically significant differences between 1st and 2nd performance levels. Differences in the varied performance levels of setters with greater values of body height and better

results of spike and block jump of the 1st performance level in comparison to 2nd and 2nd in comparison to 3rd are observed, although they are not statistically significant.

Finally, Table 3 reports position-specific normative centile values for anthropometric characteristics in terms of body height and sport-specific jumping abilities in absolute values, i.e., spike jump and block jump.

**Table 3.** Position-specific normative centile values of body height, spike jump, and block jump for junior female volleyball players.


Values of body height, spike jump, and block jump are presented in meters.

#### **4. Discussion**

The aim of this study is to investigate the anthropometric characteristics and vertical jumping abilities of junior female volleyball players according to player position and performance level. The main results of our study are as follows:

(a) there are statistically significant differences by player position in every performance level group in the variables of body height, spike jump, and block jump.

(b) Significant position-related differences in the best performance group in terms of body height are evident in all positions except between opposite and middle blocker players, with greater values of both opposites and middles in comparison to outside hitters, followed, in order, by setters and libero players. In the variables of spike and block jumps, there are no significant differences between players in the positions of opposite and middle blocker, as well as middle blocker and outside hitters, while there are statistically significant differences between opposites and outside hitters, with better results from opposite players. In all other mutual relations, there are statistically significant differences in the following order: from opposites, middles, outside hitters, setters till the libero players, and from best to worst results in spike and block jumps. In the 2nd performance group, the same conclusions were derived with the addition that in this group, there were no significant differences between opposites and outside hitters in body height, spike jump, and block jump as a consequence of the lower values of opposites of the 2nd performance level group in comparison with the 1st performance level group, which were leveled to the values of the outside hitters. In the lowest performance group (3rd) of young volleyball players, differences in the abovementioned variables between positions were even less pronounced and non-significant.

(c) There are statistically significant differences by performance level, with greater values of body height and better results of spike and block jumps of the 1st and 2nd group of opposites, middles, outsides, and liberos in comparison to the 3rd group. In the player position of opposites, there are also statistically significant differences between 1st and 2nd performance levels. Differences in the varied performance levels of setters with greater values of body height and better results of spike and block jumps of the 1st performance level in comparison to 2nd and 2nd in comparison to 3rd are observed, although they are not statistically significant.

Based on the results of the present research, the data showed that there are statistically significant differences in body height and absolute values of spike and block jumps between positions in volleyball. These findings are in accordance with research conducted by different authors [1,3,23], and they are within expectations due to player tasks on the court. At the same time, numerous studies that had taken into account the relative values of jumping abilities did not find any differences between player positions except for body height values [2,24–26]. Consequently, we can see on the basis of the norms of absolute values (Table 3), which is the critical height that athletes should reach. Elite volleyball players need to reach the threshold in the absolute values of spike and block jumps for specific positions. Such can be achieved either on the count of above-average body height and/or relative values of vertical jump in order to reach that threshold. Those players with a lower body height can compensate for their lack by an above-average jumping ability for the particular position that is targeted. In Table 3, we can see to which extent it should be expected. In such a manner, differences in relative jumping abilities between player positions are possible [19].

In this respect, relative vertical jumping ability is of great importance in volleyball regardless of the players' position, while absolute vertical jump values can differentiate players not only in terms of player position and performance level but in their career trajectories. However, maximum jump height performance in each and every jump, either in the spike or in the block, is neither necessary nor expedient. Due to player adaptation of their efforts to the game situation and efficacy of their performance throughout the game, the intensities of attack jumps at maximal capacity varies from 55–90% [27]. A higher contact height in the attack motion involves a better incidence angle of the opponent court [28]. Differences between the values of spike and block jumps, with the greater reach of spike jumps, are due to the type of the approach (frontal vs. lateral) and how the ball is contacted (one hand vs. both hands simultaneously). The fact that the aim of block performance is to increase the area by which we limit attacker options, the player needs to place both hands simultaneously on the ball when performing the block. Additionally, the reason for such height discrepancies between spike and block jumps is that the first is executed individually while a block must be performed by two or three players in a coordinated manner in which players need to adjust and harmonize their temporal and spatial actions.

Player specialization, i.e., determining the player's position, is a complex and longterm process. Based on the player's characteristics and abilities, coaches should assign the player a role on the court that would maximize the player's contribution to the team. Coaches may sometimes encounter resistance from players due to their affinities, but the specialization process should be approached thoroughly. Talent identification and development is a process based on an understanding of the tasks and responsibilities of the player regarding their position (Table 4) as well as a consideration of the body measures and abilities associated with sports performance. In pursuit of elite performance, it is of great importance to differentiate between the trainable and non-trainable qualities of a player [29–31].


**Table 4.** Player's role and subsequent tasks in volleyball according to their position.

✓: Performed task by role, roman letters indicate the field zone in which such task can be performed. <sup>1</sup> Setters do not perform spikes, but are allowed in certain situations: setter positioned in Zone IV, Zone III, and Zone II. <sup>2</sup> R1, setter in Zone I: opposite player performs spike from Zone IV, while outside hitter performs spike from Zone II. <sup>3</sup> R1, setter in Zone I: as a response to an opponent's counterattack, the opposite player performs a block from Zone IV while the outside hitter performs a block from Zone II. <sup>4</sup> if setter is not able to perform setting due to previous contact with the ball, middle blocker or libero could help.

A spike is the most attractive and efficient way of scoring a point. The success of this action depends on height of contact, ball direction, and ball speed. The main factors that determine the height of contact are standing height reached, which consists of body height and arm length, and ability to jump and reach, which consists of the ability of a player to perform technical elements in the most efficient way in terms of the utilization of motor abilities, namely, explosive muscular power. For the height of contact, the ability to jump and reach is usually monitored in volleyball [15]. With the exception of liberos, every player may spike.

Throughout the years, as demonstrated in the European and World Championships, there has been an increase in the use of the power jump serve in both men's and women's volleyball [32]. With the change in rules and the introduction of the Rally Point System, the serve as a skill has become a mighty tool for scoring a point and not just for entering the rally in the competition of two teams. Even when a team does not score a direct or ace point, through a powerful power jump serve, it can reduce the possibilities of attack from the opponent team and facilitate the organization of a counterattack. Therefore, while performing spikes, power serves (which is very similar to the spike technique), and blocks, volleyball players should possess above average body height in combination with the power and force of lower extremities in executing simple vertical jumps.

Because of the similar requirements in spike and block jumps for opposite (main hitter) and middle blockers (main blocker), the shorter outside hitters, in order to reach the critical threshold, must make up for their lack of height and standing reach by exhibiting superior relative jump heights. In this respect, absolute jump heights of spike and block are of great importance when judging the performance requirements of outsides. Those players who did not reach these thresholds cannot play at the elite level of volleyball on the position of outsides. Hence, these players were left out during the transition from junior to senior levels of competition, which resulted in their playing career coming to an end. Because of their exquisite skillfulness in serve reception and court defense, in order to keep them in the team and improve the game, in 1998, there was a change in rules and the introduction of the libero player.

Our study was able to identify differences between various playing positions in terms of body height and absolute vertical jump for both spike and block in elite junior female volleyball players. However, the main limitation of this study is that all data were retrieved from the data displayed by the official CEV site from the competition of the U19 Women's Volleyball European Championship 2020.

#### **5. Conclusions**

Player specialization, i.e., determining the player's position, is a complex and longterm process. Based on the player's characteristics and abilities, coaches should assign the player a role on the court that would maximize the player's contribution to the overall quality of the team. During that process, it is important to differentiate between the trainable and non-trainable qualities of a player.

Based on the results of this research, the data shows that there are statistically significant differences in body height and absolute values of spike and block jumps between positions in volleyball. Relative vertical jumping ability is of great importance in volleyball regardless of the players' position, while absolute vertical jump values have the power to differentiate players not only in terms of player position but also in performance level. The higher the performance level of the team, the lower the intra-positional differences in terms of height, spike jump, and block jump, and some other factors become decisive (e.g., technical–tactical skill and knowledge, decision-making quality, performance under pressure).

Deficit of body height for a particular position can be compensated by jumping ability only to some extent. The relatively large sample of subjects in our study is composed of elite (1st group), good (2nd group), and lower levels of performance (3rd group) of U19 women volleyball players. In pursuit of excellence and competition on the elite senior level, they need to reach the threshold for their age and particular position in terms of absolute spike and block jumps based on the normative values presented.

**Author Contributions:** Conceptualization, S.P.; methodology M.I.; formal analysis, Z.M.; investigation, N.L.; data curation, S.P.; writing—original draft preparation, A.B.; writing—review and editing, K.P.-B. and E.T.; visualization, E.T.; supervision, P.D.; project administration, P.D.; funding acquisition, P.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Serbian Ministry of Education, Science, and Technological Development (179011) and the Provincial Secretariat for Higher Education and Scientific Research (142-451-2094).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and the Ethical Committee approved the study of the Faculty of Sport and Physical Education (Ref. No. 46-06-02/2020-1), University of Novi Sad.

**Informed Consent Statement:** Written informed consent was not necessary for the study design.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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

**Michel Marina 1, \* , Priscila Torrado <sup>1</sup> and Raul Bescós 2**


**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.

**Keywords:** handgrip; carpi radialis; flexor digitorum superficialis; neuromuscular fatigue; motorcycle; recovery

#### **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 [1–4]. 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 [7–11]. 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.

**Citation:** 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

Academic Editor: Ewan Thomas

Received: 3 June 2021 Accepted: 21 July 2021 Published: 27 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

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 [9,12–16]. 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 [22–24], 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 [33–35], 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.

Ω 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: T<sup>1</sup> 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 (η <sup>2</sup>p) 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 (CR<sup>30</sup> and FS30), with pauses of 5 s between each contraction. In contrast, when both muscles were exerted at 50% MVCisom (CR<sup>50</sup> and FS50), after 1 min of recovery, no significant relationship was observed (*p* > 0.133). The higher correlation observed in CR<sup>30</sup> and FS<sup>30</sup> (*r* ≥ −0.71) in comparison to CR<sup>50</sup> and FS<sup>50</sup> (*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 CR<sup>50</sup> 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 CR<sup>30</sup> and FS<sup>30</sup> (*r* <sup>2</sup> ≥ 0.50) in comparison to CR<sup>50</sup> and FS<sup>50</sup> (*r* <sup>2</sup> ≤ 0.40) (Table 1).


**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.

Pearson coefficient correlation (*r*), R squared *(r*<sup>2</sup> ), 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.

**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.

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 CR<sup>30</sup> and FS<sup>30</sup> in comparison to CR<sup>50</sup> 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 CR<sup>30</sup> 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 CR<sup>50</sup> and FS50.

η

 

**η**

η


**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.

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).

**Table 3.** 2 (Time) × 2 (Muscles) × 2 (% MVCisom) ANOVA of repeated measures between the beginning (T<sup>1</sup> ) and the end (T<sup>2</sup> ) 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).


Time (T), Intensity (In) of 30% MVCisom (In30) and 50% MVCisom (In50), Muscle (M).

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 (T<sup>1</sup> 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 (η <sup>2</sup>p = 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 (η <sup>2</sup>p = 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 CR<sup>30</sup> and FS<sup>30</sup> in comparison with CR<sup>50</sup> 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 CR<sup>50</sup> and FS<sup>50</sup> 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 [59–61], 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 [21,23,43,44,59–63]. 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**

