*Article* **Strength and Power Characteristics in National Amateur Rugby Players**

**Diego Alexandre Alonso-Aubin 1, \*, Moisés Picón-Martínez <sup>1</sup> and Iván Chulvi-Medrano 2**


**\*** Correspondence: diego@wingsport.es; Tel.: +34-690-093-962

**Abstract:** Rugby players need muscular strength and power to meet the demands of the sport; therefore, a proper assessment of the performance in rugby players should include both variables. The purpose of this study was to examine the strength and power characteristics (SPC) during the squat (SQ) and bench press (BP) in national amateur rugby players and to analyze gender- and position-related differences. A total of 47 players (30 males and 17 females; age: 25.56 ± 1.14 and 23.16 ± 1.38 years, respectively) participated in the study. The one repetition-maximum (1-RM) and SPC in SQ and BP were obtained using a Smith Machine. Then, subjects performed one set of five repetitions on the SQ and BP against six relative loads (30–40–50–60–70–80% 1-RM) using a linear transducer. Differences between genders were found in 1-RM for maximal power, kilograms lifted at maximal power, maximal power, maximal strength and maximal speed in BP (*p* < 0.00) and 1-RM, kilograms lifted at maximal power, maximal power, maximal strength and maximal speed in SQ (*p* < 0.00). Comparisons between variables in SQ and BP present a significant relationship (*p* < 0.01) in SQ and BP 1-RM with kilograms lifted at maximal power *(r* = 0.86 and r = 0.84), maximal strength (*r* = 0.53 and *r* = 0.92) and maximal power (*r* = 0.76 and *r* = 0.93). This study confirms the importance of the SPC assessment for training prescription in rugby amateur players.

**Keywords:** squat; bench press; training; strength; speed

#### **1. Introduction**

Rugby is a collision sport that involves high-intensity bouts of exercise including sprint and agility activities and contact and tackling separated by short bouts of low-intensity activity [1,2]. Rugby players need speed, agility, muscular strength and power to meet the demands on the sport and these factors distinguish high- and low-level players [3,4]. Indeed, muscular strength and power are directly associated with performance, whereby the elite players demonstrate the highest muscle power values [5,6]. For example, highspeed running demands are influenced by strength and power and, consequently, by the force–power–velocity profile (FVP) characterizing the maximal mechanical capabilities of the neuromuscular system [7]. Moreover, due to the tactical and movement patterns of rugby, players should have agility skills for avoiding contact and collisions [8–11]. In the case of rugby, there are two general player positions, backs and forwards, with different physical demands [12,13]. Forwards are involved in more collisions, whereas backs are involved in more high-speed running (>5 m·s −1 ) [14]. In addition, previous studies identified that forwards are stronger and more powerful than backs [15]. Backs are reportedly faster and more agile than forwards [16,17].

Muscular strength and power are key attributes for rugby players due to the contact and collision element of the sport, alongside its relationship with those for other physical qualities [18]. Maximal strength is the vehicle that drives the development of the strength and power and allows athletes to develop greater performance on speed and agility

**Citation:** Alonso-Aubin, D.A.; Picón-Martínez, M.;

Chulvi-Medrano, I. Strength and Power Characteristics in National Amateur Rugby Players. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5615. https://doi.org/10.3390/ ijerph18115615

Academic Editor: Padulo Johnny

Received: 11 February 2021 Accepted: 21 May 2021 Published: 24 May 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/).

activities [19,20]. Thereby, when considering the ability to develop power, it is clear that high levels of muscular strength are a key factor to reach high levels of power [21,22].

Assessing the physical demands could assist in athlete development, guiding athletes' training and assisting coaches. For example, training studies that incorporate maximal or explosive strength exercises have found improvements in the sprinting speed of athletes [21]. In addition, there are several studies presenting strength and power data via Wattbike peak power output, countermovement jump or isoinertial highlighting the importance on strength and power characteristics (SPC) understanding [23].

However, studies on SPC in females are limited, which causes a gap in the knowledge about SPC in females and if there are differences in comparison to males. We hypothesize that males are stronger and more powerful than females because they are heavier and bigger [24,25]. It seems that at the elite level in females, forwards were heavier and displayed greater upper-body strength, whereas backs showed greater acceleration and maximal speed abilities [26]. Recently, similar data were reported in other studies comparing backs and forwards where high-speed demands were different, suggesting that maximal velocity running and strength and power training are important [27]. These facts highlight the importance of assessing SPC and maximizing long-term adaptation of muscle force and power via resistance training [28]. Consequently, a proper assessment of the performance in rugby players should include maximal strength and power. However, there is a gap in the research on the study of these two capacities and their relationship for rugby performance and how strength and conditioning programs should be individualized attending to position demands and gender differences. In order to assess SPC, exercises should be selected that provide a transfer to the sport in skill movement and strength. Thus, the squat (SQ) and the bench press (BP) are two of the most used and effective exercises in resistance training for strengthening the lower and the upper body for improving athletic performance [29,30].

Therefore, the purpose of this study was to examine the strength and power characteristics (SPC) on the SQ and BP in national amateur rugby players and to analyze sexand position-related differences for better strength and conditioning program designs. We hypothesize that there are differences in SPC between backs and forwards and males and females.

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

#### *2.1. Participants*

A total of 47 rugby players, with at least 5 years of experience in practice in rugby and resistance training performing parallel SQ and BP exercises, volunteered to participate in this study. Exclusion criteria included a musculoskeletal injury over the past six months, any medical condition that could limit the exercise performance, and taking steroids, drugs, medications or dietary supplements for enhancing sport performance. Subjects were national amateur rugby players and included 30 males (age 26.56 ± 1.14 years; height 1.78 ± 0.16 m; and body mass 86.85 ± 1.88 kg) and 17 females (age 23.16 ± 1.38 years; height 1.63 ± 0.16 m; and body mass 66.46 ± 2.39 kg). Then, for comparing player positions we split in forwards (age 25.57 ± 1.25 years; height 1.74 ± 0.15 m; and body mass 84.11 ± 2.43 kg; 20 males and 10 females) and backs (age 25.07 ± 1.27 years; height 1.71 ± 0.25 m; and body mass 72.23 ± 3.04 kg; 10 males and 7 females). Their average weekly training volume was 13 h\*wk−<sup>1</sup> including three days of rugby-specific training, three days of resistance training and competition. All procedures followed ethical principles for medical research involving human subjects by the World Medical Association Declaration of Helsinki (General Assembly of the World Medical Association. 2014). The study was approved by the University of Alicante Institution Ethics Committee UA-2018-06-20. All subjects were informed of the purpose of the study, and experimental procedures and potential risks of the study. They were given the opportunity to ask any questions related to the procedures. After being informed, they signed the informed consent form.

#### *2.2. Procedures*

The present research was performed in 3 different sessions, all separated by 3 days. The first visit to the laboratory was dedicated to informing the subjects about the procedures, familiarization with tests and anthropometric measurements. Maximum strength tests (1-RM) in parallel SQ and parallel BP were performed in the second session and power assessment in the third in randomized order. For the strength assessments, we used a Smith Machine (Multipower, Line Selection; Technogym, Gambettola, Italy). All the sessions were performed at the same time, between 5 p.m. and 8 p.m. All tests were supervised by a Certified Strength and Conditioning Specialist (CSCS) who has 10 years' experience in testing and training rugby athletes. Subjects were instructed to avoid any strenuous physical activity 24 h prior to each assessment and not to eat and drink water ad libitum 45 min before the assessments. Before testing, players performed a standardized 15 min warm-up that consisted of 5 min of pedaling on a cycle ergometer at an intensity of 50 watts followed by 1 set of 15 repetitions on either SQ or BP exercise at an increasing velocity with a 20-kg barbell.

#### 2.2.1. Squat and Bench Press Techniques

To standardize exercise performance during the testing sessions, an analogic goniometer was used to measure 90◦ of knee flexion (parallel-squat) and ensure a consistent stance distance during the SQ and 90◦ of elbow flexion and biacromial distance of the grip width during the BP.

To ensure the correct range of motion, straps were used to limit a greater displacement than 90◦ of knee flexion on the SQ and 90◦ of elbow flexion on the BP.

During the SQ, the load was lifted without lifting heels off the ground, keeping the back straight, eyes focused forward and feet slightly wider than shoulder-width apart with toes pointing slightly outward.

During the BP, the load was lifted without lifting the hips off the bench, with the neck and the back lying on the bench and the feet on the ground [31,32].

#### 2.2.2. Maximal Strength

Subjects completed the squat assessment first and then the bench press assessment. Subjects performed 1 set of 3–4 repetitions on the SQ and BP exercises with 4 relative loads calculated according to their previous 1-RM performed 2 weeks ago in a trainingtest session (60–70–80–90% 1-RM) for warming up. After the specific 15 min warm-up previously mentioned, subjects performed a 1-RM attempt by increasing progressively (by 10–20% in the SQ and 5–10% in the BP) the load used in 100% 1-RM. If the subject failed the 1-RM attempt, we decreased the load by 5–10% in the squat and 2.5–5% in the BP. The subjects' 1-RMs were achieved within five attempts. Subjects were given 3 min of recovery between each set and 5 min between exercises.

#### 2.2.3. Power Strength

After 72 h from the maximal strength assessment, subjects returned to the testing facility to perform 1 set of 5 repetitions on the SQ and BP exercises on 6 relative loads with an increasing intensity calculated from the data recorded in the first session (30–40– 50–60–70–80% 1-RM). Subjects were given a 3 min recovery between each set and 5 min between exercises.

Participants were told the importance of performing the concentric phase at the highest speed and effort possible. During the performance, they were not given any kind of feedback.

#### 2.2.4. Measurement Equipment and Data Analysis

The bar was properly instrumented with a linear position transducer (T-Force System, Ergotech, Murcia, Spain) that has a precision in 1000 N and a sampling frequency on 1000 Hz for maximal power recording. This device has been used to assess kinetic

and kinematic variables in resistance exercises. The system consists of a linear velocity transducer extension cable in interface with a personal computer that obtains data with an analogic–digital resolution of 14 bits. The specific software (TFDMS Version 2.35) calculates the kinematic and kinetic parameters of each repetition, and stores and provides all information from the results obtained in real time [33]. The system's software automatically calculated the bar velocity of every repetition, providing auditory and visual feedback in the same moment of realization.

The concentric phase or positive work was as fast as the subject could perform. The eccentric or negative work, and recovery phase had a duration of 3.5 s [34]. Additionally, all the measurement data were stored on a virtual disk.

Subsequently, the software analyzed the data, obtaining the following variables for both exercises: maximal power at a given percentage (Max. Power at 1-RM%), kilograms used to achieve the highest power value (Max. Power kg), maximal power (Max. Power in W), maximal strength (Max. Strength in N), maximal speed (Max. Speed), time spent reaching maximal power (Time to Max. Power), time spent reaching maximal speed (Time to Max. Speed).

#### *2.3. Statistical Analysis*

The normality of the data for each group was checked using the Shapiro–Wilk test. Due to the normal distribution, data are described as mean and standard deviation (SD). One-way ANOVA was used to determine differences between backs and forwards, and men and females. Pearson correlations were performed to determine the significance of the association between variables (0.00 to 0.30: negligible correlation; 0.30 to 0.50: low positive correlation, 0.50 to 0.70: moderate positive correlation, 0.70 to 0.90: high positive correlation, 0.90 to 1.00: very high positive correlation) [35]. Significance was set at *p* < 0.05. To assess effect size d, the Cohen test was used. The effect size indices were 0.2 = small; 0.5 = medium; 0.8 = large and 1.3 = very large [36]. Analyses were performed using SPSS® v25.0 for Mac (SPSS, Inc., Chicago, IL, USA).

#### **3. Results**

Table 1 shows differences by gender and rugby position in 1-RM for both exercises, maximal power at a given percentage (Max. Power at 1-RM%), kilograms used to achieve the highest power value (Max. Power kg), maximal power (Max. Power in W), maximal strength (Max. Strength in N), maximal speed (Max. Speed), time spent reaching maximal power (Time to Max. Power), time spent reaching maximal speed (Time to Max. Speed).


**Table 1.** Differences between genders and positions.

BP: Bench Press; SQ: Squat; s: seconds; 1-RM: one maximum repetition; kg: kilograms; W: watts; N: newtons; ms: milliseconds; \* *p* < 0.05; \*\* *p* < 0.01; d: d Cohen.

Significant differences (*p* < 0.01) were found between males and females in the BP in 1-RM (d = 14.67), kilograms used to achieve the highest power value (d = 14.77), maximal power (d = 1.84), maximal strength (d = 1.26) and maximal speed (d = 4.20) and in the SQ in kilograms used to achieve the highest power value (d = 2.96), maximal power (d = 0.50) and maximal speed (d = 3.99).

Tables 2 and 3 show the comparisons between positions by gender among variables in SQ and BP. We found significant differences (*p* < 0.01) in the BP in females in kilograms to achieve the higher power values (d = 0.76) and maximal strength (d = 0.81). Tables 4 and 5 show the correlations among variables in SQ and BP.


**Table 2.** Squat comparisons between positions by gender.

BP: Bench Press; s: seconds; 1-RM: one maximum repetition; kg: kilograms; W: watts; N: newtons; ms: milliseconds; d Cohen.

**Table 3.** Bench Press comparisons between positions by gender.



**Table 3.** *Cont.*

BP: Bench Press; s: seconds; 1-RM: one maximum repetition; kg: kilograms; W: watts; N: newtons; ms: milliseconds; \* *p* < 0.05; \*\* *p* < 0.01; d: d Cohen.

**Table 4.** Correlation among squat variables.


SQ: Squat; s: seconds; 1-RM: one maximum repetition; kg: kilograms; W: watts; N: newtons; ms: milliseconds; \* *p* < 0.05; \*\* *p* < 0.01.

**Table 5.** Correlation among bench press variables.


BP: Bench Press; s: seconds; 1-RM: one maximum repetition; kg: kilograms; W: watts; N: newtons; ms: milliseconds; \* *p* < 0.05; \*\* *p* < 0.01.

#### **4. Discussion**

The purpose of this study was to examine SPC on the SQ and BP in national amateur rugby players, analyzing possible differences in performance between genders and playing positions. One finding of this study was that the assessment of the SPC could be a useful tool for resistance-training prescription, adding the ability for coaches to prescribe training according the role demands. In addition, our study could provide the opportunity for other studies to recruit samples from multiple clubs, thus increasing sample sizes, generalizability of results and statistical power of subcategory comparisons (e.g., position, playing level or gender). This objective is one of the goals proposed in the most recent scientific literature [37].

First, there is a scarcity in the literature comparing SPC in males and females, and we found strong differences between BP and SQ in males and females, in both absolute and relative values of the different variables of the SPC, especially in 1-RM, maximal power and maximal speed in SQ and BP. These results are similar to those found in previous research conducted with adolescent rugby players where there were also differences between genders in both exercises [38,39]. These findings could be attributed to the prevalence of slower type-I and II-A fibers in females compared with males that parallels the lower contractile velocity in females compared with males and differences in thyroid hormone, estrogen and testosterone levels [40]. Supporting this, one study previously demonstrated that with different maturation status SPC could be different in males and females, as in adolescent age females could demonstrate greater power values than males [38].

Secondly, comparing different sports disciplines also shows that the variables that make up the SPC are specific not only to sports but also individually [41]. In the case of rugby, the player's role may have an important implication for the training design since the specific techniques of the sport as well as the movements associated with their playing positions may have a direct relationship with the training method to be applied [42]. Supporting this evidence, other findings confirm the influence of training and sport activity on the force and velocity capacity balance for power-oriented sports [43]. Due to these differences and the scarce studies carried on rugby it is important to determine the SPC in these players. One of the objectives of the SPC studies is to determine if there are differences between athletes based on their role during the game. We did not find strong differences between backs and forwards in our study in the SPC variables, even when we compared positions by gender, excepting in maximal speed in the BP exercise when we compared all the subjects together, and maximal power and strength in females. This could be explained by their amateur level, as other studies found differences in strength and maximal force production in BP in rugby union players because of the physical demands of these respective positions [6]. In the literature there are few studies that determine differences related to the athletes' roles [44,45]. The SPC assessment could be important to determine the factors that have an influence on performance individually on rugby players. Our data show that SQ and BP present a significant strong relationship in 1-RM and maximal power and BP and kilograms lifted at maximal power, maximal strength and maximal power. The sport position could have an influence in the performance showing differences between forwards and backs in maximal speed in BP exercise.

Our study found high correlations in SQ and BP between maximal power and maximal strength, and kilograms lifted at maximal power, suggesting that strength performance is critical for greater power values. Similar conclusions were reported in previous studies in SQ [46–48] and in BP [49,50]. Knowing the SPC of the players can also be decisive to know the level and potential of future performance of the players since the strength and power values are considered indicators of the level of development [51]. We analyzed the data and the relationships that exist between the variables of the SPC in order to determine which are the most significant in power performance to prescribe more specific training according to the objectives and for enhancing players' performance by their roles. Based on our data, maximal power in SQ and BP is strongly related to maximal strength. In this sense, the training contents can be manipulated by the coaches to make rugby players improve their performance through increased production of strength [52].

In the same way, the relationship between the force–velocity profile (FVP) and performance in sprint and jump tests has also been studied, determining that there is a high correlation between some of the FVP variables such as maximum power, speed and strength, especially in the first one [53].

Therefore, it is necessary to develop new research to understand different training protocols in order to improve the performance of the FVP associated with the specific context of rugby, including also the assessment of speed and agility since it will allow determining the possibilities of improvement in movement during game situations. In the case of trying to improve rugby performance, it is suggested that resistance training must be adjusted using SPC values close to the rugby role playing reported in high performance players. This will optimize the SPC in the different positions in rugby forwards or backs.

This is the first study analyzing the SPC in national amateur rugby players by comparing the SPC variables between gender and positions and providing SPC data in females by positions. However, some limitations should be acknowledged. Research in SPC in national amateur rugby players is a field with gaps in the literature and we need more studies for a better understanding. In our study, we only had access to 47 rugby players, but we know that a greater number of subjects would be better for improved inferences. In this sense, we have not assessed the muscle mass, and this could give a better perspective about the SPC and its relationship with the body composition. Finally, we have not included a rugby-specific test to correlate with the SPC.

#### **5. Conclusions**

This study confirms the importance of the SPC assessment for training prescription in national amateur rugby players for enhancing performance by player position or gender. In addition, this study provides data so that other investigations can compare their results and thus establish a database to make inferences about the performance of rugby players. It is confirmed that there are differences between males and females in both absolute and relative values of the different variables of the SPC, especially in 1-RM, maximal power and maximal speed in SQ and BP. These variables are also critical for sport performance and should be considered for a proper assessment and training in rugby performance.

**Author Contributions:** Conceptualization, D.A.A.-A., I.C.-M. and M.P.-M.; methodology, D.A.A.-A. and I.C.-M.; software, M.P.-M.; validation, D.A.A.-A., I.C.-M. and M.P.-M.; formal analysis, D.A.A.-A.; investigation, D.A.A.-A., I.C.-M. and M.P.-M.; resources, D.A.A.-A.; data curation, D.A.A.-A.; writing—original draft preparation, D.A.A.-A. and I.C.-M.; writing—review and editing, D.A.A.-A. and I.C.-M.; visualization, M.P.-M.; supervision, D.A.A.-A., I.C.-M. and M.P.-M.; project administration, D.A.A.-A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by University of Alicante Ethics Committee (UA-2018-06-20).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Data available on request due to restrictions, e.g., privacy or ethical.

**Acknowledgments:** We would like to thank all our participants for their time and effort.

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

#### **References**


### *Article* **The Biomechanical Characterization of the Turning Phase during a 180** ◦ **Change of Direction**

**Enrico Santoro 1 , Antonio Tessitore 1 , Chiang Liu 2 , Chi-Hsien Chen 2 , Chutimon Khemtong 3 , Mauro Mandorino 1 , Yi-Hua Lee <sup>4</sup> and Giancarlo Condello 3, \***


**Abstract:** The aim of this study was to characterize the turning phase during a modified 505 test. Forty collegiate basketball students, divided into faster and slower performers and high-playing-level and low-playing-level groups, were evaluated for the force-time characteristics (braking and/or propulsive phase) of the penultimate foot contact (PFC), final foot contact (FFC), and first accelerating foot contact (AFC), and for completion time and approach velocity. Based on the composition of the AFC, trials were classified as braking/propulsive or only propulsive. Regression analysis for the prediction of completion time was performed. The AFC contributed to reacceleration through shorter contact times and step length, and lower braking force production (*p* < 0.05). Faster performers and the high-playing-level group demonstrated (*p* < 0.05): lower completion times, higher approach velocities, longer steps length in the PFC and FFC, greater braking forces and impulses in the PFC; greater braking and propulsive forces, braking impulses, lower contact times in the FFC; greater braking and propulsive horizontal forces, horizontal impulses, lower contact times and vertical impulses in the AFC. Kinetic variables from only the FFC and AFC and approach velocity predicted 75% (braking/propulsive trials) and 76.2% (only-propulsive trials) of completion times. The characterization of the turning phase demonstrated the specific contribution of each foot contact and the possible implications for training prescription.

**Keywords:** modified 505 test; kinetic variables; completion time; foot contact; predictors

#### **1. Introduction**

Change-of-direction (COD) ability is a preplanned, multidirectional action and an important physical quality for many team sports [1–3]. It can be defined as the ability to decelerate (i.e., eccentric action) in the shortest time and quickly reaccelerate (i.e., concentric action) in a new direction while running or sprinting [1–4]. This coupling of an eccentric and concentric action also refers to the ability to properly use the stretch-shortening cycle (SSC) [5].

A COD can be performed with or without a stimulus to which to respond. Specifically, when a directional change occurs in response to a stimulus, it is referred to as agility, while it is only referred to as COD speed if the response to a stimulus is not required [1–3]. Focusing solely on the preplanned action during training and competitions, CODs are executed at different directions, speeds, and with different cutting angles. In particular, a 180 ◦ COD action frequently occurs during competitions (i.e., basketball, soccer, netball,

**Citation:** Santoro, E.; Tessitore, A.; Liu, C.; Chen, C.-H.; Khemtong, C.; Mandorino, M.; Lee, Y.-H.; Condello, G. The Biomechanical Characterization of the Turning Phase during a 180◦ Change of Direction. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5519. https://doi.org/10.3390/ ijerph18115519

Academic Editors: Ewan Thomas, Ivan Chulvi-Medrano and Elvira Padua

Received: 26 March 2021 Accepted: 18 May 2021 Published: 21 May 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/).

cricket) [6–8]. For this reason, it is widely included in fitness testing batteries for either COD speed tests (i.e., 505 test) or endurance field-based cardiorespiratory tests (i.e., Yo-Yo test, 30–15 Intermittent Fitness Test). In this context, the test's completion time is mainly used as the performance outcome. However, an exhaustive evaluation of the COD ability requires technical and biomechanical assessments [9–12], hence focusing on the "how" (i.e., quality) and not only on the "what" (i.e., time) of the COD performance to better address training prescription [13]. Therefore, the investigation of the "how" has been consistently addressed through the assessment of kinetic and kinematic variables during the critical foot contacts of COD performance [14].

Focusing solely on the 180◦ COD speed tests, the kinetic and kinematic variables of the penultimate foot contact (PFC) and final foot contact (FFC) are commonly evaluated during traditional and modified 505 tests to investigate the differences between faster and slower performers [15–17]. Recently, new research investigated for the first time the antepenultimate foot contact, demonstrating its role in facilitating the deceleration phase during a traditional 505 test [18]. However, it would be interesting to also consider the analysis of the first step after turning, henceforth called the first accelerating foot contact (AFC). However, to the best of our knowledge, previous studies did not include the analysis of this foot contact during traditional or modified 505 tests.

The available evidence demonstrates the differences between faster and slower performers (based on the completion time) during the 505 test. Faster performers showed greater horizontal braking forces for the PFC, greater horizontal propulsive forces, vertical impact forces, and shorter contact times for the FFC compared with slower performers during the modified 505 test [15]. Furthermore, faster performers demonstrated greater vertical braking and propulsive forces compared with slower performers in the COD-only step analyzed during the traditional 505 test [16]. Moreover, faster performers showed greater peak and average horizontal propulsive forces for the FFC than slower performers during the traditional and modified 505 test [17]. Finally, associations have recently been demonstrated between greater antepenultimate foot contact peak vertical, horizontal, and resultant braking forces, mean vertical, horizontal, and resultant GRFs, and horizontal total impulses in comparison with faster performers during the traditional 505 test [18].

Furthermore, the possible influence of limb asymmetries and directional dominance might exist in the traditional or modified 505 test in female and male team sports players [19,20]; however, it has not been largely confirmed in a further investigation of only female soccer players [21]. Nonetheless, contradictory results also emerged considering limb dominance as a factor associated with injury risk during COD actions [22].

Playing level might be considered a critical issue in sports performance. However, it has been demonstrated that COD speed tests are not able to discriminate between higherskilled groups [3]. In fact, previous investigations did not report significant differences between higher- and lower-performance groups in Australian football [23–25] and rugby league [26,27] players when the completion time was measured. However, there is a paucity of evidence regarding the effect of the playing level on spatial–temporal and kinetic variables during a 505 test.

Similarly, considering that several determinants (spatial–temporal and kinetic variables) may influence the performance outcome (i.e., completion time) of a 505 test, limited evidence is available for the predictors of completion time.

Taken together, the current knowledge on the 505 test can still be expanded through the inclusion of other foot contacts with those already investigated in order to define the entire turning phase, as well as in relation to several factors, such as leg dominance, COD performance, and playing level. Therefore, the objective of this study was to investigate the modified 505 test, providing an analysis of the three foot contacts (PFC, FFC, AFC) that characterize the turning phase. In particular, the purpose of this study was to examine the effects of leg preference, COD performance, and playing level on spatial–temporal and kinetic variables for each foot contact and to evaluate the prediction of the completion time from the spatial–temporal and kinetic variables. We hypothesized the existence of

differences between legs and superior performance for faster performers and the highplaying-level group during the modified 505 test.

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

#### *2.1. Study Design*

A cross-sectional study design was applied to investigate the effect of leg preference, COD performance (i.e., completion time), and playing level on spatial–temporal and kinetic variables during the modified 505 test. Furthermore, the predictors of COD performance were examined.

This study was approved by the University of Taipei Institutional Review Board (Taipei, Taiwan, reference number: IRB-2018-093). All participants gave their informed written consent, and all the experimental procedures were conducted in accordance with the Declaration of Helsinki [28].

#### *2.2. Participants*

A minimum sample size of 34 participants was determined from an a priori power analysis performed by G\*Power (version 3.1.9.2 University of Dusseldorf, Dusseldorf, Germany), considering an ANOVA test with a power of 0.95, an effect size of 0.65, and α = 0.05. Accordingly, 40 male and female collegiate students (32 males: age = 20.9 ± 2.0 years, height = 179 ± 7.3 cm, body mass = 76.1 ± 9.6 kg; 8 females: age = 21.5 ± 1.8 years, height = 164 ± 8.4 cm, body mass = 58 ± 8.6 kg) were recruited to participate in this study and were eligible in accordance with the following inclusion criteria: (a) age 18–25 years; (b) absence of known cardiovascular, pulmonary, metabolic, bone, or joint diseases; (c) no smoking; (d) no muscle and joint injuries during the last six months. Participants were asked to identify their preferred leg used to kick a ball, which was then identified as the kicking leg (KL). Consequently, the opposite leg was ascertained to be the leg used to jump off when performing a right-handed running basketball layup and was identified as the stance leg (SL) [29]. Participants were divided into faster (top 33%, *n* = 13) and slower (bottom 33%, *n* = 13) performers based on their completion time [17] to test the hypothesis of the effect of COD performance. In addition, participants were divided into a high-playing-level group (*n* = 17), engaged in basketball training and competitions at collegiate and national levels (>3 training sessions per week; >5 years of basketball experience), and a low-playing-level group (*n* = 23), engaged in basketball as a recreational activity (<3 sessions per week), to test the hypothesis of the effect of playing level.

#### *2.3. Procedures*

Participants reported to the laboratory on two occasions separated by a 72 h resting period at the same time of the day (10:00 ± 30 min), with temperature and humidity kept consistent at 24 ± 1 ◦C and 55 ± 5%, respectively. They were required to abstain from exercise during the 72 h prior to each experimental session and to abstain from alcohol and caffeine consumption during the 12 h prior.

After ascertaining the inclusion criteria, participants were familiarized with all the experimental procedures during the first experimental session. Moreover, height (cm) and body mass (kg) were measured to the nearest decimal using a Jenix DS-102 stadiometer (Dong Sahn Jenix Co., Ltd., Seoul, South Korea). During the second experimental session, participants performed the modified 505 test (Mod505) [26], in which they were required to sprint forward for 5 m, make a 180◦ COD while on the force plates, and sprint back for another 5 m. Participants were instructed: (a) to start 0.5 m from the start line with their preferred foot forward in a two-point stance; (b) to have a straight trajectory toward the force plates; (c) to make the COD with the external leg on a visual target (X) highlighted on the middle of the second force plate; (d) to exert maximal effort during the entire course of the test. Participants performed several trials (5 to 7) for each leg (alternating one trial for each leg) with a 2 min resting period in between. A trial was included in the analysis if the participants had a straight trajectory toward the force plates without prior

stuttering or prematurely turning prior to final contact and made full contact with the force plates during the three foot-contacts of the turning phase [30]. Inspection of full contacts was performed at the end of each trial using two video cameras synchronized with the Optojump photoelectric system and the force plate software showing the pushing area of the foot. The fastest four trials were considered suitable for analysis and the average value for each investigated variable was used for the statistical analysis.

Participants used the same model of basketball shoes (Adidas Pro Bounce 2019, Herzogenaurach, Germany) to reduce the variability given by the use of different types of sports shoes. Before the experimental session, they completed a standardized warmup involving 3 min of jogging on a treadmill followed by dynamic stretching, squats, frontal and lateral lunges, short accelerations, directional changes, and submaximal trials of the test.

The experimental protocol was executed in a laboratory setting (Figure S1) with the simultaneous use of two adjacent embedded force plates with a sampling rate of 2400 Hz (60 cm × 90 cm; BMS 600900 OPTIMA™ Biomechanics Measurement Series, AMTI, Watertown, MA, USA) and an Optojump photoelectric system (OptojumpNext, Microgate, Bolzano, Italy) placed beside the force plates, covering the entire 5 m course. The transmitter and receiver bars were placed 2 m apart. Force plates were covered with anti-slip tape to prevent slippage. Moreover, a set of a timing lights system (SMARTSPEED; Fusion Sports, Queensland, Australia) was placed in correspondence with the start/stop line at the hip height of participants to avoid other body parts (unless the lower torso) activating the infrared light.

#### *2.4. Data Processing*

Due to the use of two adjacent force plates, the definition of the turning phase from one direction to the new direction was achieved, consisting of three consecutive foot contacts: (1) the PFC, defined as the second last foot contact with the first force plate before moving towards a new intended direction [17]; (2) the FFC, defined as the turning foot while in contact with the second force plate to initiate the movement towards a new intended direction [17]; and (3) the AFC, defined as the first accelerating foot contact with the first force plate moving in the new intended direction. The PFC and FFC have been previously investigated [14,17,19], whilst the AFC was first evaluated in this study. A preliminary analysis was completed to verify the composition of each foot contact, using the resultant GRF. Accordingly, the PFC consisted only of a braking phase, whilst the FFC consisted of both the braking and propulsive phases, as already demonstrated in previous research [14,17,19]. In contrast, two executions have been identified for the AFC, with some trials characterized by both braking and propulsive phases and some trials encompassing only a propulsive phase. Therefore, considering the execution of the AFC, each trial for every participant was categorized as a braking/propulsive trial or only-propulsive trial and was included in the analysis in an attempt to explain possible differences for the investigated variables.

Data from force plates were collected using Cortex software (version 3.6.0; Motion Analysis Corp., Santa Rosa, CA, USA), digitally filtered at 25 Hz using a Butterworth lowpass filter, and imported and analyzed with Microsoft Excel (Microsoft Corp, Redmond, WA). The three components of the GRF were vertical (Fz), anterior-posterior (horizontal; Fx), and mediolateral (Fy). Foot contact was defined from the initial contact (touchdown), when the vertical GRF was above a threshold of 10 N, to the final contact (takeoff), when the vertical GRF was below a threshold of 10 N [10,31]. The identification of the braking and propulsive phases was based on the bimodal resultant GRF profile. The braking phase spans from the initial contact to the minimal value between the two peaks, while the propulsive phase spans from the minimal value between the two peaks to the takeoff. Therefore, for AFC classification, the braking/propulsive trials were characterized by a bimodal GRF profile with two peaks, whilst the only-propulsive trials were characterized by a unimodal GRF profile with a single peak (Figure 1).

**Figure 1.** Example of vertical ground reaction force (GRF) profiles for (**A**) penultimate foot contact (PFC), (**B**) final foot contact (FFC), and first accelerating foot contact (AFC) in case of (**C**) only-propulsive trials and (**D**) braking/propulsive trials, used for the identification of the braking and propulsive phases.

The variables obtained from the force plates for each foot contact (for either braking, propulsive, or both phases) included: braking, propulsive, and total contact time (CT); peak relative braking and propulsive GRF for both vertical and horizontal components (VGRF and HGRF); relative braking, propulsive, and total impulse for both vertical and horizontal components (VImp and HImp). All GRF and impulse variables were normalized by body mass. Moreover, peak braking and propulsive resultant GRFs were calculated using the Pythagorean theorem [17]:

ඥሺvertical force<sup>ଶ</sup>ሻ ሺhorizontal force<sup>ଶ</sup>

$$\text{resultant force} = \sqrt{\left(\text{vertical force}^2\right) + \left(\text{horizontal force}^2\right)}$$

The variables derived from the Optojump system (directly provided by the dedicated software, version 1.12.15, OptojumpNext, Microgate, Bolzano, Italy) were step length and approach velocity. Step length was calculated as the tip-to-tip distance from one foot to the next (i.e., right–left, left–right). According to the manufacturer's instructions, velocity was calculated as V = L/(Tc + Tf), where L is step distance, Tc is contact time, and Tf is flight time; all parameters were previously investigated for their reliability in gait analysis [32]. Therefore, approach velocity referred to the velocity at the last foot contact before the turning phase. A preliminary assessment of the data among trials revealed that approach velocity at the last foot contact before the turning phase was the last one with increasing velocity, then the PFC (i.e., the first foot contact of turning phase) consistently showed a decrease in velocity.

Finally, the completion time was measured to the nearest 0.01 s and used as the outcome of COD performance.

The measurement of the investigated variables demonstrated "moderate" to "excellent" internal consistency reliability, ascertained by intraclass correlation coefficients (two-way mixed effects, average measures, absolute agreement). Intraclass correlation coefficients with the 95% confidence intervals are reported in Table S1 in the Supplementary Materials. Furthermore, a recent investigation demonstrated the concurrent validity and internal consistency reliability for the use of the force plate and Optojump system in evaluating the sprint test with a 180◦ COD [31].

#### *2.5. Statistical Analysis*

Data were analyzed using the Statistical Package for the Social Sciences, version 25.0 (SPSS Inc., Chicago, IL, USA). The level of statistical significance was set at *p* < 0.05 for all computations. The normality assumption for each variable was verified using the Shapiro–Wilk test, which confirmed the normal distribution of data.

Since prior analysis showed no gender differences for the investigated variables, data from male and female participants were pooled. Moreover, since the analysis for the stance and kicking legs did not reveal differences, the data were pooled to further increase the sample size. Statistics are provided in Table S2 of the Supplementary Materials.

Differences between braking/propulsive and only-propulsive trials were investigated with paired *t*-test. Cohen's *d* effect sizes (ESs) were calculated and interpreted as trivial (<0.19), small (0.20–0.59), moderate (0.60–1.19), large (1.20–1.99), very large (2.0–4.0), and extremely large effects (>4.0) [33].

Independent sample *t*-tests were applied to ascertain differences between faster and slower performers and between the high-playing-level and low-playing-level groups for both braking/propulsive and only-propulsive trials. Hedges' *g* effect sizes (ESs) were calculated and interpreted as trivial (<0.19), small (0.20–0.59), moderate (0.60–1.19), large (1.20–1.99), very large (2.0–4.0), and extremely large effects (>4.0) [33].

A stepwise multiple regression analysis was separately executed for the braking/ propulsive and only-propulsive trials to create a model able to explain the prediction of the completion time from the spatial–temporal and kinetic variables. For braking/propulsive trials, the predictors included in the model were: step length, CT, braking VGRF, HGRF, VImp, and HImp for the PFC; step length, total, braking, and propulsive CT, braking and propulsive VGRF and HGRF, total, braking, and propulsive VImp and HImp for the FFC; step length, total, braking, and propulsive CT, braking and propulsive VGRF and HGRF, total, braking, and propulsive VImp and HImp for the AFC; approach velocity.

For only-propulsive trials, the predictors included in the model were: step length, CT, braking VGRF, HGRF, VImp, and HImp for the PFC; step length, total, braking, and propulsive CT, braking and propulsive VGRF and HGRF, total, braking, and propulsive VImp and HImp for the FFC; step length, CT, propulsive VGRF and HGRF, VImp, and HImp for the AFC; approach velocity. Multicollinearity was ascertained using tolerance and the variation inflation factor (VIF) to verify the degree of correlations among the included predictors. Values lower than 0.20 for tolerance and higher than 10 for VIF denoted the presence of multicollinearity.

#### **3. Results**

#### *3.1. Characterization of the Mod505 Performance and Turning Phase*

A descriptive analysis of the entire course of the Mod505 revealed a total number of between 8 and 10 steps to complete the test. The shortest step length was for the AFC (80.6 ± 9.9 cm), whilst the last step was the longest (154.4 ± 23.4 cm). The approach velocity at the last foot contact before the turning phase was 5.40 ± 0.47 m/s. The turning phase lasted an average of 1.22 ± 0.17 s considering all the trials, representing 44.2% of the average completion time (2.77 ± 0.14 s).

#### *3.2. Analysis of Different Executions*

Based on the execution of the AFC, the trials of every participant have been classified as braking/propulsive (65.8%) or only propulsive (34.2%). No differences emerged for completion time (braking/propulsive trials: 2.71 ± 0.13 s; only-propulsive trials: 2.73 ± 0.12 s), approach velocity (braking/propulsive trials: 5.46 ± 0.51 m/s; only-propulsive trials: 5.52 ± 0.46 m/s), and turning phase (braking/propulsive trials: 1.22 ± 0.17 s; onlypropulsive trials: 1.23 ± 0.21 s). Significant differences (*p* < 0.05) between trials emerged for braking VGRF and resultant GRF in the PFC, braking and total VImp and braking HImp in the FFC, and propulsive VGRF and HGRF, total VImp, propulsive resultant GRF, and step length in the AFC (Table 1).


**Table 1.** Comparisons of spatial–temporal and kinetic variables during the turning phase between braking/propulsive and only-propulsive trials (mean ± SD).

Note: AU = arbitrary unit; CT = contact time; ES = effect size; HGRF = horizontal ground reaction force; HImp = horizontal impulse; N/A = not available; VGRF = vertical ground reaction force; VImp = vertical impulse.

#### *3.3. Analysis of COD Performance*

Faster performers (completion time = 2.61 ± 0.07 s) revealed higher values for approach velocity (faster: 5.72 ± 0.42 m/s; slower: 5.13 ± 0.36 m/s; *p* < 0.001, ES = 1.51) compared with slower performers (completion time = 2.92 ± 0.06 s). For step length, differences between faster and slower performers emerged in the PFC (faster: 126 ± 32.2 cm; slower: 96.8 ± 31.4 cm; *p* = 0.005, ES = 0.92) and the FFC (faster: 95 ± 9.3 cm; slower: 85.2 ± 14.6 cm; *p* = 0.011, ES = 0.82) for the braking/propulsive trials, whilst only in the PFC (faster: 123.3 ± 31.2 cm; slower: 97.9 ± 37.6 cm; *p* = 0.042, ES = 0.75) for the only-propulsive trials, compared with slower performers A similar time for turning phase emerged for both braking/propulsive (faster: 1.19 ± 0.11 s; slower: 1.22 ± 0.13 s; *p* = 0.343; ES = −0.29)

and only-propulsive (faster: 1.21 ± 0.14 s; slower: 1.18 ± 0.13 s; *p* = 0.556; ES = 0.22) trials. Significant differences (*p* < 0.05) between faster and slower performers emerged for kinetic variables and are presented in Table 2. For braking/propulsive trials, faster performers exhibited: (a) greater braking VGRF, HGRF, HImp, and resultant GRF in the PFC; (b) greater braking and propulsive HGRF, braking HImp, propulsive resultant GRF, lower propulsive and total CT, and propulsive and total VImp in the FFC; (c) greater propulsive HGRF, propulsive and total HImp, propulsive resultant GRF, lower propulsive CT, and propulsive and total VImp in the AFC, compared with slower performers. For only-propulsive trials, faster performers exhibited: (a) greater braking HGRF in the PFC; (b) greater braking and propulsive HGRF, braking and total HImp, and lower propulsive VImp in the FFC; (c) greater propulsive HGRF and HImp in the AFC, compared with slower performers.

**Table 2.** Comparison of kinetic variables during the turning phase between faster and slower performers for braking/propulsive and only-propulsive trials (mean ± SD).


Note: AU = arbitrary unit; CT = contact time; ES = effect size; HGRF = horizontal ground reaction force; HImp = horizontal impulse; N/A = not available; VGRF = vertical ground reaction force; VImp = vertical impulse.

#### *3.4. Analysis of Playing Level*

A significant difference emerged for height (high playing level: 181.4 ± 6 cm; low playing level: 172.1 ± 10 cm; *p* = 0.001; ES = 1.13), but not for body mass (high playing level: 76.4 ± 9 kg; low playing level: 69.5 ± 12 kg; *p* = 0.59; ES = 0.65).

The high-playing-level group demonstrated a shorter completion time (high playing level: 2.69 ± 0.14 s; low playing level: 2.82 ± 0.11 s; *p* < 0.001, ES = −1.05), a higher approach velocity (high playing level: 5.68 ± 0.44 m/s; low playing level: 5.19 ± 0.38 m/s;

*p* < 0.001, ES = −1.21), and a longer step length for PFC (high playing level: 129.4 ± 29.5 cm; low playing level: 103.4 ± 31.6 cm; *p* < 0.001, ES = 0.85) and FFC (high playing level: 97.2 ± 7.4 cm; low playing level: 86.6 ± 10.8 cm; *p* < 0.001, ES = 1.12). A similar time for turning phase emerged for both braking/propulsive (high playing level: 1.20 ± 0.12 s; low playing level: 1.24 ± 0.17 s; *p* = 0.369, ES = −0.22) and only-propulsive (high playing level: 1.21 ± 0.16 s; low playing level: 1.23 ± 0.21 s; *p* = 0.672, ES = −0.12) trials. Significant differences (*p* < 0.05) between the high-playing-level and low-playing-level groups emerged for kinetic variables and are presented in Table 3. For braking/propulsive trials, the highplaying-level group exhibited: (a) greater braking VGRF, HGRF, VImp, HImp, and resultant GRF in the PFC; (b) greater braking and propulsive VGRF, propulsive HGRF, braking VImp, HImp, total HImp, and propulsive resultant GRF in the FFC; (c) greater propulsive VGRF and HGRF, braking HGRF, total HImp, propulsive and braking resultant GRF, and lower propulsive and total CT in the AFC, compared with the low-playing-level group. For only-propulsive trials, the high-playing-level group exhibited: (a) greater braking VGRF, HGRF, and resultant GRF in the PFC; (b) greater braking VGRF, VImp and HImp, total HImp, and braking resultant GRF in the FFC; (c) greater propulsive HGRF and HImp in the AFC, compared with the low-playing-level group.

**Table 3.** Comparison of kinetic variables during the turning phase between the high- and low-playing-level groups for braking/propulsive and only-propulsive trials (mean ± SD).


Note: AU = arbitrary unit; CT = contact time; ES = effect size; HGRF = horizontal ground reaction force; HImp = horizontal impulse; N/A = not available; VGRF = vertical ground reaction force; VImp = vertical impulse.

#### *3.5. Stepwise Multiple Regression Analysis*

Table 4 shows the steps necessary to create the model for both braking/propulsive and only-propulsive trials. For braking/propulsive trials, model five has been identified to better predict the completion time, including the five predictors (i.e., FFC propulsive HGRF, AFC propulsive HGRF, FFC propulsive VGRF, AFC Total VImp, and AFC step length) that explain 75% of the common variance (Table 5). For the only-propulsive trials, model six has been identified to better predict the completion time, including the six predictors (i.e., approach velocity, FFC braking HGRF, FFC braking VGRF, AFC propulsive HGRF, FFC total CT, and AFC propulsive VGRF) that explain 76.2% of the common variance (Table 5). Data for correlations (partial and part) and collinearity analysis have been reported for the predictors included in both models for braking/propulsive and only-propulsive trials (Table 5). In particular, the lack of multicollinearity is demonstrated from the values higher than 0.20 for tolerance and lower than 10 for VIF for all the predictors included in the models.


**Table 4.** Models derived from the stepwise multiple regression analysis.

Note: For braking/propulsive trials: 1. FFC propulsive HGRF; 2. FFC propulsive HGRF, AFC propulsive HGRF; 3. FFC propulsive HGRF, AFC propulsive HGRF, FFC propulsive VGRF; 4. FFC propulsive HGRF, AFC propulsive HGRF, FFC propulsive VGRF, AFC Total VImp; 5. FFC propulsive HGRF, AFC propulsive HGRF, FFC propulsive VGRF, AFC Total VImp, AFC step length. For only-propulsive trials: 1. Approach velocity; 2. Approach velocity, FFC braking HGRF; 3. Approach velocity, FFC braking HGRF, FFC braking VGRF; 4. Approach velocity, FFC braking HGRF, FFC braking VGRF, AFC propulsive HGRF; 5. Approach velocity, FFC braking HGRF, FFC braking VGRF, AFC propulsive HGRF, FFC total CT; 6. Approach velocity, FFC braking HGRF, FFC braking VGRF, AFC propulsive HGRF, FFC total CT, AFC propulsive VGRF. AFC = first accelerating foot contact; CT = contact time; FFC = final foot contact; HGRF = horizontal ground reaction force; SEE = standard error of estimate; VGRF = vertical ground reaction force; VImp = vertical impulse.


**Table 5.** Predictive variables of completion time.

Note: AFC = first accelerating foot contact; CI = confidence interval; CT = contact time; FFC = final foot contact; HGRF = horizontal ground reaction force; VGRF = vertical ground reaction force; VImp = vertical impulse. \* Shared contributions of the predictors. # Unique contributions of the predictors.

#### **4. Discussion**

This study intended to examine the effects of leg preference, COD performance, and playing level and to explore the prediction of the completion time from spatial– temporal and kinetic variables. The main finding of this study is a superior performance of faster performers and the high-playing-level group (Tables 2 and 3). Moreover, this study demonstrated for the first time the predictive variables of completion time (Table 5). The novelty of this study is the inclusion of the first accelerating foot contact in the analysis together with the penultimate and final foot contacts, revealing different executions. The lack of leg preference in making directional changes supports the controversy regarding the influence of interlimb asymmetries on sports performance. However, the inconsistency in evidence for interlimb asymmetries also depends on a lack of consensus regarding the definition and determination of leg preference and/or dominance [34,35].

A descriptive analysis shows that the AFC is characterized by lower values for contact time, braking GRF and impulse in both vertical and horizontal components, and resultant GRF compared with the PFC and FFC. Moreover, the AFC also revealed a shorter step length compared with the PFC and FFC. In particular, shorter contact times and step lengths might be expected to be necessary for the reacceleration in a new direction, given that AFC is the first foot contact after turning when the horizontal velocity of the center of mass is zero.

The finding of the different executions of the AFC was unexpected, with a higher proportion of the trials indicating braking before the propulsive phase, even though a full explanation has not been reached with this investigation. In fact, when braking/propulsive and only-propulsive trials were compared with spatial–temporal and kinetic variables (Table 1), marginal differences were found in the PFC and FFC, whereas in the AFC, higher values for propulsive vertical, horizontal, and resultant GRFs were found for the onlypropulsive trials. However, the total vertical impulse was higher for the braking/propulsive trials, which can be explained by the further contribution of the impulse during the braking phase. Moreover, a difference in step length emerged between trials, with the

braking/propulsive trials showing a longer AFC step length compared with the onlypropulsive ones. Therefore, the presence of the braking phase could be attributed to the longer step length of the AFC. In fact, this longer step length may require a supporting base at the initial contact resulting in the application of the braking force. This action will also require the application of the SSC with the braking phase (i.e., eccentric action) acting to store elastic strain energy, which is subsequently recovered during the propulsive phase (i.e., concentric action) [36]. It is well-documented that the enhancement of sports performance can be achieved through the amplification of the force and power produced during the shortening cycle as a consequence of the previous elastic strain energy stored during the lengthening cycle [5,36]. In contrast, in the only-propulsive trials, the shorter step length allowed for the immediate exertion of the propulsive phase without the need to accumulate elastic energy in a braking phase. This may also reflect the existence of a different foot strike pattern between the two executions. It can be speculated that in the braking/propulsive trials, the foot contact comprised the rearfoot strike pattern, whilst a forefoot strike pattern was used in the only-propulsive trials [37]. Considering this first attempt to characterize the AFC and the observation of different executions, further research to confirm the first findings obtained by this study is highly recommended.

In line with previous research on COD performance [15–18], the current study demonstrated differences between faster and slower performers. The higher approach velocities for faster performers are in accordance with previous research on 505 tests [17] and athletes with higher eccentric strength [30]. Recently, approach velocity has been recognized as an important factor influencing COD performance, together with the angle of the COD (e.g., from 45◦ to 180◦ ) [13]. Therefore, this study can contribute new evidence to the existing knowledge about approach velocity, using a photoelectric system for its determination. Further evidence is also provided for the step length, showing that a faster performance required longer steps length in the PFC and FFC, whilst no differences emerged for the AFC.

Regarding kinetic variables, several profiles and characteristics can be highlighted. For the PFC, the results demonstrate greater values for braking vertical and horizontal GRFs, horizontal impulse, and resultant GRF for faster performers, even though the contact times were similar. Therefore, for the same ground contact time, a higher force was exerted. Considering the higher approach velocities, these results might indicate that higher force production is necessary for the deceleration of the body [17]. Moreover, a higher force production has been associated with superior movement mechanisms and strength capacity, hence increasing the exit velocity during COD movements [16,38]. In particular, eccentric strength has been determined as the sole predictor of a 505 test in a sample of female basketball players [38]. Therefore, the single braking phase characterizing the PFC may explain the need for higher eccentric action to decelerate the body. For the FFC, higher values of horizontal GRF in both braking and propulsive phases, coupled with shorter total contact times, vertical propulsive, and total impulses, may confirm an efficient application of the SSC [17]. Considering that the FFC consisted of both braking and propulsive phases, faster performers might display superior ability in maximizing the application of the SSC, compared with their slower counterparts [17], based on the shorter ground contact times and impulses in the vertical component. Similarly, considering the braking/propulsive trials of the AFC, faster performers demonstrated lower values for contact time and vertical propulsive and total impulses, which can be required to accelerate the body. Therefore, the comparisons of kinetic variables between faster and slower performers may explain the action of foot contacts, with the PFC and FFC remaining critical foot contacts for reducing the momentum of the center of mass [15,19], whilst the AFC is important for the acceleration of the body in the new direction.

Higher- and lower-skilled athletes have been commonly investigated for completion time, revealing COD speed tests are not able to discriminate playing level [23–27]. Conversely, in this study, the high-playing-level group had a lower completion time and a higher approach velocity. These results may have also determined the longer step lengths and the higher values for force-related variables in both the PFC and FFC compared with

the low-playing-level group, even though similarities existed for the contact times. Greater force production during the braking phase in the PFC is required to start reducing the momentum of the center of mass due to the higher approach velocity [17]. However, contradictory results emerged in the FFC, with higher values for GRFs in both braking and propulsive phases, but also for impulses, which prevent the confirmation of a superior application of the SSC. Regarding the AFC, the high-playing-level group exhibited shorter contact times, as a result of the shorter propulsive phase, and higher values for GRFs during the propulsive phase, but only a difference for the total horizontal impulse. A superior reacceleration capacity in the new direction for the AFC is still speculated. However, the effect of playing level on spatial–temporal and kinetic variables may require further investigation to confirm the presented findings.

The regression analysis was proposed to explore the prediction of the completion time from spatial–temporal and kinetic variables. Different models emerged for braking/propulsive and only-propulsive trials, comprising variables of the FFC and AFC, with ≥75% of the variance of completion time being explained. The propulsive horizontal GRF of the AFC was the only variable in common between the two trials. This result, combined with the higher propulsive horizontal GRF values found for faster performers and the high-playing-level group in both braking/propulsive and only-propulsive trials, might highlight the role of this variable as an important determinant of COD performance. The total vertical impulse and step length of the AFC were also included in the model for the braking/propulsive trials, together with propulsive horizontal and vertical GRF of the FFC. Therefore, the predictive analysis showed the importance of including the AFC in the assessment of a 180◦ COD test for a complete characterization of the turning phase. Conversely, none of the variables of the PFC were included in both models, even though the PFC has always been considered a critical determinant of the COD performance [14]. Moreover, recent research suggests that the antepenultimate foot contact might play a superior role in deceleration compared with the PFC [18]. Unfortunately, the antepenultimate foot contact was not included in the current investigation. However, we suggest that the turning phase should be investigated for all foot contacts composition. In summary, for braking/propulsive trials, an AFC characterized by a higher propulsive horizontal GRF and step length and a lower total vertical impulse, and an FFC characterized by a higher propulsive horizontal GRF and a lower propulsive vertical GRF, can predict shorter completion time. Conversely, for only-propulsive trials, an AFC characterized by a higher propulsive horizontal GRF and lower vertical GRF, and an FFC characterized by a higher braking horizontal GRF, a lower braking vertical GRF and total contact time, and a higher approach velocity, can predict shorter completion time. It might be speculated that approach velocity may play a role in discriminating between the two different executions and could determine the other kinetic variables since it entered the predictive model only for only-propulsive trials. Therefore, due to the differences in models and predictive variables, further research is necessary to fully explain the different executions identified in this study.

The present study has some limitations that need to be addressed and can serve as guidance for future research. The present findings cannot be extended to COD tests with a different degree of angle, since the COD performance is angle- and velocity-dependent [12]. Therefore, the turning phase can be investigated with different COD tests. Approach velocity was determined with a photoelectric system which, though reliable, is not as accurate as other methods. Future research can consider the measurement of approach velocity with trunk and lower limbs' center of mass computation as proposed in previous investigations [17,30]. Moreover, 3D motion analysis has not been applied in the current study, limiting the investigation of kinematic determinants of the turning phase. It is strongly advised to replicate the current study design while adding 3D motion analysis. Although differences did not emerge between female and male participants, the different sample sizes between the two groups did not allow us to make definitive conclusions. It is advised to explore the gender differences using the same sample sizes. Similarly, all participants were basketball players, hence these findings cannot be generalized to other

team sports players. Another limitation of this study, due to the laboratory setting, is the impossibility of really detecting different strategies used by participants and the potential interaction between dominance/preference and strategy. This is considered a critical issue when investigating preplanned action in a laboratory setting and the implication for training and agility actions (i.e., unplanned COD in response to a stimulus), which occur in open-skill conditions, such as team sports [18]. However, this study implemented an experimental approach to the investigation of the turning phase in several team sports players. The present investigation suggests that the penultimate, final, and first accelerating foot contacts should be assessed for a comprehensive understanding of the turning phase. Together with previous research [15–19,21], we have evidence from four foot contacts characterizing the 180◦ COD performance. However, further research can be encouraged to extend the analysis of other foot contacts and their contribution to completion time.

#### **5. Conclusions**

The present study provided a characterization of the turning phase during the modified 505 test, demonstrating that each of the three foot contacts can play an important role in COD performance. In particular, the PFC and FFC are considered critical foot contacts for the deceleration of the body and the preparation for reacceleration in the new direction. Conversely, the AFC is the first foot contact after turning when the horizontal velocity of the center of mass is zero and the reacceleration in the new direction has to be executed. Among several spatial–temporal and kinetic variables, the propulsive horizontal GRF of the AFC can be emphasized, as it is able to indicate faster performers and the high-playing-level group and predict faster completion times in both braking/propulsive and only-propulsive trials. The findings of this study can be translated to practical implications for training. An important component of COD speed is the deceleration phase, meaning that athletes should have a high braking ability. Furthermore, an efficient COD performance can be achieved with a fast transition from the deceleration phase to the acceleration phase, meaning a fast coupling of eccentric and concentric muscle action, which is an expression of the SSC. Therefore, to achieve these goals, it is confirmed that training programs should be implemented with strength training [39]. Due to the contribution of eccentric, concentric, dynamic, and isometric strength to COD performance [38], a variety of exercises may be proposed in order to enhance the ability to change momentum and coordinate body movement within the constraints of the activity [39]. Among the several forms of strength training, the application of eccentric training for the improvement of the braking ability of athletes has been recently emphasized. Eccentric exercises can be executed under several conditions and modalities, as summarized in recent reviews, and are strongly recommended for the wide spectrum of training adaptations [40–42]. Moreover, exercises for the application of the SSC should be implemented. Change-of-direction speed can surely benefit from a variety of exercises considering machine-based, elastic band, and plyometric exercises, particularly when executed in a unilateral, multiplanar, and multidirectional fashion, to replicate the demands of team sports performance. Therefore, to be faster in performing directional changes, athletes should follow these recommendations.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/ijerph18115519/s1. Graphical representation of the laboratory setting is available as supplementary material with Figure S1. Intraclass correlation coefficients with the 95% confidence intervals of the variables investigated are reported in Table S1. The analysis of the effect of leg preference on variables investigated is provided in Table S2.

**Author Contributions:** Conceptualization, G.C. and A.T.; methodology, G.C., C.L., and A.T.; formal analysis, E.S., G.C., C.L., and A.T.; investigation, E.S., C.K., C.-H.C., and M.M.; resources, G.C., Y.- H.L., and C.L.; data curation, E.S., G.C., C.K., C.-H.C., and M.M.; writing—original draft preparation, E.S., G.C.; writing—review and editing, E.S., G.C., A.T., C.L., C.K., C.-H.C., M.M., and Y.-H.L.; visualization, G.C.; supervision, G.C.; project administration, G.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received the support of the research grant from the Ministry of Science and Technology, Taiwan (MOST 108-2410-H-845-023-MY2) for the publication fees.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of University of Taipei (Taipei, Taiwan) (protocol code IRB-2018-093—19/08/2019).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

**Acknowledgments:** The authors thank all the participants who took part in the study and all the graduate students who cooperated during the data collection for this research project.

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

#### **References**


### *Article* **Musculoskeletal Pain in Gymnasts: A Retrospective Analysis on a Cohort of Professional Athletes**

**Giacomo Farì 1,\* , Francesco Fischetti 1 , Alessandra Zonno 1 , Francesco Marra 1 , Alessia Maglie 2 , Francesco Paolo Bianchi 3 , Giuseppe Messina 4 , Maurizio Ranieri <sup>1</sup> and Marisa Megna 1**


**Abstract:** Gymnastics athletes are exposed to a high risk of injury, but also of developing musculoskeletal pain. These data are still little investigated in the available scientific literature. An online survey was distributed to 79 professional athletes who practiced artistic and rhythmic gymnastics. The survey collected demographic and anthropometric data, information about the sport practice, the training sessions, the prevalence of musculoskeletal pain gymnastics-related, and lifestyle habits. Musculoskeletal pain had a high prevalence, involving 65 of 79 athletes (82.3%). A significant correlation was found between musculoskeletal pain and the duration of sports practice, both for general pain (*p* = 0.041) and for specific districts: right wrist pain (*p* = 0.031), left wrist pain (*p* = 0.028), right shoulder (*p* = 0.039), left hip (*p* = 0.031), right thigh (*p* = 0.031), and left knee (*p* = 0.005). Another statistical association was found between right wrist pain and BMI (*p* = 0.001), and hip pain and BMI (*p* = 0.030). Hours spent in a sitting position were also correlated with the incidence of pain (*p* = 0.045). Wrist pain and right shoulder pain had a statistically significant association with the age of the athletes (right wrist pain: *p* = 0.038; left wrist pain: *p* = 0.004; right shoulder pain: *p* = 0.035). The more the gymnasts practice this sport, the more likely they are to develop musculoskeletal pain. Increased age and a higher BMI, as well as daily prolonged sitting position, seem to be potential risk factors for the onset of musculoskeletal pain. Future studies could plan training strategies aimed at preventing musculoskeletal pain associated with gymnastics, in order to promote its further spread.

**Keywords:** overload training; wrist pain; injury prevention; overuse; sitting position

#### **1. Introduction**

Gymnastics is a grueling sport that requires considerable physical and mental effort for a continuous search for harmony between biomechanics and aesthetics efforts. Current International Gymnastic Federation (IGF) disciplines include rhythmic gymnastics and artistic gymnastics, which is further divided in men's artistic gymnastics (MAG) and women's artistic gymnastics (WAG) [1].

Gymnastic elements that must be assimilated and acquired by gymnasts necessarily require the development of coordination, joint mobility, postural adaptation, strength, speed, rhythm, agility, and dynamism. In rhythmic gymnastics, a refined quality of motor control, excellent expression skills, and elegance of the technical gesture are quite important [2–5].

In order to achieve the proper skills required for a correct execution of sports gestures from an early age, high-performance training is required. The athletes usually train for,

**Citation:** Farì, G.; Fischetti, F.; Zonno, A.; Marra, F.; Maglie, A.; Bianchi, F.P.; Messina, G.; Ranieri, M.; Megna, M. Musculoskeletal Pain in Gymnasts: A Retrospective Analysis on a Cohort of Professional Athletes. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5460. https://doi.org/10.3390/ ijerph18105460

Academic Editors: Paul B. Tchounwou, Ewan Thomas, Ivan Chulvi-Medrano and Elvira Padua

Received: 29 April 2021 Accepted: 16 May 2021 Published: 20 May 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/).

on average, 25–30 h per week and, in some cases, 40 h per week. This is due to the high technical demands of this sports discipline [6]. Therefore, it is reasonable to hypothesize that rhythmic and artistic gymnastics are sports that put athletes at risk of musculoskeletal disorders such as wrist pain [5], low-back pain [2,7–11], shoulder pain [12,13], postural disorders [14,15], and many injuries [16,17], all mainly caused by overuse and repeating the same gestures several times for every type of training.

In addition, this sports practice, that usually begins at an early age, lasts throughout the growth period, including the phases of rapid growth [18]; consequently, gymnasts are exposed to injuries and to the onset of musculoskeletal pain (MP) [10] related to sports practice.

Gymnastics is affected by a high incidence of sport-related pain and lesions [7]. Since the number of those who practice these sports has increased over the years [19], there is a risk of an increase in the costs of medical care, so it is crucial to design strategies for the prevention of MP and injuries.

Moreover, it is also interesting for gymnasts to try to understand if and how lifestyles, especially in professional athletes, affect the onset of musculoskeletal pain. This is all the more interesting in a historical period such as the present one, in which the restrictions on the usual sporting activity imposed by the COVID-19 pandemic have led to inevitable postural and musculoskeletal dysfunctions [20,21].

The aim of this study is to determine the prevalence of musculoskeletal pain, differentiated by anatomical districts, in a cohort of artistic and rhythmic professional gymnasts and to investigate the main risk factors involved.

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

#### *2.1. Study Design and Participants*

The study model is that of an observational retrospective study.

All gymnasts were professional athletes. An online survey was set up using Google Forms. The survey was distributed by email on 12 June 2020 and was requested to be completed and submitted by 27 June 2020.

Informed consent was obtained from all participants involved in this study; in the case of underage athletes, consent forms were filled by parents (or by holder of the responsibility on the minor).

All the procedures were conducted in accordance with the principles set forth in the Helsinki Declaration.

#### *2.2. Procedures*

The survey consisted of multiple choice and open-ended questions divided in three different sections:

The first section provided information about the study and contained the informed consent; this section also includes demographic and anthropometric data.

The second section concerned the athletes' practice and the characteristics of the training sessions.

The third section focused on musculoskeletal pain related to specific sports activities. To define this pain, we gave gymnasts the following definition: "Any pain involving muscles, tendons, and joints that occurs in a manner closely related to the specific sports practice, and that recurs in a cyclical way following the usual gymnastic sessions, in the absence of specific traumas that can justify it". In order to delve into the origin of MP, we collected data about lifestyle habits that could also affect the onset of MP (daily hours spent in a sitting position, for usual daily activities such as working or studying).

#### *2.3. Statistical Analysis*

Continuous variables are expressed as mean ± standard deviation and range; categorical variables are expressed as proportions, with an indication of the 95% confidence interval (95% CI), where deemed appropriate. The × 100 person-months incidence rate

was calculated using the sports activity time (months) as the denominator and the number of events as the numerator; 95% CI was subsequently indicated.

Univariate logistic regression was used to evaluate the association between dichotomic outcomes and determinants; the odds ratio (OR) was calculated with the indication of 95% CI.

A *p*-value < 0.05 was considered significant for all tests.

All the statistical analyses were conducted using the Excel Real Statistics Resource Pack (Microsoft Corporation, Redmond, WA, USA).

#### **3. Results**

The cohort consisted of 79 athletes: 54 rhythmic gymnasts, 24 WAG athletes, and 1 MAG athlete, whose demographic characteristics are described in Table 1.


**Table 1.** Sample demographic characteristics.

As described in Table 2. A total of 65 out of 79 athletes (82.3%) experienced recurrent MP related to gymnastics practice.

The period of sports practice is strongly correlated with the incidence of pain (OR = 1.01; 95% CI = 1.01–1.04; *p* = 0.041). In particular, the athletes who have practiced for many years are more affected by painful wrist syndromes: wrist pain has a statistically significant association with the period of sports practice (right wrist pain: OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031; left wrist pain: OR = 1.02, 95%, CI = 1.01–1.04, *p* = 0.028), but also with the age of the athletes (right wrist pain: 1.23; 95%, CI = 1.01–1.50, *p* = 0.038; left wrist pain: 1.46, 95% CI = 1.13–1.90, *p* = 0.004). Another statistical association is between right wrist pain and BMI (OR = 1.72, 95% CI = 1.24–2.39, *p* = 0.001).

The same evidence was found for right shoulder pain. Gymnasts more prone to musculoskeletal pain in this anatomical region are the older athletes (OR = 1.30, 95% CI = 1.02–1.66, *p* = 0.035) and the ones who have practiced sports for a longer time (OR = 1.02, 95% CI = 1.01–1.04, *p* = 0.039); at the same time, the hip pain is associated with the period of sports practice (OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031) and with BMI (OR = 1.47, 95% CI = 1.04–2.07, *p* = 0.030).


**Table 2.** Prevalence of pain and incidence × 100 months-person, by anatomical district.

There is also a significant association between right thigh pain and the period of sports practice (OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031), and also between left knee pain and the period of sports practice (OR = 1.02, 95% CI = 1.01–1.04, *p* = 0.005).

A total of 43.6% of the sample spent more than 4 hours in a seated position; it emerged that the athletes who spent more daily hours in a sitting position were more exposed to MP (OR = 1.91, 95% CI = 1.01–3.59, *p* = 0.045).

#### **4. Discussion**

We found a significant correlation between musculoskeletal pain and the duration of sports practice, both for general pain (OR = 1,01, 95% CI = 1.01–1.04, *p* = 0.041) and for specific districts: right wrist pain (OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031), left wrist pain (OR = 1.02, 95% CI = 1.01–1.04, *p* = 0.028), right shoulder (OR = 1.02, 95% CI = 1.01–1.04, *p* = 0.039), left hip (OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031), right thigh (OR = 1.02, 95% CI = 1.01–1.03, *p* = 0.031), and left knee (OR = 1.02, 95% CI = 1.01–1.04, *p* = 0.005). This evidence is in line with the current scientific literature. In fact, MP is more frequent in high frequency and intensity sports [17,18] and in long sports practice [17]. In 2016, Kamada et al. [19] carried out research into the dose-response relationship between sports activity and MP in adolescents and found that each additional 1 h/wk of sports activity was associated with a 3% higher probability of having pain. Moreover, this evidence is found in relation to the duration of sports practice, regardless of age;

therefore, it also concerns younger athletes, such as those belonging to our sample [19]. The longer is the time dedicated to the sports practice, the greater is the biomechanical overload on the musculoskeletal system. Many studies stated the correlation between sports overuse and MP [22,23]. In gymnastics, if subjected to excessive stress, such as excessive load, inadequate preparation, and insufficient rest-recovery phase, the musculoskeletal system can undergo various types of overuse injuries and pain that can affect different musculoskeletal anatomical districts [22]. This is mainly detected in artistic and rhythmic gymnastics athletes. These activities particularly overload certain joints, less interested in most other sports, such as wrists, whose pain incidence increases as participation and level of competition increase [2,24–26]. Hence, the definition of "gymnast's wrist" [27]. DiFiori et al. suggest that a threshold of training intensity may be important in the development of wrist pain: they found that gymnasts with wrist pain trained more hours per week and trained at a higher skill level [5].

As the duration of sports practice increases, MP increases in another district of the upper limb as well, such as the right shoulder (probably more affected due to the prevalence of right-handed gymnasts), and in three districts of the lower limb, which are left hip, right thigh, and right knee. These findings are confirmed in the updated scientific literature [19,27,28].

Our statistical analysis points out that hours spent in a sitting position seem to be correlated with the incidence of pain (OR = 1.91, 95% CI = 1.01–3.59, *p* = 0.045) as well. These are the most important data relating to lifestyles that seem to affect the appearance of pain net of the causes directly attributable to sporting activity. Prolonged sitting is typical among the habits of contemporary society [29]. Referring to our specific sample, made up predominantly of young adolescents, the circumstances related to prolonged sitting are likely the hours spent at school or studying, the time spent at home, for example watching TV, or using a computer and a mobile phone. Often, the sitting position is incorrect, and this could have important implications for the onset of MP; in fact, pain due to prolonged incorrect postures is quite frequent in many districts of the musculoskeletal system, such as the cervical spine, with referred pain to the head, upper limbs [29], and lumbar spine [29–31], so education in appropriate sitting postures should be promoted from a young age [29]. It is desirable that future epidemiological studies on larger samples can investigate to what extent this lifestyle factor influences the onset of musculoskeletal pain in those who practice gymnastics in a professional manner, and especially if—and to what extent—sports practice can mutually influence this lifestyle factor.

Wrist pain has a statistically significant association with the age of the athletes (right wrist pain: OR = 1,23, 95% CI = 1.01–1.50, *p* = 0.038; left wrist pain: OR = 1.46; 95% CI = 1.13–1.90, *p* = 0.004). The same evidence was also found for right shoulder pain: gymnasts more prone to musculoskeletal pain in this anatomical region are the older ones (OR = 1.30; 95% CI = 1.02–1.66, *p* = 0.035). Another interesting statistical association is that between right wrist pain and BMI (OR = 1.72, 95% CI = 1.24–2.39, *p* = 0.001); at the same time, hip pain is associated with BMI (OR = 1.47, 95% CI = 1.04–2.07, *p* = 0.030). Gymnastics, unlike many other sports, requires athletes to use their upper extremities to bear large loads, exposing musculoskeletal system to repetitive biomechanical stresses, where it is not usually expected. The lower extremity is also subjected to considerable physical loading, through repetitive impacts on the ground resulting from vault takeoffs and dismounts from different heights, and during tumbling activities [19]. Our evidence is supported by Chawla et al., who noticed that wrist pain and injury are more common among athletes who are older, taller and with a larger BMI [24]. As the age increases, there occurs an increase in difficulty of the skills practiced, as well as an increase in hours and intensity of gymnastics training. This often contributes to the overuse of certain anatomical structures, causing long-term effects. In addition, as the age increases, usually the body weight of young athletes increases too, due to the physiological individual growth, resulting in higher loads on joints, already stressed by overuse [3,27,31,32]. Lastly, older gymnasts are more

susceptible to MP or injury than younger ones, because of a prolonged time of exposure to risk [33].

The findings of this research also suggest the need for gymnastics to rethink training programs [34,35], making them more suitable in terms of athletic loads so as to prevent the development of musculoskeletal pain syndromes, in particular those of chronic nature [36,37]. To achieve this, it is desirable to integrate traditional training programs with new technologies, which are increasingly widespread in kinesiology and sports rehabilitation, with therapeutic and preventive purposes [38–45]. These are already used in other areas of rehabilitation, such as neurological rehabilitation [46], in which the new frontiers of telemedicine even provide the possibility of using serious games based on virtual reality for rehabilitative purposes and in order to follow and treat patients remotely [47].

It would be desirable to periodically schedule health screenings and physical examinations for professional gymnasts, both at the beginning of their career and at high levels of competition, with the aim of creating special and tailor-made training programs. These screening measures assume a certain importance in a context where the involvement of athletes at an early age is observed, so it is necessary to identify any individual risk to develop MP or injury as soon as possible [8]. Moreover, physicians and therapists who treat gymnasts' MP need to be adequately educated about the biomechanical requirements of this sports activity, where overuse pain and injuries involve specific anatomical districts. Benjamin et al. suggest some strategies aimed at the prevention and treatment of wrist pain, according to which physical therapy should include core stabilization exercises, mobility, and stabilization exercises of shoulder and elbow, in order to better redistribute loads during activities involving the upper limbs [2]. In addition, gymnastics coaches should be strongly competent in proper training volume and adequate rest, which are critical to pain and symptom management and recovery. A medical evaluation is advisable at the first manifestation of painful symptoms, which should be considered a warning and therefore promptly investigated for an early detection of developing stress injuries [10,24,27]. Coaches are required to supervise and protect athletes' practice from early on, and in a gradual progression of technique to more complex executions [27,48,49].

These precautions, combined with greater care of one's lifestyle, could certainly allow to maintain high levels of sports performance by limiting the risk of developing painful disorders affecting the musculoskeletal system. This way, the general quality of life of gymnasts could also improve, since even the normal activities of daily life would be free from limitations that are due to algo-dysfunctional syndromes. A set of prevention strategies should minimize the risk of MP and injuries due to the competitive nature of the sport, characterized by the ever-increasing physical demands [33,50].

#### *Limitations*

The main limitation of this study is the retrospective and self-reporting nature of the survey questions. The data were therefore provided without any clinical monitoring, and, sometimes, this may have led to an inaccurate definition of the MP by participants. Moreover, the sample is small, even if it refers to a sport whose diffusion is limited compared to other sports.

On the contrary, we consider a strength of this study to be the fact that, to our knowledge, it was the first research to delve into the possible association between MP in all the possible exposed anatomical districts and gymnastics at a professional level.

#### **5. Conclusions**

Gymnastics is one of the most popular competitive sports in the world, but it also exposes athletes to develop MP and injuries. As observed in this research, many anatomical districts are subjected to MP due to sports overuse, and this is particularly evident for wrists, lumbar spine, and lower limbs.

The more gymnasts practice this sport, especially in terms of long sports practice, the more likely they are to develop pain in many musculoskeletal districts. Increased

age and BMI seem to be potential risk factors for arising MP as well. Finally, it is important to understand how, and if, lifestyle habits could affect the MP prevalence among professional gymnasts.

It is desirable that further research delves into gymnastics-related MP and designs new training strategies to prevent it, in order to limit the risk of abandonment and to improve the diffusion of these sports activities, which traditionally allow to develop psychophysical benefits when practiced from a very young age.

**Author Contributions:** Conceptualization, G.F., M.M. and A.M.; methodology, G.F., M.R. and A.Z.; software, F.P.B. and F.M.; formal analysis, G.F., F.P.B. and A.Z.; investigation, G.F., F.F., A.Z., F.M. and A.M.; resources, G.F., F.F., A.Z. and A.M.; data curation, G.F. and A.M.; writing—original draft preparation, G.F., A.Z., F.M. and A.M.; writing—critically revising the original draft, G.F., F.F., M.R., G.M. and M.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki.

**Informed Consent Statement:** Informed consent was obtained from all subjects (or from their parents in case of minors) involved in the study.

**Data Availability Statement:** The datasets used and/or analysed during the current study will be made available upon reasonable request to the corresponding author, G.F.

**Acknowledgments:** We would like to thank the participants of the study and the Apulian gymnastics associations involved in the survey distribution.

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

#### **References**


### *Article* **Hypovitaminosis D in Young Basketball Players: Association with Jumping and Hopping Performance Considering Gender**

**Borja Ricart 1,2,\* , Pablo Monteagudo 1,3, \* and Cristina Blasco-Lafarga 1**


**Abstract:** This study aimed to verify whether a group of young well-trained basketball players presented deficiencies in vitamin D concentration, and to analyze whether there was an association between vitamin D concentration and jumping and hopping performance. Gender differences were considered. Twenty-seven players from an international high-level basketball club (14 female, 16.00 ± 0.55 years; 13 male, 15.54 ± 0.52 years) participated in this cross-sectional study. Rate of force development was evaluated by means of the Abalakov test (bilateral: AbB; right leg: AbR; left leg: AbL); and the triple hop test (right leg: THR; left leg: THL). Blood samples were collected for the determination of serum 25-hydroxyvitamin D and nutritional status. Vitamin D insufficiency was found in both women (29.14 ± 6.08 ng/mL) and men (28.92 ± 6.40 ng/mL), with no gender differences regarding nutritional scores. Jumping and hopping performance was confirmed to be significantly larger in males (AbL, THR, and THL *p* < 0.005), whose CV% were always smaller. A positive correlation was found between AbB and vitamin D (*r* = 0.703) in males, whereas this correlation was negative (−0.611) for females, who also presented a negative correlation (*r* = −0.666) between THR and vitamin D. A prevalence of hypovitaminosis D was confirmed in young elite athletes training indoors. Nutritional (i.e., calciferol) controls should be conducted throughout the season. Furthermore, whilst performance seems to be affected by low levels of this vitamin in men, these deficiencies appear to have a different association with jumping and hopping in women, pointing to different performance mechanisms. Further studies accounting for differences in training and other factors might delve into these gender differences.

**Keywords:** vitamin D; explosive strength; performance; nutrition; training

Accepted: 15 May 2021 Published: 19 May 2021

Received: 22 April 2021

Academic Editor: Paul B. Tchounwou

**Citation:** Ricart, B.; Monteagudo, P.; Blasco-Lafarga, C. Hypovitaminosis D in Young Basketball Players: Association with Jumping and Hopping Performance Considering Gender. *Int. J. Environ. Res. Public Health* **2021**, *18*, 5446. https:// doi.org/10.3390/ijerph18105446

**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/).

#### **1. Introduction**

Nutrition plays an important role in the health and performance of athletes. In particular, vitamins are essential in various processes, including hemoglobin synthesis, maintenance of bone health, immune function, protection against oxidative damage, neuronal functions, and the synthesis and repair of muscle tissue during recovery from injury [1,2]. Over the last decade, the monitoring of vitamin D, or calciferol, a fat-soluble vitamin with the structure of a steroid hormone that is functionally different from all others, has been of particular interest [3]. We refer to vitamin D3, a vital isomer synthesized in the cell membrane of the epidermis and dermis as a response to solar radiation, as its other common form, D2, is derived from plants and is impossible for the human body to synthesize [4,5].

Vitamin D3 regulates the expression of more than 900 gene variants, which in turn significantly [6] impacts numerous functions related to sporting performance. Among other things, it is involved in the regulation of exercise-induced inflammation, neurological function, cardiovascular health, glucose metabolism, bone health, and skeletal muscle performance [7]. More specifically, it is attributed with an ergogenic effect on neuromuscular

137

efficiency and the muscle-contraction mechanism [8,9], as well as optimizing acute adaptive response to physical exercise [10], so that performance in athletes may be affected by deficient levels of this vitamin [11,12].

However, recent research suggests that high-performance athletes are at constant risk of vitamin D deficiency, increasing the risk of stress fractures, acute illness, and sub-optimal muscle function [3]. In addition to a possible nutritional deficit due to insufficient calorie intake in athletes with high energy needs [13], or poor diet [14], vitamin D deficiency has been linked to a lack of or drastic reduction in vitamin D production in the winter months due to a lower incidence of sun on the skin [15]. For example, Bescos and Rodriguez [16] found that more than half of one professional basketball team had hypovitaminosis D after the winter. More recently, Fishman et al. [17] found a high prevalence of vitamin D insufficiency in National Basketball Association (NBA) players.

Therefore, it seems that vitamin D deficiency is accentuated in athletes who train and compete indoors throughout the year, as is the case of basketball. Taking also into account the relationship between vitamin D and the aforementioned optimization of muscle contraction [8,9] and/or prevention of bone health issues [7], it seems that this deficiency is particularly important in a sport that involves continuous accelerations and braking, jumps and receptions [18]. The rate of force development in the lower extremity is of the utmost importance [19,20], and the risk of musculoskeletal injuries is high [21]. Moreover, jumping, which may be affected by calciferol deficit, is one of the most common actions performed in this sport [22,23], with between 40 and 60 jumps being made per athlete during a single game [24]. Jumping is also one of the most common ways of assessing player performance [25], condition-maturity level [26,27], level of functional health over the course of the season [28,29], and sporting life success [30,31].

Knowing whether basketball players are calciferol deficient from their early formative stages, and the possible relationship between their vitamin concentrations and muscle function as assessed by jumping, is therefore of interest to the medical and technical staff who care for these athletes. Although there is no evidence to suggest gender differences in vitamin D intake [32] and/or deficit [33,34], differences between male and female basketball players tend to be significant in jumping ability [35], so it is equally important to analyze these associations while taking gender into account. The aims of this study are, therefore, to test whether a group of young high-performance basketball players are vitamin D deficient (1); and to analyze whether there is any relationship between vitamin D levels and muscle strength performance as measured by two types of jumps (2), taking into account gender differences. To our knowledge, no studies have previously investigated this potential relationship.

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

#### *2.1. Participants*

This quantitative, descriptive, and correlational study involved 27 young basketball players belonging to a top-level competitive club in the ACB (Asociación de Clubes de Baloncesto) league, of whom 14 were girls (16.00 ± 0.55 years, all of them had attained menarche), and 13 were boys (15.54 ± 0.52 years). Before data collection began, both the subjects and their legal guardians were informed of the purpose of the study. Each participant signed an informed consent form, agreeing to participate in the study, which had been approved by the ethics committee of the local university (H1553774899546).

#### *2.2. Protocol*

The data collection was carried out during the regular season in the month of December, and on three alternate days of the same week. The week prior to the first assessment, the participants were informed that they should consume no stimulant drinks (caffeine or energy drinks); they could not eat two hours prior to the tests; and they should maintain their normal nutritional habits. The first evaluation session involved blood tests. In the second session, the anthropometric measurements of the players were taken and the Abalakov

vertical jump test was performed first bilaterally (both legs at a time), and then unilaterally (one leg at a time). In the final evaluation session, data on the triple hop test were collected. Prior to the jumping tests, a standardized 10-min warm-up was performed on both days, consisting of jogging, dynamic stretching, lower and upper limb strength exercises, plyometric exercises, and high-intensity running with changes of direction. No familiarization phase was carried out for the evaluation tests, as all of the athletes had already taken these at some point during the season.

#### *2.3. Assessment Tools*

#### 2.3.1. Blood Test

The method for determining the body's vitamin D status consisted of measuring the serum 25-hydroxyvitamin D concentrations [36]. For many years, there has been a consensus that blood concentrations of this metabolite reflect total body vitamin D, including endogenous synthesis by exposure to sunlight, dietary intake in supplemented or unsupplemented meals, and drug treatments [37]. The blood samples were taken by a medical professional from a hospital in the same city. The players were summoned to the medical center, along with their fathers, mothers, or legal guardians, with an overnight fast required before attending.

For the blood tests, 5 mL of venous blood were extracted from the antecubital vein of each participant. Once obtained, the blood samples were allowed to clot and then centrifuged at 3000 rpm for 10 min at room temperature to isolate the serum. The serum was aliquoted into an Eppendorf tube and conserved at −80 ◦C until biochemical analysis. Serum vitamin D concentrations were determined using the LIAISON 25(OH) Vitamin D TOTAL Assay (CLIA) (Eurofins Megalab S.A., Valencia, Spain), which is a direct competitive chemiluminescence immunoassay for human serum intended for use on the DiaSorin LIAISON automated analyzer (DiaSorin S.P.A., Saluggia, Italy). Once the laboratory tests had been performed, the reports containing the analytical data were submitted to the researchers for further analysis.

#### 2.3.2. Anthropometric Measurements

Mass (kg) and height (cm) measurements were recorded using a scale (SECA 769, CE 0123, Hamburg, Germany) and a stadiometer (SECA 220, CE 0123, Hamburg, Germany). The body mass index (BMI) of the participants was calculated using the formula mass/height<sup>2</sup> (kg/cm<sup>2</sup> ).

#### 2.3.3. Abalakov Test

In order to evaluate the rate of force development of the lower extremity, the Abalakov test [38] was performed both bilaterally (Ab) and unilaterally (Abalakov right or AbR; and Abalakov left or AbL), with the height of the jump being recorded. All players performed three jumps in each modality, with a recovery period of two minutes between the jumps [39], although only the best jump in each modality was included in the statistical analysis. The jumps were recorded using a Din-A2 contact platform (420 × 594 mm) and Chronojump software (Boscosystem®, Barcelona, Spain).

#### 2.3.4. Triple Hop Test

To evaluate the power and neuromuscular control of a horizontal jump, the participants took the triple hop test [40,41]. This test consists of three consecutive jumps on one leg, with the distance reached after the last jump being recorded [40]. Each player performed the test twice with each leg alternatively (triple hop left or THL; and triple hop right or THR), and the best jump with each leg was used in the subsequent analysis. A standard 12-metre tape measure was used to measure each jump.

#### *2.4. Statistical Analysis*

The data were analyzed using the statistics package SPSS v23 for Windows (SPSS Inc. Chicago, IL, USA). Once the normality of the sample had been analyzed (Shapiro– Wilk test), the descriptive variables were then calculated and expressed as the mean and standard deviation (mean ± *SD*). *T*-tests for independent samples or Mann Whitney U-tests were performed to analyze whether there were sex-related differences between the main study variables. *T*-tests for related samples and the Wilcoxon test were also performed to compare whether there were sex-related asymmetries between the legs. To check whether there was a relationship between vitamin D levels and performances in the jumping tests, we performed a correlation analysis (Pearson's *R* or Spearman's Rho according to the normality), both with and without controlling for the covariate BMI. Statistical significance was set at *p* < 0.05, with the absolute correlation coefficients considered being: *r* < 0.1, trivial; 0.1–0.3, low; 0.3–0.5, moderate; 0.5–0.7, strong; 0.7–0.9, very strong; >0.9, almost perfect; and 1, perfect [42].

#### **3. Results**

The final sample comprised 14 girls (16.00 ± 0.55 years, 174.20 ± 6.35 cm, 67.98 ± 6.73 kg) and 13 boys (15.54 ± 0.52 years, 190.73 ± 6.45 cm, 78.17 ± 8.87 kg). No significant differences were found between boys and girls in terms of age, but significant differences were found for weight and height (*p* < 0.05), with higher values recorded in the boys. Table 1 presents the results of the main blood composition parameters. No significant differences between boys and girls were found for any of the items, and the coefficients of variation were generally high in both cases.


**Table 1.** Blood composition variables.

CV: coefficient of variation in %; *SD*: standard deviation; TSH: serum thyroid stimulating hormone.

Table 2 shows the values obtained in the neuromuscular performance tests, with lower coefficients of variation with respect to the analytical assessment, and even greater homogeneity among the boys. When analyzing the differences by sex, significant differences (*p* < 0.01) were observed in the Abalakov test for the left leg. Significant differences were also found in the triple hop test, both for the left leg (*p* < 0.001) and right leg (*p* < 0.001). Finally, significant differences were found in boys (*p* < 0.010) between the results for the right and left legs in the Abalakov test.

Table 3 shows the correlation analyses between vitamin D concentration and the results of the neuromuscular performance tests. While in boys, a high positive correlation was found between the Abalakov test (performed in a bipedal manner) and serum vitamin D concentration, in girls this relationship was also high, but negative. When BMI was considered as a covariate, the correlation coefficient increased slightly in boys, while it decreased in girls. There was also a high negative correlation between the triple hop test performed with the right leg and vitamin D in girls, which in this case increased slightly when considering BMI.


**Table 2.** Performance variables.

CV: coefficient of variation in %; *SD*: standard deviation; AbB: Abalakov bilateral; AbL: Abalakov left; AbR: Abalakov right; THL: triple hop left; THR: triple hop right. <sup>a</sup> : Difference with the AbR of boys (*p* = 0.002); b : Difference with the AbR of girls (*p* = 0.002).

**Table 3.** Correlations between jumping and hopping and vitamin D, considering both the whole sample, and male and female athletes separately, with and without the covariate body mass index (BMI).


AbB: Abalakov bilateral; AbL: Abalakov left; AbR: Abalakov right; THL: triple hop left; THR: triple hop right; \*: *p* < 0.05; \*\*: *p* <0.01; <sup>a</sup> : BMI as a covariate.

#### **4. Discussion**

For the first objective of this study (to check whether young basketball players of a formative age suffer from vitamin D deficiency), our results confirm that both girls and boys show this deficiency at the age of 14–16, while the other components analyzed were found to be within the normal range. As for whether this deficit could influence explosive strength as assessed by jumping, the second objective of this study, the data reveals that at these ages there is no association between these variables when considering the sample as a whole. However, when taking sex into account, the data points to differences regarding the correlations in young players of the two sexes, while at the same time the expected differences are observed in the rate of force development in some of the jumps that are determining factors for basketball performance (AbL, THR, and THL).

According to the levels previously established by some authors [43], young players of both sexes already suffer vitamin D insufficiency (20–30 ng/mL), while they present normal values for the other blood components [44–48]. Our results are, therefore, consistent with other studies that have shown low concentrations of vitamin D in elite athletes [49,50], with up to 56% of one sample of athletes being below the levels considered adequate [51]. In agreement with other studies [33,34] there were no differences between sexes in the vitamin D deficiencies.

As previously noted, indoor sports involve a vitamin D deficiency rate almost twice that of outdoor sports [52]. Seasonal variation in the levels of this vitamin has also been observed [15,53]. This seasonal variation should be taken into account, as it has been observed that athletes who are vitamin D deficient during the winter are at a higher risk of having lower levels in the spring [54]. This latter period is one of the most important phases of the season since the final rankings are decided and, moreover, there are more matches, therefore leading to a greater risk of fatigue and injury [55]. Both indoor training and seasonal variation are associated with low sun and ultraviolet (UVB) exposure, the main source from which the body synthesizes this vitamin [56]. It seems important, therefore, to monitor 25(OH)D concentrations throughout the basketball season in order to mitigate any potential effects that this insufficiency may cause for the players, despite the fact that

these are initial stages in which they are still training and competing quite below the level of professional athletes [57,58].

Based on this, the second objective of this study was to find out whether lower vitamin D concentrations could influence basketball performance (by assessing the rate of force development of the players through two different types of jumps). Although we did not find sex differences regarding vitamin D, all the analyses were also performed considering the sex of the participants because individual differences in jumping ability in male and female basketball players tend to be significant [35]. Our data reinforces the importance of always considering these sex differences when analyzing performance, because although there is no association between these variables when considering the entire sample in general, the data does reveal different results for men and women.

On the one hand, there is a very strong positive correlation seen in the boys between vitamin D and the bilateral Abalakov test, with a correlation coefficient that increases even more when BMI is considered as a covariate. Some authors have argued that this vitamin increases the size and number of type II muscle fibers [54,59], which could influence an athlete's jumping ability. However, this association is negative in the case of the girls, and decreases when BMI is taken into account. These results differ from those obtained by Ward et al. [60], who concluded that vitamin D was significantly associated with muscle strength in adolescent girls, although the participants in that study were not athletes.

In this sense, it is important to emphasize that at this age, boys may be less mature than their female peers [61]. Even close to full maturity, less vitamin D does not imply less jumping ability in these young female players, but rather the opposite, suggesting that there may be other mechanisms (for example, those related to good intermuscular coordination) that help these girls to jump more. Not surprisingly, the jumps where sex-related differences were found (AbL, THL, and THR), presented the lowest coefficient of variation in the boys, with these being clearly lower for the boys than their female counterparts for these same jumps. Further studies involving larger sample sizes and a more heterogeneous performance profile for girls should confirm whether, as it appears, only their male counterparts are likely to rely more heavily on explosive force production rates, with vitamin D concentration exerting a positive influence on this variable.

Considering the previous reasoning, the game and specific training would not have highlighted differences between the right and left leg in girls when performing the Abalakov test in a unilateral manner, again contrary to that seen in boys (with a significantly better AbL than AbR, and, indeed, higher AbR and AbL than those of the more mature girls in this study). As pointed out by Jones and Bampouras [62], the dominant leg of male athletes tends to present higher strength values than the non-dominant leg, which could explain the difference recorded for our male athletes. The reason behind why we found no association between vitamin D and the unilateral tests in men could be related to a lack of stability during these movements due to coordination problems [63]; to perform well in unilateral tests, an individual must have adequate balance, coordination, muscle strength, and neuromuscular control [64], and not just rate of force development. This would account for why we only found the correlation in the bilateral test, where it is easier to coordinate movements and thereby apply a greater amount of force.

The fact that the girls did not show significant asymmetries between legs suggests that women do not tend to have a more dominant leg [65]. This information, together with the fact that the strength values (performance in cm) produced by the trainee players in our study are already similar to those obtained by professional athletes [66], could indicate that the potential for further improving this ability in women may be limited, and that jumping and hopping ability may not be the most determining factor in terms of becoming a professional player. This suggests that adequate levels of vitamin D are more important for performance in men than in women, although we should not forget the significance that this vitamin may also have for women in other aspects, such as injury prevention [67].

In the triple hop test, once again there were no differences in performance between the sexes, and only the girls showed a negative correlation with vitamin D when the test was performed with the right leg, a result that was reinforced when BMI was also factored in. New gender differences in the association between vitamin D and performance seem to indicate a different use of strength in women and men in terms of the jumping actions involved in basketball performance. Notably, the lack of control of the menstrual cycle could have influenced our results. The majority of female subjects do not menstruate on a regular basis [68], and this factor was not considered at the time of blood sampling; however, despite the high coefficient of variation, our data did not show differences in iron concentration between male and female participants.

This study has several limitations. Firstly, the cross-sectional design of this study does not allow us to determine a direct cause and effect relationship. Comprehensive nutritional monitoring would have improved our knowledge of the origin of the vitamin D deficiencies found. Similarly, it is necessary to test whether vitamin D levels vary throughout the season and whether this is associated with a change in jump values in different periods. New and less invasive assessment systems based on tear biosensing or salivary samples [69,70] could streamline the process to obtain biomarkers during the competitive season, and therefore would allow relationships between strength and vitamin D to be analyzed from a more holistic (and rapid) view. Multidisciplinary teams—included nutritionists—regardless of the level of the sport club (elite and amateur), would facilitate the interpretation of these assessments, periodizated and tailored on a regular basis, therefore promoting health and young athletic success. Secondly, a larger sample size would allow more robust correlation coefficients to be obtained, for which reason our results cannot be extrapolated to other contexts and further studies are required. Finally, the differences found between men and women suggest that future studies should analyze whether the menstrual cycle somehow affects vitamin D, and thus sports performance in female basketball players, given their high incidence of injuries [71]. Some studies have demonstrated relationships between low levels of this vitamin and the frequency of menstrual disorders [72], confirming that this is a variable to control in these stages of development.

#### **5. Conclusions**

Our results suggest that, despite their youth, trainee basketball players have insufficient vitamin D levels. Since this deficiency appears to be common in elite athletes, especially those competing indoors, various means of controlling vitamin D levels throughout the season (diet, supplementation, and sun exposure) should be considered. Furthermore, these deficiencies appear to be differentially associated with jumping performance in men and women. Thus, while performance in men does seem to be compromised by low levels of this vitamin, it would be interesting to further investigate the different role it might play in women, as vitamin D deficiency is not only related to rate of force development.

**Author Contributions:** Conceptualization, C.B.-L., B.R. and P.M.; methodology, B.R.; formal analysis, B.R., P.M. and C.B.-L.; data curation, P.M.; writing—original draft preparation, B.R., P.M. and C.B.-L.; writing—review and editing, P.M., C.B.-L., B.R.; supervision, C.B.-L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study has received financial support from the "Cátedra l'alqueria del basket" of the University of Valencia.

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Research in Humans Ethics Committee of the University of Valencia, Spain (H1553774899546).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to thank all the players and technicians from the Alquería LAB—Valencia Basket Club who have helped to develop the present study.

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

#### **References**


### *Article* **Predicting the Unknown and the Unknowable. Are Anthropometric Measures and Fitness Profile Associated with the Outcome of a Simulated CrossFit ® Competition?**

**Javier Peña 1,2,3,† , Daniel Moreno-Doutres 3, \* ,† , Iván Peña 3 , Iván Chulvi-Medrano 4 , Alberto Ortegón 5 , Joan Aguilera-Castells <sup>5</sup> and Bernat Buscà 5**


**Abstract:** The main objective of this research was to find associations between the outcome of a simulated CrossFit ® competition, anthropometric measures, and standardized fitness tests. Ten experienced male CrossFit ® athletes (age 28.8 ± 3.5 years; height 175 ± 10.0 cm; weight 80.3 ± 12.5 kg) participated in a simulated CrossFit ® competition with three benchmark workouts ("Fran", "Isabel", and "Kelly") and underwent fitness tests. Participants were tested for anthropometric measures, sit and reach, squat jump (SJ), countermovement jump (CMJ), and Reactive Strength Index (RSI), and the load (LOAD) corresponding to the highest mean power value (POWER) in the snatch, bench press, and back squat exercises was determined using incremental tests. A bivariate correlation test and k-means cluster analysis to group individuals as either high-performance (HI) or low performance (LO) via Principal Component Analysis (PCA) were carried out. Pearson's correlation coefficient two-tailed test showed that the only variable correlated with the final score was the snatch LOAD (*p* < 0.05). Six performance variables (SJ, CMJ, RSI, snatch LOAD, bench press LOAD, and back squat LOAD) explained 74.72% of the variance in a k = 2 means cluster model. When CrossFit ® performance groups HI and LO were compared to each other, *t*-test revealed no difference at a *p* ≤ 0.05 level. Snatch maximum power LOAD and the combination of six physical fitness tests partially explained the outcome of a simulated CrossFit competition. Coaches and practitioners can use these findings to achieve a better fit of the practices and workouts designed for their athletes.

**Keywords:** performance; athlete; high-intensity functional training; cross-training; functional fitness

**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/).

#### **1. Introduction**

CrossFit ® is a training method property of CrossFit ® Inc. (Washington, DC, USA), a company established in 2000 by Greg Glassman and Laura Jenai. This form of physical exercise incorporates elements from other disciplines, such as weightlifting, powerlifting, gymnastics, calisthenics, and strength athletics, while following high-intensity exercise principles and using constant variability as one of its core elements. According to data from the company, the number of official CrossFit ® affiliated gyms in the world is close to 15,000 [1], a figure that shows the worldwide interest in this exercise regime. Apart from the CrossFit ® activity aimed at the general population, CrossFit ® Inc. has developed a

**Citation:** Peña, J.; Moreno-Doutres, D.; Peña, I.; Chulvi-Medrano, I.; Ortegón, A.; Aguilera-Castells, J.; Buscà, B. Predicting the Unknown and the Unknowable. Are Anthropometric Measures and Fitness Profile Associated with the Outcome of a Simulated CrossFit ® Competition?. *Int. J. Environ. Res. Public Health* **2021**, *18*, 3692. https:// doi.org/10.3390/ijerph18073692

Received: 23 February 2021 Accepted: 29 March 2021 Published: 1 April 2021

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

competitive trend that also enjoys considerable international popularity. In 2019, 144,276 people completed all the workouts of the day (WODs) of the CrossFit Open® as prescribed or "RX" [2] (meaning that the athletes used the prescriptive weight or height, completed the prescribed number of repetitions, and followed the full standards for each movement). Alongside 15 sanctioned events, the CrossFit Open® is the only way to qualify for the CrossFit Games®, where the elite of this sport has convened every year since 2007.

Adult CrossFit® participation seems to entail similar physical demands (in terms of VO<sup>2</sup> max, muscle size, strength and endurance gains) to other high-intensity physical activities [3]. Several cohort studies have reported improvements in VO<sup>2</sup> max [4,5], body composition [6,7], and specific work capacity [8] in men and women in interventions ranging from 6 to 10 weeks. Thus, CrossFit® WODs are a demanding form of exercise, and physiologically, both aerobic and anaerobic metabolisms influence the athlete's performance [9].

General strength improvements associated with CrossFit® participation are also described in the literature with conflicting results. Significant increases in several muscular strength and endurance tests after participation in CrossFit® workouts have been reported in some studies [5], while in some others, no significant differences were noted postintervention [8].

However, all the studies mentioned above have two critical limitations highlighted in systematic reviews: a reduced number of scientific studies because the discipline is still incipient, and a lack of a high level of evidence at low risk of bias [10].

To date, several studies have highlighted that the physical stress caused by CrossFit® WODs is comparable to a 20 min high-intensity treadmill run at 90% of maximal heart rate [11] and superior to an ACSM-based training session in terms of fatigue, muscle soreness, and muscle swelling [12]. Rating of perceived exertion (RPE) seems consistently high after CrossFit® routines [12,13], and increased lactate [13–15] and pro/anti-inflammatory cytokine production [14] is also present in several scientific reports assessing these activities.

Although CrossFit® athletic competitions generate significant revenues, not many previous studies have dealt with competitive performance factors. Numerous scientific contributions have investigated the epidemiology of CrossFit® [3,16–18], with several cases of spinal injuries [19] and rhabdomyolysis [20] reported, but not many pieces of research have provided insight about the relevant elements of fitness to succeed in competitions. For instance, a study comparing the outcomes in three benchmark WODs—"Grace" (30 clean and jerks for time), "Fran" (three rounds of thrusters and pull-ups for 21, 15, and 9 repetitions), and "Cindy" (20 min of rounds of 5 pull-ups, 10 push-ups, and 15 bodyweight squats)—found that whole-body strength and anaerobic threshold exhibited association with specific CrossFit® performance [21]. In a similar analysis with 32 healthy adult males, age, group (experienced vs. inexperienced), VO<sup>2</sup> max, and anaerobic power were predictors of a 12 min as many repetitions as possible WOD with 12 throws of a 9.07 kg medicine ball at a 3.05 m target, 12 swings of a 16.38 kg kettlebell, and 12 burpee pull-ups [22]. In the same article, only CrossFit® experience was a significant predictor in a WOD with sumo deadlift high pull, a 0.5 m box jump, and a 40 m farmer's walk with 40 kg following a three-round with 21, 15, and 9 repetitions per exercise structure. Recent research has also found that absolute VO<sup>2</sup> peak values and CrossFit® Total (one repetition maximum tests for the back squat, deadlift, and overhead press) were predictors of the 19.1 CrossFit Open® workout and the benchmark "Fran" performances, respectively [23]. Body composition was revealed as the most significant success predictor in the 2018 CrossFit Open® [24].

Despite an increased number of scientific studies due to the growth in popularity of CrossFit®, there is still an important space for further research about CrossFit® athletic competitions. The main objective of this cross-sectional study was to find associations between the outcome of a simulated CrossFit® competition, anthropometric measures, and standardized fitness tests, providing insight to coaches and athletes to achieve better competitive performance.

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

#### *2.1. Participants*

A purposive sample of ten experienced male CrossFit® athletes (age 28.8 ± 3.5 years; height 175 ± 10.0 cm; weight 80.3 ± 12.5 kg; one-hand reach 223 ± 15 cm) without relevant injuries at the moment of the study and recruited from official CrossFit® affiliates volunteered to participate in the study. The inclusion criteria were set based on weekly training volume (≥5 sessions/week), competitive CrossFit® background (≥2 years), regular participation in regional (*n* = 1), national (*n* = 5), or international (*n* = 4) competitions, and their ability to perform the RX versions of the workouts (respecting the metabolic purpose of the WOD and being able to lift the weights without fatal technical flaws in the presence of fatigue). Before starting the study, we informed the participants about the experimental procedures and they signed informed consent and provided additional data by filling out a modified Physical Activity Readiness Questionnaire (PAR-Q) [25]. Procedures followed the Declaration of Helsinki and its later amendments [26] and were approved by the Research Ethics Committee of the University of Vic - Central University of Catalonia in Barcelona, Spain (ref. no. 46/2018).

#### *2.2. Experimental Procedures*

Testing was conducted over two separate sessions. In the first session, before starting a simulated CrossFit® competition, we tested the participants for anthropometric measures and a sit-and-reach flexibility test. Weight was assessed on an electronic scale (PS160, Beurer, Germany) with an accuracy of ±0.1 kg. Height was measured using a roll-up measuring tape with wall attachment (206, Seca®, Hamburg, Germany) with an accuracy of ±0.01 m. One-hand reach was assessed using a measuring tape (TM-CO2, Tacklife, New York, NY, USA). Body fat percentages were calculated using the equation of Jackson and Pollock [27] measuring the skinfold thickness at three sites (chest, abdomen, and thigh) using a caliper (Holtain Ltd. Tanner/Whitehouse Skinfold Caliper, Holtain, Dyfed, UK). One experienced anthropometrist carried out all the tests following the protocols established by the International Society for the Advancement of Kinanthropometry (ISAK). The sit-and-reach test was performed twice using a sit-and-reach box (Sit and Reach testing box, Eveque, Northwich, UK) and considering the best score as the final result in the test. Later, all of the participants completed three benchmark WODs in random order with a 30 min rest in between them, simulating a CrossFit® Competition. The three selected WODs were "Fran", "Isabel", and "Kelly", and they were performed in that same order (Table 1). These WODs were selected because they are popular benchmark WODs in the CrossFit® community and because they incorporate very diverse skills and fitness elements (Olympic lifting movements, calisthenics, pure conditioning movements, and exercises with high VO<sup>2</sup> max demands).

**Table 1.** Workouts performed in the simulated CrossFit® competition.


Every participant was assigned a certified CrossFit® judge to control their performance, and the WODs were completed in two series or "heats". Participants for the two heats in the first WOD were selected at random, while for the second and third WODs, the athletes with better accumulated scores were assigned to the second heat reproducing the usual CrossFit® competition procedures. During the second session, a week later, we carried out the rest of the measurements (Table 2). Squat jump (SJ), countermovement jump (CMJ), and a 0.7 m drop jump (DJ) were measured using a contact mat (Ergojump-Plus, Ergotest

Innovation, Norway) consisting of a switch mat connected to a digital timer (with an accuracy of ±0.001 s). Contact time and resulting height in the DJ were used to calculate Reactive Strength Index (RSI) by using the formula: RSI = Jump Height (cm)/Ground Contact Time (ms). All of the jumps were performed three times, and the best score was the final result in the tests. The loads (LOAD) corresponding to the highest mean power value (POWER) in the snatch, bench press, and back squat exercises were determined using incremental tests [28,29] and were measured with a linear encoder (MuscleLabTM, Ergotest Innovation, Stathelle, Norway) attached to the barbell. To assess the ability of the athletes to perform intermittent efforts, a Yo-Yo intermittent recovery test 2 (IR-2) was administered, and the distance covered was used to calculate VO<sup>2</sup> max (mL/min/kg) using the formula: IR-2 distance (m) × 0.0136 + 45.3 [30]. All the mentioned tests were chosen because they show ecological and construct validity, the movements used are very similar to those of CrossFit®, and the tests enabling calculations have been validated by previous scientific literature.

**Table 2.** Protocols followed in the incremental tests.


#### *2.3. Statistical Analysis*

Using a statistical package (SPSS 21 for macOS, SPSS Inc, Chicago, IL, USA), a Shapiro– Wilk test was used to determine if the sample data was normally distributed prior to conducting a bivariate correlation test between the final competition score—assigning 10 points to the best-ranked competitor in each WOD, 9 to the next one, and consecutively so until the last competitor—and the different physical condition tests conducted in the study. Significance level was established at *p* < 0.05 (α = 5%) with a 95% confidence interval. In the second term, R, a language and environment for statistical computing (R 3.5.1 GUI 1.70 for macOS, R Foundation for Statistical Computing, Vienna, Austria), was used to normalize physical tests, centering them at 0 to avoid between-variable scale differences, carrying out a k-means cluster (k = 2) analysis considering the outcome of the physical tests to group individuals as either high (HI) or low (LO) performance. Later, a *t*-test was used to compare composite WOD scores between HI and LO groups. Finally, a Principal Component Analysis (PCA) was carried out to determine the influence of each physical test on the simulated CrossFit® competition final composite score.

#### **3. Results**

The Shapiro–Wilk test showed that the variables included in the analysis were normally distributed (*p* > 0.05). A bivariate Pearson's correlation coefficient two-tailed test of significance showed that the only variable showing a very large correlation [31] with the final score of the competition was the snatch LOAD (*p* < 0.05); none of the other variables showed association with the competition outcome (Table 3). Although weekly volume of training was not significantly correlated with the final competition score (*p* = 0.142), the r-value showed a promising correlation (0.50) with this factor.


**Table 3.** Correlation coefficients, interpretation, and significance levels in the variables included in the study.

\* Denotes significant correlation (*p* < 0.05).

A k-means model established two centroids that determined the two groups, HI (*n* = 6) and LO (*n* = 4) (Figure 1). The unpaired *t*-test comparison revealed no differences between HI and LO groups in WOD scores.

**Figure 1.** Boxplot visualization of the k-means cluster analysis grouping individuals as either highperformance (HI) or low performance (LO) and showing the minimum score, first quartile, median, third quartile, and maximum score achieved in the simulated competition by every group.

PCA cluster explains 74.72% of the variance using six performance variables measured in the study (SJ, CMJ, RSI, snatch LOAD, bench press LOAD, and back squat LOAD) (Figure 2). When CrossFit® performance groups HI and LO were compared, the *t*-test revealed no difference at *p* ≤ 0.05 level.

**Figure 2.** Principal Component Analysis with concentration and confidence ellipses around each group, including the six performance measures. Each main component is obtained by linear combination of the original six variables, and every dot inside the ellipses represents one individual in the HI (*n* = 6) and LO (*n* = 4) groups. These two components explain 74.72% of the point variability.

The average values obtained in the tests included in the PCA are presented to describe the performances obtained by the athletes who participated in our study (Table 4).


**Table 4.** Average values obtained in the tests included in the PCA.

#### **4. Discussion**

The purpose of this study was to determine if a battery of standardized physical fitness tests can predict the outcome of a simulated CrossFit® competition. Competitive CrossFit® is a complex discipline, where many different skills and elements of physical fitness (endurance, stamina, strength, flexibility, power, speed, coordination, agility, balance, and accuracy) come into play to achieve success. Due to this complexity, the CrossFit® community has always accepted that the best way to assess performance (and therefore fitness levels) is to perform CrossFit® benchmark WODs and participate in CrossFit® competitions. This approach has significant limitations; specific CrossFit® workouts test more than one capacity, making it difficult to attribute the progress in a workout to all of them equally. If we improve our time or repetitions in one particular CrossFit® benchmark WOD, it is unfeasible to know if strength, skill, or conditioning was the main explanatory factor of this enhancement in performance. Additionally, CrossFit® competitive performance requires psychological and physiological settings. Thus, understanding the attributes related with CrossFit® performance can be relevant for two main reasons: it can be helpful to predict individual competitive outcomes and to work on the athletes' weaknesses, improving their performances.

Previous research has suggested a relationship between a combination of power measurements [22], whole-body strength [21], and power in the full-squat test [32], and CrossFit® performance. However, this approach has limitations. On the one hand, it is undeniable that benchmarks and competitions are specific; they reproduce the "unknown and unknow-

able" axiom of the sport. Nevertheless, using them to test fitness can be time-consuming, and for some recreational athletes, the RX standards can be unachievable. In some WODs, this changes the "testing" conditions dramatically, because it is evident that it is not the same to perform the benchmark "Fran" with a 30 kg barbell and jumping pull-ups or to use the prescribed weight and movements in the RX version. Standardized tests are valid, reliable, accurate, and sensitive to detect changes in fitness, being useful in different populations and age groups. Their main disadvantage is the need for equipment that can be expensive and, in some cases, requires training to be used. However, their application is fast, and they equalize the execution conditions for everyone.

The data reported in the present study partially support the initial hypothesis. Only the result of one incremental test, the snatch, showed a strong (but not perfect) correlation with the outcome of the competition, and this was more than likely conditioned by the fact that one of the benchmark WODs in the event ("Isabel") depended exclusively on the ability to perform this movement repeatedly with a high requirement of power. Despite this, the battery used in our study could discriminate between high (HI) and (low) LO performance athletes in the sample, explaining 74.72% of the variance with six performance variables measured. This result is consistent with that of other researchers arguing that CrossFit® experience and training level is a critical component of performance in CrossFit® workouts [22]. Weekly volume of training was not significantly correlated with the final competition score in our data, but a large correlation value (0.50) indicates that this factor can be considered as relevant in future research.

The lack of association between the individual outcome of the different fitness tests proposed and the simulated competition can be solved using a battery of tests. In one of the few investigations that we know regarding this matter, it was found that it is unfeasible to pretend that a single test of any nature can predict the result of a benchmark WOD in CrossFit® [21].

Although the benchmark WODs in this study were selected because they present very different physical condition elements (aerobic and anaerobic demands, weightlifting, gymnastics, and conditioning movements), the variables that could explain the variance were all of a similar nature; the only test assessing VO<sup>2</sup> max in our design showed no predictive power. "Fran" and "Isabel" are WODs that elite and sub-elite athletes can finish in less than five minutes, and "Kelly" lasts no longer than 20 min in these populations. This data agrees with previous research, where VO<sup>2</sup> max did not predict CrossFit® performance [21]. However, VO<sup>2</sup> max has explained 68% of the variance in the outcome of the workout "Nancy" [33], with five rounds of 400 m run. In our case, the chosen workouts had an anaerobic predominance, and the rest periods between WODs were enough to emphasize the importance of muscular power in the competitive outcome, showing an enhanced specific work capacity in the athletes [8]. In this direction, a test using four consecutive Wingate anaerobic tests has predicted CrossFit® specific performance in previous investigations [34].

We did not find any relationship between anthropometric measures and CrossFit® specific performance. This may be attributed to the participants' characteristics as expert athletes with suitable body composition (body fat 8.2 ± 2.83%) for their competitive development. The intrinsic characteristics of advanced CrossFit® athletes and the purposive sampling used in this research piece may have been a limiting factor in finding an association between body composition and competition outcome. All the athletes in our sample clearly showed a physical condition above the average among CrossFit® enthusiasts.

Flexibility levels were also shown not to be correlated with CrossFit® performance in our study. To the best of our knowledge, no previous research has included flexibility as a possible predictor of CrossFit® performance.

The present results should be interpreted with caution. The competition level of the athletes volunteering in our study (sub-elite) and the sample size are limitations to use our results to make inferences about other populations like elite athletes (CrossFit Games® caliber) or inexperienced CrossFit® recreational athletes. The selection of tests and

benchmark WODs could also be a limitation. We should also understand that although all participants were instructed to perform all the WODs at the maximum intensity, the context (a simulated competition) could be less motivating than real competition settings.

Future work on the current topic is therefore recommended to apply these findings to different cohorts, using other benchmark WODs or workouts from a real competitive event. Incorporating different standardized tests that can lead to more robust results, and a higher percentage of the variance of the outcome explained by the selected performance factors, could also be desirable.

This study set out to know in greater depth what the critical elements of physical fitness are that allow one to achieve a good result in a simulated CrossFit® competition. The load at which the maximum snatch power was achieved and the combination of six physical fitness tests (SJ, CMJ, RSI, snatch LOAD, bench press LOAD, and back squat LOAD) partially explained the outcome of a simulated CrossFit® competition with the benchmarks "Fran", "Isabel", and "Kelly". Coaches and practitioners can use these findings to improve their decision-making processes and to use these tests as an element that can allow a better fit of the practices and workouts designed for their athletes.

#### **5. Conclusions**

Results coming from this article show that isolated physical condition tests can be misleading to explain the outcome of a CrossFit® WOD. These individual tests can only be useful in cases where the benchmark WODs performed in the CrossFit® context and its results are strongly related to the execution of one particular movement. Batteries of tests can help to discriminate athletes of different levels, showing that a better physical condition expressed in the battery is partially associated with a better overall performance in the specific CrossFit® activity. These batteries should implement tests that are valid, reliable, accurate, and sensitive to detect changes in fitness, but at the same time show some level of specificity with competitive CrossFit® requirements and CrossFit® athletes' specific needs.

**Author Contributions:** Conceptualization, J.P., D.M.-D., I.P. and A.O.; methodology, J.P., D.M.-D., I.P. and A.O.; formal analysis, J.P., D.M.-D., B.B. and J.A.-C.; data curation, J.P., D.M.-D., B.B., I.C.-M. and J.A.-C.; investigation, J.P., D.M.-D., I.P. and A.O.; writing—original draft preparation, J.P., D.M.-D., B.B. and I.C.-M.; writing—review and editing, J.P., D.M.-D., B.B. and I.C.-M.; project administration, J.P., I.P. and A.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Ethical approval was given by the Research Ethics Committee of the University of Vic-Central University of Catalonia in Barcelona, Spain (ref. no. 46/2018).

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** The data that support the findings of this study are available on request from the corresponding author.

**Acknowledgments:** The authors would like to give explicit thanks to the athletes and judges that kindly volunteered in the study, as well as to the owners of the premises in which the study was carried out. We also would like to thank Amy Gibson for her assistance in the preparation of this article.

**Conflicts of Interest:** The authors declare no conflict of interest. CrossFit® is a registered trademark of CrossFit, Inc. and any use of the term in this article is adopted merely as nominative in nature. The authors are not endorsed or sponsored by CrossFit Inc. in any way.

#### **References**

