Relationship between Running Spatiotemporal Kinematics and Muscle Performance in Well-Trained Youth Female Athletes. A Cross-Sectional Study

oss-sectional study was to analyse the relationship of neuromuscular performance and spatiotemporal parameters in 18 adolescent distance athletes (age, 15.5 1.1 years). Using the OptoGait system, the power, rhythm, reactive strength index, jump ying time, and jump height of the squat jump, countermovement jump, and eight maximal hoppings test (HT 8max) and the contact time (CT), ying time (FT), step frequency, stride angle, and step length of running at different speeds were measured. Maturity offset was determined based on anthropometric variables. Analysis of variance (ANOVA) of repeated measurements showed a reduction in CT (p< 0.000) and an increase in step frequency, step length, and stride angle (p< 0.001), as the velocity increased. The HT 8maxtest showed signi cant correlations with very large effect sizes between neuromuscular performance variables (reactive strength index, power, jump ying time, jump height, and rhythm)

anthropometric variables. Analysis of variance (ANOVA) of repeated measurements showed a reduction in CT (p< 0.000) and an increase in step frequency, step length, and stride angle (p< 0.001), as the velocity increased. The HT 8maxtest showed signi cant correlations with very large effect sizes between neuromuscular performance variables (reactive strength index, power, jump ying time, jump height, and rhythm) and both step frequency and step length. Multiple linear regression found this relationship after adjusting spatiotemporal parameters with neuromuscular performance variables. Some variables of neuromuscular performance, mainly in reactive tests, were the predictors of spatiotemporal parameters (CT, FT, stride angle, and VO). Rhythm and jump ying time in the HT 8maxtest and power in the countermovement jump test are parameters that can predict variables associated with running biomechanics, such as VO, CT, FT, and stride angle. Keywords: muscleperformance; biomechanics; kinematics; plyometrics; spatiotemporal; female;youth 1. Introduction Distancerunning performance is mainly associated with physiologicalcharacteristics [1] , such as running economy [2]. Traditionally, in adult athletes and also in adolescents [3,4], it is related to metabolic ef ciency [5], the type of muscle bres [6], and cardiorespiratory ef ciency through heart rate [7] and VO2max[8], which has also been previously associ- ated with genetic variants [9,10]. However, there are other parameters, such as running biomechanics [11,12] and the ability to jump [13,14], which have also established a relation- ship with performance, suggesting that optimal movement performance and appropriate neuromuscular performance will also have a positive impact on energy cost parameters. Among thespatiotemporal parameters most related to running economy are the contact time (CT), vertical oscillation (VO), step frequency and stride angle [12,15]. Some of these variables have also been associated with the prevalence of running-relatedinjuries [16–19] together with growth-related factors, such as maturity offset and, more speci cally in female athletes, the interrelationship of energy availability, menstrual function, and bone mineral density de ned as the Female Athlete Triad [20,21]. The relationship between Int. J. Environ. Res. Public Health2021,18, 8869.

offset and, more speci cally in female athletes, the interrelationship of energy availability, menstrual function, and bone mineral density de ned as the Female Athlete Triad [20,21]. The relationship between Int. J. Environ. Res. Public Health2021,18, 8869.

Int. J. Environ. Res. Public Health2021,18, 8869 2 of 14 maturity offset and running biomechanics has been widely reported in sprint [22–25] and, more sparingly, in the distance running [26]. In short- and long-distance running, improved performance after training jumping ability has been associated with increased muscle strength and power [27], as well as stiffness of the muscular-tendinous system (which allows for the storage and use of elastic energy more ef ciently) [5,28]. Previous studies have shown that combined strength and endurance training can increase running economy, muscle strength, and performance without affecting VO2max[29], suggesting that endurance running performance may be affected by neuromuscular factors. The assessment of the jump ability can be carried out using a variety of instruments, in- cluding accelerometric systems [30], force platforms, contact mats and optical systems [31], which can also be used for running biomechanical evaluation [32]. Countermovement, rebound [33], and multi-hopping jumps are used [34] to measure jump ability, which also evaluates the neuromuscular performance and ef ciency of the stretch-shortening cycle in distance athletes [35]. Because the ability to develop strength in the shortest possible time is required in most sports [36–38], the reactive strength index has been developed as a reliable measure of force and time it takes to produce it [39]. Reliability and validity of its measurement in adolescents through the maximum hopping test has been proven [40] and has recently been recommended for development as part of the strength training of athletes, especially in women [41]. In addition, the increase in reactive strength index has been linked to performance improvements in middle-distance running [42]. Investigation of the relationship of spatiotemporal parameters with neuromuscular performance has shown different results [14,43–46], suggesting that neuromuscular fac- tors could in uence running biomechanics through kinematic spatiotemporal parameters. However, there are no studies that relate these biomechanical parameters to the neuromus- cular performance of highly trained female adolescent athletes. This prevents drawing the same conclusions for different genders and sport levels. For this reason, the main objective of this study was to analyse the relationship between the running spatiotemporal parameters with neuromuscular performance (measured

through kinematic spatiotemporal parameters. However, there are no studies that relate these biomechanical parameters to the neuromus- cular performance of highly trained female adolescent athletes. This prevents drawing the same conclusions for different genders and sport levels. For this reason, the main objective of this study was to analyse the relationship between the running spatiotemporal parameters with neuromuscular performance (measured by the ability to jump [47]) in highly trained adolescent distance runners. The authors hypothesize that higher values of neuromuscular performance are related to lower VO, stride angle, and CT and an increase in FT. 2. Materials and Methods This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the University of M¡laga (CEUMA Registration number: 56-2019-H). Researchers obtained informed consent from all subjects involved in the study. Parents/guardians signed consent before participation, and in the case of those over 18 years of age, consent was provided by participants. 2.1. Participants To conduct this cross-sectional study, 18 female adolescent athletes (age SD, 15.5 1.1 years; age range, 14–18 years; height, 164.5 8.2 cm; body mass, 55.5 7.4 kg and body mass index, 20.5 2.22 kg/m 2 ) voluntarily participated in this study. Partici- pants met the following inclusion criteria: age 14 to 18 years old, no injuries in the previous 3 months, and competed regionally, nationally, or internationally in medium-distance (800–3000 m) or long-distance (5–10 km and cross-country). Subjects were instructed to avoid training for 24 h before testing. All evaluations were carried out in a laboratory at 20–24 C, with a relative humidity of 45–55% and similar conditions for all participants. 2.2. Procedure Participants were summoned to perform the jumping and progressive running tests on the same day. First, primary anthropometric data were collected. Before starting the jumping and running tests, participants completed a warm-up phase with 10 min of

Int. J. Environ. Res. Public Health2021,18, 8869 3 of 14 continuous running and 5 min of activation exercises (knee lifts, accelerations, bracing, deep strides, and horizontal multi-hopping). The subjects then performed a battery of jumping tests: squat jump, countermovement jump, and eight maximal hoppings test (HT8max). The jumping tests were followed by a recovery period of 5 min. Subsequently, the participants conducted a running test on a motorized treadmill (Athlete 870C, Medisoft, Dinant, Belgium) in which spatiotemporal variables were measured. The grade of the slope was 1% when the spatiotemporal parameters were obtained [48]. Although footwear was not standardized among the participants, all used running shoes weighing less than 300 g. Participants had previous training experience on a treadmill [49]. They performed a standardized 10 min accommodation period divided into 5 min walking at 5 km h 1 and5 minrunning at 8 km h 1 , increasing the speed by 1 km h 1 every 5 min until 12 km h 1 . 2.3. Materials and Assessment 2.3.1. Anthropometric Assessment For descriptive purposes, the height (cm) and body mass (kg) were determined through a stadiometer and a precision scale (Seca, Hamburg, Germany), and the body mass index of the participants was calculated based on body mass and height (kg/m 2 ). All measurements were taken with participants wearing underwear. Anthropometric measurements were taken following the guidelines of the International Society for the Advancement of Kinanthropometry [50]. Each participant's maturity offset was calculated using the formula described in [51]. This assessment is a non-invasive and practical method of predicting years from peak height velocity as a measure of maturity offset, using anthropometric variables. 2.3.2. Spatiotemporal Assessment Running spatiotemporal parameters were measured with the system previously vali- dated for this purpose, OptoGait (Optogait, Microgate, Bolzano, Italy) [32,52]. The default settings for the lter 0_0 (Gait R. in: 0 and Gait R. out: 0 lter) were used. This con guration provides the least bias for time parameters in athletic walking [53]. Spatiotemporal param- eters measured for each step during the 30 s uptake interval at 9 km h 1 , 10 km h

(Optogait, Microgate, Bolzano, Italy) [32,52]. The default settings for the lter 0_0 (Gait R. in: 0 and Gait R. out: 0 lter) were used. This con guration provides the least bias for time parameters in athletic walking [53]. Spatiotemporal param- eters measured for each step during the 30 s uptake interval at 9 km h 1 , 10 km h 1 , and 11 km h 1 were the contact time (CT, in seconds; time since the foot touches the ground until the toes separate from the ground), ight time (FT, in seconds; time from the take-off of the forefoot to the initial ground contact of the next contralateral support), vertical oscillation (VO, in centimetres; change in the height of the centre of gravity during the run), step frequency (in steps per minute; number of ground contacts per minute), step length (in metres; distance between two successive contacts with the ground, nger-to- nger) and stride angle (in degrees; the angle formed by the tangent of the parabola traced by the foot to the ground during a stride). The theoretical parabola for determining the stride angle was calculated by the system using the stride length and the maximum height of the foot during a stride [15]. 2.3.3. Neuromuscular Performance Assessment Neuromuscular performance was measured by the jumping test using the same, pre- viously validated [31], system (Optogait; Microgate, Bolzano, Italy). This device measures ground contact time and ight time using photoelectric cells. Flight time during the jump was used to calculate the jump height using the body's centre of gravity. The participants carried out a familiarization session in which they could practice each of the jump protocols. The tests used in the study were in the following order: squat jump, countermovement jump, and HT8max. Two measurements were made for each jump test, and the best result obtained in each jump modality was chosen. Squat jump was performed starting from a 90 knee exion position. Participants held this position for 2 s before jumping vertically to reach maximum height after an acoustic signal. In accordance with other studies, it was visually veri

jump, and HT8max. Two measurements were made for each jump test, and the best result obtained in each jump modality was chosen. Squat jump was performed starting from a 90 knee exion position. Participants held this position for 2 s before jumping vertically to reach maximum height after an acoustic signal. In accordance with other studies, it was visually veri ed that no countermovement was performed during the squat jump [54].

Int. J. Environ. Res. Public Health2021,18, 8869 4 of 14 To complete the countermovement jump, participants descended from an initial standing position to a sitting position, immediately followed by a vertical jump. Participants were en- couraged to perform the eccentric phase of the jump as quickly as possible, with the depth of the countermovement phase selected by the participant to maximize jump height [33]. For squat jump and countermovement jump, power (using the formula proposed by Sayers et al. [55]), jump height, and jump ying time were obtained in each test. The percentage of elastic energy that contributed during the jump [56] was quanti ed by the elasticity index using the formula: Elasticity Index = (countermovement jumpH squat jumpH) 100/squat jumpH(1) HT8maxconsisted of performing eight repeated maximum vertical jumps. Participants were instructed to maximize jump height and minimize contact time with the ground during the jumps [34]. The rst jump of each test served as a countermovement jump and was therefore discounted for analysis. The remaining seven jumps were averaged to analyse the jump contact time (s), jump ying time (s), jump height (cm), reactive strength index (m s 1 ), rhythm (jumps s 1 ), and power (w kg 1 ) of each jump. Fatigue index, a variable that indicates the subject's ability to maintain maximum force during the HT8max, was calculated as: Fatigue index = (powermax powermin)/(powermax 100) (2) This percentage indicates the proportion of force the subject has maintained at the end of the continuous jumps, not the remaining power de cit. To be considered of maxi- mum intensity, the mean of the jump height of the rst three jumps needed to be higher than 95% of the jump height of the countermovement jump [47]. The reactive strength index was measured by the ratio between jump height and jump contact time (mm ms 1 ) during HT8max[39]. 2.4. Statistical Analysis Statistical analysis was performed using IBM-SPSS Statistics v. 25.0 (IBM Corp, Released 2017; Armonk, NY, USA). Data are presented as means and standard devia- tions. Normality was analysed using the Shapiro–Wilk test. An analysis of variance (ANOVA)

strength index was measured by the ratio between jump height and jump contact time (mm ms 1 ) during HT8max[39]. 2.4. Statistical Analysis Statistical analysis was performed using IBM-SPSS Statistics v. 25.0 (IBM Corp, Released 2017; Armonk, NY, USA). Data are presented as means and standard devia- tions. Normality was analysed using the Shapiro–Wilk test. An analysis of variance (ANOVA) of repeated measurements was conducted to study speed increase on spatiotem- poral parameters. The association between variables was carried out using Pearson's correlation coef cient. An estimate of the effect size accompanied by the R 2 scale was determined for Pear- son's correlations coef cient and ANOVA test. Effect sizes were classi ed as small, moder- ate, large, and very large (Table S1) [57,58]. A step-by-step multiple regression analysis was performed to determine the neuro- muscular performance variables (non-dependent) predictors of spatiotemporal variables (dependent). In all these statistical tests, a signi cant value was considered whenp< 0.05. 3. Results Summary of participants' characteristics and variables related to HT8max, squat, and countermovement jump are shown in Table. Regarding maturity offset values, all partici- pants were considered post-pubertal ( 1.0 year), even when the SE associated with the prediction equation was taken into account [51]. A direct relationship was found between maturity offset and power of countermovement jump test (r= 0.523;p= 0.026) and squat jump test (r= 0.523;p= 0.026), with an effect size of 26.5% and 27.4%, respectively.

Int. J. Environ. Res. Public Health2021,18, 8869 5 of 14 Table 1.Demographic and jumping test characteristics of the participants. Variables Mean SD Height (cm) 164.5 8.2 Body mass (kg) 55.5 7.4 Body mass index (kg/m 2 ) 20.5 2.22 Maturity offset (years) 3.1 1 Squat Jump Jump Flying Time (s) 0.45 0.04 Jump Height (cm) 25.54 4.36 Power (W) 2008.93 400.31 Countermovement Jump Jump Flying Time (s) 0.47 0.04 Jump Height (cm) 26.99 4.32 Power (W) 2107.50 391.82 8 Maximal Hopping Test (HT 8max) Jump Flying Time (s) 0.42 0.05 Jump Height (cm) 22.25 5.04 Jump contact time (s) 0.2 0.02 Rhythm (jumps s 1 ) 1.63 0.16 Reactive strength index (m s 1 ) 1.15 0.26 Power average (W Kg 1 ) 32.68 6.11 Power min (W Kg 1 ) 28.59 5.55 Power max (W Kg 1 ) 36.07 6.38 Fatigue Index (%) 20.82 5.77 Elasticity Index (%) 6.07 5.42 3.1. Running Spatiotemporal Variables The ANOVA used to determine the effect of velocity on the spatiotemporal variables indicated that, as velocity increased, CT decreased signi cantly (p< 0.001) with a linear inverse relationship (0.89). FT, VO, step length, step frequency, and stride angle increased signi cantly (p< 0.001) as velocity increased, with a linear direct relationship (0.80, 0.88, 0.92, 0.83, and 0.69, respectively). A very large effect size (from 74.9% to 88.2%) was found between these variables (Table). Table 2.Repeated measurements ANOVA. Effect of velocity on spatiotemporal variables. Variables Velocity ANOVA Linear Adjust 9 km h 1 10 km h 1 11 km h 1 fValue pValue R 2 CT (s) 0.28 0.26 0.24 85.27 ** <0.0001 0.834 0.89 FT (s) 0.10 0.11 0.12 50.63 ** <0.0001 0.749 0.80 VO (cm) 1.21 1.44 1.79 72.53 ** <0.0001 0.810 0.88 Step length (cm) 94.84 101.96 110.10 127.07 ** <0.0001 0.882 0.92 Step frequency (steps/min) 160.98 163.61 166.35 56.01 ** <0.0001 0.767 0.83 Stride angle ( ) 2.89 3.23 3.73 24.22 ** <0.0001 0.588 0.69 ** = highly signi cant; CT = contact time; FT = ying time; VO = vertical oscillation. 3.2. Linear Correlations between Spatiotemporal Variables and Jumping Tests

Step length (cm) 94.84 101.96 110.10 127.07 ** <0.0001 0.882 0.92 Step frequency (steps/min) 160.98 163.61 166.35 56.01 ** <0.0001 0.767 0.83 Stride angle ( ) 2.89 3.23 3.73 24.22 ** <0.0001 0.588 0.69 ** = highly signi cant; CT = contact time; FT = ying time; VO = vertical oscillation. 3.2. Linear Correlations between Spatiotemporal Variables and Jumping Tests The correlation of the neuromuscular performance variables with the spatiotemporal parameters is shown in Table 8maxtest and of the countermovement with squat jump tests in Table.

This study examines the link between running biomechanics and muscle performance in trained adolescent female athletes.