It was hypothesized that differences in anthropometry, physical performance, and motor coordination would be found between Belgian elite and sub-elite level female volleyball players using a retrospective analysis of test results gathered over a 5-year period. The test sample in this study consisted of 21 young female volleyball players (15.3 ± 1.5 years) who were selected to train at the Flemish Top Sports Academy for Volleyball in 2008. All players (elite, n = 13; sub-elite, n = 8) were included in the same talent development program, and the elite-level athletes were of a high to very high performance levels according to European competition level in 2013. Five multivariate analyses of variance were used. There was no significant effect of playing level on measures of anthropometry (F = 0.455, p = 0.718, (Equation is included in full-text article.)= 0.07), flexibility (F = 1.861, p = 0.188, (Equation is included in full-text article.)= 0.19), strength (F = 1.218, p = 0.355, (Equation is included in full-text article.)= 0.32); and speed and agility (F = 1.176, p = 0.350, (Equation is included in full-text article.)= 0.18). Multivariate analyses of variance revealed significant multivariate effects between playing levels for motor coordination (F = 3.470, p = 0.036, (Equation is included in full-text article.)= 0.59). A Mann-Whitney U test and a sequential discriminant analysis confirmed these results. Previous research revealed that stature and jump height are prerequisites for talent identification in female volleyball. In addition, the results show that motor coordination is an important factor in determining inclusion into the elite level in female volleyball.

The purpose of this study was to examine spatiotemporal parameters of the walk-to-run transition (WRT) and run-to-walk transition (RWT) when speed is altered with different constant accelerations. Twenty women (height: 168.9 AE 3.36 cm) performed three accelerations (0.05, 0.07 and 0.1 m s À2) and three decelerations (À0.05, À0.07 and À0.1 m s À2) on a motor-driven treadmill. The transition step in the WRT (first step with a flight phase) and RWT (first step with a double stance phase) occurred at the same speed for all accelerations but these did not occur in the same way. The most striking difference was the presence of a transition step with specific spatiotemporal characteristics in the WRT, whereas this was not observed in the RWT.The transition is not a sudden one-step-event. WRT occurred before transition and consisted of a ''pre-transition period'' and the transition step whereas RWT occurred after transition and consisted of the transition step and a ''post-transition period''. Both transition periods were characterized by an exponential evolution of step frequency and step length.Step frequency and step length showed a linear evolution before and after transition.The flight phase of the transition step in the WRT reached a minimum with comparable duration of the last flight phase in the RWT. The flight phase could be considered as an intrinsic dynamical factor of transition. Further research in kinematics, the trajectory of the body centre of mass and energy fluctuations will give more insight in these transitions. #

The purpose of this study was to examine the influence of muscular fatigue of tibialis anterior (TA) on the walk-to-run transition (WRT) and run-to-walk transition (RWT) when speed is altered at different constant accelerations (a = 0.01, 0.07 and 0.05 m s À2 ). Twenty women (height: 168.9 AE 3.36 cm) performed WRTs and RWTs on a motor-driven treadmill, before and after a protocol inducing muscular fatigue of the TA.WRT-speed decreased after TA fatigue whereas RWT-speed did not change except during the intermediate deceleration. Integrated EMG (iEMG) of the activity burst of TA around heel contact was examined in the last steps before transition, the transition step and the first steps after transition. iEMG increased before WRT, then decreased after transition to running. In the RWT the opposite was observed: iEMG increased after RWT, then decreased with decreasing walking speed. After inducing fatigue in the TA, there was a decrease in iEMG in the WRT whereas no influence of fatigue was found on iEMG in the RWT.As a result of TA fatigue, WRT occurred at a lower speed, probably to avoid over-exertion of the TA. This indicates that the TA is a likely determinant of WRT as previously reported. The RWT, on the other hand, was not altered following TA fatigue, which would indicate that WRT and RWT are determined by different factors. #

We describe a multi-segmented foot model comprising lower leg, rearfoot, midfoot, lateral forefoot, medial forefoot, and hallux for routine use in a clinical setting. The Ghent Foot Model describes the kinematic patterns of functional units of the foot, especially the midfoot, to investigate patient populations where midfoot deformation or dysfunction is an important feature, for example, rheumatoid arthritis patients. Data were obtained from surface markers by a 6 camera motion capture system at 500 Hz. Ten healthy subjects walked barefoot along a 12 m walkway at self-selected speed. Joint angles (rearfoot to shank, midfoot to rearfoot, lateral and medial forefoot to midfoot, and hallux to medial forefoot) in the sagittal, frontal, and transverse plane are reported according to anatomically based reference frames. These angles were calculated and reported during the foot rollover phases in stance, detected by synchronized plantar pressure measurements. Repeated measurements of each subject revealed low intra-subject variability, varying between 0.78 and 2.38 for the minimum values, between 0.58 and 2.18 for the maximum values, and between 0.88 and 5.88 for the ROM. The described movement patterns were repeatable and consistent with biomechanical and clinical knowledge. As such, the Ghent Foot model permits intersegment, in vivo motion measurement of the foot, which is crucial for both clinical and research applications. Keywords: multi-segment foot model; foot and ankle kinematics; gait analysis; Ghent Foot Model Multi-segmented foot models can provide better insight into foot kinematics (and to a less extent segmental foot kinetics) during gait than single segments models. Clinically, they are used to describe and analyze functional characteristics associated with foot impairments. A number of multi-segmented foot models, using either bone pins or surface markers, have been proposed. They vary according to the number of segments and functional unit definition, in the methods of calculating intersegment angles, and in the definition of the neutral foot reference position. 1,2The need exists for a clinically relevant, user-friendly multi-segment foot model that would include the lower leg, hindfoot, midfoot, medial and lateral forefoot, and the hallux, with wide utility to investigate single joint/segment joint dysfunction through to complex foot deformities. Models with <5 segments often can not describe whole foot kinematics. [3][4][5][6][7] In contrast, those with nine segments 8 are not always easily applicable in clinical settings. To our knowledge and based upon a recent review, 2 no such model is currently available.There are two main reasons why a 6 segment model is clinically relevant. First, these segments are examined clinically by physical therapists and podiatrists. The medial arch, formed by the rearfoot (talus), the medial part of the midfoot, and the first ray, plays an important role in foot function during weight bearing activities. 9 Different arch configurations are an intrinsic ris...

The aim of the present study was to evaluate the Flemish Sports Compass (FSC), a non-sport-specific generic testing battery. It was hypothesised that a set of 22 tests would have sufficient discriminant power to allocate athletes to their own sport based on a unique combination of test scores. First, discriminant analyses were applied to the 22 tests of anthropometry, physical fitness and motor coordination in 141 boys under age 18 (16.1 ± 0.8 years) and post age at peak height velocity (maturity offset = 2.674 ± 0.926) from Flemish Top Sport Academies for badminton, basketball, gymnastics, handball, judo, soccer, table tennis, triathlon and volleyball. Second, nine sequential discriminant analyses were used to assess the ability of a set of relevant performance characteristics classifying participants and non-participants for the respective sports. Discriminant analyses resulted in a 96.4% correct classification of all participants for the nine different sports. When focusing on relevant performance characteristics, 80.1% to 97.2% of the total test sample was classified correctly within their respective disciplines. The discriminating characteristics were briefly the following: flexibility in gymnastics, explosive lower-limb strength in badminton and volleyball, speed and agility in badminton, judo, soccer and volleyball, upper-body strength in badminton, basketball and gymnastics, cardiorespiratory endurance in triathletes, dribbling skills in handball, basketball and soccer and overhead-throwing skills in badminton and volleyball. The generic talent characteristics of the FSC enable the distinction of adolescent boys according to their particular sport. Implications for talent programmes are discussed.

This study investigated the link between the anthropometric, physical and motor characteristics assessed during talent identification and dropout in young female gymnasts. 3 cohorts of female gymnasts (n=243; 6?9 years) completed a test battery for talent identification. Performance-levels were monitored over 5 years of competition. Kaplan-Meier and Cox Proportional Hazards analyses were conducted to determine the survival rate and the characteristics that influence dropout respectively. Kaplan-Meier analysis indicated that only 18% of the female gymnasts that passed the baseline talent identification test survived at the highest competition level 5 years later. The Cox Proportional Hazards Model indicated that gymnasts with a score in the best quartile for a specific characteristic significantly increased chances of survival by 45?129%. These characteristics being: basic motor skills (129%), shoulder strength (96%), leg strength (53%) and 3 gross motor coordination items (45?73%). These results suggest that tests batteries commonly used for talent identification in young female gymnasts may also provide valuable insights into future dropout. Therefore, multidimensional test batteries deserve a prominent place in the selection process. The individual test results should encourage trainers to invest in an early development of basic physical and motor characteristics to prevent attrition.

Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical risk-factors of running related injuries or athletic performance during a running analysis. An interesting ground reaction force-derived variable to track is the maximal vertical instantaneous loading rate (VILR). This impact characteristic is traditionally derived from a fixed force platform, but wearable inertial sensors nowadays might approximate its magnitude while running outside the lab. The time-discrete axial peak tibial acceleration (APTA) has been proposed as a good surrogate that can be measured using wearable accelerometers in the field. This paper explores the hypothesis that applying machine learning to time continuous data (generated from bilateral tri-axial shin mounted accelerometers) would result in a more accurate estimation of the VILR. Therefore, the purpose of this study was to evaluate the performance of accelerometer-based predictions of the VILR with various machine learning models trained on data of 93 rearfoot runners. A subject-dependent gradient boosted regression trees (XGB) model provided the most accurate estimates (mean absolute error: 5.39 ± 2.04 BW•s −1 , mean absolute percentage error: 6.08%). A similar subject-independent model had a mean absolute error of 12.41 ± 7.90 BW•s −1 (mean absolute percentage error: 11.09%). All of our models had a stronger correlation with the VILR than the APTA (p < 0.01), indicating that multiple 3D acceleration features in a learning setting showed the highest accuracy in predicting the lab-based impact loading compared to APTA.

No abstract