ARE THIGH MUSCLE ACTIVATION PATTERNS DURING DROP JUMPS DEPENDENT ON SEX AND FATIGUE? A PILOT STUDY IN COMPETITIVE SOCCER PLAYERS

The purpose of this study was to identify whether sex- and fatigue-dependent effects occur in muscle activation patterns during drop jumps. Therefore, 12 (5 female, 7 male) competitive soccer players performed five drop jumps (DJs) in a rested and fatigued state. Lactate, jump height, maximum knee flexion angle and the quadriceps to hamstring muscle activation ratio (QHGRF ratio) were compared via repeated-measures MANOVA, and effect sizes were used for interpretation. In the fatigued state, jump height and maximum knee flexion angles were reduced (large effect), and the left limb showed an non-significant medium effect towards an increased QHGRF ratio. Additionally, a sex-specific medium effect of the QHGRF ratio of the left limb indicates a higher QHGRF ratio for males. This indicates a tendency towards a quadriceps-dominant landing strategy in at least one limb

Breaking new grounds in injury risk screening in soccer by deploying unsupervised learning with a special focus on sex and fatigue effects

In injury prevention, a vertical drop jump (DJ) is often used for screening athletes at risk for injury; however, the large variation in individual movement patterns might mask potentially relevant strategies when analysed on a group-based level. Two movement strategies are commonly discussed as predisposing athletes to ACL injuries: a deficient leg axis and increased leg stiffness during landing. This study investigated the individual movement pattern of 39 female and male competitive soccer players performing DJs at rest and after being fatigued. The joint angles were used to train a Kohonen self-organising map. Out of 19,596 input vectors, the SOM identified 700 unique postures. Visualising the movement trajectories and adding the latent parameters contact time, medial knee displacement (MKD) and knee abduction moment allow identification of zones with presumably increased injury risk and whether the individual movement patterns pass these zones. This information can be used, e.g., for individual screening and for feedback purposes. Additionally, an athlete's reaction to fatigue can be explored by comparing the rested and fatigued movement trajectories. The results highlight the ability of unsupervised learning to visualise movement patterns and to give further insight into an individual athlete's status without the necessity of a priori assumptions

Breaking new grounds in injury risk screening in soccer by deploying unsupervised learning with a special focus on sex and fatigue effects

In injury prevention, a vertical drop jump (DJ) is often used for screening athletes at risk for injury; however, the large variation in individual movement patterns might mask potentially relevant strategies when analysed on a group-based level. Two movement strategies are commonly discussed as predisposing athletes to ACL injuries: a deficient leg axis and increased leg stiffness during landing. This study investigated the individual movement pattern of 39 female and male competitive soccer players performing DJs at rest and after being fatigued. The joint angles were used to train a Kohonen self-organising map. Out of 19,596 input vectors, the SOM identified 700 unique postures. Visualising the movement trajectories and adding the latent parameters contact time, medial knee displacement (MKD) and knee abduction moment allow identification of zones with presumably increased injury risk and whether the individual movement patterns pass these zones. This information can be used, e.g., for individual screening and for feedback purposes. Additionally, an athlete’s reaction to fatigue can be explored by comparing the rested and fatigued movement trajectories. The results highlight the ability of unsupervised learning to visualise movement patterns and to give further insight into an individual athlete’s status without the necessity of a priori assumptions