Sports Biomechanics最新文献

Despite increased awareness of the multifactorial nature of Hamstring Strain Injury (HSI), the role of running biomechanics remains unclear. The aim of this systematic review was to investigate whether an association exists between running biomechanics and HSI. Five databases were searched from inception to January 2021. Eligibility criteria included epidemiological studies that provide data on running biomechanics in athletes who have sustained a HSI (retrospectively or prospectively) and compared to control data. Searches yielded 4,798 articles. Twelve met the selection criteria. Biomechanical analysis differed considerably across studies, thus meta-analyses was not possible. Studies largely found either no differences or contradicting findings between running biomechanics of athletes who have sustained a HSI (retrospectively or prospectively) and controls, with the exception of lateral trunk kinematics and horizontal propulsive forces. It is important to note some concern regarding the quality of included studies, particularly sample size, increasing the risk of bias associated with results. Further research utilising validated methods of biomechanical analysis, is needed to determine if an association exists between running biomechanics and HSI. Until then, definitive conclusions cannot be drawn as to whether specific biomechanical interventions should be included in injury prevention and/or rehabilitation programmes.

In cross-country ski skating, both the G2 and G4 sub-techniques involve one pole push for every second ski push but are used at largely different speed-slope ranges. The aim of this study was to compare temporal and kinematic patterns between G2 and G4 at both identical and different speed-slope conditions. A mixed model was used to analyse spatio-temporal parameters, while a combination of dynamic time warping and statistical parametric mapping was used to compare time traces. Main spatio-temporal parameters, such as cycle time, ski contact time and swing time, differed between G2 and G4 (all p < 0.01). Moreover, two forward and more pronounced acceleration phases of the centre of mass (CoM) were visible in G4 while only one acceleration phase was present in G2. The more continuous propulsion in G2 allows for maintaining a more constant speed at steep slopes and low speeds where this sub-technique is preferred. In contrast, the achievement of high speeds while skiing on flatter terrain seem to require more dynamic motion with shorter, more explosive propulsion periods allowed for in G4. In conclusion, G2 and G4 are two unique movements as characterised by fundamentally different CoM motion and should be denoted as two different sub-techniques.

The kick-start technique in competitive swimming generates a force acting on the starting platform owing to gravity, muscle contraction and resulting joint torque. To understand optimal body movement on the starting platform for maximising take-off velocity, it is necessary to investigate the joint torque in relation to the joint's rotation effects. Joint torques were calculated by inverse dynamics using kinetic and kinematic data. A one-way ANOVA showed significantly greater extensional torque for shoulders than for elbows or wrists, and for hips than for knees or ankles. The results indicated that the force of the hands was mainly influenced by extension torque at the shoulder joint. Hip joint extension torque on the front side lower limb (FSLL) was mainly used for supporting the body weight until hands off. After hands off, the front-foot force originated mainly by increases in ankle joint plantar flexion and knee joint extension torque on the FSLL. Rear side lower limb torque increases in the hip, knee and ankle joints provided the rear-foot force. This investigation clarified the magnitudes and functions of each joint torque acting on the extremities during the kick-start, providing practical information for improving starting performance.

While many parameters contribute to swimming start performance, a few have been proven to affect the overall start performance more significantly than others; these include take-off velocity, flight time, entry water distance, time underwater during descent and ascent, and free-swimming velocity. This study aims to analyse the influential trajectory of these key parameters on the overall start performance, particularly focusing on determining the optimal breakout point. Ten national-level swimmers participated in this study, which combined kinematics and statistical analysis to propose a novel start model that can be used to explore the influential trajectory of key parameters on the overall start performance and assess the effect of some training factors for performance improvement. Further, this study investigated the optimal breakout position via a mathematical model. This is the first study to provide a solution to determine this parameter. The solution is verified to be practical through trial data, and the overall start performance is improved by 0.71-3.29%, depending on the swimmer's current level. Therefore, the results can be used as a reference for swimming start training and improvement.

The aim of this study was to assess the agreement of three different automated methods of identifying force-onset (40 N, 5 SDs, and 3 SDs) with manual identification, during the isometric mid-thigh pull (IMTP). Fourteen resistance-trained participants with >6 months experience training with the power clean volunteered to take part. After three familiarisation sessions, the participants performed five maximal IMTPs separated by 1 min of rest. Fixed bias was found between 40 N and manual identification for time at force-onset. No proportional bias was present between manual identification and any automated threshold. Fixed bias between manual identification and automated was present for force at onset and F₁₅₀. Proportional but not fixed bias was found for F₅₀ between manual identification and all automated thresholds. Small to moderate differences (Hedges g = -0.487- -0.692) were found for F₉₀ between all automated thresholds and manual identification, while trivial to small differences (Hedges g = -0.122--0.279) were found between methods for F₂₀₀ and F₂₅₀. Based on these results, strength and conditioning practitioners should not use a 40 N, 5 SDs, or 3 SDs threshold interchangeably with manual identification of force-onset when analysing IMTP force-time curve data.

Combining biomechanics and motor control, the aim of this study was to investigate the limit cycle dynamics during the high bar longswing across the UK elite gymnastics pathway age groupings. Senior, junior and development gymnasts (N = 30) performed three sets of eight consecutive longswings on the high bar. The centre of mass motion was examined through Poincaré plots and recurrence quantification analysis exploring the limit cycle dynamics of the longswing. Close to one-dimensional limit cycles were displayed for the senior (correlation dimension (CD) = 1.17 ± .08), junior (CD = 1.26 ± .08) and development gymnasts (CD = 1.33 ± .14). Senior elite gymnasts displayed increased recurrence characteristics in addition to longer longswing duration (p < .01) and lower radial angular velocity of the mass centre (p < .01). All groups of gymnasts had highly recurrent and predictable limit cycle characteristics. The findings of this research support the postulation that the further practice, experience and individual development associated with the senior gymnasts contribute to the refinement of the longswing from a nonlinear dynamics perspective. These findings support the idea of functional task decomposition informing the understanding of skill and influencing coaches' decisions around skill development and physical preparation.

The aims of this study were to: (1) assess the stability (mean and normative) of the lap performance, and a set of clean swim and turn variables of junior male swimmers in the 200 m freestyle, and; (2) verify the relationship between the start, clean swim, turn, and finish phases in the 200 m freestyle. Seventy-six individual races in the 200 m freestyle at the 2019 long-course LEN European Junior Championships were analysed. Start, clean swim, turn, and finish variables were assessed. The lap performance showed a significant variance. The highest variation was verified between the first and third lap (Coefficient of Variation = 7.37%). The clean swim and the total turn also presented a significant variance. Normative stability indicated a moderate to very-high stability for all variables. All phases of the race had significant correlations with the final race time (p < 0.001). The total turn (i.e., the total time spent to perform the turn), specifically turn #3, showed the largest correlation with the total race performance. The significant correlation between all phases of the race and the final race time indicates that coaches and swimmers should customise the swimmers' preparation and race strategy at major international competitions, based on the individual characteristics of each swimmer.

Sprinting ability is important for successful performance in sports. The aim of this study was to examine the correlation between force-velocity-power relationship of a whole-body movement and sprint performance. Twelve male participants performed maximal squat jumps with additional loads ranging from 0% to 100% body weight to obtain force-velocity profiles. The mean force and velocity were calculated during the push-off phase for each jump, which resulted in a force-velocity curve. The theoretical maximal force (F₀), theoretical maximal velocity (V₀) and theoretical maximum power (P₀) were computed via extrapolation of the force and velocity data. In the second session, participants performed two 60 m sprints and the time to cover 20 m (t₂₀), time to cover 60 m (t₆₀), and maximum sprint velocity (V_max) were calculated from the best 60 m trial. Correlation analyses revealed strong and significant correlations between V₀ and t₂₀ (r = -0.60), V₀ and t₆₀ (r = -0.60), P₀ and t₂₀ (r = -0.75) and P₀ and t₆₀ (r = -0.78). Multiple linear regression indicated that P₀ explained 56%, 61% and 60% of the variability in t₂₀, t₆₀ and V_max, respectively. Our results emphasise the importance of developing power production capabilities to improve sprint performance.