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This study aimed to 1) identify what starting block outcome kinetics have the greatest relationship to 15 m start time; 2) investigate key mechanistic determinants of the block phase and how these forces are sequenced. One hundred and fifty-two high-level competitive swimmers were included in the study. Linear mixed modelling identified four on-block outcome kinetic variables (work, average power, horizontal take-off velocity (HTOV), and average acceleration) as having a very large relationship (R2 = 0.79–0.83) to 15 m start time, with average power having the most substantial impact. On-block force sequencing started with the rear leg, followed by upper limb grab forces and the front leg. Further exploration of underlying determinants was performed for average power and HTOV of the centre of mass. Multiple linear regression identified grab resultant peak force, rear resultant average force, front horizontal peak force, and resultant peak force as significant predictors of average power (R2 = 0.88). HTOV was predicted using the same variables, apart from the inclusion of rear horizontal peak force instead of rear resultant average force (R2 = 0.73). These findings may influence how strength and conditioning and skill acquisition interventions are designed to improve swim start performance. [ABSTRACT FROM AUTHOR]

An editorial is presented on leveraging game-based activities to enhance learning in biomechanics education, tackling challenges of anxiety and dropout rates in Science, Technology, Engineering, and Mathematics (STEM) majors, especially in kinesiology and sports science fields. It discusses the active learning approaches, the potential of game-based learning activities to improve engagement, and recommendations for implementing gamification strategies in university-level biomechanics courses.

As the sport of strongman is becoming increasingly popular, and such exercises are being commonly used by strength and conditioning coaches for a wide range of athletic groups, a greater understanding of the biomechanics of strongman exercises is warranted. To improve the quality of research, this systematic review summarised the research methodology used in biomechanical studies of strongman exercises and identified potential improvements to current approaches. A search of 5 databases found 10 articles adherent to the pre-defined inclusion criteria. The studies assessed 8 strongman exercises and included male participants of relatively similar body mass but varying training backgrounds. Due to the complexity of strongman exercises and the challenges in collecting advanced biomechanical data in the field, most studies used simplified measurement/analysis methods (e.g., 2D motion capture). Future strongman biomechanical studies should: assess under/un-researched strongman exercises; include a greater number of experienced and female strongman athletes; utilise more advanced (e.g., 3D motion capture and/or inertial sensor) technology so to provide a broader range and greater quality of data. Such approaches will provide strength and conditioning coaches, strongman coaches and athletes with a greater understanding of strongman exercises, thereby further improving exercise prescription, athlete performance and minimising risk of injury. [ABSTRACT FROM AUTHOR]

The aim of this study was to quantify the validity and intra-tester reliability of a novel method of kinematic measurement. The measurement target was the joint angles of an athlete performing a BMX Supercross (SX) gate start action through the first 1.2 s of movement in situ on a BMX SX ramp using a standard gate start procedure. The method employed GoPro® Hero 4 Silver (GoPro Inc., USA) cameras capturing data at 120 fps 720 p on a 'normal' lens setting. Kinovea 0.8.15 (Kinovea.org, France) was used for analysis. Tracking data was exported and angles computed in Matlab (Mathworks®, USA). The gold standard 3D method for joint angle measurement could not safely be employed in this environment, so a rigid angle was used. Validity was measured to be within 2°. Intratester reliability was measured by the same tester performing the analysis twice with an average of 55 days between analyses. Intra-tester reliability was high, with an absolute error <6° and <9 frames (0.075 s) across all angles and time points for key positions, respectively. The methodology is valid within 2° and reliable within 6° for the calculation of joint angles in the first -1.25 s. [ABSTRACT FROM AUTHOR]

The aim of this review was to determine how the findings of biomechanics and motor control/learning research may be used to improve golf performance. To be eligible, the biomechanics and motor learning studies had to use direct (ball displacement and shot accuracy) or indirect (clubhead velocity and clubface angle) golf performance outcome measures. Biomechanical studies suggested that reducing the radius path of the hands during the downswing, increasing wrist torque and/or range of motion, delaying wrist motion to late in the downswing, increasing downswing amplitude, improving sequential acceleration of body parts, improving weight transfer, and utilising X-factor stretch and physical conditioning programmes can improve clubhead velocity. Motor learning studies suggested that golf performance improved more when golfers focused on swing outcome or clubhead movement rather than specific body movements. A distributed practice approach involving multiple sessions per week of blocked, errorless practice may be best for improving putting accuracy of novice golfers, although variable practice may be better for skilled golfers. Video, verbal, or a combination of video and verbal feedback can increase mid-short iron distance in novice to mid-handicap (hcp) golfers. Coaches should not only continue to critique swing technique but also consider how the focus, structure, and types of feedback for practice may alter learning for different groups of golfers.

In the current study, we quantified biological movement variability on the start and early acceleration phase of sprinting. Ten male athletes aged 17-23 years (100-m personal best: 10.87 +/- 0.36s) performed four 10-m sprints. Two 250-Hz cameras recorded the sagittal plane action to obtain the two-dimensional kinematics of the block start and initial strides from subsequent manually digitized APAS motion analysis. Infra-red timing lights (80Hz) were used to measure the 10-m sprinting times. The coefficient of variation (CV%) calculation was adjusted to separate biological movement variability (BCV%) from estimates of variability induced by technological error (SEM%) for each individual sprinter and measure. Pearson's product-moment correlation and linear regression analysis were used to establish relationships between measures of BCV% and 10-m sprint start performance (best 10-m time) or 10-m sprint start performance consistency (10-m time BCV%) using SPSS version 12.0. Measurement error markedly inflated traditional measures of movement variability (CV%) by up to 72%. Variability in task outcome measures was considerably lower than that observed in joint rotation velocities. Consistent generation of high horizontal velocity out of the blocks led to more stable and faster starting strides.