during resisted sprinting but to carefully consider the impact of study design and computation on their conclusions. Where possible, experimentally determine friction coefficients, or consider using an “outcome” variable (eg, decrement in maximum velocity), to select and classify loading parameters
Matt R. Cross, Farhan Tinwala, Seth Lenetsky, Scott R. Brown, Matt Brughelli, Jean-Benoit Morin and Pierre Samozino
Victoria H. Stiles and Sharon J. Dixon
Research suggests that heightened impacts, altered joint movement patterns, and changes in friction coefficient from the use of artificial surfaces in sport increase the prevalence of overuse injuries. The purposes of this study were to (a) develop procedures to assess a tennis-specific movement, (b) characterize the ground reaction force (GRF) impact phases of the movement, and (c) assess human response during impact with changes in common playing surfaces. In relation to the third purpose it was hypothesized that surfaces with greatest mechanical cushioning would yield lower impact forces (PkFz) and rates of loading. Six shod volunteers performed 8 running forehand trials on each surface condition: baseline, carpet, acrylic, and artificial turf. Force plate (960 Hz) and kinematic data (120 Hz) were collected simultaneously for each trial. Running forehand foot plants are typically characterized by 3 peaks in vertical GRF prior to a foot-off peak. Group mean PkFz was significantly lower and peak braking force was significantly higher on the baseline surface compared with the other three test surfaces (p < 0.05). No significant changes in initial kinematics were found to explain unexpected PkFz results. The baseline surface yielded a significantly higher coefficient of friction compared with the other three test surfaces (p < 0.05). While the hypothesis is rejected, biomechanical analysis has revealed changes in surface type with regard to GRF variables.
Matt R. Cross, Matt Brughelli, Pierre Samozino, Scott R. Brown and Jean-Benoit Morin
To ascertain whether force-velocity-power relationships could be compiled from a battery of sled-resisted overground sprints and to clarify and compare the optimal loading conditions for maximizing power production for different athlete cohorts.
Recreational mixed-sport athletes (n = 12) and sprinters (n = 15) performed multiple trials of maximal sprints unloaded and towing a selection of sled masses (20–120% body mass [BM]). Velocity data were collected by sports radar, and kinetics at peak velocity were quantified using friction coefficients and aerodynamic drag. Individual force–velocity and power–velocity relationships were generated using linear and quadratic relationships, respectively. Mechanical and optimal loading variables were subsequently calculated and test–retest reliability assessed.
Individual force–velocity and power–velocity relationships were accurately fitted with regression models (R 2 > .977, P < .001) and were reliable (ES = 0.05–0.50, ICC = .73–.97, CV = 1.0–5.4%). The normal loading that maximized peak power was 78% ± 6% and 82% ± 8% of BM, representing a resistance of 3.37 and 3.62 N/kg at 4.19 ± 0.19 and 4.90 ± 0.18 m/s (recreational athletes and sprinters, respectively). Optimal force and normal load did not clearly differentiate between cohorts, although sprinters developed greater maximal power (17.2–26.5%, ES = 0.97–2.13, P < .02) at much greater velocities (16.9%, ES = 3.73, P < .001).
Mechanical relationships can be accurately profiled using common sled-training equipment. Notably, the optimal loading conditions determined in this study (69–96% of BM, dependent on friction conditions) represent much greater resistance than current guidelines (~7–20% of BM). This method has potential value in quantifying individualized training parameters for optimized development of horizontal power.
Øyvind Skattebo, Thomas Losnegard and Hans Kristian Stadheim
safety harness connected to an automatic emergency brake. The rolling friction coefficient ( μ = 0.021) of the roller skis (Swenor, Sarpsborg, Norway) was tested before, during, and after the experiments 21 and was unchanged during the study. Work rate was calculated as the sum of power against gravity
Pål Haugnes, Jan Kocbach, Harri Luchsinger, Gertjan Ettema and Øyvind Sandbakk
speed of ∼3 m·s −1 . The loss of speed was used to calculate the deceleration and, subsequently, the friction coefficient ( μ s = a · g −1 = 0.026), ignoring the force of air drag which was minimal at this slow speed. The wind drag coefficient ( A·C d ) incorporated in this study has previously been
Bent R. Rønnestad, Tue Rømer and Joar Hansen
MAS were calculated as the sum of the power against gravity and the power against rolling friction (friction coefficient = 0.0237) at the respective velocities as described previously. 21 HIT Sessions The HIT sessions were performed at a fixed time of day (±1 h) interspersed with 3 to 5 days
Marco Beato, Stuart A. McErlain-Naylor, Israel Halperin and Antonio Dello Iacono
different designs, inertial mechanisms, manufacturing materials, and friction coefficients. This is the main reason behind the lack of gold standard valid and reliable procedures that objectively determine the magnitude of inertial loads and associated intensities. Future studies are warranted to determine
Jorge Carlos-Vivas, Jorge Perez-Gomez, Ola Eriksrud, Tomás T. Freitas, Elena Marín-Cascales and Pedro E. Alcaraz
), which consisted of slalom sprinting with 100° COD set at 5-minute intervals. The maximum external load was set to 20% of BM, independent of equipment used, based on previous weighted vest 11 and sled 9 recommendations, as horizontal load was prescribed using the 1080 Sprint ™ . An estimated friction