The purpose of this investigation was to analyze the validity of an iPhone application (Runmatic) for measuring running mechanics. To do this, 96 steps from 12 different runs at speeds ranging from 2.77–5.55 m·s−1 were recorded simultaneously with Runmatic, as well as with an opto-electronic device installed on a motorized treadmill to measure the contact and aerial time of each step. Additionally, several running mechanics variables were calculated using the contact and aerial times measured, and previously validated equations. Several statistics were computed to test the validity and reliability of Runmatic in comparison with the opto-electronic device for the measurement of contact time, aerial time, vertical oscillation, leg stiffness, maximum relative force, and step frequency. The running mechanics values obtained with both the app and the opto-electronic device showed a high degree of correlation (r = .94–.99, p < .001). Moreover, there was very close agreement between instruments as revealed by the ICC (2,1) (ICC = 0.965–0.991). Finally, both Runmatic and the opto-electronic device showed almost identical reliability levels when measuring each set of 8 steps for every run recorded. In conclusion, Runmatic has been proven to be a highly reliable tool for measuring the running mechanics studied in this work.
Carlos Balsalobre-Fernández, Hovannes Agopyan and Jean-Benoit Morin
Jean-Benoît Morin, Georges Dalleau, Heikki Kyröläinen, Thibault Jeannin and Alain Belli
The spring-mass model, representing a runner as a point mass supported by a single linear leg spring, has been a widely used concept in studies on running and bouncing mechanics. However, the measurement of leg and vertical stiffness has previously required force platforms and high-speed kinematic measurement systems that are costly and difficult to handle in field conditions. We propose a new “sine-wave” method for measuring stiffness during running. Based on the modeling of the force-time curve by a sine function, this method allows leg and vertical stiffness to be estimated from just a few simple mechanical parameters: body mass, forward velocity, leg length, flight time, and contact time. We compared this method to force-platform-derived stiffness measurements for treadmill dynamometer and overground running conditions, at velocities ranging from 3.33 m·s–1 to maximal running velocity in both recreational and highly trained runners. Stiffness values calculated with the proposed method ranged from 0.67% to 6.93% less than the force platform method, and thus were judged to be acceptable. Furthermore, significant linear regressions (p < 0.01) close to the identity line were obtained between force platform and sine-wave model values of stiffness. Given the limits inherent in the use of the spring-mass model, it was concluded that this sine-wave method allows leg and stiffness estimates in running on the basis of a few mechanical parameters, and could be useful in further field measurements.
Olivier Girard, Franck Brocherie, Jean-Benoit Morin, Francis Degache and Grégoire P. Millet
We compared different approaches to analyze running mechanics alterations during repeated treadmill sprints. Thirteen active male athletes performed five 5-second sprints with 25 seconds of recovery on an instrumented treadmill. This approach allowed continuous measurement of running kinetics/kinematics and calculation of vertical and leg stiffness variables that were subsequently averaged over 3 distinct sections of the 5-second sprint (steps 2–5, 7–10, and 12–15) and for all steps (steps 2–15). Independently from the analyzed section, propulsive power and step frequency decreased with fatigue, while contact time and step length increased (P < .05). Except for step frequency, all mechanical variables varied (P < .05) across sprint sections. The only parameters that highly depend on running velocity (propulsive power and vertical stiffness) showed a significant interaction (P < .05) between the analyzed sections, with smaller magnitude of fatigue-induced change observed for steps 2–5. Considering all steps or only a few steps during early, middle, or late phases of 5-second sprints provides similar mechanical outcomes during repeated treadmill sprinting, although acceleration induces noticeable differences between the sections studied. Furthermore, quantifying mechanical alterations from the early acceleration phase may not be readily detectable, and is not recommended.