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Quantifying the midsole material characteristics of athletic footwear is a standard task in footwear research and development. Current material testing protocols primarily focus on the determination of cushioning properties of the heel region or the quantification of the midsole properties as one assembly. However, midsoles possess different spatial material properties that have not been quantified from previous methodologies. Therefore, new material testing methods are required to quantify the local material response of athletic footwear. We developed a cyclical force-controlled material testing protocol for the determination of non-homogeneously distributed material stiffness with a high spatial resolution. In five prototype shoes varying in their stiffness distribution, we found that the material properties can be reliably measured across the midsole. Furthermore, we observed a characteristic non-linear material response regardless of the midsole location. We found that the material stiffness increased with an increase of the applied force and that this effect is further intensified by higher testing cycles. Additionally, the obtained midsole stiffness depends on the geometry of the midsole. We explored different approaches to reduce the measurement time of the testing protocol and found that the number of measurements can be reduced by 70% using 2 D-interpolation procedures. Determining the spatial material properties of midsoles needs to be considered to understand foot-shoe interactions. Furthermore, this measurement protocol can be used for quality control within the footwear and can be adapted for considering the effects of different running styles or speeds on ground force application characteristics.
Running footwear is continuously being modified and improved; however, running-related overuse injury rates remain high. Nevertheless, novel manufacturing processes enable the production of individualized running shoes that can fit the individual needs of runners, with the potential to reduce injury risk. For this reason, it is essential to investigate functional groups of runners, a collective of runners who respond similarly to a footwear intervention. Therefore, the objective of this study was to develop a framework to identify functional groups based on their individual footwear response regarding injury-specific running-related risk factors for Achilles tendinopathy, Tibial stress fractures, Medial tibial stress syndrome, and Patellofemoral pain syndrome. In this work, we quantified the footwear response patterns of 73 female and male participants when running in three different footwear conditions using unsupervised learning (k-means clustering). For each functional group, we identified the footwear conditions minimizing the injury-specific risk factors. We described differences in the functional groups regarding their running style, anthropometric, footwear perception, and demographics. The results implied that most functional groups showed a tendency for a single footwear condition to reduce most biomechanical risk factors for a specific overuse injury. Functional groups often differed in their hip and pelvis kinematics as well as their subjective rating of the footwear conditions. The footwear intervention only partially affected biomechanical risk factors attributed to more proximal joints. Due to its adaptive nature, the framework could be applied to other footwear interventions or performance-related biomechanical variables.
This study aimed to compare a simplified calculation of the knee abduction moment with the traditional inverse dynamics calculation when athletes perform fake-cut maneuvers with different complexities. In the simplified calculation, we multiply the force vector with its lever arm to the knee, projected onto the local coordinate system of the proximal thigh, hence neglecting the inertial contributions from distal segments. We found very strong ranking consistency using Spearman’s rank correlation coefficient when using the simplified method compared to the traditional calculation. Independent of the tasks, the simplified method resulted in higher moments than the inverse dynamics. This was caused by ignoring the moment caused by segment linear acceleration generating a counteracting moment by about 7%. An alternative to the complex calculations of inverse dynamics can be used to investigate the contributions of the GRF magnitude and its lever arm to the knee.
Effect of downhill running on biomechanical risk factors associated with iliotibial band syndrome
(2022)
The purpose of this study was to identify the influence of downhill running on biomechanical risk factors for iliotibial band syndrome. We conducted a 3D motion analysis of 22 females and males running on an instrumented treadmill at four different inclinations (0%, -5%, -10%, -15%) at a speed of 3.5 m/s. We found significant differences for biomechanical risk factors associated with iliotibial band syndrome. Peak knee flexion angle at initial ground contact (p < .001), peak knee adduction angle (p = .005), and iliotibial band strain (p < .001) systematically increased with increasing slope. Downhill running increases biomechanical risk factors for iliotibial band syndrome. Our results highlight the need to consider the individual running environment in assessing overuse injury risk in runners.
This study aims to investigate the individual response concerning BRFs for AT when the mid-sole hardness underneath the rearfoot was systematically altered. We first identified FGs based on the footwear condition that minimised the risk for AT across BRFs. We then tested the FGs for differences in anthropometrics, footwear comfort, and running characteristics.