Running shoe cushioning has become a standard method for managing impact loading and consequent injuries due to running. However, despite decades of shoe technology developments and the fact that shoes have become increasingly cushioned, aimed to ease the impact on runners’ legs, running injuries have not decreased. To better understand the shoe cushioning paradox, we examined impact loading and the spring-like mechanics of running in a conventional control running shoe and a highly cushioned maximalist shoe at two training speeds, 10 and 14.5 km/h. We found that highly cushioned maximalist shoes alter spring-like running mechanics and amplify rather than attenuate impact loading. This surprising outcome was more pronounced at fast running speed (14.5 km/h), where ground reaction force impact peak and loading rate were 10.7% and 12.3% greater, respectively, in the maximalist shoe compared to the conventional shoe, whereas only a slightly higher impact peak (6.4%) was found at the 10 km/h speed with the maximalist shoe. We attribute the greater impact loading with the maximalist shoes to stiffer leg during landing compared to that of running with the conventional shoes. These discoveries may explain why shoes with more cushioning do not protect against impact-related running injuries.

. The kinematic results showed that MPI reduced the peak forefoot eversion movement in respect to both hindfoot and tibia across walking and running when compared to NORM (p<0.05-0.01). No differences were found in hindfoot eversion between conditions. The kinetic results showed no insole effects in walking, but during running MPI shifted center of pressure medially under the foot (p<0.01) leading to an increase in frontal plane moments at the hip (p<0.05) and knee (p<0.05) joints and a reduction at the ankle joint (p<0.05). These findings indicate that MPI primarily controlled the forefoot motion across walking and running. While kinetic response to MPI was more pronounced in running than walking, kinematic effects were essentially similar across both modes. This suggests that despite higher loads placed upon limp limb during running, there is no need to have a stiffer insoles to achieve similar reduction in the forefoot motion than in walking.3

Both kinematic parameters and ground reaction forces are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas ground reaction force during the support phase of running is measured by force platforms. In order to analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict ground reaction forces using a motion capture system only and this way reduce costs and complexity of the analysis. In this study, the machine learning model for vertical ground reaction force magnitude prediction based on running motion information of 128 healthy adults is pro-

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