A predictive simulation study into the effect of stack height on running biomechanics and efficiency

Predictive simulations of musculoskeletal models provide an advanced approach to investigating the relationship between shoe properties and running performance. So far, such studies have been performed retrospectively, such that independence of the experimental outcome cannot be guaranteed. Here, we aim to show that predictive simulations can be used to investigate shoe characteristics independently of experimental outcomes. Therefore, we performed a predictive simulation study and an experimental study in parallel at two different locations. Between the studies, only the mechanical shoe characteristics were shared in advance. Then, we investigated if running simulations can systematically predict the effect of midsole stack height on running energetics, kinematics, and kinetics. We developed ground-contact models for shoes with stack heights ranging from 35 to 50 mm, solved optimal control problems to generate predictive running simulations at 3.5 m/s for 28 virtual participants, and compared the results to those of the experiments. Our simulations accurately predicted the changes in metabolic cost between the 35 mm and 45 mm shoes. The 50 mm shoe reduced the metabolic cost by 3.0% on average in the simulations, while increasing it by 1.6% on average in the experiments, compared to the 35 mm shoe. Furthermore, trends in peak ankle dorsiflexion angle and peak ankle plantarflexion moment were predicted correctly for all shoes, with the 50 mm shoe showing the largest decrease in the simulations and experiments. Therefore, musculoskeletal simulations can help predict and explore the relationship between running shoe properties and running performance.