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Research is often conducted to investigate footwear mechanical properties and their effects on running biomechanics, but little is known about their influence on runner satisfaction, or how well the shoe is perceived. A tool to predict runner satisfaction in a shoe from its mechanical properties would be advantageous for footwear companies. Data in this study were from a database (n = 615 subject-shoe pairings) of satisfaction ratings (gathered after participants ran on a treadmill), and mechanical testing data for 87 unique subjects across 61 unique shoes. Random forest and elastic net logistic regression models were built to test if footwear mechanical properties and subject characteristics could predict runner satisfaction in 3 ways: degree-of-satisfaction on a 7-point Likert scale, overall satisfaction on a 3-point Likert scale, and willingness-to-purchase the shoe (yes/no response). Data were divided into training and validation sets, using an 80–20 split, to build the models and test their accuracy, respectively. Model accuracies were compared against the no-information rate (i.e. proportion of data belonging to the largest class). The models were not able to predict degree-of-satisfaction or overall satisfaction from footwear mechanical properties but could predict runner’s willingness to purchase with 68–75% accuracy. Midsole Gmax at the heel and forefoot appeared in the top five of variable importance rankings across both willingness-to-purchase models, suggesting its role as a major factor in purchase decisions. The negative regression coefficient for both heel and forefoot Gmax indicated that softer midsoles increase the likelihood of a shoe purchase. Future models to predict satisfaction may improve accuracy with the addition of more subject-specific parameters, such as running goals or foot proportions.
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.