Background: There is a lack of studies describing foot strike patterns in children and adolescents. This raises the question on what the natural foot strike pattern with less extrinsic influence should be and whether or not it is valid to make assumptions on adults based on the knowledge from children.
Objectives: To investigate the distribution of foot strike patterns in children and adolescents during running, and the association of participants' characteristics with the foot strike patterns.
Methods: This is a cross-sectional study. Videos were acquired with a high-speed camera and running speed was measured with a stopwatch. Bayesian analyses were performed to allow foot strike pattern inferences from the sample to the population distribution and a supervised machine learning procedure was implemented to develop an algorithm based on logistic mixed models aimed at classifying the participants in rearfoot, midfoot, or forefoot strike patterns.
Results: We have included 415 children and adolescents. The distribution of foot strike patterns was predominantly rearfoot for shod and barefoot assessments. Running condition (barefoot versus shod), speed, and footwear (with versus without heel elevation) seemed to influence the foot strike pattern. Those running shod were more likely to present rearfoot pattern compared to barefoot. The classification accuracy of the final algorithm ranged from 80% to 88%.
Conclusions: The rearfoot pattern was predominant in our sample. Future well-designed prospective studies are needed to understand the influence of foot strike patterns on the incidence and prevalence of running-related injuries in children and adolescents during running, and in adult runners.
Keywords: Barefoot; Kids; Runners; Shod.
Copyright © 2020 Associação Brasileira de Pesquisa e Pós-Graduação em Fisioterapia. Publicado por Elsevier España, S.L.U. All rights reserved.