What is the foot strike pattern distribution in children and adolescents during running? A cross-sectional study

Autor(es)

Giacomini, Bruno Augusto ^{[1, 2]} ; Yamato, Tie Parma ^{[3, 1, 2]} ; Lopes, Alexandre Dias ^[4] ; Hespanhol, Luiz ^{[5, 1, 2, 6]}

Afiliação do(s) autor(es)

^[1] Univ Cidade Sao Paulo UNICID, Sdo Paulo, Masters Program Phys Therapy, Rua Cesario Galena 448, BR-03071000 Sao Paulo, SP - Brazil

^[2] Univ Cidade Sao Paulo UNICID, Sdo Paulo, Doctoral Program Phys Therapy, Rua Cesario Galena 448, BR-03071000 Sao Paulo, SP - Brazil

^[3] Ctr Pain Hlth & Lifestyle CPHL, New Lambton Hts - Australia

^[4] Univ Massachusetts Lowell, Zuckerberg Coll Hlth Sci, Dept Phys Therapy & Kinesiol, Lowell, MA - USA

^[5] Amsterdam Univ Med Ctr, Locat VU Univ Med Ctr Amsterdam VUmc, Amsterdam Publ Hlth Res Inst APH, Dept Publ & Occupat Hlth DPOH, Amsterdam - Netherlands

^[6] Amsterdam Univ Med Ctr, Locat VU Univ Med Ctr Amsterdam VUmc, Amsterdam Movement Sci, Amsterdam Collaborat Hlth & Safety Sports ACHSS, Amsterdam - Netherlands

• Tipo de documento: Artigo Científico
• Fonte: BRAZILIAN JOURNAL OF PHYSICAL THERAPY; v. 25, n. 3, p. 336-343, MAY-JUN 2021.
• Citações Web of Science: 1

Resumo

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. (C) 2020 Associacao Brasileira de Pesquisa e Pos-Graduacao em Fisioterapia. Published by Elsevier Editora Ltda. All rights reserved. (AU)

• Processo FAPESP: 16/09220-1 - Avaliação do processo de desenvolvimento e implementação de um programa de prevenção de lesões da corrida
• Beneficiário: Luiz Carlos Hespanhol Junior
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 17/17484-1 - Prevalência e fatores prognósticos da dor musculoesquelética em crianças e adolescentes na cidade de São Paulo
• Beneficiário: Tiê Parma Yamato
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores