What is the foot strike pattern distribution in children and adolescents during running? A cross-sectional study.
• The distribution of foot strike patterns was predominantly rearfoot for shod and barefoot tests. • The running condition, speed, and type of footwear were associated with foot strike patterns. • Participants running shod were more likely to present a rearfoot pattern compared to those running bare...
| Publicado en: | Brazilian Journal of Physical Therapy Vol. 25; no. 3; pp. 336 - 344 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
May2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150256246&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150256246 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14133555 1FEL jtl: Brazilian Journal of Physical Therapy issn: 14133555 maglogo: N pubinfo: dt: May2021 vid: 25 iid: 3 pid: 82545 pub: Elsevier B.V. place: Philadelphia, Pennsylvania artinfo: ui: 150256246 150256246 150256246 10.1016/j.bjpt.2020.10.001 150256246 ppf: 336 ppct: 8 formats: tig: atl: What is the foot strike pattern distribution in children and adolescents during running? A cross-sectional study. aug: au: Giacomini, Bruno Augusto Yamato, Tiê Parma Lopes, Alexandre Dias Hespanhol, Luiz affil: Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de São Paulo (UNICID), São Paulo, SP, Brazil sug: subj: Running Physiology Foot Physiology Foot Physiology Human Child Adolescence Cross Sectional Studies Probability Machine Learning Program Implementation Algorithms Software Design Logistic Regression Multimethod Studies Comparative Studies Running Injuries Prevalence Incidence Child: 6-12 years Adolescent: 13-18 years ab: • The distribution of foot strike patterns was predominantly rearfoot for shod and barefoot tests. • The running condition, speed, and type of footwear were associated with foot strike patterns. • Participants running shod were more likely to present a rearfoot pattern compared to those running barefoot. 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. 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. 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. 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%. 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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