Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de São Paulo (UNICID), São Paulo, SP, Brazil.
Masters and Doctoral Programs in Physical Therapy, Universidade Cidade de São Paulo (UNICID), São Paulo, SP, Brazil; Centre for Pain Health and Lifestyle (CPHL).
Braz J Phys Ther. 2021 May-Jun;25(3):336-343. doi: 10.1016/j.bjpt.2020.10.001. Epub 2020 Oct 11.
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.
目前缺乏描述儿童和青少年足着地方式的研究。这就提出了一个问题，即在较少外在影响的情况下，自然的足着地方式应该是什么样的，以及是否可以根据儿童的知识来对成年人做出假设。
研究儿童和青少年跑步时足着地方式的分布情况，以及参与者的特征与足着地方式之间的关系。
这是一项横断面研究。使用高速摄像机获取视频，并使用秒表测量跑步速度。进行贝叶斯分析，以允许从样本推断足着地方式在人群分布中的情况，并实施监督机器学习程序，基于逻辑混合模型开发一种算法，旨在将参与者分类为后足、中足或前足着地方式。
我们共纳入了 415 名儿童和青少年。在穿鞋和赤脚评估中，足着地方式主要是后足。跑步条件（赤脚与穿鞋）、速度和鞋类（是否有鞋跟抬高）似乎影响了足着地方式。穿鞋跑步的人比赤脚跑步的人更有可能出现后足着地方式。最终算法的分类准确率在 80%至 88%之间。
在我们的样本中，后足着地方式占主导地位。未来需要进行精心设计的前瞻性研究，以了解足着地方式对儿童和青少年跑步时与跑步相关的损伤的发生率和流行率的影响，以及对成年跑步者的影响。
