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.

中文翻译：

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儿童和青少年跑步时的足部撞击模式分布是怎样的？横断面研究

背景

缺乏描述儿童和青少年足部撞击模式的研究。这就提出了一个问题，即外在影响较小的自然足部撞击模式应该是什么，以及根据儿童的知识对成人做出假设是否有效。

目标

调查儿童和青少年跑步时足部撞击模式的分布，以及参与者特征与足部撞击模式的关联。

方法

这是一项横断面研究。用高速摄像机采集视频，用秒表测量跑步速度。执行贝叶斯分析以允许从样本到人口分布的足部撞击模式推断，并实施监督机器学习程序以开发基于逻辑混合模型的算法，旨在将参与者分类为后足、中足或前足撞击模式。

结果

我们纳入了 415 名儿童和青少年。对于穿鞋和赤脚评估，足部撞击模式的分布主要是后足。跑步条件（赤脚与穿鞋）、速度和鞋类（脚后跟抬高与不抬高）似乎会影响足部撞击模式。与赤脚相比，那些跑鞋的人更有可能出现后足模式。最终算法的分类准确率在 80% 到 88% 之间。

结论

后足模式在我们的样本中占主导地位。未来需要精心设计的前瞻性研究，以了解足部撞击模式对儿童和青少年跑步期间以及成年跑步者跑步相关伤害的发生率和患病率的影响。