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首页> 外文期刊>Medicine and science in sports and exercise >Pacing strategy from high-frequency field data: More evidence for neural regulation?
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Pacing strategy from high-frequency field data: More evidence for neural regulation?

机译：高频场数据的起搏策略：神经调节的更多证据？

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摘要

Introduction: We demonstrate a methodology that uncovers an athlete's true pacing strategy from high-frequency (≤1 km) split field data, even if affected by high gradient variation on course. The method thus opens up the analysis of many previously opaque but popular undulating professional and amateur races to scientific scrutiny. Methods: The method is relatively simple to use in any standard statistical package, and execution only requires the addition of the altitude-distance trace of the event to a runner's split times (e.g., as automatically collected by a modern Global Positioning System-enabled wristwatch). In addition, as opposed to assuming a pacing function (e.g., "J shaped," "U shaped," "all-out") and testing this function on the data, the method uses a preliminary discovery step to suggest the most appropriate pacing function(s) to test on the data (if any). Results: The method is demonstrated with two novel case studies: Gebrselassie's world-record Berlin marathon (September 2008) and a unique data set taken from several years of the Six Foot Track Ultramarathon (45 km, Sydney, Australia). Conclusions: In both cases, the method reveals highly variable pacing strategies on a microscale despite remarkable symmetry on a macroscale in one case adding weight to the recent complex system perspective of the neural regulator.
机译：简介：我们演示了一种方法，该方法可以从高频（≤1 km）分割场数据中揭示运动员的真实起搏策略，即使受到赛道上高坡度变化的影响。因此，该方法打开了对许多以前不透明但流行的起伏职业和业余种族进行科学审查的分析。方法：该方法在任何标准统计数据包中都相对易于使用，执行仅需将事件的海拔距离轨迹添加到跑步者的计时中（例如，由启用了现代全球定位系统的现代手表自动收集的信息） ）。另外，与假定起搏功能（例如，“ J形”，“ U形”，“全力”）并在数据上测试此功能相反，该方法使用初步发现步骤来建议最合适的起搏用于测试数据的功能（如果有）。结果：通过两个新颖的案例研究证明了该方法：Gebrselassie的世界纪录柏林马拉松赛（2008年9月）和从几年来的六尺径超级马拉松赛（45公里，澳大利亚悉尼）获得的独特数据集。结论：在这两种情况下，尽管在宏观尺度上具有显着的对称性，该方法仍在微观尺度上揭示了高度可变的起搏策略，在一种情况下，这增加了神经调节器的最新复杂系统观点。

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