The Development of a Standardized Protocol for Quantifying Equestrian Eventing Cross-Country Ground

R. Graydon, A. Northrop, Jaime H. Martin, M. Lucey, J. Schramel, C. Peham, L. Roepstorff, J. Sinclair, S. Hobbs
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Abstract

The ground has long been cited as a key contributing factor for injury risk in the cross-country phase of eventing. The current study aimed to develop a practically useful standardized protocol for measuring eventing cross country ground. Data collection was split into three phases: Phase 1 (Validation), Phase 2 (Expansion of data set), and Phase 3 (Threshold establishment). During Phase 1, data from nine event courses were collected using an Orono Biomechanical Surface Tester (OBST), Vienna Surface Tester (VST), Lang Penetrometer, Going Stick, and moisture meter. Using linear regression, 80% of the variability in cushioning measured with the OBST was predicted from moisture and VST measurements (p < 0.001). In Phase 2, objective data from 81 event courses and subjective assessments from 180 event riders were collected. In Phase 3, k-means cluster analysis was used to classify the courses into ten clusters based on average course measurements of moisture, cushioning, firmness, stiffness, depth, and coefficient of restitution. Based on cluster membership, course average subjective data (16 courses) were compared using a General Linear Model. Significant differences (p < 0.05) in subjective impact firmness (p = 0.038) and subjective cushioning (p = 0.010) were found between clusters. These data and cluster thresholds provide an event course baseline for future comparisons.
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马术三项赛越野场地量化的标准化规程的发展
长期以来,场地一直被认为是造成越野项目中受伤风险的关键因素。目前的研究旨在开发一种实用的标准化方案来测量越野赛场地。数据收集分为三个阶段:阶段1(验证),阶段2(扩展数据集)和阶段3(建立阈值)。在第一阶段,使用Orono生物力学表面测试仪(OBST)、Vienna表面测试仪(VST)、Lang穿透仪、Going Stick和湿度仪收集了9个项目的数据。使用线性回归,用OBST测量的缓冲80%的可变性可以从湿度和VST测量中预测(p < 0.001)。第二阶段收集了81条赛事赛道的客观数据和180名赛事车手的主观评价。在阶段3中,k-means聚类分析基于平均球场测量的湿度、缓冲、硬度、刚度、深度和恢复系数,将球场分为10个聚类。基于聚类隶属度,采用一般线性模型对16门课程的平均主观数据进行比较。聚类间主观冲击稳健性(p = 0.038)和主观缓冲性(p = 0.010)差异有统计学意义(p < 0.05)。这些数据和集群阈值为将来的比较提供了事件过程基线。
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