Dingenen Bart, Staes Filip, Vanelderen Romy, Ceyssens Linde, Malliaras Peter, Barton Christian J, Deschamps Kevin
Reval Rehabilitation Research Centre, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium.
KU Leuven Musculoskeletal Rehabilitation Research Group, Department of Rehabilitation Sciences, Faculty of Kinesiology and Rehabilitation Sciences, Leuven, Belgium.
Phys Ther Sport. 2020 Jul;44:99-106. doi: 10.1016/j.ptsp.2020.04.032. Epub 2020 Apr 28.
To explore whether homogeneous subgroups could be discriminated within a population of recreational runners with a running-related injury based on running kinematics evaluated with marker-based two-dimensional video analysis.
Cross-sectional.
Research laboratory.
Fifty-three recreational runners (15 males, 38 females) with a running-related injury.
Foot and tibia inclination at initial contact, and hip adduction and knee flexion at midstance were measured in the frontal and sagittal plane with marker-based two-dimensional video analysis during shod running on a treadmill at preferred speed. The four outcome measures were clustered using K-means cluster analysis (n = 2-10). Silhouette coefficients were used to detect optimal clustering.
The cluster analysis led to the classification of two distinct subgroups (mean silhouette coefficient = 0.53). Subgroup 1 (n = 39) was characterized by significantly greater foot inclination and tibia inclination at initial contact compared to subgroup 2 (n = 14).
The existence of different subgroups demonstrate that the same running-related injury can be represented by different kinematic presentations. A subclassification based on the kinematic presentation may help clinicians in their clinical reasoning process when evaluating runners with a running-related injury and could inform targeted intervention strategy development.
基于使用基于标记的二维视频分析评估的跑步运动学,探讨在患有跑步相关损伤的休闲跑步者群体中是否可以区分出同质亚组。
横断面研究。
研究实验室。
53名患有跑步相关损伤的休闲跑步者(15名男性,38名女性)。
在跑步机上以偏好速度穿着跑鞋跑步时,使用基于标记的二维视频分析在额状面和矢状面测量初始接触时的足部和胫骨倾斜度,以及支撑中期的髋关节内收和膝关节屈曲度。使用K均值聚类分析(n = 2 - 10)对这四项观察指标进行聚类。使用轮廓系数来检测最佳聚类。
聚类分析导致将其分为两个不同的亚组(平均轮廓系数 = 0.53)。与亚组2(n = 14)相比,亚组1(n = 39)的特征是初始接触时足部倾斜度和胫骨倾斜度明显更大。
不同亚组的存在表明,相同的跑步相关损伤可以由不同的运动学表现来体现。基于运动学表现的亚分类可能有助于临床医生在评估患有跑步相关损伤的跑步者时进行临床推理过程,并可为制定有针对性的干预策略提供依据。