Sport Sciences-Performance and Technology, Department of Health Science and Technology, Aalborg University, 9220 Aalborg, Denmark.
Department of Mathematical Sciences, Aalborg University, 9220 Aalborg, Denmark.
Sensors (Basel). 2021 Nov 8;21(21):7418. doi: 10.3390/s21217418.
Patellar and Achilles tendinopathy commonly affect runners. Developing algorithms to predict cumulative force in these structures may help prevent these injuries. Importantly, such algorithms should be fueled with data that are easily accessible while completing a running session outside a biomechanical laboratory. Therefore, the main objective of this study was to investigate whether algorithms can be developed for predicting patellar and Achilles tendon force and impulse during running using measures that can be easily collected by runners using commercially available devices. A secondary objective was to evaluate the predictive performance of the algorithms against the commonly used running distance. Trials of 24 recreational runners were collected with an Xsens suit and a Garmin Forerunner 735XT at three different intended running speeds. Data were analyzed using a mixed-effects multiple regression model, which was used to model the association between the estimated forces in anatomical structures and the training load variables during the fixed running speeds. This provides twelve algorithms for predicting patellar or Achilles tendon peak force and impulse per stride. The algorithms developed in the current study were always superior to the running distance algorithm.
髌腱和跟腱病常见于跑步者。开发一种算法来预测这些结构中的累积力可能有助于预防这些损伤。重要的是，这种算法应该使用在生物力学实验室之外的跑步过程中易于获得的数据来提供动力。因此，本研究的主要目的是探讨是否可以使用跑步者使用市售设备轻松收集的测量值来开发用于预测跑步时髌腱和跟腱力和冲量的算法。次要目的是评估算法对常用跑步距离的预测性能。在三种不同的预期跑步速度下，使用 Xsens 套装和 Garmin Forerunner 735XT 收集了 24 名休闲跑步者的试验。使用混合效应多元回归模型对数据进行分析，该模型用于在固定跑步速度下对估计的解剖结构中的力与训练负荷变量之间的关系进行建模。这提供了 12 种用于预测髌腱或跟腱每步峰值力和冲量的算法。本研究中开发的算法始终优于跑步距离算法。
