Sánchez Natalia, Schweighofer Nicolas, Mulroy Sara J, Roemmich Ryan T, Kesar Trisha M, Torres-Oviedo Gelsy, Fisher Beth E, Finley James M, Winstein Carolee J
Department of Physical Therapy, Chapman University, Irvine, CA.
Fowler School of Engineering, Chapman University, Orange, CA.
bioRxiv. 2023 Oct 30:2023.05.11.540385. doi: 10.1101/2023.05.11.540385.
Walking patterns in stroke survivors are highly heterogeneous, which poses a challenge in systematizing treatment prescriptions for walking rehabilitation interventions.
We used bilateral spatiotemporal and force data during walking to create a multi-site research sample to: 1) identify clusters of walking behaviors in people post-stroke and neurotypical controls, and 2) determine the generalizability of these walking clusters across different research sites. We hypothesized that participants post-stroke will have different walking impairments resulting in different clusters of walking behaviors, which are also different from control participants.
We gathered data from 81 post-stroke participants across four research sites and collected data from 31 control participants. Using sparse K-means clustering, we identified walking clusters based on 17 spatiotemporal and force variables. We analyzed the biomechanical features within each cluster to characterize cluster-specific walking behaviors. We also assessed the generalizability of the clusters using a leave-one-out approach.
We identified four stroke clusters: a fast and asymmetric cluster, a moderate speed and asymmetric cluster, a slow cluster with frontal plane force asymmetries, and a slow and symmetric cluster. We also identified a moderate speed and symmetric gait cluster composed of controls and participants post-stroke. The moderate speed and asymmetric stroke cluster did not generalize across sites.
Although post-stroke walking patterns are heterogenous, these patterns can be systematically classified into distinct clusters based on spatiotemporal and force data. Future interventions could target the key features that characterize each cluster to increase the efficacy of interventions to improve mobility in people post-stroke.
中风幸存者的行走模式高度异质，这给为行走康复干预制定系统化治疗方案带来了挑战。
我们利用行走过程中的双侧时空和力数据创建了一个多地点研究样本，以：1）识别中风后患者和神经典型对照者的行走行为集群，以及2）确定这些行走集群在不同研究地点的普遍性。我们假设中风后参与者会有不同的行走障碍，导致不同的行走行为集群，且这些集群也与对照参与者不同。
我们从四个研究地点的81名中风后参与者收集数据，并从31名对照参与者收集数据。使用稀疏K均值聚类，我们基于17个时空和力变量识别出行走集群。我们分析了每个集群内的生物力学特征，以描述特定集群的行走行为。我们还使用留一法评估了集群的普遍性。
我们识别出四个中风集群：一个快速且不对称的集群、一个中等速度且不对称的集群、一个具有额面力不对称的缓慢集群以及一个缓慢且对称的集群。我们还识别出一个由对照者和中风后参与者组成的中等速度且对称的步态集群。中等速度且不对称的中风集群在各地点间不具有普遍性。
尽管中风后的行走模式是异质的，但基于时空和力数据，这些模式可以被系统地分类为不同的集群。未来的干预措施可以针对每个集群的关键特征，以提高改善中风后患者行动能力的干预效果。
