Patel Siddharth, Oubre Brandon, Stephen Christopher D, Schmahmann Jeremy D, Gupta Anoopum S
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 101 Merrimac St, Boston, MA, USA.
Ataxia Center, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
Cerebellum. 2025 Sep 16;24(6):156. doi: 10.1007/s12311-025-01901-3.
Digital measures derived from wearable sensors are a promising approach for assessing motor impairment in clinical trials. Submovements, which are velocity curves extracted from time series data, have been successful in characterizing impaired movement during specific motor tasks as well as from natural behavior. In this study, we evaluate the influence of different limb movements on submovement kinematic properties. Individuals with ataxia (n = 70) and healthy controls (n = 27) wore inertial sensors on their wrists and ankles and performed five neurologically-relevant tasks-finger-nose, fast alternating hand movements (AHM), finger-chase, heel-stomping, and heel-shin. A common framework was applied to extract submovements from each task and eight submovement kinematic features were analyzed. Though submovement kinematic properties changed in response to disease severity, they were primarily influenced by motor task and direction of motion. Modeling experiments revealed that accounting for task and direction of motion improved estimation of ataxia severity; the best performing model accurately estimated clinician-administered ataxia ratings (r = 0.82, 95%CI: 0.77-0.86), and found the finger-chase task to be most informative of severity. Although there were differences across tasks, in general, individuals with ataxia had submovements with lower peak accelerations and more variable kinematics. Relationships between ataxia severity and submovement durations, distances, and peak velocities were more task dependent. These results demonstrate that a common submovement analysis approach can be used to estimate ataxia severity across a wide range of motor tasks and that estimation of severity can be improved by accounting for movement type and direction of motion.
从可穿戴传感器获得的数字测量方法是临床试验中评估运动障碍的一种很有前景的方法。子运动是从时间序列数据中提取的速度曲线，已成功用于表征特定运动任务期间以及自然行为中的运动受损情况。在本研究中，我们评估了不同肢体运动对子运动运动学特性的影响。共济失调患者（n = 70）和健康对照者（n = 27）在手腕和脚踝佩戴惯性传感器，并执行五项与神经学相关的任务——指鼻试验、快速交替手部运动（AHM）、手指追逐试验、足跟跺脚试验和足跟胫试验。应用一个通用框架从每个任务中提取子运动，并分析八个子运动运动学特征。尽管子运动运动学特性随疾病严重程度而变化，但它们主要受运动任务和运动方向的影响。建模实验表明，考虑任务和运动方向可改善共济失调严重程度的估计；表现最佳的模型准确估计了临床医生评定的共济失调评分（r = 0.82，95%CI：0.77 - 0.86），并发现手指追逐试验对严重程度的信息最丰富。尽管不同任务之间存在差异，但总体而言，共济失调患者的子运动峰值加速度较低，运动学变化更大。共济失调严重程度与子运动持续时间、距离和峰值速度之间的关系更依赖于任务。这些结果表明，一种通用的子运动分析方法可用于估计广泛运动任务中的共济失调严重程度，并且通过考虑运动类型和运动方向可以改善严重程度的估计。
