Department of Kinesiology, University of North Carolina at Greensboro, Greensboro, North Carolina, United States of America.
College of Health Sciences, Old Dominion University, Norfolk, Virginia, United States of America.
PLoS One. 2022 Dec 15;17(12):e0278994. doi: 10.1371/journal.pone.0278994. eCollection 2022.
Neuromotor dysfunction after a concussion is common, but balance tests used to assess neuromotor dysfunction are typically subjective. Current objective balance tests are either cost- or space-prohibitive, or utilize a static balance protocol, which may mask neuromotor dysfunction due to the simplicity of the task. To address this gap, our team developed an Android-based smartphone app (portable and cost-effective) that uses the sensors in the device (objective) to record movement profiles during a stepping-in-place task (dynamic movement). The purpose of this study was to examine the extent to which our custom smartphone app and protocol could discriminate neuromotor behavior between concussed and non-concussed participants. Data were collected at two university laboratories and two military sites. Participants included civilians and Service Members (N = 216) with and without a clinically diagnosed concussion. Kinematic and variability metrics were derived from a thigh angle time series while the participants completed a series of stepping-in-place tasks in three conditions: eyes open, eyes closed, and head shake. We observed that the standard deviation of the mean maximum angular velocity of the thigh was higher in the participants with a concussion history in the eyes closed and head shake conditions of the stepping-in-place task. Consistent with the optimal movement variability hypothesis, we showed that increased movement variability occurs in participants with a concussion history, for which our smartphone app and protocol were sensitive enough to capture.
脑震荡后神经运动功能障碍很常见，但用于评估神经运动功能障碍的平衡测试通常是主观的。目前的客观平衡测试要么成本高昂，要么空间受限，要么使用静态平衡协议，由于任务简单，可能掩盖神经运动功能障碍。为了解决这一差距，我们的团队开发了一个基于 Android 的智能手机应用程序（便携且经济实惠），该应用程序使用设备中的传感器（客观）来记录在原地踏步任务（动态运动）期间的运动曲线。本研究的目的是研究我们的定制智能手机应用程序和协议在多大程度上可以区分脑震荡和非脑震荡参与者的神经运动行为。数据是在两个大学实验室和两个军事地点收集的。参与者包括有和没有临床诊断脑震荡的平民和军人。在参与者完成三种条件下的一系列原地踏步任务时：睁眼、闭眼和摇头，从大腿角度时间序列中得出运动学和可变性指标。我们观察到，在闭眼和摇头的原地踏步任务条件下，有脑震荡病史的参与者的大腿平均最大角速度的标准差更高。与最佳运动可变性假设一致，我们表明，有脑震荡病史的参与者的运动可变性增加，我们的智能手机应用程序和协议足以捕捉到这一点。
