FitzGerald James J, Lu Zhongjiao, Jareonsettasin Prem, Antoniades Chrystalina A
NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom.
Front Neurosci. 2018 Apr 11;12:202. doi: 10.3389/fnins.2018.00202. eCollection 2018.
Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.
直到最近，许多运动障碍的评估仍依赖于临床评分量表，尽管这些量表设计精心，但本质上是主观的且非线性的。这使得准确且真正独立于观察者的量化变得困难，并限制了敏感参数统计方法的使用。最终，能够定量测量神经问题的设备正变得 readily available。例子包括使用眼动仪测量眼球运动和加速度计测量震颤。许多应用正在为智能手机开发。其好处不仅包括更准确的疾病量化，还包括纵向研究数据的一致性、患者进入试验的准确分层，以及远程随访自动数据采集的可能性。在本综述中，我们将着眼于运动障碍，特别关注帕金森病，描述现有临床评估工具的一些局限性，并说明客观指标已经取得成功的方式。
