Gordon Mark Forrest, Grachev Igor D, Mazeh Itzik, Dolan Yonatan, Reilmann Ralf, Loupe Pippa S, Fine Shai, Navon-Perry Leehee, Gross Nicholas, Papapetropoulos Spyros, Savola Juha-Matti, Hayden Michael R
Specialty Clinical Development, Teva Pharmaceutical Industries Ltd., Frazer, Pennsylvania, USA.
Guide Pharmaceutical Consulting, LLC, Millstone Township, New Jersey, USA.
Digit Biomark. 2019 Sep 6;3(3):103-115. doi: 10.1159/000502136. eCollection 2019 Sep-Dec.
Previous studies have demonstrated the feasibility and promise of wearable sensors as objective measures of motor impairment in Parkinson disease and essential tremor. However, there are few published studies that have examined such an application in Huntington disease (HD). This report provides an evaluation of the potential to objectively quantify chorea in HD patients using wearable sensor data. Data were derived from a substudy of the phase 2 Open-PRIDE-HD study, where 17 patients were screened and 15 patients enrolled in the substudy and ultimately 10 patients provided sufficient wearable sensor data. The substudy was designed to provide high-resolution data to inform design of predictive algorithms for chorea quantification. During the entire course of the 6-month study, in addition to chorea ratings from 18 in-clinic assessments, 890 home assessments, and 1,388 responses to daily reminders, 33,000 h of high-resolution accelerometer data were captured continuously from wearable smartwatches and smartphones. Despite its limited sample size, our study demonstrates that arm chorea can be characterized using accelerometer data during static assessments. Nonetheless, the small sample size limits the generalizability of the model. The sensor-based model can quantify the chorea level with high correlation to the chorea severity reported by both clinicians and patients. In addition, our analysis shows that the chorea digital signature varies between patients. This work suggests that digital wearable sensors have the potential to support clinical development of medications in patients with movement disorders, such as chorea. However, additional data would be needed from a larger number of HD patients with a full range of chorea severity (none to severe) with and without intervention to validate this potentially predictive technology.
先前的研究已经证明了可穿戴传感器作为帕金森病和特发性震颤运动障碍客观测量方法的可行性和前景。然而,很少有已发表的研究探讨过这种应用在亨廷顿舞蹈病(HD)中的情况。本报告评估了使用可穿戴传感器数据客观量化HD患者舞蹈症的潜力。数据来自2期开放PRIDE-HD研究的一项子研究,其中17名患者接受了筛查,15名患者参加了该子研究,最终10名患者提供了足够的可穿戴传感器数据。该子研究旨在提供高分辨率数据,为舞蹈症量化预测算法的设计提供信息。在为期6个月的研究全过程中,除了来自18次门诊评估、890次家庭评估以及对每日提醒的1388次回复中的舞蹈症评分外,还从可穿戴智能手表和智能手机中持续采集了33000小时的高分辨率加速度计数据。尽管样本量有限,但我们的研究表明,在静态评估期间,手臂舞蹈症可以通过加速度计数据来表征。尽管如此,小样本量限制了该模型的普遍性。基于传感器的模型能够量化舞蹈症水平,与临床医生和患者报告的舞蹈症严重程度具有高度相关性。此外,我们的分析表明,舞蹈症数字特征在患者之间存在差异。这项工作表明,数字可穿戴传感器有潜力支持运动障碍患者(如舞蹈症患者)药物的临床开发。然而,需要从更多患有各种舞蹈症严重程度(无至严重)且有或无干预的HD患者那里获取更多数据,以验证这种潜在的预测技术。