Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial.
Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial.
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DOI：
10.1002/mds.27376
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发表时间：
2018-08
期刊：
影响因子：
--
通讯作者：
Lindemann M
中科院分区：
文献类型：
--
作者：
Lipsmeier F;Taylor KI;Kilchenmann T;Wolf D;Scotland A;Schjodt-Eriksen J;Cheng WY;Fernandez-Garcia I;Siebourg-Polster J;Jin L;Soto J;Verselis L;Boess F;Koller M;Grundman M;Monsch AU;Postuma RB;Ghosh A;Kremer T;Czech C;Gossens C;Lindemann M
背景：智能手机传感器等无处不在的数字技术有望从根本上改变帕金森病等神经系统疾病的生物医学研究和治疗监测，开创数字生物标记物的新领域。目的：本研究在临床试验中评估了基于智能手机的帕金森病数字生物标记物的可行性、可靠性和有效性。方法：在对44名帕金森病患者进行为期6个月的1b期临床试验，以及对35名年龄匹配的健康对照组进行为期45天的独立研究期间，参与者完成了六项日常运动主动测试(持续发声、静止性震颤、姿势震颤、手指敲击、平衡和步态)，然后在白天携带智能手机(被动监测)，从而能够通过陀螺仪和加速计数据评估例如行走和坐-站转换所花费的时间。结果：依从性可以接受：患者每周平均完成主动测试7次，平均3.5次。基于传感器的特征表现出中等到优秀的重测信度(平均组内相关系数 = 为0.84)。所有的主动和被动特征都显著区别于P <； 0.005的对照组。除持续发声外，所有主动测试特征都与国际帕金森病和运动障碍协会赞助的UPRDS临床严重程度评级显著相关。在被动监测中，行走时间与平均姿势不稳定和步态障碍评分有显著关系(P = 0.005)。值得注意的是，除了姿势震颤外，对于所有智能手机的主动和被动功能，监测程序检测到异常，即使是那些在现场访问时评分为在国际帕金森和运动障碍协会赞助的UPRDS项目中没有体征的帕金森患者也是如此。结论：这些发现证明了基于智能手机的数字生物标志物的可行性，并表明智能手机传感器技术为帕金森病提供了可靠、有效、临床有意义和高度敏感的表型数据。©2018作者。《运动障碍》由威利期刊公司代表国际帕金森和运动障碍协会出版。
Background: Ubiquitous digital technologies such as smartphone sensors promise to fundamentally change biomedical research and treatment monitoring in neurological diseases such as PD, creating a new domain of digital biomarkers. Objectives: The present study assessed the feasibility, reliability, and validity of smartphone‐based digital biomarkers of PD in a clinical trial setting. Methods: During a 6‐month, phase 1b clinical trial with 44 Parkinson participants, and an independent, 45‐day study in 35 age‐matched healthy controls, participants completed six daily motor active tests (sustained phonation, rest tremor, postural tremor, finger‐tapping, balance, and gait), then carried the smartphone during the day (passive monitoring), enabling assessment of, for example, time spent walking and sit‐to‐stand transitions by gyroscopic and accelerometer data. Results: Adherence was acceptable: Patients completed active testing on average 3.5 of 7 times/week. Sensor‐based features showed moderate‐to‐excellent test‐retest reliability (average intraclass correlation coefficient = 0.84). All active and passive features significantly differentiated PD from controls with P < 0.005. All active test features except sustained phonation were significantly related to corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS clinical severity ratings. On passive monitoring, time spent walking had a significant (P = 0.005) relationship with average postural instability and gait disturbance scores. Of note, for all smartphone active and passive features except postural tremor, the monitoring procedure detected abnormalities even in those Parkinson participants scored as having no signs in the corresponding International Parkinson and Movement Disorder Society–Sponsored UPRDS items at the site visit. Conclusions: These findings demonstrate the feasibility of smartphone‐based digital biomarkers and indicate that smartphone‐sensor technologies provide reliable, valid, clinically meaningful, and highly sensitive phenotypic data in Parkinson's disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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影响因子：
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