From the Oxford Parkinson's Disease Centre (OPDC) (S.A., F.B., C.L., T.R.B., M.R., C.R., J.C.K., J.R., A.L., R.W.-M, M.T.H.), University of Oxford, UK; Engineering and Applied Science (S.A., M.A.L.), Aston University, Birmingham, UK; Somerville College (S.A.), University of Oxford, UK; Nuffield Department of Clinical Neurosciences (F.B., C.L., T.R.B., M.A.L., M.T.H.), University of Oxford, UK; Population Health Sciences (M.A.L.), University of Bristol, UK; andDepartment of Computer Science (A.Z.), Johns Hopkins University, Baltimore; Department of Neurology and Neurophysiology (Z.Z., G.L., M.T.H.), Oxford University Hospitals NHS Trust, UK; Respiratory Support and Sleep Centre (T.Q.), Papworth Hospital, Cambridge, UK; Department of Neurology (G.D.), Royal Hallamshire Hospital, Sheffield, UK; and Media Lab (M.A.L.), Massachusetts Institute of Technology, Cambridge, MA.
Neurology. 2018 Oct 16;91(16):e1528-e1538. doi: 10.1212/WNL.0000000000006366. Epub 2018 Sep 19.
We sought to identify motor features that would allow the delineation of individuals with sleep study-confirmed idiopathic REM sleep behavior disorder (iRBD) from controls and Parkinson disease (PD) using a customized smartphone application.
A total of 334 PD, 104 iRBD, and 84 control participants performed 7 tasks to evaluate voice, balance, gait, finger tapping, reaction time, rest tremor, and postural tremor. Smartphone recordings were collected both in clinic and at home under noncontrolled conditions over several days. All participants underwent detailed parallel in-clinic assessments. Using only the smartphone sensor recordings, we sought to (1) discriminate whether the participant had iRBD or PD and (2) identify which of the above 7 motor tasks were most salient in distinguishing groups.
Statistically significant differences based on these 7 tasks were observed between the 3 groups. For the 3 pairwise discriminatory comparisons, (1) controls vs iRBD, (2) controls vs PD, and (3) iRBD vs PD, the mean sensitivity and specificity values ranged from 84.6% to 91.9%. Postural tremor, rest tremor, and voice were the most discriminatory tasks overall, whereas the reaction time was least discriminatory.
Prodromal forms of PD include the sleep disorder iRBD, where subtle motor impairment can be detected using clinician-based rating scales (e.g., Unified Parkinson's Disease Rating Scale), which may lack the sensitivity to detect and track granular change. Consumer grade smartphones can be used to accurately separate not only iRBD from controls but also iRBD from PD participants, providing a growing consensus for the utility of digital biomarkers in early and prodromal PD.
我们试图通过定制的智能手机应用程序，确定可用于区分经睡眠研究证实的特发性 REM 睡眠行为障碍（iRBD）患者与对照组和帕金森病（PD）患者的运动特征。
共有 334 名 PD 患者、104 名 iRBD 患者和 84 名对照组参与者完成了 7 项任务，以评估语音、平衡、步态、手指敲击、反应时间、静止性震颤和姿势性震颤。在几天内，参与者在诊所和家中的非受控条件下使用智能手机进行录制。所有参与者都接受了详细的平行诊所评估。仅使用智能手机传感器记录，我们试图（1）区分参与者是否患有 iRBD 或 PD，以及（2）确定上述 7 项运动任务中哪项最能区分组群。
在这 3 组参与者之间，基于这 7 项任务的观察到了具有统计学意义的差异。对于 3 项两两比较（1）对照组与 iRBD 组，（2）对照组与 PD 组，以及（3）iRBD 与 PD 组，平均敏感性和特异性值范围为 84.6%至 91.9%。姿势性震颤、静止性震颤和语音是总体上最具区分性的任务，而反应时间的区分性最差。
PD 的前驱形式包括睡眠障碍 iRBD，使用基于临床医生的评分量表（例如，统一帕金森病评定量表）可以检测到细微的运动障碍，这些量表可能缺乏检测和跟踪细微变化的敏感性。消费级智能手机可用于准确区分 iRBD 与对照组，以及 iRBD 与 PD 患者，这为数字生物标志物在早期和前驱性 PD 中的效用提供了越来越多的共识。
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