Del Din Silvia, Yarnall Alison J, Barber Thomas R, Lo Christine, Crabbe Marie, Rolinski Michal, Baig Fahd, Hu Michele T, Rochester Lynn
Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, UK.
Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK.
J Parkinsons Dis. 2020;10(1):283-299. doi: 10.3233/JPD-191773.
Patients with REM sleep behavior disorder (RBD) have a high risk of developing PD, and thus can be used to study prodromal biomarkers. RBD has been associated with changes in gait; quantifying these changes using wearable technology is promising; however, most data are obtained in clinical settings precluding pragmatic application.
We aimed to investigate if wearable-based, real-world gait monitoring can detect early gait changes and discriminate individuals with RBD from controls, and explore relationships between real-world gait and clinical characteristics.
63 individuals with RBD (66±10 years) and 34 controls recruited in the Oxford Parkinson's Disease Centre Discovery Study were assessed. Data were collected using a wearable device positioned on the lower back for 7 days. Real-world gait was quantified in terms of its Macrostructure (volume, pattern and variability (S2)) and Microstructure (14 characteristics). The value of Macro and Micro gait in discriminating RBD from controls was explored using ANCOVA and ROC analysis, and correlation analysis was performed between gait and clinical characteristics.
Significant differences were found in discrete Micro characteristics in RBD with reduced gait velocity, variability and rhythm (p≤0.023). These characteristics significantly discriminated RBD (AUC≥0.620), with swing time as the single strongest discriminator (AUC=0.652). Longer walking bouts discriminated best between the groups for Macro and Micro outcomes (p≤0.036).
Our results suggest that real-world gait monitoring may have utility as "risk" clinical marker in RBD participants. Real-world gait assessment is low-cost and could serve as a pragmatic screening tool to identify gait impairment in RBD.
快速眼动睡眠行为障碍（RBD）患者患帕金森病（PD）的风险很高，因此可用于研究前驱生物标志物。RBD与步态变化有关；使用可穿戴技术量化这些变化很有前景；然而，大多数数据是在临床环境中获得的，排除了实际应用。
我们旨在研究基于可穿戴设备的真实世界步态监测能否检测到早期步态变化，并区分RBD患者和对照组，以及探索真实世界步态与临床特征之间的关系。
对牛津帕金森病中心发现研究中招募的63名RBD患者（66±10岁）和34名对照组进行评估。使用放置在下背部的可穿戴设备收集数据7天。根据其宏观结构（体积、模式和变异性（S2））和微观结构（14个特征）对真实世界步态进行量化。使用协方差分析和ROC分析探讨宏观和微观步态在区分RBD和对照组中的价值，并对步态与临床特征进行相关性分析。
在RBD患者中，步态速度、变异性和节律降低的离散微观特征存在显著差异（p≤0.023）。这些特征能显著区分RBD（AUC≥0.620），其中摆动时间是最强的单一鉴别指标（AUC=0.652）。较长的步行时间在宏观和微观结果上对两组的区分效果最佳（p≤0.036）。
我们的结果表明，真实世界步态监测可能作为RBD参与者的“风险”临床标志物。真实世界步态评估成本低，可作为识别RBD患者步态障碍的实用筛查工具。
