帕金森病日常生活中的移动数字生物标志物。
Digital Biomarkers of Mobility in Parkinson's Disease During Daily Living.
机构信息
Department of Neurology, Oregon Health & Science University, Portland, OR, USA.
Department of Electrical and Computer Engineering, Portland State University, Portland, OR, USA.
出版信息
J Parkinsons Dis. 2020;10(3):1099-1111. doi: 10.3233/JPD-201914.
BACKGROUND
Identifying digital biomarkers of mobility is important for clinical trials in Parkinson's disease (PD).
OBJECTIVE
To determine which digital outcome measures of mobility discriminate mobility in people with PD from healthy control (HC) subjects over a week of continuous monitoring.
METHODS
We recruited 29 people with PD, and 27 age-matched HC subjects. Subjects were asked to wear three inertial sensors (Opal by APDM) attached to both feet and to the lumbar region, and a subset of subjects also wore two wrist sensors, for a week of continuous monitoring. We derived 43 digital outcome measures of mobility grouped into five domains. An Area Under Curve (AUC) was calculated for each digital outcome measures of mobility, and logistic regression employing a 'best subsets selection strategy' was used to find combinations of measures that discriminated mobility in PD from HC.
RESULTS
Duration of recordings was 66±14 hours in the PD and 59±16 hours in the HC. Out of a total of 43 digital outcome measures of mobility, we found six digital outcome measures of mobility with AUC > 0.80. Turn angle (AUC = 0.89, 95% CI: 0.79-0.97) and swing time variability (AUC = 0.87, 95% CI: 0.75-0.96) were the most discriminative individual measures. Turning measures were most consistently selected via the best subsets strategy to discriminate people with PD from HC, followed by gait variability measures.
CONCLUSION
Clinical studies and clinical practice with digital biomarkers of daily life mobility in PD should include turning and variability measures.
背景
识别移动的数字生物标志物对于帕金森病（PD）的临床试验很重要。
目的
确定在一周的连续监测中，哪些移动的数字结果衡量标准可以区分 PD 患者和健康对照（HC）受试者的移动能力。
方法
我们招募了 29 名 PD 患者和 27 名年龄匹配的 HC 受试者。要求受试者佩戴三个惯性传感器（APDM 的 Opal），分别附在双脚和腰部，部分受试者还佩戴两个腕部传感器，进行一周的连续监测。我们从五个领域中得出了 43 个移动的数字结果衡量标准。为每个移动的数字结果衡量标准计算了曲线下面积（AUC），并采用“最佳子集选择策略”的逻辑回归来寻找区分 PD 患者和 HC 受试者移动能力的措施组合。
结果
PD 组的记录持续时间为 66±14 小时，HC 组为 59±16 小时。在总共 43 个移动的数字结果衡量标准中，我们发现了六个 AUC>0.80 的数字移动结果衡量标准。转角（AUC=0.89，95%置信区间：0.79-0.97）和摆动时间变异性（AUC=0.87，95%置信区间：0.75-0.96）是最具区分性的个体措施。通过最佳子集策略，转弯测量是最一致地选择来区分 PD 患者和 HC，其次是步态变异性测量。
结论
在 PD 患者的日常生活移动数字生物标志物的临床研究和临床实践中，应包括转弯和变异性测量。
Zotero 插件