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.

中文翻译：

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日常生活中帕金森病活动性的数字生物标志物。

背景技术识别移动性的数字生物标志物对于帕金森病（PD）的临床试验非常重要。目的 通过一周的连续监测，确定哪些数字化的行动能力测量结果可以将帕金森病患者的行动能力与健康对照 (HC) 受试者的行动能力区分开来。方法 我们招募了 29 名 PD 患者和 27 名年龄匹配的 HC 受试者。受试者被要求在双脚和腰部区域佩戴三个惯性传感器（APDM 的 Opal），一部分受试者还佩戴两个手腕传感器，进行一周的连续监测。我们得出了 43 项流动性数字成果衡量标准，分为五个领域。计算每个移动性数字结果测量的曲线下面积 (AUC)，并使用“最佳子集选择策略”的逻辑回归来查找区分 PD 和 HC 移动性的测量组合。结果 PD 中的记录持续时间为 66±14 小时，HC 中的记录持续时间为 59±16 小时。在总共 43 个流动性数字结果衡量指标中，我们发现 6 个 AUC > 0.80 的流动性数字结果衡量指标。转动角度（AUC = 0.89，95% CI：0.79-0.97）和摆动时间变异性（AUC = 0.87，95% CI：0.75-0.96）是最具辨别力的个体指标。通过最佳子集策略最一致地选择转向测量，以区分 PD 患者和 HC 患者，其次是步态变异测量。结论 PD 日常生活活动性数字生物标志物的临床研究和临床实践应包括转向和变异性测量。