Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study.
Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study.
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DOI：
10.2196/19068
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发表时间：
2020-10-09
影响因子：
7.4
通讯作者：
Bloem BR
中科院分区：
文献类型：
--
作者：
Evers LJ;Raykov YP;Krijthe JH;Silva de Lima AL;Badawy R;Claes K;Heskes TM;Little MA;Meinders MJ;Bloem BR
可穿戴传感器已成功用于描述帕金森病（PD）患者的运动迟缓步态，但迄今为止，大多数研究都是在高度受控的实验室环境中进行的。本文旨在评估基于传感器的真实步态分析是否可用于客观和远程监测PD的运动波动。Parkinson@Home验证研究为开发数字生物标志物提供了新的参考数据集，以监测日常生活中的PD患者。具体来说，一组25名患有运动波动的PD患者和25名年龄匹配的对照组在家中及其周围进行了至少一小时的无脚本日常活动，同时记录在视频中。PD患者这样做两次：一次是在多巴胺能药物停药过夜后，另一次是在药物摄入后1小时。参与者在手腕和脚踝上，下背部和前面的裤子口袋里都戴着传感器，捕捉运动和上下文数据。基于手动视频注释从加速度计信号中提取25秒的步态段。使用Welch方法估计每个节段和器械的功率谱密度，从中推导出0.5 - 10 Hz频带内的总功率、主频宽度和节奏。使用留一受试者嵌套交叉验证评估了区分药物摄入前后以及PD患者和对照患者的能力。18例PD患者（11例男性;中位年龄65岁）和24例对照患者（13例男性;中位年龄68岁）中，有≥ 10个步态节段可用。使用物流LASSO（最小绝对收缩和选择算子）回归，我们分类了非脚本步态段是否发生在药物摄入之前或之后，平均受试者操作曲线下面积（AUC）在0.70（最少受累侧踝关节，95% CI 0.60 - 0.81）和0.82（最多受累侧踝关节，95% CI 0.72 - 0.92）。结合所有传感器位置并未显著改善分类（AUC 0.84，95% CI 0.75 - 0.93）。在所有信号特性中，0.5至10 Hz频带中的总功率对多巴胺能药物反应最灵敏。区分PD患者和对照患者通常更困难（所有传感器位置的AUC组合：0.76，95% CI 0.62 - 0.90）。视频记录显示，在现实生活中的步态中，手的位置对手腕和裤子口袋传感器的功率谱密度有很大的影响。我们提出了一个新的视频参考数据集，其中包括参与者家中和周围的无脚本活动。使用这个数据集，我们展示了使用基于传感器的分析现实生活中的步态监测电机波动与一个单一的传感器位置的可行性。未来的工作可能会评估上下文传感器控制现实世界的混杂因素的价值。
Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.
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