Kinesis Health Technologies Ltd., D04 V2N9 Dublin, Ireland.
Biomarker Department, Division of Experimental Medicine, H. Lundbeck A/S, 2500 Copenhagen, Denmark.
Sensors (Basel). 2021 Dec 22;22(1):54. doi: 10.3390/s22010054.
People with Parkinson's disease (PD) experience significant impairments to gait and balance; as a result, the rate of falls in people with Parkinson's disease is much greater than that of the general population. Falls can have a catastrophic impact on quality of life, often resulting in serious injury and even death. The number (or rate) of falls is often used as a primary outcome in clinical trials on PD. However, falls data can be unreliable, expensive and time-consuming to collect. We sought to validate and test a novel digital biomarker for PD that uses wearable sensor data obtained during the Timed Up and Go (TUG) test to predict the number of falls that will be experienced by a person with PD. Three datasets, containing a total of 1057 (671 female) participants, including 71 previously diagnosed with PD, were included in the analysis. Two statistical approaches were considered in predicting falls counts: the first based on a previously reported falls risk assessment algorithm, and the second based on elastic net and ensemble regression models. A predictive model for falls counts in PD showed a mean R value of 0.43, mean error of 0.42 and a mean correlation of 30% when the results were averaged across two independent sets of PD data. The results also suggest a strong association between falls counts and a previously reported inertial sensor-based falls risk estimate. In addition, significant associations were observed between falls counts and a number of individual gait and mobility parameters. Our preliminary research suggests that the falls counts predicted from the inertial sensor data obtained during a simple walking task have the potential to be developed as a novel digital biomarker for PD, and this deserves further validation in the targeted clinical population.
帕金森病（PD）患者的步态和平衡能力会受到严重影响；因此，帕金森病患者的跌倒率远高于一般人群。跌倒会对生活质量产生灾难性影响，经常导致严重伤害甚至死亡。跌倒次数（或发生率）通常被用作 PD 临床试验的主要结果。然而，跌倒数据的收集既不可靠，又昂贵且耗时。我们试图验证和测试一种使用可穿戴传感器数据的新型数字生物标志物，该数据是在计时起立行走（TUG）测试中获得的，用于预测 PD 患者将经历的跌倒次数。三个数据集，共包含 1057 名（671 名女性）参与者，其中 71 名先前被诊断为 PD，包括在分析中。在预测跌倒次数方面，考虑了两种统计方法：第一种基于先前报告的跌倒风险评估算法，第二种基于弹性网络和集成回归模型。当将结果平均应用于两个独立的 PD 数据集时，PD 跌倒次数的预测模型显示出 0.43 的平均 R 值、0.42 的平均误差和 30%的平均相关性。结果还表明，跌倒次数与先前报道的基于惯性传感器的跌倒风险估计之间存在很强的关联。此外，还观察到跌倒次数与许多个体步态和移动性参数之间存在显著关联。我们的初步研究表明，从简单行走任务中获得的惯性传感器数据中预测的跌倒次数有可能被开发为 PD 的新型数字生物标志物，这值得在目标临床人群中进一步验证。
