ETAware: Continuous monitoring of the functional impact of essential tremor

摘要 目标是设计和临床评估ETAware，这是一种自适应、可穿戴的系统，用于监控影响 震颤对日常生活能力(ADL)的影响评估和优化治疗干预 患有特发性震颤(ET)的患者，因为目前还没有技术可以评估其功能影响 ADL上有震颤的迹象。ETAware将专注于ET，这是最常见的运动障碍，影响着1000万人 在美国；然而，在商业上，该系统将适用于其他阻碍参与的条件 影响日常活动，影响生活质量。普萘洛尔和扑米酮是目前用于治疗的两种主要药物。 治疗特发性震颤，而外科干预措施，如脑深部刺激(DBS)和MRI引导 聚焦超声(MGFUS)是治疗难治性ET的必要手段。然而，患者对所有患者的反应 治疗的形式可能有很大的不同，需要按顺序使用不同的药物。要评估 为了确保干预措施的有效性，量化震颤的严重程度及其对ET患者日常生活能力的影响至关重要。 目前，为了显示临床改善，使用了主观问卷和评定量表。然而， 这些问卷和评分表受到临床判断和偏差的影响，仅提供信息 离散时间点，不能用于日常连续监测。ETAware将持续监控 震颤的严重程度和震颤对ADL的功能影响，这将有助于临床医生进行治疗调整， 帮助患者控制疾病管理，并通过提供 客观的生活质量测量和最小化与临床就诊和管理相关的成本 问卷和评分表。 我们之前已经将目前用于药物临床试验的可穿戴设备和移动应用程序商业化 用于监测上肢震颤的严重程度。然而，现有的系统没有量化下肢震颤或 提供任何有关震颤对ADL的影响的信息。ETAware的主要创新是1)信号 分析运动传感器数据并提供连续的功能测量的处理算法 震颤对ADL的影响，2)临床医生优化治疗、研究人员评估和开发的系统 新的治疗方法，以及患者继续参与他们的疾病管理。 为了证明第一阶段的可行性，将收集20名有特发性震颤患者的运动数据。 当他们执行基于Tetras的震颤性能和ADL任务以及其他功能任务时。 成功标准包括1)开发和验证相关的信号处理算法(r≥0.76， 均方根误差≤0.5)运动学数据与临床医生基于TETRAS的震颤表现和日常生活能力 分数，以及2)开发和验证机器学习模型，以准确(敏感度/ 识别身体活动，如走路、坐着、站着、从椅子上站起来、走 上下楼梯。

Summary The objective is to design and clinically assess ETAware, an adaptive, wearable system to monitor the impact of tremor on activities of daily living (ADL) for the evaluation and optimization of therapeutic interventions in patients with essential tremor (ET) as there is currently no technology that can assess the functional impact of tremor on ADL. ETAware will focus on ET, the most common movement disorder, affecting 10 million people in the US; however, commercially, the system will have applications for other conditions that hinder engagement in daily activities and affect quality of life. Currently, propranolol and primidone are the two main drugs used for treating essential tremor, while surgical interventions such as deep brain stimulation (DBS), and MRI-guided focused ultrasound (MgFUS) are warranted in medically refractory ET. Nevertheless, patients' responses to all forms of therapy can vary substantially, necessitating the sequential use of different drugs. To evaluate the effectiveness of interventions, it is critical to quantify both tremor severity and its impact on ADL in ET. Currently, to show clinical improvement, subjective questionnaires and rating scales are used. However, these questionnaires and rating scales are subject to clinical judgment and bias, only provide information at a discrete point in time, and cannot be utilized for daily continuous monitoring. ETAware will continuously monitor tremor severity AND the functional impact of tremor on ADL, which will help clinicians with therapy adjustments, help patients take control of their disease management, and improve research and clinical trials by providing an objective quality of life measure and minimizing the costs associated with clinic visits and the administration of questionnaires and rating scales. We have previously commercialized wearables and mobile apps that are currently used in drug clinical trials for monitoring upper limb tremor severity. However, the existing systems do not quantify lower limb tremor or give any information on the impact of tremor on ADL. The primary innovations of ETAware are 1) signal processing algorithms to analyze motion sensor data and provide continuous measurements of the functional impact of tremor on ADL, 2) a system for clinicians to optimize therapy, researchers to evaluate and develop new therapies, and patients to stay involved in their disease management. To demonstrate feasibility in Phase I, motion data will be collected from 20 patients with essential tremor while they are performing TETRAS-based tremor performance and ADL tasks as well additional functional tasks. Success criteria include 1) development and validation of signal processing algorithms that correlate (r ≥ 0.76, root-mean-square error ≤0.5) kinematic data with clinician's TETRAS-based tremor performance and ADL scores, and 2) development and validation of machine learning models that accurately (sensitivity/ specificity/AUC > 0.8) identify physical activities such as walking, sitting, standing, getting out of a chair, going up and down stairs.