ETAware: Continuous monitoring of the functional impact of essential tremor

ETAware：持续监测特发性震颤的功能影响

• 批准号: 10819790
• 负责人: Rabie Fadil
• 金额: $ 28.95万
• 依托单位: GREAT LAKES NEUROTECHNOLOGIES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10819790
• 关键词: Activities of Daily Living; Affect; Algorithms; Alzheimer' s Disease; Caring; Clinic Visits; Clinical; Clinical Trials; Clinical assessments; Data; Data Collection; Decision Making; Deep Brain Stimulation; Development; Device Approval; Disease; Disease Management; Disparity population; Drug Approval Processes; Drug usage; Dystonia; Effectiveness; Effectiveness of Interventions; Essential Tremor; Evaluation; Fingers; Focused Ultrasound; Improve Access; Individual; Judgment; Limb Tremors; Living Wills; Lower Extremity; Magnetic Resonance Imaging; Measurement; Measures; Medical; Monitor; Monitoring Clinical Trials; Motion; Movement Disorders; Observational Study; Operative Surgical Procedures; Orthostatic Hypotension; Outcome; Outcome Measure; Output; Parkinson Disease; Participant; Patient Care; Patients; Performance; Persons; Pharmaceutical Preparations; Phase; Physical activity; Primidone; Process; Propranolol; Quality of life; Questionnaires; Refractory; Research; Research Personnel; Rest; Rural Population; Sensitivity and Specificity; Severities; Stroke; Symptoms; System; Technology; Therapeutic Intervention; Time; Titrations; Tremor; Underserved Population; Upper Extremity; Validation; Visual; Walking; Work; clinical decision-making; commercialization; cost; design; effectiveness evaluation; evidence base; geographic disparity; health care availability; improved; innovation; kinematics; machine learning model; mobile application; motion sensor; novel therapeutics; patient response; sensor; signal processing; success; symptomatic improvement; treatment optimization; wearable device

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.

总结