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
基本信息
- 批准号:10819790
- 负责人:
- 金额:$ 28.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Activities of Daily LivingAffectAlgorithmsAlzheimer&aposs DiseaseCaringClinic VisitsClinicalClinical TrialsClinical assessmentsDataData CollectionDecision MakingDeep Brain StimulationDevelopmentDevice ApprovalDiseaseDisease ManagementDisparity populationDrug Approval ProcessesDrug usageDystoniaEffectivenessEffectiveness of InterventionsEssential TremorEvaluationFingersFocused UltrasoundImprove AccessIndividualJudgmentLimb TremorsLiving WillsLower ExtremityMagnetic Resonance ImagingMeasurementMeasuresMedicalMonitorMonitoring Clinical TrialsMotionMovement DisordersObservational StudyOperative Surgical ProceduresOrthostatic HypotensionOutcomeOutcome MeasureOutputParkinson DiseaseParticipantPatient CarePatientsPerformancePersonsPharmaceutical PreparationsPhasePhysical activityPrimidoneProcessPropranololQuality of lifeQuestionnairesRefractoryResearchResearch PersonnelRestRural PopulationSensitivity and SpecificitySeveritiesStrokeSymptomsSystemTechnologyTherapeutic InterventionTimeTitrationsTremorUnderserved PopulationUpper ExtremityValidationVisualWalkingWorkclinical decision-makingcommercializationcostdesigneffectiveness evaluationevidence basegeographic disparityhealth care availabilityimprovedinnovationkinematicsmachine learning modelmobile applicationmotion sensornovel therapeuticspatient responsesensorsignal processingsuccesssymptomatic improvementtreatment optimizationwearable device
项目摘要
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
项目成果
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