Use of wearable sensors to improve the early diagnosis of DLB

使用可穿戴传感器改善 DLB 的早期诊断

基本信息

批准号：
10674272
负责人：
Debby Wen Tsuang
金额：
$ 70.73万
依托单位：
SEATTLE INST FOR BIOMEDICAL/CLINICAL RES
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10674272
关键词：
Address Adverse effects Aging Alzheimer&apos s Disease Antipsychotic Agents Behavior monitoring Behavioral Biotechnology Caregiver Burden Caring Clinical Clinical Research Clinical Trials Clinical assessments Cognition Cognitive Consensus Data Dementia with Lewy Bodies Diagnosis Discrimination Disease Disease Progression Early Diagnosis Ecological momentary assessment Engineering Exclusion Exposure to Future Impaired cognition Individual Infrastructure Intervention Lead Measurement Measures Medical Memory Methods Motor Movement Nature Patients Pharmaceutical Preparations Phase Polysomnography Population Research Infrastructure Research Personnel Risk Sleep Symptoms Syndrome Thinking Time Treatment Efficacy accurate diagnosis actigraphy amnestic mild cognitive impairment clinical care clinical diagnosis diagnostic criteria diagnostic value efficacy evaluation experience feasibility testing improved informant medical specialties mild cognitive impairment novel predictive modeling prevention clinical trial rate of change risk prediction sleep behavior wearable device wearable sensor technology

项目摘要

Project Summary/Abstract Dementia with Lewy bodies (DLB) is difficult to diagnose early in its disease course due to the overlap in initial symptoms with Alzheimer's disease (AD). Many individuals with DLB therefore experience long delays in receiving an accurate diagnosis. This lack of sensitivity in the consensus diagnosis for DLB, particularly outside of specialty-care centers, means that DLB is associated with delayed interventions and increased caregiver burden. We thus propose a two-phase study that investigates the utility of combining data from wearable sensors, ecological momentary assessments (EMAs), and traditional measures as a multidomain approach for the early diagnosis of DLB. To collect and analyze these integrated objective measurements, we will establish a research infrastructure that includes an interdisciplinary team of engineers, clinicians, researchers, and biotechnology companies. In the R21, we will estimate and compare the distributions of cognitive, motor, sleep, and behavioral monitoring profiles in subjects with probable DLB (n=20) and AD dementia (n=30). If the R21 demonstrates the feasibility of using wearable sensors and EMAs in this population and their ability to improve discrimination between DLB and AD, we will proceed to the next study phase. The R33 aims to characterize and compare the trajectories of these same traditional and novel cognitive, motor, sleep, and behavioral monitoring profiles in subjects with mild cognitive impairment (MCI) and one or more core DLB features (MCI-DLB; n=75) and in subjects with amnestic MCI and no core DLB features (MCI-AD; n=25). We hypothesize that a composite measure combining information from the baseline and trajectory measures in the longitudinal R33 will improve discrimination between individuals with MCI-DLB who will convert to DLB, AD, or remain MCI. We anticipate that the results of this study will have tangible benefits to researchers, clinicians, patients, and the caretakers of patients. The improved ability to differentiate early DLB from early AD will assist researchers in selecting appropriate subjects for clinical trials of AD and related disorders (ADRD; e.g., DLB). Moreover, because of the longitudinal nature of the R33, researchers and clinicians will have accessible data on disease progression, which can be tremendously helpful in evaluating the efficacy of treatment. Most importantly, by improving the diagnosis of early DLB, clinicians will be better equipped to avoid prescribing potentially harmful treatments (e.g., antipsychotics for DLB) and to more accurately tailor current or future interventions to patients earlier in their disease course at the time that such interventions are most likely to be effective.

项目摘要/摘要由于有Lewy身体（DLB）的痴呆症在疾病病程初期很难诊断与阿尔茨海默氏病（AD）的初始症状重叠。因此，许多DLB的人经历长时间的延误，可以接受准确的诊断。这种共识缺乏敏感性 DLB的诊断，特别是在专业护理中心之外的诊断，这意味着DLB与延迟干预措施和增加的护理人员负担。因此，我们提出了一项两阶段的研究研究结合可穿戴传感器数据的效用，生态瞬间评估（EMAS）和传统措施作为DLB早期诊断的多域方法。到收集和分析这些综合客观测量，我们将建立一项研究基础设施包括一个跨学科的工程师，临床医生，研究人员和生物技术公司。在R21中，我们将估算并比较认知，运动，睡眠和可能DLB（n = 20）和AD痴呆症（n = 30）的受试者中的行为监测曲线。如果是 R21证明了在该人群中使用可穿戴传感器和EMA及其的可行性提高DLB和AD歧视的能力，我们将进入下一个研究阶段。这 R33旨在表征和比较这些传统和新颖认知的轨迹，轻度认知障碍受试者（MCI）和一个或多个核心DLB功能（MCI-DLB; n = 75），并且在具有Amnestic MCI和无核心DLB的受试者中功能（MCI-AD; n = 25）。我们假设一项复合量衡量结合了来自纵向R33中的基线和轨迹度量将改善歧视具有MCI-DLB的个人将转换为DLB，AD或保留MCI。我们预计这项研究的结果将对研究人员，临床医生，患者和患者的看护人。提高了将早期DLB与早期广告区分开的能力将帮助研究人员选择适当的AD和相关疾病临床试验的受试者（ADRD;例如DLB）。而且，由于R33的纵向性质，研究人员和临床医生将获得有关疾病进展的可访问数据，这对评估治疗的功效。最重要的是，通过改善早期DLB的诊断，临床医生将有更好的能力避免开处方潜在的有害治疗方法（例如， DLB的抗精神病药）和更准确地针对患者的当前或将来的干预措施，在他们的疾病过程中，当时这种干预措施最有效。