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

• 批准号: 10017135
• 负责人: Debby Wen Tsuang
• 金额: $ 19.02万
• 依托单位: SEATTLE INST FOR BIOMEDICAL/CLINICAL RES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10017135
• 关键词: Address; Adverse effects; Aging; Alzheimer' s Disease; Antipsychotic Agents; Behavior monitoring; Behavioral; Biotechnology; Caregiver Burden; Caring; Clinical; Clinical Research; Clinical Trials; Clinical assessments; Cognition; Cognitive; Consensus; Data; Diagnosis; Diagnostic; Discrimination; Disease; Disease Progression; Early Diagnosis; Ecological momentary assessment; Engineering; Exclusion; Exposure to; Future; Impaired cognition; Individual; Infrastructure; Intervention; Lead; Lewy Body Dementia; Measurement; Measures; Medical; Memory; Methods; Motor; Movement; Nature; Patients; Pharmaceutical Preparations; Phase; Polysomnography; Population; Research Infrastructure; Research Personnel; Risk; Sleep; Symptoms; Syndrome; Testing; Thinking; Time; Treatment Efficacy; accurate diagnosis; actigraphy; amnestic mild cognitive impairment; clinical Diagnosis; clinical care; experience; improved; informant; medical specialties; mild cognitive impairment; novel; predictive modeling; prevention clinical trial; rate of change; sleep behavior; wearable device; wearable sensor technology

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.

项目总结/摘要 路易体痴呆（DLB）在其病程早期难以诊断，这是由于 与阿尔茨海默病（AD）的初始症状重叠。因此，许多DLB患者 在接受准确诊断方面经历了长时间的延迟。共识中缺乏敏感性 DLB的诊断，特别是在特殊护理中心之外，意味着DLB与 延迟干预和增加照顾者的负担。因此，我们提出了一个两阶段的研究， 研究了结合可穿戴传感器数据的实用性，生态瞬时评估 （EMA）和传统的措施作为DLB早期诊断的多领域方法。到 收集和分析这些综合客观的测量，我们将建立一个研究 基础设施，包括工程师，临床医生，研究人员和 生物技术公司。 在R21中，我们将估计和比较认知，运动，睡眠和 在可能患有DLB（n=20）和AD痴呆（n=30）的受试者中的行为监测概况。如果 R21证明了在这一人群中使用可穿戴传感器和EMA的可行性， 为了提高DLB和AD之间的区分能力，我们将进行下一个研究阶段。的 R33旨在描述和比较这些相同的传统和新颖的认知， 轻度认知障碍（MCI）受试者的运动、睡眠和行为监测概况， 一个或多个核心DLB特征（MCI-DLB; n=75）和遗忘型MCI且无核心DLB的受试者 特征（MCI-AD; n=25）。我们假设，一个综合措施，结合信息，从 纵向R33中的基线和轨迹测量将提高区分 患有MCI-DLB的人将转换为DLB，AD或保持MCI。 我们预计这项研究的结果将对研究人员、临床医生、 病人和病人的护理人员。区分早期DLB与早期AD的能力提高 将帮助研究人员选择适当的受试者进行AD和相关疾病的临床试验 (ADRD例如，DLB）。此外，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数据，这对 评估治疗效果。最重要的是，通过改善早期DLB的诊断， 临床医生将更好地装备以避免开出潜在有害的治疗处方（例如， DLB的抗精神病药物），并更准确地为患者定制当前或未来的干预措施， 在他们的疾病过程中，在这种干预措施最有可能有效的时候。