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.

项目概要/摘要路易体痴呆 (DLB) 在病程早期很难诊断，因为初始症状与阿尔茨海默病（AD）有重叠。因此，许多患有 DLB 的人在获得准确诊断方面经历了长时间的延误。共识缺乏敏感性 DLB 的诊断，尤其是在专科护理中心之外的诊断，意味着 DLB 与延迟干预并增加护理人员的负担。因此，我们提出一项两阶段研究研究结合可穿戴传感器数据、生态瞬时评估的效用（EMA）和传统措施作为 DLB 早期诊断的多领域方法。到收集并分析这些综合客观测量结果，我们将建立一个研究基础设施，包括工程师、临床医生、研究人员和跨学科团队生物技术公司。在 R21 中，我们将估计并比较认知、运动、睡眠和可能患有 DLB (n=20) 和 AD 痴呆症 (n=30) 的受试者的行为监测概况。如果 R21 展示了在这一人群中使用可穿戴传感器和 EMA 的可行性及其效果为了提高DLB和AD之间的区分能力，我们将进入下一个研究阶段。这 R33 旨在描述和比较这些相同的传统和新颖认知的轨迹，轻度认知障碍 (MCI) 受试者的运动、睡眠和行为监测概况一项或多项核心 DLB 特征（MCI-DLB；n=75）以及遗忘性 MCI 且无核心 DLB 的受试者特征（MCI-AD；n=25）。我们假设综合措施结合了来自纵向 R33 中的基线和轨迹测量将改善之间的区分患有 MCI-DLB 的个人将转换为 DLB、AD 或保持 MCI。我们预计这项研究的结果将为研究人员、临床医生、患者以及患者的护理人员。区分早期 DLB 和早期 AD 的能力得到提高将协助研究人员选择合适的受试者进行 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，这对于治疗疾病非常有帮助评估治疗效果。最重要的是，通过改善早期 DLB 的诊断，临床医生将能够更好地避免开出潜在有害的治疗方法（例如， DLB 的抗精神病药物），并更早地更准确地为患者制定当前或未来的干预措施在他们的病程中，此类干预措施最有可能有效。