Combining information from multiple circadian activity rhythm metrics to optimally detect mild cognitive impairment using a consumer wearable

结合多个昼夜节律活动指标的信息，使用消费者可穿戴设备以最佳方式检测轻度认知障碍

• 批准号: 10478935
• 负责人: Stephen F Smagula
• 金额: $ 19.4万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-05 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478935
• 关键词: Accelerometer; Adult; Affect; Age; Aging; Algorithms; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Animals; Apple watch; Behavior Therapy; Behavioral; Big Data; Biological Assay; Biological Markers; Brain; Characteristics; Classification; Clinic; Clinical; Cognitive; Cohort Studies; Collaborations; Communities; Conflict (Psychology); Data; Dementia; Detection; Development; Diagnosis; Diagnostic; Disease; Early Diagnosis; Early Intervention; Elderly; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Fractals; Future; Goals; Health; Hispanic Community Health Study/Study of Latinos; Hour; Human Activities; Impaired cognition; Incidence; Individual; Intervention; Letters; Machine Learning; Measurement; Measures; Methods; Modeling; Monitor; Nature; Nerve Degeneration; Neuropsychology; Observational Study; Outcome; Participant; Pathogenesis; Pathology; Patients; Pattern; Persons; Phase; Population; Prevention; Prevention approach; Process; Regulation; Research; Research Personnel; Resources; Rest; Risk; Sampling; Science; Series; Signal Transduction; Sleep; Source; Structure; System; Testing; Time; Training; Translating; Translational Research; United States Dept. of Health and Human Services; Validation; Work; aged; archive data; archived data; base; circadian; cognitive function; cohort; dementia risk; diagnostic biomarker; experience; glymphatic clearance; high reward; high risk; improved; indexing; mild cognitive impairment; modifiable behavior; novel; pre-clinical; prediction algorithm; primary outcome; prototype; recruit; risk prediction; risk stratification; routine care; scale up; screening; statistical learning; systematic review; user-friendly; wearable device

Abstract: Widely-scalable methods for the earlier detection of elevated Alzheimer’s Disease and Related Dementia (ADRD) would enable earlier intervention and can help reduce/delay disease incidence. Consumer wearable technologies that passively gather “big data” signals could be leveraged to detect the early signs of elevated ADRD risk (see NOT-AG-20-017), in a relatively inexpensive and scalable fashion. One promising set of signals that can be captured by consumer wearable devices, but are currently only assessed in research settings, reflects the Circadian Activity Rhythm (CAR). Human activity follows a predictable 24-hour pattern known as the CAR. Various CAR characteristics are disrupted in ADRDs, reflect ADRD biomarkers levels (even in the pre-clinical stage), and predict future cognitive decline. However, observational studies have yet to conclusively demonstrate which CAR measure(s) best signal early-stage ADRD processes, and could help with early risk stratification. Previous studies have used subsets of the available CAR metrics to establish associations, rather than leveraging multiple metrics to improve ADRD risk prediction. We propose that using a comprehensive panel of CAR metrics could identify combinations of CAR metrics that are sensitive to ADRD risk. Furthermore, we propose that the translation of research findings into clinical screening has been difficult because CAR measurement relies on researcher-, rather than clinic-/user-, friendly systems. To fill these gaps, we propose leveraging consumer wearables, existing data, sleep/circadian science, and machine learning. Our overarching goal is to evaluate evidence for a path forward, from observing associations, towards clinically useful ADRD risk detection with consumer wearables. Our team includes experts in sleep/CAR-related health risks (Dr. Smagula, PI); neuropsychology and activity in aging (Dr. Gujral, co-I); and time series analytics/statistical learning (Dr. Krafty, co-I). We partnered with leaders of major cohorts (see letters of support) that provide the initial data. Aim 1 will compute a comprehensive panel of CAR measures in a sample of 766 adults aged 50+; then use machine learning to develop algorithms leveraging CAR measures to predict the likelihood of Mild Cognitive Impairment (MCI; a diagnostic marker of elevated ADRD risk). Aim 2 will use a new testing sample (n=25 with and n=25 without MCI) to validate if applying this algorithm to data from a consumer-wearable accurately detects MCI. Dr. Smagula already developed a working prototype measuring CARs using the Apple Watch called the Circadian Activity Profiling System. This R21 can have impact on the field of ADRD risk detection by producing: evidence regarding which CAR metrics best signal MCI; an initial algorithm that combines information regarding CARs to passively detect the likelihood of MCI; and by refining our system for collecting these signals on a popular consumer wearable (the Apple Watch). We will also develop collaborations with additional cohorts so that, if we find evidence supporting potential clinical utility of this approach, we will be prepared to develop a definitive algorithm in an R01 using data from multiple studies.

摘要：早期检测阿尔茨海默病及相关疾病的可广泛扩展的方法 痴呆症 (ADRD) 可以实现早期干预，并有助于减少/延迟消费者疾病的发生。 可以利用被动收集“大数据”信号的可穿戴技术来检测早期迹象 以一种相对便宜且可扩展的方式提高 ADRD 风险（参见 NOT-AG-20-017）。 消费者可穿戴设备可以捕获的信号，但目前仅在研究中进行评估 设置，反映了昼夜节律活动节律 (CAR)，人类活动遵循可预测的 24 小时模式。 ADRD 中的各种 CAR 特征被破坏，反映了 ADRD 生物标志物水平。 （即使在临床前阶段），并预测未来的认知能力下降但是，观察性研究尚未完成。 最终证明哪些 CAR 措施最能体现早期 ADRD 过程的信号，并且可以提供帮助 之前的研究使用了可用 CAR 指标的子集来建立早期风险分层。 我们建议使用多个指标来改进 ADRD 风险预测。 全面的 CAR 指标面板可以识别对 ADRD 敏感的 CAR 指标组合 此外，我们认为将研究结果转化为临床筛查一直很困难。 因为 CAR 测量依赖于研究人员友好的系统，而不是诊所/用户友好的系统来填补这些空白。 我们建议利用消费者可穿戴设备、现有数据、睡眠/昼夜节律科学和机器学习。 总体目标是评估从观察关联到临床应用的前进道路的证据 通过消费者可穿戴设备进行有用的 ADRD 风险检测 我们的团队包括睡眠/CAR 相关健康方面的专家。 风险（Smagula 博士，PI）；衰老过程中的神经心理学和活动（Gujral 博士，co-I）； 分析/统计学习（Krafty 博士，co-I）我们与主要群体的领导者合作（参见信件）。 支持）提供初始数据，目标 1 将计算样本中的全面 CAR 测量值。 766 名 50 岁以上的老年人；然后利用机器学习开发算法，利用 CAR 措施进行预测 轻度认知障碍（MCI；ADRD 风险升高的诊断标志物）的可能性将使用 a。 新的测试样本（n=25，带 MCI，n=25 不带 MCI）来验证是否将此算法应用于来自 消费者可穿戴设备可准确检测 MCI。Smagula 博士已经开发出一种测量工作原型。 使用 Apple Watch 的 CAR 称为昼夜节律活动分析系统，该 R21 可能会对身体产生影响。 ADRD 风险检测领域，通过提供以下证据：有关哪些 CAR 指标最能表明 MCI 的信号； 结合有关 CAR 的信息来被动检测 MCI 可能性的算法； 我们还将在流行的消费者可穿戴设备（Apple Watch）上收集这些信号。 与其他队列开展合作，以便，如果我们发现支持潜在临床效用的证据 通过这种方法，我们将准备使用多项研究的数据在 R01 中开发明确的算法。

项目成果

Association of 24-Hour Activity Pattern Phenotypes With Depression Symptoms and Cognitive Performance in Aging.

24 小时活动模式表型与抑郁症状和衰老认知表现的关联。

• 发表时间: 2022-10-01
• 影响因子: 25.8
• 作者: Smagula, Stephen F;Zhang, Gehui;Gujral, Swathi;Covassin, Naima;Li, Jingen;Taylor, Warren D;Reynolds 3rd, Charles F;Krafty, Robert T
• 通讯作者: Krafty, Robert T