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：较早发现阿尔茨海默氏病及相关的较早检测的可估计方法 痴呆症（ADRD）将进行较早的干预措施，并有助于减少/延迟疾病事件。消费者 可以利用被动地收集“大数据”信号的可穿戴技术来检测早期迹象 以相对廉价且可扩展的方式提高了ADRD风险（请参阅非AG-20-017）。一个承诺设置 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，反映了昼夜节律节奏（CAR）。人类活动遵循可预测的24小时模式 被称为汽车。各种汽车特征在ADRD中被破坏，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 结论说明哪种汽车测量最佳信号的早期ADRD过程，可以帮助 与早期风险分层。先前的研究使用了可用汽车指标的子集建立 关联，而不是利用多个指标来改善ADRD风险预测。我们建议使用 全面的汽车指标面板可以确定对ADRD敏感的汽车指标的组合 风险。此外，我们建议将研究发现转化为临床筛查很困难 因为汽车测量依赖于研究人员 - 而不是诊所/用户，友好的系统。为了填补这些空白， 我们建议利用消费者可穿戴设备，现有数据，睡眠/昼夜节律和机器学习。我们的 总体目标是评估从观察者协会到临床的前进道路的证据 使用消费者可穿戴设备的有用的ADRD风险检测。我们的团队包括睡眠/与汽车有关的健康专家 风险（Smagura博士，PI）；衰老中的神经心理学和活动（Gujral博士，Co-I）；和时间序列 分析/统计学习（Krafty博士，Co-I）。我们与主要队列的领导人合作（请参阅 支持）提供初始数据。 AIM 1将计算样本中的全面汽车措施 在766名50岁以上的成年人中；然后使用机器学习来开发利用汽车措施预测的算法 轻度认知障碍的可能性（MCI； ADRD风险升高的诊断标记）。 AIM 2将使用 新的测试样品（n = 25，n = 25，无MCI），以验证该算法对来自数据的数据。 消费者磨牙准确地检测到MCI。 Smagura博士已经开发了一个工作原型测量 使用Apple Watch的汽车称为昼夜节活动分析系统。这个R21可能会影响 通过产生ADRD风险检测的领域：关于哪种汽车指标最佳信号MCI的证据；最初 结合有关汽车的信息以被动检测MCI的可能性的算法；并通过完善 我们的系统用于在受欢迎的消费者可穿戴设备上收集这些信号（Apple Watch）。我们也会 与其他队列开发合作，以便，如果我们找到支持潜在临床实用性的证据 这种方法，我们将准备使用来自多个研究的数据在R01中开发确定的算法。

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。

• DOI: 10.1001/jamapsychiatry.2022.2573
• 发表时间: 2022
• 期刊: JAMA psychiatry
• 影响因子: 25.8
• 作者: Smagula,StephenF;Zhang,Gehui;Gujral,Swathi;Covassin,Naima;Li,Jingen;Taylor,WarrenD;Reynolds3rd,CharlesF;Krafty,RobertT
• 通讯作者: Krafty,RobertT