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

翻译后摘要：广泛可扩展的方法，早期检测升高阿尔茨海默病和相关的 老年痴呆症（ADRD）将使早期干预成为可能，并有助于减少/延迟疾病的发生。消费者 被动收集“大数据”信号的可穿戴技术可以用来检测早期迹象， ADRD风险升高（参见NOT-AG-20-017），以相对廉价和可扩展的方式。一个有希望的集合 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 生理活动节律（Circadian Activity Rhythm，CAR）人类活动遵循一个可预测的24小时模式 被称为汽车。各种CAR特征在ADRD中被破坏，反映ADRD生物标志物水平 (even在临床前阶段），并预测未来的认知能力下降。然而，观察性研究还没有 结论性地证明哪种CAR措施最能反映早期ADRD过程，并有助于 早期风险分层。以前的研究已经使用了可用的CAR指标的子集来建立 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 一个全面的CAR指标小组可以识别对ADRD敏感的CAR指标组合 风险此外，我们认为，将研究结果转化为临床筛选一直是困难的 因为CAR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/昼夜节律科学和机器学习。我们 总体目标是评估证据，从观察关联到临床 有用的ADRD风险检测与消费者可穿戴设备。我们的团队包括睡眠/CAR相关健康专家 风险（Smagula博士，PI）;神经心理学和衰老活动（Gujral博士，co-I）;以及时间序列 分析/统计学习（Krafty博士，co-I）。我们与主要群体的领导人合作（见 支持），提供初始数据。目标1将计算样本中全面的CAR指标 766名50岁以上的成年人;然后使用机器学习开发利用CAR指标预测 轻度认知障碍（MCI; ADRD风险升高的诊断标志物）的可能性。Aim 2将使用 新的测试样本（n=25，有MCI，n=25，无MCI），以确认是否将该算法应用于来自 消费者可穿戴设备准确地检测MCI。斯马古拉博士已经开发了一个工作原型， 使用Apple Watch的汽车称为昼夜活动分析系统。R21可能会影响 ADRD风险检测领域通过产生：关于哪些CAR指标最好地信号MCI的证据;初始 结合关于汽车的信息以被动检测MCI的可能性的算法;以及通过改进 我们的系统用于在流行的消费者可穿戴设备（Apple Watch）上收集这些信号。我们还将 发展与其他队列的合作，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R 01中开发一种确定的算法。

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.