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

点击翻译按钮获取中文摘要

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.