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

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $194,039

## Abstract

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...

## Key facts

- **NIH application ID:** 10478935
- **Project number:** 5R21AG074094-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Stephen F Smagula
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $194,039
- **Award type:** 5
- **Project period:** 2021-09-05 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10478935

## Citation

> US National Institutes of Health, RePORTER application 10478935, Combining information from multiple circadian activity rhythm metrics to optimally detect mild cognitive impairment using a consumer wearable (5R21AG074094-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10478935. Licensed CC0.

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