Wearable Sleep Trackers Could Predict Blood Biomarkers of Alzheimer’s Disease in At-Risk Individuals

Traditional sleep assessments, while accurate, are costly and typically capture data from just one night. In an effort to improve early diagnosis, a new five-year study will now explore whether wearable sleep trackers can predict Alzheimer's-related blood biomarkers in individuals at risk.

Researchers at the University of Massachusetts Amherst (Amherst, MA, USA) will investigate whether sleep data collected from wearables can identify patterns linked to future cognitive decline, as indicated by specific blood biomarkers. While wearable devices are not a replacement for clinical diagnostic tools for Alzheimer’s or cognitive changes, they could serve as an early detection tool to identify individuals at risk. The study will focus on participants who have a genetic predisposition to Alzheimer’s disease but are not yet showing cognitive impairment. Participants will wear three types of sleep trackers over the course of a week: the Apple Watch, the Oura Ring, and the CGX Patch, a forehead-worn electroencephalogram (EEG) device that tracks brain activity through metal electrodes.

The sleep data collected will be compared to blood tests for amyloid and tau proteins—key early biomarkers of Alzheimer’s. The assessment will be repeated two years later to track any changes. Although blood tests for Alzheimer's are becoming more reliable, identifying which individuals should undergo these tests and see a neurologist remains a challenge. Wearable devices could bridge this diagnostic gap, facilitating early detection of Alzheimer’s disease.

“Many people already wear smartwatches to sleep these days. Imagine receiving an alert from your smartwatch advising you to see a neurologist. That could be the direction we are headed,” said Joyita Dutta, professor of biomedical engineering at the University of Massachusetts Amherst, who will conduct the study. “The project will enable the integration of a wealth of new data — genetic information, wearables-derived metrics, and blood-based biomarkers to create a more comprehensive picture of the sleep-dementia axis.”

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University of Massachusetts Amherst