We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




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

By HospiMedica International staff writers
Posted on 11 Sep 2024
Print article
Image: Wearable sleep trackers and AI could predict early signs of Alzheimer’s (Photo courtesy of 123RF)
Image: Wearable sleep trackers and AI could predict early signs of Alzheimer’s (Photo courtesy of 123RF)

Sleep disturbances are a common early indicator of Alzheimer’s disease, often occurring before cognitive symptoms become apparent. 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.”

Related Links:
University of Massachusetts Amherst

Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
Flocked Fiber Swabs
Puritan® patented HydraFlock®
New
Fixed X-Ray System (RAD)
Allengers 325 - 525
New
CT Detector
PURE INSIGHT

Print article

Channels

Surgical Techniques

view channel
Image: The PM2 System with ECGuide Connector (Photo courtesy of Piccolo Medical)

Innovative Catheter Guidance Technology Aims for Zero Malpositioning

Millions of catheters are still being inserted each year without the use of guidance, posing unnecessary risks to patients. Blind insertions require confirmation through chest X-ray, which increases hospital... Read more

Patient Care

view channel
Image: The portable, handheld BeamClean technology inactivates pathogens on commonly touched surfaces in seconds (Photo courtesy of Freestyle Partners)

First-Of-Its-Kind Portable Germicidal Light Technology Disinfects High-Touch Clinical Surfaces in Seconds

Reducing healthcare-acquired infections (HAIs) remains a pressing issue within global healthcare systems. In the United States alone, 1.7 million patients contract HAIs annually, leading to approximately... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
The Atellica VTLi Patient-side Immunoassay Analyzer, a high-sensitivity troponin I test at the bedside, delivers accurate results in just 8 minutes (Photo courtesy of Siemens Healthineers)

New 8-Minute Blood Test to Diagnose or Rule Out Heart Attack Shortens ED Stay

Emergency department overcrowding is a significant global issue that leads to increased mortality and morbidity, with chest pain being one of the most common reasons for hospital admissions.... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.