Wearable Sleep Rings Accurately Estimate Vascular Age to Predict Cardiovascular Disease Risks

NUS researchers use wearable ring signals to predict vascular age with clinical accuracy, offering a new way to monitor heart health passively.

By: AXL Media

Published: Apr 8, 2026, 11:20 AM EDT

Source: Information for this report was sourced from EurekAlert

Passive Monitoring Redefines Cardiovascular Diagnostics

A team of scientists from the NUS Yong Loo Lin School of Medicine has successfully utilized passive nighttime pulse recordings to assess heart health, moving complex diagnostics from the clinic to the bedroom. By analyzing photoplethysmography signals from the Oura Ring, a popular consumer sleep tracker, researchers can now estimate vascular age, a metric that indicates how well a person's arteries are aging compared to their chronological years. According to Dr. Gizem Yilmaz, a research fellow and co-first author, these signals collected during sleep offer clinically meaningful insights that allow for scalable and longitudinal monitoring of vascular health.

Bridging the Gap Between Chronological and Biological Age

The discrepancy between a person's actual age and the health of their arteries is a critical predictor of cardiovascular disease, yet measuring this gap has traditionally required expensive, specialized equipment. The study notes that a person in their 50s might possess the arterial profile of a 70-year-old, a condition that significantly elevates the risk of cardiac events. By shifting this assessment to wearable devices, the research aims to make vital health data accessible to the general public, bypassing the high costs and logistical hurdles associated with professional laboratory screenings.

Deep Learning Precision and Clinical Validation

To ensure the reliability of the data, the researchers employed a deep learning model to process the pulse waveforms captured by the finger-worn sensor. Despite the hardware differences between consumer rings and professional fingertip sensors used in hospitals, the model predicted vascular age with a mean error of only six to seven years. The study highlighted that these ring-derived estimates showed a strong correlation with blood pressure, which remains a standard benchmark for cardiovascular wellness. This high degree of accuracy suggests that consumer grade hardware is now capable of performing sophisticated medical analysis when paired with advanced algorithms.