Can fitness trackers flag cardiovascular disease?

Wearable health technology tracks more than just your steps these days.

Researchers at The University of Texas at Arlington are launching a two-year study to explore whether data from everyday fitness trackers can help predict a person’s risk of developing cardiovascular disease. Backed by a $400,000 grant from the Texas Higher Education Coordinating Board, the study officially started on Aug. 1 and will use commercially available wearable devices to monitor physical activity, sleep and blood pressure.

Yue Liao, UTA assistant professor of kinesiology, is leading the study with co-investigators Christine Spadola, an assistant professor of social work; Souvik Roy, an associate professor of mathematics; and Matthew Brothers, a professor of kinesiology.

“We’re focusing on physical activity, sleep and blood pressure—gathering all that data to see whether we can use advanced mathematical modeling to predict a person’s cardiovascular disease risk or vascular dysfunction,” Dr. Liao said. “We want to see if we can detect early signs of cardiovascular disease in order to intervene earlier with lifestyle changes.”

A key focus of the study is sleep—an often overlooked factor in cardiovascular health. Researchers aim to analyze how sleep affects energy levels, physical activity and overnight organ function, using those data insights to deepen understanding of cardiovascular risk.

“We’re not just measuring sleep stages or duration,” Liao said, “but also continuous markers like heart rate and blood pressure during sleep to detect health risks.”

Added Dr. Spadola: “I look forward to interpreting the sleep data in ways that meaningfully reflect participants’ lived experiences.”

Among the goals of the study is to develop a machine-learning model that uses data from wearable sensors to go beyond basic fitness tracking.

“Typical diagnosis of cardiovascular disease happens after symptoms appear—often when it’s already too late,” Liao said. “But now, we have wearable sensors that can continuously monitor daily activities like sleep and blood pressure. We want to use that data to detect early trends or signals of dysfunction.”

Liao emphasized that the study relies on commercially available devices, making the approach more accessible to the general public than many traditional research tools. Additionally, because wearable devices can capture a wide range of vascular health data, participants may not need to undergo complicated lab-based assessments.

“The goal is to eventually detect vascular dysfunction using only wearable data,” she said.