The heart's response to physical activity is a vital early indicator of changes in health, particularly in cardiovascular function and mortality. Extensive research has demonstrated a connection between abnormal heart-rate recovery (HRR) and several cardiovascular diseases, including heart failure, coronary artery disease, diabetes, hypertension, and sudden cardiac death. The amount of time it takes for the heart to return to its baseline rhythm after exercise can serve as a predictor for a range of cardiovascular or metabolic disorders. However, measuring HRR traditionally involves a complex process that requires the presence of a cardiologist, a treadmill, and other costly equipment and personnel. In a new study, scientists have utilized a “smart shirt” fitted with an electrocardiogram to track HRR after exercise and developed a tool to analyze the data, predicting the risk of heart-related ailments.

Researchers at the University of Illinois Urbana-Champaign (Champaign, IL, USA) set out to create a more accessible method for assessing and predicting cardiovascular risk. If a wearable device could collect relevant data as a person goes about their daily activities and send that information to a lab or doctor for analysis, it could make early diagnoses more accessible to a larger population. To accomplish this, the researchers leveraged a smart shirt developed by Carre Technologies (Quebec, Canada). The shirt contains sensors that continuously measure heart performance, including tracking heart-rate variability and electrical activity. The study involved 38 participants, aged between 20 and 76, who walked on a treadmill with varying speeds and inclines while wearing the device. The research was conducted in Illinois during the COVID-19 lockdown in 2021.

The researchers applied machine learning and other techniques to extract meaningful cardiac health signals from the collected data, designing a system to predict individuals at higher or lower risk of cardiovascular diseases. The team set a threshold of 28 beats per minute for median heart-rate recovery, classifying participants into high-risk and low-risk groups. Additional statistical measures were used to cross-verify the results. The study’s findings, published in the IEEE Journal of Health Informatics, show that the algorithm developed for the research provided reasonably accurate results, despite the small sample size, by aligning with different traditional classifiers and cross-validation approaches.

This study marks a significant first step in using wearable devices to help individuals assess their risk for heart-related issues more easily, potentially identifying concerning trends before they progress into serious disorders or lead to sudden death. The team envisions a system where data from wearables is collected and transmitted to a doctor’s office for interpretation. This would be particularly beneficial for those living in rural areas or places with limited access to advanced healthcare facilities. Future studies using wearable technology to predict cardiovascular risk should aim to involve a larger sample size, track participants over time, and compare their heart activity during both exercise and rest. Moreover, further research should focus on integrating this technology into standard healthcare practices.

“We want to use it to provide us with some greater insight in terms of our underlying cardiovascular function,” said Manuel Hernandez, a professor of biomedical and translational sciences at the Carle Illinois College of Medicine who led the research. “And we want to make something that’s clinically actionable.” 

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