Health Electronic Assessment of Risks and Trends Using Biometric Equipment and Technology

The central hypothesis in the HEARTBEAT project posits that cardiovascular events are preceded by abnormalities in biometric signals obtained from a smartwatch. These abnormalities may include, but are not limited to, patterns of heart rate, heart rate variability, oxygen saturation, as well as raw PPG signals. By leveraging advanced analytics and machine learning algorithms on smartwatch-derived biometric data, the investigators plan to identify patterns and markers that signal an increased cardiovascular disease risk. The study will use an end-to-end decentralized approach for data collection. Huma and Alcedis, the electronic data capture (EDC) agencies, will act as the data storage facilitator for 10,000 eligible subjects. The Principal Investigators (PIs) and trained site staff members will assess healthy subjects based on their clinical opinion. If the subjects meet the eligibility criteria and are interested in participating, they will be consented by Tulane. During the study enrollment phase, a smartwatch will be provided to the subjects, who will then be trained on the study procedures, the web-based questionnaire, and the use of the wearable device. The subjects will be asked to wear the device for one year, after which they will return it to Tulane.