Observational Study to Validate VO2Max Estimation Using Wearable Biosensors in Adults Undergoing Standard Cardiopulmonary Exercise Tests

What this Trial Is About

Researchers are studying the accuracy of estimating VO2Max and other cardiopulmonary exercise test CPET measures using wearable biosensors that monitor daily activities. The study aims to develop and validate a machine learning algorithm that uses data from motion sensors and EKGs. A total of 1000 participants will enroll, including 100 healthy adults aged 18 to 80 and 900 participants undergoing CPET as part of standard care. Participants will be observed in two groups healthy individuals who undergo CPET and those receiving CPET during routine care. Data collected from these tests and wearable biosensors will feed into the algorithm development and validation process. There are no investigational treatments, as this is a non-interventional study focusing on data collection and algorithm evaluation. During the study, participants will undergo CPET assessments, and their biosensor data will be collected during daily activities. Researchers will monitor the performance of the machine learning algorithm by comparing its estimates to standard CPET results. The primary outcome is to gather data for algorithm development and validation by March 2025. Participants involvement includes consenting, following protocol procedures, and being available for the study duration.

Observational, Non-Interventional Study Supporting Validation of VO2Max Estimation Methods Using Results in Patients Receiving Standard of Care Cardiopulmonary Exercise Tests (CPET)