Harnessing Wearable Technology: Advanced Statistical and AI Methods for Enhancing Clinical Outcomes in Cardiovascular Trials

Music City Center 

As the global population ages, the need for effective treatment strategies for chronic diseases, particularly cardiovascular conditions, becomes increasingly critical. This presentation explores the innovative integration of wearable technology and biosensor data into clinical trials, focusing on their potential to enhance patient monitoring and outcome prediction. We discuss a series of analytical approaches applied to accelerometry data collected from wearable devices alongside the assessment of clinical outcomes. These methodologies enable the identification of unique clinical phenotypes within patient groups and improve predictive accuracy for clinical outcomes in heart failure. By leveraging high-resolution data from wearables, researchers can gain deeper insights into patients' physical activity patterns, sedentary behavior, and sleep, facilitating a more nuanced understanding of their relationship with clinical outcomes. Ultimately, the findings from this work underscore the importance of embracing novel technologies and advanced statistical methods to identify heterogeneous patient subgroups, thereby informing and guiding clinical development programs for improved patient care.