Wearables 3.0: Continuous Monitoring Meets Predictive Cardiovascular Risk

Wearable health technology is evolving beyond fitness tracking and reactive alerts into clinical-grade tools that continuously assess cardiovascular risk and predict adverse events before they occur. The latest generation of wearables combines sophisticated sensors, behavioral analytics, and AI models to move from simple AFib detection to complex risk stratification - identifying heart failure decompensation 48 hours in advance, flagging stroke risk through subtle pattern changes, and informing real-time treatment optimization. The challenge lies in determining which signals truly matter, how to validate predictive models across diverse populations, and how clinicians can act on continuous data without drowning in alerts.

This meeting explores how health systems, digital health companies, and pharma are operationalizing cardiovascular wearables that generate actionable clinical insights rather than just interesting data streams. Experts will examine which biometric signals prove most predictive, what AI approaches are delivering validated risk models, and how organizations are designing workflows that enable clinicians to intervene based on wearable-derived predictions.

Join us to discuss:

• What biometric signals and AI models are proving most effective for predicting cardiovascular events in real-world deployments?

• How are health systems integrating continuous wearable data into clinical workflows without creating alert fatigue or overwhelming care teams?

• What validation standards, reimbursement models, and care delivery changes are necessary to scale predictive cardiovascular wearables beyond pilot programs?