Inside WHOOP's Wearables AI Engine for Predictive Health

Discover how WHOOP is building an AI-powered health data infrastructure that is redefining how we understand human health. Emily Capodilupo, Senior Vice President of Research, Algorithms, and Data at WHOOP, explains how continuous physiological data is uncovering new opportunities in predictive health through AI, from presymptomatic disease detection to biological age scoring. She examines the governance challenges of deploying AI in a regulated environment and what it takes to build the data trust required to make it work at scale. Key Moments: How WHOOP Built Its AI and Data Foundation (00:57): Emily explains how WHOOP's early focus on elite athlete performance shaped the data collection rigor and multidisciplinary science organization that now powers its predictive health capabilities. She outlines the model she built across AI, machine learning, clinical research, and digital signal processing, and why starting with the highest-demand use case created a data foundation built to scale. The Power of Continuous Data (06:21): Emily draws on WHOOP's sleep research to show how continuous physiological data reveals patterns that would be invisible without longitudinal tracking. She shares findings linking sleep architecture to metabolic disease, cancer risk, and cognitive decline, illustrating why the depth and continuity of a data set determine what insights are actually possible. The Data Governance Challenge of Acting on Sensitive Data (13:17): Emily shares how WHOOP's respiratory rate data could detect COVID infection up to three days before symptom onset in over 80% of cases, but a denied FDA application left the company holding actionable insights it was legally prohibited from sharing. She examines the governance tension that emerges when your data capabilities move faster than the regulatory frameworks designed to govern them. Turning Complex Multi-Signal Data Into a Single Actionable Metric (27:32): Emily introduces WHOOP's Healthspan feature, which translates physiological and behavioral data across nine components into a single biological age score tied to all-cause mortality risk. She explains why distilling complex data into one number is more motivating than presenting raw risk statistics, pointing to research that shows how age-based framing drives stronger behavior change. Building Data Trust and Privacy Infrastructure at Scale (31:40): As WHOOP moves into FDA-cleared products and more sensitive data collection, Emily outlines the governance principles that underpin member trust. She argues that for any organization building on sensitive personal data, the asymmetry between earning trust and losing it should be a foundational design constraint. Key Quotes: "It takes 13 years to earn the trust and one mistake to lose it. And that kind of asymmetry is constantly top of mind." - Emily Capodilupo "We were able to show that we could detect COVID up to three days before symptom onset in over 80% of cases." - Emily Capodilupo “ WHOOP has been collecting data [for] over 12 years. We're working on a lot of new types of algorithms that are able to help people understand their bodies in ways that we might not have appreciated…even just a couple years ago.” - Emily Capodilupo "One of the ways that AI has advanced the product... is this ability to chat with WHOOP in natural language, the way you might chat to a doctor or a trainer or a coach." - Emily Capodilupo Mentions Harvard Study | Analyzing changes in respiratory rate to predict the risk of COVID-19 infection  Cornell Study Uses WHOOP Sleep Data to Monitor Patients at Risk for Alzheimer’s Can Data Help Us Sleep Better? | WHOOP There's More to Sleep than Sleep Need: The Importance of Sleep Consistency | WHOOP Cribsheet & Expecting Better 2 Books Collection Set By Emily Oster  The Family Firm: A Data-Driven Guide to Better Decision Making in the Early School Years By Emily Oster  Guest Bio </p