Source Google Research · Published · Open the original ↗
Lab notes
Using wearables to enable cardiovascular-kidney-metabolic (CKM) risk profiling for personalized coaching
Health & Bioscience
We introduce an investigational machine learning framework that translates passive wearable signals into a modifiable coachable health age, uses an action-conditioned world model to simulate personalized 12-week "what-if" habit trajectories, and expands to routine blood biomarkers to track early cardiovascular-kidney-metabolic (CKM) health.
Heart disease, kidney conditions, and metabolic disorders like type 2 diabetes aren't isolated, they form an interconnected web known as cardiovascular-kidney-metabolic (CKM) syndrome . CKM progression often begins silently years before symptoms appear, shaped by everyday habits like movement, sleep, and cardiovascular activity. Catching these subtle shifts early offers the best chance to reverse the trend . Yet while smartwatches easily track what we did yesterday, moving from passive tracking to meaningful action is much harder: How do my daily habits affect my CKM right now? And if I sleep 30 minutes longer or add a brisk walk to my routine, how much will my health improve over the next three months?
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