Apple-sponsored research paper, which relies on data from the Heart and Movement Study, explains how behavior data can often serve as a more significant health indicator relative to conventional biometric data obtained through hardware sensors. The study says that physical activity, cardiovascular fitness, and mobility metrics are especially useful for detecting transient and static health states. With that information in mind, the researchers created what they call a WBM, or wearable health behavior foundation model. It was trained on “behavioral data from wearables, using 162K participants with over 15 billion hourly measurements from the Apple Heart and Movement Study.” In short, the WBM uses patterns derived from raw sensor data to predict a person’s health state, and the study suggests this outperforms traditional detection methods that rely on data streams from sensors. The research paper also says the WBM was tested on 57 health-related tasks, and that it outperformed a traditional PPG (photoplethysmograph) model in most situations. Specifically, WBM outperforms PPG in predicting static health states such as beta blocker use, as it more reliably detects heart rate reductions during the day. It also outperformed PPG in predicting transient health states such as pregnancy, though it was unable to predict diabetes better than PPG. “Low-level sensor data outperforms behavioral data in tasks where physiological information is sufficient,” the study says. As for what all of this means in practice, Apple could adopt this type of hybrid approach as a way of building upon its existing health-related technology. In other words, using a WBM-like model alongside the existing Apple Watch PPG or ECG (electrocardiogram) sensors.