Abstract
Heart failure (HF) involves cycles of remission and exacerbation, which are poorly characterized by static disease measures. Consumer wearables have an understudied potential for daily monitoring of HF symptoms. Here we report results from an observational cohort of free-living patients over a median of 94.5 d with HF in the Ted Rogers Understanding Exacerbations of HF (TRUE-HF) study. The study measured the ability of Apple Watch data to predict peak oxygen uptake (pVO(2)) as measured using in-clinic cardiopulmonary exercise testing (CPET). A deep learning model was trained with data from 154 patients (46 women, 108 men) and validated on a held-out set of 63 patients (24 women, 39 men) for determining wearable-derived daily pVO(2), which correlated strongly with CPET-measured pVO(2) (Pearson's correlation = 0.85). Each 10% drop in wearable-derived daily pVO(2) was associated with a 3.62-fold increased hazard ratio (HR) for unplanned healthcare events (95% confidence interval (CI), 1.37-9.55; P < 0.01), which occurred at a median of 7.4 d after the first 10% drop in wearable-derived pVO(2). These findings were externally validated in an independent external cohort from the All of Us Research Program using a crossplatform model that accounted for the reduced-sensor capacities available in this external cohort. Using this reduced-sensor variant of the model, drops in wearable-derived daily pVO(2) were associated with unplanned healthcare utilization (HR 1.32, 95% CI 1.03-1.69; P = 0.03), which occurred at a median of 21 d after the first 10% drop in wearable-derived pVO(2). These results indicate that wearable-derived daily pVO(2) provides earlier and improved risk discrimination compared with existing wearable fitness estimates and established clinical markers and offers a scalable and generalizable approach for longitudinal HF research and monitoring.