A 10% drop in wearable-derived daily peak oxygen uptake (pVO2) was associated with a 3.62-fold increased risk of unplanned healthcare utilization in patients with heart failure.
Observational (n=217)
Can a smartwatch-based deep learning model accurately predict daily peak oxygen uptake (pVO2) and identify the risk of unplanned healthcare events in patients with heart failure?
A smartwatch-derived deep learning model can accurately estimate daily peak oxygen uptake and predict near-term heart failure exacerbations, offering a scalable tool for remote monitoring.
Effect estimate: HR 3.62 (95% CI 1.37-9.55)
Absolute Event Rate: 26.9% vs 3.1%
p-value: p=<0.01
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 (pVO2) 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 pVO2, which correlated strongly with CPET-measured pVO2 (Pearson's correlation = 0.85). Each 10% drop in wearable-derived daily pVO2 was associated with a 3.62-fold increased hazard ratio (HR) for unplanned healthcare events (95% confidence interval (CI), 1.37-9.55; P 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 pVO2 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 pVO2. These results indicate that wearable-derived daily pVO2 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.
“For patients with heart failure, periods of stability are often interspersed with flare-ups of symptoms such as shortness of breath or fatigue. These episodes may require medical attention to prevent hospitalization and improve quality of life. However, risk assessments for heart failure patients often rely on scheduled clinical visits or evaluation tools that take measurements at only one point in time. They don't account for the changing, episodic nature of heart failure.”
Gao et al. (2026) conducted an observational in Heart failure (n=217). TRUE-HF wearable-derived daily pVO2 monitoring vs. No drop in wearable-derived daily pVO2 was evaluated on Unplanned healthcare utilization (HR 3.62, 95% CI 1.37-9.55, p=<0.01). A 10% drop in wearable-derived daily peak oxygen uptake (pVO2) was associated with a 3.62-fold increased risk of unplanned healthcare utilization in patients with heart failure.