Wearable-derived cardiovascular health age and its way of life correlates in 442 adults

Abstract

Cardiovascular age is a robust risk-communication software that interprets complicated physiological information into an intuitive quantity, but conventional estimates require medical testing. Consumer wearables now estimate cardiorespiratory health age from photoplethysmography-derived coronary heart charge information, enabling steady, passive well being monitoring, however whether or not such estimates seize substantive way of life variation has not been examined. We characterised Cardio Age, a wearable-derived cardiorespiratory health age estimate, in 442 Ultrahuman Ring customers throughout a 12-month window ending February 2026, separating impartial way of life correlates from direct or oblique algorithmic inputs. The imply Cardio Age hole (CA hole; imply Cardio Age minus chronological age) was −1.84 ± 2.97 years, with 82.6% of individuals exhibiting youthful estimated cardiovascular ages. Independent way of life metrics with no algorithmic hyperlink to Cardio Age confirmed important associations: sleep effectivity (r = −0.194, p < 0.001), speedy eye motion (REM) sleep (r = −0.203, p < 0.001), sleep period (r = −0.200, p < 0.001), and day by day steps (r = −0.145, p = 0.003). A monotonic physique mass index (BMI) dose-response was noticed, with underweight individuals exhibiting a imply CA hole of −3.73 years versus −0.52 for overweight individuals. Extreme-group comparisons revealed that customers with the youngest cardiovascular ages slept 37 minutes longer, achieved 22 extra minutes of REM sleep, and had 1.8% increased sleep effectivity than these with the oldest cardiovascular ages (all p < 0.05). Sustained improvers over 12 months confirmed a imply CA discount of three.24 years; throughout the broader longitudinal improver and worsener teams, bettering trajectories had been accompanied by decreased resting coronary heart charge (−0.8 bpm, p < 0.001) and elevated estimated VO2 max (+1.3 mL/kg/min, p < 0.001), according to the metric’s within-person sensitivity to physiological change over time (resting coronary heart charge and estimated VO2 max are direct algorithm inputs).