Wearable-derived cardiovascular fitness age and its lifestyle correlates in 442 adults

Cardiovascular age is a powerful risk-communication tool that translates complex physiological data into an intuitive number, yet traditional estimates require clinical testing. Consumer wearables now estimate cardiorespiratory fitness age from photoplethysmography-derived heart rate data, enabling continuous, passive health monitoring, but whether such estimates capture substantive lifestyle variation has not been examined. We characterized Cardio Age, a wearable-derived cardiorespiratory fitness age estimate, in 442 Ultrahuman Ring users across a 12-month window ending February 2026, separating independent lifestyle correlates from direct or indirect algorithmic inputs. The mean Cardio Age gap (CA gap; mean Cardio Age minus chronological age) was -1.84{+/-}2.97 years, with 82.6% of participants exhibiting younger estimated cardiovascular ages. Independent lifestyle metrics with no algorithmic link to Cardio Age showed significant associations: sleep efficiency (r = -0.194, p < 0.001), rapid eye movement (REM) sleep (r = -0.203, p < 0.001), sleep duration (r = -0.200, p < 0.001), and daily steps (r = -0.145, p = 0.003). A monotonic body mass index (BMI) dose-response was observed, with underweight participants showing a mean CA gap of -3.73 years versus -0.52 for obese participants. Extreme-group comparisons revealed that users with the youngest cardiovascular ages slept 37 minutes longer, achieved 22 more minutes of REM sleep, and had 1.8% higher sleep efficiency than those with the oldest cardiovascular ages (all p < 0.05). Sustained improvers over 12 months showed a mean CA reduction of 3.24 years, accompanied by decreased resting heart rate (-0.8 bpm, p < 0.001) and increased estimated VO2 max (+1.3 mL/kg/min, p < 0.001), indicating that Cardio Age tracks physiological changes over time.