Vascular age estimation using a consumer wearable sleep tracker

Vascular aging is traditionally assessed using a combination of clinical markers, blood pressure and arterial stiffness measurement. However, measuring vascular aging with reference equipment is costly and not scalable. Nocturnal photoplethysmography (PPG) from wearable health trackers offer a scalable solution for longitudinal assessment. In this study, we evaluated the ability of a consumer wearable (Oura Ring) to detect age-related differences in PPG waveform, in comparison to a clinical-grade fingertip pulse oximeter. Healthy adults (N = 160; 78 males (49%), median age 31 years (IQR: 23)) underwent overnight polysomnography (PSG) in a sleep laboratory, during which fingertip and wearable ring PPG data were collected simultaneously. Pulse waveforms were extracted from both devices using a custom algorithm and key waveform features were compared across devices. Vascular age was estimated from pulse waveforms using a featureless deep learning model. Prediction performance was compared between the two devices. Age-related waveform changes were most prominent in PPG crest time (CT (samples)) (r = 0.64 and 0.62 for fingertip and wearable devices), while the reflection index (RI) had a weaker correlation with age for the ring sensor (r = 0.22) compared to fingertip (r = 0.58). Despite differences in waveforms between devices, the deep learning model showed comparable prediction performance with mean absolute errors (MAE (SD)) of 6.28 (1.48) and 7.25 (1.29) years, and r (SD) of 0.84 (0.07) and 0.80 (0.10) for clinical-grade and consumer-grade devices, respectively. These findings support the feasibility of using PPG waveforms from wearable devices to assess vascular age.Author summary: Vascular aging is an important marker of cardiovascular risk, traditionally assessed using clinical measurements and specialized equipment to evaluate arterial stiffness. The latter is expensive and difficult to scale for long-term or population-level monitoring. Wearable health trackers that collect nocturnal photoplethysmography (PPG) signals offer an alternative. Our study examined whether a consumer wearable device (Oura Ring) can detect age-relevant features in PPG waveforms, compared with a clinical-grade fingertip pulse oximeter. 160 healthy adults (49% male; median age 31 years) underwent overnight polysomnography, during which PPG signals were recorded simultaneously from both devices. Pulse waveforms were extracted and key waveform features were analyzed. Several PPG features showed clear age-related changes, particularly pulse crest time, which correlated strongly with age for both devices. Vascular age was then estimated using a deep-learning model applied to the PPG signals. Although waveforms differed between devices, the deep-learning model predicted vascular age with comparable accuracy using wearable and clinical-grade PPG data. Mean absolute error was approximately 6–7 years for both devices, with strong correlations between predicted and chronological age. These findings demonstrate that PPG data from consumer wearables can capture meaningful age-related vascular information, supporting their potential use for scalable, longitudinal assessment of vascular aging.