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.