{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:20:31Z","timestamp":1774894831683,"version":"3.50.1"},"reference-count":58,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T00:00:00Z","timestamp":1774828800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007419","name":"Lee Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007419","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yong Loo Lin School of Medicine","award":["Support funds for the Centre for Sleep and Cognition"],"award-info":[{"award-number":["Support funds for the Centre for Sleep and Cognition"]}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>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\u2009=\u2009160; 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\u2009=\u20090.64 and 0.62 for fingertip and wearable devices), while the reflection index (RI) had a weaker correlation with age for the ring sensor (r\u2009=\u20090.22) compared to fingertip (r\u2009=\u20090.58). 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.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001329","type":"journal-article","created":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:21:54Z","timestamp":1774891314000},"page":"e0001329","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Vascular age estimation using a consumer wearable sleep tracker"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4387-9276","authenticated-orcid":true,"given":"Gizem","family":"Yilmaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shohreh","family":"Ghorbani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ju Lynn","family":"Ong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hosein Aghayan","family":"Golkashani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. 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