{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T18:22:49Z","timestamp":1778178169818,"version":"3.51.4"},"reference-count":50,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100018044","name":"Verily Life Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018044","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sleep Research"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The goal was to evaluate the performance of a suite of 12 sleep measures generated by a multi\u2010sensor wrist\u2010worn wearable device, the verily numetric watch (VNW), in a diverse cohort. We used polysomnography (PSG) as reference during one\u2010night simultaneous recording in a sample of\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200941 (18 male, age range: 18\u201378\u2009years). We performed epoch\u2010by\u2010epoch comparisons for all measures. Key specific analyses were: core accuracy metrics for sleep versus wake classification; bias for continuous measures (Bland\u2013Altman); unweighted Cohen's kappa and accuracy for sleep stage classifications and mean count difference and linearly weighted Cohen's kappa for count metric. In addition, we performed exploratory subgroup analyses by sex, age, skin tone, body mass index and arm hair density. Sensitivity and specificity (95% CI) of sleep versus wake classification were 0.97 (0.96, 0.98) and 0.66 (0.61, 0.71), respectively. Mean total sleep time bias was 14.55\u2009min (1.61, 27.16); wake after sleep onset, \u221211.77\u2009min (\u221223.89, 1.09); sleep efficiency, 3.15% (0.68, 5.57); sleep onset latency, \u22123.24\u2009min (\u22129.38, 3.57); light\u2010sleep duration, 3.78\u2009min (\u22127.04, 15.06); deep\u2010sleep duration, 3.91\u2009min (\u22124.59, 12.60) and rapid eye movement\u2010sleep duration, 6.94\u2009min (0.57, 13.04). Mean difference for the number of awakenings, 0.17: (95% CI: \u22120.32, 0.71) and overall accuracy of sleep stage classification, 0.78 (0.51, 0.88). Most measures showed statistically significant proportional biases and\/or heteroscedasticity. Exploratory subgroup results appeared largely consistent with the overall group, although small samples precluded strong conclusions. These results support the use of VNWs in classifying sleep versus wake, sleep stages and overnight sleep measures.\n                  <\/jats:p>","DOI":"10.1111\/jsr.70036","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T20:29:44Z","timestamp":1743712184000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Performance Evaluation of the Verily Numetric Watch Sleep Suite for Digital Sleep Assessment Against In\u2010Lab Polysomnography"],"prefix":"10.1111","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5474-7674","authenticated-orcid":false,"given":"Benjamin W.","family":"Nelson","sequence":"first","affiliation":[{"name":"Verily Life Sciences  South San Francisco California USA"},{"name":"Department of Psychiatry, Division of Digital Psychiatry Harvard Medical School and Beth Israel Deaconess Medical Center  Boston Massachusetts 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