{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T09:23:44Z","timestamp":1780478624520,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"generous funding of the Novartis Institute of Biomedical Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole\u2013Kripke algorithm. For PSG, SO was defined as the first score of \u2018sleep\u2019. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of \u2018sleep\u2019. Agreement statistics and linear mixed effects regression models were used to analyze \u2018Device\u2019 and \u2018Sleep Onset Rule\u2019 main effects and interactions. Sleep\u2013wake agreement and sensitivity for all AG methods were high (89.0\u201389.5% and 97.2%, respectively); specificity was low (23.6\u201325.1%). There were no significant interactions or main effects of \u2018Sleep Onset Rule\u2019 for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep\u2013wake detection algorithms and incorporating biometric signals (e.g., heart rate).<\/jats:p>","DOI":"10.3390\/s22135041","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:38:55Z","timestamp":1656977935000},"page":"5041","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography"],"prefix":"10.3390","volume":"22","author":[{"given":"John D.","family":"Chase","sequence":"first","affiliation":[{"name":"Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA 01003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Busa","sequence":"additional","affiliation":[{"name":"Institute for Applied Life Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John W.","family":"Staudenmayer","sequence":"additional","affiliation":[{"name":"Department of Mathematics & Statistics, University of Massachusetts Amherst, Amherst, MA 01003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John R.","family":"Sirard","sequence":"additional","affiliation":[{"name":"Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA 01003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.5665\/sleep.4886","article-title":"Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for a Healthy Adult: Methodology and Discussion Consensus","volume":"38","author":"Watson","year":"2015","journal-title":"Sleep"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.sleep.2010.09.013","article-title":"Relationships between self-rated health, quality of life and sleep duration in middle aged and elderly Australians","volume":"12","author":"Magee","year":"2011","journal-title":"Sleep Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1016\/j.socscimed.2010.05.041","article-title":"Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States","volume":"71","author":"Buxton","year":"2010","journal-title":"Soc. 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