{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:53:14Z","timestamp":1783788794565,"version":"3.55.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,5,1]]},"abstract":"<jats:p>Objective To evaluate the feasibility of automatically assessing the Social Rhythm Metric (SRM), a clinically-validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), using passively-sensed data from smartphones.<\/jats:p>\n               <jats:p>Methods Seven patients with BD used smartphones for 4 weeks passively collecting sensor data including accelerometer, microphone, location, and communication information to infer behavioral and contextual patterns. Participants also completed SRM entries using a smartphone app.<\/jats:p>\n               <jats:p>Results We found that automated sensing can be used to infer the SRM score. Using location, distance traveled, conversation frequency, and non-stationary duration as inputs, our generalized model achieves root-mean-square-error of 1.40, a reasonable performance given the range of SRM score (0\u20137). Personalized models further improve performance with mean root-mean-square-error of 0.92 across users. Classifiers using sensor streams can predict stable (SRM score \u22653.5) and unstable (SRM score &amp;lt;3.5) states with high accuracy (precision: 0.85 and recall: 0.86).<\/jats:p>\n               <jats:p>Conclusions Automatic smartphone sensing is a feasible approach for inferring rhythmicity, a key marker of wellbeing for individuals with BD.<\/jats:p>","DOI":"10.1093\/jamia\/ocv200","type":"journal-article","created":{"date-parts":[[2016,3,15]],"date-time":"2016-03-15T02:43:22Z","timestamp":1458009802000},"page":"538-543","source":"Crossref","is-referenced-by-count":215,"title":["Automatic detection of social rhythms in bipolar disorder"],"prefix":"10.1093","volume":"23","author":[{"given":"Saeed","family":"Abdullah","sequence":"first","affiliation":[{"name":"Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Matthews","sequence":"additional","affiliation":[{"name":"Information Science, Gates Hall, Cornell University, Ithaca, NY 14853, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ellen","family":"Frank","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of Pittsburgh. 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