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Typically, survey based instruments including Ecologically Momentary Assessments (EMA) and Day Reconstruction Method (DRM) are the method of choice for assessing mood related health. While effective, these methods require some effort and thus both compliance rates as well as quality of responses can be limited. As an alternative, We present a study that used passively sensed data from smartphones and wearables and machine learning techniques to predict mood instabilities, an important aspect of mental health. We explored the effectiveness of the proposed method on two large-scale datasets, finding that as little as three weeks of continuous, passive recordings were sufficient to reliably predict mood instabilities.<\/jats:p>","DOI":"10.1145\/3351233","type":"journal-article","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T15:58:26Z","timestamp":1568131106000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":75,"title":["Prediction of Mood Instability with Passive Sensing"],"prefix":"10.1145","volume":"3","author":[{"given":"Mehrab Bin","family":"Morshed","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Koustuv","family":"Saha","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Richard","family":"Li","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}]},{"given":"Sidney K.","family":"D'Mello","sequence":"additional","affiliation":[{"name":"University of Colorado Boulder, Boulder, USA"}]},{"given":"Munmun","family":"De Choudhury","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, USA"}]},{"given":"Gregory D.","family":"Abowd","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, USA"}]},{"given":"Thomas","family":"Pl\u00f6tz","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, School of Interactive Computing, Atlanta, GA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.4108\/icst.pervasivehealth.2014.254959"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1192\/bjp.bp.106.030957"},{"key":"e_1_2_1_3_1","volume-title":"The mood spectrum: improving the diagnosis of bipolar disorder. 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