{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T09:06:26Z","timestamp":1793437586315,"version":"4.5.0"},"reference-count":74,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T00:00:00Z","timestamp":1728777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000874","name":"Brain and Behavior Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000874","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018529","name":"Alkermes","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018529","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Psychiatr Scand"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Effective treatment of bipolar disorder (BD) requires prompt response to mood episodes. Preliminary studies suggest that predictions based on passive sensor data from personal digital devices can accurately detect mood episodes (e.g., between routine care appointments), but studies to date do not use methods designed for broad application. This study evaluated whether a novel, personalized machine learning approach, trained entirely on passive Fitbit data, with limited data filtering could accurately detect mood symptomatology in BD patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed data from 54 adults with BD, who wore Fitbits and completed bi\u2010weekly self\u2010report measures for 9\u2009months. We applied machine learning (ML) models to Fitbit data aggregated over two\u2010week observation windows to detect occurrences of depressive and (hypo)manic symptomatology, which were defined as two\u2010week windows with scores above established clinical cutoffs for the Patient Health Questionnaire\u20108 (PHQ\u20108) and Altman Self\u2010Rating Mania Scale (ASRM) respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>As hypothesized, among several ML algorithms, Binary Mixed Model (BiMM) forest achieved the highest area under the receiver operating curve (ROC\u2010AUC) in the validation process. In the testing set, the ROC\u2010AUC was 86.0% for depression and 85.2% for (hypo)mania. Using optimized thresholds calculated with Youden's J statistic, predictive accuracy was 80.1% for depression (sensitivity of 71.2% and specificity of 85.6%) and 89.1% for (hypo)mania (sensitivity of 80.0% and specificity of 90.1%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We achieved sound performance in detecting mood symptomatology in BD patients using methods designed for broad application. Findings expand upon evidence that Fitbit data can produce accurate mood symptomatology predictions. Additionally, to the best of our knowledge, this represents the first application of BiMM forest for mood symptomatology prediction. Overall, results move the field a step toward personalized algorithms suitable for the full population of patients, rather than only those with high compliance, access to specialized devices, or willingness to share invasive data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/acps.13765","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:39:44Z","timestamp":1728866384000},"page":"434-447","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology"],"prefix":"10.1111","volume":"151","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4085-0507","authenticated-orcid":false,"given":"Jessica M.","family":"Lipschitz","sequence":"first","affiliation":[{"name":"Department of Psychiatry Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Psychiatry Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sidian","family":"Lin","sequence":"additional","affiliation":[{"name":"Graduate School of Arts and Sciences Harvard University  Cambridge Massachusetts USA"},{"name":"Harvard Kennedy School  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soroush","family":"Saghafian","sequence":"additional","affiliation":[{"name":"Harvard Kennedy School  Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chelsea K.","family":"Pike","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine E.","family":"Burdick","sequence":"additional","affiliation":[{"name":"Department of Psychiatry Brigham and Women's Hospital  Boston Massachusetts USA"},{"name":"Department of Psychiatry Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,10,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1176\/ajp.152.11.1635"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.163.2.217"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.59.6.530"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.60.3.261"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.14r09245"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/bdi.12609"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0269881116636545"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1399\u20105618.2007.00436.x"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.4088\/pcc.v09n0107"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291718001885"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/bdi.12782"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/NMD.0000000000000425"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.4321\/S0213\u201061632006000100003"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.4088\/JCP.13r08905"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2007.12.004"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.2196\/mental.5165"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2013.281078"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms21207684"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.2196\/42672"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2022.895860"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2021.08.052"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0231995"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2021.06.038"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamapsychiatry.2018.3546"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euroneuro.2024.01.003"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13142323"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e03274"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1177\/0004867418808900"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010023\u201000977\u20107"},{"key":"e_1_2_12_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2022.03.054"},{"key":"e_1_2_12_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2020.07.012"},{"key":"e_1_2_12_33_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40345\u2010021\u201000243\u20103"},{"key":"e_1_2_12_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2018.01.008"},{"key":"e_1_2_12_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2014.2307584"},{"key":"e_1_2_12_36_1","doi-asserted-by":"publisher","DOI":"10.2196\/45405"},{"key":"e_1_2_12_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2611862"},{"key":"e_1_2_12_38_1","doi-asserted-by":"crossref","unstructured":"CaoB ZhengL ZhangC et al.DeepMood: modeling mobile phone typing dynamics for mood detection.Proc 23rd ACM SIGKDD Int Conf Knowl Discov Data Min 747\u2013755.2017. doi:10.48550\/arXiv.1803.08986","DOI":"10.1145\/3097983.3098086"},{"key":"e_1_2_12_39_1","doi-asserted-by":"publisher","DOI":"10.2196\/11029"},{"key":"e_1_2_12_40_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291722002847"},{"key":"e_1_2_12_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41398\u2010024\u201002876\u20101"},{"key":"e_1_2_12_42_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40345\u2010017\u20100110\u20108"},{"key":"e_1_2_12_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746\u2010021\u201000548\u20108"},{"key":"e_1_2_12_44_1","doi-asserted-by":"publisher","DOI":"10.2196\/11223"},{"key":"e_1_2_12_45_1","doi-asserted-by":"publisher","DOI":"10.2196\/10141"},{"key":"e_1_2_12_46_1","doi-asserted-by":"publisher","DOI":"10.2196\/11334"},{"key":"e_1_2_12_47_1","doi-asserted-by":"publisher","DOI":"10.4306\/pi.2017.14.2.179"},{"key":"e_1_2_12_48_1","doi-asserted-by":"publisher","DOI":"10.2196\/10828"},{"key":"e_1_2_12_49_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjsem\u20102015\u2010000013"},{"key":"e_1_2_12_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2008.06.026"},{"key":"e_1_2_12_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006\u20103223(96)00548\u20103"},{"key":"e_1_2_12_52_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291719002137"},{"key":"e_1_2_12_53_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v045.i03"},{"key":"e_1_2_12_54_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwt312"},{"key":"e_1_2_12_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2019.01.002"},{"key":"e_1_2_12_56_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387"},{"key":"e_1_2_12_57_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v039.i05"},{"key":"e_1_2_12_58_1","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1113.001.0001"},{"key":"e_1_2_12_59_1","doi-asserted-by":"crossref","unstructured":"ChenT GuestrinC.XGBoost: a scalable tree boosting system.Proc 23rd ACM SIGKDD Int Conf Knowl Discov Data Min 785\u2013794.2016. doi:10.48550\/arXiv.1603.02754","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_12_60_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_2_12_61_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_12_62_1","first-page":"1063","article-title":"Analysis of a random forests model","volume":"13","author":"Biau G","year":"2012","journal-title":"J Mach Learn Res"},{"key":"e_1_2_12_63_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.05.018"},{"key":"e_1_2_12_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252640"},{"key":"e_1_2_12_65_1","unstructured":"DengH.Guided R\\random forest in the RRF package.2013. doi:10.48550\/arXiv.1306.0237"},{"key":"e_1_2_12_66_1","volume-title":"Forecasting: Principles and Practice","author":"Hyndman RJ","year":"2021"},{"key":"e_1_2_12_67_1","doi-asserted-by":"publisher","DOI":"10.1097\/JTO.0b013e3181ec173d"},{"key":"e_1_2_12_68_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097\u20100142(1950)3:1<32::aid\u2010cncr2820030106>3.0.co;2\u20103"},{"key":"e_1_2_12_69_1","doi-asserted-by":"publisher","DOI":"10.2196\/35807"},{"key":"e_1_2_12_70_1","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.964582"},{"key":"e_1_2_12_71_1","doi-asserted-by":"publisher","DOI":"10.1136\/eb\u20102017\u2010102760"},{"key":"e_1_2_12_72_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2016.10.005"},{"key":"e_1_2_12_73_1","doi-asserted-by":"publisher","DOI":"10.1111\/bdi.12849"},{"key":"e_1_2_12_74_1","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2014.139"},{"key":"e_1_2_12_75_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140\u20106736(15)00241\u2010X"}],"container-title":["Acta Psychiatrica Scandinavica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/acps.13765","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,2]],"date-time":"2025-02-02T19:08:14Z","timestamp":1738523294000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/acps.13765"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,13]]},"references-count":74,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1111\/acps.13765"],"URL":"https:\/\/doi.org\/10.1111\/acps.13765","archive":["Portico"],"relation":{"has-review":[{"id-type":"doi","id":"10.1111\/ACPS.13765\/v1\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v1\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v3\/review1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v3\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v2\/review2","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v3\/response1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v2\/decision1","asserted-by":"object"},{"id-type":"doi","id":"10.1111\/ACPS.13765\/v2\/review1","asserted-by":"object"}]},"ISSN":["0001-690X","1600-0447"],"issn-type":[{"value":"0001-690X","type":"print"},{"value":"1600-0447","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,13]]},"assertion":[{"value":"2024-03-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}