{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T11:41:03Z","timestamp":1777290063811,"version":"3.51.4"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digital Med"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Current approaches to psychiatric assessment are resource-intensive, requiring time-consuming evaluation by a trained clinician. Development of digital biomarkers holds promise for enabling scalable, time-sensitive, and cost-effective assessment of both psychiatric diagnosis and symptom change. The present study aimed to identify robust digital biomarkers of diagnostic status and changes in symptom severity over ~2 weeks, through re-analysis of public-use actigraphy data collected in patients with major depressive or bipolar disorder and healthy controls. Results suggest that participants\u2019 diagnostic group status (i.e., mood disorder, control) can be predicted with a high degree of accuracy (predicted correctly 89% of the time, kappa\u2009=\u20090.773), using features extracted from actigraphy data alone. Results also suggest that actigraphy data can be used to predict symptom change across ~2 weeks (<jats:italic>r<\/jats:italic>\u2009=\u20090.782, <jats:italic>p<\/jats:italic>\u2009=\u20091.04e-05). Through inclusion of digital biomarkers in our statistical model, which are generalizable to new samples, the results may be replicated by other research groups in order to validate and extend this work.<\/jats:p>","DOI":"10.1038\/s41746-019-0078-0","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T11:06:04Z","timestamp":1549019164000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":142,"title":["Digital biomarkers of mood disorders and symptom change"],"prefix":"10.1038","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8832-4741","authenticated-orcid":false,"given":"Nicholas C.","family":"Jacobson","sequence":"first","affiliation":[]},{"given":"Hilary","family":"Weingarden","sequence":"additional","affiliation":[]},{"given":"Sabine","family":"Wilhelm","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,2,1]]},"reference":[{"key":"78_CR1","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1017\/S1121189X00001421","volume":"18","author":"RC Kessler","year":"2009","unstructured":"Kessler, R. C. et al. The global burden of mental disorders: an update from the WHO World Mental Health (WMH) Surveys. Epidemiol. Psichiatr. Soc. 18, 23\u201333 (2009).","journal-title":"Epidemiol. Psichiatr. Soc."},{"key":"78_CR2","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1146\/annurev-clinpsy-032816-044949","volume":"13","author":"DC Mohr","year":"2017","unstructured":"Mohr, D. C., Zhang, M. & Schueller, S. M. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annu Rev. Clin. Psychol. 13, 23\u201347 (2017).","journal-title":"Annu Rev. Clin. Psychol."},{"key":"78_CR3","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/j.invent.2015.03.002","volume":"2","author":"J Torous","year":"2015","unstructured":"Torous, J. & Powell, A. C. Current research and trends in the use of smartphone applications for mood disorders. Internet Interv. 2, 169\u2013173 (2015).","journal-title":"Internet Interv."},{"key":"78_CR4","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja, E. et al. Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients in Proceedings of the 9th ACM on Multimedia Systems Conference (ACM). Accessed on 5 September 2018. http:\/\/datasets.simula.no\/depresjon\/.","DOI":"10.1145\/3204949.3208125"},{"key":"78_CR5","unstructured":"Garcia-Ceja, E. Personal Communication [11\/1\/2018]."},{"key":"78_CR6","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1186\/1756-0500-3-149","volume":"3","author":"JO Berle","year":"2010","unstructured":"Berle, J. O., Hauge, E. R., Oedegaard, K. J., Holsten, F. & Fasmer, O. B. Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression. BMC Res. Notes 3, 149\u2013149 (2010).","journal-title":"BMC Res. Notes"},{"key":"78_CR7","doi-asserted-by":"crossref","unstructured":"Jacobson, N. C., Chow, S. M. & Newman, M. G. The differential time-varying effect model (DTVEM): a tool for diagnosing and modeling time lags in intensive longitudinal data. Behav. Res. Methods (2018).","DOI":"10.3758\/s13428-018-1101-0"},{"key":"78_CR8","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"T Chen","year":"2016","unstructured":"Chen, T. & Guestrin, C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785\u2013794). ACM, San Francisco, California, 2016)."},{"key":"78_CR9","unstructured":"Nielsen, D. Why Does XGBoost Win \u201cEvery\u201d Machine Learning Competition? Master of Science thesis, Norwegian University of Science and Techology (2016)."},{"key":"78_CR10","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1037\/abn0000069","volume":"124","author":"M Chmielewski","year":"2015","unstructured":"Chmielewski, M., Clark, L. A., Bagby, R. M. & Watson, D. Method matters: understanding diagnostic reliability in DSM-IV and DSM-5. J. Abnorm. Psychol. 124, 764\u2013769 (2015).","journal-title":"J. Abnorm. Psychol."},{"key":"78_CR11","unstructured":"Schapire, R. E. in Proceedings of the 16th international joint conference on Artificial intelligence, Dean, T. (ed.). Vol. 2, 1401\u20131406 (Morgan Kaufmann Publishers Inc., Stockholm, Sweden, 1999)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0078-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0078-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0078-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T18:29:41Z","timestamp":1671301781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0078-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,1]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["78"],"URL":"https:\/\/doi.org\/10.1038\/s41746-019-0078-0","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,1]]},"assertion":[{"value":"2 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Mr. Jacobson is the owner of a free application published on the Google Play Store entitled \u201cMood Triggers\u201d. He does not receive any direct or indirect revenue from his ownership of the application (i.e., the application is free, there are no advertisements, and the data is only being used for research purposes). Drs. Weingarden and Wilhelm have received salary support from Telefonica Alpha, Inc. Dr. Wilhelm is a presenter for the Massachusetts General Hospital Psychiatry Academy in educational programs supported through independent medical education grants from pharmaceutical companies. Dr. Wihelm has received royalties from Elsevier Publications, Guilford Publications, New Harbinger Publications, and Oxford University Press. Dr. Wilhelm has also received speaking honoraria from various academic institutions and foundations, including the International Obsessive Compulsive Disorder Foundation and the Tourette Association of America. In addition, she received payment from the Association for Behavioral and Cognitive Therapies for her role as a Associate Editor for the <i>Behavior Therapy<\/i> journal, as well as from John Wiley & Sons, Inc. for her role as a Associate Editor for the journal <i>Depression & Anxiety<\/i>.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"3"}}