{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T20:38:21Z","timestamp":1774903101001,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T00:00:00Z","timestamp":1703116800000},"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 Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on\/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I\/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.<\/jats:p>","DOI":"10.1038\/s41746-023-00977-7","type":"journal-article","created":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T18:02:07Z","timestamp":1703181727000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Classifying and clustering mood disorder patients using smartphone data from a feasibility study"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6178-2187","authenticated-orcid":false,"given":"Carsten","family":"Langholm","sequence":"first","affiliation":[]},{"given":"Scott","family":"Breitinger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9652-7581","authenticated-orcid":false,"given":"Lucy","family":"Gray","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6262-8264","authenticated-orcid":false,"given":"Fernando","family":"Goes","sequence":"additional","affiliation":[]},{"given":"Alex","family":"Walker","sequence":"additional","affiliation":[]},{"given":"Ashley","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Cindy","family":"Stopel","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Zandi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6997-4215","authenticated-orcid":false,"given":"Mark A.","family":"Frye","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5362-7937","authenticated-orcid":false,"given":"John","family":"Torous","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,21]]},"reference":[{"key":"977_CR1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.cpr.2004.12.002","volume":"25","author":"AK Cuellar","year":"2005","unstructured":"Cuellar, A. K., Johnson, S. L. & Winters, R. Distinctions between bipolar and unipolar depression. Clin. Psychol. Rev. 25, 307\u201339 (2005).","journal-title":"Clin. Psychol. Rev."},{"key":"977_CR2","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1007\/s00406-018-0927-x","volume":"268","author":"J Angst","year":"2018","unstructured":"Angst, J. et al. Bipolar spectrum in major depressive disorders. Eur. Arch. Psychiatry Clin. Neurosci. 268, 741\u2013748 (2018).","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"977_CR3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.jad.2010.05.018","volume":"129","author":"AM Nivoli","year":"2011","unstructured":"Nivoli, A. M. et al. New treatment guidelines for acute bipolar depression: a systematic review. J. Affect. Disord. 129, 14\u201326 (2011).","journal-title":"J. Affect. Disord."},{"key":"977_CR4","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.jad.2013.07.032","volume":"152-154","author":"K Altinbas","year":"2014","unstructured":"Altinbas, K. et al. A multinational study to pilot the modified Hypomania Checklist (mHCL) in the assessment of mixed depression. J. Affect Disord. 152-154, 478\u201382 (2014).","journal-title":"J. Affect Disord."},{"key":"977_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2015.185","volume":"5","author":"MA Frye","year":"2015","unstructured":"Frye, M. A. et al. Feasibility of investigating differential proteomic expression in depression: implications for biomarker development in mood disorders. Transl. Psychiatry 5, e689 (2015).","journal-title":"Transl. Psychiatry"},{"key":"977_CR6","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1146\/annurev-neuro-101220-014053","volume":"44","author":"CM Gillan","year":"2021","unstructured":"Gillan, C. M. & Rutledge, R. B. Smartphones and the neuroscience of mental health. Annu. Rev. Neurosci. 44, 129 (2021).","journal-title":"Annu. Rev. Neurosci."},{"key":"977_CR7","doi-asserted-by":"publisher","first-page":"559954","DOI":"10.3389\/fpsyt.2021.559954","volume":"12","author":"S Melbye","year":"2021","unstructured":"Melbye, S. et al. Automatically generated smartphone data in young patients with newly diagnosed bipolar disorder and healthy controls. Front. Psychiatry 12, 559954 (2021).","journal-title":"Front. Psychiatry"},{"key":"977_CR8","doi-asserted-by":"crossref","unstructured":"Su, H. Y., Wu, C. H., Liou, C. R., Lin, E. C. & Chen, P. S. Assessment of bipolar disorder using heterogeneous data of smartphone-based digital phenotyping. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 4260\u20134264 (IEEE, 2021).","DOI":"10.1109\/ICASSP39728.2021.9415008"},{"key":"977_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-022-04013-y","volume":"22","author":"D Zarate","year":"2022","unstructured":"Zarate, D., Stavropoulos, V., Ball, M., de Sena Collier, G. & Jacobson, N. C. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 22, 421 (2022).","journal-title":"BMC Psychiatry"},{"key":"977_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s40345-020-00202-4","volume":"8","author":"S Lagan","year":"2020","unstructured":"Lagan, S. et al. Digital health developments and drawbacks: a review and analysis of top-returned apps for bipolar disorder. Int J. Bipolar Disord. 8, 39 (2020).","journal-title":"Int J. Bipolar Disord."},{"key":"977_CR11","doi-asserted-by":"publisher","first-page":"e37225","DOI":"10.2196\/37225","volume":"10","author":"E Ettore","year":"2023","unstructured":"Ettore, E. et al. Digital phenotyping for differential diagnosis of major depressive episode: narrative review. JMIR Ment. Health 10, e37225 (2023).","journal-title":"JMIR Ment. Health"},{"key":"977_CR12","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1002\/wps.21038","volume":"22","author":"LS Brady","year":"2023","unstructured":"Brady, L. S. & Larrauri, C. A. AMP SCZ Steering Committee. Accelerating Medicines Partnership\u00ae Schizophrenia (AMP\u00ae SCZ): developing tools to enable early intervention in the psychosis high risk state. World Psychiatry 22, 42\u20133 (2023).","journal-title":"World Psychiatry"},{"key":"977_CR13","doi-asserted-by":"publisher","first-page":"115015","DOI":"10.1016\/j.psychres.2022.115015","volume":"319","author":"S Chang","year":"2023","unstructured":"Chang, S., Gray, L. & Torous, J. Smartphone app engagement and clinical outcomes in a hybrid clinic. Psychiatry Res. 319, 115015 (2023).","journal-title":"Psychiatry Res."},{"key":"977_CR14","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s44220-023-00019-x","volume":"1","author":"W Zhang","year":"2023","unstructured":"Zhang, W., Sweeney, J. A., Bishop, J. R., Gong, Q. & Lui, S. Biological subtyping of psychiatric syndromes as a pathway for advances in drug discovery and personalized medicine. Nat. Ment. Health 1, 88\u201399 (2023).","journal-title":"Nat. Ment. Health"},{"key":"977_CR15","doi-asserted-by":"publisher","first-page":"e30557","DOI":"10.2196\/30557","volume":"10","author":"A Vaidyam","year":"2022","unstructured":"Vaidyam, A., Halamka, J. & Torous, J. Enabling research and clinical use of patient-generated health data (the mindLAMP Platform): digital phenotyping study. JMIR mHealth uHealth 10, e30557 (2022).","journal-title":"JMIR mHealth uHealth"},{"key":"977_CR16","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1097\/01.MLR.0000093487.78664.3C","volume":"10","author":"K Kroenke","year":"2003","unstructured":"Kroenke, K., Spitzer, R. L. & Williams, J. B. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med. Care 10, 1284\u201392 (2003).","journal-title":"Med. Care"},{"key":"977_CR17","doi-asserted-by":"crossref","unstructured":"Skapinakis, P. The 2-item Generalized Anxiety Disorder scale had high sensitivity and specificity for detecting GAD in primary care. BMJ Evid Based Mental Health 12, 317\u2013325 (2007).","DOI":"10.1136\/ebm.12.5.149"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00977-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00977-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00977-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,21]],"date-time":"2023-12-21T18:07:29Z","timestamp":1703182049000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-023-00977-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,21]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["977"],"URL":"https:\/\/doi.org\/10.1038\/s41746-023-00977-7","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,21]]},"assertion":[{"value":"24 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"M.A.F. has received honoraria from Carnot Laboratories, American Physician Institute, and has a Financial Interest Chymia LLC. J.T. is a scientific advisor for Precision Mental Wellness. J.T. is an Associate Editor and Collection Guest Editor for <i>npj Digital Medicine<\/i>. They played no role in the decision to publish this manuscript in the journal or any Collection published by the journal. They also played no role in the independent peer review of this manuscript. The remaining authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"238"}}