{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:11:45Z","timestamp":1771467105170,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["RRF-2.3.1-21-2022-00004"],"award-info":[{"award-number":["RRF-2.3.1-21-2022-00004"]}]},{"name":"European Union","award":["TKP2021-NVA-09"],"award-info":[{"award-number":["TKP2021-NVA-09"]}]},{"name":"European Union","award":["GINOP-2.3.2-15-2016-00037"],"award-info":[{"award-number":["GINOP-2.3.2-15-2016-00037"]}]},{"name":"Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund","award":["RRF-2.3.1-21-2022-00004"],"award-info":[{"award-number":["RRF-2.3.1-21-2022-00004"]}]},{"name":"Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund","award":["TKP2021-NVA-09"],"award-info":[{"award-number":["TKP2021-NVA-09"]}]},{"name":"Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund","award":["GINOP-2.3.2-15-2016-00037"],"award-info":[{"award-number":["GINOP-2.3.2-15-2016-00037"]}]},{"DOI":"10.13039\/501100000780","name":"Hungarian Government and the European Regional Development Fund","doi-asserted-by":"publisher","award":["RRF-2.3.1-21-2022-00004"],"award-info":[{"award-number":["RRF-2.3.1-21-2022-00004"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"Hungarian Government and the European Regional Development Fund","doi-asserted-by":"publisher","award":["TKP2021-NVA-09"],"award-info":[{"award-number":["TKP2021-NVA-09"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"Hungarian Government and the European Regional Development Fund","doi-asserted-by":"publisher","award":["GINOP-2.3.2-15-2016-00037"],"award-info":[{"award-number":["GINOP-2.3.2-15-2016-00037"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background and Goal: Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of our study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity. (2) Methods: Our team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer. The sensors were used by carefully selected healthy participants who were divided into three groups: Control Group (C), Cyclothymia Factor Group (CFG), and Positive Schizotypy Factor Group (PSF). From the data they collected, our team performed data cleaning operations and then used the extracted metrics to generate the feature combinations deemed most effective, along with three machine learning algorithms for categorization. (3) Results: By conducting the training, we were able to identify a set of mildly correlated traits and their order of importance based on the Shapley value that had the greatest impact on the detection of bipolarity and schizotypy according to the logistic regression, Light Gradient Boost, and Random Forest algorithms. (4) Conclusions: These results were successfully compared to the results of other researchers; we had a similar differentiation in features used by others, and successfully developed new ones that might be a good complement for further research. In the future, identifying these traits may help us identify people at risk from mental disorders early in a cost-effective, automated way.<\/jats:p>","DOI":"10.3390\/s23020958","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3321-004X","authenticated-orcid":false,"given":"\u00c1d\u00e1m","family":"Nagy","sequence":"first","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9459-912X","authenticated-orcid":false,"given":"J\u00f3zsef","family":"Dombi","sequence":"additional","affiliation":[{"name":"Department of Computer Algorithms and Artificial Intelligence, University of Szeged, 2 \u00c1rp\u00e1d Square, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6946-5795","authenticated-orcid":false,"given":"Martin Patrik","family":"F\u00fclep","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7108-9530","authenticated-orcid":false,"given":"Emese","family":"Rudics","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Szeged, 13 Dugonics Square, 6720 Szeged, 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and Control, E\u00f6tv\u00f6s L\u00f3r\u00e1nd Research Network (ELKH), Center of Excellence of the Hungarian Academy of Sciences (MTA), 13-17 Kende Street, 1111 Budapest, Hungary"},{"name":"Faculty of Economics and Business, John von Neumann University, 10 Izs\u00e1ki Street, 6000 Kecskem\u00e9t, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4316-0373","authenticated-orcid":false,"given":"Anh Tuan","family":"Hoang","sequence":"additional","affiliation":[{"name":"Institute for Computer Science and Control, Center of Excellence in Production Informatics and Control, E\u00f6tv\u00f6s L\u00f3r\u00e1nd Research Network (ELKH), Center of Excellence of the Hungarian Academy of Sciences (MTA), 13-17 Kende Street, 1111 Budapest, 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