The Two Ends of the Spectrum: Comparing Chronic Schizophrenia and Premorbid Latent Schizotypy by Actigraphy

The Two Ends of the Spectrum: Comparing Chronic Schizophrenia and Premorbid Latent Schizotypy by Actigraphy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Two Ends of the Spectrum: Comparing Chronic Schizophrenia and Premorbid Latent Schizotypy by Actigraphy Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5585827/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 May, 2025 Read the published version in BMC Psychiatry → Version 1 posted 9 You are reading this latest preprint version Abstract Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizophrenia (CS) groups using actigraphic data from two databases. At Hauke Land University Hospital, data from patients with chronic schizophrenia were collected; separately, at the University of Szeged, healthy university students were recruited and screened for PSF tendencies toward schizotypy. Several types of features are extracted from both datasets. Machine learning algorithms using different feature sets achieved nearly 90-95% for the CS group and 70-85% accuracy for the PSF. By applying model-explaining tools to the well-performing models, we could conclude the movement patterns and characteristics of the groups. Our study indicates that in the PSF liability phase of schizophrenia, actigraphic features related to sleep are most significant, but as the disease progresses, both sleep and daytime activity patterns are crucial. These variations might be influenced by medication effects in the CF group, reflecting the broader challenges in schizophrenia research, where the drug-free study of patients remains difficult. Further studies should explore these features in the prodromal and clinical High-Risk groups to refine our understanding of the development of the disorder. Actigraphy Mental Disease Machine Learning Disease development Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.zip Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in BMC Psychiatry → Version 1 posted Editor assigned by journal 14 Apr, 2025 Editorial decision: Revision requested 14 Apr, 2025 Reviews received at journal 08 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 29 Mar, 2025 Submission checks completed at journal 24 Mar, 2025 First submitted to journal 20 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5585827","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436421554,"identity":"39997eaf-e78d-415f-a6d4-692febd3357c","order_by":0,"name":"Szandra László","email":"","orcid":"","institution":"University of Szeged","correspondingAuthor":false,"prefix":"","firstName":"Szandra","middleName":"","lastName":"László","suffix":""},{"id":436421556,"identity":"fda9dde7-bcd0-4d31-8333-890d1f80f78a","order_by":1,"name":"Ádám 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