The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy
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 dat...
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BMC
2025-05-01
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| Series: | BMC Psychiatry |
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| Online Access: | https://doi.org/10.1186/s12888-025-06971-5 |
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| author | Szandra László Ádám Nagy József Dombi Emőke Adrienn Hompoth Emese Rudics Zoltán Szabó András Dér András Búzás Zsolt János Viharos Anh Tuan Hoang Vilmos Bilicki István Szendi |
| author_facet | Szandra László Ádám Nagy József Dombi Emőke Adrienn Hompoth Emese Rudics Zoltán Szabó András Dér András Búzás Zsolt János Viharos Anh Tuan Hoang Vilmos Bilicki István Szendi |
| author_sort | Szandra László |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9b30b02e810e4083b476dcc69c3a3272 |
| institution | OA Journals |
| issn | 1471-244X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Psychiatry |
| spelling | doaj-art-9b30b02e810e4083b476dcc69c3a32722025-08-20T02:29:45ZengBMCBMC Psychiatry1471-244X2025-05-0125111710.1186/s12888-025-06971-5The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphySzandra László0Ádám Nagy1József Dombi2Emőke Adrienn Hompoth3Emese Rudics4Zoltán Szabó5András Dér6András Búzás7Zsolt János Viharos8Anh Tuan Hoang9Vilmos Bilicki10István Szendi11Doctoral School of Interdisciplinary Medicine, Department of Medical Genetics, University of SzegedDepartment of Software Engineering, University of SzegedDepartment of Computer Algorithms and Artificial Intelligence, University of SzegedDepartment of Software Engineering, University of SzegedDoctoral School of Interdisciplinary Medicine, Department of Medical Genetics, University of SzegedDepartment of Software Engineering, University of SzegedHUN-REN Biological Research Centre, Institute of BiophysicsHUN-REN Biological Research Centre, Institute of BiophysicsInstitute for Computer Science and Control (SZTAKI), Center of Excellence in Production Informatics and Control, Hungarian Research Network (HUN-REN), Centre of Excellence of the Hungarian Academy of Sciences (MTA)Institute for Computer Science and Control (SZTAKI), Center of Excellence in Production Informatics and Control, Hungarian Research Network (HUN-REN), Centre of Excellence of the Hungarian Academy of Sciences (MTA)Department of Software Engineering, University of SzegedDepartment of Psychiatry, Kiskunhalas Semmelweis HospitalAbstract 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.https://doi.org/10.1186/s12888-025-06971-5ActigraphyMental diseaseMachine learningDisease development |
| spellingShingle | Szandra László Ádám Nagy József Dombi Emőke Adrienn Hompoth Emese Rudics Zoltán Szabó András Dér András Búzás Zsolt János Viharos Anh Tuan Hoang Vilmos Bilicki István Szendi The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy BMC Psychiatry Actigraphy Mental disease Machine learning Disease development |
| title | The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| title_full | The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| title_fullStr | The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| title_full_unstemmed | The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| title_short | The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| title_sort | two ends of the spectrum comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy |
| topic | Actigraphy Mental disease Machine learning Disease development |
| url | https://doi.org/10.1186/s12888-025-06971-5 |
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