László, Sz and Nagy, Á and Dombi, J and Hompoth, E A and Rudics, E and Szabó, Z and Dér, A and Búzás, A and Viharos, Zsolt János and Hoang, Anh Tuan and Bilicki, V and Szendi, I (2025) The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy. BMC PSYCHIATRY, 25 (1). ISSN 1471-244X 10.1186/s12888-025-06971-5

Text Laszlo_1_36173847_ny.pdf Download (3MB)

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. © The Author(s) 2025.

Item Type:	Article
Uncontrolled Keywords:	machine learning; mental disease; Mesterséges intelligencia; actigraphy; DISEASE DEVELOPMENT;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	04 Jun 2025 06:36
Last Modified:	04 Jun 2025 06:36
URI:	https://eprints.sztaki.hu/id/eprint/10912

Update Item