László Szandra, Nagy Ádám, Dombi József, Hompoth Emőke Adrienn, Rudics Emese, Szabó Zoltán, Dér András, Búzás András, Viharos Zsolt János, Hoang Anh Tuan, Bilicki Vilmos, Szendi István
Doctoral School of Interdisciplinary Medicine, Department of Medical Genetics, University of Szeged, Somogyi Béla Street 4., Szeged, 6720, Csongrád-Csanád, Hungary.
Department of Software Engineering, University of Szeged, Szeged, 6720, Csongrád-Csanád, Hungary.
BMC Psychiatry. 2025 May 24;25(1):531. doi: 10.1186/s12888-025-06971-5.
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.
运动活动改变是精神分裂症等精神疾病的关键症状。活动记录仪作为一种非侵入性监测方法，在早期识别方面显示出前景。本研究使用来自两个数据库的活动记录仪数据对阳性分裂型人格特质因子（PSF）组和慢性精神分裂症（CS）组进行特征描述。在豪克兰德大学医院，收集了慢性精神分裂症患者的数据；另外，在塞格德大学，招募并筛选了有分裂型人格特质倾向的健康大学生。从这两个数据集中提取了几种类型的特征。使用不同特征集的机器学习算法对CS组的准确率达到近90 - 95%，对PSF组的准确率为70 - 85%。通过将模型解释工具应用于表现良好的模型，我们可以总结出这些组的运动模式和特征。我们的研究表明，在精神分裂症的PSF易患阶段，与睡眠相关的活动记录仪特征最为显著，但随着疾病进展，睡眠和白天活动模式都很关键。这些差异可能受CF组药物作用的影响，这反映了精神分裂症研究中更广泛的挑战，即对患者进行无药物研究仍然困难。进一步的研究应在前驱期和临床高危组中探索这些特征，以完善我们对该疾病发展的理解。
