Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

查看全文
  • 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

摘要

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.