January 17, 2023
An article titled “The Actigraphy-Based Identification of Premorbid Latent Liability of Schizophrenia and Bipolar Disorder” written by Ádám Nagy et al. [1] has been accepted into the leading open access Q2 scientific journal Sensors (impact factor: 3.847).
Several studies have investigated the association of sleep, diurnal patterns, and circadian rhythms with the presence and with the risk states of mental illnesses such as schizophrenia and bipolar disorder. The goal of this study was to examine actigraphic measures to identify features that can be extracted from them so that a machine learning model can detect premorbid latent liabilities for schizotypy and bipolarity.
In the paper, the authors classified healthy university students into categories of mild disease susceptibility based on questionnaires and tested with actigraphy (the team developed a small wrist-worn measurement device that collects and identifies actigraphic data based on an accelerometer) whether the sleep-movement characteristics typical of a disease were already observable.
Identifying these traits may help us identify people at risk of developing mental disorders early in a cost-effective, automated way.
The article can be read on MDPI’s website.
[1] The paper's authors are Ádám Nagy, József Dombi, Martin Patrik Fülep, Emese Rudics, Emőke Adrienn Hompoth, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Bálint Maczák, Gergely Vadai, Zoltán Gingl, Szandra László, Vilmos Bilicki, and István Szendi.