@Article{info:doi/10.2196/56668,
author="Morita, Kentaro
and Miura, Kenichiro
and Toyomaki, Atsuhito
and Makinodan, Manabu
and Ohi, Kazutaka
and Hashimoto, Naoki
and Yasuda, Yuka
and Mitsudo, Takako
and Higuchi, Fumihiro
and Numata, Shusuke
and Yamada, Akiko
and Aoki, Yohei
and Honda, Hiromitsu
and Mizui, Ryo
and Honda, Masato
and Fujikane, Daisuke
and Matsumoto, Junya
and Hasegawa, Naomi
and Ito, Satsuki
and Akiyama, Hisashi
and Onitsuka, Toshiaki
and Satomura, Yoshihiro
and Kasai, Kiyoto
and Hashimoto, Ryota",
title="Tablet-Based Cognitive and Eye Movement Measures as Accessible Tools for Schizophrenia Assessment: Multisite Usability Study",
journal="JMIR Ment Health",
year="2024",
month="May",
day="30",
volume="11",
pages="e56668",
keywords="schizophrenia; cognitive function; eye movement; diagnostic biomarkers; digital health tools",
abstract="Background: Schizophrenia is a complex mental disorder characterized by significant cognitive and neurobiological alterations. Impairments in cognitive function and eye movement have been known to be promising biomarkers for schizophrenia. However, cognitive assessment methods require specialized expertise. To date, data on simplified measurement tools for assessing both cognitive function and eye movement in patients with schizophrenia are lacking. Objective: This study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia. Methods: Forty-four patients with schizophrenia, 67 healthy controls, and 41 patients with other psychiatric diagnoses participated in this study from 10 sites across Japan. A free-viewing eye movement task and 2 cognitive assessment tools (Codebreaker task from the THINC-integrated tool and the CognitiveFunctionTest app) were used for conducting assessments in a 12.9-inch iPad Pro. We performed comparative group and logistic regression analyses for evaluating the diagnostic efficacy of the 3 measures of interest. Results: Cognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P<.001). The Codebreaker task showed the highest classification effectiveness in distinguishing schizophrenia with an area under the receiver operating characteristic curve of 0.90. Combining cognitive and eye movement measures further improved accuracy with a maximum area under the receiver operating characteristic curve of 0.94. Cognitive measures were more effective in differentiating patients with schizophrenia from healthy controls, whereas eye movement measures better differentiated schizophrenia from other psychiatric conditions. Conclusions: This multisite study demonstrates the feasibility and effectiveness of a tablet-based app for assessing cognitive functioning and eye movements in patients with schizophrenia. Our results suggest the potential of tablet-based assessments of cognitive function and eye movement as simple and accessible evaluation tools, which may be useful for future clinical implementation. ",
issn="2368-7959",
doi="10.2196/56668",
url="https://mental.jmir.org/2024/1/e56668",
url="https://doi.org/10.2196/56668",
url="http://www.ncbi.nlm.nih.gov/pubmed/38815257"
}