TY  - JOUR
AU  - Morita, Kentaro
AU  - Miura, Kenichiro
AU  - Toyomaki, Atsuhito
AU  - Makinodan, Manabu
AU  - Ohi, Kazutaka
AU  - Hashimoto, Naoki
AU  - Yasuda, Yuka
AU  - Mitsudo, Takako
AU  - Higuchi, Fumihiro
AU  - Numata, Shusuke
AU  - Yamada, Akiko
AU  - Aoki, Yohei
AU  - Honda, Hiromitsu
AU  - Mizui, Ryo
AU  - Honda, Masato
AU  - Fujikane, Daisuke
AU  - Matsumoto, Junya
AU  - Hasegawa, Naomi
AU  - Ito, Satsuki
AU  - Akiyama, Hisashi
AU  - Onitsuka, Toshiaki
AU  - Satomura, Yoshihiro
AU  - Kasai, Kiyoto
AU  - Hashimoto, Ryota
PY  - 2024
DA  - 2024/5/30
TI  - Tablet-Based Cognitive and Eye Movement Measures as Accessible Tools for Schizophrenia Assessment: Multisite Usability Study
JO  - JMIR Ment Health
SP  - e56668
VL  - 11
KW  - schizophrenia
KW  - cognitive function
KW  - eye movement
KW  - diagnostic biomarkers
KW  - digital health tools
AB  - 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. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2024/1/e56668
UR  - https://doi.org/10.2196/56668
DO  - 10.2196/56668
ID  - info:doi/10.2196/56668
ER  -