Objective evaluation of depressive state of the youth by self-report scale combined with eye movement data
WANG Renjie1, JIA Zhilong2, ZHU Yaqin3, ZHANG Tiantian3, ZHAN Liyan3
1. Department of Research,2. Department of Medical Research, Characteristics Medical Center of Chinese People's Armed Police Force, Tianjin 300162,China; 3. Yantai Special Service Recuperation Center of Chinese People's Armed Police Force, Yantai 264000,China
Abstract：Objective To explore the objective evaluation of depressive state of the youth in a certain unit with self-report scale combined with eye movement data, and to provide reference for the implementation of psychological intervention measures. Methods The Symptom Checklist 90 (SCL-90), Self-Rating Depression Scale (SDS) and Patient Health Questionnaire-9 (PHQ-9) were used to conduct psychological assessment on 200 young people from a certain unit from April 2023 to April 2024. According to the scores, depressive states were grouped, and the youth were divided into a depression group and a normal group. The eye movement data of subjects were collected, and the eye movement features were extracted. A depression classification model was established by using machine learning algorithm tools, and the classification accuracy of the model for a certain young people's depressive state screening was verified. Results A total of 200 questionnaires were distributed, and 169 were valid, with an effective rate of 84.5%.. By comparing the test results of SCL-90 with those of soldiers and local norms, the results showed that the scores of SCL-90 were lower than those of soldiers and local norms, and the difference was statistically significant (P＜0.01) . A population classification model was established by using eye movement data, and the classification accuracy of the youth depression was 79.29%. Conclusions The machine learning algorithm classification model based on the eye movement data of the subjects can initially realize the objective identification of high-risk groups of depressive states, and assist the traditional psychological assessment scale to identify depressive states.
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2025, Vol. 36 
