Criteria for detection of possible risk factors for mental health problems in undergraduate university students

Daiki Ishimaru; Hiroyoshi Adachi; Teruhiro Mizumoto; Viktor Erdelyi; Hajime Nagahara; Shizuka Shirai; Haruo Takemura; Noriko Takemura; Mehrasa Alizadeh; Teruo Higashino; Yasushi Yagi; Manabu Ikeda

doi:10.3389/fpsyt.2023.1184156

Criteria for detection of possible risk factors for mental health problems in undergraduate university students

Front Psychiatry. 2023 Jun 29:14:1184156. doi: 10.3389/fpsyt.2023.1184156. eCollection 2023.

Authors

Affiliations

¹ Department of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan.
² Health and Counseling Center, Osaka University, Osaka, Japan.
³ Department of Information Networking, Osaka University Graduate School of Information Science and Technology, Osaka, Japan.
⁴ Institute for Datability Science, Osaka University, Osaka, Japan.
⁵ Infomedia Education Research Division, Cybermedia Center, Osaka University, Osaka, Japan.
⁶ Department of Artificial Intelligence, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan.
⁷ Department of Information Technology, Faculty of Technology, International Professional University of Technology, Osaka, Japan.
⁸ The Institute of Scientific and Industrial Research, Osaka, Japan.

Abstract

Introduction: Developing approaches for early detection of possible risk clusters for mental health problems among undergraduate university students is warranted to reduce the duration of untreated illness (DUI). However, little is known about indicators of need for care by others. Herein, we aimed to clarify the specific value of study engagement and lifestyle habit variables in predicting potentially high-risk cluster of mental health problems among undergraduate university students.

Methods: This cross-sectional study used a web-based demographic questionnaire [the Utrecht Work Engagement Scale for Students (UWES-S-J)] as study engagement scale. Moreover, information regarding life habits such as sleep duration and meal frequency, along with mental health problems such as depression and fatigue were also collected. Students with both mental health problems were classified as high risk. Characteristics of students in the two groups were compared. Univariate logistic regression was performed to identify predictors of membership. Receiver Operating Characteristic (ROC) curve was used to clarify the specific values that differentiated the groups in terms of significant predictors in univariate logistic analysis. Cut-off point was calculated using Youden index. Statistical significance was set at p < 0.05.

Results: A total of 1,644 students were assessed, and 30.1% were classified as high-risk for mental health problems. Significant differences were found between the two groups in terms of sex, age, study engagement, weekday sleep duration, and meal frequency. In the ROC curve, students who had lower study engagement with UWES-S-J score < 37.5 points (sensitivity, 81.5%; specificity, 38.0%), <6 h sleep duration on weekdays (sensitivity, 82.0%; specificity, 24.0%), and < 2.5 times of meals per day (sensitivity, 73.3%; specificity, 35.8%), were more likely to be classified into the high-risk group for mental health problems.

Conclusion: Academic staff should detect students who meet these criteria at the earliest and provide mental health support to reduce DUI among undergraduate university students.

Keywords: early detection; lifestyle habit; mental health; study engagement; university students.