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

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...

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Autores principales: Ishimaru, Daiki, Adachi, Hiroyoshi, Mizumoto, Teruhiro, Erdelyi, Viktor, Nagahara, Hajime, Shirai, Shizuka, Takemura, Haruo, Takemura, Noriko, Alizadeh, Mehrasa, Higashino, Teruo, Yagi, Yasushi, Ikeda, Manabu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338915/
https://www.ncbi.nlm.nih.gov/pubmed/37457784
http://dx.doi.org/10.3389/fpsyt.2023.1184156
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author Ishimaru, Daiki
Adachi, Hiroyoshi
Mizumoto, Teruhiro
Erdelyi, Viktor
Nagahara, Hajime
Shirai, Shizuka
Takemura, Haruo
Takemura, Noriko
Alizadeh, Mehrasa
Higashino, Teruo
Yagi, Yasushi
Ikeda, Manabu
author_facet Ishimaru, Daiki
Adachi, Hiroyoshi
Mizumoto, Teruhiro
Erdelyi, Viktor
Nagahara, Hajime
Shirai, Shizuka
Takemura, Haruo
Takemura, Noriko
Alizadeh, Mehrasa
Higashino, Teruo
Yagi, Yasushi
Ikeda, Manabu
author_sort Ishimaru, Daiki
collection PubMed
description 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.
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spelling pubmed-103389152023-07-14 Criteria for detection of possible risk factors for mental health problems in undergraduate university students Ishimaru, Daiki Adachi, Hiroyoshi Mizumoto, Teruhiro Erdelyi, Viktor Nagahara, Hajime Shirai, Shizuka Takemura, Haruo Takemura, Noriko Alizadeh, Mehrasa Higashino, Teruo Yagi, Yasushi Ikeda, Manabu Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2023-06-29 /pmc/articles/PMC10338915/ /pubmed/37457784 http://dx.doi.org/10.3389/fpsyt.2023.1184156 Text en Copyright © 2023 Ishimaru, Adachi, Mizumoto, Erdelyi, Nagahara, Shirai, Takemura, Takemura, Alizadeh, Higashino, Yagi and Ikeda. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychiatry
Ishimaru, Daiki
Adachi, Hiroyoshi
Mizumoto, Teruhiro
Erdelyi, Viktor
Nagahara, Hajime
Shirai, Shizuka
Takemura, Haruo
Takemura, Noriko
Alizadeh, Mehrasa
Higashino, Teruo
Yagi, Yasushi
Ikeda, Manabu
Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title_full Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title_fullStr Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title_full_unstemmed Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title_short Criteria for detection of possible risk factors for mental health problems in undergraduate university students
title_sort criteria for detection of possible risk factors for mental health problems in undergraduate university students
topic Psychiatry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338915/
https://www.ncbi.nlm.nih.gov/pubmed/37457784
http://dx.doi.org/10.3389/fpsyt.2023.1184156
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