Evidence map›Paper›PMID 37457784›Full record

ArticleFrontiers in psychiatry2023

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 and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 1 country.

Daiki IshimaruDepartment of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan.
Hiroyoshi AdachiDepartment of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan.
Teruhiro MizumotoDepartment of Information Networking, Osaka University Graduate School of Information Science and Technology, Osaka, Japan.
Viktor ErdelyiDepartment of Information Networking, Osaka University Graduate School of Information Science and Technology, Osaka, Japan.
Hajime NagaharaInstitute for Datability Science, Osaka University, Osaka, Japan.
Shizuka ShiraiInfomedia Education Research Division, Cybermedia Center, Osaka University, Osaka, Japan.
Haruo TakemuraInfomedia Education Research Division, Cybermedia Center, Osaka University, Osaka, Japan.
Noriko TakemuraDepartment of Artificial Intelligence, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka, Japan.
Mehrasa AlizadehDepartment of Information Technology, Faculty of Technology, International Professional University of Technology, Osaka, Japan.
Teruo HigashinoDepartment of Information Networking, Osaka University Graduate School of Information Science and Technology, Osaka, Japan.
Yasushi YagiThe Institute of Scientific and Industrial Research, Osaka, Japan.
Manabu IkedaDepartment of Psychiatry, Osaka University Graduate School of Medicine, Osaka, Japan.
The University of Osaka · JPKyushu Art Institute of Technology · JPOsaka Health Science University · JPOsaka Research Institute of Industrial Science and Technology · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Indexed as

early detectionlifestyle habitmental healthstudy engagementuniversity students

Identifiers

PMID37457784
PMCPMC10338915
OpenAlexW4382727908

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.