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World J Psychiatry. Jul 19, 2022; 12(7): 860-873
Published online Jul 19, 2022. doi: 10.5498/wjp.v12.i7.860
Influencing factors, prediction and prevention of depression in college students: A literature review
Xin-Qiao Liu, Yu-Xin Guo, Wen-Jie Zhang, Wen-Juan Gao
Xin-Qiao Liu, Yu-Xin Guo, School of Education, Tianjin University, Tianjin 300350, China
Wen-Jie Zhang, Graduate School of Education, Peking University, Beijing 100871, China
Wen-Juan Gao, Institute of Higher Education, Beihang University, Beijing 100191, China
Author contributions: Liu XQ designed the study; Liu XQ, Guo YX, Zhang WJ and Gao WJ wrote the manuscript and managed the literature analyses; all authors contributed equally to this work and have approved the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Tongyan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@pku.edu.cn
Received: February 27, 2022
Peer-review started: February 27, 2022
First decision: April 18, 2022
Revised: April 29, 2022
Accepted: June 22, 2022
Article in press: June 22, 2022
Published online: July 19, 2022
Processing time: 142 Days and 2.6 Hours
Abstract

The high prevalence of depression among college students has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication. This paper reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for college students' depression. The influencing factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of coronavirus disease 2019 has exacerbated the severity of depression among college students worldwide and poses grave challenges to the prevention and treatment of depression, given that the coronavirus has spread quickly with high infection rates, and the pandemic has changed the daily routines of college life. To predict and measure mental health, more advanced methods, such as machine algorithms and artificial intelligence, have emerged in recent years apart from the traditional commonly used psychological scales. Regarding nonpharmaceutical prevention measures, both general measures and professional measures for the prevention and treatment of college students' depression were examined in this study. Students who experience depressive disorders need family support and personalized interventions at college, which should also be supplemented by professional interventions such as cognitive behavioral therapy and online therapy. Through this literature review, we insist that the technology of identification, prediction, and prevention of depression among college students based on big data platforms will be extensively used in the future. Higher education institutions should understand the potential risk factors related to college students' depression and make more accurate screening and prevention available with the help of advanced technologies.

Keywords: Depression; Prediction; Prevention; Artificial intelligence; Big data; Machine learning

Core Tip: This study reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for depression among college students. The influencing factors can be categorized into students’ demographic characteristics, college experience, lifestyle, and social support. For the prediction of depression, methods such as machine algorithms and artificial intelligence have been employed together with the traditional psychological scales. This study summarizes general and professional measures that can be taken for the prevention and treatment of college students' depression.