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      Influencing factors, prediction and prevention of depression in college students: A literature review

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          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.

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          Most cited references126

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          Emerging adulthood. A theory of development from the late teens through the twenties.

          J Arnett (2000)
          Emerging adulthood is proposed as a new conception of development for the period from the late teens through the twenties, with a focus on ages 18-25. A theoretical background is presented. Then evidence is provided to support the idea that emerging adulthood is a distinct period demographically, subjectively, and in terms of identity explorations. How emerging adulthood differs from adolescence and young adulthood is explained. Finally, a cultural context for the idea of emerging adulthood is outlined, and it is specified that emerging adulthood exists only in cultures that allow young people a prolonged period of independent role exploration during the late teens and twenties.
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            Ordinary magic. Resilience processes in development.

            The study of resilience in development has overturned many negative assumptions and deficit-focused models about children growing up under the threat of disadvantage and adversity. The most surprising conclusion emerging from studies of these children is the ordinariness of resilience. An examination of converging findings from variable-focused and person-focused investigations of these phenomena suggests that resilience is common and that it usually arises from the normative functions of human adaptational systems, with the greatest threats to human development being those that compromise these protective systems. The conclusion that resilience is made of ordinary rather than extraordinary processes offers a more positive outlook on human development and adaptation, as well as direction for policy and practice aimed at enhancing the development of children at risk for problems and psychopathology.
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              Investigating Mental Health of US College Students During the COVID-19 Pandemic: Cross-Sectional Survey Study

              Background Evidence suggests that the COVID-19 pandemic has generally increased levels of stress and depression among the public. However, the impact on college students in the United States has not been well-documented. Objective This paper surveys the mental health status and severity of depression and anxiety of college students in a large university system in the United States during the COVID-19 pandemic. Methods An online survey was conducted among undergraduate and graduate students recruited from Texas A&M University via email. The survey consisted of two standardized scales—the Patient Health Questionnaire-9 and the General Anxiety Disorder-7—for depression and anxiety, and additional multiple-choice and open-ended questions regarding stressors and coping mechanisms specific to COVID-19. Results Among the 2031 participants, 48.14% (n=960) showed a moderate-to-severe level of depression, 38.48% (n=775) showed a moderate-to-severe level of anxiety, and 18.04% (n=366) had suicidal thoughts. A majority of participants (n=1443, 71.26%) indicated that their stress/anxiety levels had increased during the pandemic. Less than half of the participants (n=882, 43.25%) indicated that they were able to cope adequately with the stress related to the current situation. Conclusions The proportion of respondents showing depression, anxiety, and/or suicidal thoughts is alarming. Respondents reported academic-, health-, and lifestyle-related concerns caused by the pandemic. Given the unexpected length and severity of the outbreak, these concerns need to be further understood and addressed.
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                Author and article information

                Contributors
                Journal
                World J Psychiatry
                WJP
                World Journal of Psychiatry
                Baishideng Publishing Group Inc
                2220-3206
                19 July 2022
                19 July 2022
                : 12
                : 7
                : 860-873
                Affiliations
                School of Education, Tianjin University, Tianjin 300350, China. xinqiaoliu@ 123456pku.edu.cn
                School of Education, Tianjin University, Tianjin 300350, China
                Graduate School of Education, Peking University, Beijing 100871, China
                Institute of Higher Education, Beihang University, Beijing 100191, China
                Author notes

                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.

                Corresponding author: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Tongyan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@ 123456pku.edu.cn

                Article
                jWJP.v12.i7.pg860
                10.5498/wjp.v12.i7.860
                9331452
                36051603
                84d47ae6-8195-4728-8b68-24b38ce4031e
                ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.

                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.

                History
                : 27 February 2022
                : 29 April 2022
                : 22 June 2022
                Categories
                Review

                depression,prediction,prevention,artificial intelligence,big data,machine learning

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