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literature review on depression among university students

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An Exploratory Study of Students with Depression in Undergraduate Research Experiences

*Address correspondence to: Katelyn M. Cooper ( E-mail Address: [email protected] ).

Department of Biology, University of Central Florida, Orlando, FL, 32816

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Biology Education Research Lab, Research for Inclusive STEM Education Center, School of Life Sciences, Arizona State University, Tempe, AZ 85281

Depression is a top mental health concern among undergraduates and has been shown to disproportionately affect individuals who are underserved and underrepresented in science. As we aim to create a more inclusive scientific community, we argue that we need to examine the relationship between depression and scientific research. While studies have identified aspects of research that affect graduate student depression, we know of no studies that have explored the relationship between depression and undergraduate research. In this study, we sought to understand how undergraduates’ symptoms of depression affect their research experiences and how research affects undergraduates’ feelings of depression. We interviewed 35 undergraduate researchers majoring in the life sciences from 12 research-intensive public universities across the United States who identify with having depression. Using inductive and deductive coding, we identified that students’ depression affected their motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing in undergraduate research experiences. We found that students’ social connections, experiencing failure in research, getting help, receiving feedback, and the demands of research affected students’ depression. Based on this work, we articulate an initial set of evidence-based recommendations for research mentors to consider in promoting an inclusive research experience for students with depression.

INTRODUCTION

Depression is described as a common and serious mood disorder that results in persistent feelings of sadness and hopelessness, as well as a loss of interest in activities that one once enjoyed ( American Psychiatric Association [APA], 2013 ). Additional symptoms of depression include weight changes, difficulty sleeping, loss of energy, difficulty thinking or concentrating, feelings of worthlessness or excessive guilt, and suicidality ( APA, 2013 ). While depression results from a complex interaction of psychological, social, and biological factors ( World Health Organization, 2018 ), studies have shown that increased stress caused by college can be a significant contributor to student depression ( Dyson and Renk, 2006 ).

Depression is one of the top undergraduate mental health concerns, and the rate of depression among undergraduates continues to rise ( Center for Collegiate Mental Health, 2017 ). While we cannot discern whether these increasing rates of depression are due to increased awareness or increased incidence, it is clear that is a serious problem on college campuses. The percent of U.S. college students who self-reported a diagnosis with depression was recently estimated to be about 25% ( American College Health Association, 2019 ). However, higher rates have been reported, with one study estimating that up to 84% of undergraduates experience some level of depression ( Garlow et al. , 2008 ). Depression rates are typically higher among university students compared with the general population, despite being a more socially privileged group ( Ibrahim et al. , 2013 ). Prior studies have found that depression is negatively correlated with overall undergraduate academic performance ( Hysenbegasi et al. , 2005 ; Deroma et al. , 2009 ; American College Health Association, 2019 ). Specifically, diagnosed depression is associated with half a letter grade decrease in students’ grade point average ( Hysenbegasi et al. , 2005 ), and 21.6% of undergraduates reported that depression negatively affected their academic performance within the last year ( American College Health Association, 2019 ). Provided with a list of academic factors that may be affected by depression, students reported that depression contributed to lower exam grades, lower course grades, and not completing or dropping a course.

Students in the natural sciences may be particularly at risk for depression, given that such majors are noted to be particularly stressful due to their competitive nature and course work that is often perceived to “weed students out”( Everson et al. , 1993 ; Strenta et al. , 1994 ; American College Health Association, 2019 ; Seymour and Hunter, 2019 ). Science course instruction has also been described to be boring, repetitive, difficult, and math-intensive; these factors can create an environment that can trigger depression ( Seymour and Hewitt, 1997 ; Osborne and Collins, 2001 ; Armbruster et al ., 2009 ; Ceci and Williams, 2010 ). What also distinguishes science degree programs from other degree programs is that, increasingly, undergraduate research experiences are being proposed as an essential element of a science degree ( American Association for the Advancement of Science, 2011 ; President’s Council of Advisors on Science and Technology, 2012 ; National Academies of Sciences, Engineering, and Medicine [NASEM], 2017 ). However, there is some evidence that undergraduate research experiences can add to the stress of college for some students ( Cooper et al. , 2019c ). Students can garner multiple benefits from undergraduate research, including enhanced abilities to think critically ( Ishiyama, 2002 ; Bauer and Bennett, 2003 ; Brownell et al. , 2015 ), improved student learning ( Rauckhorst et al. , 2001 ; Brownell et al. , 2015 ), and increased student persistence in undergraduate science degree programs ( Jones et al. , 2010 ; Hernandez et al. , 2018 ). Notably, undergraduate research experiences are increasingly becoming a prerequisite for entry into medical and graduate programs in science, particularly elite programs ( Cooper et al. , 2019d ). Although some research experiences are embedded into formal lab courses as course-based undergraduate research experiences (CUREs; Auchincloss et al. , 2014 ; Brownell and Kloser, 2015 ), the majority likely entail working with faculty in their research labs. These undergraduate research experiences in faculty labs are often added on top of a student’s normal course work, so they essentially become an extracurricular activity that they have to juggle with course work, working, and/or personal obligations ( Cooper et al. , 2019c ). While the majority of the literature surrounding undergraduate research highlights undergraduate research as a positive experience ( NASEM, 2017 ), studies have demonstrated that undergraduate research experiences can be academically and emotionally challenging for students ( Mabrouk and Peters, 2000 ; Seymour et al. , 2004 ; Cooper et al. , 2019c ; Limeri et al. , 2019 ). In fact, 50% of students sampled nationally from public R1 institutions consider leaving their undergraduate research experience prematurely, and about half of those students, or 25% of all students, ultimately leave their undergraduate research experience ( Cooper et al. , 2019c ). Notably, 33.8% of these individuals cited a negative lab environment and 33.3% cited negative relationships with their mentors as factors that influenced their decision about whether to leave ( Cooper et al. , 2019c ). Therefore, students’ depression may be exacerbated in challenging undergraduate research experiences, because studies have shown that depression is positively correlated with student stress ( Hish et al. , 2019 ).

While depression has not been explored in the context of undergraduate research experiences, depression has become a prominent concern surrounding graduate students conducting scientific research. A recent study that examined the “graduate student mental health crisis” ( Flaherty, 2018 ) found that work–life balance and graduate students’ relationships with their research advisors may be contributing to their depression ( Evans et al. , 2018 ). Specifically, this survey of 2279 PhD and master’s students from diverse fields of study, including the biological/physical sciences, showed that 39% of graduate students have experienced moderate to severe depression. Fifty-five percent of the graduate students with depression who were surveyed disagreed with the statement “I have good work life balance,” compared to only 21% of students with depression who agreed. Additionally, the study highlighted that more students with depression disagreed than agreed with the following statements: their advisors provided “real” mentorship, their advisors provided ample support, their advisors positively impacted their emotional or mental well-being, their advisors were assets to their careers, and they felt valued by their mentors. Another recent study identified that depression severity in biomedical doctoral students was significantly associated with graduate program climate, a perceived lack of employment opportunities, and the quality of students’ research training environment ( Nagy et al. , 2019 ). Environmental stress, academic stress, and family and monetary stress have also been shown to be predictive of depression severity in biomedical doctoral students ( Hish et al. , 2019 ). Further, one study found that self-esteem is negatively correlated and stress is positively correlated with graduate student depression; presumably research environments that challenge students’ self-esteem and induce stress are likely contributing to depressive symptoms among graduate students ( Kreger, 1995 ). While these studies have focused on graduate students, and there are certainly notable distinctions between graduate and undergraduate research, the research-related factors that affect graduate student depression, including work–life balance, relationships with mentors, research environment, stress, and self-esteem, may also be relevant to depression among undergraduates conducting research. Importantly, undergraduates in the United States have reported identical levels of depression as graduate students but are often less likely to seek mental health care services ( Wyatt and Oswalt, 2013 ), which is concerning if undergraduate research experiences exacerbate depression.

Based on the literature on the stressors of undergraduate research experiences and the literature identifying some potential causes of graduate student depression, we identified three aspects of undergraduate research that may exacerbate undergraduates’ depression. Mentoring: Mentors can be an integral part of a students’ research experience, bolstering their connections with others in the science community, scholarly productivity, and science identity, as well as providing many other benefits ( Thiry and Laursen, 2011 ; Prunuske et al. , 2013 ; Byars-Winston et al. , 2015 ; Aikens et al. , 2016 , 2017 ; Thompson et al. , 2016 ; Estrada et al. , 2018 ). However, recent literature has highlighted that poor mentoring can negatively affect undergraduate researchers ( Cooper et al. , 2019c ; Limeri et al. , 2019 ). Specifically, one study of 33 undergraduate researchers who had conducted research at 10 institutions identified seven major ways that they experienced negative mentoring, which included absenteeism, abuse of power, interpersonal mismatch, lack of career support, lack of psychosocial support, misaligned expectations, and unequal treatment ( Limeri et al. , 2019 ). We hypothesize negative mentoring experiences may be particularly harmful for students with depression, because support, particularly social support, has been shown to be important for helping individuals with depression cope with difficult circumstances ( Aneshensel and Stone, 1982 ; Grav et al. , 2012 ). Failure: Experiencing failure has been hypothesized to be an important aspect of undergraduate research experiences that may help students develop some the most distinguishing abilities of outstanding scientists, such as coping with failure, navigating challenges, and persevering ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, experiencing failure and the stress and fatigue that often accompany it may be particularly tough for students with depression ( Aldwin and Greenberger, 1987 ; Mongrain and Blackburn, 2005 ). Lab environment: Fairness, inclusion/exclusion, and social support within one’s organizational environment have been shown to be key factors that cause people to either want to remain in the work place and be productive or to want to leave ( Barak et al. , 2006 ; Cooper et al. , 2019c ). We hypothesize that dealing with exclusion or a lack of social support may exacerbate depression for some students; patients with clinical depression react to social exclusion with more pronounced negative emotions than do individuals without clinical depression ( Jobst et al. , 2015 ). While there are likely other aspects of undergraduate research that affect student depression, we hypothesize that these factors have the potential to exacerbate negative research experiences for students with depression.

Depression has been shown to disproportionately affect many populations that are underrepresented or underserved within the scientific community, including females ( American College Health Association, 2018 ; Evans et al. , 2018 ), first-generation college students ( Jenkins et al. , 2013 ), individuals from low socioeconomic backgrounds ( Eisenberg et al. , 2007 ), members of the LGBTQ+ community ( Eisenberg et al. , 2007 ; Evans et al. , 2018 ), and people with disabilities ( Turner and Noh, 1988 ). Therefore, as the science community strives to be more diverse and inclusive ( Intemann, 2009 ), it is important that we understand more about the relationship between depression and scientific research, because negative experiences with depression in scientific research may be contributing to the underrepresentation of these groups. Specifically, more information is needed about how the research process and environment of research experiences may affect depression.

Given the high rate of depression among undergraduates, the links between depression and graduate research, the potentially challenging environment of undergraduate research, and how depression could disproportionately impact students from underserved communities, it is imperative to begin to explore the relationship between scientific research and depression among undergraduates to create research experiences that could maximize student success. In this exploratory interview study, we aimed to 1) describe how undergraduates’ symptoms of depression affect their research experiences, 2) understand how undergraduate research affects students’ feelings of depression, and 3) identify recommendations based on the literature and undergraduates’ reported experiences to promote a positive research experience for students with depression.

This study was done with an approved Arizona State University Institutional Review Board protocol #7247.

In Fall 2018, we surveyed undergraduate researchers majoring in the life sciences across 25 research-intensive (R1) public institutions across the United States (specific details about the recruitment of the students who completed the survey can be found in Cooper et al. (2019c) ). The survey asked students for their opinions about their undergraduate research experiences and their demographic information and whether they would be interested in participating in a follow-up interview related to their research experiences. For the purpose of this study, we exclusively interviewed students about their undergraduate research experiences in faculty member labs; we did not consider students’ experiences in CUREs. Of the 768 undergraduate researchers who completed the survey, 65% ( n = 496) indicated that they would be interested in participating in a follow-up interview. In Spring 2019, we emailed the 496 students, explaining that we were interested in interviewing students with depression about their experiences in undergraduate research. Our specific prompt was: “If you identify as having depression, we would be interested in hearing about your experience in undergraduate research in a 30–60 minute online interview.” We did not define depression in our email recruitment because we conducted think-aloud interviews with four undergraduates who all correctly interpreted what we meant by depression ( APA, 2013 ). We had 35 students agree to participate in the interview study. The interview participants represented 12 of the 25 R1 public institutions that were represented in the initial survey.

Student Interviews

We developed an interview script to explore our research questions. Specifically, we were interested in how students’ symptoms of depression affect their research experiences, how undergraduate research negatively affects student depression, and how undergraduate research positively affects student depression.

We recognized that mental health, and specifically depression, can be a sensitive topic to discuss with undergraduates, and therefore we tried to minimize any discomfort that the interviewees might experience during the interview. Specifically, we conducted think-aloud interviews with three graduate students who self-identified with having depression at the time of the interview. We asked them to note whether any interview questions made them uncomfortable. We also sought their feedback on questions given their experiences as persons with depression who had once engaged in undergraduate research. We revised the interview protocol after each think-aloud interview. Next, we conducted four additional think-aloud interviews with undergraduates conducting basic science or biology education research who identified with having depression to establish cognitive validity of the questions and to elicit additional feedback about any questions that might make someone uncomfortable. The questions were revised after each think-aloud interview until no question was unclear or misinterpreted by the students and we were confident that the questions minimized students’ potential discomfort ( Trenor et al. , 2011 ). A copy of the final interview script can be found in the Supplemental Material.

All interviews were individually conducted by one of two researchers (K.M.C. and L.E.G.) who conducted the think-aloud interviews together to ensure that their interviewing practices were as similar as possible. The interviews were approximately an hour long, and students received a $15 gift card for their participation.

Personal, Research, and Depression Demographics

All student demographics and information about students’ research experiences were collected using the survey distributed to students in Fall 2018. We collected personal demographics, including the participants’ gender, race/ethnicity, college generation status, transfer status, financial stability, year in college, major, and age. We also collected information about the students’ research experiences, including the length of their first research experiences, the average number of hours they spend in research per week, how they were compensated for research, who their primary mentors were, and the focus areas of their research.

In the United States, mental healthcare is disproportionately unavailable to Black and Latinx individuals, as well as those who come from low socioeconomic backgrounds ( Kataoka et al. , 2002 ; Howell and McFeeters, 2008 ; Santiago et al. , 2013 ). Therefore, to minimize a biased sample, we invited anyone who identified with having depression to participate in our study; we did not require students to be diagnosed with depression or to be treated for depression in order to participate. However, we did collect information about whether students had been formally diagnosed with depression and whether they had been treated for depression. After the interview, all participants were sent a link to a short survey that asked them if they had ever been diagnosed with depression and how, if at all, they had ever been treated for depression. A copy of these survey questions can be found in the Supplemental Material. The combined demographic information of the participants is in Table 1 . The demographics for each individual student can be found in the Supplemental Material.

a Students reported the time they had spent in research 6 months before being interviewed and only reported on the length of time of their first research experiences.

b Students were invited to report multiple ways in which they were treated for their depression; other treatments included lifestyle changes and meditation.

c Students were invited to report multiple means of compensation for their research if they had been compensated for their time in different ways.

d Students were asked whether they felt financially stable, particularly during the undergraduate research experience.

e Students reported who they work/worked with most closely during their research experiences.

f Staff members included lab coordinators or lab managers.

g Other focus areas of research included sociology, linguistics, psychology, and public health.

Interview Analysis

The initial interview analysis aimed to explore each idea that a participant expressed ( Charmaz, 2006 ) and to identify reoccurring ideas throughout the interviews. First, three authors (K.M.C., L.E.G., and S.E.B.) individually reviewed a different set of 10 interviews and took detailed analytic notes ( Birks and Mills, 2015 ). Afterward, the authors compared their notes and identified reoccurring themes throughout the interviews using open coding methods ( Saldaña, 2015 ).

Once an initial set of themes was established, two researchers (K.M.C. and L.E.G.) individually reviewed the same set of 15 randomly selected interviews to validate the themes identified in the initial analysis and to screen for any additional themes that the initial analysis may have missed. Each researcher took detailed analytic notes throughout the review of an interview, which they discussed after reviewing each interview. The researchers compared what quotes from each interview they categorized into each theme. Using constant comparison methods, they assigned quotes to each theme and constantly compared the quotes to ensure that each quote fit within the description of the theme ( Glesne and Peshkin, 1992 ). In cases in which quotes were too different from other quotes, a new theme was created. This approach allowed for multiple revisions of the themes and allowed the authors to define a final set of codes; the researchers created a final codebook with refined definitions of emergent themes (the final coding rubric can be found in the Supplemental Material). Once the final codebook was established, the researchers (K.M.C. and L.E.G.) individually coded seven additional interviews (20% of all interviews) using the coding rubric. The researchers compared their codes, and their Cohen’s κ interrater score for these seven interviews was at an acceptable level (κ  =  0.88; Landis and Koch, 1977 ). One researcher (L.E.G.) coded the remaining 28 out of 35 interviews. The researchers determined that data saturation had been reached with the current sample and no further recruitment was needed ( Guest et al. , 2006 ). We report on themes that were mentioned by at least 20% of students in the interview study. In the Supplemental Material, we provide the final coding rubric with the number of participants whose interview reflected each theme ( Hannah and Lautsch, 2011 ). Reporting the number of individuals who reported themes within qualitative data can lead to inaccurate conclusions about the generalizability of the results to a broader population. These qualitative data are meant to characterize a landscape of experiences that students with depression have in undergraduate research rather than to make claims about the prevalence of these experiences ( Glesne and Peshkin, 1992 ). Because inferences about the importance of these themes cannot be drawn from these counts, they are not included in the results of the paper ( Maxwell, 2010 ). Further, the limited number of interviewees made it not possible to examine whether there were trends based on students’ demographics or characteristics of their research experiences (e.g., their specific area of study). Quotes were lightly edited for clarity by inserting clarification brackets and using ellipses to indicate excluded text. Pseudonyms were given to all students to protect their privacy.

The Effect of Depressive Symptoms on Undergraduate Research

We asked students to describe the symptoms associated with their depression. Students described experiencing anxiety that is associated with their depression; this could be anxiety that precedes their depression or anxiety that results from a depressive episode or a period of time when an individual has depression symptoms. Further, students described difficulty getting out of bed or leaving the house, feeling tired, a lack of motivation, being overly self-critical, feeling apathetic, and having difficulty concentrating. We were particularly interested in how students’ symptoms of depression affected their experiences in undergraduate research. During the think-aloud interviews that were conducted before the interview study, graduate and undergraduate students consistently described that their depression affected their motivation in research, their creativity in research, and their productivity in research. Therefore, we explicitly asked undergraduate researchers how, if at all, their depression affected these three factors. We also asked students to describe any additional ways in which their depression affected their research experiences. Undergraduate researchers commonly described five additional ways in which their depression affected their research; for a detailed description of each way students’ research was affected and for example quotes, see Table 2 . Students described that their depression negatively affected their productivity in the lab. Commonly, students described that their productivity was directly affected by a lack of motivation or because they felt less creative, which hindered the research process. Additionally, students highlighted that they were sometimes less productive because their depression sometimes caused them to struggle to engage intellectually with their research or caused them to have difficulty remembering or concentrating; students described that they could do mundane or routine tasks when they felt depressed, but that they had difficulty with more complex and intellectually demanding tasks. However, students sometimes described that even mundane tasks could be difficult when they were required to remember specific steps; for example, some students struggled recalling a protocol from memory when their depression was particularly severe. Additionally, students noted that their depression made them more self-conscious, which sometimes held them back from sharing research ideas with their mentors or from taking risks such as applying to competitive programs. In addition to being self-conscious, students highlighted that their depression caused them to be overly self-critical, and some described experiencing imposter phenomenon ( Clance and Imes, 1978 ) or feeling like they were not talented enough to be in research and were accepted into a lab by a fluke or through luck. Finally, students described that depression often made them feel less social, and they struggled to socially engage with other members of the lab when they were feeling down.

The Effect of Undergraduate Research Experiences on Student Depression

We also wanted to explore how research impacted students’ feelings of depression. Undergraduates described how research both positively and negatively affected their depression. In the following sections, we present aspects of undergraduate research and examine how each positively and/or negatively affected students’ depression using embedded student quotes to highlight the relationships between related ideas.

Lab Environment: Relationships with Others in the Lab.

Some aspects of the lab environment, which we define as students’ physical, social, or psychological research space, could be particularly beneficial for students with depression.

Specifically, undergraduate researchers perceived that comfortable and positive social interactions with others in the lab helped their depression. Students acknowledged how beneficial their relationships with graduate students and postdocs could be.

Marta: “I think always checking in on undergrads is important. It’s really easy [for us] to go a whole day without talking to anybody in the lab. But our grad students are like ‘Hey, what’s up? How’s school? What’s going on?’ (…) What helps me the most is having that strong support system. Sometimes just talking makes you feel better, but also having people that believe in you can really help you get out of that negative spiral. I think that can really help with depression.”

Kelley: “I know that anytime I need to talk to [my postdoc mentors] about something they’re always there for me. Over time we’ve developed a relationship where I know that outside of work and outside of the lab if I did want to talk to them about something I could talk to them. Even just talking to someone about hobbies and having that relationship alone is really helpful [for depression].”

In addition to highlighting the importance of developing relationships with graduate students or postdocs in the lab, students described that forming relationships with other undergraduates in the lab also helped their depression. Particularly, students described that other undergraduate researchers often validated their feelings about research, which in turn helped them realize that what they are thinking or feeling is normal, which tended to alleviate their negative thoughts. Interestingly, other undergraduates experiencing the same issues could sometimes help buffer them from perceiving that a mentor did not like them or that they were uniquely bad at research. In this article, we use the term “mentor” to refer to anyone who students referred to in the interviews as being their mentors or managing their research experiences; this includes graduate students, postdoctoral scholars, lab managers, and primary investigators (PIs).

Abby: “One of my best friends is in the lab with me.  A lot of that friendship just comes from complaining about our stress with the lab and our annoyance with people in the lab. Like when we both agree like, ‘Yeah, the grad students were really off today, it wasn’t us,’ that helps. ‘It wasn’t me, it wasn’t my fault that we were having a rough day in lab; it was the grad students.’ Just being able to realize, ‘Hey, this isn’t all caused by us,’ you know? (…) We understand the stresses in the lab. We understand the details of what each other are doing in the lab, so when something doesn’t work out, we understand that it took them like eight hours to do that and it didn’t work. We provide empathy on a different level.”

Meleana: “It’s great to have solidarity in being confused about something, and it’s just that is a form of validation for me too. When we leave a lab meeting and I look at [another undergrad] I’m like, ‘Did you understand anything that they were just saying?’ And they’re like, ‘Oh, no.’ (…) It’s just really validating to hear from the other undergrads that we all seem to be struggling with the same things.”

Developing positive relationships with faculty mentors or PIs also helped alleviate some students’ depressive feelings, particularly when PIs shared their own struggles with students. This also seemed to normalize students’ concerns about their own experiences.

Alexandra: “[Talking with my PI] is helpful because he would talk about his struggles, and what he faced. A lot of it was very similar to my struggles.  For example, he would say, ‘Oh, yeah, I failed this exam that I studied so hard for. I failed the GRE and I paid so much money to prepare for it.’ It just makes [my depression] better, like okay, this is normal for students to go through this. It’s not an out of this world thing where if you fail, you’re a failure and you can’t move on from it.”

Students’ relationships with others in the lab did not always positively impact their depression. Students described instances when the negative moods of the graduate students and PIs would often set the tone of the lab, which in turn worsened the mood of the undergraduate researchers.

Abby: “Sometimes [the grad students] are not in a good mood. The entire vibe of the lab is just off, and if you make a joke and it hits somebody wrong, they get all mad. It really depends on the grad students and the leadership and the mood that they’re in.”

Interviewer: “How does it affect your depression when the grad students are in a bad mood?”

Abby: “It definitely makes me feel worse. It feels like, again, that I really shouldn’t go ask them for help because they’re just not in the mood to help out. It makes me have more pressure on myself, and I have deadlines I need to meet, but I have a question for them, but they’re in a bad mood so I can’t ask. That’s another day wasted for me and it just puts more stress, which just adds to the depression.”

Additionally, some students described even more concerning behavior from research mentors, which negatively affected their depression.

Julie: “I had a primary investigator who is notorious in the department for screaming at people, being emotionally abusive, unreasonable, et cetera. (…) [He was] kind of harassing people, demeaning them, lying to them, et cetera, et cetera. (…) Being yelled at and constantly demeaned and harassed at all hours of the day and night, that was probably pretty bad for me.”

While the relationships between undergraduates and graduate, postdoc, and faculty mentors seemed to either alleviate or worsen students’ depressive symptoms, depending on the quality of the relationship, students in this study exclusively described their relationships with other undergraduates as positive for their depression. However, students did note that undergraduate research puts some of the best and brightest undergraduates in the same environment, which can result in students comparing themselves with their peers. Students described that this comparison would often lead them to feel badly about themselves, even though they would describe their personal relationship with a person to be good.

Meleana: “In just the research field in general, just feeling like I don’t really measure up to the people around me [can affect my depression]. A lot of the times it’s the beginning of a little spiral, mental spiral. There are some past undergrads that are talked about as they’re on this pedestal of being the ideal undergrads and that they were just so smart and contributed so much to the lab. I can never stop myself from wondering like, ‘Oh, I wonder if I’m having a contribution to the lab that’s similar or if I’m just another one of the undergrads that does the bare minimum and passes through and is just there.’”

Natasha: “But, on the other hand, [having another undergrad in the lab] also reminded me constantly that some people are invested in this and meant to do this and it’s not me. And that some people know a lot more than I do and will go further in this than I will.”

While students primarily expressed that their relationships with others in the lab affected their depression, some students explained that they struggled most with depression when the lab was empty; they described that they did not like being alone in the lab, because a lack of stimulation allowed their minds to be filled with negative thoughts.

Mia: “Those late nights definitely didn’t help [my depression]. I am alone, in the entire building.  I’m left alone to think about my thoughts more, so not distracted by talking to people or interacting with people. I think more about how I’m feeling and the lack of progress I’m making, and the hopelessness I’m feeling. That kind of dragged things on, and I guess deepened my depression.”

Freddy: “Often times when I go to my office in the evening, that is when I would [ sic ] be prone to be more depressed. It’s being alone. I think about myself or mistakes or trying to correct mistakes or whatever’s going on in my life at the time. I become very introspective. I think I’m way too self-evaluating, way too self-deprecating and it’s when I’m alone when those things are really, really triggered. When I’m talking with somebody else, I forget about those things.”

In sum, students with depression highlighted that a lab environment full of positive and encouraging individuals was helpful for their depression, whereas isolating or competitive environments and negative interactions with others often resulted in more depressive feelings.

Doing Science: Experiencing Failure in Research, Getting Help, Receiving Feedback, Time Demands, and Important Contributions.

In addition to the lab environment, students also described that the process of doing science could affect their depression. Specifically, students explained that a large contributor to their depression was experiencing failure in research.

Interviewer: “Considering your experience in undergraduate research, what tends to trigger your feelings of depression?”

Heather: “Probably just not getting things right. Having to do an experiment over and over again. You don’t get the results you want. (…) The work is pretty meticulous and it’s frustrating when I do all this work, I do a whole experiment, and then I don’t get any results that I can use. That can be really frustrating. It adds to the stress. (…) It’s hard because you did all this other stuff before so you can plan for the research, and then something happens and all the stuff you did was worthless basically.”

Julie: “I felt very negatively about myself [when a project failed] and pretty panicked whenever something didn’t work because I felt like it was a direct reflection on my effort and/or intelligence, and then it was a big glaring personal failure.”

Students explained that their depression related to failing in research was exacerbated if they felt as though they could not seek help from their research mentors. Perceived insufficient mentor guidance has been shown to be a factor influencing student intention to leave undergraduate research ( Cooper et al. , 2019c ). Sometimes students talked about their research mentors being unavailable or unapproachable.

Michelle: “It just feels like [the graduate students] are not approachable. I feel like I can’t approach them to ask for their understanding in a certain situation. It makes [my depression] worse because I feel like I’m stuck, and that I’m being limited, and like there’s nothing I can do. So then I kind of feel like it’s my fault that I can’t do anything.”

Other times, students described that they did not seek help in fear that they would be negatively evaluated in research, which is a fear of being judged by others ( Watson and Friend, 1969 ; Weeks et al. , 2005 ; Cooper et al. , 2018 ). That is, students fear that their mentor would think negatively about them or judge them if they were to ask questions that their mentor thought they should know the answer to.

Meleana: “I would say [my depression] tends to come out more in being more reserved in asking questions because I think that comes more like a fear-based thing where I’m like, ‘Oh, I don’t feel like I’m good enough and so I don’t want to ask these questions because then my mentors will, I don’t know, think that I’m dumb or something.’”

Conversely, students described that mentors who were willing to help them alleviated their depressive feelings.

Crystal: “Yeah [my grad student] is always like, ‘Hey, I can check in on things in the lab because you’re allowed to ask me for that, you’re not totally alone in this,’ because he knows that I tend to take on all this responsibility and I don’t always know how to ask for help. He’s like, ‘You know, this is my lab too and I am here to help you as well,’ and just reminds me that I’m not shouldering this burden by myself.”

Ashlyn: “The graduate student who I work with is very kind and has a lot of patience and he really understands a lot of things and provides simple explanations. He does remind me about things and he will keep on me about certain tasks that I need to do in an understanding way, and it’s just because he’s patient and he listens.”

In addition to experiencing failure in science, students described that making mistakes when doing science also negatively affected their depression.

Abby: “I guess not making mistakes on experiments [is important in avoiding my depression]. Not necessarily that your experiment didn’t turn out to produce the data that you wanted, but just adding the wrong enzyme or messing something up like that. It’s like, ‘Oh, man,’ you know? You can get really down on yourself about that because it can be embarrassing.”

Commonly, students described that the potential for making mistakes increased their stress and anxiety regarding research; however, they explained that how other people responded to a potential mistake was what ultimately affected their depression.

Briana: “Sometimes if I made a mistake in correctly identifying an eye color [of a fly], [my PI] would just ridicule me in front of the other students. He corrected me but his method of correcting was very discouraging because it was a ridicule. It made the others laugh and I didn’t like that.”

Julie: “[My PI] explicitly [asked] if I had the dedication for science. A lot of times he said I had terrible judgment. A lot of times he said I couldn’t be trusted. Once I went to a conference with him, and, unfortunately, in front of another professor, he called me a klutz several times and there was another comment about how I never learn from my mistakes.”

When students did do things correctly, they described how important it could be for them to receive praise from their mentors. They explained that hearing praise and validation can be particularly helpful for students with depression, because their thoughts are often very negative and/or because they have low self-esteem.

Crystal: “[Something that helps my depression is] I have text messages from [my graduate student mentor] thanking me [and another undergraduate researcher] for all of the work that we’ve put in, that he would not be able to be as on track to finish as he is if he didn’t have our help.”

Interviewer: “Why is hearing praise from your mentor helpful?”

Crystal: “Because a lot of my depression focuses on everybody secretly hates you, nobody likes you, you’re going to die alone. So having that validation [from my graduate mentor] is important, because it flies in the face of what my depression tells me.”

Brian: “It reminds you that you exist outside of this negative world that you’ve created for yourself, and people don’t see you how you see yourself sometimes.”

Students also highlighted how research could be overwhelming, which negatively affected their depression. Particularly, students described that research demanded a lot of their time and that their mentors did not always seem to be aware that they were juggling school and other commitments in addition to their research. This stress exacerbated their depression.

Rose: “I feel like sometimes [my grad mentors] are not very understanding because grad students don’t take as many classes as [undergrads] do. I think sometimes they don’t understand when I say I can’t come in at all this week because I have finals and they’re like, ‘Why though?’”

Abby: “I just think being more understanding of student life would be great. We have classes as well as the lab, and classes are the priority. They forget what it’s like to be a student. You feel like they don’t understand and they could never understand when you say like, ‘I have three exams this week,’ and they’re like, ‘I don’t care. You need to finish this.’”

Conversely, some students reported that their research labs were very understanding of students’ schedules. Interestingly, these students talked most about how helpful it was to be able to take a mental health day and not do research on days when they felt down or depressed.

Marta: “My lab tech is very open, so she’ll tell us, ‘I can’t come in today. I have to take a mental health day.’ So she’s a really big advocate for that. And I think I won’t personally tell her that I’m taking a mental health day, but I’ll say, ‘I can’t come in today, but I’ll come in Friday and do those extra hours.’ And she’s like, ‘OK great, I’ll see you then.’  And it makes me feel good, because it helps me take care of myself first and then I can take care of everything else I need to do, which is amazing.”

Meleana: “Knowing that [my mentors] would be flexible if I told them that I’m crazy busy and can’t come into work nearly as much this week [helps my depression]. There is flexibility in allowing me to then care for myself.”

Interviewer: “Why is the flexibility helpful given the depression?”

Meleana: “Because sometimes for me things just take a little bit longer when I’m feeling down. I’m just less efficient to be honest, and so it’s helpful if I feel like I can only go into work for 10 hours in a week. It declutters my brain a little bit to not have to worry about all the things I have to do in work in addition the things that I need to do for school or clubs, or family or whatever.”

Despite the demanding nature of research, a subset of students highlighted that their research and research lab provided a sense of stability or familiarity that distracted them from their depression.

Freddy: “I’ll [do research] to run away from those [depressive] feelings or whatever. (…) I find sadly, I hate to admit it, but I do kind of run to [my lab]. I throw myself into work to distract myself from the feelings of depression and sadness.”

Rose: “When you’re sad or when you’re stressed you want to go to things you’re familiar with. So because lab has always been in my life, it’s this thing where it’s going to be there for me I guess. It’s like a good book that you always go back to and it’s familiar and it makes you feel good. So that’s how lab is. It’s not like the greatest thing in the world but it’s something that I’m used to, which is what I feel like a lot of people need when they’re sad and life is not going well.”

Many students also explained that research positively affects their depression because they perceive their research contribution to be important.

Ashlyn: “I feel like I’m dedicating myself to something that’s worthy and something that I believe in. It’s really important because it contextualizes those times when I am feeling depressed. It’s like, no, I do have these better things that I’m working on. Even when I don’t like myself and I don’t like who I am, which is again, depression brain, I can at least say, ‘Well, I have all these other people relying on me in research and in this area and that’s super important.’”

Jessica: “I mean, it just felt like the work that I was doing had meaning and when I feel like what I’m doing is actually going to contribute to the world, that usually really helps with [depression] because it’s like not every day you can feel like you’re doing something impactful.”

In sum, students highlighted that experiencing failure in research and making mistakes negatively contributed to depression, especially when help was unavailable or research mentors had a negative reaction. Additionally, students acknowledged that the research could be time-consuming, but that research mentors who were flexible helped assuage depressive feelings that were associated with feeling overwhelmed. Finally, research helped some students’ depression, because it felt familiar, provided a distraction from depression, and reminded students that they were contributing to a greater cause.

We believe that creating more inclusive research environments for students with depression is an important step toward broadening participation in science, not only to ensure that we are not discouraging students with depression from persisting in science, but also because depression has been shown to disproportionately affect underserved and underrepresented groups in science ( Turner and Noh, 1988 ; Eisenberg et al. , 2007 ; Jenkins et al. , 2013 ; American College Health Association, 2018 ). We initially hypothesized that three features of undergraduate research—research mentors, the lab environment, and failure—may have the potential to exacerbate student depression. We found this to be true; students highlighted that their relationships with their mentors as well as the overall lab environment could negatively affect their depression, but could also positively affect their research experiences. Students also noted that they struggled with failure, which is likely true of most students, but is known to be particularly difficult for students with depression ( Elliott et al. , 1997 ). We expand upon our findings by integrating literature on depression with the information that students provided in the interviews about how research mentors can best support students. We provide a set of evidence-based recommendations focused on mentoring, the lab environment, and failure for research mentors wanting to create more inclusive research environments for students with depression. Notably, only the first recommendation is specific to students with depression; the others reflect recommendations that have previously been described as “best practices” for research mentors ( NASEM, 2017 , 2019 ; Sorkness et al. , 2017 ) and likely would benefit most students. However, we examine how these recommendations may be particularly important for students with depression. As we hypothesized, these recommendations directly address three aspects of research: mentors, lab environment, and failure. A caveat of these recommendations is that more research needs to be done to explore the experiences of students with depression and how these practices actually impact students with depression, but our national sample of undergraduate researchers with depression can provide an initial starting point for a discussion about how to improve research experiences for these students.

Recommendations to Make Undergraduate Research Experiences More Inclusive for Students with Depression

Recognize student depression as a valid illness..

Allow students with depression to take time off of research by simply saying that they are sick and provide appropriate time for students to recover from depressive episodes. Also, make an effort to destigmatize mental health issues.

Undergraduate researchers described both psychological and physical symptoms that manifested as a result of their depression and highlighted how such symptoms prevented them from performing to their full potential in undergraduate research. For example, students described how their depression would cause them to feel unmotivated, which would often negatively affect their research productivity. In cases in which students were motivated enough to come in and do their research, they described having difficulty concentrating or engaging in the work. Further, when doing research, students felt less creative and less willing to take risks, which may alter the quality of their work. Students also sometimes struggled to socialize in the lab. They described feeling less social and feeling overly self-critical. In sum, students described that, when they experienced a depressive episode, they were not able to perform to the best of their ability, and it sometimes took a toll on them to try to act like nothing was wrong, when they were internally struggling with depression. We recommend that research mentors treat depression like any other physical illness; allowing students the chance to recover when they are experiencing a depressive episode can be extremely important to students and can allow them to maximize their productivity upon returning to research ( Judd et al. , 2000 ). Students explained that if they are not able to take the time to focus on recovering during a depressive episode, then they typically continue to struggle with depression, which negatively affects their research. This sentiment is echoed by researchers in psychiatry who have found that patients who do not fully recover from a depressive episode are more likely to relapse and to experience chronic depression ( Judd et al. , 2000 ). Students described not doing tasks or not showing up to research because of their depression but struggling with how to share that information with their research mentors. Often, students would not say anything, which caused them anxiety because they were worried about what others in the lab would say to them when they returned. Admittedly, many students understood why this behavior would cause their research mentors to be angry or frustrated, but they weighed the consequences of their research mentors’ displeasure against the consequences of revealing their depression and decided it was not worth admitting to being depressed. This aligns with literature that suggests that when individuals have concealable stigmatized identities, or identities that can be hidden and that carry negative stereotypes, such as depression, they will often keep them concealed to avoid negative judgment or criticism ( Link and Phelan, 2001 ; Quinn and Earnshaw, 2011 ; Jones and King, 2014 ; Cooper and Brownell, 2016 ; Cooper et al. , 2019b ; Cooper et al ., unpublished data ). Therefore, it is important for research mentors to be explicit with students that 1) they recognize mental illness as a valid sickness and 2) that students with mental illness can simply explain that they are sick if they need to take time off. This may be useful to overtly state on a research website or in a research syllabus, contract, or agreement if mentors use such documents when mentoring undergraduates in their lab. Further, research mentors can purposefully work to destigmatize mental health issues by explicitly stating that struggling with mental health issues, such as depression and anxiety, is common. While we do not recommend that mentors ask students directly about depression, because this can force students to share when they are not comfortable sharing, we do recommend providing opportunities for students to reveal their depression ( Chaudoir and Fisher, 2010 ). Mentors can regularly check in with students about how they’re doing, and talk openly about the importance of mental health, which may increase the chance that students may feel comfortable revealing their depression ( Chaudoir and Quinn, 2010 ; Cooper et al ., unpublished data ).

Foster a Positive Lab Environment.

Encourage positivity in the research lab, promote working in shared spaces to enhance social support among lab members, and alleviate competition among undergraduates.

Students in this study highlighted that the “leadership” of the lab, meaning graduate students, postdocs, lab managers, and PIs, were often responsible for establishing the tone of the lab; that is, if they were in a bad mood it would trickle down and negatively affect the moods of the undergraduates. Explicitly reminding lab leadership that their moods can both positively and negatively affect undergraduates may be important in establishing a positive lab environment. Further, students highlighted how they were most likely to experience negative thoughts when they were alone in the lab. Therefore, it may be helpful to encourage all lab members to work in a shared space to enhance social interactions among students and to maximize the likelihood that undergraduates have access to help when needed. A review of 51 studies in psychiatry supported our undergraduate researchers’ perceptions that social relationships positively impacted their depression; the study found that perceived emotional support (e.g., someone available to listen or give advice), perceived instrumental support (e.g., someone available to help with tasks), and large diverse social networks (e.g., being socially connected to a large number of people) were significantly protective against depression ( Santini et al. , 2015 ). Additionally, despite forming positive relationships with other undergraduates in the lab, many undergraduate researchers admitted to constantly comparing themselves with other undergraduates, which led them to feel inferior, negatively affecting their depression. Some students talked about mentors favoring current undergraduates or talking positively about past undergraduates, which further exacerbated their feelings of inferiority. A recent study of students in undergraduate research experiences highlighted that inequitable distribution of praise to undergraduates can create negative perceptions of lab environments for students (Cooper et al. , 2019). Further, the psychology literature has demonstrated that when people feel insecure in their social environments, it can cause them to focus on a hierarchical view of themselves and others, which can foster feelings of inferiority and increase their vulnerability to depression ( Gilbert et al. , 2009 ). Thus, we recommend that mentors be conscious of their behaviors so that they do not unintentionally promote competition among undergraduates or express favoritism toward current or past undergraduates. Praise is likely best used without comparison with others and not done in a public way, although more research on the impact of praise on undergraduate researchers needs to be done. While significant research has been done on mentoring and mentoring relationships in the context of undergraduate research ( Byars-Winston et al. , 2015 ; Aikens et al. , 2017 ; Estrada et al. , 2018 ; Limeri et al. , 2019 ; NASEM, 2019 ), much less has been done on the influence of the lab environment broadly and how people in nonmentoring roles can influence one another. Yet, this study indicates the potential influence of many different members of the lab, not only their mentors, on students with depression.

Develop More Personal Relationships with Undergraduate Researchers and Provide Sufficient Guidance.

Make an effort to establish more personal relationships with undergraduates and ensure that they perceive that they have access to sufficient help and guidance with regard to their research.

When we asked students explicitly how research mentors could help create more inclusive environments for undergraduate researchers with depression, students overwhelmingly said that building mentor–student relationships would be extremely helpful. Students suggested that mentors could get to know students on a more personal level by asking about their career interests or interests outside of academia. Students also remarked that establishing a more personal relationship could help build the trust needed in order for undergraduates to confide in their research mentors about their depression, which they perceived would strengthen their relationships further because they could be honest about when they were not feeling well or their mentors might even “check in” with them in times where they were acting differently than normal. This aligns with studies showing that undergraduates are most likely to reveal a stigmatized identity, such as depression, when they form a close relationship with someone ( Chaudoir and Quinn, 2010 ). Many were intimidated to ask for research-related help from their mentors and expressed that they wished they had established a better relationship so that they would feel more comfortable. Therefore, we recommend that research mentors try to establish relationships with their undergraduates and explicitly invite them to ask questions or seek help when needed. These recommendations are supported by national recommendations for mentoring ( NASEM, 2019 ) and by literature that demonstrates that both social support (listening and talking with students) and instrumental support (providing students with help) have been shown to be protective against depression ( Santini et al. , 2015 ).

Treat Undergraduates with Respect and Remember to Praise Them.

Avoid providing harsh criticism and remember to praise undergraduates. Students with depression often have low self-esteem and are especially self-critical. Therefore, praise can help calibrate their overly negative self-perceptions.

Students in this study described that receiving criticism from others, especially harsh criticism, was particularly difficult for them given their depression. Multiple studies have demonstrated that people with depression can have an abnormal or maladaptive response to negative feedback; scientists hypothesize that perceived failure on a particular task can trigger failure-related thoughts that interfere with subsequent performance ( Eshel and Roiser, 2010 ). Thus, it is important for research mentors to remember to make sure to avoid unnecessarily harsh criticisms that make students feel like they have failed (more about failure is described in the next recommendation). Further, students with depression often have low self-esteem or low “personal judgment of the worthiness that is expressed in the attitudes the individual holds towards oneself” ( Heatherton et al. , 2003 , p. 220; Sowislo and Orth, 2013 ). Specifically, a meta-analysis of longitudinal studies found that low self-esteem is predictive of depression ( Sowislo and Orth, 2013 ), and depression has also been shown to be highly related to self-criticism ( Luyten et al. , 2007 ). Indeed, nearly all of the students in our study described thinking that they are “not good enough,” “worthless,” or “inadequate,” which is consistent with literature showing that people with depression are self-critical ( Blatt et al. , 1982 ; Gilbert et al. , 2006 ) and can be less optimistic of their performance on future tasks and rate their overall performance on tasks less favorably than their peers without depression ( Cane and Gotlib, 1985 ). When we asked students what aspects of undergraduate research helped their depression, students described that praise from their mentors was especially impactful, because they thought so poorly of themselves and they needed to hear something positive from someone else in order to believe it could be true. Praise has been highlighted as an important aspect of mentoring in research for many years ( Ashford, 1996 ; Gelso and Lent, 2000 ; Brown et al. , 2009 ) and may be particularly important for students with depression. In fact, praise has been shown to enhance individuals’ motivation and subsequent productivity ( Hancock, 2002 ; Henderlong and Lepper, 2002 ), factors highlighted by students as negatively affecting their depression. However, something to keep in mind is that a student with depression and a student without depression may process praise differently. For a student with depression, a small comment that praises the student’s work may not be sufficient for the student to process that comment as praise. People with depression are hyposensitive to reward or have reward-processing deficits ( Eshel and Roiser, 2010 ); therefore, praise may affect students without depression more positively than it would affect students with depression. Research mentors should be mindful that students with depression often have a negative view of themselves, and while students report that praise is extremely important, they may have trouble processing such positive feedback.

Normalize Failure and Be Explicit about the Importance of Research Contributions.

Explicitly remind students that experiencing failure is expected in research. Also explain to students how their individual work relates to the overall project so that they can understand how their contributions are important. It can also be helpful to explain to students why the research project as a whole is important in the context of the greater scientific community.

Experiencing failure has been thought to be a potentially important aspect of undergraduate research, because it may provide students with the potential to develop integral scientific skills such as the ability to navigate challenges and persevere ( Laursen et al. , 2010 ; Gin et al. , 2018 ; Henry et al. , 2019 ). However, in the interviews, students described that when their science experiments failed, it was particularly tough for their depression. Students’ negative reaction to experiencing failure in research is unsurprising, given recent literature that has predicted that students may be inadequately prepared to approach failure in science ( Henry et al. , 2019 ). However, the literature suggests that students with depression may find experiencing failure in research to be especially difficult ( Elliott et al. , 1997 ; Mongrain and Blackburn, 2005 ; Jones et al. , 2009 ). One potential hypothesis is that students with depression may be more likely to have fixed mindsets or more likely to believe that their intelligence and capacity for specific abilities are unchangeable traits ( Schleider and Weisz, 2018 ); students with a fixed mindset have been hypothesized to have particularly negative responses to experiencing failure in research, because they are prone to quitting easily in the face of challenges and becoming defensive when criticized ( Forsythe and Johnson, 2017 ; Dweck, 2008 ). A study of life sciences undergraduates enrolled in CUREs identified three strategies of students who adopted adaptive coping mechanisms, or mechanisms that help an individual maintain well-being and/or move beyond the stressor when faced with failure in undergraduate research: 1) problem solving or engaging in strategic planning and decision making, 2) support seeking or finding comfort and help with research, and 3) cognitive restructuring or reframing a problem from negative to positive and engaging in self encouragement ( Gin et al. , 2018 ). We recommend that, when undergraduates experience failure in science, their mentors be proactive in helping them problem solve, providing help and support, and encouraging them. Students also explained that mentors sharing their own struggles as undergraduate and graduate students was helpful, because it normalized failure. Sharing personal failures in research has been recommended as an important way to provide students with psychosocial support during research ( NASEM, 2019 ). We also suggest that research mentors take time to explain to students why their tasks in the lab, no matter how small, contribute to the greater research project ( Cooper et al. , 2019a ). Additionally, it is important to make sure that students can explain how the research project as a whole is contributing to the scientific community ( Gin et al. , 2018 ). Students highlighted that contributing to something important was really helpful for their depression, which is unsurprising, given that studies have shown that meaning in life or people’s comprehension of their life experiences along with a sense of overarching purpose one is working toward has been shown to be inversely related to depression ( Steger, 2013 ).

Limitations and Future Directions

This work was a qualitative interview study intended to document a previously unstudied phenomenon: depression in the context of undergraduate research experiences. We chose to conduct semistructured interviews rather than a survey because of the need for initial exploration of this area, given the paucity of prior research. A strength of this study is the sampling approach. We recruited a national sample of 35 undergraduates engaged in undergraduate research at 12 different public R1 institutions. Despite our representative sample from R1 institutions, these findings may not be generalizable to students at other types of institutions; lab environments, mentoring structures, and interactions between faculty and undergraduate researchers may be different at other institution types (e.g., private R1 institutions, R2 institutions, master’s-granting institutions, primarily undergraduate institutions, and community colleges), so we caution against making generalizations about this work to all undergraduate research experiences. Future work could assess whether students with depression at other types of institutions have similar experiences to students at research-intensive institutions. Additionally, we intentionally did not explore the experiences of students with specific identities owing to our sample size and the small number of students in any particular group (e.g., students of a particular race, students with a graduate mentor as the primary mentor). We intend to conduct future quantitative studies to further explore how students’ identities and aspects of their research affect their experiences with depression in undergraduate research.

The students who participated in the study volunteered to be interviewed about their depression; therefore, it is possible that depression is a more salient part of these students’ identities and/or that they are more comfortable talking about their depression than the average population of students with depression. It is also important to acknowledge the personal nature of the topic and that some students may not have fully shared their experiences ( Krumpal, 2013 ), particularly those experiences that may be emotional or traumatizing ( Kahn and Garrison, 2009 ). Additionally, our sample was skewed toward females (77%). While females do make up approximately 60% of students in biology programs on average ( Eddy et al. , 2014 ), they are also more likely to report experiencing depression ( American College Health Association, 2018 ; Evans et al. , 2018 ). However, this could be because women have higher rates of depression or because males are less likely to report having depression; clinical bias, or practitioners’ subconscious tendencies to overlook male distress, may underestimate depression rates in men ( Smith et al. , 2018 ). Further, females are also more likely to volunteer to participate in studies ( Porter and Whitcomb, 2005 ); therefore, many interview studies have disproportionately more females in the data set (e.g., Cooper et al. , 2017 ). If we had been able to interview more male students, we might have identified different findings. Additionally, we limited our sample to life sciences students engaged in undergraduate research at public R1 institutions. It is possible that students in other majors may have different challenges and opportunities for students with depression, as well as different disciplinary stigmas associated with mental health.

In this exploratory interview study, we identified a variety of ways in which depression in undergraduates negatively affected their undergraduate research experiences. Specifically, we found that depression interfered with students’ motivation and productivity, creativity and risk-taking, engagement and concentration, and self-perception and socializing. We also identified that research can negatively affect depression in undergraduates. Experiencing failure in research can exacerbate student depression, especially when students do not have access to adequate guidance. Additionally, being alone or having negative interactions with others in the lab worsened students’ depression. However, we also found that undergraduate research can positively affect students’ depression. Research can provide a familiar space where students can feel as though they are contributing to something meaningful. Additionally, students reported that having access to adequate guidance and a social support network within the research lab also positively affected their depression. We hope that this work can spark conversations about how to make undergraduate research experiences more inclusive of students with depression and that it can stimulate additional research that more broadly explores the experiences of undergraduate researchers with depression.

Important note

If you or a student experience symptoms of depression and want help, there are resources available to you. Many campuses provide counseling centers equipped to provide students, staff, and faculty with treatment for depression, as well as university-dedicated crisis hotlines. Additionally, there are free 24/7 services such as Crisis Text Line, which allows you to text a trained live crisis counselor (Text “CONNECT” to 741741; Text Depression Hotline , 2019 ), and phone hotlines such as the National Suicide Prevention Lifeline at 1-800-273-8255 (TALK). You can also learn more about depression and where to find help near you through the Anxiety and Depression Association of American website: https://adaa.org ( Anxiety and Depression Association of America, 2019 ) and the Depression and Biopolar Support Alliance: http://dbsalliance.org ( Depression and Biopolar Support Alliance, 2019 ).

ACKNOWLEDGMENTS

We are extremely grateful to the undergraduate researchers who shared their thoughts and experiences about depression with us. We acknowledge the ASU LEAP Scholars for helping us create the original survey and Rachel Scott for her helpful feedback on earlier drafts of this article. L.E.G. was supported by a National Science Foundation (NSF) Graduate Fellowship (DGE-1311230) and K.M.C. was partially supported by a Howard Hughes Medical Institute (HHMI) Inclusive Excellence grant (no. 11046) and an NSF grant (no. 1644236). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF or HHMI.

literature review on depression among university students

Submitted: 4 November 2019 Revised: 24 February 2020 Accepted: 6 March 2020

© 2020 K. M. Cooper, L. E. Gin, et al. CBE—Life Sciences Education © 2020 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Prevalence of Depression among University Students: A Systematic Review and Meta-Analysis Study

Affiliation.

Introduction. Depression is one of the four major diseases in the world and is the most common cause of disability from diseases. The aim of this study is to estimate the prevalence of depression among Iranian university students using meta-analysis method. Materials and Methods. Keyword depression was searched in electronic databases such as PubMed, Scopus, MAGIran, Medlib, and SID. Data was analyzed using meta-analysis (random-effects model). Heterogeneity of studies was assessed using the I (2) index. Data was analyzed using STATA software Ver.10. Results. In 35 studies conducted in Iran from 1995 to 2012 with sample size of 9743, prevalence of depression in the university students was estimated to be 33% (95% CI: 32-34). The prevalence of depression among boys was estimated to be 28% (95% CI: 26-30), among girls 23% (95% CI: 22-24), single students 39% (95% CI: 37-41), and married students 20% (95% CI: 17-24). Metaregression model showed that the trend of depression among Iranian students was flat. Conclusions. On the whole, depression is common in university students with no preponderance between males and females and in single students is higher than married ones.

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

Xin-qiao liu.

School of Education, Tianjin University, Tianjin 300350, China. [email protected]

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

Corresponding author: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Tongyan Road, Jinnan District, Tianjin 300350, China. [email protected]

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.

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.

INTRODUCTION

The prevalence of depression among college students has gradually increased in recent years, even exceeding that of the general public, which has become a global phenomenon[ 1 ]. Mounting research has focused on the topic, and the consensus is that the high prevalence of depression among college students cannot be ignored. For instance, in Asia, a follow-up survey and analysis based on 1401 undergraduates in China over four consecutive years showed that approximately 20% to 40% of undergraduates suffered from depression, anxiety and stress to different degrees, and approximately 35% of them had higher depression levels than the normal population[ 2 ]. An online survey based on 7915 freshmen students at Hong Kong University in China showed that 21%, 41% and 27% of individuals had moderate or higher levels of depression, anxiety and stress, respectively, far exceeding the average in the general population[ 3 ]. The median prevalence rate for depression among 15859 college students in six ASEAN countries (Cambodia, Laos, Malaysia, Myanmar, Thailand and Vietnam) was 29.4%, and 7% to 8% of students committed suicide; despite the high prevalence of mental illness, their willingness to seek professional help was relatively low[ 4 ]. Among 642 college students in Saudi Arabia, the proportions of moderate depression, anxiety and stress were 53.6%, 65.7% and 34.3%, respectively[ 5 ]. In Africa, among 1206 Nigerian college students, 5.6% had mild depression, and 2.7% suffered severe depressive disorder[ 6 ]. In North America, 53% of 1455 American college students reported that they had experienced depression since the beginning of college, and 9% said they had considered suicide since the beginning of college[ 7 ]. Thirty percent of 7800 Canadian undergraduates reported that their psychological stress increased, and the degree of depression was significantly higher than that of the general population[ 8 ]. In Europe, more than one-third of college students from three higher education institutions in the United Kingdom suffered from long-term mental health diseases, the prevalence rate of which was higher than the average level of national surveys, and the scores of the eight dimensions of mental health, measured by the MOS 36-item short-form health survey, were all significantly lower than those of local peers aged 18 to 34[ 9 ]. In Oceania, 21.8% of 751 Australian college students reported depression, and their depression scores were higher than the standard scores of the general Australian population[ 10 ].

The global outbreak of the coronavirus disease 2019 (COVID-19) pandemic in 2020 brought in additional pressure and challenges for the prevention and treatment of depression among college students. Many reports worldwide voiced that college students had a greater probability of struggling with higher levels of depression after the pandemic. The data show that after the outbreak of the pandemic, acute stress, anxiety, and depressive symptoms were widespread among Chinese college students, and the incidence rate was significantly higher than before[ 11 ]. The prevalence rates of moderate depression and suicide-related symptoms among 212 Japanese college students were 11.7% and 6.7%, respectively[ 12 ]. Among 2031 American college students, 48.14% suffered from moderate to severe depression, 38.48% experienced moderate to severe anxiety, 18.04% had suicidal thoughts, and 71.26% reported that their stress/anxiety levels increased during the pandemic[ 13 ]. More than a quarter of Swiss university students had depressive symptoms during the pandemic, which was much higher than that of the general population and higher than that before the pandemic[ 14 ].

The transition from high school to university is full of tension and adaptation. It is a critical period for the shift from late adolescence to adulthood or emerging adulthood, which is neither adolescence nor young adulthood but theoretically and empirically distinct from both periods[ 15 ]. Arnett stressed that this is a stage full of self-exploration, instability, possibility, self-focus, and something in between[ 16 ]. At this phase, individuals will face the challenges of identity and role transformation and more diversification and complexity from families and institutions. Specifically, compared with middle schools, universities put forward higher requirements for freshmen's independence and self-regulation, such as the independence of living in a new place, the autonomy of learning patterns, and the complexity of social networks. However, confronted with these challenges, college students entering the campus for the first time often wander between independence and dependence. On the one hand, they are eager to enjoy new freedoms; on the other hand, it is difficult to eliminate their attachment and economic dependence on their parents; thus, they are often in a state of "pseudo independence"[ 17 ].

In summary, compared with teenagers and adults, college students are the key group at significantly higher risk of poor mental health. A series of factors, including family, college, studies, and social interactions, are likely to induce college students' depression. However, few publications have reviewed the literature on risk factors for college students’ depression. Given that most studies examined individual risk factors based on samples from a certain country or region, this paper reviewed the extant literature related to college students' depression and aimed to systematically present the nonpathological factors, predictions and nonpharmaceutical interventions for college students' depression to provide a reference for stakeholders worldwide.

NONPATHOLOGICAL INFLUENCING FACTORS OF DEPRESSION

The related factors can be roughly divided into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The literature review presented the specific risk factors under four categories in Table ​ Table1. 1 . Subsequently, this paper explained certain factors with controversial research conclusions.

Factors related to depression in college students

Some studies have asserted that the risk of depression in female college students is significantly higher than that in male students[ 24 , 26 , 40 , 41 ]. The possible mechanism lies in physiological differences between the sexes (such as genetic vulnerability, hormone, and cortisol levels), differences in self-concept, and different role expectations from society leading to different emotional responses and behavior patterns. Females are more likely to internalize their negative feelings, whereas males resort to externalizing behaviors such as smoking and alcoholism[ 42 - 44 ]. However, some analyses did not find significant sex differences[ 28 , 45 , 46 ]. Other studies have shown that men have a higher prevalence of depression[ 20 , 47 ]. This may be ascribed to their conservative attitudes toward mental health counseling and treatment under certain social expectations. For instance, women are more help-seeking than men and therefore tend to have more diagnoses and treatment. In particular, gregarious women are more likely to discuss their difficulties with others, such as family and friends, as a form of coping. Nevertheless, considering that societal expectations for men might be different, with those who express vulnerable emotions being regarded as weak, the depressive symptoms of men may manifest as anger and excessive indulgence in smoking and drinking, which are more acceptable masculine expressions in society[ 43 , 44 ].

Year of study

Most studies have found significant differences in the depression level of college students in different years of their education, although some found the difference to be insignificant[ 28 ]. Some research has suggested that undergraduates with lower grades suffer more from depression, which can be attributed to separation from relatives and friends, social adaptation, academic pressure, and increased investment in social activities. A survey of Chinese students showed that the highest scores for depression, anxiety and stress all appeared in the first three years of college, and students’ mental health status was relieved in the fourth year with the passage of time[ 48 ]. A survey of medical students in Saudi Arabia found that students' depression levels continued to rise from the first year of enrollment, reached maximum intensity in the third year, and then dropped significantly with graduation in the last year[ 22 ]. However, other studies found that compared with other undergraduates, senior students had a higher risk of depression. The graduation year is a critical period for individuals to further their studies or go into society, and students are faced with many new stressors, such as graduation pressure, pressure from grades and applications to other institutions, difficulties in future career planning and employment discrimination in the labor market[ 49 ]. Compared with undergraduates, postgraduates may be exposed to greater pressure in obtaining financial security, stable employment, getting married and other aspects of life, which results in a higher risk of depression[ 19 , 41 ].

The depression issues of college students can largely be attributed to their lifestyles. First, the lack of regular physical activities increases the risk of depression[ 11 , 14 ], particularly for individuals whose amount of weekly physical activity fails to meet the standards of the World Health Organization[ 20 ]. Second, substance abuse, such as excessive smoking, alcohol abuse[ 6 , 12 , 21 ], or alcohol intake[ 33 ], can cause depressive disorders, and it should be noted that their relationship might be bidirectional. Studies have shown that individuals with depression are more likely to drink obsessively to relieve their negative emotions due to their poor self-control, which will in turn trap them in a vicious cycle between excessive drinking and depressive disorders[ 32 ]. Third, unhealthy sleeping habits such as daytime sleepiness[ 20 , 34 ], poor sleep quality[ 21 ], and short[ 35 ] or long sleep duration[ 10 ] may lead to depressive symptoms. Fourth, unhealthy nutritional habits are also among the crucial factors that are strongly correlated with depression[ 36 ]. From the perspective of dietary structure and nutritional habits, individuals with depression often report excessive intake of high-fat snacks and margarine/butter/meat fat and inadequate intake of fruits, vegetables, and lean protein[ 30 ]. Overeating[ 14 ] and skipping breakfast[ 10 ], especially for males, are also related to depressive disorders.

Network usage

Relevant studies have indicated that depression in college students is associated with their time spent on the internet[ 50 , 51 ]. Those who suffer from internet addiction and dependence are more likely to struggle with depression[ 52 ], and phubbing (a portmanteau of the words “phone” and “snubbing”) has been proven to be a mediator of the relationship between depression and problematic internet use[ 53 ], mainly focusing on social networking and entertainment[ 54 ].

Social software

Some researchers believe that social software, as a complementary mode of providing social support, can provide more help for people with low social support, thus reducing the occurrence of depression[ 55 ]. However, there is increasing recognition that social networks, especially the excessive use of social media, are closely related to depression[ 56 - 60 ]. Regarding the possible contributing factors, first, individuals who frequently use social software are more likely to have a fear of missing out, and they are always worried that they will miss some important information if they do not refresh the social platform dynamics frequently. This persistent social anxiety will increase the risk of depression[ 61 ]. Second, college students who are addicted to social media are more likely to have a comparison mentality when checking the status updates of others on social network platforms, especially when they feel that others' lives are better than their own, which can result in symptoms of depression[ 62 ]. Third, it is quite impossible for those who struggle with depressive disorders to establish satisfactory interpersonal relationships in virtual space since they usually maintain poor relationships in the real world. The lack of expected support from social networks undoubtedly aggravates their depression[ 63 ].

In addition, because the COVID-19 pandemic has aggravated the depression of college students worldwide, we further analyzed the influencing factors of college students' depression against the background of the COVID-19 pandemic, apart from the general factors mentioned above: (1) Given that COVID-19 is highly contagious and uncertain, the higher risk of becoming infected with COVID-19 is closely related to individuals’ level of depression. Research has indicated that individuals who live in high-risk areas for COVID-19, have close contact with the COVID-19 virus, or have acquaintances or relatives infected with COVID-19[ 19 , 41 ] often have a higher prevalence of depression; (2) Considering that the internet serves as the main channel for college students to obtain information about COVID-19, those who browse the internet for a short time will not suffer from too much anxiety because of the small amount of information they receive. Meanwhile, students surfing the internet for a long time will be able to obtain more accurate details about COVID-19, which can prevent misunderstanding relevant information. Nevertheless, individuals with shorter browsing times often have a higher risk for depression given that they may be easily misled by the rumors and have limited time to verify the authenticity of relevant information[ 64 ]; (3) Academic stress increases the degree of depression of college students with the closure of schools, the challenges of online courses and the risk of graduation delay[ 13 , 65 ]; (4) Financial pressures include the impact of the pandemic on family economic resources[ 49 ] and the increasing uncertainty of individuals about future employment[ 13 ]; (5) Environmental changes, home study, self-isolation, isolation from relatives and friends, decreased exercise frequency, uncertainty of school reopening, regular temperature measurement, wearing masks for a long time, cancellation of package deliveries and take-out supplies and other forced changes in daily study and living habits all increase the risk of depression among college students[ 13 , 49 ]; (6) There is less family support, social support and deteriorating family relations[ 65 ]; and (7) Social confidence wanes. Research has shown that the prevalence of depression also increases when individuals lack confidence in the government[ 66 ].

PREDICTING DEPRESSION

Traditional depression prediction methods are based on various self-rated psychological scales, such as the 21-item depression, anxiety and stress scale (DASS-21) and the self-rating depression scale (SDS). A growing body of research on the reliability and validity of the DASS-21 scale has been published from throughout the world (such as in Britain, Portugal, The Netherlands, Italy, the United States, and Nepal), all of which show that the DASS-21 is a mature tool that can accurately measure the symptoms of depression, anxiety and stress in adult clinical and nonclinical samples and identify and screen people at high risk of depression[ 67 - 70 ]. Similar to the DASS-21, the prediction reliability and validity of the SDS scale for depression have also been confirmed and recognized by relevant studies[ 71 - 73 ]. These are screening tools, and when elevated scores are detected, further evaluation is needed by a clinician. Moreover, the measurement often needs to rely on the patient's own active consultation and cooperation, which is costly, time-consuming, and inaccurate, and there is a risk of social stigma for patients. In recent years, with the progress of science and technology, a series of more advanced methods of depression risk prediction and identification, such as machine learning and artificial intelligence, has emerged, which can deeply learn all types of social and behavioral characteristics of people with potential mental illness risk based on big data and then accurately simulate, identify and predict who they are. Typical methods include support vector machines, decision trees, naïve Bayes classifiers, K-nearest neighbor classifiers and logistic regression[ 74 ]. More specifically, support vector machines are applied to classify handwritten digits and organize cancer tissue samples using microarray expression data[ 75 , 76 ]. Decision trees serve as a hierarchical classifier, employing certain rules to divide the predictor space. The naïve Bayes classifier is based on Bayes’ theorem and is employed to predict class membership probabilities. K-nearest neighbor classifiers are instance-based learning classifiers that compare a new datapoint with the k nearest sample datapoints, regarding the class with the nearest neighbors to the new datapoint as the class of the datapoint. Logistic regression, as a probabilistic linear classifier, directly estimates class probabilities with the logit transform[ 74 ].

The gait feature analysis method based on machine learning has been developed as a supplementary tool to identify depression among college students. Relevant research found that the gait of depressed and nondepressed college students showed significant differences. The specific gait performance of depressed patients included reduced walking velocity, arm swing, vertical head movement and stride length, increased body sway and a slumped head posture. When the above series of features were applied to classifiers with different machine learning algorithms, the accuracy of depression screening and recognition reached 91.58%[ 77 ]. A study collected 121 campus behaviors of college students, including basic personal information, academic achievements, poverty subsidies, consumption habits, daily life, library behaviors, and eating habits, and found that 25 campus behaviors are related to depression, such as failing exams, having bad eating habits, increasing night activities, decreasing morning activities, and seldom participating in social activities (such as eating with friends). On this basis, a depression recognition method was developed by combining machine learning algorithms[ 78 ]. There is also research and development of a machine learning method to identify depression based on college students' smartphone and fitness tracker data ( e.g. , Bluetooth, calls, location, campus map, phone usage, steps, sleep), which extracts many features that can effectively identify depression, such as long-term inactivity and restless sleep at night; the recognition accuracy of this method for college students' depression can reach over 80%[ 79 ].

In addition, it is worth noting that social software has increasingly become a nonpathological risk factor for depression among college students. Addiction to social software is often more likely to induce depression, while college students at high risk of depression are more inclined to vent their negative emotions and relieve stress on various online social platforms. In this way, social network behavior analysis was developed based on machine learning as another effective way to identify and predict depression[ 80 , 81 ]. Through mining, emotion analysis and emotion recognition of personal user information data on social network platforms, we can capture the abnormal behavior patterns of people with depression, among which the most frequently used communication methods are text, emoticons, user log-in information and pictures. The selected research usually uses classic off-the-shelf classifiers to analyze the available information and combines words, such as National Research Council Canada (NRC) Word-Emoticon Association Lexicon, WordNet-Affect, Anew, and Linguistic Inquiry and Word Count tool. It is challenging to analyze the combination of temporal information and different types of information[ 82 ]. For example, some studies have conducted text analysis on the Sina Weibo data of Chinese college students. First, the behavioral differences between depressed and nondepressed individuals in language style, emoji usage, number of Weibos, followers and so on were obtained. Then, a deep neural network was applied to feature extraction and dimension reduction for college students with depression, and input data suitable for the classifier were constructed. Finally, a deeply integrated support vector machine was introduced to classify the input data, and more stable and accurate depression identification was realized[ 83 ]. Some studies collected historical behavior data of American college students using Google search and YouTube during the COVID-19 pandemic and found that there were strong correlations between depression and the following online behavior changes: long use sessions (multiple comprehensive activities with short time intervals), more online activities in the middle of the night or even staying up late, and searching for more authentic and realistic topics related to work, money or death, which verifies the feasibility of building a machine learning model based on individual behavior signals to predict college students' depression[ 84 ].

Generally, machine learning has been widely used in a series of mental health risk predictions about college students' depression, stress[ 85 ] and suicidal behavior[ 86 , 87 ]. Big data brings many benefits to the prediction of psychological states by reducing the subjectivity of human judgment or human operations to a certain extent and relieving the concerns of patients about possible social stigma and discrimination. In other words, big data and machine learning result in no prejudice in predictions. Thus, confirming depression through data and behavioral performance may be the developing trend in identifying and predicting depression among college students and an even broader population in the future. However, issues such as data privacy and data protection are unavoidable. The government needs to set stricter privacy protection policies, while a more extensive collection of personal data needs to be confirmed and approved by the collectors.

NONPHARMACEUTICAL PREVENTION OF DEPRESSION

Both general and professional measures for the prevention and treatment of depression were explored in this study. The former emphasizes the importance of multi-subject participation in the prevention and treatment of depression among college students, while the latter focuses on measures with the theoretical support of professional disciplines such as psychology.

General intervention measures

The general interventions are summarized in Table ​ Table2 2 and can be coarsely categorized into support from family, interventions by colleges and universities, cultivation of personal lifestyles, and resilience therapy.

High level of family support

A high level of family support can be used as a buffer against the influence of a high-stress reaction to prevent the development of depression[ 91 ]. In a study of 62 patients who recovered from depression, a high level of perceived emotional support from their families indicated that family support, especially emotional support, was very important for the relief and even rehabilitation of depression[ 92 ]. However, it should be noted that family support and perfect family functioning depend more on objective characteristics related to family socioeconomic status, such as parents' level of education[ 93 ]. In addition, some studies have found that the role family support plays in the prevention and treatment of depression also depends on the levels of perceived stress reactivity of individuals. Specifically, family emotional support can significantly alleviate the symptoms of depression when the perceived stress reactivity is low, but when the individual shows a high level of the perceived stress response, the effect of family emotional support in preventing depression will be greatly reduced[ 94 ].

The intervention from colleges and universities

Prior literature has shown that the faculties, peers, and social clubs on campus can help alleviate the negative effects of online games on depression. Students may seek social support from their teachers, peers, or psychological counseling centers to prevent addiction to online video games that may lead to depressive disorders[ 38 ]. Therefore, colleges and universities should build mental health services involving faculty, students, and psychological counseling centers. In addition, some studies have indicated that the implementation of related courses and projects in universities, such as resilience programs (including goal-building, mindfulness, and resilience skills), might be effective in improving college students' mental health[ 95 ].

Cultivation of healthy lifestyles

Apart from external support from family and intervention by higher education institutions, the prevention of depression also needs to rely on the patient's own efforts. Studies have shown that healthy lifestyles, including proper physical exercise, healthy sleep and diet, and regular sun exposure, can help prevent or reduce the occurrence of depression in college students[ 96 ]. For instance, students with a consistent sleep schedule and sufficient sleep duration are less likely to suffer from depression. Meanwhile, regular sun exposure aids in the synthesis of vitamin D in the body, which is crucial to release fatigue and change the negative moods that individuals with mild or moderate depression may experience[ 46 ]. Proper physical activities are also important for stress and depression relief among college students[ 97 , 98 ]. Additionally, improving diet and overall nutrition is also an effective way to treat depression[ 99 ]. In particular, eating breakfast on time helps reduce the risk of depression[ 46 ]. Certain nutrients, including zinc, magnesium, B vitamins, and cooking fats, have also been proven to be associated with depressive symptoms[ 100 - 102 ]. Therefore, colleges and universities can help prevent the occurrence of depression in college students by providing a regular diet with an adequate intake of vitamins and nutrients[ 103 ].

Resilience therapy

Some research has shown that resilience therapy can help individuals maintain mental health in the face of negative emotions and stressful events, thereby reducing the occurrence of depression[ 104 ]. Others have also found that it can reduce depressive symptoms by modulating the effects of timing and sleep quality on depression[ 105 ].

Professional intervention measures

Cognitive behavioral therapy, which aims to change individual thoughts and behaviors, has been the most widely used treatment method for depression thus far[ 106 - 110 , 113 - 115 ]. Mindfulness intervention programs[ 111 ] based on cognitive behavioral therapy and dialectal behavior group therapy[ 112 ] can effectively alleviate the depressive symptoms of college students.

In recent years, a growing number of online technologies have been applied to the treatment of depression among college students thanks to the rapid development of internet technology and mobile terminal devices[ 116 - 120 ], and some of the technologies were even skillfully combined with cognitive behavioral therapy[ 121 , 122 ]. For example, there are many apps that incorporate elements of cognitive behavioral therapy and mindfulness. A study from Switzerland revealed that apps such as MoodKit, MoodMission and MoodPrismying can successfully deliver ecological momentary interventions (EMIs) based on cognitive behavioral therapy principles to users through smartphones, thereby improving their well-being and effectively reducing the symptoms of depression. The study also noted that EMI has been generally accepted by users of different ages, sex, educational backgrounds and occupations and is expected to provide scalable global mental health solutions[ 123 ]. Compared with behavioral cognitive therapy and online interventions, the efficacy of traditional educational/personalized feedback interventions in the past has been slightly inferior. Some projects have evaluated the effectiveness of mailing personalized standardized alcohol surveys for college students' depression prevention, but unfortunately, there is no obvious improvement[ 124 ].

LIMITATIONS

Limitations of this study include the following. First, this paper analyzed relevant literature written in English, but research in other languages, such as Chinese, Japanese, German, and Italian, was not included. Second, the paper is a narrative review of extensive studies including the influencing factors, prediction, and prevention of depression in college students. We did not undertake explicit methods such as systematic reviews, nor did we involve substantial clinical results and corroborate the evidence in prior literature such as retrospective reviews. The study merely presents studies in the pertinent field by summarizing their main conclusions, which cannot be directly applied to clinical treatment.

This paper reviewed the extant literature by identifying nonpathological factors related to depression among college students, investigating methods of predicting their depressive symptoms, and summarizing nonpharmaceutical interventions. The nonpathological related factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of COVID-19 exacerbated the severity of depression among college students worldwide and posed grave challenges to the prevention and treatment of depression, given that the coronavirus spread quickly with high infection rates, changing the daily routines of college life and creating financial stress, academic stress, and long-term home isolation. Regarding the prediction of vulnerability to depression, machine algorithms and artificial intelligence based on big data have emerged in addition to the commonly used psychological scales. A series of big data, such as text, pictures, video and audio, based on individual social network behaviors was widely discussed and applied to identify and predict college students' depression. Regarding preventive measures, both general measures and professional interventions were discussed for the prevention and treatment of college students' depression, which required not only help from family, professionals, and institutions (cognitive behavioral therapy and online therapy) and society but also the individuals themselves through the cultivation of healthy habits.

Technology based on the internet and big data platforms will become more widely used in the future to identify, predict, and prevent depression among college students. Higher education institutions should clearly understand the potential risk factors related to college students' depression and employ advanced technology for more accurate screening and prevention. They should also work on increasing access to resources and clinical support considering the common difficulties in making appointments and long-term waits for psychological consultation.

Furthermore, this paper proposed two prospects for the future development of nonpharmaceutical interventions for college students' depression. First, the risk of stigma should be minimized. Many traditional precautionary measures are used to help students identify whether they suffer from depression, including e-mail, posters, campus activities, pamphlets, and first aid training courses about mental health. However, these measures may result in further concerns about the risk of stigmatization and psychological worries of students[ 125 ]. Therefore, in the future, we should avoid stigmatizing issues in the prevention of depression among college students and pay more attention to personalization and privacy in the development and application of precautionary measures. Second, the importance of general measures for the prevention and treatment of college students' depression should be combined with professional interventions such as cognitive intervention therapy and other evidence-based treatment. A meta-analysis showed that apart from cognitive behavioral therapy and mindfulness-based interventions, other measures, such as art, exercise, and peer support, are also effective in relieving depressive symptoms in college students[ 126 ].

ACKNOWLEDGEMENTS

The authors would like to thank Han T for his contribution to the language editing of the first draft of this study.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Peer-review started: February 27, 2022

First decision: April 18, 2022

Article in press: June 22, 2022

Specialty type: Psychiatry

Country/Territory of origin: China

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): C, C, C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Kaur M, United States; Radhakrishnan R, New Zealand; Rose AF, United States; Tanabe S, Japan S-Editor: Gao CC L-Editor: A P-Editor: Gao CC

Contributor Information

Xin-Qiao Liu, School of Education, Tianjin University, Tianjin 300350, China. [email protected] .

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.

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Relationship between depression and quality of life among students: a systematic review and meta-analysis

  • Michele da Silva Valadão Fernandes 1 , 7 ,
  • Carolina Rodrigues Mendonça 2 ,
  • Thays Martins Vital da Silva 3 ,
  • Priscilla Rayanne e Silva Noll 1 , 4 ,
  • Luiz Carlos de Abreu 5 &
  • Matias Noll 1 , 6  

Scientific Reports volume  13 , Article number:  6715 ( 2023 ) Cite this article

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  • Risk factors
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The objectives of this systematic review were to estimate the prevalence of depression and to identify the relationship between depression and quality of life (QOL) among high school and university students. Literature search was performed in the Scopus, Embase, PubMed, Scielo, CINAHL and Web of Science databases, following the PRISMA methodology. The results were presented through descriptive approaches and meta-analysis. Thirty-six studies met the eligibility criteria, and twenty-six were included in the meta-analysis. The prevalence of depressive symptoms was 27% (95% CI 0.21–0.33) among students, being high school and university students was 25% (95% CI 0.14–0.37) and 27% (95% CI 0.20–0.34), respectively, and most studies have shown that depression was associated with low QOL. Among the limitations of the study is the difficulty of generalizing the results found, considering the large sample of health students. New studies should be conducted considering the severity, duration, and patterns of depressive symptoms in high school and university students, to better understand the relationship between depression and QOL.

Introduction

Depression is a disorder that increasingly affects different populations, with an estimated prevalence rate of 4.4% worldwide 1 . This condition is defined as a mental disorder characterized by a persistent state of depressed mood, accompanied by other psychiatric symptoms such as fatigue and loss of energy, decreased interest or pleasure, impaired sleep, psychomotor agitation or retardation, concentration difficulties, change in appetite and weight, feelings of worthlessness or excessive guilt, or suicidal ideations 2 , 3 . Biological, psychological, cultural, and social factors can contribute to the risk of depression at some stage of life 4 , 5 , 6 , 7 . The high prevalence of depressive symptoms among high school and university students is a worrying aspect from the point of view of public health and educational policies 8 , 9 , 10 , 11 , 12 , because it interferes negatively with learning, performance, and academic success 13 , 14 , in addition to increasing the global burden of diseases 3 , 15 .

High school and university students present significant risk factors for depression, since they need to deal with academic stress on a daily basis 16 , 17 , 18 , 19 . This population is extremely concerned about school performance; emotional, family, and social conflicts; anxiety; among other aspects of life, common to adolescents and young adults, who need to adapt to changes in puberty 18 , 20 , 21 , 22 . On the other hand, interaction with a supportive environment in the educational context can contribute to the prevention and remission of depressive symptoms, improving the QOL among students 23 , 24 . Although different studies have shown that depression negatively impacts the QOL 25 , 26 , 27 , 28 , the relationship between the severity of depressive symptoms and QOL among high school and university students is unclear 21 , 29 .

Recent literature reviews have reported on the prevalence of depression in adolescents and their relationship with distinct biopsychosocial variables 4 , 22 , 30 , such as academic stress, sociodemographic correlates 12 , 31 , resilience 32 , school frequency 33 , and the school psychosocial climate 34 . Other reviews, with samples of university students, also prioritized the results of depression prevalence 35 , 36 and a wide variety of associated risk factors, such as sleep quality 37 , suicidal ideation 36 , 38 , sex 10 , 36 , 39 , socioeconomic status 40 , and sexual abuse 39 . No systematic reviews that analyzed the relationship between depression and QOL among high school and university students were found. The evaluation of QOL can contribute to preventive actions in the context of depression, since it is a multidimensional concept that covers well-being and satisfaction with different areas of life 41 , 42 , 43 .

Assessing the relationship between depression and QOL is important for a broader understanding of the nature of diseases people are exposed to 21 , 44 , 45 . Understanding how the different degrees of depression affect QOL and whether QOL interferes with the progression of the severity of depressive symptoms is necessary, since evidence shows that the trajectory of depressive symptoms vary within the same population 46 , 47 , 48 . Thus, the objectives of this study are: (1) to estimate the prevalence of depression among high school and university students and (2) to identify the relationship between depression and QOL among high school and university students through a systematic review of the literature and a meta-analysis. In addition, we aimed to summarize the evidence of the influence of depression and QOL on academic performance, absenteeism, and school dropout rates among these students. The consolidation of these findings is essential to identify and clarify the risk factors for depression among adolescents and young people. In this way, it will be possible to guide future research and interventions focusing on improving students' mental health.

Research questions

The main research questions guiding this systematic review are, “What is the prevalence of depression among high school and university students?” and “What is the evidence on the relationship between depression and QOL among high school and university students?” The secondary question guiding this review is “What are the influences of depression and QOL on academic performance, absenteeism, and school dropout rates among high school and university students?” If the high prevalence of depression among high school and university students is related to self-perception of quality of life, it is possible that this relationship is determined by specific dimensions of QOL and manifests itself in different ways among students.

Protocol and registration

The present systematic review was conducted according to the methodology for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 49 , for identification, screening, eligibility, and inclusion of studies. Details that are more specific can be found in the registration of the International Prospective Register of Systematic Reviews and in the published protocol article 50 . As the analysis was based on published articles (secondary data), ethical approval was not necessary.

This review follows the population, exposure, comparator, outcome (PECO) structure, mentioned in the recommended notification items for systematic reviews 51 . Thus, “P” represents high school and university students; “E”, depression and QOL; “C”, sex and age group; and “O”, depression and QOL 51 . Academic performance, absenteeism, and school dropout rates were also analyzed as secondary outcomes.

Search strategy and eligibility criteria

In January 2023, a researcher (reviewer 1) accessed the Scopus, Embase, PubMed, Scielo, CINAHL, and Web of Science databases, restricting the search to publications in English between 2011 and 2023. The choice to limit the search to the last 13 years was guided by the following factors: (a) focusing on recent publications in the area, particularly those that assessed depression based on the current criteria of the Diagnostic and Statistical Manual of Mental disorders (DSM-5), published in 2013 52 is more relevant, and (b) a prior analysis, based on PubMed, showed that publications and the production of research citations in this area were significantly increasing from 2011 onwards.

Table 1 shows the search strategy adapted to the different databases. The search strategy was also complemented by: (a) tracking of the references of the included studies and relevant systematic reviews, and (b) searches in Google Scholar. The main search keywords were: “high school students”, “college students” (population), “depression” (exposure/outcome) and “quality of life” (exposure/outcome).

Depression was defined as any depressive disorder based on a clinical diagnosis, according to the criteria of the International Statistical Classification of Diseases and Related Health Problems 53 , 54 or the DSM 52 , or by the evaluation of depressive symptoms through a validated inventory/self-reporting questionnaire 55 , 56 . QOL was defined, according to the criteria of the World Health Organization (WHO), as “individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” 57 .

Observational studies (cross-sectional and longitudinal) with the following characteristics were included: (a) a sample of high school and university students aged 10–33 years; (b) depression and QOL as the main outcome or exposure/risk factor; (c) reported the association between depression and QOL; (d) used a standardized questionnaire for QOL or health related QOL (HRQOL); and (e) evaluation of depression/depressive symptoms with validated instruments and/or clinical diagnosis. The age range 10 to 33 years was used based on the age of adolescents and young adults (age, 10 to 24 years as defined by WHO) 57 . The age was extended to 33 years because the average age of university students is higher in recent years.

The exclusion criteria were: (a) theses, dissertations, books, book chapters, reviews, case reports, comments, letters and editorials, duplicate articles, and articles in which the full text could not be retrieved in online databases, through library requests, or by e-mails sent to the author(s) of the study; (b) studies with specific populations (pregnant and breastfeeding women, victims of violence, amputees, inpatients, and disabled people; in disaster situations, athletes, asthmatics, diabetics, and hypertensive people; patients with HIV, cancer, arthritis, cystic fibrosis, among other chronic diseases); (c) studies with samples of mixed ages, unless data could be collected, organized or calculated separately; (d) incomplete data on the association between depression and QOL; (e) clinical trials and case–control studies; and (f) when more than one article provided data on the same sample.

Training of researchers

Before beginning the screening process, the researchers who participated in the eligibility assessments were subjected to training as to the inclusion/exclusion criteria of the study, with a practical session on eligibility assessment of 50 abstracts 58 . In addition, the researchers participated in another training session to standardize the risk of bias and the analysis of Newcastle–Ottawa Scale (NOS), evaluating five articles not included in the present study. Finally, the researchers were trained on how to correctly use the Rayyan software and standardize the procedures 58 .

Review process

After the bibliographic search, the articles retrieved in the databases were compared and the duplicates removed using EndNote X9 (Clarivate, PA, USA). In the first phase of the review, two researchers (reviewer 1 and reviewer 2) independently sorted the titles and summaries of all articles that met the inclusion and exclusion criteria. This phase was performed using Rayyan software (Rayyan Systems Inc., Cambridge, MA, USA) in blind mode 59 . Disagreements regarding the inclusion and exclusion criteria were discussed and resolved by a third researcher (reviewer 3). In the second phase, the selected articles were fully read by two researchers (reviewer 1 and reviewer 4) and evaluated to determine their eligibility. The reliability between evaluators for the inclusion and exclusion of the studies was determined by calculating the percentage of concordance and the Cohen’s kappa coefficient 58 . Finally, the eligible articles were included in the systematic review. The reference lists of the included articles were evaluated to identify possible additional studies lost in the database searches. Figure  1 shows the flowchart of this systematic review.

figure 1

Flow diagram of the selection criteria for the study. Flowchart: Adapted from the PRISMA 2020 Flow Diagram.

Risk of bias and quality assessment of individual studies

The methodological quality and risk of bias among the studies were assessed by two researchers (reviewer 1 and reviewer 2) independently and with consensus. The methodological quality of the studies was evaluated using the online version of the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) tool 60 , 61 . The strength of evidence of the studies was classified into four categories: high (four circles filled), moderate (three circles filled), low (two circles filled), or very low (one circle filled) 60 , 61 . Factors such as the risk of bias, inconsistent results, indirect evidence, imprecision, and publication bias might decrease the quality of the evidence of the studies. However, the great magnitude of the effect, the dose–response gradient, and the presence of confounders in the reduction of the effect found are factors that could increase the quality of the evidence in the studies.

The NOS for observational studies 62 was used to assess the risk of bias. The adapted scale for cross-sectional (seven items) and cohort (eight items) studies consists of three dimensions that take into account the selection of participants, the comparability of the result groups, and the evaluation of the result measurements 38 . All studies could receive a maximum of one star for each item, except for comparability, in which up to two stars could be assigned. The studies were considered as having a low risk of bias (≥ 3 points) or high risk of bias (< 3 points) 38 . In addition, we assessed whether the authors provided a statement on conflicts of interest and information on ethical approval.

Data extraction and evidence synthesis

The following information was collected from the studies using a standard data extraction spreadsheet: authors, year of publication, site/country, study design, follow-up period (longitudinal studies), characteristics of the participants (sample size, sex, and age range/mean age), instruments for the assessment of depression with respective cutoff points, QOL evaluation instruments, main findings, and association values.

Data regarding the prevalence of depression and association measures were collected, in addition to other additional results that refer to factors associated with depression and QOL. The results were categorized into two groups: (a) high school students and (b) university students. Data were collected and evaluated by two independent researchers (reviewer 1 and reviewer 4) and disagreements were resolved by a third researcher (reviewer 2).

The prevalence of depression and the results of the association between depression and QOL among students are presented as the main outcomes. The results of the prevalence of depression in the studies analyzed were presented according to the intensity of depressive symptoms. The different QOL domains evaluated were also considered in synthesizing the evidence. Secondary results are presented, including additional variables that are associated with students’ depression and QOL. We also described whether the studies presented results on the influence of depression and QOL on academic performance, absenteeism, and evasion. When possible, the differences between the sexes and age groups in terms of the prevalence of depression and the level of QOL among the students were compared.

Meta-analysis

A meta-analysis was conducted using the random effects model with data on the prevalence of depression among high school students, depression among university students, and moderate and low QOL. The data are graphically displayed in Forest plots, showing prevalence rates with their 95% confidence intervals (CIs). Publication bias was evaluated using Egger’s test. All analyses were conducted using Stata version 16.0 (StataCorp LLC, College Station, TX, USA).

Literature search and study selection

Figure  1 shows the selection process for this systematic review. In all, 12,842 articles were identified based on the eligibility criteria, and 28 additional articles were identified through lists of references and manual searches. After excluding duplicate articles, 7,877 articles were selected for title and abstract reading. There was moderate agreement (agreement = 99.4%, kappa = 0.60) between researchers and 150 articles remained for full text evaluation. After the full text analysis, 36 studies met the eligibility criteria and were included in the systematic review (Fig.  1 ). The articles included analyzed depression and QOL among high school and university students and provided information on the relationship between depression and QOL (Table 2 ) 44 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 .

Risk of bias and quality of the evidence

The NOS scale scores ranged from three to nine points. The classification of studies with lower scores 44 , 67 , 70 , 81 was related to unclear description of confounding factors, unadjusted results for confounders, and comparability between respondents and non-respondents characteristic. All studies reached scores ≥ 3 and were evaluated as having low risk of bias (Table 2 ).

The strength of the evidence classified using the GRADE methodology indicated that the studies had low (n = 19, 53%), moderate (n = 13, 36%), and high (n = 4, 11%) quality (Table 2 ). The low and moderate quality was justified by the inaccuracy of the results of observational studies, the reduced sample size, and the effect produced by these studies. Seven studies 67 , 70 , 71 , 81 , 88 , 90 , 95 , 97 did not clearly specify conflicts of interest, and two studies did not report whether ethical approval was obtained 71 , 89 (Table 2 ).

Characteristics of the studies

Table 3 presents the characteristics of the studies included in the review, grouped into the following categories: year of publication, region, study design, students' study modality, sample size and types of assessment instruments for depressive symptoms/depression and QOL. This review included studies of students of 20 nationalities and a total sample of 24,704 people. Most studies were published between 2014 and 2020 (n = 20, 55.6%), mainly with the Asian population (n = 21, 58.3%), and university students (n = 27, 75%). With the exception of a single study, all studies included samples of both sexes. The study design mainly covered cross-sectional studies (n = 15, 93.8%), with only one longitudinal study 93 . The sample size ranged from 40 participants 88 to 4,467 participants 92 , 75.0% of whom were university students (Table 3 ). The mean age of high school students ranged from 13.2 (± 2.1) 70 to 16.9 (± 1.2) years 92 , while the mean age of university students ranged from 19.0 (± 1.1) 63 to 22.8 (± 3.0) years 63 . Most of the studies included a sample of medical students 63 , 64 , 65 , 67 , 76 , 79 , 81 , 82 , 87 , 89 , 93 , 96 nursing students 80 , 95 , and health students 68 , 73 , 78 , 85 , 88 , 94 . Only six studies included a large sample of university students 44 , 66 , 69 , 84 , 86 , 97 . No study evaluated the possible influences of depression and QOL on academic performance, absenteeism, and school dropout.

Characteristics of results and main findings

The characteristics and main results are presented separately for the evaluation of depression and QOL among students, prevalence of depression and its relationship with QOL among students, other factors associated with depression and QOL among students, and meta-analysis.

Evaluation of depression and quality of life among students

Table 3 shows a summary of the instruments used to assess depressive symptoms and Table 4 lists the respective cutoff points adopted in each study. The most widely used instrument for assessing depression and depressive symptoms was the Beck Depression Inventory (BDI) (n = 9, 25.0%), with cutoff points ranging from ≥ 10 to > 15 for the presence of depressive symptoms. Other studies used a variety of instruments to assess depression and depressive symptoms, including the Depression Anxiety Stress Scale (DASS-21) (n = 6, 16.7%) and the Zung Self-Rating Depression Scale (ZUNG SDS) (n = 2, 5.5%) 65 , 85 .

Twelve studies did not specify the cutoff points adopted for the evaluation of depressive symptoms 44 , 68 , 69 , 70 , 76 , 79 , 82 , 89 , 91 , 92 , 93 , 97 . There were no studies based on the clinical diagnosis of depression, and the evaluation of depressive symptoms is prevalent through self-reporting questionnaires. The severity of depressive symptoms was evaluated only in eight studies 64 , 65 , 67 , 83 , 84 , 90 , 96 , 97 , in which the prevalence of depressive symptoms was categorized into mild, moderate, and severe/significant symptoms.

For the QOL evaluation, the most widely used instrument was the World Health Organization QOL Questionnaire (WHOQOL; WHOQOL-BREF) (n = 19, 52.8%), followed by the RAND 36-item Short Form Survey (SF-36) (n = 7, 19.4%), as specified in Table 3 . The different QOL domains evaluated by the main instruments covered the physical, environmental, psychological, and social domains (WHOQOL; WHOQOL-BREF, and SF-36), and the sub-domains related to functional capacity, general health perceptions, bodily pain, vitality, social, physical, and mental functioning, and limitations caused by emotional problems (SF-36). Although there was a certain tendency for studies to assess QOL from different domains, ten studies did not analyze these domains/sub-domains 44 , 66 , 68 , 71 , 72 , 74 , 78 , 85 , 88 , 89 .

Prevalence of depression and its relation to students’ quality of life

Table 4 shows a summary of the results on the prevalence of depression and its relationship with the students’ QOL, categorized by high school and university students, by the intensity of depressive symptoms and by instruments used in the evaluation of depression and QOL. The prevalence of depressive symptoms among high school students ranged from 8.5% among French students 71 to 43.4% among Brazilian students 77 . Among college students, the prevalence of depressive symptoms ranged from 3.3% among Indonesian students 81 to 61% among Malaysian and Brazilian students 83 , 96 . Table 5 shows the main results on the relationship between depression and QoL. Association/correlation tests for each study can be found in Supplementary File 1 .

Studies with a sample of high school students identified that QoL is negatively correlated with depression (n = 8, 100%). Only one study showed that, regarding the QoL domains, the financial resources and social support dimensions were not correlated with depression among students from Mexico 75 . In general, studies with a sample of university students found that depression is associated with low QoL (n = 11, 40.7%). In addition, depression was a predictor of QoL and vice versa. On the other hand, other studies (n = 6, 22.2%) present a varied behavior regarding the relationship between different QOL domains and the prevalence of depressive symptoms. In Thai and Malaysian students, for example, depression was associated only with the psychological and physical domains of QOL 64 , 67 , while a study with a sample of 193 Brazilian students indicated that the physical domain of QOL was unaffected by depression 96 . In two studies depression is not correlated with QOL 68 , 85 .

Three studies analyzed the relationship between depressive symptoms and QOL among German, Brazilian and Pakistani students with a longitudinal design 78 , 88 , 93 . The German students showed an increase in depression symptoms over the semesters, with highly significant correlations between depression and mental quality of life 78 . The presence of depressive symptoms among Brazilian students was negatively related to QOL in all domains, except for the physical domain 93 . It also showed that students with depression at the beginning of graduation tend to maintain depressive symptoms over time, contributing to a worse future QOL 93 . Female students were more likely to have a worse physical QOL over time 93 . On the other hand, students with depression showed improvement in QoL during the COVID-19 epidemic lockdown in Pakistan 88 .

Other factors associated with depression and quality of life among students

In addition to the main results of interest, the studies presented other important variables that are associated with depression and QOL among students, such as anxiety and academic stress. According to one study, self-esteem was positively correlated with QOL, while anxiety symptoms, and relationship with their parents were negatively correlated with QOL in high school students 92 . Another study analyzed that QOL was also correlated with low and moderate anxiety, with a high level of general well-being and with low/moderate level of educational stress 74 .

Studies have shown that among university students, QOL was negatively correlated with anxiety 44 , 67 , 94 and emotional control 44 , and positively correlated with general positive affection, emotional bonds, life satisfaction 44 , and family income 97 . Students who engaged in physical activity every day had higher scores on the HRQOL 97 .

The frequency of depressive symptoms increased with increased anxiety 63 , 85 , academic stress, sleep disorders, academic pressure 66 , and perceived stress 85 . Students with depression had higher scores for social phobia 63 and the intensity of depressive symptoms was higher in the last year of their undergraduate course 95 . In a sample of Chinese students, depression was more prevalent among medical students, followed by engineering and arts students 69 .

Seven studies evaluated depression and QOL of students during the COVID-19 pandemic 70 , 73 , 75 , 77 , 81 , 84 , 88 . In the pandemic period, the prevalence of depression ranged from 21.2% among Mexican high school students 75 to 57.9% among Indonesian university students 84 . It was observed that the COVID-19 pandemic negatively affected the mental health and QOL of students 73 , 88 and that depression symptoms were associated with poor quality of life and social isolation 70 , 75 , 77 , 81 , 88 .

Figure  2 shows the combined prevalence of depression among high school students and depression among university students. The combined prevalence of depression among students was 27% (95% CI 0.21–0.33). The prevalence of depression among High school students was 25% (95% CI 0.14–0.37). The prevalence of depression among university students was 27% (95% CI 0.20–0.34).

figure 2

Forest plot evaluating the prevalence of depression in students, using data from 26 studies. Flowchart: Elaborated by the authors.

There was a high level of statistical heterogeneity ( I 2  = 99.40%, p  < 0.001). Heterogeneity had an influence on the result of the analysis. Evidence of publication bias in the meta-analysis of the combined prevalence was found using the Egger’s regression test ( p  = 0.000).

In the meta-analysis, involving three studies, the odds ratio for the association between depression and quality of life in students was 0.009 (95% CI − 0.009 to 0.027), ( I 2  = 95.6%, p  < 0.01), not indicating a positive association 68 , 74 , 85 .

The present study systematically estimated the prevalence of depression and summarized the relationship between depression and QOL among high school and university students. The prevalence of depressive symptoms was 27% among students and most studies have shown that depressive symptoms was associated with a low QOL. Despite being relevant to research involving students, the studies did not evaluate the influence of depression and QOL on academic performance, absenteeism, and school dropout rates.

The main results show that the estimated prevalence rate of depression among university students was 27%, similar to the results of other meta-analyses that present the prevalence of depressive symptoms of 24.4% to 34.0% with the same population 11 , 35 , 36 , 38 , 40 . About 25% of high school students had depressive symptoms. Indonesian and Brazilian high school students had a higher prevalence of depressive symptoms compared to students from Mexico, Republic of Korea and France. Differences in the prevalence of depression can also be observed in different studies, where the prevalence of depression was in Chinese, 24.3% 12 , Pakistani (17.2%), and Malaysian (26.2%) students 98 , 99 . However, high school students in Indonesia had a higher prevalence of depressive symptoms, with rates of 52.7% 100 .

The findings of this review also demonstrate that high school and university students present a higher prevalence of depressive symptoms compared to large samples in distinct communities, ranging from 7.3% in countries like Australia to 20.6% in South American countries 101 . Estimates of a 12-month depression prevalence in adolescents and young adults in the United States range from 8.7% to 11.3% 102 , rates lower compared to those found in the present review.

The manifestations of depressive symptoms are not static, and they affect a distinct population of students 45 , 93 , since there are several biological, psychological, and social factors that contribute to the risk of depression, including cultural determinants that are present in the person’s life such as the context of development, parental practices, and temperament 48 , 98 . Part of the challenge relates to the heterogeneous nature of the diagnosis and condition of depression. There is an emerging notion that mood disorders lie on a spectrum 103 . In addition, individuals of different ethnicities may express depression differently. Chinese, for example, tend to deny mental health symptoms or express them somatically 104 . Given the complexity of identifying protection mechanisms and risk factors, research suggests that the dimensions of subjective well-being are complementary aspects of the evaluation of depression symptoms 25 , 105 , 106 . In addition, QOL is an important indicator for identifying groups vulnerable to depressive symptoms and the golden objective for treating depression is to improve QOL 21 .

In this review, 97.2% of the studies showed some type of association between depression and QOL, indicating that students with depressive symptoms tend to have worse QOL, or that QOL is a predictor of depression. The role of depressive symptoms as a negative predictor of QOL was documented in other reviews with adolescents 9 and university students 107 . However, the main relevance of the present study is the fact that depressive symptoms may not impact in the same way in the different domains of QOL 64 , 67 , 93 , 96 . The psychological dimension of the QOL of students seems to be the most affected; however, it is not possible to state precisely that it does not occur with the physical, environmental, and social dimensions of the QOL. This is because other factors associated with depression and QOL must be considered, such as the presence of chronic or physical diseases, for example 108 .

Data from the meta-analysis indicate that there is no positive association between depression and QOL in students, showing a possible influence of other mediators on the relationship between depression and QOL. Some people, despite experiencing depressive symptoms at some stage of life, may present adaptive mechanisms that allow them to self-manage mental suffering and demonstrate resilience 32 , 43 , 98 , 109 , 110 , 111 , 112 . The influence of different degrees of depressive symptoms may also compromise the analysis of results, but studies do not provide enough data to support this statement. Therefore, these findings are limited in clarifying the wide and complex relationship between depression and QOL among students. Further studies are needed, mainly with longitudinal design and with quality evidence.

With regard to QOL, the perception of QOL can be more positive or negative as for the meanings each person attributes to their life experiences 111 , 113 , 114 , 115 , 116 To better understand these aspects, the evaluation of QOL should consider the relationship between positive and negative psychological dimensions as independent but at the same time inter-related dimensions 25 . In this sense, a favorable educational environment may play a “barrier” role in negative psychological dimensions among students, such as stress 25 . The psychological, physical, environmental, and social domains of QOL present important differences when analyzed in terms of sex and geographic region 64 , 93 , 95 . Female students tend to present worse QOL, in addition to having the most impaired physical domain of QOL 93 , 96 , 117 , a condition that may be associated with the probability of women exercising less than men 118 . This can also be explained by the fact that different instruments are used in the evaluation of QOL and by adverse cultural or social factors.

This study also showed that students experienced intense depressive symptoms and worsened QOL during the COVID-19 pandemic. Since the establishment of social distancing/isolation measures due to the COVID-19 pandemic caused by the SARS-CoV-2 virus, students have shown considerable increases in depressive symptoms and anxiety 119 , 120 . In part, this is due to prolonged social isolation, bereavement, violence in the family context, and excessive use of the internet and social networks 121 , 122 , 123 , 124 , 125 , 126 . The existence of social distancing implemented to prevent the spread of the COVID-19 virus caused limitations in physical and social activities, including leisure activities and in the sufficiency of the family's financial 127 . In addition, the blockade and closure of schools and universities forced students to study at home, which may have contributed to increased symptoms of depression and consequent worsening of QOL 127 , 128 .

This review had some limitations. First, the assessment of depression and QOL in the studies considered different instruments, which made comparison of results difficult. Second, the most widely used instrument for the evaluation of depressive symptoms, the BDI, presented different cutoff points in the selected studies, which may reflect probable bias. In addition, screening tools are criticized for having a greater chance of false-positive results, making the burden of the disease seem worse 129 . Depressive symptoms were measured using psychometric tools that indicated the presence or absence of symptoms, but they were not able to diagnose depression. A clinical evaluation would be essential to better understand and standardize the results 21 , 42 . Third, most studies used a cross-sectional design, which does not allow definitive conclusions on causality. Longitudinal studies could demonstrate whether poor QOL is a predictor of depression or whether depression is a predictor of low QOL, in addition to clarifying how the intensity of depressive symptoms interacts with QOL and vice-versa. Fourth, the results cannot be generalized since most participants are medical, nursing and health students. Fifth, excluding gray research sources from our systematic review may resulted in loss of information on the subject. So, for future studies, we suggest to take into account the possibility to include a gray literature search as a step of the search strategy. Finally, the studies did not analyze important factors mediating in the relationship between QOL and mental health, such as socioeconomic level, stress, coping style, and personality 112 , 130 , 131 .

The strengths of this study include the specific assessment of depression, to the detriment of a wide scope of mental health problems, which allows a particular analysis of its relationship with QOL. Results from the analysis of conflicts of interest and ethical approvals, which are often omitted from the assessments, are also presented here. A meta-analysis was conducted to provide a general estimate of the prevalence of depression among high school and university students. To the best of our knowledge, this is the first systematic review that summarizes the evidence on the relationship between depression and QOL among high school and university students, allowing us to clarify the gaps in the literature and propose recommendations for future research. In addition, this is the first study that intended to analyze academic consequences, such as academic performance, absenteeism, and school dropout. However, the studies included in this review did not analyze these aspects, which indicate a lack of research on the academic consequences, from the perspective of the relationship between depression and QOL.

New studies should be conducted considering the severity, duration, and patterns of depressive symptoms in high school and university students, to better understand the relationship between depression and QOL. Future research directions also include in-depth study on the relationship between depressive symptoms and specific dimensions of QOL, considering its domains and sub-domains, identification of sociodemographic variables and the influence of coping mechanisms on the relationship between depression and QOL, and longitudinal assessment of the relationship between depression and QOL among students. Health professionals and education professionals must better understand the different aspects of the life of students who are depressed, being able to determine its origin and the protection mechanisms that can be used in punctual interventions 68 , 131 .

Depression is associated with the QOL of students; however, the relationship between depression and QOL is not clear yet. There is a need to understand whether QOL can affect the nature, duration, and intensity of depressive symptoms and the real impact of depressive symptoms on different QOL domains. The consolidation of these findings is fundamental to a more effective and integrated orientation of public health and education policies, focusing on promoting mental health and improving the students’ QOL. The multidimensional aspect that refers to the students’ mental health and QOL should be considered from a multidisciplinary and global conception, with the participation of health professionals, education professionals and the family in social and instrumental support, thus contributing to students’ academic performance and success.

Data availability

Due to sensitive data, the data can be accessed upon request to the authors (michele[email protected] (MSVF); [email protected] (MN)).

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The authors thank the Federal Institute of Education, Science and Technology of Goiano (IF Goiano) for funding this research, and the Child and Adolescent Health Research Group (GPSaCA).

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literature review on depression among university students

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Prevalence of depression and anxiety among undergraduate university students in low- and middle-income countries: a systematic review protocol

  • James January   ORCID: orcid.org/0000-0001-8074-9940 1 , 2 ,
  • Munyaradzi Madhombiro 3 ,
  • Shalote Chipamaunga 4 ,
  • Sunanda Ray 1 ,
  • Alfred Chingono 3 &
  • Melanie Abas 5  

Systematic Reviews volume  7 , Article number:  57 ( 2018 ) Cite this article

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Depression and anxiety symptoms are reported to be common among university students in many regions of the world and impact on quality of life and academic attainment. The extent of the problem of depression and anxiety among students in low- and middle-income countries (LMICs) is largely unknown. This paper details methods for a systematic review that will be conducted to explore the prevalence, antecedents, consequences, and treatments for depression and anxiety among undergraduate university students in LMICs.

Studies reporting primary data on common mental disorders among students in universities and colleges within LMICs will be included. Quality assessment of retrieved articles will be conducted using four Joanna Briggs critical appraisal checklists for prevalence, randomized control/pseudo-randomized trials, descriptive case series, and comparable cohort/case control. Meta-analysis of the prevalence of depression and anxiety will be conducted using a random effects model which will generate pooled prevalence with their respective 95% confidence intervals.

The results from this systematic review will help in informing and guiding healthcare practitioners, planners, and policymakers on the burden of common mental disorders in university students in LMICs and of appropriate and feasible interventions aimed at reducing the burden of psychological morbidity among them. The results will also point to gaps in research and help set priorities for future enquiries.

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PROSPERO CRD42017064148

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Poor mental health among university students has been a cause of concern globally. A previous systematic review indicated that university students have higher rates of depression than the general population [ 1 ]. Prevalence of depression or anxiety among health professions’ students have also been reported to be higher than in the general population in resource-constrained settings [ 2 , 3 , 4 , 5 , 6 ] and resource-rich settings [ 7 , 8 ]. Most of these studies have reported prevalence of depression or anxiety above 35% [ 1 , 2 , 4 , 5 ]. The studies have tended to focus on common mental disorders among medical students and have largely ignored university students in other fields. Understanding the burden of psychological morbidity among university and college students is imperative as there is evidence showing that cognitive, behavioral, and mindfulness interventions can be effective in reducing anxiety and depressive symptoms in these groups [ 9 , 10 ]. Such interventions are particularly useful in resource-limited settings such as low- and middle-income countries (LMICs) where antidepressants may not be easily available or the appropriate solution.

Factors implicated in psychological morbidity among students include academic pressure, demanding workloads [ 11 ], worry about own health [ 12 ], financial concerns [ 13 ], exposure to patients’ suffering in the case of medical students [ 14 , 15 ], and student abuse and mistreatment [ 16 ].

Psychological distress among students may adversely influence their academic performance and quality of life [ 17 ] and may contribute to alcohol and substance abuse, decreased empathy, and academic dishonesty [ 18 ]. In light of the risks and consequences of psychological morbidity on students and the remarkable growth in university student numbers in Sub-Saharan Africa within the last 30 years [ 19 ], there is a need to understand the prevalence and antecedents of common mental disorders among university students. University/college-based mental health well-being programs and interventions become increasingly imperative as they contribute to prevention and minimization of psychological morbidity. Additionally, there is a need to create supportive environments for students who may be having mental health difficulties during their training. Previous systematic reviews evaluating the prevalence of depressive or anxiety symptoms among health professions’ students have been conducted on studies that were carried out in the USA and Canada [ 7 ] and other high-income settings and mainly confined to English-speaking countries [ 8 ] focusing on medical students. This review will collate evidence from LMICs with regard to the burden of depression or anxiety among university student populations.

Purpose of the review

This systematic review will be conducted in an effort to answer the following key questions:

What is the documented prevalence of depression or anxiety among university students in LMICs?

Which sociodemographic and curricular factors are associated with depression or anxiety among university students in LMICs?

What are the reported short and medium term consequences of depression or anxiety among university students in LMICs?

This study protocol is structured in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). The PRISMA contains a 27-item checklist which is aimed at facilitating the development and reporting of robust systematic review protocols [ 20 ]. The systematic review was registered on the PROSPERO database (CRD42017064148).

Information sources

PubMed, PsychINFO, EMBASE and African Index Medicus, BIREME, LILACS, and MEDLINE databases will be searched for studies reporting primary data on common mental disorders (depression and/or anxiety) among students in universities and colleges within LMICs. For this study, LMICs will be defined using the World Bank Country Lending Group list for the year 2017 [ 21 ].

Search strategy

In light of the paucity of studies in LMICs, no time restrictions will be imposed on the search. Controlled vocabulary terms will be appropriately incorporated for each database. We will use the terms to search for three main concepts namely (1) undergraduate university/college students, (2) prevalence of depression or anxiety, and (3) low- and middle-income countries. A full search strategy for one database is displayed in Appendix 1 . Reference lists of retrieved articles will also be examined and additional articles added if they meet the inclusion criteria.

Eligibility criteria

Studies will be included if they report the prevalence of depression or anxiety among university/college students undertaking undergraduate degree programs. Study types will include descriptive and analytical studies such as cross-sectional and longitudinal studies, case-series analysis, and randomized control trials that include data on prevalence of depression or anxiety. We will include studies in all languages, which will be translated into English. Due to paucity of research on depression or anxiety in most LMICs, the studies will not be excluded based on how they measured depression or anxiety since it is important to understand how these conditions are being measured in different settings.

Data extraction

Two reviewers will independently screen titles and then abstracts of included articles using a piloted data extraction sheet ( Appendix 2 ). Examples of the type of data that will be extracted include study design, setting, study sample sizes, assessments used for diagnosing depression or anxiety, and prevalence of depression or anxiety. Where there will be doubts on whether a title is relevant, it will be included for retrieval. Reconciliation of disagreements on which article(s) to include will be resolved by discussion and consensus between the two reviewers, or mediation by a third person.

Assessment of methodological quality

All retrieved papers eligible for selection will undergo an assessment process conducted by two independent reviewers. Standardized critical appraisal tools will be utilized in the quality assessment. In this study, four critical appraisal tools [ 22 ] will be used to assess for quality depending on the study design. These are as follows:

The Joanna Briggs Institute (JBI) Prevalence Critical Appraisal tool [ 23 ]

The JBI critical appraisal checklist for randomized control/pseudo-randomized trials

The JBI critical appraisal checklist for descriptive/case series

The JBI critical appraisal checklist for comparable cohort/case control.

These tools were developed primarily for use in systematic reviews. Where there are disagreements between the two reviewers, a third reviewer will be engaged and discussions among the three reviewers will be used to resolve the differences.

Data analysis and synthesis

Meta-analysis of the prevalence of depression and anxiety among university students will be conducted using a random effects model which will generate pooled prevalence with their respective 95% CIs. Analyses will be conducted in Stata 14. The results from the review will be summarized and presented in text, Appendix 3 , and tables.

This systematic review will be conducted as the initial step of a longitudinal study on common mental disorders among university students in Zimbabwe. The review aims to explore the prevalence, antecedents, and consequences of depression and anxiety among university students in LMICs. The results from the review will inform and guide health care practitioners and researchers on appropriate and feasible interventions aimed at enhancing the psychological well-being of undergraduate students in resource-constrained settings.

Abbreviations

Biblioteca Regional de Medicina

Excerpta Medica dataBASE

Joanna Briggs Institute

Latin American and Caribbean Health Sciences Literature

Low- and middle-income countries

Medical Literature Analysis and Retrieval System Online

Preferred Reporting Items for Systematic Reviews and Meta-analyses

Psychological Information Database

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Acknowledgements

The authors acknowledge the support provided by Helen Jack in reviewing the protocol.

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Department of Community Medicine, College of Health Sciences, University of Zimbabwe, Box A178, Avondale, Harare, Zimbabwe

James January & Sunanda Ray

Department of Psychiatry, School of Clinical Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa

James January

Department of Psychiatry, College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe

Munyaradzi Madhombiro & Alfred Chingono

Department of Health Professions Education, College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe

Shalote Chipamaunga

Institute of Psychiatry, King’s College London, London, UK

Melanie Abas

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Contributions

JJ designed the review protocol in collaboration with MM, SC, MA, AC, and SR. JJ and MM designed the search strategy and will perform searches and conduct data selection and extraction. All authors will be involved in data analysis and interpretation of results. All authors revised and approved the final manuscript.

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Search strategy example

MESH terms will be used to search for studies and these are “college students”, OR “university students”, OR “undergraduate students”, AND “depression” OR “mental distress” OR “common mental disorder” OR “mental disorder” OR “mental health” OR “mental illness” OR “anxiety” OR “anxiety disorder” OR “anxiety symptoms” AND Afghanistan OR Benin OR “Burkina Faso” OR Burundi OR “Central African Republic” OR Chad OR Comoros OR Congo OR Eritrea OR Ethiopia OR Gambia OR Guinea OR “Guinea-Bissau” OR Haiti OR “North Korea” OR “Democratic People’s Republic of Korea” OR Liberia OR Madagascar OR Malawi OR Mali OR Mozambique OR Nepal OR Niger OR Rwanda OR Senegal OR “Sierra Leone” OR Somalia OR “South Sudan” OR Tanzania OR Togo OR Uganda OR Zimbabwe OR Armenia OR Bangladesh OR Bhutan OR Bolivia OR “Cabo Verde” OR “Cape Verde” OR Cambodia OR Cameroon OR Congo OR “Cote D’Ivoire” OR Djibouti OR Egypt OR “el Salvador” OR Ghana OR Guatemala OR Honduras OR India OR Indonesia OR Kenya OR Kiribati OR Kosovo OR Kyrgyz OR Kyrgyzstan OR Lao OR Laos OR Lesotho OR Mauritania OR Micronesia OR Moldova OR Mongolia OR Morocco OR Myanmar OR Nicaragua OR Nigeria OR Pakistan OR “Papua New Guinea” OR Philippines OR Samoa “Sao Tome” OR Principe OR “Solomon Islands” OR “Sri Lanka” OR Sudan OR Swaziland OR Syria OR “Syrian Arab Republic” OR Tajikistan OR Timor OR Tonga OR Tunisia OR Ukraine OR Uzbekistan OR Vanuatu OR Vietnam OR “West Bank” OR Gaza OR Yemen OR Zambia OR Albania OR Algeria OR “American Samoa” OR Angola OR Argentina OR Azerbaijan OR Belarus OR Belize OR Bosnia OR Herzegovina OR Botswana OR Brazil OR Bulgaria OR China OR Colombia OR “Costa Rica” OR Cuba OR Dominica OR “Dominican Republic” OR “Equatorial Guinea” OR Ecuador OR Fiji OR Gabon OR Georgia OR Grenada OR Guyana OR Iran OR Iraq OR Jamaica OR Jordan OR Kazakhstan OR Lebanon OR Libya OR Macedonia OR Malaysia OR Maldives OR “Marshall Islands” OR Mauritius OR Mexico OR Montenegro OR Namibia OR Palau OR Panama OR Paraguay OR Peru OR Romania OR Russia OR Russian OR Serbia OR “South Africa” OR “St. Lucia.”

PRISMA flow diagram

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January, J., Madhombiro, M., Chipamaunga, S. et al. Prevalence of depression and anxiety among undergraduate university students in low- and middle-income countries: a systematic review protocol. Syst Rev 7 , 57 (2018). https://doi.org/10.1186/s13643-018-0723-8

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DOI : https://doi.org/10.1186/s13643-018-0723-8

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Factors that influence mental health of university and college students in the UK: a systematic review

  • Fiona Campbell 1 ,
  • Lindsay Blank 1 ,
  • Anna Cantrell 1 ,
  • Susan Baxter 1 ,
  • Christopher Blackmore 1 ,
  • Jan Dixon 1 &
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Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.

To identify factors associated with mental health of students in higher education.

We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.

We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.

Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.

Peer Review reports

Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].

Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.

Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].

The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.

Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.

The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].

With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .

Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.

In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.

Research objectives

The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:

To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.

To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.

To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health

Methodology

Identification of relevant evidence.

The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.

We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).

Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.

Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.

Study design

We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .

Data extraction and quality appraisal

We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.

Involvement of stakeholders

We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.

Methods of synthesis

We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.

Search strategy

Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.

The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig.  1 .

figure 1

Flow diagram

The full search strategy for Medline is provided in Appendix 1 .

Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.

Design and quality appraisal of the included studies

The majority of included studies ( n  = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n  = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.

Quantitative variables

Included studies ( n  = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.

Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.

The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.

The variables described were categorised during the analyses into the following categories:

Vulnerabilities – factors that are associated with poor mental health

Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.

Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].

Sexual orientation

Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.

Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.

Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.

Family factors

Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.

The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n  = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n  = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).

Gaan et al.’s (2019) survey of LGBTQ students ( n  = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].

While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.

McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p  < 0.05) and this was related to developing poorer coping scores (OR -0.21 p  < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.

Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.

Mental health history

Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.

Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.

Buffers – factors that are protective of mental wellbeing

Psychological factors.

Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].

The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r  = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r  = -0.2—0.3) [ 43 , 49 , 54 ].

Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.

Parental factors

Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.

Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.

Mental health literacy and help seeking behaviour

Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.

University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.

Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r  = -0.6. p  < 0.01).

There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig.  2 ).

figure 2

Poor mental health – cycles of reinforcement

Social networks

Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].

Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.

Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.

Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].

The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.

Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n  = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.

The positive and negative feedback loops

The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig.  3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.

figure 3

Triggers – factors that may act in combination with other factors to lead to poor mental health

Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n  = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r  = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].

University variables

Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.

High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.

Red flags – behaviours associated with poor mental health and/or wellbeing

Engagement with learning and leisure activities.

Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig.  4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.

figure 4

Engagement and wellbeing

Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].

The inte r- related nature of these variables is depicted in Fig.  4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.

Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.

The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.

Health behaviours at university

Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].

Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.

Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.

Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r  = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r  = -0.6, -0.7) [ 66 ].

Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.

Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].

Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.

Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].

At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].

Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.

Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.

This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:

Interventions before university to improve preparation of young people and their families for the transition to university.

Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health

Interventions that set out to foster a sense of belonging and identify

Creating environments that are helpful for building social networks

Improving mental health literacy and access to high quality support services

This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n  = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.

Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.

We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.

Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.

Availability of data and materials

Further details of the study and the findings can be provided on request to the lead author ([email protected]).

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We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.

This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research  Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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All of the included authors designed the project methods and prepared a protocol. A.C. designed the search strategy. F.C, L.B and C.B screened the identified citations and undertook data extraction. S.B. led the PPI involvement. JD participated as a member of the PPI group. F.C and L.B undertook the analysis. F.C. and L.B wrote the main manuscript text. All authors reviewed the manuscript. F.C designed Figs. 2 , 3 and 4 . The author(s) read and approved the final manuscript.

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Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x

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Distress and mental health issues among college students is an emerging topic of study. The aim of this research work is to illustrate academic and social risk factors and how they prove to be predictors of anxiety and depressive disorders. The methodology used is a cumulative literature review structured over 10 systematic phases, and is replicable. Showing considerable potential for cumulative research, the relevance of this study reflects the concern of the academic community and international governments. The articles selected range from categorization of disorders in relation to mental health, to reporting the condition of rhinestones and difficulties of students in university contexts. In conclusion, the research focusses upon predisposing, concurrent or protective factors relating to the mental health of university students, so that institutions can act on concrete dynamics or propose targeted research on this topic.

Introduction

Mental health and mental health-related issues have been a matter of concern for quite a long time earning little regard and interest from the respective healthcare facilities and systems. Governments have put inadequate measures to ensure that citizens' mental challenges are handled rightfully to achieve high levels of mentally healthy people. The perpetuated issue has also developed in various sectors of society. The current situation in a learning institution is worth raising eyebrows, and therefore it deserves serious attention. Most of the undergraduate students and yet to graduate college students depict high levels of mental illness among the students, thus, depicting discomfort of the students and the level of neglect the health sector is faced with. The majority of today's people who are suffering from mental issues in society constitute college and university students ( 1 ). College students endorse high rates of mental health problems. While many colleges offer on-campus services, many students who could benefit from mental health services do not receive car. Indeed, nearly half of students who screen positive for depression, for example, do not receive treatment ( 2 ).

Adverse consequences are synonymous with undergraduate students with mental distress. The victims are likely to experience challenges such as impaired functioning in cognition, substance abuse, poor performance in their school work, and learning disabilities. They are likely to abuse drugs such as tobacco, alcohol, cigarette smoking, and other hard drugs that impair normal body functioning ( 3 ). Most of these drugs are associated with various risk behaviors, depression, and anxiety ( 3 ). This suggests that emotional discomfort raises the likelihood of developing additional mental health issues. For this reason, the prevalence of mental illnesses among university students is higher compared to people in other environments. The situation is almost similar in most colleges since they are predisposed to similar conditions and forms of livelihood. This inherent condition puts the future generation, which is inherently composed of the schooling individuals, at more risk in line with mental health and other health conditions that may arise due to the mental disorders.

Several factors have contributed to the mental distress and discomfort associated with undergraduates. For instance, sex has a significant contribution to the mental illnesses that people experience in learning institutions. The prevalence of the conditions tends to be a notch higher among female students than male students ( 4 ). Some students lack interest in fieldwork which affects their mental health in the long run. Introvert students are also more likely to fall victims and students who face various social challenges such as poverty ( 4 ). Most of the learning institutions have tight schedules and continuous sequences of study, which affects the students' performance and their mental well-being. Challenges and the predisposing factors that affect the students are bound to result from their school environment or their history; therefore, the growth environment and interaction play a significant role in determining one's health. Some of the predisposing factors are avoidable, while others are accustomed and tied to the students. Therefore, it is prudent to come up with measures to ensure control and regulation of the inherent situation of the undergraduate students who make up the future and continuity of our current society.

Common Mental Illnesses Among Undergraduates

Undergraduate students face many mental issues; however, the prevalence of some of the health conditions is a bit higher than others. Experts and researchers use terminology like “crisis” and “epidemic” to describe American college student's mental health issues today. Mood disruptions are only one of the many mental health problems that college students face. Suicide, addiction, and eating disorders are examples of significant issues ( 5 ). Although mental health specialists emphasize the need to talk about such concerns, students often regard these pressures as a typical livelihood in learning institutions. In other circumstances, individuals may be unable to seek help due to a lack of time, energy, will, or financial resources ( 5 ). It is, therefore, a challenge in coming up with a satisfactory solution to the challenges of problems. Drawing the students' goodwill and desire to have their mental issues fixed is also a challenge as some of them may feel shy or mentally healthy, and that there is no need to go through medication. Similarly, identifying the deserving students and coming up with radical measures to satisfactorily come up with a solution is also challenging since acquiring the required resources is quite expensive. However, solving the problem is arguably easy through addressing some of the major health conditions that most undergraduate students experience.

Below, we investigate some of the most common mental illnesses among college students, such as depression, anxiety, suicidal thoughts, eating disorders, and addiction.

Depression is a widespread chronic medical illness that can affect thoughts, mood, and physical health. It is characterized by low mood, lack of energy, sadness, insomnia, and an inability to enjoy life ( 6 ). Victims of the condition tend to develop varying episodes of discomfort and displeasure that destruct them from their normative activities. Students may grow poor performance and the inability to fit in with their schoolmates in co-curricular and curriculum activities. According to the ACHA's 2018 poll, 40% of American students had at least one significant depressive episode that same year ( 5 ). A person may also feel sad, hopeless, powerless, and get overwhelmed with life situations and challenges that one may be facing. Trouble in completing assignments, challenges in paying attention, and reading are also synonymous with depression among undergraduates ( 5 ). It might be challenging to spot these concerns in others since students often minimize or refuse to discuss issues that are bothering them.

In ICD-10, Generalized anxiety disorder includes anxiety neurosis, anxiety reaction, and anxiety state, but excludes neurasthenia. ICD-10 also proposes diagnostic criteria for research: (i) at least 6 months with prominent tension, worry, and feelings of apprehension about everyday events and problems; and (ii) at least four symptoms out of a list of 22 items, of which at least one item is from a list of four items of autonomic arousal (palpitations/accelerated heart rate, sweating, trembling/shaking, dry mouth).

Anxiety was identified as a significant student mental disorder by 61 percent of survey respondents in the University of Pennsylvania study published by Locke et al. ( 7 ). Anxiety disorder symptoms are frequently misdiagnosed as everyday stress or dismissed as someone overly concerned. Panic attacks might be misinterpreted as a medical ailment, like a tension headache or heart attack, depending on how your body responds to high amounts of specific chemicals ( 7 ). Since each person's symptoms present differently, what sighs the existence of anxiety to one person may not be similar in another ( 7 ). Consequently, the causes of anxiety differ from one person to another; however, some causes are common among campus students. For instance, stress, life experiences, genetics, and brain chemicals commonly cause anxiety in people ( 7 ). It, therefore, requires adequate measures of utmost keenness to ensure that the condition gets eliminated from the learners' livelihood.

The APA defines completed suicide as a self-injurious act that results in death and attempted suicide as a non-fatal, self-inflicted, potentially harmful act that is intended to result in death but may or may not result in injury ( 8 ).

Approximately 20% of university students in the United States were reported to be suicidal in 2018 ( 9 ). Therefore, it implies that the mental condition is rampant and makes up one of the major mental illnesses common among American students. According to the Los Angeles Times' Healstaff ( 10 ) report, teenagers and young adults record the highest suicide cases in America. Since the population inherently dominates the composition of the universities, it insinuates that undergraduate students register the highest number of suicide cases. Many students experience dissatisfaction and doubt, but these feelings can spiral out of control, leading some to consider suicide seriously. Suicidal ideation manifests itself in a variety of ways. Speech, temperament, and behavior are all examples of common warning indicators ( 10 ). Persons may describe themselves as stuck, burdening others, as if they have no reason to live and have no purpose to live. Suicidal ideation causes a wide range of emotions: anxiety, impatience, loss of interest in previously appreciated activities, shame, rage, and melancholy ( 11 ). People may engage in certain activities, such as giving up valued items, withdrawing from family and friends, unexpectedly visiting someone to say bye, and searching the internet for ways to commit suicide ( 11 ). They also may sleep poorly or excessively, act rashly, show anger, and increase their drug and alcohol use ( 11 ). Whenever one is seen with the symptoms, a bold and patient approach should help the victim seek medical attention from a psychiatrist and facilitate the healing process.

Eating disorders are a group of illnesses characterized by significant changes in one's eating habits and a preoccupation with a person's shape or body. Eating disorders (EDs), including anorexia nervosa, bulimia nervosa, and binge-eating disorder, constitute a class of common and deadly psychiatric disorders ( 12 ). The health conditions can entail binge eating and deprivation of food, which sometimes results in purging. According to 2018 estimates from the National Eating Disorders Association, 10–20% of female college students suffer from an eating disorder, with rates continuing to grow ( 13 ). Male students have a lower incidence rate of 4–10% ( 13 ). The typical eating disorders among undergraduates include bulimia nervosa, anorexia nervosa, and binge eating disorder. Emaciation is a specific symptom of anorexia nervosa, characterized by an excessive preoccupation with thinness, a disordered body image, and anxieties about gaining weight ( 14 ). Constant desires that occur at any time of day and lead to binge eating characterize binge eating disorder ( 14 ). This condition is frequently linked to low self-esteem and a negative body image. Bulimia nervosa is a form of binge eating condition characterized by recurrent and frequent bouts of eating abnormally large amounts of food, followed by compensatory behaviors such as purging, fasting, or excessive exercise ( 14 ). The symptoms and indications of eating preconditions differ from person to person and condition to condition, and many are dependent on the mental state of the person suffering from the problem ( 14 ). Many college students fail to seek treatment for their eating disorders since they do not have an awareness that they have one.

Alcohol and recreational substances are commonly used by college students, which can be troublesome ( 15 ). Addiction is a psychological or physical dependency pattern on one or more substances, characterized by strong cravings and substance abuse despite knowing risks and consequences ( 16 ). Alcohol is the leading cause of many disorders and deaths for campus students, while some abuse drugs to induce their studying habits ( 17 ). The recreational activities that undergraduates use alcohol and other drugs for result in addiction which causes many diseases. Besides alcohol, students also abuse marijuana, cocaine, ecstasy, and benzodiazepines ( 17 ). The dire need and desire to abuse drugs for various purported gains may lead to health complications resulting in death and body organs' failure.

Mental Illness Prevalence Among Undergraduates

Mental health disorders are common among students, with a higher incidence than in the general population. Statistically, more than half of the students in American public universities suffer from depression and anxiety ( 18 ). Similarly, a poll of undergraduate students at Coventry University in the United Kingdom found that many students had suffered mental health disorders such as anxiety and depression in 2006 ( 19 ). Maser et al. ( 20 ) showed that the prevalence of mental health disorders such as anxiety and depression cases are a notch higher in medical school compared to the general non-student community of the same age, which supports these findings. Over the last two decades, these investigations have shown that the frequency of Seasonal Affective Disorder (SAD) amongst students has remained more significant than the general population.

SAD is not only common, but it is also persistent among students. Zivin et al. ( 18 ) found that more than half of students maintain their higher anxiety and sadness over time by performing a 2-year follow-up survey research and study of students. This phenomenon could be related to a lack of SAD therapy or the persistence of pre-existing risk factors.

Methodology

The cumulative literature review, a new and rigorous research method, is divided into 10 phases ( 21 ):

1. Selection of key concepts (especially of independent and dependent variables and the relationships between them).

2. Creation of a search string (in addition to selecting the keywords, it is necessary to include and exclude the studies found through the criteria used). The final goal of this phase of the research is to find a manageable number of studies through the following procedure: (1) query two or three search engines or databases; (2) use keywords in combination with “and/or”; (3) use filters to manage the enormity of the results (comparison with a second researcher as in this study would be desirable).

3. Export of the results from the databases and a merging of all the transcribed results in the form of a bibliography on a single worksheet.

4. Selection of primary sources by eliminating duplicates and excluding irrelevant studies based on titles. This step is necessary to create a separate list of systematic reviews on the topic. It is also essential for drawing up a list of the individual choices of the cumulative review.

5. Verification of the secondary bibliography, by checking the bibliographies of all the included studies. Studies that cite primary sources, such as other systematic reviews on the topic, must therefore be included under the studies not found in the initial search.

6. Data extraction (produced by the reviewers' work), in which the characteristics of the selected studies are extrapolated. These characteristics include key variables, type of research project, context, results, year of publication, etc. The exclusion criteria are also cumulative; i.e., they are formulated on the basis of the time available and the studies retrieved from the databases.

7. Updating of the results on the basis of recent publications, which may prompt an update regarding the initial work carried out. This must be done before the conclusion of the cumulative review.

8. Verification by the second reviewer, who checks the included studies.

9. Writing of the last phase of the report.

10. Exercising due care in the publicization of the revision. This involves making the data collection work explicit within the format of the paper: keywords, extracted data, results, etc.

Each phase is illustrated below, retracing the steps taken to carry out the cumulative review, in order to make the study replicable.

In this study, research, which was based on the cumulative literature review model, followed the ten-phase model set out in the previous paragraph. Specifically, the following keywords were selected: mental disease, risk factors, university, students (phase 1). Scopus, WoS and Google Scholar were selected as search engines. The search yielded 797 results that were selected, based on comparison by researchers, using the following inclusion-exclusion criteria: inclusion of all literature reviews in the 2011–2020 period, related studies on risk factors toward mental disorders of university students (phase 2), with exclusion based on primary source titles. The raw research data was transcribed on a spreadsheet, in order to enable a global view of the studies located and to start the selection work (phase 3). The file was “cleaned up,” in order to remove duplicate contributions and create a second list of systematic reviews of the literature ( n = 4), one book, and one book chapter (phase 4). In the first file named “primary sources,” significant studies were selected and placed based on the title. The cleaned file contained n = 33 papers. For the second file containing the systematic reviews of the literature, the secondary bibliography included was consulted, and the studies already present in the first file of the present research were eliminated. In this case, 67% of the studies had already been identified in the comparison of the cumulative literature reviews. It would be desirable to build a reliability index of the cumulative review that took into account this value, i.e., the degree of replicability of the systematic studies already conducted (phase 5). Construction of a grid ( Table 1 ) was carried out, using the results of the research and data extraction by the researchers who analyzed the key variables (key variables, type of research project, context, results, year of publication, etc.), by selecting as reported in the table, only the fields relevant to the research (phase 6). The research carried out in the first months of 2021 has been updated with more recent publications that have introduced a surveys studies of mental illness among university students (effects of the COVID emergency in terms of physical, cognitive and relational consequences). On the basis of the inclusion-exclusion criteria, other ( n = 2) papers were included (phase 7). The complete file, containing the studies considered significant for the purposes of the construction of this work, was analyzed by the second researcher, in order to avoid errors in the research (phase 8). Steps 9 and 10 resulted in the production of this research paper.

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Table 1 . Summary of the results.

It is clear from the selected articles that the idea present in the introduction is confirmed: the highest number of studies were on mental disorders, as well as on alimentary disorders. On the contrary, technology and other new addictions among university students are still little studied. They were therefore excluded from this study ( Table 1 ).

Risk Factors

From the analysis of the literature, several risk factors emerge that are involved in the development of mental problems in students. In particular, it is possible to classify the main factors in: academic factors, social factors, psychological risk factors, lifestyle factors and physiological factors.

Among the academic factors, the inverse correlation between time spent in study and poor results emerges. Academic results are, in fact, correlated with job placement and other higher education programs. It follows that, at times, this can be detrimental to students' mental health. In addition, elements such as loneliness and social isolation can often induce worry and melancholic states that play a major role in learning. One example is the pandemic situation that has forced millions of students into a scarcity of relationships for a very long time. Additional pivotal factors are those of a psychological nature: disappointments, stress, and perceived anxiety can have a major impact on academic performance; in addition, abuse and mistreatment negatively affect cognitive, emotional, and social development.

The period of change characterized by entry into college often involves changes in lifestyle as well: there may be a tendency to increase intake of drugs, alcohol, and various substances that, if abused, can alter functioning patterns. One of the pivotal factors, however, is the biological makeup of the individual: genetic history and health status have high implications in mental health.

Academics Factors

SAD can get caused by a variety of university-related academic pressures. The degree's subject is one of these strongly prevalent factors. When compared to their non-medical colleagues, nursing, medical and health-related students have a greater prevalence of depression and anxiety ( 23 ). Medical and nursing students, who have both theoretical and patient-related responsibilities, typically have an enormous workload among undergrads and, as a result, experience higher anxiety and despair ( 24 ). Furthermore, students majoring in psychology and philosophy, like medical and nursing students, are more likely than others to acquire depression during their studies ( 34 ). Medical and nursing students who work with people's health may develop melancholy and anxiety due to their worries about making mistakes that could hurt them or their patients ( 23 ). Students whose degrees include practical components may travel to new locations for fieldwork and job experience, adding to their anxiety and stress ( 23 ). However, it's essential to determine whether students with underlying mental health issues are more prone to pick disciplines like philosophy or psychology or subjects that lead to caring careers like nursing and medicine.

Furthermore, some prospective students, particularly those studying nursing and medical, often lack explicit knowledge of the workload and curriculum associated with their field of study before enrolling in university, and as a result, they may become disillusioned once they begin their studies ( 23 ). It's worth noting that not all research discovered a link between the study's subject and the development of SAD ( 35 ). Variances can explain this phenomenon in sample type and size, resulting in disparities in workload and curriculum, such as courses taught in different universities across the world.

Studying for a degree at the university level can be a challenging endeavor that necessitates mental work. Mastery of the subject has been shown to negatively affect anxiety, self-esteem, and depression among college and university students, with those who have a mastery of the subject displaying less stress and anxiety ( 32 ). Additionally, students studying in a foreign environment where there is a use of a non-native language sigh high levels of anxiety and sadness during their freshman year, with their stress levels decreasing with time ( 32 ). This is due to the fact that students studying in a foreign language are typically people who have moved abroad and thus take a while in adjusting to their new form of livelihood. Domestic and international students' depression and anxiety levels can be linked to the year of study, with newcomers entering university. On the other hand, final-year students experience the highest levels of anxiety and depression, and various risk factors ( 32 ). First-year students experience SAD due to difficulties adjusting to university life, negative family experiences in the past, social isolation, and a lack of friends. Final-year students report unpredictability about their years ahead, prospective work opportunities, university debt repayment, and adjusting to life after school as major risk factors for SAD ( 32 ). As a result, as students go through their degrees and learning process, there is a change in SAD potential risk themes.

Students spend a large percentage of time engaged in academic pursuits at university, and poor academic performance can harm their mental health. Receiving worse grades during their studies can have a severe impact on student's mental health, leading to the development of SAD ( 28 ). Academic achievement throughout undergraduate education can influence degree categorization, affecting students' opportunities, including job placement or entrance to postgraduate programs. On the other hand, both the cases of students suffering from mental problem symptoms and the severity of their SAD increase during test time, indicating a direct link between academic stress and students' psychological health states ( 38 ). However, there is no direct correlation that is well-established. There are chances that depression and other related disorders such as momentary memory loss and lack of concentration are causes of bad academic marks, or that students get anxious and depressed due to their poor exam performance ( 39 ). Grades and mental health can have a reciprocal relationship, with poor mental health causing students to receive poorer grades ( 40 ), creating a vicious loop of academic performance and mental health. Interestingly, students' social connection and coherence to the campus community during exam periods decreased ( 38 ). This phenomenon can be explained by students' lower participation in university social events and clubs and a higher sense of competitiveness among their peers. Furthermore, students interact with lecturers, instructors, tutors, and other staff members both directly and indirectly; as a result, the interaction between academic staff and students can impact students' mental health. Another factor that contributes to SAD among undergrads is a bad and abusive interaction with teachers and mentors.

Part-time students are more likely to be emotionally stable and free from mental illnesses compared to full-time students. Students enrolled for part-time studies are more likely to be employed, and therefore they have a constant flow of income. Similarly, they are less likely to experience some social predisposes that may induce mental illnesses due to their schedule. Unlike full-time students, they are free-wheel and do not have a limited and timed duration to complete their courses. Their financial advantage puts them in a better position; however, they are also likely to experience other forms of predisposing factors. The negative predisposes that are more likely to cut across all students, for this reason, include the pressure accrued from school workload, phobia of performing poorly. They also entail the wrong expectations built on the courses and institutions of learning, a student's year of study, poor relationship with the staff with which a student interacts at the university.

Social Factors

In human livelihood, everyone is exposed to society and that an individual and society are two inseparable entities. Society has a significant influence on a person's thought ideology and self-actualization. Naturally, a person's description and identification of oneself gets determined by society. For this reason, society dramatically influences a person's state of mental health. Whenever a person coexists with others in a relatively fair environment or at par with the majority fortunate, the individual's mental health state is likely to be boosted. The case is dissimilar when a person belongs to a few unfortunate members of the community. Some of the social predisposes are therefore likely to perpetuate disorders in undergraduate students or otherwise breed them.

Loneliness and social isolation are a matter of concern among students, especially due to the advent of online learning, which discourages interaction. In modern society, especially after the COVID-19 pandemic, most institutions of higher learning have adopted online learning to facilitate the continuity of education and the learning process. This form of learning has hindered students' possibility of interaction with their peers in a classroom environment. This phenomenon has perpetuated the inherent social condition and situations of some students, especially introverts. According to Loades et al. ( 33 ), young adults, who make up the undergraduate population, are prone to experiencing high depression rates, which can also cause anxiety. The isolation and limitation of interaction among the young population require mitigation to ensure that the issue gets resolved at the early stages of inception ( 33 ). Self-solation and loneliness are also associated with having few friends, thus putting one at risk of experiencing mental illnesses in college. More often than not, loneliness can lead to low self-esteem and confidence, which breeds anxiety.

Social disadvantages such as poor housing and poverty pose more risk of mental disorders among students. Among learners, poverty is associated with poor performance in school in line with behavior, cognition, and attention-related issues ( 22 ). Therefore, it is associated with anxiety, schizophrenia, depression, delinquency, and other mental health disorders that are synonymous with young adults. Additionally, poverty increases one's risk of getting traumas and abuse, especially during childhood, and losing crucial family members ( 22 ). High-income inflow in a home setup reduces chances and risk of domestic violence. It, therefore, goes without saying that when the condition is otherwise, the students are likely to get exposed to unbearable environments at home, which yields mental conditions. Similarly, students coming from poor backgrounds have instilled internal pressure and desire to evict themselves from poverty. The fear of poverty and the desire to become wealthy gives students discomfort and pressure since they always think that it is likely to cost them severely ( 22 ). Students who live in poor housing facilities are also likely to develop low self-esteem and confidence. They view themselves as inferior to other classes of students ( 22 ). Other students may also discriminate and underrate them, thus brewing mental conditions that are stringent and adverse. Therefore, it is wise and socially acceptable that students should not let their social situation of poverty and poor housing ruin their idea and sense of self-esteem and confidence.

Bullying and social discrimination impose mental health conditions that may affect the students' performance and cause long-term health conditions. Bullying, especially in the school environment, affects both the victims and the perpetrators in different ways ( 41 ). It may cause trauma, behavior, and bodily implications and affect one's identity. Contemporary cyberbullying is also characterized by imposing anxiety, low self-esteem, and depression among young adults ( 41 ). The psychological discomforts and distress may yield a personal thought toward a person, thus harming oneself. The individuals are likely to behave in a manner that can trigger suicide attempts and other forms of self-harm ( 41 ). As a result of low self-esteem, a person may also become an introvert, thus interfering with one's potential to interact with other people. Perpetrators are likely to have interaction problems and the inability to socialize with their fellow students since they have instilled fear. The situation is almost similar when one experiences various forms of discrimination. Discriminated individuals end up with low self-esteem and confidence, as well as the desire to rise above their perpetrators ( 29 ). This state breeds anxiety and depression among the victims.

Psychological Risk Factors

University and college students also get exposed to various psychological stressors and displeasures that negatively impact their mental health and performance. Some social predisposes are also likely to cause psychological discomfort and resulting mental illnesses in a university or college setup. Some early childhood preconditions are also likely to impact a person psychologically, even at the tertiary level of education ( 44 ). For instance, childhood trauma, abuse, and neglect are likely to be more disastrous when a person reaches the university or college level. Trauma greatly impacts a person's thoughts and feelings about oneself and how they relate with other people in society. Students, especially females, who have gone through a traumatic experience are likely to develop mental illnesses and conditions such as post-traumatic stress disorder (PTSD), depression, or anxiety ( 45 , 46 ). Childhood maltreatment has a negative impact on cognitive, social development, and emotional development leading to problems with interaction and communication, as well as making people more prone to negative emotions in general and noticeable behavior problems like emotional maladjustment and anxiousness, hyperactivity, antisocial traits, and delinquent behaviors ( 45 ). Mistreatment during childhood is also likely to cause poor emotional intelligence, inhibited until college or university. Social support and refraining from mistreatment lead to mitigation of long-term adverse conditions such as depression and emotional self-regulation among children. Whenever the mitigation measures are not implemented, the victims are affected in adulthood. The instances are more severe among university and college students.

Long-term and severe stress is synonymously associated with causing mental illnesses among graduates. When stress becomes overwhelming and prolonged, the risks for mental health problems and medical problems increase. Long-term stress increases the risk of mental health problems such as anxiety and depression, substance use problems, sleep problems, pain, and bodily complaints such as muscle tension. Research indicates that stressful events cause significant psychological such as anxiety, distress, and depression ( 27 ). Similarly, severe and long-term academic stress leads to loss of welfare of the victims. Students suffering from academic stress are likely to perform poorly in their schoolwork ( 27 ). Poor performance perpetuates stress in the long run, as many students are accustomed to fearing academic failure and poor performance. Undergraduates may also get challenged by stressful life instances, such as breaking the law, which can cause mental discomfort and disorder. Its severity is also likely to cause other health conditions such as hypertension and asthma. It is, therefore, a predisposing factor that may inherently dominate a person's livelihood in the university.

Poor performance in school work leaves undergraduate students in thought which breeds mental illnesses. Whenever one performs poorly, there are chances that the person will get challenged mentally and develop the desire to work toward changing their results. However, failure for the same can cause a mental disorder due to the inherent academic expectations a person may develop. Similarly, mental illnesses affect a student's performance; therefore, the two risk factors are reversible, hence pausing the risk of cycle perpetuation. Attention to the students performing poorly in colleges and universities is essential in ensuring the cases of mental ill-health and continual unfolding of situations causing a cycle is fixed. This motive will help improve the learners' performance and work toward preventing some mental conditions that are likely to be incurred due to poor academic performance.

Lifestyle Factors

Moving away from family and starting a new life necessitates adaptability and flexibility for one to acclimatize to a new way of life. Most undergraduate students change their behavior and lifestyles as they leave their family setting and start a new life alongside their colleagues, friends, and classmates. SAD can be influenced by various lifestyle factors like alcohol intake, tobacco use, food habits, fitness, and drug usage. Students with mental problems consume a lot of alcohol ( 26 ). Alcohol is the most abused by undergraduates. It is synonymous with a series of mental disorders that they face. Alcohol is also addictive, and that when students overuse it, they are likely to experience various addiction disorders.

Another risk factor linked to SAD is tobacco smoking. It is widespread among students, particularly those from Eastern developing and developed nations like Japan, China, and South Korea ( 47 ). As a result of social bonding, many of the learners, especially male undergraduates, smoke, and the rate of social smoking is directly connected with SAD ( 47 ). Social smokers are less likely to give up their habit and are more likely to continue doing so, resulting in long-term detrimental psychological and physical health implications ( 47 ). Another key component in mental health among young individuals is illegal substance misuse ( 36 ). Academic stress and the social milieu in university dorms and student housing can lead students to take illegal drugs, smoke cigarettes, or consume excessive amounts of alcohol as a coping strategy, causing mental disorders ( 42 ). Students who felt supported by their university were less stressed and were less likely to engage in substance abuse, demonstrating the importance of social support in preventing and treating depression symptoms ( 42 ). It is especially important since a new social behavior or habit formed early in life might persist for a long time. Additionally, students who do not live a healthy lifestyle may experience shame, which can exacerbate their SAD symptoms ( 36 ). Rosenthal et al. ( 37 ) discovered negative behaviors associated with alcohol consumption, such as missing the next day's class, careless actions, self-harm, physical fight or verbal argument, the indulgence of unwanted sexual acts, shame, and regrets. The quantity of alcohol consumed can be the cause of depression and anxiety.

In universities and colleges, graduates adopt diverse sleeping habits that may yield mental illnesses and disorders. Many young people do not get enough sleep, causing sleep deprivation, a serious risk factor for depression and low mood ( 37 ). Students in the United States frequently report significant stress levels and inadequate sleep ( 43 ). The majority of undergraduates strive for academic brilliance, financial security, and the preservation of their lifestyle, which leads to poor sleep. Inadequate sleep can create a vicious cycle in which academic stress causes sleep deprivation. Insufficient sleep causes stress due to poor academic performance, as sleep quality and quantity are linked to academic performance ( 26 ). In general, poor sleeping habits are linked to lower learning ability, anxiety, and stress, leading to depression. Inadequate sleep, therefore, is likely to perpetuate a person's mental illness or otherwise fuel its inception.

In contrast with the predisposing factors, engaging physical exercise among students in colleges and universities is essential in protecting against mental dysfunctions. Students who claim to have limited time and fixed schedules may fail to engage in physical exercise and workouts. The development of SAD symptoms characterizes such students. Engagement in physical exercise and workouts makes the mind occupied and can also free off one's thoughts, which may cause mental illnesses. It also increases a person's interaction and enhances the social capabilities of interaction, which helps prevent some conditions. Physical exercise is also a form of therapy that requires one to exert physical exercise on the activity.

Physiobiological Factors

Physiobiological factors entail the factors that get affected directly and are related to the victim's biological composition, genetic history, and other health factors. For example, the mental health of an individual is inseparable from the family's history. Common disorders tied to an individual's family history include bipolar disorders, schizophrenia, dementia, depression, and anxiety ( 25 ). The genetic makeup determines the vulnerability of a person toward mental issues ( 25 ). People whose predecessors are associated with a certain mental illness are more likely to experience the same based on their genetic composition. Similarly, if one's family has a history of mental illness, one has likely been exposed to stressful conditions at some point in life. Growing up in a challenging environment or being abused by a parent or relative raises the risk of getting depression or anxiety ( 25 ). Epigenetics habits can also alter a person's emotions and habits, influencing people's biological composition, and it is likely to get passed to the next generation ( 30 ). Stress caused by mental health issues in great-grandparents, grandparents, or parents changes one's DNA, making them more vulnerable to difficulty ( 30 ). Furthermore, if a person's ancestors ate bad diets, had exposure to environmental pollutants, living with chronic stress, or did not receive proper prenatal nutrition, their genes—and thus an individual's—got altered, making them more likely to show mental illness health disorders.

Other biological factors such as pregnancy and birth complications, brain injury, chronic diseases, alcohol consumption, and drug abuse, as well as poor nutrition, are likely to predispose a victim to mental conditions. Some students have a history of complications during birth. Such students may inhibit the health conditions till the university or college level of study, and they are likely to cause mental disorders ( 31 ). Brain trauma and injury are also significant factors that may cause disorders in undergraduates. Some students have chronic illnesses such as diabetes and cancer, which expose them to discrimination, depression, anxiety, and low self-esteem. The diseases may also cause brain impairments, leaving the students mentally unwell ( 31 ). Usage of drugs and alcoholic drinks also influences the health status of an individual. Some drugs, such as marijuana, are associated with paranoia, resulting in adverse mental illnesses ( 31 ). Too much usage of drugs can also impair a person's eating habits which affect the learner's nutrition. It is, therefore, yields various eating disorders.

Mental health-related issues and social well-being predisposing factors are a matter of concern in the community, especially among undergraduates. The prevalence of mental disorders is a notch higher among college and university students, raising the alarm on establishing some of the causes of the phenomenon. The predisposing factors include social, psychological, biological, lifestyle-based factors and academic factors. Academic excellence pressure and exerts various emotional feelings among students. The emotions and failure to meet their expectations land students into mental conditions that may perpetuate for a while. Change of environment and desire to adjust to a new form of livelihood in the university also causes a resultant change in lifestyle. More often than not, students commence drug and substances abuse which puts them at risk. A person's history of the family's genetic composition, chronic illnesses, and injuries of the brain also causes brain challenges ( 48 ). Interaction and other socio-economic factors are also crucial to a student's mental health that, when neglected, may result in disorders. Therefore, it is wise for the community to make haste and limit instances of the unfolding of the predisposing factors to achieve high standards of mental health among the undergraduates. This move will help in creating a future society that is mentally healthy.

Author Contributions

PL: introduction. GT: methodology and conclusion. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: anxiety, depression, online learning, COVID-19, medical education, SAD, mental health, psychiatrist

Citation: Limone P and Toto GA (2022) Factors That Predispose Undergraduates to Mental Issues: A Cumulative Literature Review for Future Research Perspectives. Front. Public Health 10:831349. doi: 10.3389/fpubh.2022.831349

Received: 08 December 2021; Accepted: 24 January 2022; Published: 16 February 2022.

Reviewed by:

Copyright © 2022 Limone and Toto. 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.

*Correspondence: Giusi Antonia Toto, giusi.toto@unifg.it

This article is part of the Research Topic

The Pandemic, Undergraduate Mental Health and Academic Performance

  • Open Access
  • Published: 06 June 2023

The prevalence of depressive and anxiety symptoms among first-year and fifth-year medical students during the COVID-19 pandemic: a cross-sectional study

  • Abdullah Alshehri 1 ,
  • Badr Alshehri 2 ,
  • Omar Alghadir 2 ,
  • Abdullah Basamh 2 ,
  • Meshari Alzeer 2 ,
  • Mohammed Alshehri 2 &
  • Sameh Nasr 2  

BMC Medical Education volume  23 , Article number:  411 ( 2023 ) Cite this article

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Medical students have higher risk of psychological disorders due to the relatively stressful environment. Educators are becoming increasingly aware of the impact of stresses on the students general well-being. The objective of the current study was to examine the prevalence of and risk factors for depressive and anxiety symptoms among first-year and fifth-year medical students. Additionally, we aimed to determine whether the COVID-19 pandemic has affected students’ mental well-being.

A cross-sectional study was performed at the College of Medicine at King Saud University between September 2020 and January 2021. The target population was first-year and fifth-year medical students. Depressive symptoms were screened using the 9-item Patient Health Questionnaire (PHQ-9), while anxiety symptoms were screened using the 7-item Generalized Anxiety Disorder assessment (GAD-7). Students were also directly asked about the effect of the COVID-19 pandemic on their mental well-being. Outcomes were compared between groups using the chi-squared test and Student’s t test. Multivariate logistic regression analysis was performed to identify factors associated with depressive and anxiety symptoms.

A total of 182 medical students were included. Depressive symptoms (52.9% versus 35.8%, p  = 0.020) and anxiety symptoms (35.6% versus 26.3%, p  = 0.176) were higher in the first-year students than in the fifth-year students. Approximately 19.2% of the students were worried about acquiring COVID-19, 49.4% were worried about academic performance, and 30.8% were feeling sad, depressed or anxious during the COVID-19 pandemic. Independent risk factors for depressive symptoms included having concomitant anxiety, being worried about acquiring COVID-19, being worried about academic performance, and feeling sad, depressed or anxious. Independent risk factors for anxiety included having a lower grade point average and having concomitant depressive symptoms.

Medical students have an alarmingly high prevalence of depressive and anxiety symptoms, which might have been negatively impacted by the COVID-19 pandemic. There is a need for a special mental health program targeting new and current medical students.

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Anxiety and depressive disorders are the most common mental disorders worldwide [ 1 , 2 ]. Approximately 284 million people suffer from anxiety disorders, and 264 million people suffer from depressive disorders worldwide [ 1 ]. Depressive disorders and, to a lesser extent, anxiety disorders are listed among the top contributors to the overall global burden of disease and disability, especially among women [ 1 ]. Both diseases cause considerable negative impacts on quality of life, including the physical and psychosocial domains [ 3 ].

The mental health of medical students has received increasing attention in recent years. Several studies from Arabic and Western countries have indicated that medical students suffer from higher levels of anxiety, depression, and psychological distress than their peers in other specialties [ 4 , 5 , 6 , 7 ]. Medical education can be stressful due to academic pressure, sleep deprivation, a negative educational environment, limited faculty support, financial concerns, limited leisure time, and emotionally stressful experiences due to exposure to sick and dying patients [ 8 , 9 ]. Some studies have shown that the impact of these challenges on mental health is higher among newly joined students who face a sudden and major change in their lifestyle [ 10 , 11 ]. Examining first-year and fifth-year students may provide insight regarding the difference between both groups, as there are several possible confounding factors between them, such as the experience level, level of maturity, and ability to seek support.

The emergence of the COVID-19 pandemic represents a compelling new challenge for the mental health and psychological well-being of the whole world population and medical students in particular [ 12 , 13 , 14 , 15 , 16 ]. Estimating the burden of depressive and anxiety symptoms during the pandemic is critical for planning student mental health services [ 12 , 13 , 14 ]. Unfortunately, psychological data such as anxiety and depressive symptoms for medical students during the COVID-19 pandemic are lacking in Saudi Arabia [ 17 ]. The objective of the current study was to examine the prevalence of and risk factors for depressive and anxiety symptoms among first-year and fifth-year medical students. Additionally, we determined whether the students felt that the COVID-19 pandemic had affected their mental well-being.

The current study was conducted at the College of Medicine of King Saud University in Riyadh, Saudi Arabia. The college was established more than 40 years ago as the first medical college in the country. The college hosts more than 1400 male and female medical students.

Study design and ethical approval

A cross-sectional study was carried out between September 2020 and January 2021. The study obtained all required ethical approvals from the institutional review board at the College of Medicine, King Saud University (No. E20-5302). The students were required to provide consent to join the study. The consent emphasized voluntary participation, confidential handling of data, no sensitive data collection, and the anonymous nature of data analysis.

The target population was first-year and fifth-year medical students during the 2020–2021 academic year. Students with previously diagnosed psychiatric disorders were also included.

Sample size

It was estimated that 180 students (90 from each year) are required to detect a 20% difference in study outcomes (depressive and anxiety symptoms) between the two years using 80% power and a 95% level of significance. Given the actual number of medical students at King Saud University in the target years, the calculated sample size would be sufficient to detect 40% study outcomes (depression and/or anxiety) with 10% confidence in each group. The sample was collected using convenience sampling. Students were recruited randomly from classrooms, break areas, and building courtyards. We decided to compare first-year and fifth-year students because they represent the beginning and the end of undergraduate education in the College of Medicine. Therefore, their comparison is more likely to show differences in the measured outcomes due to several factors that distinguish between both groups, such as the experience level, level of maturity, ability to handle stress and seek support when needed, etc.

Data collection tool

A self-administered online questionnaire (using Google Forms) was developed and included sociodemographic characteristics, academic performance, and perceived probable COVID-19 impact on students’ psychology. The questionnaire was reviewed by three experts to assess face and content validity. The experts were clinicians working in the student clinic with experience in assessing the physical and psychological health of medical students. A pilot study was conducted with 10 students to check the applicability and clarity of the questions. The pilot study showed positive feedback to the questions. Additionally, previously validated psychometric tools were used to assess the study outcomes (depressive and anxiety symptoms) [ 18 , 19 ]. Students were directly asked about their feelings and academic performance during the pandemic. All included students were asked to sign informed consent forms before starting the questionnaire. Data confidentiality was protected through an online self-administered collection method, deidentification, encryption, password protection and limited access to the research team only. The questionnaire is provided as a supplementary document.

Psychometric tools

Depressive symptoms were screened using the 9-item Patient Health Questionnaire (PHQ-9), while anxiety symptoms were screened using the 7-item Generalized Anxiety Disorder assessment (GAD-7) [ 18 , 19 ]. Both tools were developed and validated to establish provisional diagnoses for depressive and anxiety symptoms (respectively) and follow response to intervention and treatment in accordance with standard definitions of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) [ 20 ]. According to the clinical literature, the PHQ-9 score of ≥ 10 indicating clinical depression, and the GAD-7 score of ≥ 10 indicating clinical anxiety [ 18 , 19 , 20 ].

Statistical analysis

Data are presented as frequencies and percentages for categorical data and mean and standard deviation (SD) for continuous data. Sociodemographic characteristics, academic performance, COVID-19-related questions, and study outcomes were compared between the study groups (first year and fifth year). Additionally, the previous factors were examined to detect the univariate risk factors for depressive and anxiety symptoms. Chi-squared or Fisher’s exact tests, as appropriate, were used to examine differences in categorical variables, while Student’s t test was used to examine differences in continuous variables. To identify factors independently associated with depressive and anxiety symptoms, multivariate logistic regression analysis models were run after adjusting for the variables that were potentially associated with awareness in univariate analysis (with p  < 0.20). Backwards elimination was used to exclude nonsignificant variables from the model. To simplify the analysis of COVID-related questions, responses of (always) and (most of the time) were analysed as (yes) and (not at all) and (sometimes), which were analysed as (no). A P value < 0.05 was considered significant. SPSS (Version 25.0. Armonk, NY: IBM Corp) was used for all statistical analyses.

A total of 182 medical students were included in the current study. Table 1 shows the sociodemographic and academic characteristics by academic year of the students. Compared with fifth-year students, first-year students had significantly younger age (19.1 ± 1.1 versus 23.1 ± 0.6 years, p  < 0.001), significantly better grade point average (GPA) (83.9% versus 43.2% for GPA 4.50 to 5.00, p  < 0.001), significantly more frequent successful academic years (97.7% versus 90.5%, p  = 0.042), significantly more frequent within-family living status (94.3% versus 88.4%, 0.037), and significantly more frequent sedentary (not active) lifestyle (29.9% versus 15.2%, p  = 0.009). There were no significant differences between students in the two academic years in gender, use of psychological therapy, sleeping duration, single social status, and current smoking.

As shown in Fig.  1 , the prevalence of depressive and anxiety symptoms in all study students was 44.0% and 30.8%, respectively. Figure  2 shows that depressive symptoms were significantly higher in the first-year students than in the fifth-year students (52.9% versus 35.8%, p  = 0.020). Anxiety symptoms were also higher in the first-year students than in the fifth-year students (35.6% versus 26.3%), but the difference did not reach statistical significance ( p  = 0.176). Approximately 25.3% of the students had both depressive and anxiety symptoms, and this prevalence was significantly higher in the first-year students than in the fifth-year students (32.2% versus 18.9%, p  = 0.040). Approximately 49.5% of the students had either depressive or anxiety symptoms, without a significant difference between students in the two academic years (56.3% versus 43.2%, p  = 0.076).

figure 1

Prevalence of depressive and anxiety symptoms in all students ( n  = 182)

figure 2

Prevalence of depressive and anxiety symptoms by the academic year of medical students (first-year n  = 87, fifth-year n  = 95)

Table 2 shows the responses to COVID-19 pandemic-related questions by the academic year of students. Approximately one-fifth of the study students were always (9.9%) or most of the time (9.3%) worried about acquiring COVID-19, without significant differences between students in the two academic years ( p  = 0.517). Almost half of the study students were always (15.9%) or most of the time (33.5%) feeling worried or anxious about their academic performance during the COVID-19 pandemic, with significantly higher levels of worry in the first-year students than in the fifth-year students (63.2% versus 36.8%, p  < 0.001). Approximately 30% of the study students always (8.8%) or most of the time (22.0%) felt sad, depressed or anxious during the COVID-19 pandemic, without significant differences between students in the two academic years ( p  = 0.648). Approximately 11.2% of the study students were diagnosed with COVID-19 (defined by a positive swab test), with a significantly lower infection rate in the first year than in the fifth year (2.4% versus 19.1%, p  < 0.001). Half of the study students had at least one of their relatives diagnosed with COVID-19, without significant differences between students in the two academic years ( p  = 0.764).

Table 3 shows the univariate and multivariate logistic regression analyses of potential predictors of depressive and anxiety symptoms. In addition to academic years, only the variables that were significantly (or almost significantly) associated with depressive or anxiety symptoms in univariate analysis are shown. First-year students were twice as likely to report depressive symptoms than fifth-year students in univariate analysis (odds ratio [OR] 2.01, 95% confidence interval [CI] 1.11–3.65, p  = 0.021) but not in multivariate analysis. Independent risk factors for depressive symptoms included having concomitant anxiety (OR = 9.57, 95% CI 3.59–25.49, p  < 0.001), being worried about acquiring COVID-19 (OR = 5.60, 95% CI 1.74–17.97, p  = 0.004), being worried about academic performance (OR = 4.33, 95% CI 1.80–10.39, p  = 0.001), and feeling sad, depressed or anxious (OR = 7.75, 95% CI 2.88–20.85, p  < 0.001). Academic year was not a significant predictor of anxiety in either univariate or multivariate analysis. Independent risk factors for anxiety included having lower GPA (OR = 2.70, 95% CI 1.03–7.08, p  = 0.044) and having concomitant depressive symptoms (OR = 13.91, 95% CI 6.08–31.81, p  < 0.001).

The current study revealed alarmingly high prevalence rates of depressive and anxiety symptoms among medical students during the COVID-19 pandemic. The current prevalence of depressive symptoms (44%) was generally similar to the prevalence reported by previous studies performed in Saudi Arabia and internationally [ 4 , 6 ]. For example, a recent review that included 18 studies from different regions of Saudi Arabia estimated the prevalence of depression among medical students to be between 31 and 78%, with an average prevalence of 51% [ 4 ]. Interestingly, only three studies included in this review used the PHQ-9 in screening for depressive symptoms (similar to the current study), with slightly lower rates of depression that ranged between 28 and 61% [ 21 , 22 , 23 ]. It should be mentioned that all the included studies in that review were conducted before the COVID-19 pandemic. Consistent with the current finding, depressive symptoms detected in medical students in Western countries using different psychometric tools were estimated to be between 6 and 66% [ 6 ].

The current prevalence of anxiety symptoms (31%) was generally similar to the prevalence reported by previous studies performed in Saudi Arabia [ 24 , 25 , 26 ] and internationally [ 6 , 24 , 26 , 27 ]. The anxiety rates in medical students in Saudi Arabia ranged between 28% and 66.3% [ 24 , 25 , 26 ]. This variability is largely caused by different study designs, use of different psychometric tools, and reporting different disease severities [ 24 , 25 , 26 ]. Consistent with the current finding, anxiety symptoms detected in medical students using different psychometric tools were estimated to be between 8 and 65% in Western countries before the COVID-19 pandemic and between 17 and 46% in developing countries after the COVID-19 pandemic [ 6 , 27 ].

Both depressive and anxiety symptoms in the current study were higher in the first-year students than in the fifth-year students; this difference was significant for depressive symptoms. Similarly, some studies have shown that depressive symptoms are higher in new medical students than in clinical students, probably because of a lack of coping mechanisms with respect to the challenging new study environment [ 10 , 11 , 28 ]. Additionally, it has been shown that the prevalence of stress among medical students is highest among first-year students, diminishes progressively until the fourth year, and then slightly increases during the fifth year [ 29 ]. The only factor that was significantly higher in first-year students and was a significant predictor of depression was worry about academic performance during the COVID-19 pandemic. The current findings support the need for special mental health support programs targeting new medical students. This is especially important with closures, quarantine, and limited psychiatric services during the pandemic [ 30 ].

The current study showed that worrying about academic performance among medical students (49%) was much higher than worrying about contracting COVID-19 (19%). Additionally, COVID-19-related worries were independent risk factors for depressive and anxiety symptoms. This finding probably indicates the significant impact of the current COVID-19 pandemic on the mental health of medical students. Consistent with this finding, several international studies have reported the significant role of the current COVID-19 pandemic in developing depressive and anxiety symptoms among medical students [ 12 , 13 , 14 ]. Similar findings have been reported in Saudi Arabia [ 17 ]. Interestingly, an increase in depressive and anxiety symptoms during the COVID-19 pandemic has been observed in both medical and nonmedical students [ 31 ].

Data on types of worry during the COVID-19 pandemic and their relationship with mental health are very limited [ 13 , 31 ]. There is differentiation between academic apprehension, which refers to worries about academic progress in the time of closures, and general apprehension, which refers to worries about the consequences of the disease on oneself, family and friends [ 13 ]. However, general but not academic apprehension was associated with depressive and anxiety symptoms in previous studies [ 13 ]. In response to the pandemic challenge, medical students can adopt different coping strategies to cope with the negative impact of the COVID-19 pandemic, including religious/spiritual coping and acceptance coping [ 32 ]. Additionally, strengthened family bonds during the lockdown were shown to improve mental health [ 33 ].

Our study is unique in quantifying the burden on the mental health of medical students in different academic years. We used validated psychometric tools to assess depressive and anxiety symptoms [ 18 , 19 ]. Additionally, we examined the impact of COVID-19-related worries on depressive and anxiety symptoms. Nevertheless, a few limitations are acknowledged. Being a single institution study, the results should be generalized with caution. The cross-sectional design used cannot prove causation but rather association. Finally, the self-reported nature of the study data cannot exclude the possibility of recall bias. However, these limitations are present in almost all similar studies and are believed to have a very minor impact on the study findings, if any. We think this study highlights these worrisome findings; however, more qualitative studies need to be performed to further understand the risk factors for mental health disorders among medical students and identify potential measures to support this population.

Conclusions

In conclusion, the current study shows an alarmingly high prevalence of depressive and anxiety symptoms among medical students during the COVID-19 pandemic. Both depressive and anxiety symptoms were significantly higher in the first-year students than in the fifth-year students, which reached statistical significance only for depressive symptoms. COVID-19-related worries were independent risk factors for depressive and anxiety symptoms. There is a need for special mental health programs targeting new medical students, especially those with limited psychiatric services during the pandemic.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

9-Item Patient Health Questionnaire

7-Item Generalized Anxiety Disorder assessment

The Diagnostic and Statistical Manual of Mental Disorders

Standard deviation

Grade point average

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Acknowledgements

We wish to extend our special thanks to Dr. Rana Alhamwy, who works in the COVID-19 clinic at King Saud University Medical City and helped us better understand its impacts on mental health among physicians and students alike in this pandemic.

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Abdullah Alshehri

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A.A., B.A., O.A., A.B., M.A., M.A., S.N. designed the study. B.A., O.A., A.B., M.A., M.A., S.N. collected the data. A.A., B.A., O.A., A.B., M.A., M.A., S.N. analyzed and interpreted the data. A.A., B.A., O.A., A.B., M.A., M.A., S.N. wrote the manuscript. All authors read and approved the final manuscript.

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The study obtained all required ethical approvals from the institutional review board at the College of Medicine, King Saud University. The students were required to provide informed consent to join the study. All methods were performed in accordance with the relevant guidelines and regulations.

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Alshehri, A., Alshehri, B., Alghadir, O. et al. The prevalence of depressive and anxiety symptoms among first-year and fifth-year medical students during the COVID-19 pandemic: a cross-sectional study. BMC Med Educ 23 , 411 (2023). https://doi.org/10.1186/s12909-023-04387-x

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Impact of COVID-19 pandemic on the mental health of university students in the United Arab Emirates: a cross-sectional study

  • Anamika Vajpeyi Misra 1 ,
  • Heba M. Mamdouh 3 , 4 ,
  • Anita Dani 2 ,
  • Vivienne Mitchell 1 ,
  • Hamid Y. Hussain 3 ,
  • Gamal M. Ibrahim 3 &
  • Wafa K. Alnakhi 3 , 5  

BMC Psychology volume  10 , Article number:  312 ( 2022 ) Cite this article

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The COVID-19 pandemic had a significant impact on the mental well-being of students worldwide. There is a scarcity of information on the mental health impact of the COVID-19 pandemic on university students in the United Arab Emirates (UAE). This study aimed to investigate the mental health impact of the COVID-19, including depression, anxiety and resilience among a sample of university students in the UAE.

A cross-sectional study using an online survey was conducted from September to November 2021. The patient health questionnaire (PHQ-9), generalized anxiety disorder-7 (GAD-7) and Connor–Davidson Resilience Scale (CD-RISC-10) were used to assess depression, anxiety, and resilience. The COVID-19 impact was assessed using a list of questions.

Only, 798 students completed the survey and were analyzed for this study. Overall, 74.8% of the students were females, 91.2% were never married, and 66.3% were UAE-nationals. Based on PHQ-9 and GAD-7 cut-off scores (≥ 10), four out of ten of the students self-reported moderate to severe depression (40.9%) and anxiety (39.1%). Significantly higher mean PHQ-9 and GAD-7 scores were found among students who were impacted by COVID-19 than those non-impacted (mean PHQ-9 = 9.51 ± 6.39 and 6.80 ± 6.34; p  = 0.001, respectively) and (mean GAD-7 = 9.03 ± 6.00 and 8.54 ± 6.02; respectively, p  < 0.001). Female students who were impacted by COVID-19 had statistically significant higher depression and anxiety scores (mean PHQ-9 of 9.14 ± 5.86 vs. 6.83 ± 6.25, respectively; p  < 0.001) than the non-impacted females (mean GAD-7 of 9.57 ± 6.32 vs. 5.15 ± 3.88, respectively; p  = 0.005). Never married students had significantly higher PHQ-9 and GAD-7 scores than ever-married (9.31 ± 6.37 vs. 6.93 ± 5.47, P  = 0.003) and (8.89 ± 6.11 vs. 7.13 ± 5.49, respectively; p  = 0.017).

Conclusions

The results of this study demonstrate that the COVID-19 pandemic has negatively impacted the mental health of this sample of university students in terms of depression and anxiety. The results highlight the need to adopt culturally appropriate interventions for university students and focus on vulnerable groups.

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Introduction

In March 2020, the World Health Organization declared the coronavirus disease 2019 (COVID-19) a world pandemic status [ 1 ]. The pattern of the virus has affected many aspects including physical wellbeing, psychosocial life, and the local and global economy [ 2 ]. The high morbidity and mortality rates and the ambiguity around the ongoing pandemic have brought up many mental sufferings for a large proportion of people worldwide [ 3 ]. In addition, the unprecedented public health interventions that were implemented across the globe, including the United Arab Emirates (UAE) caused a wide range of psychosocial impacts [ 4 ]. The societal effects of the COVID-19 pandemic are so pervasive—and yet vary so tremendously according to individual and contextual factors—that global characterization regarding its psychological impact is likely impossible [ 2 , 5 , 6 ].

Several studies have looked at the impact of epidemics on population mental health over the last few decades, and they have reported a wide range of psychological impacts [ 7 , 8 , 9 , 10 ]. Around the world, published research on the impact of the COVID-19 pandemic on mental health revealed that the pandemic is linked to an increase in the rates of depression, anxiety, stress and sleep disturbance among various population groups [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. Research endorsed that universal pandemics can endanger one’s mental well-being since only some people are resilient to change in their environment and able to seek out psychological assistance when needed. Whilst others may emphasize on the physical aspect of themselves during the pandemic time rather than their mental well-being [ 18 ]. Psychologists define resilience as the process of adapting well in the face of adversity, trauma, tragedy, threats, or significant sources of stress—such as family and relationship problems [ 19 ]. People's reactions to crises vary, yet coping strategies to manage such situations require more investigation.

It is clear that the burden of mental health adverse outcomes of the pandemic is not equally shared. Indeed, a substantially greater risk accrues to those facing ongoing stressors, such as job loss, economic distress, occupational stress, responsibilities, social isolation, interpersonal loss, and virus exposure. Moreover, specific dispositional vulnerabilities or diatheses (such as internalizing tendencies or fears of contamination) could interact with the stress and may substantially increase the risk [ 20 ].

University students’ mental health issues are not well recognized and infrequently addressed. Students at universities are often at a vulnerable age (between adolescence and early adulthood), making them sensitive to mental illnesses [ 21 ]. Research revealed that student status was associated with a higher frequency of depressive and anxiety symptoms, perceived stress, and suicidal thoughts [ 21 ]. Blanco et al. estimated in their early research that half of the college-age people they surveyed had a mental health issue [ 22 ]. Literature showed that although the physical implications of COVID-19 were milder on young adults, their mental health was negatively impacted by the pandemic [ 23 ]. Reduced socialization along with the quarantine protocols due to COVID-19 resulted in worsened mood status and increased anxiety during the pandemic [ 23 ]. Patwary et al. 2022 found in their study that more than three in four students experienced clinically significant anxiety levels during the early stages of the COVID-19 pandemic. [ 24 ]. The mental health of young people has been a concern in the UAE, where a published study of the mental health of university students in the UAE (as screened by PHQ-9) found that the prevalence of depression among university students was estimated to be 22.2% [ 25 ]. In addition, a previous pre-pandemic study from the UAE revealed significant levels of anxiety among young adults, making this group especially prone to mental health issues [ 26 ].

Higher colleges of Technology (HCT), founded in 1988, is one of the largest applied higher education institutions, with 16 campuses across the UAE. Currently, there are 21,572 students enrolled in the HCT under 72 programs [ 27 ]. During the COVID-19 pandemic, HCT remained agile and swiftly moved to the online classes and assessments, then continued the hybrid learning model of education.

Despite excellent precautionary, preventative and therapeutic healthcare measures, being put in place by the UAE government, and the lower COVID-19 infection rates than the global average (8.12%), the psychological impact of COVID-19 on the UAE population should not be overlooked [ 25 , 26 ]. Information about the influence of COVID-19 on the mental health of the different sectors of the UAE population is limited [ 28 ]. Few published research pointed to a high prevalence of anxiety, depression, and stress among the general public [ 29 ], healthcare workers [ 30 , 31 ], and the elder population [ 32 ]. However, the mental health effects on university students within the UAE are inadequately addressed. Given these situations, it is important to investigate the university students’ mental health during the COVID- pandemic to inform the possible interventions. Therefore, the current study aimed to address a number of existing gaps including the COVID-19 impact on mental health, in particular depressive and anxiety symptoms, as well as to assess the resilience of a sample of HCT university students in the UAE during the COVID-19 pandemic. It also investigated the effect of some socio-demographic characteristics and the COVID- 19 impact on the mental health of the sampled students.

Materials and methods

Study population, design and setting.

A cross-sectional study was conducted among a sample of students who were enrolled in the undergraduate and postgraduate programs of the HCT university across the UAE. A structured self-administered questionnaire was used for data collection in the current study. Participants were recruited via announcements through the email network of the HTC University. The data collection took place online from September to November 2021. The responses were extracted using an electronic survey via the google survey tool (Google Forms). Participants were asked for consent approval before participation. The median completion time for the survey was 9 min. Based on the Raosoft calculator for sample size estimation, the minimum required sample for this study was 378 with a confidence interval of 95.0 and 0.5 margin of error [ 33 ]. Out of the total survey sent, 819 students voluntarily responded with a response rate of 43%. Only, 798 students fully completed the survey and were analyzed for this study.

Variables and measures

The questionnaire included socio-demographic demographics, COVID-19 -related Items, 9-item patient health questionnaire (PHQ-9), 7-item generalized anxiety disorder (GAD-7) scale and the 10-items Connor–Davidson Resilience Scale (CD-RISC-10). The socio-demographics included gender, age groups, nationality, marital status, working status (Currently employed or not employed) and Emirate of residence within the UAE. Nationality was dichotomized to UAE nationals and non-UAE nationals. Marital status was grouped into ever-married that included married and divorced/widowed or single/ never married participants.

The impact of Covid-19 on the participants was assessed using an outcome variable (the COVID-19 impact). The variable was dichotomized into those who were impacted by COVID-19 or not impacted by COVID-19. Seven questions in the survey assessed if the respondents were impacted by COVID-19 in some way or another. “Impacted by COVID-19” was defined if the participants answered “yes”, they were diagnosed with COVID-19 themselves or a close family/ friend, witnessed a COVID-19 related death or had high exposure to COVID-19 at the workplace in the past year preceding the survey. The respondents who answered no to all of the seven questions were grouped in the category of “not impacted by COVID-19”.

Mental health assessment scales

The patient health questionnaire-9 (phq-9).

The PHQ-9 is a 9-item depression assessment module adopted from the full PHQ. The PHQ-9 has been previously recognized as a valid and reliable instrument for screening of depression in the general population and in university context [ 34 , 35 , 36 ]. It consists of nine questions probing the frequency of depressive symptoms over the past 2 weeks. Responses ranged from 0 to 3 (0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day). Total scores, obtained by summing the responses to each item, range from 0 to 27. Cut-off scores adopted in the present study included scores of ≤ 9 and ≥ 10 that suggest minimal to mild depression and moderate to severe depression on the PHQ-9, respectively [ 35 ]. The reliability of the scale among the current sample was excellent (α = 0.876).

The generalized anxiety disorder-7 (GAD-7)

The Gad-7 is widely used as a self-reporting scale to assess the symptoms of anxiety. It consists of 7-Items that measures anxiety over the past 2 weeks. Items are rated on a 4-point Likert-type scale (0 = not at all, 1 = several days, 2 = more than half the days, 3 = nearly every day). The GAD-7 score is calculated by assigning scores of 0, 1, 2, and 3, then adding together the scores for the seven questions. GAD-7 total score for the seven items ranges from 0 to 21. Cut-off scores of ≤ 9 and ≥ 10 are considered minimal to mild and moderate to severe levels of anxiety on the GAD-7, respectively [ 37 , 38 ]. The reliability of the scale among the current sample was excellent (α = 0.906).

Connor–Davidson Resilience Scale (CD-RISC-10)

CD-RISC-10 is a widely used self-reported questionnaire [ 39 , 40 ]. It consists of 10-items to assess the population’s resilience levels or their ability to tolerate and overcome adverse situations such as illness, pressure, and failure. Each item is rated on a 5-point Likert scale (0 = not true at all, 1 = rarely true, 2 = sometimes true, 3 = often true, and 4 = true nearly all of the time), with a higher total score indicates greater resilience. Due to the lack of a recognized cut-off point, resilience scores were categorized into high resilience (score ≥ 33) and normal or low resilience (score ≤ 32) [ 39 , 40 ]. The reliability of the scale among the current sample was excellent (α = 0.893).

Estimating the prevalence and the levels of depression and anxiety

Prevalence rates of depression and anxiety were determined using cut-off points based on PHQ-9 and GAD-7 scales validation [ 34 , 37 ]. In the current study, depression was defined as a total score of (≥ 10) in the PHQ-9 instrument, indicating a case of moderate to severe depression. Anxiety was defined using the GAD-7 instrument with a total score of (≥ 10), indicating a case of moderate to severe anxiety. The prevalence of depression or anxiety was estimated by dividing the number of students who exceeded the cut-off score by the total number of students who responded.

Statistical analysis

Data coding, data cleaning, and analysis have been carried out by using IBM SPSS (Version 22.0, IBM SPSS, IBM Corp, USA). Cronbach’s alpha coefficients were calculated to indicate scale reliability. Outliers were observed on the PHQ-9 scale, indicating that only four male and two female respondents had severe depression. Descriptive statistics, including means, standard deviations (+ SD) and percentages were used to illustrate participants’ demographics. The normal distribution of data was verified using box plots and histograms. Complete case analysis was considered in this study, then 12 missing cases with responses were excluded from the statistical analysis. The equality of variances was checked using Levene’s test. Independent sample T-test was used to compare the mean scores between the participant COVID-19 impact category and mean scores of the three scales (depression, anxiety and resilience). Participants’ anxiety, depression and resilience mean scores were compared with demographics characteristics using independent-samples t-test, one-way analysis of variance (ANOVA). An independent samples t-test was used to compare the mean scores of the three psychometric scales (anxiety, depression, and resilience scales) between different socio-demographic groups and between the COVID-19 impact categories, separately.

Univariate analysis of variance (ANNOVA) was used to examine if the mean score of the three psychometric scales (anxiety, depression and resilience scales) were different between the impacted by COVID-19 and non-impacted and participants’ gender. Analysis of between-subject effects was run to examine the effect of those categorized as impacted by COVID-19 and those not-impacted students revealed insignificant differences for the mean scores of GAD-7, PHQ 9 and CD-RISC 10 scales. The statistical significance of ≤ 0.05 was considered in the study, with 95% confidence intervals.

Ethical approval and consent

The study was approved by the Higher College of Technology research ethics review board. Participants gave online written consent to participate in the study prior to starting the survey.

Table 1 shows the socio-demographic characteristics of the participants. It reveals that 74.8% of the students were females, and the majority (91.2%) were single/ never married. Most of the participants (66.3%) were UAE-nationals. As for the Emirate of residence, 38.1% reported living in Abu Dhabi city. The students’ age ranged from 16 to 41 years, with the highest proportion in the age group of 19 to 25 years (63.5%). Overall, 65.5% of the students stated they were currently not employed.

The distribution of the participants by COVID-19 related questions is clarified in Fig.  1 . It was revealed that the majority of the participants (88.7%) were classified as impacted by COVID-19 (as per the COVID-19 impact questions). The vast majority of students reported they were diagnosed with COVID-19 themselves or a significant relatives/ friends (86.8%). Additionally, 27.2% of students stated they knew some close relatives/ friends who died from COVID-19 or its complications.

figure 1

Distribution of the participants by COVID-19 related questions

Prevalence estimates of depression, anxiety and resilience (as measured by PHQ- 9, GAD-7 9, and CD-RISC-10 cut-off scores) by gender among the participants were summarized in Fig.  2 . Overall, four out of ten of the participants had moderate and severe depression and anxiety (40.9% and 39.1%, respectively). A slightly higher proportion of females had moderate to severe depression and anxiety than males. It can be seen that males had higher resilience (12%) than females (9%). Prevalence estimates of depression, anxiety, and resilience by COVID-19 impact among the participants are shown in Table 2 . Based on PHQ-9 cut-off scores (≥ 10), the self-reported prevalence of moderate to severe depression symptoms was 40.9%, and it was higher in students who were categorized as impacted by COVID-19 (43.8%) than those who were not impacted (17.8%). Based on GAD-7 cut-off scores (≥ 10), the self-reported prevalence of moderate to severe anxiety was 39.1%. Students with moderate to severe anxiety symptoms categorized as impacted by COVID-19 had higher scores (40.1%) than those who were not impacted (31.8%). Few students (11.5%) self-reported high levels of resilience (based on CD-RISC-10 score ≥ 33).

figure 2

Prevalence of depression, anxiety and resilience by gender among the participants

Independent sample t-test for the comparison between the mean scores (± SD) of the psychometric scales by COVID-19 impact is presented in Table 3 . Notably, the total mean scores (± SD) of all the three psychometric scales used were below the assumed cut-off threshold of moderate to severe depression (9.10 ± 6.33) or moderate to severe anxiety (8.78 ± 6.07), and high resilience level (21.46 ± 8.80) for the participating students. Significantly higher mean PHQ-9 and GAD-7 scores were found among students who were impacted by COVID-19 than those non-impacted. No statistically significant difference was detected in the mean CD-RISC-10 scores for those who were impacted by COVID-19 and those who were non-impacted.

Independent sample t-test was used to compare the mean scores (± SD) for the three psychometric scales by socio-demographic characteristics (as shown in Table 4 ). The mean scores of the three psychometric scales (PHQ-9, GAD-7, and CD-RISC-10) were insignificantly different between male and female participants ( p  > 0.05). Participants of UAE-nationality had significantly higher mean scores ± SD for PHQ-9 than their non-national counterparts (9.63 ± 6.51 vs. 7.92 ± 5.81, respectively, p  = 0.001*). As for the employment status, currently non-employed participants had significantly higher CD-RISC-10 scores than the currently employed ones (22.01 ± 8.60 and 20.44 ± 9.10, respectively; p  = 0.018*). For the marital status single/never married participants had significantly higher PHQ-9 and GAD-7 scores than ever-married (9.31 ± 6.37 and 6.93 ± 5.47, p  = 0.003) and (8.89 ± 6.11 and 7.13 ± 5.49, respectively; p  = 0.017*).

The interaction between the effects of COVID-19 impact and gender on the mean scores (± SD) of PHQ 9, GAD-7, and CD-RISC 10 psychometric scales were examined using a two-way ANNOVA test are shown in Table 5 . There was a statistically significant interaction between the effects of gender and COVID-19 impact on both depression and anxiety scores. In particular, females who were categorized as impacted by COVID-19 (interaction term) had a significantly higher mean PHQ-9 score ± SD than those who were not impacted (9.14 ± 5.86 vs. 6.83 ± 6.25, respectively; p  < 0.001). Similarly, females who were impacted by COVID-19 had a significantly higher GAD-7 score than the ones who were non-impacted impacted (9.57 ± 6.32 vs. 5.15 ± 3.88, respectively; p  = 0.005). Resilience mean scores were almost similar in females who were impacted by COVID and those who were not. No significant differences were detected in the mean scores of any of the mental health scales studied for male participants by COVID-19 impact. The interaction between the effects of COVID-19 impact and marital status and nationality group on mean scores of PHQ 9, GAD-7, and CD-RISC 10 psychometric scales were non-significant (Additional file 1 : Appendix 1).

Prevalence of depressive and anxiety symptoms and COVID-19 impact

The current study suggested that the COVID-19 pandemic has had a significant impact on the mental health and well-being of this sample of university students, with four out of ten of the students self-reported moderate to severe depression (taking PHQ-9 cut-off scores of ≥ 10) and anxiety (GAD-7 cut-off scores of ≥ 10) symptoms. These levels were most prevalent among females and never-married students. The prevalence of depression in our study was higher than in what was reported in other studies [ 12 , 13 , 14 , 16 , 18 ]. In particular, among university students, several studies across the globe showed that the prevalence of depression varied, as low as 4% [ 41 ], and as high as 79.2% [ 42 ] depending on the severity and the instruments used [ 43 , 44 , 45 , 46 ]. In addition, according to a systemic review of published research on the mental health of young adults in the UAE between 2007 and 2017, prevalence scores ranged widely from 12.5 to 28.6% due to wide-ranging sample sizes [ 47 ].

The present study was implemented in the context of the COVID-19 pandemic. For that, our study observed higher mean PHQ-9 and GAD-7 scores among those participants who were impacted by COVID-19 than those who were categorized as not impacted. These results imply that the COVID-19 pandemic might have intensified the negative mental health impact on this sample of university students. Our findings were further supported by the results of a recent study that used PHQ-9 and the GAD-7 scales to evaluate a sample of university students during the COVID-19 pandemic and found that depressive and anxiety symptoms were prevalent in 45.2% and 38.3% of the students [ 48 ]. During stressful situations, like this pandemic, fear and anxiety about the disease can be overwhelming and it may negatively impact the mental health of all the sectors of the population [ 49 , 50 ], and students in particular [ 51 ]. Fears of infection, social distancing, vaccination drives, prolonged university closure, challenges with online learning and uncertainty over examinations all cause stress and anxiety to students worldwide [ 6 , 36 , 43 ].

The effect of socio-demographic characteristics and the COVID-19 impact on the students’ mental health

The current findings revealed that depression symptoms were more reported by females than males. As previously observed, being a female was linked to a higher risk of having elevated depressive symptoms. Gender differences in depressive symptoms are typically explained in terms of gender-role socialization processes that lead to females being more likely to adopt passive cogitative responses to negative moods [ 52 , 53 ]. Besides, women are more likely to be emotionally, socially and financially disadvantaged during crisis times like COVID-19 pandemic [ 54 , 55 ]. This finding is consistent with a large-scale, UAE population-based survey that found females had a greater risk for depression compared to males [ 56 ]. Moreover, the present findings agree with the results of similar studies that have investigated depression among population of neighboring Gulf countries [ 57 , 58 , 59 ].

This study revealed that never-married university students had significantly higher depression and anxiety symptoms than their ever-married counterparts. Research speculated that marriage has been found to be associated with better mental well-being compared to other relationship statuses [ 60 , 61 , 62 ]. Moreover, a study confirmed that positive family-level factors (e.g. positive parenting, healthy family functions and environment) were associated with decreased depression and anxiety [ 62 ]. The married respondents enjoyed more positive family-level factors than the respondents who were not married. The unique nature of COVID-19 which offered the reduced opportunity for social interaction in single respondents while the home-bound married respondents had a robust companionship could be one of the reasons behind such findings [ 63 ]. This finding is consistent with other research [ 2 , 30 , 47 , 50 , 64 ], however, some claimed that the strength of association between single status and depression was modified by age and gender [ 50 , 65 , 66 ]

Although the research on the association between depression and ethnicity is inconclusive [ 67 , 68 , 69 , 70 ], our findings indicate that PHQ-9 is sensitive to ethnicity/ nationality, whereas UAE-national students had higher PHQ-9 scores than non-UAE national ones. The differences in the prevalence of depression outcomes may depend on whether the studies were adjusted for other factors that might be associated with depression or not [ 71 , 72 ]. Factors like sociodemographic and economic profiles should be adjusted carefully. Early research provided evidence of measurement invariance of the PHQ-9 scale regarding ethnicity, implying that the observed inequalities in depressive symptoms may not be attributed to the ethnicity factor alone [ 73 ]. Some considerations can be made based on descriptions of social and cultural norms at large. Contrary to the present findings, no significant difference was observed between Emirati and non-Emirati patients in the frequency of depressive disorders using PHQ-9 [ 2 , 74 ]. Our results could reflect the need to investigate the association between nationality/ ethnicity and reporting of depression symptoms among the UAE population at large.

Resilience scores and COVID-19

The majority of the students surveyed in the present study demonstrated low to normal levels of resilience (CD-RISC 10 cut-off score of ≥ 32). It has been observed that the levels of resilience vary widely according to the sample size and the assessment tool used [ 75 , 76 ]. However, considering the current total sample mean resilience score of 21.46 (± 8.80) indicates that our sample had a lower mean score than the reported mean score of 31.8 in the general population [ 77 ] and 30.97 (± 5.46) in a specific sample during COVID-19 [ 76 ]. Furthermore, the present findings highlighted that students who were categorized as impacted by COVID-19 had significantly lower resilience levels than those who were not impacted by COVID-19. Similarly, research reported that the COVID-19 stress and fear had a significant inverse correlation with resilience and that students' academic stress is negatively related to social support and resilience [ 78 ].

The present findings also showed that the resilience mean score was higher in non-employed students than in currently employed ones. This could be directly related to the increased stress levels caused by COVID-19 at the workplace. Working students might be exposed to different stressors at the workplace, including COVID-19, particularly in settings that require close human contact [ 20 ]. COVID-19 pandemic implied increased demand at the workplace in regards to the online work, travel restrictions, testing, sanitization, and vaccination drives. The UAE government applied strict work safety guidelines during the pandemic [ 79 ]. As a result, extended online working hours, added to the college's academic expectations, higher risk exposure to COVID-19 infection, changing work culture and balancing study and work could have contributed to reduced resilience in currently employed students in this study [ 80 , 81 ].

Strengths and limitations

The current study has many strengths. The novelty of the data that were collected primarily during the pandemic for this study cannot be argued, as this study added evidence to the pool of research on the mental health impact of the pandemic among a sample of university students in the UAE. The use of validated psychometric scales allows us to presume that the levels of depression and anxiety reported in this sample significantly exceeded the previously reported numbers for similar samples and could be related to COVID-19 pandemic. In addition, assessment of the demographic variables allows us to report on groups which appear to be at greatest mental health burden, and suggest a role for future interventions.

However, this study has similar limitations to other cross-sectional self-reported surveys that investigate sensitive mental health issues. First, the results represent the views of a single university student population in the UAE, that may limit the generalizability of the results. As potential participants were selected by a convenience sampling, non-random selection of the sample may limit the generalizability of this study. Another limitation may arise as there may have been a relevant difference between the students who chose to participate in the study and those who did not. It was also possible that the social desirability bias might have led some students to respond to survey items in ways that they believed were the most socially desirable [ 82 ]. Some responses also might have been influenced by confidentiality concerns as study was conducted by faculty members. Hence the above reasons might lead to some students answering in ways that they believed were the most socially desirable. Instead, it is possible that students with depression and anxiety symptoms were more willing to answer as a result of their fears about their studies.

Moreover, as the study experienced relatively high non-response rate and missing data, bias may have been introduced. However, neither of these factors should affect the attitude of those students who responded for the survey. In addition, the data was adequality managed at statistical handling to address the true values and impacts of the measured variables. Females were overrepresented in this study as in many other university settings in the UAE, which may affect the observed prevalence of depressive and anxiety in this sample. Lastly, the cross-sectional design makes it difficult to have causal relationships.

The results of this study revealed that the COVID-19 pandemic has negatively impacted the mental health of this sample of university students in terms of depression and anxiety. Based on PHQ-9 and GAD-7 cut-off scores, prevalence estimates highlight that moderate to severe depression as well as anxiety symptoms were self-identified by four out of ten of the sampled students. The COVID-19 pandemic was remarkably linked to significantly higher depression and anxiety symptoms among this sample. The assessment of demographic variables revealed that differences based on gender, marital status and nationality affected the mental health of this sample and suggest a role for future interventions. This study also showed that only one in ten of the students revealed high resilience levels, however, differences in the mean CD-RISC-10 scores by COVID-19 impact were not statistically significant. In contrast, the students who were not affected by COVID-19 had a lower level of resilience. The results also revealed no significant differences in anxiety, depression, and resilience levels by gender, except when COVID-19 impact was taken as an interaction term, which further emphasize the negative impact of COVID-19 on students’ mental health.

As for the policy implications, the application of the validated PHQ-9 and GAD-7 scales are recommended as initial screening tools, however, detected cases should be later assessed using more comprehensive instruments. Besides, mental healthcare providers should offer continuous monitoring of the psychological status of university students, in particular for the vulnerable groups, and provide the required mental health support at the university setting. Strategies for could focus on increasing the availability of mental health support interventions. The results of this study highlight the importance of developing a university culture in which students could have an opportunity to communicate their mental health concerns in confidential and comfortable ways. Hotline and virtual consultations could be introduced to ensure the students confidentially and privacy. Though a huge information on students' mental health has been gathered since March 2020, research on the psychological and behavioral effects of lockdowns should still be done when the epidemic ends. Further research can include follow-ups of this sample and similar samples from various colleges and university students to allow accurate detection of the true impact of the COVID-19 pandemic on this targeted population.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available because the data analysis is ongoing to study variables other than covered in this study. The data that supports the findings of this study are available upon request, but restrictions apply to the availability of these data. Data are however available from the authors upon reasonable request and with permission of the Higher Colleges of Technology.

Abbreviations

One-way analysis of variance

Connor–Davidson Resilience Scale

Coronavirus disease 2019

Higher Colleges of Technology

Generalized anxiety disorder

Patient health questionnaire

  • United Arab Emirates

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Acknowledgements

The authors would like to thank all the students who voluntarily shared their time and took part. The team also express gratitude to Professor Dr Gregory Blatch, the Executive Dean of Health Science Department and Dr Heyam Dalky for supporting this research as the Chair for Sharjah Women College’s Research Committee.

The current research was launched in late 2021 under the seed Grant Number [103802] from HCT. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of HCT.”

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The interaction between the effects of COVID-19 impact, marital status and nationality groups on mean scores of PHQ 9, GAD-7, and CD-RISC 10 psychometric scales.

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Vajpeyi Misra, A., Mamdouh, H.M., Dani, A. et al. Impact of COVID-19 pandemic on the mental health of university students in the United Arab Emirates: a cross-sectional study. BMC Psychol 10 , 312 (2022). https://doi.org/10.1186/s40359-022-00986-3

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The role of blood groups, vaccine type and gender in predicting the severity of side effects among university students receiving COVID-19 vaccines

  • Ohoud S. Almalki 1 ,
  • Eman Y. Santali 2 ,
  • Abdulaziz A. Alhothali 3 ,
  • Ashraf A. Ewis 4 , 5 ,
  • Abeer Shady 6 ,
  • Ahmed Ibrahim Fathelrahman 1 &
  • Sayed F. Abdelwahab 6  

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On March 11 th , 2020, the World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) a pandemic. To control the pandemic, billions of vaccine doses have been administered worldwide. Predictors of COVID-19 vaccine-related side effects are inconsistently described in the literature. This study aimed to identify the predictors of side effects’ severity after COVID-19 vaccination among young adult students at Taif University (TU) in Saudi Arabia. An online, anonymous questionnaire was used. Descriptive statistics were calculated for numerical and categorical variables. Possible correlations with other characteristics were identified using the chi-square test. The study included 760 young adult participants from TU. Pain at the injection site (54.7%), headache (45.0%), lethargy and fatigue (43.3%), and fever (37.5%) were the most frequently reported COVID-19 vaccine-related side effects after the first dose. The most frequent side effects were reported among the 20–25-year-old age group for all doses of all vaccines. Females experienced remarkably more side effects after the second ( p  < 0.001) and third doses ( p  = 0.002). Moreover, ABO blood groups significantly correlated with vaccine-related side effects after the second dose ( p  = 0.020). The participants' general health status correlated with the side effects after the first and second doses ( p  < 0.001 and 0.022, respectively). The predictors of COVID-19 vaccine-related side effects in young, vaccinated people were blood group B, female gender, vaccine type, and poor health status.

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Introduction

Recently, the world has been facing one of the most widespread and significant public health crises due to the novel coronavirus disease 2019 (COVID-19) that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On March 11 th , 2020, the World Health Organization (WHO) [ 1 ] declared COVID-19 a pandemic. Worldwide spread was rapid, and each infection presents an opportunity for new virus mutations. The pandemic’s social, economic, and psychological impacts are devastating [ 2 , 3 ]. Two years into the pandemic, the coronavirus has now confirmed more than 5.5 million deaths worldwide, according to data from Johns Hopkins University (February 15 th , 2022). Life had come to a standstill, and there was an urgent need to find a way back to normal life and prevent rapid spread of the virus, which could lead to hospitalization, intubation, and possibly death from acute respiratory distress syndrome. Vaccination is an important safeguard against COVID-19 infection [ 4 ] and plays an important role in boosting population immunity, preventing severe illness, and mitigating health crises [ 5 ]. To face the pandemic, Saudi Arabia’s government adopted several public health policies, including vaccination, lockdown, enforcement of social distancing, virtual learning in educational institutions, and home delivery of health services.

After the WHO declared COVID-19 a pandemic in March 2020, the Food and Drug Administration entities in different countries issued an emergency use authorization (EUA) for COVID-19 vaccines [ 6 ] for individuals who are 18 years and older [ 6 , 7 ]. The first COVID-19 vaccine received EUA in December 2020 in the United States [ 8 , 9 ]. Billions of vaccine doses have been administered worldwide [ 10 ]. However, recognizing and tracking the side effects of COVID-19 vaccines is challenging, as some individuals have concerns about the vaccine’s safety [ 11 ]. Although clinical trials have ensured vaccine safety and efficacy, vaccines developed using the new mRNA and recombinant vaccine technology in a short period of time has raised concerns about their safety among the public.

Different studies have found common vaccine side effects, such as pain at the injection site, swelling, redness, fever, itching, headache, fatigue, joint and muscle pain, and night sweats [ 12 , 13 , 14 ]. However, it has been reported that side effects decreased after multiple-dose administration. Most importantly, there was a need to rapidly develop a vaccine to prevent the fast spread of the virus. Communities worldwide are protected from infectious diseases each year due to vaccination [ 15 ]. Therefore, the benefits of immunization outweigh the risks. Several pharmaceutical and biotechnology companies competed to be the first to develop and commercialize COVID-19 vaccines [ 16 ]. Pfizer-BioNTech, and Moderna quickly pioneered the development of vaccines using previously unapproved mRNAs. Pfizer-BioNTech was the first to cross the border in the US on December 11 th , 2020, to obtain an EUA, and Moderna® announced the EUA a week later [ 17 , 18 ]. The development process to commercialization, which normally takes 7–10 years, was completed in 10 months. A viral vector vaccine was also under development. Johnson & Johnson received the EUA of its vaccine in the USA in March 2021. AstraZeneca’s vaccine is also a viral vector and is in use in several countries, although some have decided to stop deploying it after reports of blood clots in vaccinated individuals [ 19 ]. Although most of the reported side effects are minor, people still believe in conspiracy theories and misinformation about the COVID-19 vaccines [ 10 , 20 ]. This requires extensive research to explore the factors associated with higher risk of vaccines-related side effects. Potential predictors of vaccination side effects are age, gender, and ABO blood groups, among others [ 7 , 21 , 22 , 23 , 24 ]. However, existing studies show variability in identifying predictors of the severity of COVID-19 vaccine-related side effects. In Saudi Arabia, Almalki et al conducted a nationwide survey to assess the relationships between such variables and the severity of adverse reactions. The researchers identified the education level and nationality of the participants following the first dose, gender following the second dose, and general health status after all of the doses as significant predictors of severe adverse reactions [ 20 ]. The study found no significant correlation between the severity of adverse vaccine-related reactions and ABO blood-type groups. In our study, we wanted to replicate the methodological approach used in the previous study of the general population by focusing on a special population consisting of university students to determine whether the previous findings would be supported.

The objectives of our present study were to identify the side effects that Taif University (TU) students reported after receiving COVID-19 vaccines; compare the side effects’ severity, onset, and duration by vaccine manufacturer; and identify the associated predictors.

Materials and methods

Settings and participants.

This was a cross-sectional study conducted using a self-reported online questionnaire to investigate the predictors of severity of COVID-19 vaccine-related side effects in young population in the Kingdom of Saudi Arabia (KSA). A convenient sampling strategy was used to select students from TU living in the region from January 2022 to July 2022. The questionnaire was distributed to the students and their groups through social media using a link to the Google form. Also, the deanship of scientific research at TU emailed the questionnaire link to the students using official emails. The inclusion criteria included participants who had received at least one dose of COVID-19 vaccine of any type ( n = 760). The study protocol was approved by the Research Ethics Committee of TU (Approval No. 43-172). All participants provided online consent for participation in the study.

A previously published validated questionnaire was used, which was administered in Arabic for optimal comprehension by the participants [ 25 ]. The questionnaire consisted of three sections. Section one included the demographic characteristics e.g., gender, age, nationality, social status, college, academic level, whether the participant has a family member who is working in the healthcare sector, and city of residence. Section two (seven questions) included questions about the clinical characteristics of the participants, such as student’s blood group, Rhesus (Rh) factor, self-reported general health status, COVID-19 vaccination status, history of COVID-19 infection, and the presence of comorbidities that mandate continuous administration of medication (e.g., asthma, diabetes, and hypertension). Section three was about self-reported COVID-19 vaccine-related side effects, including type, onset, duration, and severity after each of the three doses of the COVID-19 vaccine. Side effects’ severities were self-reported on a Likert scale from 1 to 10 and were classified as mild (1-3), moderate (4-7), and severe (8-10).

Statistical analyses

Descriptive statistics were performed for the numerical and categorical variables to establish the frequency and percentage distribution of the demographic data and clinical characteristics. A chi-square test was performed for the cross-tabulation to the vaccine type and the severity of side effect-related variables and for investigating the correlation with other sociodemographic and clinical characteristics. A p -value of less than 0.05 was considered significant. All analyses were performed using the Statistical Package for Social Sciences (SPSS) software for Windows (Version 25.0, IBM Corp., Armonk, NY, USA).

A total of 760 adult participants from TU were included in the study. Most of them were 20–25 years old (79%, n = 600), 50.9% of them were females ( n = 387), 97% ( n = 737) were Saudi citizens, 42.8% ( n = 325) were students in the medical colleges (Medicine, Dentistry, Pharmacy, and Applied Medical Sciences), 57.8% ( n = 439) were between the 4 th to 6 th academic year, and 92.5% ( n = 703) were from Taif. These data and other sociodemographic data are shown in Table 1 .

Regarding clinical characteristics, 40% ( n = 304) had type O blood group. Rh factor was positive in 44.3% of the participants ( n = 337), while it was unknown in 366 subjects (48.2%). Most of the participants ( n = 476, 62.6%) received three doses of COVID-19 vaccine, while one-third were infected with COVID-19 since the start of the pandemic, of whom approximately 21% were infected after vaccination. The Pfizer-BioNTech vaccine was the first dose for 76.3% ( n = 580) of them. Most of the participants (91.1%, n = 692) had no history of any chronic disease, and 706 of them (92.9%) reported having good health status (Table 2 ).

The most frequent COVID-19 vaccine-related side effects after the first dose were pain at the injection site (54.7%), headache (45.0%), lethargy and fatigue (43.3%), and fever (37.5%). However, severe side effects rarely occurred after the first dose, such as low blood pressure (1.1%), heartbeat disturbance (2.6%), thrombosis (0.9%), and seizures (0.3%). The most frequent side effects after the second dose were pain at the injection site (49.7%), fatigue (48.5%), and fever (48.5%). Furthermore, the most frequent side effects after the third dose were pain at the injection site (49.6%), fatigue (47.7%), and fever (42.2%). Severe side effects were also rare after the second and third doses. Table 3 shows the frequency of each side effect for the three doses.

Receiving the Pfizer-BioNTech vaccine resulted in more mild-to-moderate side effects compared to those of the other manufacturers (Table 4 ). This was found separately after the first, second, and third doses given that a respondent may have received doses from various manufacturers. However, slightly less than half of all respondents who reported the types of vaccine (48.84%) received all three doses from Pfizer-BioNTech, and the majority (81.2%) received at least two doses from Pfizer-BioNTech. This finding remained the same when analyses included those who received vaccines in the second and third doses from the same manufacturers compared to those who received mixed types (Supplemental Table 1 ). Moreover, side effects usually started within 8 hours of receiving each dose of the three types of vaccines used in KSA and lasted for 1–3 days, with significant differences between the three vaccine types.

Table 5 shows the correlation between the severity of the vaccine-related side effects and the demographic characteristics of the study participants. The finding showed that the most frequent side effects were reported among the 20–25-year age group for all doses of all types of vaccines. Side effects were significantly more among females after the second ( p < 0.001) and third doses ( p = 0.005). Also, nationality showed a significant correlation with COVID-19 vaccine-related side effects after the first dose of the vaccine ( p = 0.043). Additionally, the educational level showed a significant correlation with COVID-19 vaccine-related side effects, with a higher frequency in those in the 4 th to 6 th academic year, especially after the first dose ( p = 0.008).

ABO blood groups showed a significant correlation with vaccine-related side effects after the second dose ( p = 0.020). Also, the general health status of the participants showed a significant correlation with vaccine-related side effects after the first ( p < 0.001) and second doses of the vaccines ( p = 0.022). However, the Rh factor or the presence of chronic diseases showed no correlation with vaccine-related side effects after the three doses of the vaccine (Table 6 ).

Multivariate regression analysis shows a significant correlation between the severity of the vaccine-related side effects and female gender ( p = 0.025), vaccine type ( p = 0.014 for AstraZeneca), poor general health status ( p = 0.012) and Blood group B ( p = 0.017 for severe side effects, Table 7 ).

This study sought to identify predictors of the severity of side effects after COVID-19 vaccines among young adult students at Taif University (TU). Since the pandemic, several COVID-19 vaccines have been licensed [ 17 ], and the success of a launch often depends on people’s willingness to accept it considering the highlighted safety profile, taking into account the fact that the development of vaccines and the technology used utilized either mRNA technology or a virus belonging to the adenovirus family, genetically modified with a gene encoding a specific SARS-CoV-2 protein [ 26 ]. This study indicated that the severity of COVID-19 vaccine-related side effects depend on several factors, such as the COVID-19 vaccine type, gender, ABO blood groups, age, and general health status.

The study results suggested that both females and males experienced side effects that mostly started within 8 hours after vaccination, lasted between one and three days, and were mainly limited to pain at the injection site, headache, fatigue, and fever, while serious side effects were rare. These results are consistent with what has been reported in the literature [ 27 , 28 ]. In this study, we also found that both females and males reported side effects to the vaccine over the three doses, but females showed more side effects that were between moderate and severe than their male counterparts. These findings are in agreement with previous international studies reporting fewer side effects in males, which could be attributed to psychological variation between genders [ 29 , 30 ].

Our study did not find any correlation between the severity of side effects and chronic illnesses. This observation could be attributed to the fact that the study participants were young and that most of them were free from chronic illnesses. Interestingly, we found that being of blood group B, receiving AstraZeneca vaccine, and being in poor general health were significant predictors of the occurrence and severity of vaccine-related side effects. The results were in partial disagreement with two local studies, one by Alessa et al., which included 612 surgeons, and one by Almalki et al., which included 1180 adult participants in the community across KSA, and neither study reported an association between the appearance or severity of COVID-19 vaccine-related side effects and blood type [ 7 , 20 ]. However, both studies supported our finding that the severity of side effects was associated with gender and vaccine type. It is worth noting that Alessa et al. and Almalki et al. included older and broader age groups than those in our study whereas we recruited students from Taif University who resided in the cities of Taif and Makkah, with about 95% of the participants in our study being younger than 25 years old. Also, most of the students enrolled herein are from Taif and Makkah (~97%), and they differ in marital status, education level, and general health status whereas more than 40% of the participants in Almalki et al. study [ 20 ], were from other cities of Saudi Arabia and differ in demographic characteristics, which could lead to such conflicting findings in the two studies. On the other hand, a history of COVID-19 infection was not a predictor of the occurrence or severity of COVID-19 vaccine-related side effects compared to non-infected participants. These results are consistent with those Alessa et al. [ 7 ] and Almalki et al. [ 20 ] reported.

On the other hand, our results contradict what was reported by Beatty et al. [ 30 ], who found that people with past COVID-19 history before vaccination had greater odds of developing adverse effects or more severe forms of adverse effects after COVID-19 vaccination.

Our study found that being of blood group B was a predictor of developing more severe forms of side effects after vaccination. It is also possible to read such findings from the opposite perspective, saying: group A individuals were less susceptible to severe side effects of the vaccine. To our knowledge, this was the first study to discern this association, compared to other local studies that included broader and older age groups [ 7 , 25 ], and to those conducted internationally. In this regard, according to growing data, the ABO blood group system could be involved in the immuno-pathogenesis of SARS-CoV-2 infection, with blood group O people being less likely to test positive and group A and B individuals having both a higher sensitivity to infection and a tendency toward severe disease. Also, subjects of blood group A may have unfavorable prognoses. In addition, several observational studies, genome-wide association reports, and country-level meta-regression analyses all provide evidence of a link between ABO group and susceptibility to SARS-CoV-2/COVID-19 infection, as reviewed elsewhere [ 31 , 32 , 33 , 34 ]. Though it was not evident in Almalki et al.’s study, the relationship our study found between blood group and severity of vaccine side effects might be linked to certain age groups (i.e., revealing a possible interaction between age and blood groups). The role of age as a moderator for other variables effects is well known and widely hypothesized, tested, or proven in medical literature [ 28 , 29 ]. However, we could not determine whether Almalki et al’s study did not find such a relationship because the relationship was not present at all or was present but could not be detected due to the small sample size. The latter option is particularly possible because the former study included a wider range of variable ages, and the significance of the relationship is only detectable when the pattern differs more between different groups than within groups. In our current study, the age variations are much smaller, with all participants being aged between 18 to 28.

This study has some limitations due to the nature of self-reporting and its cross-sectional design. Participants may over- or underestimate their self-reported side effects. There is evidence from literature supports an existence of a nocebo response, which can be defined as a negative reaction or symptom that is believed to be reported because of an individual’s expectation of developing negative events after receiving a medical intervention such as a drug or vaccine [ 35 , 36 ]. However, it is to be noted that a large percentage of our participants did not report any side effects and the reported side effects were low in frequency in most cases, nullifying the presence of nocebo effect. The cross-sectional design cannot assess the temporal relationships and prospective follow-up studies will be needed to confirm findings. Also, we included only TU students, which may limit the generalizability of the study to others. In addition, other factors that might affect the development and severity of the vaccine-related side effects, such as ethnicity, pregnancy, history of asthma at baseline, receiving an influenza shot in the previous year, and social status, were not addressed in the study.

This study provided real-world data about predictors of COVID-19 vaccine-related side effects across three brands of COVID-19 vaccines (AstraZeneca, Moderna, and Pfizer-BioNTech) in young, vaccinated people in KSA for the three doses. Also, we were able to investigate predictors of COVID-19 vaccine-related side effects in a more focused special population as we included young participants who were between 18 and 26 years old to explore the age’s effect on the appearance and severity of COVID-19 vaccine-related side effects. Future studies with larger sizes and more diverse population characteristics are needed to confirm the relationship between blood group and side effect severity, explore the mechanism therein, and test the age–blood group interaction we hypothesize.

Conclusions

Blood group B, female gender, vaccine type, and poor health status were among the predictors of COVID-19 vaccine-related side effects in young COVID-19 vaccinated people in KSA. Future national digital post-marketing surveillance for COVID-19 vaccine-related side effects should be conducted to better understand the role of ABO blood groups and other predictors in the safety of COVID-19 vaccines in young vaccinated individuals due to the inconsistency in the literature in reporting predictors of COVID-19 vaccine-related side effects.

Availability of data and materials

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation at [email protected]. Competing interests: The authors declare no competing interests.

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Acknowledgements

The researchers would like to acknowledge the Deanship of Scientific Research at Taif University for providing technical assistance for this study.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Department of Clinical Pharmacy, College of Pharmacy, Taif University, Taif, 21944, Saudi Arabia

Ohoud S. Almalki & Ahmed Ibrahim Fathelrahman

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Eman Y. Santali

College of Pharmacy, Taif University, Taif, 21944, Kingdom of Saudi Arabia

Abdulaziz A. Alhothali

Department of Public Health and Occupational Medicine, Faculty of Medicine, Minia University, Minia, 61511, Egypt

Ashraf A. Ewis

Department of Public Health, Faculty of Health Sciences-AlQunfudah, Umm Al-Qura University, Makkah, 28821, Saudi Arabia

Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, Taif, 21944, Saudi Arabia

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OSA and SFA: conceptualization and project administration. OSA, ES, AA, AE, AS, AF and SFA: methodology, software, data curation, and formal analysis. OSA, ES, AS and SFA: validation and writing-original draft preparation. OSA and SFA: investigation. OSA, ES, AE, AF and SFA: writing-review and editing. ES and AS: visualization. OSA, and SFA: supervision. All authors have read and agreed to the published version of the manuscript.

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Supplemental Table 1. Characteristics of COVID 19 vaccine related side effects by manufacturer.

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Almalki, O.S., Santali, E.Y., Alhothali, A.A. et al. The role of blood groups, vaccine type and gender in predicting the severity of side effects among university students receiving COVID-19 vaccines. BMC Infect Dis 23 , 378 (2023). https://doi.org/10.1186/s12879-023-08363-0

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