a thesis about depression

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An Exploratory Study of Students with Depression in Undergraduate Research Experiences

• Katelyn M. Cooper
• Logan E. Gin
• M. Elizabeth Barnes
• Sara E. Brownell

*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.

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

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7 Depression Research Paper Topic Ideas

In psychology classes, it's common for students to write a depression research paper. Researching depression may be beneficial if you have a personal interest in this topic and want to learn more, or if you're simply passionate about this mental health issue. However, since depression is a very complex subject, it offers many possible topics to focus on, which may leave you wondering where to begin.

If this is how you feel, here are a few research titles about depression to help inspire your topic choice. You can use these suggestions as actual research titles about depression, or you can use them to lead you to other more in-depth topics that you can look into further for your depression research paper.

What Is Depression?

Everyone experiences times when they feel a little bit blue or sad. This is a normal part of being human. Depression, however, is a medical condition that is quite different from everyday moodiness.

Your depression research paper may explore the basics, or it might delve deeper into the  definition of clinical depression  or the  difference between clinical depression and sadness .

What Research Says About the Psychology of Depression

Studies suggest that there are biological, psychological, and social aspects to depression, giving you many different areas to consider for your research title about depression.

Types of Depression

There are several different types of depression  that are dependent on how an individual's depression symptoms manifest themselves. Depression symptoms may vary in severity or in what is causing them. For instance, major depressive disorder (MDD) may have no identifiable cause, while postpartum depression is typically linked to pregnancy and childbirth.

Depressive symptoms may also be part of an illness called bipolar disorder. This includes fluctuations between depressive episodes and a state of extreme elation called mania. Bipolar disorder is a topic that offers many research opportunities, from its definition and its causes to associated risks, symptoms, and treatment.

Causes of Depression

The possible causes of depression are many and not yet well understood. However, it most likely results from an interplay of genetic vulnerability  and environmental factors. Your depression research paper could explore one or more of these causes and reference the latest research on the topic.

For instance, how does an imbalance in brain chemistry or poor nutrition relate to depression? Is there a relationship between the stressful, busier lives of today's society and the rise of depression? How can grief or a major medical condition lead to overwhelming sadness and depression?

Who Is at Risk for Depression?

This is a good research question about depression as certain risk factors may make a person more prone to developing this mental health condition, such as a family history of depression, adverse childhood experiences, stress , illness, and gender . This is not a complete list of all risk factors, however, it's a good place to start.

The growing rate of depression in children, teenagers, and young adults is an interesting subtopic you can focus on as well. Whether you dive into the reasons behind the increase in rates of depression or discuss the treatment options that are safe for young people, there is a lot of research available in this area and many unanswered questions to consider.

Depression Signs and Symptoms

The signs of depression are those outward manifestations of the illness that a doctor can observe when they examine a patient. For example, a lack of emotional responsiveness is a visible sign. On the other hand, symptoms are subjective things about the illness that only the patient can observe, such as feelings of guilt or sadness.

An illness such as depression is often invisible to the outside observer. That is why it is very important for patients to make an accurate accounting of all of their symptoms so their doctor can diagnose them properly. In your depression research paper, you may explore these "invisible" symptoms of depression in adults or explore how depression symptoms can be different in children .

How Is Depression Diagnosed?

This is another good depression research topic because, in some ways, the diagnosis of depression is more of an art than a science. Doctors must generally rely upon the patient's set of symptoms and what they can observe about them during their examination to make a diagnosis. 

While there are certain  laboratory tests that can be performed to rule out other medical illnesses as a cause of depression, there is not yet a definitive test for depression itself.

If you'd like to pursue this topic, you may want to start with the Diagnostic and Statistical Manual of Mental Disorders (DSM). The fifth edition, known as DSM-5, offers a very detailed explanation that guides doctors to a diagnosis. You can also compare the current model of diagnosing depression to historical methods of diagnosis—how have these updates improved the way depression is treated?

Treatment Options for Depression

The first choice for depression treatment is generally an antidepressant medication. Selective serotonin reuptake inhibitors (SSRIs) are the most popular choice because they can be quite effective and tend to have fewer side effects than other types of antidepressants.

Psychotherapy, or talk therapy, is another effective and common choice. It is especially efficacious when combined with antidepressant therapy. Certain other treatments, such as electroconvulsive therapy (ECT) or vagus nerve stimulation (VNS), are most commonly used for patients who do not respond to more common forms of treatment.

Focusing on one of these treatments is an option for your depression research paper. Comparing and contrasting several different types of treatment can also make a good research title about depression.

A Word From Verywell

The topic of depression really can take you down many different roads. When making your final decision on which to pursue in your depression research paper, it's often helpful to start by listing a few areas that pique your interest.

From there, consider doing a little preliminary research. You may come across something that grabs your attention like a new study, a controversial topic you didn't know about, or something that hits a personal note. This will help you narrow your focus, giving you your final research title about depression.

Remes O, Mendes JF, Templeton P. Biological, psychological, and social determinants of depression: A review of recent literature . Brain Sci . 2021;11(12):1633. doi:10.3390/brainsci11121633

National Institute of Mental Health. Depression .

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition . American Psychiatric Association.

National Institute of Mental Health. Mental health medications .

Ferri, F. F. (2019). Ferri's Clinical Advisor 2020 E-Book: 5 Books in 1 . Netherlands: Elsevier Health Sciences.

By Nancy Schimelpfening Nancy Schimelpfening, MS is the administrator for the non-profit depression support group Depression Sanctuary. Nancy has a lifetime of experience with depression, experiencing firsthand how devastating this illness can be.  

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What Is a Good Thesis Statement About Depression?

Do you need to compose an informative or an argumentative essay on depression? One of the vital parts of your paper is a thesis statement on depression. Note there are various types of thesis statements, and what you use depends on the type of essay you are writing. A thesis summarizes the concept that you write on your research paper or the bottom line that you will write in your essay. It should elaborate more on the depression topics for the research paper you are working on. But at times, you might have a hard time writing your thesis statement.

Good Thesis Statement about Teenage Depression

Bipolar disorder thesis statements about depression, interesting thesis statements about depression, interesting thesis statement about diagnosis and treatment of depression, thesis statement about stress and depression, free thesis statements about depression and anxiety, get help with your depression research paper.

Here is a list of thesis statements to have an easier time writing your essay. They cover different topics, making it easy to select what excites you. Here we go!

Are you writing about teenagers and how they are always overthinking about their future, and they end up getting depressed? You need to write a good thesis statement for a depression research paper. That will help your depression argumentative essay stand out. Here are some thesis statement for depression to check out.

• There is a link between depression and alcohol among teenagers and the various ways to control it.
• Teenagers dealing with mood disorders eat and sleep more than usual, getting less interested in regular activities.
• Mediation is an effective way to reach out to adolescents that show heightened symptoms of depression.
• Self-blaming attributions are social cognitive mechanisms among adolescents.
• Peer victimization causes high-stress levels among adolescents and has negative psychological consequences.

Choosing a good depression thesis statement on bipolar disorder can be hectic. Research on bipolar will require a good thesis statement for mental health. Choose a thesis statement about mental health awareness here.

• People with Bipolar depression have more difficulties getting quality sleep.
• Bipolar disorder influences every aspect of a person’s life and changes their quality of life.
• Bipolar disorder causes depressive moods or lows of mental disorder.
• Bipolar is a severe mental issue that can negatively impact your moods, self-esteem, and behavior.
• Psychological evaluations play a significant role in diagnosing bipolar disorder.

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• The effects of human psychology are viewed in the form of depression.
• Clinical psychology can help to bring outpatients who have depression.
• Treating long-term depression in bipolar patients is possible.
• Bipolar patients are drained to the roots of depression.
• Well-established rehabilitation centers can help bring drug addicts from depression.

Are you thinking of writing a thesis on depression and how to treat it? If so, you need to have an excellent thesis statement about mental health that will impress your professor. Read this list to find a thesis you need for your research paper.

• There are different ways to diagnose and treat depression from its early stage.
• People who show signs of depression from an early stage and seek treatment are likely to recover instead of those who do not show early signs.
• After you receive treatment for depression, putting the right measure in place is one of the best and effective ways to ensure that you do not get it again for the second time.
• Anxiety can interfere with daily living, and it can get anyone from children to adults.
• Besides medication, you need a lifestyle change and acceptance to treat depression.

Is your research about stress and how it can impact mental health? Getting a thesis statement for depression research paper that impresses your examiners can be challenging. Choose a thesis statement for your mental illness research paper below.

• Although it is normal for various situations to cause stress, having constant stress can have detrimental effects.
• To survive the modern industrial society, you need to have stress management strategies.
• The challenges of understanding and adapting to the changing environment can lead to stress.
• Lack of proper stress management will lead to inefficiency in everything people do.
• Stress does not come unless there are underlying stressors in your life.

Our team of writers is well-conversant about a free thesis statement about anxiety you can use. The best anxiety thesis statement will help you get the best grades. Here is a list of statements that stands out:

• Many factors can lead to early anxiety, but the leading cause of anxiety in adolescents is directly linked to families.
• Anxiety is a severe mental disorder that can occur without any apparent triggers.
• Long-term depression and anxiety can impact your mental health, but you can recover if you seek treatment.
• Depression and anxiety are not interlinked, and it is essential to learn how to differentiate them on practical grounds.
• Society has a role to play in helping people come out of depression and anxiety.

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Social media use and depression in adolescents: a scoping review

There have been increases in adolescent depression and suicidal behaviour over the last two decades that coincide with the advent of social media (SM) (platforms that allow communication via digital media), which is widely used among adolescents. This scoping review examined the bi-directional association between the use of SM, specifically social networking sites (SNS), and depression and suicidality among adolescents. The studies reviewed yielded four main themes in SM use through thematic analysis: quantity of SM use, quality of SM use, social aspects associated with SM use, and disclosure of mental health symptoms. Research in this field would benefit from use of longitudinal designs, objective and timely measures of SM use, research on the mechanisms of the association between SM use and depression and suicidality, and research in clinical populations to inform clinical practice.

Introduction

Over the past several decades, adolescent depression and suicidal behaviours have increased considerably. In the USA, depression diagnoses among youth increased from 8.7% in 2005 to 11.3% in 2014 ( Mojtabai, Olfson, & Han, 2016 ). Additionally, suicide is the second leading cause of death among youth between the ages of 10 and 34 ( Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, 2017 ), with a 47.5% increase since 2000 ( Miron, Yu, Wilf-Miron, & Kohane, 2019 ). One suggested cause for this rise in adolescent depression and suicide is the advent of social media (SM) ( McCrae, Gettings, & Purssell, 2017 ; Twenge, Joiner, Rogers, & Martin, 2018 ).

The term ‘social media’ describes types of media that involve digital platforms and interactive participation. SM includes forms such as email, text, blogs, message boards, connection sites (online dating), games and entertainment, apps, and social networking sites (SNS) ( Manning, 2014 ). Over the past decade, SNS platforms designed to help people communicate and share information online have become ubiquitous. Among youth, 97% of all adolescents between the ages of 13 and 17 use at least one of the following seven SNS platforms: YouTube (85% of adolescents), Instagram (72%), Snapchat (69%), Facebook (51%), Twitter (32%), Tumblr (9%) or Reddit (7%) ( Pew Research Center, 2018a ).

Concerns have arisen around the effects of SM on adolescents’ mental health, due to SM’s association with decreased face-to-face interpersonal interactions ( Baym, 2010 ; Kraut et al., 1998 ; Nie, Hillygus, & Erbring, 2002 ; Robinson, Kestnbaum, Neustadtl, & Alvarez, 2002 ), addiction-like behaviours ( Anderson, Steen, & Stavropoulos, 2017 ), online bullying ( Kowalski, Limber, & Agatston, 2012 ), social pressure through increased social comparisons ( Guernsey, 2014 ), and contagion effect through increased exposure to suicide stories on SM ( Bell, 2014 ).

Conversely, others have described potential benefits of SM use in adolescents such as feelings of greater connection to friends and interactions with more diverse groups of people who can provide support ( Pew Research Center, 2018b ). In fact, higher internet use has been associated with positive social well-being, higher use of communication tools, and increased face-to-face conversations and social contacts in college students ( Baym, Zhang, & Lin, 2004 ; Kraut et al., 2002 ; Wang & Wellman, 2010 ). These findings suggest that internet use, including SM, may provide opportunities for social connection and access to information ( Reid Chassiakos et al., 2016 ).

Recent systematic reviews examining the association between online technologies and depression have found a ‘general correlation’ between SM use and depression in adolescents, but with conflicting findings in some domains (e.g. the association between time spent on SM and mental health problems), overall limited quality of the evidence ( Keles, McCrae, & Grealish, 2019 ), and a relative absence of studies designed to show causal effects ( Best, Manktelow, & Taylor, 2014 ). The scope of search in these reviews is broader in topic, including online technologies other than SM ( Best et al., 2014 ) or focussed on a select number of studies in order to meet the requirements of a systematic review ( Keles et al., 2019 ). With this scoping review, we aim to expand the inclusion of studies with a range of designs, while narrowing the scope of the topic of SM to those studies that specifically included SNS use. Additionally, we aim to expand the understanding and potential research gaps on the bi-directional association between SM and depression and suicidal behaviours in adolescents, including studies that consider SM use as a predictor as well as an outcome. A better understanding of this relationship can inform interventions and screenings related to SM use in clinical settings.

This scoping review was initiated by a research team including 3 mental health professionals with clinical expertise in treating depression and suicidality in adolescents. We followed the framework suggested by Arksey and O’Malley (2005) for scoping reviews. The review included five steps: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) charting the data; and (5) collating, summarizing and reporting the results.

Research question

The review was guided by the question: What is known from the existing literature about the association between depression and suicidality and use of SNS among adolescents? Given that much of the literature used SM and SNS interchangeably, this review used the term ‘social media’ or ‘SM’ when it was difficult to discern if the authors were referring exclusively to SNS.

Data sources and search strategy

The team conceived the research question through a series of discussions, and the first author (CV) consulted an informationist to identify the appropriate search terms and databases. A search of the database PsychINFO limited to peer-reviewed articles was conducted on 5 June 2019 (see Table 1 for search strategy). No additional methods were identified through other sources. The search was broad to include articles measuring depression as an outcome variable, and as a co-variate or independent variable. There was no restriction on the type of study design included, and English and Spanish language articles were included in the search. Articles were organized using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia).

Search strategy.

Eligibility criteria

(1) The study examined SM (versus internet use in general) and made specific mention of SNS; (2) participants were between the ages of 10 and 18. If adults were included, the majority of the study population was between 10–18 years of age, or the mean participant age was 18 or younger; (3) the study examined the association between SM use and depression and/or suicidality; (4) the study included at least one measure of depression; and (5) if the focus of the study was on SM addiction or cyberbullying, it included mention and a measure of depressive symptoms. We did not include articles in which: (1) the study primarily focussed on media use other than SM, or that did not specifically mention inclusion of SNS (e.g. studies that focussed only on TV, video game, smartphone use, blogging, email); (2) included primarily adult population; (3) was not an original study, but a case report, review, commentary, erratum, or letter to the editor; (4) focussed on addiction and cyberbullying exclusively without a depression measure; and (5) the method used was content analysis of SM posts without specification of the population age range.

Title and abstract relevance screening

The search yielded 728 articles of which six duplicates were removed. One author (CV) screened the remainder of the articles by title and abstract and a second author (TL) reviewed every 25th article for agreement. All authors screened full-text articles and extracted data from those that met the inclusion criteria. The authors met over the course of the full-text review process to resolve conflicts and maintain consistency among the authors themselves and with the research question. Of the total number of studies included for full-text review, 505 articles were excluded. Out of the 223 full-text studies assessed for eligibility, 175 were excluded. A total of 42 articles were eligible for review (see Figure 1 : PRISMA flow chart for details). A form was developed to extract the characteristics of each study that included author and year of publication, objectives of the study, study method, country where the study was conducted, depression scale used, number of participants, participant age, and results (see Table 2 for details).

PRISMA flow chart of data selection process.

Data charting form including author and year of publication, objectives of the study, method used, country where the study was conducted, depression scale used, number of participants, participant age, results and main social media focus.

AIU = Addictive internet Use; BIU = Borderline Addictive Internet Use; BSMAS = Bergen Social Media Addiction Scale; BIU = Borderline addictive internet use; CBP = Cyberbullying Perpetration; CERM = Cuestionario de Experiencias Relacionadas con el móvil (Questionnaire of Experiences Related to the cellphone); DIB = Dysfunctional Internet Behaviour; DSM-IV = Diagnostic and Statistical Manual of Mental Disorders (4th edition, Text Revision); FOMO = Fear of Missing Out; HVSM = Highly Visual Social Media; SNI = Intensity of social network use; IA = Internet Addiction; IAB = Internet Addictive Behaviour; OSNA = Online social networking addiction; PSMU = Problematic Social Media Use; RADS-2 = Reynolds Adolescent Depression Scale - Version 2; SITBs = self-injurious thoughts and behaviours; SNS = social networking sites.

Data summary and synthesis

After reviewing the table, each study was labelled according to the main focus of research related to SM, based on the objectives, variables used, and results of the study. The topics were classified into nine different categories based on the main SM focus of the article; categories were discussed and reviewed by two authors (TL and CV) ( Table 2 ). All authors then discussed the categories and grouped them into four main themes of studies looking at SM and depression in adolescents.

A total of 42 studies published between 2011 and 2019 met the inclusion criteria. Of the studies included, 16 were conducted in European Countries, 14 in the USA, 5 in Asia, 3 in Canada, 2 in Australia, and 2 in Latin American Countries. The number of participants per study ranged from 23 in a qualitative study (94 in the smallest quantitative study) to 118,545 participants in the largest study ( Table 2 ).

The studies reviewed were grouped into four themes with nine categories according to the main focus of the research. The themes and categories were: (1) quantity of SNS use: effects of the frequency of SM use and problematic SM use (or evidence of addictive engagement with SM); (2) quality of SM use: characteristics of SNS use and social comparisons; (3) social aspects of SM use: cyberbullying, social support, and parental involvement; and (4) disclosure of mental health symptoms: online disclosure and prediction of symptoms and suicide contagion effect ( Figure 2 ).

Number of studies by theme (quantity, quality, social and disclosure) and time period (2011–2012, 2013–2014, 2015–2016 and 2017–2018).

Quantity of SM use

The majority of studies ( n = 24) examined quantity of SM use by measuring either frequency or time spent on SM ( n = 17), or problematic or addictive engagement with SM ( n = 7).

Frequency of use

The majority of studies found a positive correlation between time spent on SNS and higher levels of The majority of studies found a positive correlation between time spent on SNS and higher levels of depression ( Akkın Gürbüz, Demir, Gökalp Özcan, Kadak, & Poyraz, 2017 ; Marengo, Longobardi, Fabris & Settanni, 2018 ; Pantic et al., 2012 ; Twenge et al., 2018 ; Woods & Scott, 2016 ). Higher frequency of SM use (≥2 h a day) was also found to be positively associated with suicidal ideation ( Sampasa-Kanyinga & Lewis, 2015 ) and attempts ( Sampasa-Kanyinga & Hamilton, 2015 ), in addition to deficits in self-regulation ( Lee, Ho, & Lwin, 2017 ). Factors such as the number of SM accounts and the frequency of checking SM ( Barry, Sidoti, Briggs, Reiter, & Lindsey, 2017 ) were associated with a variety of symptoms, including depression.

A study ( Oberst, Wegmann, Stodt, Brand, & Chamarro, 2017 ) examining SM use as an outcome suggested that depression may affect SM use both directly, and indirectly, mediated by the Fear of Missing Out (or the apprehension of missing rewarding experiences that others might be enjoying) ( Przybylski, Murayama, DeHaan, & Gladwell, 2013 ). Adolescents with depression were also found to have more difficulty regulating their SM use ( Lee et al., 2017 ).

Longitudinal studies suggested a reciprocal relationship between quantity of SM use and depression. Frison and Eggermont (2017) found that frequency of Instagram browsing at baseline predicted depressed mood six months later and depressed mood at baseline predicted later frequency of photo posting. Additionally, heavy use (>4 h per day) of the internet to communicate (including social networking) and play games (gaming) predicted depressive symptoms a year later ( Romer, Bagdasarov, & More, 2013 ). Further, depressive symptoms predicted increased internet use and decreased participation in non-screen activities (e.g. sports). Finally, Salmela-Aro, Upadyaya, Hakkarainen, Lonka, and Alho (2017) found that school burnout increased the risk for later excessive internet use and depressive symptoms. Conversely, Houghton et al. (2018) found small, positive bi-directional associations between depressive symptoms and screen use 1 year later, but their final model did not support a longitudinal association.

Yet, not all studies found a positive association between frequency of use and depressed mood. While Blomfield-Neira and Barber (2014) reported a link between adolescents having a SM profile and depressed mood, they found no correlation between SM frequency of use and depressed mood. Rather, investment in SM (a measure of how important SM is to an adolescent) was linked to poorer adjustment, lower self-esteem and depressed mood. Moderate SM use (a stable trend in the time spent on SM during adolescence and into early adulthood that did not interfere with functioning) was associated with better emotional self-regulation ( Coyne, Padilla-Walker, Holmgren, & Stockdale, 2018 ) and healthier development, especially when used to acquire information ( Romer et al., 2013 ). Finally, Rodriguez Puentes and Parra (2014) found a positive association between SM and externalizing behaviours, but no significant association between SM use and depression.

Additionally, age moderated the effects of frequency of use on depression. For example, in one study, older adolescents with higher SM use had higher ‘offline’ social competence, while younger adolescents with higher SM use had more internalizing problems and diminished academics and activities ( Tsitsika, Janikian, et al., 2014 ).

Problematic SM use

Seven studies explored problematic use or engagement with SM or the internet in an addictive manner (a dysfunctional pattern of behaviour similar to that of impulse control disorders, which causes distress and/or functional impairment) ( Critselis et al., 2014 ).

An addiction-like pattern of internet use (including SM use) was associated with emotional maladjustment ( Critselis et al., 2014 ), internalizing and externalizing symptoms ( Tsitsika, Tzavela, et al., 2014 ), and depressive mood ( Van Rooij, Ferguson, Van de Mheen, & Schoenmakers, 2017 ). Further, depressive mood predicted problematic internet use (both SM and gaming, independently) ( Kırcaburun et al., 2018 ; Van Rooij et al., 2017 ).

Bányai et al. (2017) assessed the prevalence of problematic internet use conducting a latent profile analysis to describe classes of users and found that the class described as ‘at risk’ for problematic internet and SM use tended to be female, use the internet for longer periods, and have lower self-esteem and more depressive symptoms. Yet, while Banjanin, Banjanin, Dimitrijevic, and Pantic (2015) found a positive correlation between internet addiction and depression in high school students (particularly for females), no such correlation was found with engagement with SM (measured by number of pictures posted).

Several studies examined mediators of the association of problematic SM use and depression. Wang et al. (2018) found that rumination mediated the relationship between SM addiction and adolescent depression, with a stronger effect among adolescents with low self-esteem. Additionally, insomnia partially mediated the association between SM addiction and depressive symptoms ( Li et al., 2017 ). Woods and Scott (2016) found that nighttime-specific SM use (in addition to overall use and emotional investment in SM) was associated with poorer sleep quality, anxiety and depressive symptoms. Finally, problematic SM use mediated the association between depressive symptoms and cyberbullying perpetration ( Kırcaburun et al., 2018 ).

Quality of SNS use

In addition to the frequency of adolescents’ engagement with SM, another focus of research has been the ways in which adolescents engage with SM. Of the studies selected, four primarily examined engagement styles with SM and two specifically examined social comparisons with other users.

Characteristics of SM use

The ways in which adolescents use SM may also have an effect on depression. One study ( Frison & Eggermont, 2016 ) characterized SM use as public (e.g. updating one’s status on a profile) vs private (e.g. messaging), and active (e.g. interacting with others on SM) vs passive (e.g. browsing on SM) and found that public Facebook use was associated with adolescent depressed mood. Among girls, passive use of Facebook yielded negative outcomes such as depressed mood, while active use yielded positive outcomes such as perceived social support ( Frison & Eggermont, 2016 ). A longitudinal study of Flemish adolescents by the same group ( Frison & Eggermont, 2017 ) found passive SM use at baseline to predict depressive symptoms 7 months later, while depressive symptoms predicted active use of SM. Interestingly, there was no association between depressive symptoms and Facebook use (frequency of use, network size, self-presentation, and peer interaction) in a study conducted among healthy adolescents ( Morin-Major et al., 2016 ).

Romer et al. (2013) found that the types of internet activities utilized (e.g. SNS, blogs, etc.) were associated with the frequency of self-reported depression-like symptoms. Additionally, using the internet for information searching was associated with higher grades, more frequent participation in clubs, and lower reports of depressive symptoms, while using the internet more than 4 h per day to communicate or play games was associated with greater depression-like symptoms, suggesting that Internet use for acquiring information is associated with healthy development.

A qualitative study further explored positive and negative aspects of SM use among adolescents diagnosed with clinical depression ( Radovic, Gmelin, Stein, & Miller, 2017 ). Participants described positive SM use as including searching for positive content (e.g. entertainment, humour, content creation) or social connection, while they described negative SM use as sharing risky behaviours, cyberbullying, or making self-denigrating comparisons with others. Furthermore, this study found that adolescents’ use of SM shifted from negative to positive during the course of treatment.

Social comparisons

Two studies examined social comparisons made through SM and the association with depression. Nesi and Prinstein (2015) found that technology-based social comparison and feedback-seeking were associated with depressive symptoms, even when controlling for the effects of overall frequency of technology use, offline excessive reassurance-seeking, and prior depressive symptoms. This association was strongest among females and adolescents low in popularity (as measured by peer report). Niu et al. (2018) found that negative social comparisons mediated the association between Qzone use (a Chinese SM site) and depression, and that the association between Qzone use and negative social comparisons was stronger among individuals with low self-esteem. However, there was no direct effect of Qzone use on depression. An additional study that primarily focussed on studying frequency of use ( Marengo et al., 2018 ) found that increased use of highly visual SM (e.g. Instagram) predicted internalizing symptoms and body image concerns in a student sample. Moreover, in this study, the effect of highly visual SM on internalizing symptoms was mediated by body image concerns.

Social aspects of SM use

Several studies looked at the social aspects of engagement with SM, either by evaluating the effects of cybervictimization ( n = 4) on depression, parental involvement both through monitoring of SM use or direct engagement with the adolescent ( n = 3), and aspects of social support received by the adolescent within and outside of SNS ( n = 2).

Cyberbullying/cybervictimization

Four studies examined cyberbullying via SM and depressive symptoms. Duarte, Pittman, Thorsen, Cunningham, and Ranney (2018) found that symptoms of depression, post-traumatic stress disorder, and suicidal ideation were more prevalent among participants who reported any past-year cyberbullying (either victimization or perpetration). After adjusting for a range of demographic factors, only lesbian, gay, and bisexual status correlated with cyberbullying involvement or adverse mental health outcomes. Another study found that cyberbullying victimization fully mediated the association between SM use and psychological distress and suicide attempts ( Sampasa-Kanyinga & Hamilton, 2015 ). Furthermore, a 12-month longitudinal study found that cybervictimization predicted later depressive symptoms ( Cole et al., 2016 ). Depressive symptoms have also been shown to be a risk factor (rather than an outcome) for cybervictimization on Facebook ( Frison, Subrahmanyam, & Eggermont, 2016 ), showing evidence of the bi-directionality of this association.

Social support

While many studies examined potential negative effects of SM use, some studies examined the positive effects of SM use on youth outcomes, including social support. Frison and Eggermont (2015) found that adolescents seeking social support through Facebook had improved depressive symptoms if support was received, but worsened symptoms if support was not received. This pattern was not found in non-virtual social support contexts, suggesting differences in online and traditional social support contexts. A later study that primarily focussed on the characteristics of SM use ( Frison & Eggermont, 2016 ) found that perception of online support was particularly protective against depressive symptoms in girls with ‘active’ Facebook use (e.g. those who update their status or instant message on Facebook). Finally, Frison et al. (2016) showed that support from friends can be a protective factor of Facebook victimization.

Parental involvement/parental monitoring

Studies examining parent and family role in adolescent SM use and its outcomes were heterogeneous. One study ( Coyne, Padilla-Walker, Day, Harper, & Stockdale, 2014 ) explored adolescent use of SM with parents and found lower internalizing behaviours in participants who used SNS with their parents (mediated by feelings of parent/child connection). Another study ( Fardouly, Magson, Johnco, Oar, & Rapee, 2018 ) examined parent control over preadolescents’ time spent on SM and found no association between parental control and preadolescent depressive symptoms.

Family relationships offline were also associated with adolescent outcomes. Isarabhakdi and Pewnil (2016) examined adolescents’ engagement with offline relationships and found improved mental health outcomes with higher involvement in family activities and with peers, while internet use did not significantly improve mental well-being. This finding suggests that in-person support systems were more effective for the promotion of mental well-being. Interestingly, in Szwedo, Mikami, and Allen (2011) , negative interactions with mothers during early adolescence were associated with youth preferring online versus face-to-face communication, experiencing more negative interactions on webpages, and forming close friendships with someone they met online 7 years later. An additional study that primarily focussed on suicide contagion ( Tseng & Yang, 2015 ) found that family support was protective for both males and females, while friend support was protective only for females. However, ‘significant other’ support was a risk factor for suicidal plans among females.

Disclosure of mental health symptoms on SM

A few of the studies selected focussed on studying the disclosure of depressive symptoms on SM and explored the potential of disclosure of symptoms of distress on SM to predict depression and suicide, in addition to the phenomenon of suicide contagion.

Online disclosure and prediction of mental health symptoms

Although content analysis is a method theorized to have potential to predict and prevent non-suicidal and suicidal self-injurious behaviours, the data are mixed. Ophir, Asterhan, and Schwarz (2019) examined the predictive validity of explicit references to personal distress in adolescents’ Facebook postings, comparing these postings with external, self-report measures of psychological distress (e.g. depression) and found that most depressed adolescents did not publish explicit references to depression. Additionally, adolescents published less verbal content than adult users of SNS. Conversely, Akkın Gürbüz et al. (2017) found that while disclosures of depressed mood were frequent among both depressed and non-depressed adolescents, those who were depressed shared more negative feelings, anhedonia, and suicidal thoughts on SM than those who were not depressed.

Suicide contagion effect

One longitudinal study examined suicide contagion effects ( Dunlop, More, & Romer, 2011 ) finding that even though traditional SNS (e.g. Facebook or MySpace) were a significant source of exposure to suicide stories, this exposure was not associated with increases in suicidal ideation one year later. On the other hand, exposure to online discussion forums (including self-help forums) did predict increases in suicidal ideation over time. Notably, this study found that in a quarter of the sample, the exposure to suicide stories took place through SM. Another study ( Tseng & Yang, 2015 ) found that higher importance attributed to web communication (e.g. chatting or making friends online) was associated with increased risk of self-injurious thoughts and behaviours in boys.

The recent rise in the prevalence of depression and suicide among adolescents has coincided with an increase in screen-related activities, including SM use ( Twenge et al., 2018 ), sparking an interest in investigating the effects of SM use on adolescent mental health. This interest has given rise to a broad scope of research, ranging from observational to experimental and qualitative studies through interviews or analysis of SM content, and systematic studies. This scoping review aimed to understand the breadth of research in the area of depression and SM (with a focus on SNS) and to identify the existing research gaps.

We identified four main themes of research, including (1) the quantity of SM use; (2) the quality of SM use; (3) social aspects associated with SM use; and (4) SM as a tool for disclosure of mental health symptoms and potential for prediction and prevention of depression and suicide outcomes.

Most research on SM and depressive symptoms has focussed on the effects of frequency of SM use and problematic SM use. The majority of articles included in this review demonstrated a positive and bi-directional association between frequency of SM use and depression and in some instances even suicidality. Yet some questions remain to be determined, including to what degree adolescents’ personal vulnerabilities and characteristics of SM use moderate the association between SM use and depression or suicidality, and whether other environmental factors, such as family support and/or monitoring, or cultural differences influence this association. Although moderate SM use may be associated with better self-regulation, it is unclear if this is due to moderate users being better at self-regulation.

Findings from the studies examining problematic SM use were consistent with prior studies linking problematic internet use with a variety of psychosocial outcomes including depressive symptoms ( Reid Chassiakos et al., 2016 ). Though limited in number, studies reviewed here suggested that problematic or addictive SM use may be more common in females ( Banyai et al., 2017 ; Kırcaburun et al., 2018 ) and in those starting use at a younger age ( Tsitsika, Janikian, et al., 2014 ). These findings suggest a possible role of screening for addictive SM use, with a particular focus on risk stratification for younger and female adolescents.

With respect to the effects of patterns and types of SM use, studies reviewed here suggest possible differential effects between passive and active, and private versus public SM use. This suggests that screening only for time spent on SM may be insufficient. Moreover, though there are types of SM use that have adverse mental health effects for adolescents (e.g. addictive patterns, nighttime use), other types of SM use, such as for information searching or receiving social support, may have a positive effect ( Coyne et al., 2018 ; Frison & Eggermont, 2016 ; Romer et al., 2013 ). Furthermore, over time, depressed adolescents can successfully shift their use of SM from negative (e.g. cyberbullying) to positive (e.g. searching for humour), possibly through increasing awareness of the effect of SM use on their mood ( Radovic et al., 2017 ). Given the ubiquity of SM use, these results suggest that interventions targeting changes in adolescents’ use of SM may be fruitful in improving their mental health.

Consistent with prior research ( Feinstein et al., 2013 ), studies examining social comparisons found significant associations between social comparisons made via SM and depression. The tendency of individuals to share more positive depictions of themselves on SM ( Subrahmanyam & Greenfield, 2008 ), and the increased opportunities for comparisons ( Steers, Wickham, & Acitelli, 2014 ) may suggest a confluence of risks for depression and an important avenue for interventions. Moreover, the studies reviewed and previous findings ( Buunk & Gibbons, 2007 ) suggest that individuals with low self-esteem may be at higher risk for the negative effects of social comparisons on mental health.

As previously shown ( Cénat et al., 2014 ), most studies found cyberbullying (either perpetration or victimization) was either associated with mental health problems ( Cole et al., 2016 ; Duarte et al., 2018 ) or moderated the relationship between SM use and depression and suicidality ( Sampasa-Kanyinga & Hamilton, 2015 ). Additionally, cyberbullying may be a distinctive form of victimization that requires further investigation in order to understand its impact on adolescent mental health ( Dempsey, Sulkowski, Nichols, & Storch, 2009 ).

Studies examining social support highlight the association of both depressed mood and low in-person social support with social networking and online support-seeking ( Frison & Eggermont, 2015 ). Moreover, while social support online can be beneficial ( Frison & Eggermont, 2015 ), excessive reliance on online communication and support may be problematic ( Twenge et al., 2018 ). Of note, parental involvement both positively and negatively affected SM use and adolescent outcomes. These mixed findings suggest a need to include parental relationships in research (both via online and ‘offline’ communication), to better understand their role in adolescents’ SM use and depression.

Surprisingly, depressed adolescents were not more likely to publish explicit references to depression on SM platforms than their healthy peers ( Ophir et al., 2019 ) which suggests that screening for depression via SM may not be useful when used alone. However, some depressed adolescents posted more negative feelings, anhedonia and suicidal ideation ( Akkın Gürbüz et al., 2017 ), suggesting that SM may be used as a supplemental tool to track the course of depressive mood over time and start discussions about mental health.

Suicide contagion effect is a relatively understudied area, despite concerns raised that increased exposure to SM may amplify this effect ( Bell, 2014 ). Given that adolescents are particularly vulnerable to the group contagion effect of suicide ( Stack, 2003 ) and the potential for increased exposure to suicide stories online ( Dunlop et al., 2011 ), interventions to limit this exposure could decrease suicide contagion.

The studies reviewed identified several potential moderators of the association between SM use and adolescent depression, including age and gender. The differential effects of SM use on mental health depending on the age of the adolescent ( Tsitsika, Tzavela, et al., 2014 ) are not surprising given the developmental differences in social and mood regulation skills between younger and older adolescents. Likewise, potential mediators of the effects of SM on mental health such as social comparisons ( Niu et al., 2018 ), body image concerns ( Marengo et al., 2018 ), perceived support online ( Frison & Eggermont, 2015 ), and parent–child relationship ( Coyne et al., 2014 ) may also be important targets for future interventions.

The studies reviewed present several limitations. Most studies were cross-sectional and could not elucidate the directionality of the association between SM use and depression. Most of the studies included self-report rather than clinician-administered measures of depression, and retrospective reports, asking participants to report on past activities. Newer methods that measure actual (and not just reported) use (e.g. news feed activity, number of likes and comments) and more frequent and timely reports of SM use (e.g. diaries) could more accurately explain these associations. Another limitation is that many of the studies recruited participants in schools, limiting the generalizability to clinical samples. It is possible that those students not in school were spending more time on SM and/or experiencing more depressive symptoms. Most studies included general assessments of SM without specifying whether the use was limited to SNS or other forms of SM or internet use. While we tried to narrow our search to studies that explicitly included questions on SNS use, many also asked about other types of SM use. Separating the different types of SM use may be difficult when asking for adolescents’ self-reports, but more immediate measures of mood symptoms and SNS use could be more specific and informative. Finally, while some studies included contextual factors such as the educational and family environments, other contextual factors such as ethnicity and cultural context are areas of potential for investigation.

Conclusions

In summary, extensive research on the quantity and quality of SM use has shown an association between SM use and depression in adolescents. Given that most studies are cross-sectional, longitudinal research would help assess the direction of this association. At the same time, some aspects of SM use may have a beneficial effect on adolescent well-being, such as the ability to have diversity of friendships and easily accessed supports. Furthermore, the use of SM content to detect symptoms has potential in depression and suicide prevention. Finally, moderators of the association between SM and adolescent depression and suicidality (e.g. gender, age, parental involvement) are areas to explore that would allow more targeted interventions. Since SM will remain an important facet of adolescents’ lives, a better understanding of the mechanisms of its relationship with depression could be beneficial to increase exposure to mental health interventions and promote well-being.

Acknowledgements

The authors acknowledge the help of Jaime Blanck, MLIS, MPA for her help with the search and retrieval of full-text articles.

Disclosure statement

Dr. Vidal is supported by the Stravos Niarchos Foundation. Ms. Lhaksampa and Dr. Miller are supported by the Once Upon a Time Foundation. Drs. Miller and Dr. Platt are supported by the Patient-Centered Outcomes Research Institute (PCORI). Dr. Platt is supported by the NIMH 1K23MH118431 and the Robert Wood Johnson Foundation.

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Home — Essay Samples — Nursing & Health — Psychiatry & Mental Health — Depression

Essays About Depression

Depression essay topic examples.

Explore topics like the impact of stigma on depression, compare it across age groups or in literature and media, describe the emotional journey of depression, discuss how education can help, and share personal stories related to it. These essay ideas offer a broad perspective on depression, making it easier to understand and engage with this important subject.

Argumentative Essays

Argumentative essays require you to analyze and present arguments related to depression. Here are some topic examples:

• 1. Argue whether mental health stigma contributes to the prevalence of depression in society.
• 2. Analyze the effectiveness of different treatment approaches for depression, such as therapy versus medication.

Example Introduction Paragraph for an Argumentative Essay: Depression is a pervasive mental health issue that affects millions of individuals worldwide. This essay delves into the complex relationship between mental health stigma and the prevalence of depression in society, examining the barriers to seeking help and the consequences of this stigma.

Example Conclusion Paragraph for an Argumentative Essay: In conclusion, the analysis of mental health stigma's impact on depression underscores the urgent need to challenge and dismantle the stereotypes surrounding mental health. As we reflect on the far-reaching consequences of stigma, we are called to create a society that fosters empathy, understanding, and open dialogue about mental health.

Compare and Contrast Essays

Compare and contrast essays enable you to examine similarities and differences within the context of depression. Consider these topics:

• 1. Compare and contrast the symptoms and risk factors of depression in adolescents and adults.
• 2. Analyze the similarities and differences between the portrayal of depression in literature and its depiction in modern media.

Example Introduction Paragraph for a Compare and Contrast Essay: Depression manifests differently in various age groups and mediums of expression. This essay embarks on a journey to compare and contrast the symptoms and risk factors of depression in adolescents and adults, shedding light on the unique challenges faced by each demographic.

Example Conclusion Paragraph for a Compare and Contrast Essay: In conclusion, the comparison and contrast of depression in adolescents and adults highlight the importance of tailored interventions and support systems. As we contemplate the distinct challenges faced by these age groups, we are reminded of the need for age-appropriate mental health resources and strategies.

Descriptive Essays

Descriptive essays allow you to vividly depict aspects of depression, whether it's the experience of the individual or the societal impact. Here are some topic ideas:

• 1. Describe the emotional rollercoaster of living with depression, highlighting the highs and lows of the experience.
• 2. Paint a detailed portrait of the consequences of untreated depression on an individual's personal and professional life.

Example Introduction Paragraph for a Descriptive Essay: Depression is a complex emotional journey that defies easy characterization. This essay embarks on a descriptive exploration of the emotional rollercoaster that individuals with depression experience, delving into the profound impact it has on their daily lives.

Example Conclusion Paragraph for a Descriptive Essay: In conclusion, the descriptive portrayal of the emotional rollercoaster of depression underscores the need for empathy and support for those grappling with this condition. Through this exploration, we are reminded of the resilience of the human spirit and the importance of compassionate understanding.

Persuasive Essays

Persuasive essays involve arguing a point of view related to depression. Consider these persuasive topics:

• 1. Persuade your readers that incorporating mental health education into the school curriculum can reduce the prevalence of depression among students.
• 2. Argue for or against the idea that employers should prioritize the mental well-being of their employees to combat workplace depression.

Example Introduction Paragraph for a Persuasive Essay: The prevalence of depression underscores the urgent need for proactive measures to address mental health. This persuasive essay asserts that integrating mental health education into the school curriculum can significantly reduce the prevalence of depression among students, offering them the tools to navigate emotional challenges.

Example Conclusion Paragraph for a Persuasive Essay: In conclusion, the persuasive argument for mental health education in schools highlights the potential for early intervention and prevention. As we consider the well-being of future generations, we are called to prioritize mental health education as an essential component of a holistic education system.

Narrative Essays

Narrative essays offer you the opportunity to tell a story or share personal experiences related to depression. Explore these narrative essay topics:

• 1. Narrate a personal experience of overcoming depression or supporting a loved one through their journey.
• 2. Imagine yourself in a fictional scenario where you advocate for mental health awareness and destigmatization on a global scale.

Example Introduction Paragraph for a Narrative Essay: Personal experiences with depression can be transformative and enlightening. This narrative essay delves into a personal journey of overcoming depression, highlighting the challenges faced, the support received, and the lessons learned along the way.

Example Conclusion Paragraph for a Narrative Essay: In conclusion, the narrative of my personal journey through depression reminds us of the resilience of the human spirit and the power of compassion and understanding. As we reflect on our own experiences, we are encouraged to share our stories and contribute to the ongoing conversation about mental health.

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Depression, known as major depressive disorder or clinical depression, is a psychological condition characterized by enduring feelings of sadness and a significant loss of interest in activities. It is a mood disorder that affects a person's emotional state, thoughts, behaviors, and overall well-being.

Its origin can be traced back to ancient civilizations, where melancholia was described as a state of sadness and melancholy. In the 19th century, depression began to be studied more systematically, and terms such as "melancholic depression" and "nervous breakdown" emerged. The understanding and classification of depression have evolved over time. In the early 20th century, Sigmund Freud and other psychoanalysts explored the role of unconscious conflicts in the development of depression. In the mid-20th century, the Diagnostic and Statistical Manual of Mental Disorders (DSM) was established, providing a standardized criteria for diagnosing depressive disorders.

Biological Factors: Genetic predisposition plays a role in depression, as individuals with a family history of the disorder are at a higher risk. Psychological Factors: These may include a history of trauma or abuse, low self-esteem, pessimistic thinking patterns, and a tendency to ruminate on negative thoughts. Environmental Factors: Adverse life events, such as the loss of a loved one, financial difficulties, relationship problems, or chronic stress, can increase the risk of depression. Additionally, living in a socioeconomically disadvantaged area or lacking access to social support can be contributing factors. Health-related Factors: Chronic illnesses, such as cardiovascular disease, diabetes, and chronic pain, are associated with a higher risk of depression. Substance abuse and certain medications can also increase vulnerability to depression. Developmental Factors: Certain life stages, including adolescence and the postpartum period, bring about unique challenges and changes that can contribute to the development of depression.

Depression is characterized by a range of symptoms that affect an individual's emotional, cognitive, and physical well-being. These characteristics can vary in intensity and duration but generally include persistent feelings of sadness, hopelessness, and a loss of interest or pleasure in activities once enjoyed. One prominent characteristic of depression is a noticeable change in mood, which can manifest as a constant feeling of sadness or emptiness. Individuals may also experience a significant decrease or increase in appetite, leading to weight loss or gain. Sleep disturbances, such as insomnia or excessive sleepiness, are common as well. Depression can impact cognitive functioning, causing difficulties in concentration, decision-making, and memory recall. Negative thoughts, self-criticism, and feelings of guilt or worthlessness are also common cognitive symptoms. Furthermore, physical symptoms may arise, including fatigue, low energy levels, and a general lack of motivation. Physical aches and pains, without an apparent medical cause, may also be present.

The treatment of depression typically involves a comprehensive approach that addresses both the physical and psychological aspects of the condition. It is important to note that the most effective treatment may vary for each individual, and a personalized approach is often necessary. One common form of treatment is psychotherapy, which involves talking to a mental health professional to explore and address the underlying causes and triggers of depression. Cognitive-behavioral therapy (CBT) is a widely used approach that helps individuals identify and change negative thought patterns and behaviors associated with depression. In some cases, medication may be prescribed to help manage depressive symptoms. Antidepressant medications work by balancing neurotransmitters in the brain that are associated with mood regulation. It is crucial to work closely with a healthcare provider to find the right medication and dosage that suits an individual's needs. Additionally, lifestyle changes can play a significant role in managing depression. Regular exercise, a balanced diet, sufficient sleep, and stress reduction techniques can all contribute to improving mood and overall well-being. In severe cases of depression, when other treatments have not been effective, electroconvulsive therapy (ECT) may be considered. ECT involves administering controlled electric currents to the brain to induce a brief seizure, which can have a positive impact on depressive symptoms.

1. According to the World Health Organization (WHO), over 264 million people worldwide suffer from depression, making it one of the leading causes of disability globally. 2. Depression can affect people of all ages, including children and adolescents. In fact, the prevalence of depression in young people is increasing, with an estimated 3.3 million adolescents in the United States experiencing at least one major depressive episode in a year. 3. Research has shown that there is a strong link between depression and other physical health conditions. People with depression are more likely to experience chronic pain, cardiovascular diseases, and autoimmune disorders, among other medical conditions.

The topic of depression holds immense significance and should be explored through essays due to its widespread impact on individuals and society as a whole. Understanding and raising awareness about depression is crucial for several reasons. Firstly, depression affects a significant portion of the global population, making it a pressing public health issue. Exploring its causes, symptoms, and treatment options can contribute to better mental health outcomes and improved quality of life for individuals affected by this condition. Additionally, writing an essay about depression can help combat the stigma surrounding mental health. By promoting open discussions and providing accurate information, essays can challenge misconceptions and foster empathy and support for those experiencing depression. Furthermore, studying depression allows for a deeper examination of its complex nature, including its psychological, biological, and sociocultural factors. Lastly, essays on depression can highlight the importance of early detection and intervention, promoting timely help-seeking behaviors and reducing the burden of the condition on individuals and healthcare systems. By shedding light on this critical topic, essays have the potential to educate, inspire action, and contribute to the overall well-being of individuals and society.

1. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. 2. World Health Organization. (2017). Depression and other common mental disorders: Global health estimates. World Health Organization. 3. Kessler, R. C., Bromet, E. J., & Quinlan, J. (2013). The burden of mental disorders: Global perspectives from the WHO World Mental Health Surveys. Cambridge University Press. 4. Beck, A. T., Rush, A. J., Shaw, B. F., & Emery, G. (1979). Cognitive therapy of depression. Guilford Press. 5. Nierenberg, A. A., & DeCecco, L. M. (2001). Definitions and diagnosis of depression. The Journal of Clinical Psychiatry, 62(Suppl 22), 5-9. 6. Greenberg, P. E., Fournier, A. A., Sisitsky, T., Pike, C. T., & Kessler, R. C. (2015). The economic burden of adults with major depressive disorder in the United States (2005 and 2010). Journal of Clinical Psychiatry, 76(2), 155-162. 7. Cuijpers, P., Berking, M., Andersson, G., Quigley, L., Kleiboer, A., & Dobson, K. S. (2013). A meta-analysis of cognitive-behavioural therapy for adult depression, alone and in comparison with other treatments. Canadian Journal of Psychiatry, 58(7), 376-385. 8. Hirschfeld, R. M. A. (2014). The comorbidity of major depression and anxiety disorders: Recognition and management in primary care. Primary Care Companion for CNS Disorders, 16(2), PCC.13r01611. 9. Rush, A. J., Trivedi, M. H., Wisniewski, S. R., Nierenberg, A. A., Stewart, J. W., Warden, D., ... & Fava, M. (2006). Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: A STAR*D report. American Journal of Psychiatry, 163(11), 1905-1917. 10. Kendler, K. S., Kessler, R. C., Walters, E. E., MacLean, C., Neale, M. C., Heath, A. C., & Eaves, L. J. (1995). Stressful life events, genetic liability, and onset of an episode of major depression in women. American Journal of Psychiatry, 152(6), 833-842.

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Deep learning for prediction of depressive symptoms in a large textual dataset

• Original Article
• Open access
• Published: 27 August 2021
• Volume 34 , pages 721–744, ( 2022 )

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• Md Zia Uddin   ORCID: orcid.org/0000-0002-5215-1834 1 ,
• Kim Kristoffer Dysthe 2 ,
• Asbjørn Følstad 1 &
• Petter Bae Brandtzaeg 1 , 2  

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Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

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1 Introduction

Depression, or depressive disorder, is a common disease. According to the World Health Organization (WHO), the number of people with depression was estimated at more than 300 million affected worldwide [ 1 ]. Depression may severely impact well-being and functioning at work, school, and family, and can even lead to self-harm. Adolescent depression is associated with mood disorders and severe mental illness in adult life [ 2 , 3 ]. Nearly 0.8 million people die from suicide each year and suicide is the fourth leading cause of death in 15–19-year-olds, according to WHO [ 1 ]. Amongst the top major diseases causing disability or incapability, five are mental illnesses—depression being the most prominent of these [ 4 ]. Hence, the disease burden due to depression is vast. The prevalence of depression in the adult population is approximately 5% across cultures, and 20% in its milder forms (i.e., partial symptoms, mild depression, and probable depression) [ 5 ]. Among adults, those most at risk are within the middle-aged population. Also, the world-wide occurrence of depression is increasing, with a rise of 18% between 2005 and 2015. However, early professional intervention can improve mental symptoms (e.g., absence of self-confidence and rumination) and resolve somatic problems (e.g., gastrointestinal problems and sleeping disorders) in most of the cases [ 6 , 7 ].

Early detection of depressive symptoms followed by assessment and treatment can considerably improve chances for curbing symptoms and the underlying disease; mitigate negative implications for well-being and health as well as personal, economic, and social life [ 7 , 8 , 9 , 10 ]. However, detection of depressive symptoms is challenging and resource demanding. Current approaches are mainly based on clinical interviews and questionnaire surveys by hospitals or agencies [ 11 ], where psychological evaluation tables are utilized to make predictions on mental disorder. This approach is mostly based on one-to-one questionnaires and can roughly diagnose the psychological disorder for depression.

An alternative approach to interview or questionnaire-based predictions of depression is the analysis of informal texts provided by users. Previous studies in clinical psychology have shown that the relationship between the user of a language (e.g., speaker or writer) and their text is meaningful and has potential for the future [ 12 ]. A recent study by Havigerová et al. indicate a potential for text-based detection of persons at risk for depression, using a sample of informal text written about a holiday [ 12 ]. Hence, online records and data are increasingly seen as a valuable data source in supporting health care with decision support. The approach to identify depression symptoms from informal texts is promising, as it allows for benefitting from recent advances in natural language processing and Artificial Intelligence (AI). AI applied for natural language processing employs linguistics and computing techniques to help machines to understand underlying phenomena such as sentiments or emotions from texts. In that case, the core intent is to analyse opinions, ideas, and thoughts via the assignment of polarities either negative or positive.

Previous work has found that automatic analysis of depression symptoms from texts can be applied in, for example, sentiment retrieval from suicide notes and detecting insulting or depressive words or sentences in conversations or blog posts [ 13 , 14 , 15 , 16 , 7 , 18 ]. However, there is still substantial untapped potential in research on extracting depressive symptoms from texts. Key challenges include portraying significant cues of depression from texts. Also, there is a substantial hurdle in detecting depression symptoms from short texts.

To contribute towards solving these challenges, we aim to develop an automatic algorithm for detecting depression symptoms in texts, using a text-based sample of young people seeking advice about self-perceived depressive symptoms. We believe our automatic detection approach, describing the problems of the users in natural language, can be a substantial contribution to this research field. Hence, the current study focuses on how symptoms of depression are manifested through text in natural language using AI.

To visualize sample data of different groups in different applications, Linear Discriminant Analysis (LDA) is a good tool for data visualization based on discriminations [ 19 , 20 , 21 , 22 ]. It works on grouping of samples of similar classes. It tries to find the directions where the classes are best separated by considering minimizing the within-class scatter while maximizing the between-class scatter. LDA has already been used in various practical applications such as facial emotion recognition and human activity recognition. LDA projects the sample data of different classes onto a lower-dimensional vector space. Thus, the ratios of the between-class scatter and the within-class scatter is maximized to achieve highest discrimination.

Deep neural network has been contributing a lot recently in enormous fields of research, especially in pattern recognition and AI [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. Though it is more robust than typical neural networks, it however consists of two major disadvantages. The first disadvantage is overfitting problem most of the time. The last one is taking much time for modelling the underlying data. The first successful deep learning algorithm was deep belief network that consisted of Restricted Boltzmann Machines (RBMs) that made the training quite faster than other previous learning approaches. Later, convolutional neural networks (CNN) was proposed and got popular especially in image processing fields. It showed better discriminative power compared to other approaches. CNN also extracts features alongside training the data. It has some convolutional stacks to generate a progressive hierarchy of abstract features via convolution, pooling, tangent squashing, rectifier, and normalization [ 24 ]. CNN is mostly applied for image and video pattern analysis rather than temporal information decoding. Hence, it has not been adopted for time-sequential data analysis. Recurrent Neural Networks (RNNs) is however a better choice than CNN since it consists of better discriminative power over others in case of sequential data and pattern analysis [ 30 ]. Since the basic RNNs usually consist of vanishing gradient problem due to long-term dependencies when it handles high-dimensional and time-sequential data, Long Short-Term Memory (LSTM) was introduced in RNN to overcome it. Hence, this work utilizes the advantage of LSTM-based RNN to model different emotional states in text data.

Among different approaches to analyse physical and mental states of human being from different data sources, machine learning has been very widely used [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Since machine learning models are progressively being employed to do significant predictions in crucial contexts day by day, the demand of transparency rises in such contexts from the various stakeholders in AI industry [ 42 ]. The high risk in this regard is making and applying the AI decisions that are unjustifiable and lacks explanations of the models' behaviour. Hence, explanations of the output of a model are vital. For example, specialists in precision medicine fields need further information from the machine learning models than simple prediction for supporting their diagnosis. Such necessities may also arise in other fields as well, such as medical emergencies. Hence, focusing merely on the performances of the AI models, gradually makes the systems towards unacceptance in some cases. Therefore, current research has highlighted the importance of explainable Artificial Intelligence (XAI) for establishing trust in machine learning-based decisions through the explanations of the black-box models. Popular state-of-the-art explanation algorithms include Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and layer-wise relevance propagation (LRP). From which, LIME is very light-weight and yet tries to generate quick and satisfactory post-hoc explanations. Hence, this work adopts LIME to see the explanations (i.e., importance of the features) once the decision is provided by the model.

1.1 Contribution

This work focuses on processing text data, features, and depression symptoms text recognition with the target of chatbot as a smart application. Figure  1 shows a schematic setup of a text-based depression symptoms text detection system in a smart application where a user provides a query in text format and a server processes the text to apply feature extraction and deep learning. Based on the results, the server can suggest further advices to the user. Figure  2 shows the basic architecture of the proposed system consisting of training and testing procedure for the classification of texts describing symptoms of depression. In the training part, text data from all the users is obtained and then the features are trained using RNN. In the testing part, features from a sample test are applied to the trained model to take the decision whether the user describes depression or not. LDA is applied to show the robustness of the proposed features compared to other traditional ones. Finally, we apply one of the most popular algorithms (i.e., LIME) for post-hoc, local, and meaningful explanations of the machine learning decision regarding the existence of a potential depression or not, in the text. The contribution of the paper can be summarized as bellow:

A large dataset of text is obtained from a public Norwegian online information channel: ung.no .

Novel features are extracted representing the possible symptoms of depression defined by the experts from medical and psychology domains.

RNN is applied based on LSTM, attention, and dense layers for modelling the emotional states.

The machine learning decisions are explained using a state-of-the-art XAI approach, LIME to see the importance of the features.

A schematic setup for classifying texts containing symptoms of depression

Flowcharts of the proposed depression prediction system from text

2 Data collection and processing

To reliably detect symptoms of mental health issues, the collection of data for the detection model is crucial. For instance, data from social media such as Facebook status updates does not seem to be sufficiently detailed to develop reliable models to decode emotional states from data [ 43 ]. For this work, we obtained a large text-based dataset from a public Norwegian information website: ung.no . At ung.no, youth have the opportunity to post questions anonymously in Norwegian about their various challenges and problems in their everyday life. In response, corresponding professional experts (e.g. doctors, psychologist, nurses etc.) provide answers and offer advise. These questions and answers are published online and publicly available for everyone. Prior to submitting a question on ung.no , young people pre-define and categorize the topic of their post. We focused herein on the category “Mental health and emotions”. Even if the texts are relatively short, they typically describe the activating factors leading to the mental state and the ensuing symptoms and behaviour. First, a proportion of the texts describe depressive conditions already diagnosed by a health professional. Second, many of the texts describe the narrative and the ensuing symptoms, either asking if it could represent depression or suggesting depression as a possible diagnosis. We believe these texts to present self-perceived depressive symptoms. Previous research suggest that self-perceived mental states correspond well with later clinical diagnoses [ 44 , 45 , 46 ]. Last, some of the texts describe the narratives and the succeeding mental states without mentioning a possible depression. The staff interpret the texts as describing symptoms of depression. Accordingly, the data is classified into categories, depression being one of them. Then, a trained GP went through the posts, confirming descriptions of depressive symptoms. A list of sentences and words are summarized analysing the messages in the database where they may indicate the person having depression. A medical practitioner validated the sentences and words. Table 1 shows some important features from “Appendix” representing the possible sentences and/or words may occur in the queries by the youth having depression.

The sentences and words are used to obtain features for each message of the dataset. Five translated and paraphrased examples of depression texts derived from a Norwegian text dataset at ung.no are shown in Fig.  3 . The Norwegian dataset consists of 277,552 free-text posts in different categories including depression texts. From that dataset, we utilized 11,807 and 21,470 posts of different length for our two different experiments in this work. For feature extraction process to model depression and non-depression machine learning model, we augment all the feature rows of “Appendix” first. Then, all rows in the “Appendix” are tokenized word by word and stemmed for feature extraction process. The stemmed words from the list of symptoms are represented as F  = " all hat meg alt er jæv … noe mer å lev for" . To extract features from a text input, one-hot process is applied on the stemmed words of the input text based on each word of F (i.e., 1 if a word from F is present and 0 otherwise). Thus, the features for the texts represents binary patterns to be applied with machine learning model of depression prediction. The collection of 189 unique words extracted from the list of possible symptoms is shown in Fig.  4 where the words in Norwegian are in alphabetical order in Fig.  4 a and the corresponding translated words are in Fig.  4 b. Unique extractions of stemmed words are listed to illustrate the diversity of possible words associated with symptoms of depression.

Five translated and paraphrased examples of depression posts derived from the Norwegian dataset used in the work

Unique words extracted from the stemmed words of possible symptoms reported in APENDIX A: a Norwegian words in alphabetical order and b translated in English

The symptoms presented in “Appendix” are obtained with the help of Norwegian professionals (e.g., medical doctors and psychologists). However, the way of expressing the emotions in Norwegian texts may be linguistically different from other languages. Therefore, professionals in those languages can contribute to building dataset and features in modelling depression and non-depression. To be noted, the English texts are shown in Table 1 , Figs.  3 , 4 , and “Appendix” only for the readability of international readers and researchers. Otherwise, whole approaches from input text to emotional state modelling via feature processing, is done based on the Norwegian language.

The main reason to go for using one-hot on the robust depression features rather than traditional ones such as typical one-hot and Term Frequency—Inverse Document Frequency (TF-IDF) [ 47 ] that are related to typical word frequencies rather than word importance is, the features describing depression symptoms are much more important than just word frequencies to predict depression in the text. Figure  5 shows the algorithm for one-hot feature extraction based on the unique feature words in the list of depression symptoms. Thus, the one-hot binary features based on the depression symptoms for the i th text in the dataset can be represented as L i .

The algorithm of one-hot depression symptom feature extraction

3 Linear discriminant analysis (LDA) for visualization

To visualize different features, we adopt linear discriminant analysis (LDA) here. LDA is basically an eigenvalue decomposition problem trying to maximize the inter-class scatterings of the samples whereas minimizing the inner-class scatterings of them. The formulas for the inter-class scattering, \(M_{B}\) and inner-class scattering matrix, \(M_{W}\) are shown as follows:

where c is the total number of classes, \(N_{i}\) the number samples in class \(C_{i}\) , \(m_{k}\) the feature vectors from class C , \(m_{i}\) the mean of class i , and \(m_{j}\) the mean of all feature vectors. The LDA feature space representing the optimal discrimination matrix can be found by maximizing the ratio of the determinant of \(M_{B}\) and \(M_{W}\) as

where Q basically represents the set of discriminant vectors. Thus, the discriminant ratio of inner as well as inter-class samples of different classes can be found by solving an eigenvalue problem as

where \(\Lambda\) is the eigenvalue matrix in the singular value decomposition process. Figures  6 , 7 , 8 , and 9 show the feature visualizations using 3-D plots of typical one-hot in LDA, typical TF-IDF in LDA, and proposed features in PCA, and proposed features in LDA features spaces, respectively. In the figures, the proposed features (i.e., Fig.  9 ) shows superior clustering of the samples of same class and better separation among the samples of different classes compare to the two other approaches, indicating the robustness of the proposed features in this regard. However, the traditional PCA projection on the features Thus, the text feature matrix F is projected to the LDA feature space \(Q_{{{\text{opt}}}}\) as

3-D plot after LDA on the traditional one-hot features of two emotional states

3-D plot after LDA on the traditional TF-IDF features of two emotional states

3-D plot after PCA on the proposed robust features of two emotional states

3-D plot after LDA on the proposed robust features of two emotional states

4 Deep recurrent neural network (RNN) for modelling emotional states

Emotional states can be represented as time-sequential words in text data while conversating with others. Hence, a machine learning model capable of encoding time-sequential data is quite suitable for such kind of work. Hence, Recurrent Neural Networks (RNNs) is adopted in this work. RNN can be considered as most popular deep learning approaches used to model time-sequential information [ 22 ]. RNNs basically consists of recurrent connections between history to present state and hidden states. That is a quite important role of the memory in neural networks. The usual RNN algorithms very often face a vanishing gradient problem, a limitation of processing long-term data which is mostly known as Long-Term Dependencies. To overcome the problem, Long Short-Term Memory (LSTM) was developed [ 23 ]. Figure  10 shows a sample deep neural network consists of 50 LSTM units.

A basic structure of LSTM-based RNN

Each LSTM memory block has a cell state as well as three gates, which are input, forget, and the output gates. The input gate F t can be represented as

where W is weight matrix, b bias vectors, and β a logistic function. The forget gate F can be expressed as

The long-term memory is stored in a cell state vector S that is expressed as

The output gate V produces the output for the unit and can be expressed as

The hidden state H is expressed as

We adopt an attention layer over the LSTM units before applying dense layer [ 48 ] as

The attention technique is basically used for emphasising important information in the current task rather than other useless information. Hence, it can be applied on top of the LSTM layers to improve the model's accuracy. Finally, the output can be determined using a softmax function as

where W and b represent weights and bias, respectively. Figures  11 and 12 show the algorithms for training and prediction of depression or non-depression through RNN, respectively.

The algorithm of training features from all texts with RNN

The algorithm of testing of a test text message with the trained RNN

5 Experimental results and discussion

For experiments, two text datasets were obtained from the queries and answers from ung.no website. The dataset comprises of several categories including depression texts. The annotations of the messages were done with the help of professionals such as medical doctors and psychologists. All the experiments are done on a computer that has Intel(R) Core(TM) i7-7700HQ CPU with the speed of 2.80 GHz and 2.81 GHz, memory of 32 GB, Windows® 10 operating system, and TensorFlow deep learning tool version 2.4.1.

5.1 First dataset and experiments

From the whole collection of texts of different categories, 11,807 of them were extracted for the first dataset and experiments that consisted of 1820 texts categorized as depression texts (describing symptoms of depression) and the other 9987 as non-depression texts (not describing symptoms of depression). Tables 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 represent the classification reports of tenfold used in the experiments where each fold consist of 90% data as training and rest as testing. Figures 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 show the confusion matrices of each fold. Figure  23 depicts the accuracy and loss for 100 epochs during the training of the ten different folds. The overall training of the folds looks good except a little negligible fluctuation. Figure  24 shows the attention-based LSTM model used in this work where there are 53,358 parameters represented by an LSTM layer with 50 memory units, an attention layer, and a dense layer for 2 different emotional states (i.e., depression and non-depression).

Confusion matrix of fold-1 in the first dataset using proposed approach

Confusion matrix of fold-2 in the first dataset using proposed approach

Confusion matrix of fold-3 in the first dataset using proposed approach

Confusion matrix of fold-4 in the first dataset using proposed approach

Confusion matrix of fold-5 in the first dataset using proposed approach

Confusion matrix of fold-6 in the first dataset using proposed approach

Confusion matrix of fold-7 in the first dataset using proposed approach

Confusion matrix of fold-8 in the first dataset using proposed approach

Confusion matrix of fold-9in the first dataset using proposed approach

Confusion matrix of fold-10 in the first dataset using proposed approach

a Accuracy and b loss of 10-folds during experiments on the first dataset using the proposed approach

The emotional state model structure and parameters based on attention over LSTM units

5.2 Comparison with traditional approaches

We compared the proposed approach with traditional approaches where the proposed one showed superior results than others. We first applied traditional machine learning approaches using different features (i.e., typical one-hot, TF-IDF, and proposed features) with other conventional machine learning algorithms (i.e., logistic regression, decision trees, support vector machines (SVM), typical large artificial neural network (ANN), DBN, and CNN) but could not achieve more than 91% of mean accuracy as shown in Table 12 . Furthermore, we tried LSTM with the traditional as well as proposed features to decode and model the time-sequential information to determine the emotional states. Table 13 and chart in Fig.  25 show the performance of three different approaches to the first dataset where the proposed approach shows the superiority by achieving 98% of mean accuracy over two other approaches.

Performance of three different approaches to the first dataset

Besides, another straight-forward approach was applied where the direct presence of the symptoms from “Appendix” was checked to take the binary decision of depression or non-depression. This approach was applied on the whole dataset rather than splitting into training and testing since it was a simple rule-based classification. The direct presence of one or more symptoms-based approach achieved the accuracy of 84.20% where 1684 depression texts were correctly classified among a total of 1807 depression texts and 1730 non-depression texts correctly classified among 10,000 non-depression text. Since there are different ways to express self-depression in texts of different length, it is hard to apply just a binary rule to determine the depression in the text. Hence, it is better to combine the base words from all the symptoms to define collection of features for depression to apply some complicated algorithms such as sequence-based machine learning algorithm using LSTM-based RNN that has been applied in this work.

5.3 Second dataset and experiments

For the second dataset, a total of 21,470 text samples were obtained consisting of depression—and non-depressions texts. From which, 1470 were depression texts and rest of the 20,000 were non-depression texts. We applied fivefold cross validation for the second phase experiments with the proposed approach, i.e. using RNN on the robust features. Only the results using the proposed approach is reported here since it showed the best results than the other approaches as shown in the experiments of the first phase, i.e. first dataset. Figures 26 , 27 , 28 , 29 , 30 represent the confusion matrices of fivefold used in the second experiments where each fold consist of 80% data as training and rest 20% as testing. The experimental results show a remarkable performance of the proposed features followed by one-hot and LSTM where the mean recall rate of depression and non-depression is 0.98 and 0.99, respectively. The mean accuracy is 0.99 that shows the robustness of the proposed approach.

Confusion matrix of fold-1 in the second dataset using proposed approach

Confusion matrix of fold-2 in the second dataset using proposed approach

Confusion matrix of fold-3 in the second dataset using proposed approach

Confusion matrix of fold-4 in the second dataset using proposed approach

Confusion matrix of fold-5 in the second dataset using proposed approach

In summary, the above experimental results show the overall efficiency of the proposed depression prediction system using depression symptom-based features and time-sequential LSTM-based machine learning model. The proposed system shows better results than existing latest approaches for depression prediction. For instance, in [ 12 ], the work is basically based on a measuring scale considering depression, anxiety and stress, which is a point-based measuring scale obtained by writing four different kind of letters by the candidates. The candidates collected by formal advertisements were asked to write these letters whereas in our database, the participants wrote the text spontaneously expressing their necessity to seek assistance over a national portal. The model used [ 12 ] is logistic regression, a simple and basic machine learning model which is usually simple linear model and hence, should not generally fit well where the sample data is distributed non-linearly. On the contrary, our work adopted time-sequential LSTM-based machine learning model that can separate both linearly and nonlinearly distributed samples from different classes. The proposed approach also overpowers other popular deep learning models such as DBN and CNN which are usually used for non-sequential event modelling.

5.4 XAI to explain the ML decisions

Humans are basically restrained to accept approaches that are not interpretable or trustworthy, pushes the demand for transparent AI to increase. Hence, focusing only on performance of the AI models, gradually makes the systems towards unacceptance. Though there is a trade-off between the performance and transparency of machine learning models, improvements in the understanding of the models via explainability can however lead to the correction of the model's deficiencies as well. Therefore, with the target of overcoming the limitations of accepting the current generation AI models, XAI should focus on machine learning techniques to produce more and more explainable models while upholding a high level of accuracy. Besides, they can also make it happen for humans to appropriately understand, trust, and manage the emerging AI phenomena as much as possible. Explainability is a main factor to gain confidence of whether a model would act as intended for a given problem. Most certainly, it is a property of any explainable model. Local explanations in AI models handle explainability by dividing the model's complex solutions space into several less complex solution subspaces which are relevant for the whole model. These explanations can utilize some approaches with the differentiating property to explain the model to some basic extent.

Most of the techniques of model simplification are based on rule extraction techniques. The most popular contributions for local post-hoc explanation is based on the approach called Local Interpretable Model-Agnostic Explanations (LIME) [ 35 ]. LIME basically generates locally linear models for the predictions of a machine learning model to explain it. It falls under category of the rule-based local explanations by simplification. Explanations by simplification builds a whole new system based on the trained model to be explained. Then, the new simplified model usually tries to optimize its resemblance to its predecessor model functions while reducing the complexity and at the same time, keeping a similar performance. Therefore, once the machine learning decision is obtained, XAI algorithm LIME is applied to see the importance of the features and probabilities towards the decision. Hence, we can understand the presence of the feature importance in the input for the decision, that helps understanding the outcomes of the system. Figure  31 shows the total class probabilities, top 10 features, their probabilities, and automatically highlighted features in a sample input text using LIME. As can be seen in right side of the figure, features towards depression get higher weights altogether than non-depression class, indicates the person to be in depression mode. The input text, features, and highlights were originally in Norwegian language since the database is from a Norwegian national portal to interact with youth, but the figure shows the corresponding translated text in English for better readability and understanding of the approach. According to the decision from machine learning model and explanations from LIME, the sample text consists of depression. To be noted, the ground truth for the sample text in the figure was the same as the model's prediction (i.e., depression), indicating the robustness of the model's decision and explanation.

Total class probabilities, top 10 features, their probabilities, and automatically highlighted features in a sample input text using LIME

Furthermore, Fig.  32 shows summarized probabilities of top 10 features for a paraphrased non-depression example text using LIME. In the figure, left side represents the original part after applying the algorithm and right side the corresponding representation in English for better readability as well as understandability. The overall probabilities of the non-depression text from the machine learning model for depression and non-depression classes were 0.001 and 0.999, respectively.

a Probabilities of top 10 features for a normal non-depression text (on the top) using LIME and b corresponding English on the right

6 Conclusion

To automatically detect depression symptoms in text for decision support in health care is important. In this work, a multimodal human depression prediction approach has been investigated based on one-hot approach on robust features based on describing depression symptoms and deep learning method, RNN. First, the young users' text data has been obtained from ung.no, a public information channel targeting young people in Norway. Then, one-hot method is applied after sequentially extracting the words from different sentences and words representing the symptoms of depression. Furthermore, the one-hot features have been applied to train a deep RNN based on LSTM method to model two different emotional states: depression and non-depression. Finally, the trained RNN has been used for predicting the underlying emotional state in unknown sensor text data. Using the proposed approach, 98% and 99% mean prediction performance has been achieved on first and second dataset consists of around 11,807 and 21,807 texts, respectively. Whereas, the traditional approaches could achieve maximum of 91% mean recognition performance, indicating the robustness of the proposed approach. The proposed approach outperforms the other traditional approaches such as using the proposed features with logistic regression, DBN, and CNN models as well as using typical one-hot and TF-IDF features with RNN. Besides, an XAI algorithm, LIME has been utilized to see whether the proposed system generates meaningful explanations to support its decision. Thus, the features used in this work can be used to support the machine learning decisions and to contribute to design effective user interface for better affective care. The deep learning-based efficient system can be explored in greater levels with comprehensive dataset. Detection of depression symptoms in texts can be applied in mental health care services for real-time analysing and predicting normal as well as severe states of mood disorders in smart environments combined with latest technologies. For instance, smart chatbot systems providing informational support about depression can be a feasible solution for both health professionals working with youth and youths struggling with mental health issues.

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Acknowledgements

This work was supported by the Research Council of Norway under Grant Number 262848.

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Md Zia Uddin, Asbjørn Følstad & Petter Bae Brandtzaeg

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Uddin, M.Z., Dysthe, K.K., Følstad, A. et al. Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Comput & Applic 34 , 721–744 (2022). https://doi.org/10.1007/s00521-021-06426-4

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Received : 18 March 2021

Accepted : 17 August 2021

Published : 27 August 2021

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DOI : https://doi.org/10.1007/s00521-021-06426-4

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