thesis on depression and anxiety

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• March 01, 2024 | VOL. 181, NO. 3 CURRENT ISSUE pp.171-254
• February 01, 2024 | VOL. 181, NO. 2 pp.83-170
• January 01, 2024 | VOL. 181, NO. 1 pp.1-82

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The Critical Relationship Between Anxiety and Depression

• Ned H. Kalin , M.D.

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Anxiety and depressive disorders are among the most common psychiatric illnesses; they are highly comorbid with each other, and together they are considered to belong to the broader category of internalizing disorders. Based on statistics from the Substance Abuse and Mental Health Services Administration, the 12-month prevalence of major depressive disorder in 2017 was estimated to be 7.1% for adults and 13.3% for adolescents ( 1 ). Data for anxiety disorders are less current, but in 2001–2003, their 12-month prevalence was estimated to be 19.1% in adults, and 2001–2004 data estimated that the lifetime prevalence in adolescents was 31.9% ( 2 , 3 ). Both anxiety and depressive disorders are more prevalent in women, with an approximate 2:1 ratio in women compared with men during women’s reproductive years ( 1 , 2 ).

Across all psychiatric disorders, comorbidity is the rule ( 4 ), which is definitely the case for anxiety and depressive disorders, as well as their symptoms. With respect to major depression, a worldwide survey reported that 45.7% of individuals with lifetime major depressive disorder had a lifetime history of one or more anxiety disorder ( 5 ). These disorders also commonly coexist during the same time frame, as 41.6% of individuals with 12-month major depression also had one or more anxiety disorder over the same 12-month period. From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ). Data from the well-known Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study demonstrate comorbidity at the symptom level, as 53% of the patients with major depression had significant anxiety and were considered to have an anxious depression ( 9 ).

Anxiety and depressive disorders are moderately heritable (approximately 40%), and evidence suggests shared genetic risk across the internalizing disorders ( 10 ). Among internalizing disorders, the highest level of shared genetic risk appears to be between major depressive disorder and generalized anxiety disorder. Neuroticism is a personality trait or temperamental characteristic that is associated with the development of both anxiety and depression, and the genetic risk for developing neuroticism also appears to be shared with that of the internalizing disorders ( 11 ). Common nongenetic risk factors associated with the development of anxiety and depression include earlier life adversity, such as trauma or neglect, as well as parenting style and current stress exposure. At the level of neural circuits, alterations in prefrontal-limbic pathways that mediate emotion regulatory processes are common to anxiety and depressive disorders ( 12 , 13 ). These findings are consistent with meta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety and major depression, in circuits involving emotion regulation ( 13 ), executive function ( 14 ), and cognitive control ( 15 ).

Anxiety disorders and major depression occur during development, with anxiety disorders commonly beginning during preadolescence and early adolescence and major depression tending to emerge during adolescence and early to mid-adulthood ( 16 – 18 ). In relation to the evolution of their comorbidity, studies demonstrate that anxiety disorders generally precede the presentation of major depressive disorder ( 17 ). A European community-based study revealed, beginning at age 15, the developmental relation between comorbid anxiety and major depression by specifically focusing on social phobia (based on DSM-IV criteria) and then asking the question regarding concurrent major depressive disorder ( 18 ). The findings revealed a 19% concurrent comorbidity between these disorders, and in 65% of the cases, social phobia preceded major depressive disorder by at least 2 years. In addition, initial presentation with social phobia was associated with a 5.7-fold increased risk of developing major depressive disorder. These associations between anxiety and depression can be traced back even earlier in life. For example, childhood behavioral inhibition in response to novelty or strangers, or an extreme anxious temperament, is associated with a three- to fourfold increase in the likelihood of developing social anxiety disorder, which in turn is associated with an increased risk to develop major depressive disorder and substance abuse ( 19 ).

It is important to emphasize that the presence of comor‐bid anxiety symptoms and disorders matters in relation to treatment. Across psychiatric disorders, the presence of significant anxiety symptoms generally predicts worse outcomes, and this has been well demonstrated for depression. In the STAR*D study, patients with anxious major depressive disorder were more likely to be severely depressed and to have more suicidal ideation ( 9 ). This is consistent with the study by Kessler and colleagues ( 5 ), in which patients with anxious major depressive disorder, compared with patients with nonanxious major depressive disorder, were found to have more severe role impairment and more suicidal ideation. Data from level 1 of the STAR*D study (citalopram treatment) nicely illustrate the impact of comorbid anxiety symptoms on treatment. Compared with patients with nonanxious major depressive disorder, those 53% of patients with an anxious depression were less likely to remit and also had a greater side effect burden ( 20 ). Other data examining patients with major depressive disorder and comorbid anxiety disorders support the greater difficulty and challenge in treating patients with these comorbidities ( 21 ).

This issue of the Journal presents new findings relevant to the issues discussed above in relation to understanding and treating anxiety and depressive disorders. Drs. Conor Liston and Timothy Spellman, from Weill Cornell Medicine, provide an overview for this issue ( 22 ) that is focused on understanding mechanisms at the neural circuit level that underlie the pathophysiology of depression. Their piece nicely integrates human neuroimaging studies with complementary data from animal models that allow for the manipulation of selective circuits to test hypotheses generated from the human data. Also included in this issue is a review of the data addressing the reemergence of the use of psychedelic drugs in psychiatry, particularly for the treatment of depression, anxiety, and PTSD ( 23 ). This timely piece, authored by Dr. Collin Reiff along with a subgroup from the APA Council of Research, provides the current state of evidence supporting the further exploration of these interventions. Dr. Alan Schatzberg, from Stanford University, contributes an editorial in which he comments on where the field is in relation to clinical trials with psychedelics and to some of the difficulties, such as adequate blinding, in reliably studying the efficacy of these drugs ( 24 ).

In an article by McTeague et al. ( 25 ), the authors use meta-analytic strategies to understand the neural alterations that are related to aberrant emotion processing that are shared across psychiatric disorders. Findings support alterations in the salience, reward, and lateral orbital nonreward networks as common across disorders, including anxiety and depressive disorders. These findings add to the growing body of work that supports the concept that there are common underlying factors across all types of psychopathology that include internalizing, externalizing, and thought disorder dimensions ( 26 ). Dr. Deanna Barch, from Washington University in St. Louis, writes an editorial commenting on these findings and, importantly, discusses criteria that should be met when we consider whether the findings are actually transdiagnostic ( 27 ).

Another article, from Gray and colleagues ( 28 ), addresses whether there is a convergence of findings, specifically in major depression, when examining data from different structural and functional neuroimaging modalities. The authors report that, consistent with what we know about regions involved in emotion processing, the subgenual anterior cingulate cortex, hippocampus, and amygdala were among the regions that showed convergence across multimodal imaging modalities.

In relation to treatment and building on our understanding of neural circuit alterations, Siddiqi et al. ( 29 ) present data suggesting that transcranial magnetic stimulation (TMS) targeting can be linked to symptom-specific treatments. Their findings identify different TMS targets in the left dorsolateral prefrontal cortex that modulate different downstream networks. The modulation of these different networks appears to be associated with a reduction in different types of symptoms. In an editorial, Drs. Sean Nestor and Daniel Blumberger, from the University of Toronto ( 30 ), comment on the novel approach used in this study to link the TMS-related engagement of circuits with symptom improvement. They also provide a perspective on how we can view these and other circuit-based findings in relation to conceptualizing personalized treatment approaches.

Kendler et al. ( 31 ), in this issue, contribute an article that demonstrates the important role of the rearing environment in the risk to develop major depression. Using a unique design from a Swedish sample, the analytic strategy involves comparing outcomes from high-risk full sibships and high-risk half sibships where at least one of the siblings was home reared and one was adopted out of the home. The findings support the importance of the quality of the rearing environment as well as the presence of parental depression in mitigating or enhancing the likelihood of developing major depression. In an accompanying editorial ( 32 ), Dr. Myrna Weissman, from Columbia University, reviews the methods and findings of the Kendler et al. article and also emphasizes the critical significance of the early nurturing environment in relation to general health.

This issue concludes with an intriguing article on anxiety disorders, by Gold and colleagues ( 33 ), that demonstrates neural alterations during extinction recall that differ in children relative to adults. With increasing age, and in relation to fear and safety cues, nonanxious adults demonstrated greater connectivity between the amygdala and the ventromedial prefrontal cortex compared with anxious adults, as the cues were being perceived as safer. In contrast, neural differences between anxious and nonanxious youths were more robust when rating the memory of faces that were associated with threat. Specifically, these differences were observed in the activation of the inferior temporal cortex. In their editorial ( 34 ), Dr. Dylan Gee and Sahana Kribakaran, from Yale University, emphasize the importance of developmental work in relation to understanding anxiety disorders, place these findings into the context of other work, and suggest the possibility that these and other data point to neuroscientifically informed age-specific interventions.

Taken together, the papers in this issue of the Journal present new findings that shed light onto alterations in neural function that underlie major depressive disorder and anxiety disorders. It is important to remember that these disorders are highly comorbid and that their symptoms are frequently not separable. The papers in this issue also provide a developmental perspective emphasizing the importance of early rearing in the risk to develop depression and age-related findings important for understanding threat processing in patients with anxiety disorders. From a treatment perspective, the papers introduce data supporting more selective prefrontal cortical TMS targeting in relation to different symptoms, address the potential and drawbacks for considering the future use of psychedelics in our treatments, and present new ideas supporting age-specific interventions for youths and adults with anxiety disorders.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

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25 McTeague LM, Rosenberg BM, Lopez JW, et al. : Identification of common neural circuit disruptions in emotional processing across psychiatric disorders . Am J Psychiatry 2020 ; 177:411–421 Link ,  Google Scholar

26 Caspi A, Moffitt TE : All for one and one for all: mental disorders in one dimension . Am J Psychiatry 2018 ; 175:831–844 Link ,  Google Scholar

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28 Gray JP, Müller VI, Eickhoff SB, et al. : Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies . Am J Psychiatry 2020 ; 177:422–434 Link ,  Google Scholar

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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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METHODS article

Types of anxiety and depression: theoretical assumptions and development of the anxiety and depression questionnaire.

• 1 Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland
• 2 SWPS University of Social Sciences and Humanities, Warsaw, Poland

The present paper is addressed to (1) the validation of a recently proposed typology of anxiety and depression, and (2) the presentation of a new tool—the Anxiety and Depression Questionnaire (ADQ)—based on this typology. Empirical data collected across two stages—construction and validation—allowed us to offer the final form of the ADQ, designed to measure arousal anxiety, apprehension anxiety, valence depression, anhedonic depression, and mixed types of anxiety and depression. The results support the proposed typology of anxiety and depression and provide evidence that the ADQ is a reliable and valid self-rating measure of affective types, and accordingly its use in scientific research is recommended.

Introduction: Anxiety and Depression as Personality Types

This paper is aimed at presenting the validity of a newly proposed typology of anxiety and depression, formulated within the systemic approach to personality ( Fajkowska, 2013 , 2015 ) which employed General System Theory (e.g., von Bertalanffy, 1968 ) and the self-report instrument that grew within this theory. The article is divided into three sections. In the Introduction section, the theoretical background of this instrument is demonstrated. In the empirical part of the paper, we report the results of our development of the Anxiety and Depression Questionnaire (ADQ) across construction (Study 1) and validation (Study 2) stages. Finally, in the Discussion section we advocate the theoretical and applied value of this theory and the usefulness of the ADQ in research and practice.

An appropriate point of departure might be the question of why we need another theory and questionnaire to describe, explain, and differentiate between anxiety and depression.

First, the presented theory allows for examining anxiety and depression in a general, not only clinical population. It seems to be very important in light of the latest meta-analysis (e.g., Ayuso-Mateos et al., 2010 ). Among others points, it demonstrated that the consequences of anxiety/depression for the general well-being in non-clinical populations when the main/full range of clinical criteria of anxiety/depression are not identified (e.g., low intensity of symptoms, low number of symptoms) are comparable with clinical populations. This implies the significance of analyzing the mechanisms of non-clinical forms of anxiety/depression and assessing them in the self-report instruments. As a review of the appropriate literature suggests, there are not many approaches and questionnaires that fulfill this need (see Fajkowska, 2013 ).

Second, the proposed theory represents a belief that non-clinical forms of anxiety/depression can be seen as relatively stable personality characteristics and reflects the newest results of the studies on cognitive and affective mechanisms in anxiety/depression (e.g., Eysenck and Fajkowska, 2017 for a review). Therefore, the questionnaire developed within it permits more precise hypotheses related to the origin of anxiety/depression to be formulated, supports the understanding of different consequences of functioning in these phenomena, and allows them to be evaluated on the basis of their maladaptive mechanisms (e.g., attentional, cf. Arditte and Joormann, 2014 ).

Third, the central finding in previous studies of anxiety and depression is the high degree of comorbidity that occurs between them (e.g., Gorman, 1996 ). Possible explanations of this co-occurrence relate to the poor discriminant validity of measures (e.g., Fox, 2008 ) and the fact that both phenomena are associated with negative affect (e.g., Watson, 2000 ), stressful life events ( Naragon-Gainey and Watson, 2011 ), and impaired cognitive processes or a common biological/genetic diathesis ( Watson and Kendall, 1989 ; Fox, 2008 ).

However, despite a set of nonspecific features, anxiety and depression are clearly not identical phenomena. The theory demonstrated here advocates that the differences between them might be best viewed through their heterogeneous and multilayered nature, adaptive functions, and relations with regulatory processes, positive affect, and motivation or complex cognitive processes (cf. Fajkowska, 2013 ). More precisely, differentiation should be improved by reducing the importance of overlapping features and by giving greater weight to distinctive aspects of these affective phenomena.

To meet all these points, Fajkowska (2013 , 2015) suggests grouping anxiety and depression based on two criteria:

a) The specificity of their structural composition; anxiety and depression are proposed to be seen as personality types embodying groups of traits (cf. Eysenck, 1998 ). Generally, both personality trait and type are defined as a hierarchical system, organized into three levels: complex inner mechanisms, components/structures, and behavioral markers (see Figure 1A ). In this sense trait and type are equivalent, where types are structurally higher-order systems than traits, embracing a larger grouping of internal mechanisms and components than traits. Thus, the understanding of anxiety and depression as structurally complex personality types distinguishes this approach from the theory of Spielberger (1983) , where anxiety is a homogeneous personality state or trait, and from the cognitive theories of depression (e.g., Beck, 1976 ; Bower, 1981 ; Teasdale, 1983 ) postulating its processual nature. In this approach a matched set of specific structural components and underlying processes are involved in building a particular type of anxiety or depression.

b) The dominant functions (reactive or regulative) they play in stimulation processing (a transformation of arousal and activation, which arises as an effect of flowing stimulation, e.g., sensory, emotional, cognitive, leading to changes within different systems of the organism, e.g., motor, cognitive, or motivational). The dominant functions of a trait or type in stimulation processing might be considered as the emergent properties located between the level of structures and behavioral markers (see Figure 1A ). In other words, these functions are rooted in structures and can be identified through overt reactions and behaviors (cf. Fajkowska, 2013 ). Traits/types with a reactive dominant (e.g., anxiety, Spielberger, 1983 ) inform about individual differences in the reception of flowing stimulation; they denote a high sensitivity or vigilance (e.g., sensory) to stimuli and rather automatic and immediate readiness to activity (reaction, behavior), and relate to energy expenditure (in a particular time range). For instance, the reactive function in anxiety can be identified through its associations with hypervigilance to threatening material or social evaluation (e.g., Eysenck, 2006 ). Traits/types with a regulative dominant indicate individual differences in energy expenditure (in a particular range of time) and more strategic than automatic/immediate directing and monitoring of the flowing stimulation, adequately to the organism's capacities for stimulation processing. For example, the regulative function in openness ( Costa and McCrae, 1992 ) can be identified through its associations with creative and innovative strategies used to pursue one's goals ( DeYoung, 2010 ). Additionally, the structural complexity of traits/types influences their controlling functions, which implies that different controlling functions might coexist in one trait (e.g., reactive-regulative in neuroticism, Eysenck, 1998 ). Thus, here anxiety and depression contribute to stimulation processing in that they relate to arousal, activation, and activity in different neurobiological and physiological systems (cf. Robinson and Compton, 2006 ). Therefore, it is further suggested that anxiety and depression can be differentiated according to the different functions they reveal in stimulation processing.

Figure 1 . The organization of (A) personality trait/type, (B) anxiety types, (C) depression types according to the three-level hierarchy (cf. Fajkowska, 2013 , 2015 ).

Although Fajkowska (2013) acknowledges that to some extent her categorization capitalizes on prior (neuro-)psychological models of emotion (cf. Heller, 1993a , b ; Watson, 2000 ), these approaches seem to be rather categorical, while she suggests a dimensional typology (e.g., Eysenck, 1970 ; Strelau, 2014 p. 44–45). It enables the shared or separate structural components to be captured and to explain overlapping or distinctive functions in stimulation processing among types of anxiety and depression. Moreover, the current classification of anxiety and depression offered by the DSM-5 ( American Psychiatric Association, 2013 ), although more dimensional than the previous DSM, seems not to be very supportive in solving some of the cardinal theoretical concerns in this area, e.g., specificity of the structure of affect or specificity of attentional biases in both anxiety and depression. Thus, a promising avenue to provide possible solutions to these concerns might be, as suggested here, an alternative grouping.

Types of Anxiety

Structural composition.

The starting point for the identification of anxiety types is to point to relevant processes and mechanisms (the lowest level) that contribute to structures of anxiety types (the middle level), and to associate them with the relevant behavioral markers (the highest level; see Figure 1B ).

• Complex inner mechanisms —With respect to the appropriate literature, Fajkowska (2013 , 2015) assumes that somatic and cognitive processes are key for anxiety structuralization. The repetitive interactions among cognitive mechanisms (e.g., connected with attentional and working memory systems) and among somatic mechanisms (related to affective and motivational systems) lead to more integrated cognitive and somatic entities, from which emerge two essential elements that compose anxiety types: somatic-related arousal and cognitive-related apprehension (see Figure 1B ).

• Components/structures —Thus, by interacting with each other, different levels of arousal and apprehension produce different types of anxiety at the level of structures (see Figure 1B ). When the proportion between the degree of apprehension and degree of arousal is in favor of arousal, it suggests the Arousal Type of anxiety . When it is in favor of apprehension it produces the Apprehension Type of anxiety , and relatively equal (but high) levels of apprehension and arousal build the Mixed Type of anxiety [in previous publications ( Fajkowska, 2013 , 2015 ) used a misleading term, Balanced Type of anxiety, which suggested a positive type, but in fact it is composed of two disruptive elements].

Anxious arousal is described (cf. Watson et al., 1995 ; Watson, 2000 ) as being distinguished by symptoms of physiological hyperarousal and somatic tension, while anxious apprehension is primarily characterized by worry and verbal rumination, typically about future events ( Barlow, 1991 ; Heller, 1993a , b ; Heller et al., 1997 ; Heller and Nitschke, 1998 ). However, the relation between autonomic reactivity and anxious apprehension is not clear. Some studies report the connection of worrisome thoughts with elevated autonomic responsiveness (e.g., Nitschke et al., 1999 ), while others with autonomic rigidity (e.g., Thayer et al., 1996 ). The latter one seems to be more convincing, as worrisome thoughts are seen as a strategy for avoiding emotional arousal.

Panic attacks, phobias, high-stress states, and state anxiety as defined by self-report, behavioral, or physiological response systems would be covered by the Arousal Type (cf. Heller and Nitschke, 1998 ; Watson, 2000 ; American Psychiatric Association, 2013 ). It seems probable that the Apprehension Type would be characteristic of generalized anxiety states (GAD) and trait anxiety as identified by self-reports of anxious apprehension and worry on various questionnaires (cf. Heller and Nitschke, 1998 ; American Psychiatric Association, 2013 ). Theoretically, the Mixed Type might be identified among all the categories of anxiety mentioned above.

• Behavioral markers —The dominance of a particular component (arousal or apprehension) in a particular type of anxiety specifically determines the manner of stimulation processing, as well as patterns of response to stimulation across different response systems. More precisely, with reference to a review of the literature, it may be concluded that the typical patterns of attentional stimulation processing (that is reactions, behavioral acts) in the Arousal Type of anxiety are associated with (a) increased “early” attentional vigilance to threat (usually in clinical anxiety) and “later,” but unconscious, attentional avoidance of threat (usually in the non-patient group) (e.g., Calvo and Eysenck, 2000 ; Fox et al., 2002 ; Mathews and MacLeod, 2002 ; Wilson and MacLeod, 2003 ; Hock and Krohne, 2004 ; Heim-Dreger et al., 2006 ; Fisher et al., 2010 ); (b) elevated autonomic reactivity in the presence of threat (e.g., Sapolsky, 1992 ; Nitschke et al., 1999 ; Lovallo and Gerin, 2003 ; Hock and Krohne, 2004 ; Applehans and Luecken, 2006 ; Heim-Dreger et al., 2006 ; Fisher et al., 2010 ); and (c) right-hemisphere involvement in threatening stimuli processing (e.g., Heller et al., 1997 ; Compton et al., 2003 ; Engels et al., 2007 ; Mathersul et al., 2008 ). Accordingly, the typical patterns of stimulation processing in the Apprehension Type of anxiety are associated with (a) reduced attentional control and related impairment to the effectiveness of stimulation processing and avoidance of threatening stimuli (in clinical and nonclinical groups and trait anxiety; e.g., Laguna et al., 2004 ) (b) reduction in autonomic reactivity (e.g., Hoehn Saric et al., 1989 ; Borkovec and Ray, 1998 ) (c) impairment/inhibition of emotional processing, both on an attentional and physiological level (e.g., Stöber, 1998 ) and (d) left-hemisphere involvement in stimulation processing (cf. Tucker et al., 1978 ; Baxter et al., 1987 ; Swedo et al., 1989 ; Wu et al., 1991 ; Heller and Nitschke, 1997 , 1998 ; Wagner, 1999 ; Fletcher and Henson, 2001 ; Nitschke and Heller, 2002 ; Hofmann et al., 2005 ).

Dominant Functions

Thus, recognizing the presented above behavioral markers allows us to establish the dominant controlling function of each type: reactive rather than regulative in arousal anxiety (identified through more automatic stimulation processing related to attentional vigilance-avoidance, and also through elevated autonomic reactivity) and regulative rather than reactive in apprehension anxiety (identified through more strategic but ineffective stimulation processing related to reduced attentional control). It is assumed that the Mixed Type of anxiety is a functionally balanced type that represents a reactive-regulative function in stimulation processing.

Types of Depression

With reference to the identification of depression types (the middle level), the crucial mechanisms contributing to the formation of their structure are proposed (the lowest level) along with their related behavioral markers (the highest level; see Figure 1C ).

• Complex inner mechanisms —In congruence with the relevant literature, Fajkowska (2013) proposed that cognitive and emotional-motivational processes are crucial in the formation of the structure of depression subtypes. The recurring interactions among cognitive mechanisms (connected with valence undersensitivity in attentional systems, e.g., Davidson et al., 1995 ), emotional mechanisms (linked with negative emotional experience, e.g., Beck et al., 1979 ) and the repetitive interactions among motivational mechanisms (associated with impaired control, anhedonia, reduction in response to reward-related stimuli, and a lack of positive reinforcement, e.g., Sloan et al., 2001 ), coupled with a deficit in approach behavior (e.g., Henriques and Davidson, 2000 ) lead to more integrated entities, from which in turn emerges more cognitive-related valence insensitivity and more emotion- and motivation-related anhedonia (see Figure 1C ).

• Components/structures —Thus, dynamic interactions between the higher-ordered components—anhedonia and valence insensitivity—produce three types of depression: the Valence Type of depression , where the degree of valence insensitivity dominates the degree of anhedonia; the Anhedonic Type of depression , where the degree of valence insensitivity is dominated by the degree of anhedonia; and the Mixed Type of depression (previously named Balanced Type of depression, cf. Fajkowska, 2013 ), with a structure resting on a relative balance between (high levels of) the two components.

Thus, the valence insensitivity to stimulation is typical for non-melancholic forms of depression, while anhedonia is the key feature of melancholic depression ( Heller and Nitschke, 1998 ; Watson, 2000 ), i.e., the inability to experience pleasure in all activities and a lack of responsiveness to pleasurable stimulation. However, melancholic and non-melancholic depression share many symptoms related to anhedonia, such as sadness, indecisiveness, feelings of guilt, and valence-related insensitivity such as inaccuracy in emotion recognition or inability to differentiate emotional states ( Fajkowska, 2013 ).

All these types might be present in both nonclinical (depressed mood) and clinical forms of depression. The Valence Type embraces non-melancholic subtypes of depression, while the Anhedonic Type covers the melancholic subtypes (e.g., MDD) suggested by the DSM-5 ( American Psychiatric Association, 2013 ). The Valence Type is treated here as an exogenous and state-like type, primarily connected with a biased cognitive system on account of the content or valence of stimulation. It is also connected with very high negative affectivity (see Fajkowska, 2013 for a review). The Anhedonic Type is relevant to an endogenous and trait-like type (cf. Rubino et al., 2009 ) and is primarily connected with impaired control in stimulation processing, motivational deficits, very high negative affect and very low positive affect ( Watson, 2000 ). The Mixed Type of depression is a matter for future research.

• Behavioral markers —A review of the literature allows for the conclusion that specific patterns of attentional stimulation processing in the Valence Type depression are related to (a) attentional avoidance reflected in valence insensitivity to emotional and social material (e.g., Gotlib et al., 2000 ; Watson, 2000 ; Fox, 2008 ), and (b) increased right-hemisphere activity in stimulation processing (e.g., Heller and Nitschke, 1997 ; Parker et al., 1999 ; Nitschke et al., 2001 ; Sato et al., 2001 ; Tembler and Schüßler, 2009 ; Hecht, 2010 ). On the basis of both theoretical and empirical evidence, it turns out that specific patterns of stimulation processing in the Anhedonic Type of depression are related to (a) impaired attentional control, or sustained attention over positive as well as negative material (e.g., Bargh et al., 1988 ; Gotlib and MacLeod, 1997 ; Westra and Kuiper, 1997 ; Egeland et al., 2003 ; Marszał-Wiśniewska and Fajkowska-Stanik, 2005 ; Withall et al., 2009 ; Bourke et al., 2010 ), and (b) decreased left-hemisphere activity in stimulation processing (e.g., Bench et al., 1992 ; Heller, 1993a ; Bruder, 1995 ; Hecht, 2010 ; Schock et al., 2011 ).

Again, the identification of the above presented behavioral markers allowed the dominant controlling functions of each subtype to be established: reactive rather than regulative in valence depression (identified through more automatic stimulation processing related to attentional avoidance of stimuli), and regulative rather than reactive in anhedonic depression (identified through more strategic but ineffective stimulation processing related to reduced attentional control and inability to sustain attention over stimulation). In the Mixed Type of depression the mixed, reactive-regulative function over stimulation processing is postulated.

Operationalization of the Anxiety and Depression Types

The evidence discussed here provides important information that has contributed to the development of precise definitions of anxiety types according to their structural components and functions in controlling stimulation (see Figure 2A ):

Figure 2 . Operationalization of (A) anxiety types, (B) depression types (cf. Fajkowska, 2013 , 2015 ).

Reactive Arousal Type of anxiety is composed of:

• Somatic Reactivity —Elevated autonomic reactivity, psychophysiological hyperarousal and somatic tension (e.g., trembling hands, palpitations, sweating, gastric problems, shortening breath) in the presence of actual or anticipated threat or negative stimulation.

• Panic/Phobia —Presence of panic symptoms, distress (e.g., related to fear of heights or new situations or objects) and phobias (e.g., social).

• Attentional Vigilance/Avoidance —“Early” attentional vigilance toward threat (very fast identification of threat or negative social signals appearing in the attentional field), usually in clinical forms of anxiety, and “later” attentional avoidance of this threat (rather instinctive than intentional withdrawal from dangerous and threatening situations present in the attentional field for some time), usually in the non-patient groups.

Regulative Apprehension Type of anxiety is composed of:

• Worrisome thoughts —Referring to physical, emotional, or symbolic threats to the self; they relate to social evaluation of one's behavior or competences; sometimes their content may include general world problems;

• Somatic Reactivity —Elevated autonomic reactivity in the presence of threat, or because of worrisome thoughts; it goes with reduced capacities of emotional processing on autonomic and somatic levels.

• Attentional control —Reduced attentional control, that relates to difficulties in attentional (a) shifting, (b) focusing, and (c) disengagement from negative experiences; (d) it undergoes distracting thoughts, and (e) reveals itself in impaired inhibition in processing of negative emotional material, connected with failure or negative experiences or events;

Mixed Type of anxiety represents balanced apprehension and arousal elements and balanced reactive and regulative functions of stimulation processing. Speculatively, it covers specific patterns of stimulation processing of both primary types (arousal and apprehension) and their activation might be situation-dependent.

Figure 2B summarizes definitions of the three types of depression according to their structural components and the functions they play in stimulation processing.

Reactive Valence Type of depression goes with:

• Negative Affect —Manifested in increased level of anxiety, tension, hostility, anger, sadness, self-sensitivity, and social avoidance.

• Attentional Avoidance —Identified through (a) valence insensitivity to emotional and social material, i.e., delayed or constricted attention allocation toward emotional material, inaccurate recognition of emotional material regardless of its (positive or negative) content and (b) insensitivity to social material, including emotions appearing in the social context.

Regulative Anhedonic Type of depression includes:

• Emotional-Motivational Deficits —Revealed in (a) the inability to experience pleasure and decreased reactivity to pleasurable things and events, (b) difficulties in goal achievement and loss of interest in pursuing goal-directed activities, and (c) failure in delivering sufficient pleasure or reward following approach behaviors.

• Positive Affect —Extremely low positive affect; very low level of positive emotions, e.g., self-confidence, happiness, hope, or satisfaction.

• Negative Affect —Extremely high negative affect; very high level of negative emotions, e.g., sadness, guilt, shame, sense of loss, disappointment, anxiety, and loneliness.

• Attentional Control —Impaired attentional control, indicating (a) decrement in sustained vigilance to emotional material, (b) slower and inaccurate response to emotional material (e.g., slower reactions to positive material and inaccurate recognition of negative material), (c) inability to sustain effort in processing emotional material (regardless of its valence), and (d) difficulties in attentional focusing.

Mixed Type of depression is defined through the relative balance of the valence and anhedonia elements, and balanced reactive and regulative functions of stimulation processing. It most probably comprises specific patterns of stimulation processing of both primary (valence or anhedonic) types, and their activation might depend on the specific situation.

Even though some of the structural components overlap across various affective types, they do not always mean the same. Somatic reactivity is a component very specific for anxiety (cf. Watson, 2000 ), thus it appears in both types of anxiety. However, it has different causes and expressions. In arousal anxiety it is a primary element, while in the apprehension type it is not a crucial one, it is caused by worrisome thoughts and is rather expressed as reduced somatic reactivity. Next, attentional control is present in both regulative types, i.e., apprehension anxiety and anhedonic depression. In apprehension anxiety it appears as an effect of worrisome thoughts and primarily indicates impaired inhibition functioning, while in anhedonic depression it appears as an effect of emotional-motivational deficits and negatively influences prolonged and sustained attention. Finally, negative affect is a part of the structure in both depression types. Nonetheless, hostility and anger are typical for valence depression, while for anhedonic depression it is guilt and shame.

With reference to dominant functions in controlling stimulation processing, one should expect similarities in patterns of stimulation processing (e.g., in attentional processing) across types, i.e., reactive types, regulative types and functionally balanced types, and differences within types, i.e., between reactive and regulative types (see Figure 2 ).

Anxiety and Depression Questionnaire (ADQ)

A psychometric study was conducted with the aims of revising the postulated anxiety and depression types ( Fajkowska, 2013 , 2015 ) and of constructing an instrument, the Anxiety and Depression Questionnaire (ADQ), which corresponds to the six affective types. Consequently, we proposed four scales of the ADQ to directly measure arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD). However, in line with the theory ( Fajkowska, 2013 , 2015 ), the mixed types of anxiety and depression should be regarded, respectively, as the ratio of arousal anxiety to apprehension anxiety and valence depression to anhedonic depression, and they have the status of secondary types (cf. balance of the nervous processes as a secondary trait/scale, Strelau et al., 1999 ).

General Plan of Research and Analysis

The elaboration and development of the ADQ has been divided into construction (Study 1) and validation (Study 2) stages.

The aim of Study 1 was the generation of items and delivering psychometric characteristics of the preliminary version of the ADQ. Thus, for four scales of the ADQ we evaluated (a) the discriminatory power of items to answer the question to what degree the particular test positions differentiate among individuals; (b) confirmatory factor analysis to test the theoretically suggested structure of the particular ADQ scales; (c) intercorrelations of subscales within the scales of the ADQ to check if theoretically predicted relations among them are supported by empirical data; and (d) internal consistency for all scales of the ADQ to measure the extent to which all of the items of a certain scale measure the same latent variable. With these data we made appropriate corrections to propose the final form of the ADQ.

The Study 2 was organized around demonstration of the quantitative description of items and scales of the ADQ, reliability and validity of the ADQ, and verification of the assumed position of the affective types among other personality characteristics.

Construction Stage—Study 1

Extensive research aimed at constructing the ADQ consisted of the generation of items, linguistic evaluation of items, evaluation of content validity, on-line administration of the questionnaire to respondents, and elaboration of the experimental version of the questionnaire based on the results from testing of discriminatory power of items, confirmatory factor analysis, intercorrelational analysis of subscales, and internal consistency.

Materials and Methods

Generation of the adq items.

The first development stage involved the generation of items (experts, n = 4; Ph.D. students of psychology, n = 4) that will form the four scales of the ADQ. This generation was guided by methodological requirements underlying the construction of personality inventories (cf. Zawadzki, 2006 ) and operational definitions of each affective type. Additionally, about five percent of the total number of items was taken, mostly in slightly modified versions, from other inventories [e.g., Attentional Control Scale (ACS), ( Fajkowska and Derryberry, 2010 ), Mood and Anxiety Symptom Questionnaire (MASQ), ( Watson, 2000 ), The Penn State Worry Questionnaire, ( Meyer et al., 1990 )].

The linguistic analysis of items, as well as the assessment of the content validity (sorting items into types and subscales, experts, n = 4), led to the development of an item pool for each scale. Arousal anxiety consists of 64 items grouped into subscales of Somatic Reactivity (SR, 35 items), Panic/Phobia (PP, 18), and Attentional Vigilance/Avoidance (AVA, 11), while apprehension anxiety has 89 items in subscales of Worrisome Thoughts (WT, 22), Attentional Control (AC, 44), Attentional Avoidance (AA, 13), and Somatic Reactivity (rSR, 10; “r” means that items indicate reduced somatic activity). It should be noted that although Attentional Avoidance is not mentioned in the definition of apprehension anxiety, we introduced this scale experimentally as some studies report significant connections between attentional avoidance and apprehension ( Laguna et al., 2004 ).

Valence depression consists of 71 items clustered around Negative Affect (NA, 50) and Attentional Avoidance (AA, 21), and for anhedonic depression there are 139 statements in four subscales of Emotional-Motivational Deficits (EMD, 87), Positive Affect (PA, 12), Negative Affect (NA, 26), and Attentional Control (AC, 14). Attention was paid during all stages of item generation to keep a balanced keying within each of the scales.

Participants

Since the theory assumes that affective types are personality traits rather than purely clinical disorders, the study was conducted on two general, non-clinical samples. Both samples matched the demographic structure of the Polish population.

The first sample ( N = 1,109) consisted of 546 males (49.2%) and 563 females (50.8%) with a mean age of 39.19 ( SD = 13.59; range 18–65 years). Participants filled out the two scales of the ADQ measuring anxiety types: ADQ-ArA and ADQ-ApA.

39.7% held university degrees, 36.6% finished high school, 15.9% vocational school, and 7.8% elementary school. The participants were of various professional and educational backgrounds, including university students, high-school students, working people, white-collar workers, part-time workers, unemployed, and pensioners. 49.3% of the participants came from villages and small towns, 20% from cities, and 30.7% from big cities and metropolises. 26.1% reported that they had experienced anxiety disorders in the past, and 15.3% suffered from anxiety at the moment of the study. Moreover, 10.1% of respondents benefited from psychological and psychiatric help, and 3.4% were hospitalized because of severe anxiety. The respondents specified different phobias, panic attacks, separation anxiety, and anxiety coexisting with other disorders and states (e.g., depression, traumatic experiences and addictions, reaction to rape, violence, and unsuccessful social and family relations).

The second sample contained 1,086 participants (549 [50.6%] females and 537 [49.4%] males) who were on average 39.01 years old ( SD = 13.33; range 18–65 years). They completed the ADQ-VD and the ADQ-AD scales, assessing depression types.

36.6% of the participants completed university education, 36.1% finished high school, 20.3% vocational school, and the remaining 7% elementary school. They represented different professions and schools, including university students, high-school students, working people, white-collar workers, part-time workers, and pensioners. 48.8% of the sample represented inhabitants of villages and small towns, 20.4% of cities, and 30.8% of big cities and metropolises. 22.9% of individuals acknowledged suffering from depression in the past and 12% in the present. In addition, 12.3% received help from psychological and psychiatric services, and 4.5% underwent hospitalization because of depression. They reported reactive depression (e.g., because of loss of a close person or job, difficult family relationships), bipolar depression, major depression (with dominating sadness, lack of values in life, low self-esteem, suicidal thoughts) and depression concomitant with other disorders (e.g., alcoholism, psychosis, borderline personality).

This study was approved by the ethics committee of the Institute of Psychology, Polish Academy of Sciences. Participants provided their written informed consent before the on-line procedure was activated. Participants were recruited from an on-line research panel and every person who completed the full procedure on-line received points that were exchangeable for rewards. The order of the questionnaires was randomized across subjects. Altogether they contained 153 (anxiety questionnaires) and 210 (depression questionnaires) agree-disagree items that allow the assessment of arousal anxiety and apprehension anxiety in the first sample, and valence and anhedonic depression in the second sample.

All of the items included in the arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) scales are summed to score (1 point per diagnostic item Agree/Disagree), though extra calculations are required for the AC subscales of apprehension anxiety (ADQ-ApA) and for the anhedonic depression (ADQ-AD) scales. To calculate the scores on these scales, the obtained score should be subtracted from the maximum possible score in the given scale, because we are interested in evaluating decreased attentional control (while the items measure the strength of attentional control).

Statistical Analysis

We performed analyses on the discriminatory power of items (Youle's Phi coefficients; ϕ, Phi), confirmatory factor analysis (CFA), intercorrelations of subscales (Pearson's r ) and internal consistency (Cronbach's α coefficients) in the first and second sample separately.

Discriminatory Power of Items

Discriminatory power of items was used as the criterion for excluding the preliminarily selected items from the ADQ. For all scales of the ADQ, the number of items that was kept depended on their Youle's Phi coefficients (ϕ,). We decided to apply the value ϕ ≥ 0.30 as it is a correlation between the item score and the overall scale score reduced by this item, which is usually lower than the correlation between item and the total scale score, and values 0.30 and above indicate good and very good discrimination (see Drwal and Brzozowski, 1995 ). The number of remaining items in each subscale of each ADQ scale was sufficient for further statistical analysis (ArA = 42, ApA = 62, VD = 36, AD = 69).

Confirmatory Factor Analysis

Confirmatory factor analysis (CFA) was used to verify the factor structure of the set of introduced variables ( Kline, 2005 ). More precisely, we tested the theoretically suggested structures of the affective types. We expected that the models including the number of factors derived from the theory will show a better fit than other (e.g., one-factor) models. The analyses were performed in Mplus ( Muthén and Muthén, 1998 ), which enables models of binary data to be built (see Górniak, 2000 ; Hox, 2002 ). Because χ 2 values for the models fit across all scales of the ADQ were significant (suggesting a lack of fit between the hypothesized models and the data) and due to the sensitivity of χ 2 in large samples, other fit indices were assessed and reported, namely: Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA); Tucker Lewis Index (TLI) ( Kline, 2005 ).

In arousal anxiety (ADQ-ArA) we compared a one-factor solution with a three-factor solution. The fit indexes reflected the improvement in fit of the three-factor model [RMSEA = 0.051; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.93; χ ( 816 ,   N   =   1 , 109 ) 2 = 3211.13, p < 0.001] over the alternative one [RMSEA = 0.054; CFI = 0.91; TLI = 0.91; χ 2 /df = 4.24, χ ( 819 ,   N   =   1 , 109 ) 2 = 3474.33, p < 0.001]. All items loaded significantly onto their respective factors (loadings ranging from 0.66 to 0.84 on the SR subscale, from 0.43 to 0.87 on the PP subscale, and between 0.29 and 0.86 on the AVA subscale). None of the test positions were eliminated from further analysis.

To assess the factor structure of the ADQ-ApA, the fit of one-factor and four-factor models was examined. As predicted, the model fit for the four factors [RMSEA = 0.055; CFI = 0.87; TLI = 0.87; χ 2 /df = 4.33; χ ( 1 , 823 ,   N   =   1 , 109 ) 2 = 7908.17, p < 0.001] was better than for one factor [RMSEA = 0.057; CFI = 0.86; TLI = 0.85; χ 2 /df = 4.56; χ ( 1 , 829 ,   N   =   1 , 109 ) 2 = 8354.69, p < 0.001]. Given that the four-factor model did not reach the fit parameters (CFI, TLI) over 0.90, items with the lowest factor loadings were removed. Indeed, the fit indexes improved [RMSEA = 0.053; CFI = 0.92; TLI = 0.92; χ 2 /df = 4.16; χ ( 1 , 420 ,   N   =   1 , 109 ) 2 = 5918.256, p < 0.001]; however, the left items were not differentiated in their meaning as the factor analysis favors items similar in their content. Thus, for the sake of better psychological and theoretical rationality of items, we decided to keep all of them for subsequent elaboration of the ADQ-ApA. As a result, factor loadings for the WT items ranged from 0.49 to 0.89, for the AC varied from 0.36 to 0.85, for the AA extended from 0.43 to 0.86, and for the rSR oscillated from 0.47 to 0.87.

In case of the ADQ-VD we tested one-factor and two-factor models. Contrasting with the first model, the latter one had a good fit [respectively, RMSEA = 0.071; CFI = 0.88; TLI = 0.87; χ 2 /df = 6.46; χ ( 816 ,   N   =   1 , 109 ) 2 = 3211.133, p < 0.001; and RMSEA = 0.057; CFI = 0.92; TLI = 0.92; χ 2 /df = 4.56; χ ( 594 ,   N   =   1 , 086 ) 2 = 3838.70, p < 0.001]. The lowest factor loading was 0.58 and the highest 0.86 on the NA factor, while on the AA factor we had a range of factor loadings from 0.59 to 0.84.

The hypothetical model of the structure of the ADQ-AD was grounded on four factors, but we examined three competing models of one-factor, three-factors (EMD, NA—boosted by the items of PA, treated as reversed, and of AC), and four-factors (EMD, NA, PA, and AC). The findings suggested that the four-factor solution was the best one [RMSEA = 0.049; CFI = 0.93; TLI = 0.93; χ 2 /df = 3.63; χ ( 2 , 271 ,   N   =   1 , 086 ) 2 = 8254.56, p < 0.001] compared to the one- and three-factor models [respectively, RMSEA = 0.051; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.82; χ ( 2 , 277 ,   N   =   1 , 086 ) 2 = 8717.44, p < 0.001; and RMSEA = 0.050; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.71; χ ( 2 , 274 ,   N   =   1 , 086 ) 2 = 8441.85, p < 0.001]. The final assessment concerned the factor loadings. For the EMD they ranged from 0.64 to 0.89, for the NA from 0.74 to 0.93, for the PA from 0.63 to 0.93, and for the AC from 0.32 to 0.91. Two items of the AC subscale with the lowest factor loadings were kept after linguistic correction.

Intercorrelations of Subscales

With reference to the relevant data ( Fajkowska, 2013 , for a review see: Watson, 2000 ), we expected positive correlations (Pearson's r ) among the ADQ-ArA subscales. Indeed, the analyses revealed a positive relationship between the SR subscale and the PP subscale (0.32, p < 0.01), the SR and the AVA subscales (0.28, p < 0.01), and the PP and the AVA subscales (0.18, p < 0.01).

Supported by the neuropsychological models of emotions (e.g., Heller, 1993a , b ; Heller and Nitschke, 1998 ; for a review see: Fajkowska, 2013 ) in the case of the ADQ-ApA, we predicted (a) positive relations among subscales WT, AA, and rSR, and (b) negative relations between the AC subscale and WT, AA, and rSR subscales. The obtained results (Pearson's r ) confirmed these speculations to some extent. As predicted, the WT subscale correlated positively with the AA subscale (0.23, p < 0.01) and negatively with the AC subscale (−0.44, p < 0.01). However, contrary to expectations rSR correlated negatively with the WT and AA subscales (−0.44, p < 0.01 and −0.23, p < 0.01, respectively). Many psychophysiological studies reveal that anxious apprehension, unlike other anxious states, are not associated with a greater response of the autonomic system but rather with autonomic rigidity (e.g., Hoehn Saric et al., 1989 ; Thayer et al., 1996 ). However, in the long-term perspective it was observed that in addition to worry, physical symptoms and elevated physiological arousal often accompany anxious apprehension (e.g., Nitschke et al., 1999 ; Laguna et al., 2004 ). These outcomes are in line with our findings. Thus, the elevated autonomic responsiveness relates to decreased attentional control (positive correlation of rSR with AC, 0.32, p < 0.01).

With regards to the appropriate data presented in the literature (see Fajkowska, 2013 for a review), we anticipated a positive but weak correlation between subscales of the ADQ-VD, i.e., between the NA and AA subscale. The findings (Pearson's r ) are in accordance with predictions (0.35, p < 0.01). The attentional insensitivity (or avoidance) to valence of emotional and social material is connected with negative affectivity, which is typical for e.g., anxiety or non-melancholic types of depression (see Fajkowska, 2013 ).

Turning to the relations among subscales of the ADQ-AD, the results of other studies suggest that we should expect positive and moderate associations between EMD and NA, as well as AC and PA, and negative ones between EMD, AC, and PA (see Fajkowska, 2013 ), and non-significant relations between NA and PA, defined here as affective traits (see Watson, 2000 ). The collected data (Pearson's r ) partially confirmed these expectations. It was found that negative affect relates negatively to positive affect (−0.59, p < 0.01). The bipolar relation between NA and PA reflected in this study might be explained by the fact that both affects were explored with items representing very intensive negative and positive states. In other words, a high or very high level of NA implies a low or very low level of PA, and vice versa ( Watson and Tellegen, 1985 ; Watson, 2000 ).

The data revealed a positive relation between EMD and NA (0.71, p < 0.01) and a negative relation between EMD and PA (−0.65, p < 0.01), which Watson (2000) also documented in his research. Motivational deficits (among others understood as the loss of appetitive behaviors and interest in pursuing goal-directed activities) relate to the very specific for this type of depression (a) marked reduction in experiencing pleasure and extremely low PA, and (b) high NA, nonspecific for it ( Watson, 2000 ).

In addition, several studies have shown that effective attentional control is subjected to positive mood, while negative affect has an adverse effect on it (see Fajkowska, 2013 for a review), which is congruent with the results of our study (0.65, p < 0.01 and −0.63, p < 0.01, correlations between AC and PA, AC and NA, respectively). The association between EMD and AC should be negative, and it was (−0.63, p < 0.01). There is a conflict between the intentional and effortful, effective attentional control (cf. Fajkowska and Derryberry, 2010 ) and emotional-motivational deficits—difficulties in pursuing goals and tasks, putting effort into realizing them, and troubles in undertaking and initiating activities.

Internal Consistency

The results showed high Cronbach's alphas for all scales of the ADQ (42-item ADQ-ArA = 0.93; 62-item ADQ-ApA = 0.91; 36-item ADQ-VD = 0.93; 69-item ADQ-AD = 0.82). High and moderate internal consistency was also observed across all subscales of each ADQ scale, namely: arousal anxiety (ADQ-ArA: 19-item SR = 0.91; 15-item PP = 0.86; 8-item AVA = 0.53), apprehension anxiety (ADQ-ApA: 16-item WT = 0.90; 29-item AC = 0.91; 8-item AA = 0.53; 9-item rSR = 0.79), valence depression (ADQ-VD: 21-item NA = 0.92; 15-item AA = 0.87), and anhedonic depression (ADQ-AD: 33-item EMD = 0.95; 12-item PA = 0.91; 12-item NA = 0.92; 12-item AC = 0.76). The two shortest subscales—AVA from the ADQ-ArA and AA from the ADQ-ApA—showed the lowest internal consistency.

Final Remarks

Based on the results from the analysis of discriminatory power of items and from the CFA, we proposed an experimental version of the ADQ. However, a few final corrections were made. Some items were excluded (e.g., with the lowest factor loadings), some linguistically corrected, and some moved to different subscales, and the subscale of AA was removed from the ADQ-ApA due to having very weak psychometric parameters (results cumulated from all analyses). The intercorrelational analysis among the subscales of the ADQ-ApA revealed that the rSR subscale is not valid. Thus, supported by these results and the appropriate results from the other studies (e.g., Laguna et al., 2004 ), we decided to transform this subscale into one assessing elevated somatic reactivity in apprehension anxiety.

Moreover, we added some filler items to the arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) scales to balance keying within each of the scales. Generally, it was not always possible to find a sufficient number of well-balanced items.

Validation Stage—Study 2

The second stage was designed as a validation study in order to ensure that the developed questionnaires of affective types are valid and reliable; the second aim was to verify the theoretically postulated location of the affective types among other personality constructs. Consequently, we report item and scale statistics, present evidence on the intercorrelations between subscales of each scale of the ADQ, and also assess the content and construct validity (that is factor structure, convergent, and divergent validity with well-established measures of related personality constructs, and theory-consistent group differences) and the stability and reliability of the questionnaires.

Four Scales of the ADQ

The final form of the ADQ (please see the Supplementary Material) is composed of four scales, directly measuring arousal anxiety, apprehension anxiety, valence depression, and anhedonic depression, and indirectly measuring mixed types of anxiety and depression. There is a dichotomous response format for all items (Agree/Disagree). The scoring system is identical as previously described.

ADQ-Arousal Anxiety (ADQ-ArA): 45 items (including 4 fillers), 3 subscales

• Somatic Reactivity (SR, 22 items); e.g., When something scares me, I feel a sudden attack of heat or cold.

• Panic/Phobia (PP, 14 items); e.g., I do not panic, even in the face of threats and dangers (reversed).

• Attentional Vigilance/Avoidance (AVA, 5 items); e.g., When I notice a potential threat, I automatically withdraw from the given situation.

• Fillers (4 items), e.g., I enjoy reading books.

ADQ-Apprehension Anxiety (ADQ-ApA): 48 items, 3 subscales

• Worrisome Thoughts (WT, 14 items); e.g., I am not in the habit of worrying excessively (reversed).

• Attentional Control (AC, 23 items); e.g., I cannot concentrate on a difficult task if there are noises around.

• Somatic Reactivity (SR, 11 items); e.g., When facing danger, I often feel like my legs “turn to jelly.”

ADQ-Valence Depression (ADQ-VD): 40 items (including 4 fillers), 2 subscales

• Negative Affect (21 items); e.g., I often get angry.

• Attentional Avoidance (15 items); e.g., I find it difficult to notice that someone is sad.

• Fillers (4 items); e.g., I prefer to travel by car rather than by train.

ADQ-Anhedonic Depression (ADQ-AD): 64 items, 4 subscales

• Emotional-Motivational Deficits (EMD; 31 items); e.g., I can start new things without difficulty (reversed).

• Positive Affect (PA; 13 items); e.g., I often smile honestly and joke.

• Negative Affect (NA; 12 items); e.g., I often feel sad.

• Attentional Control (AC; 8 items); e.g., Emotional events distract me so much that I later have trouble concentrating.

Table 1 demonstrates the socio-demographic characteristics of the validation, non-clinical sample ( N = 1,632). The sample matched the demographic structure of the Polish population. Participants who provided an unusually high number of identical answers on any questionnaire were removed from analyses. This procedure was used for each questionnaire separately. For ADQ and EPQ-R we removed participants with M +2 SD of identical answers, and for the remaining questionnaires we removed those who had zero variance in their answers, as the M +2 SD procedure turned out to be ineffective. As a result, from 4 to 11.8% ( M = 7.6%) of the participants were removed from the original sample ( N = 1,632), hence the differences in N s across analyses.

Table 1 . Socio-demographic characteristics of the sample.

Except demographic questions, participants were asked about whether they suffered (now or in the past) from anxiety or depression, and if “yes” they were questioned about psychotherapy, pharmacotherapy, hospitalization, professional diagnosis, and causes of these disorders. Respectively, 23.5 and 19.9% of individuals admitted that they experienced anxiety or depression disorders in the past. 13.3% of them reported that they had suffered from anxiety and 11.1% from depression in the moment of the study. Furthermore, owing to anxiety or depression, correspondingly 9.5 and 11.7% of respondents reported having used psychological and psychiatric help. 4.1 and 5% had been hospitalized because of severe anxiety or depression, respectively. The participants stated different phobias, panic attacks, separation anxiety, and anxiety coexisting with other disorders and states, reactive depression, bipolar depression, major depression, and depression associated with other disorders.

The ethics committee of the Institute of Psychology, Polish Academy of Sciences approved this on-line study and the consent procedure elaborated to it. The procedure of participants' recruitment and data collection were the same as in Study 1.

Respondents across two separate sessions completed a battery of on-line self-report techniques, randomized across subjects, and across sessions:

- Four scales of the ADQ.

- State–Trait Anxiety Inventory (STAI), which consists of two 20-item subscales: one measuring state anxiety and the other measuring trait anxiety ( Spielberger, 1983 ; Wrześniewski and Sosnowski, 1996 ).

- Beck Depression Inventory (BDI-II), composed of 21 questions assessing intensity of depressive symptoms ( Beck et al., 1996 ; Zawadzki et al., 2009 ).

- Behavioral Inhibition System/Behavioral Approach System scales (BIS/BAS scales)—the 24-item measure assessing dispositional BIS and BAS sensitivities. It includes three BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness ( Carver and White, 1994 ; Müller and Wytykowska, 2005 ).

- Positive and Negative Affect Schedule—Expanded Form (PANAS-X) a 60-item questionnaire comprising two higher level scales reflecting the valence of affect, that is Positive Affect (PA) and Negative Affect (NA) scales, and 11 lower level scales reflecting their specific content: Fear, Sadness, Guilt, Hostility (Basic Negative Emotion Scales); Joviality, Self-Assurance, Attentiveness (Basic Positive Emotion Scales); Shyness, Fatigue, Surprise, and Serenity (Other Affective States) ( Watson and Clark, 1994 ; Fajkowska and Marszał-Wiśniewska, 2009 ).

- Cognitive Emotion Regulation Questionnaire (CERQ), a multidimensional technique constructed in order to identify the cognitive emotion regulation strategies someone uses after having experienced negative or traumatic events. It contains 36 items measuring nine different cognitive coping strategies, including four non-adaptive: Self-blame, Rumination, Catastrophizing, and Other blame and five adaptive ones: Acceptance, Positive refocusing, Refocus on planning, Positive reappraisal, Putting into perspective ( Garnefski et al., 2002 ; Marszał-Wiśniewska and Fajkowska, 2010 ).

- Attentional Control Scale (ACS)—The 20-item ACS measures the ability to focus perceptual attention, switch attention between tasks, and flexibly control thought ( Derryberry and Reed, 2002 ; Fajkowska and Derryberry, 2010 ).

- Eysenck Personality Questionnaire Revised—Short Version (EPQ-R [S])—short version contains 48 items from the full EPQ-R. Includes scales: Psychoticism (P), Extraversion (E), Neuroticism (N), and Lie (L) ( Eysenck et al., 1985 ; Jaworowska, 2011 ).

To provide a general statistical description of items and scales of the ADQ we elaborated means, standard deviations, Cronbach's α coefficients, the discriminatory power of items (Youle's Phi coefficients; ϕ), intercorrelations of subscales (Pearson's r ) on the total sample, the means, standard deviations, and t -tests showing sex differences, and the prevalence of affective types in women and men.

The content validity of the ADQ was assessed with inter-rater agreement Fleiss' kappa (κ), while the construct validity was evaluated with confirmatory factor analysis (CFA). Pearson's r and t -tests were used to examine, respectively, the structure, convergent, and divergent validity of the test and theory-consistent group differences. In addition, the test-retest (r tt ) correlations were used to test the stability of the ADQ.

Items and Scales Statistics

Table 2 summarizes the means, standard deviations, and Cronbach's α coefficients of the total sample. It shows that the internal consistencies of each scale of the ADQ are very high, with Cronbach's α coefficients ranging from 0.92 (for the ADQ-VD scale) to 0.96 (for the ADQ-ApA scale and the ADQ-AD scale). Excepting the AVA subscale of the ADQ-ArA, the αs are also high for the subscales of each ADQ scale (ranges from the 0.93 for the EMD subscale of the ADQ-AD to 0.73 for the AC subscale of the ADQ-AD). Apparently, the lowest numbers of items within subscales can explain the lowest α's (cf. the AVA from the ADQ-ArA or the AC from the ADQ-AD). According to the Spearman-Brown formula, these scales would achieve reliability of around 0.80 with 13 (instead of 5) and 12 (instead of 8) items, respectively.

Table 2 . Means ( M ), standard deviations ( SD ), and Cronbach's α coefficients (on the total sample) of the four scales of the ADQ and their subscales.

Table 3 demonstrates the means, standard deviations, as well as t -test results between the sexes, separately evaluated for each scale of the ADQ. It informs that women scored significantly higher on both types of anxiety. There were no significant sex differences on valence and anhedonic depression.

Table 3 . Means ( M ), standard deviations ( SD ), and t -test comparisons between men and women of the four scales of the ADQ and their subscales.

We extracted the “pure types” by controlling the level of the other three affective types. For example, we identified arousal anxiety when the individuals scored above the median in ADQ-ArA and below the median in the other three types (ADQ-ApA, ADQ-VD, ADQ-AD). Mixed types, on the other hand, were built of individuals who scored above the median on both types of anxiety (ADQ-ArA, ADQ-ApA) or depression (ADQ-VD, ADQ-AD). Interestingly, as Table 4 indicates, men reported all types of depression more frequently than women, while women declared arousal and mixed types of anxiety more frequently than men.

Table 4 . The prevalence of “pure” affective types in women and men.

The discriminatory power coefficients of items from the four ADQ scales are reported in Table 5 . Similarly to the construction stage, we calculated the Youle's Phi coefficients and proposed the value ≥0.30 as indicative of good and very good discrimination.

Table 5 . Discriminatory power of items (Yule ϕ, phi-coefficient; on total sample) measuring arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD).

The results suggest high item discrimination coefficients, ranging from 0.33 (the AVA subscale) to 0.68 (the SR subscale) in the ADQ-ArA, from 0.30 (the AC subscale) to 0.69 (the WT subscale) in the ADQ-ApA, from 0.37 (the NA subscale) to 0.63 (the NA subscale) in the ADQ-VD, and from 0.34 (the EMD subscale) to 0.69 (the NA subscale) in the ADQ-AD.

In order to check the obtained intercorrelations among subscales composing adequate scales of the ADQ in study 1, we analyzed them in study 2. Table 6 demonstrates that generally all of the results are confirmed. However, comparing these findings to the findings from study 1, all correlation coefficients, across all scales of the questionnaire, increased.

Table 6 . Intercorrelations among subscales in the four scales of the ADQ.

Validity of the ADQ

In the case of content validity , we examined the extent to which the particular scales of the ADQ represent all proposed facets of arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD) constructs. Thus, three experts evaluated whether items of the ADQ-ArA and ADQ-AD assess the defined content of arousal anxiety and anhedonic depression, and another three experts decided if the test positions of the ADQ-ApA and ADQ-VD cover the content of apprehension anxiety and valence depression, respectively. Precisely, the raters were instructed to assign the items measuring the adequate construct (e.g., arousal anxiety) to the distinguished facets of that construct (e.g., SR, PP, AVA). The inter-rater agreement was measured with Fleiss' kappa ( Fleiss, 1971 ) using an online calculator ( Geertzen, 2012 ). Kappa (κ) ranges from 0 to 1, with higher values showing greater inter-rater reliability of agreement. For all tested versions of the ADQ the κ-values were very high: ADQ-ArA, κ = 0.93; ADQ- ApA, κ = 0.90; ADQ-VD, κ = 0.94; ADQ-AD, κ = 0.98. Then the raters' sorting was compared to the scoring keys in order to replace, remove or linguistically correct problematic items.

The CFA was used to evaluate the factorial validity and authorize the results from Study 1. Along with these findings we tested (Mplus; Muthén and Muthén, 1998 ) the same models; however, they were formed on the corrected versions of the ADQ elaborated according to the results from Study 1 (e.g., they have a different number of items because some of them were removed from the original versions, different number of subscales). Again, χ 2 values for the models fit across all version of the ADQ were significant, thus other fit indices were reported ( Kline, 2005 ).

The results of the CFA for all scales of ADQ are presented in Tables 7 , 8 . As can be seen, the three-factor model showed the best fit in case of the ADQ-ArA with all indices reaching satisfactory levels. The data clearly demonstrated that all the items are proper markers of the expected single factor. More precisely, for the SR factor range from 0.42 to 0.87, for the PP factor from 0.54 to 0.82, and for the AVA from 0.49 to 0.74.

Table 7 . Goodness of fit indices for the two models of Anxiety and Depression Questionnaire—Arousal Anxiety (ADQ-ArA); for the two models of Anxiety and Depression Questionnaire—Apprehension Anxiety (ADQ-ApA); for the two models of Anxiety and Depression Questionnaire—Valence Depression (ADQ-VD), and for the three models of Anxiety and Depression Questionnaire—Anhedonic Depression (ADQ-AD).

Table 8 . Factor loadings of items for the three-factor model of Anxiety and Depression Questionnaire—Arousal Anxiety (ADQ-ArA), for the three-factor model of Anxiety and Depression Questionnaire—Apprehension Anxiety (ADQ-ApA), for the two-factor model of Anxiety and Depression Questionnaire—Valence Depression (ADQ-VD), and for the four-factor model of Anxiety and Depression Questionnaire—Anhedonic Depression (ADQ-AD).

In case of the ADQ-ApA, the three-factor model had better fit parameters than the one-factor model. The factors loadings for items of the three-factor model are high: from 0.51 to 0.87 for the WT subscale, from 0.41 to 0.85 for the AC subscale, and from 0.61 to 0.84 for the SR subscale.

Again, the two-factor model for the ADQ-VD seemed to be a better fit than the one-factor solution, and all of the items loaded high on the adequate factor: for NA from 0.53 to 0.80, and for AA from 0.55 to 0.81.

The best solution for the ADQ-AD is the four-factor model and the factor loadings of items for this model reach a satisfactory level. More specifically, for the EMD subscale from 0.51 to 0.85, for the PA subscale from 0.59 to 0.86, for the NA subscale from 0.70 to 0.88, and for the AC subscale from 0.52 to 0.88.

Additionally, in order to place the proposed affective types among other related personality constructs, we assessed the convergent and divergent validity with well-recognized measures. We predicted that state-like arousal anxiety and trait-like apprehension anxiety are both positively related to state anxiety and trait anxiety (STAI). However, the correlation between state anxiety and arousal anxiety should be higher than the correlation between trait anxiety and arousal anxiety, and opposite relations should be identified for apprehension anxiety. As can be seen from Table 9 , the obtained data confirmed these predictions.

Table 9 . Correlations between arousal anxiety (ADQ-ArA) and state and trait anxiety (STAI), extraversion and neuroticism (EPQ-R [S]), positive and negative affect (PANAS-X; instruction “always”), and correlations between apprehension anxiety (ADQ-ApA) and state and trait anxiety (STAI), extraversion and neuroticism (EPQ-R [S]), positive and negative affect (PANAS-X; instruction “always”), and attentional control (ACS).

The review of results from other sources showed that a moderate negative correlation with extraversion and a moderate and high positive correlation with neuroticism (EPQ-R[S]) is usually obtained for both state and trait anxiety (STAI) (see Fajkowska, 2013 ). Thus, similar relations between extraversion, neuroticism, arousal anxiety, and apprehension anxiety could be expected, which is actually reflected in the results showed in Table 9 .

According to the tripartite model of anxiety and depression proposed by Clark and Watson ( Burns and Eidelson, 1998 ; Watson, 2000 ), anxiety relates to Negative Affect (NA) but is not connected with Positive Affect (PA), while depression is associated with both affects by correlating negatively with PA and positively with NA. The results of our studies only partially support this model. Both types of anxiety moderately and positively related to NA; however, they also correlated moderately and negatively with PA (see Table 9 ). But both types of depression, low and negatively (valence depression) and moderately and negatively (anhedonic depression) related to PA, and moderately and positively to NA (see Table 11 ). Nonetheless, there are some studies matching our (but not Clark and Watson's) findings (e.g., Burns and Eidelson, 1998 ; Fajkowska and Marszał-Wiśniewska, 2009 ).

Fajkowska and Derryberry (2010) provided evidence that anxious and depressive subjects scored significantly lower on effortful attentional control than non-anxious and non-depressive individuals. The results from our study are congruent with their findings. As Tables 9 , 10 show, apprehension anxiety and anhedonic depression are negatively correlated with attentional control.

Table 10 . Correlations between valence depression (ADQ-VD) and depressive tendencies (BDI); extraversion and neuroticism (EPQ-R [S]) and correlations between anhedonic depression (ADQ-AD) and depressive tendencies (BDI), extraversion and neuroticism (EPQ-R [S]), and attentional control (ACS).

The Mood and Anxiety Symptom Questionnaire (MASQ; Watson, 2000 ) can be used to assess anhedonic depression. The MASQ Anhedonic Depression Subscale displays good convergent validity with the Beck Depression Inventory (BDI; cf. Kendall et al., 1987 ). Therefore, we predicted a higher and positive correlation between anhedonic depression and depression measured by BDI, and low or moderate between valence depression and depression measured by BDI. This forecast is supported by the data presented in Table 10 .

Generally, in most studies low extraversion and high neuroticism are found in clinical and nonclinical depression (e.g., Watson et al., 1999 ; Kotov et al., 2010 ; Fajkowska, 2013 ). Thus, it implies that we should expect that both types of depression would be negatively related to extraversion and positively to neuroticism. Indeed, the data presented in Table 10 support this hypothesis.

The structure of both types of depression refers to the NA; however, its content is depression-type specific (cf. definition of valence and anhedonic depression). We predicted that valence depression should correlate higher with hostility than anhedonic depression, while anhedonic depression would be more strongly related to sadness and guilt. Fear should not differentiate depressions. The obtained data supported these speculations (cf. Table 11 ).

Table 11 . Correlations between valence depression (ADQ-VD; N = 1,424) and anhedonic depression (ADQ-AD; N = 1,428) with PANAS-X (instruction “always”).

In addition, the very low PA is a part of the structure of anhedonic depression, thus we expected stronger negative relations between anhedonic depression and the Basic Positive Scales (Joviality, Self-assurance, and Attentiveness) than between valence depression and these scales. Again, these expectations are reflected in our empirical data (cf. Table 11 ).

Finally, as the emotional-motivational deficit defines anhedonic depression we also predicted stronger relations between Other Affective States, especially those referring to low energetic states (Fatigue, Serenity), and anhedonic depression than between Other Affective States and valence depression. As Table 11 shows, the predictions were confirmed.

We assumed that reactive types, that is arousal anxiety (ArA) and valence depression (VD), should be more weakly associated with adaptive and nonadaptive cognitive strategies of emotion regulation than regulative types, that is apprehension anxiety (ApA) and anhedonic depression (AD). Data presented in Table 12 qualified our predictions.

Table 12 . Correlations between reactive types—arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD), regulative types—apprehension anxiety (ADQ-ApA), anhedonic depression (ADQ-AD) and adaptive and nonadaptive strategies of emotion regulation (CERQ).

The next step in assessing construct validity was to analyze the theory-consistent group differences . Gray (1981) proposed two systems of controlling behavioral activity, that is the behavioral inhibition system (BIS) and the behavioral activation system (BAS). The BIS is thought to regulate aversive motives, in which the goal is to move away from something unpleasant, while the BAS is understood to regulate appetitive motives, in which the goal is to move toward something desirable ( Carver and White, 1994 ).

It claims that the amygdala provides inputs to the BIS and may relay its outputs to the hypothalamus and autonomic nervous system, thereby mediating anxious arousal. Sustained activation of the BIS may therefore account for some features of anxiety and be related to panic (cf. Barlow, 2004 , p. 210). Thus, we assumed that high arousal-anxious individuals would score higher on the BIS than low arousal-anxious ones. And it is clear from the results that high arousal-anxious subjects ( n = 265) are higher ( M = 3.05, SD = 0.47) on the BIS ( Carver and White, 1994 ; Müller and Wytykowska, 2005 ) scale than low arousal-anxious participants ( n = 313; M = 2.50, SD = 0.47), t (576) = 14.00, p < 0.001, d = 1.18. However, as data from one study ( Moser et al., 2013 ) indicated, we should expect higher BIS in apprehension anxiety (measured by STAI) than in arousal anxiety (measured by MASQ). Their study showed that apprehension anxiety correlates three times higher with BIS than arousal anxiety because the former one is most closely associated with error monitoring. The findings from our study employing ADQ-ArA and ADQ-ApA measures are in accord with the cited studies: high apprehension-anxious individuals ( n = 134) are higher ( M = 2.88, SD = 0.39) on the BIS scale than high arousal-anxious participants ( n = 133; M = 2.72, SD = 0.44), t (265) = 3.49, p < 0.001, d = 0.44. The high-apprehension subjects ( n = 298) obtained higher scores on BIS than low-apprehension individuals ( n = 301; M = 3.12, SD = 0.46, and M = 2.47, SD = 0.37, respectively; t (597) = 17.16, p < 0.001, d = 1.40).

In addition, high activity of the BIS means a higher level of sensitivity to nonreward, punishment, and novel experience, which results in a natural avoidance of such environments in order to prevent negative experiences such as fear, anxiety, frustration, and sadness. Thus, it should be predicted that individuals with a high level of valence depression would show a higher level of BIS comparing to individuals with a low level of valence depression and high-anhedonic depressive, because the negative affect building this type of depression relates to the aforementioned range of negative emotions. Indeed, results of the analysis aimed at these differences showed that individuals with high valence depression ( n = 296), measured with ADQ-VD, revealed a higher BIS level ( M = 2.92, SD = 0.43) than subjects with low valence depression ( n = 319; M = 2.51, SD = 0.42), t (613) = 10.84, p < 0.001, d = 0.87, and participants with high valence depression ( n = 122) scored higher on the BIS scale than those with high anhedonic depression as assessed by the ADQ-AD ( n = 124; M = 2.87, SD = 0.40, and M = 2.75, SD = 0.47, respectively), t (244) = 2.13, p < 0.05, d = 0.30).

Bijttebier et al. (2009) summarized the studies that examined the relationship between sensitivity of the BIS and BAS systems and a broad range of psychiatric disorders. Among others, they found that low BAS sensitivity characterized anhedonic depression. Along with these results we assessed the differences in BAS level and three BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness ( Carver and White, 1994 ; Müller and Wytykowska, 2005 ) between high anhedonic-depressive ( n = 263) and low anhedonic-depressive ( n = 300) individuals, and between high anhedonic-depressive ( n = 124) and high valence-depressive ( n = 122). Anhedonic depression and valence depression were assessed by the ADQ-VD and ADQ-AD, respectively. As expected, the results showed that high anhedonic-depressive individuals scored significantly lower on the BAS ( M = 2.52, SD = 0.50) than low anhedonic-depressive ( M = 2.86, SD = 0.43), t (561) = 8.54, p < 0.001, d = 0.72, and high valence-depressive individuals ( M = 2.54, SD = 0.48, and M = 2.77, SD = 0.43, respectively), t (244) = 3.89, p < 0.001, d = 0.50. The high anhedonic-depressive participants were significantly lower ( M = 2.85, SD = 0.54) on the BAS Drive scale than low anhedonic-depressive participants ( M = 5.41, SD = 1.82), t (561) = 7.87, p < 0.001, d = 0.68, and they were lower on the BAS Fun Seeking ( M = 2.58, SD = 0.55) and BAS Reward Responsiveness ( M = 2.98, SD = 0.51) scales than low anhedonic-depressive individuals ( M = 2.88, SD = 0.45, t (561) = 6.99, p < 0.001, d = 0.58 and M = 3.27, SD = 0.38, t (561) = 7.76, p < 0.001, d = 0.66, respectively). Also, the high anhedonic-depressive participants were lower on BAS drive, BAS Fun Seeking, and BAS Reward Responsiveness scales than high valence-depressive individuals [ M = 2.47, SD = 0.55 vs. M = 2.72, SD = 0.56, t (244) = 3.54, p < 0.001, d = 0.45; M = 2.62, SD = 0.53 vs. M = 2.82, SD = 0.49, t (244) = 3.21, p < 0.001, d = 0.39; M = 3.03, SD = 0.45 vs. M = 3.20, SD = 0.46, t (244) = 2.86, p < 0.01, d = 0.37, respectively).

Estimation of the Questionnaire Test-Retest Reliability

The test-retest (r tt ) reliabilities were evaluated on smaller groups (randomly selected from the total sample) that filled out the ADQ scales again after 5 weeks from the initial study. According to the results, the r tt reliabilities (see Table 13 ) are high for all scales of the ADQ, varying from 0.70 (ADQ-ArA) to 0.89 (ADQ-ApA). Moreover, coefficients for the tests measuring state-like arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) are lower (0.70, 0.79, respectively) than for the tests assessing trait-like apprehension anxiety (ADQ-ApA) and anhedonic depression (APQ-AD); 0.89 and 0.88, respectively. Coefficients of 0.70 are considered satisfactory for personality states ( Spielberger, 1983 ). Additionally, the test-retest reliability of most subscales of each ADQ scale is high or moderate, except for poor reliability (0.45) of the subscale of the ADQ-ArA version that assesses attentional vigilance/avoidance (AVA).

Table 13 . Test-retest (r tt ) reliabilities of the four scales of ADQ and their subscales (five week retest interval).

The aim of the present studies was to validate a recently proposed typology of anxiety and depression operationalized within the systemic approach to personality trait and personality type ( Fajkowska, 2013 ) and to develop a questionnaire based on it. This typology has been offered as a supplement to the widely accepted categorizations (e.g., Spielberger, 1983 ; Heller, 1993a , b ; Watson, 2000 ; American Psychiatric Association, 2013 ) with the intention to advance knowledge in differential and overlapping features between anxiety and depression, and in differential and overlapping adaptive meanings of both phenomena, especially in non-clinical forms of anxiety/depression. In this approach, anxiety and depression are seen as complex personality types and their new grouping refers to their specific structural composition (mechanisms, components, and behavioral markers) and the dominant functions they play in stimulation processing (reactive, regulative). Hence, six affective types are proposed: arousal anxiety, apprehension anxiety, mixed anxiety, valence depression, anhedonic depression, and mixed depression. It is assumed that differences and similarities in structural components and dominant functions in stimulation processing in various affective types are connected with differences and similarities in their adaptive meanings. This line of reasoning suggests that one can expect more out-group than in-group similarities or more in-group than out-group differences. This theoretical proposition concerning a new typology of anxiety and depression has led us to develop a questionnaire that corresponds fully to this model. The empirical data gathered across the two stages—construction and validation—allowed us to offer the final form of the Anxiety and Depression Questionnaire (ADQ). The ADQ is composed of four multidimensional versions, directly assessing Arousal Type of anxiety (ADQ-ArA), Apprehension Type of anxiety (ADQ-ApA), Valence Type of depression (ADQ-VD), and Anhedonic Type of depression (ADQ-AD), and indirectly evaluating Mixed Type of anxiety (MA) and Mixed Type of depression (MD). The results showed that all scales of the ADQ are valid and reliable measurements.

The gender differences we found with the ADQ were to some extent similar to those found by other authors (see Fox, 2008 for a review). The prevalence of mood disorders is generally much higher among women than men. In our studies women scored higher on both types of anxiety, but we did not observe sex differences in valence and anhedonic depressions. However, when we used the “pure types” (where we controlled the level of the other three affective disorders) the results indicated that men showed all types of depression more frequently than women, while women more frequently reported arousal and mixed types of anxiety. The probable explanations include the sensitivity of the measurement we used, or it might be possible that these results reflect social and cultural changes predisposing men to be more vulnerable to mood disorders, especially to depression, than women. This issue needs further and cross-cultural studies.

All scales of the ADQ are characterized by high homogeneity (cf. discrimination coefficients and Cronbach's α coefficients). However, across two studies, the intercorrelations among subscales composing the anhedonic depression scale of ADQ seem to be much higher than expected. The scales with the highest intercorrelations were emotional-motivational deficits (EMD) with both affects, positive (PA; −0.65 in Study 1 and −0.82 in Study 2) and negative (NA; 0.71 in Study 1 and 0.84 in Study 2). It might suggest that motivational and affective systems are not separable or that the selected items in these subscales need further elaboration. Nonetheless, future validation studies should provide more information on how to deal with this puzzle.

The results of content validity supported the theoretical assumptions regarding the internal structure of the proposed affective types. In addition, the CFA sustained the adequacy of these theoretical assumptions.

Apart from that, all scales of the ADQ also have good convergent and divergent validity. It is shown by ADQ-ArA's higher correlation with STAI state anxiety than STAI trait anxiety, ADQ-ApA's higher correlations with STAI trait anxiety than STAI state anxiety, ADQ-VD's lower correlation with BDI and higher ADQ-AD's correlation with BDI, which measures anhedonia as is assumed. Moreover, as expected, all types of anxiety and depression related positively to neuroticism and negatively to extraversion (EPQ-R[S]).

Contrary to the predictions stemming from the tripartite model of anxiety and depression ( Watson, 2000 ), both types of anxiety and both types of depression related negatively to PA and positively to NA (PANAS-X). Our results are in line with other studies (see Fajkowska and Marszał-Wiśniewska, 2009 for a review) showing that anxiety is related to positive affect. However, the expected correlational patterns were found for valence and anhedonic depression with respect to the proposed theoretical structure of these affective types. Valence depression correlated higher with hostility than anhedonic depression, and anhedonic depression was more strongly related to sadness and guilt than valence depression. Moreover, anhedonic depression showed higher negative correlations with the basic positive emotions, and stronger positive correlations with fatigue and serenity than valence depression (PANAS-X).

Referring to the identified dominant functions in controlling stimulation, reactive or regulative, in affective types, we discovered that reactive types (arousal anxiety and valence depression) are more weakly related to strategies of emotion regulation (CERQ) than regulative types (apprehension anxiety and anhedonic depression). These results also support theoretical assumptions regarding the fact that regulative personality traits or types can be recognized through their correspondence to different strategies.

The ADQ is also characterized by satisfactory construct validity as measured by means of the theory-consistent group differences. There were significant differences in BIS levels (BIS/BAS scales) among high arousal-anxious, low arousal-anxious, high apprehension-anxious, and low-apprehension anxious. BIS relates to arousal, panic, and also to monitoring errors. In line with the results obtained by other authors (e.g., Moser et al., 2013 ), a higher level of BIS is more typical for apprehension anxiety as it is more associated with error monitoring than arousal anxiety. Obviously, both types of anxiety revealed a higher BIS level than individuals low on both arousal and apprehension anxiety. In addition, as BIS goes with fear, anxiety, frustration, and sadness ( Gray, 1981 ), it was not surprising that it was higher in valence depression than in anhedonic depression and in low valence depression.

Finally, significant differences were found for the level of BAS and BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness in scoring higher on anhedonic depression compared to low anhedonic-depressive and valence-depressive. It is explained by the fact that low BAS is associated with anhedonia, i.e., with difficulties in goal achievement, impossibility to experience pleasure, and failure in delivering sufficient reward following approach behaviors.

It should also be added that the stability of all scales of the ADQ as measured by the test-retest technique is satisfactory.

Certain limitations of our study should be noted. First relates to the self-reported data that could be associated with several potential sources of bias and requires replication and confirmation with experimental procedures. Second, these studies were time consuming and demanding for the participants. Thus, a possibility existed that they clicked through the questionnaires without much reflection (although it seemed that the number of such participants was not especially high, and we tried to exclude these cases from analyses). Third, in statistical techniques like Cronbach's α or factor analysis (CFA), high parameter values sometimes indicated redundancy in scales. Even though we were struggling for both (a) the content differentiation of the scales and subscales and (b) good psychometric parameters, it was not always possible to achieve. Fourth, we used a median split for grouping participants and that method has its serious limitations; thus for further analysis we recommend considering different approaches (e.g., means and standard deviations). Fifth, the number of items measuring attentional subscales is too low, although all definitional aspects of each attentional construct are covered. This is usually the problem when one operationalizes processual elements of personality (cf. Pavlovian Temperament Survey; Attentional Control Scale). Definitely it needs further elaboration. Finally, there are more processes involved in producing anxiety and depression than taken here into consideration. We are aware that this model is far from being complete but it suggests the right direction—understanding affective types as complex, three-level systems.

Despite these limitations, we received satisfactory empirical support for the proposed typology of anxiety and depression. From the viewpoint of developing a reliable and valid instrument for self-ratings of affective types, the results provide evidence to support the good psychometric status of the ADQ as a measure for evaluation of proposed types of anxiety and depression. It should be pointed out, however, that the research described in this paper is not intended to provide normative data. Although the present studies are based on large samples, we believe that the pattern of results needs further replications.

Future research should also consider the utility of the ADQ and the extent to which it may be generalized in a variety of applied settings, for example clinical, educational, and work settings. Such studies may potentially reveal interesting information regarding the usefulness of the ADQ in both research and practice.

Author Contributions

MF: Provided the theoretical framework and supervised the project; MF, ED, and AW: Designed the questionnaire and contributed to the study design; ED: Analyzed the data and supervised data collection; MF: Drafted the manuscript; ED and AW: Provided critical revisions.

Conflict of Interest Statement

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

Acknowledgments

This research was supported by Grant 2012/07/E/HS6/04071 from the National Science Centre, Poland. We thank Dr. Joanna Kantor-Martynuska for her involvement in the construction of the Anxiety and Depression Questionnaire. ED has obtained funding under a Ph.D. scholarship from the National Science Center, Poland, grant number 2016/20/T/HS6/00598.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.02376/full#supplementary-material

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Keywords: anxiety types, depression types, anxiety and depression questionnaire, measurement, reliability, validity, personality types

Citation: Fajkowska M, Domaradzka E and Wytykowska A (2018) Types of Anxiety and Depression: Theoretical Assumptions and Development of the Anxiety and Depression Questionnaire. Front. Psychol . 8:2376. doi: 10.3389/fpsyg.2017.02376

Received: 31 August 2017; Accepted: 29 December 2017; Published: 23 January 2018.

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*Correspondence: Małgorzata Fajkowska, [email protected]

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• Published: 13 July 2021

Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students

• Emily N. Satinsky 1 ,
• Tomoki Kimura 2 ,
• Mathew V. Kiang 3 , 4 ,
• Rediet Abebe 5 , 6 ,
• Scott Cunningham 7 ,
• Hedwig Lee 8 ,
• Xiaofei Lin 9 ,
• Cindy H. Liu 10 , 11 ,
• Igor Rudan 12 ,
• Srijan Sen 13 ,
• Mark Tomlinson 14 , 15 ,
• Miranda Yaver 16 &
• Alexander C. Tsai 1 , 11 , 17  

Scientific Reports volume  11 , Article number:  14370 ( 2021 ) Cite this article

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• Epidemiology
• Health policy
• Quality of life

University administrators and mental health clinicians have raised concerns about depression and anxiety among Ph.D. students, yet no study has systematically synthesized the available evidence in this area. After searching the literature for studies reporting on depression, anxiety, and/or suicidal ideation among Ph.D. students, we included 32 articles. Among 16 studies reporting the prevalence of clinically significant symptoms of depression across 23,469 Ph.D. students, the pooled estimate of the proportion of students with depression was 0.24 (95% confidence interval [CI], 0.18–0.31; I 2  = 98.75%). In a meta-analysis of the nine studies reporting the prevalence of clinically significant symptoms of anxiety across 15,626 students, the estimated proportion of students with anxiety was 0.17 (95% CI, 0.12–0.23; I 2  = 98.05%). We conclude that depression and anxiety are highly prevalent among Ph.D. students. Data limitations precluded our ability to obtain a pooled estimate of suicidal ideation prevalence. Programs that systematically monitor and promote the mental health of Ph.D. students are urgently needed.

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Introduction

Mental health problems among graduate students in doctoral degree programs have received increasing attention 1 , 2 , 3 , 4 . Ph.D. students (and students completing equivalent degrees, such as the Sc.D.) face training periods of unpredictable duration, financial insecurity and food insecurity, competitive markets for tenure-track positions, and unsparing publishing and funding models 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 —all of which may have greater adverse impacts on students from marginalized and underrepresented populations 13 , 14 , 15 . Ph.D. students’ mental health problems may negatively affect their physical health 16 , interpersonal relationships 17 , academic output, and work performance 18 , 19 , and may also contribute to program attrition 20 , 21 , 22 . As many as 30 to 50% of Ph.D. students drop out of their programs, depending on the country and discipline 23 , 24 , 25 , 26 , 27 . Further, while mental health problems among Ph.D. students raise concerns for the wellbeing of the individuals themselves and their personal networks, they also have broader repercussions for their institutions and academia as a whole 22 .

Despite the potential public health significance of this problem, most evidence syntheses on student mental health have focused on undergraduate students 28 , 29 or graduate students in professional degree programs (e.g., medical students) 30 . In non-systematic summaries, estimates of the prevalence of clinically significant depressive symptoms among Ph.D. students vary considerably 31 , 32 , 33 . Reliable estimates of depression and other mental health problems among Ph.D. students are needed to inform preventive, screening, or treatment efforts. To address this gap in the literature, we conducted a systematic review and meta-analysis to explore patterns of depression, anxiety, and suicidal ideation among Ph.D. students.

Flowchart of included articles.

The evidence search yielded 886 articles, of which 286 were excluded as duplicates (Fig.  1 ). An additional nine articles were identified through reference lists or grey literature reports published on university websites. Following a title/abstract review and subsequent full-text review, 520 additional articles were excluded.

Of the 89 remaining articles, 74 were unclear about their definition of graduate students or grouped Ph.D. and non-Ph.D. students without disaggregating the estimates by degree level. We obtained contact information for the authors of most of these articles (69 [93%]), requesting additional data. Three authors clarified that their study samples only included Ph.D. students 34 , 35 , 36 . Fourteen authors confirmed that their study samples included both Ph.D. and non-Ph.D. students but provided us with data on the subsample of Ph.D. students 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 . Where authors clarified that the sample was limited to graduate students in non-doctoral degree programs, did not provide additional data on the subsample of Ph.D. students, or did not reply to our information requests, we excluded the studies due to insufficient information (Supplementary Table S1 ).

Ultimately, 32 articles describing the findings of 29 unique studies were identified and included in the review 16 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 (Table 1 ). Overall, 26 studies measured depression, 19 studies measured anxiety, and six studies measured suicidal ideation. Three pairs of articles reported data on the same sample of Ph.D. students 33 , 38 , 45 , 51 , 53 , 56 and were therefore grouped in Table 1 and reported as three studies. Publication dates ranged from 1979 to 2019, but most articles (22/32 [69%]) were published after 2015. Most studies were conducted in the United States (20/29 [69%]), with additional studies conducted in Australia, Belgium, China, Iran, Mexico, and South Korea. Two studies were conducted in cross-national settings representing 48 additional countries. None were conducted in sub-Saharan Africa or South America. Most studies included students completing their degrees in a mix of disciplines (17/29 [59%]), while 12 studies were limited to students in a specific field (e.g., biomedicine, education). The median sample size was 172 students (interquartile range [IQR], 68–654; range, 6–6405). Seven studies focused on mental health outcomes in demographic subgroups, including ethnic or racialized minority students 37 , 41 , 43 , international students 47 , 50 , and sexual and gender minority students 42 , 54 .

In all, 16 studies reported the prevalence of depression among a total of 23,469 Ph.D. students (Fig.  2 ; range, 10–47%). Of these, the most widely used depression scales were the PHQ-9 (9 studies) and variants of the Center for Epidemiologic Studies-Depression scale (CES-D, 4 studies) 63 , and all studies assessed clinically significant symptoms of depression over the past one to two weeks. Three of these studies reported findings based on data from different survey years of the same parent study (the Healthy Minds Study) 40 , 42 , 43 , but due to overlap in the survey years reported across articles, these data were pooled. Most of these studies were based on data collected through online surveys (13/16 [81%]). Ten studies (63%) used random or systematic sampling, four studies (25%) used convenience sampling, and two studies (13%) used multiple sampling techniques.

Pooled estimate of the proportion of Ph.D. students with clinically significant symptoms of depression.

The estimated proportion of Ph.D. students assessed as having clinically significant symptoms of depression was 0.24 (95% confidence interval [CI], 0.18–0.31; 95% predictive interval [PI], 0.04–0.54), with significant evidence of between-study heterogeneity (I 2  = 98.75%). A subgroup analysis restricted to the twelve studies conducted in the United States yielded similar findings (pooled estimate [ES] = 0.23; 95% CI, 0.15–0.32; 95% PI, 0.01–0.60), with no appreciable difference in heterogeneity (I 2  = 98.91%). A subgroup analysis restricted to the studies that used the PHQ-9 to assess depression yielded a slightly lower prevalence estimate and a slight reduction in heterogeneity (ES = 0.18; 95% CI, 0.14–0.22; 95% PI, 0.07–0.34; I 2  = 90.59%).

Nine studies reported the prevalence of clinically significant symptoms of anxiety among a total of 15,626 Ph.D. students (Fig.  3 ; range 4–49%). Of these, the most widely used anxiety scale was the 7-item Generalized Anxiety Disorder scale (GAD-7, 5 studies) 64 . Data from three of the Healthy Minds Study articles were pooled into two estimates, because the scale used to measure anxiety changed midway through the parent study (i.e., the Patient Health Questionnaire-Generalized Anxiety Disorder [PHQ-GAD] scale was used from 2007 to 2012 and then switched to the GAD-7 in 2013 40 ). Most studies (8/9 [89%]) assessed clinically significant symptoms of anxiety over the past two to four weeks, with the one remaining study measuring anxiety over the past year. Again, most of these studies were based on data collected through online surveys (7/9 [78%]). Five studies (56%) used random or systematic sampling, two studies (22%) used convenience sampling, and two studies (22%) used multiple sampling techniques.

Pooled estimate of the proportion of Ph.D. students with clinically significant symptoms of anxiety.

The estimated proportion of Ph.D. students assessed as having anxiety was 0.17 (95% CI, 0.12–0.23; 95% PI, 0.02–0.41), with significant evidence of between-study heterogeneity (I 2  = 98.05%). The subgroup analysis restricted to the five studies conducted in the United States yielded a slightly lower proportion of students assessed as having anxiety (ES = 0.14; 95% CI, 0.08–0.20; 95% PI, 0.00–0.43), with no appreciable difference in heterogeneity (I 2  = 98.54%).

Six studies reported the prevalence of suicidal ideation (range, 2–12%), but the recall windows varied greatly (e.g., ideation within the past 2 weeks vs. past year), precluding pooled estimation.

Additional stratified pooled estimates could not be obtained. One study of Ph.D. students across 54 countries found that phase of study was a significant moderator of mental health, with students in the comprehensive examination and dissertation phases more likely to experience distress compared with students primarily engaged in coursework 59 . Other studies identified a higher prevalence of mental ill-health among women 54 ; lesbian, gay, bisexual, transgender, and queer (LGBTQ) students 42 , 54 , 60 ; and students with multiple intersecting identities 54 .

Several studies identified correlates of mental health problems including: project- and supervisor-related issues, stress about productivity, and self-doubt 53 , 62 ; uncertain career prospects, poor living conditions, financial stressors, lack of sleep, feeling devalued, social isolation, and advisor relationships 61 ; financial challenges 38 ; difficulties with work-life balance 58 ; and feelings of isolation and loneliness 52 . Despite these challenges, help-seeking appeared to be limited, with only about one-quarter of Ph.D. students reporting mental health problems also reporting that they were receiving treatment 40 , 52 .

Risk of bias

Twenty-one of 32 articles were assessed as having low risk of bias (Supplementary Table S2 ). Five articles received one point for all five categories on the risk of bias assessment (lowest risk of bias), and one article received no points (highest risk). The mean risk of bias score was 3.22 (standard deviation, 1.34; median, 4; IQR, 2–4). Restricting the estimation sample to 12 studies assessed as having low risk of bias, the estimated proportion of Ph.D. students with depression was 0.25 (95% CI, 0.18–0.33; 95% PI, 0.04–0.57; I 2  = 99.11%), nearly identical to the primary estimate, with no reduction in heterogeneity. The estimated proportion of Ph.D. students with anxiety, among the 7 studies assessed as having low risk of bias, was 0.12 (95% CI, 0.07–0.17; 95% PI, 0.01–0.34; I 2  = 98.17%), again with no appreciable reduction in heterogeneity.

In our meta-analysis of 16 studies representing 23,469 Ph.D. students, we estimated that the pooled prevalence of clinically significant symptoms of depression was 24%. This estimate is consistent with estimated prevalence rates in other high-stress biomedical trainee populations, including medical students (27%) 30 , resident physicians (29%) 65 , and postdoctoral research fellows (29%) 66 . In the sample of nine studies representing 15,626 Ph.D. students, we estimated that the pooled prevalence of clinically significant symptoms of anxiety was 17%. While validated screening instruments tend to over-identify cases of depression (relative to structured clinical interviews) by approximately a factor of two 67 , 68 , our findings nonetheless point to a major public health problem among Ph.D. students. Available data suggest that the prevalence of depressive and anxiety disorders in the general population ranges from 5 to 7% worldwide 69 , 70 . In contrast, prevalence estimates of major depressive disorder among young adults have ranged from 13% (for young adults between the ages of 18 and 29 years in the 2012–2013 National Epidemiologic Survey on Alcohol and Related Conditions III 71 ) to 15% (for young adults between the ages of 18 and 25 in the 2019 U.S. National Survey on Drug Use and Health 72 ). Likewise, the prevalence of generalized anxiety disorder was estimated at 4% among young adults between the ages of 18 and 29 in the 2001–03 U.S. National Comorbidity Survey Replication 73 . Thus, even accounting for potential upward bias inherent in these studies’ use of screening instruments, our estimates suggest that the rates of recent clinically significant symptoms of depression and anxiety are greater among Ph.D. students compared with young adults in the general population.

Further underscoring the importance of this public health issue, Ph.D. students face unique stressors and uncertainties that may put them at increased risk for mental health and substance use problems. Students grapple with competing responsibilities, including coursework, teaching, and research, while also managing interpersonal relationships, social isolation, caregiving, and financial insecurity 3 , 10 . Increasing enrollment in doctoral degree programs has not been matched with a commensurate increase in tenure-track academic job opportunities, intensifying competition and pressure to find employment post-graduation 5 . Advisor-student power relations rarely offer options for recourse if and when such relationships become strained, particularly in the setting of sexual harassment, unwanted sexual attention, sexual coercion, and rape 74 , 75 , 76 , 77 , 78 . All of these stressors may be magnified—and compounded by stressors unrelated to graduate school—for subgroups of students who are underrepresented in doctoral degree programs and among whom mental health problems are either more prevalent and/or undertreated compared with the general population, including Black, indigenous, and other people of color 13 , 79 , 80 ; women 81 , 82 ; first-generation students 14 , 15 ; people who identify as LGBTQ 83 , 84 , 85 ; people with disabilities; and people with multiple intersecting identities.

Structural- and individual-level interventions will be needed to reduce the burden of mental ill-health among Ph.D. students worldwide 31 , 86 . Despite the high prevalence of mental health and substance use problems 87 , Ph.D. students demonstrate low rates of help-seeking 40 , 52 , 88 . Common barriers to help-seeking include fears of harming one’s academic career, financial insecurity, lack of time, and lack of awareness 89 , 90 , 91 , as well as health care systems-related barriers, including insufficient numbers of culturally competent counseling staff, limited access to psychological services beyond time-limited psychotherapies, and lack of programs that address the specific needs either of Ph.D. students in general 92 or of Ph.D. students belonging to marginalized groups 93 , 94 . Structural interventions focused solely on enhancing student resilience might include programs aimed at reducing stigma, fostering social cohesion, and reducing social isolation, while changing norms around help-seeking behavior 95 , 96 . However, structural interventions focused on changing stressogenic aspects of the graduate student environment itself are also needed 97 , beyond any enhancements to Ph.D. student resilience, including: undercutting power differentials between graduate students and individual faculty advisors, e.g., by diffusing power among multiple faculty advisors; eliminating racist, sexist, and other discriminatory behaviors by faculty advisors 74 , 75 , 98 ; valuing mentorship and other aspects of “invisible work” that are often disproportionately borne by women faculty and faculty of color 99 , 100 ; and training faculty members to emphasize the dignity of, and adequately prepare Ph.D. students for, non-academic careers 101 , 102 .

Our findings should be interpreted with several limitations in mind. First, the pooled estimates are characterized by a high degree of heterogeneity, similar to meta-analyses of depression prevalence in other populations 30 , 65 , 103 , 104 , 105 . Second, we were only able to aggregate depression prevalence across 16 studies and anxiety prevalence across nine studies (the majority of which were conducted in the U.S.) – far fewer than the 183 studies included in a meta-analysis of depression prevalence among medical students 30 and the 54 studies included in a meta-analysis of resident physicians 65 . These differences underscore the need for more rigorous study in this critical area. Many articles were either excluded from the review or from the meta-analyses for not meeting inclusion criteria or not reporting relevant statistics. Future research in this area should ensure the systematic collection of high-quality, clinically relevant data from a comprehensive set of institutions, across disciplines and countries, and disaggregated by graduate student type. As part of conducting research and addressing student mental health and wellbeing, university deans, provosts, and chancellors should partner with national survey and program institutions (e.g., Graduate Student Experience in the Research University [gradSERU] 106 , the American College Health Association National College Health Assessment [ACHA-NCHA], and HealthyMinds). Furthermore, federal agencies that oversee health and higher education should provide resources for these efforts, and accreditation agencies should require monitoring of mental health and programmatic responses to stressors among Ph.D. students.

Third, heterogeneity in reporting precluded a meta-analysis of the suicidality outcomes among the few studies that reported such data. While reducing the burden of mental health problems among graduate students is an important public health aim in itself, more research into understanding non-suicidal self-injurious behavior, suicide attempts, and completed suicide among Ph.D. students is warranted. Fourth, it is possible that the grey literature reports included in our meta-analysis are more likely to be undertaken at research-intensive institutions 52 , 60 , 61 . However, the direction of bias is unpredictable: mental health problems among Ph.D. students in research-intensive environments may be more prevalent due to detection bias, but such institutions may also have more resources devoted to preventive, screening, or treatment efforts 92 . Fifth, inclusion in this meta-analysis and systematic review was limited to those based on community samples. Inclusion of clinic-based samples, or of studies conducted before or after specific milestones (e.g., the qualifying examination or dissertation prospectus defense), likely would have yielded even higher pooled prevalence estimates of mental health problems. And finally, few studies provided disaggregated data according to sociodemographic factors, stage of training (e.g., first year, pre-prospectus defense, all-but-dissertation), or discipline of study. These factors might be investigated further for differences in mental health outcomes.

Clinically significant symptoms of depression and anxiety are pervasive among graduate students in doctoral degree programs, but these are understudied relative to other trainee populations. Structural and clinical interventions to systematically monitor and promote the mental health and wellbeing of Ph.D. students are urgently needed.

This systematic review and meta-analysis follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach (Supplementary Table S3 ) 107 . This study was based on data collected from publicly available bibliometric databases and did not require ethical approval from our institutional review boards.

Eligibility criteria

Studies were included if they provided data on either: (a) the number or proportion of Ph.D. students with clinically significant symptoms of depression or anxiety, ascertained using a validated scale; or (b) the mean depression or anxiety symptom severity score and its standard deviation among Ph.D. students. Suicidal ideation was examined as a secondary outcome.

We excluded studies that focused on graduate students in non-doctoral degree programs (e.g., Master of Public Health) or professional degree programs (e.g., Doctor of Medicine, Juris Doctor) because more is known about mental health problems in these populations 30 , 108 , 109 , 110 and because Ph.D. students face unique uncertainties. To minimize the potential for upward bias in our pooled prevalence estimates, we excluded studies that recruited students from campus counseling centers or other clinic-based settings. Studies that measured affective states, or state anxiety, before or after specific events (e.g., terrorist attacks, qualifying examinations) were also excluded.

If articles described the study sample in general terms (i.e., without clarifying the degree level of the participants), we contacted the authors by email for clarification. Similarly, if articles pooled results across graduate students in doctoral and non-doctoral degree programs (e.g., reporting a single estimate for a mixed sample of graduate students), we contacted the authors by email to request disaggregated data on the subsample of Ph.D. students. If authors did not reply after two contact attempts spaced over 2 months, or were unable to provide these data, we excluded these studies from further consideration.

Search strategy and data extraction

PubMed, Embase, PsycINFO, ERIC, and Business Source Complete were searched from inception of each database to November 5, 2019. The search strategy included terms related to mental health symptoms (e.g., depression, anxiety, suicide), the study population (e.g., graduate, doctoral), and measurement category (e.g., depression, Columbia-Suicide Severity Rating Scale) (Supplementary Table S4 ). In addition, we searched the reference lists and the grey literature.

After duplicates were removed, we screened the remaining titles and abstracts, followed by a full-text review. We excluded articles following the eligibility criteria listed above (i.e., those that were not focused on Ph.D. students; those that did not assess depression and/or anxiety using a validated screening tool; those that did not report relevant statistics of depression and/or anxiety; and those that recruited students from clinic-based settings). Reasons for exclusion were tracked at each stage. Following selection of included articles, two members of the research team extracted data and conducted risk of bias assessments. Discrepancies were discussed with a third member of the research team. Key extraction variables included: study design, geographic region, sample size, response rate, demographic characteristics of the sample, screening instrument(s) used for assessment, mean depression or anxiety symptom severity score (and its standard deviation), and the number (or proportion) of students experiencing clinically significant symptoms of depression or anxiety.

Risk of bias assessment

Following prior work 30 , 65 , the Newcastle–Ottawa Scale 111 was adapted and used to assess risk of bias in the included studies. Each study was assessed across 5 categories: sample representativeness, sample size, non-respondents, ascertainment of outcomes, and quality of descriptive statistics reporting (Supplementary Information S5 ). Studies were judged as having either low risk of bias (≥ 3 points) or high risk of bias (< 3 points).

Analysis and synthesis

Before pooling the estimated prevalence rates across studies, we first transformed the proportions using a variance-stabilizing double arcsine transformation 112 . We then computed pooled estimates of prevalence using a random effects model 113 . Study specific confidence intervals were estimated using the score method 114 , 115 . We estimated between-study heterogeneity using the I 2 statistic 116 . In an attempt to reduce the extent of heterogeneity, we re-estimated pooled prevalence restricting the analysis to studies conducted in the United States and to studies in which depression assessment was based on the 9-item Patient Health Questionnaire (PHQ-9) 117 . All analyses were conducted using Stata (version 16; StataCorp LP, College Station, Tex.). Where heterogeneity limited our ability to summarize the findings using meta-analysis, we synthesized the data using narrative review.

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Acknowledgements

We thank the following investigators for generously sharing their time and/or data: Gordon J. G. Asmundson, Ph.D., Amy J. L. Baker, Ph.D., Hillel W. Cohen, Dr.P.H., Alcir L. Dafre, Ph.D., Deborah Danoff, M.D., Daniel Eisenberg, Ph.D., Lou Farrer, Ph.D., Christy B. Fraenza, Ph.D., Patricia A. Frazier, Ph.D., Nadia Corral-Frías, Ph.D., Hanga Galfalvy, Ph.D., Edward E. Goldenberg, Ph.D., Robert K. Hindman, Ph.D., Jürgen Hoyer, Ph.D., Ayako Isato, Ph.D., Azharul Islam, Ph.D., Shanna E. Smith Jaggars, Ph.D., Bumseok Jeong, M.D., Ph.D., Ju R. Joeng, Nadine J. Kaslow, Ph.D., Rukhsana Kausar, Ph.D., Flavius R. W. Lilly, Ph.D., Sarah K. Lipson, Ph.D., Frances Meeten, D.Phil., D.Clin.Psy., Dhara T. Meghani, Ph.D., Sterett H. Mercer, Ph.D., Masaki Mori, Ph.D., Arif Musa, M.D., Shizar Nahidi, M.D., Ph.D., Arthur M. Nezu, Ph.D., D.H.L., Angelo Picardi, M.D., Nicole E. Rossi, Ph.D., Denise M. Saint Arnault, Ph.D., Sagar Sharma, Ph.D., Bryony Sheaves, D.Clin.Psy., Kennon M. Sheldon, Ph.D., Daniel Shepherd, Ph.D., Keisuke Takano, Ph.D., Sara Tement, Ph.D., Sherri Turner, Ph.D., Shawn O. Utsey, Ph.D., Ron Valle, Ph.D., Caleb Wang, B.S., Pengju Wang, Katsuyuki Yamasaki, Ph.D.

A.C.T. acknowledges funding from the Sullivan Family Foundation. This paper does not reflect an official statement or opinion from the County of San Mateo.  

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A.C.T. conceptualized the study and provided supervision. T.K. conducted the search. E.N.S. contacted authors for additional information not reported in published articles. E.N.S. and T.K. extracted data and performed the quality assessment appraisal. E.N.S. and A.C.T. conducted the statistical analysis and drafted the manuscript. T.K., M.V.K., R.A., S.C., H.L., X.L., C.H.L., I.R., S.S., M.T. and M.Y. contributed to the interpretation of the results. All authors provided critical feedback on drafts and approved the final manuscript.

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Satinsky, E.N., Kimura, T., Kiang, M.V. et al. Systematic review and meta-analysis of depression, anxiety, and suicidal ideation among Ph.D. students. Sci Rep 11 , 14370 (2021). https://doi.org/10.1038/s41598-021-93687-7

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Types of Anxiety and Depression: Theoretical Assumptions and Development of the Anxiety and Depression Questionnaire

Małgorzata fajkowska.

1 Institute of Psychology, Polish Academy of Sciences, Warsaw, Poland

Ewa Domaradzka

Agata wytykowska.

2 SWPS University of Social Sciences and Humanities, Warsaw, Poland

Associated Data

The present paper is addressed to (1) the validation of a recently proposed typology of anxiety and depression, and (2) the presentation of a new tool—the Anxiety and Depression Questionnaire (ADQ)—based on this typology. Empirical data collected across two stages—construction and validation—allowed us to offer the final form of the ADQ, designed to measure arousal anxiety, apprehension anxiety, valence depression, anhedonic depression, and mixed types of anxiety and depression. The results support the proposed typology of anxiety and depression and provide evidence that the ADQ is a reliable and valid self-rating measure of affective types, and accordingly its use in scientific research is recommended.

Introduction: anxiety and depression as personality types

This paper is aimed at presenting the validity of a newly proposed typology of anxiety and depression, formulated within the systemic approach to personality (Fajkowska, 2013 , 2015 ) which employed General System Theory (e.g., von Bertalanffy, 1968 ) and the self-report instrument that grew within this theory. The article is divided into three sections. In the Introduction section, the theoretical background of this instrument is demonstrated. In the empirical part of the paper, we report the results of our development of the Anxiety and Depression Questionnaire (ADQ) across construction (Study 1) and validation (Study 2) stages. Finally, in the Discussion section we advocate the theoretical and applied value of this theory and the usefulness of the ADQ in research and practice.

An appropriate point of departure might be the question of why we need another theory and questionnaire to describe, explain, and differentiate between anxiety and depression.

First, the presented theory allows for examining anxiety and depression in a general, not only clinical population. It seems to be very important in light of the latest meta-analysis (e.g., Ayuso-Mateos et al., 2010 ). Among others points, it demonstrated that the consequences of anxiety/depression for the general well-being in non-clinical populations when the main/full range of clinical criteria of anxiety/depression are not identified (e.g., low intensity of symptoms, low number of symptoms) are comparable with clinical populations. This implies the significance of analyzing the mechanisms of non-clinical forms of anxiety/depression and assessing them in the self-report instruments. As a review of the appropriate literature suggests, there are not many approaches and questionnaires that fulfill this need (see Fajkowska, 2013 ).

Second, the proposed theory represents a belief that non-clinical forms of anxiety/depression can be seen as relatively stable personality characteristics and reflects the newest results of the studies on cognitive and affective mechanisms in anxiety/depression (e.g., Eysenck and Fajkowska, 2017 for a review). Therefore, the questionnaire developed within it permits more precise hypotheses related to the origin of anxiety/depression to be formulated, supports the understanding of different consequences of functioning in these phenomena, and allows them to be evaluated on the basis of their maladaptive mechanisms (e.g., attentional, cf. Arditte and Joormann, 2014 ).

Third, the central finding in previous studies of anxiety and depression is the high degree of comorbidity that occurs between them (e.g., Gorman, 1996 ). Possible explanations of this co-occurrence relate to the poor discriminant validity of measures (e.g., Fox, 2008 ) and the fact that both phenomena are associated with negative affect (e.g., Watson, 2000 ), stressful life events (Naragon-Gainey and Watson, 2011 ), and impaired cognitive processes or a common biological/genetic diathesis (Watson and Kendall, 1989 ; Fox, 2008 ).

However, despite a set of nonspecific features, anxiety and depression are clearly not identical phenomena. The theory demonstrated here advocates that the differences between them might be best viewed through their heterogeneous and multilayered nature, adaptive functions, and relations with regulatory processes, positive affect, and motivation or complex cognitive processes (cf. Fajkowska, 2013 ). More precisely, differentiation should be improved by reducing the importance of overlapping features and by giving greater weight to distinctive aspects of these affective phenomena.

To meet all these points, Fajkowska ( 2013 , 2015 ) suggests grouping anxiety and depression based on two criteria:

The organization of (A) personality trait/type, (B) anxiety types, (C) depression types according to the three-level hierarchy (cf. Fajkowska, 2013 , 2015 ).

• The dominant functions (reactive or regulative) they play in stimulation processing (a transformation of arousal and activation, which arises as an effect of flowing stimulation, e.g., sensory, emotional, cognitive, leading to changes within different systems of the organism, e.g., motor, cognitive, or motivational). The dominant functions of a trait or type in stimulation processing might be considered as the emergent properties located between the level of structures and behavioral markers (see Figure ​ Figure1A). 1A ). In other words, these functions are rooted in structures and can be identified through overt reactions and behaviors (cf. Fajkowska, 2013 ). Traits/types with a reactive dominant (e.g., anxiety, Spielberger, 1983 ) inform about individual differences in the reception of flowing stimulation; they denote a high sensitivity or vigilance (e.g., sensory) to stimuli and rather automatic and immediate readiness to activity (reaction, behavior), and relate to energy expenditure (in a particular time range). For instance, the reactive function in anxiety can be identified through its associations with hypervigilance to threatening material or social evaluation (e.g., Eysenck, 2006 ). Traits/types with a regulative dominant indicate individual differences in energy expenditure (in a particular range of time) and more strategic than automatic/immediate directing and monitoring of the flowing stimulation, adequately to the organism's capacities for stimulation processing. For example, the regulative function in openness (Costa and McCrae, 1992 ) can be identified through its associations with creative and innovative strategies used to pursue one's goals (DeYoung, 2010 ). Additionally, the structural complexity of traits/types influences their controlling functions, which implies that different controlling functions might coexist in one trait (e.g., reactive-regulative in neuroticism, Eysenck, 1998 ). Thus, here anxiety and depression contribute to stimulation processing in that they relate to arousal, activation, and activity in different neurobiological and physiological systems (cf. Robinson and Compton, 2006 ). Therefore, it is further suggested that anxiety and depression can be differentiated according to the different functions they reveal in stimulation processing.

Although Fajkowska ( 2013 ) acknowledges that to some extent her categorization capitalizes on prior (neuro-)psychological models of emotion (cf. Heller, 1993a , b ; Watson, 2000 ), these approaches seem to be rather categorical, while she suggests a dimensional typology (e.g., Eysenck, 1970 ; Strelau, 2014 p. 44–45). It enables the shared or separate structural components to be captured and to explain overlapping or distinctive functions in stimulation processing among types of anxiety and depression. Moreover, the current classification of anxiety and depression offered by the DSM-5 (American Psychiatric Association, 2013 ), although more dimensional than the previous DSM, seems not to be very supportive in solving some of the cardinal theoretical concerns in this area, e.g., specificity of the structure of affect or specificity of attentional biases in both anxiety and depression. Thus, a promising avenue to provide possible solutions to these concerns might be, as suggested here, an alternative grouping.

Types of anxiety

Structural composition.

The starting point for the identification of anxiety types is to point to relevant processes and mechanisms (the lowest level) that contribute to structures of anxiety types (the middle level), and to associate them with the relevant behavioral markers (the highest level; see Figure ​ Figure1B 1B ).

• Complex inner mechanisms —With respect to the appropriate literature, Fajkowska ( 2013 , 2015 ) assumes that somatic and cognitive processes are key for anxiety structuralization. The repetitive interactions among cognitive mechanisms (e.g., connected with attentional and working memory systems) and among somatic mechanisms (related to affective and motivational systems) lead to more integrated cognitive and somatic entities, from which emerge two essential elements that compose anxiety types: somatic-related arousal and cognitive-related apprehension (see Figure ​ Figure1B 1B ).
• Components/structures —Thus, by interacting with each other, different levels of arousal and apprehension produce different types of anxiety at the level of structures (see Figure ​ Figure1B). 1B ). When the proportion between the degree of apprehension and degree of arousal is in favor of arousal, it suggests the Arousal Type of anxiety . When it is in favor of apprehension it produces the Apprehension Type of anxiety , and relatively equal (but high) levels of apprehension and arousal build the Mixed Type of anxiety [in previous publications (Fajkowska, 2013 , 2015 ) used a misleading term, Balanced Type of anxiety, which suggested a positive type, but in fact it is composed of two disruptive elements].

Anxious arousal is described (cf. Watson et al., 1995 ; Watson, 2000 ) as being distinguished by symptoms of physiological hyperarousal and somatic tension, while anxious apprehension is primarily characterized by worry and verbal rumination, typically about future events (Barlow, 1991 ; Heller, 1993a , b ; Heller et al., 1997 ; Heller and Nitschke, 1998 ). However, the relation between autonomic reactivity and anxious apprehension is not clear. Some studies report the connection of worrisome thoughts with elevated autonomic responsiveness (e.g., Nitschke et al., 1999 ), while others with autonomic rigidity (e.g., Thayer et al., 1996 ). The latter one seems to be more convincing, as worrisome thoughts are seen as a strategy for avoiding emotional arousal.

Panic attacks, phobias, high-stress states, and state anxiety as defined by self-report, behavioral, or physiological response systems would be covered by the Arousal Type (cf. Heller and Nitschke, 1998 ; Watson, 2000 ; American Psychiatric Association, 2013 ). It seems probable that the Apprehension Type would be characteristic of generalized anxiety states (GAD) and trait anxiety as identified by self-reports of anxious apprehension and worry on various questionnaires (cf. Heller and Nitschke, 1998 ; American Psychiatric Association, 2013 ). Theoretically, the Mixed Type might be identified among all the categories of anxiety mentioned above.

• Behavioral markers —The dominance of a particular component (arousal or apprehension) in a particular type of anxiety specifically determines the manner of stimulation processing, as well as patterns of response to stimulation across different response systems. More precisely, with reference to a review of the literature, it may be concluded that the typical patterns of attentional stimulation processing (that is reactions, behavioral acts) in the Arousal Type of anxiety are associated with (a) increased “early” attentional vigilance to threat (usually in clinical anxiety) and “later,” but unconscious, attentional avoidance of threat (usually in the non-patient group) (e.g., Calvo and Eysenck, 2000 ; Fox et al., 2002 ; Mathews and MacLeod, 2002 ; Wilson and MacLeod, 2003 ; Hock and Krohne, 2004 ; Heim-Dreger et al., 2006 ; Fisher et al., 2010 ); (b) elevated autonomic reactivity in the presence of threat (e.g., Sapolsky, 1992 ; Nitschke et al., 1999 ; Lovallo and Gerin, 2003 ; Hock and Krohne, 2004 ; Applehans and Luecken, 2006 ; Heim-Dreger et al., 2006 ; Fisher et al., 2010 ); and (c) right-hemisphere involvement in threatening stimuli processing (e.g., Heller et al., 1997 ; Compton et al., 2003 ; Engels et al., 2007 ; Mathersul et al., 2008 ). Accordingly, the typical patterns of stimulation processing in the Apprehension Type of anxiety are associated with (a) reduced attentional control and related impairment to the effectiveness of stimulation processing and avoidance of threatening stimuli (in clinical and nonclinical groups and trait anxiety; e.g., Laguna et al., 2004 ) (b) reduction in autonomic reactivity (e.g., Hoehn Saric et al., 1989 ; Borkovec and Ray, 1998 ) (c) impairment/inhibition of emotional processing, both on an attentional and physiological level (e.g., Stöber, 1998 ) and (d) left-hemisphere involvement in stimulation processing (cf. Tucker et al., 1978 ; Baxter et al., 1987 ; Swedo et al., 1989 ; Wu et al., 1991 ; Heller and Nitschke, 1997 , 1998 ; Wagner, 1999 ; Fletcher and Henson, 2001 ; Nitschke and Heller, 2002 ; Hofmann et al., 2005 ).

Dominant functions

Thus, recognizing the presented above behavioral markers allows us to establish the dominant controlling function of each type: reactive rather than regulative in arousal anxiety (identified through more automatic stimulation processing related to attentional vigilance-avoidance, and also through elevated autonomic reactivity) and regulative rather than reactive in apprehension anxiety (identified through more strategic but ineffective stimulation processing related to reduced attentional control). It is assumed that the Mixed Type of anxiety is a functionally balanced type that represents a reactive-regulative function in stimulation processing.

Types of depression

With reference to the identification of depression types (the middle level), the crucial mechanisms contributing to the formation of their structure are proposed (the lowest level) along with their related behavioral markers (the highest level; see Figure ​ Figure1C 1C ).

• Complex inner mechanisms —In congruence with the relevant literature, Fajkowska ( 2013 ) proposed that cognitive and emotional-motivational processes are crucial in the formation of the structure of depression subtypes. The recurring interactions among cognitive mechanisms (connected with valence undersensitivity in attentional systems, e.g., Davidson et al., 1995 ), emotional mechanisms (linked with negative emotional experience, e.g., Beck et al., 1979 ) and the repetitive interactions among motivational mechanisms (associated with impaired control, anhedonia, reduction in response to reward-related stimuli, and a lack of positive reinforcement, e.g., Sloan et al., 2001 ), coupled with a deficit in approach behavior (e.g., Henriques and Davidson, 2000 ) lead to more integrated entities, from which in turn emerges more cognitive-related valence insensitivity and more emotion- and motivation-related anhedonia (see Figure ​ Figure1C 1C ).
• Components/structures —Thus, dynamic interactions between the higher-ordered components—anhedonia and valence insensitivity—produce three types of depression: the Valence Type of depression , where the degree of valence insensitivity dominates the degree of anhedonia; the Anhedonic Type of depression , where the degree of valence insensitivity is dominated by the degree of anhedonia; and the Mixed Type of depression (previously named Balanced Type of depression, cf. Fajkowska, 2013 ), with a structure resting on a relative balance between (high levels of) the two components.

Thus, the valence insensitivity to stimulation is typical for non-melancholic forms of depression, while anhedonia is the key feature of melancholic depression (Heller and Nitschke, 1998 ; Watson, 2000 ), i.e., the inability to experience pleasure in all activities and a lack of responsiveness to pleasurable stimulation. However, melancholic and non-melancholic depression share many symptoms related to anhedonia, such as sadness, indecisiveness, feelings of guilt, and valence-related insensitivity such as inaccuracy in emotion recognition or inability to differentiate emotional states (Fajkowska, 2013 ).

All these types might be present in both nonclinical (depressed mood) and clinical forms of depression. The Valence Type embraces non-melancholic subtypes of depression, while the Anhedonic Type covers the melancholic subtypes (e.g., MDD) suggested by the DSM-5 (American Psychiatric Association, 2013 ). The Valence Type is treated here as an exogenous and state-like type, primarily connected with a biased cognitive system on account of the content or valence of stimulation. It is also connected with very high negative affectivity (see Fajkowska, 2013 for a review). The Anhedonic Type is relevant to an endogenous and trait-like type (cf. Rubino et al., 2009 ) and is primarily connected with impaired control in stimulation processing, motivational deficits, very high negative affect and very low positive affect (Watson, 2000 ). The Mixed Type of depression is a matter for future research.

• Behavioral markers —A review of the literature allows for the conclusion that specific patterns of attentional stimulation processing in the Valence Type depression are related to (a) attentional avoidance reflected in valence insensitivity to emotional and social material (e.g., Gotlib et al., 2000 ; Watson, 2000 ; Fox, 2008 ), and (b) increased right-hemisphere activity in stimulation processing (e.g., Heller and Nitschke, 1997 ; Parker et al., 1999 ; Nitschke et al., 2001 ; Sato et al., 2001 ; Tembler and Schüßler, 2009 ; Hecht, 2010 ). On the basis of both theoretical and empirical evidence, it turns out that specific patterns of stimulation processing in the Anhedonic Type of depression are related to (a) impaired attentional control, or sustained attention over positive as well as negative material (e.g., Bargh et al., 1988 ; Gotlib and MacLeod, 1997 ; Westra and Kuiper, 1997 ; Egeland et al., 2003 ; Marszał-Wiśniewska and Fajkowska-Stanik, 2005 ; Withall et al., 2009 ; Bourke et al., 2010 ), and (b) decreased left-hemisphere activity in stimulation processing (e.g., Bench et al., 1992 ; Heller, 1993a ; Bruder, 1995 ; Hecht, 2010 ; Schock et al., 2011 ).

Again, the identification of the above presented behavioral markers allowed the dominant controlling functions of each subtype to be established: reactive rather than regulative in valence depression (identified through more automatic stimulation processing related to attentional avoidance of stimuli), and regulative rather than reactive in anhedonic depression (identified through more strategic but ineffective stimulation processing related to reduced attentional control and inability to sustain attention over stimulation). In the Mixed Type of depression the mixed, reactive-regulative function over stimulation processing is postulated.

Operationalization of the anxiety and depression types

The evidence discussed here provides important information that has contributed to the development of precise definitions of anxiety types according to their structural components and functions in controlling stimulation (see Figure ​ Figure2A 2A ):

Operationalization of (A) anxiety types, (B) depression types (cf. Fajkowska, 2013 , 2015 ).

Reactive Arousal Type of anxiety is composed of:

• Somatic Reactivity —Elevated autonomic reactivity, psychophysiological hyperarousal and somatic tension (e.g., trembling hands, palpitations, sweating, gastric problems, shortening breath) in the presence of actual or anticipated threat or negative stimulation.
• Panic/Phobia —Presence of panic symptoms, distress (e.g., related to fear of heights or new situations or objects) and phobias (e.g., social).
• Attentional Vigilance/Avoidance —“Early” attentional vigilance toward threat (very fast identification of threat or negative social signals appearing in the attentional field), usually in clinical forms of anxiety, and “later” attentional avoidance of this threat (rather instinctive than intentional withdrawal from dangerous and threatening situations present in the attentional field for some time), usually in the non-patient groups.

Regulative Apprehension Type of anxiety is composed of:

• Worrisome thoughts —Referring to physical, emotional, or symbolic threats to the self; they relate to social evaluation of one's behavior or competences; sometimes their content may include general world problems;
• Somatic Reactivity —Elevated autonomic reactivity in the presence of threat, or because of worrisome thoughts; it goes with reduced capacities of emotional processing on autonomic and somatic levels.
• Attentional control —Reduced attentional control, that relates to difficulties in attentional (a) shifting, (b) focusing, and (c) disengagement from negative experiences; (d) it undergoes distracting thoughts, and (e) reveals itself in impaired inhibition in processing of negative emotional material, connected with failure or negative experiences or events;

Mixed Type of anxiety represents balanced apprehension and arousal elements and balanced reactive and regulative functions of stimulation processing. Speculatively, it covers specific patterns of stimulation processing of both primary types (arousal and apprehension) and their activation might be situation-dependent.

Figure ​ Figure2B 2B summarizes definitions of the three types of depression according to their structural components and the functions they play in stimulation processing.

Reactive Valence Type of depression goes with:

• Negative Affect —Manifested in increased level of anxiety, tension, hostility, anger, sadness, self-sensitivity, and social avoidance.
• Attentional Avoidance —Identified through (a) valence insensitivity to emotional and social material, i.e., delayed or constricted attention allocation toward emotional material, inaccurate recognition of emotional material regardless of its (positive or negative) content and (b) insensitivity to social material, including emotions appearing in the social context.

Regulative Anhedonic Type of depression includes:

• Emotional-Motivational Deficits —Revealed in (a) the inability to experience pleasure and decreased reactivity to pleasurable things and events, (b) difficulties in goal achievement and loss of interest in pursuing goal-directed activities, and (c) failure in delivering sufficient pleasure or reward following approach behaviors.
• Positive Affect —Extremely low positive affect; very low level of positive emotions, e.g., self-confidence, happiness, hope, or satisfaction.
• Negative Affect —Extremely high negative affect; very high level of negative emotions, e.g., sadness, guilt, shame, sense of loss, disappointment, anxiety, and loneliness.
• Attentional Control —Impaired attentional control, indicating (a) decrement in sustained vigilance to emotional material, (b) slower and inaccurate response to emotional material (e.g., slower reactions to positive material and inaccurate recognition of negative material), (c) inability to sustain effort in processing emotional material (regardless of its valence), and (d) difficulties in attentional focusing.

Mixed Type of depression is defined through the relative balance of the valence and anhedonia elements, and balanced reactive and regulative functions of stimulation processing. It most probably comprises specific patterns of stimulation processing of both primary (valence or anhedonic) types, and their activation might depend on the specific situation.

Even though some of the structural components overlap across various affective types, they do not always mean the same. Somatic reactivity is a component very specific for anxiety (cf. Watson, 2000 ), thus it appears in both types of anxiety. However, it has different causes and expressions. In arousal anxiety it is a primary element, while in the apprehension type it is not a crucial one, it is caused by worrisome thoughts and is rather expressed as reduced somatic reactivity. Next, attentional control is present in both regulative types, i.e., apprehension anxiety and anhedonic depression. In apprehension anxiety it appears as an effect of worrisome thoughts and primarily indicates impaired inhibition functioning, while in anhedonic depression it appears as an effect of emotional-motivational deficits and negatively influences prolonged and sustained attention. Finally, negative affect is a part of the structure in both depression types. Nonetheless, hostility and anger are typical for valence depression, while for anhedonic depression it is guilt and shame.

With reference to dominant functions in controlling stimulation processing, one should expect similarities in patterns of stimulation processing (e.g., in attentional processing) across types, i.e., reactive types, regulative types and functionally balanced types, and differences within types, i.e., between reactive and regulative types (see Figure ​ Figure2 2 ).

Anxiety and depression questionnaire (ADQ)

A psychometric study was conducted with the aims of revising the postulated anxiety and depression types (Fajkowska, 2013 , 2015 ) and of constructing an instrument, the Anxiety and Depression Questionnaire (ADQ), which corresponds to the six affective types. Consequently, we proposed four scales of the ADQ to directly measure arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD). However, in line with the theory (Fajkowska, 2013 , 2015 ), the mixed types of anxiety and depression should be regarded, respectively, as the ratio of arousal anxiety to apprehension anxiety and valence depression to anhedonic depression, and they have the status of secondary types (cf. balance of the nervous processes as a secondary trait/scale, Strelau et al., 1999 ).

General plan of research and analysis

The elaboration and development of the ADQ has been divided into construction (Study 1) and validation (Study 2) stages.

The aim of Study 1 was the generation of items and delivering psychometric characteristics of the preliminary version of the ADQ. Thus, for four scales of the ADQ we evaluated (a) the discriminatory power of items to answer the question to what degree the particular test positions differentiate among individuals; (b) confirmatory factor analysis to test the theoretically suggested structure of the particular ADQ scales; (c) intercorrelations of subscales within the scales of the ADQ to check if theoretically predicted relations among them are supported by empirical data; and (d) internal consistency for all scales of the ADQ to measure the extent to which all of the items of a certain scale measure the same latent variable. With these data we made appropriate corrections to propose the final form of the ADQ.

The Study 2 was organized around demonstration of the quantitative description of items and scales of the ADQ, reliability and validity of the ADQ, and verification of the assumed position of the affective types among other personality characteristics.

Construction stage—study 1

Extensive research aimed at constructing the ADQ consisted of the generation of items, linguistic evaluation of items, evaluation of content validity, on-line administration of the questionnaire to respondents, and elaboration of the experimental version of the questionnaire based on the results from testing of discriminatory power of items, confirmatory factor analysis, intercorrelational analysis of subscales, and internal consistency.

Materials and methods

Generation of the adq items.

The first development stage involved the generation of items (experts, n = 4; Ph.D. students of psychology, n = 4) that will form the four scales of the ADQ. This generation was guided by methodological requirements underlying the construction of personality inventories (cf. Zawadzki, 2006 ) and operational definitions of each affective type. Additionally, about five percent of the total number of items was taken, mostly in slightly modified versions, from other inventories [e.g., Attentional Control Scale (ACS), (Fajkowska and Derryberry, 2010 ), Mood and Anxiety Symptom Questionnaire (MASQ), (Watson, 2000 ), The Penn State Worry Questionnaire, (Meyer et al., 1990 )].

The linguistic analysis of items, as well as the assessment of the content validity (sorting items into types and subscales, experts, n = 4), led to the development of an item pool for each scale. Arousal anxiety consists of 64 items grouped into subscales of Somatic Reactivity (SR, 35 items), Panic/Phobia (PP, 18), and Attentional Vigilance/Avoidance (AVA, 11), while apprehension anxiety has 89 items in subscales of Worrisome Thoughts (WT, 22), Attentional Control (AC, 44), Attentional Avoidance (AA, 13), and Somatic Reactivity (rSR, 10; “r” means that items indicate reduced somatic activity). It should be noted that although Attentional Avoidance is not mentioned in the definition of apprehension anxiety, we introduced this scale experimentally as some studies report significant connections between attentional avoidance and apprehension (Laguna et al., 2004 ).

Valence depression consists of 71 items clustered around Negative Affect (NA, 50) and Attentional Avoidance (AA, 21), and for anhedonic depression there are 139 statements in four subscales of Emotional-Motivational Deficits (EMD, 87), Positive Affect (PA, 12), Negative Affect (NA, 26), and Attentional Control (AC, 14). Attention was paid during all stages of item generation to keep a balanced keying within each of the scales.

Participants

Since the theory assumes that affective types are personality traits rather than purely clinical disorders, the study was conducted on two general, non-clinical samples. Both samples matched the demographic structure of the Polish population.

The first sample ( N = 1,109) consisted of 546 males (49.2%) and 563 females (50.8%) with a mean age of 39.19 ( SD = 13.59; range 18–65 years). Participants filled out the two scales of the ADQ measuring anxiety types: ADQ-ArA and ADQ-ApA.

39.7% held university degrees, 36.6% finished high school, 15.9% vocational school, and 7.8% elementary school. The participants were of various professional and educational backgrounds, including university students, high-school students, working people, white-collar workers, part-time workers, unemployed, and pensioners. 49.3% of the participants came from villages and small towns, 20% from cities, and 30.7% from big cities and metropolises. 26.1% reported that they had experienced anxiety disorders in the past, and 15.3% suffered from anxiety at the moment of the study. Moreover, 10.1% of respondents benefited from psychological and psychiatric help, and 3.4% were hospitalized because of severe anxiety. The respondents specified different phobias, panic attacks, separation anxiety, and anxiety coexisting with other disorders and states (e.g., depression, traumatic experiences and addictions, reaction to rape, violence, and unsuccessful social and family relations).

The second sample contained 1,086 participants (549 [50.6%] females and 537 [49.4%] males) who were on average 39.01 years old ( SD = 13.33; range 18–65 years). They completed the ADQ-VD and the ADQ-AD scales, assessing depression types.

36.6% of the participants completed university education, 36.1% finished high school, 20.3% vocational school, and the remaining 7% elementary school. They represented different professions and schools, including university students, high-school students, working people, white-collar workers, part-time workers, and pensioners. 48.8% of the sample represented inhabitants of villages and small towns, 20.4% of cities, and 30.8% of big cities and metropolises. 22.9% of individuals acknowledged suffering from depression in the past and 12% in the present. In addition, 12.3% received help from psychological and psychiatric services, and 4.5% underwent hospitalization because of depression. They reported reactive depression (e.g., because of loss of a close person or job, difficult family relationships), bipolar depression, major depression (with dominating sadness, lack of values in life, low self-esteem, suicidal thoughts) and depression concomitant with other disorders (e.g., alcoholism, psychosis, borderline personality).

This study was approved by the ethics committee of the Institute of Psychology, Polish Academy of Sciences. Participants provided their written informed consent before the on-line procedure was activated. Participants were recruited from an on-line research panel and every person who completed the full procedure on-line received points that were exchangeable for rewards. The order of the questionnaires was randomized across subjects. Altogether they contained 153 (anxiety questionnaires) and 210 (depression questionnaires) agree-disagree items that allow the assessment of arousal anxiety and apprehension anxiety in the first sample, and valence and anhedonic depression in the second sample.

All of the items included in the arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) scales are summed to score (1 point per diagnostic item Agree/Disagree), though extra calculations are required for the AC subscales of apprehension anxiety (ADQ-ApA) and for the anhedonic depression (ADQ-AD) scales. To calculate the scores on these scales, the obtained score should be subtracted from the maximum possible score in the given scale, because we are interested in evaluating decreased attentional control (while the items measure the strength of attentional control).

Statistical analysis

We performed analyses on the discriminatory power of items (Youle's Phi coefficients; ϕ, Phi), confirmatory factor analysis (CFA), intercorrelations of subscales (Pearson's r ) and internal consistency (Cronbach's α coefficients) in the first and second sample separately.

Discriminatory power of items

Discriminatory power of items was used as the criterion for excluding the preliminarily selected items from the ADQ. For all scales of the ADQ, the number of items that was kept depended on their Youle's Phi coefficients (ϕ,). We decided to apply the value ϕ ≥ 0.30 as it is a correlation between the item score and the overall scale score reduced by this item, which is usually lower than the correlation between item and the total scale score, and values 0.30 and above indicate good and very good discrimination (see Drwal and Brzozowski, 1995 ). The number of remaining items in each subscale of each ADQ scale was sufficient for further statistical analysis (ArA = 42, ApA = 62, VD = 36, AD = 69).

Confirmatory factor analysis

Confirmatory factor analysis (CFA) was used to verify the factor structure of the set of introduced variables (Kline, 2005 ). More precisely, we tested the theoretically suggested structures of the affective types. We expected that the models including the number of factors derived from the theory will show a better fit than other (e.g., one-factor) models. The analyses were performed in Mplus (Muthén and Muthén, 1998 ), which enables models of binary data to be built (see Górniak, 2000 ; Hox, 2002 ). Because χ 2 values for the models fit across all scales of the ADQ were significant (suggesting a lack of fit between the hypothesized models and the data) and due to the sensitivity of χ 2 in large samples, other fit indices were assessed and reported, namely: Comparative Fit Index (CFI); Root Mean Square Error of Approximation (RMSEA); Tucker Lewis Index (TLI) (Kline, 2005 ).

In arousal anxiety (ADQ-ArA) we compared a one-factor solution with a three-factor solution. The fit indexes reflected the improvement in fit of the three-factor model [RMSEA = 0.051; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.93; χ ( 816 ,   N   =   1 , 109 ) 2 = 3211.13, p < 0.001] over the alternative one [RMSEA = 0.054; CFI = 0.91; TLI = 0.91; χ 2 /df = 4.24, χ ( 819 ,   N   =   1 , 109 ) 2 = 3474.33, p < 0.001]. All items loaded significantly onto their respective factors (loadings ranging from 0.66 to 0.84 on the SR subscale, from 0.43 to 0.87 on the PP subscale, and between 0.29 and 0.86 on the AVA subscale). None of the test positions were eliminated from further analysis.

To assess the factor structure of the ADQ-ApA, the fit of one-factor and four-factor models was examined. As predicted, the model fit for the four factors [RMSEA = 0.055; CFI = 0.87; TLI = 0.87; χ 2 /df = 4.33; χ ( 1 , 823 ,   N   =   1 , 109 ) 2 = 7908.17, p < 0.001] was better than for one factor [RMSEA = 0.057; CFI = 0.86; TLI = 0.85; χ 2 /df = 4.56; χ ( 1 , 829 ,   N   =   1 , 109 ) 2 = 8354.69, p < 0.001]. Given that the four-factor model did not reach the fit parameters (CFI, TLI) over 0.90, items with the lowest factor loadings were removed. Indeed, the fit indexes improved [RMSEA = 0.053; CFI = 0.92; TLI = 0.92; χ 2 /df = 4.16; χ ( 1 , 420 ,   N   =   1 , 109 ) 2 = 5918.256, p < 0.001]; however, the left items were not differentiated in their meaning as the factor analysis favors items similar in their content. Thus, for the sake of better psychological and theoretical rationality of items, we decided to keep all of them for subsequent elaboration of the ADQ-ApA. As a result, factor loadings for the WT items ranged from 0.49 to 0.89, for the AC varied from 0.36 to 0.85, for the AA extended from 0.43 to 0.86, and for the rSR oscillated from 0.47 to 0.87.

In case of the ADQ-VD we tested one-factor and two-factor models. Contrasting with the first model, the latter one had a good fit [respectively, RMSEA = 0.071; CFI = 0.88; TLI = 0.87; χ 2 /df = 6.46; χ ( 816 ,   N   =   1 , 109 ) 2 = 3211.133, p < 0.001; and RMSEA = 0.057; CFI = 0.92; TLI = 0.92; χ 2 /df = 4.56; χ ( 594 ,   N   =   1 , 086 ) 2 = 3838.70, p < 0.001]. The lowest factor loading was 0.58 and the highest 0.86 on the NA factor, while on the AA factor we had a range of factor loadings from 0.59 to 0.84.

The hypothetical model of the structure of the ADQ-AD was grounded on four factors, but we examined three competing models of one-factor, three-factors (EMD, NA—boosted by the items of PA, treated as reversed, and of AC), and four-factors (EMD, NA, PA, and AC). The findings suggested that the four-factor solution was the best one [RMSEA = 0.049; CFI = 0.93; TLI = 0.93; χ 2 /df = 3.63; χ ( 2 , 271 ,   N   =   1 , 086 ) 2 = 8254.56, p < 0.001] compared to the one- and three-factor models [respectively, RMSEA = 0.051; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.82; χ ( 2 , 277 ,   N   =   1 , 086 ) 2 = 8717.44, p < 0.001; and RMSEA = 0.050; CFI = 0.92; TLI = 0.92; χ 2 /df = 3.71; χ ( 2 , 274 ,   N   =   1 , 086 ) 2 = 8441.85, p < 0.001]. The final assessment concerned the factor loadings. For the EMD they ranged from 0.64 to 0.89, for the NA from 0.74 to 0.93, for the PA from 0.63 to 0.93, and for the AC from 0.32 to 0.91. Two items of the AC subscale with the lowest factor loadings were kept after linguistic correction.

Intercorrelations of subscales

With reference to the relevant data (Fajkowska, 2013 , for a review see: Watson, 2000 ), we expected positive correlations (Pearson's r ) among the ADQ-ArA subscales. Indeed, the analyses revealed a positive relationship between the SR subscale and the PP subscale (0.32, p < 0.01), the SR and the AVA subscales (0.28, p < 0.01), and the PP and the AVA subscales (0.18, p < 0.01).

Supported by the neuropsychological models of emotions (e.g., Heller, 1993a , b ; Heller and Nitschke, 1998 ; for a review see: Fajkowska, 2013 ) in the case of the ADQ-ApA, we predicted (a) positive relations among subscales WT, AA, and rSR, and (b) negative relations between the AC subscale and WT, AA, and rSR subscales. The obtained results (Pearson's r ) confirmed these speculations to some extent. As predicted, the WT subscale correlated positively with the AA subscale (0.23, p < 0.01) and negatively with the AC subscale (−0.44, p < 0.01). However, contrary to expectations rSR correlated negatively with the WT and AA subscales (−0.44, p < 0.01 and −0.23, p < 0.01, respectively). Many psychophysiological studies reveal that anxious apprehension, unlike other anxious states, are not associated with a greater response of the autonomic system but rather with autonomic rigidity (e.g., Hoehn Saric et al., 1989 ; Thayer et al., 1996 ). However, in the long-term perspective it was observed that in addition to worry, physical symptoms and elevated physiological arousal often accompany anxious apprehension (e.g., Nitschke et al., 1999 ; Laguna et al., 2004 ). These outcomes are in line with our findings. Thus, the elevated autonomic responsiveness relates to decreased attentional control (positive correlation of rSR with AC, 0.32, p < 0.01).

With regards to the appropriate data presented in the literature (see Fajkowska, 2013 for a review), we anticipated a positive but weak correlation between subscales of the ADQ-VD, i.e., between the NA and AA subscale. The findings (Pearson's r ) are in accordance with predictions (0.35, p < 0.01). The attentional insensitivity (or avoidance) to valence of emotional and social material is connected with negative affectivity, which is typical for e.g., anxiety or non-melancholic types of depression (see Fajkowska, 2013 ).

Turning to the relations among subscales of the ADQ-AD, the results of other studies suggest that we should expect positive and moderate associations between EMD and NA, as well as AC and PA, and negative ones between EMD, AC, and PA (see Fajkowska, 2013 ), and non-significant relations between NA and PA, defined here as affective traits (see Watson, 2000 ). The collected data (Pearson's r ) partially confirmed these expectations. It was found that negative affect relates negatively to positive affect (−0.59, p < 0.01). The bipolar relation between NA and PA reflected in this study might be explained by the fact that both affects were explored with items representing very intensive negative and positive states. In other words, a high or very high level of NA implies a low or very low level of PA, and vice versa (Watson and Tellegen, 1985 ; Watson, 2000 ).

The data revealed a positive relation between EMD and NA (0.71, p < 0.01) and a negative relation between EMD and PA (−0.65, p < 0.01), which Watson ( 2000 ) also documented in his research. Motivational deficits (among others understood as the loss of appetitive behaviors and interest in pursuing goal-directed activities) relate to the very specific for this type of depression (a) marked reduction in experiencing pleasure and extremely low PA, and (b) high NA, nonspecific for it (Watson, 2000 ).

In addition, several studies have shown that effective attentional control is subjected to positive mood, while negative affect has an adverse effect on it (see Fajkowska, 2013 for a review), which is congruent with the results of our study (0.65, p < 0.01 and −0.63, p < 0.01, correlations between AC and PA, AC and NA, respectively). The association between EMD and AC should be negative, and it was (−0.63, p < 0.01). There is a conflict between the intentional and effortful, effective attentional control (cf. Fajkowska and Derryberry, 2010 ) and emotional-motivational deficits—difficulties in pursuing goals and tasks, putting effort into realizing them, and troubles in undertaking and initiating activities.

Internal consistency

The results showed high Cronbach's alphas for all scales of the ADQ (42-item ADQ-ArA = 0.93; 62-item ADQ-ApA = 0.91; 36-item ADQ-VD = 0.93; 69-item ADQ-AD = 0.82). High and moderate internal consistency was also observed across all subscales of each ADQ scale, namely: arousal anxiety (ADQ-ArA: 19-item SR = 0.91; 15-item PP = 0.86; 8-item AVA = 0.53), apprehension anxiety (ADQ-ApA: 16-item WT = 0.90; 29-item AC = 0.91; 8-item AA = 0.53; 9-item rSR = 0.79), valence depression (ADQ-VD: 21-item NA = 0.92; 15-item AA = 0.87), and anhedonic depression (ADQ-AD: 33-item EMD = 0.95; 12-item PA = 0.91; 12-item NA = 0.92; 12-item AC = 0.76). The two shortest subscales—AVA from the ADQ-ArA and AA from the ADQ-ApA—showed the lowest internal consistency.

Final remarks

Based on the results from the analysis of discriminatory power of items and from the CFA, we proposed an experimental version of the ADQ. However, a few final corrections were made. Some items were excluded (e.g., with the lowest factor loadings), some linguistically corrected, and some moved to different subscales, and the subscale of AA was removed from the ADQ-ApA due to having very weak psychometric parameters (results cumulated from all analyses). The intercorrelational analysis among the subscales of the ADQ-ApA revealed that the rSR subscale is not valid. Thus, supported by these results and the appropriate results from the other studies (e.g., Laguna et al., 2004 ), we decided to transform this subscale into one assessing elevated somatic reactivity in apprehension anxiety.

Moreover, we added some filler items to the arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) scales to balance keying within each of the scales. Generally, it was not always possible to find a sufficient number of well-balanced items.

Validation stage—study 2

The second stage was designed as a validation study in order to ensure that the developed questionnaires of affective types are valid and reliable; the second aim was to verify the theoretically postulated location of the affective types among other personality constructs. Consequently, we report item and scale statistics, present evidence on the intercorrelations between subscales of each scale of the ADQ, and also assess the content and construct validity (that is factor structure, convergent, and divergent validity with well-established measures of related personality constructs, and theory-consistent group differences) and the stability and reliability of the questionnaires.

Four scales of the ADQ

The final form of the ADQ (please see the Supplementary Material ) is composed of four scales, directly measuring arousal anxiety, apprehension anxiety, valence depression, and anhedonic depression, and indirectly measuring mixed types of anxiety and depression. There is a dichotomous response format for all items (Agree/Disagree). The scoring system is identical as previously described.

ADQ-Arousal Anxiety (ADQ-ArA): 45 items (including 4 fillers), 3 subscales

• Somatic Reactivity (SR, 22 items); e.g., When something scares me, I feel a sudden attack of heat or cold.
• Panic/Phobia (PP, 14 items); e.g., I do not panic, even in the face of threats and dangers (reversed).
• Attentional Vigilance/Avoidance (AVA, 5 items); e.g., When I notice a potential threat, I automatically withdraw from the given situation.
• Fillers (4 items), e.g., I enjoy reading books.

ADQ-Apprehension Anxiety (ADQ-ApA): 48 items, 3 subscales

• Worrisome Thoughts (WT, 14 items); e.g., I am not in the habit of worrying excessively (reversed).
• Attentional Control (AC, 23 items); e.g., I cannot concentrate on a difficult task if there are noises around.
• Somatic Reactivity (SR, 11 items); e.g., When facing danger, I often feel like my legs “turn to jelly.”

ADQ-Valence Depression (ADQ-VD): 40 items (including 4 fillers), 2 subscales

• Negative Affect (21 items); e.g., I often get angry.
• Attentional Avoidance (15 items); e.g., I find it difficult to notice that someone is sad.
• Fillers (4 items); e.g., I prefer to travel by car rather than by train.

ADQ-Anhedonic Depression (ADQ-AD): 64 items, 4 subscales

• Emotional-Motivational Deficits (EMD; 31 items); e.g., I can start new things without difficulty (reversed).
• Positive Affect (PA; 13 items); e.g., I often smile honestly and joke.
• Negative Affect (NA; 12 items); e.g., I often feel sad.
• Attentional Control (AC; 8 items); e.g., Emotional events distract me so much that I later have trouble concentrating.

Table ​ Table1 1 demonstrates the socio-demographic characteristics of the validation, non-clinical sample ( N = 1,632). The sample matched the demographic structure of the Polish population. Participants who provided an unusually high number of identical answers on any questionnaire were removed from analyses. This procedure was used for each questionnaire separately. For ADQ and EPQ-R we removed participants with M +2 SD of identical answers, and for the remaining questionnaires we removed those who had zero variance in their answers, as the M +2 SD procedure turned out to be ineffective. As a result, from 4 to 11.8% ( M = 7.6%) of the participants were removed from the original sample ( N = 1,632), hence the differences in N s across analyses.

Socio-demographic characteristics of the sample.

M, mean; SD, Standard deviation .

Except demographic questions, participants were asked about whether they suffered (now or in the past) from anxiety or depression, and if “yes” they were questioned about psychotherapy, pharmacotherapy, hospitalization, professional diagnosis, and causes of these disorders. Respectively, 23.5 and 19.9% of individuals admitted that they experienced anxiety or depression disorders in the past. 13.3% of them reported that they had suffered from anxiety and 11.1% from depression in the moment of the study. Furthermore, owing to anxiety or depression, correspondingly 9.5 and 11.7% of respondents reported having used psychological and psychiatric help. 4.1 and 5% had been hospitalized because of severe anxiety or depression, respectively. The participants stated different phobias, panic attacks, separation anxiety, and anxiety coexisting with other disorders and states, reactive depression, bipolar depression, major depression, and depression associated with other disorders.

The ethics committee of the Institute of Psychology, Polish Academy of Sciences approved this on-line study and the consent procedure elaborated to it. The procedure of participants' recruitment and data collection were the same as in Study 1.

Respondents across two separate sessions completed a battery of on-line self-report techniques, randomized across subjects, and across sessions:

• - Four scales of the ADQ.
• - State–Trait Anxiety Inventory (STAI), which consists of two 20-item subscales: one measuring state anxiety and the other measuring trait anxiety (Spielberger, 1983 ; Wrześniewski and Sosnowski, 1996 ).
• - Beck Depression Inventory (BDI-II), composed of 21 questions assessing intensity of depressive symptoms (Beck et al., 1996 ; Zawadzki et al., 2009 ).
• - Behavioral Inhibition System/Behavioral Approach System scales (BIS/BAS scales)—the 24-item measure assessing dispositional BIS and BAS sensitivities. It includes three BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness (Carver and White, 1994 ; Müller and Wytykowska, 2005 ).
• - Positive and Negative Affect Schedule—Expanded Form (PANAS-X) a 60-item questionnaire comprising two higher level scales reflecting the valence of affect, that is Positive Affect (PA) and Negative Affect (NA) scales, and 11 lower level scales reflecting their specific content: Fear, Sadness, Guilt, Hostility (Basic Negative Emotion Scales); Joviality, Self-Assurance, Attentiveness (Basic Positive Emotion Scales); Shyness, Fatigue, Surprise, and Serenity (Other Affective States) (Watson and Clark, 1994 ; Fajkowska and Marszał-Wiśniewska, 2009 ).
• - Cognitive Emotion Regulation Questionnaire (CERQ), a multidimensional technique constructed in order to identify the cognitive emotion regulation strategies someone uses after having experienced negative or traumatic events. It contains 36 items measuring nine different cognitive coping strategies, including four non-adaptive: Self-blame, Rumination, Catastrophizing, and Other blame and five adaptive ones: Acceptance, Positive refocusing, Refocus on planning, Positive reappraisal, Putting into perspective (Garnefski et al., 2002 ; Marszał-Wiśniewska and Fajkowska, 2010 ).
• - Attentional Control Scale (ACS)—The 20-item ACS measures the ability to focus perceptual attention, switch attention between tasks, and flexibly control thought (Derryberry and Reed, 2002 ; Fajkowska and Derryberry, 2010 ).
• - Eysenck Personality Questionnaire Revised—Short Version (EPQ-R [S])—short version contains 48 items from the full EPQ-R. Includes scales: Psychoticism (P), Extraversion (E), Neuroticism (N), and Lie (L) (Eysenck et al., 1985 ; Jaworowska, 2011 ).

To provide a general statistical description of items and scales of the ADQ we elaborated means, standard deviations, Cronbach's α coefficients, the discriminatory power of items (Youle's Phi coefficients; ϕ), intercorrelations of subscales (Pearson's r ) on the total sample, the means, standard deviations, and t -tests showing sex differences, and the prevalence of affective types in women and men.

The content validity of the ADQ was assessed with inter-rater agreement Fleiss' kappa (κ), while the construct validity was evaluated with confirmatory factor analysis (CFA). Pearson's r and t -tests were used to examine, respectively, the structure, convergent, and divergent validity of the test and theory-consistent group differences. In addition, the test-retest (r tt ) correlations were used to test the stability of the ADQ.

Items and scales statistics

Table ​ Table2 2 summarizes the means, standard deviations, and Cronbach's α coefficients of the total sample. It shows that the internal consistencies of each scale of the ADQ are very high, with Cronbach's α coefficients ranging from 0.92 (for the ADQ-VD scale) to 0.96 (for the ADQ-ApA scale and the ADQ-AD scale). Excepting the AVA subscale of the ADQ-ArA, the αs are also high for the subscales of each ADQ scale (ranges from the 0.93 for the EMD subscale of the ADQ-AD to 0.73 for the AC subscale of the ADQ-AD). Apparently, the lowest numbers of items within subscales can explain the lowest α's (cf. the AVA from the ADQ-ArA or the AC from the ADQ-AD). According to the Spearman-Brown formula, these scales would achieve reliability of around 0.80 with 13 (instead of 5) and 12 (instead of 8) items, respectively.

Means ( M ), standard deviations ( SD ), and Cronbach's α coefficients (on the total sample) of the four scales of the ADQ and their subscales.

ADQ-ArA, Anxiety and Depression Questionnaire—Arousal Anxiety; SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; ADQ-ApA, Anxiety and Depression Questionnaire—Apprehension Anxiety; WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity; ADQ-VD, Anxiety and Depression Questionnaire—Valence Depression; NA, Negative Affect; AA, Attentional Avoidance; ADQ-AD, Anxiety and Depression Questionnaire—Anhedonic Depression; MD, Motivational Deficit; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control .

Table ​ Table3 3 demonstrates the means, standard deviations, as well as t -test results between the sexes, separately evaluated for each scale of the ADQ. It informs that women scored significantly higher on both types of anxiety. There were no significant sex differences on valence and anhedonic depression.

Means ( M ), standard deviations ( SD ), and t -test comparisons between men and women of the four scales of the ADQ and their subscales.

ADQ-ArA, Anxiety and Depression Questionnaire-Arousal Anxiety; SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; ADQ-ApA, Anxiety and Depression Questionnaire-Apprehension Anxiety; WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity; ADQ-VD, Anxiety and Depression Questionnaire-Valence Depression; NA, Negative Affect; AA, Attentional Avoidance; ADQ-AD, Anxiety and Depression Questionnaire-Anhedonic Depression; EMD, Emotional-Motivational Deficits; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control;

We extracted the “pure types” by controlling the level of the other three affective types. For example, we identified arousal anxiety when the individuals scored above the median in ADQ-ArA and below the median in the other three types (ADQ-ApA, ADQ-VD, ADQ-AD). Mixed types, on the other hand, were built of individuals who scored above the median on both types of anxiety (ADQ-ArA, ADQ-ApA) or depression (ADQ-VD, ADQ-AD). Interestingly, as Table ​ Table4 4 indicates, men reported all types of depression more frequently than women, while women declared arousal and mixed types of anxiety more frequently than men.

The prevalence of “pure” affective types in women and men.

M, mean, SD, standard deviation .

The discriminatory power coefficients of items from the four ADQ scales are reported in Table ​ Table5. 5 . Similarly to the construction stage, we calculated the Youle's Phi coefficients and proposed the value ≥0.30 as indicative of good and very good discrimination.

Discriminatory power of items (Yule ϕ, phi-coefficient; on total sample) measuring arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD).

Anxiety scales: SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity. Depression scales: NA, Negative Affect; AA, Attentional Avoidance; EMD, Emotional-Motivational Deficit; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control .

The results suggest high item discrimination coefficients, ranging from 0.33 (the AVA subscale) to 0.68 (the SR subscale) in the ADQ-ArA, from 0.30 (the AC subscale) to 0.69 (the WT subscale) in the ADQ-ApA, from 0.37 (the NA subscale) to 0.63 (the NA subscale) in the ADQ-VD, and from 0.34 (the EMD subscale) to 0.69 (the NA subscale) in the ADQ-AD.

In order to check the obtained intercorrelations among subscales composing adequate scales of the ADQ in study 1, we analyzed them in study 2. Table ​ Table6 6 demonstrates that generally all of the results are confirmed. However, comparing these findings to the findings from study 1, all correlation coefficients, across all scales of the questionnaire, increased.

Intercorrelations among subscales in the four scales of the ADQ.

ADQ-ArA, Anxiety and Depression Questionnaire—Arousal Anxiety; SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; ADQ-ApA, Anxiety and Depression Questionnaire—Apprehension Anxiety; WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity; ADQ-VD, Anxiety and Depression Questionnaire—Valence Depression; NA, Negative Affect; AA, Attentional Avoidance; ADQ-AD, Anxiety and Depression Questionnaire—Anhedonic Depression; EMD, Emotional-Motivational Deficit; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control; Pearson's r ,

Validity of the ADQ

In the case of content validity , we examined the extent to which the particular scales of the ADQ represent all proposed facets of arousal anxiety (ADQ-ArA), apprehension anxiety (ADQ-ApA), valence depression (ADQ-VD), and anhedonic depression (ADQ-AD) constructs. Thus, three experts evaluated whether items of the ADQ-ArA and ADQ-AD assess the defined content of arousal anxiety and anhedonic depression, and another three experts decided if the test positions of the ADQ-ApA and ADQ-VD cover the content of apprehension anxiety and valence depression, respectively. Precisely, the raters were instructed to assign the items measuring the adequate construct (e.g., arousal anxiety) to the distinguished facets of that construct (e.g., SR, PP, AVA). The inter-rater agreement was measured with Fleiss' kappa (Fleiss, 1971 ) using an online calculator (Geertzen, 2012 ). Kappa (κ) ranges from 0 to 1, with higher values showing greater inter-rater reliability of agreement. For all tested versions of the ADQ the κ-values were very high: ADQ-ArA, κ = 0.93; ADQ- ApA, κ = 0.90; ADQ-VD, κ = 0.94; ADQ-AD, κ = 0.98. Then the raters' sorting was compared to the scoring keys in order to replace, remove or linguistically correct problematic items.

The CFA was used to evaluate the factorial validity and authorize the results from Study 1. Along with these findings we tested (Mplus; Muthén and Muthén, 1998 ) the same models; however, they were formed on the corrected versions of the ADQ elaborated according to the results from Study 1 (e.g., they have a different number of items because some of them were removed from the original versions, different number of subscales). Again, χ 2 values for the models fit across all version of the ADQ were significant, thus other fit indices were reported (Kline, 2005 ).

The results of the CFA for all scales of ADQ are presented in Tables ​ Tables7, 7 , ​ ,8. 8 . As can be seen, the three-factor model showed the best fit in case of the ADQ-ArA with all indices reaching satisfactory levels. The data clearly demonstrated that all the items are proper markers of the expected single factor. More precisely, for the SR factor range from 0.42 to 0.87, for the PP factor from 0.54 to 0.82, and for the AVA from 0.49 to 0.74.

Goodness of fit indices for the two models of Anxiety and Depression Questionnaire—Arousal Anxiety (ADQ-ArA); for the two models of Anxiety and Depression Questionnaire—Apprehension Anxiety (ADQ-ApA); for the two models of Anxiety and Depression Questionnaire—Valence Depression (ADQ-VD), and for the three models of Anxiety and Depression Questionnaire—Anhedonic Depression (ADQ-AD).

Factor loadings of items for the three-factor model of Anxiety and Depression Questionnaire—Arousal Anxiety (ADQ-ArA), for the three-factor model of Anxiety and Depression Questionnaire—Apprehension Anxiety (ADQ-ApA), for the two-factor model of Anxiety and Depression Questionnaire—Valence Depression (ADQ-VD), and for the four-factor model of Anxiety and Depression Questionnaire—Anhedonic Depression (ADQ-AD).

ADQ-ArA: SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; ADQ-ApA: WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity; ADQ-VD: NA, Negative Affect; AA, Attentional Avoidance; ADQ-AD: EMD, Emotional- Motivational Deficit; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control; loadings below 0.40 are omitted .

In case of the ADQ-ApA, the three-factor model had better fit parameters than the one-factor model. The factors loadings for items of the three-factor model are high: from 0.51 to 0.87 for the WT subscale, from 0.41 to 0.85 for the AC subscale, and from 0.61 to 0.84 for the SR subscale.

Again, the two-factor model for the ADQ-VD seemed to be a better fit than the one-factor solution, and all of the items loaded high on the adequate factor: for NA from 0.53 to 0.80, and for AA from 0.55 to 0.81.

The best solution for the ADQ-AD is the four-factor model and the factor loadings of items for this model reach a satisfactory level. More specifically, for the EMD subscale from 0.51 to 0.85, for the PA subscale from 0.59 to 0.86, for the NA subscale from 0.70 to 0.88, and for the AC subscale from 0.52 to 0.88.

Additionally, in order to place the proposed affective types among other related personality constructs, we assessed the convergent and divergent validity with well-recognized measures. We predicted that state-like arousal anxiety and trait-like apprehension anxiety are both positively related to state anxiety and trait anxiety (STAI). However, the correlation between state anxiety and arousal anxiety should be higher than the correlation between trait anxiety and arousal anxiety, and opposite relations should be identified for apprehension anxiety. As can be seen from Table ​ Table9, 9 , the obtained data confirmed these predictions.

Correlations between arousal anxiety (ADQ-ArA) and state and trait anxiety (STAI), extraversion and neuroticism (EPQ-R [S]), positive and negative affect (PANAS-X; instruction “always”), and correlations between apprehension anxiety (ADQ-ApA) and state and trait anxiety (STAI), extraversion and neuroticism (EPQ-R [S]), positive and negative affect (PANAS-X; instruction “always”), and attentional control (ACS).

Pearson's r ,

The review of results from other sources showed that a moderate negative correlation with extraversion and a moderate and high positive correlation with neuroticism (EPQ-R[S]) is usually obtained for both state and trait anxiety (STAI) (see Fajkowska, 2013 ). Thus, similar relations between extraversion, neuroticism, arousal anxiety, and apprehension anxiety could be expected, which is actually reflected in the results showed in Table ​ Table9 9 .

According to the tripartite model of anxiety and depression proposed by Clark and Watson (Burns and Eidelson, 1998 ; Watson, 2000 ), anxiety relates to Negative Affect (NA) but is not connected with Positive Affect (PA), while depression is associated with both affects by correlating negatively with PA and positively with NA. The results of our studies only partially support this model. Both types of anxiety moderately and positively related to NA; however, they also correlated moderately and negatively with PA (see Table ​ Table9). 9 ). But both types of depression, low and negatively (valence depression) and moderately and negatively (anhedonic depression) related to PA, and moderately and positively to NA (see Table 11 ). Nonetheless, there are some studies matching our (but not Clark and Watson's) findings (e.g., Burns and Eidelson, 1998 ; Fajkowska and Marszał-Wiśniewska, 2009 ).

Fajkowska and Derryberry ( 2010 ) provided evidence that anxious and depressive subjects scored significantly lower on effortful attentional control than non-anxious and non-depressive individuals. The results from our study are congruent with their findings. As Tables ​ Tables9, 9 , ​ ,10 10 show, apprehension anxiety and anhedonic depression are negatively correlated with attentional control.

Correlations between valence depression (ADQ-VD) and depressive tendencies (BDI); extraversion and neuroticism (EPQ-R [S]) and correlations between anhedonic depression (ADQ-AD) and depressive tendencies (BDI), extraversion and neuroticism (EPQ-R [S]), and attentional control (ACS).

The Mood and Anxiety Symptom Questionnaire (MASQ; Watson, 2000 ) can be used to assess anhedonic depression. The MASQ Anhedonic Depression Subscale displays good convergent validity with the Beck Depression Inventory (BDI; cf. Kendall et al., 1987 ). Therefore, we predicted a higher and positive correlation between anhedonic depression and depression measured by BDI, and low or moderate between valence depression and depression measured by BDI. This forecast is supported by the data presented in Table ​ Table10 10 .

Generally, in most studies low extraversion and high neuroticism are found in clinical and nonclinical depression (e.g., Watson et al., 1999 ; Kotov et al., 2010 ; Fajkowska, 2013 ). Thus, it implies that we should expect that both types of depression would be negatively related to extraversion and positively to neuroticism. Indeed, the data presented in Table ​ Table10 10 support this hypothesis.

The structure of both types of depression refers to the NA; however, its content is depression-type specific (cf. definition of valence and anhedonic depression). We predicted that valence depression should correlate higher with hostility than anhedonic depression, while anhedonic depression would be more strongly related to sadness and guilt. Fear should not differentiate depressions. The obtained data supported these speculations (cf. Table ​ Table11 11 ).

Correlations between valence depression (ADQ-VD; N = 1,424) and anhedonic depression (ADQ-AD; N = 1,428) with PANAS-X (instruction “always”).

In addition, the very low PA is a part of the structure of anhedonic depression, thus we expected stronger negative relations between anhedonic depression and the Basic Positive Scales (Joviality, Self-assurance, and Attentiveness) than between valence depression and these scales. Again, these expectations are reflected in our empirical data (cf. Table ​ Table11 11 ).

Finally, as the emotional-motivational deficit defines anhedonic depression we also predicted stronger relations between Other Affective States, especially those referring to low energetic states (Fatigue, Serenity), and anhedonic depression than between Other Affective States and valence depression. As Table ​ Table11 11 shows, the predictions were confirmed.

We assumed that reactive types, that is arousal anxiety (ArA) and valence depression (VD), should be more weakly associated with adaptive and nonadaptive cognitive strategies of emotion regulation than regulative types, that is apprehension anxiety (ApA) and anhedonic depression (AD). Data presented in Table ​ Table12 12 qualified our predictions.

Correlations between reactive types—arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD), regulative types—apprehension anxiety (ADQ-ApA), anhedonic depression (ADQ-AD) and adaptive and nonadaptive strategies of emotion regulation (CERQ).

The next step in assessing construct validity was to analyze the theory-consistent group differences . Gray ( 1981 ) proposed two systems of controlling behavioral activity, that is the behavioral inhibition system (BIS) and the behavioral activation system (BAS). The BIS is thought to regulate aversive motives, in which the goal is to move away from something unpleasant, while the BAS is understood to regulate appetitive motives, in which the goal is to move toward something desirable (Carver and White, 1994 ).

It claims that the amygdala provides inputs to the BIS and may relay its outputs to the hypothalamus and autonomic nervous system, thereby mediating anxious arousal. Sustained activation of the BIS may therefore account for some features of anxiety and be related to panic (cf. Barlow, 2004 , p. 210). Thus, we assumed that high arousal-anxious individuals would score higher on the BIS than low arousal-anxious ones. And it is clear from the results that high arousal-anxious subjects ( n = 265) are higher ( M = 3.05, SD = 0.47) on the BIS (Carver and White, 1994 ; Müller and Wytykowska, 2005 ) scale than low arousal-anxious participants ( n = 313; M = 2.50, SD = 0.47), t (576) = 14.00, p < 0.001, d = 1.18. However, as data from one study (Moser et al., 2013 ) indicated, we should expect higher BIS in apprehension anxiety (measured by STAI) than in arousal anxiety (measured by MASQ). Their study showed that apprehension anxiety correlates three times higher with BIS than arousal anxiety because the former one is most closely associated with error monitoring. The findings from our study employing ADQ-ArA and ADQ-ApA measures are in accord with the cited studies: high apprehension-anxious individuals ( n = 134) are higher ( M = 2.88, SD = 0.39) on the BIS scale than high arousal-anxious participants ( n = 133; M = 2.72, SD = 0.44), t (265) = 3.49, p < 0.001, d = 0.44. The high-apprehension subjects ( n = 298) obtained higher scores on BIS than low-apprehension individuals ( n = 301; M = 3.12, SD = 0.46, and M = 2.47, SD = 0.37, respectively; t (597) = 17.16, p < 0.001, d = 1.40).

In addition, high activity of the BIS means a higher level of sensitivity to nonreward, punishment, and novel experience, which results in a natural avoidance of such environments in order to prevent negative experiences such as fear, anxiety, frustration, and sadness. Thus, it should be predicted that individuals with a high level of valence depression would show a higher level of BIS comparing to individuals with a low level of valence depression and high-anhedonic depressive, because the negative affect building this type of depression relates to the aforementioned range of negative emotions. Indeed, results of the analysis aimed at these differences showed that individuals with high valence depression ( n = 296), measured with ADQ-VD, revealed a higher BIS level ( M = 2.92, SD = 0.43) than subjects with low valence depression ( n = 319; M = 2.51, SD = 0.42), t (613) = 10.84, p < 0.001, d = 0.87, and participants with high valence depression ( n = 122) scored higher on the BIS scale than those with high anhedonic depression as assessed by the ADQ-AD ( n = 124; M = 2.87, SD = 0.40, and M = 2.75, SD = 0.47, respectively), t (244) = 2.13, p < 0.05, d = 0.30).

Bijttebier et al. ( 2009 ) summarized the studies that examined the relationship between sensitivity of the BIS and BAS systems and a broad range of psychiatric disorders. Among others, they found that low BAS sensitivity characterized anhedonic depression. Along with these results we assessed the differences in BAS level and three BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness (Carver and White, 1994 ; Müller and Wytykowska, 2005 ) between high anhedonic-depressive ( n = 263) and low anhedonic-depressive ( n = 300) individuals, and between high anhedonic-depressive ( n = 124) and high valence-depressive ( n = 122). Anhedonic depression and valence depression were assessed by the ADQ-VD and ADQ-AD, respectively. As expected, the results showed that high anhedonic-depressive individuals scored significantly lower on the BAS ( M = 2.52, SD = 0.50) than low anhedonic-depressive ( M = 2.86, SD = 0.43), t (561) = 8.54, p < 0.001, d = 0.72, and high valence-depressive individuals ( M = 2.54, SD = 0.48, and M = 2.77, SD = 0.43, respectively), t (244) = 3.89, p < 0.001, d = 0.50. The high anhedonic-depressive participants were significantly lower ( M = 2.85, SD = 0.54) on the BAS Drive scale than low anhedonic-depressive participants ( M = 5.41, SD = 1.82), t (561) = 7.87, p < 0.001, d = 0.68, and they were lower on the BAS Fun Seeking ( M = 2.58, SD = 0.55) and BAS Reward Responsiveness ( M = 2.98, SD = 0.51) scales than low anhedonic-depressive individuals ( M = 2.88, SD = 0.45, t (561) = 6.99, p < 0.001, d = 0.58 and M = 3.27, SD = 0.38, t (561) = 7.76, p < 0.001, d = 0.66, respectively). Also, the high anhedonic-depressive participants were lower on BAS drive, BAS Fun Seeking, and BAS Reward Responsiveness scales than high valence-depressive individuals [ M = 2.47, SD = 0.55 vs. M = 2.72, SD = 0.56, t (244) = 3.54, p < 0.001, d = 0.45; M = 2.62, SD = 0.53 vs. M = 2.82, SD = 0.49, t (244) = 3.21, p < 0.001, d = 0.39; M = 3.03, SD = 0.45 vs. M = 3.20, SD = 0.46, t (244) = 2.86, p < 0.01, d = 0.37, respectively).

Estimation of the questionnaire test-retest reliability

The test-retest (r tt ) reliabilities were evaluated on smaller groups (randomly selected from the total sample) that filled out the ADQ scales again after 5 weeks from the initial study. According to the results, the r tt reliabilities (see Table ​ Table13) 13 ) are high for all scales of the ADQ, varying from 0.70 (ADQ-ArA) to 0.89 (ADQ-ApA). Moreover, coefficients for the tests measuring state-like arousal anxiety (ADQ-ArA) and valence depression (ADQ-VD) are lower (0.70, 0.79, respectively) than for the tests assessing trait-like apprehension anxiety (ADQ-ApA) and anhedonic depression (APQ-AD); 0.89 and 0.88, respectively. Coefficients of 0.70 are considered satisfactory for personality states (Spielberger, 1983 ). Additionally, the test-retest reliability of most subscales of each ADQ scale is high or moderate, except for poor reliability (0.45) of the subscale of the ADQ-ArA version that assesses attentional vigilance/avoidance (AVA).

Test-retest (r tt ) reliabilities of the four scales of ADQ and their subscales (five week retest interval).

ADQ-ArA, Anxiety and Depression Questionnaire—Arousal Anxiety; SR, Somatic Reactivity; PP, Panic/Phobia; AVA, Attentional Vigilance/Avoidance; ADQ-ApA, Anxiety and Depression Questionnaire—Apprehension Anxiety; WT, Worrisome Thoughts; AC, Attentional Control; SR, Somatic Reactivity; ADQ-VD, Anxiety and Depression Questionnaire—Valence Depression; NA, Negative Affect; AA, Attentional Avoidance; ADQ-AD, Anxiety and Depression Questionnaire—Anhedonic Depression; EMD, Emotional-Motivational Deficits; PA, Positive Affect; NA, Negative Affect; AC, Attentional Control ;

The aim of the present studies was to validate a recently proposed typology of anxiety and depression operationalized within the systemic approach to personality trait and personality type (Fajkowska, 2013 ) and to develop a questionnaire based on it. This typology has been offered as a supplement to the widely accepted categorizations (e.g., Spielberger, 1983 ; Heller, 1993a , b ; Watson, 2000 ; American Psychiatric Association, 2013 ) with the intention to advance knowledge in differential and overlapping features between anxiety and depression, and in differential and overlapping adaptive meanings of both phenomena, especially in non-clinical forms of anxiety/depression. In this approach, anxiety and depression are seen as complex personality types and their new grouping refers to their specific structural composition (mechanisms, components, and behavioral markers) and the dominant functions they play in stimulation processing (reactive, regulative). Hence, six affective types are proposed: arousal anxiety, apprehension anxiety, mixed anxiety, valence depression, anhedonic depression, and mixed depression. It is assumed that differences and similarities in structural components and dominant functions in stimulation processing in various affective types are connected with differences and similarities in their adaptive meanings. This line of reasoning suggests that one can expect more out-group than in-group similarities or more in-group than out-group differences. This theoretical proposition concerning a new typology of anxiety and depression has led us to develop a questionnaire that corresponds fully to this model. The empirical data gathered across the two stages—construction and validation—allowed us to offer the final form of the Anxiety and Depression Questionnaire (ADQ). The ADQ is composed of four multidimensional versions, directly assessing Arousal Type of anxiety (ADQ-ArA), Apprehension Type of anxiety (ADQ-ApA), Valence Type of depression (ADQ-VD), and Anhedonic Type of depression (ADQ-AD), and indirectly evaluating Mixed Type of anxiety (MA) and Mixed Type of depression (MD). The results showed that all scales of the ADQ are valid and reliable measurements.

The gender differences we found with the ADQ were to some extent similar to those found by other authors (see Fox, 2008 for a review). The prevalence of mood disorders is generally much higher among women than men. In our studies women scored higher on both types of anxiety, but we did not observe sex differences in valence and anhedonic depressions. However, when we used the “pure types” (where we controlled the level of the other three affective disorders) the results indicated that men showed all types of depression more frequently than women, while women more frequently reported arousal and mixed types of anxiety. The probable explanations include the sensitivity of the measurement we used, or it might be possible that these results reflect social and cultural changes predisposing men to be more vulnerable to mood disorders, especially to depression, than women. This issue needs further and cross-cultural studies.

All scales of the ADQ are characterized by high homogeneity (cf. discrimination coefficients and Cronbach's α coefficients). However, across two studies, the intercorrelations among subscales composing the anhedonic depression scale of ADQ seem to be much higher than expected. The scales with the highest intercorrelations were emotional-motivational deficits (EMD) with both affects, positive (PA; −0.65 in Study 1 and −0.82 in Study 2) and negative (NA; 0.71 in Study 1 and 0.84 in Study 2). It might suggest that motivational and affective systems are not separable or that the selected items in these subscales need further elaboration. Nonetheless, future validation studies should provide more information on how to deal with this puzzle.

The results of content validity supported the theoretical assumptions regarding the internal structure of the proposed affective types. In addition, the CFA sustained the adequacy of these theoretical assumptions.

Apart from that, all scales of the ADQ also have good convergent and divergent validity. It is shown by ADQ-ArA's higher correlation with STAI state anxiety than STAI trait anxiety, ADQ-ApA's higher correlations with STAI trait anxiety than STAI state anxiety, ADQ-VD's lower correlation with BDI and higher ADQ-AD's correlation with BDI, which measures anhedonia as is assumed. Moreover, as expected, all types of anxiety and depression related positively to neuroticism and negatively to extraversion (EPQ-R[S]).

Contrary to the predictions stemming from the tripartite model of anxiety and depression (Watson, 2000 ), both types of anxiety and both types of depression related negatively to PA and positively to NA (PANAS-X). Our results are in line with other studies (see Fajkowska and Marszał-Wiśniewska, 2009 for a review) showing that anxiety is related to positive affect. However, the expected correlational patterns were found for valence and anhedonic depression with respect to the proposed theoretical structure of these affective types. Valence depression correlated higher with hostility than anhedonic depression, and anhedonic depression was more strongly related to sadness and guilt than valence depression. Moreover, anhedonic depression showed higher negative correlations with the basic positive emotions, and stronger positive correlations with fatigue and serenity than valence depression (PANAS-X).

Referring to the identified dominant functions in controlling stimulation, reactive or regulative, in affective types, we discovered that reactive types (arousal anxiety and valence depression) are more weakly related to strategies of emotion regulation (CERQ) than regulative types (apprehension anxiety and anhedonic depression). These results also support theoretical assumptions regarding the fact that regulative personality traits or types can be recognized through their correspondence to different strategies.

The ADQ is also characterized by satisfactory construct validity as measured by means of the theory-consistent group differences. There were significant differences in BIS levels (BIS/BAS scales) among high arousal-anxious, low arousal-anxious, high apprehension-anxious, and low-apprehension anxious. BIS relates to arousal, panic, and also to monitoring errors. In line with the results obtained by other authors (e.g., Moser et al., 2013 ), a higher level of BIS is more typical for apprehension anxiety as it is more associated with error monitoring than arousal anxiety. Obviously, both types of anxiety revealed a higher BIS level than individuals low on both arousal and apprehension anxiety. In addition, as BIS goes with fear, anxiety, frustration, and sadness (Gray, 1981 ), it was not surprising that it was higher in valence depression than in anhedonic depression and in low valence depression.

Finally, significant differences were found for the level of BAS and BAS-related scales: BAS Drive, BAS Fun Seeking, and BAS Reward Responsiveness in scoring higher on anhedonic depression compared to low anhedonic-depressive and valence-depressive. It is explained by the fact that low BAS is associated with anhedonia, i.e., with difficulties in goal achievement, impossibility to experience pleasure, and failure in delivering sufficient reward following approach behaviors.

It should also be added that the stability of all scales of the ADQ as measured by the test-retest technique is satisfactory.

Certain limitations of our study should be noted. First relates to the self-reported data that could be associated with several potential sources of bias and requires replication and confirmation with experimental procedures. Second, these studies were time consuming and demanding for the participants. Thus, a possibility existed that they clicked through the questionnaires without much reflection (although it seemed that the number of such participants was not especially high, and we tried to exclude these cases from analyses). Third, in statistical techniques like Cronbach's α or factor analysis (CFA), high parameter values sometimes indicated redundancy in scales. Even though we were struggling for both (a) the content differentiation of the scales and subscales and (b) good psychometric parameters, it was not always possible to achieve. Fourth, we used a median split for grouping participants and that method has its serious limitations; thus for further analysis we recommend considering different approaches (e.g., means and standard deviations). Fifth, the number of items measuring attentional subscales is too low, although all definitional aspects of each attentional construct are covered. This is usually the problem when one operationalizes processual elements of personality (cf. Pavlovian Temperament Survey; Attentional Control Scale). Definitely it needs further elaboration. Finally, there are more processes involved in producing anxiety and depression than taken here into consideration. We are aware that this model is far from being complete but it suggests the right direction—understanding affective types as complex, three-level systems.

Despite these limitations, we received satisfactory empirical support for the proposed typology of anxiety and depression. From the viewpoint of developing a reliable and valid instrument for self-ratings of affective types, the results provide evidence to support the good psychometric status of the ADQ as a measure for evaluation of proposed types of anxiety and depression. It should be pointed out, however, that the research described in this paper is not intended to provide normative data. Although the present studies are based on large samples, we believe that the pattern of results needs further replications.

Future research should also consider the utility of the ADQ and the extent to which it may be generalized in a variety of applied settings, for example clinical, educational, and work settings. Such studies may potentially reveal interesting information regarding the usefulness of the ADQ in both research and practice.

Author contributions

MF: Provided the theoretical framework and supervised the project; MF, ED, and AW: Designed the questionnaire and contributed to the study design; ED: Analyzed the data and supervised data collection; MF: Drafted the manuscript; ED and AW: Provided critical revisions.

Conflict of interest statement

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

Acknowledgments

This research was supported by Grant 2012/07/E/HS6/04071 from the National Science Centre, Poland. We thank Dr. Joanna Kantor-Martynuska for her involvement in the construction of the Anxiety and Depression Questionnaire. ED has obtained funding under a Ph.D. scholarship from the National Science Center, Poland, grant number 2016/20/T/HS6/00598.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2017.02376/full#supplementary-material

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Anxiety Disorders and Depression Essay (Critical Writing)

Introduction, description section, feelings section, action plan, reference list.

Human beings become anxious in different situations that are uncertain to them. Depression and anxiety occur at a similar time. Anxiety is caused due to an overwhelming fear of an expected occurrence of an event that is unclear to a person. More than 25 million people globally are affected by anxiety disorders. People feel anxious in moments such as when making important decisions, before facing an interview panel, and before taking tests. Anxiety disorders are normally brained reactions to stress as they alert a person of impending danger. Most people feel sad and low due to disappointments. Feelings normally overwhelm a person leading to depression, especially during sad moments such as losing a loved one or divorce. When people are depressed, they engage in reckless behaviors such as drug abuse that affect them physically and emotionally. However, depression manifests in different forms in both men and women. Research shows that more women are depressed compared to men. This essay reflects on anxiety disorders and depression regarding from a real-life experience extracted from a publication.

“Every year almost 20% of the general population suffers from a common mental disorder, such as depression or an anxiety disorder” (Cuijpers et al. 2016, p.245). I came across a publication by Madison Jo Sieminski available who was diagnosed with depression and anxiety disorders (Madison 2020). She explains how she was first diagnosed with anxiety disorders and depression and how it felt unreal at first. She further says that she developed the need to get a distraction that would keep her busy so that she won’t embrace her situation. In her case, anxiety made her feel that she needed to do more, and everything needed to be perfect.

Madison further said that the struggle with anxiety is that it never seemed to happen, but it happened eventually. She always felt a feeling of darkness and loneliness. She could barely stay awake for more than 30 minutes for many days. Anxiety and depression made her question herself if she was good enough, and this resulted in tears in her eyes due to the burning sensation and overwhelmed emotions. In her own words, she said, “Do I deserve to be here? What is my purpose?” (Madison 2020). Anxiety made her lose confidence in herself and lowered her self-esteem. She could lay in bed most of the time and could not take any meal most of the days.

Madison said that since the sophomore year of high school, all was not well, and she suddenly felt someone in her head telling her to constantly worry and hold back from everything. She could wake up days when she could try a marathon to keep her mind busy. However, she sought help on 1 January 2020, since she felt her mental health was important, and she needed to be strong. She was relieved from her biggest worries, and what she thought was failure turned into a biggest achievement. She realized that her health needed to be her priority. Even after being diagnosed with depression disorders, she wanted to feel normal and have a normal lifestyle like other people.

Madison was happy with her decision to seek medical help even though she had her doubts. She was happy that she finally took that step to see a doctor since she was suffering in silence. She noted that the background of her depression and anxiety disorders was her family. It was kind of genetic since her mom also struggled with depression and anxiety disorders. Her mom was always upset, and this broke her heart. She said it took her years to better herself, but she still had bad days. Madison decided to take the challenge regarding her mother’s experience. Also, Madison said she was struggling to get over depression since her childhood friends committed suicide, and it affected her deeply. She also told the doctor how she often thought of harming herself. The doctor advised her on the different ways she could overcome her situation after discovering she had severe depression and anxiety disorders.

After going through Madison’s story, I was hurt by the fact that he had to go through that for a long time, and something tragic could have happened if she had not resorted to medical help. I felt emotional by the fact that she constantly blamed herself due to her friends who committed suicide, and she decided to accumulate all the pain and worries. The fact that I have heard stories of how people commit suicide due to depression and anxiety disorders made me have a somber mood considering her case. In this case, you will never know what people are going through in their private lives until they decide to open up. We normally assume every person is okay, yet they fight their demons and struggle to look okay. Hence, it won’t cost any person to check up on other people, especially if they suddenly change their social characters.

Madison’s story stood out for me since she had struggled since childhood to deal with depression and anxiety disorders. In her case, she was unable to seek help first even when she knew that she was suffering in silence (Madison 2020). However, most people find it hard to admit they need help regardless of what they are going through, like Madison. People who are depressed cannot work as they lack the motivation to do anything. In my knowledge, depression affects people close to you, including your family and friends. Depression also hurts those who love someone suffering from it. Hence, it is complex to deal with. Madison’s situation stood out for me since her childhood friends committed suicide, and she wished silently she could be with them. Hence, this leads to her constant thoughts of harming herself. Childhood friends at one point can become your family even though you are not related by blood due to the memories you share.

Depression and Anxiety disorders have been common mental health concerns globally for a long time. Depression and anxiety disorders create the impression that social interactions are vague with no meaning. It is argued by Cuijpers (2016, p.245) that people who are depressed normally have personality difficulties as they find it hard to trust people around them, including themselves. In this case, Madison spent most of her time alone, sleeping, and could not find it necessary to hang around other people. Negativity is the order of the day as people depressed find everything around them not interesting.

People who are depressed find it easy to induce negativity in others. Hence, they end up being rejected. Besides, if someone is depressed and is in a relationship, he/she may be the reason for ending the relationship since they would constantly find everything offensive. Research shows that people who are clinically depressed, such as Madison, prefer sad facial expressions to happy facial expressions. Besides, most teenagers in the 21 st century are depressed, and few parents tend to notice that. Also, most teenagers lack parental love and care since their parents are busy with their job routines and have no time to engage their children. Research has shown that suicide is the second cause of death among teenagers aged between 15-24 years due to mental disorders such as suicide and anxiety disorders.

Despite depression being a major concern globally, it can be controlled and contained if specific actions are taken. Any person needs to prioritize their mental health to avoid occurrences of depression and anxiety orders. Emotional responses can be used to gauge if a person is undergoing anxiety and depression. The best efficient way to deal with depression and anxiety is to sensitize people about depression through different media platforms (Cuijpers et al. 2016). A day in a month should be set aside where students in colleges are sensitized on the symptoms of depression and how to cope up with the situation. Some of the basic things to do to avoid anxiety and depression include; talking to someone when you are low, welcoming humor, learning the cause of your anxiety, maintaining a positive attitude, exercising daily, and having enough sleep.

Depression and anxiety disorders are different forms among people, such as irritability and nervousness. Most people are diagnosed with depression as a psychiatric disorder. Technology has been a major catalyst in enabling depression among people as they are exposed to many negative experiences online. Besides, some people are always motivated by actions of other people who seem to have given up due to depression. Many people who develop depression normally have a history of anxiety disorders. Therefore, people with depression need to seek medical attention before they harm themselves or even commit suicide. Also, people need to speak out about what they are going through to either their friends or people they trust. Speaking out enables people to relieve their burden and hence it enhances peace.

Cuijpers, P., Cristea, I.A., Karyotaki, E., Reijnders, M. and Huibers, M.J., 2016. How effective are cognitive behavior therapies for major depression and anxiety disorders? A meta‐analytic update of the evidence . World Psychiatry 15(3), pp. 245-258.

Madison, J. 2020. Open Doors .

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Bibliography

IvyPanda . "Anxiety Disorders and Depression." June 16, 2022. https://ivypanda.com/essays/anxiety-disorders-and-depression/.

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COVID-19 Pandemic Effects on Depression, Anxiety, and Stress of Hemodialysis Patients

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• May 4, 2021
• Affiliation: School of Nursing
• Hemodialysis patients are disproportionally at risk for depression and anxiety due to changes in functional status and financial and practical concerns. More recently in the year 2020, the Coronavirus Disease (COVID-19) brought many behavioral and lifestyle changes related to quarantine policies. The purpose of this study was to evaluate changes in depression, anxiety, and stress among hemodialysis patients during the COVID-19 pandemic. This retrospective cohort study recruited 13 participants, ranging from age 18 to 60. Paper surveys were distributed to patients during treatment within an inpatient dialysis unit. This study used a modified version of the DASS-21 scale for depression, anxiety, and stress “before” the pandemic, as well as “during” the pandemic. A majority of participants’ final scores for depression and anxiety remained unchanged across the two time periods (53.8% and 69.2%, respectively). Of those participants, 53.8% demonstrated an increase in final stress scores with the emergence of the pandemic. Findings suggest that increases in stress among hemodialysis patients may be related to variables of the COVID-19 pandemic. In addition to personal risks and vulnerabilities, managing end stage renal disease and facing a global disease may explain the sample’s stress scores. Caregivers and healthcare providers should routinely assess hemodialysis patients’ cognitive, behavioral and emotional wellbeing. Further evaluation of exacerbating factors is needed to explore the overall effects of the COVID-19 pandemic among the population.
• April 29, 2021
• https://doi.org/10.17615/06z8-9h35
• Honors Thesis
• In Copyright
• Bachelor of Science

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• UNC-Chapel Hill Coronavirus Research

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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.

When writing your essay, ensure that the thesis statement for mental health is fascinating. You will impress your professors if you get the right depression research paper outline as your thesis statement. Here is a depression thesis statement you can use.

• 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.

How do you write a research paper about depression and how it affects mental health? Before choosing a thesis statement on mental health, have a clear understanding of the essay that you are writing. That will help you get the best thesis to make our essay stand out.

But don’t keep stressing out about your thesis statement for mental illness research paper. We have your work cut out because our skilled writers have compiled a list of thesis statements about mental health and depression topics for research paper writing. We will also suggest correct thesis statements for your essay homework or assignment.

If you are still unsure of the statement to use, get in touch with us today. We have a team of skilled and experienced writers that can help you with your essay or research project and ensure that you get the best grades.

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10 New Thesis Statement about Depression & Anxiety | How to Write One?

Did you know according to the National Institute of Mental Health; it is estimated that approximately 8.4% of adults are patients of major depression in the US? Well, depression is a common illness globally that affects a lot of people. Yet, the reasons for this psychological sickness vary from person to person and numerous studies are being conducted to discover more about depression.

Therefore, college and university students are currently assigned to write research papers, dissertations, essays, and a thesis about depression. However, writing essays on such topics aims to increase the awareness of physical and mental well-being among youth and help them find solutions.

However, a lot of students find it pretty challenging to write a thesis statement about depression and seek someone to write my essay . No worries! In this article, you will learn about what is a good thesis statement about mental health and some effective methods and approaches to write a killer headline and compose an astonishing essay about depression.

5 Thesis Statement About Depression:

• “The complexity of depression, which includes biological, psychological, and environmental components, emphasizes the need for individualized treatment plans that consider each person’s particular requirements.”
• “Depression in the workplace not only affects an individual’s productivity but also carries economic implications, emphasizing the importance of fostering a mental health-friendly work environment.”
• “Alternative, holistic approaches to mental health care have the potential to be more successful as the link between creative expressions, such as art therapy, and depression management becomes more commonly recognized.”
• “It is critical to enhance geriatric mental health treatment and reduce the stigma associated with mental illness in older people since depression in senior populations is typically underdiagnosed and mistreated.”
• “The link between early childhood adversity and the risk of developing depression later in life accentuates the importance of early intervention and support systems for children exposed to adverse experiences.”

5 Thesis Statements about Anxiety & Depression :

• “Depression and anxiety Co-occurring disorders are a major concern in mental health, necessitating integrated treatment options that meet the unique challenges that co-occurring diseases provide.”
• “The utilization of technology-driven therapies, such as smartphone apps and telehealth services, is a realistic approach of addressing persons suffering from anxiety and depression, while also increasing access to mental health care.”
• “The examination of the gut-brain connection and its potential role in anxiety and depression showcases a burgeoning area of research that could lead to novel treatments emphasizing nutrition and gut health.”
• “Adolescents who experience both anxiety and depression face a serious issue that calls for comprehensive school-based mental health programs and preventative measures to promote young people’s mental health.”
• “Exploring the impact of sociocultural factors and the role of community support systems in the experience of anxiety and depression provides insights into the development of culturally sensitive mental health interventions.”

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Follow 7 Proven Methods to Compose Thesis Statement about Depression

A thesis is the overview of the concepts and ideas that you will write in your research paper or in the essay. Yet, a thesis statement about anxiety focuses more on the stress and depression topics for the paper you’re working on, which can be written by following the tips given below.

Nonetheless, you can compose an outline by covering the points mentioned below:

1. Pick a good study topic and perform a basic reading. Look for some intriguing statistics and try to come up with creative ways to approach your subject. Examine a few articles for deficiencies in understanding.

2. Make a list of your references and jot down when you come across a noteworthy quotation. You can cite them in your paper as references. Organize all of the information you’ve acquired in one location.

3. In one phrase, state the purpose of your essay. Consider what you want to happen when other people read your article.

4. Examine your notes and construct a list of all the key things you wish to emphasize. Make use of brainstorming strategies and jot down any ideas that come to mind.

5. Review and revise the arguments and write a thesis statement for a research paper or essay about depression.

6. Organize your essay by organizing the list of points. Arrange the points in a logical sequence. Analyze all elements to ensure that they are all relevant to your goal.

7. Reread all of your statements and arrange your outline in a standard manner, such as a bulleted list.

Final Words

So, what is an ideal way to write a thesis statement about depression for your research paper or essay? We hope you have a thorough idea of the essay you’re writing before picking a thesis statement about mental well-being. That will assist you in developing the greatest thesis for our essay.

But don’t get too worked up over your thesis statement for a research paper on mental disorders. Our professional subject experts have produced a list of thesis statements about mental health and depression themes for research paper writing, so you’ve got your job cut out for you. For your essay assignments or assignments, we will also offer appropriate thesis statements.

If you’re still confused about which statement to use, contact us right away. We have a staff of highly qualified and seasoned writers who can assist you with your essay or research work and guarantee that you receive the highest possible score.

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