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7 Depression Research Paper Topic Ideas

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

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

What Is Depression?

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

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

What Research Says About the Psychology of Depression

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

Types of Depression

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

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

Causes of Depression

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

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

Who Is at Risk for Depression?

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

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

Depression Signs and Symptoms

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

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

How Is Depression Diagnosed?

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

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

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

Treatment Options for Depression

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

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

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

A Word From Verywell

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

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

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

National Institute of Mental Health. Depression .

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

National Institute of Mental Health. Mental health medications .

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

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

The Critical Relationship Between Anxiety and Depression

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

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Essays About Depression

Depression essay topic examples.

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

Argumentative Essays

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

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

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

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

Compare and Contrast Essays

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

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

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

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

Descriptive Essays

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

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

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

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

Persuasive Essays

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

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

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

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

Narrative Essays

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

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

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

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

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

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

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

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

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

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

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

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

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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.

Student-level demographics, research demographics, and depression demographics of the 35 interview participants

Student-level demographicsInterview participants = 35 (%)Research demographicsInterview participants = 35 (%)Depression demographicsInterview participants = 35 (%)
 Female27 (77%) Less than 6 months7 (20%) Yes21 (60%)
 Male7 (23%) 6 months6 (17%) No10 (29%)
 Declined to state1 (3%) 1 year11 (31%) Declined to state4 (11%)
 1.5 years4 (11%)
 Asian9 (26%) 2 years2 (6%) Medication15 (43%)
 Black1 (3%) 3 years3 (9%) Counseling17 (49%)
 Latinx5 (14%) 3.5 years1 (3%) Other2 (6%)
 Middle Eastern1 (3%) Declined to state1 (3%) No treatment15 (43%)
 Mixed race1 (3%)  Declined to state2 (6%)
 White17 (49%) 1–5 hours6 (17%)
 Declined to state1 (3%) 6–10 hours16 (46%)
 11–15 hours7 (20%)
 First generation10 (29%) 16 + hours5 (14%)
 Continuing generation24 (69%) Declined to state1 (3%)
 Declined to state1 (3%)
 Money13 (37%)
 Transfer5 (14%) Course credit24 (69%)
 Nontransfer29 (83%) Volunteer7 (20%)
 Declined to state1 (3%) Declined to state2 (6%)
 No6 (17%) PI9 (26%)
 Yes, but only sometimes12 (34%) Postdoc3 (9%)
 Yes16 (46%) Graduate student14 (40%)
 Declined to state1 (3%) Staff member 7 (20%)
 Undergraduate student1 (3%)
 First year1 (3%) Declined to state1 (3%)
 Second year5 (14%)
 Third year6 (17%) Cell/molecular biology4 (11%)
 Fourth year or greater22 (63%) Ecology/evolution9 (26%)
 Declined to state1 (3%) Genetics5 (14%)
 Immunology4 (11%)
 Biology32 (91%) Neuroscience3 (9%)
 Biochemistry2 (6%) Physiology/health3 (9%)
 Declined to state1 (3%) Other 6 (17%)
 Declined to state1 (3%)
 18–195 (14%)
 20–2117 (49%)
 22–2311 (31%)
 24 or older1 (3%)
 Declined to state1 (3%)

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.

Ways in which students report that depression affected their undergraduate research experience with example student quotes

DescriptionExample quote 1Example quote 2
Motivation and productivity
Lack of motivation in researchStudents describe that their depression can cause them to feel unmotivated to do research.Crystal: “[Depression] can make it hard to motivate myself to keep doing [research] because when I get into [depression] it doesn’t matter. [All my organisms] are going to die and everything’s going to go horribly sideways and why do I even bother? And then that can descend into a state of just sadness or apathy or a combination of the two.”Naomi: “I don’t feel as motivated to do the research because I just don’t feel like doing anything. [Depression] definitely does not help with the motivation.”
Less productiveStudents describe that depression can cause them to be less productive, less efficient, or to move slower than usual.Marta: “I think at times when [my depression is] really, really bad, I’ll just find myself just sitting at my desk looking busy but not actually doing anything. (…) And I think that obviously affects productivity because I’m not really doing anything.”Julie: “I think I literally moved and thought slower. (…) I think that if I could redo all of that time while not depressed, I would have gotten so much more done. I feel like so much of this stalling I had on various projects was because of [my depression].”
Creativity and risk-taking
Lack of creativity in researchStudents describe that depression can cause them to be less creative in their research.Michelle: “In that depressive episode, I probably won’t be even using my brain in that, sort of, [creative] sense. My mind will probably be just so limited and blank and I won’t even want to think creatively.”Amy: “I think [depression] definitely has super negatively impacted my research creativity. I just feel like I’m not as creative with my problem solving skills when I am depressed as when I am not depressed.”
Held back from taking risks or contributing thoughts and ideasStudents describe that their depression can hold them back from sharing an idea with their lab mates or from taking risks like applying for competitive positions or trying something in research that might not work.Marta: “[Depression affects my research] because I’m so scared to take a risk. That has really put a very short cap on what I’ve been able to do. And maybe I would’ve been able to get internships at institutions like my peers. But instead, because I was so limited by my depression, it kept me from doing that.”Christian: “That’s where I think [depression] definitely negatively affects what I have accomplished just because I feel personally that I could have achieved more if I wasn’t held down, I guess, by depression. So, I feel like I would’ve been able to put myself out there more and take more risks, reaching out to others to take opportunities when I was in lab.”
Engagement and concentration
Struggle to intellectually engageStudents describe that they struggle to do research activities that require intellectual engagement when they are feeling depressed.Freddy: “I find mechanical things like actually running an experiment in the lab, I can pretty much do regardless of how I’m feeling. But things that require a ton of mental energy, like analyzing data, doing statistics, or actually writing, was [ ] a lot more difficult if I was feeling depressed.”Rose: “When you’re working on a research project you’re like ‘I wonder what this does? Or why is that the way it is?,’ and then you’ll read more articles and talk to a few people. And when I’m depressed, I don’t care. I’m like this is just another thing I have to do.”
Difficulty concentrating or rememberingStudents describe that, because of their depression, they can have difficulty concentrating or remembering when they are conducting research.Julie: “My memory absolutely goes to hell, especially my short-term memory. My attention span nosedives. Later, I will look back on work and have no idea how any of that made sense to me.”Adrianna: “Yeah. [Sometimes when I’m depressed] it’s like, ‘Oh, I forgot a step,’ or ‘Oh, I mislabeled the tube.’ It’s like, okay, I got to slow down even more and pay more attention. But it’s really hard to get myself to focus.”
Self-perception and socializing
Overly self-criticalStudents describe that depression causes them to have low self-esteem or to be overly self-critical.Heather: “I guess [my depression can cause me to] beat myself up about different things. Especially when the experiment didn’t really work. I guess blaming myself to the point where it was unhealthy about different things. If I had an experiment and it didn’t work, even if I was working with someone else, then I’d put all the blame on myself. I guess [your depression] worsens it because you just feel worse about yourself mentally.”Taylor: “I feel like I’m sort of not good enough, right? And I’ve sort of fooled [my research advisor] for letting me into their lab, and that I should just stop. I guess that’s really how [my depression] would relate directly to research.”
Less socialStudents describe that their depression can cause them to not want to interact with others in the lab or to be less social in general.Adrianna: “There are days I’m emotionally flat and obviously those I just don’t engage in conversation as much and [my lab mates] are probably like, ‘Oh, she’s just under the weather.’ I don’t know. It just affects my ability to want to sit down and talk to somebody.”Michelle: “When I’m depressed I won’t talk as much, so [my lab mates and I] won’t have a conversation.”

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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research essay about depression

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

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

American Psychological Association Logo

Everyone experiences sadness at times. But depression is something more. Depression is extreme sadness or despair that lasts more than days. It interferes with the activities of daily life and can cause physical symptoms such as pain, weight loss or gain, sleeping pattern disruptions, or lack of energy.

People with depression may also experience an inability to concentrate, feelings of worthlessness or excessive guilt, and recurrent thoughts of death or suicide.

Depression is the most common mental disorder. Fortunately, depression is treatable. A combination of therapy and antidepressant medication can help ensure recovery.

Adapted from the Encyclopedia of Psychology

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Evolution and Emerging Trends in Depression Research From 2004 to 2019: A Literature Visualization Analysis

1 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China

Xuemei Tian

2 School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China

Xianrui Wang

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

Depression has become a major threat to human health, and researchers around the world are actively engaged in research on depression. In order to promote closer research, the study of the global depression knowledge map is significant. This study aims to map the knowledge map of depression research and show the current research distribution, hotspots, frontiers, and trends in the field of depression research, providing researchers with worthwhile information and ideas. Based on the Web of Science core collection of depression research from 2004 to 2019, this study systematically analyzed the country, journal, category, author, institution, cited article, and keyword aspects using bibliometric and data visualization methods. A relationship network of depression research was established, highlighting the highly influential countries, journals, categories, authors, institutions, cited articles, and keywords in this research field. The study identifies great research potential in the field of depression, provides scientific guidance for researchers to find potential collaborations through collaboration networks and coexistence networks, and systematically and accurately presents the hotspots, frontiers, and shortcomings of depression research through the knowledge map of global research on depression with the help of information analysis and fusion methods, which provides valuable information for researchers and institutions to determine meaningful research directions.

Introduction

Health issues are becoming more and more important to people due to the continuous development of health care. The social pressures on people are becoming more and more pronounced in a social environment that is developing at an increasing rate. Prolonged exposure to stress can have a negative impact on brain development ( 1 ), and depression is one of the more typical disorders that accompany it. Stress will increase the incidence of depression ( 2 ), depression has become a common disease ( 3 ), endangering people's physical health. Depression is a debilitating mental illness with mood disorders, also known as major depression, clinical depression, or melancholia. In human studies of the disease, it has been found that depression accounts for a large proportion of the affected population. According to the latest data from the World Health Organization (WHO) statistics in 2019, there are more than 350 million people with depression worldwide, with an increase of about 18% in the last decade and an estimated lifetime prevalence of 15% ( 4 ), it is a major cause of global disability and disease burden ( 5 ), and depression has quietly become a disease that threatens hundreds of millions of people worldwide.

Along with the rise of science communication research, the quantification of science is also flourishing. As a combination of “data science” and modern science, bibliometrics takes advantage of the explosive growth of research output in the era of big data, and uses topics, authors, publications, keywords, references, citations, etc. as research targets to reveal the current status and impact of the discipline more accurately and scientifically. Whereas, there is not a wealth of bibliometric studies related to depression. Fusar-Poli et al. ( 6 ) used bibliometrics to systematically evaluate cross-diagnostic psychiatry. Hammarström et al. ( 7 ) used bibliometrics to analyze the scientific quality of gender-related explanatory models of depression in the medical database PubMed. Tran et al. ( 8 ) used the bibliometric analysis of research progress and effective interventions for depression in AIDS patients. Wang et al. ( 9 ) used bibliometric methods to analyze scientific studies on the comorbidity of pain and depression. Shi et al. ( 10 ) performed a bibliometric analysis of the top 100 cited articles on biomarkers in the field of depression. Dongping et al. ( 11 ) used bibliometric analysis of studies on the association between depression and gut flora. An Chunping et al. ( 12 ) analyzed the literature on acupuncture for depression included in PubMed based on bibliometrics. Yi and Xiaoli ( 13 ) used a bibliometric method to analyze the characteristics of the literature on the treatment of depression by Chinese medicine in the last 10 years. Zhou and Yan ( 14 ) used bibliometric method to analyze the distribution of scientific and technological achievements on depression in Peoples R China. Guaijuan ( 15 ) performed a bibliometric analysis of the interrelationship between psoriasis and depression. Econometric analysis of the relationship between vitamin D deficiency and depression was performed by Yunzhi et al. ( 16 ) and Shauni et al. ( 17 ) performed a bibliometric analysis of domestic and international research papers on depression-related genes from 2003 to 2007. A previous review of depression-related bibliometric studies revealed that there is no bibliometric analysis of global studies in the field of depression, including country network analysis, journal network analysis, category network analysis, author network analysis, institutional network analysis, literature co-citation analysis, keyword co-presentation analysis, and cluster analysis.

The aim of this study was to conduct a comprehensive and systematic literature-based data mining and metrics analysis of depression-related research. More specifically, this analysis focuses on cooperative network and co-presentation analysis, based on the 36,477 papers included in the Web of Science Core Collection database from 2004 to 2019, and provides an in-depth analysis of cooperative network, co-presentation network, and co-citation through modern metrics and data visualization methods. Through the mining of key data, the data correlation is further explored, and the results obtained can be used to scientifically and reasonably predict the depression-related information. This study aims to show the spatial and temporal distribution of research countries, journals, authors, and institutions in the field of depression in a more concise manner through a relational network. A deeper understanding of the internal structure of the research community will help researchers and institutions to establish more accurate and effective global collaborations, in line with the trend of human destiny and globalization. In addition, the study will allow for the timely identification of gaps in current research. A more targeted research direction will be established, a more complete picture of the new developments in the field of depression today will be obtained, and the research protocol will be informed for further adjustments. The results of these analyses will help researchers understand the evolution of this field of study. Overall, this paper uses literature data analysis to find research hotspots in the field of depression, analyze the knowledge structure within different studies, and provide a basis for predicting research frontiers. This study analyzed the literature in the field of depression using CiteSpace 5.8.R2 (64-bit) to analyze collaborative networks, including country network analysis, journal network analysis, category network analysis, researcher network analysis, and institutional network analysis using CiteSpace 5.8.R2 (64-bit). In addition, literature co-citation, keyword co-presentation, and cluster analysis of depression research hotspots were also performed. Thus, exploring the knowledge dimensions of the field, quantifying the research patterns in the field, and uncovering emerging trends in the field will help to obtain more accurate and complete information. The large amount of current research results related to depression will be presented more intuitively and accurately with the medium of information technology, and the scientific evaluation of research themes and trend prediction will be provided from a new perspective.

Data Sources

The data in this paper comes from the Web of Science (WoS) core collection. The time years were selected as 2004–2019. First, the literature was retrieved after entering “depression” using the title search method. A total of 73,829 articles, excluding “depression” as “suppression,” “decline,” “sunken,” “pothole,” “slump,” “low pressure,” “frustration.” The total number of articles with other meanings such as “depression” was 5,606, and the total number of valid articles related to depression was 68,223. Next, the title search method was used to search for studies related to “major depressive disorder” not “depression,” and a total of 8,070 articles were retrieved. For the two search strategies, a total of 76,293 records were collected. The relevant literature retrieved under the two methods were combined and exported in “plain text” file format. The exported records included: “full records and references cited.” CiteSpace processed the data to obtain 41,408 valid records, covering all depression-related research articles for the period 2004–2019, and used this as the basis for analysis.

Processing Tools

CiteSpace ( 18 ), developed by Chao-Mei Chen, a professor in the School of Information Science and Technology at Drexel University, is a Java-based program with powerful data visualization capabilities and is one of the most widely used knowledge mapping tools. The software version used in this study is CiteSpace 5.8.R2 (64-bit).

Methods of Analysis

This study uses bibliometrics and data visualization as analytical methods. First, the application of bibliometrics to the field of depression helped to identify established and emerging research clusters, demonstrating the value of research in this area. Second, data visualization provides multiple perspectives on the data, presenting correlations in a clearer “knowledge graph” that can reveal underestimated and overlooked trends, patterns, and differences ( 19 ). CiteSpace is mainly based on the “co-occurrence clustering idea,” which extracts the information units (keywords, authors, institutions, countries, journals, etc.) in the data by classification, and then further reconstructs the data in the information units to form networks based on different types and strengths of connections (e.g., keyword co-occurrence, author collaboration, etc.). The resulting networks include nodes and links, where the nodes represent the information units of the literature and the links represent the existence of connections (co-occurrence) between the nodes. Finally, the network is measured, statistically analyzed, and presented in a visual way. The analysis needs to focus on: the overall structure of the network, key nodes and paths. The key evaluation indicators in this study are: betweenness centrality, year, keyword frequency, and burst strength. Betweenness centrality (BC) is the number of times a node acts as the shortest bridge between two other nodes. The higher the number of times a node acts as an “intermediary,” the greater its betweenness centrality. Betweenness centrality is a measure of the importance of articles found and measured by nodes in the network by labeling the category (or authors, journals, institutions, etc.) with purple circles. There may be many shortest paths between two nodes in the network, and by counting all the shortest paths of any two nodes in the network, if many of the shortest paths pass through a node, then the node is considered to have high betweenness centrality. In CiteSpace, nodes with betweenness centrality over 0.1 are called critical nodes. Year, which represents the publication time of the article. Frequency, which represents the number of occurrences. Burst strength, an indicator used to measure articles with sudden rise or sudden decline in citations. Nodes with high burst strength usually represent a shift in a certain research area and need to be focused on, and the burst article points are indicated in red. The nodes and their sizes and colors are first analyzed initially, and further analyzed by betweenness centrality indicators for evaluation. Each node represents an article, and the larger the node, the greater the frequency of the keyword word and the greater the relevance to the topic. Similarly, the color of the node represents time: the warmer the color, the more recent the time; the colder the color, the older the era; the node with a purple outer ring is a node with high betweenness centrality; the color of each annual ring can determine the time distribution: the color of the annual ring represents the corresponding time, and the thickness of one annual ring is proportional to the number of articles within the corresponding time division; the dominant color can reflect the relative concentration of the emergence time; the node The appearance of red annual rings in the annual rings means hot spots, and the frequency of citations has been or is still increasing rapidly.

Large-Scale Assessment

Country analysis.

During the period 2004–2019, a total of 157 countries/territories have conducted research on depression, which is about 67.38% of 233 countries/territories worldwide. This shows that depression is receiving attention from many countries/regions around the world. Figure 1 shows the geographical distribution of published articles for 157 countries. The top 15 countries are ranked according to the number of articles published. Table 1 lists the top 15 countries with the highest number of publications in the field of depression worldwide from 2004 to 2019. These 15 countries include 4 Asian countries (Peoples R China, Japan, South Korea, Turkey), 2 North American countries (USA, Canada), 1 South American country (Brazil), 7 European countries (UK, Germany, Netherlands, Italy, France, Spain, Sweden), and 1 Oceania country (Australia).

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Geographical distributions of publications, 2004–2019.

The top 15 productive countries.

1USA15,36027.137.870.200.982004
2UK3,8526.801.840.210.202004
3Peoples R China3,8026.724.630.080.012005
4Australia3,2435.730.930.350.022004
5Germany3,1565.571.890.170.092006
6Canada2,6794.731.170.230.092004
7Netherlands2,1463.790.670.320.032006
8Japan1,5532.741.540.100.052007
9Italy1,4472.561.180.120.082007
10South Korea1,3042.300.840.160.032007
11France1,2232.161.290.090.102007
12Spain1,1652.060.950.120.012007
13Brazil1,1542.040.650.180.072007
14Turkey1,1121.960.450.250.002007
15Sweden1,0661.880.440.240.012005

TP, total publications; TP R (%), the ratio of the amount of the publications in the country to the publications in the word during 2004–2019; BC, betweenness centrality; TPA (million), total publications in all areas; TPA R (%), the ratio of the amount of publications in depression to publications in all areas .

Overall, the main distribution of these articles is in USA and some European countries, such as UK, Germany, Netherlands, Italy, France, Spain, and Sweden. This means that these countries are more interested and focused on research on depression compared to others. The total number of publications across all research areas in the Web of Science core collection is similar to the distribution of depression research areas, with the trend toward USA, UK, and Peoples R China as leading countries being unmistakable, and USA has been a leader in the field of depression, with far more articles published than any other country. It can also be seen that USA is the country with the highest betweenness centrality in the network of national collaborations analyzed in this paper. USA research in the field of depression is closely linked to global research, and is an important part of the global collaborative network for depression research. As of 2019, the total number of articles published in depression performance research in USA represents 27.13% of the total number of articles published in depression worldwide, which is ~4 times more than the second-place country, UK, which is far ahead of other countries. Peoples R China, as the third most published country, has a dominant number of articles, but its betweenness centrality is 0.01, reflecting the fact that Peoples R China has less collaborative research with other countries, so Peoples R China should strengthen its foreign collaborative research and actively establish global scientific research partnerships to seek development and generate breakthroughs in cooperation. The average percentage of scientific research on depression in each country is about 0.19%, also highlighting the urgent need to address depression as one of the global human health problems. The four Asian countries included in the top 15 countries are Peoples R China, Japan, South Korea, and Turkey, with Peoples R China ranking third with 6.72% of the total number of all articles counted. The distribution may be explained by the fact that Peoples R China is the largest developing country with a rapid development rate as the largest. Along with the steady rise in the country's economic power, people are creating economic benefits and their health is becoming a consumable commodity. The lifetime prevalence and duration of depression varies by country and region ( 2 ), but the high prevalence and persistence of depression worldwide confirms the increasing severity of the disease worldwide. The WHO estimates that more than 300 million people, or 4.4% of the world's population, suffer from depression ( 20 ), with the number of people suffering from depression increasing at a patient rate of 18.4% between 2005 and 2015. Depression, one of the most prevalent mental illnesses of our time, has caused both physical and psychological harm to many people, and it has become the leading cause of disability worldwide today, and in this context, there is increased interest and focus on research into depression. It is expected that a more comprehensive understanding of depression and finding ways to prevent and cope with the occurrence of this disease can help people get rid of the pain and shadow brought by depression, obtain a healthy and comfortable physical and mental environment and physical health, and make Chinese contributions to the cause of human health. Undoubtedly, the occurrence of depressive illnesses in the context of irreversible human social development has stimulated a vigorous scientific research environment on depression in Peoples R China and other developing countries and contributed to the improvement of research capacity in these countries. Moreover, from a different perspective, the geographical distribution of articles in this field also represents the fundamental position of the country in the overall scientific and academic research field.

Growth Trend Analysis

Figure 2 depicts the distribution of 38,433 articles from the top 10 countries in terms of the number of publications and the trend of growth during 2004–2019.

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The distribution of publications in top 10 productive countries, 2004–2019. Source: author's calculation. National development classification criteria refer to “Human Development Report 2020” ( 21 ).

First, the number of articles published per year for the top 10 countries in terms of productivity was counted and then the white bar chart in Figure 2 was plotted, with the year as the horizontal coordinate and total publications as the vertical coordinate, showing the distribution of the productivity of articles in the field of depression per year. The total number of publications for the period 2004–2019 is 38,433. Based on the white bars and line graphs in Figure 2 , we can divide this time period into three growth periods. The number of publications in each growth period is calculated based on the number of publications per year. As can be seen from the figure, the period 2004–2019 can be divided into three main growth periods, namely 2004–2009, 2010–2012, and 2013–2019, the first growth period being from 2004 to 2009, the number of publications totaled 6,749, accounting for 23.97% of all publications; from 2010 to 2012, the number of publications totaled 8,236, accounting for 17.56% of all publications; and from 2013 to 2019, the number of publications totaled 22,473, accounting for 58.47% of all publications. Of these, 2006 was the first year of sharp growth with an annual growth rate of 19.97%, 2009 was the second year of sharp growth with an annual growth rate of 17.64%, and 2008 was the third year of sharp growth with an annual growth rate of 16.09%. In the last 5 years, 2019 has also shown a sharp growth trend with a growth rate of 14.34%. Notably, in 2010 and 2013, there was negative growth with the growth rate of −3.39 and −1.45%. In the last 10 years, depression research has become one of the most valuable areas of human research. It can also be noted that the number of publications in the field of depression in these 10 countries has been increasing year after year.

Second, the analysis is conducted from the perspective of national development, divided into developed and developing countries, as shown in the orange bar chart in Figure 2 , where the horizontal coordinate is year and the vertical coordinate is total publications, comparing the article productivity variability between developed and developing countries. The top 10 most productive countries in the field of depression globally include nine developed countries and one developing country, respectively. During the period 2004–2019, 34,631 papers were published in developed countries and 3,802 papers were published in developing countries, with developed countries accounting for 90.11% of the 38,433 articles and developing countries accounting for 9.89%, and the total number of publications in developed countries was about 9 times higher than that in developing countries. During the period 2004–2019, the number of publications in developed countries showed negative growth in 2 years (2010 and 2013) with growth rates of −3.39 and −1.45%, respectively. The rest of the years showed positive growth with growth rates of 1.52% (2005), 19.97 (2006), 8.11 (2007), 12.70 (2008), 17.64 (2009), 13.22 (2011), 10.17 (2012), 16.09 (2014), 10.46 (2015), 4.10 (2016), 1.59 (2017), 3.91 (2018), and 14.34 (2019), showing three periods of positive growth: 2004–2009, 2011–2012, and 2014–2019, with the highest growth rate of 19.97% in 2006. Recent years have also shown a higher growth trend, with a growth rate of 14.34% in 2019. It is worth noting that developing countries have been showing positive growth in the number of articles in the period 2004–2019, with annual growth rates of 81.25 (2005), 17.24 (2006), 35.29 (2007), 19.57 (2008), 65.45 (2009), 13.19 (2010), 29.13 (2011), 54.89 (2012), 12.14 (2013), 36.36 (2014), 14.92 (2015), 16.02 (2016), 10.24 (2017), 21.17 (2018), and 31.37 (2019), with the highest growth rate of 81.25% in 2005. In the field of depression research, developed countries are still the main force and occupy an important position.

Further, 10 countries with the highest productivity in the field of depression are compared, total publications in the vertical coordinate, and the colored scatter plot contains 10 colored dots, representing 10 different countries. On the one hand, the variability of the contributions of different countries in the same time frame can be compared horizontally. On the other hand, it is possible to compare vertically the variability of the growth of different countries over time. Among them, USA, with about 40.29% of the world's publications in the field of depression, has always been a leader in the field of depression with its rich research results. Peoples R China, as the only developing country, ranks 3rd in the top 10 countries with high production of research papers in the field of depression, and Peoples R China's research in the field of depression has shown a rapid growth trend, and by 2016, it has jumped to become the 2nd largest country in the world, with the number of published papers increasing year by year, which has a broad prospect and great potential for development.

Distribution of Periodicals

Table 2 lists the top 15 journals in order of number of journal co-citations. In the field of depression, the top 15 cited journals accounted for 19.06% of the total number of co-citations, nearly one in five of the total number of journal co-citations. In particular, the top 3 journals were ARCH GEN PSYCHIAT (ARCHIVES OF GENERAL PSYCHIATRY), J AFFECT DISORDERS (JOURNAL OF AFFECTIVE DISORDERS), and AM J PSYCHIAT (AMERICAN JOURNAL OF PSYCHIATRY), with co-citation counts of 20,499, 20,302, and 20,143, with co-citation rates of 2.09, 2.07, and 2.06%, respectively. The main research area of ARCH GEN PSYCHIAT is Psychiatry; the main research area of the journal J AFFECT DISORDERS is Neurosciences and Neurology, Psychiatry; AM J PSYCHIAT is the main research area of Psychiatry, and the three journals have “psychiatry” in common, making them the most frequently co-cited journals in the field of depression.

The top 15 co-cited journals.

1ARCH GEN PSYCHIAT20,4992.090.02
2J AFFECT DISORDERS20,3022.070.07
3AM J PSYCHIAT20,1432.060.01
4BIOL PSYCHIAT15,5381.590.04
5BRIT J PSYCHIAT15,1091.540.01
6PSYCHOL MED13,1831.350
7J CLIN PSYCHIAT12,7781.300.01
8JAMA-J AM MED ASSOC11,8681.210.02
9ACTA PSYCHIAT SCAND10,1711.040
10LANCET9,1790.940
11PSYCHIAT RES8,2310.840
12PLOS ONE7,7040.790
13NEUROPSYCHOPHARMACOL7,6160.780.01
14DIAGN STAT MAN MENT7,5530.770
15PSYCHOSOM MED6,9200.710.01

TP, total publications; TP R (%), the ratio of the amount of the journal's publications to the total publications; BC, betweenness centrality .

Figure 3 shows the network relationship graph of the cited journals from 2004 to 2019. The figure takes g-index as the selection criteria, the scale factor k = 25 to include more nodes. Each node of the graph represents each journal, the node size represents the number of citation frequencies, the label size represents the size of the betweenness centrality of the journal in the network, and the links between journals represent the co-citation relationships. The journal co-citation map reflects the structure of the journals, indicating that there are links between journals and that the journals include similar research topics. These journals included research topics related to neuroscience, psychiatry, neurology, and psychology. The journal with betweenness centrality size in the top 1 was ARCH GEN PSYCHIAT, with betweenness centrality size of 0.07, and impact shadows of 14.48. ARCH GEN PSYCHIAT, has research themes of Psychiatry. In all, these journals in Figure 3 occupy an important position in the journal's co-citation network and have strong links with other journals.

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Prominent journals involved in depression. The betweenness centrality of a node in the network measures the importance of the position of the node in the network. Two types of nodes may have high betweenness centrality scores: (1) Nodes that are highly connected to other nodes, (2) Nodes are positioned between different groups of nodes. The lines represent the link between two different nodes.

Distribution of Categories

Table 3 lists the 15 most popular categories in the field of depression research during the period 2004–2019. In general, the main disciplines involved are neuroscience, psychology, pharmacy, medicine, and health care, which are closely related to human life and health issues. Of these, psychiatry accounted for 20.78%, or about one-five, making it the most researched category. The study of depression focuses on neuroscience, reflecting the essential characteristics of depression as a category of mental illness and better reflecting the fact that depression is an important link in the human public health care. In addition, Table 3 shows that the category with the highest betweenness centrality is Neuroscience, followed by Public, Environment & Occupational Health, and then Pharmacology & Pharmacy, with betweenness centrality of 0.16, 0.13, and 0.11, respectively. It is found that the research categories of depression are also centered on disciplines such as neuroscience, public health and pharmacology, indicating that research on depression requires a high degree of integration of multidisciplinary knowledge and integration of information from various disciplines in order to have a more comprehensive and in-depth understanding of the depression.

The top 15 productive categories, 2004–2019.

1PSYCHIATRY19,80420.780.07
2NEUROSCIENCES & NEUROLOGY12,35512.960.01
3CLINICAL NEUROLOGY7,2977.660.03
4NEUROSCIENCES6,8487.190.16
5PSYCHOLOGY4,2844.490.09
6PHARMACOLOGY & PHARMACY3,1243.280.11
7GENERAL &INTERNAL MEDICINE2,6822.810
8MEDICINE, GENERAL, & INTERNAL2,5322.660.06
9PSYCHOLOGY, CLINICAL2,3402.460
10PUBLIC, ENVIRONMENTAL, & OCCUPATIONAL HEALTH2,0872.190.13
11GERIATRICS & GERONTOLOGY2,0462.150.01
12GERONTOLOGY1,5581.630
13NURSING1,4541.530.07
14HEALTH CARE SCIENCES & SERVICES1,3801.450.08
15SCIENCE & TECHNOLOGY—OTHER TOPICS1,3011.370.04

TP, total publications; TP R (%), the ratio of the amount of the category's publications to the total publications; BC, betweenness centrality .

Figure 4 shows the nine categories with the betweenness centrality in the category research network, with Neuroscience being the node with the highest betweenness centrality in this network, meaning that Neuroscience is most strongly linked to all research categories in the field of depression research. Depression is a debilitating psychiatric disorder with mood disorders. It is worth noting that the development of depression not only has psychological effects on humans, but also triggers many somatic symptoms that have a bad impact on their daily work and life, giving rise to the second major mediating central point of research with public health as its theme. The somatization symptoms of depression often manifest as abnormalities in the cardiovascular system, and many studies have looked at the pathology of the cardiovascular system in the hope of finding factors that influence the onset of depression, mechanisms that trigger it or new ways to treat it. Thus, depression involves not only the nervous system, but also interacts with the human cardiovascular system, for example, and the complexity of depression dictates that the study of depression is an in-depth study based on complex systems.

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Prominent categories involved in depression, 2004–2019. The betweenness centrality of a node in the network measures the importance of the position of the node in the network. Two types of nodes may have high betweenness centrality scores: (1) Nodes that are highly connected to other nodes, (2) Nodes are positioned between different groups of nodes. The lines represent the link between two different nodes.

Author Statistics

The results of the analysis showed that there were many researchers working in the field of depression over the past 16 years, and 63 of the authors published at least 30 articles related to depression. Table 4 lists the 15 authors with the highest number of articles published. It includes the rank of the number of articles published, author, country, number of articles published in depression-related studies, total number of articles included in Web of Science, total number of citations, average number of citations, and H-index. According to the statistics, seven of the top 15 authors are from USA, three from the Netherlands, one from Canada, one from Australia, one from New Zealand, one from Italy, and one from Germany. From this, it can be seen that these productive authors are from developed countries, thus it can be inferred that developed countries have a better research environment, more advanced research technology and more abundant research funding. The evaluation indicators in the author co-occurrence network are frequency, betweenness centrality and time of first appearance. The higher the frequency, i.e., the higher the number of collaborative publications, the more collaboration, the higher the information dissemination rate, the three authors with the highest frequency in this author co-occurrence network are MAURIZIO FAVA, BRENDA W. J. H. PENNINX, MADHUKAR H. TRIVEDI; the higher the betweenness centrality, i.e., the closer the relationship with other authors, the more collaboration, the higher the information dissemination rate, the three authors with the highest betweenness centrality are the three authors with the highest betweenness centrality are MICHAEL E. THASE, A. JOHN RUSH; the time of first appearance, i.e., the longer the influence generated by the author's research, the higher the information dissemination rate; in addition, the impact factor and citations can also reflect the information dissemination efficiency of the authors.

The top 15 authors in network of co-authorship, 2004–2019.

1MAURIZIO FAVAUSA20060.092501,07323.3051,09447.62105
2BRENDA W. J. H PENNINXNetherlands20080.0518472525.3870,41397.12129
3MADHUKAR H. TRIVEDIUSA20060.0215180218.8340,17150.0993
4MICHAEL E. THASEUSA20060.2114198014.3954,42355.53109
5PIM CUIJPERSNetherlands20060.111361818.2841,42967.04108
6CHARLES F.USA20070.0510053118.8313,89026.1663
7A. JOHN RUSHUSA20060.119491310.3064,23770.36116
8MICHAEL BERKAustralia20070.049467713.8827,53240.6779
9DAVID C. STEFFENSUSA20060.038647118.2619,15640.6772
10BERNHARD T. BAUNENew Zealand20080.048255414.8033,36560.2376
11ALESSANDRO SERRETTIItaly20070.02758588.7421,56325.1369
12AARTJAN T. F. BEEKMANNetherlands20070.037470010.5732,97247.1092
13VOLKER AROLTGermany20060.017362211.7420,16532.4277
14ROGER S. MCINTYRECanada20140.09737709.4821,63928.1072
15DAVID MISCHOULONUSA20080.027130023.677,10423.6844

BC, betweenness centrality; TP, total publications; AP, publications in all areas; DP (%), the ratio of the publications about depression in 2004–2019 to the publications about all areas in all times; TC, total citation; CPP (%), citations per publication .

The timezone view ( Figure 5 ) in the author co-occurrence network clearly shows the updates and interactions of author collaborations, for example. All nodes are positioned in a two-dimensional coordinate with the horizontal axis of time, and according to the time of first posting, the nodes are set in different time zones, and their positions are sequentially upward with the time axis, showing a left-to-right, bottom-up knowledge evolution diagram. The time period 2004–2019 is divided into 16 time zones, one for each year, and each circle in the figure represents an author, and the time zone in which the circle appears is the year when the author first published an article in the data set of this study. The closer the color, the warmer the color, the closer the time, the colder the color, the older the era, the thickness of an annual circle, and the number of articles within the corresponding time division is proportional, the dominant color can reflect the relative concentration of the emergence time, the nodes appear in the annual circle of the red annual circle, that is, on behalf of the hot spot, the frequency of being cited was or is still increasing sharply. Nodes with purple outer circles are nodes with high betweenness centrality. The time zone view demonstrates the growth of author collaboration in the field, and it can be found from the graph that the number of author collaborations increases over time, and the frequency of publications in the author collaboration network is high; observe that the thickness of the warm annual rings in the graph is much greater than the thickness of the cold annual rings, which represents the increase of collaboration in time; there are many authors in all time zones, which indicates that there are many research collaborations and achievements in the field, and the field is in a period of collaborative prosperity. The linkage relationship between the sub-time-periods can be seen by the linkage relationship between the time periods, and it can be found from the figure that there are many linkages in the field in all time periods, which indicates that the author collaboration in the field of depression research is strong.

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Timezone view of the author's co-existing network in depression, 2004–2019. The circle represents the author, the time zone in which the circle appears is the year in which the author first published in this study dataset, the radius of the circle represents the frequency of appearance, the color represents the different posting times, the lines represent the connections between authors, and the time zone diagram shows the evolution of author collaboration.

Institutional Statistics

Table 5 lists the top 15 research institutions in network of co-authors' institutions. These include 10 American research institutions, two Netherlands research institutions, one UK research institution, one Canadian research institution and one Australian research institution, all of which, according to the statistics, are from developed countries. Of these influential research institutions, 66.7% are from USA. Figure 6 shows the collaborative network with these influential research institutions as nodes. Kings Coll London (0.2), Univ Michigan (0.17), Univ Toronto (0.15), Stanford Univ (0.14), Univ Penn (0.14), Univ Pittsburgh (0.14), Univ Melbourne (0.12), Virginia Commonwealth Univ (0.12), Columbia Univ (0.1), Duke Univ (0.1), Massachusetts Gen Hosp (0.1), Vrije Univ Amsterdam (0.1), with betweenness centrality >0.1. Kings Coll London has a central place in this collaborative network and is influential in the field of depression research. Table 6 lists the 15 institutions with the strong burst strength. The top 3 institutions are all from USA. Univ Copenhagen, Univ Illinois, Harvard Med Sch, Boston Univ, Univ Adelaide, Heidelberg Univ, Univ New South Wales, and Icahn Sch Med Mt Sinai have had strong burst strength in recent years. It suggests that these institutions may have made a greater contribution to the field of depression over the course of this year and more attention could be paid to their research.

The top 15 institutions in network of co-authors' institutions, 2004–2019.

1Univ PittsburghUSA1,0080.14
2Kings Coll LondonUK9080.2
3Harvard UnivUSA9070.01
4Univ TorontoCanada8130.15
5Columbia UnivUSA8000.1
6Univ MelbourneAustralia6780.12
7Univ Calif Los AngelesUSA6710.05
8Univ PennUSA6230.14
9Vrije Univ AmsterdamNetherlands6130.1
10Duke UnivUSA6120.1
11Univ WashingtonUSA6080.03
12Univ MichiganUSA6080.17
13Massachusetts Gen HospUSA5990.1
14Univ GroningenNetherlands5570.07
15Stanford UnivUSA5570.14

TP, total publications; BC, betweenness centrality .

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Prominent institutions involved in depression, 2004–2019. The betweenness centrality of a node in the network measures the importance of the position of the node in the network. Two types of nodes may have high betweenness centrality scores: (1) Nodes that are highly connected to other nodes, (2) Nodes are positioned between different groups of nodes. The lines represent the link between two different nodes.

The top 15 institutions with the strongest citation bursts, 2004–2019.

Univ Texas200490.3220042007
Indiana Univ200443.0320042010
Eli Lilly & Co.200441.9320042010
Univ Munich200440.3420042012
Cornell Univ200434.4520042008
Univ Texas SW Med Ctr Dallas200462.320082013
Charite200436.6120102014
Univ Copenhagen200436.7220142019
Univ Illinois200436.1320152019
Harvard Med Sch2004122.0820162019
Boston Univ200437.2520162019
Univ Adelaide200435.8620162019
Heidelberg Univ200433.120162019
Univ New South Wales200447.620172019
Icahn Sch Med Mt Sinai200443.0520172019

Burst denote the citation burst strength; blue thin lines denote the whole period of 2004962019, which provide a useful means to trace the development of research focus; the location and length of red thick lines denote the start and end time during the whole period of the bursts and how long the burst lasts .

Summing up the above analysis, it can be seen that the research institutions in USA are at the center of the depression research field, are at the top of the world in terms of quantity and quality of research, and are showing continuous growth in vitality. Research institutions in USA, as pioneers among all research institutions, lead and drive the development of depression research and play an important role in cutting-edge research in the field of depression.

Article Citations

Table 7 lists the 16 articles that have been cited more than 1,000 times within the statistical range of this paper from 2004 to 2019. As can be seen from the table, the most cited article was written by Dowlati et al. from Canada and published in BIOLOGICAL PSYCHIATRY 2010, which was cited 2,556 times. In addition, 11 of these 16 highly cited articles were from the USA. Notably, two articles by Kroenke, K as first author appear in this list, ranked 7th and 11th, respectively. In addition, there are three articles from Canada, one article from Switzerland, and one article from the UK. And interestingly, all of these countries are developed countries. It can be reflected that developed countries have ample research experience and high quality of research in the field of depression research. On the other hand, it also reflects that depression is a key concern in developed countries. These highly cited articles provide useful information to many researchers and are of high academic and exploratory value.

The top 15 frequency cited articles, 2004–2019.

1A meta-analysis of cytokines in major depression ( )Dowlati, Y2010Canada2,556BIOLOGICAL PSYCHIATRY
2Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice ( )Trivedi, MH2006USA2,354AMERICAN JOURNAL OF PSYCHIATRY
3Deep brain stimulation for treatment-resistant depression ( )Mayberg, HS2005Canada2,314NEURON
4Depression, chronic diseases, and decrements in health: results from the World Health Surveys ( )Moussavi, S2006Switzerland2,219LANCET
5A randomized trial of an N-methyl-D-aspartate antagonist in treatment-resistant major depression ( )Zarate, CA2007USA2,088ARCHIVES OF GENERAL PSYCHIATRY
6The molecular neurobiology of depression ( )Krishnan, V2008USA1,691NATURE
7The PHQ-8 as a measure of current depression in the general population ( )Kroenke, K2009USA1,602JOURNAL OF AFFECTIVE DISORDERS
85-HTTLPR polymorphism impacts human cingulate-amygdala interactions: a genetic susceptibility mechanism for depression ( )Pezawas, L2005USA1,447NATURE NEUROSCIENCE
9Resting-state functional connectivity in major depression: Abnormally increased contributions from subgenual cingulate cortex and thalamus ( )Greicius, MD2007USA1,403BIOLOGICAL PSYCHIATRY
10Sustained hippocampal chromatin regulation in a mouse model of depression and antidepressant action ( )Tsankova, NM2006USA1,242NATURE NEUROSCIENCE
11An Ultra-Brief Screening Scale for anxiety and depression: the PHQ-4 ( )Kroenke, K2009USA1,173PSYCHOSOMATICS
12Fluoxetine, cognitive-behavioral therapy, and their combination for adolescents with depression—Treatment for adolescents with depression study (TADS) randomized controlled trial ( )March, J2004USA1,155JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
13Epidemiology of major depressive disorder—Results from the National Epidemiologic Survey on Alcoholism and Related Conditions ( )Hasin, DS2005USA1,155ARCHIVES OF GENERAL PSYCHIATRY
14Cognition and depression: current status and future directions ( )Gotlib, IH2010USA1,131ANNUAL REVIEW OF CLINICAL PSYCHOLOGY, VOL. 6
15Antenatal risk factors for postpartum depression: a synthesis of recent literature ( )Robertson, E2004Canada1,084GENERAL HOSPITAL PSYCHIATRY
16Prevalence of depression, anxiety, and adjustment disorder in oncological, hematological, and palliative-care settings: a meta-analysis of 94 interview-based studies ( )Mitchell, AJ2011UK1,072LANCET ONCOLOGY

TP, total publications (citations) .

Research Hotspots Ang Frontiers

Keyword analysis.

The keyword analysis of depression yielded the 25 most frequent keywords in Table 8 and the keyword co-occurrence network in Figure 7 . Also, the data from this study were detected by burst, the 25 keywords with the strongest burst strength were obtained in Table 9 . These results bring out the popular and cutting-edge research directions in the field clearly.

Top 25 frequent keywords in the period of 2004–2019.

1Symptom20047,3350.6
2Disorder20047,0710.25
3Major depression20045,8830.28
4Prevalence20045,4550.27
5Meta-analysis20043,2120.08
6Anxiety20043,1530.02
7Risk20043,0400.01
8Scale20042,7790.03
9Association20042,7590
10Quality of life20042,7560.04
11Health20042,7530
12Risk factor20042,4390.12
13Stress20042,0560.11
14Validity20041,8730.03
15Validation20041,8190.02
16Mental health20041,8170.04
17Women20041,8020.03
18Double blind20041,7600.18
19Brain20041,6260.07
20Population20041,6050.01
21Disease20041,5000.02
22Impact20041,4990.06
23Primary care20041,4770.04
24Mood20041,4590.01
25Efficacy20041,4560.04

Count, number of times the article has been cited; BC, betweenness centrality .

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Keyword co-occurrence network in depression, 2004–2019.

Top 25 keywords with strongest citation bursts in the period of 2004–2019.

Fluoxetine2004111.220042007
Community2004110.0820042007
Antidepressant treatment200494.2820092011
Severity200488.3520142019
Meta-analysis200486.4220172019
People200485.3320152017
Follow up200484.4620042013
Expression200479.7820172019
Trial200472.7920062008
Epidemiology200466.9320122015
Model200464.420132019
United States200463.420102012
Adolescent200463.1320142015
Serotonin reuptake inhibitor200462.2120082009
Late life depression200459.7120092010
Disability200452.2920072008
Myocardial infarction200450.5920082009
Placebo200449.3220062007
Hospital anxiety200443.3320082013
Illness200442.320042005
Major depression200442.2220122013
Dementia200441.8120052007
Prefrontal cortex200440.9320162019
Psychiatric disorder200435.3420042008
Management200435.0820162017

Burst denote the citation burst strength; blue thin lines denote the whole period of 2004–2019, which provide a useful means to trace the development of research focus; the location and length of red thick lines denote the start and end time during the whole period of the bursts and how long the burst lasts .

The articles on depression during 2004–2019 were analyzed in 1-year time slices, and the top 25 keywords with the highest frequency of occurrence were selected from each slice to obtain the keyword network shown in Table 8 . The top 25 keywords with the highest frequencies were: symptom, disorder, major depression, prevalence, meta-analysis, anxiety, risk, scale, association, quality of life, health, risk factor, stress, validity, validation, mental health, women, double blind, brain, population, disease, impact, primary care, mood, and efficacy. High-frequency nodes respond to popular keywords and are an important basis for the field of depression research.

Figure 7 shows the co-occurrence network mapping of keywords regarding depression research. Each circle in the figure is a node representing a keyword, and the greater the betweenness centrality, the more critical the position of the node in the network. The top 10 keywords in terms of betweenness centrality are: symptom (0.6), major depression (0.28), prevalence (0.27), disorder (0.25), double blind (0.18), risk factor (0.12), stress (0.11), children (0.1), schizophrenia (0.1), and expression (0.1). Nodes with high betweenness centrality reflect that the keyword forms a co-occurrence relationship with multiple other keywords in the domain. A higher betweenness centrality indicates that it is more related to other keywords, and therefore, the node plays an important role in the study. Relatively speaking, these nodes represent the main research directions in the field of depression; they are also the key research directions in this period, and to a certain extent, represent the research hotspots in this period.

Burst detection was performed on the keywords, and the 25 keywords with the strongest strength were extracted, as shown in Table 9 . These keywords contain: fluoxetine, community, follow up, illness, psychiatric disorder, dementia, trial, placebo, disability, serotonin reuptake inhibitor, myocardial infarction, hospital anxiety, antidepressant treatment, late life depression, United States, epidemiology, major depression, model, severity, adolescent, people, prefrontal cortex, management, meta-analysis, and expression. The keywords that burst earlier include fluoxetine (2004), community (2004), follow up (2004), illness (2004), and psychiatric disorder (2004), are keywords that imply that researchers focused on themes early in the field of depression. As researchers continue to explore, the study of depression is changing day by day, and the keywords that have burst in recent years are people (2015), prefrontal cortex (2016), management (2016), meta-analysis (2017), and expression (2017). Reflecting the fact that depression research in recent years has mainly focused on human subjects, the focus has been on the characterization of populations with depression onset. The relationship between depression and the brain has aroused the curiosity of researchers, what exactly are the causes that trigger depression and what are the effects of depression for the manifestation of depression have caused a wide range of discussions in the research community, and the topics related to it have become the most popular studies and have been the focus of research in recent years. All of these research areas showed considerable growth, indicating that research into this area is gaining traction, suggesting that it is becoming a future research priority. The keywords with the strongest burst strength are fluoxetine (111.2), community (110.08), antidepressant treatment (94.28), severity (88.35), meta-analysis (86.42), people (85.33), and follow up (84.46). The rapid growth of research based on these keywords indicates that these topics are the most promising and interesting. The keywords that has been around the longest burst are follow up (2004–2013), model (2013–2019), hospital anxiety (2008–2013), severity (2014–2019), and psychiatric disorder (2004–2008), researchers have invested a lot of research time in these research directions, making many research results, and responding to the exploratory value and significance of research on these topics. At the same time, the longer duration of burst also proves that these research directions have research potential and important value.

Research Hotspots

Hotspots must mainly have the characteristics of high frequency, high betweenness centrality, strong burst, and time of emergence can be used as secondary evaluation indicators. The higher the number of occurrences, the higher the degree of popularity and attention. The higher betweenness centrality means the greater the influence and the higher the importance. Nodes with strong burst usually represent key shift nodes and need to be focused on. The time can be dynamically adjusted according to the target time horizon of the analysis. Thus, based on the results of statistical analysis, it is clear that the research hotspots in the field of depression can be divided into four main areas: etiology (external factors, internal factors), impact (quality of life, disease symptoms, co-morbid symptoms), treatment (interventions, drug development, care modalities), and assessment (population, size, symptoms, duration of disease, morbidity, mortality, effectiveness).

Risk factors for depression include a family history of depression, early life abuse and neglect, and female sexuality and recent life stressors. Physical illnesses also increase the risk of depression, particularly increasing the prevalence associated with metabolic (e.g., cardiovascular disease) and autoimmune disorders.

Research on the etiology of depression can be divided into internal and external factors. In recent years, researchers have increasingly focused on the impact of external factors on depression. Depression is influenced by environmental factors related to social issues, such as childhood experiences, social interactions, and lifestyles. Adverse childhood experiences are risk factors for depression and anxiety in adolescence ( 37 ) and are a common pathway to depression in adults ( 38 ). Poor interpersonal relationships with classmates, family, teachers, and friends increase the prevalence of depression in adolescents ( 39 ). Related studies assessed three important, specific indicators of the self-esteem domain: social confidence, academic ability, and appearance ( 40 ). The results suggest that these three dimensions of self-esteem are key risk factors for increased depressive symptoms in Chinese adolescents. The vulnerability model ( 41 ) suggests that low self-esteem is a causal risk factor for depression, and low self-esteem is thought to be one of the main causes of the onset and progression of depression, with individuals who exhibit low self-esteem being more likely to develop social anxiety and social withdrawal, and thus having a sense of isolation ( 42 ), which in turn leads to subsequent depression. Loneliness predicts depression in adolescents. Individuals with high levels of loneliness experience more stress and tension from psychological and physical sources in their daily lives, which, combined with insufficient care from society, can lead to depression ( 43 ). A mechanism of association exists between life events and mood disorders, with negative life events being directly associated with depressive symptoms ( 44 ). In a cross-sectional study conducted in Shanghai, the prevalence of depression was higher among people who worked longer hours, and daily lifestyle greatly influenced the prevalence of depression ( 45 ). A number of studies in recent years have presented a number of interesting ideas, and they suggest that depression is related to different environmental factors, such as temperature, sunlight hours, and air pollution. Environmental factors have been associated with suicidal behavior. Traffic noise is a variable that triggers depression and is associated with personality disorders such as depression ( 46 ). The harmful effects of air pollution on mental health, inhalation of air pollutants can trigger neuroinflammation and oxidative stress and induce dopaminergic neurotoxicity. A study showed that depression was associated with an increase in ambient fine particulate matter (PM2.5) ( 47 ).

Increased inflammation is a feature of many diseases and even systemic disorders, such as some autoimmune diseases [e.g., type 1 diabetes ( 48 ) or rheumatoid arthritis ( 49 )] and infectious diseases [e.g., hepatitis and sepsis ( 50 )], are associated with an inflammatory response and have been found to increase the risk of depression. A growing body of evidence supports a bidirectional association between depression and inflammatory processes, with stressors and pathogens leading to excessive or prolonged inflammatory responses when combined with predisposing factors (e.g., childhood adversity and modifying factors such as obesity). The resulting illnesses (e.g., pain, sleep disorders), depressive symptoms, and negative health (e.g., poor diet, sedentary lifestyle) may act as mediating pathways leading to inflammation and depression. In terms of mechanistic pathways, cytokines induce depression by affecting different mood-related processes. Elevated inflammatory signals can dysregulate the metabolism of neurotransmitters, damaging neurons, and thus altering neural activity in the brain. In addition cytokines can modulate depression by regulating hormone levels. Inflammation can have different effects on different populations depending on individual physiology, and even lower levels of inflammation may have a depressive effect on vulnerable individuals. This may be due to lower parasympathetic activity, poorer sensitivity to glucocorticoid inhibitory feedback, a greater response to social threat in the anterior oral cortex or amygdala and a smaller hippocampus. Indeed, these are all factors associated with major depression that can affect the sensitivity to the inhibitory consequences of inflammatory stimuli.

Depression triggers many somatization symptoms, which can manifest as insomnia, menopausal syndrome, cardiovascular problems, pain, and other somatic symptoms. There is a link between sleep deprivation and depression, with insomnia being a trigger and maintenance of depression, and more severe insomnia and chronic symptoms predicting more severe depression. Major depression is considered to be an independent risk factor for the development of coronary heart disease and a predictor of cardiovascular events ( 51 ). Patients with depression are extremely sensitive to pain and have increased pain perception ( 52 ) and is associated with an increased risk of suicide ( 53 , 54 ), and generally the symptoms of these pains are not relieved by medication.

Studies have shown that depression triggers an inflammatory response, promoting an increase in cytokines in response to stressors vs. pathogens. For example, mild depressive symptoms have been associated with an amplified and prolonged inflammatory response ( 55 , 56 ) following influenza vaccination in older adults and pregnant women. Among women who have recently given birth, those with a lifetime history of major depression have greater increases in both serum IL-6 and soluble IL-6 receptors after delivery than women without a history of depression ( 57 ). Pro-inflammatory agents, such as interferon-alpha (IFN-alpha), for specific somatization disorders [e.g., hepatitis C or malignant melanoma ( 58 , 59 )], although effective for somatic disorders, pro-inflammatory therapy often leads to psychiatric side effects. Up to 80% of patients treated with IFN-α have been reported to suffer from mild to moderate depressive symptoms.

Clinical trials have shown better antidepressant treatment with anti-inflammatory drugs compared to placebo, either as monotherapy ( 60 , 61 ) or as an add-on treatment ( 62 – 65 ) to antidepressants ( 66 , 67 ). However, findings like whether NSAIDs can be safely used in combination with antidepressants are controversial. Patients with depression often suffer from somatic co-morbidities, which must be included in the benefit/risk assessment. It is important to consider the type of medication, duration of treatment, and dose, and always balance the potential treatment effect with the risk of adverse events in individual patients. Depression, childhood adversity, stressors, and diet all affect the gut microbiota and promote gut permeability, another pathway that enhances the inflammatory response, and effective depression treatment may have profound effects on mood, inflammation, and health. Early in life gut flora colonization is associated with hypothalamic-pituitary-adrenal (HPA) axis activation and affects the enteric nervous system, which is associated with the risk of major depression, gut flora dysbiosis leads to the onset of TLR4-mediated inflammatory responses, and pro-inflammatory factors are closely associated with depression. Clinical studies have shown that in the gut flora of depressed patients, pro-inflammatory bacteria such as Enterobacteriaceae and Desulfovibrio are enriched, while short-chain fatty acid producing bacteria are reduced, and some of these bacterial taxa may transmit peripheral inflammation into the brain via the brain-gut axis ( 68 ). In addition, gut flora can affect the immune system by modulating neurotransmitters (5-hydroxytryptamine, gamma-aminobutyric acid, norepinephrine, etc.), which in turn can influence the development of depression ( 69 ). Therefore, antidepressant drugs targeting gut flora are a future research direction, and diet can have a significant impact on mood by regulating gut flora.

As the molecular basis of clinical depression remains unclear, and treatments and therapeutic effects are limited and associated with side effects, researchers have worked to discover new treatment modalities for depression. High-amplitude low-frequency musical impulse stimulation as an additional treatment modality seems to produce beneficial effects ( 70 ). Studies have found electroconvulsive therapy to be one of the most effective antidepressant treatment therapies ( 71 ). Physical exercise can promote molecular changes that lead to a shift from a chronic pro-inflammatory to an anti-inflammatory state in the peripheral and central nervous system ( 72 ). Aromatherapy is widely used in the treatment of central nervous system disorders ( 73 ). By activating the parasympathetic nervous system, qigong can be effective in reducing depression ( 74 ). The exploration of these new treatment modalities provides more reference options for the treatment of depression.

Large-scale assessments of depression have found that the probability of developing depression varies across populations. Depression affects some specific populations more significantly, for example: adolescents, mothers, and older adults. Depression is one of the disorders that predispose to adolescence, and depression is associated with an increased risk of suicide among college students ( 75 ). Many women develop depression after childbirth. Depression that develops after childbirth is one of the most common complications for women in the postpartum period ( 76 ). The health of children born to mothers who suffer from postpartum depression can also be adversely affected ( 77 ). Depression can cause many symptoms within the central nervous system, especially in the elderly population ( 78 ).

Furthermore, one of the most consistent findings of the association between inflammation and depression is the elevated levels of peripheral pro-inflammatory markers in depressed individuals, and peripheral pro-inflammatory marker levels can also be used as a basis for the assessment of depressed patients. Studies have shown that the following pro-inflammatory markers have been found to be at increased levels in depressed individuals: CRP ( 79 , 80 ), IL-6 ( 22 , 79 , 81 , 82 ), TNF–α, and interleukin-1 receptor antagonist (IL-1ra) ( 79 , 82 ), however, this association is not unidirectional and the subsequent development of depression also increases pro-inflammatory markers ( 82 , 83 ). These biomarkers are of great interest, and depressed patients with increased inflammatory markers may represent a relatively drug-resistant population.

Frontier Analysis

The exploration and analysis of frontier areas of depression were based on the results of the analysis of the previous section on keywords. According to the evaluation index and analysis idea of this study, the frontier research topics need to have the following four characteristics: low to medium frequency, strong burst, high betweenness centrality, and the research direction in recent years. Therefore, combining the results of keyword analysis and these characteristics, it can be found that the frontier research on depression also becomes clear.

Research on Depression Characterized by Psychosexual Disorders

Exploration of biological mechanisms based on depression-associated neurological disorders and analysis of depression from a neurological perspective have always been the focus of research. Activation of neuroinflammatory pathways may contribute to the development of depression ( 84 ). A research model based on the microbial-gut-brain axis facilitates the neurobiology of depression ( 85 ). Some probiotics positively affect the central nervous system due to modulation of neuroinflammation and thus may be able to modulate depression ( 86 ). The combination of environmental issues and the neurobiological study of depression opens new research directions ( 46 ).

Research on Relevant Models of Depression

How to develop a model that meets the purpose of the study determines the outcome of the study and has become the direction that researchers have been exploring in recent years. Martínez et al. ( 87 ) developed a predictive model to assess factors that modify the treatment pathway for postpartum depression. Nie et al. ( 88 ) extended the work on predictive modeling of treatment-resistant depression to establish a predictive model for treatment-resistant depression. Rational modeling methods and behavioral testing facilitate a more comprehensive exploration of depression, with richer studies and more scientifically valid findings.

Research and Characterization of the Depressed Patient Population

Current research on special groups and depression has received much attention. In a study of a group of children, 4% were found to suffer from depression ( 89 ). The diagnosis and treatment of mental health disorders is an important component of pediatric care. Second, some studies of populations with distinct characteristics have been based primarily on female populations. Maternal perinatal depression is also a common mental disorder with a prevalence of over 10% ( 90 ). In addition, geriatric depression is a chronic and specific disorder ( 91 ). Studies based on these populations highlight the characteristics of the disorder more directly than large-scale population explorations and are useful for conducting extended explorations from specific to generalized.

Somatic Comorbidities Associated With Depression

Depression often accompanies the onset and development of many other disorders, making the study of physical comorbidities associated with depression a new landing place for depression research. Depression is a complication of many neurological or psychopathological disorders. Depression is a common co-morbidity of glioblastoma multiforme ( 92 ). Depression is an important disorder associated with stroke ( 93 ). Chronic liver disease is associated with depression ( 94 ). The link between depressive and anxiety states and cancer has been well-documented ( 95 ). In conclusion, depression is associated with an increased risk of lung, oral, prostate, and skin cancers, an increased risk of cancer-specific death from lung, bladder, breast, colorectal, hematopoietic system, kidney, and prostate cancers, and an increased risk of all-cause mortality in lung cancer patients. The early detection and effective intervention of depression and its complications has public health and clinical implications.

Research on Mechanisms of Depression

Research based on the mechanisms of depression includes the study of disease pathogenesis, the study of drug action mechanisms, and the study of disease treatment mechanisms. Research on the pathogenesis of depression has focused more on the study of the hypothalamic-pituitary-adrenal axis. Social pressure can change the hypothalamic-pituitary-adrenal axis ( 96 ). Studies on the mechanism of action of drugs are mostly based on their effects on the central nervous system. The antidepressant effects of Tanshinone IIA are mediated by the ERK-CREB-BDNF pathway in the hippocampus of mice ( 97 ). Research on the mechanisms of depression treatment has also centered on the central nervous system. It has been shown that the vagus nerve can transmit signals to the brain that can lead to a reduction in depressive behavior ( 98 ).

In this study, based on the 2004–2019 time period, this wealth of data is effectively integrated through data analysis and processing to reproduce the research process in a particular field and to co-present global trends in homogenous fields while organizing past research.

Journals that have made outstanding contributions in this field include ARCH GEN PSYCHIAT, J AFFECT DISORDERS and AM J PSYCHIAT. PSYCHIATRY, NEUROSCIENCES & NEUROLOGY and CLINICAL NEUROLOGY are the three most popular categories. The three researchers with the highest number of articles were MAURIZIO FAVA (USA), BRENDA W. J. H. PENNINX (NETHERLANDS) and MADHUKAR H TRIVEDI (USA). Univ Pittsburgh (USA), Kings Coll London (UK) and Harvard Univ (USA) are three of the most productive and influential research institutions. A Meta-Analysis of Cytokines in Major Depression, Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: Implications for clinical practice and Deep brain stimulation for treatment-resistant depression are key articles. Through keyword analysis, a distribution network centered on depression was formed. Although there are good trends in the research on depression, there are still many directions to be explored in depth. Some recommendations regarding depression are as follows.

(1) The prevention of depression can be considered by focusing on treating external factors and guiding the individual.

Faced with the rising incidence of depression worldwide and the difficulty of treating depression, researchers can think more about how to prevent the occurrence of depression. Depressed moods are often the result of stress, not only social pressures on the individual, but also environmental pressures in the developmental process, which in turn have an unhealthy relationship with the body and increase the likelihood of depression. The correlation between external factors and depression is less well-studied, but the control of external factors may be more effective in the short term than in the long term, and may be guided by self-adjustment to avoid major depressive disorder.

(2) The measurement and evaluation of the degree of depression should be developed in the direction of precision.

In the course of research, it has been found that the Depression Rating Scale is mostly used for the detection and evaluation of depression. This kind of assessment is more objective, but it still lacks accuracy, and the research on measurement techniques and methods is less, which is still at a low stage. Patients with depression usually have a variety of causes, conditions, and duration of illness that determine the degree of depression. Therefore, whether these scales can truly accurately measure depression in depressed patients needs further consideration. Accurate measurement is an important basis for evidence-based treatment of depression, and thus how to achieve accurate measurement of depression is a research direction that researchers can move toward.

Therefore, there is an urgent need for further research to address these issues.

A systematic analysis of research in the field of depression in this study concludes that the distribution of countries, journals, categories, authors, institutions, and citations may help researchers and research institutions to establish closer collaboration, develop appropriate publication plans, grasp research hotspots, identify valuable research ideas, understand current emerging research, and determine research directions. In addition, there are still some limitations that can be overcome in future work. First, due to the lack of author and address information in older published articles, it may not be possible to accurately calculate their collaboration; second, although the data scope of this paper is limited to the Web of Science, it can adequately meet our objectives.

Data Availability Statement

Author contributions.

HW conceived and designed the analysis, collected the data, performed the analysis, and wrote the paper. XT, XW, and YW conceived and designed the analysis. All authors contributed to the article and approved the submitted version.

This work was supported by the National Natural Science Foundation of China under Grant No. 81973495.

Conflict of Interest

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

Publisher's Note

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Essay on Depression

Introductions

In today’s study, there are several psychological conditions associated with technology and lifestyle we engage in every day. According to the psychiatrist’s experts, depression can be defined as a constant sensation of sadness and loss of concentration, which hampers individuals from doing everyday activities. Biologically, depression is a psychological abnormality affecting mental health due to continuous stress, feelings, deep thoughts, and behaviors that may contribute anomalous thinking. Basing the research, depression can be of two types, primary and secondary. Primary depression is mainly related to personal behaviors, such as altered mood due to overthinking, thus resulting in stress. On the other hand, secondary depression is promoted mainly by environmental factors depending on the type of people you interact with. Different types of depression with different signs and symptoms; however, it turns the level at which depression will affect a person. There have been definitive discussion concerning the causes, signs/symptoms, and preventions of depression disorder. Depression is a form of the condition that has affected almost every individual internationally. The research article’s primary focus is to have a definitive discussion on how depression disorder has affected people, especially in today’s period.

Identification and diagnosis of depression

According to medical experts, depression is mainly characterized by stress, sadness, strange feelings, and behavioral conditions. In this research, we need to ask ourselves how depression disorder can be identified and diagnosed, especially in early phase. According to professor Weiss 2015, depression disorder can only be determined depending on personal behaviors and feelings since it’s an invisible condition. Firstly, there is a need to have precise observations and interviews regarding the person suspected of having depression disorder. Depression and bipolar disorders have Closs similarities, and therefore, they share the same characteristics. During the identification of depression, it’s good to recognize the type because there are several types of depression, and each has its features and how it can be handled (Lidz  et al.; 2016). For example, persistent depression disorder has long-term effects thus can last for about 24 months. Therefore, the patient needs special care and close attention to ensure the level of depression is gradually lowering.

To have a positive diagnosis in any disorder, proper identification must be made to understand the type of medication the patient needs. In understanding depression disorder, several patients show sadness and anomalous anxiety, significantly if they are affected by chronic illness conditions. The National Institute of Mental Health (NIMH) argues that despair and clinical depression conditions deploy signs that continue within a given period and have antagonistic impacts that limit patients’ ability to function. Understanding the depression might be challenging, especially to the nursing staff, though there is a need to recognize some indicative characteristics since they can be considered during the conditions’ treatment.

According to Petersen  et al.; 2017, depression can affect any individual regardless of age and size; however, the degree of depression will depend on its stress. The other depression diagnosis is to seek medical care, which provides the affected patients with appropriate treatment to lower depression and anxiety. According to professor Weiss’s arguments, depression and stress are a form of humiliation that affects people due to their relatives’ denunciations. It is believed that this form of stigma is facilitated mainly by lack of parental care to most younger people. It is not only to the younger people, but stigmatization can also affect any individual regardless of age or class. The psychotherapists provide a therapeutic diagnosis to the individual experiencing persistent depressive disorder by assessing the signs, symptoms, and impairment conditions related to the situation.

In response to other medical professors, mental health psychologists may either apply the same strategy depending on their state or condition. Most people who have depression are assessed to get medical statistics of the client, thus permitting the specialists and nurses to think towards a mechanism to diagnose the condition (Stanners et al.; 2014). To recognize depression conditions, therapeutic experts use repetitive indicative practices to know the disorder’s type and how to approach the situation. In this scenario, assessments are made basing the appearances and the expected behavior of the persons. The depression condition mostly depends on the condition’s appearance, which will influence the affected individual’s mood, especially sadness and anxiety.

According to Calandra, Graziano, Barghi & Bonino, 2019 depression and stress condition testing can be appropriately used through pen and paper or electronic forms, including procedures based on indicating diagnostic criteria to the affected individuals. This publication shows that time and reasonable depression disorder diagnosis restraints, limiting psychiatrists from conducting ethical diagnostic assessments. It has been found that most clinics related to depression evaluate patients primarily using an unstructured approach. As per the medical guidelines, any medical expert must have enough training before conducting any psychological assessment on the people who reacted with bipolar disorders.

Petersen& Madsen are the experts who have been concerned with chronic diagnosis basically for the people facing depression conditions. From their arguments, people with a psychiatric disorder who have arbitrarily slapped have higher fusibility to other psychological conditions such as stress; thus, they need care and medication (Cruwys et al.; 2014). Depression abnormality is a definitive disorder that requires a lot of care and frequent medicines, especially to patients in a critical state. Therefore, according to this research, it’s our responsibility to ensure patients living with depression issues have been cared for. Basing the National Institute of Health, psychological diagnosis arguments mainly depend on fallible subjective assessment rather than biological and interview tests. Understanding depression condition makes it simpler to carry out the required diagnosis, limiting the chances of stress and sadness to the affected person.

Diagnosis in depression is cognitively and emotionally crucial in the medical ritual ideas since it provides directive aspects to the affected individuals. Our daily duty and assertation provide noticeable and affordable treatments, especially to the people experiencing depression and stress. Biologically, anxiety and depression. Basing this article’s arguments, depression and stress have adverse effects on psychology, requiring immediate medications and care. Doctors and nurses are expected to show explicit concerns towards people affected by this condition. According to my perspective, there is a need to develop a sense of humanity, especially to the people who have been slapped, depression, and stress condition.

Calandri, E., Graziano, F., Barghi, M., & Bonino, S. (2019). Young adults’ adjustment to a recent diagnosis of multiple sclerosis: The role of identity satisfaction and self-efficacy.  Disability and health journal ,  12 (1), 72-78.

Cruwys, T., Haslam, S. A., Dingle, G. A., Haslam, C., & Jetten, J. (2014). Depression and social identity: An integrative review.  Personality and Social Psychology Review ,  18 (3), 215-238.

Lidz, C. W., & Parker, L. S. (2016). Issues of ethics and identity in the diagnosis of late-life depression.  Ethics & behavior ,  13 (3), 249-262.

Petersen, A., & Madsen, O. J. (2017). Depression: Diagnosis and suffering as a process.  Nordic Psychology ,  69 (1), 19-32.

Stanners, M. N., Barton, C. A., Shakib, S., & Winfield, H. R. (2014). Depression diagnosis and treatment amongst multimorbid patients: a thematic analysis.  BMC family practice ,  15 (1), 1-6.

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The association between coffee consumption and risk of incident depression and anxiety: Exploring the benefits of moderate intake

Affiliations.

  • 1 School of Public Health, Hangzhou Normal University, Hangzhou, China; Hangzhou International Urbanology Research Center & Center for Urban Governance Studies, Hangzhou, China.
  • 2 School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
  • 3 Hangzhou International Urbanology Research Center & Center for Urban Governance Studies, Hangzhou, China.
  • 4 Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
  • 5 Yanjing medical college, Capital Medical University, Beijing, China.
  • 6 School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
  • 7 School of Public Health, Hangzhou Normal University, Hangzhou, China; Hangzhou International Urbanology Research Center & Center for Urban Governance Studies, Hangzhou, China. Electronic address: [email protected].
  • 8 School of Public Health, Hangzhou Normal University, Hangzhou, China; Hangzhou International Urbanology Research Center & Center for Urban Governance Studies, Hangzhou, China. Electronic address: [email protected].
  • PMID: 37352747
  • DOI: 10.1016/j.psychres.2023.115307

Accumulating evidence has reported the associations of coffee consumption with physical conditions and mortality, but the associations with mental disorders were limited. The objective of this study was to examine the associations of coffee consumption with incident depression and anxiety, and to assess whether the associations differed by coffee subtypes (instant, ground, and decaffeinated coffee) or additives (milk, sugar-sweetened, and artificial-sweetened). In this prospective cohort study, we utilized data from the UK Biobank and included a total of 146,566 participants who completed the touchscreen questionnaire at baseline between 2006 and 2010. During the follow-up, incident depression and anxiety were measured in 2016 using the Patient Health Questionnaire (PHQ)-9 and the Generalised Anxiety Disorder Assessment (GAD)-7, respectively. Multivariable-adjusted logistic regression models and restricted cubic splines were used to assess the associations. Approximately 80.7% of participants reported consuming coffee, and most drank 2 to 3 cups per day (41.2%). We found J-shaped associations between coffee consumption and both incident depression and anxiety, with the lowest risk of the mental disorders occurring at around 2-3 cups per day. Results were similar for participants who drank 2-3 cups of ground coffee, milk-coffee, or unsweetened coffee. Our findings highlight that 2-3 cups of coffee consumption could be recommended as part of a healthy lifestyle to improve mental health.

Keywords: Anxiety; Coffee additives; Coffee intake; Coffee subtypes; Depression.

Copyright © 2023 Elsevier B.V. All rights reserved.

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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What Role Does Salt Play in Depression and Anxiety? A Prospective Cohort of 276,138 Individuals

30 Pages Posted: 23 Jul 2024

Wenzhou Medical University - Department of Neurology

Chunyang Pang

Lingfei gao, junwei zhang.

Wenzhou Medical University - Department of Pediatrics

Binbin Deng

Background: Despite the numerous studies highlighting the detrimental health effects of excessive salt intake, research remains scarce in the field of mental health. This study aims to explore the relationship between the frequency of salt added to food and the risk of incident anxiety and depression. Methods: The prospective cohort study comprised 276,138 individuals from the UK Biobank who were initially free from anxiety and depression. Cox proportional hazard models were used to investigate the relationship between the frequency of salt added to food (categorized as never/rarely, sometimes, usually, and always) and the incident depression and anxiety. Results: Within the analyzed population of 276,138 individuals, the average age was 56.6 years, with males accounting for 48.6% of the cohort. During a median follow-up of 14.6 years, 10,486 individuals developed depression, and 10,786 individuals developed anxiety. The multivariable-adjusted hazard ratios (HRs) for incident depression were 1 (reference), 1.10 (95%CI, 1.04-1.16), 1.19 (1.11-1.28), and 1.45 (1.32-1.59) (p trend < 0.001). For incident anxiety, the adjusted HRs were 1 (reference), 1.08 (1.03-1.14), 1.08 (1.01-1.16), and 1.24 (1.12-1.37) (p trend < 0.001). Adequate vegetable and fruit intake showed a modifying effect on the risk of incident depression and anxiety (p for interaction = 0.049 and 0.031, respectively). Conclusions: Higher frequency of salt added to food is linked to increased risk of anxiety and depression, while adequate vegetable and fruit intake may mitigate the detrimental effects of excessive salt consumption. Funding: This work was supported by the National Natural Science Foundation of China (grant number 81901273, 12101460) and the Zhejiang Provincial Natural Science Foundation (grant number ZCLY24H0903). Declaration of Interest: The authors have stated explicitly that there are no conflicts of interest in connection with this article. Ethical Approval: All participants provided informed consent, and the study received approval from the Northwest-Multicenter Research Ethical Committee.

Keywords: Salt, Sodium, depression, Anxiety, UK Biobank

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Researching: Impacts of Childhood Trauma Research Paper

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Annotated Bibliography

Copeland, William E., et al. “Association of Childhood Trauma Exposure With Adult Psychiatric Disorders And Functional Outcomes.” JAMA Network Open 1.7 (2018): e184493-e184493.

The researchers found that childhood trauma exposure was associated with worse adult mental health outcomes, including higher rates of major depression, bipolar disorder, posttraumatic stress disorder (PTSD), generalized anxiety disorder (GAD), and panic disorder. Childhood trauma exposure was also associated with lower levels of social functioning and life satisfaction in adulthood. The authors conclude that their findings suggest that childhood trauma exposure is an important risk factor for poorer adult mental health outcomes. In particular, their results support screening for PTSD and GAD among people with histories of childhood trauma exposure and early interventions for those with these diagnoses to improve their long-term functioning.

The article is reliable because it has been peer-reviewed. The authors are medical doctors who have had their work published in reputable journals and have no financial ties to the topic. The article was also published by JAMA Network Open, one of the world’s most highly respected professional organizations, which adds credibility to their findings. The article compares itself with other articles on the same topic. It uses meta-analysis to combine data from previous studies to reach a consensus about whether or not childhood trauma causes psychiatric disorders later in life. This is an effective way to compare multiple studies because it reduces bias from individual researchers who may be more inclined than others to find certain results.

The article is relevant in explaining how childhood trauma can resurface adolescence and adulthood. In the study, the authors found that childhood trauma exposure was significantly associated with an increased risk of psychiatric disorders, including depression, anxiety, and substance use disorder. The findings revealed that childhood trauma exposure was associated with poorer functional outcomes in adulthood. The authors after concluded that childhood trauma exposure might be a risk factor for psychiatric disorders and poorer functional outcomes in adulthood.

Godoy, Lucas C., et al. “Association of Adverse Childhood Experiences with Cardiovascular Disease Later in Life: A Review.” JAMA Cardiology 6.2 (2021): 228-235.

The article’s authors concluded that there is strong evidence that exposure to adverse childhood experiences (ACEs) is associated with cardiovascular disease and coronary heart disease risk factors in adulthood. The authors point out that ACEs include abuse, neglect, household dysfunction, and other types of childhood trauma. These experiences can affect a child’s developing brain in many ways, including altering the development of neural networks involved in stress response regulation and cognition. The authors state that there is evidence from several studies showing a strong relationship between ACEs and health outcomes, including physical and mental health problems.

The authors of the articles are experts and they also cited several sources of information in their article, which gives credibility to their claims. They use evidence from their studies to support their arguments. The information presented in this article is unbiased because the authors do not mention any personal beliefs or opinions about the topic. The content in this article can be compared with other sources of information on the same topic because it provides a balanced view of the subject matter. The authors discuss both sides of the argument without taking sides or making judgments about them. They stated that there was a need for more research to clarify the potential mechanisms linking ACEs to poor health outcomes later in life.

The article is helpful to the topic of study since it points out a connection between childhood trauma and cardiovascular disease later in life. It also explains that children exposed to trauma are more likely to experience poor cardiovascular health as adults. The researchers discovered that people who experienced various types of childhood adversity were more likely to have heart disease later on in life when compared to those who did not experience any childhood adversity while growing up.

Jaworska-Andryszewska, Paulina, and Janusz K. Rybakowski. “Childhood Trauma In Mood Disorders: Neurobiological Mechanisms And Implications For Treatment.” Pharmacological Reports 71.1 (2019): 112-120.

The article argued that childhood trauma significantly impacts the development of mental health problems later in life, especially depression. They further argue that due to advances in neuroscience, there is more information about the relationship between childhood trauma and major depressive disorder (MDD). They also argue that genetic and environmental factors like childhood trauma cause MDD. Furthermore, they state that many biological mechanisms are involved in MDD, such as neuroinflammation, oxidative stress, and neurogenesis impairment. These biological mechanisms can be triggered by exposure to traumatic experiences during childhood. They discuss treatment options, including psychotherapy, cognitive behavioral therapy, or medication like selective serotonin reuptake inhibitors.

The article is a reliable source of information since experts write it in their field and have many publications on similar topics. They also have a lot of experience in the field, which is evident from their publication record. The authors cite other sources, making them more credible because they provide evidence for their claims. The authors provide an extensive review of research on mood disorders and childhood trauma, which gives us a clear picture of what happens to our brains during these experiences and how they may cause mood disorders later in life.

The article fits the research topic since it discusses how childhood trauma can resurface in adolescence and adulthood and harm a person’s mental health and well-being. The article further provides an overview of neurobiological mechanisms involved in childhood trauma, which include dysregulation of the hypothalamic–pituitary–adrenal axis (HPA), altered neurotransmitter systems, emotional dysregulation, and decreased neurogenesis. The authors discuss how childhood maltreatment is associated with an increased risk of developing mood disorders such as depression, anxiety, PTSD, and substance abuse disorders later in life.

Kim, J. S., et al. “Childhood Trauma.” Solution-Focused Brief Therapy with Clients Managing Trauma , 2018, pp. 189-199.

The article examines the effects of childhood trauma on adults who have experienced it. The authors provide evidence from their research and other studies indicating that individuals who have been traumatized in childhood are at risk for developing mental health issues later in life. The authors explain how trauma can affect development and how that can contribute to mental health issues later in life. They also describe how exposure to trauma harms brain development and functioning and can lead to difficulties with emotion regulation, memory processing, and attention span. According to them, childhood trauma is strongly associated with depression, anxiety disorders, dissociative disorders, substance abuse, and personality disorders.

The article is well-researched and referenced, making it a reliable source. The authors have a Ph.D. in Clinical Psychology and an MSW. The authors of this article have also written many other books, including Solution-Focused Brief Therapy with Clients Managing Trauma. The article cites other research, which shows that the authors have done their research and can support their claims. The article has some biases because the authors are not neutral; instead, they have a point of view on trauma and how it affects people’s lives. The article’s research supports previous findings that childhood trauma plays an important role in understanding adult mental health problems.

The article is relevant to the research study since it discusses how trauma is not a one-time event but rather an ongoing experience that negatively impacts a person’s life. The authors discussed how the different types of trauma could impact an individual in adulthood. The article also highlighted the importance of addressing trauma early on so that they do not continue to negatively affect an individual’s life later on in life. The authors also stated that adolescents and young adults are more susceptible to developing mental health problems due to traumatic experiences such as abuse or neglect.

Painter, K., and M. Scannapieco. “Childhood Trauma.” Understanding the Mental Health Problems of Children and Adolescents , 2021, pp. 49-63.

The article’s authors discussed the relationship between childhood trauma and mental health problems. According to their research, there are different types of childhood trauma, including physical abuse, sexual abuse, emotional abuse, and neglect. Painter and Scannapieco (2019) stated that children who experienced physical abuse during childhood were more likely to develop mental health problems. They added that children who experienced sexual abuse had an increased risk of developing depression later in life. The researchers also mentioned that children who experienced emotional abuse had a higher risk of developing anxiety disorders than those who had not experienced any childhood trauma. Finally, the researcher explained that neglected children had an increased risk of developing substance use disorders later in life compared with those who had not been neglected during childhood.

The authors of this article are both respected experts in the field of psychology and mental health. This fact alone lends credence to their work since it shows that they are knowledgeable about their field. They also provide citations for their research and studies, which means that other experts have reviewed their work. From these two facts, this article is reliable. On the other hand, Painter and Scannapieco do not cite any studies from other researchers who might disagree with their conclusions or methods used in conducting their research, suggesting some bias.

The article can be used for the topic because it is a great source for researching the negative impact of childhood trauma on adulthood. It starts by discussing how trauma can affect the development of children. It details different types of trauma that can occur, such as sexual abuse and neglect. It also explains how these experiences affect people later in life. The article provides examples of children exposed to trauma who have developed psychiatric disorders as adults.

Popovic, David, et al. “Childhood Trauma in Schizophrenia: Current Findings and Research Perspectives.” Frontiers in Neuroscience 13 (2019): 274.

The author described some of the common symptoms that are associated with schizophrenia. These include delusions, hallucinations, and disorganized speech. The author then explains how childhood trauma affects the brain development of children and adolescents. He mentions that there is evidence that early-life stress can affect brain development by causing brain cells to die or grow too fast in certain areas of the brain. He also states that these changes can lead to later mental health problems such as depression or anxiety disorders. In addition, he mentions that survivors of childhood abuse often experience PTSD symptoms like flashbacks and nightmares after they have experienced stressful events later in life, such as divorce or getting fired from their jobs.

The authors of this article are all experts in their fields, having written many other articles related to psychology and mental health. The sources cited by the authors were also reliable as they were peer-reviewed journals and books. These sources have been used by other researchers and professionals working in the field of psychology, which means that these researchers have found the information useful enough to reference their works with it. There was no bias present in this article because it was written based on scientific research rather than personal opinion or experience.

The article is relevant to the topic since it provides a better understanding of how trauma can affect the brain. The researchers explain how traumatic experiences can alter brain pathways, leading to mental illness later in life. The authors also discuss how childhood trauma has been linked to other mental health issues like depression and post-traumatic stress disorder (PTSD). They provide insight into how these disorders may be connected through similar brain pathways. They also discuss how childhood trauma may affect social functioning later in life, leading to isolation and loneliness that could further develop mental health issues.

Simonetti, Alessio. “ Electroencephalography and Childhood Trauma .” Childhood Trauma in Mental Disorders , 2020, pp. 79–103., Web.

The article discusses how childhood trauma can hurt an adult’s brain activity using electroencephalography (EEG). The author notes that brain activity does not only affect the person’s emotional state but also their cognitive abilities. This is evident when looking at how children experience trauma in their childhood. Children are more vulnerable to experiencing trauma compared to adults due to their limited understanding of what is happening. The author notes that most people who experience trauma during their childhood will grow up to become adults with mental disorders and other health problems. This is because they cannot properly process the events that took place during their childhood. Furthermore, they may find it difficult to trust other individuals or themselves. This makes them feel insecure about themselves and thus causes them to behave abnormally.

The reliability of this article can be assessed by looking at its credibility and reputation among the readers. The author is a well-known researcher in the field of neuroscience who has published many articles on this topic before and after this one was published. The author also cited other research done by other scientists that supports his findings here and uses proper citations when doing so, which makes it easier for other researchers to find out more about his research because they can find out where he got his information.

The article best fits the research topic since it discusses the effects of early childhood trauma on the brain and how it affects children’s behavior in adulthood. The article starts by describing how the brain is affected by trauma and how it causes changes in behavior. It then describes how electroencephalography (EEG) can be used to monitor electrical activity in the brain to help determine whether a child has experienced trauma.

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Cannabigerol (CBG) is a phytocannabinoid increasing in popularity, with preclinical research indicating it has anxiolytic and antidepressant effects. However, there are no published clinical trials to corroborate these findings in humans. The primary objective of this study was to examine acute effects of CBG on anxiety, stress, and mood. Secondary objectives were to examine whether CBG produces subjective drug effects or motor and cognitive impairments. A double-blind, placebo-controlled cross-over field trial was conducted with 34 healthy adult participants. Participants completed two sessions (with a one-week washout period) via Zoom. In each, they provided ratings of anxiety, stress, mood, and subjective drug effects prior to double-blind administration of 20 mg hemp-derived CBG or placebo tincture (T0). These ratings were collected again after participants ingested the product and completed an online survey (T1), the Trier Social Stress Test (T2), a verbal memory test and the DRUID impairment app (T3). Relative to placebo, there was a significant main effect of CBG on overall reductions in anxiety as well as reductions in stress at T1. CBG also enhanced verbal memory relative to placebo. There was no evidence of subjective drug effects or impairment. CBG may represent a novel option to reduce stress and anxiety in healthy adults.

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Introduction.

The rapid proliferation of the new legal cannabis market has instigated producers to cultivate an array of novel products to satisfy consumers’ growing interest. While products high in delta-9-tetrahydrocannabinol (THC) continue to saturate the market 1 , a growing body of consumers are seeking alternative non-intoxicating products carrying the promise of easing what ails them. Producers are responding to this demand by isolating various minor phytocannabinoids and terpenes and marketing them with bold, but largely unsubstantiated, claims of their therapeutic potential. While cannabidiol (CBD) continues to be the dominant non-intoxicating cannabinoid of interest to both consumers and researchers, cannabigerol (CBG) has rapidly increased in popularity 2 .

CBG is a minor phytocannabinoid and its acidic form (CBGA) is often referred to as “the mother of all cannabinoids” as it is a precursor to numerous other phytocannabinoids, including THC, CBD, and cannabichromene (CBC). CBG received little research interest initially, due in part to the overwhelming focus on the effects of THC and CBD. Subsequent pre-clinical investigations involving the administration of CBG to animals, however, have demonstrated a broad spectrum of potential therapeutic effects including potent antibiotic 3 and antifungal activity 4 . CBG also appears to have anti-hypertensive effects 5 , it reduces intra-ocular pressure 6 and keratinocytes in a psoriasis model 7 , it has possible efficacy in inflammatory bowel disease 8 and it may have analgesic effects 9 . Moreover, CBG has been demonstrated to have antidepressant-like effects in a rodent tail suspension model 10 while lacking cannabimimetic effects indictive of THC 10 , 11 .

In stark contrast to the impressive body of preclinical research, there has been a dearth of research examining effects of CBG on humans. To help fill this gap, we recently published a study in which 127 experienced CBG users were surveyed on their use of CBG-dominant products, including their use patterns, the perceived therapeutic effects of CBG, as well as its potential side effects 12 . Participants most frequently reported using CBG to manage anxiety (attested to by 51% of the sample), chronic pain (41%), depression (33%), and insomnia (31%). Moreover, most of the sample indicated that CBG was more effective than conventional medications for treating anxiety (78%), chronic pain (74%), depression (80%), and insomnia (73%). Only a minority reported experiencing side effects such as dry eyes (9%), dry mouth (16.5%), sleepiness (15%), and increased appetite (12%). While provocative, these findings are limited by their retrospective, self-report, nature, and the use of preparations of varying CBG composition. As such, they require corroboration via double-blind, placebo-controlled clinical trials.

However, to date there are only two published clinical trials on the effects CBG in humans. One examined the effects of an oral formulation containing CBG (50 mg), CBD (30 mg), and the terpene beta caryophyllene (25 mg) on experimentally induced delayed onset muscle soreness 13 . The other study examined the impacts of dietary fat (low fat meal [< 5 g] vs. high fat meal [> 30 g]) and oral delivery systems (isolate vs. emulsification) on CBG pharmacokinetics 14 .

Current study

The primary objective of the current study was to use a double-blind, placebo-controlled cross-over trial to investigate the acute effects of CBG on anxiety, stress, and mood. The secondary objectives were to assess subjective drug effects (intoxication, drug effect, drug liking), potential side effects (dry eyes, dry mouth, sleepiness, appetite, racing heart/heart palpitations) as well as to determine whether CBG produces motor or cognitive impairments. We hypothesized that CBG would decrease anxiety, decrease stress, and elevate mood. Further, we hypothesized that ratings of subjective drug effects would be low and that CBG would not produce motor or cognitive impairments.

A double-blind, placebo-controlled cross-over field trial was used to address our primary and secondary objectives. There was a one-week wash out period between the two testing sessions. Orally administered CBG has been demonstrated to have a half-life of 2–6 h. in rodents and dogs 15 , 16 , thus this 1-week washout period would be ample to clear any residual drug effects. We used a 1:1 allocation ratio and potential carry over effects were controlled for by adding order of drug/placebo administration as a covariate in the statistical analyses.

Data collection commenced March 2022 and was completed November 2023. The study was pre-registered on ClinicalTrials.gov [NCT05257044] (25/02/2022) and was approved by the Washington State University (WSU) Institutional Review Board (IRB). All participants provided informed consent and the research was conducted in accordance with the Declaration of Helsinki. The trial was conducted remotely via Zoom from The Health & Cognition (THC) lab at WSU. The use of Zoom helped enhance feasibility and generalizability by allowing us to recruit a more diverse sample of participants residing at various locations across the state rather than limiting recruitment to our small university town.

Participants

Results of an a priori power analysis using G-Power indicated that a sample size of 34 would be required to achieve power of 0.80 to detect medium-sized effects (i.e., \(d\) = 0.50) with alpha set at 0.05.

Inclusion/exclusion criteria

Participants were required to be at least 21 years of age, reside in Washington State, have a smartphone, and have access to a computer with a webcam connected to stable internet in a private environment. They also had to be fluent in English and able to see, hear, and read. Participants could not have any chronic neurological disorders, head injuries involving a loss of consciousness for more than 10 min, or have a diagnosed intellectual disorder, psychotic disorder, autism spectrum disorder, or bipolar disorder. Participants were also excluded if they were pregnant or breastfeeding, or if they reported any illicit drug use (except cannabis) in the past 2 months. To reduce risks of adverse reactions, participants had to report prior experience with cannabis-based products (i.e., ≥ 10 lifetime uses, use in the past month) and report no serious prior adverse reactions to CBG (e.g., panic attacks, psychosis). Finally, participants had to agree to abstain from using products containing cannabis or CBG for a minimum of 24 h prior to the testing session. Initially, participants were required to report no heavy alcohol use, no illicit drug use in the past 3 months (rather than 2 months) and prior experience using CBG (rather than with cannabis-based products). They were also initially required to abstain from use of cannabis and CBG for a minimum of 72 h prior to the testing session. These four criteria were changed 5 months into the trial to bolster recruitment and minimize cannabis withdrawal symptoms as only two participants were enrolled and tested prior to this change.

The CBG tincture was composed of 10 mg/ml CBG, 0.89 mg/ml CBGA, 0.35 mg/ml β-caryophyllene (0.51 mg/ml total terpenoids). It contained < 0.001 mg/g THC and CBD and was derived from a CBG-dominant hemp plant containing less than the 0.3% THC limit imposed in the U.S. Agricultural Improvement Act of 2018. It was extracted from inflorescences in 190 proof (95%) ethanol. It was donated by Shango Los from a single batch of material with a certificate of analysis provided from Analytical 360 Analysis Laboratory (Yakima, WA; see Certificate of Analysis in Supplemental Materials S1 ). Single 3 ml amber vials were prepared with this material containing 2 ml each representing a dose of 20 mg of CBG. Chartreuse liqueur (55% ethanol by volume) prepared by Les Pères Chartreux, France 1 ml, diluted with 1 ml filtered water was employed as the placebo as it provided a reasonable match for the green color and herbal/ethanol taste of the CBG tincture. Opaque vials were labeled with color coded tabs (Blue = CBG, Yellow = Placebo) in double-blind fashion and mailed directly to study participants. To dilute the taste, participants were instructed to mix the contents of the vial in a small glass of water prior to ingestion.

Online questionnaires

Demographic questionnaire.

Participants responded to a small series of items designed to assess demographic characteristics. Specifically, they were asked to provide information pertaining to their gender, age, ethnicity, education, personal income, work status, and marital status.

Daily sessions, frequency, age of onset, and quantity of cannabis use inventory (DFAQ-CU)

The DFAQ-CU is a 41-item inventory, with 24 core items that assess frequency, age of onset, and quantity of cannabis flower, quantity of cannabis concentrates, and quantity of edibles typically used 17 . The remaining items measure other aspects of cannabis use not commonly measured by other cannabis use scales (e.g., forms of cannabis; methods of administration; use for medical, recreational, or combined purposes). The subscales have shown acceptable internal consistency as well as good predictive, convergent, and discriminant validity 17 . This inventory was simply used to describe the cannabis using patterns of participants.

State-trait anxiety inventory (STAI)

The STAI is an inventory for measuring state and trait anxiety 18 . The inventory contains two parts, one containing 20 items designed to assess state anxiety and another containing 20 items designed to assess trait anxiety. The state anxiety section assesses how individuals feel in the moment. The trait anxiety section assesses how individuals generally feel. We computed state and trait anxiety scores according to Spielberger et al. 18 . Possible scores on each section range from 20 to 80, with higher scores reflecting more anxiety. The inventory has been demonstrated to have high reliability coefficients 18 . In the present study, Cronbach’s alpha exceeded 0.90 for trait anxiety and state anxiety at baseline as well as for the repeated assessments of state anxiety in both conditions.

Depression anxiety stress scales (DASS-21)

Levels of depression, anxiety, and stress were assessed using the DASS-21, which is a self-report inventory designed to measure these different, but related constructs 19 . Each of the three subscales contained seven statements and participants indicate how much each statement has applied to them in the past week. Scores for each subscale were computed by summing the scores for each subscale and multiplying the sum by two, with higher scores representing stronger endorsement of that dimension of distress. The DASS-21 and its subscales have been shown to possess good psychometric properties 20 , 21 . In the present study, Cronbach’s alpha for depression was 0.92 in the CBG condition and 0.94 in the placebo condition, for anxiety it was 0.75 in the CBG condition and 0.69 in the placebo condition, and for stress it was 0.90 in the CBG condition and 0.91 in the placebo condition.

Subjective state ratings

Participants rated their subjective levels of anxiety and stress using a 0 (not at all) to 10 (extremely) visual analogue scales (VAS), and they rated their subjective mood using a 0 (extremely negative) to 10 (extremely positive) VAS. Cronbach’s alpha was 0.94 for anxiety ratings in both conditions, it was 0.90 for stress in the CBG condition and 0.92 for stress in the placebo condition, and it was 0.88 for mood in the CBG condition and 0.90 for mood in the placebo condition.

Subjective drug effect ratings

Participants rated their levels of intoxication using 0 (not at all intoxicated) to 10 (extremely intoxicated) VAS, the level of drug effects they were experiencing using 0 (none) to 10 (a lot) VAS, and their liking of the drug effects using 0 (dislike very much), 5 (neutral) to 10 (like very much) VAS. Similarly, they rated their level of dry eyes, dry mouth, sleepiness, appetite, and racing heart/heart palpitations using 0 (none) to 10 (extremely) VAS. Cronbach’s alpha exceeded 0.90 for ratings of dry eyes, dry mouth, sleepiness, and appetite in both conditions and drug effect ratings in the placebo condition; it exceeded 0.80 for intoxication ratings in the CBG condition; it exceeded 0.70 for heart palpitations in both conditions, drug effect ratings in the CBG condition and drug liking ratings in the placebo condition. Finally, Cronbach’s alpha was 0.66 for drug liking ratings in the CBG condition and it was 0.68 for intoxication ratings in the placebo condition.

Online trier social stress test

Participants completed an online version of the Trier Social Stress Test (TSST) 22 . Participants were instructed to mentally prepare a 5-min speech describing why they would be a good candidate for their ideal job. They were informed that the speech would be recorded and reviewed by a panel of judges trained in public speaking (although no such recording was made). They were sent to a Zoom breakout room for 10-min to prepare their speech. The research assistant (RA) put on a white lab coat while they were in the breakout room. Participants were brought back to the main Zoom room and were asked to deliver their speech to the RA who maintained a neutral expression. They were instructed to speak for the entire 5-min period and were prompted to continue if they stopped for more than 20 secs. Immediately upon completing the speech, they were told they would need to complete a 5-min math test. For this test they were instructed to sequentially subtract the number 13 from 1,022. Each time they made a mistake they were informed they made a mistake and were instructed to start over from 1,022. Prior research has found that this online version of the TSST significantly and robustly increases state anxiety, perceived stress, blood pressure, and heartrate compared to baseline 22 .

California verbal learning test-II

The standardized California Verbal Learning Test II (CVLT-II) 23 was used to assess verbal free recall. Participants were asked to listen to and immediately recall a list of 16 words three times in a row (Trials 1–3). They were then asked to listen to and immediately recall a list of 16 new words (List B). Immediately following recall of List B, participants were required to recall the words from List A (Short Delay). Alternate forms of the lists were used in the first and second testing sessions to reduce practice effects. Cronbach’s alpha was 0.84 in the CBG condition and 0.91 in the placebo condition.

DRUID application

The benchmark version of the Driving under the influence of drugs (DRUID) mobile application contains four brief tasks (each under 45 s), completed on a smartphone, that measure cognitive and motor impairment. Past research has shown that the DRUID is a sensitive test of impairment that detected significant differences between placebo and both oral and vaporized THC 24 . Prior to completing the tests participants were instructed to stand up and to hold their phone in one hand and to tap the screen with their other hand. For the first task, circles and squares are briefly flashed on the screen and participants are instructed to tap the location on the screen where each circle flashed and to tap a white oval at the top of the screen whenever they see a square as quickly as possible. The instructions change mid-way through the task and participants are instructed to tap the location on the screen where each square flashed and to tap the white oval at the top of the screen whenever they see a circle as quickly as possible. For the second task, circles briefly appear on the screen, and participants are instructed to tap where they saw each circle as quickly as possible and to press STOP at the top of the screen when they have estimated that 30 secs have passed. For the third task, participants are instructed to keep their finger on a moving circle and count the number of squares that briefly appear on the screen. For the fourth task, participants are instructed to raise their left foot off the floor and balance on their right foot while holding their phone in their left hand for 15 s. Finally, they switch sides and raise their right foot off the floor while holding their phone in their right hand for 15 s. The app accesses data from the accelerometer in the smartphones to measure stability and balance during the latter task. The primary outcome measure is a global impairment score, with higher scores indicating worse reaction times and balance. Scores above 57 are indicative of impairment 24 as they have previously been shown to be associated with blood-alcohol concentrations of 0.08% 25 . Cronbach’s alpha was 0.93 in the present study.

Recruitment

Participants were recruited using advertisements posted in cannabis dispensaries, at WSU, in the community, and on social media as well as by emailing cannabis users who had completed other THC lab studies. Prospective participants were informed that we were interested in understanding the acute effects of CBG and were directed to complete a brief online Qualtrics screening survey that included bot detection and contained questions probing the various inclusion/exclusion criteria.

Pre-testing session

Eligible participants were invited to a pre-testing Zoom session with either the principal investigator or RA to ensure they could access Zoom on a secure stable internet connection in a personal environment, obtain informed consent, download the DRUID app and complete the baseline trials, schedule their testing sessions, assign their ID code, and provide them with the contact information of a CBG producer who shipped color-coded vials of hemp-derived CBG and placebo directly to them. Participants were instructed to abstain from using any cannabis products including CBG for a minimum of 24 h prior to their testing session. This 24-h period was selected to avoid picking up any acute effects of cannabis while also ensuring participants were not experiencing withdrawal symptoms which typically peak 2–6 days after abstinence from cannabis 26 .

Testing sessions

Approximately one-week after the pre-testing Zoom session participants met with a RA—who was blinded to the color-codes used for the drug and placebo—on Zoom for their first testing session. First, the RA confirmed that participants had abstained from use of cannabis and CBG for a minimum of 24 h prior to the testing session. As depicted in Fig.  1 , participants then provided baseline (T0) ratings of their subjective state (anxiety, stress, mood, STAI state anxiety). Next, participants were instructed to ingest one of the color-coded vials. Half the participants were assigned odd ID codes and were randomly assigned to ingest the blue vial containing 20 mg CBG first and the other half were assigned even ID codes and were assigned to ingest the yellow vial containing 20 mg placebo first. To help disguise the taste, participants were instructed to mix the contents of the vial in a small glass of water prior to oral ingestion. After observing the participant ingest the product, the RA instructed them to complete an online survey that contained measures of their demographic characteristics; anxiety, depression, and stress levels; as well as cannabis and CBG use patterns.

figure 1

Overview of procedure for each testing session.

After completing the online questionnaire, participants provided T1 ratings of their subjective state and subjective drug effects. The mean time between T0 and T1 ratings was 20 min. Participants subsequently completed the Trier Social Stress Test. Following the stress manipulation participants provided T2 ratings of their subjective state and subjective drug effects. The mean time between T0 and T2 ratings was approximately 45 min. Participants then completed the California Verbal Learning Test-II and the DRUID app. Finally, participants provided T3 ratings of their subjective state and subjective drug effects. The mean time between T0 and T3 ratings was approximately 60 min.

One-week after the first testing session, participants completed the second testing session. This session was identical to the first testing session except those participants who ingested the blue vial containing CBG in the first session, ingested the yellow vial containing placebo in the second session. Those who ingested the yellow vial containing placebo in the first session, ingested the blue vial containing CBG in the second session. Neither the participant nor the RA knew which color code corresponded to CBG and placebo.

Data and statistical analysis

Percentages, means, and standard deviations were used to determine the demographic characteristics and cannabis use patterns of the sample. Mean DASS subscale scores, mean subjective state (mood, anxiety, stress) ratings, mean STAI state and trait anxiety scores, and mean subjective drug effect ratings were computed at baseline.

Change scores were created by subtracting baseline (T0) scores from T1, T2, and T3 scores for the subjective state ratings (anxiety, stress, mood), STAI state anxiety scores, and subjective drug effect ratings (dry eyes, dry mouth, sleepiness, appetite, heart palpitations/racing heart). Since intoxication scores were all 0 at baseline (T0) and drug effect and drug liking ratings were not obtained at baseline, raw post-drug administration ratings at T1, T2, and T3 were used in analyses of these secondary outcomes.

To determine the effects of CBG vs. placebo on subjective state ratings, STAI state anxiety scores, and subjective drug effect ratings (dry eyes, dry mouth, sleepiness, appetite, heart palpitations/racing heart) a series of 2 × 3 repeated-measures ANCOVAs were conducted with condition (CBG, placebo), and time (T1, T2, T3) as within-subjects factors, order of drug administration as a covariate, and changes (difference from T0 to T1, T2, T2) in ratings as the dependent variables.

To determine the effects of CBG vs. placebo on intoxication, drug effect, and drug liking ratings a series of 2 × 3 repeated-measures ANCOVAs were conducted with condition (CBG, placebo), and time (T1, T2, T3) as within-subjects factors, order of drug administration as a covariate, and ratings of each indicator of drug effects as the dependent variables.

To examine the acute effects of CBG on verbal memory, a 2 × 5 repeated-measures analysis of covariance (ANCOVA) was conducted with condition (CBG, placebo), and CVLT-II trial (Trial 1, Trial 2, Trial 3, Trial 1B, Short-Delay) as within-subjects factors, order of drug administration as a covariate, and number of words correctly recalled as dependent variable.

Finally, to assess the effects of CBG vs. placebo on impairment, a one-way repeated measures ANCOVA was conducted to compare DRUID scores at baseline, in the CBG condition, and in the placebo condition, while controlling for order.

Pairwise deletion was used to handle the small amount (< 1%) of missing data. Alpha was set to 0.05 and effect sizes of 0.01 are interpreted as small, 0.06 are considered medium, and 0.14 and above are considered large 27 . Data were analyzed using IBM SPSS v.27.

Ethics approval

The study was approved by the Washington State University (WSU) Institutional Review Board (IRB).

The sample of 34 eligible participants ranged in age from 21 to 60 ( M  = 30.06; SD  = 10.50). Table 1 shows the remaining demographic characteristics of the sample. Figure  2 shows a Consort flow diagram of the number of participants screened, determined ineligible, and randomized.

figure 2

Consort flow diagram. Consort flow diagram showing the total number of people assessed for eligibility, randomized, tested, and analyzed with reasons for exclusions.

The majority of the sample reported no prior experience using CBG ( n  = 22, 64.7%). All participants endorsed prior experience using cannabis. Further, all participants indicated that they had abstained from cannabis use in the 24 h prior to each testing session with a mean period of abstinence of 96 h and a median of 48 h. Participants reported using cannabis 0–7 days of the past week ( M  = 3.62, SD  = 2.45) and 1–31 days of the past month ( M  = 18.65, SD  = 10.86). Participants reported using cannabis for 2 to 40 years in duration ( M  = 10.79, SD  = 9.29) and being aged 8 to 29 when they first tried cannabis ( M  = 16.25, SD  = 3.42). Most of the sample ( n  = 28, 82.4%) reported using cannabis for recreational purposes, with the remainder ( n  = 6; 17.6%) reporting using for recreational and medicinal purposes (none reported using for only medicinal purposes). The remaining cannabis and use patterns of the sample are provided in Table 2 .

Baseline measures

Participants’ baseline DASS or STAI subscale scores; subjective state ratings of mood, anxiety, and stress; ratings of intoxication and subjective drug effects prior to drug administration are provided in Table 3 . Importantly, all participants gave baseline intoxication ratings of 0 in both conditions consistent with the required 24-h period of abstinence from cannabis and CBG.

Effects of CBG vs. placebo on subjective state ratings

A 2 × 3 repeated-measures ANCOVA on changes in subjective anxiety ratings (changes from T0 to T1, T2, T3) revealed a moderately large-sized statistically significant main effect of condition, F (1, 32) = 4.88, p  = 0.034, \({\upeta }_{\text{p}}^{2}\) = 0.132, and a large-sized statistically significant main effect of time, F (2, 64) = 7.76, p  < 0.001, \({\upeta }_{\text{p}}^{2}\) = 0.195. The interaction between condition and time was small in magnitude and was not statistically significant, F (2, 64) = 1.56, p  = 0.217, \({\upeta }_{\text{p}}^{2}\) = 0.047. As depicted in Fig.  3 A, the main effect of condition reflects overall larger reductions in self-reported feelings of anxiety in the CBG condition compared to the placebo condition.

figure 3

Changes in subjective state ratings following drug administration. Lines represent mean changes in subjective ratings of anxiety ( A ), stress ( B ), mood ( C ), and STAI state anxiety scores ( D ) from baseline (T0) to T1, T2, and T3. Error bars represent standard errors of the mean.

A 2 × 3 repeated measures ANCOVA on changes in subjective stress ratings revealed a large-sized statistically significant main effect of time, F (2, 64) = 8.20, p  < 0.001, \({\upeta }_{\text{p}}^{2}\) = 0.204, and a small-sized main effect of condition that was not statistically significant, F (1, 32) = 1.19, p  = 0.283, \({\upeta }_{\text{p}}^{2}\) = 0.036. This main effect of time was qualified by a moderately large-sized statistically significant interaction between condition and time, F (2, 64) = 4.90, p  = 0.011, \({\upeta }_{\text{p}}^{2}\) = 0.133. Probing of this interaction revealed a moderately large-sized simple effect of condition on change in subjective stress ratings at T1, F (1, 32) = 5.02, p  = 0.032, \({\upeta }_{\text{p}}^{2}\) = 0.136. In contrast, there were no significant differences in changes in stress ratings in the CBG and placebo conditions at T2, F (1, 32) = 0.31, p  = 0.583, \({\upeta }_{\text{p}}^{2}\) = 0.010, or T3, F (1, 32) = 2.26, p  = 0.142, \({\upeta }_{\text{p}}^{2}\) = 0.066 (see Fig.  3 B).

The 2 × 3 repeated measures ANCOVA on changes in subjective mood ratings revealed no significant main effects of time, F (2, 64) = 2.56, p  = 0.085, \({\upeta }_{\text{p}}^{2}\) = 0.074, or condition, F (1, 32) = 3.25, p  = 0.081, \({\upeta }_{\text{p}}^{2}\) = 0.092, and no condition x time interaction, F (2, 64) = 2.27, p  = 0.111, \({\upeta }_{\text{p}}^{2}\) = 0.066 (Fig.  3 C).

The 2 × 3 repeated-measures ANCOVA on changes in STAI state anxiety total scores (changes from T0 to T1, T2, T3) revealed no significant main effect of condition, F (1, 32) = 2.47, p  = 0.126, \({\upeta }_{\text{p}}^{2}\) = 0.072, but a large-sized statistically significant main effect of time, F (2, 64) = 7.54, p  = 0.001, \({\upeta }_{\text{p}}^{2}\) = 0.191, that was qualified by a medium sized statistically significant interaction between condition and time, F (2, 64) = 3.68, p  = 0.031, \({\upeta }_{\text{p}}^{2}\) = 0.103. Nevertheless, probing of this interaction revealed no significant effects of drug condition on changes in STAI state anxiety scores at T1, F (1, 32) = 0.09, p  = 0.764, \({\upeta }_{\text{p}}^{2}\) = 0.003; T2, F (1, 32) = 3.10, p  = 0.088, \({\upeta }_{\text{p}}^{2}\) = 0.088; or T3, F (1, 32) = 3.74, p  = 0.062, \({\upeta }_{\text{p}}^{2}\) = 0.105 (Fig.  3 D).

Supplemental Fig. S1 further depicts raw score (rather than change score) ratings of anxiety, stress, mood and STAI anxiety scores from T0, T1, T2, and T3.

Effects of CBG vs. placebo on subjective drug effect ratings

There were no significant effects of condition [ F (1, 32) = 3.72, p  = 0.063, \({\upeta }_{\text{p}}^{2}\) = 0.104], time [ F (2, 64) = 0.32, p  = 0.724, \({\upeta }_{\text{p}}^{2}\) = 0.010], or condition x time interaction [ F (2, 64) = 0.34, p  = 0.714, \({\upeta }_{\text{p}}^{2}\) = 0.010] on subjective ratings of intoxication. There were no significant effects of condition [ F (2, 64) = 2.99, p  = 0.093, \({\upeta }_{\text{p}}^{2}\) = 0.085], time [ F (1, 32) = 1.71, p  = 0.188, \({\upeta }_{\text{p}}^{2}\) = 0.051], or condition x time interaction [ F (2, 64) = 0.04, p  = 0.958, \({\upeta }_{\text{p}}^{2}\) = 0.001] on drug liking ratings. There were no significant effects of condition [ F (2, 64) = 0.73, p  = 0.789, \({\upeta }_{\text{p}}^{2}\) = 0.002], time [ F (1, 32) = 1.08, p  = 0.345, \({\upeta }_{\text{p}}^{2}\) = 0.033], or condition x time interaction [ F (2, 64) = 0.25, p  = 0.779, \({\upeta }_{\text{p}}^{2}\) = 0.008] on drug liking ratings. Moreover, intoxication (Fig.  4 A) and drug effect (Fig.  4 B) ratings remained low (under 2 out of 10) in both conditions at all timepoints, and drug liking ratings (Fig.  4 C) remained neutral (5 out of 10) in both conditions at all timepoints.

figure 4

Intoxication, drug effect, and drug liking ratings following drug administration. Lines represent mean self-reported intoxication ( A ), drug effect ( B ), drug liking ( C ) ratings following drug/placebo administration. Error bars represent standard errors of the mean.

There were no significant effects of condition [ F (1, 32) = 0.22, p  = 0.640, \({\upeta }_{\text{p}}^{2}\) = 0.007], time [ F (2, 64) = 0.17, p  = 0.847, \({\upeta }_{\text{p}}^{2}\) = 0.005], or condition x time interaction [ F (2, 64) = 0.68, p  = 0.508, \({\upeta }_{\text{p}}^{2}\) = 0.021] on changes in dry eye ratings (see Fig.  5 A). There were no significant effects of condition [ F (1, 32) = 0.59, p  = 0.448, \({\upeta }_{\text{p}}^{2}\) = 0.018], time [ F (2, 64) = 1.85, p  = 0.166, \({\upeta }_{\text{p}}^{2}\) = 0.055], or condition x time interaction [ F (2, 64) = 1.86, p  = 0.164, \({\upeta }_{\text{p}}^{2}\) = 0.055] on changes in dry mouth ratings (see Fig.  5 B). There were no significant effects of condition [ F (1, 32) = 0.03, p  = 0.863, \({\upeta }_{\text{p}}^{2}\) = 0.001], time [ F (2, 64) = 1.35, p  = 0.267, \({\upeta }_{\text{p}}^{2}\) = 0.040], or condition x time interaction [ F (2, 64) = 2.39, p  = 0.100, \({\upeta }_{\text{p}}^{2}\) = 0.069] on changes in sleepiness ratings (see Fig.  5 C). There was a significant main effect of time [ F (2, 64) = 7.05, p  = 0.002, \({\upeta }_{\text{p}}^{2}\) = 0.181], but no significant effect of condition [ F (1, 32) = 0.15, p  = 0.701, \({\upeta }_{\text{p}}^{2}\) = 0.005], or condition x time interaction [ F (2, 64) = 0.28, p  = 0.754, \({\upeta }_{\text{p}}^{2}\) = 0.009] on changes in appetite ratings. As depicted in Fig.  5 D, the main effect of time simply reflects an increase in appetite over time in both conditions. There was a significant main effect of time [ F (2, 64) = 4.19, p  = 0.020, \({\upeta }_{\text{p}}^{2}\) = 0.116], but no significant effect of condition [ F (1, 32) = 0.18, p  = 0.674, \({\upeta }_{\text{p}}^{2}\) = 0.006], or condition x time interaction [ F (2, 64) = 0.17, p  = 0.843, \({\upeta }_{\text{p}}^{2}\) = 0.005] on changes in heart palpitation/racing heart ratings. As depicted in Fig.  5 E, the main effect of time simply reflects a slight increase in heart palpitations following the Trier Social Stress Test (from T1 to T2).

figure 5

Subjective drug effect ratings following drug administration. Lines represent self-reported changes (from T0) in dry eyes ( D ), dry mouth ( E ), sleepiness ( F ), appetite ( G ), and heart palpitations ( H ) following drug administration. Error bars represent standard errors of the mean.

Verbal memory

A 2 × 5 repeated measures ANCOVA with condition (CBG, placebo) and trial as within-subjects factors and order as a covariate revealed a moderate-sized effect of condition that was statistically significant, F (1, 30) = 4.17, p  = 0.050, \({\upeta }_{\text{p}}^{2}\) = 0.122 and a large-sized effect of time that was statistically significant, F (4, 120) = 70.14, p  < 0.001, \({\upeta }_{\text{p}}^{2}\) = 0.700. The interaction between condition and time was small and not statistically significant, F (4, 120) = 0.38, p  = 0.820, \({\upeta }_{\text{p}}^{2}\) = 0.013. As depicted in Fig.  6 , verbal memory test performance was significantly better in the CBG condition.

figure 6

Effects of CBG vs placebo memory. Lines represent mean number of words recalled in Trial 1, Trial 2, Trial 3, List B, and the Short Delay recall trial of the CVLT-II. Error bars show standard errors of the mean.

DRUID impairment

The one-way repeated measures ANCOVA on DRUID impairment scores revealed a small sized effect of condition that was not statistically significant, F (2, 64) = 0.76, p  = 0.474, \({\upeta }_{\text{p}}^{2}\) = 0.023 (see Fig. 7 ).

figure 7

Effects of CBG vs placebo on impairment. Bars represent mean global impairment scores at Baseline (T0), following ingesting of CBG and Placebo. Circles represent individual scores in each condition. Error bars represent standard errors of the mean. The dashed line represents a score of 57 which indicates significant impairment.

The rapid proliferation of the legal cannabis market has provoked producers to cultivate an array of novel products to satisfy consumers' growing interests, including products dominant in CBG. Pre-clinical research indicates CBG may have a broad spectrum of therapeutic effects that have recently been supported by self-report data from a large sample of CBG users 12 . The present study represents the first human clinical trial to examine the acute effects of CBG on anxiety, stress, and mood. Results indicate that CBG reduces global feelings of anxiety and stress and that it may enhance memory in the absence of intoxication, impairment, or subjective drug effects.

Most notably, there was a significant main effect of CBG on subjective state ratings of anxiety. Specifically, there was a mean decrease in anxiety ratings of 0.95 on a 10-point scale which represents a 26.5% reduction in the already low levels of baseline anxiety ( M  = 3.59 on a 0–10 VAS) in the CBG condition. In contrast there was a 0.66-point reduction in anxiety ratings in the placebo condition (which represents a 22.5% reduction in anxiety in the placebo condition). These findings are in line with our survey findings wherein 51% of CBG users reported using CBG to manage anxiety and 78% claimed it was more effective than conventional anxiety medications 12 . These effects may stem from CBG’s effects on 5-HT 1A 28 , 29 and/or GABA 30 . Nevertheless, enthusiasm for these findings is dampened by the lack of significant effect of CBG on STAI state anxiety ratings. This pattern of findings may reflect a tendency for CBG to reduce the global impression of feelings of anxiety, rather than affecting the specific sub-components of anxiety tapped by the STAI (e.g., jittery, confused, indecisive) and further indicate that replication of these findings is necessary.

Consistent with these effects, there was also a significant effect of CBG on subjective stress ratings at T1 (prior to the stressor). While our power analysis indicated we were sufficiently powered to detect medium-sized effects, a larger sample size and/or higher dose may be needed to robustly detect acute effects of CBG on subjective stress ratings. Our use of a field trial precluded our ability to examine physiological indicators of stress (e.g., cortisol, electrodermal activity, alpha-amylase) but we plan to conduct a follow-up laboratory study which will include such measures. As no human clinical trials with CBG had been published at the time we commenced the study our dose selection was guided by anecdotal reports of people using the same CBG tincture, observations that CBG is commonly sold in 10 mg units, as well as by common doses of CBG reported in clinical observations 31 , 32 . However, given recent clinical trials using larger doses (25 mg 12 and 50 mg 13 ), our dose may have been somewhat conservative.

Our prior survey study showed that over 30% of CBG users were self-medicating for depression and 80% reported CBG is more effective than conventional antidepressants 12 which is consistent with preclinical research demonstrating that CBG has antidepressant-like effects in a rodent tail suspension model 10 . Nonetheless, results from the present clinical trial failed to support our hypothesis that CBG would enhance mood. It is possible that our sensitivity to detect such effects was reduced by the administration of a single-item indicator of mood to a non-clinical sample. Indeed, the overall mean baseline depression levels on the DASS indicates that the sample was in the normal to mild range of depression 19 and the overall baseline mood ratings suggest participants were in a positive mood state prior to drug administration. Future research employing a more comprehensive measure of depression and a larger sample of clinical patients with higher baseline depression is needed to attempt to reconcile these apparently contradictory findings.

CBG did not produce cognitive or motor impairments on the DRUID app. Prior placebo-controlled research reported DRUID scores around 57 following oral administration of 25 mg of THC and vaporized administration of 20 mg of THC 24 which is on par with the threshold of 57 for detecting significant impairment using this app 25 . In contrast, participants’ mean DRUID score following administration of CBG was much lower (43.1) and was remarkably consistent with the mean DRUID score detected at baseline and in the placebo condition. As shown in Fig.  7 , a small number of people surpassed the 57-score threshold for impairment at baseline and in both the CBG and placebo conditions which is likely attributable to preexisting conditions (e.g., one participant reported issues with balance, another had a leg injury).

One of the most robust detrimental effects of THC is on verbal memory 33 which guided our decision to include a test of verbal memory in the present study. We hypothesized that CBG would not impair memory, but our finding that CBG significantly enhanced verbal memory was unexpected. Inspection of Fig.  6 shows that the effect was most pronounced on trials 2 & 3 with participants in the CBG condition recalling on average 0.5 words more on trial 2 and 1-word more on trial 3, than they recalled in the placebo condition. Trials 2 & 3 represents learning trials which indicates that CBG may enhance learning. Nevertheless, these surprising findings warrant further corroboration. Future studies attempting to replicate these findings should include tests of verbal memory with additional learning trials, as well as other tests of memory (e.g., working memory) and cognition (e.g., executive functioning, attention) to further elucidate the effects of CBG on cognition. It would also be interesting to examine whether CBG might offset the detrimental effects of THC on verbal memory, as CBD was initially purported to do 34 , 35 , until multiple attempts to replicate those findings failed 36 , 37 , 38 .

Consistent with the lack of impairment detected in the CBG condition, participants reported no intoxicating effects of CBG. The overall mean intoxication rating (combined across the three time points) was less than 1 on a 0–10 VAS in both the CBG and placebo conditions. Similarly, overall mean ratings of subjective drug effects were low in both conditions and drug liking effects were neutral in both conditions at all timepoints suggesting a low potential for abuse. Further, there were no significant effects of CBG on changes in dry eyes, dry mouth, sleepiness, appetite, or heart palpitation ratings, with ratings changing less than 1-point from baseline over the course of the experiment. This suggests that CBG was well tolerated and did not elicit adverse acute effects that are typically associated with THC administration. Our decision to assess effects on these outcomes was informed by our prior survey study 12 that revealed that while the plurality (44%) indicated CBG has no side effects, small percentages of participants endorsed experiencing dry eyes, dry mouth, sleepiness, and increased appetite after using CBG-dominant cannabis, perhaps because some were using cannabis containing both CBG and THC. It is also important to note that five individuals who completed our screening survey indicated they had previously experienced a severe adverse reaction to CBG. While the extent to which these events were triggered by CBG taken in combination with THC are unknown, they suggest we should be cautious in our interpretation of these null findings. Additional caution should be taken when interpreting results from the present study because we conducted this study remotely via Zoom so there were no physical exams or assessments of vital signs, participants were limited to reporting on a set of pre-determined potential adverse events and were not asked about these in an open-ended manner, these assessments were limited to the first 20–60 min after dosing and only a single, relatively modest dose was assessed which may have missed some of the peak effects 14 . As such, these results cannot be extrapolated to other potential adverse effects, later timepoints, higher doses, or repeated/chronic dosing. Future research should examine a broader array of potential drug effects and side effects using physiological measures (heart rate monitors, blood pressure monitors) across longer periods of time prior to reaching strong conclusions that CBG is safe and not associated with side effects.

While our use of a gold-standard double-blind, placebo-controlled cross-over design increases internal validity and our use of Zoom to naturalistically test participants in their home environments enhances ecological validity, the study is not without its limitations. These limitations include our use of a non-clinical sample of cannabis users, a lack of correction for multiple statistical comparisons, modest effects of the stress manipulation, as well as the use of a relatively modest dose and the early timing of assessments. Specifically, as a prudent first step we recruited a moderate-sized community sample rather than a clinical sample of patients with anxiety or depression. A larger sized clinical sample may have increased our sensitivity to detect significant effects of CBG on mood, stress, and state anxiety but may have also increased risks as potential drug interactions with SSRIs are unknown. We also used a sample of experienced cannabis users (for ethical reasons) and as such must be very cautious in generalizing the findings to cannabis naïve individuals. Although potential interactions between THC and CBG are unknown, our use of experienced cannabis users may have also diminished our sensitivity to detect effects due to a general level of tolerance with cannabinoids. We also did not correct for the multiple statistical comparisons that were conducted as the study was not powered to do so. As such, some findings may represent Type I errors and replication is required to corroborate them. Further, while the acute stress manipulation significantly increased stress from T1 to T2, average subjective stress ratings remained below the baseline (T0) after the stressor. While the online version of the TSST has been previously validated 22 , a stress manipulation performed in the lab (rather than in participants’ comfortable home environments via Zoom) may have produce larger increases in subjective stress. However, the relatively modest increases in subjective stress following the TSST may also reflect our use of a sample of experienced cannabis users, as prior research has shown that cannabis users demonstrate blunted cortisol and subjective stress responses to acute stressors even in a laboratory environment 39 .

At the time we commenced the study there had been no published clinical trials of CBG in humans, as such, we had little to guide us on the most appropriate dose or timing of assessments. As indicated previously, our dose selection was guided by anecdotal evidence from people using this tincture, standard doses of CBG in marketed products, and clinical observations of efficacy at similar doses 31 , 32 . However, given recent clinical trials using larger doses of CBG (25 mg 12 and 50 mg 13 ) our dose may have been too conservative to reveal optimal effects. Repeated dosing may have also increased potential effects on stress, anxiety, and mood. Further, while recent data from a human pharmacokinetic study of CBG 14 demonstrated rises in plasma concentrations as early as 20 min after oral administration, these concentrations peaked from 45 min to nearly 2 h. (depending on dietary fat). As such our assessments from 20 to 60 min after dosing may have failed to capture peak effects. Additionally, we did not restrict participant’s diets in anyway, but recent evidence indicates that eating a meal high in dietary fat prior to ingestion of CBG increases plasma levels of CBG 14 . Our failure to control diet may have resulted in random variability in the potency of the product that could have contributed to random error that further diminished sensitivity to detect significant effects.

In conclusion, results of this double-blind, placebo-controlled, cross-over field trial indicate that 20 mg of hemp-derived CBG reduces subjective ratings of anxiety and stress in healthy cannabis-using adults in the absence of motor or cognitive impairment, intoxication, or other subjective drug effects (e.g., heart palpitations, dry mouth). Additional research is needed to corroborate these novel findings as well as to extend them to a clinical population of patients with anxiety disorders.

Data availability

Data are available upon reasonable request to [email protected].

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This research was funded by CReDO Science ( https://credo-science.com ) who are working with CBG. The funder had no role in the analyses or interpretation of the findings.

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Contributions

C. Cuttler helped to conceive of the idea and design the study, recruited participants, trained research assistants, oversaw data collection, checked the data for data entry errors, analyzed the data, and prepared the manuscript. A. Stueber advertised the study, helped collect and enter data, helped interpret the results and prepare the manuscript. Z. Cooper helped to design the study and prepare the manuscript. E. Russo helped to conceive of the idea and design the study, identified the CBG distributor who provided the study drug, assisted with writing the manuscript.

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Correspondence to Carrie Cuttler .

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Competing interests.

Outside of this work, ZDC reports receiving study drug from Canopy Growth Corp and True Terpenes, and study-related materials from Storz & Bickel. She served a scientific consultant for Canopy Growth Corporation in 2021 and on the scientific advisory board for FSD Pharma in 2020. ZDC’s research is funded by grants from the National Institute on Drug Abuse, National Center for Complementary and Integrative Health, California Department of Cannabis Control, Center for Medicinal Cannabis Research, and the California Highway Patrol. ER is founder and CEO of CReDO science which formulates and works with CBG. ZDC and ER had no role in the analyses or interpretation of the findings. Dr. Cuttler reports receiving study-related materials from Storz & Bickel as well as research funds from Huxley Health and Healer. Amanda Stueber declares no conflicts of interest.

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Cuttler, C., Stueber, A., Cooper, Z.D. et al. Acute effects of cannabigerol on anxiety, stress, and mood: a double-blind, placebo-controlled, crossover, field trial. Sci Rep 14 , 16163 (2024). https://doi.org/10.1038/s41598-024-66879-0

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  • Li, Dingheng
  • Yang, Xinyun

Previous epidemiological studies have reported associations between vitamin D and postpartum depression (PPD); however, the findings are inconsistent. This study employs bidirectional Mendelian Randomization (MR) to investigate the causal link between serum 25-hydroxyvitamin D [25(OH)D] levels and PPD. By utilizing genetic data from cohorts, this research aims to provide a more robust understanding of the potential relationship between vitamin D and PPD, addressing a critical gap in the current literature. A bidirectional MR analysis was conducted to investigate the genetic association between serum 25(OH)D and PPD using summary statistics extracted from GWAS datasets. The study included data from 15,668 patients with PPD and 376,755 healthy controls of European ancestry. The GWAS data for 25(OH)D were obtained from two studies within the UK Biobank, encompassing 496,946 and 79,366 participants. The primary analysis employed the inverse-variance weighted (IVW) method, while supplementary MR estimates were derived through the MR-Egger and weighted median (WME) methods. Furthermore, sensitivity analyses were implemented to ensure robustness and reliability, including Cochran's Q test, MR-PRESSO, MR-Egger intercept test, and the leave-one-out test. The MR study revealed no substantial genetic correlation between serum 25(OH)D levels and PPD (OR = 1.065, 95%CI = 0.878–1.293, P = 0.522 for set A; OR = 0.978, 95 % CI = 0.669–1.430, P = 0.910 for set B). Additionally, in the reverse analysis, we did not observe a significant causal impact of PPD on serum 25(OH)D (OR = 1.001, 95%CI = 0.974–1.028, P = 0.951 for set A; OR = 1.011, 95%CI = 0.992–1.031, P = 0.261 for set B). The results obtained from MR-Egger and WME analyses concord with those derived from the IVW method. Conducting leave-one-out tests did not identify any single nucleotide polymorphism that might have influenced the MR results, confirming the robustness and reliability of the findings. The results suggest the absence of a causal link between vitamin D concentrations and PPD. Inconsistent observations in previous observational studies may be attributed to residual confounding.

  • Mendelian randomization;
  • Postpartum depression;
  • Serum vitamin D;

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