research paper of mental health

September 5, 2024

Managing PTSD with exercise: What do clinicians think?

Researchers from UCL highlight the need for more guidance for trauma clinicians on how to effectively use exercise to help treat PTSD

Image credit: Pete Linforth from Pixabay

Patient expectations are a feature of treatment strategy rather than a source of bias

Ralph Horwitz and colleagues suggest that expectations could be a useful factor in the management of mental health 

Image credit: germ, by congerdesign from Pixabay

Climate & mental health: A roadmap to global heat resilience

The Wellcome Trust and The Physiological Society share their plans to help tackle mental health effects of climate change

Image credit: Environmental, by Pete Linforth from Pixabay

Is it time to change mental health crisis responses?

Psychiatrist Rupinder Legha advocates for change to mental health crisis management

Image credit: Police, Max from Pixabay

Equipping the next generation of clinicians for addressing conflict mental health: A role for Geopsychiatry

Section Editor Joseph El-Khoury and colleagues share recommendations for training mental health professionals in conflict medicine

Image credit: War, by Alexa from Pixabay

mental health psychology

Gender differences in severity and parental estimation of adolescent’s pandemic-related stress in the United States

Kristen D. Holtz and colleagues find that parents could be more likely to underestimate the mental health needs of their daughters compared to their sons.

Image credit: Family, by Pexels from Pixabay

What are the effects of diagnostic labels?

Nick Haslam and colleagues examine how diagnostic labels affect empathy, help-seeking and stigma

Image credit: Woman, by lisa runnels from Pixabay

behavioural medicine and mental health economics

Cost-effectiveness of automated digital CBT ( Daylight ) for generalized anxiety disorder: A Markov simulation model in the United States

The cost-effectiveness and cost-benefits of digital CBT are assessed by Christopher Miller and colleagues

Image credit: Hands, by Pexels from Pixabay

epidemiology of mental health

Prevalence and covariates of depression among older adults in Nepal: A systematic review and meta-analysis

Image credit: woman, by Pexels from Pixabay

public mental health & policy

Psychological support for older crime victims - understanding barriers using Metropolitan Police data

Image credit: Hands, by Sabine van Erp, Pixabay

socio-economics & political approaches

The role of social determinants of health in mental health: An examination of the moderating effects of race, ethnicity, and gender on depression through the all of us research program dataset

Image credit: Line, by Peggy from Pixabay

Mental health psychology

Psychopathy, psychological distress, and treatment history among perpetrators of intimate partner femicide, homicide, and other violent crimes in Buenos Aires, Argentina

Image credit: Barbed wire, by vero_vig_050 from Pixabay

Community Case Studies - NTDs

Get to know our sections neurodiversity & mental health, get to know our sections epidemiology of mental health, get to know our sections community mental health, journeys in mental health: an anonymous blog series on lived experiences from plos mental health, journeys in mental health for world refugee day, june 2024, schizophrenia, overturning a ‘death sentence’, mental health awareness month at plos mental health, publish with plos.

• Submission Instructions
• Submit Your Manuscript

Connect with Us

• PLOS Mental Health on Twitter
• PLOS on Facebook

Get new content from PLOS Mental Health in your inbox

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 03 October 2022

How COVID-19 shaped mental health: from infection to pandemic effects

• Brenda W. J. H. Penninx   ORCID: orcid.org/0000-0001-7779-9672 1 , 2 ,
• Michael E. Benros   ORCID: orcid.org/0000-0003-4939-9465 3 , 4 ,
• Robyn S. Klein 5 &
• Christiaan H. Vinkers   ORCID: orcid.org/0000-0003-3698-0744 1 , 2  

Nature Medicine volume  28 ,  pages 2027–2037 ( 2022 ) Cite this article

43k Accesses

165 Citations

491 Altmetric

Metrics details

• Epidemiology
• Infectious diseases
• Neurological manifestations
• Psychiatric disorders

The Coronavirus Disease 2019 (COVID-19) pandemic has threatened global mental health, both indirectly via disruptive societal changes and directly via neuropsychiatric sequelae after SARS-CoV-2 infection. Despite a small increase in self-reported mental health problems, this has (so far) not translated into objectively measurable increased rates of mental disorders, self-harm or suicide rates at the population level. This could suggest effective resilience and adaptation, but there is substantial heterogeneity among subgroups, and time-lag effects may also exist. With regard to COVID-19 itself, both acute and post-acute neuropsychiatric sequelae have become apparent, with high prevalence of fatigue, cognitive impairments and anxiety and depressive symptoms, even months after infection. To understand how COVID-19 continues to shape mental health in the longer term, fine-grained, well-controlled longitudinal data at the (neuro)biological, individual and societal levels remain essential. For future pandemics, policymakers and clinicians should prioritize mental health from the outset to identify and protect those at risk and promote long-term resilience.

Similar content being viewed by others

A longitudinal analysis of the impact of the COVID-19 pandemic on the mental health of middle-aged and older adults from the Canadian Longitudinal Study on Aging

Global prevalence of mental health issues among the general population during the coronavirus disease-2019 pandemic: a systematic review and meta-analysis

The effects of the COVID-19 pandemic on neuropsychiatric symptoms in dementia and carer mental health: an international multicentre study

In 2019, the COVID-19 outbreak was declared a pandemic by the World Health Organization (WHO), with 590 million confirmed cases and 6.4 million deaths worldwide as of August 2022 (ref. 1 ). To contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, many national and local governments implemented often drastic restrictions as preventive health measures. Consequently, the pandemic has not only led to potential SARS-CoV-2 exposure, infection and disease but also to a wide range of policies consisting of mask requirements, quarantines, lockdowns, physical distancing and closure of non-essential services, with unprecedented societal and economic consequences.

As the world is slowly gaining control over COVID-19, it is timely and essential to ask how the pandemic has affected global mental health. Indirect effects include stress-evoking and disruptive societal changes, which may detrimentally affect mental health in the general population. Direct effects include SARS-CoV-2-mediated acute and long-lasting neuropsychiatric sequelae in affected individuals that occur during primary infection or as part of post-acute COVID syndrome (PACS) 2 —defined as symptoms lasting beyond 3–4 weeks that can involve multiple organs, including the brain. Several terminologies exist for characterizing the effects of COVID-19. PACS also includes late sequalae that constitute a clinical diagnosis of ‘long COVID’ where persistent symptoms are still present 12 weeks after initial infection and cannot be attributed to other conditions 3 .

Here we review both the direct and indirect effects of COVID-19 on mental health. First, we summarize empirical findings on how the COVID-19 pandemic has impacted population mental health, through mental health symptom reports, mental disorder prevalence and suicide rates. Second, we describe mental health sequalae of SARS-CoV-2 virus infection and COVID-19 disease (for example, cognitive impairment, fatigue and affective symptoms). For this, we use the term PACS for neuropsychiatric consequences beyond the acute period, and will also describe the underlying neurobiological impact on brain structure and function. We conclude with a discussion of the lessons learned and knowledge gaps that need to be further addressed.

Impact of the COVID-19 pandemic on population mental health

Independent of the pandemic, mental disorders are known to be prevalent globally and cause a very high disease burden 4 , 5 , 6 . For most common mental disorders (including major depressive disorder, anxiety disorders and alcohol use disorder), environmental stressors play a major etiological role. Disruptive and unpredictable pandemic circumstances may increase distress levels in many individuals, at least temporarily. However, it should be noted that the pandemic not only resulted in negative stressors but also in positive and potentially buffering changes for some, including a better work–life balance, improved family dynamics and enhanced feelings of closeness 7 .

Awareness of the potential mental health impact of the COVID-19 pandemic is reflected in the more than 35,000 papers published on this topic. However, this rapid research output comes with a cost: conclusions from many papers are limited due to small sample sizes, convenience sampling with unclear generalizability implications and lack of a pre-COVID-19 comparison. More reliable estimates of the pandemic mental health impact come from studies with longitudinal or time-series designs that include a pre-pandemic comparison. In our description of the evidence, we, therefore, explicitly focused on findings from meta-analyses that include longitudinal studies with data before the pandemic, as recently identified through a systematic literature search by the WHO 8 .

Self-reported mental health problems

Most studies examining the pandemic impact on mental health used online data collection methods to measure self-reported common indicators, such as mood, anxiety or general psychological distress. Pooled prevalence estimates of clinically relevant high levels of depression and anxiety symptoms during the COVID-19 pandemic range widely—between 20% and 35% 9 , 10 , 11 , 12 —but are difficult to interpret due to large methodological and sample heterogeneity. It also is important to note that high levels of self-reported mental health problems identify increased vulnerability and signal an increased risk for mental disorders, but they do not equal clinical caseness levels, which are generally much lower.

Three meta-analyses, pooling data from between 11 and 61 studies and involving ~50,000 individuals or more 13 , 14 , 15 , compared levels of self-reported mental health problems during the COVID-19 pandemic with those before the pandemic. Meta-analyses report on pooled effect sizes—that is, weighted averages of study-level effect sizes; these are generally considered small when they are ~0.2, moderate when ~0.5 and large when ~0.8. As shown in Table 1 , meta-analyses on mental health impact of the COVID-19 pandemic reach consistent conclusions and indicate that there has been a heterogeneous, statistically significant but small increase in self-reported mental health problems, with pooled effect sizes ranging from 0.07 to 0.27. The largest symptom increase was found when using specific mental health outcome measures assessing depression or anxiety symptoms. In addition, loneliness—a strong correlate of depression and anxiety—showed a small but significant increase during the pandemic (Table 1 ; effect size = 0.27) 16 . In contrast, self-reported general mental health and well-being indicators did not show significant change, and psychotic symptoms seemed to have decreased slightly 13 . In Europe, alcohol purchase decreased, but high-level drinking patterns solidified among those with pre-pandemic high drinking levels 17 . When compared to pre-COVID levels, no change in self-reported alcohol use (effect size = −0.01) was observed in a recent meta-analysis summarizing 128 studies from 58 (predominantly European and North American) countries 18 .

What is the time trajectory of self-reported mental health problems during the pandemic? Although findings are not uniform, various large-scale studies confirmed that the increase in mental health problems was highest during the first peak months of the pandemic and smaller—but not fully gone—in subsequent months when infection rates declined and social restrictions eased 13 , 19 , 20 . Psychological distress reports in the United Kingdom increased again during the second lockdown period 15 . Direct associations between anxiety and depression symptom levels and the average number of daily COVID-19 cases were confirmed in the US Centers for Disease Control and Prevention (CDC) data 21 . Studies that examined longer-term trajectories of symptoms during the first or even second year of the COVID-19 pandemic are more sparse but revealed stability of symptoms without clear evidence of recovery 15 , 22 . The exception appears to be for loneliness, as some studies confirmed further increasing trends throughout the first COVID-19 pandemic year 22 , 23 . As most published population-based studies were conducted in the early time period in which absolute numbers of SARS-CoV2-infected individuals were still low, the mental health impacts described in such studies are most likely due to indirect rather than direct effects of SARS-CoV-2 infection. However, it is possible that, in longer-term or later studies, these direct and indirect effects may be more intertwined.

The extent to which governmental policies and communication have impacted on population mental health is a relevant question. In cross-country comparisons, the extent of social restrictions showed a dose–response relationship with mental health problems 24 , 25 . In a review of 33 studies worldwide, it was concluded that governments that enacted stringent measures to contain the spread of COVID-19 benefitted not only the physical but also the mental health of their population during the pandemic 26 , even though more stringent policies may lead to more short-term mental distress 25 . It has been suggested that effective communication of risks, choices and policy measures may reduce polarization and conspiracy theories and mitigate the mental health impact of such measures 25 , 27 , 28 .

In sum, the general pattern of results is that of an increase in mental health symptoms in the population, especially during the first pandemic months, that remained elevated throughout 2020 and early 2021. It should be emphasized that this increase has a small effect size. However, even a small upward shift in mental health problems warrants attention as it has not yet shown to be returned to pre-pandemic levels, and it may have meaningful cumulative consequences at the population level. In addition, even a small effect size may mask a substantial heterogeneity in mental health impact, which may have affected vulnerable groups disproportionally (see below).

Mental disorders, self-harm and suicide

Whether the observed increase in mental health problems during the COVID-19 pandemic has translated into more mental disorders or even suicide mortality is not easy to answer. Mental disorders, characterized by more severe, disabling and persistent symptoms than self-reported mental health problems, are usually diagnosed by a clinician based on the International Classification of Diseases, 10th Revision (ICD-10) or the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria or with validated semi-structured clinical interviews. However, during the COVID-19 pandemic, research systematically examining the population prevalence of mental disorders has been sparse. Unfortunately, we can also not strongly rely on healthcare use studies as the pandemic impacted on healthcare provision more broadly, thereby making figures of patient admissions difficult to interpret.

On a global scale and based on imputations and modeling from survey data of self-reported mental health problems, the Global Burden of Disease (GBD) study 29 estimated that the COVID-19 pandemic has led to a 28% (95% uncertainty interval (UI): 25–30) increase in major depressive disorders and a 26% (95% UI: 23–28) increase in anxiety disorders. It should be noted that these estimations come with high uncertainty as the assumption that transient pandemic-related increases in mental symptoms extrapolate into incident mental disorders remains disputable. So far, only four longitudinal population-based studies have measured and compared current mental (that is, depressive and anxiety) disorder prevalence—defined using psychiatric diagnostic criteria—before and during the pandemic. Of these, two found no change 30 , 31 , one found a decrease 32 and one found an increase in prevalence of these disorders 33 . These studies were local, limited to high-income countries, often small-scale and used different modes of assessment (for example, online versus in-person) before and during the pandemic. This renders these observational results uncertain as well, but their contrast to the GBD calculations 29 is striking.

Time-series analysis of monthly suicide trends in 21 middle-income to high-income countries across the globe yielded no evidence for an increase in suicide rates in the first 4 months of the pandemic, and there was evidence of a fall in rates in 12 countries 34 . Also in the United States, there was a significant decrease in suicide mortality in the first pandemic months but a slight increase in mortality due to drug overdose and homicide 35 . A living systematic review 36 also concluded that, throughout 2020, there was no observed increase in suicide rates in 20 studies conducted in North America, Europe and Asia. Analyses of electronic health record data in the primary care setting showed reduced rates of self-harm during the first COVID-19 pandemic year 37 . In contrast, emergency department visits for self-harm behavior were unchanged 38 or increased 39 . Such inconsistent findings across healthcare settings may reflect a reluctance in healthcare-seeking behavior for mental healthcare issues. In the living systematic review, eight of 11 studies that examined service use data found a significant decrease in reported self-harm/suicide attempts after COVID lockdown, which returned to pre-lockdown levels in some studies with longer follow-up (5 months) 36 .

In sum, although calculations based on survey data predict a global increase of mental disorder prevalence, objective and consistent evidence for an increased mental disorder, self-harm or suicide prevalence or incidence during the first pandemic year remains absent. This observation, coupled with the only small increase in mental health symptom levels in the overall population, may suggest that most of the general population has demonstrated remarkable resilience and adaptation. However, alternative interpretations are possible. First, there is a large degree of heterogeneity in the mental health impact of COVID-19, and increased mental health in one group (for example, due to better work–family balance and work flexibility) may have masked mental health problems in others. Various societal responses seen in many countries, such as community support activities and bolstering mental health and crisis services, may have had mitigating effects on the mental health burden. Also, the relationship between mental health symptom increases during stressful periods and its subsequent effects on the incidence of mental disorders may be non-linear or could be less visible due to resulting alternative outcomes, such as drug overdose or homicide. Finally, we cannot rule out a lag-time effect, where disorders may take more time to develop or be picked up, especially because some of the personal financial or social consequences of the COVID pandemic may only become apparent later. It should be noted that data from low-income countries and longer-term studies beyond the first pandemic year are largely absent.

Which individuals are most affected by the COVID-19 pandemic?

There is substantial heterogeneity across studies that evaluated how the COVID pandemic impacted on mental health 13 , 14 , 15 . Although our society as a whole may have the ability to adequately bounce back from pandemic effects, there are vulnerable people who have been affected more than others.

First, women have consistently reported larger increases in mental health problems in response to the COVID-19 pandemic than men 13 , 15 , 29 , 40 , with meta-analytic effect sizes being 44% 15 to 75% 13 higher. This could reflect both higher stress vulnerability or larger daily life disruptions due to, for example, increased childcare responsibilities, exposure to home violence or greater economic impact due to employment disruptions that all disproportionately fell to women 41 , thereby exacerbating the already existing pre-pandemic gender inequalities in depression and anxiety levels. In addition, adolescents and young adults have been disproportionately affected compared to younger children and older adults 12 , 15 , 29 , 40 . This may be the result of unfavorable behavioral and social changes (for example, school closure periods 42 ) during a crucial development phase where social interactions outside the family context are pivotal. Alarmingly, even though suicide rates did not seem to increase at the population level, studies in China 43 and Japan 44 indicated significant increases in suicide rates in children and adolescents.

Existing socio-cultural disparities in mental health may have further widened during the COVID pandemic. Whether the impact is larger for individuals with low socio-economic status remains unclear, with contrasting meta-analyses pointing toward this group being protected 15 or at increased risk 40 . Earlier meta-analyses did not find that the mental health impact of COVID-19 differed across Europe, North America, Asia and Oceania 13 , 14 , but data are lacking from Africa and South America. Nevertheless, a large-scale within-country comparison in the United States found that the mental health of Black, Hispanic and Asian respondents worsened relatively more during the pandemic compared to White respondents. Moreover, White respondents were more likely to receive professional mental healthcare during the pandemic, and, conversely, Black, Hispanic, and Asian respondents demonstrated higher levels of unmet mental healthcare needs during this time 45 .

People with pre-existing somatic conditions represent another vulnerable group in which the pandemic had a greater impact (pooled effect size of 0.25) 13 . This includes people with conditions such as epilepsy, multiple sclerosis or cardiometabolic disease as well as those with multiple comorbidities. The disproportionate impact may reflect this groupʼs elevated COVID-19 risk and, consequently, more perceived stress and fear of infection, but it could also reflect disruptions of regular healthcare services.

Healthcare workers faced increased workload, rapidly changing and challenging work environments and exposure to infections and death, accompanied by fear of infecting themselves and their families. High prevalences of (subthreshold) depression (13% 46 ), depressive symptoms (31% 47 ), (subthreshold) anxiety (16% 46 ), anxiety symptoms (23% 47 ) and post-traumatic stress disorder (~22% 46 , 47 ) have been reported in healthcare workers. However, a meta-analysis did not find a larger mental health impact of the pandemic as compared to the general population 40 , and another meta-analysis (of 206 studies) found that the mental health status of healthcare workers was similar to or even better than that of the general population during the first COVID year 48 . However, it is important to note that these meta-analyses could not differentiate between frontline and non-frontline healthcare workers.

Finally, individuals with pre-existing mental disorders may be at increased risk for exacerbation of mental ill-health during the pandemic, possibly due to disease history—illustrating a higher genetic and/or environmental vulnerability—but also due to discontinuity of mental healthcare. Already before the pandemic, mental health systems were under-resourced and disorganized in most countries 6 , 49 , but a third of all WHO member states reported disruptions to mental and substance use services during the first 18 months of the pandemic 50 , with reduced, shortened or postponed appointments and limited capacity for acute inpatient admissions 51 , 52 . Despite this, there is no clear evidence that individuals with pre-existing mental disorders are disproportionately affected by pandemic-related societal disruptions; the effect size for pandemic impact on self-reported mental health problems was similar in psychiatric patients and the general population 13 . In the United States, emergency visits for ten different mental disorders were generally stable during the pandemic compared to earlier periods 53 . In a large Dutch study 22 , 54 with multiple pre-pandemic and during-pandemic assessments, there was no difference in symptom increase among patients relative to controls (see Fig. 1 for illustration). In absolute terms, however, it is important to note that psychiatric patients show much higher symptom levels of depression, anxiety, loneliness and COVID-fear than healthy controls. Again, variation in mental health changes during the pandemic is large: next to psychiatric patients who showed symptom decrease due to, for example, experiencing relief from social pressures, there certainly have been many patients with symptom increases and relapses during the pandemic.

Trajectories of mean depressive symptoms (QIDS score), anxiety symptoms (BAI score), loneliness (De Jong questionnaire score) and Fear of COVID-19 score before and during the first year of the COVID-19 pandemic in healthy controls (blue line, n  = 378) and in patients with depressive and/or anxiety disorders (red line, n  = 908). The x -axis indicates time with one pre-COVID assessment (averaged over up to five earlier assessments conducted between 2006 and 2019) and 11 online assessments during April 2020 through February 2021. Symbols indicate the mean score during the assessment with 95% CIs. As compared to pre-COVID assessment scores, the figure shows a statistically significant increase of depression and loneliness symptoms during the first pandemic peak (April 2020) in healthy controls but not in patients (for more details, see refs. 22 , 54 ). Asterisks indicate where subsequent wave scores differ from the prior wave scores ( P  < 0.05). The figure also illustrates the stability of depressive and anxiety symptoms during the first COVID year, a significant increase in loneliness during this period and fluctuations of Fear of COVID-19 score that positively correlate with infection rates in the Netherlands. Raw data are from the Netherlands Study of Depression and Anxiety (NESDA), which were re-analyzed for the current plots to illustrate differences between two groups (healthy controls versus patients). BAI, Beck Anxiety Inventory; QIDS, Quick Inventory of Depressive Symptoms.

Impact of COVID-19 infection and disease on mental health and the brain

Not only the pandemic but also COVID-19 itself can have severe impact on the mental health of affected individuals and, thus, of the population at large. Below we describe acute and post-acute neuropsychiatric sequelae seen in patients with COVID-19 and link these to neurobiological mechanisms.

Neuropsychiatric sequelae in individuals with COVID-19

Common symptoms associated with acute SARS-CoV-2 infection include headache, anosmia (loss of sense of smell) and dysgeusia (loss of sense of taste). The broader neuropsychiatric impact is dependent on infection severity and is very heterogeneous (Table 2 ). It ranges from no neuropsychiatric symptoms among the large group of asymptomatic COVID-19 cases to milder transient neuropsychiatric symptoms, such as fatigue, sleep disturbance and cognitive impairment, predominantly occurring among symptomatic patients with COVID-19 (ref. 55 ). Cognitive impairment consists of sustained memory impairments and executive dysfunction, including short-term memory loss, concentration problems, word-finding problems and impaired daily problem-solving, colloquially termed ‘brain fog’ by patients and clinicians. A small number of infected individuals become severely ill and require hospitalization. During hospital admission, the predominant neuropsychiatric outcome is delirium 56 . Delirium occurs among one-third of hospitalized patients with COVID-19 and among over half of patients with COVID-19 who require intensive care unit (ICU) treatment. These delirium rates seem similar to those observed among individuals with severe illness hospitalized for other general medical conditions 57 . Delirium is associated with neuropsychiatric sequalae after hospitalization, as part of post-intensive care syndrome 58 , in which sepsis and inflammation are associated with cognitive dysfunction and an increased risk of a broad range of psychiatric symptoms, from anxiety to depression and psychotic symptoms with hallucinations 59 , 60 .

A subset of patients with COVID-19 develop PACS 61 , which can include neuropsychiatric symptoms. A large meta-analysis summarizes 51 studies involving 18,917 patients with a mean follow-up of 77 days (range, 14–182 days) 62 . The most prevalent neuropsychiatric symptom associated with COVID-19 was sleep disturbance, with a pooled prevalence of 27.4%, followed by fatigue (24.4%), cognitive impairment (20.2%), anxiety symptoms (19.1%), post-traumatic stress symptoms (15.7%) and depression symptoms (12.9%) (Table 2 ). Another meta-analysis that assessed patients 12 weeks or more after confirmed COVID-19 diagnosis found that 32% experienced fatigue, and 22% experienced cognitive impairment 63 . To what extent neuropsychiatric symptoms are truly unique for patients with COVID remains unclear from these meta-analyses, as hardly any study included well-matched controls with other types of respiratory infections or inflammatory conditions.

Studies based on electronic health records have examined whether higher levels of neuropsychiatric symptoms truly translate into a higher incidence of clinically overt mental disorders 64 , 65 . In a 1-year follow-up using the US Veterans Affairs database, 153,848 survivors of SARS-CoV-2 infection exhibited an increased incidence of any mental disorder with a relative risk of 1.46 and, specifically, 1.35 for anxiety disorders, 1.39 for depressive disorders and 1.38 for stress and adjustment disorders, compared to a contemporary group and a historical control group ( n  = 5,859,251) 65 . In absolute numbers, the incident risk difference attributable to SARS-CoV-2 for mental disorders was 64 per 1,000 individuals. Taquet et al. 64 analyzed electronic health records from the US-based TriNetX network with over 81 million patients and 236,379 COVID-19 survivors followed for 6 months. In absolute numbers, 6-month incidence of hospital contacts related to diagnoses of anxiety, affective disorder or psychotic disorder was 7.0%, 4.5% and 0.4%, respectively. Risks of incident neurological or psychiatric diagnoses were directly correlated with COVID-19 severity and increased by 78% when compared to influenza and by 32% when compared to other respiratory tract infections. In contrast, a medical record study involving 8.3 million adults confirmed that neuropsychiatric disorders were significantly elevated among COVID-19 hospitalized individuals but to a similar extent as in hospitalized patients with other severe respiratory disease 66 . In line with this, a study using language processing of clinical notes in electronic health records did not find an increase in fatigue, mood and anxiety symptoms among COVID-19 hospitalized individuals when compared to hospitalized patients for other indications and adjusted for sociodemographic features and hospital course 67 . It is important to note that research based only on hospital records might be influenced by increased health-seeking behavior that could be differential across care settings or by increased follow-up by hospitals of patients with COVID-19 (compared to patients with other conditions).

Consequently, whether PACS symptoms form a unique pattern due to specific infection with SARS-CoV-2 remains debatable. Prospective case–control studies that do not rely on hospital records but measure the incidence of neuropsychiatric symptoms and diagnoses after COVID-19 are still scarce, but they are critical for distinguishing causation and confounding when characterizing PACS and the uniqueness of neuropsychiatric sequalae after COVID-19 (ref. 68 ). Recent studies with well-matched control groups illustrate that long-term consequences may not be so unique, as they were similar to those observed in patients with other diseases of similar severity, such as after acute myocardial infarction or in ICU patients 56 , 66 . A first prospective follow-up study of COVID-19 survivors and control patients matched on disease severity, age, sex and ICU admission found similar neuropsychiatric outcomes, regarding both new-onset psychiatric diagnosis (19% versus 20%) and neuropsychiatric symptoms (81% versus 93%). However, moderate but significantly worse cognitive outcomes 6 months after symptom onset were found among survivors of COVID-19 (ref. 69 ). In line with this, a longitudinal study of 785 participants from the UK Biobank showed small but significant cognitive impairment among individuals infected with SARS-CoV-2 compared to matched controls 70 .

Numerous psychosocial mechanisms can lead to neuropsychiatric sequalae of COVID-19, including functional impairment; psychological impact due to, for example, fear of dying; stress of being infected with a novel pandemic disease; isolation as part of quarantine and lack of social support; fear/guilt of spreading COVID-19 to family or community; and socioeconomic distress by lost wages 71 . However, there is also ample evidence that neurobiological mechanisms play an important role, which is discussed below.

Neurobiological mechanisms underlying neuropsychiatric sequelae of COVID-19

Acute neuropsychiatric symptoms among patients with severe COVID-19 have been found to correlate with the level of serum inflammatory markers 72 and coincide with neuroimaging findings of immune activation, including leukoencephalopathy, acute disseminated encephalomyelitis, cytotoxic lesions of the corpus callosum or cranial nerve enhancement 73 . Rare presentations, including meningitis, encephalitis, inflammatory demyelination, cerebral infarction and acute hemorrhagic necrotizing encephalopathy, have also been reported 74 . Hospitalized patients with frank encephalopathies display impaired blood-brain barrier (BBB) integrity with leptomeningeal enhancement on brain magnetic resonance images 75 . Studies of postmortem specimens from patients who succumbed to acute COVID-19 reveal significant neuropathology with signs of hypoxic damage and neuroinflammation. These include evidence of BBB permeability with extravasation of fibrinogen, microglial activation, astrogliosis, leukocyte infiltration and microhemorrhages 76 , 77 . However, it is still unclear to what extent these findings differ from patients with similar illness severity due to acute non-COVID illness, as these brain effects might not be virus-specific effects but rather due to cytokine-mediated neuroinflammation and critical illness.

Post-acute neuroimaging studies in SARS-CoV-2-recovered patients, as compared to control patients without COVID-19, reveal numerous alterations in brain structure on a group level, although effect sizes are generally small. These include minor reduction in gray matter thickness in the various regions of the cortex and within the corpus collosum, diffuse edema, increases in markers of tissue damage in regions functionally connected to the olfactory cortex and reductions in overall brain size 70 , 78 . Neuroimaging studies of post-acute COVID-19 patients also report abnormalities consistent with micro-structural and functional alterations, specifically within the hippocampus 79 , 80 , a brain region critical for memory formation and regulating anxiety, mood and stress responses, but also within gray matter areas involving the olfactory system and cingulate cortex 80 . Overall, these findings are in line with ongoing anosmia, tremors, affect problems and cognitive impairment.

Interestingly, despite findings mentioned above, there is little evidence of SARS-CoV-2 neuroinvasion with productive replication, and viral material is rarely found in the central nervous system (CNS) of patients with COVID-19 (refs. 76 , 77 , 81 ). Thus, neurobiological mechanisms of SARS-CoV-2-mediated neuropsychiatric sequelae remain unclear, especially in patients who initially present with milder forms of COVID-19. Symptomatic SARS-CoV-2 infection is associated with hypoxia, cytokine release syndrome (CRS) and dysregulated innate and adaptive immune responses (reviewed in ref. 82 ). All these effects could contribute to neuroinflammation and endothelial cell activation (Fig. 2 ). Examination of cerebrospinal fluid in patients with neuroimaging findings revealed elevated levels of pro-inflammatory, BBB-destabilizing cytokines, including interleukin-6 (IL-6), IL-1, IL-8 and mononuclear cell chemoattractants 83 , 84 . Whether these cytokines arise from the periphery, due to COVID-19-mediated CRS, or from within the CNS, is unclear. As studies generally lack control patients with other severe illnesses, the specificity of such findings to SARS-CoV-2 also remains unclear. Systemic inflammatory processes, including cytokine release, have been linked to glial activation with expression of chemoattractants that recruit immune cells, leading to neuroinflammation and injury 85 . Cerebrospinal fluid concentrations of neurofilament light, a biomarker of neuronal damage, were reportedly elevated in patients hospitalized with COVID-19 regardless of whether they exhibited neurologic diseases 86 . Acute thromboembolic events leading to ischemic infarcts are also common in patients with COVID-19 due to a potentially increased pro-coagulant process secondary to CRS 87 .

(1) Elevation of BBB-destabilizing cytokines (IL-1β and TNF) within the serum due to CRS or local interactions of mononuclear and endothelial cells. (2) Virus-induced endotheliitis increases susceptibility to microthrombus formation due to platelet activation, elevation of vWF and fibrin deposition. (3) Cytokine, mononuclear and endothelial cell interactions promote disruption of the BBB, which may allow entry of leukocytes expressing IFNg into the CNS (4), leading to microglial activation (5). (6) Activated microglia may eliminate synapses and/or express cytokines that promote neuronal injury. (7) Injured neurons express IL-6 which, together with IL-1β, promote a ‘gliogenic switch’ in NSCs (8), decreasing adult neurogenesis. (9) The combination of microglial (and possibly astrocyte) activation, neuronal injury and synapse loss may lead to dysregulation of NTs and neuronal circuitry. IFNg, interferon-g; NSC, neural stem cell; NT, neurotransmitter; TJ, tight junction; TNF, tumor necrosis factor; vWF, von Willebrand factor.

It is also unclear whether hospitalized patients with COVID-19 may develop brain abnormalities due to hypoxia or CRS rather than as a direct effect of SARS-CoV-2 infection. Hypoxia may cause neuronal dysfunction, cerebral edema, increased BBB permeability, cytokine expression and onset of neurodegenerative diseases 88 , 89 . CRS, with life-threatening levels of serum TNF-α and IL-1 (ref. 90 ) could also impact BBB function, as these cytokines destabilize microvasculature endothelial cell junctional proteins critical for BBB integrity 91 . In mild SARS-CoV-2 infection, circulating immune factors combined with mild hypoxia might impact BBB function and lead to neuroinflammation 92 , as observed during infection with other non-neuroinvasive respiratory pathogens 93 . However, multiple studies suggest that the SARS-CoV-2 spike protein itself may also induce venous and arterial endothelial cell activation and endotheliitis, disrupt BBB integrity or cross the BBB via adoptive transcytosis 94 , 95 , 96 .

Reducing neuropsychiatric sequelae of COVID-19

The increased risk of COVID-19-related neuropsychiatric sequalae was most pronounced during the first pandemic peak but reduced over the subsequent 2 years 64 , 97 . This may be due to reduced impact of newer SARS-CoV-2 strains (that is, Omicron) but also protective effects of vaccination, which limit SARS-CoV-2 spread and may, thus, prevent neuropsychiatric sequalae. Fully vaccinated individuals with breakthrough infections exhibit a 50% reduction in PACS 98 , even though vaccination does not improve PACS-related neuropsychiatric symptoms in patients with a prior history of COVID-19 (ref. 99 ). As patients with pre-existing mental disorders are at increased risk of SARS-CoV-2 infection, they deserve to be among the prioritization groups for vaccination efforts 100 .

Adequate treatment strategies for neuropsychiatric sequelae of COVID-19 are needed. As no specific evidence-based intervention yet exists, the best current treatment approach is that for neuropsychiatric sequelae arising after other severe medical conditions 101 . Stepped care—a staged approach of mental health services comprising a hierarchy of interventions, from least to most intensive, matched to the individual’s need—is efficacious with monitoring of mental health and cognitive problems. Milder symptoms likely benefit from counseling and holistic care, including physiotherapy, psychotherapy and rehabilitation. Individuals with moderate to severe symptoms fulfilling psychiatric diagnoses should receive guideline-concordant care for these disorders 61 . Patients with pre-existing mental disorders also deserve special attention when affected by COVID-19, as they have shown to have an increased risk of COVID-19-related hospitalization, complications and death 102 . This may involve interventions to address their general health, any unfavorable socioenvironmental factors, substance abuse or treatment adherence issues.

Lessons learned, knowledge gaps and future challenges

Ultimately, it is not only the millions of people who have died from COVID-19 worldwide that we remember but also the distress experienced during an unpredictable period with overstretched healthcare systems, lockdowns, school closures and changing work environments. In a world that is more and more globalized, connectivity puts us at risk for future pandemics. What can be learned from the last 2 years of the COVID-19 pandemic about how to handle future and longstanding challenges related to mental health?

Give mental health equal priority to physical health

The COVID-19 pandemic has demonstrated that our population seems quite resilient and adaptive. Nevertheless, even if society as a whole may bounce back, there is a large group of people whose mental health has been and will be disproportionately affected by this and future crises. Although various groups, such as the WHO 8 , the National Health Commission of China 103 , the Asia Pacific Disaster Mental Health Network 104 and a National Taskforce in India 105 , developed mental health policies early on, many countries were late in realizing that a mental health agenda deserves immediate attention in a rapidly evolving pandemic. Implementation of comprehensive and integrated mental health policies was generally inconsistent and suboptimal 106 and often in the shadow of policies directed at containing and reducing the spread of SARS-CoV-2. Leadership is needed to convey the message that mental health is as important as physical health and that we should focus specific attention and early interventions on those at the highest risk. This includes those vulnerable due to factors such as low socioeconomic status, specific developmental life phase (adolescents and young adults), pre-existing risk (poor physical or somatic health and early life trauma) or high exposure to pandemic-related (work) changes—for example, women and healthcare personnel. This means that not only should investment in youth and reducing health inequalities remain at the top of any policy agenda but also that mental health should be explicitly addressed from the start in any future global health crisis situation.

Communication and trust is crucial for mental health

Uncertainty and uncontrollability during the pandemic have challenged rational thinking. Negative news travels fast. Communication that is vague, one-sided and dishonest can negatively impact on mental health and amplify existing distress and anxiety 107 . Media reporting should not overemphasize negative mental health impact—for example, putative suicide rate increases or individual negative experiences—which could make situations worse than they actually are. Instead, communication during crises requires concrete and actionable advice that avoids polarization and strengthens vigilance, to foster resilience and help prevent escalation to severe mental health problems 108 , 109 .

Rapid research should be collaborative and high-quality

Within the scientific community, the topic of mental health during the pandemic led to a multitude of rapid studies that generally had limited methodological quality—for example, cross-sectional designs, small or selective sampling or study designs lacking valid comparison groups. These contributed rather little to our understanding of the mental health impact of the emerging crisis. In future events that have global mental health impact, where possible, collaborative and interdisciplinary efforts with well-powered and well-controlled prospective studies using standardized instruments will be crucial. Only with fine-grained determinants and outcomes can data reliably inform mental health policies and identify who is most at risk.

Do not neglect long-term mental health effects

So far, research has mainly focused on the acute and short-term effects of the pandemic on mental health, usually spanning pandemic effects over several months to 1 year. However, longer follow-up of how a pandemic impacts population mental health is essential. Can societal and economic disruptions after the pandemic increase risk of mental disorders at a later stage when the acute pandemic effects have subsided? Do increased self-reported mental health problems return to pre-pandemic levels, and which groups of individuals remain most affected in the long-term? We need to realize that certain pandemic consequences, particularly those affecting income and school/work careers, may become visible only over the course of several years. Consequently, we should maintain focus and continue to monitor and quantify the effects of the pandemic in the years to come—for example, by monitoring mental healthcare use and suicide. This should include specific at-risk populations (for example, adolescents) and understudied populations in low-income and middle-income countries.

Pay attention to mental health consequences of infectious diseases

Even though our knowledge on PACS is rapidly expanding, there are still many unanswered questions related to who is at risk, the long-term course trajectories and the best ways to intervene early. Consequently, we need to be aware of the neuropsychiatric sequelae of COVID-19 and, for that matter, of any infectious disease. Clinical attention and research should be directed toward alleviating potential neuropsychiatric ramifications of COVID-19. Next to clinical studies, studies using human tissues and appropriate animal models are pivotal to determine the CNS region-specific and neural-cell-specific effects of SARS-CoV-2 infection and the induced immune activation. Indeed, absence of SARS-CoV-2 neuroinvasion is an opportunity to learn and discover how peripheral neuroimmune mechanisms can contribute to neuropsychiatric sequelae in susceptible individuals. This emphasizes the importance of an interdisciplinary approach where somatic and mental health efforts are combined but also the need to integrate clinical parameters after infection with biological parameters (for example, serum, cerebrospinal fluid and/or neuroimaging) to predict who is at risk for PACS and deliver more targeted treatments.

Prepare mental healthcare infrastructure for pandemic times

If we take mental health seriously, we should not only monitor it but also develop the resources and infrastructure necessary for rapid early intervention, particularly for specific vulnerable groups. For adequate mental healthcare to be ready for pandemic times, primary care, community mental health and public mental health should be prepared. In many countries, health services were not able to meet the population’s mental health needs before the pandemic, which substantially worsened during the pandemic. We should ensure rapid access to mental health services but also address the underlying drivers of poor mental health, such as mitigating risks of unemployment, sexual violence and poverty. Collaboration in early stages across disciplines and expertise is essential. Anticipating disruption to face-to-face services, mental healthcare providers should be more prepared for consultations, therapy and follow-up by telephone, video-conferencing platforms and web applications 51 , 52 . The pandemic has shown that an inadequate infrastructure, pre-existing inequalities and low levels of technological literacy hindered the use and uptake of e-health, both in healthcare providers and in patients across different care settings. The necessary investments can ensure rapid upscaling of mental health services during future pandemics for those individuals with a high mental health need due to societal changes, government measures, fear of infection or infection itself.

Even though much attention has been paid to the physical health consequences of COVID-19, mental health has unjustly received less attention. There is an urgent need to prepare our research and healthcare infrastructures not only for adequate monitoring of the long-term mental health effects of the COVID-19 pandemic but also for future crises that will shape mental health. This will require collaboration to ensure interdisciplinary and sound research and to provide attention and care at an early stage for those individuals who are most vulnerable—giving mental health equal priority to physical health from the very start.

WHO Coronavirus (COVID-19) Dashboard (WHO, 2022; https://covid19.who.int/

Rando, H. M. et al. Challenges in defining long COVID: striking differences across literature, electronic health records, and patient-reported information. Preprint at https://www.medrxiv.org/content/10.1101/2021.03.20.21253896v1 (2021).

Nalbandian, A. et al. Post-acute COVID-19 syndrome. Nat. Med. 27 , 601–615 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Abbafati, C. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222 (2020).

Article   Google Scholar  

Penninx, B. W., Pine, D. S., Holmes, E. A. & Reif, A. Anxiety disorders. Lancet 397 , 914–927 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Herrman, H. et al. Time for united action on depression: a Lancet –World Psychiatric Association Commission. Lancet 399 , 957–1022 (2022).

Article   PubMed   Google Scholar  

Radka, K., Wyeth, E. H. & Derrett, S. A qualitative study of living through the first New Zealand COVID-19 lockdown: affordances, positive outcomes, and reflections. Prev. Med. Rep. 26 , 101725 (2022).

Mental Health and COVID-19: Early Evidence of the Pandemic’s Impact (WHO, 2022).

Dragioti, E. et al. A large-scale meta-analytic atlas of mental health problems prevalence during the COVID-19 early pandemic. J. Med. Virol. 94 , 1935–1949 (2022).

Zhang, S. X. et al. Mental disorder symptoms during the COVID-19 pandemic in Latin America—a systematic review and meta-analysis. Epidemiol. Psychiatr. Sci. 31 , e23 (2022).

Zhang, S. X. et al. Meta-analytic evidence of depression and anxiety in Eastern Europe during the COVID-19 pandemic. Eur. J. Psychotraumatol . 13 , 2000132 (2022).

Racine, N. et al. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. 175 , 1142–1150 (2021).

Robinson, E., Sutin, A. R., Daly, M. & Jones, A. A systematic review and meta-analysis of longitudinal cohort studies comparing mental health before versus during the COVID-19 pandemic in 2020. J. Affect. Disord. 296 , 567–576 (2022).

Article   CAS   PubMed   Google Scholar  

Prati, G. & Mancini, A. D. The psychological impact of COVID-19 pandemic lockdowns: a review and meta-analysis of longitudinal studies and natural experiments. Psychol. Med. 51 , 201–211 (2021).

Patel, K. et al. Psychological distress before and during the COVID-19 pandemic among adults in the United Kingdom based on coordinated analyses of 11 longitudinal studies. JAMA Netw. Open 5 , e227629 (2022).

Ernst, M. et al. Loneliness before and during the COVID-19 pandemic: a systematic review with meta-analysis. Am. Psychol . 77 , 660–677 (2022).

Kilian, C. et al. Changes in alcohol use during the COVID-19 pandemic in Europe: a meta-analysis of observational studies. Drug Alcohol Rev . 41 , 918–931 (2022).

Acuff, S. F., Strickland, J. C., Tucker, J. A. & Murphy, J. G. Changes in alcohol use during COVID-19 and associations with contextual and individual difference variables: a systematic review and meta-analysis. Psychol. Addict. Behav. 36 , 1–19 (2022).

Varga, T. V. et al. Loneliness, worries, anxiety, and precautionary behaviours in response to the COVID-19 pandemic: a longitudinal analysis of 200,000 Western and Northern Europeans. Lancet Reg. Health Eur . 2 , 100020 (2021).

Fancourt, D., Steptoe, A. & Bu, F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: a longitudinal observational study. Lancet Psychiatry 8 , 141–149 (2021).

Jia, H. et al. National and state trends in anxiety and depression severity scores among adults during the COVID-19 pandemic—United States, 2020–2021. MMWR Morb. Mortal. Wkly. Rep. 70 , 1427–1432 (2021).

Kok, A. A. L. et al. Mental health and perceived impact during the first Covid-19 pandemic year: a longitudinal study in Dutch case–control cohorts of persons with and without depressive, anxiety, and obsessive-compulsive disorders. J. Affect. Disord. 305 , 85–93 (2022).

Su, Y. et al. Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: a systematic review and meta-analysis. Int. Psychogeriatr. https://doi.org/10.1017/S1041610222000199 (2022).

Knox, L., Karantzas, G. C., Romano, D., Feeney, J. A. & Simpson, J. A. One year on: what we have learned about the psychological effects of COVID-19 social restrictions: a meta-analysis. Curr. Opin. Psychol. 46 , 101315 (2022).

Aknin, L. B. et al. Policy stringency and mental health during the COVID-19 pandemic: a longitudinal analysis of data from 15 countries. Lancet Public Health 7 , e417–e426 (2022).

Lee, Y. et al. Government response moderates the mental health impact of COVID-19: a systematic review and meta-analysis of depression outcomes across countries. J. Affect. Disord. 290 , 364–377 (2021).

Wu, J. T. et al. Nowcasting epidemics of novel pathogens: lessons from COVID-19. Nat. Med. 27 , 388–395 (2021).

Brooks, S. K. et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 395 , 912–920 (2020).

Santomauro, D. F. et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet 398 , 1700–1712 (2021).

Knudsen, A. K. S. et al. Prevalence of mental disorders, suicidal ideation and suicides in the general population before and during the COVID-19 pandemic in Norway: a population-based repeated cross-sectional analysis. Lancet Reg. Health Eur . 4 , 100071 (2021).

Ayuso-Mateos, J. L. et al. Changes in depression and suicidal ideation under severe lockdown restrictions during the first wave of the COVID-19 pandemic in Spain: a longitudinal study in the general population. Epidemiol. Psychiatr. Sci . 30 , e49 (2021).

Vloo, A. et al. Gender differences in the mental health impact of the COVID-19 lockdown: longitudinal evidence from the Netherlands. SSM Popul. Health 15 , 100878 (2021).

Winkler, P. et al. Prevalence of current mental disorders before and during the second wave of COVID-19 pandemic: an analysis of repeated nationwide cross-sectional surveys. J. Psychiatr. Res. 139 , 167–171 (2021).

Pirkis, J. et al. Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries. Lancet Psychiatry 8 , 579–588 (2021).

Faust, J. S. et al. Mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicides during the pandemic, March–August 2020. JAMA 326 , 84–86 (2021).

John, A. et al. The impact of the COVID-19 pandemic on self-harm and suicidal behaviour: update of living systematic review. F1000Res. 9 , 1097 (2020).

Steeg, S. et al. Temporal trends in primary care-recorded self-harm during and beyond the first year of the COVID-19 pandemic: time series analysis of electronic healthcare records for 2.8 million patients in the Greater Manchester Care Record. EClinicalMedicine 41 , 101175 (2021).

Rømer, T. B. et al. Psychiatric admissions, referrals, and suicidal behavior before and during the COVID-19 pandemic in Denmark: a time-trend study. Acta Psychiatr. Scand. 144 , 553–562 (2021).

Holland, K. M. et al. Trends in US emergency department visits for mental health, overdose, and violence outcomes before and during the COVID-19 pandemic. JAMA Psychiatry 78 , 372–379 (2021).

Kunzler, A. M. et al. Mental burden and its risk and protective factors during the early phase of the SARS-CoV-2 pandemic: systematic review and meta-analyses. Global Health 17 , 34 (2021).

Flor, L. S. et al. Quantifying the effects of the COVID-19 pandemic on gender equality on health, social, and economic indicators: a comprehensive review of data from March, 2020, to September, 2021. Lancet 399 , 2381–2397 (2022).

Viner, R. et al. School closures during social lockdown and mental health, health behaviors, and well-being among children and adolescents during the first COVID-19 wave: a systematic review. JAMA Pediatr. 176 , 400–409 (2022).

Zheng, X. Y. et al. Trends of injury mortality during the COVID-19 period in Guangdong, China: a population-based retrospective analysis. BMJ Open 11 , e045317 (2021).

Tanaka, T. & Okamoto, S. Increase in suicide following an initial decline during the COVID-19 pandemic in Japan. Nat. Hum. Behav. 5 , 229–238 (2021).

Thomeer, M. B., Moody, M. D. & Yahirun, J. Racial and ethnic disparities in mental health and mental health care during the COVID-19 pandemic. J. Racial Ethn. Health Disparities https://doi.org/10.1007/s40615-021-01006-7 (2022).

Hill, J. E. et al. The prevalence of mental health conditions in healthcare workers during and after a pandemic: systematic review and meta-analysis. J. Adv. Nurs. 78 , 1551–1573 (2022).

Marvaldi, M., Mallet, J., Dubertret, C., Moro, M. R. & Guessoum, S. B. Anxiety, depression, trauma-related, and sleep disorders among healthcare workers during the COVID-19 pandemic: a systematic review and meta-analysis. Neurosci. Biobehav. Rev. 126 , 252–264 (2021).

Phiri, P. et al. An evaluation of the mental health impact of SARS-CoV-2 on patients, general public and healthcare professionals: a systematic review and meta-analysis. EClinicalMedicine 34 , 100806 (2021).

Jorm, A. F., Patten, S. B., Brugha, T. S. & Mojtabai, R. Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries. World Psychiatry 16 , 90–99 (2017).

Third Round of the Global Pulse Survey on Continuity of Essential Health Services during the COVID-19 Pandemic (WHO, 2021).

Baumgart, J. G. et al. The early impacts of the COVID-19 pandemic on mental health facilities and psychiatric professionals. Int. J. Environ. Res. Public Health 18 , 8034 (2021).

Raphael, J., Winter, R. & Berry, K. Adapting practice in mental healthcare settings during the COVID-19 pandemic and other contagions: systematic review. BJPsych Open 7 , e62 (2021).

Anderson, K. N. et al. Changes and inequities in adult mental health-related emergency department visits during the COVID-19 pandemic in the US. JAMA Psychiatry 79 , 475–485 (2022).

Pan, K. Y. et al. The mental health impact of the COVID-19 pandemic on people with and without depressive, anxiety, or obsessive-compulsive disorders: a longitudinal study of three Dutch case–control cohorts. Lancet Psychiatry 8 , 121–129 (2021).

Dantzer, R., O’Connor, J. C., Freund, G. G., Johnson, R. W. & Kelley, K. W. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat. Rev. Neurosci. 9 , 46–56 (2008).

Nersesjan, V. et al. Central and peripheral nervous system complications of COVID-19: a prospective tertiary center cohort with 3-month follow-up. J. Neurol. 268 , 3086–3104 (2021).

Wilson, J. E. et al. Delirium. Nat. Rev. Dis. Prim . 6 , 90 (2020).

Rawal, G., Yadav, S. & Kumar, R. Post-intensive care syndrome: an overview. J. Transl. Intern. Med. 5 , 90–92 (2017).

Pandharipande, P. P. et al. Long-term cognitive impairment after critical illness. N. Engl. J. Med. 369 , 1306–1316 (2013).

Girard, T. D. et al. Long-term cognitive impairment after hospitalization for community-acquired pneumonia: a prospective cohort study. J. Gen. Intern. Med. 33 , 929–935 (2018).

Crook, H., Raza, S., Nowell, J., Young, M. & Edison, P. Long covid—mechanisms, risk factors, and management. BMJ 374 , n1648 (2021).

Badenoch, J. B. et al. Persistent neuropsychiatric symptoms after COVID-19: a systematic review and meta-analysis. Brain Commun . 4 , fcab297 (2021).

Ceban, F. et al. Fatigue and cognitive impairment in post-COVID-19 syndrome: a systematic review and meta-analysis. Brain Behav. Immun. 101 , 93–135 (2022).

Taquet, M., Geddes, J. R., Husain, M., Luciano, S. & Harrison, P. J. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. Lancet Psychiatry 8 , 416–427 (2021).

Xie, Y., Xu, E. & Al-Aly, Z. Risks of mental health outcomes in people with covid-19: cohort study. BMJ 376 , e068993 (2022).

Kieran Clift, A. et al. Neuropsychiatric ramifications of severe COVID-19 and other severe acute respiratory infections. JAMA Psychiatry 79 , 690–698 (2022).

Castro, V. M., Rosand, J., Giacino, J. T., McCoy, T. H. & Perlis, R. H. Case–control study of neuropsychiatric symptoms following COVID-19 hospitalization in 2 academic health systems. Mol. Psych. (in the press).

Amin-Chowdhury, Z. & Ladhani, S. N. Causation or confounding: why controls are critical for characterizing long COVID. Nat. Med. 27 , 1129–1130 (2021).

Nersesjan, V. et al. Neuropsychiatric and cognitive outcomes in patients 6 months after COVID-19 requiring hospitalization compared with matched control patients hospitalized for non-COVID-19 illness. JAMA Psychiatry 79 , 486–497 (2022).

Douaud, G. et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature 604 , 697–707 (2022).

Zhang, H. et al. Psychological experience of COVID-19 patients: a systematic review and qualitative meta-synthesis. Am. J. Infect. Control 50 , 809–819 (2022).

Mazza, M. G. et al. Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav. Immun. 89 , 594–600 (2020).

Moonis, G. et al. The spectrum of neuroimaging findings on CT and MRI in adults With COVID-19. AJR Am. J. Roentgenol. 217 , 959–974 (2021).

Asadi-Pooya, A. A. & Simani, L. Central nervous system manifestations of COVID-19: a systematic review. J. Neurol. Sci . 413 , 116832 (2020).

Lersy, F. et al. Cerebrospinal fluid features in patients with Coronavirus Disease 2019 and neurological manifestations: correlation with brain magnetic resonance imaging findings in 58 patients. J. Infect. Dis. 223 , 600–609 (2021).

Thakur, K. T. et al. COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital. Brain 144 , 2696–2708 (2021).

Cosentino, G. et al. Neuropathological findings from COVID-19 patients with neurological symptoms argue against a direct brain invasion of SARS-CoV-2: a critical systematic review. Eur. J. Neurol. 28 , 3856–3865 (2021).

Tian, T. et al. Long-term follow-up of dynamic brain changes in patients recovered from COVID-19 without neurological manifestations. JCI Insight 7 , e155827 (2022).

Lu, Y. et al. Cerebral micro-structural changes in COVID-19 patients—an MRI-based 3-month follow-up study. EClinicalMedicine 25 , 100484 (2020).

Qin, Y. et al . Long-term microstructure and cerebral blood flow changes in patients recovered from COVID-19 without neurological manifestations. J. Clin. Invest . 131 , e147329 (2021).

Matschke, J. et al. Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. 19 , 919–929 (2020).

Shivshankar, P. et al. SARS-CoV-2 infection: host response, immunity, and therapeutic targets. Inflammation 45 , 1430–1449 (2022).

Manganotti, P. et al. Cerebrospinal fluid and serum interleukins 6 and 8 during the acute and recovery phase in COVID-19 neuropathy patients. J. Med. Virol. 93 , 5432–5437 (2021).

Farhadian, S. et al. Acute encephalopathy with elevated CSF inflammatory markers as the initial presentation of COVID-19. BMC Neurol . 20 , 248 (2020).

Francistiová, L. et al. Cellular and molecular effects of SARS-CoV-2 linking lung infection to the brain. Front. Immunol . 12 , 730088 (2021).

Paterson, R. W. et al. Serum and cerebrospinal fluid biomarker profiles in acute SARS-CoV-2-associated neurological syndromes. Brain Commun . 3 , fcab099 (2021).

Cryer, M. J. et al. Prothrombotic milieu, thrombotic events and prophylactic anticoagulation in hospitalized COVID-19 positive patients: a review. Clin. Appl. Thromb. Hemost . 28 , 10760296221074353 (2022).

Nalivaeva, N. N. & Rybnikova, E. A. Editorial: Brain hypoxia and ischemia: new insights into neurodegeneration and neuroprotection. Front. Neurosci . 13 , 770 (2019).

Brownlee, N. N. M., Wilson, F. C., Curran, D. B., Lyttle, N. & McCann, J. P. Neurocognitive outcomes in adults following cerebral hypoxia: a systematic literature review. NeuroRehabilitation 47 , 83–97 (2020).

Del Valle, D. M. et al. An inflammatory cytokine signature predicts COVID-19 severity and survival. Nat. Med. 26 , 1636–1643 (2020).

Daniels, B. P. et al. Viral pathogen-associated molecular patterns regulate blood–brain barrier integrity via competing innate cytokine signals. mBio 5 , e01476-14 (2014).

Reynolds, J. L. & Mahajan, S. D. SARS-COV2 alters blood brain barrier integrity contributing to neuro-inflammation. J. Neuroimmune Pharmacol. 16 , 4–6 (2021).

Bohmwald, K., Gálvez, N. M. S., Ríos, M. & Kalergis, A. M. Neurologic alterations due to respiratory virus infections. Front. Cell. Neurosci . 12 , 386 (2018).

Khaddaj-Mallat, R. et al. SARS-CoV-2 deregulates the vascular and immune functions of brain pericytes via spike protein. Neurobiol. Dis . 161 , 105561 (2021).

Qian, Y. et al. Direct activation of endothelial cells by SARS-CoV-2 nucleocapsid protein is blocked by simvastatin. J Virol. 95 , e0139621 (2021).

Rhea, E. M. et al. The S1 protein of SARS-CoV-2 crosses the blood–brain barrier in mice. Nat. Neurosci. 24 , 368–378 (2021).

Magnúsdóttir, I. et al. Acute COVID-19 severity and mental health morbidity trajectories in patient populations of six nations: an observational study. Lancet Public Health 7 , e406–e416 (2022).

Antonelli, M. et al. Risk factors and disease profile of post-vaccination SARS-CoV-2 infection in UK users of the COVID Symptom Study app: a prospective, community-based, nested, case–control study. Lancet Infect. Dis. 22 , 43–55 (2022).

Wisnivesky, J. P. et al. Association of vaccination with the persistence of post-COVID symptoms. J. Gen. Intern. Med . 37 , 1748–1753 (2022).

De Picker, L. J. et al. Severe mental illness and European COVID-19 vaccination strategies. Lancet Psychiatry 8 , 356–359 (2021).

Cohen, G. H. et al. Comparison of simulated treatment and cost-effectiveness of a stepped care case-finding intervention vs usual care for posttraumatic stress disorder after a natural disaster. JAMA Psychiatry 74 , 1251–1258 (2017).

Vai, B. et al. Mental disorders and risk of COVID-19-related mortality, hospitalisation, and intensive care unit admission: a systematic review and meta-analysis. Lancet Psychiatry 8 , 797–812 (2021).

Xiang, Y. T. et al. Timely mental health care for the 2019 novel coronavirus outbreak is urgently needed. Lancet Psychiatry 7 , 228 (2020).

Newnham, E. A. et al. The Asia Pacific Disaster Mental Health Network: setting a mental health agenda for the region. Int. J. Environ. Res. Public Health 17 , 6144 (2020).

Article   CAS   PubMed Central   Google Scholar  

Dandona, R. & Sagar, R. COVID-19 offers an opportunity to reform mental health in India. Lancet Psychiatry 8 , 9–11 (2021).

Qiu, D. et al. Policies to improve the mental health of people influenced by COVID-19 in China: a scoping review. Front. Psychiatry 11 , 588137 (2020).

Su, Z. et al. Mental health consequences of COVID-19 media coverage: the need for effective crisis communication practices. Global Health 17 , 4 (2021).

Petersen, M. B. COVID lesson: trust the public with hard truths. Nature 598 , 237 (2021).

van der Bles, A. M., van der Linden, S., Freeman, A. L. J. & Spiegelhalter, D. J. The effects of communicating uncertainty on public trust in facts and numbers. Proc. Natl Acad. Sci. USA 117 , 7672–7683 (2020).

Titze-de-Almeida, R. et al. Persistent, new-onset symptoms and mental health complaints in Long COVID in a Brazilian cohort of non-hospitalized patients. BMC Infect. Dis. 22 , 133 (2022).

Carfì, A., Bernabei, R. & Landi, F. Persistent symptoms in patients after acute COVID-19. JAMA 324 , 603–605 (2020).

Bliddal, S. et al. Acute and persistent symptoms in non-hospitalized PCR-confirmed COVID-19 patients. Sci. Rep. 11 , 13153 (2021).

Kim, Y. et al. Post-acute COVID-19 syndrome in patients after 12 months from COVID-19 infection in Korea. BMC Infect. Dis . 22 , 93 (2022).

Download references

Acknowledgements

The authors thank E. Giltay for assistance on data analyses and production of Fig. 1 . B.W.J.H.P. discloses support for research and publication of this work from the European Union’s Horizon 2020 research and innovation programme-funded RESPOND project (grant no. 101016127).

Author information

Authors and affiliations.

Department of Psychiatry, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Brenda W. J. H. Penninx & Christiaan H. Vinkers

Amsterdam Public Health, Mental Health Program and Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam, The Netherlands

Biological and Precision Psychiatry, Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark

Michael E. Benros

Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

Departments of Medicine, Pathology & Immunology and Neuroscience, Center for Neuroimmunology & Neuroinfectious Diseases, Washington University School of Medicine, St. Louis, MO, USA

Robyn S. Klein

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Brenda W. J. H. Penninx .

Ethics declarations

Competing interests.

The authors declare no conflicts of interest.

Peer review

Peer review information.

Nature Medicine thanks Jane Pirkis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Karen O’Leary, in collaboration with the Nature Medicine team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Penninx, B.W.J.H., Benros, M.E., Klein, R.S. et al. How COVID-19 shaped mental health: from infection to pandemic effects. Nat Med 28 , 2027–2037 (2022). https://doi.org/10.1038/s41591-022-02028-2

Download citation

Received : 06 June 2022

Accepted : 26 August 2022

Published : 03 October 2022

Issue Date : October 2022

DOI : https://doi.org/10.1038/s41591-022-02028-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Mental health disturbance in preclinical medical students and its association with screen time, sleep quality, and depression during the covid-19 pandemic.

• Tjhin Wiguna
• Valerie Josephine Dirjayanto
• Erik Kinzie

BMC Psychiatry (2024)

Level of patient contact and Impact of Event scores among Canadian healthcare providers during the COVID-19 pandemic

• Iris Gutmanis
• Ayodele Sanni
• Brenda L Coleman

BMC Health Services Research (2024)

A repeated cross-sectional pilot study of the relationship between perceived a community with shared future for doctor-patient and benefit finding: the mediating role of health self-consciousness and moderating role of anxiety

• Fenwick Feng Jing

BMC Psychology (2024)

Protocol for a pilot cluster randomised controlled trial of a multicomponent sustainable return to work IGLOo intervention

• Oliver Davis
• Jeremy Dawson
• Fehmidah Munir

Pilot and Feasibility Studies (2024)

Comparing temporal changes and predictors of different types of mental health and socio-emotional wellbeing outcomes during COVID-19: an overlapping panel study of Spanish residents

• Brenda Robles
• Maria A. Barceló

BMC Public Health (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Advertisement

Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice

• Published: 20 April 2020
• Volume 5 , pages 245–257, ( 2020 )

Cite this article

• John A. Naslund 1 ,
• Ameya Bondre 2 ,
• John Torous 3 &
• Kelly A. Aschbrenner 4  

406k Accesses

221 Citations

186 Altmetric

19 Mentions

Explore all metrics

Avoid common mistakes on your manuscript.

Introduction

Social media has become a prominent fixture in the lives of many individuals facing the challenges of mental illness. Social media refers broadly to web and mobile platforms that allow individuals to connect with others within a virtual network (such as Facebook, Twitter, Instagram, Snapchat, or LinkedIn), where they can share, co-create, or exchange various forms of digital content, including information, messages, photos, or videos (Ahmed et al. 2019 ). Studies have reported that individuals living with a range of mental disorders, including depression, psychotic disorders, or other severe mental illnesses, use social media platforms at comparable rates as the general population, with use ranging from about 70% among middle-age and older individuals to upwards of 97% among younger individuals (Aschbrenner et al. 2018b ; Birnbaum et al. 2017b ; Brunette et al. 2019 ; Naslund et al. 2016 ). Other exploratory studies have found that many of these individuals with mental illness appear to turn to social media to share their personal experiences, seek information about their mental health and treatment options, and give and receive support from others facing similar mental health challenges (Bucci et al. 2019 ; Naslund et al. 2016b ).

Across the USA and globally, very few people living with mental illness have access to adequate mental health services (Patel et al. 2018 ). The wide reach and near ubiquitous use of social media platforms may afford novel opportunities to address these shortfalls in existing mental health care, by enhancing the quality, availability, and reach of services. Recent studies have explored patterns of social media use, impact of social media use on mental health and wellbeing, and the potential to leverage the popularity and interactive features of social media to enhance the delivery of interventions. However, there remains uncertainty regarding the risks and potential harms of social media for mental health (Orben and Przybylski 2019 ) and how best to weigh these concerns against potential benefits.

In this commentary, we summarized current research on the use of social media among individuals with mental illness, with consideration of the impact of social media on mental wellbeing, as well as early efforts using social media for delivery of evidence-based programs for addressing mental health problems. We searched for recent peer reviewed publications in Medline and Google Scholar using the search terms “mental health” or “mental illness” and “social media,” and searched the reference lists of recent reviews and other relevant studies. We reviewed the risks, potential harms, and necessary safety precautions with using social media for mental health. Overall, our goal was to consider the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care, and enhancing existing mental health services, while balancing the need for safety. Given this broad objective, we did not perform a systematic search of the literature and we did not apply specific inclusion criteria based on study design or type of mental disorder.

Social Media Use and Mental Health

In 2020, there are an estimated 3.8 billion social media users worldwide, representing half the global population (We Are Social 2020 ). Recent studies have shown that individuals with mental disorders are increasingly gaining access to and using mobile devices, such as smartphones (Firth et al. 2015 ; Glick et al. 2016 ; Torous et al. 2014a , b ). Similarly, there is mounting evidence showing high rates of social media use among individuals with mental disorders, including studies looking at engagement with these popular platforms across diverse settings and disorder types. Initial studies from 2015 found that nearly half of a sample of psychiatric patients were social media users, with greater use among younger individuals (Trefflich et al. 2015 ), while 47% of inpatients and outpatients with schizophrenia reported using social media, of which 79% reported at least once-a-week usage of social media websites (Miller et al. 2015 ). Rates of social media use among psychiatric populations have increased in recent years, as reflected in a study with data from 2017 showing comparable rates of social media use (approximately 70%) among individuals with serious mental illness in treatment as compared with low-income groups from the general population (Brunette et al. 2019 ).

Similarly, among individuals with serious mental illness receiving community-based mental health services, a recent study found equivalent rates of social media use as the general population, even exceeding 70% of participants (Naslund et al. 2016 ). Comparable findings were demonstrated among middle-age and older individuals with mental illness accessing services at peer support agencies, where 72% of respondents reported using social media (Aschbrenner et al. 2018b ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study (Abdel-Baki et al. 2017 ).

Individuals who self-identified as having a schizophrenia spectrum disorder responded to a survey shared through the National Alliance of Mental Illness (NAMI) and reported that visiting social media sites was one of their most common activities when using digital devices, taking up roughly 2 h each day (Gay et al. 2016 ). For adolescents and young adults ages 12 to 21 with psychotic disorders and mood disorders, over 97% reported using social media, with average use exceeding 2.5 h per day (Birnbaum et al. 2017b ). Similarly, in a sample of adolescents ages 13–18 recruited from community mental health centers, 98% reported using social media, with YouTube as the most popular platform, followed by Instagram and Snapchat (Aschbrenner et al. 2019 ).

Research has also explored the motivations for using social media as well as the perceived benefits of interacting on these platforms among individuals with mental illness. In the sections that follow (see Table 1 for a summary), we consider three potentially unique features of interacting and connecting with others on social media that may offer benefits for individuals living with mental illness. These include: (1) Facilitate social interaction; (2) Access to a peer support network; and (3) Promote engagement and retention in services.

Facilitate Social Interaction

Social media platforms offer near continuous opportunities to connect and interact with others, regardless of time of day or geographic location. This on demand ease of communication may be especially important for facilitating social interaction among individuals with mental disorders experiencing difficulties interacting in face-to-face settings. For example, impaired social functioning is a common deficit in schizophrenia spectrum disorders, and social media may facilitate communication and interacting with others for these individuals (Torous and Keshavan 2016 ). This was suggested in one study where participants with schizophrenia indicated that social media helped them to interact and socialize more easily (Miller et al. 2015 ). Like other online communication, the ability to connect with others anonymously may be an important feature of social media, especially for individuals living with highly stigmatizing health conditions (Berger et al. 2005 ), such as serious mental disorders (Highton-Williamson et al. 2015 ).

Studies have found that individuals with serious mental disorders (Spinzy et al. 2012 ) as well as young adults with mental illness (Gowen et al. 2012 ) appear to form online relationships and connect with others on social media as often as social media users from the general population. This is an important observation because individuals living with serious mental disorders typically have few social contacts in the offline world and also experience high rates of loneliness (Badcock et al. 2015 ; Giacco et al. 2016 ). Among individuals receiving publicly funded mental health services who use social media, nearly half (47%) reported using these platforms at least weekly to feel less alone (Brusilovskiy et al. 2016 ). In another study of young adults with serious mental illness, most indicated that they used social media to help feel less isolated (Gowen et al. 2012 ). Interestingly, more frequent use of social media among a sample of individuals with serious mental illness was associated with greater community participation, measured as participation in shopping, work, religious activities, or visiting friends and family, as well as greater civic engagement, reflected as voting in local elections (Brusilovskiy et al. 2016 ).

Emerging research also shows that young people with moderate to severe depressive symptoms appear to prefer communicating on social media rather than in-person (Rideout and Fox 2018 ), while other studies have found that some individuals may prefer to seek help for mental health concerns online rather than through in-person encounters (Batterham and Calear 2017 ). In a qualitative study, participants with schizophrenia described greater anonymity, the ability to discover that other people have experienced similar health challenges and reducing fears through greater access to information as important motivations for using the Internet to seek mental health information (Schrank et al. 2010 ). Because social media does not require the immediate responses necessary in face-to-face communication, it may overcome deficits with social interaction due to psychotic symptoms that typically adversely affect face-to-face conversations (Docherty et al. 1996 ). Online social interactions may not require the use of non-verbal cues, particularly in the initial stages of interaction (Kiesler et al. 1984 ), with interactions being more fluid and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction (Indian and Grieve 2014 ). Furthermore, many individuals with serious mental disorders can experience symptoms including passive social withdrawal, blunted affect, and attentional impairment, as well as active social avoidance due to hallucinations or other concerns (Hansen et al. 2009 ), thus potentially reinforcing the relative advantage, as perceived by users, of using social media over in person conversations.

Access to a Peer Support Network

There is growing recognition about the role that social media channels could play in enabling peer support (Bucci et al. 2019 ; Naslund et al. 2016b ), referred to as a system of mutual giving and receiving where individuals who have endured the difficulties of mental illness can offer hope, friendship, and support to others facing similar challenges (Davidson et al. 2006 ; Mead et al. 2001 ). Initial studies exploring use of online self-help forums among individuals with serious mental illnesses have found that individuals with schizophrenia appeared to use these forums for self-disclosure and sharing personal experiences, in addition to providing or requesting information, describing symptoms, or discussing medication (Haker et al. 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness (Vayreda and Antaki 2009 ). More recently, in a review of online social networking in people with psychosis, Highton-Williamson et al. ( 2015 ) highlight that an important purpose of such online connections was to establish new friendships, pursue romantic relationships, maintain existing relationships or reconnect with people, and seek online peer support from others with lived experience (Highton-Williamson et al. 2015 ).

Online peer support among individuals with mental illness has been further elaborated in various studies. In a content analysis of comments posted to YouTube by individuals who self-identified as having a serious mental illness, there appeared to be opportunities to feel less alone, provide hope, find support and learn through mutual reciprocity, and share coping strategies for day-to-day challenges of living with a mental illness (Naslund et al. 2014 ). In another study, Chang ( 2009 ) delineated various communication patterns in an online psychosis peer-support group (Chang 2009 ). Specifically, different forms of support emerged, including “informational support” about medication use or contacting mental health providers, “esteem support” involving positive comments for encouragement, “network support” for sharing similar experiences, and “emotional support” to express understanding of a peer’s situation and offer hope or confidence (Chang 2009 ). Bauer et al. ( 2013 ) reported that the main interest in online self-help forums for patients with bipolar disorder was to share emotions with others, allow exchange of information, and benefit by being part of an online social group (Bauer et al. 2013 ).

For individuals who openly discuss mental health problems on Twitter, a study by Berry et al. ( 2017 ) found that this served as an important opportunity to seek support and to hear about the experiences of others (Berry et al. 2017 ). In a survey of social media users with mental illness, respondents reported that sharing personal experiences about living with mental illness and opportunities to learn about strategies for coping with mental illness from others were important reasons for using social media (Naslund et al. 2017 ). A computational study of mental health awareness campaigns on Twitter provides further support with inspirational posts and tips being the most shared (Saha et al. 2019 ). Taken together, these studies offer insights about the potential for social media to facilitate access to an informal peer support network, though more research is necessary to examine how these online interactions may impact intentions to seek care, illness self-management, and clinically meaningful outcomes in offline contexts.

Promote Engagement and Retention in Services

Many individuals living with mental disorders have expressed interest in using social media platforms for seeking mental health information (Lal et al. 2018 ), connecting with mental health providers (Birnbaum et al. 2017b ), and accessing evidence-based mental health services delivered over social media specifically for coping with mental health symptoms or for promoting overall health and wellbeing (Naslund et al. 2017 ). With the widespread use of social media among individuals living with mental illness combined with the potential to facilitate social interaction and connect with supportive peers, as summarized above, it may be possible to leverage the popular features of social media to enhance existing mental health programs and services. A recent review by Biagianti et al. ( 2018 ) found that peer-to-peer support appeared to offer feasible and acceptable ways to augment digital mental health interventions for individuals with psychotic disorders by specifically improving engagement, compliance, and adherence to the interventions and may also improve perceived social support (Biagianti et al. 2018 ).

Among digital programs that have incorporated peer-to-peer social networking consistent with popular features on social media platforms, a pilot study of the HORYZONS online psychosocial intervention demonstrated significant reductions in depression among patients with first episode psychosis (Alvarez-Jimenez et al. 2013 ). Importantly, the majority of participants (95%) in this study engaged with the peer-to-peer networking feature of the program, with many reporting increases in perceived social connectedness and empowerment in their recovery process (Alvarez-Jimenez et al. 2013 ). This moderated online social therapy program is now being evaluated as part of a large randomized controlled trial for maintaining treatment effects from first episode psychosis services (Alvarez-Jimenez et al. 2019 ).

Other early efforts have demonstrated that use of digital environments with the interactive peer-to-peer features of social media can enhance social functioning and wellbeing in young people at high risk of psychosis (Alvarez-Jimenez et al. 2018 ). There has also been a recent emergence of several mobile apps to support symptom monitoring and relapse prevention in psychotic disorders. Among these apps, the development of PRIME (Personalized Real-time Intervention for Motivational Enhancement) has involved working closely with young people with schizophrenia to ensure that the design of the app has the look and feel of mainstream social media platforms, as opposed to existing clinical tools (Schlosser et al. 2016 ). This unique approach to the design of the app is aimed at promoting engagement and ensuring that the app can effectively improve motivation and functioning through goal setting and promoting better quality of life of users with schizophrenia (Schlosser et al. 2018 ).

Social media platforms could also be used to promote engagement and participation in in-person services delivered through community mental health settings. For example, the peer-based lifestyle intervention called PeerFIT targets weight loss and improved fitness among individuals living with serious mental illness through a combination of in-person lifestyle classes, exercise groups, and use of digital technologies (Aschbrenner et al. 2016b , c ). The intervention holds tremendous promise as lack of support is one of the largest barriers towards exercise in patients with serious mental illness (Firth et al. 2016 ), and it is now possible to use social media to counter such. Specifically, in PeerFIT, a private Facebook group is closely integrated into the program to offer a closed platform where participants can connect with the lifestyle coaches, access intervention content, and support or encourage each other as they work towards their lifestyle goals (Aschbrenner et al. 2016a ; Naslund et al. 2016a ). To date, this program has demonstrated preliminary effectiveness for meaningfully reducing cardiovascular risk factors that contribute to early mortality in this patient group (Aschbrenner, Naslund, Shevenell, Kinney, et al., 2016), while the Facebook component appears to have increased engagement in the program, while allowing participants who were unable to attend in-person sessions due to other health concerns or competing demands to remain connected with the program (Naslund et al. 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from real world community mental health services settings (Aschbrenner et al. 2018a ).

These examples highlight the promise of incorporating the features of popular social media into existing programs, which may offer opportunities to safely promote engagement and program retention, while achieving improved clinical outcomes. This is an emerging area of research, as evidenced by several important effectiveness trials underway (Alvarez-Jimenez et al. 2019 ; Aschbrenner et al. 2018a ), including efforts to leverage online social networking to support family caregivers of individuals receiving first episode psychosis services (Gleeson et al. 2017 ).

Challenges with Social Media for Mental Health

The science on the role of social media for engaging persons with mental disorders needs a cautionary note on the effects of social media usage on mental health and wellbeing, particularly in adolescents and young adults. While the risks and harms of social media are frequently covered in the popular press and mainstream news reports, careful consideration of the research in this area is necessary. In a review of 43 studies in young people, many benefits of social media were cited, including increased self-esteem and opportunities for self-disclosure (Best et al. 2014 ). Yet, reported negative effects were an increased exposure to harm, social isolation, depressive symptoms, and bullying (Best et al. 2014 ). In the sections that follow (see Table 1 for a summary), we consider three major categories of risk related to use of social media and mental health. These include: (1) Impact on symptoms; (2) Facing hostile interactions; and (3) Consequences for daily life.

Impact on Symptoms

Studies consistently highlight that use of social media, especially heavy use and prolonged time spent on social media platforms, appears to contribute to increased risk for a variety of mental health symptoms and poor wellbeing, especially among young people (Andreassen et al. 2016 ; Kross et al. 2013 ; Woods and Scott 2016 ). This may partly be driven by the detrimental effects of screen time on mental health, including increased severity of anxiety and depressive symptoms, which have been well documented (Stiglic and Viner 2019 ). Recent studies have reported negative effects of social media use on mental health of young people, including social comparison pressure with others and greater feeling of social isolation after being rejected by others on social media (Rideout and Fox 2018 ). In a study of young adults, it was found that negative comparisons with others on Facebook contributed to risk of rumination and subsequent increases in depression symptoms (Feinstein et al. 2013 ). Still, the cross-sectional nature of many screen time and mental health studies makes it challenging to reach causal inferences (Orben and Przybylski 2019 ).

Quantity of social media use is also an important factor, as highlighted in a survey of young adults ages 19 to 32, where more frequent visits to social media platforms each week were correlated with greater depressive symptoms (Lin et al. 2016 ). More time spent using social media is also associated with greater symptoms of anxiety (Vannucci et al. 2017 ). The actual number of platforms accessed also appears to contribute to risk as reflected in another national survey of young adults where use of a large number of social media platforms was associated with negative impact on mental health (Primack et al. 2017 ). Among survey respondents using between 7 and 11 different social media platforms compared with respondents using only 2 or fewer platforms, there were 3 times greater odds of having high levels of depressive symptoms and a 3.2 times greater odds of having high levels of anxiety symptoms (Primack et al. 2017 ).

Many researchers have postulated that worsening mental health attributed to social media use may be because social media replaces face-to-face interactions for young people (Twenge and Campbell 2018 ) and may contribute to greater loneliness (Bucci et al. 2019 ) and negative effects on other aspects of health and wellbeing (Woods and Scott 2016 ). One nationally representative survey of US adolescents found that among respondents who reported more time accessing media such as social media platforms or smartphone devices, there were significantly greater depressive symptoms and increased risk of suicide when compared with adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities (Twenge et al. 2018 ). For individuals living with more severe mental illnesses, the effects of social media on psychiatric symptoms have received less attention. One study found that participation in chat rooms may contribute to worsening symptoms in young people with psychotic disorders (Mittal et al. 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood (Berry et al. 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies (Orben and Przybylski 2019 ) and emphasize the need to explore how social media may contribute to symptom severity and whether protective factors may be identified to mitigate these risks.

Facing Hostile Interactions

Popular social media platforms can create potential situations where individuals may be victimized by negative comments or posts. Cyberbullying represents a form of online aggression directed towards specific individuals, such as peers or acquaintances, which is perceived to be most harmful when compared with random hostile comments posted online (Hamm et al. 2015 ). Importantly, cyberbullying on social media consistently shows harmful impact on mental health in the form of increased depressive symptoms as well as worsening of anxiety symptoms, as evidenced in a review of 36 studies among children and young people (Hamm et al. 2015 ). Furthermore, cyberbullying disproportionately impacts females as reflected in a national survey of adolescents in the USA, where females were twice as likely to be victims of cyberbullying compared with males (Alhajji et al. 2019 ). Most studies report cross-sectional associations between cyberbullying and symptoms of depression or anxiety (Hamm et al. 2015 ), though one longitudinal study in Switzerland found that cyberbullying contributed to significantly greater depression over time (Machmutow et al. 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there were over 3 times greater odds of facing online harassment in the last year compared with youth who reported mild or no depressive symptoms (Ybarra 2004 ). Similarly, in a 2018 national survey of young people, respondents ages 14 to 22 with moderate to severe depressive symptoms were more likely to have had negative experiences when using social media and, in particular, were more likely to report having faced hostile comments or being “trolled” from others when compared with respondents without depressive symptoms (31% vs. 14%) (Rideout and Fox 2018 ). As these studies depict risks for victimization on social media and the correlation with poor mental health, it is possible that individuals living with mental illness may also experience greater hostility online compared to individuals without mental illness. This would be consistent with research showing greater risk of hostility, including increased violence and discrimination, directed towards individuals living with mental illness in in-person contexts, especially targeted at those with severe mental illnesses (Goodman et al. 1999 ).

A computational study of mental health awareness campaigns on Twitter reported that while stigmatizing content was rare, it was actually the most spread (re-tweeted) demonstrating that harmful content can travel quickly on social media (Saha et al. 2019 ). Another study was able to map the spread of social media posts about the Blue Whale Challenge, an alleged game promoting suicide, over Twitter, YouTube, Reddit, Tumblr, and other forums across 127 countries (Sumner et al. 2019 ). These findings show that it is critical to monitor the actual content of social media posts, such as determining whether content is hostile or promotes harm to self or others. This is pertinent because existing research looking at duration of exposure cannot account for the impact of specific types of content on mental health and is insufficient to fully understand the effects of using these platforms on mental health.

Consequences for Daily Life

The ways in which individuals use social media can also impact their offline relationships and everyday activities. To date, reports have described risks of social media use pertaining to privacy, confidentiality, and unintended consequences of disclosing personal health information online (Torous and Keshavan 2016 ). Additionally, concerns have been raised about poor quality or misleading health information shared on social media and that social media users may not be aware of misleading information or conflicts of interest especially when the platforms promote popular content regardless of whether it is from a trustworthy source (Moorhead et al. 2013 ; Ventola 2014 ). For persons living with mental illness, there may be additional risks from using social media. A recent study that specifically explored the perspectives of social media users with serious mental illnesses, including participants with schizophrenia spectrum disorders, bipolar disorder, or major depression, found that over one third of participants expressed concerns about privacy when using social media (Naslund and Aschbrenner 2019 ). The reported risks of social media use were directly related to many aspects of everyday life, including concerns about threats to employment, fear of stigma and being judged, impact on personal relationships, and facing hostility or being hurt (Naslund and Aschbrenner 2019 ). While few studies have specifically explored the dangers of social media use from the perspectives of individuals living with mental illness, it is important to recognize that use of these platforms may contribute to risks that extend beyond worsening symptoms and that can affect different aspects of daily life.

In this commentary, we considered ways in which social media may yield benefits for individuals living with mental illness, while contrasting these with the possible harms. Studies reporting on the threats of social media for individuals with mental illness are mostly cross-sectional, making it difficult to draw conclusions about direction of causation. However, the risks are potentially serious. These risks should be carefully considered in discussions pertaining to use of social media and the broader use of digital mental health technologies, as avenues for mental health promotion or for supporting access to evidence-based programs or mental health services. At this point, it would be premature to view the benefits of social media as outweighing the possible harms, when it is clear from the studies summarized here that social media use can have negative effects on mental health symptoms, can potentially expose individuals to hurtful content and hostile interactions, and can result in serious consequences for daily life, including threats to employment and personal relationships. Despite these risks, it is also necessary to recognize that individuals with mental illness will continue to use social media given the ease of accessing these platforms and the immense popularity of online social networking. With this in mind, it may be ideal to raise awareness about these possible risks so that individuals can implement necessary safeguards, while highlighting that there could also be benefits. Being aware of the risks is an essential first step, before then recognizing that use of these popular platforms could contribute to some benefits like finding meaningful interactions with others, engaging with peer support networks, and accessing information and services.

To capitalize on the widespread use of social media and to achieve the promise that these platforms may hold for supporting the delivery of targeted mental health interventions, there is need for continued research to better understand how individuals living with mental illness use social media. Such efforts could inform safety measures and also encourage use of social media in ways that maximize potential benefits while minimizing risk of harm. It will be important to recognize how gender and race contribute to differences in use of social media for seeking mental health information or accessing interventions, as well as differences in how social media might impact mental wellbeing. For example, a national survey of 14- to 22-year olds in the USA found that female respondents were more likely to search online for information about depression or anxiety and to try to connect with other people online who share similar mental health concerns when compared with male respondents (Rideout and Fox 2018 ). In the same survey, there did not appear to be any differences between racial or ethnic groups in social media use for seeking mental health information (Rideout and Fox 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males (Booker et al. 2018 ), highlighting the need to explore unique experiences between gender groups to inform tailored programs and services. Research shows that lesbian, gay, bisexual, or transgender individuals frequently use social media for searching for health information and may be more likely compared with heterosexual individuals to share their own personal health experiences with others online (Rideout and Fox 2018 ). Less is known about use of social media for seeking support for mental health concerns among gender minorities, though this is an important area for further investigation as these individuals are more likely to experience mental health problems and online victimization when compared with heterosexual individuals (Mereish et al. 2019 ).

Similarly, efforts are needed to explore the relationship between social media use and mental health among ethnic and racial minorities. A recent study found that exposure to traumatic online content on social media showing violence or hateful posts directed at racial minorities contributed to increases in psychological distress, PTSD symptoms, and depression among African American and Latinx adolescents in the USA (Tynes et al. 2019 ). These concerns are contrasted by growing interest in the potential for new technologies including social media to expand the reach of services to underrepresented minority groups (Schueller et al. 2019 ). Therefore, greater attention is needed to understanding the perspectives of ethnic and racial minorities to inform effective and safe use of social media for mental health promotion efforts.

Research has found that individuals living with mental illness have expressed interest in accessing mental health services through social media platforms. A survey of social media users with mental illness found that most respondents were interested in accessing programs for mental health on social media targeting symptom management, health promotion, and support for communicating with health care providers and interacting with the health system (Naslund et al. 2017 ). Importantly, individuals with serious mental illness have also emphasized that any mental health intervention on social media would need to be moderated by someone with adequate training and credentials, would need to have ground rules and ways to promote safety and minimize risks, and importantly, would need to be free and easy to access.

An important strength with this commentary is that it combines a range of studies broadly covering the topic of social media and mental health. We have provided a summary of recent evidence in a rapidly advancing field with the goal of presenting unique ways that social media could offer benefits for individuals with mental illness, while also acknowledging the potentially serious risks and the need for further investigation. There are also several limitations with this commentary that warrant consideration. Importantly, as we aimed to address this broad objective, we did not conduct a systematic review of the literature. Therefore, the studies reported here are not exhaustive, and there may be additional relevant studies that were not included. Additionally, we only summarized published studies, and as a result, any reports from the private sector or websites from different organizations using social media or other apps containing social media–like features would have been omitted. Although, it is difficult to rigorously summarize work from the private sector, sometimes referred to as “gray literature,” because many of these projects are unpublished and are likely selective in their reporting of findings given the target audience may be shareholders or consumers.

Another notable limitation is that we did not assess risk of bias in the studies summarized in this commentary. We found many studies that highlighted risks associated with social media use for individuals living with mental illness; however, few studies of programs or interventions reported negative findings, suggesting the possibility that negative findings may go unpublished. This concern highlights the need for a future more rigorous review of the literature with careful consideration of bias and an accompanying quality assessment. Most of the studies that we described were from the USA, as well as from other higher income settings such as Australia or the UK. Despite the global reach of social media platforms, there is a dearth of research on the impact of these platforms on the mental health of individuals in diverse settings, as well as the ways in which social media could support mental health services in lower income countries where there is virtually no access to mental health providers. Future research is necessary to explore the opportunities and risks for social media to support mental health promotion in low-income and middle-income countries, especially as these countries face a disproportionate share of the global burden of mental disorders, yet account for the majority of social media users worldwide (Naslund et al. 2019 ).

Future Directions for Social Media and Mental Health

As we consider future research directions, the near ubiquitous social media use also yields new opportunities to study the onset and manifestation of mental health symptoms and illness severity earlier than traditional clinical assessments. There is an emerging field of research referred to as “digital phenotyping” aimed at capturing how individuals interact with their digital devices, including social media platforms, in order to study patterns of illness and identify optimal time points for intervention (Jain et al. 2015 ; Onnela and Rauch 2016 ). Given that most people access social media via mobile devices, digital phenotyping and social media are closely related (Torous et al. 2019 ). To date, the emergence of machine learning, a powerful computational method involving statistical and mathematical algorithms (Shatte et al. 2019 ), has made it possible to study large quantities of data captured from popular social media platforms such as Twitter or Instagram to illuminate various features of mental health (Manikonda and De Choudhury 2017 ; Reece et al. 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression (De Choudhury et al. 2013 ) as well as detecting users’ mood and affective states (De Choudhury et al. 2012 ), while photos posted to Instagram can yield insights for predicting depression (Reece and Danforth 2017 ). The intersection of social media and digital phenotyping will likely add new levels of context to social media use in the near future.

Several studies have also demonstrated that when compared with a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns (Birnbaum et al. 2017a ), including more frequent discussion of tobacco use (Hswen et al. 2017 ), symptoms of depression and anxiety (Hswen et al. 2018b ), and suicide (Hswen et al. 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala et al. 2017 ). Each of these examples offers early insights into the potential to leverage widely available online data for better understanding the onset and course of mental illness. It is possible that social media data could be used to supplement additional digital data, such as continuous monitoring using smartphone apps or smart watches, to generate a more comprehensive “digital phenotype” to predict relapse and identify high-risk health behaviors among individuals living with mental illness (Torous et al. 2019 ).

With research increasingly showing the valuable insights that social media data can yield about mental health states, greater attention to the ethical concerns with using individual data in this way is necessary (Chancellor et al. 2019 ). For instance, data is typically captured from social media platforms without the consent or awareness of users (Bidargaddi et al. 2017 ), which is especially crucial when the data relates to a socially stigmatizing health condition such as mental illness (Guntuku et al. 2017 ). Precautions are needed to ensure that data is not made identifiable in ways that were not originally intended by the user who posted the content as this could place an individual at risk of harm or divulge sensitive health information (Webb et al. 2017 ; Williams et al. 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, and the target individuals with mental illness throughout the collection of data, development of predictive algorithms, and interpretation of findings (Chancellor et al. 2019 ).

In recognizing that many individuals living with mental illness use social media to search for information about their mental health, it is possible that they may also want to ask their clinicians about what they find online to check if the information is reliable and trustworthy. Alternatively, many individuals may feel embarrassed or reluctant to talk to their clinicians about using social media to find mental health information out of concerns of being judged or dismissed. Therefore, mental health clinicians may be ideally positioned to talk with their patients about using social media and offer recommendations to promote safe use of these sites while also respecting their patients’ autonomy and personal motivations for using these popular platforms. Given the gap in clinical knowledge about the impact of social media on mental health, clinicians should be aware of the many potential risks so that they can inform their patients while remaining open to the possibility that their patients may also experience benefits through use of these platforms. As awareness of these risks grows, it may be possible that new protections will be put in place by industry or through new policies that will make the social media environment safer. It is hard to estimate a number needed to treat or harm today given the nascent state of research, which means the patient and clinician need to weigh the choice on a personal level. Thus, offering education and information is an important first step in that process. As patients increasingly show interest in accessing mental health information or services through social media, it will be necessary for health systems to recognize social media as a potential avenue for reaching or offering support to patients. This aligns with growing emphasis on the need for greater integration of digital psychiatry, including apps, smartphones, or wearable devices, into patient care and clinical services through institution-wide initiatives and training clinical providers (Hilty et al. 2019 ). Within a learning healthcare environment where research and care are tightly intertwined and feedback between both is rapid, the integration of digital technologies into services may create new opportunities for advancing use of social media for mental health.

As highlighted in this commentary, social media has become an important part of the lives of many individuals living with mental disorders. Many of these individuals use social media to share their lived experiences with mental illness, to seek support from others, and to search for information about treatment recommendations, accessing mental health services and coping with symptoms (Bucci et al. 2019 ; Highton-Williamson et al. 2015 ; Naslund et al. 2016b ). As the field of digital mental health advances, the wide reach, ease of access, and popularity of social media platforms could be used to allow individuals in need of mental health services or facing challenges of mental illness to access evidence-based treatment and support. To achieve this end and to explore whether social media platforms can advance efforts to close the gap in available mental health services in the USA and globally, it will be essential for researchers to work closely with clinicians and with those affected by mental illness to ensure that possible benefits of using social media are carefully weighed against anticipated risks.

Abdel-Baki, A., Lal, S., Charron, D.-C., Stip, E., & Kara, N. (2017). Understanding access and use of technology among youth with first-episode psychosis to inform the development of technology-enabled therapeutic interventions. Early Intervention in Psychiatry, 11 (1), 72–76.

PubMed   Google Scholar  

Ahmed, Y. A., Ahmad, M. N., Ahmad, N., & Zakaria, N. H. (2019). Social media for knowledge-sharing: a systematic literature review. Telematics and Informatics, 37 , 72–112.

Google Scholar  

Alhajji, M., Bass, S., & Dai, T. (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: data from the 2015 youth risk behavior survey. Global Pediatric Health, 6 , 2333794X19868887.

PubMed   PubMed Central   Google Scholar  

Alvarez-Jimenez, M., Bendall, S., Lederman, R., Wadley, G., Chinnery, G., Vargas, S., Larkin, M., Killackey, E., McGorry, P., & Gleeson, J. F. (2013). On the HORYZON: moderated online social therapy for long-term recovery in first episode psychosis. Schizophrenia Research, 143 (1), 143–149.

Alvarez-Jimenez, M., Gleeson, J., Bendall, S., Penn, D., Yung, A., Ryan, R., et al. (2018). Enhancing social functioning in young people at ultra high risk (UHR) for psychosis: a pilot study of a novel strengths and mindfulness-based online social therapy. Schizophrenia Research, 202 , 369–377.

Alvarez-Jimenez, M., Bendall, S., Koval, P., Rice, S., Cagliarini, D., Valentine, L., et al. (2019). HORYZONS trial: protocol for a randomised controlled trial of a moderated online social therapy to maintain treatment effects from first-episode psychosis services. BMJ Open, 9 (2), e024104.

Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychology of Addictive Behaviors, 30 (2), 252.

Aschbrenner, K. A., Naslund, J. A., & Bartels, S. J. (2016a). A mixed methods study of peer-to-peer support in a group-based lifestyle intervention for adults with serious mental illness. Psychiatric Rehabilitation Journal, 39 (4), 328–334.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Kinney, E., & Bartels, S. J. (2016b). A pilot study of a peer-group lifestyle intervention enhanced with mHealth technology and social media for adults with serious mental illness. The Journal of Nervous and Mental Disease, 204 (6), 483–486.

Aschbrenner, K. A., Naslund, J. A., Shevenell, M., Mueser, K. T., & Bartels, S. J. (2016c). Feasibility of behavioral weight loss treatment enhanced with peer support and mobile health technology for individuals with serious mental illness. Psychiatric Quarterly, 87 (3), 401–415.

Aschbrenner, K. A., Naslund, J. A., Gorin, A. A., Mueser, K. T., Scherer, E. A., Viron, M., et al. (2018a). Peer support and mobile health technology targeting obesity-related cardiovascular risk in young adults with serious mental illness: protocol for a randomized controlled trial. Contemporary Clinical Trials, 74 , 97–106.

Aschbrenner, K. A., Naslund, J. A., Grinley, T., Bienvenida, J. C. M., Bartels, S. J., & Brunette, M. (2018b). A survey of online and mobile technology use at peer support agencies. Psychiatric Quarterly , 1–10.

Aschbrenner, K. A., Naslund, J. A., Tomlinson, E. F., Kinney, A., Pratt, S. I., & Brunette, M. F. (2019). Adolescents’ use of digital technologies and preferences for mobile health coaching in mental health settings. Frontiers in Public Health. 7 , 178.

Badcock, J. C., Shah, S., Mackinnon, A., Stain, H. J., Galletly, C., Jablensky, A., & Morgan, V. A. (2015). Loneliness in psychotic disorders and its association with cognitive function and symptom profile. Schizophrenia Research, 169 (1–3), 268–273.

Batterham, P. J., & Calear, A. J. (2017). Preferences for internet-based mental health interventions in an adult online sample: findings from ann online community survey. JMIR Mental Health, 4 (2), e26.

Bauer, R., Bauer, M., Spiessl, H., & Kagerbauer, T. (2013). Cyber-support: an analysis of online self-help forums (online self-help forums in bipolar disorder). Nordic Journal of Psychiatry, 67 (3), 185–190.

Berger, M., Wagner, T. H., & Baker, L. C. (2005). Internet use and stigmatized illness. Social Science & Medicine, 61 (8), 1821–1827.

Berry, N., Lobban, F., Belousov, M., Emsley, R., Nenadic, G., & Bucci, S. (2017). # WhyWeTweetMH: understanding why people use Twitter to discuss mental health problems. Journal of Medical Internet Research, 19 (4), e107.

Berry, N., Emsley, R., Lobban, F., & Bucci, S. (2018). Social media and its relationship with mood, self-esteem and paranoia in psychosis. Acta Psychiatrica Scandinavica, 138 , 558–570.

Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: a systematic narrative review. Children and Youth Services Review, 41 , 27–36.

Biagianti, B., Quraishi, S. H., & Schlosser, D. A. (2018). Potential benefits of incorporating peer-to-peer interactions into digital interventions for psychotic disorders: a systematic review. Psychiatric Services, 69 (4), 377–388.

Bidargaddi, N., Musiat, P., Makinen, V.-P., Ermes, M., Schrader, G., & Licinio, J. (2017). Digital footprints: facilitating large-scale environmental psychiatric research in naturalistic settings through data from everyday technologies. Molecular Psychiatry, 22 (2), 164.

Birnbaum, M. L., Ernala, S. K., Rizvi, A. F., De Choudhury, M., & Kane, J. M. (2017a). A collaborative approach to identifying social media markers of schizophrenia by employing machine learning and clinical appraisals. Journal of Medical Internet Research, 19 (8), e289.

Birnbaum, M. L., Rizvi, A. F., Correll, C. U., Kane, J. M., & Confino, J. (2017b). Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders. Early Intervention in Psychiatry, 11 (4), 290–295.

Booker, C. L., Kelly, Y. J., & Sacker, A. (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health, 18 (1), 321.

Brunette, M., Achtyes, E., Pratt, S., Stilwell, K., Opperman, M., Guarino, S., & Kay-Lambkin, F. (2019). Use of smartphones, computers and social media among people with SMI: opportunity for intervention. Community Mental Health Journal , 1–6.

Brusilovskiy, E., Townley, G., Snethen, G., & Salzer, M. S. (2016). Social media use, community participation and psychological well-being among individuals with serious mental illnesses. Computers in Human Behavior, 65 , 232–240.

Bucci, S., Schwannauer, M., & Berry, N. (2019). The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice, 92 (2), 277–297.

Chancellor, S., Birnbaum, M. L., Caine, E. D., Silenzio, V. M., & De Choudhury, M. (2019). A taxonomy of ethical tensions in inferring mental health states from social media. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 79–88.

Chang, H. J. (2009). Online supportive interactions: using a network approach to examine communication patterns within a psychosis social support group in Taiwan. Journal of the American Society for Information Science and Technology, 60 (7), 1504–1517.

Davidson, L., Chinman, M., Sells, D., & Rowe, M. (2006). Peer support among adults with serious mental illness: a report from the field. Schizophrenia Bulletin, 32 (3), 443–450.

De Choudhury, M., Gamon, M., & Counts, S. (2012). Happy, nervous or surprised? classification of human affective states in social media. Paper presented at the sixth international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Meedia, 435–438.

De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. Paper presented at the seventh international Association for Advancement of Artificial Intelligence (AAAI) Conference on Weblogs and Social Media, 128–137.

Docherty, N. M., Hawkins, K. A., Hoffman, R. E., Quinlan, D. M., Rakfeldt, J., & Sledge, W. H. (1996). Working memory, attention, and communication disturbances in schizophrenia. Journal of Abnormal Psychology, 105 (2), 212–219.

Ernala, S. K., Rizvi, A. F., Birnbaum, M. L., Kane, J. M., & De Choudhury, M. (2017). Linguistic markers indicating therapeutic outcomes of social media disclosures of schizophrenia. Proceedings of the ACM on Human-Computer Interaction, 1 (1), 43.

Feinstein, B. A., Hershenberg, R., Bhatia, V., Latack, J. A., Meuwly, N., & Davila, J. (2013). Negative social comparison on Facebook and depressive symptoms: rumination as a mechanism. Psychology of Popular Media Culture, 2 (3), 161.

Firth, J., Cotter, J., Torous, J., Bucci, S., Firth, J. A., & Yung, A. R. (2015). Mobile phone ownership and endorsement of “mHealth” among people with psychosis: a meta-analysis of cross-sectional studies. Schizophrenia Bulletin, 42 (2), 448–455.

Firth, J., Rosenbaum, S., Stubbs, B., Gorczynski, P., Yung, A. R., & Vancampfort, D. (2016). Motivating factors and barriers towards exercise in severe mental illness: a systematic review and meta-analysis. Psychological Medicine, 46 (14), 2869–2881.

Gay, K., Torous, J., Joseph, A., Pandya, A., & Duckworth, K. (2016). Digital technology use among individuals with schizophrenia: results of an online survey. JMIR Mental Health, 3 (2), e15.

Giacco, D., Palumbo, C., Strappelli, N., Catapano, F., & Priebe, S. (2016). Social contacts and loneliness in people with psychotic and mood disorders. Comprehensive Psychiatry, 66 , 59–66.

Gleeson, J., Lederman, R., Herrman, H., Koval, P., Eleftheriadis, D., Bendall, S., Cotton, S. M., & Alvarez-Jimenez, M. (2017). Moderated online social therapy for carers of young people recovering from first-episode psychosis: study protocol for a randomised controlled trial. Trials, 18 (1), 27.

Glick, G., Druss, B., Pina, J., Lally, C., & Conde, M. (2016). Use of mobile technology in a community mental health setting. Journal of Telemedicine and Telecare, 22 (7), 430–435.

Goodman, L. A., Thompson, K. M., Weinfurt, K., Corl, S., Acker, P., Mueser, K. T., & Rosenberg, S. D. (1999). Reliability of reports of violent victimization and posttraumatic stress disorder among men and women with serious mental illness. Journal of Traumatic Stress: Official Publication of the International Society for Traumatic Stress Studies, 12 (4), 587–599.

Gowen, K., Deschaine, M., Gruttadara, D., & Markey, D. (2012). Young adults with mental health conditions and social networking websites: seeking tools to build community. Psychiatric Rehabilitation Journal, 35 (3), 245–250.

Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18 , 43–49.

Haker, H., Lauber, C., & Rössler, W. (2005). Internet forums: a self-help approach for individuals with schizophrenia? Acta Psychiatrica Scandinavica, 112 (6), 474–477.

Hamm, M. P., Newton, A. S., Chisholm, A., Shulhan, J., Milne, A., Sundar, P., Ennis, H., Scott, S. D., & Hartling, L. (2015). Prevalence and effect of cyberbullying on children and young people: a scoping review of social media studies. JAMA Pediatrics, 169 (8), 770–777.

Hansen, C. F., Torgalsbøen, A.-K., Melle, I., & Bell, M. D. (2009). Passive/apathetic social withdrawal and active social avoidance in schizophrenia: difference in underlying psychological processes. The Journal of Nervous and Mental Disease, 197 (4), 274–277.

Highton-Williamson, E., Priebe, S., & Giacco, D. (2015). Online social networking in people with psychosis: a systematic review. International Journal of Social Psychiatry, 61 (1), 92–101.

Hilty, D. M., Chan, S., Torous, J., Luo, J., & Boland, R. J. (2019). Mobile health, smartphone/device, and apps for psychiatry and medicine: competencies, training, and faculty development issues. Psychiatric Clinics, 42 (3), 513–534.

Hswen, Y., Naslund, J. A., Chandrashekar, P., Siegel, R., Brownstein, J. S., & Hawkins, J. B. (2017). Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia. Psychiatry Research, 257 , 479–484.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018a). Monitoring online discussions about suicide among Twitter users with schizophrenia: exploratory study. JMIR Mental Health, 5 (4), e11483.

Hswen, Y., Naslund, J. A., Brownstein, J. S., & Hawkins, J. B. (2018b). Online communication about depression and anxiety among twitter users with schizophrenia: preliminary findings to inform a digital phenotype using social media. Psychiatric Quarterly, 89 (3), 569–580.

Indian, M., & Grieve, R. (2014). When Facebook is easier than face-to-face: social support derived from Facebook in socially anxious individuals. Personality and Individual Differences, 59 , 102–106.

Jain, S. H., Powers, B. W., Hawkins, J. B., & Brownstein, J. S. (2015). The digital phenotype. Nature Biotechnology, 33 (5), 462–463.

Kiesler, S., Siegel, J., & McGuire, T. W. (1984). Social psychological aspects of computer-mediated communication. American Psychologist, 39 , 1123–1134.

Kross, E., Verduyn, P., Demiralp, E., Park, J., Lee, D. S., Lin, N., Shablack, H., Jonides, J., & Ybarra, O. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS One, 8 (8), e69841.

Lal, S., Nguyen, V., & Theriault, J. (2018). Seeking mental health information and support online: experiences and perspectives of young people receiving treatment for first-episode psychosis. Early Intervention in Psychiatry, 12 (3), 324–330.

Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among US young adults. Depression and Anxiety, 33 (4), 323–331.

Machmutow, K., Perren, S., Sticca, F., & Alsaker, F. D. (2012). Peer victimisation and depressive symptoms: can specific coping strategies buffer the negative impact of cybervictimisation? Emotional and Behavioural Difficulties, 17 (3–4), 403–420.

Manikonda, L., & De Choudhury, M. (2017). Modeling and understanding visual attributes of mental health disclosures in social media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 170–181.

Mead, S., Hilton, D., & Curtis, L. (2001). Peer support: a theoretical perspective. Psychiatric Rehabilitation Journal, 25 (2), 134–141.

Mereish, E. H., Sheskier, M., Hawthorne, D. J., & Goldbach, J. T. (2019). Sexual orientation disparities in mental health and substance use among Black American young people in the USA: effects of cyber and bias-based victimisation. Culture, Health & Sexuality, 21 (9), 985–998.

Miller, B. J., Stewart, A., Schrimsher, J., Peeples, D., & Buckley, P. F. (2015). How connected are people with schizophrenia? Cell phone, computer, email, and social media use. Psychiatry Research, 225 (3), 458–463.

Mittal, V. A., Tessner, K. D., & Walker, E. F. (2007). Elevated social Internet use and schizotypal personality disorder in adolescents. Schizophrenia Research, 94 (1–3), 50–57.

Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15 (4), e85.

Naslund, J. A., & Aschbrenner, K. A. (2019). Risks to privacy with use of social media: understanding the views of social media users with serious mental illness. Psychiatric Services, 70 (7), 561–568.

Naslund, J. A., Grande, S. W., Aschbrenner, K. A., & Elwyn, G. (2014). Naturally occurring peer support through social media: the experiences of individuals with severe mental illness using YouTube. PLoS One, 9 (10), e110171.

Naslund, J. A., Aschbrenner, K. A., & Bartels, S. J. (2016). How people living with serious mental illness use smartphones, mobile apps, and social media. Psychiatric Rehabilitation Journal, 39 (4), 364–367.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016a). Feasibility and acceptability of Facebook for health promotion among people with serious mental illness. Digital Health, 2 , 2055207616654822.

PubMed Central   Google Scholar  

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., & Bartels, S. J. (2016b). The future of mental health care: peer-to-peer support and social media. Epidemiology and Psychiatric Sciences, 25 (2), 113–122.

Naslund, J. A., Aschbrenner, K. A., McHugo, G. J., Unützer, J., Marsch, L. A., & Bartels, S. J. (2019). Exploring opportunities to support mental health care using social media: A survey of social media users with mental illness. Early Intervention in Psychiatry, 13 (3), 405–413.

Naslund, J. A., Aschbrenner, K. A., Marsch, L. A., McHugo, G. J., & Bartels, S. J. (2018). Facebook for supporting a lifestyle intervention for people with major depressive disorder, bipolar disorder, and schizophrenia: an exploratory study. Psychiatric Quarterly, 89 (1), 81–94.

Naslund, J. A., Gonsalves, P. P., Gruebner, O., Pendse, S. R., Smith, S. L., Sharma, A., & Raviola, G. (2019). Digital innovations for global mental health: opportunities for data science, task sharing, and early intervention. Current Treatment Options in Psychiatry , 1–15.

Onnela, J.-P., & Rauch, S. L. (2016). Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health. Neuropsychopharmacology, 41 (7), 1691–1696.

Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3 (2), 173–182.

Patel, V., Saxena, S., Lund, C., Thornicroft, G., Baingana, F., Bolton, P., et al. (2018). The Lancet Commission on global mental health and sustainable development. The Lancet, 392 (10157), 1553–1598.

Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Computers in Human Behavior, 69 , 1–9.

Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6 (1), 15.

Reece, A. G., Reagan, A. J., Lix, K. L., Dodds, P. S., Danforth, C. M., & Langer, E. J. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific Reports, 7 (1), 13006.

Rideout, V., & Fox, S. (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Retrieved from San Francisco, CA: https://www.hopelab.org/reports/pdf/a-national-survey-by-hopelab-and-well-being-trust-2018.pdf . Accessed 10 Jan 2020.

Saha, K., Torous, J., Ernala, S. K., Rizuto, C., Stafford, A., & De Choudhury, M. (2019). A computational study of mental health awareness campaigns on social media. Translational behavioral medicine, 9 (6), 1197–1207.

Schlosser, D. A., Campellone, T., Kim, D., Truong, B., Vergani, S., Ward, C., & Vinogradov, S. (2016). Feasibility of PRIME: a cognitive neuroscience-informed mobile app intervention to enhance motivated behavior and improve quality of life in recent onset schizophrenia. JMIR Research Protocols, 5 (2).

Schlosser, D. A., Campellone, T. R., Truong, B., Etter, K., Vergani, S., Komaiko, K., & Vinogradov, S. (2018). Efficacy of PRIME, a mobile app intervention designed to improve motivation in young people with schizophrenia. Schizophrenia Bulletin, 44 (5), 1010–1020.

Schrank, B., Sibitz, I., Unger, A., & Amering, M. (2010). How patients with schizophrenia use the internet: qualitative study. Journal of Medical Internet Research, 12 (5), e70.

Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019). Use of digital mental health for marginalized and underserved populations. Current Treatment Options in Psychiatry, 6 (3), 243–255.

Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine, 49 (9), 1426–1448.

Spinzy, Y., Nitzan, U., Becker, G., Bloch, Y., & Fennig, S. (2012). Does the Internet offer social opportunities for individuals with schizophrenia? A cross-sectional pilot study. Psychiatry Research, 198 (2), 319–320.

Stiglic, N., & Viner, R. M. (2019). Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open, 9 (1), e023191.

Sumner, S. A., Galik, S., Mathieu, J., Ward, M., Kiley, T., Bartholow, B., et al. (2019). Temporal and geographic patterns of social media posts about an emerging suicide game. Journal of Adolescent Health, 65 (1), 94–100.

Torous, J., & Keshavan, M. (2016). The role of social media in schizophrenia: evaluating risks, benefits, and potential. Current Opinion in Psychiatry, 29 (3), 190–195.

Torous, J., Chan, S. R., Tan, S. Y.-M., Behrens, J., Mathew, I., Conrad, E. J., et al. (2014a). Patient smartphone ownership and interest in mobile apps to monitor symptoms of mental health conditions: a survey in four geographically distinct psychiatric clinics. JMIR Mental Health, 1 (1), e5.

Torous, J., Friedman, R., & Keshavan, M. (2014b). Smartphone ownership and interest in mobile applications to monitor symptoms of mental health conditions. JMIR mHealth and uHealth, 2 (1), e2.

Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., et al. (2019). Creating a digital health smartphone app and digital phenotyping platform for mental health and diverse healthcare needs: an interdisciplinary and collaborative approach. Journal of Technology in Behavioral Science, 4 (2), 73–85.

Trefflich, F., Kalckreuth, S., Mergl, R., & Rummel-Kluge, C. (2015). Psychiatric patients' internet use corresponds to the internet use of the general public. Psychiatry Research, 226 , 136–141.

Twenge, J. M., & Campbell, W. K. (2018). Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Preventive Medicine Reports, 12 , 271–283.

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among US adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6 (1), 3–17.

Tynes, B. M., Willis, H. A., Stewart, A. M., & Hamilton, M. W. (2019). Race-related traumatic events online and mental health among adolescents of color. Journal of Adolescent Health, 65 (3), 371–377.

Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and anxiety in emerging adults. Journal of Affective Disorders, 207 , 163–166.

Vayreda, A., & Antaki, C. (2009). Social support and unsolicited advice in a bipolar disorder online forum. Qualitative Health Research, 19 (7), 931–942.

Ventola, C. L. (2014). Social media and health care professionals: benefits, risks, and best practices. Pharmacy and Therapeutics, 39 (7), 491–520.

We Are Social. (2020). Digital in 2020. Retrieved from https://wearesocial.com/global-digital-report-2019 . Accessed 10 Jan 2020.

Webb, H., Jirotka, M., Stahl, B. C., Housley, W., Edwards, A., Williams, M., ... & Burnap, P. (2017). The ethical challenges of publishing Twitter data for research dissemination . Paper presented at the proceedings of the 2017 ACM on Web Science Conference, 339–348.

Williams, M. L., Burnap, P., & Sloan, L. (2017). Towards an ethical framework for publishing twitter data in social research: taking into account users’ views, online context and algorithmic estimation. Sociology, 51 (6), 1149–1168.

Woods, H. C., & Scott, H. (2016). # Sleepyteens: social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 , 41–49.

Ybarra, M. L. (2004). Linkages between depressive symptomatology and internet harassment among young regular Internet users. Cyberpsychology & Behavior, 7 (2), 247–257.

Download references

Dr. Naslund is supported by a grant from the National Institute of Mental Health (U19MH113211). Dr. Aschbrenner is supported by a grant from the National Institute of Mental Health (1R01MH110965-01).

Author information

Authors and affiliations.

Department of Global Health and Social Medicine, Harvard Medical School, 641 Huntington Avenue, Boston, MA, 02115, USA

John A. Naslund

Digital Mental Health Research Consultant, Mumbai, India

Ameya Bondre

Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

John Torous

Department of Psychiatry, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

Kelly A. Aschbrenner

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to John A. Naslund .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Naslund, J.A., Bondre, A., Torous, J. et al. Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice. J. technol. behav. sci. 5 , 245–257 (2020). https://doi.org/10.1007/s41347-020-00134-x

Download citation

Received : 19 October 2019

Revised : 24 February 2020

Accepted : 17 March 2020

Published : 20 April 2020

Issue Date : September 2020

DOI : https://doi.org/10.1007/s41347-020-00134-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

Transforming the understanding and treatment of mental illnesses.

Información en español

Celebrating 75 Years! Learn More >>

Research funded by nimh, research conducted at nimh (intramural research program), priority research areas.

• Research Resources

The National Institute of Mental Health (NIMH), part of the National Institutes of Health (NIH), is the lead federal agency for research on mental disorders, supporting research that aims to transform the understanding and treatment of mental illnesses through basic and clinical research. Learn more about NIMH-funded research areas, policies, resources, initiatives, and research conducted by NIMH.

  Notify the NIMH Press Team about NIMH-funded research that has been submitted to a journal for publication, and we may be able to promote the findings.

NIMH Strategic Plan

The NIMH Strategic Plan for Research outlines the Institute's research goals and priorities over the next five years. Learn more about the Strategic Plan.

NIMH supports research at universities, medical centers, and other institutions via grants, contracts, and cooperative agreements. Learn more about NIMH research areas, policies, resources, and initiatives:

• Research Domain Criteria (RDoC)
• Policies and Procedures
• Inside NIMH - Funding and Research News

The Division of Intramural Research Programs (IRP) is the internal research division of the NIMH. Over 40 research groups conduct basic neuroscience research and clinical investigations of mental illnesses, brain function, and behavior at the NIH campus in Bethesda, Maryland. Learn more about research conducted at NIMH:

• Intramural Investigators
• Intramural Research Groups
• Fellowships and Training
• Office of the Scientific Director
• Collaborations and Partnerships
• Join a Study
• IRP News and Events

• NIMH Priority Research Areas
• Highlighted Research Initiatives

Resources for Researchers

• Psychosocial Research at NIMH: A Primer
• Genomics Research Guidance for Grant Applicants

NIMH Women Leading Mental Health Research

Diversity in the scientific workforce enhances excellence, creativity, and innovation. NIMH and the National Institutes of Health (NIH) are committed to increasing diversity in the scientific workforce. Learn more about early-career women scientists whose NIMH-funded research is playing a role in advancing our mission of transforming the understanding and treatment of mental illnesses.

ORIGINAL RESEARCH article

Mental health and well-being of university students: a bibliometric mapping of the literature.

• 1 Graduate School of Education, Nazarbayev University, Nur-Sultan, Kazakhstan
• 2 Nazarbayev University School of Medicine, Nur-Sultan, Kazakhstan
• 3 Psychological Counseling Center, Nazarbayev University, Nur-Sultan, Kazakhstan

The purpose of this study is to map the literature on mental health and well-being of university students using metadata extracted from 5,561 journal articles indexed in the Web of Science database for the period 1975–2020. More specifically, this study uses bibliometric procedures to describe and visually represent the available literature on mental health and well-being in university students in terms of the growth trajectory, productivity, social structure, intellectual structure, and conceptual structure of the field over 45 years. Key findings of the study are that research on mental health and well-being in university students: (a) has experienced a steady growth over the last decades, especially since 2010; (b) is disseminated in a wide range of journals, mainly in the fields of psychology, psychiatry, and education research; (c) is published by scholars with diverse geographical background, although more than half of the publications are produced in the United States; (d) lies on a fragmented research community composed by multiple research groups with little interactions between them; (e) is relatively interdisciplinary and emerges from the convergence of research conducted in the behavioral and biomedical sciences; (f) tends to emphasize pathogenic approaches to mental health (i.e., mental illness); and (g) has mainly addressed seven research topics over the last 45 years: positive mental health, mental disorders, substance abuse, counseling, stigma, stress, and mental health measurement. The findings are discussed, and the implications for the future development of the field are highlighted.

Introduction

The entrance to the university marks a period of transition for young people. Through this transition, students face new challenges, such as making independent decisions about their lives and studies, adjusting to the academic demands of an ill-structured learning environment, and interacting with a diverse range of new people. In addition, many students must, often for the first time, leave their homes and distance themselves from their support networks ( Cleary et al., 2011 ). These challenges can affect the mental health and well-being of higher education students. Indeed, there is evidence that a strain on mental health is placed on students once they start at the university, and although it decreases throughout their studies ( Macaskill, 2013 ; Mey and Yin, 2015 ), it does not return to pre-university levels ( Cooke et al., 2006 ; Bewick et al., 2010 ). Also, the probabilities of experiencing common psychological problems, such as depression, anxiety, and stress, increase throughout adolescence and reach a peak in early adulthood around age 25 ( Kessler et al., 2007 ) which makes university students a particularly vulnerable population.

The interest in mental health and well-being in university students has grown exponentially in the last decades. This is likely due to three interrelated challenges. First, although university students report levels of mental health similar to their non-university counterparts ( Blanco et al., 2008 ), recent studies suggest an increase and severity of mental problems and help-seeking behaviors in university students around the world in the last decade ( Wong et al., 2006 ; Hunt and Eisenberg, 2010 ; Verger et al., 2010 ; Auerbach et al., 2018 ; Lipson et al., 2019 ). Some researchers refer to these trends as an emerging “mental health crisis” in higher education ( Kadison and DiGeronimo, 2004 ; Evans et al., 2018 ). Second, psychological distress in early adulthood is associated with adverse short-term outcomes, such as poor college attendance, performance, engagement, and completion (e.g., King et al., 2006 ; Antaramian, 2015 ), and others in the long term, such as dysfunctional relationship ( Kerr and Capaldi, 2011 ), recurrent mental health problems, university dropout, lower rates of employment, and reduced personal income ( Fergusson et al., 2007 ). Third, there is a widespread agreement that higher education institutions offer unique opportunities to promote the mental health and well-being of young adults as they provide a single integrated setting that encompasses academic, professional, and social activities, along with health services and other support services ( Eisenberg et al., 2009 ; Hunt and Eisenberg, 2010 ). However, the majority of university students experiencing mental health problems and low levels of well-being are not receiving treatment ( Blanco et al., 2008 ; Eisenberg et al., 2011 ; Lipson et al., 2019 ) and, while universities continue to expand, there is a growing concern that the services available to provide support to students are not developing at an equivalent rate ( Davy et al., 2012 ).

In response to the increasing volume of research on the mental health and well-being of university students, there have been several attempts to synthesize the accumulating knowledge in the field and to provide an illustration of the theoretical core and structure of the field using traditional content analysis of the literature (e.g., Kessler et al., 2007 ; Gulliver et al., 2010 ; Hunt and Eisenberg, 2010 ; Sharp and Theiler, 2018 ). This study aims to extend the understanding of mental health in university students by providing a bird’s eye view of the research conducted in this field in recent decades using a bibliometric approach. Bibliometric overviews provide an objective and systematic approach to discover knowledge flows and patterns in the structure of a field ( Van Raan, 2014 ) reveal its scientific roots, identify emerging thematic areas and gaps in the literature ( Skute et al., 2019 ) and, ultimately, contribute to moving the field forward. Accordingly, this study employs several bibliometric indicators to explore the evolution of the field based on publication and citation trends, key actors and venues contributing to the advancement of research on mental health and well-being of university students, and the structure of the field in terms of patterns of scientific collaborations, disciplines underlying the foundations of the field, and recurrent research themes explored in the literature. This is important because, despite significant advances in the field, research on mental health and well-being remains a diverse and fragmented body of knowledge ( Pellmar and Eisenberg, 2000 ; Bailey, 2012 ; Wittchen et al., 2014a ). Indeed, mental health and well-being are nebulous concepts and their history and development are quite intricate, with a multitude of perspectives and contributions emerging from various disciplines and contexts (see section “Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview”). Therefore, mapping research on mental health and well-being in university students is essential to identify contributions and challenges to the development of the field, to help guide policy, research, and practice toward areas, domains, populations, and contexts that should be further explored, and to provide better care of students at higher education institutions ( Naveed et al., 2017 ).

Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview

This section provides an overview of the different perspectives adopted in the literature to conceptualize mental health, well-being, and other relevant constructs in order to identify the glossary of key terms that will be used in the search strategy to create a comprehensive corpus of documents on mental health and well-being in university students for this bibliometric review.

Perspectives on Mental Health and Mental Illness

There is no general agreement on the definition of mental health. For a long time, the term mental health has been used as a euphemism for mental illness ( Manwell et al., 2015 ). However, mental health and mental illness are regarded as distinct constructs nowadays and two main perspectives differentiating between mental health and illness are available in the literature. The continuum approach considers that mental health and mental illness are the two opposite poles of a continuum. Thus, there are various degrees of health and illness between these poles, with most of us falling somewhere in between. The categorical approach, on the other hand, represents mental health and illness as a dichotomy. People who manifest mental illness symptoms would belong to that category and labeled correspondingly, while those absent of these symptoms can be considered as mentally healthy ( Scheid and Brown, 2010 ).

Disciplinary Approaches to the Conceptualization of Mental Health/Illness

Conceptualizations of mental health/illness are largely dependent on the theoretical and paradigmatic foundations of the disciplines from which they emerge. In this context, the field has progressively evolved through the accumulation of knowledge generated in a diverse range of disciplines in the biomedical, behavioral, and social sciences. Biomedical disciplines are grounded in the medical paradigm focused on disease and (ab)normality and often emphasize dichotomous conceptions of mental health/illness ( Scheid and Brown, 2010 ). Research on mental health and well-being in this domain has been traditionally conducted from a psychiatric perspective, which aims to understand the dysfunctionality in the brain that leads to psychiatric symptoms and to also offer a pharmacological treatment to correct neuronal dysfunctions. Consequently, psychiatrists have historically considered mental health as a disease of the brain (e.g., depression), similar to any other physical disease, caused by genetic, biological, or neurological factors ( Schwartz and Corcoran, 2010 ). While the prevalence of psychiatric approaches to mental health is currently incontestable, the development of other biomedical disciplines has tremendously contributed to the progression of the field in recent decades. For example, Insel and Wang (2010) argue that insights gained from genetics and neuroscience contribute to the reconceptualization of “the disorders of the mind as disorders of the brain and thereby transform the practice of psychiatry.” (1979). In addition to that, other disciplines such as behavioral medicine have made important contributions to the field, although it has recently argued that mental health and behavioral medicine should be as two separate fields ( Dekker et al., 2017 ).

Within the behavioral sciences, the study of mental health focuses on the distinct psychological processes and mechanisms that prompt thoughts, feelings, and behaviors ( Peterson, 2010 ). Clinical psychology has the longest tradition in the psychological study of mental health and tends to focus on the assessment and treatment of mental illness and disorders that can alleviate psychological distress or promote positive states of being ( Haslam and Lusher, 2011 ). However, significant contributions to the field have also emerged from other branches of psychology less focused on psychopathology, including personality and social psychology, psychoanalysis, humanistic psychology, and cognitive psychology ( Peterson, 2010 ). Despite the diversity of theories, principles, and methodological approaches to understanding mental health within the behavioral sciences, these disciplines acknowledge that mental health have a biological basis and reside in the social context, and tend to prioritize continuum approaches to mental health ( Scheid and Brown, 2010 ).

Perspectives from the social sciences complement the biomedical and behavioral approaches by considering the influence of social and cultural environments in mental health/illness ( Horwitz, 2010 ). For example, sociologists are interested in how social circumstances (e.g., level of support available) affect levels of mental health/illness and how social structures shape the understanding and response to mental health issues [see Compton and Shim (2015) for an overview of the social determinants of mental health]. Similarly, medical anthropologists attend to the mental health beliefs and practices that form the cultural repertory within and across populations ( Foster, 1975 ). Beyond sociology and anthropology, social researchers in the fields of business and economics, family and ethnic studies, and educational research have also played a key role in advancing research on mental health in different directions.

The Importance of the Context in Mental Health

Certainly, most notions of mental health/illness in the literature derive from prevailing psychiatric and psychological traditions developed in Western countries ( Gopalkrishnan, 2018 ). However, cultural values and traditions do shape how mental health and mental illness are conceptualized across contexts ( Vaillant, 2012 ). In this regard, Eshun and Gurung (2009) pointed out that “culture influences how individuals manifest symptoms, communicate their symptoms, cope with psychological challenges, and their willingness to seek treatment.” (4). Fernando (2019) argued that issues related to the ‘mind’ developed and are often interpreted very differently in non-Western and Low- and Middle-Income Countries (LMICs). For example, cultures explain the manifestation of certain feelings and behaviors based on a range of motives including biological, psychological, social, religious, spiritual, supernatural, and cosmic. Failure to acknowledge alternative non-Western approaches to mental health and mental illness has resulted in imbalances of knowledge exchange and the permeation of dominating Western narratives into LMICs (i.e., so-called medical imperialism) ( Timimi, 2010 ; Summerfield, 2013 ). To address this issue, scholars have advocated for a greater willingness to embrace pluralism in the conceptualization of mental health and illness, which might help people to engage with particular forms of support that they deem to be appropriate for them, and to explore how knowledge and practices developed in LMICs can benefit those living in higher-income countries (i.e., knowledge “counterflow”) (see White et al., 2014 ).

Prioritizing Positive Mental Health: The Science of Well-Being

Despite the diversity of disciplinary and contextual approaches to mental health, current definitions of mental health have two things in common. First, mental health is considered from a biopsychosocial point of view that incorporates biological, psychological, and social factors. Second, mental health implies something beyond the absence of mental illness (e.g., Bhugra et al., 2013 ; Galderisi et al., 2015 ). An example is the definition by the World Health Organization which refers to mental health as “a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community” ( World Health Organization, 2004 ). This definition contributed to substantial progress in research and practice in the field as it expanded the notion of mental health beyond the absence of mental illness and integrated the presence of positive features ( Galderisi et al., 2015 ).

Research on positive mental health is relatively new but has grown rapidly in the last decades fueled by advocates of positive medicine and psychology, who have argued for a change of paradigm from medical and psychopathological-oriented models of mental health that focus on disorders and illness toward more strength-based approaches, which pay more attention to what is right about people and positive attributes and assets ( Kobau et al., 2011 ). In this regard, the term mental well-being has been progressively incorporated into the study of mental health to account for the positive aspects of mental health beyond the absence of negative factors. While there is not a universally accepted definition of well-being, two perspectives have dominated the discourses on well-being in the literature: subjective well-being (SBW) and psychological well-being (PWB). SWB is based on hedonic perspectives of pleasure and represents “people’s beliefs and feelings that they are living a desirable and rewarding life” ( Diener, 2012 ). SBW is strongly linked with the idea of happiness and is typically understood as the personal experience of high levels of positive affect, low levels of negative affect, and high satisfaction with one’s life ( Deci and Ryan, 2008 ). PWB is grounded in Aristotelian ideas about eudaimonia, i.e., self-realization, with the ultimate aim in life being to strive to realize one’s true potential ( Ryff and Singer, 2008 ). PWB has been broadly defined as a state of positive psychological functioning and encompasses six dimensions: purpose in life (i.e., the extent to which respondents felt their lives had meaning, purpose, and direction); autonomy (i.e., whether they viewed themselves as living in accord with their own convictions); personal growth (i.e., the extent to which they were making use of their personal talents and potential); environmental mastery (i.e., how well they were managing their life situations); positive relationships (i.e., the depth of connection they had in ties with significant others); and self-acceptance (i.e., the knowledge and acceptance they had of themselves, including awareness of personal limitations) ( Ryff, 1989 ).

Integrating Mental Health, Mental Illness, and Well-Being

The contribution of positive mental health frameworks to the advancement of the field has been undeniable. However, definitions that overemphasize positive emotions and productive functioning as key indicators of mental health have been recently challenged because of the potential they have to discriminate against individuals and groups that, for example, might not be able to work productively or function within the environment because of individual physical characteristics or contextual constraints ( Galderisi et al., 2015 ). To address these issues, Keyes has successfully integrated the notions of mental illness, mental health, well-being, and other related terms in the literature into a conceptual framework that allows for a more comprehensive understanding of mental health ( Keyes, 2005 , 2007 ; Keyes and Michalec, 2010 ). The model argues that neither pathogenic approaches focusing on the negative (e.g., mental illness) nor salutogenic approaches focusing on the positive (e.g., well-being) can alone accurately describe the mental health of a person ( Keyes and Michalec, 2010 ). Instead, the model proposes that mental illness and well-being represent two correlated but differentiated latent continua in defining mental health. More specifically, mental illness and well-being lie on two separate spectra, the first going from absent to present mental illness and the second running from low to high well-being ( Slade, 2010 ). The absence of mental illness, therefore, does not necessarily imply high levels of well-being. Correspondingly, low levels of well-being do not always indicate the presence of mental illness. Further, in this model, mental health is defined as not only the absence of mental illness, not the mere presence of high well-being. Complete mental health (i.e., flourishing) is a result of experiencing low mental illness and high levels of well-being. Incomplete mental health (i.e., languishing), on the other hand, refers to the absence of mental illness symptoms and low reported levels of well-being. Two other conditions are possible within this framework. Incomplete mental illness (i.e., struggling) refers to high levels of well-being accompanied by high mental illness symptoms. Lastly, complete mental illness (i.e., floundering) accounts for low levels of well-being and high mental illness symptoms ( Keyes and Lopez, 2002 ).

The Present Study

In light of the complexity of the constructs of mental health and well-being and the multiple theoretical, disciplinary, and contextual approaches to their conceptualization, this study seeks to map out the terrain of international research and scholarship on mental health and university students for the period 1975–2020. More specifically, this study aims to provide new insights into the development and current state of mental health research in university students by mapping and visually representing the literature on mental health and well-being of university students over the last 45 years in terms of the growth trajectory, productivity, and social, intellectual, and conceptual structure of the field. First, the study describes the development of research mental health and well-being in university students examining the trends in publication and citation data between 1975 and 2020 (i.e., growth trajectory). Second, the study identifies the core journals and the research areas contributing most to the development of the field, as well as the key authors and countries leading the generation and dissemination of research on mental health and well-being in university populations (i.e., productivity). Third, the study outlines the networks of scientific collaboration between authors, and countries (i.e., social structure). Fourth, the scientific disciplines underlying the intellectual foundations of research on mental health and well-being in university settings (i.e., intellectual structure) are uncovered. Fifth, the study elucidates the topical foci (i.e., conceptual structure) of the research on the mental health and well-being of university students over the last 45 years.

Materials and Methods

A bibliometric approach was used in this study to map the literature on mental health and well-being in university students over the last 45 years using metadata extracted from four indexes of the Web of Science (WoS): The Science Citation Index-Expanded (SCI-Expanded); the Social Sciences Citation Index (SSCI); the Arts & Humanities Citation Index (A&HCI); and the Emerging Sources Citation Index (ESCI). Several reasons justified the selection of the WoS database in this study. First, the WoS remains as the standard and most widely used for bibliometric analysis ( Meho and Yang, 2007 ). Second, the WoS is a multidisciplinary database and includes publications on mental health and well-being emerging from distinctive research areas and disciplines published in more than 20,000 journals ( McVeigh, 2009 ). Using specialized databases such as PubMed would introduce biases into the search strategy favoring biomedical research disciplines. Still, it is important to note that interdisciplinary databases such as WoS and Scopus discriminate against publications in the Social Sciences and Humanities and publications in languages other than the English language ( Mongeon and Paul-Hus, 2016 ), so the picture provided by WoS is still imperfect. Third, while other databases might provide wider coverage, WoS includes publication and citation information from 1900. For example, Scopus has complete citation information only from 1996 ( Li et al., 2010 ). Moreover, Google Scholar provides results of inconsistent accuracy in terms of citations, and citation analyses in PubMed are not available ( Falagas et al., 2008 ). Fourth, WoS has demonstrated better accuracy in its journal classification system compared to Scopus database ( Wang and Waltman, 2016 ).

The methodological approach used in this study is presented in Figure 1 and further elaborated in the following paragraphs.

Figure 1. Methodological framework.

Search Strategy

To create a comprehensive corpus of documents on the mental health and well-being of university students, three parallel searches were performed, which accounted for the multiple approaches and perspectives that have been used in the field, as identified in the Section “Conceptualization of Mental Health, Mental Illness, and Well-Being: An Overview.” All the searches were conducted in the last week of January 2020. The first search aimed at capturing research on mental health broadly and included one single keyword in the topic field: [“mental health”]. The second search was implemented to capture research focusing on pathogenic approaches to mental health. Key terms used in the literature to refer to the negative side of mental health, as well as the most frequent mental health problems experienced by university students, were introduced in this search in the title field: [“mental illness,” “mental disorder ∗ ,” “mental distress,” “psychological distress,” “psychopathology,” “depression,” “anxiety,” “stress,” “suicide,” “eating disorder ∗ ,” “substance use”]. In the third search, keywords reflecting salutogenic approaches to mental health were input. These included terms related to mental health from a positive mental health perspective (i.e., well-being). These key terms were added in the title field and included the following: [“well-being,” “wellbeing,” “wellness,” “life satisfaction,” “happiness,” “positive affect,” “purpose in life,” “personal growth,” “self-determination”].

To retrieve research relevant only to higher education students, another set of keywords was imputed in all three searches in the title field. These included: [“university,” “college,” “higher education,” “tertiary education,” “post-secondary education,” “postsecondary education,” “undergrad ∗ student,” “grad ∗ student,” “master’s student,” “doctoral student,” “Ph.D. student”]. The Boolean operator OR was used between keywords in all the three searches to secure a higher number of relevant hits. Also, asterisks were used as wildcards to account for multiple variations in several keywords (e.g., disorder and disorder-s). All searches were limited to journal articles published between 1975 and 2020 (both inclusive). No restrictions on language were implemented in the search.

The search strategy retrieved a total of 6,356 hits ( n search 1 = 2782; n search 2 = 2814, n search 3 = 760). After the removal of duplicates, 5,561 research articles were finally selected and retained for the study. For each of the documents obtained in the search, the authors extracted metadata about the title of the paper, the year of publication, the journal, the number of citations, and the authors’ name, organization, and country. Also, the title, the abstract, the author’s keywords, and cited references were retrieved.

Data Analysis Procedures

The corpus of the literature was then analyzed using descriptive and bibliometric approaches to provide an overall picture of the evolution and current state of the research on mental health and wellbeing in university settings. Frequency counts of the number of publications and citations per year were obtained to describe the growth trajectory of research on the mental health and well-being of university students. Rank ordered tables were produced to describe the productivity of the field in terms of core journals and research areas, as well as leading scholars and countries contributing to the development of the field.

Bibliometric analyses in VOSViewer software were implemented to examine and visually represent the social, intellectual, and conceptual structure of the field. VOSViewer is a freely available computer software for viewing and constructing bibliometric maps 1 . In VOSViewer, the units of analysis are journals, publications, citations, authors, or countries, depending on the focus of the analysis. The units of analysis are represented in the maps as circular nodes. The size of the node accounts for volume (e.g., number of publications in the dataset by an author) and the position represents the similarity with other nodes in the map. Closer nodes are more alike than nodes far apart from each other. The lines connecting nodes represent the relationship between nodes and their thickness indicates the strength of that relationship. Finally, the color of the node denotes the cluster to which each node has been allocated. Nodes are clustered together based on relatedness ( Van Eck et al., 2010 ). The software uses a distance-based approach to constructing the bibliometric maps in three steps ( Van Eck and Waltman, 2014 ). In the first step, the software normalizes the differences between nodes. In the second step, the software builds a two-dimensional map where the distance between the nodes reflects the similarity between these nodes. In the third step, VOSViewer groups closely related nodes into clusters ( Van Eck and Waltman, 2014 ).

A series of co-authorship analyses were performed to examine the social structure of research on mental health and well-being in university students. In these analyses, the units of analysis were authors and countries/territories. Each node in the map represents an author or a country/territory and the lines connecting them reflect the relationship between nodes. Clusters represent networks of scientific collaboration, which might be interpreted as groups of authors or countries frequently publishing together (e.g., research groups in the case of authors).

Co-citation analysis of journals was implemented to explore the intellectual structure of the field. Here, the units of analysis were journals in the dataset and the map reflects co-citation relationships between journals. Two journals are co-cited if there is a third journal citing these two. The more times a pair of journals are cited by other journals, the stronger their co-citation relationship will be. Frequently co-cited journals are assumed to share theoretical and semantical grounds. Therefore, in our study, clusters of frequently co-cited journals can be interpreted as disciplines underlying the foundations of research on mental health and well-being in university students.

Finally, a co-occurrence analysis of keywords was used to uncover the conceptual structure of the field. The units of analysis, in this case, were the authors’ keywords. The more often two keywords appear in the same record, the stronger their co-occurrence relationship. Clusters of co-occurring keywords represent in this study the topical foci (i.e., knowledge base) that have been addressed in the literature in mental health and well-being in university students in the last 45 years.

Findings and Discussion

Growth trajectory: evolution of publications and citations in the field.

The developmental patterns of a particular field can be well demonstrated by trends in publications and citations. The 5,561 publications in the dataset have been cited 87,096 times, with an average of 15.6 citations per item. Figure 2 shows the growth trajectory of publication data of research on mental health and well-being in university students from 1975 to January 2020. Overall, the trends demonstrate a gradual increase in the scholarly interest in the mental health of university students over the last 45 years that can be organized in three stages: an emergence stage, in which publications rose slowly (1975–2000); a fermentation stage, with a notable increase in publications in the field (2000–2010); and a take-off stage, during which the number of records published per year in the field has almost risen 10 times (2010–2020). The steady increase of publications in the last 15 years coincides with the first calls for attention on the increase and severity of mental problems and help-seeking behaviors of college students ( Kadison and DiGeronimo, 2004 ; Evans et al., 2018 ), potentially indicating a growing interest in exploring the epidemiology of mental disorders and the role of universities in promoting the mental health and well-being of students. A similar pattern has also been observed in a recent bibliometric study examining global research on mental health both in absolute terms and as a proportion of all papers published in medicine and across disciplines, which certainly reflects an increase in the general interest in the field ( Larivière et al., 2013 ).

Figure 2. Growth of research on mental health and well-being of university students.

Productivity I: Core Journals and Research Areas

In total, 1,560 journals published the 5,561 records included in the dataset. Table 1 presents the ten core journals in the field. The Journal of American College stands out as the main publication venue in the field, accumulating around 5% of the publications in the dataset ( n = 270). Psychological Reports and Journal of College Student Development also stand out, publishing 119 and 102 studies, respectively. The Journal of Counseling Psychology ranks fourth in the list with 83 records. Despite being an interdisciplinary and relatively young journal, Plos One appears in the top five journal publishing research on mental health and well-being in university students.

Table 1. Core journals ranked by number of records.

The top research areas contributing to the publication of research on the mental health and well-being of university students are presented in Table 2 . Nearly half of the records in the dataset are published in psychology journals. Another influential research area in the field is psychiatry , which captures almost 20% of the publications. Journals on education and educational research also accumulate a considerable number of publications in the field (15%). Other relevant research areas in the field are connected with health and medicine, including public environmental occupational health , substance abuse , general internal medicine , neurosciences neurology , health care sciences services , and nursing . Finally, the field is also grounded, although to a lower extent, in the publications emerging from journals in the social sciences , family studies , and social work research.

Table 2. Top research areas ranked by number of records.

All in all, the productivity analysis for journals and research areas showed that most research on mental health and well-being in university students is disseminated in journals in the “psy disciplines”’ (i.e., psychology and psychiatry) ( McAvoy, 2014 ), which is consistent with previous research on mental health in general populations (e.g., Haslam and Lusher, 2011 ). However, our findings demonstrated that the volume of research in psychology doubles that of research emerging from psychiatric journals. This contrasts with the findings by Haslam and Lusher (2011) , who demonstrated that psychiatry journals had a greater influence on mental health research compared to clinical psychology journals and that psychiatry journals accumulate a higher volume of research and citations on mental health research. This is probably because our study includes publications emerging from all branches of psychology, unlike the study by Haslam and Lusher, which included only journals in the field of clinical psychology. Additionally, mental health services in higher education are typically provided by counseling centers led and staffed by non-medical professionals (e.g., psychologists, social workers, counselors, and family therapists) who tend to adopt developmental models of practice grounded in the behavioral sciences and focused on adjustment issues, vocational training, employment, and other personal needs rather than diagnosis and symptom reduction, more common in the biomedical sciences (i.e., psychiatry) ( LeViness et al., 2018 ; Mitchell et al., 2019 ).

Productivity II: Leading Authors and Countries/Territories

The 5,561 publications in the dataset were published by a total of 16,161 authors from 119 countries worldwide. Table 3 shows the researchers with the highest number of publications in the field. D. Eisenberg appears as the most productive researcher, followed by K. Peltzer and S. Pengpid. Authors on the list come from diverse geographical backgrounds. Five of the authors work at three different American universities (University of Michigan, Harvard Medical School, and Boston University), two researchers work at KU Leuven University (Belgium), and two other authors are affiliated to the same two universities in Thailand and South Africa. Other prolific researchers are affiliated with higher education institutions in the Netherlands, Egypt, and Germany.

Table 3. Leading authors ranked by number of records.

Countries and territories leading research on mental health and well-being of university students are presented in Table 4 . The United States is the indisputable leader in this field, publishing more than half of the records in the dataset. This is nearly 10 times the number of publications produced in China, which occupies the second position in the ranking and accounts for nearly 6% of the volume of research in the dataset. Three predominantly English speaking countries/territories complete the top five of the ranking: Canada (265 records), Australia (254), and England (243). The rest of the countries in the list are situated in Europe (Spain, Germany, Turkey), Western Asia (Iran), Africa (South Africa), and East Asia (Japan), which demonstrates that research on college students’ mental health and well-being is a matter of concern in different regions of the world, at least to some extent.

Table 4. Leading countries/territories ranked by number of records.

Overall, the productivity analysis for authors and countries indicated that the research of mental health and well-being of university students occurs in a variety of locations around the world, especially in developed countries, and in a very prominent way, in the United States. This is not surprising since it is in those countries where better infrastructures and more abundant resources for research are available ( Wong et al., 2006 ), and a more lasting tradition in the study of mental health, in general, exists ( Gopalkrishnan, 2018 ). However, Larivière et al. (2013) found that the productivity of the United States on mental health research has dropped significantly and remained stable in other two English speaking countries (the United Kingdom and Canada) since 1980. On the contrary, the number of publications from European countries and the five major emerging national economies (Brazil, Russia, India, China, and South Africa), has experienced remarkable growth, and collectively account nearly for half of the publications in the field. Still, the predominance of knowledge generated in the developed world today, which tends to be grounded on psychiatric and psychological perspectives, might be eclipsing non-traditional views on mental health and well-being that are popular in other regions of the world and, therefore, limiting the development of effective initiatives that align better with local norms, values, and needs in LMICs ( Timimi, 2010 ; Summerfield, 2013 ).

Social Structure: Networks of Scientific Collaboration

Research collaboration is regarded as an indicator of quality research and a means to improve research productivity and academic impact (i.e., citations) ( Kim, 2006 ; Abramo et al., 2009 ). In particular, international research collaboration is considered a key contributor to the social construction of science and the evolution of scientific disciplines ( Coccia and Wang, 2016 ). There is recent evidence that national and international research collaborations have been accelerating in recent years ( Gazni et al., 2012 ; Wagner et al., 2015 ), especially in applied fields such as medical and psychological disciplines ( Coccia and Bozeman, 2016 ). In this study, co-authorship analyses were performed to find out patterns in the scientific collaboration between researchers and countries/territories on the mental health and well-being of university students.

Figure 3 demonstrates collaborative ties among authors who published at least 5 articles in the dataset ( n = 179). The map shows the existence of multiple productive collaborative networks of five or more researchers contributing to the development of the field. The largest collaboration network (red cluster) represents an international research group composed of 15 scholars affiliated to universities in the United States, Belgium, and Netherlands. This cluster groups some of the leading scholars in the field, including R. P. Auerbach, R. Brauffaerts, R. C. Kressler, and P. Cuijpers. Moreover, researchers in this cluster lead The WHO World Mental Health International College Student (WMH-ICS) Initiative, a large scale international project aimed at promoting the mental health and well-being of college students around the world through generating epidemiological data of mental health issues in university students worldwide, designing web-based interventions for the prevention and promotion of mental health, and disseminating evidence-based interventions ( Cuijpers et al., 2019 ). The second biggest cluster (green) represents an intra-national research network that includes 10 researchers from eight different higher education institutions in the United States. The dark blue cluster represents an institutional collaborative network, including nine researchers from the School of Public Health, Puerto Rico. Other prominent clusters in the map represent collaborative research networks between eight (olive color) and seven researchers (turquoise, violet, orange, and mellow mauve). This contrasts, however, with the limited collaboration that exists between clusters. Only four of the clusters on the map demonstrate some kind of scientific collaboration in the field (light blue, pink, brown, and yellow).

Figure 3. Collaborative research networks between researchers. Only researchers with five or more publications were considered in the analysis ( n = 179).

Cross-country collaboration networks in mental health and well-being of university students study are presented in Figure 4 . Research collaborations between countries with 20 or more publications were considered in this analysis ( n = 45). The United States occupies the central position of the map and shares collaborative ties with all other countries/territories, forming a cluster together with China, South Korea, and Taiwan. Overall, the results suggest that international collaborations in the field are framed to a large extent by cultural, linguistic, and geographical proximity. For instance, the largest cluster (red) is formed by two European countries (Spain and Portugal) and other South American countries with whom they share historical and cultural backgrounds. Other European countries form the purple cluster. Similarly, the blue cluster clearly brings together predominantly English-speaking countries and territories, while the green cluster agglomerates a range of Asian countries.

Figure 4. Collaborative research networks between countries and territories. Only countries with 20 or more publications were considered in the analysis ( n = 45).

Collectively, the results of our study suggest that research collaboration in the field of mental health and well-being in university students remains relatively scarce and localized to date. The social structure of the field at the author level could be described as an archipelago formed by a large number of islands (research groups) of different composition and size but with few bridges connecting them, which suggests a relatively fragmented research community. Moreover, while the existence of international collaborative networks was evident in the analysis, they seem to be formed within national borders, between researchers in neighboring countries/territories, or between countries that share cultural, linguistic, and historical heritages. This may be due to the important role that cultural and traditional values play in the conceptualization of mental health and well-being across contexts ( Eshun and Gurung, 2009 ; Vaillant, 2012 ; Fernando, 2019 ). Also, language differences, divergent cross-national institutional and organizational traditions, and increased costs of extramural collaboration, have been found to complicate the formation and continuity of research partnerships in health research ( Hooper et al., 2005 ; Freshwater et al., 2006 ). Nevertheless, limited within- and between-country research collaboration arguably poses challenges to the development of a field in terms of lost opportunities to challenge assumptions taken for granted and move toward fresh perspectives, push boundaries in methods and techniques, meet diverse groups of people from differing cultures and get immersed in those cultures, share information, resources, and skills, and address common mental health problems through the pooling of resources ( Rolfe et al., 2004 ; Freshwater et al., 2006 ).

Intellectual Structure: Disciplines Underlying the Foundations of the Field

Interdisciplinarity is considered as a valuable approach to address the complex and multidimensional nature of health and well-being ( Mabry et al., 2008 ). Buckton (2015) argues that the integration of medical, psychological, and social sciences have contributed to generate “new insights into theory, practice, and research in mental health and development.” (3). To examine the disciplines underlying research on the mental health and well-being of university students, a journal co-citation analysis was performed. In this analysis, only journals with at least 50 citations were considered ( n = 593). The nodes on the map represent journals and their size reflects the number of co-citation relationships with other journals. Colors account for journal clusters, which agglutinate journals with higher co-citation relationships and stronger semantic connectedness. Clusters were interpreted and labeled accounting for the WoS categorization of the journals with the highest co-citation links within each cluster. For example, if the Journal of Personality and Social Psychology , the Journal of Counseling Psychology , and Personality and Individual Differences clustered together, this group was interpreted as the personality, social, and counseling psychology cluster.

In general, the findings of this study suggest that research on mental health and well-being in university students is interdisciplinary, to a certain extent, and mainly emerges from the convergence of research conducted in the behavioral and biomedical sciences, as it has been suggested elsewhere ( Schumann et al., 2014 ; Wittchen et al., 2014b ). More specifically, the map shows that the research in the mental health and well-being of university students is constructed through the integration of knowledge generated in five interconnected disciplines (see Figure 5 ). To the left of the map, the red cluster integrates journals on personal, social, and counseling psychology . To the right, the blue cluster represents the contribution of psychiatric journals to research to the formation and development of the field. At the top, the yellow cluster groups journals on substance abuse and issues related to alcohol consumption, addiction, and interpersonal violence. At the bottom of the map, journals covering topics on eating behaviors, sleep, and other issues related to physical health converge on the green cluster. At the center of the map is the purple cluster, which includes journals in the area of clinical psychology and behavioral therapy .

Figure 5. Map of clustered network journals based on co-citation data. Only publications with 50 or more citations were considered in the analysis ( n = 593).

More broadly, the findings suggest that biomedical sciences contribute to a large extent to the composition of the field. Psychiatric research emerged in our study as an obvious building block in the study of university students’ mental health and well-being, which is not surprising considering the historical contributions of biomedical disciplines to mental health research ( Schwartz and Corcoran, 2010 ). Within the behavioral sciences, personality and social psychology, which explores processes and mechanisms through which social phenomena influence mental health and well-being ( Sánchez Moreno and Barrón López de Roda, 2003 ), appears as a key discipline underlying the foundations of the field. Surprisingly, clinical psychology journals occupy a central position in the map and demonstrate co-citation relationships with journals from all other clusters but make up the most dispersed network and account for a considerably lower volume of co-citation relationships in the field. This suggests that clinical psychology journals are more subordinate to journals in other disciplines in terms of citations flows, and ultimately, play a less unique role in research on the mental health and well-being of university students, as suggested by Haslam and Lusher (2011) . Interestingly, research arising from the social sciences (e.g., sociology and anthropology) does not seem to make a distinctive contribution to the intellectual structure of the field, which suggests that the influence of social contexts and cultures on university students’ mental health and well-being (e.g., inequality, social norms, public policies, cultural beliefs, and values) is an underexplored research area. Still, the density of co-citation network relationships within and between clusters is particularly noteworthy, considering the lack of common language between disciplines, the absence of a shared philosophy of practice on mental health, and the tensions between medical, psychological, and social explanations of mental distress ( Bailey, 2012 ).

Conceptual Structure: Topical Foci Addressed in the Literature Over the Last 45 Years

The topical foci of research on the mental health and well-being of university students during the 1975–January 2020 period are presented in Figure 6 . The map offers a visual representation of the co-occurrence analysis of author keywords of all the publications included in the dataset. Only the most frequently occurring keywords (25+ occurrences) were considered in the analysis ( n = 84). Items that were not related to others and do not belong to the existing clusters were excluded. The size of the nodes indicates the occurrence of author keywords in the dataset and the thickness of edges represents the co-occurrence strength between pairs of keywords.

Figure 6. Topical foci in mental health and well-being of university students research. Only keywords with 25 or more occurrences were considered in the analysis ( n = 84).

The most frequent keywords in the dataset, excluding students’ descriptors (e.g., college students and university students), refer to common mental health challenges experienced by university students such as depression ( n = 612), anxiety ( n = 353), and stress ( n = 341). Salutogenic-related keywords such as well-being and life satisfaction occurred less often ( n = 138, n = 113, respectively), suggesting that pathogenic approaches to the exploration of mental health issues in higher education are more widespread. More broadly, seven general themes seem to summarize the topical foci of interest in the field of mental health and well-being of university students over the last 45 years. First, there has been a general interest in positive mental health , as denoted by frequently co-occurring key terms such as well-being, self-esteem, life satisfaction, social support, emotional intelligence, and happiness (red cluster). Second, mental disorders stand as another theme widely addressed in the literature, with a special emphasis on depression, anxiety, and to a lesser extent, suicide and suicidal ideation (green cluster). A third topical area in this field has been substance abuse , most predominantly alcohol consumption (blue cluster). The fourth theme reflects college counseling for mental health , including interventions and protective factors such as mindfulness, stress management, spirituality, and help-seeking (yellow cluster). Other topics reflected in the map are mental illness stigma (purple), stress (e.g., psychological distress and coping) (light blue), and mental health measurement (orange).

This study provides a comprehensive overview of the research on university students’ mental health and well-being in the last 45 years using bibliometric indicators. In general, the results reveal interesting trends in the evolution of the field over the last four decades and promising scientific patterns toward a better understanding of the mental health and well-being of university students internationally. First, the interest in the mental health and well-being of university students has grown in the last decades and in a very significant way during the last 10 years, indicating that this area has not still reached its maturity period and will continue developing in the future. Second, research in the field is relatively interdisciplinary and emerges from the convergence of research conducted in several disciplines within the behavioral and biomedical sciences. Third, research in this field is produced by a community of productive researchers coming from several regions around the world, most notably in the United States, which secures a generation of scholars that will continue shaping the field in the years to come. Fourth, over the last 45 years, researchers have been able to address a multitude of research topics in the field, including positive mental health, mental disorders, substance abuse, counseling, stigma, stress, and mental health measurement.

However, this study also identified some issues that could be hindering the development of the study of the mental health and well-being of university students. For example, the research available overrepresents theoretical and disciplinary approaches from the developed world. Additional studies on the field from developing economies and LMICs are needed to provide a more comprehensive picture and ensure a fair representation of the multiple perspectives available in the field. Such studies would inform administrators and practitioners on how to broaden and enrich available programs and initiatives to promote mental health and well-being in higher education contexts in order to offer alternative forms of support that university students find appropriate for their social and cultural values. Moreover, the research community contributing to the development of the field is relatively fragmented. There are multiple research groups but little research collaborations between them and, at the international level, these connections tend to be limited by geographic, cultural, and language proximity. In this context, more actions like the WMH-ICS Initiative could provide a partial solution to this problem by strengthening national and international research partnerships and facilitating knowledge exchange across regions. Also, special issues in the core journals in the field inviting cross-cultural studies on the topic could contribute to promoting research collaboration across regions and research in less represented countries. The field would also benefit from a greater volume of research from the social sciences and humanities exploring the influence of social, cultural, economic, and educational factors on the conceptualization, manifestation, and experience of mental health and well-being. Moreover, more studies emerging from disciplines such as sociology, anthropology, business, and education, would likely increase the permeability of positive mental health concepts into the field and contribute to the promotion of salutogenic approaches to the study of mental health and well-being of university students.

This study has several limitations. First, publications were retrieved only from the WoS database, which limits the generalizability of the findings. Second, WoS provides stronger coverage of Life Sciences, Biomedical Sciences, and Engineering, and includes a disproportionate number of publications in the English language ( Mongeon and Paul-Hus, 2016 ). This could partially explain the low number of publications emerging from the Social Sciences, the Arts, and the Humanities, and research conducted in non-English speaking countries in the present study. Third, only journal articles were retrieved for analysis, excluding other relevant publications in the field such as reviews, book chapters, and conference proceedings. Future studies could replicate the findings of this study using alternative databases (e.g., Scopus and PubMed) or a combination of them, as well as different filters in the search strategy, to provide an alternative coverage of research conducted in the field. Nevertheless, we believe that the bibliometric approach used in this study offers novel insights about the development and current status of the field and some of the challenges that undermine its progression.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Author Contributions

DH-T and LI contributed to conception and design of the study, organized the database, and performed the statistical analysis. DH-T, LI, and JS wrote the first draft of the manuscript. NL, AC, AA, YN, and AM wrote the sections of the manuscript.

This research was funded by the Nazarbayev University Faculty-Development Competitive Research Grants Program (Reference Number 240919FD3902).

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.

• ^ www.vosviewer.com

Abramo, G., D’Angelo, C. A., and Di Costa, F. (2009). Research collaboration and productivity: Is there correlation? High. Educ. 57, 155–171. doi: 10.1007/s10734-008-9139-z

CrossRef Full Text | Google Scholar

Antaramian, S. (2015). Assessing psychological symptoms and well-being: application of a dual-factor mental health model to understand college student performance. J. Psychoeduc. Assess. 33, 419–429. doi: 10.1177/0734282914557727

Auerbach, R. P., Mortier, P., Bruffaerts, R., Alonso, J., Benjet, C., Cuijpers, P., et al. (2018). WHO world mental health surveys international college student project: prevalence and distribution of mental disorders. J. Abnorm. Psychol. 127, 623–638. doi: 10.1037/abn0000362

PubMed Abstract | CrossRef Full Text | Google Scholar

Bailey, D. (2012). Interdisciplinary Working in Mental Health. Basingstoke: Palgrave Macmillan.

Google Scholar

Bewick, B. M., Koutsopoulou, G., Miles, J., Slaa, E., and Barkham, M. (2010). Changes in undergraduate students’ psychological wellbeing as they progress through university. Stud. High. Educ. 35, 633–645. doi: 10.1080/03075070903216643

Bhugra, D., Till, A., and Sartorius, N. (2013). What is mental health? Int. J. Soc. Psychiatr. 59, 3–4. doi: 10.1177/0020764012463315

Blanco, C., Okuda, M., Wright, C., Hasin, D. S., Grant, B. F., Liu, S. M., et al. (2008). Mental health of college students and their non-college-attending peers: results from the national epidemiologic study on alcohol and related conditions. Arch. Gen. Psychiatry 65, 1429–1437. doi: 10.1001/archpsyc.65.12.1429

Buckton, A. (2015). Conversations between anthropology and psychiatry: drawing out the best from interdisciplinarity in global mental health. Australas. Psychiatry 23, 3–5. doi: 10.1177/1039856215608291

Cleary, M., Walter, G., and Jackson, D. (2011). Not always smooth sailing: mental health issues associated with the transition from high school to college. Issues Ment. Health Nurs. 32, 250–254. doi: 10.3109/01612840.2010.548906

Coccia, M., and Bozeman, B. (2016). Allometric models to measure and analyze the evolution of international research collaboration. Scientometrics 108, 1065–1084. doi: 10.1007/s11192-016-2027-x

Coccia, M., and Wang, L. (2016). Evolution and convergence of the patterns of international scientific collaboration. Proc. Natl. Acad. Sci. U.S.A. 113, 2057–2061. doi: 10.1073/pnas.1510820113

Compton, M. T., and Shim, R. S. (2015). The social determinants of mental health. Focus 13, 419–425. doi: 10.1176/appi.focus.20150017

Cooke, R., Bewick, B. M., Barkham, M., Bradley, M., and Audin, K. (2006). Measuring, monitoring and managing the psychological well-being of first year university students. Br. J. Guid. Couns. 34, 505–517. doi: 10.1080/03069880600942624

Cuijpers, P., Auerbach, R. P., Benjet, C., Bruffaerts, R., Ebert, D., Karyotaki, E., et al. (2019). The world health organization world mental health international college student initiative: an overview. Int. J. Methods Psychiatr. Res. 28:e1762. doi: 10.1002/mpr.1761

Davy, C., Dobson, A., Lawrence-Wood, E., Lorimer, M., Moores, K., Lawrence, A., et al. (2012). The Middle East Area of Operations (MEAO) Health Study: Prospective Study Report. Adelaide: University of Adelaide.

Deci, E. L., and Ryan, R. M. (2008). Self-determination theory: a macrotheory of human motivation, development, and health. Can. Psychol. 49, 182–185. doi: 10.1037/a0012801

Dekker, J., Stauder, A., and Penedo, F. J. (2017). Proposal for an update of the definition and scope of behavioral medicine. Int. J. Behav. Med. 24, 1–4. doi: 10.1007/s12529-016-9610-7

Diener, E. (2012). New findings and future directions for subjective well-being research. Am. Psychol. 67, 590–597. doi: 10.1037/a0029541

Eisenberg, D., Downs, M. F., Golberstein, E., and Zivin, K. (2009). Stigma and help seeking for mental health among college students. Med. Care Res. Rev. 66, 522–541. doi: 10.1177/1077558709335173

Eisenberg, D., Hunt, J., Speer, N., and Zivin, K. (2011). Mental health service utilization among college students in the United States. J. Nerv. Ment. Dis. 199, 301–308. doi: 10.1097/NMD.0b013e3182175123

Eshun, S., and Gurung, A. R. (2009). “Introduction to culture and psychopathology,” in Culture and Mental Health: Sociocultural Influences, Theory, and Practice , eds S. Eshun and R. A. R. Gurung (Hoboken, NJ: Wiley and Sons), 3–18.

Evans, T. M., Bira, L., Gastelum, J. B., Weiss, L. T., and Vanderford, N. L. (2018). Evidence for a mental health crisis in graduate education. Nat. Biotechnol. 36, 282–284. doi: 10.1038/nbt.4089

Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., and Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. FASEB J. 22, 338–342. doi: 10.1096/fj.07-9492LSF

Fergusson, D. M., Boden, J. M., and Horwood, L. J. (2007). Recurrence of major depression in adolescence and early adulthood, and later mental health, educational and economic outcomes. Br. J. Psychiatry 191, 335–342. doi: 10.1192/bjp.bp.107.036079

Fernando, S. (2019). Developing mental health services in the global south. Int. J. Ment. Health 48, 338–345. doi: 10.1080/00207411.2019.1706237

Foster, G. M. (1975). Medical anthropology: some contrasts with medical sociology. Soc. Sci. Med. 9, 427–432. doi: 10.1016/0037-7856(75)90070-0

Freshwater, D., Sherwood, G., and Drury, V. (2006). International research collaboration: issues, benefits and challenges of the global network. J. Res. Nurs. 11, 295–303. doi: 10.1177/1744987106066304

Galderisi, S., Heinz, A., Kastrup, M., Beezhold, J., and Sartorius, N. (2015). Toward a new definition of mental health. World Psychiatry 14, 231–233. doi: 10.1002/wps.20231

Gazni, A., Sugimoto, C. R., and Didegah, F. (2012). Mapping world scientific collaboration: authors, institutions, and countries. J. Am. Soc. Inf. Sci. Technol. 63, 323–335. doi: 10.1002/asi.21688

Gopalkrishnan, N. (2018). Cultural diversity and mental health: considerations for policy and practice. Front. Public Health 6:179. doi: 10.3389/fpubh.2018.00179

Gulliver, A., Griffiths, K. M., and Christensen, H. (2010). Perceived barriers and facilitators to mental health help-seeking in young people: a systematic review. BMC Psychiatry 10:113. doi: 10.1186/1471-244X-10-113

Haslam, N., and Lusher, D. (2011). The structure of mental health research: networks of influence among psychiatry and clinical psychology journals. Psychol. Med. 41, 2661–2668. doi: 10.1017/S0033291711000821

Hooper, T. I., Smith, T. C., Gray, G. C., Al Qahtani, M. S., Memish, Z. A., Barrett, D. H., et al. (2005). Saudi Arabia–United States collaboration in health research: a formula for success. Am. J. Infect. Control 33, 192–196. doi: 10.1016/j.ajic.2005.01.006

Horwitz, A. V. (2010). “An overview of sociological perspectives on the definitions, causes, and responses to mental health and illness,” in A Handbook for the Study of Mental Health: Social Contexts, Theories and Systems , eds T. L. Scheid and T. N. Brown (Cambridge: Cambridge University Press), 6–19. doi: 10.1017/9781316471289.004

Hunt, J., and Eisenberg, D. (2010). Mental health problems and help-seeking behaviors among college students. J. Adolesc. Health 46, 3–10. doi: 10.1016/j.jadohealth.2009.08.008

Insel, T. R., and Wang, P. S. (2010). Rethinking mental illness. JAMA 303, 1970–1971.

Kadison, R., and DiGeronimo, T. F. (2004). College of the Overwhelmed: The Campus Mental Health Crisis and What to do About It. San-Francisco, CA: Jossey-Bass.

Kerr, D. C., and Capaldi, D. M. (2011). Young men’s intimate partner violence and relationship functioning: long-term outcomes associated with suicide attempt and aggression in adolescence. Psychol. Med. 41, 759–769. doi: 10.1017/S0033291710001182

Kessler, R. C., Amminger, G. P., Aguilar-Gaxiola, S., Alonso, J., Lee, S., and Ustun, T. B. (2007). Age of onset of mental disorders: a review of recent literature. Curr. Opin. Psychiatr. 20, 359–364. doi: 10.1097/YCO.0b013e32816ebc8c

Keyes, C. L. (2005). Mental illness and/or mental health? Investigating axioms of the complete state model of health. J. Consult. Clin. Psychol. 73, 539–548. doi: 10.1037/0022-006X.73.3.539

Keyes, C. L. (2007). Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. Am. Psychol. 62, 95–108. doi: 10.1037/0003-066X.62.2.95

Keyes, C. L., and Lopez, S. J. (2002). “Toward a science of mental health,” in Handbook of Positive Psychology , eds C. R. Synder and S. J. Lopez (Oxford: Oxford University Press), 45–62.

Keyes, C. L. M., and Michalec, B. (2010). “Viewing mental health from the complete state paradigm,” in A Handbook for the Study of Mental Health: Social Contexts, Theories and Systems , eds T. L. Scheid and T. N. Brown (Cambridge: Cambridge University Press), 125–134. doi: 10.1017/cbo9780511984945.010

Kim, K. W. (2006). Measuring international research collaboration of peripheral countries: taking the context into consideration. Scientometrics 66, 231–240. doi: 10.1007/s11192-006-0017-0

King, K. M., Meehan, B. T., Trim, R. S., and Chassin, L. (2006). Marker or mediator? The effects of adolescent substance use on young adult educational attainment. Addiction 101, 1730–1740. doi: 10.1111/j.1360-0443.2006.01507.x

Kobau, R., Seligman, M. E., Peterson, C., Diener, E., Zack, M. M., Chapman, D., et al. (2011). Mental health promotion in public health: perspectives and strategies from positive psychology. Am. J. Public Health 101, e1–e9. doi: 10.2105/AJPH.2010.300083

Larivière, V., Diepeveen, S., Chonaill, S. N., Macaluso, B., Pollitt, A., and Grant, J. (2013). International comparative performance of mental health research, 1980–2011. Eur. Neuropsychopharm. 23, 1340–1347. doi: 10.1016/j.euroneuro.2013.01.006

LeViness, P., Bershad, C., Gorman, K., Braun, L., and Murray, T. (2018). The Association for University and College Counseling Center Directors Annual Survey. Available online at: https://www.aucccd.org/assets/documents/Survey/2018%20AUCCCD%20Survey-Public-June%2012-FINAL.pdf (accessed February 24, 2020).

Li, J., Burnham, J. F., Lemley, T., and Britton, R. M. (2010). Citation analysis: comparison of Web of Science, Scopus, SciFinder, and Google Scholar. J. Electron. Resour. Med. Lib. 7, 196–217. doi: 10.1080/15424065.2010.505518

Lipson, S. K., Lattie, E. G., and Eisenberg, D. (2019). Increased rates of mental health service utilization by US college students: 10-year population-level trends (2007–2017). Psychiatr. Serv. 70, 60–63. doi: 10.1176/appi.ps.201800332

Mabry, P. L., Olster, D. H., Morgan, G. D., and Abrams, D. B. (2008). Interdisciplinarity and systems science to improve population health: a view from the NIH Office of Behavioral and Social Sciences Research. Am. J. Prev. Med. 35, S211–S224. doi: 10.1016/j.amepre.2008.05.018

Macaskill, A. (2013). The mental health of university students in the United Kingdom. Br. J. Guid. Couns. 41, 426–441. doi: 10.1080/03069885.2012.743110

Manwell, L. A., Barbic, S. P., Roberts, K., Durisko, Z., Lee, C., Ware, E., et al. (2015). What is mental health? Evidence towards a new definition from a mixed methods multidisciplinary international survey. BMJ Open 5, 1–11. doi: 10.1136/bmjopen-2014-007079

McAvoy, J. (2014). “Psy disciplines,” in Encyclopedia of Critical Psychology , ed. T. Teo (New York, NY: Springer), 1527–1529. doi: 10.1007/978-1-4614-5583-7_611

McVeigh, M. (2009). “Citation indexes and the Web of Science,” in Encyclopedia of Library and Information Sciences , 3rd Edn, eds M. J. Bates and M. N. Maack (Cambridge: Chandos Publishing), 1027–1037. doi: 10.1081/e-elis3-120044569

Meho, L. I., and Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: web of science versus Scopus and Google scholar. J. Am. Soc. Inf. Sci. Technol. 58, 2105–2125. doi: 10.1002/asi.20677

Mey, S. C., and Yin, C. J. (2015). Mental health and wellbeing of the undergraduate students in a research university: a Malaysian experience. Soc. Indic. Res. 122, 539–551. doi: 10.1007/s11205-014-0704-9

Mitchell, S. L., Oakley, D. R., and Dunkle, J. H. (2019). White paper: a multidimensional understanding of effective university and college counseling center organizational structures. J. Coll. Stud. Psychother. 33, 89–106. doi: 10.1080/87568225.2019.1578941

Mongeon, P., and Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics 106, 213–228. doi: 10.1007/s11192-015-1765-5

Naveed, S., Waqas, A., Majeed, S., Zeshan, M., Jahan, N., and Sheikh, M. H. (2017). Child psychiatry: a scientometric analysis 1980-2016. F1000Res . 6:1293. doi: 10.12688/f1000research.12069.1

Pellmar, T. C., and Eisenberg, L. (2000). Bridging Disciplines in the Brain, Behavioral, and Clinical Sciences. Washington, DC: IOM.

Peterson, P. (2010). “Psychological approaches to mental illness,” in A Handbook for the Study of Mental Health: Social Contexts, Theories and Systems , eds T. L. Scheid and T. N. Brown (Cambridge: Cambridge University Press), 106–124.

Rolfe, M. K., Bryar, R. M., Hjelm, K., Apelquist, J., Fletcher, M., and Anderson, B. L. (2004). International collaborations to address common problems in healthcare: processes practicalities and power. Int. Nurs. Rev. 51, 140–148. doi: 10.1111/j.1466-7657.2004.00237.x

Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of psychological well-being. J. Pers. Soc. Psychol. 57, 1069–1081. doi: 10.1037/0022-3514.57.6.1069

Ryff, C. D., and Singer, B. H. (2008). Know thyself and become what you are: a eudaimonic approach to psychological well-being. J. Happiness Stud. 9, 13–39. doi: 10.1007/s10902-006-9019-0

Sánchez Moreno, E., and Barrón López de Roda, A. (2003). Social psychology of mental health: the social structure and personality perspective. Span. J. Psychol. 6, 3–11. doi: 10.1017/s1138741600005163

Scheid, T. L., and Brown, T. N. (2010). A Handbook for the Study of Mental Health: Social Contexts, Theories and Systems. Cambridge: Cambridge University Press.

Schumann, G., Binder, E., Holte, A., de Kloet, E. R., Odegaard, K. J., Robbins, T. W., et al. (2014). Stratified medicine for mental disorders. Eur. Neuropsychopharm. 24, 5–50. doi: 10.1016/j.euroneuro.2013.09.010

Schwartz, S., and Corcoran, C. (2010). “Biological theories of psychiatric disorders: a sociological approach,” in A Handbook for the Study of Mental Health: Social Contexts, Theories and Systems , eds T. L. Scheid and T. N. Brown (Cambridge: Cambridge University Press), 64–88. doi: 10.1017/cbo9780511984945.007

Sharp, J., and Theiler, S. (2018). A review of psychological distress among university students: pervasiveness, implications and potential points of intervention. Int. J. Adv. Couns. 40, 193–212. doi: 10.1007/s10447-018-9321-7

Skute, I., Zalewska-Kurek, K., Hatak, I., and de Weerd-Nederhof, P. (2019). Mapping the field: a bibliometric analysis of the literature on university-industry collaborations. J. Technol. Transfer 44, 916–947. doi: 10.1007/s10961-017-9637-1

Slade, M. (2010). Mental illness and well-being: the central importance of positive psychology and recovery approaches. BMC Health Serv. Res. 10:26. doi: 10.1186/1472-6963-10-26

Summerfield, D. (2013). Global mental health is an oxymoron and medical imperialism. Br. Med. J. 346:f3509. doi: 10.1136/bmj.f3509

Timimi, S. (2010). The McDonaldization of childhood: children’s mental health in neo-liberal market cultures. Transcult. Psychiatry 47, 686–706. doi: 10.1177/1363461510381158

Vaillant, G. E. (2012). Positive mental health: Is there a cross-cultural definition? World Psychiatry 11, 93–99. doi: 10.1016/j.wpsyc.2012.05.006

Van Eck, N. J., and Waltman, L. (2014). “Visualizing bibliometric networks,” in Measuring Scholarly Impact: Methods and Practice , eds Y. Ding, R. Rousseau, and D. Wolfram (Cham: Springer), 285–320. doi: 10.1007/978-3-319-10377-8_13

Van Eck, N. J., Waltman, L., Noyons, E. C. M., and Buter, R. K. (2010). Automatic term identification for bibliometric mapping. Scientometrics 82, 581–596. doi: 10.1007/s11192-010-0173-0

Van Raan, A. F. (2014). “Advances in bibliometric analysis: research performance assessment and science mapping,” in Bibliometrics: Use and Abuse in the Review of Research Performance , Vol. 3, eds W. Blockmans, L. Engwall, and D. Weaire (London: Portland Press Ltd), 17–28.

Verger, P., Guagliardo, V., Gilbert, F., Rouillon, F., and Kovess-Masfety, V. (2010). Psychiatric disorders in students in six French universities: 12-month prevalence, comorbidity, impairment and help-seeking. Soc. Psychiatry Psychiatr. Epidemiol. 45, 189–199. doi: 10.1007/s00127-009-0055-z

Wagner, C. S., Park, H. W., and Leydesdorff, L. (2015). The continuing growth of global cooperation networks in research: a conundrum for national governments. PLoS One 10:e0131816. doi: 10.1371/journal.pone.0131816

Wang, Q., and Waltman, L. (2016). Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus. J. Informetr. 10, 347–364. doi: 10.1016/j.joi.2016.02.003

White, R., Jain, S., and Giurgi-Oncu, C. (2014). Counterflows for mental well-being: what high-income countries can learn from low and middle-income countries. Int. Rev. Psychiatr. 26, 602–606. doi: 10.3109/09540261.2014.939578

Wittchen, H. U., Knappe, S., Andersson, G., Araya, R., Banos Rivera, R. M., Barkham, M., et al. (2014a). The need for a behavioural science focus in research on mental health and mental disorders. Int. J. Methods Psychiatr. Res. 23, 28–40.

Wittchen, H. U., Knappe, S., and Schumann, G. (2014b). The psychological perspective on mental health and mental disorder research: introduction to the ROAMER work package 5 consensus document. Int. J. Methods Psychiatr. Res. 23, 15–27. doi: 10.1002/mpr.1408

Wong, J. G., Cheung, E. P., Chan, K. K., Ma, K. K., and Wa Tang, S. (2006). Web-based survey of depression, anxiety and stress in first-year tertiary education students in Hong Kong. Aust. N. Z. J. Psychiatry 40, 777–782. doi: 10.1080/j.1440-1614.2006.01883.x

World Health Organization (2004). Promoting Mental Health: Concepts, Emerging Evidence, Practice (Summary Report). Geneva: World Health Organization.

Keywords : mental health, mental illness, well-being, psychological distress, university students, higher education, bibliometric review, VOSViewer

Citation: Hernández-Torrano D, Ibrayeva L, Sparks J, Lim N, Clementi A, Almukhambetova A, Nurtayev Y and Muratkyzy A (2020) Mental Health and Well-Being of University Students: A Bibliometric Mapping of the Literature. Front. Psychol. 11:1226. doi: 10.3389/fpsyg.2020.01226

Received: 03 March 2020; Accepted: 11 May 2020; Published: 09 June 2020.

Reviewed by:

Copyright © 2020 Hernández-Torrano, Ibrayeva, Sparks, Lim, Clementi, Almukhambetova, Nurtayev and Muratkyzy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Daniel Hernández-Torrano, [email protected] ; [email protected]

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

Why the reliance on data? Findings and statistics from research studies can impact us emotionally, add credibility to an article, and ground us in the real world. However, the importance of research findings reaches far beyond providing knowledge to the general population. Research and evaluation studies — those studies that assess a program’s impact — are integral to promoting mental health and reducing the burden of mental illness in different populations.

Mental health research identifies biopsychosocial factors — how biological, psychological and social functioning are interacting — detecting trends and social determinants in population health. That data greatly informs the current state of mental health in the U.S. and around the world. Findings from such studies also influence fields such as public health, health care and education. For example, mental health research and evaluation can impact public health policies by assisting public health professionals in strategizing policies to improve population mental health.

Research helps us understand how to best promote mental health in different populations. From its definition to how it discussed, mental health is seen differently in every community. Thus, mental health research and evaluation not only reveals mental health trends but also informs us about how to best promote mental health in different racial and ethnic populations. What does mental health look like in this community? Is there stigma associated with mental health challenges? How do individuals in the community view those with mental illness? These are the types of questions mental health research can answer.

Data aids us in understanding whether the mental health services and resources that are available meet mental health needs. Many times the communities where needs are the greatest are the ones where there are limited services and resources available. Mental health research and evaluation informs public health professionals and other relevant stakeholders of the gaps that currently exist so they can prioritize policies and strategies for communities where gaps are the greatest.

Research establishes evidence for the effectiveness of public health policies and programs. Mental health research and evaluation help develop evidence for the effectiveness of healthcare policies and strategies as well as mental health promotion programs. This evidence is crucial for showcasing the value and return on investment for programs and policies, which can justify local, state and federal expenditures. For example, mental health research studies evaluating the impact of Mental Health First Aid (MHFA) have revealed that individuals taking the course show increases in knowledge about mental health, greater confidence to assist others in distress, and improvements in their own mental wellbeing. They have been fundamental in assisting organizations and instructors in securing grant funding to bring MHFA to their communities.

The findings from mental health research and evaluation studies provide crucial information about the specific needs within communities and the impacts of public education programs like MHFA. These studies provide guidance on how best to improve mental health in different contexts and ensure financial investments go towards programs proven to improve population mental health and reduce the burden of mental illness in the U.S.

In 2021, in a reaffirmation of its dedication and commitment to mental health and substance use research and community impact, Mental Health First Aid USA introduced MHFA Research Advisors. The group advises and assists Mental Health First Aid USA on ongoing research and future opportunities related to individual MHFA programs, including Youth MHFA, teen MHFA and MHFA at Work.

Through this advisory group and evaluation efforts at large, Mental Health First Aid USA will #BeTheDifference for mental health research and evaluation across communities in the US.

Learn more about MHFA Research Advisors and how you can share your research with us.

Get the latest MHFA blogs, news and updates delivered directly to your inbox so you never miss a post.

Share and help spread the word.

Related stories.

No related posts.

This paper is in the following e-collection/theme issue:

Published on 30.8.2024 in Vol 11 (2024)

Evaluation of Digital Mental Health Technologies in the United States: Systematic Literature Review and Framework Synthesis

Authors of this article:

• Julianna Catania 1 , MPH   ; 
• Steph Beaver 1 , MChem   ; 
• Rakshitha S Kamath 1 , MS, MSL   ; 
• Emma Worthington 2 , MPH   ; 
• Minyi Lu 3 , PhD   ; 
• Hema Gandhi 3 , PhD   ; 
• Heidi C Waters 3 , PhD   ; 
• Daniel C Malone 4 , PhD  

1 Costello Medical, Boston, MA, United States

2 Costello Medical, Cambridge, United Kingdom

3 Otsuka Pharmaceutical Development & Commercialization Inc, Princeton, NJ, United States

4 Department of Pharmacotherapy, Skaggs College of Pharmacy, University of Utah, Salt Lake City, UT, United States

Corresponding Author:

Daniel C Malone, PhD

Department of Pharmacotherapy

Skaggs College of Pharmacy

University of Utah

30 S 2000 East

Salt Lake City, UT, 84112

United States

Phone: 1 801 581 6257

Email: [email protected]

Background: Digital mental health technologies (DMHTs) have the potential to enhance mental health care delivery. However, there is little information on how DMHTs are evaluated and what factors influence their use.

Objective: A systematic literature review was conducted to understand how DMHTs are valued in the United States from user, payer, and employer perspectives.

Methods: Articles published after 2017 were identified from MEDLINE, Embase, PsycINFO, Cochrane Library, the Health Technology Assessment Database, and digital and mental health congresses. Each article was evaluated by 2 independent reviewers to identify US studies reporting on factors considered in the evaluation of DMHTs targeting mental health, Alzheimer disease, epilepsy, autism spectrum disorder, or attention-deficit/hyperactivity disorder. Study quality was assessed using the Critical Appraisal Skills Program Qualitative and Cohort Studies Checklists. Studies were coded and indexed using the American Psychiatric Association’s Mental Health App Evaluation Framework to extract and synthesize relevant information, and novel themes were added iteratively as identified.

Results: Of the 4353 articles screened, data from 26 unique studies from patient, caregiver, and health care provider perspectives were included. Engagement style was the most reported theme (23/26, 88%), with users valuing DMHT usability, particularly alignment with therapeutic goals through features including anxiety management tools. Key barriers to DMHT use included limited internet access, poor technical literacy, and privacy concerns. Novel findings included the discreetness of DMHTs to avoid stigma.

Conclusions: Usability, cost, accessibility, technical considerations, and alignment with therapeutic goals are important to users, although DMHT valuation varies across individuals. DMHT apps should be developed and selected with specific user needs in mind.

Introduction

Digital health comprises a broad range of technologies, including mobile health, health information technology, wearable devices, and personalized medicine, which serve as tools to enhance health care delivery. Recently, several digital mental health (MH) therapeutics, a category of digital MH technologies (DMHTs), have received US Food and Drug Administration (FDA) approval to prevent, manage, or treat a medical disorder or disease based on evidence from superiority trials and compliance with technical guidelines [ 1 , 2 ]. However, most DMHTs, particularly apps, fall outside FDA jurisdiction because they are not intended to diagnose, treat, or prevent disease and because they are “low risk” in that they would not cause harm in the event of malfunction [ 3 ]. Due to this lack of regulatory framework, few DMHTs are supported by published efficacy studies. One study found that only 16% of MH apps recommended by college counseling centers were supported by efficacy studies published in peer-reviewed journals [ 4 ].

Nonetheless, many health care providers (HCPs) use MH apps in clinical practice. Up to 83% of behavioral health providers in a small study covering the Greater Boston area reported using apps as part of their clinical care, particularly mindfulness apps for patient anxiety management [ 5 ]. As many DMHTs are currently widely used in clinical practice without undergoing any formal assessment for quality or relevance, understanding how DMHTs should be assessed based on factors impacting their value from the perspective of key stakeholders, such as patients, caregivers, providers, payers, and employers, could improve the selection of DMHTs for use by patients, thereby increasing care quality and outcomes for those seeking MH support.

To address identified gaps, a systematic literature review (SLR) was conducted using a published framework to synthesize emerging themes from mixed methods evidence in order to understand how digital health solutions, encompassing both digital therapeutics and direct-to-consumer digital health technologies, are valued, with a focus on MH disorders, Alzheimer disease, epilepsy, autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD) in the United States.

The SLR was performed in accordance with a prespecified protocol and reported in line with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 6 , 7 ]. The protocol was not registered.

Search Strategy

Electronic databases, encompassing MEDLINE (including MEDLINE In-Process, MEDLINE Daily, and MEDLINE Epub Ahead of Print); Embase; the Cochrane Library (including Cochrane Database of Systematic Reviews and Cochrane Central Register of Controlled Trials); PsycINFO; and the Health Technology Assessment Database, were selected in alignment with this SLR’s target indications and were searched on June 17, 2022. The search terms included combinations of free-text and Medical Subject Heading or Emtree terms related to indications of interest, DMHTs, and relevant outcomes or assessment types (eg, technology assessments and cost; Tables S1-S5 in Multimedia Appendix 1 ). Searches were limited to studies performed in the United States and to those published from 2017 onward.

Manual hand searches of gray literature, namely, the bibliographies of relevant SLRs identified from the electronic database searches and key conference proceedings (2019-2022), were performed to identify additional studies of relevance (Table S6 in Multimedia Appendix 1 ). The FDA website was also searched to identify factors involved in the FDA’s appraisal of relevant MH apps, which could supplement the factors identified in this SLR (Table S7 in Multimedia Appendix 1 ).

Study Selection

Studies were included in the SLR if they met prespecified criteria defined using the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework, which is appropriate for mixed methods research questions. Eligible studies were published in the English language, were set in the United States, and reported quantitative or qualitative outcomes relating to the factors considered in the evaluation of DMHTs. Only studies published in 2017 or later were included because of the rapidly evolving research area. Eligible studies reported on MH, Alzheimer disease, epilepsy, ASD, or ADHD from user, payer, or employer perspectives (Table S8 in Multimedia Appendix 1 ). While the primary focus of the SLR was MH, neurological conditions were also of interest because their pathologies, symptoms, and treatment strategies can overlap with those of mental illnesses. Alzheimer disease, epilepsy, ASD, and ADHD were selected because they are highly researched and represent diverse types of neurological conditions.

The titles and abstracts of records were assessed for inclusion against these eligibility criteria by 2 independent reviewers, and discrepancies were resolved by consensus, with arbitration by a third reviewer if necessary. Full texts of potentially relevant articles were acquired and screened using the same methodology.

Study Prioritization

Due to the large volume of the evidence identified, additional eligibility criteria were applied to prioritize primary research on theoretical DMHT valuation factors. In line with the thematic framework synthesis objective, theoretical valuation factors were defined as user or DMHT attributes that impact interaction with or perception of DMHTs; therefore, studies that reported only efficacy outcomes, such as mental illness symptom improvement, were deprioritized for full-text review. Secondary research was also deprioritized for full-text review. Studies that reviewed a select app against a framework and studies that reported only the outcomes specific to a select app were deprioritized for data extraction. For example, a study reporting the usability of a specific app’s features would have been deprioritized, while a study reporting what types of features increase MH app usability in general would not.

Data Extraction

All relevant data were extracted into a prespecified Microsoft Excel grid, and a quality assessment was performed for each study. Studies that reported only qualitative data were assessed with the Critical Appraisal Skills Program Qualitative Studies Checklist. Studies that reported only quantitative data were evaluated with the Critical Appraisal Skills Program Cohort Study Checklist, and studies reporting both qualitative and quantitative data were evaluated with both checklists [ 8 ]. Data extractions and quality assessments were performed by a single individual for each study, with the information verified by a second independent individual. Discrepancies were resolved by consensus, with arbitration by a third individual if necessary.

Framework Synthesis

A framework synthesis approach was undertaken to synthesize qualitative and quantitative data identified from the SLR. In line with the “best fit” framework synthesis approach, data were indexed deductively against an existing framework where possible, and novel themes were added inductively as needed [ 9 , 10 ]. The American Psychiatric Association (APA) Mental Health App Evaluation framework was considered the most appropriate framework to address the research objectives of this SLR because its key valuation themes were developed using psychiatrist and patient input, are broadly shared by other evaluation frameworks, are widely acknowledged in the literature, and have been described as durable and adaptable [ 11 - 13 ].

The APA model follows a hierarchical and chronological order whereby the evaluator moves through the framework using prompting questions (eg, “Does the app work offline?”). For this SLR, these questions were either thematically grouped into subthemes or left as prompting questions, as appropriate. The framework was therefore ultimately adapted into 3 levels: themes, subthemes, and more granular valuation criteria. It should be emphasized that this SLR did not aim to formally develop an updated framework to be used in practice by HCPs and their patients but rather was used to form a theoretical basis for understanding DMHT valuation factors, for which novel themes were expected to emerge.

A data-based convergent approach was used to synthesize quantitative and qualitative data [ 14 ]. Data were initially indexed deductively against the prespecified themes within the data collection instrument and then further synthesized within Docear [ 15 ], a mind-map software used to organize and connect data and concepts. Indexing was performed by 1 reviewer and checked by a second independent reviewer. New themes and subthemes that emerged from the literature through inductive coding were added post hoc to the thematic framework, with all extracted data then considered against both the prespecified and novel themes. The evidence identified for each theme was synthesized narratively, taking into consideration the context and design of each study.

Included Studies

A total of 4974 records were retrieved from the electronic databases. Of the 3374 (67.83%) unique records identified following deduplication across the databases, 2891 (85.68%) were excluded based on the eligibility criteria, and an additional 456 (13.52%) were deprioritized because they were not directly related to the topic of interest for this SLR. Excluded and deprioritized full texts are listed in Tables S9 and S10 in Multimedia Appendix 1 , respectively. Therefore, 27 (0.54%) articles were included from the electronic database searches. In addition, 1 article reporting on the same study as an already-included conference abstract was identified during supporting targeted searches and included as a supplementary record, resulting in a total of 28 articles reporting on 26 unique studies (Figure S1 in Multimedia Appendix 1 ). No relevant FDA appraisals were identified in the supplementary search.

Of the 26 included studies, 8 (31%) were quantitative, 12 (46%) were qualitative, and 6 (23%) used a mixed methods approach. While 5 (19%) studies assessed prospective cohorts, 22 (85%) used a cross-sectional approach, including 1 (4%) study that contained both a prospective cohort and a cross-sectional cohort ( Table 1 ). All studies (26/26, 100%) investigated a user perspective, with none specifically investigating payer or employer perspectives. Only 1 (4%) study, which examined ingestible sensor pills and smart pill dispensers to track adherence, investigated a DMHT that was not an app [ 16 ].

a Only information relevant to this systematic literature review is reported in this table.

b MDD: major depressive disorder.

c BD: bipolar disorder.

d OCD: obsessive-compulsive disorder.

e MH: mental health.

f General users are participants who were not necessarily diagnosed with indications of interest.

g ASD: autism spectrum disorder.

h HCP: Health care provider.

i mHealth: mobile health.

j PTSD: posttraumatic stress disorder.

k VA: Veterans Affairs.

l ADHD: attention-deficit/hyperactivity disorder.

m PHQ-9: Personal Health Questionnaire-9.

n GAD-7: Generalized Anxiety Disorder-7.

o DMHT: digital mental health technology.

Most frequently, studies focused on indications for mood, anxiety, or psychotic disorders (15/26, 58%), with other indications of focus including ADHD (2/26, 8%), ASD (1/26, 4%), and epilepsy (1/26, 4%). No relevant studies focused on Alzheimer disease were identified.

A total of 8 (31%) studies assessed the perspectives toward DMHTs of general population participants who were not necessarily diagnosed with relevant conditions [ 19 , 28 , 29 , 33 - 37 ]. Of these populations, several were identified as having an increased risk of MH conditions, such as patients with cancer [ 28 ], college students [ 29 , 34 ], deaf or hard-of-hearing individuals [ 19 ], and people who were unemployed or furloughed during the COVID-19 pandemic [ 33 ]. In addition, 1 (4%) study included a mix of patients who were above and below the referral criteria for psychotherapy for depression [ 37 ].

Thematic Analysis

Evidence was identified for all 5 themes included in the APA framework: engagement style (23/26, 88%), background and accessibility (16/26, 62%), privacy and security (13/26, 50%), therapeutic goal (12/26, 46%), and clinical foundation (8/26, 31%; Table 2 ). Five novel criteria were identified and added to the framework post hoc, 1 each under engagement style (forgetting or feeling unmotivated to use DMHTs) and privacy and security (personal image and stigma) and 3 under background and accessibility (willingness to pay, insurance restrictions, and cost savings compared with professional care).

a Novel findings that emerged from this systematic literature review.

b These subthemes and criteria were included in the American Psychiatric Association’s framework but were not reported on by studies included in this systematic literature review.

c HCP: health care provider.

Theme 1: Engagement Style

Engagement style was the most reported theme, with evidence identified from 23 (88%) of the 26 studies. Engagement style encompasses how and why users do or do not interact with DMHTs. The long-term usability subtheme was reported by 96% (22/23) of studies, short-term usability by 12 (52%) studies, and customizability by 7 (30%) studies. Findings from short- and long-term usability subthemes were highly interconnected.

A total of 4 studies reported that ease of use promoted short-term DMHT engagement. In the study by Schueller et al [ 35 ], 89.6% of a general population of smartphone users reported ease of use for MH apps as “important” or “very important,” and users qualitatively reported dislike of “overwhelming,” difficult-to-navigate apps. In addition, users valued apps that were “simplistic” [ 34 ], fit into their daily schedules, and were available when needed (eg, during acute symptom experiences) [ 5 , 25 ]. Select supporting qualitative data are presented in Table 3 .

a ASD: autism spectrum disorder.

b MH: mental health.

c ADHD: attention-deficit/hyperactivity disorder

d BD: bipolar disorder.

e Novel criteria identified by this systematic literature review.

f CHA: Cambridge Health Alliance.

g EMR: electronic medical record.

h HCP: health care provider.

Users valued DMHT features that aligned with their needs and priorities, as reflected by findings within the long-term usability subtheme. Across 9 studies, quantitative and qualitative findings demonstrated high interest in anxiety management features such as relaxation tools, breathing exercises, and mindfulness or meditation activities, and 10 studies identified interest in mood, symptom, or sleep tracking ( Tables 3 and 4 ). While most studies (24/26, 92%) focused on MH, patients with epilepsy also reported high interest in features to record seizure dates and types [ 17 ]. Importantly, users in 2 studies emphasized the need for developers to tailor DMHTs to the needs and priorities of the target population ( Table 3 ) [ 28 , 31 ]. Relatedly, mixed attitudes were reported toward positive affirmations and words of encouragement, with many users expressing interest but others emphasizing the value of a human component to DMHTs or cautioning against blanket encouragement and automated messages that could feel insincere [ 19 , 25 , 31 ].

a A 5-point Likert scale (0-4) was used.

b Not available.

c A 5-point Likert scale (1-5) was used.

d MH: mental health.

e DMHT: digital mental health technology.

f MDD: major depressive disorder.

g PTSD: posttraumatic stress disorder.

Both patients and caregivers expressed interest in psychoeducational content that aligned with their needs and priorities. When surveyed, >60% of veterans with anxiety or major depressive disorder (MDD), patients with epilepsy, young adults with psychosis, and essential and furloughed workers during the COVID-19 pandemic expressed interest in relevant psychoeducational content [ 17 , 22 , 32 , 33 ]. In contrast, only 4% of college students in another study reported using an MH app for information about MH, although an MH diagnosis was not required for study participation [ 29 ].

Caregivers of young adults with psychosis, caregivers of children with ADHD, and spouses and partners of people with bipolar disorder (BD) were all interested in information related to caring for the individual with the given disorder, such as information on psychological and pharmacological treatments, symptoms and symptom changes, and the MH system [ 21 , 24 , 26 ]. Comparatively smaller, but still notable, proportions of caregivers of patients with psychosis were interested in caregiver-focused information; for instance, 62% to 69% were interested in relaxation exercises, stress and mood management, and community events for caregivers, while 85% to 90% were interested in the aforementioned patient-focused information [ 21 ].

Information delivery–style preference was captured under the short-term usability subtheme. One study in young adults with psychosis and another study with their caregivers revealed that delivering information in a variety of formats was important; when presented with nonmutually exclusive options, >50% of both populations were interested in text content, video content, audio content, and discussion boards [ 21 , 22 ].

Social interaction promoted long-term engagement. Qualitatively, 3 studies found that users valued learning about similar experiences from others via social media–like features, which normalized their experiences and could provide new symptom management strategies ( Table 3 ) [ 28 , 31 , 36 ]. Similarly, 67% of both young adults with psychosis and deaf or hard-of-hearing survey participants (N=9) reported interest in peer support via chat features [ 19 , 22 ]. However, a comparatively smaller proportion of veterans with anxiety or MDD (48.3% of the full cohort and 51.3% of the smartphone user subgroup) were interested in peer support [ 32 ].

Overall, users endorsed social features to support their MH. In the study by Casarez et al [ 24 ], spouses and partners of people with BD likewise desired features to communicate with other caregivers and also emphasized that DMHTs could facilitate conversation and understanding with patients, a sentiment echoed by peer support specialists by Storm et al [ 38 ] ( Table 3 ). However, one oncology HCP cautioned that similar to support groups, “very strict guidelines of what is said” should be implemented to manage potential risks from shared social media–like content, although little additional context was provided [ 28 ].

Spouses and partners of people with BD also suggested both in-app information on accessing professional resources and direct counseling for the patient at times when other support might be inaccessible [ 24 ]. More than half of all workers, employed or unemployed during the COVID-19 pandemic, likewise endorsed links to resources, counseling, and crisis support as DMHT features, and 81.6% of young adults with psychosis endorsed a feature to communicate with professional experts [ 22 , 33 ]. Importantly, compared with patients attending public clinics, patients attending private psychiatric clinics expressed a higher comfort level for in-app communication with HCPs, suggesting demographic differences in the valuation of access to professional support through DMHTs [ 39 ].

A total of 9 studies reported an interest in DMHT reminders and notifications. Across 3 studies, >70% of patients or caregivers were interested in appointment reminders [ 17 , 21 , 22 ]. In addition, 73% and 68% of patients with epilepsy reported interest in reminders for medication refills and adherence, respectively [ 17 ]. Beyond apps, caregivers of patients with MDD, BD, and schizophrenia preferred an ingestible pill sensor that tracked medication adherence, physical activity, mood, and rest 9.79 (95% CI 4.81-19.9), 7.47 (95% CI 3.81-14.65), and 6.71 (95% CI 3.29-13.69) times more than a nondigital pill organizer, respectively [ 16 ]. Qualitatively, patients and caregivers also appreciated reminders, especially if reasonably timed or delivered via text messages [ 27 , 31 ].

Short-term DMHT engagement was also supported by games and graphics, which could communicate information in an accessible way [ 24 ], provide tools for stress management [ 17 , 33 ], and be used therapeutically with children [ 20 , 30 ]. However, some HCPs and caregivers expressed concerns that graphics and games may be distracting for certain children ( Table 3 ) [ 20 ].

In a novel finding, 3 studies reported forgetfulness or lack of motivation as an influence on DMHT engagement. In some cases, disuse was related to stress, other MH symptoms, or poor technical literacy ( Table 3 ) [ 5 , 25 , 31 ]. In contrast, “forgetting to use” DMHTs and “lack of motivation” were perceived as relatively small barriers to use in the study by Stiles-Shields et al [ 37 ].

The third subtheme under engagement style was customizability, which was generally valued by users; 70.9% of a general population of smartphone users noted customization was an important factor [ 35 ]. Similarly, 9.4% of all surveyed veterans and 10.9% of those with smartphones reported disliking a prior DMHT due to a lack of personalization [ 32 ]. Users specifically wanted to be able to opt out of irrelevant features, customize audiovisual and design elements, add personal notes to tracked mood data, and provide ongoing feedback to facilitate personalization [ 20 , 24 , 28 , 31 , 34 ].

Theme 2: Background and Accessibility

A total of 16 (62%) studies reported findings related to DMHT background and accessibility, which considers the developer of the DMHT, as well as functionality and accessibility. Of these, 12 (75%) studies reported on the technical considerations subtheme, 9 (56%) on costs, and 2 (13%) on stability.

Under technical considerations, 9 studies assessed diverse accessibility concerns. Broadly, Storm et al [ 38 ] emphasized that DMHTs should be developed in consideration of patients’ social, cognitive, and environmental needs to avoid overwhelming users. Specifically, 2 studies reported language as a barrier. Deaf or hard-of-hearing participants recommended visual content presentation, such as videos and icons, alongside text and American Sign Language translations where possible [ 19 ]. Similarly, when discussing English-only apps, 1 provider stated as follows: “language is a barrier for some [patients]” [ 5 ]. Mobility issues related to MH symptoms or other conditions and technical literacy, such as difficulties remembering passwords and navigating smartphones or apps, created accessibility barriers as well ( Table 3 ) [ 5 , 25 , 27 , 28 ]. Additional concerns included apps that restricted use based on geographic location [ 19 ], user difficulty in finding relevant, useful apps [ 32 ], and limited mobile device memory for downloading apps [ 5 , 19 ].

Offline functionality, reported by 6 studies, was also captured under the technical considerations subtheme. A majority (5/9, 56%) of participants included in the study by Borghouts et al [ 19 ] expressed concern about their mobile data plans when using their devices. Correspondingly, “availability of Wi-Fi” was noted as a top barrier to the use of apps for depression by Stiles-Shields et al [ 37 ], and several veterans in another study reported that home Wi-Fi connectivity facilitated app use by eliminating cellular data fees [ 25 , 37 ]. Quotes from patients and HCPs echoed the concern about apps without offline functionality ( Table 3 ) [ 23 , 30 ].

Data fees were also captured under the costs subtheme, with hidden or additional costs described as a barrier to app use by 2 studies [ 26 , 37 ]. Parents of children with ADHD reported that difficulty paying phone bills could result in their phones being shut off, limiting DMHT use; one MH clinic administrator stated as follows: “We often encounter parents’ phones being shut off because they haven’t paid their bill...If the app were free or low cost, I imagine it could be very helpful” [ 26 ]. In addition to hidden costs, this quote identifies up-front app costs as a barrier. Quantitatively, more than half of a general population of surveyed college students expressed that cost was a top concern for the use of MH apps [ 34 ]. Qualitative findings from 2 additional studies likewise identified cost as a barrier to DMHT use [ 25 , 27 ].

Three novel cost attributes were identified by this SLR: willingness to pay, insurance restrictions, and cost savings compared with professional care. Four studies, 3 of which focused on apps, explored willingness to pay for DMHTs from a user perspective. Willingness to pay varied based on user preference; some surveyed college students and smartphone users among general populations valued free apps due to financial restrictions or uncertainty around app effectiveness, although 1 student commented that the quality of free trials might be inferior [ 34 , 35 ]. Some smartphone users also voiced a limit on how much they would be willing to spend for an app subscription ( Table 3 ) [ 35 ]. Forma et al [ 16 ] found that caregivers were willing to pay US $255.04 (95% CI US $123.21-US $386.86) more per month for a pill with an ingestible sensor that tracked medication adherence, physical activity, and rest and could connect to an app that also collected self-reported mood data. Moreover, the caregivers were willing to pay US $124.50 (95% CI US $48.18-US $200.81) more per month for an app-connected pill organizer alone than for a nondigital pill organizer [ 16 ]. In contrast, some veterans expressed total disinterest in paid apps, with 1 user citing poor technical literacy (“don’t have the knowledge”) in addition to cost as affecting willingness to pay [ 25 ].

In another novel finding, a speech-language pathologist working with children with ASD preferred a single app including multiple features over separate apps for particular features due to insurance restrictions: “I agree that teaching Apps should be an in-App feature versus their own app because sometimes insurance doesn’t allow us to open the iPads purchased through insurance” [ 20 ]. Although no further detail was provided for this finding, it suggests that there may be restrictions on the use of other apps on devices purchased under insurance, which may have implications for DMHT use in formal care settings due to the lack of financial support.

In a third novel cost-related finding, a small number of participants from a general population of students (3.6%) in one study preferred using an MH app to seeing an MH professional due to cost savings [ 29 ].

A total of 13% (2/16) of studies reported on the subtheme of app stability and technical difficulties, with crashes and poor display quality decreasing DMHT value [ 35 , 37 ]. Participants in the study by Schueller et al [ 35 ] reported that technical difficulties were often an issue for apps developed by medical institutions, which might be effective and safe but less usable than apps from other developers.

Theme 3: Privacy and Security

A total of 13 (50%) out of 26 studies reported findings related to the privacy and security theme, which covered the use and protection of user data by DMHTs. Subthemes were reported relatively equally: data collection and storage (5/13, 38%), personal health information (PHI; 5/13, 38%), privacy policies (4/13, 31%), general privacy (3/13, 23%), and security measures (3/13, 23%).

Quantitative and qualitative findings on general privacy (ie, evidence not categorized under any specific subtheme), the data collection and storage subtheme, and the privacy policies subtheme revealed heterogeneous concerns ( Table 3 ). A total of 74% of a general population of college students reported privacy as a top concern for MH apps, although further details on the specific area of concern were unclear [ 34 ]. In the study by Stiles-Shields et al [ 37 ], participants were highly concerned with data access but less so with general privacy. Echoing the concerns about data collection and storage, 59.1% of veterans with anxiety or MDD in 1 study were concerned about in-app PHI protection [ 32 ]; however, a qualitative study in veterans with posttraumatic stress disorder, alcohol use disorder, or MDD reported that a relatively small number of participants expressed privacy concerns. In the latter study, reasons for the concerns included distrust in Veterans Affairs, belief that digital data are inherently not confidential, and fear of phone hacking [ 25 ]. From an HCP perspective, none of the surveyed behavioral health HCPs agreed with the statement “My patients are concerned about data security,” despite multiple patients within the same study reporting privacy concerns [ 5 ].

Still, privacy policies were important overall, with 70.5% of smartphone MH app users rating having a privacy policy as “very important” or “important” [ 35 ]. Melcher et al [ 34 ] found that although users valued data protection, some reported a lack of awareness about data privacy, and others were concerned about obscure privacy policies and PHI use. As noted in the data collection and storage subtheme, veteran concerns about government use of PHI were heterogeneous [ 25 ].

A novel valuation factor not included in the APA framework related to user concern with PHI privacy and security regarding MH diagnoses and MH app use is a desire to upkeep their personal image or avoid stigma ( Table 3 ) [ 5 , 25 , 29 , 40 ]. For instance, 21.1% of a general college student population preferred MH app use to seeing an MH professional due to anonymity or reduced stigma [ 29 ]. One participant in a study of Veterans Affairs health service users described access to professional care via MH apps as convenient because they could avoid disclosing their use of MH services to explain leaving work early for an appointment [ 25 ].

In line with the overarching concern about PHI privacy and security, users valued app security measures. Schueller et al [ 35 ] reported that 74.2% of users rated data encryption as “important” or “very important.” Users in another study perceived the level of privacy protection as the highest for apps using a combination of a generic app name (ie, not reflecting the indicated MH disorder); easily hidden modules; and secure, user-authenticated web portals for making module changes [ 40 ]. Behavioral health clinic staff echoed the importance of discreet MH app names ( Table 3 ) [ 30 ].

Theme 4: Therapeutic Goal

There were 12 (46%) studies that reported on the factors relating to the integration of DMHTs with users’ therapeutic goals. The clinical actionability and therapeutic alliance subthemes were reported by 83% (10/12) and 58% (7/12) of studies, respectively.

A total of 9 studies reported the value of clinically actionable insights from apps where the users could acquire and practice new skills to make positive changes in their lives ( Table 3 ). For instance, patient and caregiver app users reported interests in “daily tips,” “new ideas,” and “solutions or recommendations” for symptom management [ 26 , 27 , 36 ]. Furthermore, an app that could serve as a resource for multiple management strategies was preferable [ 26 , 28 , 31 ]. Quantitatively, 4% of patients receiving acute treatment in a partial hospitalization program for MH conditions, including mood and psychotic disorders, reported that the primary purpose of their DMHT use was therapy skills practice [ 18 ]. HCPs similarly appreciated that DMHTs could facilitate patients practicing skills outside of formal treatment sessions [ 5 ]. In particular, clinicians from a youth behavioral health clinic noted that DMHTs might be especially beneficial for young users because they could be conveniently and discreetly incorporated into their daily lives [ 30 ].

Users valued easy data sharing with clinicians, particularly for mood- or symptom-tracking features, which could improve communication and the accuracy of symptom reporting during clinical visits [ 5 , 25 - 27 , 34 , 36 ]. For instance, 53% of a general college student population believed that the potential to share information with their clinician was “one of the top benefits” of using DMHTs [ 34 ]. In addition, many HCPs reported active use or interest in the use of DMHTs in clinical practice to facilitate asynchronous communication and increase patient engagement with treatments outside of formal appointments; however, some preferred traditional care strategies for their personalization and flexibility ( Table 3 ) [ 5 , 26 , 30 ].

Theme 5: Clinical Foundation

A total of 8 (31%) studies reported findings related to the clinical foundation of DMHTs, that is, their utility and appropriateness for patients. Clinical validity was the most reported subtheme, with evidence identified from 6 (75%) studies; 2 (25%) studies reported on the user feedback subtheme and 2 (25%) on the impressions of use subtheme, which captured users’ perceptions of app content as accurate and relevant.

Across subthemes, users valued evidence of DMHT benefit or efficacy from various sources. A total of 71.8% of surveyed veterans said that they would use a DMHT if they “saw proof that it worked” for their MH conditions [ 32 ]. Similarly, among the 811 general population participants surveyed, 69.5% ranked direct research evidence as “important” or “very important” for DMHT, and 66.8% ranked indirect research evidence the same [ 35 ]. Qualitative data identified recommendations from HCPs or academic institutions, as well as evidence of DMHT benefit from publications or research studies, as specific sources for clinically valid evidence of benefits ( Table 3 ) [ 27 , 34 , 35 ].

In addition to academic and professional support, the user feedback subtheme captured user interest in whether DMHTs were beneficial for peers or recommended by other trusted individuals. Patients with depression reported that other users’ experiences influenced their app use, with one user wanting to know “...if other people had success using it” [ 27 ]. Quantitatively, user ratings and user reviews were ranked as “important” or “very important” factors in DMHT use by 59.4% and 58.7% of the general population participants, respectively [ 35 ].

Quality Assessment

The risk of bias was overall moderate. Of the 14 studies including quantitative components, only 1 (7%) used relevant validated outcome measurement instruments [ 33 ]; all others used custom questionnaires. Of the 18 studies with qualitative components, 4 (22%) were at risk of selection bias due to participants being exclusively recruited using web-based postings and research registries [ 33 - 35 , 37 ], and only 1 (6%) considered the relationship between researcher and participant when interpreting the results [ 36 ]. Full quality assessments for qualitative and quantitative study components can be found in Tables S11 and S12 in Multimedia Appendix 1 , respectively.

Principal Findings

This SLR aimed to identify and synthesize qualitative and quantitative evidence on how DMHTs are valued by users, payers, and employers in the United States. Evidence from users with or without diagnosed relevant disorders, caregivers, and HCPs was captured across a wide range of demographics. No study reported evaluating an app from a payer or employer perspective. Furthermore, all but one included study focused on mobile apps.

No relevant appraisals of DMHTs were identified from the FDA website searches; however, 8 relevant FDA approval labels or notifications for MH apps or guidance documents for industry and FDA staff were identified. The content of these materials overlapped with some valuation factors identified in this SLR, including evidence of clinical efficacy and safety, app maintenance, and privacy and security.

Engagement style, although not covered by the FDA materials, was the most reported theme by the studies included in this SLR and was found to overlap heavily with other themes. Engagement may be a key consideration for app developers, as app user retention can be low: 1 study showed that >90% of users had abandoned free MH apps within 30 days of installation [ 41 ]. Engagement is also a key clinical concern in terms of DMHT efficacy; one meta-analysis of 25 studies showed that increased use of DMHT modules was significantly associated with positive outcomes regardless of the target MH condition [ 42 ]. The findings of this SLR may therefore be informative to both DMHT designers and HCPs who integrate DMHTs into clinical care by providing insight on DMHT valuation and thus how use and benefit can be improved. For instance, users valued DMHTs that were easy to use and aligned with their needs and priorities, particularly through features that supported their therapeutic goals. In addition, content presented through multiple delivery modes, such as both text and visuals, promoted engagement as well as accessibility.

However, engagement and feature preference varied across populations. For instance, DMHT valuation was affected by technical literacy, which may relate to user demographics; in this SLR, veterans repeatedly emphasized technical literacy as a barrier to DMHT use [ 25 ]. Similarly, offline functionality may be more important for some users. Although 85% of the total United States population owns smartphones, only 59% of Medicare beneficiaries have access to a smartphone with a wireless plan. Moreover, beneficiaries who are older, less educated, disabled, or Black or Hispanic have even lower digital access [ 43 , 44 ]. These findings emphasize the importance of customizability and suggest that app development and selection in the clinical setting should consider the demographics of the target population, particularly in relation to ease of use and offline functionality.

Background and accessibility findings also identified up-front and hidden costs as barriers to DMHT use, with the willingness to pay varying among individuals. This has important implications for app development, considering that many MH apps currently on the market are direct-to-consumer sales and require out-of-pocket payment. App developers often take this approach as it does not require the accumulation of formal evidence of clinical benefit for FDA approval [ 45 ], but it may present a financial barrier to use for consumers.

Privacy and security, reported by 13 (50%) out of 26 studies, was a prevalent theme, with users primarily concerned with data and PHI security within apps. This finding reflects wider research; a 2019 review of 116 depression-related apps retrieved from iTunes and Google Play stores in 2017 found that only 4% of the identified apps had acceptable transparency in privacy and security, with many completely lacking a privacy policy [ 46 ]. Similarly, 39% of MH apps recommended by college counseling centers had no privacy policy, and of those with a policy, 88% collected user data, and 49% shared that data with third parties [ 4 ]. Most evidence identified in this SLR under this theme, as well as findings previously published in the wider literature, focuses on these remote privacy risks. However, local privacy concerns are also important to users. In particular, inconspicuous naming and the ability to hide sensitive modules within MH apps were rated as highly important by both patients and HCPs to maintain user privacy. Users emphasized a desire to avoid the stigma associated with mental illness, which was also reflected by the findings in the engagement style theme: more young adults with psychosis were more interested in in-app messaging with other patients in psychosis recovery (67.1%) than a provider and family member together (47.3%) or their personal support network (59.8%) [ 22 ]. Similarly, youths were interested in apps that could be used discreetly in school or other public settings to avoid potential MH stigma. This is a key, novel finding of this SLR, considering that many app or DMHT components on the market are named after their target disorder.

The use of DMHTs to achieve therapeutic goals was discussed from patient, caregiver, and HCP perspectives, all of which valued DMHTs that had evidence of efficacy, presented clinically actionable information, and facilitated patient-clinician relationships. Of the 5 studies that explored how HCPs value DMHTs in clinical practice, 2 (40%) were restricted to the oncology or ASD settings and were not readily generalizable to wider MH settings [ 20 , 28 ]. In other studies, providers reported interest in using DMHTs to facilitate asynchronous communication with patients and their caregivers, promote patient skill practice, and improve care for children through the use of games and visuals [ 26 , 30 ]. However, while HCPs overall believed that DMHTs improved care, some believed that their clinical training allowed for care personalization beyond what DMHTs could provide. Feature customizability and receipt of input from HCPs and users during app development and testing may be a way to mitigate these concerns, as well as concerns about safety and efficacy, as many available apps do not appropriately address user health concerns [ 47 ].

Findings additionally suggested that training and resources on DMHTs would be beneficial to ensure that HCPs were equipped to integrate DMHTs into their practices [ 5 ]. Collaboration between DMHT specialists and HCPs, along with a shift from randomized controlled trials to effectiveness-implementation hybrid trials, may be a way to streamline the integration of DMHTs into clinical care and provide more training and resources for HCPs [ 30 , 48 ].

This review followed a prespecified protocol and used systematic methods in line with the York Centre for Reviews and Dissemination guidelines [ 49 ] to conduct an exhaustive search of the literature, identifying evidence relevant to the review objectives from multiple databases and supplementary sources. The framework synthesis approach allowed for the inclusion and analysis of both qualitative and quantitative data, providing a detailed picture of not only what DMHT features users value but why they value them, especially in areas where valuation varies across patient demographics. In addition, the APA framework is a robust model created with patient and HCP input that incorporates key valuation themes broadly shared by other frameworks and widely acknowledged in the literature [ 11 - 13 ].

Limitations

Methodological limitations should be considered when interpreting the findings of this SLR. Only publications in English and in United States populations were included. As perceptions of value are influenced by factors including cultures, laws, and health care settings, the findings of this SLR should not be generalized to other countries. For instance, trust in HCPs and rates of longstanding relationships between patients and primary care providers are lower in the United States than in many European nations [ 50 , 51 ], which could impact the type of support users want from DMHTs (ie, engagement style) or interest in DMHT integration with therapeutic goals.

In addition to the prespecified eligibility criteria, deprioritization strategies were implemented due to the large volume of the identified evidence, and this may have resulted in missing relevant articles. In particular, the deprioritization of secondary research and opinion pieces likely led to the exclusion of relevant discussion around payer perspectives and reimbursement, for which no evidence was included in this SLR. Furthermore, although unlikely, there may have been reporting biases in the included studies due to missing results, which this SLR was not able to assess.

This SLR identified no evidence for 3 subthemes included in the APA framework: business model (background and accessibility), which covers DMHT funding sources and potential sources of conflict, medical claims (background and accessibility), which examines whether DMHTs claim to be medical and the trustworthiness of their creators, and data ownership, access, and export (therapeutic goal), which includes sharing data with eHealth records or wellness devices (eg, Apple HealthKit [Apple Inc], Fitbit [Google LLC]). The valuation of these subthemes should be evaluated in future research.

Conclusions

In summary, app usability, cost, accessibility and other technical considerations, and alignment with therapeutic goals were the most reported valuation factors identified by this SLR. Many studies also reported user preference for apps that incorporated privacy and security features that provided protection from stigma. However, individual DMHTs and their features are valued differently across individuals based on demographics and personal preferences. MH apps should be developed and selected with these specific user needs in mind. Feature customizability and input from users and HCPs during development may improve app usability and clinical benefit.

Acknowledgments

The authors thank Max Lee, Costello Medical, US, for medical writing and editorial assistance based on the authors’ input and direction.

Conflicts of Interest

DCM is a consultant for Otsuka Pharmaceutical Development & Commercialization (OPDC) Inc for this project and has received consulting funds from Pear Therapeutics, Sanofi, Avidity, Sarepta, Novartis, and BioMarin. ML, HG, and HCW are employees of OPDC. JC, SB, RSK, and EW are employees of Costello Medical. This research was funded by OPDC.

Electronic database and supplementary search terms, systematic literature review eligibility criteria, publications excluded or deprioritized at full-text review, quality assessments of included studies, and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of the identified publications.

PRISMA checklist.

• Content of premarket submissions for device software functions: guidance for industry and Food and Drug Administration staff. U.S. Food and Drug Administration. Jun 14, 2023. URL: https://www.fda.gov/media/153781/download [accessed 2024-07-19]
• Patel NA, Butte AJ. Characteristics and challenges of the clinical pipeline of digital therapeutics. NPJ Digit Med. Dec 11, 2020;3(1):159. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Policy for device software functions and mobile medical applications: guidance for industry and Food and Drug Administration staff. U.S. Food and Drug Administration. URL: https://www.fda.gov/media/80958/download [accessed 2023-01-06]
• Melcher J, Torous J. Smartphone apps for college mental health: a concern for privacy and quality of current offerings. Psychiatr Serv. Nov 01, 2020;71(11):1114-1119. [ CrossRef ] [ Medline ]
• Hoffman L, Benedetto E, Huang H, Grossman E, Kaluma D, Mann Z, et al. Augmenting mental health in primary care: a 1-year study of deploying smartphone apps in a multi-site primary care/behavioral health integration program. Front Psychiatry. 2019;10:94. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Tong A, Flemming K, McInnes E, Oliver S, Craig J. Enhancing transparency in reporting the synthesis of qualitative research: ENTREQ. BMC Med Res Methodol. Nov 27, 2012;12(1):181. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. Jul 21, 2009;339(jul21 1):b2535. [ FREE Full text ] [ CrossRef ] [ Medline ]
• CASP critical appraisal checklists. Critical Appraisal Skills Programme. URL: https://casp-uk.net/casp-tools-checklists/ [accessed 2022-01-06]
• Carroll C, Booth A, Cooper K. A worked example of "best fit" framework synthesis: a systematic review of views concerning the taking of some potential chemopreventive agents. BMC Med Res Methodol. Mar 16, 2011;11(1):29. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Carroll C, Booth A, Leaviss J, Rick J. "Best fit" framework synthesis: refining the method. BMC Med Res Methodol. Mar 13, 2013;13(1):37. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kolasa K, Kozinski G. How to value digital health interventions? a systematic literature review. Int J Environ Res Public Health. Mar 23, 2020;17(6):2119. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lagan S, Aquino P, Emerson MR, Fortuna K, Walker R, Torous J. Actionable health app evaluation: translating expert frameworks into objective metrics. NPJ Digit Med. 2020;3:100. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lagan S, Emerson MR, King D, Matwin S, Chan SR, Proctor S, et al. Mental health app evaluation: updating the American Psychiatric Association's framework through a stakeholder-engaged workshop. Psychiatr Serv. Sep 01, 2021;72(9):1095-1098. [ CrossRef ] [ Medline ]
• Hong QN, Pluye P, Bujold M, Wassef M. Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence. Syst Rev. Mar 23, 2017;6(1):61. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Beel J, Gipp B, Langer S, Genzmehr M. Docear: an academic literature suite for searching, organizing and creating academic literature. In: Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries. 2011. Presented at: JCDL '11; June 13-17, 2011:565-566; Ottawa, ON. URL: https://dl.acm.org/doi/10.1145/1998076.1998188 [ CrossRef ]
• Forma F, Chiu K, Shafrin J, Boskovic DH, Veeranki SP. Are caregivers ready for digital? caregiver preferences for health technology tools to monitor medication adherence among patients with serious mental illness. Digit Health. 2022;8:20552076221084472. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Afra P, Bruggers CS, Sweney M, Fagatele L, Alavi F, Greenwald M, et al. Mobile software as a medical device (SaMD) for the treatment of epilepsy: development of digital therapeutics comprising behavioral and music-based interventions for neurological disorders. Front Hum Neurosci. 2018;12:171. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Beard C, Silverman AL, Forgeard M, Wilmer MT, Torous J, Björgvinsson T. Smartphone, social media, and mental health app use in an acute transdiagnostic psychiatric sample. JMIR Mhealth Uhealth. Jun 07, 2019;7(6):e13364. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Borghouts J, Neary M, Palomares K, de Leon C, Schueller SM, Schneider M, et al. Understanding the potential of mental health apps to address mental health needs of the deaf and hard of hearing community: mixed methods study. JMIR Hum Factors. Apr 11, 2022;9(2):e35641. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Boster JB, McCarthy JW. Designing augmentative and alternative communication applications: the results of focus groups with speech-language pathologists and parents of children with autism spectrum disorder. Disabil Rehabil Assist Technol. May 10, 2018;13(4):353-365. [ CrossRef ] [ Medline ]
• Buck B, Chander A, Monroe-DeVita M, Cheng SC, Stiles B, Ben-Zeev D. Mobile health for caregivers of young adults with early psychosis: a survey study examining user preferences. Psychiatr Serv. Aug 01, 2021;72(8):955-959. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Buck B, Chander A, Tauscher J, Nguyen T, Monroe-DeVita M, Ben-Zeev D. mHealth for young adults with early psychosis: user preferences and their relationship to attitudes about treatment-seeking. J Technol Behav Sci. 2021;6(4):667-676. [ CrossRef ] [ Medline ]
• Carpenter-Song E, Noel VA, Acquilano SC, Drake RE. Real-world technology use among people with mental illnesses: qualitative study. JMIR Ment Health. Nov 23, 2018;5(4):e10652. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Casarez RL, Barlow E, Iyengar SM, Soares JC, Meyer TD. Understanding the role of m-health to improve well-being in spouses of patients with bipolar disorder. J Affect Disord. May 01, 2019;250:391-396. [ CrossRef ] [ Medline ]
• Connolly SL, Miller CJ, Koenig CJ, Zamora KA, Wright PB, Stanley RL, et al. Veterans' attitudes toward smartphone app use for mental health care: qualitative study of rurality and age differences. JMIR Mhealth Uhealth. Aug 22, 2018;6(8):e10748. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cummings JR, Gaydos LM, Mensa-Kwao A, Song M, Blake SC. Perspectives on caregiver-focused mHealth technologies to improve mental health treatment for low-income youth with ADHD. J Technol Behav Sci. Mar 9, 2019;4(1):6-16. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Dinkel D, Harsh Caspari J, Fok L, Notice M, Johnson DJ, Watanabe-Galloway S, et al. A qualitative exploration of the feasibility of incorporating depression apps into integrated primary care clinics. Transl Behav Med. Sep 15, 2021;11(9):1708-1716. [ CrossRef ] [ Medline ]
• Huberty J, Bhuiyan N, Neher T, Joeman L, Mesa R, Larkey L. Leveraging a consumer-based product to develop a cancer-specific mobile meditation app: prototype development study. JMIR Form Res. Jan 14, 2022;6(1):e32458. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kern A, Hong V, Song J, Lipson SK, Eisenberg D. Mental health apps in a college setting: openness, usage, and attitudes. Mhealth. Jun 2018;4:20. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Knapp AA, Cohen K, Nicholas J, Mohr DC, Carlo AD, Skerl JJ, et al. Integration of digital tools into community mental health care settings that serve young people: focus group study. JMIR Ment Health. Aug 19, 2021;8(8):e27379. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kornfield R, Meyerhoff J, Studd H, Bhattacharjee A, Williams JJ, Reddy MC, et al. Meeting users where they are: user-centered design of an automated text messaging tool to support the mental health of young adults. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 2022. Presented at: CHI '22; April 29-May 5 2022:1-6; New Orleans, LA. URL: https://dl.acm.org/doi/abs/10.1145/3491102.3502046
• Lipschitz J, Miller CJ, Hogan TP, Burdick KE, Lippin-Foster R, Simon SR, et al. Adoption of mobile apps for depression and anxiety: cross-sectional survey study on patient interest and barriers to engagement. JMIR Ment Health. Jan 25, 2019;6(1):e11334. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mata-Greve F, Johnson M, Pullmann MD, Friedman EC, Griffith Fillipo I, Comtois KA, et al. Mental health and the perceived usability of digital mental health tools among essential workers and people unemployed due to COVID-19: cross-sectional survey study. JMIR Ment Health. Aug 05, 2021;8(8):e28360. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Melcher J, Camacho E, Lagan S, Torous J. College student engagement with mental health apps: analysis of barriers to sustained use. J Am Coll Health. Oct 13, 2022;70(6):1819-1825. [ CrossRef ] [ Medline ]
• Schueller SM, Neary M, O'Loughlin K, Adkins EC. Discovery of and interest in health apps among those with mental health needs: survey and focus group study. J Med Internet Res. Jun 11, 2018;20(6):e10141. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Schueller SM, Neary M, Lai J, Epstein DA. Understanding people's use of and perspectives on mood-tracking apps: interview study. JMIR Ment Health. Aug 11, 2021;8(8):e29368. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Stiles-Shields C, Montague E, Lattie EG, Kwasny MJ, Mohr DC. What might get in the way: barriers to the use of apps for depression. Digit Health. Jun 08, 2017;3:2055207617713827. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Storm M, Venegas M, Gocinski A, Myers A, Brooks J, Fortuna KL. Stakeholders' perspectives on partnering to inform the software development lifecycle of smartphone applications for people with serious mental illness: enhancing the software development lifecycle through stakeholder engagement. In: Proceedings of the 2021 IEEE Global Humanitarian Technology Conference. 2021. Presented at: GHTC '21; October 19-23, 2021:195-199; Seattle, WA. URL: https://ieeexplore.ieee.org/document/9612444 [ CrossRef ]
• Torous J, Wisniewski H, Liu G, Keshavan M. Mental health mobile phone app usage, concerns, and benefits among psychiatric outpatients: comparative survey study. JMIR Ment Health. Nov 16, 2018;5(4):e11715. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhou L, Parmanto B. User preferences for privacy protection methods in mobile health apps: a mixed-methods study. Int J Telerehabil. Dec 08, 2020;12(2):13-26. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with mental health apps: systematic search and panel-based usage analysis. J Med Internet Res. Sep 25, 2019;21(9):e14567. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gan DZ, McGillivray L, Han J, Christensen H, Torok M. Effect of engagement with digital interventions on mental health outcomes: a systematic review and meta-analysis. Front Digit Health. 2021;3:764079. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mobile fact sheet. Pew Research Center. 2021. URL: https://www.pewresearch.org/internet/fact-sheet/mobile/ [accessed 2024-04-29]
• Roberts ET, Mehrotra A. Assessment of disparities in digital access among Medicare beneficiaries and implications for telemedicine. JAMA Intern Med. Oct 01, 2020;180(10):1386-1389. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Powell AC, Torous JB, Firth J, Kaufman KR. Generating value with mental health apps. BJPsych Open. Feb 05, 2020;6(2):e16. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Loughlin K, Neary M, Adkins EC, Schueller SM. Reviewing the data security and privacy policies of mobile apps for depression. Internet Interv. Mar 2019;15:110-115. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Akbar S, Coiera E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. J Am Med Inform Assoc. Feb 01, 2020;27(2):330-340. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. Mar 2012;50(3):217-226. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Systematic reviews: CRD's guidance for undertaking reviews in health care. Centre for Reviews and Dissemination. 2008. URL: https://www.york.ac.uk/media/crd/Systematic_Reviews.pdf [accessed 2024-04-29]
• Gumas ED, Lewis C, Horstman C, Gunja MZ. Finger on the pulse: the state of primary care in the U.S. and nine other countries. The Commonwealth Fund. URL: https:/​/www.​commonwealthfund.org/​publications/​issue-briefs/​2024/​mar/​finger-on-pulse-primary-care-us-nine-countries [accessed 2024-04-29]
• Huang EC, Pu C, Chou YJ, Huang N. Public trust in physicians-health care commodification as a possible deteriorating factor: cross-sectional analysis of 23 countries. Inquiry. Mar 05, 2018;55:46958018759174. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by J Torous; submitted 15.02.24; peer-reviewed by A Mathieu-Fritz, K Stawarz; comments to author 05.05.24; revised version received 20.06.24; accepted 21.06.24; published 30.08.24.

©Julianna Catania, Steph Beaver, Rakshitha S Kamath, Emma Worthington, Minyi Lu, Hema Gandhi, Heidi C Waters, Daniel C Malone. Originally published in JMIR Mental Health (https://mental.jmir.org), 30.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.

The Declining Mental Health of the Young in the UK

We show the incidence of mental ill-health has been rising especially among the young in the years and especially so in Scotland. The incidence of mental ill-health among young men in particular, started rising in 2008 with the onset of the Great Recession and for young women around 2012. The age profile of mental ill-health shifts to the left, over time, such that the peak of depression shifts from mid-life, when people are in their late 40s and early 50s, around the time of the Great Recession, to one’s early to mid-20s in 2023. These trends are much more pronounced if one drops the large number of proxy respondents in the UK Labour Force Surveys, indicating fellow family members understate the poor mental health of respondents, especially if those respondents are young. We report consistent evidence from the Scottish Health Surveys and UK samples from Eurobarometer surveys. Our findings are consistent with those for the United States and suggest that, although smartphone technologies may be closely correlated with a decline in young people’s mental health, increases in mental ill-health in the UK from the late 1990s suggest other factors must also be at play.

David G. Blanchflower and Alex Bryson would like to thank the Human Development Report Office, United Nations Development Programme for support. The copyright for all research commissioned by the Human Development Report Office will be held by UNDP. We thank the ESRC Data Archive for access to the data. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

• DOI: 10.55041/ijsrem29138
• Corpus ID: 268429998

An Investigation on Students Learnings & Mental Health Influenced by Artificial Intelligence

• Ijsrem Journal
• Published in INTERANTIONAL JOURNAL OF… 7 March 2024
• Computer Science, Education, Psychology

Related Papers

Showing 1 through 3 of 0 Related Papers

The Prevailing Causes of Mental Health Illnesses in Pakistan and the Way Forward to Deal with the Menace

12 Pages Posted: 4 Sep 2024

Mentor Pakistan

Independent.

Date Written: August 06, 2024

This research article investigates the complex causes behind the mental health crisis in Pakistan, shedding light on the intersection of socioeconomic , cultural, and biological factors that contribute to widespread mental health disorders. The study examines the pervasive stigma surrounding mental illness, the inadequacy of mental health services, and the impact of economic hardships and social pressures on mental well-being. It also explores the effects of the COVID-19 pandemic and the particular vulnerabilities of marginalized groups, such as women, children, and the elderly. By critically analyzing existing challenges and evaluating current mental health initiatives, this article aims to offer actionable recommendations for improving mental health outcomes in Pakistan. The research underscores the importance of a multi-faceted approach that includes public awareness, policy reform, and expanding mental health services to address this pressing issue effectively.

Suggested Citation: Suggested Citation

Mentor Pakistan (Contact Author)

Do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, social sciences education ejournal.

Subscribe to this fee journal for more curated articles on this topic

Law & Psychology eJournal

Clinical & counseling psychology ejournal, comparative political economy: social welfare policy ejournal, digital health ejournal, political economy - development: health ejournal, medical & mental health sociology ejournal.

The Prevalence of Mental Health Issues among the Tertiary Level Students of Bangladesh: A Qualitative Study on Public Universities of Bangladesh

• August 2024
• 2(3):122-146

• Islamic University (Bangladesh)

• Comilla University

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Sadika Akhter

• Sohel Daria

• Int J Ment Health Addiction

• BMC Int Health Hum Right

• William G. Axinn

• Muhammad Faisol Chowdhury
• Tasnim Rezoana Tanim
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Stress, Anxiety, and Depression Among Undergraduate Students during the COVID-19 Pandemic and their Use of Mental Health Services

Jungmin lee.

1 Department of Educational Policy Studies and Evaluation, University of Kentucky, 597 S. Upper Street, 131 Taylor Education Building, Lexington, KY 40506-0001 USA

Hyun Ju Jeong

2 Department of Integrated Strategic Communication, University of Kentucky, Lexington, KY USA

3 Division of Biomedical Informatics, University of Kentucky, Lexington, KY USA

Associated Data

Not applicable.

The coronavirus 2019 (COVID-19) has brought significant changes to college students, but there is a lack of empirical studies regarding how the pandemic has affected student mental health among college students in the U.S. To fill the gap in the literature, this study describes stress, anxiety, and depression symptoms for students in a public research university in Kentucky during an early phase of COVID-19 and their usage of mental health services. Results show that about 88% of students experienced moderate to severe stress, with 44% of students showing moderate to severe anxiety and 36% of students having moderate to severe depression. In particular, female, rural, low-income, and academically underperforming students were more vulnerable to these mental health issues. However, a majority of students with moderate or severe mental health symptoms never used mental health services. Our results call for proactively reaching out to students, identifying students at risk of mental health issues, and providing accessible care.

The coronavirus 2019 (COVID-19) has brought significant and sudden changes to college students. To protect and prevent students, faculty, and staff members from the disease, higher education institutions closed their campus in the spring of 2020 and made a quick transition to online classes. Students were asked to evacuate on a short notice, adjust to new online learning environments, and lose their paid jobs in the middle of the semester. The pandemic has also raised concerns among college students about the health of their family and friends (Brown & Kafka, 2020 ). Because all these changes were unprecedented and intensive, they caused psychological distress among students, especially during the first few months of the pandemic. There is abundant anecdotal evidence describing students’ stress and emotional difficulties as impacted by COVID-19, but there are only a few empirical studies available that directly measure college student mental health since the outbreak (e.g., Huckins et al., 2020 ; Kecojevic et al., 2020 ; Son et al., 2020 ). Most existing studies focus on mental health for general populations (e.g., Gao et al., 2020 ) or health care workers (e.g., Chen et al., 2020 ), whose results may not be applicable to college students. Given that college students are particularly vulnerable to mental health issues (e.g., Kitzrow, 2003 ), it is important to explore their mental health during this unprecedented crisis.

In this study, we describe the prevalence of stress, anxiety, and depression for undergraduate students in a public research university during the six weeks after the COVID-19 outbreak alongside their usage of mental health services. Using a self-administered online survey, we measured stress, anxiety, and depression levels with well-established clinical tools and asked the extent to which college students used on-campus and off-campus mental health services for the academic year. Our results revealed that more than eight out of ten students surveyed experienced modest or severe stress, and approximately 36–44% of respondents showed moderate or severe anxiety and depression. However, more than 60% of students with moderate or severe stress, anxiety, or depression had never utilized mental health services on- or off-campus. Although focusing on a single institution, this paper is one of the few studies that empirically examine mental health of college students in the U.S. during the early phase of the pandemic. Findings from this paper reassure the seriousness of student mental health during the pandemic and call for a proactive mental health assessment and increased support for college students.

Literature Review

Covid-19 and student mental health.

Empirical studies reported a high prevalence of college mental health issues during the early phase of COVID-19 around the world (Cao et al., 2020 ; Chang et al., 2020 ; Liu et al., 2020 , Rajkumar, 2020 ; Saddik et al., 2020 ). In the U.S. a few, but a growing number of empirical surveys and studies were conducted to assess college students’ mental health during the pandemic. Three nationwide surveys conducted across the U.S. conclude that college student mental health became worse during the pandemic. According to an online survey administered by Active Minds in mid-April of 2020, 80% of college students across the country reported that COVID-19 negatively affected their mental health, with 20% reporting that their mental health had significantly worsened (Horn, 2020 ). It is also concerning that 56% of students did not know where to go if they had immediate needs for professional mental health services (Horn, 2020 ). Another nationwide survey conducted from late-May to early-June also revealed that 85% of college students felt increased anxiety and stress during the pandemic, but only 21% of respondents sought a licensed counselor or a professional (Timely MD, n.d. ) According to the Healthy Minds Network’s survey (2020), which collected data from 14 college campuses across the country between March and May of 2020, the percentage of students with depression increased by 5.2% compared to the year before. However, 58.2% of respondents never tried mental health care and about 60% of students felt that it became more difficult to access to mental health care since the pandemic. These survey results clearly illustrate that an overwhelming majority of college students in the U.S. have experienced mental health problems during the early phase of COVID-19, but far fewer students utilized professional help. Despite the timely and valuable information, only Healthy Minds Network ( 2020 ) used clinical tools to measure student mental health, and none of them explored whether student characteristics were associated with mental health symptoms.

To date, only a few scholarly research studies focus on college student mental health in the U.S. since the COVID-19 outbreak. Huckins et al. ( 2020 ) have longitudinally tracked 178 undergraduate students at Dartmouth University for the 2020 winter term (from early-January to late-March of 2020) and found elevated anxiety and depression scores during mid-March when students were asked to leave the campus due to the pandemic. The evacuation decision coincided with the final week, which could have intensified student anxiety and depression. The anxiety and depression scores gradually decreased once the academic term was over, but they were still significantly higher than those measured during academic breaks in previous years. Conducting semi-structured interviews with 195 students at a large public university in Texas, Son et al. ( 2020 ) found that 71% of students surveyed reported increased stress and anxiety due to the pandemic, but only 5% of them used counseling services. The rest of the students explained that they did not use counseling services because they assumed that others would have similar levels of stress and anxiety, they did not feel comfortable talking with unfamiliar people or over the phone, or they did not trust counseling services in general. Common stressors included concerns about their own health or their loved ones’, sleep disruption, reduced social interactions, and difficulty in concentration. Based on a survey from 162 undergraduate students in New Jersey, Kecojevic et al. ( 2020 ) found that female students had a significantly higher level of stress than male students and that upper-class undergraduate students showed a higher level of anxiety than first-year students. Having difficulties in focusing on academic work led to increased levels of stress, anxiety, and depression (Kecojevic et al., 2020 ).

College Student Mental Health and Usage of Mental Health Services Before COVID-19

College student mental health has long been studied in education, psychology, and medicine even before the pandemic. The general consensus of the literature is that college student mental health is in crisis, worsening in number and severity over time. Before the pandemic in the academic year of 2020, more than one-third of college students across the country were diagnosed by mental health professionals for having at least one mental health symptom (American College Health Association, 2020 ). Anxiety (27.7%) and depression (22.5%) were most frequently diagnosed. The proportion of students with mental health problems is on the rise as well. Between 2009 and 2015, the proportion of students with anxiety or depression increased by 5.9% and 3.2%, respectively (Oswalt et al., 2020 ). Similarly, between 2012 and 2020, scores for depression, general anxiety, and social anxiety have constantly increased among those who visited counseling centers on college campuses (Center for College Mental Health [CCMH], 2021 ).

Some groups are more vulnerable to mental health problems than others. For example, female and LGBTQ students tend to report a higher prevalence of mental health issues than male students (Eisenberg et al., 2007b ; Evans et al., 2018 ; Wyatt et al., 2017 ). However, there is less conclusive evidence on the difference across race or ethnicity. It is well-supported that Asian students and international students report fewer mental health problems than White students and domestic students, but there are mixed results regarding the difference between underrepresented racial minority students (i.e., African-American, Hispanic, and other races) and White students (Hyun et al., 2006 ; Hyun et al., 2007 ). Many researchers find either insignificant differences (e.g., Eisenberg et al., 2007b ) or fewer mental health issues reported for underrepresented minority students compared to White students (e.g., Wyatt et al., 2017 ). This may not necessarily mean that racial minority students tend to have fewer mental health problems, but it may reflect their cultural tendency against disclosing one’s mental health issues to others (Hyun et al., 2007 ; Wyatt & Oswalt, 2013 ). In terms of age, some studies (e.g., Eisenberg et al., 2007b ) reveal that students who are 25 years or older tend to have fewer mental health issues than younger students, while others find it getting worse throughout college (Wyatt et al., 2017 ). Lastly, financial stress significantly increases depression, anxiety, and suicidal thoughts among college students (Eisenberg et al., 2007b ).

Despite the high prevalence of mental health issues, college students tend to underutilize mental health services (Cage et al., 2018 ; Hunt & Eisenberg, 2010 ; Lipson et al., 2019 ; Oswalt et al., 2020 ). The Healthy Minds Study 2018–2019, which collected data from 62,171 college students across the country, reports that 57% of students with positive anxiety or depression screens have not used counseling or therapy, and 64% of them have not taken any psychotropic medications within the past 12 months (Healthy Minds, 2019 ). Even when students had visited a counseling center, about one-fourth of them did not return for a scheduled appointment, and another 14.1% of students declined further services (CCMH, 2021 ). When asked the barriers that prevented them from seeking mental health services, students reported a lack of perceived needs for help (41%), preference to deal with mental health issues on their own or with families and friends (27%), a lack of time (23%), financial difficulty (15%), and a lack of information about where to go (10%). Students who never used mental health services were not sure if their insurance covered mental health treatment or were more skeptical about the effectiveness of treatment (Eisenberg et al., 2007a ). Stigma, students’ view about getting psychological help for themselves, is another significant barrier in seeking help and utilizing mental health services (Cage et al., 2018 ).

Current Study

While previous studies have advanced our understanding of student mental health and their usage of mental health services, we find a lack of empirical studies on these matters, particularly in the context of COVID-19. The goal of this study is to fill the gap with specific investigations into the prevalence and pattern of U.S. college student mental health with regard to counseling service use during the early phase of COVID-19. First, very few studies focus on college students and their mental health during the pandemic, and most nationwide surveys conducted in the U.S. did not use clinically validated tools to measure student mental health. In this study, we have employed the three clinical measures to assess stress, anxiety, and depression, which are the most prevalent mental health problems among college student populations (Leviness et al., 2017 ). Secondly, it should be noted that while empirical research conducted in U.S. institutions clearly demonstrate that college students were under serious mental distress during the pandemic (Huckins et al., 2020 ; Son et al., 2020 ; Kecojevic et al., 2020 ), such studies have relatively small sample sizes and rarely examined whether particular groups were more vulnerable than others during the pandemic. To overcome such limitations, the present study has recruited a relatively large number of students from all degree-seeking students enrolled at the study institution. Further, given the high prevalence of mental health issues, we have identified vulnerable student groups and provided suggestions regarding necessary support for these students in an effort to reduce mental health disparity. Lastly, previous studies (e.g., Healthy Minds, 2019 ) show that college students, even those with mental health issues, tended to underutilize counseling services before the pandemic. Yet, there is limited evidence regarding whether this continued to be the case during COVID-19. Our study provides empirical evidence regarding the utilization of mental health services during the early phase of the pandemic and identifies its predictors. Based on the preceding discussions, we address the following research questions in this study:

First, how prevalent were stress, anxiety, and depression among college students during the early phase of the pandemic? Second, to what extent have students utilized mental health services on- and off-campus? Third, what are the predictors of mental health symptoms and the usage of mental health services?

We collected data via a self-administered online survey. This survey was designed to measure student mental health, the usage of mental health services, and demographics. The survey was sent to all degree-seeking students enrolled in a public research university in Kentucky for the spring of 2020. An invitation email was first sent on March 23, which was two days after the university announced campus closure, and two more reminder emails were sent in mid-April and late-April. The survey was available until May 8th, which was the last day of the semester.

A total of 2691 students (out of 24,146 qualified undergraduate and graduate degree-seeking students enrolled for the semester) responded to the survey. The response rate was 11.14%, but this is acceptable as it is within the range of Internet survey response rates, which is anywhere from 1 to 30% (Wimmer & Dominick, 2006 ). We deleted responses from 632 students who did not answer any mental health questions, which left 2059 valid students for the analysis. In this study, we focused on undergraduate students because they are significantly different from graduate students in terms of demographics (e.g., racial composition, age, and income) and major stressors (Wyatt & Oswalt, 2013 ). As a result, 1412 undergraduate students are included in our sample. 90% of these students had complete data. The rest of students skipped a couple of questions (usually related to their residency) but answered most of the question. Thus, we conducted multiple imputation, created ten imputed data sets, and ran regression models using these imputed data (Allison, 2002 ). Our regression results using imputed data are qualitatively similar to the estimates using original data; however, for comparison, we also provided the regression estimates using original data in Appendix Tables  6 and ​ and7. 7 . Please note that we still used original data for descriptive research questions (presented in Tables  1 , ​ ,2, 2 , and ​ and4) 4 ) to accurately describe the prevalence of mental health symptoms and use of counseling services.

Descriptive statistics of sample characteristics

Descriptive statistics for stress, anxiety, and depression prevalence

Usage of mental health services among students with moderate or severe symptoms

Ordinal logistic regression models for severity of mental health symptoms (original data)

Odds ratio are reported, and numbers in parentheses are standard error

+ p  < 0.1, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Logistic regression models predicting the usage of mental health services (original data)

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table  1 provides descriptive statistics for students in our data. Female (73%), White (86%), and students who are below 25 years old (95%) are the vast majority of our sample. About one in four students are rural students and/or students from Appalachian areas (27%) and first-generation students (23%). Wealthier students (whose family income was $100,000 or more) make up about 44% of the sample (44%). Compared to the undergraduate student population at the study site, female students (56.3% at the study site) are overrepresented in our study. The proportion of White students is slightly higher in our sample (86%) than the study population (84%), and that of first-generation students is slightly lower in our sample (23%) than that in the study population (26%).

There are five key outcome variables for this study. The first three outcome variables are stress, anxiety, and depression, and the other two variables are the extent to which students used on-campus and off-campus mental health services for the academic year, respectively. Our mental health measures are well-established and widely used in a clinical setting. For stress, we used the Perceived Stress Scale (PSS) that includes ten items asking students’ feelings and perceived stress measured on a 5-point Likert scale from 0 (strongly disagree) to 4 (strongly agree) (Cohen et al., 1983 ). Using the sum of scores from the ten items, the cut-off score for low, moderate, and high stress is 13, 26, and 40, respectively. PSS scale was used in hundreds of studies and validated in many languages (Samaha & Hawi, 2016 ). PSS also has a high internal consistency reliability. Of the recent studies that used the instrument to measure mental health of U.S. college students, Cronbach’s alpha was around 0.83 to 0.87, which exceeded the commonly used cut-off of 0.70 (Adams et al., 2016 ; Burke et al., 2016 ; Samaha & Hawi, 2016 ).

We used the General Anxiety Disorder 7-item (GAD-7) scale to measure anxiety. This is a brief self-report scale to identify probable cases of anxiety disorders (Spitzer et al., 2006 ). The GAD scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. In a clinical setting, anyone with a score of 10 or above are recommended for further evaluation. GAD is moderately good at screening three other common anxiety disorders - panic disorder (sensitivity 74%, specificity 81%), social anxiety disorder (sensitivity 72%, specificity 80%), and post-traumatic stress disorder (sensitivity 66%, specificity 81%) (Spitzer et al., 2006 ) In their recent study, Johnson, et al. ( 2019 ) validated that “the GAD-7 has excellent internal consistency, and the one-factor structure in a heterogeneous clinical population was supported” (p. 1).

Lastly, depression was assessed with the eight-item Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form (Pilkonis et al., 2014 ). A score less than 17 is considered as none to slight depression, a score between 17 and 21 is considered as mild depression, a score between 22 and 32 is considered as moderate depression, and a score of 33 or above is considered as severe depression. PROMIS depression scale is a universal, rather than a disease-specific, measure that was developed using item response theory to promote greater precision and reduce respondent burden (Shensa et al., 2018 ). The scale has been correlated and validated with other commonly used depression instruments, including the Center for Epidemiological Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI-II), and the Patient Health Questionnaire (PHQ-9) (Lin et al., 2016 ).

When it comes to the usage of psychological and counseling services, we asked students to indicate the extent to which they used free on-campus resources (e.g., counseling center) and off-campus paid health professional services (e.g., psychiatrists) anytime during the academic year on a scale of 1 (never) to 5 (very often), respectively. These questions do not specifically ask if students utilized these services after the COVID-19 outbreak, but responses for these questions indicate whether and how often students had used any of these services for the academic year until they responded to our survey.

We also collected data about student demographics and characteristics including student gender, race or ethnicity, age, class levels (freshman, sophomore, junior, and senior), first generation student status (1 = neither parent has a bachelor’s degree, 0 = at least one parent with a bachelor’s degree), family income, residency (rural and/or Appalachian students, international students), GPAs, and perceived stigma about seeking counseling or therapy (i.e., “I am afraid of what my family and friends will say or think of me if I seek counseling/therapy”) measured on a 5-point Likert scale. We used these variables to see if they were associated with a high level of stress, anxiety, and depression and the usage of mental health services.

We used descriptive statistics, ordinal logistic regression, and logistic regression models in this study. To address the first and second research questions, we used descriptive statistics and presented the prevalence of stress, anxiety, and depression as well as the frequency of using mental health services. For the third research question, we adopted ordinal logistic regression and logistic regression models depending on outcome variables. We used ordinal logistic regression models to identify correlates of different levels of stress, anxiety, and depression, which were measured in ordinal variables (e.g., mild, moderate, and severe). For the usage of mental health service outcomes, we employed logistic regression models. Because more than two-thirds of students in the sample never utilized either type of mental health services, we re-coded the usage variables into binary variables (1 = used services, 0 = never used services) and ran logistic regression models.

Limitations

Our study is not without limitations. First, we do not claim a causal relationship in this study, but we describe the state of mental health for students soon after the COVID-19 outbreak. We acknowledge that many students may have suffered from mental health problems before the pandemic, with some experiencing escalation after the outbreak (e.g., Horn, 2020 ). Even if our study does not provide a causal relationship, we believe that it is important to measure and document student mental health during the pandemic so that practitioners can be aware of the seriousness of this issue and consider ways to better serve students. Secondly, our study results may not be applicable to students in other institutions or states. We collected data from a public research university in Kentucky where the number of confirmed cases and deaths were relatively lower than other states such as New York. The study site mainly serves traditional college students who attend college right after high school, who live on campus, and who do not have dependents. Therefore, mental health for students at other types of institutions or in other states could be different from what is presented in our study.

Prevalence of Stress, Anxiety, and Depression

Table  2 shows the prevalence of stress, anxiety, and depression. Overall, a majority of students experienced psychological distress during the early phase of the pandemic. When it comes to stress, about 63% of students had a moderate level of stress, and another 24.61% of students fell into a severe stress category. Only 12% of students had a low level of stress. In other words, more than eight in ten students in the survey experienced moderate to severe stress during the pandemic. This result is comparable to the Active Minds’ survey results that report 91% of college students reported experiencing feelings of stress and anxiety since the pandemic (Horn, 2020 ).

In terms of anxiety, approximately 24% and 21% of students in our study had moderate and severe anxiety disorders, respectively. Given that those who scored 10 or above on the GAD-7 scale (moderate to severe category) are recommended to meet with professionals (Spitzer et al., 2006 ), this finding implies that nearly half of students in this study needed to get professional help. This proportion of students with moderate to severe anxiety is almost double that for university students in China (e.g., Chang et al., 2020 ) or the United Arab Emirates soon after the COVID-19 outbreak (Saddik et al., 2020 ). Lastly, approximately 30% and 6% of students suffered from moderate and severe depression, respectively. These proportions are far higher than college students in China measured during the pandemic (Chang et al., 2020 ) but slightly higher than a nationwide sample of U.S. college students assessed before the pandemic (Healthy Minds, 2019 ). Given that our study measured these mental health symptoms for the first six weeks of the pandemic, we speculate that the proportion of students with moderate or severe depression would increase over time.

In order to explore predictors of a higher level of stress, anxiety, and depression, we ran ordinal logistic regression models as presented in Table  3 . Overall, it is clear and consistent that the odds of experiencing a higher level of stress, anxiety, and depression (e.g., severe than moderate, moderate than mild, etc.) were significantly greater for female students by a factor of 1.489, 1.723, and 1.246 than the odds for male students when other things were held constant. This gender difference in mental health symptoms is quite consistent with other studies before and during the pandemic (Eisenberg et al., 2007a ; Kecojevic et al., 2020 ). When it comes to race or ethnicity, the odds of experiencing a higher level of stress, anxiety, and depression for African-American students were almost as half as the odds for White students. However, there was no significant difference in the odds for Hispanic and Asian students compared to White students. Student class level was significantly related to stress and anxiety levels: The odds were greater for upper-class students than lower class students. This result is consistent with Kecojevic et al. ( 2020 ), which reported significantly higher levels of anxiety among upper-class students compared to freshman students. It may reflect that one of major stressors for college students during the pandemic is the uncertain future of their education and job prospects, which would be a bigger concern for upper-class students (Timely MD, n.d.).

Ordinal logistic regression models for severity of mental health symptoms (imputed data)

One’s rurality, family income, and GPA were significantly associated with the severity of mental health symptoms. The odds of experiencing a severe level of anxiety and depression were 1.325 and 1.270 times higher among rural students than urban and suburban students. With every one unit increase in family income or students’ GPAs, the odds of experiencing a more severe stress, anxiety, and depression significantly decreased. This result suggests that students from disadvantaged backgrounds were even more vulnerable to psychological distress during the early phase of the pandemic. The negative association between GPAs and mental distress levels was consistent with previous studies that showed that college students were very concerned about their academic performances and had difficulty in concentration during the early phase of the pandemic (Kecojevic et al., 2020 ; Son et al., 2020 ).

Usage of Mental Health Services

In Table  4 , we first describe the extent to which students with moderate to severe symptoms of stress, anxiety, or depression used mental health services on- and off-campus during the academic year. The university in this study has provided free counseling services for students, and the counseling services have continued to be available for students in the state via phone or Internet even after the university was closed after the outbreak. Table ​ Table4 4 presents the frequency of students using on-campus mental health services (Panel A) and off-campus paid mental health services (Panel B) on a five-point scale. For this table, we limited the sample to students with moderate to severe symptoms of stress, anxiety, or depression to focus on students who were in need of these services. Surprisingly, a majority of these students never used mental health services on- and off-campus even when their stress, anxiety, or depression scores indicated that they needed professional help. More than 60% of students with moderate to severe symptoms never used on-campus services, and more than two-thirds of students never used off-campus mental health services. This underutilization of mental health resources is concerning but not surprising given that college students tended not to use counseling services before and during the pandemic as presented in previous studies (e.g., CCMH, 2021 ; Healthy minds, 2019 ; Son et al., 2020 ).

In order to explore predictors of the usage of mental health services, we ran logistic regression models as shown in Table  5 . We included all students in these regression models to see whether a severity of mental health symptoms was related to the usage of mental health services. Table ​ Table5 5 presents the results for the usage of any mental health services, on-campus mental health services, and off-campus mental health services, respectively. Overall, stress, anxiety, and depression levels were positively associated with using mental health services on- and off-campus: With every one unit increase in each of these mental health symptoms, the odds of using on- and off-campus mental health services significantly increased. This result is relieving as it suggests that students who were in great need of these services actually used them. Other than mental health symptoms, there were different predictors for utilizing on-campus and off-campus services. African-American and Hispanic students were significantly more likely to use on-campus services than White students. The odds of using on-campus mental health services were 3.916 times higher for African-American students and 2.032 times higher for Hispanic students than White students. This result is interesting given that the odds of having severe mental distress were significantly lower for African-American students than White students, according to Table ​ Table3. 3 . It may suggest that African-American students reported relatively lower levels of mental health symptoms as they had been using on-campus mental health services at higher rates. The odds of using on-campus mental health services were 2.269 times higher for international students than domestic students, but there was no significant difference in the odds of using off-campus services between the two groups. Students’ age was significantly associated with the usage of on-campus and off-campus mental health services: The odds of using on-campus services were significantly lower for older students, while the odds of utilizing off-campus services were significantly higher for older students compared to younger students. When it comes to using off-campus mental health services, the odds were significantly higher for female students, older students, and upper-class students than male students, younger students, and lower classman students. Students who were concerned with stigma associated with getting counseling and therapy were less likely to utilize off-campus mental health services.

Logistic regression models predicting the usage of mental health services (imputed data)

Discussions

Our paper describes the prevalence of stress, anxiety, and depression among a sample of undergraduate students in a public research university during an early phase of the COVID-19 outbreak. Using well-established clinical tools, we find that stress, anxiety, and depression were the pervasive problems for college student population during the pandemic. In particular, female, rural, low-income, and academically low-performing students were more vulnerable to psychological distress. Despite its prevalence, about two-thirds of students with moderate to severe symptoms had not utilized mental health services on- and off-campus. These key findings are very concerning considering that mental health is strongly associated with student well-being, academic outcomes, and retention (Bruffaerts et al., 2018 ; Wyatt et al., 2017 ).

Above all, we reiterate that college student mental health is in crisis during the pandemic and call for increased attention and interventions on this issue. More than eight in ten students in our study had moderate to severe stress, and more than one thirds of students experienced moderate to severe anxiety and/or depression. This is much worse than American college students before the COVID-19 (e.g., American College Health Association, 2020 ) and postsecondary students in other countries during the pandemic (e.g., Chang et al., 2020 ; Saddik et al., 2020 ). In particular, rural students, low-income students, and students with low GPAs were more vulnerable to psychological distress. These students have already faced multiple barriers in pursuing higher education (e.g., Adelman, 2006 ; Byun et al., 2012 ), and additional mental health issues would put them at a high risk of dropping out of college. Lastly, although they were dropped from the main analysis due to the small sample size ( n  = 17), it is still noteworthy that a significantly higher proportion of LGBTQ students in our sample experienced severe stress, anxiety, and depression, which calls for significant attention and care for these students.

Despite the high prevalence of mental health problems, a majority of students with moderate to severe symptoms never used mental health services during the academic year, even though the university provided free counseling services. This result could be partially explained by the fact that the university’s counseling center switched to virtual counseling since the COVID-19 outbreak, which was available only for students who stayed within the state due to the license restriction across state boarders. This transition could limit access to necessary care for out-of-state students, international students, or students in remote areas where telecommunications or the internet connection is not very stable. Even worse, these students may also have limited access to off-campus health professionals due to the geographic restrictions (rural students), limited insurance coverage (international students), or a lack of financial means. Our results support that international students relied significantly more on on-campus resources than domestic students. We urge practitioners and policy makers to provide additional mental health resources that are accessible, affordable, and available for students regardless of their locations, insurance, and financial means, such as informal peer conversation groups or regular check-ins via phone calls or texts.

It is also important to point out that the overall usage of both on-campus and off-campus mental health services was generally low even before the COVID-19 outbreak. Previous studies consistently report that college students underutilize mental health services not only because of a lack of information, financial means, or available seats but also because of a paucity of perceived needs or stigma related to revealing one’s mental health issues to others (Cage et al., 2018 ; Eisenberg et al., 2007a ; Son et al., 2020 ). Our results support this finding by demonstrating that stigma one associated with getting counseling or therapy negatively influenced their utilization of off-campus mental health services. Considering these barriers, practitioners should deliver a clear message publicly that mental health problems are very common among college students and that it is natural and desirable to seek professional help if students feel stressed out, anxious, or depressed. In order to identify students with mental health needs and raise awareness among students, it can be also considered to administer a short and validated assessment in classes that enroll a large number of students (e.g., in a freshman seminar course), inform the entire class of how to interpret their scores on their own, and provide a list of available resources for those who may be interested. This would give students a chance to self-check their mental health without revealing their identities and seek help, if necessary.

We recommend that future researchers longitudinally track students and see whether the prevalence of mental health problems changes over time. Longitudinal studies are generally scarce in student mental health literature, but the timing of assessment can influence mental health symptoms reported (Huckins et al., 2020 ). The survey for our study was sent out right after the university of this study was closed due to the pandemic. It is possible that students may adjust to the outbreak over time and feel better, or that their stress may add up as the disease progresses. Tracking students over time can illustrate whether and how their mental health changes, especially depending on the way the pandemic unfolds combined with the cycle of an academic year. Secondly, there should be more studies that evaluate the effect of an intervention program on student mental health. Hunt and Eisenberg ( 2010 ) point out that little has been known about the efficacy of intervention programs while almost every higher education institution offers multiple mental health resources and counseling programs. During this pandemic, it can be a unique opportunity to implement virtual mental health interventions and evaluate their efficacy. Future research on virtual counseling and mental health interventions would guide practices to accommodate mental health needs for students who exclusively take online courses or part-time students who spend most of their time off campus. Lastly, we recommend future research investigate the extent of mental health service utilization among students with mental health needs. Existing surveys and studies on this topic usually rely on responses from those who visit a counseling center or students who respond to their surveys. Neither of these groups accurately represents those who are in need of professional help because there may be a number of students who are not aware of their mental health issues or do not want to reveal it. An effective treatment should first start with identifying those in need.

Our study highlights that college students are stressed, anxious, and depressed in the wake of COVID-19. Although college students have constantly reported mental health issues (e.g., American College Health Association, 2020 ), it is remarkable to note that the broad spectrum of COVID-19-related challenges may mitigate the overall quality of their psychological wellbeing. This is particularly the case for at-risk students (rural, international, low-income, and low-achieving students) who have already faced multiple challenges. We also present that a majority of students with mental health needs have never utilized on- and off-campus services possibly due to the limited access or potential stigma associated with mental health care. Systematic efforts with policy makers and practitioners are requested in this research to overcome the potential barriers. All these findings, based on the clinical assessment of student mental health during the early phase of the pandemic, will benefit scholars and practitioners alike. As many colleges and universities across the country have re-opened their campus for the 2020–2021 academic year, students, especially those who take in-person classes, would be concerned about the disease and continuing their study in this unprecedented time. On top of protecting students from the disease by promoting wearing masks and social distancing, it is imperative to pay attention to their mental health and make sure that they feel safe and healthy. To this end, higher education institutions should proactively reach out to all student populations, identify students at risk of mental health issues, and provide accessible and affordable care.

Biographies

is Assistant Professor of Higher Education at the University of Kentucky. She studies higher education policy, program, and practice and their effects on student success.

is an Assistant Professor of Integrated Strategic Communication at the University of Kentucky. She earned her Ph.D. in Media and Information Studies at Michigan State University. Her research interests include prosocial campaigns, consumer wellbeing, and civic engagement.

is an associate professor in the Division of Biomedical Informatics in the College of Medicine at the University of Kentucky. Dr. Kim’s current research includes: consumer health informatics, personal health information management, and health information seeking behaviors. She uses clinical natural language professing techniques and survey methodologies to better understand patients’ health knowledge and their health information uses and behaviors.

Author’s Contribution

The order of the authors in the title page reflects the share of each author’s contribution to the manuscript.

Data Availability

Code availability, declarations.

The authors declare that they have no conflicts of interest.

All authors agree to publish this paper.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jungmin Lee, Email: [email protected] .

Hyun Ju Jeong, Email: [email protected] .

Sujin Kim, Email: ude.yku@miknijus .

• Adams DR, Meyers SA, Beidas RS. The relationship between financial strain, perceived stress, psychological symptoms, and academic and social integration in undergraduate students. Journal of American College Health. 2016; 64 (5):362–370. doi: 10.1080/07448481.2016.1154559. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. U.S. Department of Education. https://files.eric.ed.gov/fulltext/ED490195.pdf . Accessed 15 Dec 2020.
• Allison, P. D. (2002). Missing data . Sage Publications .
• American College Health Association (2020). American college health association-National college health assessment III: Undergraduate student reference group executive summary spring 2020 . https://www.acha.org/documents/ncha/NCHA-III_Spring_2020_Undergraduate_Reference_Group_Executive_Summary.pdf . Accessed 9 Mar 2021.
• Brown, S., & Kafka, A. C. (2020). Covid-19 has worsened the student mental-health crisis. Can resilience training fix it? The Chronicle of Higher Education . https://www.chronicle.com/article/Covid-19-Has-Worsened-the/248753 . Accessed 3 Aug 2020.
• Bruffaerts R, Mortier P, Kiekens G, Auerbach RP, Cuijpers P, Demyttenaere K, Green JG, Nock MK, Kessler RC. Mental health problems in college freshmen: Prevalence and academic functioning. Journal of Affective Disorders. 2018; 225 (1):97–103. doi: 10.1016/j.jad.2017.07.044. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burke TJ, Ruppel EK, Dinsmore DR. Moving away and reaching out: Young adults’ relational maintenance and psychosocial well-being during the transition to college. Journal of Family Communication. 2016; 16 (2):180–187. doi: 10.1080/15267431.2016.1146724. [ CrossRef ] [ Google Scholar ]
• Byun S, Meece JL, Irvin MJ. Rural-nonrural disparities in postsecondary educational attainment revisited. American Educational Research Journal. 2012; 49 (3):412–437. doi: 10.3102/0002831211416344. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cage E, Stock M, Sharpington A, Pitman E, Batchelor R. Barriers to accessing support for mental health issues at university. Studies in Higher Education. 2018; 45 (8):1–13. doi: 10.1080/03075079.2018.1544237. [ CrossRef ] [ Google Scholar ]
• Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, Zheng J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research. 2020; 287 :1–5. doi: 10.1016/j.psychres.2020.112934. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Center for Collegiate Mental Health. (2021). 2020 Annual Report. https://ccmh.memberclicks.net/assets/docs/2020%20CCMH%20Annual%20Report.pdf . Accessed 10 Mar 2021.
• Chang J, Yuan Y, Wang D. Mental health status and its influencing factors among college students during the epidemic of COVID-19. Nan Fang Yi Ke Da Xue Xue Bao. 2020; 40 (2):159–163. doi: 10.12122/j.issn.1673-4254.2020.02.06. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen Q, Liang M, Li Y, Guo J, Fei D, Wang L, He L, Sheng C, Cai Y, Li X, Wang J, Zhang Z. Mental health care for medical staff in China during the COVID-19 outbreak. The Lancet Psychiatry. 2020; 7 (4):e15–e16. doi: 10.1016/S2215-0366(20)30078-X. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24 (4), 385–396. 10.2307/2136404. [ PubMed ]
• Eisenberg, D., Golberstein, E., & Gollust, S. E. (2007a). Help-seeking and access to mental health care in a university student population. Medical Care, 45 (7), 594–601. www.jstor.org/stable/40221479 . Accessed 19 June 2020. [ PubMed ]
• Eisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. American Journal of Orthopsychiatry. 2007; 77 (4):534–542. doi: 10.1037/0002-9432.77.4.534. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Evans TM, Bira L, Gastelum JB, Weiss LT, Vanderford NL. Evidence for a mental health crisis in graduate education. Nature Biotechnology. 2018; 36 (3):282–284. doi: 10.1038/nbt.4089. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, Wang I, Fu H, Dai J. Mental health problems and social media exposure during COVID-19 outbreak. PLoS One. 2020; 15 (4):1–10. doi: 10.1371/journal.pone.0231924. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Healthy Minds. (2019). The healthy minds study: 2018–2019 data report . https://healthymindsnetwork.org/wp-content/uploads/2019/09/HMS_national-2018-19.pdf . Accessed 17 Aug 2020.
• Healthy Minds Network. (2020). The impact of COVID 19 on college students well-being . https://www.acha.org/documents/ncha/Healthy_Minds_NCHA_COVID_Survey_Report_FINAL.pdf . Accessed 1 Mar 2021.
• Horn, A. (2020) . Active minds and association of college and university educators release guide on practical approaches for supporting student wellbeing and mental health. Active Minds . https://www.activeminds.org/wp-content/uploads/2020/04/Active-Minds-ACUE-Release_Faculty-Guide_April-2020.pdf . Accessed 1 Mar 2021.
• Huckins JF, daSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, Wu J, Obuchi M, Murphy EI, Meyer ML, Wagner DD, Holtzheimer PE, Campbell AT. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: Longitudinal smartphone and ecological momentary assessment study. Journal of Medical Internet Research. 2020; 22 (6):1–13. doi: 10.2196/20185. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hunt J, Eisenberg D. Mental health problems and help-seeking behavior among college students. Journal of Adolescent Health. 2010; 46 (1):3–10. doi: 10.1016/j.jadohealth.2009.08.008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hyun JK, Quinn BC, Madon T, Lustig S. Graduate student mental health: Needs assessment and utilization of counseling services. Journal of College Student Development. 2006; 47 (3):247–266. doi: 10.1353/csd.2006.0030. [ CrossRef ] [ Google Scholar ]
• Hyun JK, Quinn BC, Madon T, Lustig S. Mental health need, awareness, and use of counseling services among international graduate students. Journal of American College Health. 2007; 56 (2):109–118. doi: 10.3200/JACH.56.2.109-118. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnson SU, Ulvenes PG, Øktedalen T, Hoffart A. Psychometric properties of the GAD-7 in a heterogeneous psychiatric sample. Frontiers in Psychology. 2019; 10 :1–8. doi: 10.3389/fpsyg.2019.01713. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kecojevic A, Basch CH, Sullivan M, Davi NK. The impact of the COVID-19 epidemic on mental health of undergraduate students in New Jersey, cross-sectional study. PLoS One. 2020; 15 (9):1–16. doi: 10.1371/journal.pone.0239696. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kitzrow, M. A. (2003). The mental health needs of today's college students: Challenges and recommendations. Journal of Student Affairs Research and Practice , 41 (1), 167–181. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.2605&rep=rep1&type=pdf . Accessed 19 June 2020.
• Leviness, P., Bershad, C., & Gorman, K. (2017). Annual survey 2017. The Association for University and College Counseling Center Directors. https://www.aucccd.org/assets/documents/Governance/2017%20aucccd%20survey-public-apr26.pdf . Accessed 19 June 2020.
• Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, Hoffman BL, Giles LM, Primack BA. Association between social media use and depression among U.S. young adults. Depression and Anxiety. 2016; 33 (4):323–331. doi: 10.1002/da.22466. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lipson SK, Lattie EG, Eisenberg D. Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007–2017) Psychiatric Services. 2019; 70 (1):60–63. doi: 10.1176/appi.ps.201800332. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu S, Liu Y, Liu Y. Somatic symptoms and concern regarding COVID-19 among Chinese college and primary school students: A cross-sectional survey. Psychiatry Research. 2020; 289 :1–5. doi: 10.1016/j.psychres.2020.113070. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oswalt SB, Lederer AM, Chestnut-Steich K, Day C, Halbritter A, Ortiz D. Trends in college students’ mental health diagnoses and utilization of services, 2009–2015. Journal of American College Health. 2020; 68 (1):41–51. doi: 10.1080/07448481.2018.1515748. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pilkonis PA, Yu L, Dodds NE, Johnston KL, Maihoefer CC, Lawrence SM. Validation of the depression item bank from the patient-reported outcomes measurement information system (PROMIS®) in a three-month observational study. Journal of Psychiatric Research. 2014; 56 :112–119. doi: 10.1016/j.jpsychires.2014.05.010. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rajkumar RP. COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry. 2020; 52 :1–5. doi: 10.1016/j.ajp.2020.102066. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Saddik B, Hussein A, Sharif-Askari FS, Kheder W, Temsah MH, Koutaich RA, Haddad ES, Al-Roub NM, Marhoon FA, Hamid Q, Halwani R. Increased levels of anxiety among medical and non-medical university students during the COVID-19 pandemic in the United Arab Emirates. Risk Management and Healthcare Policy. 2020; 13 :2395–2406. doi: 10.2147/RMHP.S273333. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior. 2016; 57 :321–325. doi: 10.1016/j.chb.2015.12.045. [ CrossRef ] [ Google Scholar ]
• Shensa A, Sidani JE, Escobar-Viera CG, Chu KH, Bowman ND, Knight JM, Primack BA. Real-life closeness of social media contacts and depressive symptoms among university students. Journal of American College Health. 2018; 66 (8):747–753. doi: 10.1080/07448481.2018.1440575. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research. 2020; 22 (9):1–14. doi: 10.2196/21279. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine. 2006; 166 (10):1092–1097. doi: 10.1001/archinte.166.10.1092. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Timely MD. (n.d.). What really has college students stressed during COVID-19 . Timely MD. https://www.timely.md/wp-content/uploads/2020/06/TimelyMD-Student-Survey-June-2020.pdf . Accessed 15 Feb 2021.
• Wimmer, R. D., & Dominick, J. R. (2006). Mass communication research: An introduction. Wadsworth.
• Wyatt TJ, Oswalt SB. Comparing mental health issues among undergraduate and graduate students. American Journal of Health Education. 2013; 44 (2):96–107. doi: 10.1080/19325037.2013.764248. [ CrossRef ] [ Google Scholar ]
• Wyatt TJ, Oswalt SB, Ochoa Y. Mental health and academic performance of first-year college students. International Journal of Higher Education. 2017; 6 (2):178–187. doi: 10.5430/ijhe.v6n3p178. [ CrossRef ] [ Google Scholar ]

Research & Studies

• Research Videos
• WTC Medical Working Group

WTC Medical Working Group , a group of scientists and medical experts appointed by former Mayor Bloomberg, met from 2007 to 2013 to review 9/11 health research, identify gaps in research and services, and advise city government on ways to communicate health risk information.

Find Current Studies on 9/11 health funded under the Zadroga Act .

WTC Scientific Bibliography  lists WTC-related research published in the past years, by category:

• Mental Health Effects  
• Physical Effects  
• Exposures  
• Tower Survivors and Fatalities  
• Residential, Commercial and Building Occupants  

Find published reports about 9/11 health from the World Trade Center Health Program.