a research on mental health

August 20, 2024

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

Very early onset dementias: Importance of differentiating from schizophrenia spectrum disorders

In our first Essay, McCormick and colleagues explain the importance of accurately differentiating between very early onset dementia and psychosis 

Image credit: Medical, by Darko Stojanovic from Pixabay

Bridging the gap between tradition and innovation in psychotherapy: The promise of awareness integration theory

Psychotherapist Foojan Zeine outlines a new, holistic psychotherapy framework - AIT

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Is it time to change mental health crisis responses?

Psychiatrist Rupinder Legha advocates for change to mental health crisis management

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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

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public mental health & policy

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

Researchers at UCL collaborate with the Metropolitan police to understand help-seeking behavior in older victims of crime

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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

Researchers from University of Austin, including Section Editor Craig Watkins, demonstrate the need for deliberate sampling plans to examine the needs of marginalized communities

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Supporting our community

Introducing ' PLOS Mental Health : The Bigger Picture'

A new quarterly seminar series led by our Senior Editorial Board and Executive Editor, which highlights work in our journal authored by early career researchers and those from underrepresented regions

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community mental health

The mental health of parent versus non-parent post-secondary students

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public mental health and policy

Psychosocial interventions for persons affected by Leprosy: A systematic review

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neurodiversity and mental health

“Actually, even me I wouldn’t think that it is there” exploring the knowledge and attitudes of health professionals towards autism spectrum disorders in Uganda

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Mental health psychology

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

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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.

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• 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:

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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:

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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.

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.

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Mental health

Mental health is a state of mind characterized by emotional well-being, good behavioral adjustment, relative freedom from anxiety and disabling symptoms, and a capacity to establish constructive relationships and cope with the ordinary demands and stresses of life.

Adapted from APA Dictionary of Psychology

Resources from APA

Mental health in a warming world

Kim Meidenbauer, PhD, and Amruta Nori-Sarma, PhD, discuss how extreme heat affects mental health, emotions, cognitive abilities, and behavior

How heat affects the mind

The impact of extreme heat on behavior and cognition, and policy and infrastructure changes needed to protect mental health

Understanding the mind of a serial killer

Louis Schlesinger, PhD, talks about what we really know about serial killers’ motivations and their methods.

Broadening the scope of psychology

Understanding societal structures and policies is key to improving community well-being

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Mental health parity

APA Services works to ensure that health plans provide equitable coverage for mental and behavioral health services.

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Treatment of Posttraumatic Stress ...

The Thriving Therapist

Deliberate Practice in Systemic Family Therapy

The Impostor Phenomenon

Magination Press children’s books

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A Perfectionist's Guide to Not Being Perfect

All the Feelings Under the Sun

Qué Hacer Cuando te Preocupas Demasiado

Priscilla Pack Rat

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Combined Use of Alcohol and Cannabis

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Psychology and Virtual Reality

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Psychiatric Rehabilitation Journal

Researching better mental health for everyone.

Without research it's just guesswork.

You can help fund research that leads to better treatments, better support and faster diagnoses for everyone affected by mental illnesses. This benefits the entire future of mental health — and that benefits us all.

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Our ultimate goal is to prevent mental illnesses from developing in the first place by developing evidence based early-intervention strategies.

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Your Mental Health

MQ's book features stories from celebrities with lived experience of mental illnesses, such as Gemma Styles, Alistair Campbell and Claire Eastham, and the latest research findings from the experts.

Find out about conditions such as PTSD, depression, eating disorders and anxiety, what treatments are available and how it really feels to live with them.

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Young peoples mental health is entering a dangerous phase experts warn.

by Bryony Doughty | 19/8/24 | Science and research

Young peoples mental health has been declining over the past two decades and is now entering a dangerous phase, suggests a new Lancet Psychiatry Commission.

The Sleep Struggle: How Loneliness Fuels Nightmares and Affects Health

by Craig Perryman | 19/8/24 | Science and research

People who are lonely are more likely to experience bad dreams. The research highlights a connection between loneliness and sleep disorders, both of which are significant public health concerns linked to higher risks of heart disease, stroke, and premature death....

Rebuilding Me: A Journey Through Heartbreak and Healing

by MQ Mental Health | 13/8/24 | Personal stories

Content warning: Mentions of self-harm, suicide, or suicidal thoughts Written by volunteer Ben White, this blog details his journey through a depressive period, and the strength it took to change his life for the better. My name is Ben, I am a 28-year-old medical...

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JMIR Mental Health

Internet interventions, technologies, and digital innovations for mental health and behavior change. jmir mental health is the official journal of the society of digital psychiatry . .

John Torous, MD, MBI, Harvard Medical School, USA

JMIR Mental Health ( JMH, ISSN 2368-7959 ,  ( Journal Impact Factor™ 4.8 , (Journal Citation Reports™ from Clarivate, 2024))  is a premier, open-access, peer-reviewed journal indexed in PubMed Central and PubMed,  MEDLINE ,  Scopus , Sherpa/Romeo,  DOAJ , EBSCO/EBSCO Essentials, ESCI,  PsycINFO ,  CABI  and SCIE.

J MIR Mental Health  has a unique focus on digital health and Internet/mobile interventions, technologies, and electronic innovations (software and hardware) for mental health, addictions, online counseling, and behavior change. This includes formative evaluation and system descriptions, theoretical papers, review papers, viewpoint/vision papers, and rigorous evaluations related to digital psychiatry, e-mental health, and clinical informatics in psychiatry/psychology.

JMIR Mental Health received a CiteScore of 10.8, placing it in the 92nd percentile (#43 of 567) as a Q1 journal in the field of Psychiatry and Mental Health.

Recent Articles

The rising prevalence of mental health issues in children, adolescents, and young adults has become an escalating public health issue, impacting approximately 10%-20% of young people on a global scale. Positive psychology interventions (PPIs) can act as powerful mental health promotion tools to reach wide-ranging audiences that might otherwise be challenging to access. This increased access would enable prevention of mental disorders and promotion of widespread well-being by enhancing self-efficacy, thereby supporting the achievement of tangible objectives.

The COVID-19 social distancing guidelines resulted in a dramatic transition to telephone and video technologies to deliver substance use disorder (SUD) treatment. Before COVID-19, the question was, “Would telehealth would ever take hold for SUD services?” Now that social distancing guidelines have been lifted, the question is, “Will telehealth remain a commonly used care modality?” The principal purpose of the investigation was to examine the extent to which telehealth use in SUD service settings persisted following the lifting of COVID-19 safety distancing recommendations. Additionally, the study aimed to explore practitioners’ perceptions of telehealth convenience and value after its regular implementation.

Digital cognitive behavioral therapy for insomnia (dCBTi) is an effective intervention for treating insomnia. The findings regarding its efficacy compared to face-to-face cognitive behavioral therapy for insomnia are inconclusive but suggest that dCBTi might be inferior. The lack of human support and low treatment adherence are believed to be barriers to dCBTi achieving its optimal efficacy. However, there has yet to be a direct comparative trial of dCBTi with different types of coaching support.

Due to recent advances in artificial intelligence, large language models (LLMs) have emerged as a powerful tool for a variety of language-related tasks, including sentiment analysis, and summarization of provider-patient interactions. However, there is limited research on these models in the area of crisis prediction.

Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.

Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials’ samples consisted of predominantly non-Hispanic, White individuals.

Global rates of mental health concerns are rising and there is increasing realization that existing models of mental healthcare will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this review, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs’ application to mental health and encourage adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. Especially critical is ensuring that mental health LLMs are fine-tuned for mental health, enhance mental health equity, adhere to ethical standards, and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.

Digital exclusion, characterized by a lack of access to digital technology, connectivity, or digital skills, disproportionally affects marginalized groups. An important domain impacted by digital exclusion is access to health care. During COVID-19, health care services had to restrict face-to-face contact to limit the spread of the virus. The subsequent shift toward remote delivery of mental health care exacerbated the digital divide, with limited access to remote mental health care delivery. In response, Camden and Islington National Health Service Foundation Trust launched the innovative Digital Inclusion Scheme (DIS).

Comprehensive session summaries enable effective continuity in mental health counseling, facilitating informed therapy planning. However, manual summarization presents a significant challenge, diverting experts’ attention from the core counseling process. Leveraging advances in automatic summarization to streamline the summarization process addresses this issue because this enables mental health professionals to access concise summaries of lengthy therapy sessions, thereby increasing their efficiency. However, existing approaches often overlook the nuanced intricacies inherent in counseling interactions.

Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses.

Fetal alcohol spectrum disorder (FASD) is a common developmental disability that requires lifelong and ongoing support, but is often difficult to find due to a lack of trained professionals, limited funding and support available. Technology could provide cost-effective, accessible, and effective support to those living with FASD and their caregivers.

Telehealth implementation can be challenging for persons with serious mental illness (SMI), which may impact their quality of care and health outcomes. The literature on telehealth’s impacts on SMI care outcomes is mixed, necessitating further investigation.

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Many Americans continue to experience mental health difficulties as pandemic enters second year

Note: For the latest information on this topic, read our 2022 post .

One year into the societal convulsions caused by the coronavirus pandemic , about a fifth of U.S. adults (21%) are experiencing high levels of psychological distress, including nearly three-in-ten (28%) among those who say the outbreak has changed their lives in “a major way.” The share of the public experiencing psychological distress has edged down slightly since March 2020 but remains elevated among some groups in the population. Concerns about both the personal health and the financial threats from the pandemic are associated with high levels of psychological distress.

This assessment of the public’s psychological reaction to the COVID-19 outbreak is based on surveys of members of Pew Research Center’s American Trends Panel (ATP) conducted online several times since March 2020. The mental health questions were included on three surveys. The first survey was conducted with 11,537 U.S. adults March 19-24, 2020; a second survey with the question series was conducted April 20-26, 2020, with a sample of 10,139 adults; and the most recent survey was conducted Feb. 16-21, 2021, among 10,121 adults. This analysis also includes questions asked in a survey conducted Jan. 19-24, 2021, with a sample of 10,334 adults.

The ATP is an online survey panel that is recruited through national random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The surveys are weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Here is more information about the ATP.

The psychological distress index used here measures the total amount of mental distress that individuals reported experiencing in the past seven days. The low distress category in the index includes about half of the sample; very few in that group said they were experiencing any of the types of distress most or all of the time. The middle category includes roughly one-quarter of the sample, while the high distress category includes 21%, down slightly from 24% in March 2020. A large majority of those in the high distress group reported experiencing at least one type of distress most or all of the time in the past seven days.

The questions used to measure the levels of psychological distress were developed with the help of the COVID-19 and mental health measurement group from Johns Hopkins Bloomberg School of Public Health (JHSPH): M. Daniele Fallin (JHSPH), Calliope Holingue (Kennedy Krieger Institute, JHSPH), Renee Johnson (JHSPH), Luke Kalb (Kennedy Krieger Institute, JHSPH), Frauke Kreuter (University of Maryland, Ludwig-Maximilians University of Munich), Elizabeth Stuart (JHSPH), Johannes Thrul (JHSPH) and Cindy Veldhuis (Columbia University).

Here are the mental health questions used for this analysis, along with responses, and the detailed survey methodology statements for March 2020 , late April 2020 and February 2021 .

The index of psychological distress is based on a set of five questions asking about anxiety, sleeplessness, depression, loneliness and physical symptoms of distress. Except for the last item, the questions do not explicitly mention the pandemic. But experts have documented that fear and isolation associated with the pandemic have been responsible for a surge of anxiety and depression over the past year. And on the one item that asks about physical reactions when thinking about coronavirus outbreak – such as sweating, trouble breathing, nausea or a pounding heart – 17% report having such reactions at least “some or a little of the time” in the past week.

The five items were combined to create an index, which was then grouped into three categories: high, medium and low distress. The questions were part of a survey conducted online Feb. 16-21 among 10,121 members of Pew Research Center’s American Trends Panel. Most of those interviewed had also participated in last year’s surveys about reactions to the pandemic.

High levels of distress are being experienced by those who say the coronavirus outbreak is a major threat to their personal financial situation (34% high distress) or to their personal health (28%). Psychological distress is especially common among adults ages 18 to 29 (32%), those with lower family incomes (31%) and those who have a disability or health condition that keeps them from participating fully in work, school, housework or other activities (36%).

The share of adults falling into the high distress group (21%) is now slightly lower than in March of last year: It was 24% then, near the beginning of coronavirus-related lockdowns in the U.S.  

Underneath the relative stability of the index, considerable change has occurred. About six-in-ten (61%) of those interviewed in both April 2020 and February 2021 remained in the same category of the index. Just over a fifth (22%) moved from a higher to a lower category of distress, while 16% moved from a lower to a higher category. Of all panelists interviewed in both April 2020 and February 2021, 12% were classified as high in psychological distress in both interviews and 40% were low in both.

Young people have been a particular group of concern during the pandemic for mental health professionals, and young adults stand out in the current survey for exhibiting higher levels of psychological distress than other age groups. The shutdowns have disrupted job opportunities, college experiences, and the mixing and mingling that marks the transition to adulthood. Among adults ages 18 to 29, women (36%) and those with lower incomes (39%) are especially likely to be in the high distress group. In this age group, those who are unmarried fare worse than the married (35% vs. 23% experienced high levels of distress, respectively).

Adults ages 18 to 29 are especially likely to report anxiety, depression or loneliness compared with other age groups. For example, 45% of those under 30 describe being “nervous, anxious or on edge” at least “occasionally or a moderate amount of time” during the past seven days; among those 30 and older, 28% do so. 

Not surprisingly, psychological distress is higher among those who express concern about becoming ill with COVID-19 or believe that the disease is a major threat to their personal health. Among those who are “very concerned” that they might get infected and require hospitalization, 27% score high in psychological distress, compared with just 14% among those who are not too or not at all concerned. Similarly, 27% of those who see the disease as a major threat to their personal health score high in psychological distress, compared with 11% who say it is not a threat. Distress levels are also higher among those who perceive the sign-up process for a coronavirus vaccine in their area as unfair or who say that it has not been easy to find information about the process.

As much as concern about the health implications of the pandemic may be affecting the mental health status of many Americans, financial troubles are also a strong correlate of psychological distress. Pew Research Center surveys, including this one, have documented the substantial negative impact of the pandemic on the financial situation of many Americans. A January survey found that more than four-in-ten adults said that they or someone in their household had lost a job or wages since the beginning of the outbreak, and significant shares of the unemployed acknowledged the emotional toll it had taken.

Among those interviewed in the current survey who say that the pandemic is a major threat to their personal financial situation, 34% are classified as being in high psychological distress. Even greater levels of distress are observed among those who said in a January interview that they worry about how to pay their bills “every day” (40% high psychological distress) or who said they are in “poor” shape financially (44%).

Note: Here are the mental health questions used for this analysis, along with responses, and the detailed survey methodology statements for March 2020 , late April 2020 and February 2021 .

• Coronavirus (COVID-19)
• COVID-19 & Science
• Happiness & Life Satisfaction

Scott Keeter is a senior survey advisor at Pew Research Center .

How Americans View the Coronavirus, COVID-19 Vaccines Amid Declining Levels of Concern

Online religious services appeal to many americans, but going in person remains more popular, about a third of u.s. workers who can work from home now do so all the time, how the pandemic has affected attendance at u.s. religious services, mental health and the pandemic: what u.s. surveys have found, most popular.

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Mental health

• Affordable, effective and feasible strategies exist to promote, protect and restore mental health.
• The need for action on mental health is indisputable and urgent.
• Mental health has intrinsic and instrumental value and is integral to our well-being.
• Mental health is determined by a complex interplay of individual, social and structural stresses and vulnerabilities.

Concepts in mental health

Mental health is a state of mental well-being that enables people to cope with the stresses of life, realize their abilities, learn well and work well, and contribute to their community. It is an integral component of health and well-being that underpins our individual and collective abilities to make decisions, build relationships and shape the world we live in. Mental health is a basic human right. And it is crucial to personal, community and socio-economic development.

Mental health is more than the absence of mental disorders. It exists on a complex continuum, which is experienced differently from one person to the next, with varying degrees of difficulty and distress and potentially very different social and clinical outcomes.

Mental health conditions include mental disorders and psychosocial disabilities as well as other mental states associated with significant distress, impairment in functioning, or risk of self-harm. People with mental health conditions are more likely to experience lower levels of mental well-being, but this is not always or necessarily the case.

Determinants of mental health

Throughout our lives, multiple individual, social and structural determinants may combine to protect or undermine our mental health and shift our position on the mental health continuum.

Individual psychological and biological factors such as emotional skills, substance use and genetics can make people more vulnerable to mental health problems.

Exposure to unfavourable social, economic, geopolitical and environmental circumstances – including poverty, violence, inequality and environmental deprivation – also increases people’s risk of experiencing mental health conditions.

Risks can manifest themselves at all stages of life, but those that occur during developmentally sensitive periods, especially early childhood, are particularly detrimental. For example, harsh parenting and physical punishment is known to undermine child health and bullying is a leading risk factor for mental health conditions.

Protective factors similarly occur throughout our lives and serve to strengthen resilience. They include our individual social and emotional skills and attributes as well as positive social interactions, quality education, decent work, safe neighbourhoods and community cohesion, among others.

Mental health risks and protective factors can be found in society at different scales. Local threats heighten risk for individuals, families and communities. Global threats heighten risk for whole populations and include economic downturns, disease outbreaks, humanitarian emergencies and forced displacement and the growing climate crisis.

Each single risk and protective factor has only limited predictive strength. Most people do not develop a mental health condition despite exposure to a risk factor and many people with no known risk factor still develop a mental health condition. Nonetheless, the interacting determinants of mental health serve to enhance or undermine mental health.

Mental health promotion and prevention

Promotion and prevention interventions work by identifying the individual, social and structural determinants of mental health, and then intervening to reduce risks, build resilience and establish supportive environments for mental health. Interventions can be designed for individuals, specific groups or whole populations.

Reshaping the determinants of mental health often requires action beyond the health sector and so promotion and prevention programmes should involve the education, labour, justice, transport, environment, housing, and welfare sectors. The health sector can contribute significantly by embedding promotion and prevention efforts within health services; and by advocating, initiating and, where appropriate, facilitating multisectoral collaboration and coordination.

Suicide prevention is a global priority and included in the Sustainable Development Goals. Much progress can be achieved by limiting access to means, responsible media reporting, social and emotional learning for adolescents and early intervention. Banning highly hazardous pesticides is a particularly inexpensive and cost–effective intervention for reducing suicide rates.

Promoting child and adolescent mental health is another priority and can be achieved by policies and laws that promote and protect mental health, supporting caregivers to provide nurturing care, implementing school-based programmes and improving the quality of community and online environments. School-based social and emotional learning programmes are among the most effective promotion strategies for countries at all income levels.

Promoting and protecting mental health at work is a growing area of interest and can be supported through legislation and regulation, organizational strategies, manager training and interventions for workers.

Mental health care and treatment

In the context of national efforts to strengthen mental health, it is vital to not only protect and promote the mental well-being of all, but also to address the needs of people with mental health conditions.

This should be done through community-based mental health care, which is more accessible and acceptable than institutional care, helps prevent human rights violations and delivers better recovery outcomes for people with mental health conditions. Community-based mental health care should be provided through a network of interrelated services that comprise:

• mental health services that are integrated in general health care, typically in general hospitals and through task-sharing with non-specialist care providers in primary health care;
• community mental health services that may involve community mental health centers and teams, psychosocial rehabilitation, peer support services and supported living services; and
• services that deliver mental health care in social services and non-health settings, such as child protection, school health services, and prisons.

The vast care gap for common mental health conditions such as depression and anxiety means countries must also find innovative ways to diversify and scale up care for these conditions, for example through non-specialist psychological counselling or digital self-help.

WHO response

All WHO Member States are committed to implementing the “Comprehensive mental health action plan 2013–2030" , which aims to improve mental health by strengthening effective leadership and governance, providing comprehensive, integrated and responsive community-based care, implementing promotion and prevention strategies, and strengthening information systems, evidence and research. In 2020, WHO’s “Mental health atlas 2020” analysis of country performance against the action plan showed insufficient advances against the targets of the agreed action plan.

WHO’s “World mental health report: transforming mental health for all” calls on all countries to accelerate implementation of the action plan. It argues that all countries can achieve meaningful progress towards better mental health for their populations by focusing on three “paths to transformation”:

• deepen the value given to mental health by individuals, communities and governments; and matching that value with commitment, engagement and investment by all stakeholders, across all sectors;
• reshape the physical, social and economic characteristics of environments – in homes, schools, workplaces and the wider community – to better protect mental health and prevent mental health conditions; and
• strengthen mental health care so that the full spectrum of mental health needs is met through a community-based network of accessible, affordable and quality services and supports.

WHO gives particular emphasis to protecting and promoting human rights, empowering people with lived experience and ensuring a multisectoral and multistakeholder approach.

WHO continues to work nationally and internationally – including in humanitarian settings – to provide governments and partners with the strategic leadership, evidence, tools and technical support to strengthen a collective response to mental health and enable a transformation towards better mental health for all. 

• World mental health report: transforming mental health for all
• Comprehensive Mental Health Action Plan 2013─2030
• Mental health atlas 2020

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Mental Health Headlines

More than one-third of adults with medical debt and depression or anxiety delayed mental health care in previous 12 months.

Medical debt is common among adults with depression or anxiety, contributing to the mental health treatment gap.

How to Offer Support and Find Strength on a Trauma Anniversary

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Mental Health in the Workplace: A Conversation Bridging Research and Practice

A mental health researcher and an HR professional have a wide-ranging conversation on how to better promote mental health in the work place.

What We Do in the Department of Mental Health

We are the only department of mental health in a school of public health in the U.S. We bring together experts across disciplines—from childhood adversity to aging, workplace health to global health—to understand mental health, prevent and manage mental health and substance use disorders, and advocate for improved access to mental health services. Dedicated to advancing research and public health practice, not clinical training, our faculty are committed to educating the next generation of mental health researchers and professionals and giving students the foundational skills to improve mental health in their communities and globally.

Department of Mental Health Highlights

One-of-a-kind department.

We are the only department dedicated to mental health in a school of public health in the U.S.

Spanning the Life Course

Faculty, staff, and students carry out research on   childhood, adulthood, and later life mental and behavioral health, and study the determinants of mental health at each stage of the continuum from mental wellness to mental illness.

Teaching and Training

We are committed to educating and mentoring students to become leaders in public mental health practice and research.

From Research to Implementation

We identify determinants, develop and test interventions, and evaluate strategies for implementing and sustaining these interventions through evidence-based policy. 

Mental Health Programs

The Department of Mental Health offers a doctoral program, a master's program in health science, and a combined bachelor’s/master’s program. We also offer postdoctoral training, two certificate programs, and a special Summer Institute.

Bachelor's/Master of Health Science (Bachelor's/MHS)

The Bachelor's/MHS program gives Public Health Studies majors at Johns Hopkins University an opportunity to seamlessly extend their undergraduate studies to graduate-level coursework and research.

Master of Health Science (MHS)

The MHS in Mental Health is a nine-month degree program that provides a foundation in the research methods and content-area knowledge essential to public mental health.

Doctor of Philosophy (PhD)

The PhD program provides advanced training in the application of research methods to understand and enhance public mental health.

Centers in the Department of Mental Health

The Department of Mental Health houses several centers that bring together faculty, students, and community partners across multiple departments and schools in pursuit of improving public mental health.

Center for Mental Health and Addiction Policy

Johns hopkins center on aging and health, moore center for the prevention of child sexual abuse, wendy klag center for autism and developmental disabilities, renee m. johnson, phd, mph.

Dr. Johnson uses social epidemiology and behavioral science methods to investigate injury and violence, substance use, and overdose prevention.

Our Commitment to IDARE

In the Department of Mental Health, our faculty, staff, students, and postdoctoral fellows are committed to fostering an environment that values diversity in its many forms. We are advancing research and training on how racism and structural disadvantages increase risk for mental and behavioral health issues, while uncovering innovative strategies that enhance equity and justice in health programs, policies, and interventions.

The road to true diversity, equity, and inclusion is one that involves not one or two changes, but a consistent, ongoing effort. We believe it is our responsibility to improve mental health and well-being for all members of our community. Creating an environment where all people feel safe, supported, and included is an essential step in accomplishing this goal.

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Children’s Mental Health Research

• Mental health in the community
• Different data sources
• National data sets

Research on children’s mental health in the community

Project to learn about youth – mental health.

The Project to Learn About Youth – Mental Health (PLAY-MH) analyzed information collected from four communities. The focus was to study attention-deficit/hyperactivity disorder (ADHD)  and other  externalizing and internalizing  disorders, as well as  tic disorders  in school-aged children. The purpose was to learn more about public health prevention and intervention strategies to support children’s health and development.

Read about the results of the Play-MH study

Study questions included:

• What percentage of children in the community had one or more externalizing, internalizing, or tic disorders?
• How frequently did these disorders appear together?
• What types of treatment were children receiving in their communities?

This project used the same methodology as the original Project to Learn about ADHD in Youth (PLAY) project.  Read more about the original study approach here .

Other research

Read more about research on

• Tourette syndrome

CDC and partner agencies are working to understand the prevalence of mental disorders in children and how they impact their lives. Currently, it is not known exactly how many children have any mental disorder, or how often different disorders occur together, because no national dataset is available that looks at all mental, emotional, or behavioral disorders together.

Research on prevalence

What is It and Why is It Important?

Using different data sources

Healthcare providers, public health researchers, educators, and policy makers can get information about the prevalence of children’s mental health disorders from a variety of sources. Data sources, such as national surveys, community-based studies, and administrative claims data (like healthcare insurance claims), use different study methods and provide different types of information, each with advantages and disadvantages. Advantages and disadvantages for different data sources include the following:

• National surveys have large sample sizes that are needed to create estimates at the national and state levels. However, they also generally use a parent’s report of the child’s diagnosis, which means that the healthcare provider has to give an accurate diagnosis and the parent has to accurately remember what it was.
• Community-based studies offer the opportunity to observe children’s symptoms, which means that even children who have not been diagnosed or do not have the right diagnosis could be found. However, these studies are typically done in small geographic areas, so findings are not necessarily the same in other communities.
• Administrative claims are typically very large datasets with information on diagnosis and treatment directly from the providers, which allows tracking changes over time. Because they are recorded for billing purposes, diagnoses or services that would not be reimbursed from the specific health insurance might not be recorded in the data.

Using different sources of data together provides more information because it is possible to describe the following:

• Children with a diagnosed condition compared to children who have the same symptoms, but are not diagnosed
• Differences between populations with or without health insurance
• How estimates for mental health disorders change over time

Read more about using different data sources.

National data on children’s mental health

A comprehensive report from the Centers for Disease Control and Prevention (CDC), Mental Health Surveillance Among Children — United States, 2013 – 2019 , described federal efforts on monitoring mental disorders, and presented estimates of the number of children with specific mental disorders as well as for positive indicators of mental health. The report was developed in collaboration with the Substance Abuse and Mental Health Services Administration (SAMHSA ), the National Institute of Mental Health (NIMH ), and the Health Resources and Services Administration (HRSA ). It represents an update to the first ever cross-agency children’s mental health surveillance report in 2013.

Read a summary of the findings for the current report using data from 2012-2019

Read a summary of the findings for the first report using data from 2005-2011 .

The goal is now to build on the strengths of federal agencies serving children with mental disorders to:

• Develop better ways to document how many children have these disorders,
• Better understand the impacts of mental disorders,
• Inform needs for treatment and intervention strategies, and
• Promote the mental health of children.

This report is an important step on the road to recognizing the impact of childhood mental disorders and developing a public health approach to address children’s mental health.

Holbrook JR, Bitsko RB, Danielson ML, Visser SN. Interpreting the Prevalence of Mental Disorders in Children: Tribulation and Triangulation. Health Promotion Practice. Published online November 15, 2016 https://www.ncbi.nlm.nih.gov/pubmed/27852820 .

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We are passionate about the transformative power of mental health research to create change in people’s lives, their communities and workplaces regarding services and policy.

We aim to contribute to the mental health evidence base by:

• undertaking original research
• synthesising existing knowledge 
• translating this research to further understanding of what works, transferring interventions to other populations and places at a scale that maximises benefit and impact, and including mental health in all policies

Our research covers work in England, Scotland, Wales and Northern Ireland. We have offices in London, Glasgow, Cardiff and Belfast and conduct various evaluations and research in these areas.

Mental health A-Z

Explore our A to Z topics on mental health, where you can find more information on specific mental health problems and some of the things that can have an impact –good and bad – on our mental health.

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OPEN is a diverse online community of people we ask to inform us what we do, through anything from quick feedback on a social media post to fully participating in a research project.

Featured reports

Experiences of poverty stigma and mental health in the uk.

In this report, we examine how experiences of poverty stigma are related to mental health outcomes in the UK. Our aim was to understand the impact of poverty stigma and who is most affected in order to inform solutions.

The economic case for investing in the prevention of mental health conditions in the UK

Our report with the London School of Economics and Political Science provides evidence from the UK and around the world that proves prevention interventions work.

Surviving or Thriving? The state of the UK's mental health

This report aims to understand the prevalence of self-reported mental health problems, levels of positive and negative mental health in the population, and the actions people take to deal with the stressors in their lives.

Uncertain times: Anxiety in the UK and how to tackle it

This briefing looks at the prevalence and rates of anxiety amongst different groups of people, and the current key drivers and risk factors for anxiety. It also considers the main ways of coping with anxiety and provides recommendations to governments across the UK for preventing anxiety.

Prevention and mental health report

This report provides an understanding of the evidence for prevention and mental health so that we can address the greatest health challenge of our times.

Tackling social inequalities to reduce mental health problems

This report describes the extent of inequalities contributing to poor mental health in the UK today. It explains how certain circumstances interact with our own risks and discusses communities that are facing vulnerabilities.

Fundamental facts about mental health 2016

A comprehensive summary of mental health research, providing a unique handbook of key facts and figures, covering all key areas of mental health.

Relationships in the 21st century: the forgotten foundation of mental health and well-being

This report explores relationships across the life course, why they matter and the importance of being part of a community.

The impact of traumatic events on mental health

This report explores the impact traumatic events have on our mental health and provides advice on coping with trauma.

See more research reports

Our research projects

Thriving learners.

This research study is one of the largest of its kind, looking into student mental health across Scotland.

See Me Scotland

See Me is Scotland's national programme to tackle mental health stigma across Scotland, with the Mental Health Foundation providing the research and evaluation for the programme.

The Mental Health Foundation is the evaluation and learning partner for Barnardo’s Core Priority Programme in mental health and wellbeing.

Gaming and Mental Health

Using funding from Jingle Jam, we conducted an exciting new study exploring the link between video games and mental well-being.

Coronavirus: Mental Health in the Pandemic Study

Between 2020-2022, working with partners, the Mental Health Foundation led a ground-breaking, long-term, UK-wide research study of how the pandemic affected people’s mental health.

Coronavirus: Mental Health in the Pandemic Study in Scotland

Explore the Scotland Research team’s work as part of the UK-wide study.

Coronavirus and Adolescent Mental Health Study

The Mental Health Foundation and Swansea University led a repeated cross-sectional study of how the COVID-19 pandemic affected adolescents' mental health (people aged 13 to 19) across Great Britain.

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New USC study sheds light on adolescent mental health crisis in the United States

Results emphasize the interconnectedness of mental health, attendance and school grades—a necessary reality for schools to grapple with.

Key Findings

This study suggests:

• Teen girls and pre-teen boys exhibit distress differently, with pre-teen boys struggling with externalizing behaviors and hyperactivity, while teen girls are experiencing symptoms of anxiety and depression.
• Students who are on track to be chronically absent or who are earning Cs are three or more times as likely to face mental health challenges as those with fewer absences or As and Bs.
• Black and lower-income families report fewer school mental health services, but are more likely to utilize them when available.
• Nearly 20 percent of families without access to mental health services would enroll their children if offered.

The mental health of children in the United States has reached a critical juncture, with rising rates of teen suicides, emergency room visits and anxiety and depression among youth. Contributing factors include the social isolation of the pandemic, academic disruptions, family challenges, economic impacts and social media’s inescapable influence.

Today, researchers with USC Dornsife College of Letters, Arts and Sciences and USC Rossier School of Education released a new report titled “A Nation’s Children at Risk: Insights on Children’s Mental Health from the Understanding America Study” that examined the current state of adolescent mental health in the United States.

In a nationally representative sample of U.S. families, this new report examines adolescent mental health through the lens of their school experiences and parental perspectives. The study delved into mental health scores across multiple demographic groups and explored the correlation between scores, school attendance and course grades. Importantly, the study also investigated the availability of mental health resources in schools to support students in need.

Study co-authors Amie Rapaport , Morgan Polikoff , Anna Saavedra and Daniel Silver presented the finding’s implications and offered recommendations in their report.

“Our data supports the interconnected nature of student needs; to improve academic outcomes, schools must need to also prioritize mental health and attendance,” said Rapaport, co-director with the Center for Applied Research in Education (CARE) and research scientist with the Center for Economic and Social Research (CESR) both with USC Dornsife.

The study suggests that when students receive mental health support in school, 75 percent of parents report that these services are beneficial, with 72 percent expressing satisfaction. However, disparities in service availability exist, with service availability more than 20 percentage points greater in schools serving more White and higher-income households. This despite the fact that lower-income families are more than 5 times as likely as higher-income families to take up the services in schools when offered.

“While there is a growing awareness of the mental health struggles faced by adolescents, our study underscores that different student groups are experiencing different struggles–clearly, a one-size-fits-all solution to this problem will not work,” said Polikoff, USC Rossier professor of education and co-faculty director of the USC EdPolicy Hub .

Among the study’s implications:

• While the mental health struggles of our nation’s adolescents often are in the headlines, the report sheds light on the unique challenges faced by different subgroups of children.
• The study recommends a need for targeted allocation of resources to address mental health needs in schools.
• The correlation between mental health struggles and academic outcomes-including the approximately threefold increase in mental health warning flags among students chronically absent or with lower grades - underscores the importance of comprehensive support systems for students.

“The study shows that there is substantial unmet need for mental health services in schools, especially for the most disadvantaged students–states and the federal government need to step in and provide resources and guidance to address this crisis,” said Saavedra, a research scientist with CESR and co-director at CARE.

The study was supported by the Peter G. Peterson Foundation Pandemic Policy Research Fund at the USC Schaeffer Center for Health Policy & Economics .

Morgan Polikoff

• Professor of Education

USC EdPolicy Hub

Article Type

Article topics.

• K–12 education policy

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Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

Alexander j. scott.

a Keele University, School of Psychology, Keele, UK

Thomas L. Webb

c Department of Psychology, The University of Sheffield, UK

Marrissa Martyn-St James

b School of Health and Related Research (ScHARR), The University of Sheffield, UK

Georgina Rowse

d Clinical Psychology Unit, Department of Psychology, The University of Sheffield, UK

Scott Weich

Associated data.

All data and analysis code are freely available on the Open Science Framework under a creative commons 4.0 license (for access, see [ 73 ]).

The extent to which sleep is causally related to mental health is unclear. One way to test the causal link is to evaluate the extent to which interventions that improve sleep quality also improve mental health. We conducted a meta-analysis of randomised controlled trials that reported the effects of an intervention that improved sleep on composite mental health, as well as on seven specific mental health difficulties. 65 trials comprising 72 interventions and N  = 8608 participants were included. Improving sleep led to a significant medium-sized effect on composite mental health ( g+  = −0.53), depression ( g+  = −0.63), anxiety ( g+  = −0.51), and rumination ( g+  = −0.49), as well as significant small-to-medium sized effects on stress ( g+  = −0.42), and finally small significant effects on positive psychosis symptoms ( g+  = −0.26). We also found a dose response relationship, in that greater improvements in sleep quality led to greater improvements in mental health. Our findings suggest that sleep is causally related to the experience of mental health difficulties. Future research might consider how interventions that improve sleep could be incorporated into mental health services, as well as the mechanisms of action that explain how sleep exerts an effect on mental health.

Does improving sleep lead to better mental health? A meta-analysis of randomised controlled trials

Problems sleeping are common. A review of several hundred epidemiological studies [ 1 ] concluded that nearly one-third of the general population experience symptoms of insomnia (defined as difficulties falling asleep and/or staying asleep), between 4% and 26% experience excessive sleepiness, and between 2% and 4% experience obstructive sleep apnoea. Additionally, a recent study of over 2000 participants reported that the prevalence of ‘general sleep disturbances’ was 32% [ 2 ] and Chattu et al. concluded on the basis of a large systematic review of the evidence that public and health professionals need to be more aware of the adverse effects of poor sleep [ 3 ]. Mental health problems are also common, with around 17% of adults experiencing mental health difficulties of varying severities [ 4 ], and evidence from large nationally representative studies suggesting that mental health difficulties are on the increase [ 5 ]. Sleep and mental health are, therefore, global public health challenges in their own right, with each having substantive impacts on both individuals and society [ 3 , 6 , 7 ]. However, problems sleeping and mental health difficulties are also intrinsically linked [ 8 , 9 ]. It was previously assumed that mental health difficulties led to problems sleeping [ 10 , 11 ]; however, the reverse may also be true [ 12 ], such that poor sleep contributes to the onset, recurrence, and maintenance of mental health difficulties [ [13]∗ , [14] , [15]∗ , [16] , [17] ]. Therefore, the extent to which there is a causal relation between (poor) sleep and (worse) mental health and the possibility that interventions designed to improve sleep might be able to reduce mental health difficulties warrants investigation.

Evidence on the relationship between sleep and mental health

The association between sleep and mental health is well documented [ 9 , 13 , [18] , [19] , [20] , [21] , [22] , [23]∗ ]. For example, people with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of depression and anxiety, respectively [ 24 ]. Furthermore, a meta-analysis of 21 longitudinal studies reported that people with insomnia at baseline had a two-fold risk of developing depression at follow-up compared with people who did not experience insomnia [ 13 ]. Although research most commonly studies the associations between insomnia and depression and anxiety, there is also evidence that problems sleeping are associated with a variety of mental health difficulties. For example, poor sleep has also been associated with post-traumatic stress [ 25 ], eating disorders [ 26 ], and psychosis spectrum experiences such as delusions and hallucinations [ 23 , 27 ]. Studies have also found that specific sleep disorders, such as sleep apnoea [ 28 ], circadian rhythm disruption [ 29 ], restless leg syndrome [ 30 ], excessive daytime sleepiness and narcolepsy [ 31 , 32 ], sleepwalking [ 33 ], and nightmares [ 34 ] are all more prevalent in those experiencing mental health difficulties.

Unfortunately, most research on the association between sleep and mental health is observational in design. While informative, inferring causation from such studies is difficult. For example, cross-sectional designs tell us that variables are associated in some way, but they cannot say whether one variable precedes the other in a causal chain [ 35 ]. Longitudinal designs provide stronger evidence, but are prone to residual confounding [ [36] , [37] , [38] ] and other forms of bias that limit causal inference [ [39] , [40] , [41] , [42] , [43] ]. The best evidence is provided by studies that randomly allocate participants to experimental and control conditions to minimise the effects of potential confounds [ 44 , 45 ]. Therefore, to establish whether sleeping problems are causally associated with mental health difficulties, it is necessary to experimentally manipulate sleep to see whether changes in sleep lead to changes in mental health over time (i.e., the interventionist approach to causation, [ 46 ]).

Many RCTs have examined the effect of interventions designed to improve sleep (typically cognitive behavioural therapy for insomnia, CBTi), on mental health (typically depression and anxiety). There have also been attempts to meta-analyse some of these RCTs and quantify their effects on mental health outcomes [ [47]∗ , [48] , [49] , [50] ]. However, even these meta-analyses do not permit robust conclusions as to the causal impact of sleep on mental health outcomes for several reasons. First, previous reviews have included studies that did not successfully manipulate sleep (i.e., the intervention did not improve sleep relative to controls). It is not possible to conclude whether sleep is causally linked to mental health if the experimental manipulation of sleep is unsuccessful [ 51 ]. Indeed, these studies simply tell us that it can sometimes be difficult to improve sleep in the first place. Second, reviews have tended to examine the effect of interventions targeting sleep on mental health at the first post-intervention time point. This is problematic for two reasons; 1) there is no temporal lag between the measurement of sleep and measurement of mental health (a key tenet of causal inference); and 2) effects are limited to the short-term where they are likely to be strongest. Third, the focus of previous reviews has been limited to depression and anxiety only, and typically limited to CBTi interventions. Therefore, the effect of improving sleep on other mental health outcomes, using different approaches to intervention, is limited. Finally, to date there has been no or limited attempts to investigate variables that influence – or moderate – the impact of interventions that improve sleep on mental health. It is crucial that the impact of such variables is systematically examined to understand whether the effect of improving sleep on mental health differs across populations, settings, and study designs.

The present review: an interventionist approach to causation

The present review sought to address these issues to provide an accurate and robust estimate of the effect of changes in sleep quality (i.e., as a result of an intervention) on changes in mental health. To test this empirically, we identified randomised controlled trials that successfully manipulated sleep in an intervention group relative to controls, and then measured mental health at a later follow-up point. We did not limit the scope of interventions to CBTi, or the measures of mental health to solely depression and/or anxiety. Instead, we included any intervention designed to improve sleep that produced a statistically significant effect on sleep quality relative to controls and examined the effect of that improvement in sleep on any subsequent mental health outcome. To better isolate the effect of improved sleep on mental health, we excluded interventions that included specific elements targeting mental health (e.g., CBT elements for depression). Given the (potentially) high degree of heterogeneity between studies that this approach might create, we examined the effect of different study characteristics and outcomes using moderation analyses. Our primary hypothesis is that interventions that significantly improve sleep will lead to significantly improved mental health at follow-up.

Eligibility criteria

To be included in the present review, studies needed to 1) be a randomized controlled trial that tested an intervention designed to improve sleep; 2) produce a statistically significant effect on sleep quality when compared to a control group or an alternative treatment, 3) report a measure of mental health subsequent to the measure of sleep quality, 4) report sufficient data to compute an effect size representing the impact of the intervention on both sleep quality and mental health, 5) be written in English, or translatable using available resources. In order to reliably and validly assess the independent contribution of changes in sleep on mental health outcomes among adult populations, studies were excluded if 1) the intervention contained elements that specifically target a mental health problem in addition to elements that target sleep; or 2) recruited children and young people (i.e., <18 years of age).

Search strategy

First, we searched MEDLINE (1946 to present), Embase (1974 to present), PsycINFO (1967 to present), and The Cochrane Library (1898 to present) using the Cochrane Highly Sensitive Search Strategy (i.e., HSSS, [ 52 ]) to identify RCTs that included terms relating to sleep quality and/or sleep disorders, and mental health (see Table 1 for a list of the search terms and Supplementary Material 1 for an example search strategy). Second, the reference lists of extant reviews of the relationship between sleep and mental health were searched for any potential articles. Third, a search for any unpublished or ongoing studies was conducted by searching online databases including White Rose Online, The National Research Register, WHO approved clinical trial databases (e.g., ISRCTN), and PROSPERO. Searches were originally conducted in May 2019 and then updated in February 2021.

Table 1

Search terms used to identify RCT's that examined the effect of improving sleep on mental health.

Notes : HSSS for RCTs = highly sensitive search strategy for randomised controlled trials, OCD = obsessive compulsive disorder, PTSD = post-traumatic stress disorder.

Data management and study selection

We followed PRISMA guidelines [ 53 ] when selecting studies. The first phase of screening removed duplicate records and records that were clearly ineligible based on the title and/or abstract. The second phase of screening cross-referenced full-text versions of articles against the inclusion criteria, with eligible records included in the present review, and ineligible records excluded along with reasons for exclusion. Records were screened by two members of the review team, and a sub-sample of 10% of each reviewer's records were second checked by the other reviewer, with almost perfect agreement between the reviewers ( kappa  = 1.00 and 0.99).

Data extraction

Data was extracted from included studies using a standardized form and an accompanying manual detailing each variable for extraction. In addition to extracting statistical data to compute effect sizes, data pertaining to source characteristics of included studies (e.g., publication status, year, impact factor), characteristics of the sample (e.g., age, type of mental health problem), the study (e.g., the nature of the comparison group, length of follow-up), and the intervention (e.g., intervention type, mode of delivery) was also extracted.

Outcomes and prioritization

Measuring improvements in sleep.

The concept of ‘improved sleep’ is multifaceted and can mean different things to different people [ [54] , [55] , [56] ]. Consequently, one challenge for the proposed review was to ensure that included studies assessed a similar notion of improved sleep so that they could be meaningfully combined using a single metric. Therefore, we specified that primary studies reported a measure that reflected the overall quality of sleep experienced by participants. The concept of sleep quality can also be subjective [ 54 ]; however, broadly speaking, sleep quality consists of sleep continuity (e.g., sleep onset, sleep maintenance, and number of awakenings) and daytime impact (e.g., the extent to which the person feels refreshed on waking and throughout the day, see [ 54 , 57 ]). We used the following hierarchy to decide which outcome measure(s) to use to estimate an effect size (in descending order of prioritization); 1) self-report measures of global sleep quality (e.g., the Pittsburgh Sleep Quality Index); 2) outcomes specific to a given sleep disorder that assess sleep continuity and impact on daily life (e.g., the Insomnia Severity Index); and 3) individual components of self-reported sleep continuity aggregated to form a single composite effect size (e.g., the average effect of intervention on sleep onset latency (SoL) and wake after sleep onset (WASO)).

Measuring mental health

We examined the effect of improving sleep on 1) composite mental health (which included all mental health outcomes reported across studies, see Table 2 for outcomes), and 2) specific mental health difficulties in isolation (e.g., depression separately from other mental health outcomes). We computed the between-group effect of improving sleep on each mental health outcome reported by the study at the furthest follow-up point available. This strategy provides a stringent test of the effect of improving sleep on mental health outcomes in the sense that any changes need to have been maintained over time. In line with previous reviews [ 58 ], these effect sizes were then averaged to form a ‘composite’ measure of mental health. As with the measures of sleep quality, we prioritized self-report measures of mental health rather than observer-rated measures, as arguably it is the subjective experience of mental health problems that is most important [ 59 ].

Table 2

Summary of studies included in the review.

Note : ∗ p  < 0.05, ∗∗ p  < 0.01, ∗∗∗ p  < 0.001. CBTi = cognitive behavioural therapy for insomnia, dx = diagnosis, IRT = image rehearsal therapy, MH = mental health, n e  = number of participants in intervention group, n c  = number of participants in the control group, PTSD = post-traumatic stress disorder, TaU = treatment as usual, WLC = wait list control. ab Subscript indicates that the study reports multiple eligible interventions in the same study, in these situations both interventions were included as separate studies in the analysis and the control was halved accordingly.

Risk of bias

Risk of bias was assessed using the risk of bias assessment criteria developed by the Cochrane Collaboration [ 60 ]. RCTs were classified as being at overall risk of bias according to three of the six domains – 1) allocation concealment, 2) blinding of outcome assessment and 3) completeness of outcome data (attrition). RCTs judged as being at low risk of bias for all three domains were judged at overall low risk of bias. RCTs judged as being at high risk for any of the three domains were judged as overall high risk of bias. RCTs judged as a mix of low and unclear risk on these three domains, or all unclear were judged as unclear with respect to risk of bias.

Estimating effect sizes

Hedges g and the associated standard error were estimated using the means and standard deviations reported by each of the primary studies. Where means and standard deviations were not reported, effect sizes were estimated by converting relevant summary statistics into Hedges g . Where studies reported multiple outcome measures for the same/similar constructs (e.g., several measures of depression), effect sizes were computed for each outcome and then meta-analysed in their own right to form one overall effect.

Meta-analytic approach

All analyses were conducted in R [ 61 ], using the ‘ esc ’ [ 62 ], ‘ meta’ [ 63 ], ‘ metafor’ [ 64 ], ‘ dmetar ’ [ 65 ], and ‘ robvis ’ [ 66 ] packages. The pooled, sample-weighted, average effect size was computed using a random effects model as effect sizes between studies are likely to vary considerably [ 67 ]. Following Cohen's recommendations [ 68 ], g  = 0.20 was taken to represent a ‘small’ effect size, g  = 0.50 a ‘medium’ effect size and g  = 0.80 a ‘large’ effect size. The I 2 statistic was used to assess heterogeneity of effect sizes across the included studies and was interpreted according to the classifications suggested by Higgins et al. [ 69 ], where I 2  = 25% indicates low heterogeneity, I 2  = 50% indicates moderate heterogeneity, and I 2  = 75% indicates high heterogeneity. Publication bias was assessed via visual inspection of a funnel plot and Egger's test [ 70 ]. Additionally, Orwin's formula [ 71 ] was used to determine the fail-safe n . Finally, outliers were defined as any effect size for which the confidence intervals did not overlap with the confidence interval of the pooled effect [ 72 ]. We conducted a sensitivity analysis examining the effect of outliers for each outcome by rerunning the analysis with any outlying effect sizes removed.

Subgroup analyses

Moderation analysis was conducted to identify variables that were associated with the effect of improving sleep on mental health outcomes. A minimum of three studies representing each moderator level category was required in order to conduct moderation analysis. For categorical variables, the analysis was based on a mixed effects model, in that the pooling of effect sizes within each moderator level was based on a random effects model, while the comparison of effect sizes between levels was based on a fixed effects model. The Q statistic was then used to assess whether effect sizes were significantly different between moderator levels. For continuous variables, sample-weighted meta-regression was used to investigate the impact of the moderator on mental health effect sizes.

Data availability statement

Study selection.

Fig. 1 shows the flow of records through the review. Systemic searches of the published and grey literature retrieved a total of 21,733 records, which was reduced to 15,139 after duplicates were removed. Of these records, 14,687 (97%) were excluded in the first stage of screening, leaving 452 full-text records to be screened. Of these records, 387 (86%) were cross-referenced against the review eligibility criteria and excluded (see Fig. 1 for a breakdown of reasons and Supplementary Materials 2 for a list of the studies excluded at this stage), leaving 65 records for inclusion in the meta-analysis.

PRISMA diagram showing the flow of studies through the review.

Study characteristics

Table 2 describes key characteristics of the included studies. The 65 studies provided 72 comparisons between an intervention that successfully improved sleep quality vs. a control group.

Participants

A total of N  = 8608 participants took part across the 72 interventions. 38 of the comparisons (53%) included participants with a comorbid physical or mental health problem, while 31 (43%) reported no comorbid health problems, and 3 (4%) reported insufficient detail to make a judgement. Of the 38 comparisons including participants with comorbid health problems, 18 (47%) reported mental health diagnoses, and 20 (53%) had physical health problems.

Outcome measures

The majority of comparisons (61, 85%) reported a measure of depression, but 33 (46%) reported a measure of anxiety, 6 (8%) reported a measure of stress, 5 (7%) reported measures of psychosis spectrum experiences (e.g., total, positive, and negative symptoms), 9 (13%) reported a measure of general mood, 2 (3%) reported post-traumatic stress disorder outcomes, 2 (3%) reported measures of suicidal ideation, 4 (6%) reported rumination outcomes, and 1 (2%) reported a measure of psychological burnout.

Interventions and comparisons

Most interventions were multi-component CBTi (53, 74%), but interventions also involved acupuncture (7, 10%), pharmacological treatments (2, 3%), sleep hygiene alone (2, 3%), sleep restriction alone (2, 3%), Tai Chi (2, 3%), CBT for nightmares (1, 2%), herbal remedies (1, 2%), walking (1, 2%), and yoga (1, 2%). Interventions were most often compared against an active control group (34, 47%), but were also compared to waitlist control groups (25, 35%), and groups receiving treatment as usual (13, 18%). On average participants’ mental health was followed-up 20.5 weeks post-intervention (median = 12 weeks post-intervention), with the earliest follow-up being 4-weeks post-intervention, and the furthest follow-up 156-weeks (three years) post intervention.

Manipulation check: did sleep quality improve significantly in the intervention group relative to controls?

Before we examined the effect of improving sleep quality on subsequent mental health, we confirmed that studies included in the review successfully improved sleep quality. The interventions had large and statistically significant effects on sleep quality at the earliest follow-up point reported ( g +  = −1.07, 95% CI = −1.26 to −0.88, p  < 0.001), although heterogeneity between studies was substantial ( I 2  = 79%, Q  = 331.93, p  < 0.001). After twelve outlying effect sizes were removed, the effect of the interventions on sleep quality remained large and statistically significant ( g +  = −0.97, 95% CI = −1.07 to −0.88, p  < 0.001), and heterogeneity was reduced to moderate levels ( I 2  = 43%, Q  = 102.32, p  < 0.001). These findings suggest that the primary studies included in the present review successfully manipulated sleep quality, even after accounting for outliers.

What effect do improvements in sleep quality have on mental health?

Table 3 presents the effect of improving sleep quality on composite mental health outcomes, and on measures of depression, anxiety, stress, psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout.

Table 3

The effect of improving sleep on mental health outcomes.

Notes : ∗∗∗ p  < 0.001, ∗ p  < 0.05, PANSS = Positive and Negative Symptoms Scale, PTSD = Post Traumatic Stress Disorder.

Composite mental health

On average, the 72 interventions that successfully improved sleep quality had a statistically significant, medium-sized effect on subsequent composite mental health outcomes, ( g +  = −0.53, 95% CI = −0.68 to −0.38, p  < 0.001); however, there was substantial heterogeneity between the effect sizes, ( I 2  = 76%, Q  = 291.94, p  < 0.001). After re-running the analysis with eleven outlying effect sizes removed, the effect of improving sleep on composite mental health outcomes was small-to-medium sized but still statistically significant, ( g +  = −0.42, 95% CI = −0.49 to −0.34, p  < 0.001) and now relatively homogeneous ( I 2  = 20%, Q  = 75.24, p  = 0.0888). See Fig. 2 for a forest plot.

Forest plot showing the effect of improving sleep on composite mental health outcomes.

Interventions that successfully improved sleep quality had a statistically significant, medium-sized effect on depression across 61 comparisons, ( g +  = −0.63, 95% CI = −0.83 to −0.43, p  < 0.001); however, once again, there was substantial heterogeneity, ( I 2  = 81%, Q  = 322.09, p  < 0.001). After re-running the analysis with nine outlying effect sizes removed, the effect of improving sleep on depression remained medium-sized, ( g +  = −0.47, 95% CI = −0.57 to −0.37, p  < 0.001), with moderate heterogeneity, ( I 2  = 32%, Q  = 74.86, p  = 0.0164). See Fig. 3 for a forest plot.

Forest plot showing the effect of improving sleep on depression.

Interventions that successfully improved sleep quality had a statistically significant, small-to-medium sized effect on anxiety across 35 comparisons, ( g +  = −0.50, 95% CI = −0.76 to −0.24, p  < 0.001), with substantial levels of heterogeneity, ( I 2  = 82%, Q  = 187.02, p  < 0.001). After re-running the analysis with four outlying effect sizes removed, the effect improving sleep on anxiety outcomes was small-to-medium sized, but still statistically significant, ( g +  = −0.38, 95% CI = −0.49 to −0.27, p  < 0.001), with lower levels of heterogeneity, ( I 2  = 43%, Q  = 52.49, p  = 0.0067). See Fig. 4 for a forest plot.

Forest plot showing the effect of improving sleep on anxiety.

Interventions that successfully improved sleep quality had a statistically significant, small-to-medium sized effect on stress ( g +  = −0.42, 95% CI = −0.79 to −0.05, p  = 0.033), across six comparisons. There were moderate levels of heterogeneity ( I 2  = 55%, Q  = 11.05, p  = 0.05), but there were no outlying effect sizes. See Fig. 5 for a forest plot.

Forest Plot Showing the Effect of Improving Sleep on Stress, Suicidal Ideation, PTSD, and rumination.

Psychosis spectrum experiences

Interventions that successfully improved sleep quality had a small effect on total symptoms as indicated by the PANSS ( g +  = −0.17, 95% CI = −0.53 to 0.19, p  = 0.18) across three comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 0.41, p  = 0.813). Interventions that successfully improved sleep quality had a small effect on positive symptoms ( g +  = −0.26, 95% CI = −0.43 to −0.08, p  = 0.014) across five comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 1.71, p  = 0.788). Finally, interventions that successfully improved sleep quality had a small effect on negative symptoms ( g +  = −0.28, 95% CI = −3.22 to 2.65, p  = 0.436) across k  = 2 comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 1, p  = 0.318). See Fig. 6 for a forest plot.

Forest plot showing the effect of improving sleep on psychosis spectrum outcomes.

Suicidal ideation

Interventions that successfully improved sleep quality had a small, adverse effect on suicidal ideation ( g +  = 0.10, 95% CI = −3.74 to 3.94, p  = 0.804) across two comparisons. There were low levels of heterogeneity ( I 2  = 20%, Q  = 1.25, p  = 0.263) and no outlying effect sizes. See Fig. 5 for a forest plot.

Post-traumatic stress disorder (PTSD)

Interventions that successfully improved sleep quality had a medium-to-large effect on PTSD ( g +  = −0.72, 95% CI = −2.90 to 1.46, p  = 0.149) across two comparisons, with zero heterogeneity ( I 2  = 0%, Q  = 0.59, p  = 0.442). See Fig. 5 for a forest plot.

Interventions that successfully improved sleep quality had a statistically significant, medium sized effect on rumination ( g +  = −0.49, 95% CI = −0.93 to −0.04, p  = 0.041) across four comparisons, with moderate heterogeneity ( I 2  = 36%, Q  = 4.65, p  = 0.1991). See Fig. 5 for a forest plot.

Only one study reported the effect of improving sleep on burnout finding almost zero effect ( g  = −0.03, CI = −0.58 to 0.52, p  = 0.917).

Moderators of the effect of improving sleep quality on composite mental health outcomes

Table 4 presents the findings of analyses evaluating categorical moderators of the effect of improving sleep quality on composite mental health outcomes and Table 5 presents analyses evaluating continuous moderators using meta-regression. Studies that found significant effects of the intervention on sleep quality reported larger effects on subsequent composite mental health, ( g  = −0.53, 95% CI = −0.68 to −0.38, p  < 0.001), than studies that did not find a significant effect of the intervention on sleep quality, ( g  = −0.12, 95% CI = −0.24 to 0.01, p  = 0.0522), a difference that was statistically significant, ( Q  = 17.59, p  < 0.001). This finding strengthens the notion that improvements in sleep are behind improvements in mental health. The effect of improving sleep on mental health was larger in studies with shorter follow-up periods, (i.e., <6 months, g + = −0.60), than in studies with longer follow-ups, (i.e., 6 months, g + = −0.18, Q  = 10.75, p  < 0.01). Furthermore, interventions that were delivered face-to-face by a clinician or therapist were associated with significantly larger effects on mental health, ( g + = −0.63), than those that were self-administered by participants, ( g + = −0.34, Q  = 4.50, p  < 0.05). Finally, there was significant variation in the size of the effect between countries ( Q  = 53.69, p  < 0.001). No other statistically significant categorical moderator effects were found. Regarding continuous moderators, meta-regression revealed a statistically significant dose–response effect for the association between the effect of interventions on sleep quality and the effect on subsequent mental health outcomes ( B  = 0.77, 95% CI = 0.52 to 1.02, p  < 0.001), suggesting that greater improvements in sleep led to greater improvements in mental health. No other continuous variables significantly moderated the effect of improving sleep on mental health.

Table 4

Categorical moderators of the effect of improving sleep on composite mental health outcomes.

Notes : CBTi = cognitive behavioural therapy for insomnia, MH = Mental Health, PH = Physical Health, TaU = treatment as usual, WLC = wait list control.

∗ p  < 0.05, ∗∗ p  < 0.01, ∗∗∗ p  < 0.001.

Table 5

Continuous moderators of the effect of improving sleep on composite mental health outcomes.

Post-hoc moderation analysis

Is the smaller effect of improving sleep on mental health at longer follow-ups associated with smaller effects on sleep quality.

We conducted further (unplanned) post-hoc analysis to investigate whether the smaller effect of improving sleep on mental health at longer follow-ups was accompanied by a reduction in the improvements to sleep quality. Studies reporting the effect of the intervention at shorter follow-ups reported larger improvements in sleep quality, ( g  = −1.03, 95% CI = −1.27 to −0.78, p  < 0.001), than those reporting longer follow-ups ( g  = −0.44, 95% CI = −0.62 to −0.27, p  < 0.001), a difference that was statistically significant, ( Q  = 14.38, p  < 0.001). This suggests that the smaller effect of improving sleep on mental health at longer follow-ups might be driven by a smaller effect of the interventions on sleep quality at longer follow-ups.

Can some of the effect of improved mental health be explained by CBTi modules that target processes associated with mental health?

Finally, although the present review excluded interventions that specifically and directly targeted mental health, some CBTi protocols include modules that might target similar processes associated with some mental health difficulties (rumination around sleep, catastrophizing over the effect of poor sleep etc.). Therefore, we compared CBTi interventions with modules that could target processes associated with mental health vs. interventions that did not include these modules (e.g., sleep restriction alone, sleep hygiene alone, herbal tea, and pharmacological intervention). There were no significant differences in the effect of improved sleep quality on mental health between CBTi interventions including modules addressing processes associated with mental health ( g  = −0.44, 95% CI = −0.59 to −0.29, p  < 0.001), relative to those that did not ( g  = −0.48, 95% CI = −0.65 to −0.32, p  < 0.001, Q  = 2.51, p  = 0.285). This finding suggests that it is the beneficial effect of improved sleep quality that confers improvements in mental health rather than the inclusion of modules that target processes associated with mental health commonly seen in CBTi protocols.

Risk of bias assessments

Fig. 7 summarizes the weighted assessment of risk of bias. Individual risk of bias judgements for included studies are presented in Supplementary Material 3 . Ten studies (15%) were judged as having low risk of bias, 29 studies (45%) were judged as high risk of bias, and 26 studies (40%) were judged as unclear. The methodological quality of the included studies was not associated with the effect of improving sleep on composite mental health outcomes, Q  = 0.72, p  = 0.395.

Weighted risk of bias summary plot.

Publication bias

A funnel plot of the effect of improving sleep quality on composite outcomes revealed asymmetry in the effect sizes (Egger's regression = −1.09, 95% CI = −1.91 to −0.28, p  < 0.05, see Fig. 8 ). Duval and Tweedie's [ 74 ] trim and fill procedure was therefore used to address the asymmetry. Ten studies were imputed resulting in a statistically significant, small-to-medium sized adjusted effect of improving sleep on composite mental health outcomes ( g +  = −0.35, 95% CI = −0.55 to −0.16, p  < 0.001). Orwin's failsafe n test suggested that an additional 4101 comparisons producing null effects would be needed to reduce the average effect of improving sleep on composite outcomes to zero. Taken together these results suggest that the effect of improving sleep on composite mental health is robust to possible publication bias.

Contrast enhanced funnel plot for the effect of improving sleep on composite mental health (solid grey markers) with imputed studies (hollow markers).

The present review used meta-analysis to synthesize the effect of 72 interventions that improved sleep quality relative to a control condition on subsequent mental health. The findings revealed that improving sleep quality had, on average, a medium-sized effect on mental health, including clear evidence that improving sleep reduced depression, anxiety, and stress. A dearth of primary studies of other mental health difficulties (e.g., psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout) mean that it is premature to draw definitive conclusions in these areas. It was also notable that we found a dose–response relationship between improvements in sleep quality and subsequent mental health, such that greater improvements in sleep led to greater improvements in mental health. Although there was some evidence of publication bias, the effects remained robust to correction. Taken together, the findings suggest that improving sleep leads to better mental health, therefore providing strong evidence that sleep plays a causal role in the experience of mental health difficulties.

Sleep as a transdiagnostic treatment target

The present findings support the idea that targeting sleep promotes mental health across a range of populations and experiences. The effect of improving sleep quality on composite mental health was medium-sized and statistically significant, regardless of the presence of physical and/or mental health comorbidities. This finding is particularly important given the healthcare challenges associated with multimorbidity [ 75 ] and mental and physical health problems often co-occur [ [76] , [77] , [78] , [79] ], something that appears to be increasing [ 80 ]. Consequently, it is important that the benefits of improving sleep on mental health occur even in the presence of comorbid health complaints, as was reported in the present research. Improving sleep has also been shown to improve aspects of physical health, including fatigue [ 81 ], chronic pain [ 82 , 83 ], and overall health related quality of life [ 84 ] and could reduce the cost of healthcare. For example, offering a digital CBTi intervention (Sleepio) to primary care patients was associated with an average saving of £70.44 per intervention user [ 85 ], and cost savings following sleep intervention have also been specifically reported in people with comorbid mental health difficulties such as depression [ 86 ].

Another finding to suggest that targeting sleep could promote mental health across a range of populations and experiences, is that we found no difference in the effect of improving sleep quality on mental health between those with clinically defined mental health difficulties and those with non-clinical experiences or between those recruited from clinical vs. community settings, with both groups receiving significant benefits of improved sleep on mental health. This suggests that improving sleep could prove helpful across a range of mental health severities, thus broadening the possible impact of sleep interventions within healthcare services. Finally, there is growing evidence that sleep disturbances predict the development of mental health difficulties in the future. For example, shorter and more variable sleep has been shown to be longitudinally associated with more severe hallucinations and delusional ideation in those at high-risk of psychosis [ 87 ]. The present research found that improving sleep has a significant beneficial impact on future mental health in those with non-clinical experiences, raising the possibility that delivering interventions that improve sleep early might limit the risk of developing (or exacerbating) substantive mental health difficulties. Indeed, less severe mild-to-moderate presentations of mental health difficulties can develop over time into more severe mental health diagnoses [ 88 , 89 ], therefore improving sleep might be one tool that can be used in combination with others to limit the risk of transition.

Strengths and limitations

The present review has several strengths. First, it provides a comprehensive and up-to-date search of RCTs examining the effect of improving sleep on a variety of subsequent mental health outcomes. Indeed, with 65 RCTs and N  = 8608 participants, the present review is one of the largest studies of the effect of improving sleep on mental health to date. Second, the review was specifically designed to test the causal association between sleep and mental health (i.e., RCTs only, successful sleep improvement required, temporal lag between measures etc.). To our knowledge, the review is the first to adopt this approach in the field of sleep and mental health, although the general approach has been used in other fields [ 90 ]. Finally, we provide an analysis of possible moderators of the effect of improving sleep on mental health, identifying several key moderators of the effect.

However, there are limitations that must be considered when interpreting the findings. First, relatively few studies examined the effect of improving sleep over the long term. Those that did report longer follow-ups generally found smaller effects (although still statistically significant), most likely due to the diminishing effects of interventions on sleep quality over time [ 91 ]. Consequently, it is important that interventions targeting sleep quality as a route to improving mental health seek to maintain their beneficial effects. Second, there were few primary studies for some of the outcomes included in this review. Consequently, in lieu of more studies reporting these outcomes, the inferences that we can make for mental health outcomes other than depression and anxiety are more limited. Third, although the intention of the present review was to include a broad range of sleep disturbances, most of the analyses are based on CBT interventions for insomnia. This might be due to the relationship between insomnia and mental health being the one that is historically most studied. However, it may be that our focus on sleep quality precluded some studies that do not focus on insomnia from inclusion. For example, different sleep disorders have different conceptualisations of improvement that might not include sleep quality. For example, the timing of sleep is particularly important in circadian rhythm disorders and daytime sleepiness is a key outcome in sleep apnoea research. Future research might consider examining the effect of improving specific sleep disorders on mental health by conceptualising improvements using sleep disorder specific outcomes.

Future directions

The present review highlighted several areas for future research in terms of both research and theory, and the implementation of findings in practice. First, given that mental health was measured on average around 20.5 weeks post-intervention in the primary studies, and that the effect of improving sleep on mental health significantly reduced over time, future research should examine the effect of improving sleep on mental health over the longer term. Second, although not uncommon, the majority of RCTs included in the present review were at high risk, or unclear risk or bias. Consequently, in addition to studying the effect of improving sleep over the longer term, on a range of mental health difficulties beyond depression and anxiety, we need more research at lower risk of methodological bias.

Finally, although the present research provides evidence for a causal association between sleep and mental health, it is less clear how sleep affects mental health. One potential mechanism is whether and how people regulate their emotions (e.g., in response to negative events). Indeed, evidence suggests that poor sleep can amplify the adverse effect of negative life events [ 92 , 93 ], dull the beneficial impact of positive events [ 94 ], and is associated with more frequent use of emotion regulation strategies that might be detrimental to good mental health [ 95 ]. By extension, although we are unaware of RCTs testing the effect of improved sleep on emotion regulation, changes in sleep are prospectively associated with changes in aspects of emotion regulation [ 96 , 97 ], while experimentally induced sleep deprivation is adversely linked to poorer emotion regulation [ 96 , 97 ]. Contemporary perspectives on emotion regulation (e.g., the action control perspective), draw on research on how people regulate their behaviour, to propose that regulating emotions involves three tasks, 1) identifying the need to regulate, 2) deciding whether and how to regulate, and 3) enacting a regulation strategy [ 98 ]. We propose that poor sleep quality has the potential to adversely affect anyone (or all) of the three tasks involved in effectively regulating emotions, which might go some way toward explaining the relationship between poor sleep and mental health. Therefore, we would recommend that future research includes measures of aspects of emotion regulation (e.g., the Difficulties in Emotion Regulation Scale, [ 99 ]) within experimental and longitudinal designs to elucidate possible mechanisms by which improvements in sleep benefit mental health.

In terms of practice and implementation, evidence on the effect of sleep on mental health also supports calls for routine screening and treatment of problems with sleep. Both the Royal Society for Public Health (RSPH) and the Mental Health Foundation (MHF) recommend that primary health care training should include awareness of, and skills in assessing, sleep problems [ 100 , 101 ]. Despite this and a growing body of evidence, there has been little progress to date [ 102 ]. This may reflect under-appreciation of the importance of sleep [ 103 ] and lack of training and skills in assessing and managing sleep problems [ [104] , [105] , [106] , [107] , [108] ], as well as limited time and resources [ 103 , 109 ]. Therefore, a profitable next step might be to explore barriers and facilitators to assessing sleep and delivering effective interventions in specific care settings, from both the patient and clinician perspective. Indeed, the present review also highlighted a dearth of trials that tested the effect of improving sleep on mental health outcomes in ‘real world’ settings (e.g., within existing clinical and community health services). Although some researchers are taking important steps in this area [ [110] , [111] , [112] ], there is a clear need for more trials of interventions in clinical services so that the effectiveness and implementation of such interventions in routine care can be better understood.

Conclusions

Taken together, the present research supports the view that sleep is causally related to the experience of mental health difficulties, and therefore that sleep represents a viable treatment target that can confer significant benefits to mental health, as it has been found to do for physical health. We found that improving sleep was associated with better mental health regardless of the severity of mental health difficulty (i.e., clinical vs. non-clinical) or the presence of comorbid health conditions. Poor sleep is almost ubiquitous within mental health services [ 102 , 108 , 113 , 114 ], is causally related to the experience of mental health difficulties, and represents a potential treatment target [ 105 , 115 , 116 ]. Consequently, equipping health professionals with greater knowledge and resources to support sleep is an essential next step. Future research should consider how interventions that improve sleep could be better incorporated into routine mental health care, as well as the possible mechanisms of action that might explain how sleep exerts its effects on mental health.

Research agenda

To fully harness the effect of improved sleep on mental health, it is important that future research:

• 1. Explores the barriers and possible solutions to incorporating interventions that improve sleep into mental health care services.
• 2. Tests the effect of improving sleep on mental health outcomes beyond depression and anxiety, and over the long term, using designs at low risk of methodological bias.
• 3. Investigates the possible mechanisms of action that might explain how sleep exerts its effects on the experience of mental health difficulties.

Practice points

• • Sleep is causally related to the experience of mental health difficulties and represents a viable transdiagnostic treatment target for those experiencing mental health difficulties.
• • Improving sleep has beneficial effects on the experience of mental health difficulties, regardless of the severity of those difficulties, or the presence of comorbid health conditions.
• • Healthcare professionals aiming to improve mental health (particularly depression, anxiety, and stress) should consider interventions designed to improve sleep, particularly cognitive behavioral therapy for insomnia where the evidence base is strongest.

This research was funded by the National Institute for Health Research under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG- 0817-20027). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Acknowledgements

We thank Anthea Sutton (Information Resources Group Manager, The University of Sheffield) for her help developing the systematic search strategy and managing the records.

∗ The most important references are denoted by an asterisk.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.smrv.2021.101556 .

Appendix ASupplementary data

The following are the Supplementary data to this article:

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References ∗

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55 research questions about mental health

Last updated

11 March 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Short on time? Get an AI generated summary of this article instead

Research in the mental health space helps fill knowledge gaps and create a fuller picture for patients, healthcare professionals, and policymakers. Over time, these efforts result in better quality care and more accessible treatment options for those who need them.

Use this list of mental health research questions to kickstart your next project or assignment and give yourself the best chance of producing successful and fulfilling research.

• Why does mental health research matter?

Mental health research is an essential area of study. It includes any research that focuses on topics related to people’s mental and emotional well-being.

As a complex health topic that, despite the prevalence of mental health conditions, still has an unending number of unanswered questions, the need for thorough research into causes, triggers, and treatment options is clear.

Research into this heavily stigmatized and often misunderstood topic is needed to find better ways to support people struggling with mental health conditions. Understanding what causes them is another crucial area of study, as it enables individuals, companies, and policymakers to make well-informed choices that can help prevent illnesses like anxiety and depression.

• How to choose a strong mental health research topic

As one of the most important parts of beginning a new research project, picking a topic that is intriguing, unique, and in demand is a great way to get the best results from your efforts.

Mental health is a blanket term with many niches and specific areas to explore. But, no matter which direction you choose, follow the tips below to ensure you pick the right topic.

Prioritize your interests and skills

While a big part of research is exploring a new and exciting topic, this exploration is best done within a topic or niche in which you are interested and experienced.

Research is tough, even at the best of times. To combat fatigue and increase your chances of pushing through to the finish line, we recommend choosing a topic that aligns with your personal interests, training, or skill set.

Consider emerging trends

Topical and current research questions are hot commodities because they offer solutions and insights into culturally and socially relevant problems.

Depending on the scope and level of freedom you have with your upcoming research project, choosing a topic that’s trending in your area of study is one way to get support and funding (if you need it).

Not every study can be based on a cutting-edge topic, but this can be a great way to explore a new space and create baseline research data for future studies.

Assess your resources and timeline

Before choosing a super ambitious and exciting research topic, consider your project restrictions.

You’ll need to think about things like your research timeline, access to resources and funding, and expected project scope when deciding how broad your research topic will be. In most cases, it’s better to start small and focus on a specific area of study.

Broad research projects are expensive and labor and resource-intensive. They can take years or even decades to complete. Before biting off more than you can chew, consider your scope and find a research question that fits within it.

Read up on the latest research

Finally, once you have narrowed in on a specific topic, you need to read up on the latest studies and published research. A thorough research assessment is a great way to gain some background context on your chosen topic and stops you from repeating a study design. Using the existing work as your guide, you can explore more specific and niche questions to provide highly beneficial answers and insights.

• Trending research questions for post-secondary students

As a post-secondary student, finding interesting research questions that fit within the scope of your classes or resources can be challenging. But, with a little bit of effort and pre-planning, you can find unique mental health research topics that will meet your class or project requirements.

Examples of research topics for post-secondary students include the following:

How does school-related stress impact a person’s mental health?

To what extent does burnout impact mental health in medical students?

How does chronic school stress impact a student’s physical health?

How does exam season affect the severity of mental health symptoms?

Is mental health counseling effective for students in an acute mental crisis?

• Research questions about anxiety and depression

Anxiety and depression are two of the most commonly spoken about mental health conditions. You might assume that research about these conditions has already been exhausted or that it’s no longer in demand. That’s not the case at all.

According to a 2022 survey by Centers for Disease Control and Prevention (CDC), 12.5% of American adults struggle with regular feelings of worry, nervousness, and anxiety, and 5% struggle with regular feelings of depression. These percentages amount to millions of lives affected, meaning new research into these conditions is essential.

If either of these topics interests you, here are a few trending research questions you could consider:

Does gender play a role in the early diagnosis of anxiety?

How does untreated anxiety impact quality of life?

What are the most common symptoms of anxiety in working professionals aged 20–29?

To what extent do treatment delays impact quality of life in patients with undiagnosed anxiety?

To what extent does stigma affect the quality of care received by people with anxiety?

Here are some examples of research questions about depression:

Does diet play a role in the severity of depression symptoms?

Can people have a genetic predisposition to developing depression?

How common is depression in work-from-home employees?

Does mood journaling help manage depression symptoms?

What role does exercise play in the management of depression symptoms?

• Research questions about personality disorders

Personality disorders are complex mental health conditions tied to a person’s behaviors, sense of self, and how they interact with the world around them. Without a diagnosis and treatment, people with personality disorders are more likely to develop negative coping strategies during periods of stress and adversity, which can impact their quality of life and relationships.

There’s no shortage of specific research questions in this category. Here are some examples of research questions about personality disorders that you could explore:

What environments are more likely to trigger the development of a personality disorder?

What barriers impact access to care for people with personality disorders?

To what extent does undiagnosed borderline personality disorder impact a person’s ability to build relationships?

How does group therapy impact symptom severity in people with schizotypal personality disorder?

What is the treatment compliance rate of people with paranoid personality disorder?

• Research questions about substance use disorders

“Substance use disorders” is a blanket term for treatable behaviors and patterns within a person’s brain that lead them to become dependent on illicit drugs, alcohol, or prescription medications. It’s one of the most stigmatized mental health categories.

The severity of a person’s symptoms and how they impact their ability to participate in their regular daily life can vary significantly from person to person. But, even in less severe cases, people with a substance use disorder display some level of loss of control due to their need to use the substance they are dependent on.

This is an ever-evolving topic where research is in hot demand. Here are some example research questions:

To what extent do meditation practices help with craving management?

How effective are detox centers in treating acute substance use disorder?

Are there genetic factors that increase a person’s chances of developing a substance use disorder?

How prevalent are substance use disorders in immigrant populations?

To what extent do prescription medications play a role in developing substance use disorders?

• Research questions about mental health treatments

Treatments for mental health, pharmaceutical therapies in particular, are a common topic for research and exploration in this space.

Besides the clinical trials required for a drug to receive FDA approval, studies into the efficacy, risks, and patient experiences are essential to better understand mental health therapies.

These types of studies can easily become large in scope, but it’s possible to conduct small cohort research on mental health therapies that can provide helpful insights into the actual experiences of the people receiving these treatments.

Here are some questions you might consider:

What are the long-term effects of electroconvulsive therapy (ECT) for patients with severe depression?

How common is insomnia as a side effect of oral mental health medications?

What are the most common causes of non-compliance for mental health treatments?

How long does it take for patients to report noticeable changes in symptom severity after starting injectable mental health medications?

What issues are most common when weaning a patient off of an anxiety medication?

• Controversial mental health research questions

If you’re interested in exploring more cutting-edge research topics, you might consider one that’s “controversial.”

Depending on your own personal values, you might not think many of these topics are controversial. In the context of the research environment, this depends on the perspectives of your project lead and the desires of your sponsors. These topics may not align with the preferred subject matter.

That being said, that doesn’t make them any less worth exploring. In many cases, it makes them more worthwhile, as they encourage people to ask questions and think critically.

Here are just a few examples of “controversial” mental health research questions:

To what extent do financial crises impact mental health in young adults?

How have climate concerns impacted anxiety levels in young adults?

To what extent do psychotropic drugs help patients struggling with anxiety and depression?

To what extent does political reform impact the mental health of LGBTQ+ people?

What mental health supports should be available for the families of people who opt for medically assisted dying?

• Research questions about socioeconomic factors & mental health

Socioeconomic factors—like where a person grew up, their annual income, the communities they are exposed to, and the amount, type, and quality of mental health resources they have access to—significantly impact overall health.

This is a complex and multifaceted issue. Choosing a research question that addresses these topics can help researchers, experts, and policymakers provide more equitable and accessible care over time.

Examples of questions that tackle socioeconomic factors and mental health include the following:

How does sliding scale pricing for therapy increase retention rates?

What is the average cost to access acute mental health crisis care in [a specific region]?

To what extent does a person’s environment impact their risk of developing a mental health condition?

How does mental health stigma impact early detection of mental health conditions?

To what extent does discrimination affect the mental health of LGBTQ+ people?

• Research questions about the benefits of therapy

Therapy, whether that’s in groups or one-to-one sessions, is one of the most commonly utilized resources for managing mental health conditions. It can help support long-term healing and the development of coping mechanisms.

Yet, despite its popularity, more research is needed to properly understand its benefits and limitations.

Here are some therapy-based questions you could consider to inspire your own research:

In what instances does group therapy benefit people more than solo sessions?

How effective is cognitive behavioral therapy for patients with severe anxiety?

After how many therapy sessions do people report feeling a better sense of self?

Does including meditation reminders during therapy improve patient outcomes?

To what extent has virtual therapy improved access to mental health resources in rural areas?

• Research questions about mental health trends in teens

Adolescents are a particularly interesting group for mental health research due to the prevalence of early-onset mental health symptoms in this age group.

As a time of self-discovery and change, puberty brings plenty of stress, anxiety, and hardships, all of which can contribute to worsening mental health symptoms.

If you’re looking to learn more about how to support this age group with mental health, here are some examples of questions you could explore:

Does parenting style impact anxiety rates in teens?

How early should teenagers receive mental health treatment?

To what extent does cyberbullying impact adolescent mental health?

What are the most common harmful coping mechanisms explored by teens?

How have smartphones affected teenagers’ self-worth and sense of self?

• Research questions about social media and mental health

Social media platforms like TikTok, Instagram, YouTube, Facebook, and X (formerly Twitter) have significantly impacted day-to-day communication. However, despite their numerous benefits and uses, they have also become a significant source of stress, anxiety, and self-worth issues for those who use them.

These platforms have been around for a while now, but research on their impact is still in its infancy. Are you interested in building knowledge about this ever-changing topic? Here are some examples of social media research questions you could consider:

To what extent does TikTok’s mental health content impact people’s perception of their health?

How much non-professional mental health content is created on social media platforms?

How has social media content increased the likelihood of a teen self-identifying themselves with ADHD or autism?

To what extent do social media photoshopped images impact body image and self-worth?

Has social media access increased feelings of anxiety and dread in young adults?

• Mental health research is incredibly important

As you have seen, there are so many unique mental health research questions worth exploring. Which options are piquing your interest?

Whether you are a university student considering your next paper topic or a professional looking to explore a new area of study, mental health is an exciting and ever-changing area of research to get involved with.

Your research will be valuable, no matter how big or small. As a niche area of healthcare still shrouded in stigma, any insights you gain into new ways to support, treat, or identify mental health triggers and trends are a net positive for millions of people worldwide.

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