research papers on mental illness

August 29, 2024

What are the effects of diagnostic labels?

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

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

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

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

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Introduction

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

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

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

Social Media Use and Mental Health

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

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

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

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

Facilitate Social Interaction

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

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

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

Access to a Peer Support Network

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

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

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

Promote Engagement and Retention in Services

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

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

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

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

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

Challenges with Social Media for Mental Health

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

Impact on Symptoms

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

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

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

Facing Hostile Interactions

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

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

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

Consequences for Daily Life

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

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

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

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

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

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

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

Future Directions for Social Media and Mental Health

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

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

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

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

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

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

PubMed   Google Scholar  

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

Google Scholar  

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

PubMed   PubMed Central   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PubMed Central   Google Scholar  

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Research landscape analysis on dual diagnosis of substance use and mental health disorders: key contributors, research hotspots, and emerging research topics

• Waleed M. Sweileh 1  

Annals of General Psychiatry volume  23 , Article number:  32 ( 2024 ) Cite this article

Metrics details

Substance use disorders (SUDs) and mental health disorders (MHDs) are significant public health challenges with far-reaching consequences on individuals and society. Dual diagnosis, the coexistence of SUDs and MHDs, poses unique complexities and impacts treatment outcomes. A research landscape analysis was conducted to explore the growth, active countries, and active journals in this field, identify research hotspots, and emerging research topics.

A systematic research landscape analysis was conducted using Scopus to retrieve articles on dual diagnosis of SUDs and MHDs. Inclusion and exclusion criteria were applied to focus on research articles published in English up to December 2022. Data were processed and mapped using VOSviewer to visualize research trends.

A total of 935 research articles were found. The number of research articles on has been increasing steadily since the mid-1990s, with a peak of publications between 2003 and 2012, followed by a fluctuating steady state from 2013 to 2022. The United States contributed the most articles (62.5%), followed by Canada (9.4%). The Journal of Dual Diagnosis , Journal of Substance Abuse Treatment , and Mental Health and Substance Use Dual Diagnosis were the top active journals in the field. Key research hotspots include the comorbidity of SUDs and MHDs, treatment interventions, quality of life and functioning, epidemiology, and the implications of comorbidity. Emerging research topics include neurobiological and psychosocial aspects, environmental and sociocultural factors, innovative interventions, special populations, and public health implications.

Conclusions

The research landscape analysis provides valuable insights into dual diagnosis research trends, active countries, journals, and emerging topics. Integrated approaches, evidence-based interventions, and targeted policies are crucial for addressing the complex interplay between substance use and mental health disorders and improving patient outcomes.

Introduction

Substance use disorders (SUDs) refer to a range of conditions characterized by problematic use of psychoactive substances, leading to significant impairment in physical, psychological, and social functioning [ 1 ]. These substances may include alcohol, tobacco, illicit drugs (e.g., cocaine, opioids, cannabis), and prescription medications. The global burden of SUDs is substantial, with far-reaching consequences on public health, socio-economic development, and overall well-being. For instance, alcohol abuse accounts for 3 million deaths worldwide annually, while the opioid crisis has escalated to unprecedented levels in certain regions, such as North America, resulting in tens of thousands of overdose deaths per year [ 2 , 3 , 4 ]. Mental health disorders (MHDs) encompass a wide range of conditions that affect mood, thinking, behavior, and emotional well-being [ 5 ]. Examples of MHDs include depression, anxiety disorders, post-traumatic stress disorder (PTSD), bipolar disorder, schizophrenia, and eating disorders. These conditions can significantly impair an individual's ability to function, negatively impacting their quality of life, relationships, and overall productivity [ 6 , 7 , 8 ]. Furthermore, certain MHD such as major depressive disorder and anxiety are often associated with specific affective temperaments, hopelessness, and suicidal behavior and grasping such connections can help in crafting customized interventions to reduce suicide risk [ 9 ]. In addition, a systematic review of 18 studies found that demoralization with somatic or psychiatric disorders is a significant independent risk factor for suicide and negative clinical outcomes across various populations [ 10 ]. The coexistence of SUDs and MHDs, often referred to as dual diagnosis or comorbidity, represents a complex and prevalent phenomenon that significantly impacts affected individuals and healthcare systems [ 11 , 12 , 13 , 14 , 15 ]. For instance, individuals with depression may be more likely to self-medicate with alcohol or drugs to cope with emotional distress [ 16 ]. Similarly, PTSD has been linked to increased rates of substance abuse, as individuals attempt to alleviate the symptoms of trauma [ 17 , 18 ]. Moreover, chronic substance use can lead to changes in brain chemistry, increasing the risk of developing MHDs or exacerbating existing conditions [ 17 , 19 , 20 , 21 ]. The coexistence of SUDs and MHDs presents unique challenges from a medical and clinical standpoint. Dual diagnosis often leads to more severe symptoms, poorer treatment outcomes, increased risk of relapse, and higher rates of hospitalization compared to either disorder alone [ 22 ]. Additionally, diagnosing and treating dual diagnosis cases can be complex due to overlapping symptoms and interactions between substances and psychiatric medications. Integrated treatment approaches that address both conditions simultaneously are essential for successful recovery and improved patient outcomes [ 20 ]. Patients grappling with dual diagnosis encounter a multifaceted web of barriers when attempting to access essential mental health services. These barriers significantly compound the complexity of their clinical presentation. The first barrier pertains to stigma, where societal prejudices surrounding mental health and substance use disorders deter individuals from seeking help, fearing discrimination or social repercussions [ 23 ]. A lack of integrated care, stemming from fragmented healthcare systems, poses another significant hurdle as patients often struggle to navigate separate mental health and addiction treatment systems [ 24 ]. Insurance disparities contribute by limiting coverage for mental health services and imposing strict criteria for reimbursement [ 25 ]. Moreover, there is a shortage of adequately trained professionals equipped to address both substance use and mental health issues, creating a workforce barrier [ 26 ]. Geographical disparities in access further hinder care, particularly in rural areas with limited resources [ 27 ]. These barriers collectively serve to exacerbate the clinical complexity of patients with dual diagnosis, and ultimately contributing to poorer outcomes.

A research landscape analysis involves a systematic review and synthesis of existing literature on a specific topic to identify key trends, knowledge gaps, and research priorities [ 28 , 29 ]. Scientific research landscape analysis, is motivated by various factors. First, the rapid growth of scientific literature poses a challenge for researchers to stay up-to-date with the latest developments in their respective fields. Research landscape analysis provides a structured approach to comprehend the vast body of literature, identifying crucial insights and emerging trends. Additionally, it plays a vital role in identifying knowledge gaps, areas with limited research, or inadequate understanding. This pinpointing allows researchers to focus on critical areas that demand further investigation, fostering more targeted and impactful research efforts [ 30 ]. Furthermore, in the realm of policymaking and resource allocation, evidence-based decision-making is crucial. Policymakers and funding agencies seek reliable information to make informed decisions about research priorities. Research landscape analysis offers a comprehensive view of existing evidence, facilitating evidence-based decision-making processes [ 28 ]. When it comes to the research landscape analysis of dual diagnosis of SUDs and MHDs, there are several compelling justifications to explore this complex comorbidity and gain a comprehensive understanding of its interplay and impact on patient outcomes. Firstly, the complexity of the interplay between SUDs and MHDs demands a comprehensive examination of current research to unravel the intricacies of this comorbidity [ 31 ]. Secondly, dual diagnosis presents unique challenges for treatment and intervention strategies due to the overlapping symptoms and interactions between substances and psychiatric medications. A research landscape analysis can shed light on effective integrated treatment approaches and identify areas for improvement [ 18 ]. Moreover, the public health impact of co-occurring SUDs and MHDs is substantial, resulting in more severe symptoms, poorer treatment outcomes, increased risk of relapse, and higher rates of hospitalization. Understanding the research landscape can inform public health policies and interventions to address this issue more effectively [ 32 ]. Lastly, the holistic approach of research landscape analysis enables a comprehensive understanding of current knowledge, encompassing epidemiological data, risk factors, treatment modalities, and emerging interventions. This integrative approach can lead to more coordinated and effective care for individuals with dual diagnosis [ 22 ]. Based on the above argument, the current study aims to conduct a research landscape analysis of dual diagnosis of SUDs and MHDs. The research landscape analysis bears a lot of significance for individuals and society. First and foremost, it’s a beacon of hope for individuals seeking help. Research isn’t just about dry statistics; it's about finding better ways to treat and support those facing dual diagnosis. By being informed about the latest breakthroughs, healthcare professionals can offer more effective, evidence-backed care, opening the door to improved treatment outcomes and a brighter future for those they serve. Beyond the individual level, this understanding has profound societal implications. It has the power to chip away at the walls of stigma that often surround mental health and substance use issues. Greater awareness and knowledge about the complexities of dual diagnosis can challenge stereotypes and biases, fostering a more compassionate and inclusive society. Additionally, society allocates resources based on research findings. When we understand the prevalence and evolving nature of dual diagnosis, policymakers and healthcare leaders can make informed decisions about where to channel resources most effectively. This ensures that the needs of individuals struggling with co-occurring disorders are not overlooked or under-prioritized. Moreover, research helps identify risk factors and early warning signs related to dual diagnosis. Armed with this information, we can develop prevention strategies and early intervention programs, potentially reducing the incidence of co-occurring disorders and mitigating their impact. Legal and criminal justice systems also stand to benefit. Understanding dual diagnosis trends can inform policies related to diversion programs, treatment alternatives to incarceration, and the rehabilitation of individuals with co-occurring disorders, potentially reducing rates of reoffending. Moreover, dual diagnosis research contributes to public health planning by highlighting the need for integrated mental health and addiction services. This knowledge can guide the development of comprehensive healthcare systems that offer holistic care to individuals with co-occurring disorders. Families and communities, too, are vital players in this narrative. With a grasp of research findings, they can provide informed, empathetic, and effective support to their loved ones, contributing to better outcomes.

The present research landscape analysis of dual diagnosis of SUDs and MHDs was conducted using a systematic approach to retrieve, process, and analyze relevant articles. The following methodology outlines the key steps taken to address the research questions:

Research Design The present study constitutes a thorough and robust analysis of the research landscape concerning the dual diagnosis of SUD and MHD. It's important to note that the research landscape analysis differs from traditional systematic or scoping reviews. In conducting research landscape analysis, we made deliberate methodological choices aimed at achieving both timely completion and unwavering research quality. These choices included a strategic decision to focus our search exclusively on a single comprehensive database, a departure from the customary practice of utilizing multiple databases. Furthermore, we streamlined the quality control process by assigning specific quality checks to a single author, rather than following the conventional dual-reviewer approach. This approach prioritized efficiency and expediency without compromising the rigor of our analysis. To expedite the research process further, we opted for a narrative synthesis instead of a quantitative one, ensuring that we provide a succinct yet highly informative summary of the available evidence. We place a premium on research transparency and, as such, are committed to sharing the detailed search string employed for data retrieval. This commitment underscores our dedication to fostering reproducibility and transparency in research practices.

Ethical considerations Since the research landscape analysis involved the use of existing and publicly available literature, and no human subjects were directly involved, no formal ethical approval was required.

Article retrieval Scopus, a comprehensive bibliographic database, was utilized to retrieve articles related to the dual diagnosis of SUDs and MHDs. Scopus is a multidisciplinary abstract and citation database that covers a wide range of scientific disciplines, including life sciences, physical sciences, social sciences, and health sciences. It includes content from thousands of scholarly journals.

Keywords used To optimize the search process and ensure the inclusion of pertinent articles, a set of relevant keywords and equivalent terms were employed. Keywords for “dual diagnosis” included dual diagnosis, co-occurring disorders, comorbid substance use, comorbid addiction, coexisting substance use, combined substance use, simultaneous substance use, substance use and psychiatric, co-occurring substance use and psychiatric, concurrent substance use and mental, coexisting addiction and mental, combined addiction and mental, simultaneous addiction and mental, substance-related and psychiatric, comorbid mental health and substance use, co-occurring substance use and psychiatric, concurrent mental health and substance use, coexisting mental health and substance use, combined mental health and substance use, simultaneous mental health and substance use, substance-related and coexisting psychiatric, comorbid psychiatric and substance abuse, co-occurring mental health and substance-related, concurrent psychiatric and substance use, coexisting psychiatric and substance abuse, combined psychiatric and substance use, simultaneous psychiatric and substance use, substance-related and concurrent mental, substance abuse comorbidity. Keywords for “Substance use disorders” included substance abuse, substance dependence, drug use disorders, addiction, substance-related disorders, drug abuse, opioid use disorder, cocaine use disorder, alcohol use disorder, substance misuse, substance use disorder, substance-related, substance addiction. Keywords for “Mental health disorders” included psychiatric disorders, mental illnesses, mental disorders, emotional disorders, psychological disorders, schizophrenia, depression, PTSD, ADHD, anxiety, bipolar disorder, eating disorders, personality disorders, mood disorders, psychotic disorders, mood and anxiety disorders, mental health conditions. To narrow down the search to focus specifically on dual diagnosis, we adopted a strategy that involved the simultaneous presence of SUDs and MHDs in the presence of specific keywords in the titles and abstracts such as “dual,” “co-occurring,” “concurrent,” “co-occurring disorders,” “dual disorders,” “dual diagnosis,” “comorbid psychiatric,” “cooccurring psychiatric,” “comorbid*,” and “coexisting”.

Inclusion and exclusion criteria To maintain the study’s focus and relevance, specific inclusion and exclusion criteria were applied. Included articles were required to be research article, written in English, and published in peer-reviewed journals up to December 31, 2022, Articles focusing on animal studies, internet addiction, obesity, pain, and validity of instruments and tools were excluded.

Flow chart of the search strategy Supplement 1 shows the overall search strategy and the number of articles retrieved in each step. The total number of research articles that met the inclusion and exclusion criteria were 935.

Validation of search strategy The effectiveness of our search strategy was rigorously assessed through three distinct methods, collectively demonstrating its ability to retrieve pertinent articles while minimizing false positives. First, to gauge precision, we meticulously examined a sample of 30 retrieved articles, scrutinizing their alignment with our research question and their contributions to the topic of dual diagnosis. This manual review revealed that the majority of the assessed articles were highly relevant to our research focus. Second, for a comprehensive evaluation, we compared the articles obtained through our search strategy with a set of randomly selected articles from another source. This set comprised 10 references sourced from Google Scholar [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ], and the aim was to determine if our strategy successfully identified articles selected at random from an alternative database. Impressively, our analysis showed that the search strategy had a notably high success rate in capturing these randomly selected articles. Lastly, to further corroborate the relevance of our retrieved articles, we investigated the research interests of the top 10 active authors and the subject scope of the top 10 active journals. This exploration confirmed that their areas of expertise and the journal scopes were in alignment with the field of mental health and/or substance use disorders. These three validation methods collectively reinforce the reliability of our search strategy, affirming that the vast majority of the retrieved articles are indeed pertinent to our research inquiry.

Data processing and mapping Data extracted from the selected articles were processed and organized using Microsoft Excel. Information on the titles/abstracts/author keywords, year of publication, journal name, authors, institution and country affiliation, and number of citations received by the article were extracted. To visualize and analyze the research landscape, VOSviewer, a bibliometric analysis tool, was employed [ 43 ]. This software enables mapping and clustering of co-occurring terms, authors, and countries, providing a comprehensive overview of the dual diagnosis research domain.

Interpreting VOSviewer maps and generating research topics

We conducted a rigorous analysis and generated a comprehensive research landscape using VOSviewer, a widely acclaimed software tool renowned for its expertise in mapping research domains. We seamlessly integrated pertinent data extracted from the Scopus database, including publication metadata, into VOSviewer to delve into the frequency of author keywords and terminologies. The resulting visualizations provided us with profound insights into the intricate web of interconnected research topics and their relationships within the field. Interpreting VOSviewer maps is akin to navigating a vibrant and interconnected tapestry of knowledge. Each term or keyword in the dataset is depicted as a point on the map, represented by a circle or node. These nodes come in varying sizes and colors and are interconnected by lines of differing thicknesses. The size of a node serves as an indicator of the term’s significance or prevalence within the dataset. Larger nodes denote that a specific term is frequently discussed or plays a pivotal role in the body of research, while smaller nodes signify less commonly mentioned concepts. The colors assigned to these nodes serve a dual purpose. Firstly, they facilitate the categorization of terms into thematic groups, with terms of the same color typically belonging to the same cluster or sharing a common thematic thread. Secondly, they aid in the identification of distinct research clusters or thematic groups within the dataset. For instance, a cluster of blue nodes might indicate that these terms are all associated with a particular area of research. The spatial proximity of nodes on the map reflects their closeness in meaning or concept. Nodes positioned closely together share a robust semantic or contextual connection and are likely to be co-mentioned in research articles or share a similar thematic focus. Conversely, nodes situated farther apart indicate less commonality in terms of their usage in the literature. The lines that link these nodes represent the relationships between terms. The thickness of these lines provides insights into the strength and frequency of these connections. Thick lines indicate that the linked terms are frequently discussed together or exhibit a robust thematic association, while thinner lines imply weaker or less frequent connections. In essence, VOSviewer maps offer a visual narrative of the underlying structure and relationships within your dataset. By examining node size and color, you can pinpoint pivotal terms and thematic clusters. Simultaneously, analyzing the distance between nodes and line thickness unveils the semantic closeness and strength of associations between terms. These visual insights are invaluable for researchers seeking to unearth key concepts, identify research clusters, and track emerging trends within their field of study.

Growth pattern, active countries, and active journals

The growth pattern of the 935 research articles on dual diagnosis of substance use disorders and mental health disorders shows an increasing trend in the number of published articles over the years. Starting from the late 1980s and early 1990s with only a few publications, the research interest gradually picked up momentum, and the number of articles has been consistently rising since the mid-1990s. Table 1 shows the number of articles published in three different periods. The majority of publications (52.2%) were produced between 2003 and 2012, indicating a significant surge in research during that decade. The subsequent period from 2013 to 2022 saw a continued interest in the subject, accounting for 35.5% of the total publications. The number of articles published per year during the period from 2013 to 2022 showed a fluctuating steady state with an average of approximately 33 articles per year. The earliest period from 1983 to 2002 comprised 12.3% of the total publications, reflecting the initial stages of research and the gradual development of interest in the field.

Out of the total 935 publications, the United States contributed the most with 585 publications, accounting for approximately 62.5% of the total research output. Canada follows with 88 publications, making up around 9.4% of the total. The United Kingdom and Australia also made substantial contributions with 70 and 53 publications, accounting for 7.5 and 5.7%, respectively. Table 2 shows the top 10 active countries.

Based on the list of top active journals in the field of dual diagnosis of substance use and mental health disorders, it is evident that there are several reputable and specialized journals that focus on this important area of research (Table  3 ). These journals cover a wide range of topics related to dual diagnosis, including comorbidity, treatment approaches, intervention strategies, and epidemiological studies. The Journal of Dual Diagnosis appears to be a leading and comprehensive platform for research on dual diagnosis. It covers a broad spectrum of studies related to substance use disorders and mental health conditions. The Journal of Substance Abuse Treatment ranked second while the Mental Health and Substance Use Dual Diagnosis journal ranked third and seems to be dedicated specifically to the intersection of substance use disorder and mental health disorders, providing valuable insights and research findings related to comorbidities and integrated treatment approaches.

Most frequent author keywords

Mapping author keywords with a minimum occurrence of five (n = 96) provides insights in research related to dual diagnosis. Figure  1 shows the 96 author keywords and their links with other keywords. The number of occurrences represent the number of times each author keyword appears in the dataset, while the total link strength (TLS) indicates the combined strength of connections between keywords based on their co-occurrence patterns. The most frequent author keywords with high occurrences and TLS represent the key areas of focus in research on the dual diagnosis of substance use and mental health disorders.

“Comorbidity” is the most frequent keyword, with 144 occurrences and a high TLS of 356. This reflects the central theme of exploring the co-occurrence of substance use disorders and mental health conditions and their complex relationship. “Substance use disorder” and “dual diagnosis” are also highly prevalent keywords with 122 and 101 occurrences, respectively. These terms highlight the primary focus on studying individuals with both substance use disorders and mental health disorders, underscoring the significance of dual diagnosis in research. “Co-occurring disorders” and “substance use disorders” are frequently used, indicating a focus on understanding the relationship between different types of disorders and the impact of substance use on mental health. Several specific mental health disorders such as “schizophrenia,” “depression,” “bipolar disorder,” and “PTSD” are prominent keywords, indicating a strong emphasis on exploring the comorbidity of these disorders with substance use. “Mental health” and “mental illness” are relevant keywords, reflecting the broader context of research on mental health conditions and their interaction with substance use. “Treatment” is a significant keyword with 34 occurrences, indicating a focus on investigating effective interventions and treatment approaches for individuals with dual diagnosis. “Addiction” and “recovery” are important keywords, highlighting the interest in understanding the addictive nature of substance use and the potential for recovery in this population. The mention of “veterans” as a keyword suggests a specific focus on the dual diagnosis of substance use and mental health disorders in the veteran population. “Integrated treatment” is an important keyword, indicating an interest in studying treatment approaches that address both substance use and mental health disorders together in an integrated manner.

Network visualization map of author keywords with a minimum occurrence of five in the retrieved articles on dual diagnosis of substance use and mental health disorders

Most impactful research topics

To have an insight into the most impactful research topics on dual diagnosis, the top 100 research articles were visualized and the terms with the largest node size and TLS were used to. To come up with the five most common investigated research topics:

Dual diagnosis and comorbidity of SUDs and MHDs: This topic focuses on the co-occurrence of substance use disorders and various mental health conditions, such as schizophrenia, bipolar disorder, PTSD, anxiety disorders, and major depressive disorder. This research topic explored the prevalence, characteristics, and consequences of comorbidity in different populations, including veterans, adolescents, and individuals experiencing homelessness [ 13 , 19 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ].

Treatment and interventions for co-occurring disorders: This topic involves studies on different treatment approaches and interventions for individuals with dual diagnosis. These interventions may include motivational interviewing, cognitive-behavioral therapy, family intervention, integrated treatment models, assertive community treatment, and prolonged exposure therapy. The goal is to improve treatment outcomes and recovery for individuals with co-occurring substance use and mental health disorders [ 48 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].

Quality of life and functioning in individuals with dual diagnosis: This research topic explores the impact of dual diagnosis on the quality of life and functioning of affected individuals. It assesses the relationship between dual diagnosis and various aspects of well-being, including social functioning, physical health, and overall quality of life [ 60 , 61 , 62 , 63 , 64 ].

Epidemiology and prevalence of co-occurring disorders: This topic involves population-based studies that investigate the prevalence of comorbid substance use and mental health disorders. It examines the demographic and clinical correlates of dual diagnosis, as well as risk factors associated with the development of co-occurring conditions [ 50 , 52 , 60 , 65 , 66 , 67 ].

Implications and consequences of comorbidity: This research topic explores the consequences of comorbidity between substance use and mental health disorders, such as treatment utilization, service access barriers, criminal recidivism, and the impact on suicidality. It also investigates the implications of comorbidity for treatment outcomes and the potential risks associated with specific comorbidities [ 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ].

Emerging research topics

Upon scrutinizing the titles, abstracts, author keywords, and a visualization map of the 100 recently published articles, the research themes listed below came to the forefront. It’s worth noting that some of the research themes in the 100 recently published articles were not groundbreaking; rather, they represented a natural progression of ongoing research endeavors, and that is why they were not listed as emerging research themes. For instance, there was a continuation of research into the prevalence and epidemiology of co-occurring mental illnesses and substance use disorders and characteristics of various cases of co-morbid cases of SUDs and MHDs. The list below included such emergent themes. It might seem that certain aspects within these research themes duplicate the initial research topics, but it’s crucial to emphasize that this is not the case. For example, both themes delve into investigations concerning treatment, yet the differentiation lies in the treatment approach adopted.

Neurobiological and psychosocial aspects of dual diagnosis: This research topic focuses on exploring the neurobiological etiology and underlying mechanisms of comorbid substance use and mental health disorders. It investigates brain regions, neurotransmitter systems, hormonal pathways, and other neurobiological factors contributing to the development and maintenance of dual diagnosis. Additionally, this topic may examine psychosocial aspects, such as trauma exposure, adverse childhood experiences, and social support, that interact with neurobiological factors in the context of comorbidity [ 76 ].

Impact of environmental and sociocultural factors on dual diagnosis: This research topic delves into the influence of environmental and sociocultural factors on the occurrence and course of comorbid substance use and mental health disorders. It may explore how cultural norms, socioeconomic status, access to healthcare, and societal attitudes toward mental health and substance use affect the prevalence, treatment outcomes, and quality of life of individuals with dual diagnosis [ 77 , 78 ].

New interventions and treatment approaches for dual diagnosis: This topic involves studies that propose and evaluate innovative interventions and treatment approaches for individuals with dual diagnosis. These interventions may include novel psychotherapeutic techniques, pharmacological treatments, digital health interventions, and integrated care models. The research aims to improve treatment effectiveness, adherence, and long-term recovery outcomes in individuals with comorbid substance use and mental health disorders [ 79 , 80 , 81 , 82 , 83 , 84 ].

Mental health and substance use in special populations with dual diagnosis: This research topic focuses on exploring the prevalence and unique characteristics of comorbid substance use and mental health disorders in specific populations, such as individuals with eating disorders, incarcerated individuals, and people with autism spectrum disorder. It aims to identify the specific needs and challenges faced by these populations and develop tailored interventions to address their dual diagnosis [ 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 ].

Public health implications and policy interventions for dual diagnosis: This topic involves research that addresses the public health implications of dual diagnosis and the need for policy interventions to address this complex issue. It may include studies on the economic burden of comorbidity, the impact on healthcare systems, and the evaluation of policy initiatives aimed at improving prevention, early intervention, and access to integrated care for individuals with dual diagnosis [ 81 , 96 , 97 , 98 , 99 , 100 , 101 ].

Comparison in research topics

The comparison between the most impactful research topics and emerging research topics in the field of dual diagnosis reveals intriguing insights into the evolving landscape of this critical area of study (Table  4 ). In the most impactful research topics, there is a strong emphasis on the epidemiology of dual diagnosis, indicating a well-established foundation in understanding the prevalence, characteristics, and consequences of comorbid SUDs and MHDs. Treatment and interventions also receive considerable attention, highlighting the ongoing efforts to improve outcomes and recovery for individuals with dual diagnosis. Quality of life and medical consequences are additional focal points, reflecting the concern for the holistic well-being of affected individuals and the health-related implications of comorbidity.

On the other hand, emerging research topics signify a shift towards newer methods and interventions. The exploration of neurobiology in the context of dual diagnosis reflects a growing interest in unraveling the underlying neurobiological mechanisms contributing to comorbidity. This shift suggests a deeper understanding of the neural pathways and potential targets for intervention. The consideration of dual diagnosis in special groups underscores a recognition of the unique needs and challenges faced by specific populations, such as individuals with autism spectrum disorder. This tailored approach acknowledges that one size does not fit all in addressing dual diagnosis. Finally, the exploration of environmental and psychosocial contexts highlights the importance of socio-cultural factors, policy interventions, and societal attitudes in shaping the experience of individuals with dual diagnosis, signaling a broader perspective that extends beyond clinical interventions. In summary, while the most impactful research topics have laid a strong foundation in epidemiology, treatment, quality of life, and medical consequences, the emerging research topics point to a promising future with a deeper dive into the neurobiology of dual diagnosis, a focus on special populations, and a broader consideration of the environmental and psychosocial context. This evolution reflects the dynamic nature of dual diagnosis research as it strives to advance our understanding and improve the lives of those affected by comorbid substance use and mental health disorders.

The main hypothesis underlying the study was that dual diagnosis, or the comorbidity of SUDs and MHDs, was historically underrecognized and under-researched. Over time, however, there has been a significant increase in understanding, appreciation, and research into this complex interplay in clinical settings. This was expected to manifest through a growing number of publications, increased attention to integrated treatment approaches, and a heightened recognition of the complexities and public health implications associated with dual diagnosis. The study aims to analyze this progression and its implications through a research landscape analysis, identifying key trends, knowledge gaps, and research priorities. The research landscape analysis of the dual diagnosis of SUDs and MHDs has unveiled a substantial and evolving body of knowledge, with a notable rise in publications since the mid-1990s and a significant surge between 2003 and 2012. This growing research interest underscores the increasing recognition of the importance and complexity of dual diagnosis in clinical and public health contexts. The United States has emerged as the most active contributor, followed by Canada, the United Kingdom, and Australia, with specialized journals such as the Journal of Dual Diagnosis playing a pivotal role in disseminating research findings. Common keywords such as “comorbidity,” “substance use disorder,” “dual diagnosis,” and specific mental health disorders highlight the primary focus areas, with impactful research topics identified as the comorbidity of SUDs and MHDs, treatment and interventions, quality of life, epidemiology, and the implications of comorbidity. Emerging research themes emphasize neurobiological and psychosocial aspects, the impact of environmental and sociocultural factors, innovative treatment approaches, and the needs of special populations with dual diagnosis, reflecting a shift towards a more holistic and nuanced understanding. The study highlights a shift from traditional epidemiological studies towards understanding the underlying mechanisms and broader social determinants of dual diagnosis, with a need for continued research into integrated treatment models, specific needs of diverse populations, and the development of tailored interventions.

The findings of this research landscape analysis have significant implications for clinical practice, public health initiatives, policy development, and future research endeavors. Clinicians and healthcare providers working with individuals with dual diagnosis can benefit from the identified research hotspots, as they highlight crucial aspects that require attention in diagnosis, treatment, and support. The prominence of treatment and intervention topics indicates the need for evidence-based integrated approaches that address both substance use and mental health disorders concurrently [ 102 , 103 , 104 ]. The research on the impact of dual diagnosis on quality of life and functioning underscores the importance of holistic care that addresses psychosocial and functional well-being [ 63 ]. For public health initiatives, understanding the prevalence and epidemiological aspects of dual diagnosis is vital for resource allocation and the development of effective prevention and early intervention programs. Policymakers can use the research landscape analysis to inform policies that promote integrated care, reduce barriers to treatment, and improve access to mental health and substance abuse services [ 15 , 105 ]. Furthermore, the identification of emerging topics offers opportunities for investment in research areas that are gaining momentum and importance.

The present study lays a robust groundwork, serving as a catalyst for the advancement of research initiatives and the formulation of comprehensive policies and programs aimed at elevating the quality of life for individuals grappling with the intricate confluence of SUDs and MHDs. Within the realm of significance, it underscores a critical imperative—the urgent necessity to revolutionize the landscape of tailored mental health services offered to patients harboring this challenging comorbidity. The paper distinctly illuminates the exigency for a heightened quantity of research endeavors that delve deeper into unraveling the temporal intricacies underpinning the relationship between SUDs and MHDs. In so doing, it not only unveils potential risk factors but also delves into the far-reaching consequences of treatment modalities over the extended course of time. This illumination, therefore, not only beckons but virtually ushers in a promising trajectory for prospective research endeavors, a path designed to uncover the intricate and evolving journey of dual diagnosis. A profound implication of this study is the direct applicability of its findings in the corridors of policymaking. By leveraging the insights encapsulated within the paper, policymakers stand uniquely equipped to sculpt policies that unequivocally champion the cause of integrated care. The remarkable emphasis on themes of treatment and intervention, permeating the research's core, emphatically underscores the urgent demand for dismantling barriers obstructing access to mental health and substance abuse services. It is incumbent upon policymakers to heed this call, for policies fostering the integration of care can inexorably elevate the outcomes experienced by patients grappling with dual diagnosis. Furthermore, this study artfully directs policymakers to allocate their resources judiciously by identifying burgeoning areas of research that are surging in prominence and pertinence. These emergent topics, discerned within the study, are not just topics; they are emblematic of windows of opportunity. By investing in these areas, policymakers can tangibly bolster research initiatives that are primed to tackle the multifaceted challenges inherent in the realm of dual diagnosis, addressing both current exigencies and future prospects. Additionally, the paper furnishes the foundational blueprint essential for the development of screening guidelines and clinical practice protocols that truly grasp the complexity of dual diagnosis. Clinical practitioners and healthcare establishments would be remiss not to harness this invaluable information to augment their own practices, thereby delivering more effective and empathetic care to individuals contending with dual diagnosis. In essence, this study serves as the compass guiding the way toward a more compassionate, comprehensive, and efficacious approach to mental health and substance abuse care for those in need.

The current landscape analysis of reveals significant implications and highlights the growing research interest in this field since the late 1980s. This increasing trend underscores the complexities and prevalence of comorbid conditions, which necessitate focused research and intervention strategies. The results can be generalized to guide future research priorities, inform clinical guidelines, shape healthcare policies, and provide a framework for other countries to adapt and build upon in their context.

The key take-home message emphasizes the importance of recognizing the high prevalence and intricate relationship between SUDs and MHDs, necessitating integrated and tailored treatment approaches. Additionally, the study advocates for employing efficient research methodologies to synthesize vast amounts of literature and identify emerging trends, focusing on quality of life, treatment outcomes, and the broader socio-cultural and policy contexts to improve care and support for individuals with dual diagnosis. Finally, the research underscores the critical need for continued focus on dual diagnosis, advocating for comprehensive, integrated, and innovative approaches to research, clinical practice, and policymaking to improve outcomes for affected individuals.

Despite the comprehensive approach adopted in this research landscape analysis, several limitations must be acknowledged. The exclusive reliance on Scopus, while extensive, inherently limits the scope of the analysis, potentially omitting relevant articles indexed in other databases such as the Chinese scientific database, thus not fully representing the entire research landscape on dual diagnosis of SUDs and MHDs. Assigning quality control responsibilities to a single author, rather than employing a dual-reviewer system, may introduce bias and affect the reliability of the quality assessment. Although this approach was chosen to expedite the process, it might have compromised the thoroughness of quality checks. The use of narrative synthesis instead of a quantitative synthesis limits the ability to perform meta-analytical calculations that could provide more robust statistical insights. This choice was made for efficiency, but it may affect the depth of the analysis and the generalizability of the conclusions. The reliance on specific keywords to retrieve articles means that any relevant studies not containing these exact terms in their titles or abstracts may have been overlooked, potentially leading to an incomplete representation of the research domain. The restriction to English-language articles and peer-reviewed journals may exclude significant research published in other languages or in non-peer-reviewed formats, introducing linguistic and publication type bias that could skew the results towards predominantly English-speaking regions and established academic journals. The inclusion of articles up to December 31, 2022, means that any significant research published after this date is not considered, potentially missing the latest developments in the field. The validation of the search strategy using a small sample of 30 articles and a comparison with 10 randomly selected articles from Google Scholar may not be sufficient to comprehensively assess the effectiveness of the search strategy; a larger sample size might provide a more accurate validation. Some of the research topics and findings may be specific to particular populations (e.g., veterans) and might not be generalizable to other groups, highlighting the need for caution when extrapolating the results to broader contexts. Although no formal ethical approval was required due to the use of existing literature, ethical considerations related to the interpretation and application of findings must still be acknowledged, particularly in terms of representing vulnerable populations accurately and sensitively. Acknowledging these limitations is crucial for interpreting the findings of this research landscape analysis and for guiding future research efforts to address these gaps and enhance the robustness and comprehensiveness of studies on the dual diagnosis of SUDs and MHDs.

In conclusion, the research landscape analysis of dual diagnosis of substance abuse and mental health disorders provides valuable insights into the growth, active countries, and active journals in this field. The identification of research hotspots and emerging topics informs the scientific community about prevailing interests and potential areas for future investigation. Addressing research gaps can lead to a more comprehensive understanding of dual diagnosis, while the implications of the findings extend to clinical practice, public health initiatives, policy development, and future research priorities. This comprehensive understanding is crucial in advancing knowledge, improving care, and addressing the multifaceted challenges posed by dual diagnosis to individuals and society.

Availability of data and materials

All data presented in this manuscript are available on the Scopus database using the search query listed in the methodology section.

Hasin DS, O’Brien CP, Auriacombe M, Borges G, Bucholz K, Budney A, et al. DSM-5 criteria for substance use disorders: recommendations and rationale. Am J Psychiatry. 2013;170(8):834–51. https://doi.org/10.1176/appi.ajp.2013.12060782 .

Article   PubMed   PubMed Central   Google Scholar  

Collaborators GA. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2018;392(10152):1015–35. https://doi.org/10.1016/s0140-6736(18)31310-2 .

Article   Google Scholar  

Ayalew M, Tafere M, Asmare Y. Prevalence, trends, and consequences of substance use among university students: implication for intervention. Int Q Community Health Educ. 2018;38(3):169–73. https://doi.org/10.1177/0272684x17749570 .

Article   PubMed   Google Scholar  

Raftery D, Kelly PJ, Deane FP, Baker AL, Ingram I, Goh MCW, et al. Insight in substance use disorder: a systematic review of the literature. Addict Behav. 2020;111:106549. https://doi.org/10.1016/j.addbeh.2020.106549 .

Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. 2005;62(6):593–602. https://doi.org/10.1001/archpsyc.62.6.593 .

Hossain MM, Nesa F, Das J, Aggad R, Tasnim S, Bairwa M, et al. Global burden of mental health problems among children and adolescents during COVID-19 pandemic: an umbrella review. Psychiatry Res. 2022;317:114814. https://doi.org/10.1016/j.psychres.2022.114814 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Kurdyak P, Patten S. The burden of mental illness and evidence-informed mental health policy development. Can J Psychiatry. 2022;67(2):104–6. https://doi.org/10.1177/07067437211021299 .

Stumbrys D, Jasilionis D, Pūras D. The burden of mental health-related mortality in the Baltic States in 2007–2018. BMC Public Health. 2022;22(1):1776. https://doi.org/10.1186/s12889-022-14175-9 .

Pompili M, Innamorati M, Gonda X, Serafini G, Sarno S, Erbuto D, et al. Affective temperaments and hopelessness as predictors of health and social functioning in mood disorder patients: a prospective follow-up study. J Affect Disord. 2013;150(2):216–22. https://doi.org/10.1016/j.jad.2013.03.026 .

Costanza A, Vasileios C, Ambrosetti J, Shah S, Amerio A, Aguglia A, et al. Demoralization in suicide: a systematic review. J Psychosom Res. 2022;157:110788. https://doi.org/10.1016/j.jpsychores.2022.110788 .

Arias F, Szerman N, Vega P, Mesías B, Basurte I, Rentero D. Bipolar disorder and substance use disorders. Madrid study on the prevalence of dual disorders/pathology. Adicciones. 2017;29(3):186–94. https://doi.org/10.2088/adicciones.782 .

Brewer S, Godley MD, Hulvershorn LA. Treating mental health and substance use disorders in adolescents: what is on the menu? Curr Psychiatry Rep. 2017;19(1):5. https://doi.org/10.1007/s11920-017-0755-0 .

Jones CM, McCance-Katz EF. Co-occurring substance use and mental disorders among adults with opioid use disorder. Drug Alcohol Depend. 2019;197:78–82. https://doi.org/10.1016/j.drugalcdep.2018.12.030 .

Murthy P, Mahadevan J, Chand PK. Treatment of substance use disorders with co-occurring severe mental health disorders. Curr Opin Psychiatry. 2019;32(4):293–9. https://doi.org/10.1097/yco.0000000000000510 .

Saddichha S, Schütz CG, Sinha BN, Manjunatha N. Substance use and dual diagnosis disorders: future epidemiology, determinants, and policies. Biomed Res Int. 2015;2015:145905. https://doi.org/10.1155/2015/145905 .

Hammen C. Adolescent depression: stressful interpersonal contexts and risk for recurrence. Curr Dir Psychol Sci. 2009;18(4):200–4. https://doi.org/10.1111/j.1467-8721.2009.01636.x .

Wolitzky-Taylor K, Bobova L, Zinbarg RE, Mineka S, Craske MG. Longitudinal investigation of the impact of anxiety and mood disorders in adolescence on subsequent substance use disorder onset and vice versa. Addict Behav. 2012;37(8):982–5. https://doi.org/10.1016/j.addbeh.2012.03.026 .

Mueser KT, Drake RE, Wallach MA. Dual diagnosis: a review of etiological theories. Addict Behav. 1998;23(6):717–34.

Article   CAS   PubMed   Google Scholar  

Hartz SM, Pato CN, Medeiros H, Cavazos-Rehg P, Sobell JL, Knowles JA, et al. Comorbidity of severe psychotic disorders with measures of substance use. JAMA Psychiat. 2014;71(3):248–54. https://doi.org/10.1001/jamapsychiatry.2013.3726 .

Carroll KM, Kiluk BD, Nich C, Babuscio TA, Brewer JA, Potenza MN, et al. Cognitive function and treatment response in a randomized clinical trial of computer-based training in cognitive-behavioral therapy. Subst Use Misuse. 2011;46(1):23–34. https://doi.org/10.3109/10826084.2011.521069 .

Drake RE, Mueser KT. Psychosocial approaches to dual diagnosis. Schizophr Bull. 2000;26(1):105–18. https://doi.org/10.1093/oxfordjournals.schbul.a033429 .

Ruggeri M, Leese M, Thornicroft G, Bisoffi G, Tansella M. Definition and prevalence of severe and persistent mental illness. Br J Psychiatry. 2000;177:149–55. https://doi.org/10.1192/bjp.177.2.149 .

Reavley NJ, Jorm AF. Stigmatizing attitudes towards people with mental disorders: findings from an Australian national survey of mental health literacy and stigma. Aust N Z J Psychiatry. 2011;45(12):1086–93. https://doi.org/10.3109/00048674.2011.621061 .

Torrey WC, Tepper M, Greenwold J. Implementing integrated services for adults with co-occurring substance use disorders and psychiatric illnesses: a research review. J Dual Diagn. 2011;7(3):150–61. https://doi.org/10.1080/15504263.2011.592769 .

Bouchery EE, Harwood HJ, Dilonardo J, Vandivort-Warren R. Type of health insurance and the substance abuse treatment gap. J Subst Abuse Treat. 2012;42(3):289–300. https://doi.org/10.1016/j.jsat.2011.09.002 .

Abuse S, Administration MHS. National Mental Health Services Survey (N-MHSS): 2014. Data on mental health treatment facilities. Department of Health and Human Services, Substance Abuse and Mental Health …; 2014.

Shiner B, Gottlieb D, Rice K, Forehand JA, Snitkin M, Watts BV. Evaluating policies to improve access to mental health services in rural areas. J Rural Health. 2022;38(4):805–16. https://doi.org/10.1111/jrh.12674 .

Ioannidis JP, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet. 2014;383(9912):166–75. https://doi.org/10.1016/s0140-6736(13)62227-8 .

Bornmann L, Bowman BF, Bauer J, Marx W, Schier H, Palzenberger MJBbHmiosi. (2014): 11 Bibliometric standards for evaluating research institutes in the natural sciences.201.

Hicks D, Wouters P, Waltman L, de Rijcke S, Rafols I. Bibliometrics: the Leiden manifesto for research metrics. Nature. 2015;520(7548):429–31. https://doi.org/10.1038/520429a .

Ziedonis D, Brady K. Dual diagnosis in primary care. Detecting and treating both the addiction and mental illness. Med Clin North Am. 1997;81(4):1017–36. https://doi.org/10.1016/s0025-7125(05)70561-7 .

Kim JI, Kim B, Kim BN, Hong SB, Lee DW, Chung JY, et al. Prevalence of psychiatric disorders, comorbidity patterns, and repeat offending among male juvenile detainees in South Korea: a cross-sectional study. Child Adolesc Psychiatry Ment Health. 2017;11:6. https://doi.org/10.1186/s13034-017-0143-x .

Astals M, Domingo-Salvany A, Buenaventura CC, Tato J, Vazquez JM, Martín-Santos R, et al. Impact of substance dependence and dual diagnosis on the quality of life of heroin users seeking treatment. Subst Use Misuse. 2008;43(5):612–32. https://doi.org/10.1080/10826080701204813 .

Buckley PF. Prevalence and consequences of the dual diagnosis of substance abuse and severe mental illness. J Clin Psychiatry. 2006;67(Suppl 7):5–9.

PubMed   Google Scholar  

Buckley PF, Brown ES. Prevalence and consequences of dual diagnosis. J Clin Psychiatry. 2006;67(7):e01. https://doi.org/10.4088/jcp.0706e01 .

Canaway R, Merkes M. Barriers to comorbidity service delivery: the complexities of dual diagnosis and the need to agree on terminology and conceptual frameworks. Aust Health Rev. 2010;34(3):262–8. https://doi.org/10.1071/ah08723 .

Edward KL, Munro I. Nursing considerations for dual diagnosis in mental health. Int J Nurs Pract. 2009;15(2):74–9. https://doi.org/10.1111/j.1440-172X.2009.01731.x .

Healey C, Peters S, Kinderman P, McCracken C, Morriss R. Reasons for substance use in dual diagnosis bipolar disorder and substance use disorders: a qualitative study. J Affect Disord. 2009;113(1–2):118–26. https://doi.org/10.1016/j.jad.2008.05.010 .

Horsfall J, Cleary M, Hunt GE, Walter G. Psychosocial treatments for people with co-occurring severe mental illnesses and substance use disorders (dual diagnosis): a review of empirical evidence. Harv Rev Psychiatry. 2009;17(1):24–34. https://doi.org/10.1080/10673220902724599 .

Kerfoot KE, Petrakis IL, Rosenheck RA. Dual diagnosis in an aging population: prevalence of psychiatric disorders, comorbid substance abuse, and mental health service utilization in the department of veterans affairs. J Dual Diagn. 2011;7(1–2):4–13. https://doi.org/10.1080/15504263.2011.568306 .

Morojele NK, Saban A, Seedat S. Clinical presentations and diagnostic issues in dual diagnosis disorders. Curr Opin Psychiatry. 2012;25(3):181–6. https://doi.org/10.1097/YCO.0b013e328351a429 .

Thylstrup B, Johansen KS. Dual diagnosis and psychosocial interventions–introduction and commentary. Nord J Psychiatry. 2009;63(3):202–8. https://doi.org/10.1080/08039480802571069 .

van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38. https://doi.org/10.1007/s11192-009-0146-3 .

Arndt S, Tyrrell G, Flaum M, Andreasen NC. Comorbidity of substance abuse and schizophrenia: the role of pre-morbid adjustment. Psychol Med. 1992;22(2):379–88. https://doi.org/10.1017/S0033291700030324 .

Barnes TRE, Mutsatsa SH, Hutton SB, Watt HC, Joyce EM. Comorbid substance use and age at onset of schizophrenia. Br J Psychiatry. 2006;188:237–42. https://doi.org/10.1192/bjp.bp.104.007237 .

Brady KT, Killeen T, Saladin ME, Dansky B, Becker S. Comorbid substance abuse and posttraumatic stress disorder: characteristics of women in treatment. Am J Addict. 1994;3(2):160–4. https://doi.org/10.1111/j.1521-0391.1994.tb00383.x .

Brière FN, Rohde P, Seeley JR, Klein D, Lewinsohn PM. Comorbidity between major depression and alcohol use disorder from adolescence to adulthood. Compr Psychiatry. 2014;55(3):526–33. https://doi.org/10.1016/j.comppsych.2013.10.007 .

Brown PJ, Stout RL, Mueller T. Substance use disorder and posttraumatic stress disorder comorbidity: addiction and psychiatric treatment rates. Psychol Addict Behav. 1999;13(2):115–22. https://doi.org/10.1037/0893-164X.13.2.115 .

Bulik CM, Klump KL, Thornton L, Kaplan AS, Devlin B, Fichter MM, et al. Alcohol use disorder comorbidity in eating disorders: a multicenter study. J Clin Psychiatry. 2004;65(7):1000–6. https://doi.org/10.4088/JCP.v65n0718 .

Compton WM, Conway KP, Stinson FS, Grant BF. Changes in the prevalence of major depression and comorbid substance use disorders in the United States between 1991–1992 and 2001–2002. Am J Psychiatry. 2006;163(12):2141–7. https://doi.org/10.1176/ajp.2006.163.12.2141 .

Green AI, Drake RE, Brunette MF, Noordsy DL. Schizophrenia and co-occurring substance use disorder. Am J Psychiatry. 2007;164(3):402–8. https://doi.org/10.1176/ajp.2007.164.3.402 .

Morgenstern J, Langenbucher J, Labouvie E, Miller KJ. The comorbidity of alcoholism and personality disorders in a clinical population: prevalence rates and relation to alcohol typology variables. J Abnorm Psychol. 1997;106(1):74–84. https://doi.org/10.1037/0021-843X.106.1.74 .

Back SE, Waldrop AE, Brady KT. Treatment challenges associated with comorbid substance use and posttraumatic stress disorder: clinicians’ perspectives. Am J Addict. 2009;18(1):15–20. https://doi.org/10.1080/10550490802545141 .

Brown PJ, Recupero PR, Stout R. PTSD substance abuse comorbidity and treatment utilization. Addict Behav. 1995;20(2):251–4. https://doi.org/10.1016/0306-4603(94)00060-3 .

Harris KM, Edlund MJ. Use of mental health care and substance abuse treatment among adults with co-occurring disorders. Psychiatr Serv. 2005;56(8):954–9. https://doi.org/10.1176/appi.ps.56.8.954 .

Hien DA, Cohen LR, Miele GM, Litt LC, Capstick C. Promising treatments for women with comorbid PTSD and substance use disorders. Am J Psychiatry. 2004;161(8):1426–32. https://doi.org/10.1176/appi.ajp.161.8.1426 .

Manwani SG, Szilagyi KA, Zablotsky B, Hennen J, Griffin ML, Weiss RD. Adherence to pharmacotherapy in bipolar disorder patients with and without co-occurring substance use disorders. J Clin Psychiatry. 2007;68(8):1172–6. https://doi.org/10.4088/JCP.v68n0802 .

Minkoff K. An integrated treatment model for dual diagnosis of psychosis and addiction. Hosp Community Psychiatry. 1989;40(10):1031–6. https://doi.org/10.1176/ps.40.10.1031 .

Smith JP, Book SW. Comorbidity of generalized anxiety disorder and alcohol use disorders among individuals seeking outpatient substance abuse treatment. Addict Behav. 2010;35(1):42–5. https://doi.org/10.1016/j.addbeh.2009.07.002 .

Kamali M, Kelly L, Gervin M, Browne S, Larkin C, O’Callaghan E. The prevalence of comorbid substance misuse and its influence on suicidal ideation among in-patients with schizophrenia. Acta Psychiatr Scand. 2000;101(6):452–6. https://doi.org/10.1034/j.1600-0447.2000.101006452.x .

Padgett DK, Gulcur L, Tsemberis S. Housing first services for people who are homeless with co-occurring serious mental illness and substance abuse. Res Soc Work Pract. 2006;16(1):74–83. https://doi.org/10.1177/1049731505282593 .

Schmidt LM, Hesse M, Lykke J. The impact of substance use disorders on the course of schizophrenia-A 15-year follow-up study. Dual diagnosis over 15 years. Schizophr Res. 2011;130(1–3):228–33. https://doi.org/10.1016/j.schres.2011.04.011 .

Singh J, Mattoo SK, Sharan P, Basu D. Quality of life and its correlates in patients with dual diagnosis of bipolar affective disorder and substance dependence. Bipolar Disord. 2005;7(2):187–91. https://doi.org/10.1111/j.1399-5618.2004.00173.x .

Urboanoski KA, Cairney J, Bassani DG, Rush BR. Perceived unmet need for mental health care for Canadians with co-occurring mental and substance use disorders. Psychiatr Serv. 2008;59(3):283–9. https://doi.org/10.1176/appi.ps.59.3.283 .

Kingston REF, Marel C, Mills KL. A systematic review of the prevalence of comorbid mental health disorders in people presenting for substance use treatment in Australia. Drug Alcohol Rev. 2017;36(4):527–39. https://doi.org/10.1111/dar.12448 .

Klinkenberg WD, Caslyn RJ, Morse GA, Yonker RD, McCudden S, Ketema F, et al. Prevalence of human immunodeficiency virus, hepatitis B, and hepatitis C among homeless persons with co-occurring severe mental illness and substance use disorders. Compr Psychiatry. 2003;44(4):293–302. https://doi.org/10.1016/S0010-440X(03)00094-4 .

Wallace C, Mullen PE, Burgess P. Criminal offending in schizophrenia over a 25-year period marked by deinstitutionalization and increasing prevalence of comorbid substance use disorders. Am J Psychiatry. 2004;161(4):716–27. https://doi.org/10.1176/appi.ajp.161.4.716 .

Bronisch T, Wittchen HU. Suicidal ideation and suicide attempts: comorbidity with depression, anxiety disorders, and substance abuse disorder. Eur Arch Psychiatry Clin Neurosci. 1994;244(2):93–8. https://doi.org/10.1007/BF02193525 .

Hatzenbuehler ML, Keyes KM, Narrow WE, Grant BF, Hasin DS. Racial/ethnic disparities in service utilization for individuals with co-occurring mental health and substance use disorders in the general population: Results from the national epidemiologic survey on alcohol and related conditions. J Clin Psychiatry. 2008;69(7):1112–21. https://doi.org/10.4088/JCP.v69n0711 .

Hodgins S, Tiihonen J, Ross D. The consequences of conduct disorder for males who develop schizophrenia: associations with criminality, aggressive behavior, substance use, and psychiatric services. Schizophr Res. 2005;78(2–3):323–35. https://doi.org/10.1016/j.schres.2005.05.021 .

Hunt GE, Bergen J, Bashir M. Medication compliance and comorbid substance abuse in schizophrenia: impact on community survival 4 years after a relapse. Schizophr Res. 2002;54(3):253–64. https://doi.org/10.1016/S0920-9964(01)00261-4 .

Link BG, Struening EL, Rahav M, Phelan JC, Nuttbrock L. On stigma and its consequences: evidence from a longitudinal study of men with dual diagnoses of mental illness and substance abuse. J Health Soc Behav. 1997;38(2):177–90. https://doi.org/10.2307/2955424 .

Priester MA, Browne T, Iachini A, Clone S, DeHart D, Seay KD. Treatment access barriers and disparities among individuals with co-occurring mental health and substance use disorders: an integrative literature review. J Subst Abuse Treat. 2016;61:47–59. https://doi.org/10.1016/j.jsat.2015.09.006 .

Talamo A, Centorrino F, Tondo L, Dimitri A, Hennen J, Baldessarini RJ. Comorbid substance-use in schizophrenia: relation to positive and negative symptoms. Schizophr Res. 2006;86(1–3):251–5. https://doi.org/10.1016/j.schres.2006.04.004 .

Teplin LA, Elkington KS, McClelland GM, Abram KM, Mericle AA, Washburn JJ. Major mental disorders, substance use disorders, comorbidity, and HIV-AIDS risk behaviors in juvenile detainees. Psychiatr Serv. 2005;56(7):823–8. https://doi.org/10.1176/appi.ps.56.7.823 .

Hinckley JD, Danielson CK. Elucidating the neurobiologic etiology of comorbid PTSD and substance use disorders. Brain Sci. 2022. https://doi.org/10.3390/brainsci12091166 .

Jarnecke AM, Saraiya TC, Brown DG, Richardson J, Killeen T, Back SE. Examining the role of social support in treatment for co-occurring substance use disorder and posttraumatic stress disorder. Addict Behav Rep. 2022. https://doi.org/10.1016/j.abrep.2022.100427 .

Kyster NB, Tranberg K, Osler M, Hjorthøj C, Mårtensson S. The influence of childhood aspirations on the risk of developing psychotic disorders, substance use disorders, and dual diagnosis in adulthood based on the Metropolit 1953 Danish male birth cohort. Eur Child Adolesc Psychiatry. 2022. https://doi.org/10.1007/s00787-022-02091-7 .

Cunill R, Castells X, González-Pinto A, Arrojo M, Bernardo M, Sáiz PA, et al. Clinical practice guideline on pharmacological and psychological management of adult patients with attention deficit and hyperactivity disorder and comorbid substance use. Adicciones. 2022;34(2):168–78. https://doi.org/10.2088/adicciones.1569 .

Margolese HC, Boucher M, Therrien F, Clerzius G. Treatment with aripiprazole once-monthly injectable formulation is effective in improving symptoms and global functioning in schizophrenia with and without comorbid substance use—a post hoc analysis of the ReLiAM study. BMC Psychiatry. 2022. https://doi.org/10.1186/s12888-022-04397-x .

Minkoff K, Covell NH. Recommendations for integrated systems and services for people with co-occurring mental health and substance use conditions. Psychiatric Serv. 2022;73(6):686–9. https://doi.org/10.1176/appi.ps.202000839 .

Oliva V, De Prisco M, Pons-Cabrera MT, Guzmán P, Anmella G, Hidalgo-Mazzei D, et al. Machine learning prediction of comorbid substance use disorders among people with bipolar disorder. J Clin Med. 2022. https://doi.org/10.3390/jcm11143935 .

Somohano VC, Kaplan J, Newman AG, O’Neil M, Lovejoy T. Formal mindfulness practice predicts reductions in PTSD symptom severity following a mindfulness-based intervention for women with co-occurring PTSD and substance use disorder. Addict Sci Clin Pract. 2022. https://doi.org/10.1186/s13722-022-00333-2 .

Watkins LE, Patton SC, Drexler K, Rauch SAM, Rothbaum BO. Clinical effectiveness of an intensive outpatient program for integrated treatment of comorbid substance abuse and mental health disorders. Cogn Behav Pract. 2022. https://doi.org/10.1016/j.cbpra.2022.05.005 .

Bertulies-Esposito B, Ouellet-Plamondon C, Jutras-Aswad D, Gagnon J, Abdel-Baki A. The impact of treatment orders for residential treatment of comorbid severe substance use disorders for youth suffering from early psychosis: a case series. Int J Ment Heal Addict. 2021;19(6):2233–44. https://doi.org/10.1007/s11469-020-00317-w .

Butler A, Nicholls T, Samji H, Fabian S, Lavergne MR. Prevalence of mental health needs, substance use, and co-occurring disorders among people admitted to prison. Psychiatric Serv. 2022;73(7):737–44. https://doi.org/10.1176/appi.ps.202000927 .

Henderson JL, Wilkins LK, Hawke LD, Wang W, Sanches M, Brownlie EB, et al. Longitudinal emergence of concurrent mental health and substance use concerns in an ontario school-based sample: the research and action for teens study. J Can Acad Child Adolesc Psychiatry. 2021;30(4):249–63.

PubMed   PubMed Central   Google Scholar  

Huang JS, Yang FC, Chien WC, Yeh TC, Chung CH, Tsai CK, et al. Risk of substance use disorder and its associations with comorbidities and psychotropic agents in patients with autism. JAMA Pediatr. 2021. https://doi.org/10.1001/jamapediatrics.2020.5371 .

Lu W, Muñoz-Laboy M, Sohler N, Goodwin RD. Trends and disparities in treatment for co-occurring major depression and substance use disorders among US adolescents from 2011 to 2019. JAMA Netw Open. 2021. https://doi.org/10.1001/jamanetworkopen.2021.30280 .

Melkonian AJ, Flanagan JC, Calhoun CD, Hogan JN, Back SE. Craving moderates the effects of intranasal oxytocin on anger in response to social stress among veterans with co-occurring posttraumatic stress disorder and alcohol use disorder. J Clin Psychopharmacol. 2021;41(4):465–9. https://doi.org/10.1097/JCP.0000000000001434 .

Otasowie J. Co-occurring mental disorder and substance use disorder in young people: aetiology, assessment and treatment. BJPsych Adv. 2021;27(4):272–81. https://doi.org/10.1192/bja.2020.64 .

Saraiya TC, Badour CL, Jones AC, Jarnecke AM, Brown DG, Flanagan JC, et al. The role of posttraumatic guilt and anger in integrated treatment for PTSD and co-occurring substance use disorders among primarily male veterans. Psychol Trauma: Theory Res Pract Policy. 2022. https://doi.org/10.1037/tra0001204 .

Walhout SJN, Zanten JV, DeFuentes-Merillas L, Sonneborn CKME, Bosma M. Patients with autism spectrum disorder and co-occurring substance use disorder: a clinical intervention study. Subst Abuse: Res Treat. 2022. https://doi.org/10.1177/11782218221085599 .

Walker D, Infante AA, Knight D. Examining the impact of mental health, substance use, and co-occurring disorders on juvenile court outcomes. J Res Crime Delinq. 2022;59(6):820–53. https://doi.org/10.1177/00224278221084981 .

Warfield SC, Pack RP, Degenhardt L, Larney S, Bharat C, Ashrafioun L, et al. The next wave? Mental health comorbidities and patients with substance use disorders in under-resourced and rural areas. J Subst Abuse Treat. 2021. https://doi.org/10.1016/j.jsat.2020.108189 .

Hien DA, Fitzpatrick S, Saavedra LM, Ebrahimi CT, Norman SB, Tripp J, et al. What’s in a name? A data-driven method to identify optimal psychotherapy classifications to advance treatment research on co-occurring PTSD and substance use disorders. Eur J Psychotraumatol. 2022. https://doi.org/10.1080/20008198.2021.2001191 .

Leonhardt M, Brodahl M, Cogan N, Lien L. How did the first COVID-19 lockdown affect persons with concurrent mental health and substance use disorders in Norway? A qualitative study. BMC Psychiatry. 2022. https://doi.org/10.1186/s12888-022-03812-7 .

Leonhardt M, Kyrdalen E, Holstad A, Hurlen-Solbakken H, Chiu MYL, Lien L. Norwegian cross-cultural adaptation of the social and communities opportunities profile-mini for persons with concurrent mental health and substance use disorders. J Psychosoc Rehabil Mental Health. 2022. https://doi.org/10.1007/s40737-022-00309-0 .

Sell L, Lund HL, Johansen KS. Past, present, and future labor market participation among patients admitted to hospital with concurrent substance use and mental health disorder, and what we can learn from it. J Occup Environ Med. 2022;64(12):1041–5. https://doi.org/10.1097/JOM.0000000000002633 .

Sverdlichenko I, Hawke LD, Henderson J. Understanding the service needs of youth with opioid use: a descriptive study of demographics and co-occurring substance use and mental health concerns. J Subst Abuse Treat. 2022. https://doi.org/10.1016/j.jsat.2021.108592 .

Yerriah J, Tomita A, Paruk S. Surviving but not thriving: Burden of care and quality of life for caregivers of patients with schizophrenia spectrum disorders and comorbid substance use in South Africa. Early Interv Psychiatry. 2022;16(2):153–61. https://doi.org/10.1111/eip.13141 .

Murthy P, Chand P. Treatment of dual diagnosis disorders. Curr Opin Psychiatry. 2012;25(3):194–200. https://doi.org/10.1097/YCO.0b013e328351a3e0 .

Schneier M. Better treatment for dual diagnosis patients. Psychiatr Serv. 2000;51(9):1079. https://doi.org/10.1176/appi.ps.51.9.1079 .

Tirado Muñoz J, Farré A, Mestre-Pintó J, Szerman N, Torrens M. Dual diagnosis in depression: treatment recommendations. Adicciones. 2018;30(1):66–76. https://doi.org/10.2088/adicciones.868 .

Carrà G, Clerici M. Dual diagnosis–policy and practice in Italy. Am J Addict. 2006;15(2):125–30. https://doi.org/10.1080/10550490500528340 .

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Sweileh, W.M. Research landscape analysis on dual diagnosis of substance use and mental health disorders: key contributors, research hotspots, and emerging research topics. Ann Gen Psychiatry 23 , 32 (2024). https://doi.org/10.1186/s12991-024-00517-x

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Implications of cash transfer programs for mental health promotion among families facing significant stressors: using ecological systems theory to explain successes of conditional and unconditional programs.

1. Introduction

2. implications of cash transfer programs for mental health, 2.1. conditional cash transfer programs, 2.2. unconditional cash transfer programs, 3. applying ecological systems theory to child development and family well-being: implications for cash transfer programs, 3.1. individual level, 3.2. microsystem, 3.3. exosystem and macrosystem, 4. a need to address stigma and consider structural factors, 5. overview and summary, 6. a call for transnational research to explore connections between cash transfer programs and mental health outcomes among families, 6.1. research agenda, 6.1.1. line of inquiry, 6.1.2. multi-country study, 6.2. policy agenda, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Salmeron-Gomez, D.; Engilbertsdottir, S.; Antonio, J.; Leiva, C.; Newhouse, D.; Stewart, D. Global Trends in Child Monetary Poverty According to International Poverty Lines. 2023. Available online: https://www.unicef.org/mena/media/23631/file/Global%20trends%20in%20child%20monetary%20poverty.pdf (accessed on 22 August 2024).
• Reiss, F. Socioeconomic inequalities and mental health problems in children and adolescents: A systematic review. Soc. Sci. Med. 2013 , 90 , 24–31. [ Google Scholar ] [ CrossRef ]
• Tracy, M.; Zimmerman, F.J.; Galea, S.; McCauley, E.; Stoep, A.V. What explains the relation between family poverty and childhood depressive symptoms? J. Psychiatr. Res. 2008 , 42 , 1163–1175. [ Google Scholar ] [ CrossRef ]
• McLaughlin, K.A.; Breslau, J.; Green, J.G.; Lakoma, M.D.; Sampson, N.A.; Zaslavsky, A.M.; Kessler, R.C. Childhood socio-economic status and the onset, persistence, and severity of DSM-IV mental disorders in a US national sample. Soc. Sci. Med. 2011 , 73 , 1088–1096. [ Google Scholar ] [ CrossRef ]
• Hatcher, A.M.; Gibbs, A.; Jewkes, R.; McBride, R.S.; Peacock, D.; Christofides, N. Effect of childhood poverty and trauma on adult depressive symptoms among young men in peri-urban South African settlements. J. Adolesc. Health 2019 , 64 , 79–85. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Evans, G.W.; De France, K. Childhood poverty and psychological well-being: The mediating role of cumulative risk exposure. Dev. Psychopathol. 2022 , 34 , 911–921. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, K.; Zhang, L. Cumulative effects of poverty on children’s social-emotional development: Absolute poverty and relative poverty. Community Ment. Health J. 2022 , 58 , 930–943. [ Google Scholar ] [ CrossRef ]
• Wolf, K.; Schmitz, J. Scoping review: Longitudinal effects of the COVID-19 pandemic on child and adolescent mental health. Eur. Child Adolesc. Psychiatry 2023 , 33 , 1257–1312. [ Google Scholar ] [ CrossRef ]
• Fegert, J.M.; Vitiello, B.; Plener, P.L.; Clemens, V. Challenges and burden of the Coronavirus 2019 (COVID-19) pandemic for child and adolescent mental health: A narrative review to highlight clinical and research needs in the acute phase and the long return to normality. Child Adolesc. Psychiatry Ment. Health 2020 , 14 , 20. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Santabárbara, J.; Lasheras, I.; Lipnicki, D.M.; Bueno-Notivol, J.; Pérez-Moreno, M.; López-Antón, R.; De la Cámara, C.; Lobo, A.; Gracia-García, P. Prevalence of anxiety in the COVID-19 pandemic: An updated meta-analysis of community-based studies. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021 , 109 , 110207. [ Google Scholar ] [ CrossRef ]
• Hoagwood, K.E.; Kelleher, K.J. A marshall plan for children’s mental health after COVID-19. Psychiatr. Serv. 2020 , 71 , 1216–1217. [ Google Scholar ] [ CrossRef ]
• de Figueiredo, C.S.; Sandre, P.C.; Portugal, L.C.L.; Mázala-De-Oliveira, T.; da Silva Chagas, L.; Raony, Í.; Ferreira, E.S.; Giestal-De-Araujo, E.; dos Santos, A.A.; Bomfim, P.O.-S. COVID-19 pandemic impact on children and adolescents’ mental health: Biological, environmental, and social factors. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021 , 106 , 110171. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Conger, R.D.; Conger, K.J. Resilience in midwestern families: Selected findings from the first decade of a prospective, longitudinal study. J. Marriage Fam. 2002 , 64 , 361–373. [ Google Scholar ] [ CrossRef ]
• Engle, P.L.; Black, M.M. The effect of poverty on child development and educational outcomes. Ann. N. Y. Acad. Sci. 2008 , 1136 , 243–256. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Clauss-Ehlers, C.S. In search of an evidence-based approach to understand and promote effective parenting practices. Couple Fam. Psychol. Res. Pract. 2017 , 6 , 135–153. [ Google Scholar ] [ CrossRef ]
• Wolfe, B.L.; Haveman, R. The determinants of Children Attainments: A Review of Methods and Findings. J. Econ. Lit. 1995 , 33 , 1829–1878. Available online: https://www.researchgate.net/publication/4901914 (accessed on 22 August 2024).
• Davis-Kean, P.E. The influence of parent education and family income on child achievement: The indirect role of parental expectations and the home environment. J. Fam. Psychol. 2005 , 19 , 294–304. [ Google Scholar ] [ CrossRef ]
• Bauer, A.; Baltra, R.A.; Pabon, M.A.; Díaz, Y.; Garman, E.; Hessel, P.; Lund, C.; Malvasi, P.; Matijasevich, A.; McDaid, D.; et al. Examining the dynamics between young people’s mental health, poverty and life chances in six low- and middle-income countries: Protocol for the CHANCES-6 study. Soc. Psychiatry Psychiatr. Epidemiol. 2021 , 56 , 1687–1703. [ Google Scholar ] [ CrossRef ]
• Thompson, H. Cash transfer programs can promote child protection outcomes. Child. Abus. Negl. 2014 , 38 , 360–371. [ Google Scholar ] [ CrossRef ]
• Arnold, C.; Conway, T.; Greenslade, M. Cash Transfers: Evidence Paper ; Department for International Development: London, UK, 2011; Available online: https://gsdrc.org/document-library/cash-transfers-evidence-paper/ (accessed on 22 August 2024).
• Wolf, S.; Aber, L.J.; Morris, P.A. Drawing on psychological theory to understand and improve antipoverty policies: The case of conditional cash transfers. Psychol. Public Policy Law 2013 , 19 , 3–14. [ Google Scholar ] [ CrossRef ]
• Palomar-Lever, J.; Victorio-Estrada, A. Determinants of subjective well-being in adolescent children of recipients of the oportunidades human development program in Mexico. Soc. Indic. Res. 2014 , 118 , 103–124. [ Google Scholar ] [ CrossRef ]
• Hunter, W.; Patel, L.; Sugiyama, N.B. How family and child cash transfers can empower women: Comparative lessons from Brazil and South Africa. Glob. Soc. Policy 2021 , 21 , 258–277. [ Google Scholar ] [ CrossRef ]
• Neufeld, L.M. The Oportunidades Program and child growth: Mexico perspectives. In Handbook of Growth and Growth Monitoring in Health and Disease ; Springer: Berlin/Heidelberg, Germany, 2011; pp. 1659–1671. [ Google Scholar ] [ CrossRef ]
• Neves, J.A.; de Vasconcelos, F.d.A.C.; Machado, M.L.; Recine, E.; Garcia, G.S.; de Medeiros, M.A.T. The Brazilian cash transfer program (Bolsa Família): A tool for reducing inequalities and achieving social rights in Brazil. Glob. Public. Health 2022 , 17 , 26–42. [ Google Scholar ] [ CrossRef ]
• Fernald, L.C.; Gertler, P.J.; Neufeld, L.M. Role of cash in conditional cash transfer programmes for child health, growth, and development: An analysis of Mexico’s Oportunidades. Lance 2008 , 371 , 828–837. [ Google Scholar ] [ CrossRef ]
• Ozer, E.J.; Fernald, L.C.H.; Manley, J.G.; Gertler, P.J. Effects of a conditional cash transfer program on children’s behavior problems. Pediatrics 2009 , 123 , e630–e637. [ Google Scholar ] [ CrossRef ]
• Fernald, L.C.H.; Gunnar, M.R. Poverty-alleviation program participation and salivary cortisol in very low-income children. Soc. Sci. Med. 2009 , 68 , 2180–2189. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ozer, E.J.; Fernald, L.C.H.; Weber, A.; Flynn, E.P.; VanderWeele, T.J. Does alleviating poverty affect mothers’ depressive symptoms? A quasi-experimental investigation of Mexico’s Oportunidades programme. Int. J. Epidemiol. 2011 , 40 , 1565–1576. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ziebold, C.; Paula, C.S.; Santos, I.S.; Barros, F.C.; Munhoz, T.N.; Lund, C.; McDaid, D.; Araya, R.; Bauer, A.; Garman, E.; et al. Conditional cash transfers and adolescent mental health in Brazil: Evidence from the 2004 Pelotas Birth Cohort. J. Glob Health 2021 , 11 , 04066. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Prencipe, L.; Houweling, T.A.J.; van Lenthe, F.J.; Palermo, T. Do conditional cash transfers improve mental health? Evidence from Tanzania’s governmental social protection program. J. Adolesc. Health 2021 , 69 , 797–805. [ Google Scholar ] [ CrossRef ]
• Riccio, J.A.; Dechausay, N.; Greenberg, D.M.; Miller, C.; Rucks, Z.; Verma, N. Findings from New York City’s Conditional Cash Transfer Program ; MDRC: New York, NY, USA, 2010; Available online: https://ssrn.com/abstract=1786981 (accessed on 22 August 2024).
• Morris, P.A.; Aber, J.L.; Wolf, S.; Berg, J. Impacts of family rewards on adolescents’ mental health and problem behavior: Understanding the full range of effects of a conditional cash transfer program. Prev. Sci. 2017 , 18 , 326–336. [ Google Scholar ] [ CrossRef ]
• Zaneva, M.; Guzman-Holst, C. Reeves, and L. Bowes. The impact of monetary poverty alleviation programs on children’s and adolescents’ mental health: A systematic review and meta-analysis across low-, middle-, and high-income countries. J. Adolesc. Health 2022 , 71 , 147–156. [ Google Scholar ] [ CrossRef ]
• Powell-Jackson, T.; Pereira, S.K.; Dutt, V.; Tougher, S.; Haldar, K.; Kumar, P. Cash transfers, maternal depression and emotional well-being: Quasi-experimental evidence from India’s Janani Suraksha Yojana programme. Soc. Sci. Med. 2016 , 162 , 210–218. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ohrnberger, J.; Anselmi, L.; Fichera, E.; Sutton, M. The effect of cash transfers on mental health: Opening the black box—A study from South Africa. Soc. Sci. Med. 2020 , 260 , 113181. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fernald, L.C.H.; Hidrobo, M. Effect of Ecuador’s cash transfer program (Bono de Desarrollo Humano) on child development in infants and toddlers: A randomized effectiveness trial. Soc. Sci. Med. 2011 , 72 , 1437–1446. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Natali, L.; Handa, S.; Peterman, A.; Seidenfeld, D.; Tembo, G. Does money buy happiness? Evidence from an unconditional cash transfer in Zambia. SSM Popul. Health 2018 , 4 , 225–235. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Rojas, N.M.; Yoshikawa, H.; Gennetian, L.; Lemus Rangel, M.; Melvin, S.; Noble, K.; Duncan, G.; Magunson, K. Exploring the experiences and dynamics of an unconditional cash transfer for low-income mothers: A mixed-methods study. J. Child. Poverty 2020 , 26 , 64–84. [ Google Scholar ] [ CrossRef ]
• de Walque, D.; Fernald, L.; Gertler, P.; Hidrobo, M. Cash transfers and child and adolescent development. In Child and Adolescent Health and Development Disease Control Priorities , 3rd ed.; Bundy, D.A.P., de Silva, N., Horton, S., Jamison, D.T., Patton, G.C., Eds.; World Bank: Washington, DC, USA, 2017; Volume 8, pp. 325–342. [ Google Scholar ] [ CrossRef ]
• Angeles, G.; de Hoop, J.; Handa, S.; Kilburn, K.; Milazzo, A.; Peterman, A. Government of Malawi’s unconditional cash transfer improves youth mental health. Soc. Sci. Med. 2019 , 225 , 108–119. [ Google Scholar ] [ CrossRef ]
• Kilburn, K.; Thirumurthy, H.; Halpern, C.T.; Pettifor, A.; Handa, S. Effects of a large-scale unconditional cash transfer program on mental health outcomes of young people in Kenya. J. Adolesc. Health 2016 , 58 , 223–229. [ Google Scholar ] [ CrossRef ]
• Marr, C.; Cox, K.; Hingtgen, S.; Windham, K.; Sherman, A. American Rescue Plan Act includes Critical Expansions of Child Tax Credit and EITC ; Center on Budget and Policy Priorities: Washington, DC, USA, 2021; Available online: https://www.cbpp.org/research/federal-tax/american-rescue-plan-act-includes-critical-expansions-of-child-tax-credit-and (accessed on 22 August 2024).
• Goldin, J.; Michelmore, K. Who benefits from the child tax credit? Natl. Tax. J. 2022 , 75 , 123–147. [ Google Scholar ] [ CrossRef ]
• Glasner, B.; Jiménez-Solomon, O.; Collyer, S.M.; Garfinkel, I.; Wimer, C.T. No evidence the Child Tax Credit expansion had An effect on the well-being and mental health of parents. Health Aff. 2022 , 41 , 1607–1615. [ Google Scholar ] [ CrossRef ]
• Batra, A.; Jackson, K.; Hamad, R. Effects of the 2021 Expanded Child Tax Credit on adults’ mental health: A quasi-experimental study. Health Aff. 2023 , 42 , 74–82. [ Google Scholar ] [ CrossRef ]
• Kovski, N.; Pilkauskas, N.V.; Michelmore, K.; Shaefer, H.L. Unconditional cash transfers and mental health symptoms among parents with low incomes: Evidence from the 2021 child tax credit. SSM Popul. Health 2021 , 22 , 101420. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Collyer, S.; Gandhi, J.; Garfinkel, I.; Ross, S.; Waldfogel, J.; Wimer, C. The Effects of the 2021 Monthly Child Tax Credit on Child and Family Well-Being: Evidence from New York City. Socius 2022 , 8 , 23780231221141165. [ Google Scholar ] [ CrossRef ]
• Kauhanen, L.; Yunus, W.M.A.W.M.; Lempinen, L.; Peltonen, K.; Gyllenberg, D.; Mishina, K.; Gilbert, S.; Bastola, K.; Brown, J.S.L.; Sourander, A. A systematic review of the mental health changes of children and young people before and during the COVID-19 pandemic. Eur. Child. Adolesc. Psychiatry 2023 , 32 , 995–1013. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Clauss-Ehlers, C.S.; Sood, A.B.; Weist, M.D. Social Justice for Children and Young People: International Perspectives ; Cambridge University Press: Cambridge, UK, 2020. [ Google Scholar ]
• Adato, M.; Devereux, S.; Sabates-Wheeler, R. Accessing the ‘Right’ Kinds of Material and Symbolic Capital: The Role of Cash Transfers in Reducing Adolescent School Absence and Risky Behaviour in South Africa. J. Dev. Stud. 2016 , 52 , 1132–1146. [ Google Scholar ] [ CrossRef ]
• Bourdieu, P. The forms of capital. In Handbook of Theory and Research for the Sociology of Education ; Richardson, J., Ed.; Greenwood: New York, NY, USA, 1986; pp. 241–258. [ Google Scholar ]
• Heinrich, C.; Hoddinott, J. Reducing adolescent risky behaviors in a high-risk context: The effects of unconditional cash transfers in South Africa. Econ. Dev. Cult. Chang. 2017 , 65 , 619–652. [ Google Scholar ] [ CrossRef ]
• Bronfenbrenner, U. Toward an experimental ecology of human development. Am. Psychol. 1977 , 32 , 513–531. [ Google Scholar ] [ CrossRef ]
• Bronfenbrenner, U. Developmental research, public policy, and the ecology of childhood. Child. Dev. 1974 , 45 , 1–5. [ Google Scholar ] [ CrossRef ]
• Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design ; Harvard University Press: Cambridge, UK, 1979. [ Google Scholar ]
• Morris, P.; Aber, J.L.; Wolf, S.; Berg, J. Using Incentives to Change How Teenagers Spend Their Time: The Effect of New York City’s Conditional Cash Transfer Program ; MDRC: New York, NY, USA, 2012. [ Google Scholar ]
• Clauss-Ehlers, C.S.; Garagiola, E.R. Cultural competence and cultural humility as foundations for meaningful engagement among an educational system of care for school stakeholders. In Handbook of School Mental Health: Innovations in Science and Practice ; Springer International Publishing: Berlin/Heidelberg, Germany, 2023; pp. 265–282. [ Google Scholar ] [ CrossRef ]
• Cookson, T. Unjust Conditions: Women’s Work and the Hidden Cost of Cash Transfer Programs ; University of California Press: Oakland, CA, USA, 2018. [ Google Scholar ]
• Roelen, K. Receiving social assistance in low- And middle-income countries: Negating shame or producing stigma? J. Soc. Policy 2020 , 49 , 705–723. [ Google Scholar ] [ CrossRef ]
• Samuels, F.; Stavropoulou, M. ‘Being able to breathe again’: The effects of cash transfer programmes on psychosocial wellbeing. In The Social and Political Potential of Cash Transfers ; Routledge: London, UK, 2018; pp. 13–28. [ Google Scholar ] [ CrossRef ]
• Gaarder, M. Conditional versus unconditional cash: A commentary. J. Dev. Eff. 2012 , 4 , 130–133. [ Google Scholar ] [ CrossRef ]
• Haman, O.B. Conditional and unconditional cash transfer programs: The recent experiences around the world. Int. J. Res. Innov. Soc. Sci. 2019 , 3 , 210–219. Available online: https://hdl.handle.net/102.100.100/555946 (accessed on 22 August 2024).
• Ladhani, S.; Sitter, K.C. Conditional cash transfers: A critical review. Dev. Policy Rev. 2020 , 38 , 28–41. [ Google Scholar ] [ CrossRef ]
• Swiss Academy of Sciences (SCNAT). Commission for Research Partnerships with Developing Countries (KFPE). Available online: https://kfpe.scnat.ch/en/about_kfpe (accessed on 22 August 2024).
• Stöckli, B.; Wiesmann, U.; Lys, J.-A. A Guide for Transboundary Research Partnerships: 11 Principles , 3rd ed. (1st ed. 2012); Swiss Commission for Research Partnerships with Developing Countries (KFPE): Bern, Switzerland, 2018; Available online: https://portal-cdn.scnat.ch/asset/4d8372ee-27d7-558a-b9db-77bf409ceef4/KFPE-11P-7Q-3d2018.pdf?b=370a2788-58f3-581e-85d7-2418b87c662e&v=8ef687bc-7b14-5a4f-ad9e-bf494cddc1d7_0&s=gLwVbtPON190F6auadVe-DPd_IOknFA_pssGzeBGB68h8nfbZIcnUaztPkFbUOKFef8V2w2-5NzkK61J2WEdN4JXXheHl2gX1f6cLTTro8WFmDkhlKvHQjqRQDZdPmPo3whl0-P3PWWEWWinhvJ5hWLGC5hYwVTAPC0kLEC-F7g (accessed on 22 August 2024).

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Lesser, T.L.; Matalon, M.; Clauss-Ehlers, C.S. Implications of Cash Transfer Programs for Mental Health Promotion among Families Facing Significant Stressors: Using Ecological Systems Theory to Explain Successes of Conditional and Unconditional Programs. Behav. Sci. 2024 , 14 , 770. https://doi.org/10.3390/bs14090770

Lesser TL, Matalon M, Clauss-Ehlers CS. Implications of Cash Transfer Programs for Mental Health Promotion among Families Facing Significant Stressors: Using Ecological Systems Theory to Explain Successes of Conditional and Unconditional Programs. Behavioral Sciences . 2024; 14(9):770. https://doi.org/10.3390/bs14090770

Lesser, Tali L., Maya Matalon, and Caroline S. Clauss-Ehlers. 2024. "Implications of Cash Transfer Programs for Mental Health Promotion among Families Facing Significant Stressors: Using Ecological Systems Theory to Explain Successes of Conditional and Unconditional Programs" Behavioral Sciences 14, no. 9: 770. https://doi.org/10.3390/bs14090770

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The relationship between physical exercise and brain health is a burgeoning field of research in neuroscience, with a pivotal impact on our understanding of cognitive well-being, mental health, and aging. Existing studies evidence the positive influences of regular physical activity on brain health, suggesting its implications on learning, memory, and mood. Despite significant advancements, comprehensive analysis incorporating broader perspectives and deeper explorations remain scarce. The objective of this Research Topic is to create an enriching platform for focused discourse on the interconnection between physical exercise and brain health. The goal is to bring together theoretical and experimental research papers that depict a comprehensive overview of recent developments, examine the mechanistic underpinnings of the exercise-brain interaction, and delve into the future potential of this promising area. We welcome contributions that explore, but are not limited to, the following themes: • Impact of various types of exercises on mental health and cognitive functions. • Role of physical activity in stress, anxiety, and mood disorders management. • The molecular and neurochemical effects of exercise on the brain. • Exercise mitigating neurodegenerative disorders and age-related cognitive decline. • Effects of physical exercise on brain development and neuroplasticity. Manuscript types desired for this topic are Original Research, Review, Systematic Review, Mini Review, Perspective, and Opinion articles. Emphasis is on rigorous and high-quality methodology, analysis, and data presentation. The Research Topic places a high priority on interdisciplinary approaches and the potential practical implications of research findings.

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Mental Health, Substance Use, and Child Maltreatment

Child maltreatment is a pressing concern in the United States, with more than four million children referred to child protective services in 2022. Reducing child maltreatment is a national health objective given the substantial, negative consequences for children who experience maltreatment, both in the short- and long-term. Parental mental health and substance use disorders are strongly associated with child maltreatment. In this study, we use administrative data over the period 2004 to 2021 to study the relationship between the number of mental health and substance use treatment centers per county and child maltreatment reports. Our findings provide evidence that better access to mental health and substance use treatment reduces child maltreatment reports. In particular, an 8% increase in the supply of treatment would reduce maltreatment reports by 1%. These findings suggest that recent and ongoing efforts by the federal government to expand mental health and substance use treatment availability may lead to reduced child maltreatment.

All authors contributed equally to this study. Authors are listed in alphabetical order. Research reported in this publication was supported by the National Institute on Mental Health of the National Institutes of Health under Award Number 1R01MH132552 (PI: Johanna Catherine Maclean). Dr. Meinhofer acknowledges support from the Foundation for Opioid Response Efforts GR00015582 and the National Institute on Drug Abuse K01DA051777. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health or the Foundation for Opioid Response Efforts. We thank Douglas Webber and Jiaxin Wei for excellent comments. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• 10 February 2020

Scrutinizing the effects of digital technology on mental health

• Jonathan Haidt &

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The topic in brief

• There is an ongoing debate about whether social media and the use of digital devices are detrimental to mental health.

• Adolescents tend to be heavy users of these devices, and especially of social media.

• Rates of teenage depression began to rise around 2012, when adolescent use of social media became common (Fig. 1).

• Some evidence indicates that frequent users of social media have higher rates of depression and anxiety than do light users.

• But perhaps digital devices could provide a way of gathering data about mental health in a systematic way, and make interventions more timely.

Figure 1 | Depression on the rise. Rates of depression among teenagers in the United States have increased steadily since 2012. Rates are higher and are increasing more rapidly for girls than for boys. Some researchers think that social media is the cause of this increase, whereas others see social media as a way of tackling it. (Data taken from the US National Survey on Drug Use and Health, Table 11.2b; go.nature.com/3ayjaww )

JONATHAN HAIDT: A guilty verdict

A sudden increase in the rates of depression, anxiety and self-harm was seen in adolescents — particularly girls — in the United States and the United Kingdom around 2012 or 2013 (see go.nature.com/2up38hw ). Only one suspect was in the right place at the right time to account for this sudden change: social media. Its use by teenagers increased most quickly between 2009 and 2011, by which point two-thirds of 15–17-year-olds were using it on a daily basis 1 . Some researchers defend social media, arguing that there is only circumstantial evidence for its role in mental-health problems 2 , 3 . And, indeed, several studies 2 , 3 show that there is only a small correlation between time spent on screens and bad mental-health outcomes. However, I present three arguments against this defence.

First, the papers that report small or null effects usually focus on ‘screen time’, but it is not films or video chats with friends that damage mental health. When research papers allow us to zoom in on social media, rather than looking at screen time as a whole, the correlations with depression are larger, and they are larger still when we look specifically at girls ( go.nature.com/2u74der ). The sex difference is robust, and there are several likely causes for it. Girls use social media much more than do boys (who, in turn, spend more of their time gaming). And, for girls more than boys, social life and status tend to revolve around intimacy and inclusion versus exclusion 4 , making them more vulnerable to both the ‘fear of missing out’ and the relational aggression that social media facilitates.

Second, although correlational studies can provide only circumstantial evidence, most of the experiments published in recent years have found evidence of causation ( go.nature.com/2u74der ). In these studies, people are randomly assigned to groups that are asked to continue using social media or to reduce their use substantially. After a few weeks, people who reduce their use generally report an improvement in mood or a reduction in loneliness or symptoms of depression.

The best way forward

Third, many researchers seem to be thinking about social media as if it were sugar: safe in small to moderate quantities, and harmful only if teenagers consume large quantities. But, unlike sugar, social media does not act just on those who consume it. It has radically transformed the nature of peer relationships, family relationships and daily activities 5 . When most of the 11-year-olds in a class are on Instagram (as was the case in my son’s school), there can be pervasive effects on everyone. Children who opt out can find themselves isolated. A simple dose–response model cannot capture the full effects of social media, yet nearly all of the debate among researchers so far has been over the size of the dose–response effect. To cite just one suggestive finding of what lies beyond that model: network effects for depression and anxiety are large, and bad mental health spreads more contagiously between women than between men 6 .

In conclusion, digital media in general undoubtedly has many beneficial uses, including the treatment of mental illness. But if you focus on social media, you’ll find stronger evidence of harm, and less exculpatory evidence, especially for its millions of under-age users.

What should we do while researchers hash out the meaning of these conflicting findings? I would urge a focus on middle schools (roughly 11–13-year-olds in the United States), both for researchers and policymakers. Any US state could quickly conduct an informative experiment beginning this September: randomly assign a portion of school districts to ban smartphone access for students in middle school, while strongly encouraging parents to prevent their children from opening social-media accounts until they begin high school (at around 14). Within 2 years, we would know whether the policy reversed the otherwise steady rise of mental-health problems among middle-school students, and whether it also improved classroom dynamics (as rated by teachers) and test scores. Such system-wide and cross-school interventions would be an excellent way to study the emergent effects of social media on the social lives and mental health of today’s adolescents.

NICK ALLEN: Use digital technology to our advantage

It is appealing to condemn social media out of hand on the basis of the — generally rather poor-quality and inconsistent — evidence suggesting that its use is associated with mental-health problems 7 . But focusing only on its potential harmful effects is comparable to proposing that the only question to ask about cars is whether people can die driving them. The harmful effects might be real, but they don’t tell the full story. The task of research should be to understand what patterns of digital-device and social-media use can lead to beneficial versus harmful effects 7 , and to inform evidence-based approaches to policy, education and regulation.

Long-standing problems have hampered our efforts to improve access to, and the quality of, mental-health services and support. Digital technology has the potential to address some of these challenges. For instance, consider the challenges associated with collecting data on human behaviour. Assessment in mental-health care and research relies almost exclusively on self-reporting, but the resulting data are subjective and burdensome to collect. As a result, assessments are conducted so infrequently that they do not provide insights into the temporal dynamics of symptoms, which can be crucial for both diagnosis and treatment planning.

By contrast, mobile phones and other Internet-connected devices provide an opportunity to continuously collect objective information on behaviour in the context of people’s real lives, generating a rich data set that can provide insight into the extent and timing of mental-health needs in individuals 8 , 9 . By building apps that can track our digital exhaust (the data generated by our everyday digital lives, including our social-media use), we can gain insights into aspects of behaviour that are well-established building blocks of mental health and illness, such as mood, social communication, sleep and physical activity.

Stress and the city

These data can, in turn, be used to empower individuals, by giving them actionable insights into patterns of behaviour that might otherwise have remained unseen. For example, subtle shifts in patterns of sleep or social communication can provide early warning signs of deteriorating mental health. Data on these patterns can be used to alert people to the need for self-management before the patterns — and the associated symptoms — become more severe. Individuals can also choose to share these data with health professionals or researchers. For instance, in the Our Data Helps initiative, individuals who have experienced a suicidal crisis, or the relatives of those who have died by suicide, can donate their digital data to research into suicide risk.

Because mobile devices are ever-present in people’s lives, they offer an opportunity to provide interventions that are timely, personalized and scalable. Currently, mental-health services are mainly provided through a century-old model in which they are made available at times chosen by the mental-health practitioner, rather than at the person’s time of greatest need. But Internet-connected devices are facilitating the development of a wave of ‘just-in-time’ interventions 10 for mental-health care and support.

A compelling example of these interventions involves short-term risk for suicide 9 , 11 — for which early detection could save many lives. Most of the effective approaches to suicide prevention work by interrupting suicidal actions and supporting alternative methods of coping at the moment of greatest risk. If these moments can be detected in an individual’s digital exhaust, a wide range of intervention options become available, from providing information about coping skills and social support, to the initiation of crisis responses. So far, just-in-time approaches have been applied mainly to behaviours such as eating or substance abuse 8 . But with the development of an appropriate research base, these approaches have the potential to provide a major advance in our ability to respond to, and prevent, mental-health crises.

These advantages are particularly relevant to teenagers. Because of their extensive use of digital devices, adolescents are especially vulnerable to the devices’ risks and burdens. And, given the increases in mental-health problems in this age group, teens would also benefit most from improvements in mental-health prevention and treatment. If we use the social and data-gathering functions of Internet-connected devices in the right ways, we might achieve breakthroughs in our ability to improve mental health and well-being.

Nature 578 , 226-227 (2020)

doi: https://doi.org/10.1038/d41586-020-00296-x

Twenge, J. M., Martin, G. N. & Spitzberg, B. H. Psychol. Pop. Media Culture 8 , 329–345 (2019).

Article   Google Scholar  

Orben, A. & Przybylski, A. K. Nature Hum. Behav. 3 , 173–182 (2019).

Article   PubMed   Google Scholar  

Odgers, C. L. & Jensen, M. R. J. Child Psychol. Psychiatry https://doi.org/10.1111/jcpp.13190 (2020).

Maccoby, E. E. The Two Sexes: Growing Up Apart, Coming Together Ch. 2 (Harvard Univ. Press, 1999).

Google Scholar  

Nesi, J., Choukas-Bradley, S. & Prinstein, M. J. Clin. Child. Fam. Psychol. Rev. 21 , 267–294 (2018).

Rosenquist, J. N., Fowler, J. H. & Christakis, N. A. Molec. Psychiatry 16 , 273–281 (2011).

Orben, A. Social Psychiatry Psychiatr. Epidemiol. https://doi.org/10.1007/s00127-019-01825-4 (2020).

Mohr, D. C., Zhang, M. & Schueller, S. M. Annu. Rev. Clin. Psychol. 13 , 23–47 (2017).

Nelson, B. W. & Allen, N. B. Perspect. Psychol. Sci. 13 , 718–733 (2018).

Nahum-Shani, I. et al. Ann. Behav. Med. 52 , 446–462 (2018).

Allen, N. B., Nelson, B. W., Brent, D. & Auerbach, R. P. J. Affect. Disord. 250 , 163–169 (2019).

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Competing Interests

N.A. has an equity interest in Ksana Health, a company he co-founded and which has the sole commercial licence for certain versions of the Effortless Assessment of Risk States (EARS) mobile-phone application and some related EARS tools. This intellectual property was developed as part of his research at the University of Oregon’s Center for Digital Mental Health (CDMH).

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

Published on 3.9.2024 in Vol 26 (2024)

This is a member publication of University of Oxford (Jisc)

Value of Engagement in Digital Health Technology Research: Evidence Across 6 Unique Cohort Studies

Authors of this article:

Original Paper

• Sarah M Goodday 1, 2 , MSc, PhD   ; 
• Emma Karlin 1 , MSc   ; 
• Alexa Brooks 1 , MS, RD   ; 
• Carol Chapman 3 , MPH   ; 
• Christiana Harry 1 , MPH   ; 
• Nelly Lugo 1 , BS   ; 
• Shannon Peabody 1 , BA   ; 
• Shazia Rangwala 4 , MPH   ; 
• Ella Swanson 1 , BS   ; 
• Jonell Tempero 1 , BS, MS   ; 
• Robin Yang 1 , MS   ; 
• Daniel R Karlin 1, 5, 6 , MA, MD   ; 
• Ron Rabinowicz 7, 8 , MD   ; 
• David Malkin 7, 9 , MD   ; 
• Simon Travis 10 , Prof Dr   ; 
• Alissa Walsh 10 , MD   ; 
• Robert P Hirten 11 , MD   ; 
• Bruce E Sands 11 , MS, MD   ; 
• Chetan Bettegowda 12 , MD, PhD   ; 
• Matthias Holdhoff 13 , MD, PhD   ; 
• Jessica Wollett 12 , MS   ; 
• Kelly Szajna 12 , BSc, RN   ; 
• Kallan Dirmeyer 12 , BS   ; 
• Anna Dodd 14 , MS   ; 
• Shawn Hutchinson 14 , MS   ; 
• Stephanie Ramotar 14 , BSc   ; 
• Robert C Grant 14 , MD, PhD   ; 
• Adrien Boch 15 , MA   ; 
• Mackenzie Wildman 16 , PhD   ; 
• Stephen H Friend 2, 4 , MD, PhD  

1 4YouandMe, Seattle, WA, United States

2 Department of Psychiatry, University of Oxford, Oxford, United Kingdom

3 Crohn's & Colitis Foundation, New York, NY, United States

4 Section of Urology and Renal Transplantation, Virginia Mason Francisan Health, Seattle, WA, United States

5 MindMed Inc, New York, NY, United States

6 Tufts University School of Medicine, Boston, MA, United States

7 Department of Paediatrics, University of Toronto, Toronto, ON, Canada

8 Department of Pediatric Hematology/Oncology, Schneider Children's Medical Center of Israel, Petach-Tikva, Israel

9 Department of Pediatrics, University of Toronto, Toronto, ON, Canada

10 Gasteroentology Unit, Oxford University Hospitals NHS Foundation Trust and Biomedical Research Centre, Oxford, United Kingdom

11 The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

12 Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, United States

13 The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD, United States

14 Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada

15 Evidation Health Inc, Santa Mateo, CA, United States

16 Sage Bionetworks, Seattle, WA, United States

Corresponding Author:

Sarah M Goodday, MSc, PhD

2901 3rd Ave

Seattle, WA, 98121

United States

Phone: 1 (206) 928 8243

Email: [email protected]

Background: Wearable digital health technologies and mobile apps (personal digital health technologies [DHTs]) hold great promise for transforming health research and care. However, engagement in personal DHT research is poor.

Objective: The objective of this paper is to describe how participant engagement techniques and different study designs affect participant adherence, retention, and overall engagement in research involving personal DHTs.

Methods: Quantitative and qualitative analysis of engagement factors are reported across 6 unique personal DHT research studies that adopted aspects of a participant-centric design. Study populations included (1) frontline health care workers; (2) a conception, pregnant, and postpartum population; (3) individuals with Crohn disease; (4) individuals with pancreatic cancer; (5) individuals with central nervous system tumors; and (6) families with a Li-Fraumeni syndrome affected member. All included studies involved the use of a study smartphone app that collected both daily and intermittent passive and active tasks, as well as using multiple wearable devices including smartwatches, smart rings, and smart scales. All studies included a variety of participant-centric engagement strategies centered on working with participants as co-designers and regular check-in phone calls to provide support over study participation. Overall retention, probability of staying in the study, and median adherence to study activities are reported.

Results: The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies. Median adherence to study activities varied by study population. Severely ill cancer populations and postpartum mothers showed the lowest adherence to personal DHT research tasks, largely the result of physical, mental, and situational barriers. Except for the cancer and postpartum populations, median adherences for the Oura smart ring, Garmin, and Apple smartwatches were over 80% and 90%, respectively. Median adherence to the scheduled check-in calls was high across all but one cohort (50%, IQR 20%-75%: low-engagement cohort). Median adherence to study-related activities in this low-engagement cohort was lower than in all other included studies.

Conclusions: Participant-centric engagement strategies aid in participant retention and maintain good adherence in some populations. Primary barriers to engagement were participant burden (task fatigue and inconvenience), physical, mental, and situational barriers (unable to complete tasks), and low perceived benefit (lack of understanding of the value of personal DHTs). More population-specific tailoring of personal DHT designs is needed so that these new tools can be perceived as personally valuable to the end user.

Introduction

Wearable digital health technologies (DHTs) [ 1 , 2 ] and mobile apps facilitate the remote, real-world assessment of health including objective signs of disease that are typically confined to health care visits and health care provider interpretation. These specific categories of DHTs, herein referred to as “personal DHTs,” hold promise for transforming health research through the new ability to capture high-resolution, high-frequency, in-the-moment health-related multimodal information in decentralized ways. Through the provision of personal DHTs in clinical care, individuals could be better empowered to navigate their health outside the health care system with greater accessibility, agency, and accuracy than currently possible [ 1 , 2 ]. One of the largest challenges in the future of digital health that involves the use of personal DHTs is end-user engagement. While direct comparisons of engagement in personal DHT research are challenging due to the heterogeneous reporting of retention and adherence factors, and a lack of consensus on a definition of “engagement” [ 3 - 6 ], accumulating evidence supports that so far engagement in the use of personal DHTs has been poor. Specifically, retention in personal DHT research studies and the use of health-related apps is low across diverse populations and applications [ 7 - 9 ]. Further, there is evidence of attrition biases in personal DHT research resulting in insufficient representation of minority populations [ 7 ]. In addition to poor retention, personal DHT research studies have low adherence to completing active app-based tasks resulting in large amounts of missing data. This missing data problem results in challenges in artificial intelligence models from insufficient volumes of data to follow individual patterns, and limits app-based context “label” data. This “label” data is crucial for validating passively collected information from personal DHTs, particularly given the early state of the field and as the utility of certain approaches such as knowledge graphs and large language models emerge.

Several personal DHTs health research studies have started to surface [ 7 - 12 ], resulting in the identification of barriers to engagement. These barriers include technical problems with the technology and in collecting the data, usability, privacy concerns, and digital literacy. Many of these barriers point to a need to retain a human element in the research process, and to include an aspect of co-designing with end users. Emerging personal DHT research studies that show better engagement retain some form of “human-in-the-loop” (regular contact with research staff) and co-design or end-user approach [ 11 - 15 ]. Among these studies, retention rates of 80% and higher have been observed, while average adherence to wearable device use and daily app surveys have been shown to be >90% and 70%, respectively [ 11 - 15 ].

The promise of digital health rests on the assumption that end users can be engaged in the long-term use of personal DHTs for health monitoring, yet this remains to be seen among most existing research applications. There have been increasing international calls for the inclusion of patients in the design and conduct of health research [ 16 - 18 ], and this seems particularly relevant for digital health research where the patient is the end user of these new remote tools. In this paper, we report on engagement across 6 unique personal DHT health research studies that adopted different aspects of a participant-centric design, but each with distinct population and design features. The objective is to describe how participant engagement techniques and different personal DHT designs affect participant adherence, retention, and overall engagement in personal DHT health research.

Study Design

In total, 6 personal DHT research studies are included in this quantitative and qualitative analysis of engagement that span diverse populations including a frontline health care population (the stress and recovery in frontline health care workers study) [ 11 ]; a conception, pregnancy, and postpartum population (Better Understanding the Metamorphosis of Pregnancy [BUMP] study) [ 19 ]; and populations with different diseases including Crohn disease (stress in Crohn: forecasting symptom transitions study), Li-Fraumeni syndrome (stress and LFS: a feasibility study of wearable technologies to detect stress in families with LFS), and patients with pancreatic and central nervous system (CNS) tumors (help enable real-time observations [HERO] in pancreatic [PANC] and CNS tumors studies) [ 20 ].

All of these studies were conducted by 4YouandMe—a US-based nonprofit (charitable) organization. 4YouandMe specializes in open-source research into the application of personal DHTs for health and wellness [ 20 ]. 4YouandMe has a particular focus on leveraging personal DHTs to empower the patient in navigating their unique disease or life transitional period. These 6 studies were included in this analysis as they reflect all of the completed studies by 4YouandMe at the time of this analysis. Characteristics of these studies can be found in Table 1 and additional methodological detail can be found in Multimedia Appendix 1 . All studies involved the use of a bespoke study smartphone app built by 4YouandMe and the use of the Oura smart ring, the Garmin smartwatch, the Apple smartwatch, an Empatica smartwatch, and the Bodyport Cardiac Scale. Details of these devices can be found in Multimedia Appendix 2 ).

a MSSM: Mount Sinai School of Medicine.

b HERO-CNS: help enable real-time observations—central nervous system.

c CNS: central nervous system.

d HERO-PANC: help enable real-time observations—pancreatic cancer.

e n=24, 2 unknown.

f Until withdrawal, progression, death, or study completion (October 31, 2022).

g LFS: Li-Fraumeni syndrome.

h BUMP: Better Understanding the Metamorphosis of Pregnancy.

i BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.

Ethical Considerations

All included studies were approved by the local institutional research ethics boards (REB) at their local sites ( Multimedia Appendix 1 ): stress and recovery in frontline health care workers study (institutional review board [IRB], Advarra [4UCOVID1901, Pro00043205]), BUMP study (IRB Advarra Pro00047893), stress in Crohn (Oxford site: Hampshire-A IRAS ID: 269286, Mount Sinai School of Medicine [MSSM] site: IRB of MSSM: GCO 19-1543 | IRB-19-02298), stress and LFS (Sick Kids: REB: 1000072240), HERO-CNS (John Hopkins Medicine IRB IRB00253818), and HERO-PANC (University Hospital Network REB: 20-5211).

Statistical Analysis

Definitions of adherence in digital health research studies are heterogeneous [ 3 - 6 ]. Consistent criteria for adherence across all included studies were attempted. While many different wearable features could be used as the basis for the use of the device, features that were most reliably monitored were selected. For studies using the Oura smart ring, daily adherence was defined as at least one sleep data event present for the prior night. The Oura ring was only expected to be worn at night for many of the included studies, which is why sleep data were used as the indicator for adherence. For studies using the Garmin smartwatch, daily adherence was defined as step data present for that day. For the Empatica smartwatch, daily adherence was defined as at least one data event (worn properly in a day). Adherence to the Bodyport Cardiac Scale was defined as the proportion of days where a weight event was present divided by the total number of expected follow-up days. Adherence to in-app task completion was defined as the proportion of tasks completed when prompted in the app divided by the total number of tasks that should have been completed over study follow-up. For example, all included studies had a daily survey. In a study with a minimum of 4 months of follow-up expected from participants, the total number of expected daily surveys is approximately 120. For a weekly app survey, the total number of expected surveys for a 4-month study follow-up would be 16. Adherence to biweekly check-in calls was defined as the proportion of calls completed divided by the total number of expected calls over study follow-up. Medians and ranges are described since the adherence distributions were nonnormally distributed. All adherence estimations were performed only among retained participants.

Differences in adherence and retention by sociodemographic characteristics were estimated using χ 2 , Fisher exact, Mann-Whitney U , and ANOVA tests where appropriate among studies that have sufficient sample sizes (stress and recovery, BUMP, and stress in Crohn). Survival probabilities using the Kaplan-Meier approach were calculated to display the probability of retention over the course of each included study. Retention (total proportion of participants completing the study among all enrolled) is also reported. Additional information on how retention was calculated for each unique study can be found in Multimedia Appendix 3 .

Description of Included Studies

Study design characteristics of all studies are described in Table 1 . All studies included the use of at least one wearable device plus a study app that involved daily, as well as intermittent surveys (daily question prompts, validated questionnaires) and active tasks (cognitive active or physical function tasks [eg, walk tests], video diaries). In all included studies, participants were required to use their own Android or iPhone smartphone for study activities. Recruitment mechanisms differed across studies with some including remote recruitment through digital advertisements on social media, professional organizations and newsletters, and patient portals (stress and recovery, and BUMP), while others recruited patients in-person through specialty clinics (stress in Crohn, HERO studies, and stress and LFS). The daily burden of app active tasks across studies ranged from 2 to 7 minutes. Study follow-up periods across studies ranged from 4 to 18 months. Across all studies except the stress and LFS study, participants were offered to keep some of the study wearable devices (most often the ring and the watch). Further, 2 studies included the option for modest financial compensation (BUMP and stress in Crohn).

All studies included an engagement strategy that centered around a biweekly phone check-in with a consistent engagement specialist that served the purpose of supporting participants, helping them with onboarding, resolving potential technological problems, and discussing and collecting study experience feedback. Additionally, all included studies implemented different strategies that focused on working with participants as co-designers. These strategies included making app changes that were driven by direct participant feedback during active follow-up, offering a “your data” section in the app that allowed participants to track key symptoms over time, hosting optional investigator-participant Zoom calls where participants could meet the study team, receive study updates, preliminary results, and could offer more feedback, and inviting participants to contribute to and be listed as coauthors on published work.

Adherence by Study Population

Median adherence in engagement phone check-in calls, wearable device use, daily app survey completion, and in-app active tasks can be found in Table 2 . Median adherence varied across study populations. The stress in Crohn–MSSM site had a lower adherence on the engagement check-in calls (50%) compared to other studies, many of which had 100% adherence on these calls ( Table 2 ). This study site is herein referred to as the low-engagement cohort. In this low-engagement cohort, median adherence to completing daily app surveys, to wearing the Empatica smartwatch, and to using the Bodyport Cardiac Scale were lower than all other study cohorts that included these studies’ activities (except the BUMP-postpartum cohort). Further, median adherence to using the Oura smart ring was lower in the low-engagement cohort compared to other cohorts except for the postpartum and severely ill cancer populations.

The HERO studies included the most severely ill participants including patients with active diagnoses of CNS and pancreatic tumors. Some HERO participants were undergoing chemotherapy, some had therapy-related complications, some had infections, and some had progressive, life-threatening tumor growth. While the total number of participants in these studies was low, these studies showed low adherence on the daily survey (<55%) and wearable device use (<65% HERO-CNS only). Interestingly, HERO-PANC participants exhibited high wearable device use median adherence (83.3%, IQR 51%-93.2%, Oura and 95.5%, IQR 75.2%-99.2%, Garmin), despite the health status of this population. Further, median adherence to in-app cognitive active tasks was higher among the HERO studies compared to most other studies. Engagement check-in call adherence was also high in the HERO studies. Among the BUMP postpartum cohort, there was consistently lower adherence on all study tasks except for the engagement check-in calls compared to other studies, particularly in comparison to the BUMP prenatal cohort. Specifically, median adherence to the Oura ring, Garmin smartwatch use, and the Bodyport Cardiac Scale in the BUMP-prenatal cohort compared to the BUMP postpartum cohort dropped from 87.2% (IQR 68.7%-96.7%) to 55% (IQR 5.5%-83.7%), 96.7% (IQR 82.9%-100%) to 62.5% (IQR 12.3%-96.4%), and 74.7% (IQR 52%-87.3%) to 33.1% (IQR 8.9%-67.7%), respectively ( Table 2 ).

a BUMP-C: Better Understanding the Metamorphosis of Pregnancy—Conception.

b BUMP: Better Understanding the Metamorphosis of Pregnancy.

c BUMP-POST: Better Understanding the Metamorphosis of Pregnancy—Postpartum.

d SINC: stress in Crohn.

e MSSM: Mount Sinai School of Medicine.

f HERO-CNS: help enable real-time observations—central nervous system.

g HERO-PANC: help enable real-time observations—pancreatic cancer.

h LFS: Li-Fraumeni syndrome.

i ES: engagement specialist.

j Not available.

k EBT: emotional bias test.

Adherence by Study Activity

There were differences in adherence rates across different study activities. Adherence to wearable device use was consistently higher across studies compared to in-app activities, which is not surprising given the passive nature of these devices. Excluding the postpartum and HERO-CNS study, median adherence to Oura ring use was >80% across all studies, and as high as 99% (IQR 94.9%-99.6%; stress in Crohn-Oxford site; Table 2 ). There were also differences in adherence across specific wearable devices. Garmin and Apple smartwatch adherence was >95% in BUMP pregnant individuals and HERO-PANC participants, while median adherence for the Empatica Watch was lower among the studies that used this device (stress in Crohn-Oxford, 72.5%, IQR 37.1%-96.8%; stress in Crohn-MSSM, low-engagement cohort, 26%, IQR 6.2%-64.1%; and stress in LFS, 86.8%, IQR 0.7%-0.9%). Median adherence to the Bodyport Cardiac Scale was 74.7% (IQR 52%-87.3%) among BUMP pregnant individuals and 79.5% (IQR 52.7%-88.4%) in HERO-PANC participants ( Table 2 ). Excluding the postpartum and HERO study populations and the low-engagement cohort, in-app daily survey adherence was >60% for all studies ( Table 2 ). Finally, adherence to in-app active tasks was lower in general compared to other activities such as wearable device use or in-app surveys. Tasks that involved walking (gait and walk task) or speaking (video diaries) showed lower adherence compared to other active tasks (eg, cognitive and emotional bias tasks; Table 2 ).

Adherence by Study Recruitment and Engagement Strategy

There did not appear to be any meaningful difference in median adherence rates across study activities by study recruitment methods (in-clinic vs remote) or follow-up time. Further, 2 studies that included modest financial compensation in addition to engagement strategies showed higher adherence rates compared to some of the other studies (ie, BUMP and stress in Crohn), but the impact of compensation is difficult to disentangle from other study characteristics such as population differences, and these studies did not show superior adherence rates compared to the stress and recovery study that did not offer financial compensation.

The median proportion of participants retained in the study across the 6 studies was 77.2% (IQR 72.6%-88%; Table 3 ). The probability of staying in the study stayed above 80% for all studies during the first month of study participation and stayed above 50% for the entire active study period across all studies ( Multimedia Appendix 4 ).

b Only includes participants who were enrolled in the Better Understanding the Metamorphosis of Pregnancy—Conception-specific app.

c BUMP: Better Understanding the Metamorphosis of Pregnancy.

d MSSM: Mount Sinai School of Medicine.

e HERO-CNS: help enable real-time observations—central nervous system.

f HERO-PANC: help enable real-time observations—pancreatic cancer.

g Help enable real-time observations—pancreatic cancer has unique factors to consider when interpreting the proportion retained until study completion, since the study aimed to monitor patients until they developed progressive disease or died, or the study end date (October 31, 2022; see Multimedia Appendix 3 ).

Adherence and Retention by Participant Sociodemographic Characteristics

Median adherence for the Oura smart ring, a smartwatch (Garmin, Apple, and Empatica), and the Bodyport Cardiac Scale was lower among younger participants compared to older participants across most studies ( Multimedia Appendix 5 ). Specifically, Oura smart ring adherence was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.03) and stress in Crohn-MSSM studies ( P =.02), and was lower in the BUMP-C and stress and recover studies, but this difference was not statistically significant at P =.59 and P =.08, respectively. Median adherence for Apple smartwatch use was significantly lower in those aged 18-25 years compared to those aged ≥26 years in the BUMP study ( P =.02), while median adherence for Garmin smartwatch use was lower but not statistically significant ( P =.06). Median adherence for the Bodyport Cardiac Scale was significantly lower in those aged 18-25 years compared to those aged ≥26 years in BUMP ( P <.005) and stress in Crohn-MSSM ( P <.006).

In the BUMP study, Black or African American ethnicity had significantly higher median adherence to completing the in-app daily survey compared to other race or ethnicity groups ( P =.01). This trend was observed in the stress and recovery study ( P =.07) and the stress in Crohn-MSSM study ( P =.24), although the difference was not statistically significant. In contrast, median adherence to Oura smart ring, smartwatch, and Bodyport Cardiac Scale use was lower among Black or African American individuals compared to other race or ethnicity groups, although these differences were not statistically significant ( Multimedia Appendix 5 ).

Retention did not significantly differ by age group or gender ( Multimedia Appendix 6 ).

Retention likelihood was significantly different by race or ethnicity groups in BUMP-C ( P <.001) and BUMP ( P= .001). Specifically, participants of White ethnicity were more likely to stay in the study in both BUMP-C and BUMP, while participants reporting their race or ethnicity as either unknown or not reporting this item were less likely to be retained ( Multimedia Appendix 6 ).

Barriers to Engagement (Qualitative Synthesis of Participant Feedback)

Figure 1 describes key themes that impacted participant retention, adherence, and overall engagement that cut across all included studies. These themes include participant burden and forgetfulness, digital literacy, physical and mental barriers, personal and altruistic benefits, and privacy and confidentiality. Qualitative feedback from participants, research staff, and investigators across these 5 themes is summarized in Multimedia Appendix 7 . The top three barriers to engagement in active study tasks were (1) participant burden and in particular fatigue with the repetitiveness of tasks; (2) physical or mental and situational barriers that prevented the ability to complete tasks; and (3) personal and altruistic benefit, namely the perception that the use of the personal DHTs was not personally useful for a health benefit or a lack of understanding as to why and how certain features (eg, heart rate variability) could be useful to track for health benefit. Qualitative feedback from participants in the 2 cohorts demonstrating lower adherence (HERO-PANC and BUMP post partum) suggested that while participants were highly engaged, they were either too ill, distracted, or tired to complete many of the study activities while navigating a serious illness or the early postpartum period.

Principal Findings

Evidence across 6 unique and diverse studies involving the longitudinal use of personal DHTs supports that participant-centric engagement strategies aid in participant retention and maintaining good adherence in some populations. These strategies centered around (1) human contact with an engagement specialist as often as every 2 weeks, (2) investigator-participant meetings during active study follow-up, (3) offering returned symptom data in the app, (4) inviting participants to contribute as coauthors in published work, and (5) real-time modifications to the study app based on participant feedback.

In the majority of included studies, the probability of staying in the study stayed above 90% for the first month and stayed above 50% for active study periods for all studies. Lower retention or adherence was observed among studies that included a severely ill cancer population and a postpartum population. Barriers to participation in these cohorts were largely the result of physical and situational roadblocks. Excluding studies of a severely ill and postpartum population and the low-engagement cohort in the stress in Crohn study, adherence to Oura smart ring and Garmin smartwatch use was 80% and as high as 99% in some cohorts, while adherence to the Bodyport Cardiac Scale was 75% in a pregnant population. This supports that different populations can successfully be engaged in the use of active app assessments and wearable devices in the long term with adequate support.

Retention and adherence rates observed in these studies are higher than typically reported by other personal DHT research studies [ 7 - 9 , 12 , 13 , 21 ]. For example, a review of 8 large app-based DHT research studies in the United States reported that the probability of staying in the study dropped to or below 50% after the first 4 weeks of participation for all included studies [ 7 ]. Further, across the 8 included studies in this review, >50% of participants did not engage with the app for at least 7 days. Another large app-based study in the United States, the Warfighter Analytics Using Smartphones for Health study that collected daily active and passive app data reported a median retention of 45.2% (38/84 days), while the probability of staying in the study hit 50% at approximately 5.5 weeks [ 10 ]. A large app-based study in the United Kingdom (cloudy with a chance of pain study) involving daily active app assessments reported that 64% of participants fell into the low engagement or no engagement categories after baseline [ 12 ]. The RADAR study [ 14 ], a multinational study involving active and passive assessments from an app, and a Fitbit reported comparable retention results among participants with major depression to those reported here. This study reported a retention rate of 54.6% for 43 weeks of study participation; however, the probability of staying in the study stayed above 75% for the first several months of participation (~6 months). While the active app assessments in this study only included assessments every 2 weeks as opposed to daily assessments, this study additionally included aspects of a participant-centric design, which may have contributed to the higher reported retention [ 15 ].

Taken together, in comparison to other published personal DHT research studies, the 6 studies included in this paper reflect higher levels of engagement. Importantly, the included studies in this analysis involved high burden designs in comparison to other studies that request, for example, weekly or biweekly active tasks of participants [ 14 ] or only involve the use of a smartwatch. Specifically, across the included studies here, participants were expected to complete on average 4.6 (SD 1.62) minutes a day of app activities in addition to continuously using multiple wearable devices.

While different variations of participant-centric strategies were used across the 6 included studies, a key common feature was a biweekly check-in call with an engagement specialist. These calls served the purpose of providing support and building rapport with participants, working through onboarding and technological issues with study devices, tracking adherence, and receiving study-related feedback from participants. Numerous challenges arise in the conduct of remote, personal DHT research, and without frequent check-in and semiregular data monitoring by research staff, knowledge of these issues is a black box. The most significant drop in retention in personal DHT research studies tends to be during the first few weeks of participation [ 7 ]. These early onboarding weeks are crucial in working with participants to ensure they can get into a rhythm of participation. The passive sensing nature of personal DHTs has much potential to inform new objective measures of health, however, are not always intuitively understood as personally important for unique diseases (eg, heart rate variability or phone screen time). Personal DHT studies allow for “light touch” research approaches that enable data collection without traditional research coordinator contact, but this may come with a cost that inadvertently creates a less engaging study environment for participants and limits the opportunity to help participants understand the value in their participation. Of the included 6 studies, 1 cohort had much lower engagement on the check-in calls (50% adherence) compared to other included studies and, in turn, consistently demonstrated lower adherence to study-related activities. Still, even with extensive engagement designs, populations that had physical, mental, and situational barriers to study task completion (ie, severely ill, postpartum mothers) showed lower adherence to wearable device use and active smartphone tasks compared to other study populations. Top reported barriers to engagement included participant burden, physical, mental, and situational barriers, and low perceived value of personal DHTs for health care. These engagement barriers have been reported in previous literature [ 8 , 9 ] relating to DHT research and in the use of DHT interventions. However, the conveyed importance of the perceived value of the approach among participants in the current analysis is noteworthy. Given the foreign nature of personal DHTs for many individuals, particularly older populations, further work is needed to co-design and educate end users on the potential value of self-monitoring unique health-related data.

Irrespective of the engagement approach, adherence to in-app surveys and tasks was lower than wearable device use, which is not surprising given the higher burden related to in-app activities. The self-reported information captured from frequent or momentary in-app assessments is extremely valuable as context information. This context information or “label” data is useful for validating objectively captured information, yet remains the most difficult to capture in sufficient detail. Further, certain in-app activity adherences were consistently lower than others. Namely, activities that required the user to be active (walk in a straight line or complete a video diary) were low across studies. Still, adherence to daily in-app surveys was >60% for all studies excluding the postpartum and HERO study populations.

Limitations

This quantitative and qualitative analysis compared observational data across different digital health studies. However, no true comparison cohort that did not include engagement strategies was included. Therefore, the inferred casualty of participant check-ins with engagement specialists on retention and adherence rates cannot be not concluded. We are formally testing whether the biweekly check-in significantly increases adherence and retention in an ongoing study with an appropriate comparison arm without check-in support (NCT05753605). One of the included studies (stress and recovery) was conducted during the early 2020 COVID-19 pandemic. There is some evidence that engagement in research was higher during the early pandemic time periods [ 22 ]. It cannot be ruled out that the higher observed retention and adherence in this study compared to others was not due to this potential time period bias. The stress in the Crohn-Oxford site included a population of patients some of whom were already engaged in the use of web-based monitoring of symptoms. In turn, this could have contributed to the high retention and higher adherence observed at this site compared to the other stress in the Crohn-MSSM site. The results presented on barriers to engagement were primarily qualitative and collected from conversations with participants, research staff, and investigators across studies.

Conclusions

Globally, mobile apps are used for a variety of purposes in everyday life, while the use of smartwatches for activity monitoring is gaining increasing popularity. However, the use of these tools for health remains a challenge. These findings support that human support via phone and other participant-centric engagement strategies centered on giving back to participants and working with them as co-designers can support sufficient retention and adherence in personal DHT research across diverse populations. This has implications for the utility and potential necessity of a digital support worker in digital health care, as highlighted by others [ 23 ]. A power of personal DHTs is enabling the patient to be in control of their health through self-monitoring, but this new role comes with a responsibility. This important shift in role from doctor to patient outlines how crucial it is to include patients in the early design phase of personal DHT health research. Further work is needed to inform app designs that support habitual forming activities around task completion so that app-related activities become a part of participants’ daily routine and are perceived as personally valuable.

Acknowledgments

The stress and recovery study was supported in part by the Bill & Melinda Gates Foundation (INV-016651). The stress in Crohn study was funded by the Leona M. and Harry B. Helmsley Charitable Trust (1911-03376). The help enable real-time observation (HERO)–central nervus system study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP). The HERO–pancreatic cancer study was funded by the Mark Foundation for Cancer Research through an ASPIRE award (19-024-ASP), Pancreatic Cancer Canada, the Princess Margaret Cancer Foundation, and 4YouandMe. The Better Understanding the Metamorphosis of Pregnancy (BUMP) study was funded by 4YouandMe and Sema4 along with supplemental in-kind contributions from coalition partners (Evidation Health, Vector Institute, Cambridge Cognition, and Bodyport). The stress and LFS study was funded by in-kind contributions from 4YouandMe, SickKids Hospital, and the Vector Institute.

Conflicts of Interest

CB is a consultant for Depuy Synthes, Bionaut Labs, Galectin Therapeutics, Haystack Oncology, and Privo Technologies. CB is a cofounder of Belay Diagnostics and OrisDx. DRK is an officer, employee, and shareholder of MindMed; a consultant at Tempus, Nightware, and Limitless; and board member of Sonara. RPH is an advisory board member at Bristol Meyers Squibb. MH is an advisory board member for Servier, AnHeart, and Bayer; steering committee member for Novartis; honoraria from Novartis; data safety monitoring committee member for Advarra and Parexel. RG received a graduate scholarship from Pfizer and provided consulting or advisory roles for Astrazeneca, Tempus, Eisai, Incyte, Knight Therapeutics, Guardant Health, and Ipsen. The others declare no conflicts of interest.

Study descriptions.

Study wearable devices.

Retention calculations.

Probability of retaining in the study across studies.

Median adherence to study activities stratified by sociodemographic characteristics.

Sociodemographic differences in participants who were retained versus not retained.

Qualitative feedback from participants, research staff, and investigators surrounding barriers to engagement in digital health research, summarized across 6 unique studies.

• Friend SH, Ginsburg GS, Picard RW. Wearable digital health technology. N Engl J Med. 2023;389(22):2100-2101. [ CrossRef ] [ Medline ]
• Goodday SM, Geddes JR, Friend SH. Disrupting the power balance between doctors and patients in the digital era. Lancet Digit Health. 2021;3(3):e142-e143. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ng MM, Firth J, Minen M, Torous J. User engagement in mental health apps: a review of measurement, reporting, and validity. Psychiatr Serv. 2019;70(7):538-544. [ FREE Full text ] [ CrossRef ] [ Medline ]
• White KM, Williamson C, Bergou N, Oetzmann C, de Angel V, Matcham F, et al. A systematic review of engagement reporting in remote measurement studies for health symptom tracking. NPJ Digit Med. 2022;5(1):82. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Daniore P, Nittas V, von Wyl V. Enrollment and retention of participants in remote digital health studies: scoping review and framework proposal. J Med Internet Res. 2022;24(9):e39910. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chan A, Chan D, Lee H, Ng CC, Yeo AHL. Reporting adherence, validity and physical activity measures of wearable activity trackers in medical research: a systematic review. Int J Med Inform. 2022;160:104696. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med. 2020;3:21. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Simblett S, Matcham F, Siddi S, Bulgari V, Barattieri di San Pietro C, Hortas López J, et al. RADAR-CNS Consortium. Barriers to and facilitators of engagement with mHealth technology for remote measurement and management of depression: qualitative analysis. JMIR mHealth uHealth. 2019;7(1):e11325. [ FREE Full text ] [ CrossRef ] [ Medline ]
• O'Connor S, Hanlon P, O'Donnell CA, Garcia S, Glanville J, Mair F. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: a systematic review of qualitative studies. BMC Med Inform Decis Mak. 2016;16(1):120. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Li SX, Halabi R, Selvarajan R, Woerner M, Fillipo IG, Banerjee S, et al. Recruitment and retention in remote research: learnings from a large, decentralized real-world study. JMIR Form Res. 2022;6(11):e40765. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Goodday SM, Karlin E, Alfarano A, Brooks A, Chapman C, Desille R, Stress And Recovery Participants, et al. An alternative to the light touch digital health remote study: the stress and recovery in frontline COVID-19 health care workers study. JMIR Form Res. 2021;5(12):e32165. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Druce KL, McBeth J, van der Veer SN, Selby DA, Vidgen B, Georgatzis K, et al. Recruitment and ongoing engagement in a UK smartphone study examining the association between weather and pain: cohort study. JMIR mHealth uHealth. 2017;5(11):e168. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Reade S, Spencer K, Sergeant JC, Sperrin M, Schultz DM, Ainsworth J, et al. Cloudy with a chance of pain: engagement and subsequent attrition of daily data entry in a smartphone pilot study tracking weather, disease severity, and physical activity in patients with rheumatoid arthritis. JMIR mHealth uHealth. 2017;5(3):e37. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Matcham F, Leightley D, Siddi S, Lamers F, White KM, Annas P, et al. RADAR-CNS consortium. Remote Assessment of Disease and Relapse in Major Depressive Disorder (RADAR-MDD): recruitment, retention, and data availability in a longitudinal remote measurement study. BMC Psychiatry. 2022;22(1):136. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhang Y, Pratap A, Folarin AA, Sun S, Cummins N, Matcham F, et al. Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study. NPJ Digit Med. Feb 17, 2023;6(1):25. [ CrossRef ] [ Medline ]
• How we engage the public. Wellcome. URL: https://wellcome.org/what-we-do/public-engagement [accessed 2024-02-02]
• Canada's strategy for patient-oriented research. Canadian Institutes of Health Research. URL: https://cihr-irsc.gc.ca/e/44000.html [accessed 2024-02-02]
• NIHR launches new centre for engagement and dissemination. National Institute of Health and Care Research. URL: https://www.nihr.ac.uk/news/nihr-launches-new-centre-for-engagement-and-dissemination/24576 [accessed 2024-02-02]
• Goodday SM, Karlin E, Brooks A, Chapman C, Karlin DR, Foschini L, et al. Better Understanding of the Metamorphosis of Pregnancy (BUMP): protocol for a digital feasibility study in women from preconception to postpartum. NPJ Digit Med. Mar 30, 2022;5(1):40. [ CrossRef ] [ Medline ]
• 4YouandMe. URL: http://4youandme.org [accessed 2023-06-07]
• Nowell WB, Curtis JR, Zhao H, Xie F, Stradford L, Curtis D, et al. Participant engagement and adherence to providing smartwatch and patient-reported outcome data: digital tracking of rheumatoid arthritis longitudinally (DIGITAL) real-world study. JMIR Hum Factors. 2023;10:e44034. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Vargo D, Zhu L, Benwell B, Yan Z. Digital technology use during COVID-19 pandemic: a rapid review. Human Behav and Emerg Tech. Dec 28, 2020;3(1):13-24. [ CrossRef ]
• Perret S, Alon N, Carpenter-Song E, Myrick K, Thompson K, Li S, et al. Standardising the role of a digital navigator in behavioural health: a systematic review. Lancet Digit Health. 2023;5(12):e925-e932. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, T de Azevedo Cardoso; submitted 06.03.24; peer-reviewed by C Godoy Jr; comments to author 05.04.24; revised version received 12.04.24; accepted 29.05.24; published 03.09.24.

©Sarah M Goodday, Emma Karlin, Alexa Brooks, Carol Chapman, Christiana Harry, Nelly Lugo, Shannon Peabody, Shazia Rangwala, Ella Swanson, Jonell Tempero, Robin Yang, Daniel R Karlin, Ron Rabinowicz, David Malkin, Simon Travis, Alissa Walsh, Robert P Hirten, Bruce E Sands, Chetan Bettegowda, Matthias Holdhoff, Jessica Wollett, Kelly Szajna, Kallan Dirmeyer, Anna Dodd, Shawn Hutchinson, Stephanie Ramotar, Robert C Grant, Adrien Boch, Mackenzie Wildman, Stephen H Friend. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.09.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Social Media Use and Its Connection to Mental Health: A Systematic Review

Fazida karim.

1 Psychology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

2 Business & Management, University Sultan Zainal Abidin, Terengganu, MYS

Azeezat A Oyewande

3 Family Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

4 Family Medicine, Lagos State Health Service Commission/Alimosho General Hospital, Lagos, NGA

Lamis F Abdalla

5 Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

Reem Chaudhry Ehsanullah

Safeera khan.

Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for quality. Eight papers were cross-sectional studies, three were longitudinal studies, two were qualitative studies, and others were systematic reviews. Findings were classified into two outcomes of mental health: anxiety and depression. Social media activity such as time spent to have a positive effect on the mental health domain. However, due to the cross-sectional design and methodological limitations of sampling, there are considerable differences. The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies.

Introduction and background

Human beings are social creatures that require the companionship of others to make progress in life. Thus, being socially connected with other people can relieve stress, anxiety, and sadness, but lack of social connection can pose serious risks to mental health [ 1 ].

Social media

Social media has recently become part of people's daily activities; many of them spend hours each day on Messenger, Instagram, Facebook, and other popular social media. Thus, many researchers and scholars study the impact of social media and applications on various aspects of people’s lives [ 2 ]. Moreover, the number of social media users worldwide in 2019 is 3.484 billion, up 9% year-on-year [ 3 - 5 ]. A statistic in Figure  1  shows the gender distribution of social media audiences worldwide as of January 2020, sorted by platform. It was found that only 38% of Twitter users were male but 61% were using Snapchat. In contrast, females were more likely to use LinkedIn and Facebook. There is no denying that social media has now become an important part of many people's lives. Social media has many positive and enjoyable benefits, but it can also lead to mental health problems. Previous research found that age did not have an effect but gender did; females were much more likely to experience mental health than males [ 6 , 7 ].

Impact on mental health

Mental health is defined as a state of well-being in which people understand their abilities, solve everyday life problems, work well, and make a significant contribution to the lives of their communities [ 8 ]. There is debated presently going on regarding the benefits and negative impacts of social media on mental health [ 9 , 10 ]. Social networking is a crucial element in protecting our mental health. Both the quantity and quality of social relationships affect mental health, health behavior, physical health, and mortality risk [ 9 ]. The Displaced Behavior Theory may help explain why social media shows a connection with mental health. According to the theory, people who spend more time in sedentary behaviors such as social media use have less time for face-to-face social interaction, both of which have been proven to be protective against mental disorders [ 11 , 12 ]. On the other hand, social theories found how social media use affects mental health by influencing how people view, maintain, and interact with their social network [ 13 ]. A number of studies have been conducted on the impacts of social media, and it has been indicated that the prolonged use of social media platforms such as Facebook may be related to negative signs and symptoms of depression, anxiety, and stress [ 10 - 15 ]. Furthermore, social media can create a lot of pressure to create the stereotype that others want to see and also being as popular as others.

The need for a systematic review

Systematic studies can quantitatively and qualitatively identify, aggregate, and evaluate all accessible data to generate a warm and accurate response to the research questions involved [ 4 ]. In addition, many existing systematic studies related to mental health studies have been conducted worldwide. However, only a limited number of studies are integrated with social media and conducted in the context of social science because the available literature heavily focused on medical science [ 6 ]. Because social media is a relatively new phenomenon, the potential links between their use and mental health have not been widely investigated.

This paper attempt to systematically review all the relevant literature with the aim of filling the gap by examining social media impact on mental health, which is sedentary behavior, which, if in excess, raises the risk of health problems [ 7 , 9 , 12 ]. This study is important because it provides information on the extent of the focus of peer review literature, which can assist the researchers in delivering a prospect with the aim of understanding the future attention related to climate change strategies that require scholarly attention. This study is very useful because it provides information on the extent to which peer review literature can assist researchers in presenting prospects with a view to understanding future concerns related to mental health strategies that require scientific attention. The development of the current systematic review is based on the main research question: how does social media affect mental health?

Research strategy

The research was conducted to identify studies analyzing the role of social media on mental health. Google Scholar was used as our main database to find the relevant articles. Keywords that were used for the search were: (1) “social media”, (2) “mental health”, (3) “social media” AND “mental health”, (4) “social networking” AND “mental health”, and (5) “social networking” OR “social media” AND “mental health” (Table  1 ).

Out of the results in Table  1 , a total of 50 articles relevant to the research question were selected. After applying the inclusion and exclusion criteria, duplicate papers were removed, and, finally, a total of 28 articles were selected for review (Figure  2 ).

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Inclusion and exclusion criteria

Peer-reviewed, full-text research papers from the past five years were included in the review. All selected articles were in English language and any non-peer-reviewed and duplicate papers were excluded from finally selected articles.

Of the 16 selected research papers, there were a research focus on adults, gender, and preadolescents [ 10 - 19 ]. In the design, there were qualitative and quantitative studies [ 15 , 16 ]. There were three systematic reviews and one thematic analysis that explored the better or worse of using social media among adolescents [ 20 - 23 ]. In addition, eight were cross-sectional studies and only three were longitudinal studies [ 24 - 29 ].The meta-analyses included studies published beyond the last five years in this population. Table  2  presents a selection of studies from the review.

IGU, internet gaming disorder; PSMU, problematic social media use

This study has attempted to systematically analyze the existing literature on the effect of social media use on mental health. Although the results of the study were not completely consistent, this review found a general association between social media use and mental health issues. Although there is positive evidence for a link between social media and mental health, the opposite has been reported.

For example, a previous study found no relationship between the amount of time spent on social media and depression or between social media-related activities, such as the number of online friends and the number of “selfies”, and depression [ 29 ]. Similarly, Neira and Barber found that while higher investment in social media (e.g. active social media use) predicted adolescents’ depressive symptoms, no relationship was found between the frequency of social media use and depressed mood [ 28 ].

In the 16 studies, anxiety and depression were the most commonly measured outcome. The prominent risk factors for anxiety and depression emerging from this study comprised time spent, activity, and addiction to social media. In today's world, anxiety is one of the basic mental health problems. People liked and commented on their uploaded photos and videos. In today's age, everyone is immune to the social media context. Some teens experience anxiety from social media related to fear of loss, which causes teens to try to respond and check all their friends' messages and messages on a regular basis.

On the contrary, depression is one of the unintended significances of unnecessary use of social media. In detail, depression is limited not only to Facebooks but also to other social networking sites, which causes psychological problems. A new study found that individuals who are involved in social media, games, texts, mobile phones, etc. are more likely to experience depression.

The previous study found a 70% increase in self-reported depressive symptoms among the group using social media. The other social media influence that causes depression is sexual fun [ 12 ]. The intimacy fun happens when social media promotes putting on a facade that highlights the fun and excitement but does not tell us much about where we are struggling in our daily lives at a deeper level [ 28 ]. Another study revealed that depression and time spent on Facebook by adolescents are positively correlated [ 22 ]. More importantly, symptoms of major depression have been found among the individuals who spent most of their time in online activities and performing image management on social networking sites [ 14 ].

Another study assessed gender differences in associations between social media use and mental health. Females were found to be more addicted to social media as compared with males [ 26 ]. Passive activity in social media use such as reading posts is more strongly associated with depression than doing active use like making posts [ 23 ]. Other important findings of this review suggest that other factors such as interpersonal trust and family functioning may have a greater influence on the symptoms of depression than the frequency of social media use [ 28 , 29 ].

Limitation and suggestion

The limitations and suggestions were identified by the evidence involved in the study and review process. Previously, 7 of the 16 studies were cross-sectional and slightly failed to determine the causal relationship between the variables of interest. Given the evidence from cross-sectional studies, it is not possible to conclude that the use of social networks causes mental health problems. Only three longitudinal studies examined the causal relationship between social media and mental health, which is hard to examine if the mental health problem appeared more pronounced in those who use social media more compared with those who use it less or do not use at all [ 19 , 20 , 24 ]. Next, despite the fact that the proposed relationship between social media and mental health is complex, a few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are required to clarify the underlying factors that help examine why social media has a negative impact on some peoples’ mental health, whereas it has no or positive effect on others’ mental health.

Conclusions

Social media is a new study that is rapidly growing and gaining popularity. Thus, there are many unexplored and unexpected constructive answers associated with it. Lately, studies have found that using social media platforms can have a detrimental effect on the psychological health of its users. However, the extent to which the use of social media impacts the public is yet to be determined. This systematic review has found that social media envy can affect the level of anxiety and depression in individuals. In addition, other potential causes of anxiety and depression have been identified, which require further exploration.

The importance of such findings is to facilitate further research on social media and mental health. In addition, the information obtained from this study can be helpful not only to medical professionals but also to social science research. The findings of this study suggest that potential causal factors from social media can be considered when cooperating with patients who have been diagnosed with anxiety or depression. Also, if the results from this study were used to explore more relationships with another construct, this could potentially enhance the findings to reduce anxiety and depression rates and prevent suicide rates from occurring.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

September National Health Observances: Healthy Aging, Sickle Cell Disease, and More

Each month, we feature select National Health Observances (NHOs) that align with our priorities for improving health across the nation. In September, we’re raising awareness about healthy aging, sickle cell disease, substance use recovery, and HIV/AIDS. 

Below, you’ll find resources to help you spread the word about these NHOs with your audiences. 

• Healthy Aging Month Each September, we celebrate Healthy Aging Month to promote ways people can stay healthy as they age. Explore our healthy aging resources , bookmark the Healthy People 2030 and Older Adults page , share our Move Your Way® materials for older adults , and check out the Physical Activity Guidelines for Americans Midcourse Report . You can also share resources related to healthy aging from the National Institute on Aging — and register for the 2024 National Healthy Aging Symposium to hear from experts on innovations to improve the health and well-being of older adults.
• National Recovery Month The Substance Abuse and Mental Health Services Administration (SAMHSA) sponsors National Recovery Month to raise awareness about mental health and addiction recovery. Share our MyHealthfinder resources on substance use and misuse — and be sure to check out Healthy People 2030’s evidence-based resources related to drug and alcohol use . 
• National Sickle Cell Awareness Month National Sickle Cell Awareness Month is a time to raise awareness and support people living with sickle cell disease. Help your community learn about sickle cell disease by sharing these resources from the National Heart, Lung, and Blood Institute (NHLBI) . You can also encourage new and expecting parents to learn about screening their newborn baby for sickle cell . And be sure to view our Healthy People 2030 objectives on improving health for people who have blood disorders .
• National HIV/AIDS and Aging Awareness Day (September 18) On September 18, we celebrate HIV/AIDS and Aging Awareness Day to encourage older adults to get tested for HIV. Share CDC’s Let’s Stop HIV Together campaign to help promote HIV testing, prevention, and treatment. MyHealthfinder also has information for consumers about getting tested for HIV and actionable questions for the doctor about HIV testing . Finally, share these evidence-based resources on sexually transmitted infections from Healthy People 2030.
• National Gay Men’s HIV/AIDS Awareness Day (September 27) National Gay Men’s HIV/AIDS Awareness Day on September 27 highlights the impact of HIV on gay and bisexual men and promotes strategies to encourage testing. Get involved by sharing CDC’s social media toolkit and HIV information to encourage men to get tested — and share our MyHealthfinder resources to help people get tested for HIV and talk with their doctor about testing .

We hope you’ll join us in promoting these important NHOs with your networks to help improve health across the nation!

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