social health research paper

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

John a. naslund.

a Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA

Ameya Bondre

b CareNX Innovations, Mumbai, India

John Torous

c Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA

Kelly A. Aschbrenner

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

Social media platforms are popular venues for sharing personal experiences, seeking information, and offering peer-to-peer support among individuals living with mental illness. With significant shortfalls in the availability, quality, and reach of evidence-based mental health services across the United States and globally, social media platforms may afford new opportunities to bridge this gap. However, caution is warranted, as numerous studies highlight risks of social media use for mental health. In this commentary, we 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. Specifically, we summarize current research on the use of social media among mental health service users, and early efforts using social media for the delivery of evidence-based programs. We also review the risks, potential harms, and necessary safety precautions with using social media for mental health. To conclude, we explore opportunities using data science and machine learning, for example by leveraging social media for detecting mental disorders and developing predictive models aimed at characterizing the aetiology and progression of mental disorders. These various efforts using social media, as summarized in this commentary, hold promise for improving the lives of individuals living with mental disorders.

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, Ahmad, Ahmad, & Zakaria, 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, Naslund, Grinley, et al., 2018 ; M. L. Birnbaum, Rizvi, Correll, Kane, & Confino, 2017 ; Brunette et al., 2019 ; Naslund, Aschbrenner, & Bartels, 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, Schwannauer, & Berry, 2019 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016b ).

Across the United States 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 & 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, Druss, Pina, Lally, & Conde, 2016 ; Torous, Chan, et al., 2014 ; Torous, Friedman, & Keshavan, 2014 ). 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, Kalckreuth, Mergl, & Rummel-Kluge, 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, Stewart, Schrimsher, Peeples, & Buckley, 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 to 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, Aschbrenner, & Bartels, 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, Naslund, Grinley, et al., 2018 ). Similar results, with 68% of those with first episode psychosis using social media daily were reported in another study ( Abdel-Baki, Lal, D.-Charron, Stip, & Kara, 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 hours each day ( Gay, Torous, Joseph, Pandya, & Duckworth, 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 hours per day ( M. L. Birnbaum et al., 2017 ). 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.

Summary of potential benefits and challenges with social media for mental health

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 & 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, Wagner, & Baker, 2005 ), such as serious mental disorders ( Highton-Williamson, Priebe, & Giacco, 2015 ).

Studies have found that individuals with serious mental disorders ( Spinzy, Nitzan, Becker, Bloch, & Fennig, 2012 ) as well as young adults with mental illness ( Gowen, Deschaine, Gruttadara, & Markey, 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, Palumbo, Strappelli, Catapano, & Priebe, 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, Townley, Snethen, & Salzer, 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 & 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 & 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, Sibitz, Unger, & Amering, 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, Siegel, & McGuire, 1984 ), with interactions being more fluid, and within the control of users, thereby overcoming possible social anxieties linked to in-person interaction ( Indian & 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, Torgalsbøen, Melle, & Bell, 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, Aschbrenner, 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, Chinman, Sells, & Rowe, 2006 ; Mead, Hilton, & Curtis, 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, Lauber, & Rössler, 2005 ), while users with bipolar disorder reported using these forums to ask for help from others about their illness ( Vayreda & 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, Grande, Aschbrenner, & Elwyn, 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, Bauer, Spiessl, & Kagerbauer, 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, Nguyen, & Theriault, 2018 ), connecting with mental health providers ( M. L. Birnbaum et al., 2017 ), 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, Quraishi, & Schlosser, 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, Naslund, Shevenell, Kinney, & Bartels, 2016 ; Aschbrenner, Naslund, Shevenell, Mueser, & Bartels, 2016 ). The intervention holds tremendous promise as lack of support is one of the largest barriers toward 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, Naslund, & Bartels, 2016 ; Naslund, Aschbrenner, Marsch, & Bartels, 2016a ). To date, this program has demonstrate 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, Aschbrenner, Marsch, McHugo, & Bartels, 2018 ). This lifestyle intervention is currently being evaluated in a randomized controlled trial enrolling young adults with serious mental illness from a variety of real world community mental health services settings ( Aschbrenner, Naslund, Gorin, et al., 2018 ).

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, Naslund, Gorin, et al., 2018 ), 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 well being, 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, Manktelow, & Taylor, 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 & 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 & 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 & 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 & 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, Flannery, & Ohannessian, 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 to respondents using only 2 or fewer platforms, there was a 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 & Campbell, 2018 ), and may contribute to greater loneliness ( Bucci et al., 2019 ), and negative effects on other aspects of health and wellbeing ( Woods & 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 was significantly greater depressive symptoms and increased risk of suicide when compared to adolescents who reported spending more time on non-screen activities, such as in-person social interaction or sports and recreation activities ( Twenge, Joiner, Rogers, & Martin, 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, Tessner, & Walker, 2007 ), while another study of patients with psychosis found that social media use appeared to predict low mood ( Berry, Emsley, Lobban, & Bucci, 2018 ). These studies highlight a clear relationship between social media use and mental health that may not be present in general population studies ( Orben & 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 to 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 United States, where females were twice as likely to be victims of cyberbullying compared to males ( Alhajji, Bass, & Dai, 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, Perren, Sticca, & Alsaker, 2012 ).

For youth ages 10 to 17 who reported major depressive symptomatology, there was over 3 times greater odds of facing online harassment in the last year compared to 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 to respondents without depressive symptoms (31% vs. 14%) ( Rideout & 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 & 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 & 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 & 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 also highlighting that there could also be benefits. For individuals with mental illness who use social media, being aware of the risks is an essential first step, and then highlighting ways that use of these popular platforms could also contribute to some benefits, ranging from 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 United States 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 to male respondents ( Rideout & 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 & Fox, 2018 ). Social media use also appears to have a differential impact on mental health and emotional wellbeing between females and males ( Booker, Kelly, & Sacker, 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 to heterosexual individuals to share their own personal health experiences with others online ( Rideout & 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 more likely to experience online victimization when compared to heterosexual individuals ( Mereish, Sheskier, Hawthorne, & Goldbach, 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 United States ( Tynes, Willis, Stewart, & Hamilton, 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, Hunter, Figueroa, & Aguilera, 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. Though 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 United States, as well as from other higher income settings such as Australia or the United Kingdom. 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, Powers, Hawkins, & Brownstein, 2015 ; Onnela & 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, Hutchinson, & Teague, 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 & De Choudhury, 2017 ; Reece et al., 2017 ). Specifically, conversations on Twitter have been analyzed to characterize the onset of depression ( De Choudhury, Gamon, Counts, & Horvitz, 2013 ) as well as detecting users’ mood and affective states ( De Choudhury, Gamon, & Counts, 2012 ), while photos posted to Instagram can yield insights for predicting depression ( Reece & 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 to a control group, Twitter users with a self-disclosed diagnosis of schizophrenia show unique online communication patterns ( Michael L Birnbaum, Ernala, Rizvi, De Choudhury, & Kane, 2017 ), including more frequent discussion of tobacco use ( Hswen et al., 2017 ), symptoms of depression and anxiety ( Hswen, Naslund, Brownstein, & Hawkins, 2018b ), and suicide ( Hswen, Naslund, Brownstein, & Hawkins, 2018a ). Another study found that online disclosures about mental illness appeared beneficial as reflected by fewer posts about symptoms following self-disclosure (Ernala, Rizvi, Birnbaum, Kane, & De Choudhury, 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, Birnbaum, Caine, Silenzio, & De Choudhury, 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, Yaden, Kern, Ungar, & Eichstaedt, 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, Burnap, & Sloan, 2017 ). Promising approaches for minimizing these risks include supporting the participation of individuals with expertise in privacy, clinicians, as well as 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, Chan, Torous, Luo, & Boland, 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, Aschbrenner, 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 United States 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.

Acknowledgements

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).

Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.

Conflict of Interest

The authors have nothing to disclose.

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REVIEW article

Social media use and mental health and well-being among adolescents – a scoping review.

• 1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway
• 2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway
• 3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.

Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.

Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.

Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

In just a few decades, the use of social media have permeated most areas of our society. For adolescents, social media play a particularly large part in their lives as indicated by their extensive use of several different social media platforms ( Ofcom, 2018 ). Furthermore, the use of social media and types of platforms offered have increased at such a speed that there is reason to believe that scientific knowledge about social media in relation to adolescents’ health and well-being is scattered and incomplete ( Orben, 2020 ). Nevertheless, research findings indicating the potential negative effects of social media on mental health and well-being are frequently reported in traditional media (newspapers, radio, TV) ( Bell et al., 2015 ). Within the scientific community, however, there are ongoing debates regarding the impact and relevance of social media in relation to mental health and well-being. For instance, Twenge and Campbell (2019) stated that use of digital technology and social media have a negative impact on well-being, while Orben and Przybylski (2019) argued that the association between digital technology use and adolescent well-being is so small that it is more or less inconsequential. Research on social media use is a new focus area, and it is therefore important to get an overview of the studies performed to date, and describe the subject matter studies have investigated in relation to the effect of social media use on adolescents mental health and well-being. Also, research gaps in this emerging research field is important to highlight as it may guide future research in new and meritorious directions. A scoping review is therefore deemed necessary to provide a foundation for further research, which in time will provide a knowledge base for policymaking and service delivery.

This scoping review will help provide an overall understanding of the main foci of research within the field of social media and mental health and well-being among adolescents, as well as the type of data sources and research instruments used so far. Furthermore, we aim to highlight potential gaps in the research literature ( Arksey and O’Malley, 2005 ). Even though a large number of studies on social media use and mental health with different vantage points has been conducted over the last decade, we are not aware of any broad-sweeping scoping review covering this area.

This scoping review aims to give an overview of the main research questions that have been focused on with regard to use of social media among adolescents in relation to mental health and well-being. Both quantitative and qualitative studies are of interest. Three specific secondary research questions will be addressed and together with the main research question serve as a template for organizing the results:

• Which aspects of mental health and well-being have been the focus or foci of research so far?

• Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?

• Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

In the present review, adolescents are defined as those between 13 and 19 years of age. We chose the mean age of 13 as our lower limit as nearly all social media services require users to be at least 13 years of age to access and use their services ( Childnet International, 2018 ). All pertinent studies which present results relevant for this age range is within the scope of this review. For social media we used the following definition by Kietzmann et al. (2011 , p. 1): “Social media employ mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.” We also employed the typology described by Kaplan and Haenlein’s classification scheme across two axes: level of self-presentation and social presence/media richness ( Kaplan and Haenlein, 2010 ). The current scoping review adheres to guidelines and recommendations stated by Tricco et al. (2018) .

See protocol for further details about the definitions used ( Schønning et al., 2020 ).

Data Sources and Search Strategy

A literature search was performed in OVID Medline, OVID Embase, OVID PsycINFO, Sociological Abstracts (proquest), Social Services Abstracts (proquest), ERIC (proquest), and CINAHL. The search strategy combined search terms for adolescents, social media and mental health or wellbeing. The database-controlled vocabulary was used for searching subject headings, and a large spectrum of synonyms with appropriate truncations was used for searching title, abstract, and author keywords. A filter for observational studies was applied to limit the results. The search was also limited to publications from 2014 to current. The search strategy was translated between each database. An example of full strategy for Embase is attached as Supplementary Material .

Study Selection: Exclusion and Inclusion Criteria

The exclusion and inclusion criteria are detailed in the protocol ( Schønning et al., 2020 ). Briefly, we included English language peer-reviewed quantitative- or qualitative papers or systematic reviews published within the last 5 years with an explicit focus on mental health/well-being and social media. Non-empirical studies, intervention studies, clinical studies and publications not peer-reviewed were excluded. Intervention studies and clinical studies were excluded as we sought to not introduce too much heterogeneity in design and our focus was on observational studies. The criteria used for study selection was part of an iterative process which was described in detail in the protocol ( Schønning et al., 2020 ). As per the study protocol ( Schønning et al., 2020 ), and in line with scoping review guidelines ( Peters et al., 2015 , 2017 ; Tricco et al., 2018 ), we did not assess methodological quality or risk of bias of the included studies.

The selection process is illustrated by a flow-chart indicating the stages from unsorted search results to the number of included studies (see Figure 1 ). Study selection was accomplished and organized using the Rayyan QCRI software 1 . The inclusion and exclusion process was performed independently by VS and JCS. The interrater agreement was κ = 0.87, indicating satisfactory agreement.

Figure 1. Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

Details of the data extracted is described in the protocol. Three types of information were extracted, bibliographic information, information about study design and subject matter information. Subject matter information included aim of study, how social media and mental health/well-being was measured, and main findings of the study.

Visualization of Words From the Titles of the Included Studies

The most frequently occurring words and bigrams in the titles of the included studies are presented in Figures 2 , 3 . The following procedure was used to generate Figure 1 : First, a text file containing all titles were imported into R as a data frame ( R Core Team, 2014 ). The data frame was processed using the “tidy text”-package with required additional packages ( Silge and Robinson, 2016 ). Second, numbers and commonly used words with little inherent meaning (so called “stop words,” such as “and,” “of,” and “in”), were removed from the data frame using the three available lexicons in the “tidy-text”-package ( Silge and Robinson, 2016 ). Furthermore, variations of “adolescents” (e.g., “adolescent,” “adolescence,” and “adolescents”) and “social media” (e.g., “social media,” “social networking,” “online social networks”) were removed from the data frame. Third, the resulting data frame was sorted based on frequency of unique words, and words occurring only once were removed. The final data frame is presented as a word cloud in Figure 1 ( N = 113). The same procedure as described above was employed to generate commonly occurring bigrams (two words occurring adjacent to each other), but without removing bigrams occurring only once ( N = 231). The word clouds were generated using the “wordcloud2”-package in R ( Lang and Chien, 2018 ). For Figure 1 , shades of blue indicate word frequencies >2 and green a frequency of 2. For Figure 2 , shades of blue indicate bigram frequencies of >1 and green a frequency of 1.

Figure 2. Word cloud from the titles of the included studies. Most frequent words, excluding variations of “adolescence” and “social media.” N = 113. Shades of blue indicate word frequencies >2 and green a frequency of 2. The size of each word is indicative of its relative frequency of occurrence.

Figure 3. Word cloud from the titles of the included studies. Bigrams from the titles of the included studies, excluding variations of “adolescence” and “social media.” N = 231. Shades of blue indicate bigram frequencies of >1 and green a frequency of 1. The size of each bigram is indicative of its relative frequency of occurrence.

Characteristics of the Included Studies

Of 7927 unique studies, 79 (1%) met our inclusion criteria ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 , 2015 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Throuvala et al., 2019 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Among the included studies, 74 (94%) are quantitative ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Fahy et al., 2016 ; Ferguson et al., 2014 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 , 2017 ; Geusens and Beullens, 2017 , 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Houghton et al., 2018 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Koo et al., 2015 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marchant et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Merelle et al., 2017 ; Neira and Barber, 2014 ; Nesi et al., 2017a , b ; Niu et al., 2018 ; Nursalam et al., 2018 ; Oberst et al., 2017 ; O’Connor et al., 2014 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Spears et al., 2015 ; Tiggemann and Slater, 2017 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ), three are qualitative ( O’Reilly et al., 2018 ; Burnette et al., 2017 ; Throuvala et al., 2019 ), and two use mixed methods ( Best et al., 2015 ; Holfeld and Mishna, 2019 ) (see Supplementary Tables 1 , 2 in the Supplementary Material for additional details extracted from all included studies). In relation to study design, 45 (57%) used a cross-sectional design ( Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Koo et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Tiggemann and Slater, 2017 ; Wolke et al., 2017 ; Yan et al., 2017 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Fredrick and Demaray, 2018 ; Geusens and Beullens, 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ; Twenge and Campbell, 2019 ), 17 used a longitudinal design ( Cross et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 ; Kim, 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Booker et al., 2018 ; Houghton et al., 2018 ; van den Eijnden et al., 2018 ; Holfeld and Mishna, 2019 ), seven were systematic reviews ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Fisher et al., 2016 ; Marchant et al., 2017 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ), two were meta-analyses ( Foody et al., 2017 : Curtis et al., 2018 ), one was a causal-comparative study ( Jafarpour et al., 2017 ), one was a review article ( Richards et al., 2015 ), one used a time-lag design ( Twenge et al., 2018 ), one was a scoping review ( Hamm et al., 2015 ), three used a focus-group interview design ( Burnette et al., 2017 ; O’Reilly et al., 2018 ; Throuvala et al., 2019 ), and one study used a combined survey and focus-group design ( Best et al., 2014 ).

The most common study settings were schools [ N = 42 (54%)] ( Best et al., 2014 ; Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Frison and Eggermont, 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Geusens and Beullens, 2017 , 2018 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Przybylski and Bowes, 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; de Lenne et al., 2018 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ). Fourteen of the included studies were based on data from a home setting ( Cross et al., 2015 ; Koo et al., 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Barry et al., 2017 ; Frison and Eggermont, 2017 ; Oberst et al., 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; Marques et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ). Eleven publications were reviews or meta-analyses and included primary studies from different settings ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ). One study used both a home and school setting ( Erreygers et al., 2018 ), and 11 (14%) of the included studies did not mention the study setting for data collection ( Ferguson et al., 2014 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Przybylski and Weinstein, 2017 ; Wolke et al., 2017 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ).

Mental Health Foci of Included Studies

For a visual overview of the mental health foci of the included studies see Figures 2 , 3 . Most studies had a focus on different negative aspects of mental health, as evident from the frequently used terms in Figures 2 , 3 . The most studied aspect was depression, with 23 (29%) studies examining the relationship between social media use and depressive symptoms ( Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Banjanin et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Larm et al., 2017 ; Nesi et al., 2017a ; Salmela-Aro et al., 2017 ; Fredrick and Demaray, 2018 ; Houghton et al., 2018 ; Niu et al., 2018 ; Twenge et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ). Twenty of the included studies focused on different aspects of good mental health, such as well-being, happiness, or quality of life ( Best et al., 2014 , 2015 ; Bourgeois et al., 2014 ; Ferguson et al., 2014 ; Cross et al., 2015 ; Koo et al., 2015 ; Richards et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Foerster and Roosli, 2017 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Lai et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Nineteen studies had a more broad-stroke approach, and covered general mental health or psychiatric problems ( Aboujaoude et al., 2015 ; Hanprathet et al., 2015 ; Hase et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Fisher et al., 2016 ; Barry et al., 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Merelle et al., 2017 ; Oberst et al., 2017 ; Wolke et al., 2017 ; Marengo et al., 2018 ; Marques et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Holfeld and Mishna, 2019 ; Kim et al., 2019 ; Larm et al., 2019 ). Eight studies examined the link between social media use and body dissatisfaction and eating disorder symptoms ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; de Vries et al., 2016 ; Burnette et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Marengo et al., 2018 ; Wartberg et al., 2018 ). Anxiety was the focus of seven studies ( O’Connor et al., 2014 ; Koo et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Colder Carras et al., 2017 ; Yan et al., 2017 ), and 13 studies included a focus on the relationship between alcohol use and social media use ( O’Connor et al., 2014 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Brunborg et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Curtis et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Critchlow et al., 2019 ; Kim et al., 2019 ). Seven studies examined the effect of social media use on sleep ( Harbard et al., 2016 ; Woods and Scott, 2016 ; Yan et al., 2017 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Larm et al., 2019 ). Five studies saw how drug use and social media use affected each other ( O’Connor et al., 2014 ; Merelle et al., 2017 ; Sampasa-Kanyinga et al., 2018 ; Kim et al., 2019 ; Larm et al., 2019 ). Self-harm and suicidal behavior was the focus of eleven studies ( O’Connor et al., 2014 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Kim, 2017 ; Marchant et al., 2017 ; Merelle et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Memon et al., 2018 ; Twenge et al., 2018 ; Kim et al., 2019 ). Other areas of focus other than the aforementioned are loneliness, self-esteem, fear of missing out and other non-pathological measures ( Neira and Barber, 2014 ; Banyai et al., 2017 ; Barry et al., 2017 ; Colder Carras et al., 2017 ).

Social Media Metrics of Included Studies

The studies included in the current scoping review often focus on specific, widely used, social media and social networking services, such as 31 (39%) studies focusing on Facebook ( Bourgeois et al., 2014 ; Meier and Gray, 2014 ; Banjanin et al., 2015 ; Cross et al., 2015 ; Hanprathet et al., 2015 ; Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Banyai et al., 2017 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Larm et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a , b ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ), 11 on Instagram ( Sampasa-Kanyinga and Lewis, 2015 ; Boyle et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Frison and Eggermont, 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), 11 including Twitter ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Spears et al., 2015 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Merelle et al., 2017 ; Nesi et al., 2017a ; Memon et al., 2018 ; Sampasa-Kanyinga et al., 2018 ), and five studies asking about Snapchat ( Boyle et al., 2016 ; Barry et al., 2017 ; Brunborg et al., 2017 ; Nesi et al., 2017a ; Marengo et al., 2018 ). Eight studies mentioned Myspace ( Richards et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; de Vries et al., 2016 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Larm et al., 2017 ; Booker et al., 2018 ; Sampasa-Kanyinga et al., 2018 ) and two asked about Tumblr ( Barry et al., 2017 ; Nesi et al., 2017a ). Other media such as Skype ( Merelle et al., 2017 ), Youtube ( Richards et al., 2015 ), WhatsApp ( Brunborg et al., 2017 ), Ping ( Merelle et al., 2017 ), Bebo ( Booker et al., 2018 ), Hyves ( de Vries et al., 2016 ), Kik ( Brunborg et al., 2017 ), Ask ( Brunborg et al., 2017 ), and Qzone ( Niu et al., 2018 ) were only included in one study each.

Almost half ( n = 34, 43%) of the included studies focus on use of social network sites or online communication in general, without specifying particular social media sites, leaving this up to the study participants to decide ( Best et al., 2014 , 2015 ; Ferguson et al., 2014 ; Neira and Barber, 2014 ; O’Connor et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Fahy et al., 2016 ; Woods and Scott, 2016 ; Burnette et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Foody et al., 2017 ; Geusens and Beullens, 2017 , 2018 ; Jafarpour et al., 2017 ; Kim, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Przybylski and Weinstein, 2017 ; Salmela-Aro et al., 2017 ; Yan et al., 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; Erreygers et al., 2018 ; Nursalam et al., 2018 ; Scott and Woods, 2018 ; van den Eijnden et al., 2018 ; Wartberg et al., 2018 ; Critchlow et al., 2019 ; Holfeld and Mishna, 2019 ; Larm et al., 2019 ; Throuvala et al., 2019 ; Twenge and Campbell, 2019 ). Seven of the included studies examined the relationship between virtual game worlds or socially oriented video games and mental health ( Ferguson et al., 2014 ; Best et al., 2015 ; Spears et al., 2015 ; Yan et al., 2017 ; van den Eijnden et al., 2018 ; Larm et al., 2019 ; Twenge and Campbell, 2019 ).

In the 79 studies included in this scoping review, several approaches to measuring social media use are utilized. The combination of frequency and duration of social media use is by far the most used measurement of social media use, and 44 (56%) of the included studies collected data on these parameters ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Neira and Barber, 2014 ; Banjanin et al., 2015 ; Best et al., 2015 ; Hanprathet et al., 2015 ; Sampasa-Kanyinga and Lewis, 2015 ; Tseng and Yang, 2015 ; Boyle et al., 2016 ; de Vries et al., 2016 ; Frison and Eggermont, 2016 , 2017 ; Harbard et al., 2016 ; Sampasa-Kanyinga and Chaput, 2016 ; Woods and Scott, 2016 ; Banyai et al., 2017 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Foerster and Roosli, 2017 ; Jafarpour et al., 2017 ; Kim, 2017 ; Larm et al., 2017 , 2019 ; Merelle et al., 2017 ; Nesi et al., 2017b ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Tiggemann and Slater, 2017 ; Yan et al., 2017 ; Booker et al., 2018 ; de Lenne et al., 2018 ; Erreygers et al., 2018 ; Houghton et al., 2018 ; Lai et al., 2018 ; Marengo et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Settanni et al., 2018 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Twenge and Campbell, 2019 ). Eight studies focused on the relationship between social media addiction or excessive use and mental health ( Banjanin et al., 2015 ; Tseng and Yang, 2015 ; Banyai et al., 2017 ; Merelle et al., 2017 ; Nursalam et al., 2018 ; Settanni et al., 2018 ; Wang et al., 2018 ). Bergen Social Media Addiction Scale is a commonly used questionnaire amongst the included studies ( Hanprathet et al., 2015 ; Banyai et al., 2017 ; Settanni et al., 2018 ). Seven studies asked about various specific actions on social media, such as liking or commenting on photos, posting something or participating in a discussion ( Meier and Gray, 2014 ; Koo et al., 2015 ; Nesi et al., 2017b ; Geusens and Beullens, 2018 ; Marques et al., 2018 ; van den Eijnden et al., 2018 ; Critchlow et al., 2019 ).

Five studies had a specific and sole focus on the link between social media use and alcohol, and examined how various alcohol-related social media use affected alcohol intake ( Boyle et al., 2016 ; Geusens and Beullens, 2017 , 2018 ; Nesi et al., 2017b ; Critchlow et al., 2019 ). Some studies had a more theory-based focus and investigated themes such as peer comparison, social media intrusion or pro-social behavior on social media and its effect on mental health ( Bourgeois et al., 2014 ; Rousseau et al., 2017 ; de Lenne et al., 2018 ). One of the included studies looked into night-time specific social media use ( Scott and Woods, 2018 ) and one looked into pre-bedtime social media behavior ( Harbard et al., 2016 ) to study the link between this use and sleep.

Amongst the 79 included studies, only six (8%) studies had participants of one gender ( Ferguson et al., 2014 ; Meier and Gray, 2014 ; Best et al., 2015 ; Burnette et al., 2017 ; Jafarpour et al., 2017 ; Tiggemann and Slater, 2017 ). Sixteen studies (20%) did not mention the gender distribution of the participants ( Aboujaoude et al., 2015 ; Best et al., 2015 ; Hamm et al., 2015 ; Richards et al., 2015 ; Fisher et al., 2016 ; Woods and Scott, 2016 ; Foody et al., 2017 ; Marchant et al., 2017 ; Przybylski and Weinstein, 2017 ; Curtis et al., 2018 ; Erfani and Abedin, 2018 ; John et al., 2018 ; Memon et al., 2018 ; O’Reilly et al., 2018 ; Twenge et al., 2018 ; Twenge and Campbell, 2019 ). Several of these were meta-analyses or reviews ( Aboujaoude et al., 2015 ; Best et al., 2014 ; Curtis et al., 2018 ; Foody et al., 2017 ; John et al., 2018 ; Erfani and Abedin, 2018 ; Wallaroo, 2020 ). The studies that included both genders as participants generally had a well-balanced gender distribution with no gender below 40% of the participants. Eight of the studies did not report gender-specific results ( Harbard et al., 2016 ; Nesi et al., 2017b ; Curtis et al., 2018 ; de Lenne et al., 2018 ; Niu et al., 2018 ; Nursalam et al., 2018 ; Wang et al., 2018 ; Twenge and Campbell, 2019 ). Of the included studies, gender was seldom examined as an explanatory variable, and other sociodemographic variables (e.g., ethnicity, socioeconomic status) were not included at all.

Implicit Causation Based on Direction of Association

Sixty-one (77%) of the included studies has social media use as the independent variable and some of the mentioned measurements of mental health as the dependent variable ( Aboujaoude et al., 2015 ; Banjanin et al., 2015 ; Banyai et al., 2017 ; Barry et al., 2017 ; Best et al., 2014 ; Booker et al., 2018 ; Bourgeois et al., 2014 ; Boyle et al., 2016 ; Brunborg et al., 2017 ; Colder Carras et al., 2017 ; Critchlow et al., 2019 ; Cross et al., 2015 ; Curtis et al., 2018 ; de Lenne et al., 2018 ; de Vries et al., 2016 ; Erfani and Abedin, 2018 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foerster and Roosli, 2017 ; Fredrick and Demaray, 2018 ; Frison and Eggermont, 2016 ; Geusens and Beullens, 2018 ; Hamm et al., 2015 ; Hanprathet et al., 2015 ; Harbard et al., 2016 ; Hase et al., 2015 ; Holfeld and Mishna, 2019 ; Jafarpour et al., 2017 ; John et al., 2018 ; Kim et al., 2019 ; Kim, 2017 ; Lai et al., 2018 ; Larm et al., 2017 , 2019 ; Marengo et al., 2018 ; Marques et al., 2018 ; Meier and Gray, 2014 ; Memon et al., 2018 ; Neira and Barber, 2014 ; Nesi et al., 2017b ; Niu et al., 2018 ; Nursalam et al., 2018 ; O’Connor et al., 2014 ; O’Reilly et al., 2018 ; Przybylski and Bowes, 2017 ; Przybylski and Weinstein, 2017 ; Richards et al., 2015 ; Sampasa-Kanyinga and Chaput, 2016 ; Sampasa-Kanyinga and Lewis, 2015 ; Sampasa-Kanyinga et al., 2018 ; Scott and Woods, 2018 ; Spears et al., 2015 ; Tseng and Yang, 2015 ; Twenge and Campbell, 2019 ; Twenge et al., 2018 ; van den Eijnden et al., 2018 ; Wang et al., 2018 ; Wartberg et al., 2018 ; Wolke et al., 2017 ; Woods and Scott, 2016 ; Yan et al., 2017 ). Most of the included studies hypothesize social media use pattern will affect youth mental health in certain ways. The majority of the included studies tend to find a correlation between more frequent social media use and poor well-being and/or mental health (see Supplementary Table 2 ). The strength of this correlation is however heterogeneous as social media use is measured substantially different across studies. Four (5%) of the included studies focus explicitly on how mental health can affect social media use ( Merelle et al., 2017 ; Nesi et al., 2017a ; Erreygers et al., 2018 ; Settanni et al., 2018 ). Fourteen studies included a mediating factor or focus on reciprocal relationships between social media use and mental health ( Ferguson et al., 2014 ; Koo et al., 2015 ; Tseng and Yang, 2015 ; Frison and Eggermont, 2017 ; Geusens and Beullens, 2017 ; Marchant et al., 2017 ; Oberst et al., 2017 ; Rousseau et al., 2017 ; Salmela-Aro et al., 2017 ; Tiggemann and Slater, 2017 ; Houghton et al., 2018 ; Marques et al., 2018 ; Niu et al., 2018 ; Wang et al., 2018 ). An example is a cross-sectional study by Ferguson et al. (2014) suggesting that exposure to social media contribute to later peer competition which was found to be a predictor of negative mental health outcomes such as eating disorder symptoms.

Cyberbullying as a Nexus

Thirteen of the 79 (17%) included studies investigated cyberbullying as the measurement of social media use ( Aboujaoude et al., 2015 ; Cross et al., 2015 ; Hamm et al., 2015 ; Hase et al., 2015 ; Spears et al., 2015 ; Fahy et al., 2016 ; Fisher et al., 2016 ; Foody et al., 2017 ; Przybylski and Bowes, 2017 ; Wolke et al., 2017 ; Fredrick and Demaray, 2018 ; John et al., 2018 ; Holfeld and Mishna, 2019 ). Most of the systematic reviews and meta-analyses included focused on cyberbullying. A cross-sectional study from 2017 suggests that cyberbullying has similar negative effects as direct or relational bullying, and that cyberbullying is “mainly a new tool to harm victims already bullied by traditional means” ( Wolke et al., 2017 ). A meta-analysis from 2016 concludes that “peer cybervictimization is indeed associated with a variety of internalizing and externalizing problems among adolescents” ( Fisher et al., 2016 ). A systematic review from 2018 concludes that both victims and perpetrators of cyberbullying are at greater risk of suicidal behavior compared with non-victims and non-perpetrators ( John et al., 2018 ).

Strengths and Limitations of Present Study

The main strength of this scoping review lies in the effort to give a broad overview of published research related to use of social media, and mental health and well-being among adolescents. Although a range of reviews on screen-based activities in general and mental health and well-being exist ( Dickson et al., 2018 ; Orben, 2020 ), they do not necessarily discern between social media use and other types of technology-based media. Also, some previous reviews tend to be more particular regarding mental health outcome ( Best et al., 2014 ; Seabrook et al., 2016 ; Orben, 2020 ), or do not focus on adolescents per se ( Seabrook et al., 2016 ). The main limitation is that, despite efforts to make the search strategy as comprehensive and inclusive as possible, we probably have not been able to identify all relevant studies – this is perhaps especially true when studies do include relevant information about social media and mental health/well-being, but this information is part of sub-group analyses or otherwise not the main aim of the studies. In a similar manner, related to qualitative studies, we do not know if our search strategy were as efficient in identifying studies of relevance if this was not the main theme or focus of the study. Despite this, we believe that we were able to strike a balance between specificity and sensitivity in our search strategy.

Description of Central Themes and Core Concepts

The findings from the present scoping review on social media use and mental health and well-being among adolescents revealed that the majority (about 3/4) of the included studies focused on social media and pathology. The core concepts identified are social media use and its statistical association with symptoms of depression, general psychiatric symptoms and other symptoms of psychopathology. Similar findings were made by Keles et al. (2020) in a systematic review from 2019. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature, even though some studies focused on well-being which also includes positive aspects of mental health. Studies focusing on screen-based media in general and well-being is more prevalent than studies linking social media specifically with well-being ( Orben, 2020 ). The notion that excessive social media use is associated with poor mental health is well established within mainstream media. Our observation that this preconception seems to be the starting point for much research is not conducive to increased knowledge, but also alluded to elsewhere ( Coyne et al., 2020 ).

Why the Focus on Poor Mental Health/Pathology?

The relationship between social media and mental health is likely to be complex, and social media use can be beneficial for maintaining friendships and enriching social life ( Seabrook et al., 2016 ; Birkjær and Kaats, 2019 ; Coyne et al., 2020 ; Orben, 2020 ). This scoping review reveals that the majority of studies focusing on effects of social media use has a clearly stated focus on pathology and detrimental results of social media use. Mainstream media and the public discourse has contributed in creating a culture of fear around social media, with a focus on its negative elements ( Ahn, 2012 ; O’Reilly et al., 2018 ). It is difficult to pin-point why the one-sided focus on the negative effects of social media has been established within the research literature. But likely reasons are elements of “moral panic,” and reports of increases in mental health problems among adolescents in the same period that social media were introduced and became wide-spread ( Birkjær and Kaats, 2019 ). The phenomenon of moral panic typically resurges with the introduction and increasing use of new technologies, as happened with video games, TV, and radio ( Mueller, 2019 ).

The Metrics of Social Media

Social media trends change rapidly, and it is challenging for the research field to keep up. The included studies covered some of the most frequently used social media, but the amount of studies focusing on each social media did not accurately reflect the contemporary distribution of users. Even though sites such as Instagram and Snapchat were covered in some studies, the coverage did not do justice to the amount of users these sites had. Newer social media sites such as TikTok were not mentioned in the included studies even though it has several hundred million daily users ( Mediakix, 2019 ; Wallaroo, 2020 ).

Across the included studies there was some variation in how social media were gauged, but the majority of studies focused on the mere frequency and duration of use. There were little focus on separating between different forms of (inter)actions on social media, as these can vary between being a victim of cyberbullying to participating in healthy community work. Also, few studies differentiated between types of actions (i.e., posting, scrolling, reading), active and passive modes of social media use (i.e., production versus consumption, and level of interactivity), a finding similar to other reports ( Seabrook et al., 2016 ; Verduyn et al., 2017 ; Orben, 2020 ). There is reason to believe that different modes of use on social media platforms are differentially associated with mental health, and a recent narrative review highlight the need to address this in future research ( Orben, 2020 ). One of the included studies found for instance that it is not the total time spent on Facebook or the internet, but the specific amount of time allocated to photo-related activities that is associated with greater symptoms of eating disorders such as thin ideal internalization, self-objectification, weight dissatisfaction, and drive for thinness ( Meier and Gray, 2014 ). This observation can possibly be explained by social comparison mechanisms ( Appel et al., 2016 ) and passive use of social media ( Verduyn et al., 2017 ). The lack of research differentiating social media use and its association with mental health is an important finding of this scoping review and will hopefully contribute to this being included in future studies.

Few studies examined the motivation behind choosing to use social media, or the mental health status of the users when beginning a social media session. It has been reported that young people sometimes choose to enter sites such as Facebook and Twitter as an escape from threats to their mental health such as experiencing overwhelming pressure in daily life ( Boyd, 2014 ). This kind of escapism can be explained through uses and gratifications theory [see for instance ( Coyne et al., 2020 )]. On the other hand, more recent research suggest that additional motivational factors may include the need to control relationships, content, presentation, and impressions ( Throuvala et al., 2019 ), and it is possible that social media use can act as an reinforcement of adolescents’ current moods and motivations ( Birkjær and Kaats, 2019 ). Regardless, it seems obvious that the interplay between online and offline use and underlying motivational mechanisms needs to be better understood.

There has also been some questions about the accuracy when it comes to deciding the amount and frequency of one’s personal social media use. Without measuring duration and frequency of use directly and objectively it is unlikely that subjective self-report of general use is reliable ( Kobayashi and Boase, 2012 ; Scharkow, 2016 , 2019 ; Naab et al., 2019 ). Especially since the potential for social media use is almost omnipresent and the use itself is diverse in nature. Also, due to processes such as social desirability, it is likely that some participants report lower amounts of social media use as excessive use is seen largely undesirable ( Krumpal, 2013 ). Inaccurate reporting of prior social media use could also be a threat to the validity of the reported numbers and thus bias the results reported. Real-time tracking of actual use and modes of use is therefore recommended in future studies to ensure higher accuracy of these aspects of social media use ( Coyne et al., 2020 ; Orben, 2020 ), despite obvious legal and ethical challenges. Another aspect of social media use which does not seem to be addressed is potential spill-over effects, where use of social media leads to potential interest in or thinking about use of – and events or contents on – social media when the individual is offline. When this aspect has been addressed, it seems to be in relation to preoccupations and with a focus on excessive use or addictive behaviors ( Griffiths et al., 2014 ). Conversely, given the ubiquitous and important role of social media, experiences on social media – for better or for worse – are likely to be interconnected with the rest of an individual’s lived experience ( Birkjær and Kaats, 2019 ).

The Studies Seem to Implicitly Think That the Use of Social Media “Causes”/“Affects” Mental Health (Problems)

Most of the included studies establish an implicit causation between social media and mental health. It is assumed that social media use has an impact on mental health. The majority of studies included establish some correlation between more frequent use of social media and poor well-being/mental health, as evident from Supplementary Table 2 . As formerly mentioned, most of the included studies are cross-sectional and cannot shed light into temporality or cause-and-effect. In total, only 16 studies had a longitudinal design, using different types of regression models, latent growth curve models and cross-lagged models. Yet there seems to be an unspoken expectation that the direction of the association is social media use affecting mental health. The reason for this supposition is unclear, but again it is likely that the mainstream media discourse dominated by mostly negative stories and reports of social media use has some impact together with the observed moral panic.

With the increased popularity of social media and internet arrived a reduction of face-to-face contact and supposed increased social isolation ( Kraut et al., 1998 ; Espinoza and Juvonen, 2011 ). This view is described as the displacement hypothesis [see for instance ( Coyne et al., 2020 )]. Having a thriving social life and community with meaningful relations are for many considered vital for well-being and good mental health, and the supposed reduction of sociality were undoubtedly met with skepticism by some. Social media use has increased rapidly among young people over the last two decades along with reports that mental health problems are increasing. Several studies report that there is a rising prevalence of symptom of anxiety and depression among our adolescents ( Bor et al., 2014 ; Olfson et al., 2015 ). The observation that increases in social media use and mental health issues happened in more or less the same time period can have contributed to focus on how use of social media affects mental health problems.

The existence of an implicit causation is supported by the study variables chosen and the lack of positively worded outcomes. Depression, anxiety, alcohol use, psychiatric problems, suicidal behavior and eating disorders are amongst the most studied outcome-variables. On the other side of the spectrum we have well-being, which can oscillate from positive to negative, whilst the measures of pathology only vary from “ill” to “not ill” with positive outcomes not possible.

What Is the Gap in the Literature?

The current literature on social media and mental health among youth is still developing and has several gaps and shortcomings, as evident from this scoping review and other publications ( Seabrook et al., 2016 ; Coyne et al., 2020 ; Keles et al., 2020 ; Orben, 2020 ). Some of the gaps and shortcomings in the field we propose solutions for has been identified in a systematic review from 2019 by Keles et al. (2020) . The majority of the included studies in the current scoping review were cross-sectional, were limited in their inclusion of potential confounders and 3rd variables such as sociodemographics and personality, preventing knowledge about possible cause-and-effect between social media and mental health. There is a lack of longitudinal studies examining the effects of social media over extended periods of time, as well as investigations longitudinally of how mental health impacts social media use. However, since the formal search was ended for this scoping review, some innovative studies have emerged using longitudinal data ( Brunborg and Andreas, 2019 ; Orben et al., 2019 ; Coyne et al., 2020 ). More high quality longitudinal studies of social media use and mental health could help us identify the patterns over time and help us learn about possible cause-and-effect relationships, as well as disentangling between- and within-person associations ( Coyne et al., 2020 ; Orben, 2020 ). Furthermore, both social media use and mental health are complex phenomena in themselves, and future studies need to consider which aspects they want to investigate when trying to understand their relationship. Mechanisms linking social media use and eating disorders are for instance likely to be different than mechanisms linking social media use and symptoms of ADHD.

Our literature search also revealed a paucity of qualitative studies exploring the why’s and how’s of social media use in relation to mental health among adolescents. Few studies examine how youth themselves experience and perceive the relationship between social media and mental health, and the reasons for their continued and frequent use. Qualitatively oriented studies would contribute to a deeper understanding of adolescent’s social media sphere, and their thoughts about the relationship between social media use and mental health [see for instance ( Burnette et al., 2017 )]. For instance, O’Reilly et al. (2018) found that adolescents viewed social media as a threat to mental well-being, and concluded that they buy into the idea that “inherently social media has negative effects on mental wellbeing” and seem to “reify the moral panic that has become endemic to contemporary discourses.” On the other hand, Weinstein found using both quantitative and qualitative data that adolescents’ perceptions of the relationship between social media use and well-being probably is more nuanced, and mostly positive. Another clear gap in the research literature is the lack of focus on potentially positive aspects of social media use. It is obvious that there are some positive sides of the use of social media, and these also need to be investigated further ( Weinstein, 2018 ; Birkjær and Kaats, 2019 ). Gender-specific analyses are also lacking in the research literature, and there is reason to believe that social media use have different characteristics between the genders with different relationships to mental health. In fact, recent findings indicate that not only gender should be considered an important factor when investigating the role of social media in adolescents’ lives, but individual characteristics in general ( Orben et al., 2019 ; Orben, 2020 ). Analyses of socioeconomic status and geographic location are also lacking and it is likely that these factors might play a role the potential association between social media use and mental health. And finally, several studies point to the fact that social media potentially could be a fruitful arena for promoting mental well-being among youth, and developing mental health literacy to better equip our adolescents for the challenges that will surely arise ( O’Reilly et al., 2018 ; Teesson et al., 2020 ).

Research into the association between social media use and mental health and well-being among adolescents is rapidly emerging. The field is characterized by a focus on the association between social media use and negative aspects of mental health and well-being, and where studies focusing on the potentially positive aspects of social media use are lacking. Presently, the majority of studies in the field are quantitatively oriented, with most utilizing a cross-sectional design. An increase in qualitatively oriented studies would add to the field of research by increasing the understanding of adolescents’ social-media life and their own experiences of its association with mental health and well-being. More studies using a longitudinal design would contribute to examining the effects of social media over extended periods of time and help us learn about possible cause-and-effect relationships. Few studies look into individual factors, which may be important for our understanding of the association. Social media use and mental health and well-being are complex phenomena, and future studies could benefit from specifying the type of social media use they focus on when trying to understand its link to mental health. In conclusion, studies including more specific aspects of social media, individual differences and potential intermediate variables, and more studies using a longitudinal design are needed as the research field matures.

Author Contributions

JS conceptualized the review approach and provided general guidance to the research team. VS and JS drafted the first version of this manuscript. JS, GH, and LA developed the draft further based on feedback from the author group. All authors reviewed and approved the final version of the manuscript and have made substantive intellectual contributions to the development of this manuscript.

This review was partly funded by Regional Research Funds in Norway, funding #RFF297031. No other specific funding was received for the present project. The present project is associated with a larger innovation-project lead by Bergen municipality in Western Norway related to the use of social media and mental health and well-being. The innovation-project is funded by a program initiated by the Norwegian Directorate of Health, and in Vestland county coordinated by the County Council (County Authority). The project aims to explore social media as platform for health-promotion among adolescents.

Conflict of Interest

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

Acknowledgments

We would like to thank Bergen municipality, Hordaland County Council and Western Norway University of Applied Sciences for their collaboration and help with the review. We would also like to thank Senior Librarian Marita Heinz at the Norwegian Institute for Public Health for vital help conducting the literature search.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01949/full#supplementary-material

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Keywords : scoping review, social media, mental health, adolescence, well-being

Citation: Schønning V, Hjetland GJ, Aarø LE and Skogen JC (2020) Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review. Front. Psychol. 11:1949. doi: 10.3389/fpsyg.2020.01949

Received: 11 March 2020; Accepted: 14 July 2020; Published: 14 August 2020.

Reviewed by:

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*Correspondence: Viktor Schønning, [email protected]

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• Published: 06 July 2023

Pros & cons: impacts of social media on mental health

• Ágnes Zsila 1 , 2 &
• Marc Eric S. Reyes   ORCID: orcid.org/0000-0002-5280-1315 3  

BMC Psychology volume  11 , Article number:  201 ( 2023 ) Cite this article

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The use of social media significantly impacts mental health. It can enhance connection, increase self-esteem, and improve a sense of belonging. But it can also lead to tremendous stress, pressure to compare oneself to others, and increased sadness and isolation. Mindful use is essential to social media consumption.

Social media has become integral to our daily routines: we interact with family members and friends, accept invitations to public events, and join online communities to meet people who share similar preferences using these platforms. Social media has opened a new avenue for social experiences since the early 2000s, extending the possibilities for communication. According to recent research [ 1 ], people spend 2.3 h daily on social media. YouTube, TikTok, Instagram, and Snapchat have become increasingly popular among youth in 2022, and one-third think they spend too much time on these platforms [ 2 ]. The considerable time people spend on social media worldwide has directed researchers’ attention toward the potential benefits and risks. Research shows excessive use is mainly associated with lower psychological well-being [ 3 ]. However, findings also suggest that the quality rather than the quantity of social media use can determine whether the experience will enhance or deteriorate the user’s mental health [ 4 ]. In this collection, we will explore the impact of social media use on mental health by providing comprehensive research perspectives on positive and negative effects.

Social media can provide opportunities to enhance the mental health of users by facilitating social connections and peer support [ 5 ]. Indeed, online communities can provide a space for discussions regarding health conditions, adverse life events, or everyday challenges, which may decrease the sense of stigmatization and increase belongingness and perceived emotional support. Mutual friendships, rewarding social interactions, and humor on social media also reduced stress during the COVID-19 pandemic [ 4 ].

On the other hand, several studies have pointed out the potentially detrimental effects of social media use on mental health. Concerns have been raised that social media may lead to body image dissatisfaction [ 6 ], increase the risk of addiction and cyberbullying involvement [ 5 ], contribute to phubbing behaviors [ 7 ], and negatively affects mood [ 8 ]. Excessive use has increased loneliness, fear of missing out, and decreased subjective well-being and life satisfaction [ 8 ]. Users at risk of social media addiction often report depressive symptoms and lower self-esteem [ 9 ].

Overall, findings regarding the impact of social media on mental health pointed out some essential resources for psychological well-being through rewarding online social interactions. However, there is a need to raise awareness about the possible risks associated with excessive use, which can negatively affect mental health and everyday functioning [ 9 ]. There is neither a negative nor positive consensus regarding the effects of social media on people. However, by teaching people social media literacy, we can maximize their chances of having balanced, safe, and meaningful experiences on these platforms [ 10 ].

We encourage researchers to submit their research articles and contribute to a more differentiated overview of the impact of social media on mental health. BMC Psychology welcomes submissions to its new collection, which promises to present the latest findings in the emerging field of social media research. We seek research papers using qualitative and quantitative methods, focusing on social media users’ positive and negative aspects. We believe this collection will provide a more comprehensive picture of social media’s positive and negative effects on users’ mental health.

Data Availability

Not applicable.

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Ágnes Zsila was supported by the ÚNKP-22-4 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund.

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Zsila, Á., Reyes, M.E.S. Pros & cons: impacts of social media on mental health. BMC Psychol 11 , 201 (2023). https://doi.org/10.1186/s40359-023-01243-x

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Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic

• Abouzar Nazari   ORCID: orcid.org/0000-0003-2155-5438 1 ,
• Maede Hosseinnia   ORCID: orcid.org/0000-0002-2248-7011 2 ,
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Social media causes increased use and problems due to their attractions. Hence, it can affect mental health, especially in students. The present study was conducted with the aim of determining the relationship between the use of social media and the mental health of students.

Materials and methods

The current cross-sectional study was conducted in 2021 on 781 university students in Lorestan province, who were selected by the Convenience Sampling method. The data was collected using a questionnaire on demographic characteristics, social media, problematic use of social media, and mental health (DASS-21). Data were analyzed in SPSS-26 software.

Shows that marital status, major, and household income are significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Also, problematic use of social media (β = 3.54, 95% CI: (3.23, 3.85)) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). Income and social media use (β = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Major was significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status).

This study indicated that social media had a direct relationship with mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects.

Peer Review reports

• Social media

Social media is one of the newest and most popular internet services, which has caused significant progress in the social systems of different countries in recent years [ 1 , 2 ]. The use of the Internet has become popular among people in such a way that its use has become inevitable and has made life difficult for those who use it excessively [ 3 ]. Social media has attracted the attention of millions of users around the world owing to the possibility of fast communication, access to a large amount of information, and its widespread dissemination [ 4 ]. Facebook, WhatsApp, Instagram, and Twitter are the most popular media that have attractive and diverse spaces for online communication among users, especially the young generation [ 5 , 6 ].

According to studies, at least 55% of the world’s population used social media in 2022 [ 7 ]. Iranian statistics also indicate that 78.5% of people use at least one social media. WhatsApp, with 71.1% of users, Instagram, with 49.4%, and Telegram, with 31.6% are the most popular social media among Iranians [ 8 , 9 ].

The use of social media has increased significantly in all age groups due to the origin of the COVID-19 pandemic [ 10 ] .It affected younger people, especially students, due to educational and other purposes [ 11 , 12 ]. Because of the sudden onset of the COVID-19 pandemic, educational institutions and learners had to accept e-learning as the only sustainable education option [ 13 ]. The rapid migration to E-learning has brought several challenges that can have both positive and negative consequences [ 14 ].

Unlike traditional media, where users are passive, social media enables people to create and share content; hence, they have become popular tools for social interaction [ 15 ].The freedom to choose to participate in the company of friends, anonymity, moderation, encouragement, the free exchange of feelings, and network interactions without physical presence and the constraints of the real world are some of the most significant factors that influence users’ continued activity in social media [ 16 ]. In social media, people can interact, maintain relationships, make new friends, and find out more about the people they know offline [ 17 ]. However, this popularity has resulted in significant lifestyle changes, as well as intentional or unintentional changes in various aspects of human social life [ 18 ]. Despite many advantages, the high use of social media brings negative physical, psychological, and social problems and consequences [ 19 ], but despite the use and access of more people to the Internet, its consequences and crises have been ignored [ 20 ].

Use of social media and mental health

Spending too much time on social media can easily become problematic [ 21 ]. Excessive use of social media, called problematic use, has symptoms similar to addiction [ 22 , 23 ]. Problematic use of social media represents a non-drug-related disorder in which harmful effects emerge due to preoccupation and compulsion to over-participate in social media platforms despite its highly negative consequences [ 24 , 25 , 26 ], which leads to adverse consequences of mental health, including anxiety, depression, lower well-being, and lower self-esteem [ 27 , 28 , 29 ].

Mental health & use of social media

Mental health is the main pillar of healthy human societies, which plays a vital role in ensuring the dynamism and efficiency of any society in such a way that other parts of health cannot be achieved without mental health [ 30 ]. According to World Health Organization’s (WHO) definition, mental health refers to a person’s ability to communicate with others [ 31 ]. Some researchers believe that social relationships can significantly affect mental health and improve quality of life by creating a sense of belonging and social identity [ 32 ]. It is also reported that people with higher social interactions have higher physical and mental health [ 33 ].

Scientific evidence also shows that social media affect people’s mental health [ 34 ]. Social studies and critiques often emphasize the investigation of the negative effects of Internet use [ 35 ]. For example, Kim et al. studied 1573 participants aged 18–64 years and reported that Internet addiction and social media use were associated with higher levels of depression and suicidal thoughts [ 36 ]. Zadar also studied adults and reported that excessive use of social media and the Internet was correlated with stress, sleep disturbances, and personality disorders [ 37 ]. Richards et al. reported the negative effects of the Internet and social media on the health and quality of life of adolescents [ 38 ]. There have been numerous studies that examine Internet addiction and its associated problems in young people [ 39 , 40 ], as well as reports of the effects of social media use on young people’s mental health [ 41 , 42 ].

A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. But no study has investigated the effects of social media on the mental health of students from a more traditional province with lower individualism and higher levels of social support (where they were thought to have lower social media use and better mental health) during the COVID-19 pandemic. As social media became more and more vital to university students’ social lives during the lockdowns, students were likely at increased risk of social media addiction, which could harm their mental health. University students depended more on social media due to the limitations of face-to-face interactions. In addition, previous studies were conducted exclusively on students in specific fields. However, in our study, all fields, including medical and non-medical science fields were investigated.

The present study was conducted to determine the relationship between the use of social media and mental health in students in Lorestan Province during the COVID-19 pandemic.

Study design and participants

The current study was descriptive-analytical, cross-sectional, and conducted from February to March 2022 with a statistical population made up of students in all academic grades at universities in Lorestan Province (19 scientific and academic centers, including centers under the supervision of the Ministry of Health and the Ministry of Science).

Sample size

According to the convenience sampling method, 781 people were chosen as participants in the present study. During the sampling, a questionnaire was created and uploaded virtually on Porsline’s website, and then the questionnaire link was shared in educational and academic groups on social media for students to complete the questionnaire under inclusion criteria (being a student at the University of Lorestan and consenting to participate in the study).

The research tools included the demographic information questionnaire, the standard social media use questionnaire, and the mental health questionnaire.

Demographic information

The demographic information age, gender, ethnicity, province of residence, urban or rural, place of residence, semester, and the field of study, marital status, household income, education level, and employment status were recorded.

Psychological assessment

The students were subjected to the Persian version of the Depression Anxiety Stress Scale (DASS21). It consists of three self-report scales designed to measure different emotional states. DASS21 questions were adjusted according to their importance and the culture of Iranian students. The DASS21 scale was scored on a four-point scale to assess the extent to which participants experienced each condition over the past few weeks. The scoring method was such that each question was scored from 0 (never) to 3 (very high). Samani (2008) found that the questionnaire has a validity of 0.77 and a Cronbach’s alpha of 0.82 [ 43 ].

Use of social media questionnaire

Among the 13 questions on social media use in the questionnaire, seven were asked on a Likert scale (never, sometimes, often, almost, and always) that examined the problematic use of social media, and six were asked about how much time users spend on social media. Because some items were related to the type of social media platform, which is not available today, and users now use newer social media platforms such as WhatsApp and Instagram, the questionnaires were modified by experts and fundamentally changed, and a 22-item questionnaire was obtained that covered the frequency of using social media. Cronbach’s alpha was equal to 0.705 for the first part, 0.794 for the second part, and 0.830 for all questions [ 44 ]. Considering the importance of the problematic use of the social media, six questions about the problematic use were measured separately.

To confirm the validity of the questionnaire, a panel of experts with CVR 0.49 and CVI 0.70 was used. Its reliability was also obtained (0.784) using Cronbach’s alpha coefficient. Finally, the questionnaire was tested in a class with 30 students to check the level of difficulty and comprehension of the questionnaire. Finally, a 22-item questionnaire was obtained, of which six items were about the problematic use of social media and the remaining 16 questions were about the rate and frequency of using social media. Cronbach’s alpha was 0.705 for the first part, including questions about the problematic use of the social media, and 0.794 for the second part, including questions about the rate and frequency of using the social media. The total Cronbach’s alpha for all questions was 0.830. Six questions about the problematic use of social media were measured separately due to the importance of the problematic use of social media. Also, a separate score was considered for each question. The scores of these six questions on the problematic use of the social media were summed, and a single score was obtained for analysis.

Statistical analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 26.0 (SPSS Inc., Chicago, IL, USA). The normal distribution of continuous variables was analyzed using the Kolmogorov-Smirnov test, histogram, and P-P diagram, which showed that they are not normally distributed. Descriptive statistics were calculated for all variables. Comparison between groups was done using Mann-Whitney and Kruskal-Wallis non-parametric tests. Multiple linear regression analysis was used to investigate the relationship between mental health, problematic use of social media, and social media use (The result of merging the Frequency of using social media and Time to use social media). Generalized Linear Models (GLM) were used to assess the association between mental health with the use of social media and problematic use of social media. Due to the high correlation (r = 0.585, p = < 0.001) between the use of social media and problematic use of social media, collinearity, we run two separate GLM models. Regression coefficients (β) and adjusted β (β*) with 95% CI and P-value were reported.

A total of 781 participants completed the questionnaires, of which 64.4% were women and 71.3% were single. The minimum age of the participants was 17 years, the maximum age was 45 years, and about half of them (48.9%) were between 21 and 25 years old. A total of 53.4% of the participants had bachelor’s degrees. The income level of 23.2% of participants was less than five million Tomans (the currency of Iran), and 69.7% of the participants were unemployed. 88.1% were living with their families and 70.8% were studying in non-medical fields. 86% of the participants lived in the city, and 58.9% were in their fourth semester or higher. Considering that the research was conducted in a Lorish Province, 43.8% of participants were from the Lorish ethnicity.

The mean total score of mental health was 12.30 with a standard deviation of 30.38, and the mean total score of social media was 14.5557 with a standard deviation of 7.74140.

Table  1 presents a comparison of the mean problematic use of social media and mental health with demographic variables. Considering the non-normality of the hypothesis H0, to compare the means of the independent variables, Mann-Whitney non-parametric tests (for the variables of gender, the field of study, academic semester, employment status, province of residence, and whether it is rural or urban) and Kruskal Wallis (for the variables age, ethnicity, level of education, household income and marital status). According to the obtained results, it was found that the score of problematic use of social media is significantly higher in women, the age group less than 20 years, unemployed, non-native students, dormitory students, and students living with friends or alone, Fars students, students with a household income level of fewer than 7 million Tomans(Iranian currency), and single, divorced, and widowed students were higher than the other groups(P < 0.05).

By comparing the mean score of mental health with demographic variables using non-parametric Mann-Whitney and Kruskal Wallis tests, it was found that there is a significant difference between the variable of poor mental health and all demographic variables (except for the semester variable), residence status (rural or urban) and education level. (There was a significant relationship (P < 0.05). In such a way that the mental health condition was worse in women, age group less than 20 years old, non-medical science, unemployed, non-native, and dormitory students. Also, Fars students, divorced, widowed, and students with a household income of fewer than 5 million Tomans (Iranian currency) showed poorer mental health status. (Table  1 ).

The final model shows that marital status, field, and household income were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Being single (β* = -23.03, 95% CI: (-33.10, -12.96), being married (β* = -38.78, 95% CI: -51.23, -26.33), was in Medical sciences fields (β* = -8.15, 95% CI: -11.37, -4.94), and have income 7–10 million (β* = -5.66, 95% CI: -9.62, -1.71) were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Problematic use of social media (β* = 3.54, 95% CI: (3.23, 3.85) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). (Table  2 )

Age, income, and use of social media (β* = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Marital status and field were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Age groups < 20 years (β* = 6.36, 95% CI: 0.78, 11.95) and income group < 5 million (β* = 6.58, 95% CI: 1.47, 11.70) increased mental health scores. Being single (β* = -34.72, 95% CI: -47.06, -38.78), being married (β* = -38.78, 95% CI: -51.23, -26.33) and in medical sciences fields (β* = -8.17, 95% CI: -12.09, -4.24) decreased DASS21 scores. (Table  3 )

The main purpose of this study was to determine the relationship between social media use and mental health among students during the COVID-19 pandemic.

University students are more reliant on social media because of the limitations of in-person interactions [ 45 ]. Since social media has become more and more vital to the social lives of university students during the pandemic, students may be at increased risk of social media addiction, which may be harmful to their mental health [ 14 ].

During non-adulthood, peer relations and approval are critical and social media seems to meet these needs. For example, connection and communication with friends make them feel better and happier, especially during the COVID-19 pandemic and national lockdowns where face-to-face communication was restricted [ 46 ]. Kele’s study showed that the COVID-19 pandemic has increased the time spent on social media, and the frequency of online activities [ 47 ].

Because of the COVID-19 pandemic, e-learning became the only sustainable option for students [ 13 ]. This abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also important to some university students [ 48 ].

Staying at home, having nothing else to do, and being unable to go out and meet with friends due to the lockdown measures increased the time spent on social media and the frequency of online activities, which influenced their mental health negatively [ 49 ]. These reasons may explain the findings of previous studies that found an increase in depression and anxiety among adolescents who were healthy before the COVID-19 pandemic [ 50 ].

According to the results, there was a statistically significant relationship between social media use and mental health in students, in such a way that one Unit increase in the score of social media use enhanced the score of mental health. These two variables were directly correlated. Consistent with the current study, many studies have shown a significant relationship between higher use of social media and lower mental health in students [ 45 , 51 , 52 , 53 , 54 ].

Inconsistent with the findings of the present study, some previous studies reported the positive effect of social media use on mental health [ 55 , 56 , 57 ]. The differences in findings could be attributed to the time and location of the studies. Anderson’s study in France in 2018 found no significant relationship between social media use and mental health. This may be because of the differences between the tools for measuring the ability to detect fake news and health literacy and the scales of the research [ 4 ].

The present study showed that the impact of using social media on the mental health of students was higher than Lebni’s study, which was conducted in 2020 [ 25 ]. Also, in Dost Mohammad’s study in 2018, the effect of using social media on the mental health of students was reported to be lower than in the present study [ 58 ]. Entezari’s study in 2021, was also lower than the present study [ 59 ]. It seems that the excessive use of social media during the COVID-19 pandemic was the reason for the greater effects of social media on students’ mental health.

The use of social media has positive and negative characteristics. Social media is most useful for rapidly disseminating timely information via widely accessible platforms [ 4 ]. Among the types of studies, at least one shows an inverse relationship between the use of social media and mental health [ 53 ]. While social media can serve as a tool for fostering connection during periods of physical isolation, the mental health implications of social media being used as a news source are tenuous [ 45 ].

The results of the GLM analysis indicated that there was a statistically significant relationship between the problematic use of social media and mental health in students in such a way that one-unit increase in the score of problematic use of social media enhanced the mental health score, and it was found that the two variables had a direct relationship. Consistent with our study, Boer’s study showed that problematic use of social media may highlight the potential risk to adolescent mental health [ 60 ]. Malaeb also reported that the problematic use of social media had a positive relationship with mental health [ 61 ], but that study was conducted on adults and had a smaller sample size before the COVID-19 pandemic.

Saputri’s study found that excessive social media use likely harms the mental health of university students since students with higher social media addiction scores had a greater risk of experiencing mild depression [ 62 ]. A systematic literature review before the COVID-19 pandemic (2019) found that the time spent by adolescents on social media was associated with depression, anxiety, and psychological distress [ 63 ]. Marino’s study (2018) reported a significant correlation between the problematic use of social media by students and psychological distress [ 64 ].

Social media has become more vital for students’ social lives owing to online education during the COVID-19 pandemic. Therefore, this group is more at risk of addiction to social media and may experience more mental health problems than other groups. Lebni also indicated that students’ higher use of the Internet led to anxiety, depression, and adverse mental health, but the main purpose of the study was to investigate the effects of such factors on student’s academic performance [ 25 ]. Previous studies indicated that individuals who spent more time on social media had lower self-esteem and higher levels of anxiety and depression [ 65 , 66 ]. In the present study, students with higher social media addiction scores were at higher mental health risk. Such a finding was consistent with research by Gao et al., who found that the excessive use of social media during the pandemic had adverse effects on social health [ 14 ]. Cheng et al. indicated that using the Internet, especially for communication with people, can harm mental health by changing the quality of social relationships, face-to-face communication, and changes in social support [ 24 ].

A reason for the significant relationship between social media use and mental health in students during the COVID-19 pandemic in the present study was probably the students’ intentional or unintentional use of online communication. Unfortunately, social media published information, which might be incorrect, in this pandemic that caused public fear and threatened mental health.

During the pandemic, social media played essential roles in learning and leisure activities. Due to electronic education, staying at home, and long leisure time, students had more time, frequency, and opportunities to use social media in this pandemic. Such a high reliance on social media may threaten student’s mental health. Lee et al. conducted a study during the COVID-19 pandemic and confirmed that young people who used social media had higher symptoms of depression and loneliness than before the COVID-19 pandemic [ 67 ].

The present study showed that there was a significant positive relationship between problematic use of social media and gender, so that women were more willing to use social media, probably because they had more opportunities to use social media as they stayed at home more than men; hence, they were more exposed to problematic use of social media. Consistent with our study, Andreassen reported that being a woman was an important factor in social media addiction [ 68 ]. In contrast to our study, Azizi’s study in Iran showed that male students use social media significantly more than female students, possibly due to differences in demographic variables in each population [ 69 ].

Moreover, there was a significant relationship between age and problematic use of social media in that people younger than 20 were more willing to use social media in a problematic way. Consistent with the present study, Perrin also indicated that younger people further used social media [ 70 ].

According to the findings, unemployed students used social media more than employed ones, probably because they had more time to spend in virtual space, leading to higher use and the possibility of problematic use of social media [ 71 ].

Moreover, non-native students were more willing to use the social media probably because students who lived far away from their families used social media problematically due to the lack of family control over hours of use and higher opportunities [ 72 ] .

The results showed that rural students have a greater tendency to use social Medias than urban students. Inconsistent with this finding, Perrin reported that urban people were more willing to use the social media. The difference was probably due to different research times and places or different target groups [ 70 ].

According to the current study, people with low household income were more likely to use social media, most likely because low-income people seek free information and services due to a lack of access to facilities and equipment in the real world or because they seek assimilation with people around them. Inconsistent with our findings, Hruska et al. reported that people with high household income levels made much use of social media [ 73 ], probably because of cultural, economic, and social differences or different information measurement tools.

Furthermore, single, divorced, and widowed students used social media more than married students. This is because they spend more time on social media due to the need for more emotional attention, the search for a life partner, or a feeling of loneliness. This also led to the problematic use of social media [ 74 ].

According to the results, Fars people used social media more than other ethnic groups, but this difference was insignificant. This finding was consistent with Perrin’s study, but the population consisted of people aged 18 to 65 [ 70 ].

In the current study, there was a significant relationship between gender and mental health, so that women had lower mental health than men. The difference was in health sociology. Consistent with the present study, Ghasemi et al. indicated that it appeared necessary to pay more attention to women’s health and create an opportunity for them to use health services [ 75 ].

The findings revealed that unemployed students had lower mental health than employed students, most likely because unemployed individuals have lower mental health due to not having a job and being economically dependent on others, as well as feeling incompetent at times. Consistent with the present study, Bialowolski reported that unemployment and low income caused mental disorders and threatened mental health [ 76 ].

According to this study, non-native students have lower mental health than native students because they live far from their families. The family plays an imperative role in improving the mental health of their children, and mental health requires their support. Also, the economic, social, and support problems caused by being away from the family have endangered their mental health [ 77 ].

Another important factor of the current study was that married people had higher mental health than single people. In addition, divorced and widowed students had lower mental health [ 78 ]. Possibly due to the social pressure they suffer in Iranian society. Furthermore, they received lower emotional support than married people. Therefore, their lower mental health seemed logical [ 79 , 80 , 81 ]. A large study in a European population also reported differences in the likelihood of mood, anxiety, and personality disorders between separated/divorced and married mothers [ 82 ].

A key point confirmed in other studies is the relationship between low incomes with mental health. A meta-analysis by Lorant indicated that economic and social inequalities caused mental disorders [ 83 ]. Safran also reported that the probability of developing mental disorders in people with low socioeconomic status is up to three times higher than that of people with the highest socioeconomic status [ 84 ]. Bialowolski’s study was consistent with the current study but Bialowolski’s study examined employees [ 76 ].

The present study was conducted during the COVID-19 pandemic and therefore had limitations in accessing students. Another limitation was the use of self-reporting tools. Participants may show positive self-presentation by over- or under-reporting their social media-related behaviors and some mental health-related items, which may directly or indirectly lead to social desirability bias, information bias, and reporting bias. Small sample sizes and convenience sampling limit student population representativeness and generalizability. This study was based on cross-sectional data. Therefore, the estimation results should be seen as associative rather than causative. Future studies would need to investigate causal effects using a longitudinal or cohort design, or another causal effect research design.

The findings of this study indicated that the high use of social media affected students’ mental health. Furthermore, the problematic use of the social media had a direct relationship with mental health. Variables such as age, gender, income level, marital status, and unemployment of non-native students had significant relationships with social media use and mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects. It is imperative to better understand the relationship between social media use and mental health symptoms among young people to prevent such a negative outcome.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to express their gratitude to all academic officials of Lorestan universities and Mr. Mohsen Amani for their cooperation in data collection.

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Maede Hosseinnia

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Abouzar Nazari and Maedeh Hossennia designed the study, collected the data and drafted the manuscript. Samaneh Torkian performed the statistical analysis and prepared the tables. Gholamreza Garmaroudi, as the responsible author, supervised the entire study. All authors reviewed and edited the draft manuscript and approved the final version.

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Nazari, A., Hosseinnia, M., Torkian, S. et al. Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic. BMC Psychiatry 23 , 458 (2023). https://doi.org/10.1186/s12888-023-04859-w

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Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review

• C. E. Hall 1 , 2 ,
• H. Wehling 1 ,
• J. Stansfield 3 ,
• J. South 3 ,
• S. K. Brooks 2 ,
• N. Greenberg 2 , 4 ,
• R. Amlôt 1 &
• D. Weston 1  

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The ability of the public to remain psychologically resilient in the face of public health emergencies and disasters (such as the COVID-19 pandemic) is a key factor in the effectiveness of a national response to such events. Community resilience and social capital are often perceived as beneficial and ensuring that a community is socially and psychologically resilient may aid emergency response and recovery. This review presents a synthesis of literature which answers the following research questions: How are community resilience and social capital quantified in research?; What is the impact of community resilience on mental wellbeing?; What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, What types of interventions enhance community resilience and social capital?

A scoping review procedure was followed. Searches were run across Medline, PsycInfo, and EMBASE, with search terms covering both community resilience and social capital, public health emergencies, and mental health. 26 papers met the inclusion criteria.

The majority of retained papers originated in the USA, used a survey methodology to collect data, and involved a natural disaster. There was no common method for measuring community resilience or social capital. The association between community resilience and social capital with mental health was regarded as positive in most cases. However, we found that community resilience, and social capital, were initially negatively impacted by public health emergencies and enhanced by social group activities.

Several key recommendations are proposed based on the outcomes from the review, which include: the need for a standardised and validated approach to measuring both community resilience and social capital; that there should be enhanced effort to improve preparedness to public health emergencies in communities by gauging current levels of community resilience and social capital; that community resilience and social capital should be bolstered if areas are at risk of disasters or public health emergencies; the need to ensure that suitable short-term support is provided to communities with high resilience in the immediate aftermath of a public health emergency or disaster; the importance of conducting robust evaluation of community resilience initiatives deployed during the COVID-19 pandemic.

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For the general population, public health emergencies and disasters (e.g., natural disasters; infectious disease outbreaks; Chemical, Biological, Radiological or Nuclear incidents) can give rise to a plethora of negative outcomes relating to both health (e.g. increased mental health problems [ 1 , 2 , 3 , 4 ]) and the economy (e.g., increased unemployment and decreased levels of tourism [ 4 , 5 , 6 ]). COVID-19 is a current, and ongoing, example of a public health emergency which has affected over 421 million individuals worldwide [ 7 ]. The long term implications of COVID-19 are not yet known, but there are likely to be repercussions for physical health, mental health, and other non-health related outcomes for a substantial time to come [ 8 , 9 ]. As a result, it is critical to establish methods which may inform approaches to alleviate the longer-term negative consequences that are likely to emerge in the aftermath of both COVID-19 and any future public health emergency.

The definition of resilience often differs within the literature, but ultimately resilience is considered a dynamic process of adaptation. It is related to processes and capabilities at the individual, community and system level that result in good health and social outcomes, in spite of negative events, serious threats and hazards [ 10 ]. Furthermore, Ziglio [ 10 ] refers to four key types of resilience capacity: adaptive, the ability to withstand and adjust to unfavourable conditions and shocks; absorptive, the ability to withstand but also to recover and manage using available assets and skills; anticipatory, the ability to predict and minimize vulnerability; and transformative, transformative change so that systems better cope with new conditions.

There is no one settled definition of community resilience (CR). However, it generally relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ]. Social capital (SC) is considered a major determinant of CR [ 12 , 13 ], and reflects strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats. SC is often broken down into further categories [ 15 ], for example: cognitive SC (i.e. perceptions of community relations, such as trust, mutual help and attachment) and structural SC (i.e. what actually happens within the community, such as participation, socialising) [ 16 ]; or, bonding SC (i.e. connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ]) and bridging SC (i.e. acquaintances or individuals loosely connected that span different social groups [ 18 ]). Generally, CR is perceived to be primarily beneficial for multiple reasons (e.g. increased social support [ 18 , 19 ], protection of mental health [ 20 , 21 ]), and strengthening community resilience is a stated health goal of the World Health Organisation [ 22 ] when aiming to alleviate health inequalities and protect wellbeing. This is also reflected by organisations such as Public Health England (now split into the UK Health Security Agency and the Office for Health Improvement and Disparities) [ 23 ] and more recently, CR has been targeted through the endorsement of Community Champions (who are volunteers trained to support and to help improve health and wellbeing. Community Champions also reflect their local communities in terms of population demographics for example age, ethnicity and gender) as part of the COVID-19 response in the UK (e.g. [ 24 , 25 ]).

Despite the vested interest in bolstering communities, the research base establishing: how to understand and measure CR and SC; the effect of CR and SC, both during and following a public health emergency (such as the COVID-19 pandemic); and which types of CR or SC are the most effective to engage, is relatively small. Given the importance of ensuring resilience against, and swift recovery from, public health emergencies, it is critically important to establish and understand the evidence base for these approaches. As a result, the current review sought to answer the following research questions: (1) How are CR and SC quantified in research?; (2) What is the impact of community resilience on mental wellbeing?; (3) What is the impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?

By collating research in order to answer these research questions, the authors have been able to propose several key recommendations that could be used to both enhance and evaluate CR and SC effectively to facilitate the long-term recovery from COVID-19, and also to inform the use of CR and SC in any future public health disasters and emergencies.

A scoping review methodology was followed due to the ease of summarising literature on a given topic for policy makers and practitioners [ 26 ], and is detailed in the following sections.

Identification of relevant studies

An initial search strategy was developed by authors CH and DW and included terms which related to: CR and SC, given the absence of a consistent definition of CR, and the link between CR and SC, the review focuses on both CR and SC to identify as much relevant literature as possible (adapted for purpose from Annex 1: [ 27 ], as well as through consultation with review commissioners); public health emergencies and disasters [ 28 , 29 , 30 , 31 ], and psychological wellbeing and recovery (derived a priori from literature). To ensure a focus on both public health and psychological research, the final search was carried across Medline, PsycInfo, and EMBASE using OVID. The final search took place on the 18th of May 2020, the search strategy used for all three databases can be found in Supplementary file 1 .

Selection criteria

The inclusion and exclusion criteria were developed alongside the search strategy. Initially the criteria were relatively inclusive and were subject to iterative development to reflect the authors’ familiarisation with the literature. For example, the decision was taken to exclude research which focused exclusively on social support and did not mention communities as an initial title/abstract search suggested that the majority of this literature did not meet the requirements of our research question.

The full and final inclusion and exclusion criteria used can be found in Supplementary file 2 . In summary, authors decided to focus on the general population (i.e., non-specialist, e.g. non-healthcare worker or government official) to allow the review to remain community focused. The research must also have assessed the impact of CR and/or SC on mental health and wellbeing, resilience, and recovery during and following public health emergencies and infectious disease outbreaks which affect communities (to ensure the research is relevant to the review aims), have conducted primary research, and have a full text available or provided by the first author when contacted.

Charting the data

All papers were first title and abstract screened by CH or DW. Papers then were full text reviewed by CH to ensure each paper met the required eligibility criteria, if unsure about a paper it was also full text reviewed by DW. All papers that were retained post full-text review were subjected to a standardised data extraction procedure. A table was made for the purpose of extracting the following data: title, authors, origin, year of publication, study design, aim, disaster type, sample size and characteristics, variables examined, results, restrictions/limitations, and recommendations. Supplementary file 3 details the charting the data process.

Analytical method

Data was synthesised using a Framework approach [ 32 ], a common method for analysing qualitative research. This method was chosen as it was originally used for large-scale social policy research [ 33 ] as it seeks to identify: what works, for whom, in what conditions, and why [ 34 ]. This approach is also useful for identifying commonalities and differences in qualitative data and potential relationships between different parts of the data [ 33 ]. An a priori framework was established by CH and DW. Extracted data was synthesised in relation to each research question, and the process was iterative to ensure maximum saturation using the available data.

Study selection

The final search strategy yielded 3584 records. Following the removal of duplicates, 2191 records remained and were included in title and abstract screening. A PRISMA flow diagram is presented in Fig.  1 .

PRISMA flow diagram

At the title and abstract screening stage, the process became more iterative as the inclusion criteria were developed and refined. For the first iteration of screening, CH or DW sorted all records into ‘include,’ ‘exclude,’ and ‘unsure’. All ‘unsure’ papers were re-assessed by CH, and a random selection of ~ 20% of these were also assessed by DW. Where there was disagreement between authors the records were retained, and full text screened. The remaining papers were reviewed by CH, and all records were categorised into ‘include’ and ‘exclude’. Following full-text screening, 26 papers were retained for use in the review.

Study characteristics

This section of the review addresses study characteristics of those which met the inclusion criteria, which comprises: date of publication, country of origin, study design, study location, disaster, and variables examined.

Date of publication

Publication dates across the 26 papers spanned from 2008 to 2020 (see Fig.  2 ). The number of papers published was relatively low and consistent across this timescale (i.e. 1–2 per year, except 2010 and 2013 when none were published) up until 2017 where the number of papers peaked at 5. From 2017 to 2020 there were 15 papers published in total. The amount of papers published in recent years suggests a shift in research and interest towards CR and SC in a disaster/ public health emergency context.

Graph to show retained papers date of publication

Country of origin

The locations of the first authors’ institutes at the time of publication were extracted to provide a geographical spread of the retained papers. The majority originated from the USA [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ], followed by China [ 42 , 43 , 44 , 45 , 46 ], Japan [ 47 , 48 , 49 , 50 ], Australia [ 51 , 52 , 53 ], The Netherlands [ 54 , 55 ], New Zealand [ 56 ], Peru [ 57 ], Iran [ 58 ], Austria [ 59 ], and Croatia [ 60 ].

There were multiple methodological approaches carried out across retained papers. The most common formats included surveys or questionnaires [ 36 , 37 , 38 , 42 , 46 , 47 , 48 , 49 , 50 , 53 , 54 , 55 , 57 , 59 ], followed by interviews [ 39 , 40 , 43 , 51 , 52 , 60 ]. Four papers used both surveys and interviews [ 35 , 41 , 45 , 58 ], and two papers conducted data analysis (one using open access data from a Social Survey [ 44 ] and one using a Primary Health Organisations Register [ 56 ]).

Study location

The majority of the studies were carried out in Japan [ 36 , 42 , 44 , 47 , 48 , 49 , 50 ], followed by the USA [ 35 , 37 , 38 , 39 , 40 , 41 ], China [ 43 , 45 , 46 , 53 ], Australia [ 51 , 52 ], and the UK [ 54 , 55 ]. The remaining studies were carried out in Croatia [ 60 ], Peru [ 57 ], Austria [ 59 ], New Zealand [ 56 ] and Iran [ 58 ].

Multiple different types of disaster were researched across the retained papers. Earthquakes were the most common type of disaster examined [ 45 , 47 , 49 , 50 , 53 , 56 , 57 , 58 ], followed by research which assessed the impact of two disastrous events which had happened in the same area (e.g. Hurricane Katrina and the Deepwater Horizon oil spill in Mississippi, and the Great East Japan earthquake and Tsunami; [ 36 , 37 , 38 , 42 , 44 , 48 ]). Other disaster types included: flooding [ 51 , 54 , 55 , 59 , 60 ], hurricanes [ 35 , 39 , 41 ], infectious disease outbreaks [ 43 , 46 ], oil spillage [ 40 ], and drought [ 52 ].

Variables of interest examined

Across the 26 retained papers: eight referred to examining the impact of SC [ 35 , 37 , 39 , 41 , 46 , 49 , 55 , 60 ]; eight examined the impact of cognitive and structural SC as separate entities [ 40 , 42 , 45 , 48 , 50 , 54 , 57 , 59 ]; one examined bridging and bonding SC as separate entities [ 58 ]; two examined the impact of CR [ 38 , 56 ]; and two employed a qualitative methodology but drew findings in relation to bonding and bridging SC, and SC generally [ 51 , 52 ]. Additionally, five papers examined the impact of the following variables: ‘community social cohesion’ [ 36 ], ‘neighbourhood connectedness’ [ 44 ], ‘social support at the community level’ [ 47 ], ‘community connectedness’ [ 43 ] and ‘sense of community’ [ 53 ]. Table  1 provides additional details on this.

How is CR and SC measured or quantified in research?

The measures used to examine CR and SC are presented Table  1 . It is apparent that there is no uniformity in how SC or CR is measured across the research. Multiple measures are used throughout the retained studies, and nearly all are unique. Additionally, SC was examined at multiple different levels (e.g. cognitive and structural, bonding and bridging), and in multiple different forms (e.g. community connectedness, community cohesion).

What is the association between CR and SC on mental wellbeing?

To best compare research, the following section reports on CR, and facets of SC separately. Please see Supplementary file 4  for additional information on retained papers methods of measuring mental wellbeing.

• Community resilience

CR relates to the ability of a community to withstand, adapt and permit growth in adverse circumstances due to social structures, networks and interdependencies within the community [ 11 ].

The impact of CR on mental wellbeing was consistently positive. For example, research indicated that there was a positive association between CR and number of common mental health (i.e. anxiety and mood) treatments post-disaster [ 56 ]. Similarly, other research suggests that CR is positively related to psychological resilience, which is inversely related to depressive symptoms) [ 37 ]. The same research also concluded that CR is protective of psychological resilience and is therefore protective of depressive symptoms [ 37 ].

• Social capital

SC reflects the strength of a social network, community reciprocity, and trust in people and institutions [ 14 ]. These aspects of community are usually conceptualised primarily as protective factors that enable communities to cope and adapt collectively to threats.

There were inconsistencies across research which examined the impact of abstract SC (i.e. not refined into bonding/bridging or structural/cognitive) on mental wellbeing. However, for the majority of cases, research deems SC to be beneficial. For example, research has concluded that, SC is protective against post-traumatic stress disorder [ 55 ], anxiety [ 46 ], psychological distress [ 50 ], and stress [ 46 ]. Additionally, SC has been found to facilitate post-traumatic growth [ 38 ], and also to be useful to be drawn upon in times of stress [ 52 ], both of which could be protective of mental health. Similarly, research has also found that emotional recovery following a disaster is more difficult for those who report to have low levels of SC [ 51 ].

Conversely, however, research has also concluded that when other situational factors (e.g. personal resources) were controlled for, a positive relationship between community resources and life satisfaction was no longer significant [ 60 ]. Furthermore, some research has concluded that a high level of SC can result in a community facing greater stress immediately post disaster. Indeed, one retained paper found that high levels of SC correlate with higher levels of post-traumatic stress immediately following a disaster [ 39 ]. However, in the later stages following a disaster, this relationship can reverse, with SC subsequently providing an aid to recovery [ 41 ]. By way of explanation, some researchers have suggested that communities with stronger SC carry the greatest load in terms of helping others (i.e. family, friends and neighbours) as well as themselves immediately following the disaster, but then as time passes the communities recover at a faster rate as they are able to rely on their social networks for support [ 41 ].

Cognitive and structural social capital

Cognitive SC refers to perceptions of community relations, such as trust, mutual help and attachment, and structural SC refers to what actually happens within the community, such as participation, socialising [ 16 ].

Cognitive SC has been found to be protective [ 49 ] against PTSD [ 54 , 57 ], depression [ 40 , 54 ]) mild mood disorder; [ 48 ]), anxiety [ 48 , 54 ] and increase self-efficacy [ 59 ].

For structural SC, research is again inconsistent. On the one hand, structural SC has been found to: increase perceived self-efficacy, be protective of depression [ 40 ], buffer the impact of housing damage on cognitive decline [ 42 ] and provide support during disasters and over the recovery period [ 59 ]. However, on the other hand, it has been found to have no association with PTSD [ 54 , 57 ] or depression, and is also associated with a higher prevalence of anxiety [ 54 ]. Similarly, it is also suggested by additional research that structural SC can harm women’s mental health, either due to the pressure of expectations to help and support others or feelings of isolation [ 49 ].

Bonding and bridging social capital

Bonding SC refers to connections among individuals who are emotionally close, and result in bonds to a particular group [ 17 ], and bridging SC refers to acquaintances or individuals loosely connected that span different social groups [ 18 ].

One research study concluded that both bonding and bridging SC were protective against post-traumatic stress disorder symptoms [ 58 ]. Bridging capital was deemed to be around twice as effective in buffering against post-traumatic stress disorder than bonding SC [ 58 ].

Other community variables

Community social cohesion was significantly associated with a lower risk of post-traumatic stress disorder symptom development [ 35 ], and this was apparent even whilst controlling for depressive symptoms at baseline and disaster impact variables (e.g. loss of family member or housing damage) [ 36 ]. Similarly, sense of community, community connectedness, social support at the community level and neighbourhood connectedness all provided protective benefits for a range of mental health, wellbeing and recovery variables, including: depression [ 53 ], subjective wellbeing (in older adults only) [ 43 ], psychological distress [ 47 ], happiness [ 44 ] and life satisfaction [ 53 ].

Research has also concluded that community level social support is protective against mild mood and anxiety disorder, but only for individuals who have had no previous disaster experience [ 48 ]. Additionally, a study which separated SC into social cohesion and social participation concluded that at a community level, social cohesion is protective against depression [ 49 ] whereas social participation at community level is associated with an increased risk of depression amongst women [ 49 ].

What is the impact of Infectious disease outbreaks / disasters and emergencies on community resilience?

From a cross-sectional perspective, research has indicated that disasters and emergencies can have a negative effect on certain types of SC. Specifically, cognitive SC has been found to be impacted by disaster impact, whereas structural SC has gone unaffected [ 45 ]. Disaster impact has also been shown to have a negative effect on community relationships more generally [ 52 ].

Additionally, of the eight studies which collected data at multiple time points [ 35 , 36 , 41 , 42 , 47 , 49 , 56 , 60 ], three reported the effect of a disaster on the level of SC within a community [ 40 , 42 , 49 ]. All three of these studies concluded that disasters may have a negative impact on the levels of SC within a community. The first study found that the Deepwater Horizon oil spill had a negative effect on SC and social support, and this in turn explained an overall increase in the levels of depression within the community [ 40 ]. A possible explanation for the negative effect lays in ‘corrosive communities’, known for increased social conflict and reduced social support, that are sometimes created following oil spills [ 40 ]. It is proposed that corrosive communities often emerge due to a loss of natural resources that bring social groups together (e.g., for recreational activities), as well as social disparity (e.g., due to unequal distribution of economic impact) becoming apparent in the community following disaster [ 40 ]. The second study found that SC (in the form of social cohesion, informal socialising and social participation) decreased after the 2011 earthquake and tsunami in Japan; it was suggested that this change correlated with incidence of cognitive decline [ 42 ]. However, the third study reported more mixed effects based on physical circumstances of the communities’ natural environment: Following an earthquake, those who lived in mountainous areas with an initial high level of pre-community SC saw a decrease in SC post disaster [ 49 ]. However, communities in flat areas (which were home to younger residents and had a higher population density) saw an increase in SC [ 49 ]. It was proposed that this difference could be due to the need for those who lived in mountainous areas to seek prolonged refuge due to subsequent landslides [ 49 ].

What types of intervention enhance CR and SC and protect survivors?

There were mixed effects across the 26 retained papers when examining the effect of CR and SC on mental wellbeing. However, there is evidence that an increase in SC [ 56 , 57 ], with a focus on cognitive SC [ 57 ], namely by: building social networks [ 45 , 51 , 53 ], enhancing feelings of social cohesion [ 35 , 36 ] and promoting a sense of community [ 53 ], can result in an increase in CR and potentially protect survivors’ wellbeing and mental health following a disaster. An increase in SC may also aid in decreasing the need for individual psychological interventions in the aftermath of a disaster [ 55 ]. As a result, recommendations and suggested methods to bolster CR and SC from the retained papers have been extracted and separated into general methods, preparedness and policy level implementation.

General methods

Suggested methods to build SC included organising recreational activity-based groups [ 44 ] to broaden [ 51 , 53 ] and preserve current social networks [ 42 ], introducing initiatives to increase social cohesion and trust [ 51 ], and volunteering to increase the number of social ties between residents [ 59 ]. Research also notes that it is important to take a ‘no one left behind approach’ when organising recreational and social community events, as failure to do so could induce feelings of isolation for some members of the community [ 49 ]. Furthermore, gender differences should also be considered as research indicates that males and females may react differently to community level SC (as evidence suggests males are instead more impacted by individual level SC; in comparison to women who have larger and more diverse social networks [ 49 ]). Therefore, interventions which aim to raise community level social participation, with the aim of expanding social connections and gaining support, may be beneficial [ 42 , 47 ].

Preparedness

In order to prepare for disasters, it may be beneficial to introduce community-targeted methods or interventions to increase levels of SC and CR as these may aid in ameliorating the consequences of a public health emergency or disaster [ 57 ]. To indicate which communities have low levels of SC, one study suggests implementing a 3-item scale of social cohesion to map areas and target interventions [ 42 ].

It is important to consider that communities with a high level of SC may have a lower level of risk perception, due to the established connections and supportive network they have with those around them [ 61 ]. However, for the purpose of preparedness, this is not ideal as perception of risk is a key factor when seeking to encourage behavioural adherence. This could be overcome by introducing communication strategies which emphasise the necessity of social support, but also highlights the need for additional measures to reduce residual risk [ 59 ]. Furthermore, support in the form of financial assistance to foster current community initiatives may prove beneficial to rural areas, for example through the use of an asset-based community development framework [ 52 ].

Policy level

At a policy level, the included papers suggest a range of ways that CR and SC could be bolstered and used. These include: providing financial support for community initiatives and collective coping strategies, (e.g. using asset-based community development [ 52 ]); ensuring policies for long-term recovery focus on community sustainable development (e.g. community festival and community centre activities) [ 44 ]; and development of a network amongst cooperative corporations formed for reconstruction and to organise self-help recovery sessions among residents of adjacent areas [ 58 ].

This scoping review sought to synthesise literature concerning the role of SC and CR during public health emergencies and disasters. Specifically, in this review we have examined: the methods used to measure CR and SC; the impact of CR and SC on mental wellbeing during disasters and emergencies; the impact of disasters and emergencies on CR and SC; and the types of interventions which can be used to enhance CR. To do this, data was extracted from 26 peer-reviewed journal articles. From this synthesis, several key themes have been identified, which can be used to develop guidelines and recommendations for deploying CR and SC in a public health emergency or disaster context. These key themes and resulting recommendations are summarised below.

Firstly, this review established that there is no consistent or standardised approach to measuring CR or SC within the general population. This finding is consistent with a review conducted by the World Health Organization which concludes that despite there being a number of frameworks that contain indicators across different determinants of health, there is a lack of consensus on priority areas for measurement and no widely accepted indicator [ 27 ]. As a result, there are many measures of CR and SC apparent within the literature (e.g., [ 62 , 63 ]), an example of a developed and validated measure is provided by Sherrieb, Norris and Galea [ 64 ]. Similarly, the definitions of CR and SC differ widely between researchers, which created a barrier to comparing and summarising information. Therefore, future research could seek to compare various interpretations of CR and to identify any overlapping concepts. However, a previous systemic review conducted by Patel et al. (2017) concludes that there are nine core elements of CR (local knowledge, community networks and relationships, communication, health, governance and leadership, resources, economic investment, preparedness, and mental outlook), with 19 further sub-elements therein [ 30 ]. Therefore, as CR is a multi-dimensional construct, the implications from the findings are that multiple aspects of social infrastructure may need to be considered.

Secondly, our synthesis of research concerning the role of CR and SC for ensuring mental health and wellbeing during, or following, a public health emergency or disaster revealed mixed effects. Much of the research indicates either a generally protective effect on mental health and wellbeing, or no effect; however, the literature demonstrates some potential for a high level of CR/SC to backfire and result in a negative effect for populations during, or following, a public health emergency or disaster. Considered together, our synthesis indicates that cognitive SC is the only facet of SC which was perceived as universally protective across all retained papers. This is consistent with a systematic review which also concludes that: (a) community level cognitive SC is associated with a lower risk of common mental disorders, while; (b) community level structural SC had inconsistent effects [ 65 ].

Further examination of additional data extracted from studies which found that CR/SC had a negative effect on mental health and wellbeing revealed no commonalities that might explain these effects (Please see Supplementary file 5 for additional information)

One potential explanation may come from a retained paper which found that high levels of SC result in an increase in stress level immediately post disaster [ 41 ]. This was suggested to be due to individuals having greater burdens due to wishing to help and support their wide networks as well as themselves. However, as time passes the levels of SC allow the community to come together and recover at a faster rate [ 41 ]. As this was the only retained paper which produced this finding, it would be beneficial for future research to examine boundary conditions for the positive effects of CR/SC; that is, to explore circumstances under which CR/SC may be more likely to put communities at greater risk. This further research should also include additional longitudinal research to validate the conclusions drawn by [ 41 ] as resilience is a dynamic process of adaption.

Thirdly, disasters and emergencies were generally found to have a negative effect on levels of SC. One retained paper found a mixed effect of SC in relation to an earthquake, however this paper separated participants by area in which they lived (i.e., mountainous vs. flat), which explains this inconsistent effect [ 49 ]. Dangerous areas (i.e. mountainous) saw a decrease in community SC in comparison to safer areas following the earthquake (an effect the authors attributed to the need to seek prolonged refuge), whereas participants from the safer areas (which are home to younger residents with a higher population density) saw an increase in SC [ 49 ]. This is consistent with the idea that being able to participate socially is a key element of SC [ 12 ]. Overall, however, this was the only retained paper which produced a variable finding in relation to the effect of disaster on levels of CR/SC.

Finally, research identified through our synthesis promotes the idea of bolstering SC (particularly cognitive SC) and cohesion in communities likely to be affected by disaster to improve levels of CR. This finding provides further understanding of the relationship between CR and SC; an association that has been reported in various articles seeking to provide conceptual frameworks (e.g., [ 66 , 67 ]) as well as indicator/measurement frameworks [ 27 ]. Therefore, this could be done by creating and promoting initiatives which foster SC and create bonds within the community. Papers included in the current review suggest that recreational-based activity groups and volunteering are potential methods for fostering SC and creating community bonds [ 44 , 51 , 59 ]. Similarly, further research demonstrates that feelings of social cohesion are enhanced by general social activities (e.g. fairs and parades [ 18 ]). Also, actively encouraging activities, programs and interventions which enhance connectedness and SC have been reported to be desirable to increase CR [ 68 ]. This suggestion is supported by a recent scoping review of literature [ 67 ] examined community champion approaches for the COVID-19 pandemic response and recovery and established that creating and promoting SC focused initiatives within the community during pandemic response is highly beneficial [ 67 ]. In terms of preparedness, research states that it may be beneficial for levels of SC and CR in communities at risk to be assessed, to allow targeted interventions where the population may be at most risk following an incident [ 42 , 44 ]. Additionally, from a more critical perspective, we acknowledge that ‘resilience’ can often be perceived as a focus on individual capacity to adapt to adversity rather than changing or mitigating the causes of adverse conditions [ 69 , 70 ]. Therefore, CR requires an integrated system approach across individual, community and structural levels [ 17 ]. Also, it is important that community members are engaged in defining and agreeing how community resilience is measured [ 27 ] rather than it being imposed by system leads or decision-makers.

In the aftermath of the pandemic, is it expected that there will be long-term repercussions both from an economic [ 8 ] and a mental health perspective [ 71 ]. Furthermore, the findings from this review suggest that although those in areas with high levels of SC may be negatively affected in the acute stage, as time passes, they have potential to rebound at a faster rate than those with lower levels of SC. Ongoing evaluation of the effectiveness of current initiatives as the COVID-19 pandemic progresses into a recovery phase will be invaluable for supplementing the evidence base identified through this review.

• Recommendations

As a result of this review, a number of recommendations are suggested for policy and practice during public health emergencies and recovery.

Future research should seek to establish a standardised and validated approach to measuring and defining CR and SC within communities. There are ongoing efforts in this area, for example [ 72 ]. Additionally, community members should be involved in the process of defining how CR is measured.

There should be an enhanced effort to improve preparedness for public health emergencies and disasters in local communities by gauging current levels of SC and CR within communities using a standardised measure. This approach could support specific targeting of populations with low levels of CR/SC in case of a disaster or public health emergency, whilst also allowing for consideration of support for those with high levels of CR (as these populations can be heavily impacted initially following a disaster). By distinguishing levels of SC and CR, tailored community-centred approaches could be implemented, such as those listed in a guide released by PHE in 2015 [ 73 ].

CR and SC (specifically cognitive SC) should be bolstered if communities are at risk of experiencing a disaster or public health emergency. This can be achieved by using interventions which aim to increase a sense of community and create new social ties (e.g., recreational group activities, volunteering). Additionally, when aiming to achieve this, it is important to be mindful of the risk of increased levels of CR/SC to backfire, as well as seeking to advocate an integrated system approach across individual, community and structural levels.

It is necessary to be aware that although communities with high existing levels of resilience / SC may experience short-term negative consequences following a disaster, over time these communities might be able to recover at a faster rate. It is therefore important to ensure that suitable short-term support is provided to these communities in the immediate aftermath of a public health emergency or disaster.

Robust evaluation of the community resilience initiatives deployed during the COVID-19 pandemic response is essential to inform the evidence base concerning the effectiveness of CR/ SC. These evaluations should continue through the response phase and into the recovery phase to help develop our understanding of the long-term consequences of such interventions.

Limitations

Despite this review being the first in this specific topic area, there are limitations that must be considered. Firstly, it is necessary to note that communities are generally highly diverse and the term ‘community’ in academic literature is a subject of much debate (see: [ 74 ]), therefore this must be considered when comparing and collating research involving communities. Additionally, the measures of CR and SC differ substantially across research, including across the 26 retained papers used in the current review. This makes the act of comparing and collating research findings very difficult. This issue is highlighted as a key outcome from this review, and suggestions for how to overcome this in future research are provided. Additionally, we acknowledge that there will be a relationship between CR & SC even where studies measure only at individual or community level. A review [ 75 ] on articulating a hypothesis of the link to health inequalities suggests that wider structural determinants of health need to be accounted for. Secondly, despite the final search strategy encompassing terms for both CR and SC, only one retained paper directly measured CR; thus, making the research findings more relevant to SC. Future research could seek to focus on CR to allow for a comparison of findings. Thirdly, the review was conducted early in the COVID-19 pandemic and so does not include more recent publications focusing on resilience specifically in the context of COVID-19. Regardless of this fact, the synthesis of, and recommendations drawn from, the reviewed studies are agnostic to time and specific incident and contain critical elements necessary to address as the pandemic moves from response to recovery. Further research should review the effectiveness of specific interventions during the COVID-19 pandemic for collation in a subsequent update to this current paper. Fourthly, the current review synthesises findings from countries with individualistic and collectivistic cultures, which may account for some variation in the findings. Lastly, despite choosing a scoping review method for ease of synthesising a wide literature base for use by public health emergency researchers in a relatively tight timeframe, there are disadvantages of a scoping review approach to consider: (1) quality appraisal of retained studies was not carried out; (2) due to the broad nature of a scoping review, more refined and targeted reviews of literature (e.g., systematic reviews) may be able to provide more detailed research outcomes. Therefore, future research should seek to use alternative methods (e.g., empirical research, systematic reviews of literature) to add to the evidence base on CR and SC impact and use in public health practice.

This review sought to establish: (1) How CR and SC are quantified in research?; (2) The impact of community resilience on mental wellbeing?; (3) The impact of infectious disease outbreaks, disasters and emergencies on community resilience and social capital?; and, (4) What types of interventions enhance community resilience and social capital?. The chosen search strategy yielded 26 relevant papers from which we were able extract information relating to the aims of this review.

Results from the review revealed that CR and SC are not measured consistently across research. The impact of CR / SC on mental health and wellbeing during emergencies and disasters is mixed (with some potential for backlash), however the literature does identify cognitive SC as particularly protective. Although only a small number of papers compared CR or SC before and after a disaster, the findings were relatively consistent: SC or CR is negatively impacted by a disaster. Methods suggested to bolster SC in communities were centred around social activities, such as recreational group activities and volunteering. Recommendations for both research and practice (with a particular focus on the ongoing COVID-19 pandemic) are also presented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Social Capital

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This study was supported by the National Institute for Health Research Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between Public Health England, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, Public Health England, the UK Health Security Agency or the Department of Health and Social Care [Grant number: NIHR20008900]. Part of this work has been funded by the Office for Health Improvement and Disparities, Department of Health and Social Care, as part of a Collaborative Agreement with Leeds Beckett University.

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Hall, C.E., Wehling, H., Stansfield, J. et al. Examining the role of community resilience and social capital on mental health in public health emergency and disaster response: a scoping review. BMC Public Health 23 , 2482 (2023). https://doi.org/10.1186/s12889-023-17242-x

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Published on 11.4.2024 in Vol 26 (2024)

Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

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, 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.

• COVID-19 and your mental health

Worries and anxiety about COVID-19 can be overwhelming. Learn ways to cope as COVID-19 spreads.

At the start of the COVID-19 pandemic, life for many people changed very quickly. Worry and concern were natural partners of all that change — getting used to new routines, loneliness and financial pressure, among other issues. Information overload, rumor and misinformation didn't help.

Worldwide surveys done in 2020 and 2021 found higher than typical levels of stress, insomnia, anxiety and depression. By 2022, levels had lowered but were still higher than before 2020.

Though feelings of distress about COVID-19 may come and go, they are still an issue for many people. You aren't alone if you feel distress due to COVID-19. And you're not alone if you've coped with the stress in less than healthy ways, such as substance use.

But healthier self-care choices can help you cope with COVID-19 or any other challenge you may face.

And knowing when to get help can be the most essential self-care action of all.

Recognize what's typical and what's not

Stress and worry are common during a crisis. But something like the COVID-19 pandemic can push people beyond their ability to cope.

In surveys, the most common symptoms reported were trouble sleeping and feeling anxiety or nervous. The number of people noting those symptoms went up and down in surveys given over time. Depression and loneliness were less common than nervousness or sleep problems, but more consistent across surveys given over time. Among adults, use of drugs, alcohol and other intoxicating substances has increased over time as well.

The first step is to notice how often you feel helpless, sad, angry, irritable, hopeless, anxious or afraid. Some people may feel numb.

Keep track of how often you have trouble focusing on daily tasks or doing routine chores. Are there things that you used to enjoy doing that you stopped doing because of how you feel? Note any big changes in appetite, any substance use, body aches and pains, and problems with sleep.

These feelings may come and go over time. But if these feelings don't go away or make it hard to do your daily tasks, it's time to ask for help.

Get help when you need it

If you're feeling suicidal or thinking of hurting yourself, seek help.

• Contact your healthcare professional or a mental health professional.
• Contact a suicide hotline. In the U.S., call or text 988 to reach the 988 Suicide & Crisis Lifeline , available 24 hours a day, seven days a week. Or use the Lifeline Chat . Services are free and confidential.

If you are worried about yourself or someone else, contact your healthcare professional or mental health professional. Some may be able to see you in person or talk over the phone or online.

You also can reach out to a friend or loved one. Someone in your faith community also could help.

And you may be able to get counseling or a mental health appointment through an employer's employee assistance program.

Another option is information and treatment options from groups such as:

• National Alliance on Mental Illness (NAMI).
• Substance Abuse and Mental Health Services Administration (SAMHSA).
• Anxiety and Depression Association of America.

Self-care tips

Some people may use unhealthy ways to cope with anxiety around COVID-19. These unhealthy choices may include things such as misuse of medicines or legal drugs and use of illegal drugs. Unhealthy coping choices also can be things such as sleeping too much or too little, or overeating. It also can include avoiding other people and focusing on only one soothing thing, such as work, television or gaming.

Unhealthy coping methods can worsen mental and physical health. And that is particularly true if you're trying to manage or recover from COVID-19.

Self-care actions can help you restore a healthy balance in your life. They can lessen everyday stress or significant anxiety linked to events such as the COVID-19 pandemic. Self-care actions give your body and mind a chance to heal from the problems long-term stress can cause.

Take care of your body

Healthy self-care tips start with the basics. Give your body what it needs and avoid what it doesn't need. Some tips are:

• Get the right amount of sleep for you. A regular sleep schedule, when you go to bed and get up at similar times each day, can help avoid sleep problems.
• Move your body. Regular physical activity and exercise can help reduce anxiety and improve mood. Any activity you can do regularly is a good choice. That may be a scheduled workout, a walk or even dancing to your favorite music.
• Choose healthy food and drinks. Foods that are high in nutrients, such as protein, vitamins and minerals are healthy choices. Avoid food or drink with added sugar, fat or salt.
• Avoid tobacco, alcohol and drugs. If you smoke tobacco or if you vape, you're already at higher risk of lung disease. Because COVID-19 affects the lungs, your risk increases even more. Using alcohol to manage how you feel can make matters worse and reduce your coping skills. Avoid taking illegal drugs or misusing prescriptions to manage your feelings.

Take care of your mind

Healthy coping actions for your brain start with deciding how much news and social media is right for you. Staying informed, especially during a pandemic, helps you make the best choices but do it carefully.

Set aside a specific amount of time to find information in the news or on social media, stay limited to that time, and choose reliable sources. For example, give yourself up to 20 or 30 minutes a day of news and social media. That amount keeps people informed but not overwhelmed.

For COVID-19, consider reliable health sources. Examples are the U.S. Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO).

Other healthy self-care tips are:

• Relax and recharge. Many people benefit from relaxation exercises such as mindfulness, deep breathing, meditation and yoga. Find an activity that helps you relax and try to do it every day at least for a short time. Fitting time in for hobbies or activities you enjoy can help manage feelings of stress too.
• Stick to your health routine. If you see a healthcare professional for mental health services, keep up with your appointments. And stay up to date with all your wellness tests and screenings.
• Stay in touch and connect with others. Family, friends and your community are part of a healthy mental outlook. Together, you form a healthy support network for concerns or challenges. Social interactions, over time, are linked to a healthier and longer life.

Avoid stigma and discrimination

Stigma can make people feel isolated and even abandoned. They may feel sad, hurt and angry when people in their community avoid them for fear of getting COVID-19. People who have experienced stigma related to COVID-19 include people of Asian descent, health care workers and people with COVID-19.

Treating people differently because of their medical condition, called medical discrimination, isn't new to the COVID-19 pandemic. Stigma has long been a problem for people with various conditions such as Hansen's disease (leprosy), HIV, diabetes and many mental illnesses.

People who experience stigma may be left out or shunned, treated differently, or denied job and school options. They also may be targets of verbal, emotional and physical abuse.

Communication can help end stigma or discrimination. You can address stigma when you:

• Get to know people as more than just an illness. Using respectful language can go a long way toward making people comfortable talking about a health issue.
• Get the facts about COVID-19 or other medical issues from reputable sources such as the CDC and WHO.
• Speak up if you hear or see myths about an illness or people with an illness.

COVID-19 and health

The virus that causes COVID-19 is still a concern for many people. By recognizing when to get help and taking time for your health, life challenges such as COVID-19 can be managed.

• Mental health during the COVID-19 pandemic. National Institutes of Health. https://covid19.nih.gov/covid-19-topics/mental-health. Accessed March 12, 2024.
• Mental Health and COVID-19: Early evidence of the pandemic's impact: Scientific brief, 2 March 2022. World Health Organization. https://www.who.int/publications/i/item/WHO-2019-nCoV-Sci_Brief-Mental_health-2022.1. Accessed March 12, 2024.
• Mental health and the pandemic: What U.S. surveys have found. Pew Research Center. https://www.pewresearch.org/short-reads/2023/03/02/mental-health-and-the-pandemic-what-u-s-surveys-have-found/. Accessed March 12, 2024.
• Taking care of your emotional health. Centers for Disease Control and Prevention. https://emergency.cdc.gov/coping/selfcare.asp. Accessed March 12, 2024.
• #HealthyAtHome—Mental health. World Health Organization. www.who.int/campaigns/connecting-the-world-to-combat-coronavirus/healthyathome/healthyathome---mental-health. Accessed March 12, 2024.
• Coping with stress. Centers for Disease Control and Prevention. www.cdc.gov/mentalhealth/stress-coping/cope-with-stress/. Accessed March 12, 2024.
• Manage stress. U.S. Department of Health and Human Services. https://health.gov/myhealthfinder/topics/health-conditions/heart-health/manage-stress. Accessed March 20, 2020.
• COVID-19 and substance abuse. National Institute on Drug Abuse. https://nida.nih.gov/research-topics/covid-19-substance-use#health-outcomes. Accessed March 12, 2024.
• COVID-19 resource and information guide. National Alliance on Mental Illness. https://www.nami.org/Support-Education/NAMI-HelpLine/COVID-19-Information-and-Resources/COVID-19-Resource-and-Information-Guide. Accessed March 15, 2024.
• Negative coping and PTSD. U.S. Department of Veterans Affairs. https://www.ptsd.va.gov/gethelp/negative_coping.asp. Accessed March 15, 2024.
• Health effects of cigarette smoking. Centers for Disease Control and Prevention. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/health_effects/effects_cig_smoking/index.htm#respiratory. Accessed March 15, 2024.
• People with certain medical conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-with-medical-conditions.html. Accessed March 15, 2024.
• Your healthiest self: Emotional wellness toolkit. National Institutes of Health. https://www.nih.gov/health-information/emotional-wellness-toolkit. Accessed March 15, 2024.
• World leprosy day: Bust the myths, learn the facts. Centers for Disease Control and Prevention. https://www.cdc.gov/leprosy/world-leprosy-day/. Accessed March 15, 2024.
• HIV stigma and discrimination. Centers for Disease Control and Prevention. https://www.cdc.gov/hiv/basics/hiv-stigma/. Accessed March 15, 2024.
• Diabetes stigma: Learn about it, recognize it, reduce it. Centers for Disease Control and Prevention. https://www.cdc.gov/diabetes/library/features/diabetes_stigma.html. Accessed March 15, 2024.
• Phelan SM, et al. Patient and health care professional perspectives on stigma in integrated behavioral health: Barriers and recommendations. Annals of Family Medicine. 2023; doi:10.1370/afm.2924.
• Stigma reduction. Centers for Disease Control and Prevention. https://www.cdc.gov/drugoverdose/od2a/case-studies/stigma-reduction.html. Accessed March 15, 2024.
• Nyblade L, et al. Stigma in health facilities: Why it matters and how we can change it. BMC Medicine. 2019; doi:10.1186/s12916-019-1256-2.
• Combating bias and stigma related to COVID-19. American Psychological Association. https://www.apa.org/topics/covid-19-bias. Accessed March 15, 2024.
• Yashadhana A, et al. Pandemic-related racial discrimination and its health impact among non-Indigenous racially minoritized peoples in high-income contexts: A systematic review. Health Promotion International. 2021; doi:10.1093/heapro/daab144.
• Sawchuk CN (expert opinion). Mayo Clinic. March 25, 2024.

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Mental health care is hard to find, especially for people with Medicare or Medicaid

Rhitu Chatterjee

With rates of suicide and opioid deaths rising in the past decade and children's mental health declared a national emergency , the United States faces an unprecedented mental health crisis. But access to mental health care for a significant portion of Americans — including some of the most vulnerable populations — is extremely limited, according to a new government report released Wednesday.

The report, from the Department of Health and Human Services' Office of Inspector General, finds that Medicare and Medicaid have a dire shortage of mental health care providers.

The report looked at 20 counties with people on Medicaid, traditional Medicare and Medicare Advantage plans, which together serve more than 130 million enrollees — more than 40% of the U.S. population, says Meridith Seife , the deputy regional inspector general and the lead author of the report.

Medicaid serves people on low incomes, and Medicare is mainly for people 65 years or older and those who are younger with chronic disabilities.

The report found fewer than five active mental health care providers for every 1,000 enrollees. On average, Medicare Advantage has 4.7 providers per 1,000 enrollees, whereas traditional Medicare has 2.9 providers and Medicaid has 3.1 providers for the same number of enrollees. Some counties fare even worse, with not even a single provider for every 1,000 enrollees.

"When you have so few providers available to see this many enrollees, patients start running into significant problems finding care," says Seife.

The findings are especially troubling given the level of need for mental health care in this population, she says.

"On Medicare, you have 1 in 4 Medicare enrollees who are living with a mental illness," she says. "Yet less than half of those people are receiving treatment."

Among people on Medicaid, 1 in 3 have a mental illness, and 1 in 5 have a substance use disorder. "So the need is tremendous."

The results are "scary" but "not very surprising," says Deborah Steinberg , senior health policy attorney at the nonprofit Legal Action Center. "We know that people in Medicare and Medicaid are often underserved populations, and this is especially true for mental health and substance use disorder care."

Among those individuals able to find and connect with a provider, many see their provider several times a year, according to the report. And many have to drive a long way for their appointments.

"We have roughly 1 in 4 patients that had to travel more than an hour to their appointments, and 1 in 10 had to travel more than an hour and a half each way," notes Seife. Some patients traveled two hours each way for mental health care, she says.

Mental illnesses and substance use disorders are chronic conditions that people need ongoing care for, says Steinberg. "And when they have to travel an hour, more than an hour, for an appointment throughout the year, that becomes unreasonable. It becomes untenable."

"We know that behavioral health workforce shortages are widespread," says Heather Saunders , a senior research manager on the Medicaid team at KFF, the health policy research organization. "This is across all payers, all populations, with about half of the U.S. population living in a workforce shortage."

But as the report found, that's not the whole story for Medicare and Medicaid. Only about a third of mental health care providers in the counties studied see Medicare and Medicaid patients. That means a majority of the workforce doesn't participate in these programs.

This has been well documented in Medicaid, notes Saunders. "Only a fraction" of providers in provider directories see Medicaid patients, she says. "And when they do see Medicaid patients, they often only see a few."

Lower reimbursement rates and a high administrative burden prevent more providers from participating in Medicaid and Medicare, the report notes.

"In the Medicare program, they set a physician fee rate," explains Steinberg. "Then for certain providers, which includes clinical social workers, mental health counselors and marriage and family therapists, they get reimbursed at 75% of that rate."

Medicaid reimbursements for psychiatric services are even lower when compared with Medicare , says Ellen Weber , senior vice president for health initiatives at the Legal Action Center.

"They're baking in those discriminatory standards when they are setting those rates," says Steinberg.

The new report recommends that the Centers for Medicare & Medicaid Services (CMS) take steps to increase payments to providers and lower administrative requirements. In a statement, CMS said it has responded to those recommendations within the report.

According to research by Saunders and her colleagues at KFF, many states have already started to take action on these fronts to improve participation in Medicaid.

Several have upped their payments to mental health providers. "But the scale of those increases ranged widely across states," says Saunders, "with some states limiting the increase to one provider type or one type of service, but other states having rate increases that were more across the board."

Some states have also tried to simplify and streamline paperwork, she adds. "Making it less complex, making it easier to understand," says Saunders.

But it's too soon to know whether those efforts have made a significant impact on improving access to providers.

CMS has also taken steps to address provider shortages, says Steinberg.

"CMS has tried to increase some of the reimbursement rates without actually fixing that structural problem," says Steinberg. "Trying to add a little bit here and there, but it's not enough, especially when they're only adding a percent to the total rate. It's a really small increase."

The agency has also started covering treatments and providers it didn't use to cover before.

"In 2020, Medicare started covering opioid treatment programs, which is where a lot of folks can go to get medications for their substance use disorder," says Steinberg.

And starting this year, Medicare also covers "mental health counselors, which includes addiction counselors, as well as marriage and family therapists," she adds.

While noteworthy and important, a lot more needs to be done, says Steinberg. "For example, in the substance use disorder space, a lot of addiction counselors do not have a master's degree. And that's one of their requirements to be a counselor in the Medicare program right now."

Removing those stringent requirements and adding other kinds of providers, like peer support specialists, is key to improving access. And the cost of not accessing care is high, she adds.

"Over the past two decades, [in] the older adult population, the number of overdose deaths has increased fourfold — quadrupled," says Steinberg. "So this is affecting people. It is causing deaths. It is causing people to go to the hospital. It increases [health care] costs."

• Centers for Medicare & Medicaid Services
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Gender medicine ‘built on shaky foundations’, Cass review finds

Analysis finds most research underpinning clinical guidelines, hormone treatments and puberty blockers to be low quality

Review of gender services has major implications for mental health services

The head of the world’s largest review into children’s care has said that gender medicine is “built on shaky foundations”.

Dr Hilary Cass, the paediatrician commissioned to conduct a review of the services provided by the NHS to children and young people questioning their gender identity, said that while doctors tended to be cautious in implementing new findings in emerging areas of medicine, “quite the reverse happened in the field of gender care for children”.

Cass commissioned the University of York to conduct a series of analyses as part of her review.

Two papers examined the quality and development of current guidelines and recommendations for managing gender dysphoria in children and young people. Most of the 23 clinical guidelines reviewed were not independent or evidence based, the researchers found.

A third paper on puberty blockers found that of 50 studies, only one was of high quality.

Similarly, of 53 studies included in a fourth paper on the use of hormone treatment, only one was of sufficiently high quality, with little or only inconsistent evidence on key outcomes.

Here are the main findings of the reviews:

Clinical guidelines

Increasing numbers of children and young people experiencing gender dysphoria are being referred to specialist gender services. There are various guidelines outlining approaches to the clinical care of these children and adolescents.

In the first two papers, the York researchers examined the quality and development of published guidelines or clinical guidance containing recommendations for managing gender dysphoria in children and young people up to the age of 18.

They studied a total of 23 guidelines published in different countries between 1998 and 2022. All but two were published after 2010.

Most of them lacked “an independent and evidence-based approach and information about how recommendations were developed”, the researchers said.

Few guidelines were informed by a systematic review of empirical evidence and they lack transparency about how their recommendations were developed. Only two reported consulting directly with children and young people during their development, the York academics found.

“Healthcare services and professionals should take into account the poor quality and interrelated nature of published guidance to support the management of children and adolescents experiencing gender dysphoria/incongruence,” the researchers wrote.

Writing in the British Medical Journal (BMJ) , Cass said that while medicine was usually based on the pillars of integrating the best available research evidence with clinical expertise, and patient values and preferences, she “found that in gender medicine those pillars are built on shaky foundations”.

She said the World Professional Association of Transgender Healthcare (WPATH) had been “highly influential in directing international practice, although its guidelines were found by the University of York’s appraisal to lack developmental rigour and transparency”.

In the foreword to her report, Cass said while doctors tended to be cautious in implementing new findings “quite the reverse happened in the field of gender care for children”.

In one example, she said a single Dutch medical study, “suggesting puberty blockers may improve psychological wellbeing for a narrowly defined group of children with gender incongruence”, had formed the basis for their use to “spread at pace to other countries”. Subsequently, there was a “greater readiness to start masculinising/feminising hormones in mid-teens”.

She added: “Some practitioners abandoned normal clinical approaches to holistic assessment, which has meant that this group of young people have been exceptionalised compared to other young people with similarly complex presentations. They deserve very much better.”

Both papers repeatedly pointed to a key problem in this area of medicine: a dearth of good data.

She said: “Filling this knowledge gap would be of great help to the young people wanting to make informed choices about their treatment.”

Cass said the NHS should put in place a “full programme of research” looking at the characteristics, interventions and outcomes of every young person presenting to gender services, with consent routinely sought for enrolment in a research study that followed them into adulthood.

Gender medicine was “an area of remarkably weak evidence”, her review found, with study results also “exaggerated or misrepresented by people on all sides of the debate to support their viewpoint”.

Alongside a puberty blocker trial, which could be in place by December, there should be research into psychosocial interventions and the use of the masculinising and feminising hormones testosterone and oestrogen, the review found.

Hormone treatment

Many trans people who seek medical intervention in their transition opt to take hormones to masculinise or feminise their body, an approach that has been used in transgender adults for decades.

“It is a well-established practice that has transformed the lives of many transgender people,” the Cass review notes, adding that while these drugs are not without long-term problems and side-effects, for many they are dramatically outweighed by the benefits.

For birth-registered females, the approach means taking testosterone, which brings about changes including the growth of facial hair and a deepening of the voice, while for birth-registered males, it involves taking hormones including oestrogen to promote changes including the growth of breasts and an increase in body fat. Some of these changes may be irreversible.

However, in recent years a growing proportion of adolescents have begun taking these cross-sex, or gender-affirming, hormones, with the vast majority who are prescribed puberty blockers subsequently moving on to such medication.

This growing take-up among young people has led to questions over the impact of these hormones in areas ranging from mental health to sexual functioning and fertility.

Now researchers at the University of York have carried out a review of the evidence, comprising an analysis of 53 previously published studies, in an attempt to set out what is known – and what is not – about the risks, benefits and possible side-effects of such hormones on young people.

All but one study, which looked at side-effects, were rated of moderate or low quality, with the researchers finding limited evidence for the impact of such hormones on trans adolescents with respect to outcomes, including gender dysphoria and body satisfaction.

The researchers noted inconsistent findings around the impact of such hormones on growth, height, bone health and cardiometabolic effects, such as BMI and cholesterol markers. In addition, they found no study assessed fertility in birth-registered females, and only one looked at fertility in birth-registered males.

“These findings add to other systematic reviews in concluding there is insufficient and/or inconsistent evidence about the risks and benefits of hormone interventions in this population,” the authors write.

However, the review did find some evidence that masculinising or feminising hormones might help with psychological health in young trans people. An analysis of five studies in the area suggested hormone treatment may improve depression, anxiety and other aspects of mental health in adolescents after 12 months of treatment, with three of four studies reporting an improvement around suicidality and/or self-harm (one reported no change).

But unpicking the precise role of such hormones is difficult. “Most studies included adolescents who received puberty suppression, making it difficult to determine the effects of hormones alone,” the authors write, adding that robust research on psychological health with long-term follow-up was needed.

The Cass review has recommended NHS England should review the current policy on masculinising or feminising hormones, advising that while there should be the option to provide such drugs from age 16, extreme caution was recommended, and there should be a clear clinical rationale for not waiting until an individual reached 18.

Puberty blockers

Treatments to suppress puberty in adolescents became available through routine clinical practice in the UK a decade ago.

While the drugs have long been used to treat precocious puberty – when children start puberty at an extremely young age – they have only been used off-label in children with gender dysphoria or incongruence since the late 1990s. The rationale for giving puberty blockers, which originated in the Netherlands, was to buy thinking time for young people and improve their ability to smooth their transition in later life.

Data from gender clinics reported in the Cass review showed the vast majority of people who started puberty suppression went on to have masculinising or feminising hormones, suggesting that puberty blockers did not buy people time to think.

To understand the broader effects of puberty blockers, researchers at the University of York identified 50 papers that reported on the effects of the drugs in adolescents with gender dysphoria or incongruence. According to their systematic review, only one of these studies was high quality, with a further 25 papers regarded as moderate quality. The remaining 24 were deemed too weak to be included in the analysis.

Many of the reports looked at how well puberty was suppressed and the treatment’s side-effects, but fewer looked at whether the drugs had their intended benefits.

Of two studies that investigated gender dysphoria and body satisfaction, neither found a change after receiving puberty blockers. The York team found “very limited” evidence that puberty blockers improved mental health.

Overall, the researchers said “no conclusions” could be drawn about the impact on gender dysphoria, mental and psychosocial health or cognitive development, though there was some evidence bone health and height may be compromised during treatment.

Based on the York work, the Cass review finds that puberty blockers offer no obvious benefit in helping transgender males to help their transition in later life, particularly if the drugs do not lead to an increase in height in adult life. For transgender females, the benefits of stopping irreversible changes such as a deeper voice and facial hair have to be weighed up against the need for penile growth should the person opt for vaginoplasty, the creation of a vagina and vulva.

In March, NHS England announced that children with gender dysphoria would no longer receive puberty blockers as routine practice. Instead, their use will be confined to a trial that the Cass review says should form part of a broader research programme into the effects of masculinising and feminising hormones.

• Transgender
• Young people

More on this story

Cass review must be used as ‘watershed moment’ for NHS gender services, says Streeting

‘This isn’t how good scientific debate happens’: academics on culture of fear in gender medicine research

Five thousand children with gender-related distress awaiting NHS care in England

Ban on children’s puberty blockers to be enforced in private sector in England

What Cass review says about surge in children seeking gender services

Adult transgender clinics in England face inquiry into patient care

‘Children are being used as a football’: Hilary Cass on her review of gender identity services

Thousands of children unsure of gender identity ‘let down by NHS’, report finds

What are the key findings of the NHS gender identity review?

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