Skip to main content Accessibility help
×
Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-25T13:54:13.225Z Has data issue: false hasContentIssue false

9 - Depression and Anxiety in the Context of Digital Media

from Part III - Digital Media and Adolescent Mental Disorders

Published online by Cambridge University Press:  30 June 2022

Jacqueline Nesi
Affiliation:
Brown University, Rhode Island
Eva H. Telzer
Affiliation:
University of North Carolina, Chapel Hill
Mitchell J. Prinstein
Affiliation:
University of North Carolina, Chapel Hill

Summary

Today’s adolescents are often considered to be digital natives given the near-ubiquity of their access and use of these technologies. In the context of the near-ubiquity of digital media, studies have endeavored to understand the relationship between digital media use and common mental health concerns of depression and anxiety. This chapter begins with an overview of depression and anxiety among adolescents. After providing that background, the chapter reviews the state of the science of the relationship between these two critical mental health issues and social media use. Both potential risks and benefits of social media use on mental health are explored. Finally, throughout the chapter we consider other factors that may influence these relationships between digital media use, depression and anxiety. The chapter concludes with considering clinical implications and future research directions.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2022
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC-ND 4.0 https://creativecommons.org/cclicenses/

Over the past two decades, scientists have strived to understand the relationship between digital media use and two common mental illnesses in adolescents: depression and anxiety. The lifetime prevalence of depression or anxiety among youth increased from 5.4% in 2003, to 8% in 2007, to 8.4% in 2012 (Bitsko et al., Reference Bitsko, Holbrook and Ghandour2018). In this chapter we begin by defining depression and anxiety, and addressing the state of the science around the relationship between these two mental illnesses and social media use. We then consider both potential problematic digital media behaviors for depression and anxiety, as well as potential benefits of social media for youth with these conditions. Throughout this chapter we consider other factors that may influence the proposed relationships among digital media use, depression, and anxiety. We conclude the chapter with considerations of clinical implications and future research directions.

Theories of Depression and Anxiety

This chapter will frequently discuss symptoms of major depressive disorder (MDD) and symptoms of generalized anxiety disorder (GAD). We will refer to these as “depression” and “anxiety” for short, but keep in mind that, first, there are many types of depression and anxiety; and second, the research described here is not limited to participants with clinically significant MDD or GAD, but often simply with depressive or anxious symptoms.

The fifth edition of the Diagnostic and Statistical Manual (DSM-5) defines a major depressive episode as a period of at least two weeks during which an individual experiences either a depressed mood or a markedly diminished interest in normal activities (American Psychiatric Association, 2013). In adolescents, the depressed mood many manifest as irritability. Also key to the diagnosis of depression is decreased performance or increased distress in a major area of life, such as school, work, or relationships. In contrast, GAD is characterized as a period of at least six months during which a child experiences excessive and uncontrollable worry, worry that is inappropriate or out of proportion to the anticipated event. In children, this worry is often about competence or performance. Additional symptoms of GAD include restlessness, difficulty concentrating, or sleep disturbance.

There are many theories to explain the development, maintenance, and treatment of depression and anxiety. It will be helpful to have a working theory of depression and anxiety to understand its relationship to digital media use; as such, we will describe two example theories here. However, keep in mind that there are many qualified theories to describe the etiology and maintenance of mental illness – many more than can be discussed in this handbook.

One such theory is cognitive theory. According to cognitive theory, “cognition is at the core of human suffering” (Sommers-Flanagan & Sommers-Flanagan, Reference Sommers-Flanagan and Sommers-Flanagan2018, p. 273). Factors such as early life events, genetic predisposition, and caregiver modeling lead individuals to develop rigid and negative beliefs about the self, other people, and the world at large. When faced with a life stressor, an individual’s core beliefs are triggered and present as automatic thoughts. Over time, the repeated activation of automatic thoughts results in information processing, emotions, and behaviors that are consistent with depression or anxiety. Core beliefs consistent with depression or anxiety might include “I’m unlovable,” “I’m powerless,” or “I’m defective.” There are many critical events during adolescence – and, relevant to this chapter, events on social media – that might activate thoughts like these. According to cognitive theory, depression and anxiety can be reduced through the conscious revision of automatic thoughts and the core beliefs underlying them. This “validity testing” is often performed in partnership with a therapist or another trusted person.

A second theory through which we will view depression and anxiety, in relation to digital media use, is multicultural theory. This is not so much a theory for the etiology of illness as it is a lens, or an orientation, that all theorists must integrate in order to effectively explain and diagnose illness as well as guide treatment (Bitsko et al., Reference Bitsko, Holbrook and Ghandour2018). In short, multicultural theory suggests that mental illness develops in response to the oppressive nature of the dominant culture. According to multicultural theorist Derald Wing Sue, “people of color from the moment of birth are subjected to multiple racial micro-aggressions, from the media, peers, neighbors, friends, teachers and even in the educational process” (Sue et al., Reference Sue, Rivera, Capodilupo, Lin and Torino2010, p. 212). It is easy to imagine how symptoms of depression and anxiety might silently develop in response to these social forces. Although multicultural counseling does not emphasize diagnosis (in part because psychopathology has been defined using Westernized notions of normativity) treatment is possible. Healing from depression and anxiety, from a multicultural lens, must integrate culturally responsive processes and practices, often in community with culturally competent others.

State of the Science: Social Media, Depression, and Anxiety

Over the past decade, the empirical literature and lay news media have addressed at length associations between social media, depression, and anxiety. The sheer volume of studies in this area has led to a recent upswing in published systematic reviews on this topic. Two such systematic reviews found a small positive association between social media use and these two mental illnesses; however, these reviews noted that the quality and practical significance of these studies are often low, and they are typically not designed to capture the nuance of the effect (Keles et al., Reference Keles, McCrae and Grealish2019; Piteo & Ward, Reference Piteo and Ward2020). Another 2016 systematic review analyzed 70 studies looking at the relationship between social media use and depression or anxiety, and found that while passive use of social media was not associated with depression, specific behaviors (e.g., self-comparison) were (Seabrook et al., Reference Seabrook, Kern and Rickard2016). In sum, recent systematic reviews indicate that research to date is not designed to piece apart the nuanced relationship between social media use and mental health.

Challenges in Studying Depression and Social Media

The relationship between social media, depression, and anxiety is a challenging area of research for several reasons. As illnesses that wax and wane over time, assessments of depression or anxiety at a single time point may not fully capture the illness experience. A critical approach is to use measurements of mental illness that are shown to be valid measurements of the illness in question, such as the Center for Epidemiologic Studies.

The Depression Scale for Children (CES-DC), the Patient Health Questionnaire-9 (PHQ 9), the Generalized Anxiety Disorder scale – 7 (GAD 7), and the Screen for Child Anxiety Related Emotional Disorders (SCARED) are all empirically supported measures of depression or anxiety in adolescents and young adults (Cannon et al., Reference Cannon, Tiffany, Coon, Scholand, McMahon and Leppert2007; Keles et al., Reference Keles, McCrae and Grealish2019; Kroenke et al., Reference Kroenke, Spitzer and Williams2001; Piteo & Ward, Reference Piteo and Ward2020; Richardson et al., Reference Richardson, McCauley and Grossman2010; Weissman et al., Reference Weissman, Orvaschel and Padian1980). Despite the availability of valid and reliable screening tools for depression and anxiety, some studies have not employed such tools and have selected instead ad hoc measurement tools, making the results of these studies difficult to interpret (Twenge & Campbell, Reference Twenge and Campbell2019).

A second challenge when studying depression and social media is determining a measurement approach for social media use. Most commonly, studies focus on quantity of social media use in terms of hours or minutes. Screen time is typically measured using self-reported estimates, which are often inaccurate (Ellis, Reference Ellis2019; Moreno, Jelenchick, et al., Reference Moreno, Christakis and Egan2012). Other studies have employed passive observation to understand screen time, which involves asking a participant to download an application on their phone to track their social media use (Messner et al., Reference Messner, Sariyska and Mayer2019). However, these studies tend to capture time on a specific device, and adolescent technology use is known to incorporate multiple devices. It is recommended that future studies focus on other aspects of adolescents’ technology experiences, such as quality or importance placed on use. These measurement approaches are less common, and present new ways to examine media’s relationship to adolescent health.

Third, many studies examining social media use, depression, and anxiety do not focus on normative social media use. There is a wealth of studies measuring problematic social media use or social media “addiction,” specifically, as opposed to various qualities of normative use (Duradoni et al., Reference Duradoni, Innocenti and Guazzini2020; Hussain et al., Reference Hussain, Wegmann, Yang and Montag2020). While these constructs are relevant to adolescent mental health, positive associations between problematic social media use and mental illness may not apply to normative social media use (Przepiorka & Blachnio, Reference Przepiorka and Blachnio2020). Thus, much of the research on social media use and depression or anxiety among adolescents actually comments on nonnormative use, and the implications for the general population of adolescents cannot be inferred.

Key Hypotheses on the Relationship between Social Media, Depression, and Anxiety

As we consider several key hypotheses in the literature on the relationship between social media screen time, depression, and anxiety, we ask you to keep in mind the measurement and study design issues that may influence these study findings.

The first hypothesis posits that there is a positive linear relationship between social media, depression, and anxiety. That is, as social media use increases, so does risk for anxiety and depression. From a cognitive theoretical perspective, it may be that exposure to certain stimuli on social media (e.g., a photo-shopped image, a photo from a party to which one was not invited, a heartbreaking news story) might activate or reinforce existing negative beliefs about oneself (“I’m worthless”) or the world (“everything is out of control”). Further, time spent on social media might displace time spent on other behaviors, behaviors that may have resulted in mental health-promoting thoughts (e.g., “I have a knack for piano” or “I’m a good teammate”). The “crowding out” hypothesis explains positive associations between depression and screen time by saying that screen time is related to depression when it occurs at the expense of other beneficial activities (McDool et al., Reference McDool, Powell, Roberts and Taylor2020; Twenge, Joiner, Martin, & Rogers, Reference Twenge, Joiner, Martin and Rogers2018). As discussed, multiple systematic reviews support a weak positive relationship between social media use and adolescent depression or anxiety. However, because the studies reviewed are often cross-sectional, it is difficult to ascertain whether social media use causes depression or anxiety, or whether the presence of anxiety or depression makes one more prone to use social media (and less prone, for example, to activities such as exercise, in-person socialization, vocational pursuits, or recreation). Certain studies have detected greater risk for anxiety and depression after a certain threshold of social media use is met, which has been cited as three hours per day (Riehm et al., Reference Riehm, Feder and Tormohlen2019), four hours per day (Barman et al., Reference Barman, Mukhopadhyay and Bandyopadhyay2018), and nearly five hours per day (O’Keeffe et al., Reference O’Keeffe and Clarke-Pearson2011).

A second hypothesis states that a U-shaped curve best captures the relationship between internalizing symptoms and social media use, with negative mental health associated with very low or very high use. There is some empirical support for this hypothesis (Belanger et al., Reference Belanger, Akre, Berchtold and Michaud2011; Liu et al., Reference Liu, Wu and Yao2016; Moreno, Jelenchick, et al., Reference Moreno, Christakis and Egan2012). From a multicultural perspective on anxiety or depression, adolescents with nonnormative (very high or very low) use may be in other ways alienated from the dominant culture. High social media use might indicate a lack of participation in other areas of life, while very low social media involvement might signify estrangement from what now constitutes a developmentally appropriate activity: social media. Further, youth at the very high and very low ends of social media may be socioeconomically disadvantaged; they may live in low-resource settings, without consistent access to the Internet, or in contexts where social media use is the only activity available. Financial hardship, or disempowerment, is associated with depression and anxiety (Selfhout et al., Reference Selfhout, Branje, Delsing, ter Bogt and Meeus2009). Further, the U-shaped curve may also result from the stimuli encountered on social media. Frequent social media use puts minority youth at risk of daily, and sometimes hourly, evidence of minority oppression in the form of news media. Similarly, those who choose to stay off of social media may be trying to avoid these stimuli. Importantly, detecting the U-shaped curve requires the use of analytic approaches beyond traditional linear regression. Therefore, it is possible that studies presumed to support a linear positive relationship actually support the U-shaped curve hypothesis.

A third hypothesis is that there is no relationship between social media and depression, or social media and anxiety. More specifically, this hypothesis suggests that there is no population-level clinically significant relationship between these illnesses and social media use. Rather, certain subgroups may be at elevated risk for depression and anxiety due to social media use (Radovic et al., Reference Radovic, Gmelin, Stein and Miller2017) while for others there is no relationship, and for still others social media use actually promotes mental health. At a population level, this variability cannot be detected. From a multicultural perspective, this makes sense; one would never expect to find a “population level” effect of social media on depression or anxiety, in a world where oppression (and consequential mental illness) is not equally distributed. A white, cisgender child from a middle-class household is likely to see aspects of their own life reflected online; in contrast, those with any number of minority identities may feel “othered” by going online. The “no relationship” hypothesis is supported by several studies that have identified no population-level statistically significant association between social media use and depression or emotional problems (Anjum et al., Reference Anjum, Hossain, Sikder, Uddin and Rahim2019; Fardouly et al., Reference Fardouly, Magson, Johnco, Oar and Rapee2018; Ferguson, Reference Ferguson2021; Jelenchick et al., Reference Jelenchick, Eickhoff and Moreno2013). Given the additional difficulty of publishing statistically insignificant findings, it may be that more studies have detected the “null” relationship than have been published.

The fourth and final hypothesis is “it’s complicated,” which mirrors a common relationship status adolescents themselves like to use. The majority of studies focus on screen time as a measure of social media use, and it may be that other aspects of social media use relate more to depression and anxiety than does screen time. This hypothesis finds theoretical support from a cognitive perspective of anxiety and depression. Different online behaviors generate different thoughts, thoughts that may either reinforce or challenge beliefs about the self. Cognitive theory further states that avoidance is a key behavior maintaining illnesses like anxiety and depression. If an adolescent scrolls through social media primarily as a means of avoiding – rather than confronting – dreaded stimuli, social media use would likely contribute to the maintenance of anxiety. In contrast, a youth who uses their Finsta (Fake Instagram) to air the “less acceptable” sides of themselves may learn over time, through this online “validity testing,” that what they thought were unacceptable features are warmly received by peers. Last, children who already have depression or anxiety might assign social media different worth; they may compulsively check social media for evidence in support of their own worth, while a child who affords social media no such power would not feel this attachment toward use.

Few studies have been designed to test Hypothesis 4; that is, few assess the specific features or intentions underlying adolescents’ social media use. As such, it remains to be seen how the specific uses of social media differentially relate to depression and anxiety. A recently developed tool to measure the quality of use, the Adolescents’ Digital Technology Interactions and Importance scale, is a promising means of evaluating the importance that adolescents assign to different affordances of technology, including technology to bridge online/offline experiences and preferences, technology to go outside one’s identity or offline environment. and technology for social connection (Moreno et al., Reference Moreno, Binger, Zhao and Eickhoff2020). Tools like these are needed to understand the nuanced relationship between social media use on depression and anxiety in adolescents.

Where Are We Now?

A 2020 paper synthesized data from systematic reviews and meta-analyses between 2014 and 2019. This included cohort, longitudinal, and ecological momentary assessment studies (Odgers & Jensen, Reference Odgers and Jensen2020). They authors concluded that most research has been correlational, focused on adults, and has led to a mix of conflicting results. They also observed that most studies report “small associations … that do not offer a way of distinguishing cause from effect and, as estimated, are unlikely to be of clinical or practical significance” (p. 336). It has become increasingly evident that the current literature may not support Hypothesis 1, but that Hypotheses 2–4 above remain available for more nuanced and high-quality studies to address.

Potentially Problematic Digital Media Behaviors for Depression and Anxiety

As discussed, the relationship between screen time and depression and anxiety in adolescents is not straightforward. Thus, rather than focusing on time spent on social media, an alternative approach is to focus on specific digital behaviors and their relationships to depression and anxiety. Problematic or addictive social media use is discussed elsewhere in this handbook. The present chapter will discuss problematic aspects of normative use that are associated with depression and anxiety among adolescents. As a reminder, depression is often characterized by symptoms such as low mood, fatigue, diminished pleasure in activities, and thoughts of death, while anxiety is characterized by symptoms like excessive worry, sleep disturbance, and restlessness. In this section, we will examine how symptoms of depression and anxiety are related to specific adverse experiences on social media, including exposure to cyberbullying, troubling news media, and certain types of highly visual social media. Next, we will consider other variables that strengthen the observed relationships between social media use, depression, and anxiety, including fear of missing out, sleep, and gender.

Risk 1: Adverse Online Experiences

Cyberbullying

The majority of teens have experienced an instance of cyber-victimization at some point. The most common categories of cyber-victimization include name-calling, spreading of rumors, and receiving explicit or unwanted images. However, cyberbullying is less common – and often more serious (Anderson, Reference Anderson2018). Cyberbullying has occurred when cyber-victimization is repeated, intentional, and unwanted (Ansary, Reference Ansary2020). Unsurprisingly, experiencing cyberbullying is linked to depression and anxiety (Alhajji et al., Reference Alhajji, Bass and Dai2019; Barry et al., Reference Barry, Briggs and Sidoti2019; Tian et al., Reference Sommers-Flanagan and Sommers-Flanagan2018; Willenberg et al., Reference Willenberg, Wulan and Medise2020). Indeed, a previous study found that online harassment was key to explaining the observed relationship between social media use and depressive symptoms (Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018). From a cognitive theoretical perspective, experiences of cyber-victimization may cause or reinforce core beliefs associated with depression and anxiety (e.g., “I am unlovable,” “I am powerless”).

It is also important to adopt a multicultural perspective when understanding the relationship between cyberbullying and depression or anxiety. Adolescents of color frequently experience online racism, including online micro-aggressions, discrimination, and hate crimes (Moreno et al., Reference Moreno, Chassiakos and Cross2016). Some research suggests, however, that racial minority adolescents are actually less likely to report cyberbullying (Alhajji et al., Reference Alhajji, Bass and Dai2019; Edwards et al., Reference Edwards, Kontostathis and Fisher2016). It is unclear whether this finding is due to a real difference in the prevalence of cyberbullying; increased stigma in certain racial or ethnic groups around reporting cyberbullying; or because racism is so common to minority adolescents’ online experience that they do not recognize it as cyberbullying. Just as offline experiences of racism and bullying are linked to symptoms of depression and anxiety, so these symptoms can emerge from the same interactions online (Cannon et al., Reference Cannon, Tiffany, Coon, Scholand, McMahon and Leppert2007).

Adolescent females and members of sexual minority groups are also more at risk of upsetting experiences online (Kroenke et al., Reference Kroenke, Spitzer and Williams2001; Richardson et al., Reference Richardson, McCauley and Grossman2010). Research suggests females are more likely to report cyberbullying and are more negatively affected by it (Alhajji et al., Reference Alhajji, Bass and Dai2019; Rice et al., Reference Rice, Petering and Rhoades2015). Adolescent females are also more likely to be victims of digital intimate partner violence (Burns et al., Reference Burns, Birrell and Bismark2016). In combination with poor sleep, experiences of cyberbullying have been shown to fully explain the relationship between high social media use and psychological distress among females (Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018).

News Media

Research shows that 77% of adolescents obtain their news through social media (Robb, Reference Robb2020). The news is frequently troubling and, as such, exposure to it, via social media, may elevate symptoms of anxiety and depression for certain adolescents. This may be especially true for members of stigmatized or disenfranchised groups, as well as people with existing depressive or anxiety symptoms (Caporino et al., Reference Caporino, Exley and Latzman2020; Sahoo et al., Reference Sahoo, Rani, Shah, Singh, Mehra and Grover2020; Weinstein, Reference Weinstein2018). In the year 2020, for example, adolescents in the United States could not open their most-used social media apps without confronting news of a global pandemic, racial injustice, wildfires across California and Oregon, and a highly contentious election. Black and Hispanic or Latino teens describe finding the news to be more important, and feeling more affected by the news, than their white counterparts (Mundt et al., Reference Mundt, Ross and Burnett2018). Adolescents living in the United States who identify as black, transgender, or undocumented risk facing news of injustice against themselves or others with their same identities nearly every time they log into social media (Campbell & Valera, Reference Campbell and Valera2020; Ince et al., Reference Ince, Rojas and Davis2017; Leopold & Bell, Reference Leopold and Bell2017; Robb, Reference Robb2020). While social media is a platform on which many people can and do effectively advocate for social justice and raise awareness about social injustice, both cognitive and multicultural theories help to explain why encounters with news on social media might perpetuate depression and anxiety. The news can reinforce negative beliefs about the world (it’s dangerous), oneself (I’m powerless), and one’s future (people like me don’t make it very far).

Risk 2: Highly Visual Social Media

Exposure to certain highly visual social media (HVSM) is a risk factor for depression and anxiety in some adolescents. Undoubtedly, visual social media can be positive. However, in this section, we use HVSM as shorthand for risky HVSM – for example, media that enables users to modify or “improve” their appearance before uploading (Weissman et al., Reference Weissman, Orvaschel and Padian1980). Many of the most popular social media platforms for adolescents (Instagram, Snapchat, and most recently TikTok) are visual platforms that allow for appearance modification (Anderson & Jiang, 2018). While use of HVSM has also been associated with disordered eating, the relationship between social media and disordered eating is covered elsewhere in this handbook. The present section will explore the relationship between HVSM and depression and anxiety.

Some of the thoughts and feelings that characterize depression and anxiety may be triggered by exposure to HVSM. Feelings of worthlessness or fears of inadequacy may be sparked or exacerbated by frequent exposure to visually “perfected” images.

Youth may compare themselves to the people they “follow,” and find themselves lacking (Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018). From a multicultural perspective, visual media are uniquely able to transmit messages from the dominant culture: how to look, how to behave, and the types of people and behaviors that are deserving of praise.

People with existing tendencies toward poor body image are at particular risk of depression or anxiety as a result of exposure to HVSM (Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018). The tendency to be bothered if tagged in an unflattering picture is associated with depression among college students (Robinson et al., Reference Robinson, Bonnette and Howard2019). HVSM also allows for taking, editing, and uploading pictures of oneself online, which has been linked to anxiety in college students (Mills et al., Reference Mills, Musto, Williams and Tiggemann2018; Wick & Keel, Reference Wick and Keel2020). Appearance-related social comparisons, which are uniquely afforded by HVSM, have been associated with depression (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020; Hawes et al., Reference Hawes, Zimmer-Gembeck and Campbell2020). Engaging with “pro-ana” (pro-anorexia) media or “thinspiration,” which often contains depictions of thinness, “clean” foods, and calorie-deficient diets, has been linked with depression and anxiety (Fitzsimmons-Craft et al., Reference Fitzsimmons-Craft, Krauss, Costello, Floyd, Wilfley and Cavazos-Rehg2020; Jennings et al., Reference Jennings, LeBlanc, Kisch, Lancaster and Allen2020). From a cognitive perspective, social media may reinforce the negative belief that one’s worth is tied to bodily appearance. If viewers perceive themselves as failing to meet these standards, depressive or anxious symptoms may increase. It may also be that adolescents who are anxious or depressed and dissatisfied with their bodies are more likely to engage with HVSM in pursuit of information (a like, comment, or share) challenges or confirms of their self-beliefs.

However, despite the theoretical justification and some empirical support, a recent scoping review on HVSM and depression found that the relationship between HVSM and depression is inconclusive (McCrory et al., Reference McCrory, Best and Maddock2020). It may be that the relationships between HVSM and depression are simply better explained by other variables. The absence of an effect may also be due in part to a lack of research studies designed to detect this effect: research on social media use does not always distinguish between HVSM and other social media, let alone differentiate between positive and negative forms of visual social media. Further, research typically relies on quantitative methods to evaluate the relationship between HVSM and depression and anxiety, which lacks richness and possibility for participants to elaborate on their experiences.

The relationship between social media use and depression or anxiety also may be dependent on a variety of factors that increase risk for internalizing symptoms. Several of these potential moderators are discussed below.

Fear of Missing Out

Fear of missing out, or FOMO, is defined as the “pervasive apprehension that others might be having rewarding experiences from which one is absent” (Przybylski et al., Reference Przybylski, Murayama, DeHaan and Gladwell2013, p. 1841). FOMO in adolescents has been independently associated with both depression and anxiety and, less consistently, with social media use (Barry et al., Reference Barry, Sidoti, Briggs, Reiter and Lindsey2017; Franchina et al., Reference Franchina, Vanden Abeele, van Rooij, Lo Coco and De Marez2018; Przybylski et al., Reference Przybylski, Murayama, DeHaan and Gladwell2013). Given that social media is a place where the (often enviable) experiences of others are constantly on display, it is not difficult to explain the link between social media use and FOMO. More complex is to explain why adolescents who are higher in FOMO are more at risk for depression or anxiety as a consequence of social media use (Fabris et al., Reference Fabris, Marengo, Longobardi and Settanni2020). It may be that, for adolescents with tendencies toward FOMO, exposure to friends’ and influencers’ “highlight reels” creates feelings of worthlessness, worry, and dissatisfaction with one’s own daily life. From a cognitive perspective, scrolling through social media might trigger automatic thoughts, such as “no one invites me to anything” or “my life sucks in comparison with hers.” The action of scrolling through social media may also be motivated by FOMO, as depressed or anxious adolescents search for evidence to assuage or confirm the belief that they are missing out.

At present, research is mixed on whether FOMO affects the relationship between social media use and depression or anxiety in adolescents. While there is ample evidence that FOMO is associated with problematic social media use and problematic smartphone use (Franchina et al., Reference Franchina, Vanden Abeele, van Rooij, Lo Coco and De Marez2018; Przepiorka & Blachnio, Reference Przepiorka and Blachnio2020), there is insufficient evidence that FOMO explains or strengthens the relationship between typical use and depression or anxiety at a population level. In some cases, this absence of an effect may be due to insufficient measures of social media; as discussed, insufficient measures focus solely on time spent, rather than activities performed or experiences had while online.

Sleep

Sleep is critical to consider in any study of social media and mental illness. Indeed, sleep is perhaps the most consistently supported variable to explain the relationship between social media use and depression or anxiety (Alonzo et al., Reference Alonzo, Hussain, Anderson and Stranges2019; Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Lemola et al., Reference Lemola, Perkinson-Gloor and Hagmann-von Arx2015; Oshima et al., Reference Oshima, Nishida and Shimodera2012). However, studies suggesting that sleep explains the relationship between social media use and depression and anxiety have been largely cross-sectional, meaning directionality is subject to interpretation. Before exploring these hypotheses, it is important to note that poor sleep (e.g., sleeping too much or too little, trouble falling asleep) is actually a symptom of both depression and anxiety. Thus, sleep trouble is central to the experience of depression and anxiety for many people.

One hypothesis suggests that social media use causes sleeplessness, which in turn causes or exacerbates symptoms of depression or anxiety. Social media may cause sleeplessness by displacing sleeping hours and delaying bedtime (Quante et al., Reference Quante, Khandpur, Kontos, Bakker, Owens and Redline2019). The blue light exposure associated with social media use may disrupt melatonin and cause wakefulness (Blass et al., Reference Blass, Anderson, Kirkorian, Pempek, Price and Koleini2006; Levenson, Reference Levenson2016; Wahnschaffe et al., Reference Wahnschaffe, Haedel and Rodenbeck2013). It may be that social media is uniquely stimulating as compared to nonsocial online activities, given that it contains a wealth of self-relevant social information and capacity for social interaction.

A second hypotheses interprets the association in the reverse direction. That is, it may be that adolescents who are already depressed or anxious are more prone to sleep disruption. In turn, disrupted sleep leads to social media use, perhaps as adolescents seek distraction or support online. However, this hypothesis is contentious. Some research has shown that the relationship between poor sleep and social media use cannot be explained by existing depression or anxiety (Twenge & Campbell, Reference Twenge and Campbell2019; Woods & Scott, Reference Woods and Scott2016). Thus, all teens – not just those who are anxious or depressed – may benefit from finding soothing activities that are less stimulating than social media, and from following the American Academy of Pediatrics’ recommendations to keep devices out of bedrooms at nighttime (Moreno et al., Reference Moreno, Chassiakos and Cross2016).

Gender

Some studies have suggested that gender may influence the effect of social media use on depression and anxiety. Several individual studies have found that adolescent females are more likely than males to experience depression associated with social media use (Barthorpe et al., Reference Barthorpe, Winstone, Mars and Moran2020; Kelly et al., Reference Kelly, Zilanawala, Booker and Sacker2018; Twenge, Joiner, Rogers, & Martin, Reference Twenge, Joiner, Martin and Rogers2018; Twenge & Martin, Reference Twenge and Martin2020; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg2018). From a multicultural theoretical perspective, the potentially unique negative effects of social media for adolescent females finds support. The design of social media (including affordances for appearance feedback and negative self-comparison) may be uniquely oppressive to adolescent females, particularly females of color, who face great pressure from the dominant culture to conform to a certain beauty ideal (Coyne et al., Reference Coyne, Padilla-Walker, Holmgren and Stockdale2019; Messner et al., Reference Messner, Sariyska and Mayer2019). Under multicultural theory, symptoms of anxiety and depression (e.g., guilt, worry, restlessness, diminished pleasure) should not be understood as reflecting psychopathology, but instead reflecting reasonable reactions to the dominant culture.

However, summaries of research found in recent systematic reviews conclude no consistent effect of gender on the relationship between social media use and internalizing symptoms, and typically conclude that more research is needed on this topic (Keles et al., Reference Keles, McCrae and Grealish2019; Piteo & Ward, Reference Piteo and Ward2020). A meta-analysis evaluated 67 independent samples of a combined 19,652 participants, and found that the effect of gender on the relationship between time spent on social media and psychological well-being was insignificant (Huang, Reference Huang2017). A recent review of reviews reported something slightly different: after controlling for confounding variables, the least-depressed adolescent females in the sample had “slightly increased risk for depressive symptoms with daily social media use”(Odgers & Jensen, Reference Odgers and Jensen2020, p. 341).

While the moderating effect of gender is inconclusive, research supports that males and females do use social media differently (Boyle et al., Reference Boyle, LaBrie, Froidevaux and Witkovic2016). Research suggests that females spend more time online and are more likely to say they are nearly constant online users compared to adolescent males (50% vs. 39%) (Anderson & Jiang, 2018; Duggan, Reference Duggan2013). Females are more likely than males to use social media for self-expression, including expression of joy and pride, as well as expression of negative emotions, such as worry, stress, and depression (Egan & Moreno, Reference Egan and Moreno2011; Moreno, Christakis, et al., Reference Moreno, Christakis and Egan2012; Waterloo et al., Reference Waterloo, Baumgartner, Peter and Valkenburg2018). Given that females compared to males are likely to use online platforms for emotional expression, it is also possible that females with depression turn to social media more readily for support. Thus, it may be that social media use does not predict depressive symptoms, but greater depressive symptoms predict more frequent social media use, especially among females (Heffer et al., Reference Heffer, Good, Daly, MacDonell and Willoughby2019).

Potentially Beneficial Digital Media Behaviors for Depression and Anxiety

Certain uses of social media may promote mental health among adolescents. As previously mentioned, cognitive theory would suggest that online experiences that confirm positive beliefs about the self, and those that challenge or invalidate negative beliefs, are likely to reduce depression or anxiety. From a multicultural perspective, uses of social media that create identity-affirming alternatives to offline spaces may mitigate depression and anxiety. However, the hypothesis that social media use directly reduces anxiety or depression is difficult to test. Similar to studies that try to assess whether social media use directly increases depression or anxiety, there are methodologic barriers to these assessments. That being said, empirical research does support a positive association between social media use and adolescents’ mental health. This is especially true for adolescents with depression and anxiety, adolescents with unique and marginalized identities, as well as typical adolescents who seek to maintain or promote mental wellness online.

Benefits of Social Media for the Typical Adolescent

Typical adolescents report using social media in ways that may ward off depressive and anxious symptoms, both by seeking information related to these symptoms and by finding support and connection online (Rideout et al., Reference Rideout, Fox and Trust2018). Research has shown that adolescents often feel happy, amused, or closer to friends while using social media (Weinstein, Reference Weinstein2018; Wenninger et al., Reference Wenninger, Krasnova and Buxmann2019). While studies over the years have repeatedly demonstrated social networks and support contribute to overall and mental health, too much online social networking may put one at risk for negative experiences, cognitions, and emotions (Ahn, Reference Ahn2012; Longobardi et al., Reference Longobardi, Settanni, Fabris and Marengo2020; Negriff, Reference Negriff2019; Rajani et al., Reference Rajani, Berman and Rozanski2011). Those who experience isolation, stress, and unmet needs in their offline worlds may find corrective experiences or buffering effects by going online (Nick et al., Reference Nick, Cole, Cho, Smith, Carter and Zelkowitz2018; Prochnow et al., Reference Prochnow, Patterson and Hartnell2020). One qualitative interview study found that young people naturally and organically developed close-knit communities of close friends, often in the form of private Instagram accounts, on which privacy was a priority and emotional disclosure was safe and commonplace (Gibson & Trnka, Reference Gibson and Trnka2020). These findings support the positive, adaptive, and strategic use of social media for typical adolescents.

Benefits of Social Media for Adolescents with Depression and Anxiety

Adolescents with depression and anxiety use social media differently than their mentally well peers (Radovic et al., Reference Radovic, Gmelin, Stein and Miller2017). Thus, the commonly cited associations between social media use, depression, and anxiety may be explained in part by the unique offerings of social media for depressed and anxious youth. Youth have described feeling motivated to share their depression online because it is perceived as easier than sharing in-person, and because they are hoping to connect with others who understand and have had similar experiences (Carey et al., Reference Carey, Carreiro and Chapman2018; Rideout et al., Reference Rideout, Fox and Trust2018). A systematic narrative review of 28 studies on online help-seeking among adolescents found that adolescents commonly cited anonymity, ease of access, and sense of community as driving motivators to find mental health support online (Pretorius et al., Reference Pretorius, Chambers and Coyle2019). Thus, youth who are already experiencing depression and anxiety may find particular mental health benefits by going online.

Benefits of Social Media for Marginalized Adolescents

Social media may be particularly beneficial to marginalized adolescents, for whom it may not be safe, feasible, or appealing to find support in the offline world. This may include homeless youth, as well as racial, sexual, and gender minority youth. From a multicultural perspective, the possibility of finding support for mental illness while remaining anonymous may help adolescents to overcome shame and stigma, imposed by the dominant culture, around help-seeking. Further, in the wide world of online support, adolescents may be more likely to find support that is tailored to their cultural values and worldview.

A scoping review of 19 studies on individuals experiencing homelessness and their social media use found that for homeless youth, seeking help online minimized barriers and prejudices often encountered in-person (Calvo & Carbonell, Reference Calvo and Carbonell2019). Perhaps for the same reason, sexual and gender minority youth are significantly more likely than straight and cisgender youth to go online for information about depression and anxiety (Marengo et al., Reference Marengo, Longobardi, Fabris and Settanni2018; Rideout et al., Reference Rideout, Fox and Trust2018). Transgender youth have affirmed that social media is a place to garner emotional, informational, and “appraisal” support, or the validation in seeing their same experience reflected in others (Selkie et al., Reference Selkie, Adkins, Masters, Bajpai and Shumer2020). In sum, both qualitative and quantitative research studies support that homeless youth, as well as sexual and gender minority youth, use social media to find affirming communities and avoid discrimination (Craig et al., Reference Craig, McInroy, McCready and DeCesare2015; Escobar-Viera et al., Reference Escobar-Viera, Shensa and Hamm2020; Jenzen, Reference Jenzen2017).

Another area of study has focused on experiences of racial minority youth. Perhaps due to a lack of culturally competent healthcare providers offline, black youth are more likely to go online to share their health stories (Rideout et al., Reference Rideout, Fox and Trust2018). One study interviewed 25 racially and economically diverse undergraduate students to understand the empowering and disempowering aspects of social media (Brough et al., Reference Brough, Literat and Ikin2020). Interviewees noted that social media allowed them to find and connect with similar others (e.g., by using the #blackLGBTQ hashtag), as well as to represent their voice both by sharing their own stories and observing as others share theirs. However, the same youth noted that social media can have the opposite effect, encouraging conformity to the dominant culture and exposing them to lifestyles that were not relatable (Brough et al., Reference Brough, Literat and Ikin2020). Thus, while social media may have unique affordances for marginalized youth, its potential to “other” its end users could also worsen mental health symptoms.

With the exception of the studies mentioned above, there is less support for the differential use of social media by racial or ethnic minorities. None of the recent systematic reviews on the relationship between social media use and internalizing symptoms mention race, although two call for more diverse samples (Odgers & Jensen, Reference Odgers and Jensen2020; Orben, Reference Orben2020). This suggests that there is little conclusive evidence on the differential use of social media by race, as well as any differences in associated mental health outcomes, whether positive or negative. Given that certain racial and ethnic groups may have fewer opportunities for culturally competent in-person mental health care and support, it is important to understand how they have built alternative spaces online.

Future Research Directions

After describing the literature to date, including studies that examine the relationships between depression and social media, problematic behaviors and experiences on social media, variables that may affect the relationship between social media and mental health, as well as the ways in which social media may alleviate symptoms of depression and anxiety, it is time to consider future research directions. The content above has noted gaps in the current understanding of these topics and exciting opportunities for future research in this area.

From the evidence surrounding depression and social media, we conclude with four critical considerations to move the research forward. These include improved assessments, advanced and nuanced analysis approaches, interpreting results with regard to their practical significance, and improving transparency in linking findings to conclusions. Further, we recommend that future studies incorporate measurements and hypotheses to address potential positive and negative associations between social media use, depression, and anxiety.

First, for improved assessments, many studies of depression do not use validated measurements for depression, leading to findings with limited clinical implications. Further, assessing technology use has most often focused on self-reported quantity of use, leading to biased and inaccurate assessments. Knowing that the vast majority of youth carry smartphones in their pockets, and often use devices passively (for example, walking while listening to music) and other devices simultaneously (for example, performing schoolwork on one’s laptop while using a smartphone as a calculator), accurately reporting the time spent on technology is next to impossible and not always meaningful. Improving technology assessments may involve further considerations of quality of use, such as through understanding emotional investment in use, importance placed on use, and the extent to which device use displaces other activities. Further, because offline activities are limited (either due to availability of resources or, the reality of offline discrimination, or recently, by the global COVD-19 pandemic), technology use may not be a marker of risk so much as a necessary path for education, entertainment, support, and connection.

Second, for advanced and nuanced analytic approaches, many previous studies have used population-level analyses such as linear or logistic regression across single populations. Future studies should consider more nuanced analysis approaches, such as quadratic analysis or latent class analysis to identify differences within groups. This approach would allow for detection and appreciation of individual differences that shape interactions with technology (Orben, Reference Orben2020).

Understanding of practical significance, represented by statistical effect sizes, is also important, as many studies of media identify small effect sizes that are unlikely to drive clinical illness states. Putting these results into context is critical to help readers understand what behaviors are necessary to modify, and what behaviors lose practical significance in the context of an adolescent’s whole health.

Finally, we recommend that researchers evaluating social media, depression, and anxiety consider hypotheses that incorporate the potential for both positive and negative health effects, especially within at-risk subgroups. One study using this approach found that 46% of adolescent participants indicated that social media had a positive effect on their mood, while 41% reported neither a positive nor negative effect, and only 6% reported a negative effect (Wright et al., Reference Wright, Garside, Allgar, Hodkinson and Thorpe2020). Measuring diverse uses and motivations for use, alongside validated measures of depression and anxiety, would allow for fuller consideration of social media’s effects on a study population, subgroup or individual. Specifically, the social media use among racial minority youth is underexplored. Thus, research should aim to understand the effects of social media on mental health within subgroups and individuals, especially individuals who are frequent targets of discrimination.

Clinical and Intervention Resources

Resources to promote healthy social media use may benefit both clinicians working with adolescents, and interventionists seeking new approaches to test.

There are several key tools and concepts that can be considered toward these goals:

  1. 1. The American Academy of Pediatrics policy statement, “Media Use among School-aged Children and Adolescents,” recommended that parents establish media use rules to promote safe and healthy media use (Moreno et al., Reference Moreno, Chassiakos and Cross2016). The policy statement proposed that families create a Family Media Use Plan to select and engage with media use rules. This plan is available online and includes a Media Use Plan in which families can select family rules and expectations around media use. It also includes a Media Time Calculator that allows teens to plan and consider how they spend their time during a given day, including time for media use.

  2. 2. Healthy Internet Use Model. The Healthy Internet Use Model focuses on three key concepts: balance, boundaries, and communication (Moreno, Reference Moreno2013).

    • Balance: The balance between online and offline time is a critical concept to discuss with youth. Spending time offline, including hanging out with friends, exercising, or spending time outside, is critical to adolescent development. Further, achieving balance provides protection against concerns such as problematic technology use.

    • Boundaries: Boundaries refers to setting limits around what youth are willing to display about themselves online or on social media, as well as setting limits in where adolescents spend their time online. Discussing guidelines on what types of personal information are not appropriate to post on social media sites with teens can help prevent them from several online safety risks. These risks include being targets of bullying, unwanted solicitation, or embarrassment.

    • Communication: Just as with many tenets of adolescent health, parents should discuss social media and technology with their adolescents early and often. Establishing home rules for social media and technology use as soon as the child begins using these tools is an important way to promote healthy technology use from the beginning.

Further, adolescents should be advised that social media can promote mental health but can also make it worse. Social media can negatively affect health when it displaces other health-promoting activities, like sleep and physical activity. However, social media use that falls within normative ranges should not be the focus of modification. Rather, adolescent patients should be encouraged to pursue those aspects of social media use that research suggests promote mental health, while reducing or eliminating social media use associated with depression and anxiety.

References

Ahn, J. (2012). The effect of social network sites on adolescents’ social and academic development: Current theories and controversies. Journal of the American Society for Information Science and Technology, 62(8), 14351445. https://doi.org/10.1002/asi.21540Google Scholar
Alhajji, M., Bass, S., & Dai, T. (2019). Cyberbullying, mental health, and violence in adolescents and associations with sex and race: Data from the 2015 Youth Risk Behavior Survey. Global Pediatric Health, 6, Article 2333794X19868887. https://doi.org/10.1177/2333794X19868887Google Scholar
Alonzo, R. T., Hussain, J., Anderson, K., & Stranges, S. (2019). Interplay between social media use, sleep quality and mental health outcomes in youth: A systematic review. Sleep Medicine, 64(64), S365S365. https://doi.org/10.1016/j.smrv.2020.101414CrossRefGoogle Scholar
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®) (5th ed.). http://www.psych.org/MainMenu/Research/DSMIV.aspxGoogle Scholar
Anderson, M. (2018, September 27). A majority of teens have experienced some form of cyberbullying. https://www.pewresearch.org/internet/2018/09/27/a-majority-of-teens-have-experienced-some-form-of-cyberbullying/Google Scholar
Anderson, M., & Jiang, J. (2018, May 31). Teens, social media, and technology 2018. Pew Research Center. http://www.pewinternet.org/2018/05/31/teens-social-media-technology-2018/Google Scholar
Anjum, A., Hossain, S., Sikder, T., Uddin, M. E., & Rahim, D. A. (2019, November 6). Investigating the prevalence of and factors associated with depressive symptoms among urban and semi-urban school adolescents in Bangladesh: A pilot study. International Health. https://doi.org/10.1093/inthealth/ihz092CrossRefGoogle Scholar
Ansary, N. S. (2020). Cyberbullying: Concepts, theories, and correlates informing evidence-based best practices for prevention. Aggression and Violent Behavior, 50, Article 101343. https://doi.org/ARTN 101343 10.1016/j.avb.2019.101343CrossRefGoogle Scholar
Barman, L., Mukhopadhyay, D. K., & Bandyopadhyay, G. K. (2018). Use of social networking site and mental disorders among medical students in Kolkata, West Bengal. Indian Journal of Psychiatry, 60(3), 340345. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_210_18Google Scholar
Barry, C. T., Briggs, S. M., & Sidoti, C. L. (2019). Adolescent and parent reports of aggression and victimization on social media: Associations with psychosocial adjustment. Journal of Child and Family Studies, 28(8), 22862296. https://doi.org/10.1007/s10826–019-01445-1Google Scholar
Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., & Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. Journal of Adolescence, 61, 111. https://doi.org/10.1016/j.adolescence.2017.08.005Google Scholar
Barthorpe, A., Winstone, L., Mars, B., & Moran, P. (2020). Is social media screen time really associated with poor adolescent mental health? A time use diary study. Journal of Affective Disorders, 274, 864870. https://doi.org/10.1016/j.jad.2020.05.106CrossRefGoogle ScholarPubMed
Belanger, R. E., Akre, C., Berchtold, A., & Michaud, P. A. (2011). A U-shaped association between intensity of internet use and adolescent health. Pediatrics, 127(2), e330–335. https://doi.org/10.1542/peds.2010-1235Google Scholar
Bitsko, R. H., Holbrook, J. R., Ghandour, R. M., et al. (2018). Epidemiology and impact of health care provider-diagnosed anxiety and depression among US children. Journal of Developmental and Behavioral Pediatrics, 39(5), 395403. https://doi.org/10.1097/Dbp.0000000000000571CrossRefGoogle ScholarPubMed
Blass, E. M., Anderson, D. R., Kirkorian, H. L., Pempek, T. A., Price, I., & Koleini, M. F. (2006). On the road to obesity: Television viewing increases intake of high-density foods. Physiology & Behavior, 88(4–5), 597604. https://doi.org/10.1016/j.physbeh.2006.05.035Google Scholar
Boyle, S. C., LaBrie, J. W., Froidevaux, N. M., & Witkovic, Y. D. (2016). Different digital paths to the keg? How exposure to peers’ alcohol-related social media content influences drinking among male and female first-year college students. Addictive Behaviors, 57, 2129. https://doi.org/10.1016/j.addbeh.2016.01.011Google Scholar
Brough, M., Literat, I., & Ikin, A. (2020). “Good social media?”: Underrepresented youth perspectives on the ethical and equitable design of social media platforms. Social Media + Society, 6(2). https://doi.org/10.1177/2056305120928488CrossRefGoogle Scholar
Burns, J. M., Birrell, E., Bismark, M., et al. (2016). The role of technology in Australian youth mental health reform. Australian Health Review, 40(5), 584590. https://doi.org/10.1071/AH15115CrossRefGoogle ScholarPubMed
Calvo, F., & Carbonell, X. (2019). Is Facebook use healthy for individuals experiencing homelessness? A scoping review on social networking and living in the streets. Journal of Mental Health, 28(5), 505519. https://doi.org/10.1080/09638237.2019.1608927CrossRefGoogle ScholarPubMed
Campbell, F., & Valera, P. (2020). “The only thing new is the cameras”: A study of US college students’ perceptions of police violence on social media. Journal of Black Studies, 51(7), 654670. https://doi.org/Artn 0021934720935600 10.1177/0021934720935600Google Scholar
Cannon, D. S., Tiffany, S. T., Coon, H., Scholand, M. B., McMahon, W. M., & Leppert, M. F. (2007). The PHQ-9 as a brief assessment of lifetime major depression. Psychological Assessment, 19(2), 247251. https://doi.org/10.1037/1040-3590.19.2.247Google Scholar
Caporino, N. E., Exley, S., & Latzman, R. D. (2020). Youth anxiety about political news. Child Psychiatry & Human Development, 51(5), 683698. https://doi.org/10.1007/s10578–020-00972-zCrossRefGoogle ScholarPubMed
Carey, J. L., Carreiro, S., Chapman, B., et al. (2018). SoMe and self harm: The use of social media in depressed and suicidal youth. Proceedings of the 51st Annual Hawaii International Conference on System Sciences, 2018, 33143319. https://doi.org/10.24251/HICSS.2018.420Google ScholarPubMed
Choukas-Bradley, S., Nesi, J., Widman, L., & Galla, B. M. (2020). The Appearance-Related Social Media Consciousness Scale: Development and validation with adolescents. Body Image, 33, 164174. https://doi.org/10.1016/j.bodyim.2020.02.017CrossRefGoogle ScholarPubMed
Coyne, S. M., Padilla-Walker, L. M., Holmgren, H. G., & Stockdale, L. A. (2019). Instagrowth: A longitudinal growth mixture model of social media time use across adolescence. Journal of Research on Adolescence, 29(4), 897907. https://doi.org/10.1111/jora.12424Google Scholar
Craig, S. L., McInroy, L., McCready, L. T., & DeCesare, D. M. (2015). Connecting without fear: Clinical implications of the consumption of information and communication technologies by sexual minority youth and young adults. Clinical Social Work, 43, 159168. https://doi.org/10.1007/s10615-014-0505-2CrossRefGoogle Scholar
Duggan, M. (2013). Social networking fact sheet. Pew Research Center’s Internet Project library survey. http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/Google Scholar
Duradoni, M., Innocenti, F., & Guazzini, A. (2020). Well-being and social media: A systematic review of Bergen Addiction Scales. Future Internet, 12(2), Article 24. https://doi.org/ARTN 24 10.3390/fi12020024Google Scholar
Edwards, L., Kontostathis, A. E., & Fisher, C. (2016). Cyberbullying, race/ethnicity and mental health outcomes: A review of the literature. Media and Communication, 4(3), 7178. https://doi.org/10.17645/mac.v4i3.525Google Scholar
Egan, K. G., & Moreno, M. A. (2011). Prevalence of stress references on college freshmen Facebook profiles. Computers, Informatics, Nursing, 29(10), 586592. https://doi.org/10.1097/NCN.0b013e3182160663Google Scholar
Ellis, D. A. (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. Computers in Human Behavior, 97, 6066. https://doi.org/10.1016/j.chb.2019.03.006CrossRefGoogle Scholar
Escobar-Viera, C., Shensa, A., Hamm, M., et al. (2020). “I don’t feel like the odd one”: Utilizing content analysis to compare the effects of social media use on well-being among sexual minority and nonminority US young adults. American Journal of Health Promotion, 34(3), 285293. https://doi.org/10.1177/0890117119885517CrossRefGoogle ScholarPubMed
Fabris, M. A., Marengo, D., Longobardi, C., & Settanni, M. (2020). Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: The role of stress associated with neglect and negative reactions on social media. Addictive Behaviors, 106. https://doi.org/ARTN 106364 10.1016/j.addbeh.2020.106364Google Scholar
Fardouly, J., Magson, N. R., Johnco, C. J., Oar, E. L., & Rapee, R. M. (2018). Parental control of the time preadolescents spend on social media: Links with preadolescents’ social media appearance comparisons and mental health. Journal of Youth and Adolescence, 47(7), 14561468. https://doi.org/10.1007/s10964–018-0870-1Google Scholar
Ferguson, C. J. (2021). Links between screen use and depressive symptoms in adolescents over 16 years: Is there evidence for increased harm? Developmental Science, 24(1), e13008. https://doi.org/10.1111/desc.13008CrossRefGoogle ScholarPubMed
Fitzsimmons-Craft, E. E., Krauss, M. J., Costello, S. J., Floyd, G. M., Wilfley, D. E., & Cavazos-Rehg, P. A. (2020). Adolescents and young adults engaged with pro-eating disorder social media: Eating disorder and comorbid psychopathology, health care utilization, treatment barriers, and opinions on harnessing technology for treatment. Eating and Weight Disorders, 25(6), 16811692. https://doi.org/10.1007/s40519–019-00808-3CrossRefGoogle Scholar
Franchina, V., Vanden Abeele, M., van Rooij, A. J., Lo Coco, G., & De Marez, L. (2018). Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. International Journal of Environmental Research and Public Health, 15(10), Article 2319. https://doi.org/10.3390/ijerph15102319CrossRefGoogle ScholarPubMed
Gibson, K., & Trnka, S. (2020). Young people’s priorities for support on social media: “It takes trust to talk about these issues”. Computers in Human Behavior, 102, 238247. https://doi.org/10.1016/j.chb.2019.08.030Google Scholar
Hawes, T., Zimmer-Gembeck, M. J., & Campbell, S. M. (2020). Unique associations of social media use and online appearance preoccupation with depression, anxiety, and appearance rejection sensitivity. Body Image, 33, 6676. https://doi.org/10.1016/j.bodyim.2020.02.010Google Scholar
Heffer, T., Good, M., Daly, O., MacDonell, E., & Willoughby, T. (2019). The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: An empirical reply to Twenge et al. (2018). Clinical Psychological Science, 7(3), 462470. https://doi.org/10.1177/2167702618812727Google Scholar
Huang, C. (2017). Time spent on social network sites and psychological well-being: A meta-analysis. Cyberpsychology, Behavior, and Social Networking, 20(6), 346354. https://doi.org/10.1089/cyber.2016.0758CrossRefGoogle ScholarPubMed
Hussain, Z., Wegmann, E., Yang, H. B., & Montag, C. (2020). Social networks use disorder and associations with depression and anxiety symptoms: A systematic review of recent research in China. Frontiers in Psychology, 11, Article 211. https://doi.org/ARTN 21110.3389/fpsyg.2020.00211Google Scholar
Ince, J., Rojas, F., & Davis, C. A. (2017). The social media response to Black Lives Matter: How Twitter users interact with Black Lives Matter through hashtag use. Ethnic and Racial Studies, 40(11), 18141830. https://doi.org/10.1080/01419870.2017.1334931CrossRefGoogle Scholar
Jelenchick, L. A., Eickhoff, J. C., & Moreno, M. A. (2013). “Facebook depression?”: Social networking site use and depression in older adolescents. Journal of Adolescent Health, 52(1), 128130. https://doi.org/10.1016/j.jadohealth.2012.05.008Google Scholar
Jennings, A. F., LeBlanc, H., Kisch, K., Lancaster, S., & Allen, J. (2020). Blurred boundaries between pro-anorexia and fitspiration media? Diverging cognitive and emotional effects. Eating Disorders, 1–11. https://doi.org/10.1080/10640266.2020.1712634Google Scholar
Jenzen, O. (2017). Trans youth and social media: Moving between counterpublics and the wider web. Gender, Place and Culture, 24(11), 16261641. https://doi.org/10.1080/0966369X.2017.1396204Google Scholar
Keles, B., McCrae, N., & Grealish, A. (2019). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 7993. https://doi.org/10.1080/02673843.2019.1590851Google Scholar
Kelly, Y., Zilanawala, A., Booker, C., & Sacker, A. (2018). Social media use and adolescent mental health: Findings from the UK Millennium Cohort Study. EClinicalMedicine, 6, 5968. https://doi.org/10.1016/j.eclinm.2018.12.005Google Scholar
Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606613. https://doi.org/10.1046/j.1525-1497.2001.016009606.xCrossRefGoogle ScholarPubMed
Lemola, S., Perkinson-Gloor, N., Hagmann-von Arx, P., et al. (2015). Morning cortisol secretion in school-age children is related to the sleep pattern of the preceding night. Psychoneuroendocrinology, 52, 297301. https://doi.org/10.1016/j.psyneuen.2014.12.007Google Scholar
Leopold, J., & Bell, M. P. (2017). News media and the racialization of protest: An analysis of Black Lives Matter articles. Equality, Diversity and Inclusion, 36(8), 720735. https://doi.org/10.1108/Edi-01-2017-0010Google Scholar
Levenson, J. C. (2016). The association between social media use and sleep disturbance among young adults. Preventative Medicine, 85, 3641. https://doi.org/10.1016/j.ypmed.2016.01.001Google Scholar
Liu, M., Wu, L., & Yao, S. (2016). Dose-response association of screen time-based sedentary behaviour in children and adolescents and depression: A meta-analysis of observational studies. British Journal of Sports Medicine, 50(20), 12521258. https://doi.org/10.1136/bjsports-2015-095084Google Scholar
Longobardi, C., Settanni, M., Fabris, M. A., & Marengo, D. (2020). Follow or be followed: Exploring the links between Instagram popularity, social media addiction, cyber victimization, and subjective happiness in Italian adolescents. Children and Youth Services Review, 113, Article 104955. https://doi.org/10.1016/j.childyouth.2020.104955Google Scholar
Marengo, D., Longobardi, C., Fabris, M. A., & Settanni, M. (2018). Highly-visual social media and internalizing symptoms in adolescence: The mediating role of body image concerns. Computers in Human Behavior, 82, 6369. https://doi.org/10.1016/j.chb.2018.01.003Google Scholar
McCrory, A., Best, P., & Maddock, A. (2020). The relationship between highly visual social media and young people’s mental health: A scoping review. Children and Youth Services Review, 115. https://doi.org/ARTN 105053 10.1016/j.childyouth.2020.105053CrossRefGoogle Scholar
McDool, E., Powell, P., Roberts, J., & Taylor, K. (2020). The internet and children’s psychological wellbeing. Journal of Health Economics, 69, Article 102274. https://doi.org/10.1016/j.jhealeco.2019.102274Google Scholar
Messner, E. M., Sariyska, R., Mayer, B., et al. (2019). Insights: Future implications of passive smartphone sensing in the therapeutic context. Verhaltenstherapie, 29(3), 155165. https://doi.org/10.1159/000501735Google Scholar
Mills, J. S., Musto, S., Williams, L., & Tiggemann, M. (2018). “Selfie” harm: Effects on mood and body image in young women. Body Image, 27, 8692. https://doi.org/10.1016/j.bodyim.2018.08.007Google Scholar
Moreno, M. A. (2013). Sex, drugs ‘n Facebook: A parent’s toolkit for promoting healthy internet use. Hunter House, Inc.Google Scholar
Moreno, M. A., Binger, K., Zhao, Q., & Eickhoff, J. (2020). Measuring interests not minutes: Development and validation of the Adolescents’ Digital Technology Interactions and Importance Scale (ADTI). Journal of Medical Internet Research, 22(2), e16736. https://doi.org/10.2196/16736CrossRefGoogle Scholar
Moreno, M. A., Chassiakos, Y. R., Cross, C., et al. (2016). Media use in school-aged children and adolescents. Pediatrics, 138(5). https://doi.org/ARTNe2016259210.1542/peds.2016-2592Google Scholar
Moreno, M. A., Christakis, D. A., Egan, K. G., et al. (2012). A pilot evaluation of associations between displayed depression references on Facebook and self-reported depression using a clinical scale. Journal of Behavioral Health Services & Research, 39(3), 295304. https://doi.org/10.1007/s11414–011-9258-7Google Scholar
Moreno, M. A., Jelenchick, L., Koff, R., & Eickhoff, J. (2012). Depression and internet use among older adolescents: An experience sampling approach. Psychology, 3(9), 743748. https://doi.org/10.4236/psych.2012.329112Google Scholar
Mundt, M., Ross, K., & Burnett, C. M. (2018). Scaling social movements through social media: The case of Black Lives Matter. Social Media + Society, 4(4). https://doi.org/Artn 2056305118807911 10.1177/2056305118807911CrossRefGoogle Scholar
Negriff, S. (2019). Depressive symptoms predict characteristics of online social networks. Journal of Adolescent Health, 65(1), 101106. https://doi.org/10.1016/j.jadohealth.2019.01.026Google Scholar
Nick, E. A., Cole, D. A., Cho, S. J., Smith, D. K., Carter, T. G., & Zelkowitz, R. L. (2018). The Online Social Support Scale: Measure development and validation. Psychological Assessment, 30(9), 11271143. https://doi.org/10.1037/pas0000558Google Scholar
O’Keeffe, G. S., Clarke-Pearson, K., & Council on Communications & Media. (2011). The impact of social media on children, adolescents, and families. Pediatrics, 127(4), 800804. https://doi.org/10.1542/peds.2011-0054Google Scholar
Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age: Facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 61(3), 336348. https://doi.org/10.1111/jcpp.13190Google Scholar
Orben, A. (2020). Teenagers, screens and social media: A narrative review of reviews and key studies. Social Psychiatry and Psychiatric Epidemiology, 55(4), 407414. https://doi.org/10.1007/s00127–019-01825-4Google Scholar
Oshima, N., Nishida, A., Shimodera, S., et al. (2012). The suicidal feelings, self-injury, and mobile phone use after lights out in adolescents. Journal of Pediatric Psychology, 37(9), 10231030. https://doi.org/10.1093/jpepsy/jss072Google Scholar
Piteo, E. M., & Ward, K. (2020). Review: Social networking sites and associations with depressive and anxiety symptoms in children and adolescents – A systematic review. Child and Adolescent Mental Health, 25(4), 201216. https://doi.org/10.1111/camh.12373Google Scholar
Pretorius, C., Chambers, D., & Coyle, D. (2019). Young people’s online help-seeking and mental health difficulties: Systematic narrative review. Journal of Medical Internet Research, 21(11), e13873. https://doi.org/10.2196/13873Google Scholar
Prochnow, T., Patterson, M. S., & Hartnell, L. (2020). Social support, depressive symptoms, and online gaming network communication. Mental Health and Social Inclusion, 24(1), 4958. https://doi.org/10.1108/Mhsi-11-2019-0033Google Scholar
Przepiorka, A., & Blachnio, A. (2020). The role of Facebook intrusion, depression, and future time perspective in sleep problems among adolescents. Journal of Research on Adolescence, 30(2), 559569. https://doi.org/10.1111/jora.12543Google Scholar
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 18411848. https://doi.org/10.1016/j.chb.2013.02.014Google Scholar
Quante, M., Khandpur, N., Kontos, E. Z., Bakker, J. P., Owens, J. A., & Redline, S. (2019). “Let’s talk about sleep”: A qualitative examination of levers for promoting healthy sleep among sleep-deprived vulnerable adolescents. Sleep Medicine, 60, 8188. https://doi.org/10.1016/j.sleep.2018.10.044Google Scholar
Radovic, A., Gmelin, T., Stein, B. D., & Miller, E. (2017). Depressed adolescents’ positive and negative use of social media. Journal of Adolescence, 55, 515. https://doi.org/10.1016/j.adolescence.2016.12.002Google Scholar
Rajani, R., Berman, D. S., & Rozanski, A. (2011). Social networks: Are they good for your health? The era of Facebook and Twitter. QJM, 104(9), 819820. https://doi.org/10.1093/qjmed/hcr078Google Scholar
Rice, E., Petering, R., Rhoades, H., et al. (2015). Cyberbullying perpetration and victimization among middle-school students. American Journal of Public Health, 105(3), E66E72. https://doi.org/10.2105/Ajph.2014.302393Google Scholar
Richardson, L. P., McCauley, E., Grossman, D. C., et al. (2010). Evaluation of the Patient Health Questionnaire-9 item for detecting major depression among adolescents. Pediatrics, 126(6), 11171123. https://doi.org/10.1542/peds.2010-0852Google Scholar
Rideout, V., Fox, S., & Trust, T. W. (2018). Digital health practices, social media use, and mental well-being among teens and young adults in the US. https://digitalcommons.psjhealth.org/publications/1093Google Scholar
Riehm, K. E., Feder, K. A., Tormohlen, K. N., et al. (2019). Associations between time spent using social media and internalizing and externalizing problems among US youth. JAMA Psychiatry, 76(12), 12661273. https://doi.org/10.1001/jamapsychiatry.2019.2325CrossRefGoogle ScholarPubMed
Robb, M. B. (2020). Teens and the news: The influencers, celebrities, and platforms they say matter most. Common Sense Media. https://www.commonsensemedia.org/research/teens-and-the-news-the-influencers-celebrities-and-platforms-they-say-matter-most-2020Google Scholar
Robinson, A., Bonnette, A., Howard, K., et al. (2019). Social comparisons, social media addiction, and social interaction: An examination of specific social media behaviors related to major depressive disorder in a millennial population. Journal of Applied Biobehavioral Research, 24(1), e12158. https://doi.org/ARTNe1215810.1111/jabr.12158Google Scholar
Sahoo, S., Rani, S., Shah, R., Singh, A. P., Mehra, A., & Grover, S. (2020). COVID-19 pandemic-related anxiety in teenagers. Indian Journal of Psychiatry, 62(3), 328330. https://doi.org/10.4103/psychiatry.IndianJPsychiatry_327_20Google Scholar
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: A systematic review. JMIR Mental Health, 3(4), e50. https://doi.org/10.2196/mental.5842Google Scholar
Selfhout, M. H., Branje, S. J., Delsing, M., ter Bogt, T. F., & Meeus, W. H. (2009). Different types of internet use, depression, and social anxiety: The role of perceived friendship quality. Journal of Adolescence, 32(4), 819833. https://doi.org/10.1016/j.adolescence.2008.10.011CrossRefGoogle ScholarPubMed
Selkie, E., Adkins, V., Masters, E., Bajpai, A., & Shumer, D. (2020). Transgender adolescents’ uses of social media for social support. Journal of Adolescent Health, 66(3), 275280. https://doi.org/10.1016/j.jadohealth.2019.08.011Google Scholar
Sommers-Flanagan, J., & Sommers-Flanagan, R. (2018). Counseling and psychotherapy theories in context and practice: Skills, strategies, and techniques. John Wiley & Sons.Google Scholar
Sue, D. W., Rivera, D. P., Capodilupo, C. M., Lin, A. I., & Torino, G. C. (2010). Racial dialogues and White trainee fears: Implications for education and training. Cultural Diversity and Ethnic Minority Psychology, 16(2), 206214. https://doi.org/10.1037/a0016112Google Scholar
Tian, L., Yan, Y., & Huebner, E. S. (2018). Effects of cyberbullying and cybervictimization on early adolescents’ mental health: Differential mediating roles of perceived peer relationship stress. Cyberpsychology, Behavior, and Social Networking, 21(7), 429436. https://doi.org/10.1089/cyber.2017.0735Google Scholar
Twenge, J. M., & Campbell, W. K. (2019). Media use is linked to lower psychological well-being: Evidence from three datasets. Psychiatric Quarterly, 90(2), 311331. https://doi.org/10.1007/s11126–019-09630-7Google Scholar
Twenge, J. M., Joiner, T. E., Martin, G., & Rogers, M. L. (2018). Amount of time online is problematic if it displaces face-to-face social interaction and sleep. Clinical Psychological Science, 6(4), 456457. https://doi.org/10.1177/2167702618778562Google Scholar
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 317. https://doi.org/10.1177/2167702617723376Google Scholar
Twenge, J. M., & Martin, G. N. (2020). Gender differences in associations between digital media use and psychological well-being: Evidence from three large datasets. Journal of Adolescence, 79, 91102. https://doi.org/10.1016/j.adolescence.2019.12.018Google Scholar
Wahnschaffe, A., Haedel, S., Rodenbeck, A., et al. (2013). Out of the lab and into the bathroom: Evening short-term exposure to conventional light suppresses melatonin and increases alertness perception. International Journal of Molecular Science, 14(2), 25732589. https://doi.org/10.3390/ijms14022573Google Scholar
Waterloo, S. F., Baumgartner, S. E., Peter, J., & Valkenburg, P. M. (2018). Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp. New Media & Society, 20(5), 18131831. https://doi.org/https://doi.org/10.1177/1461444817707349Google Scholar
Weinstein, E. (2018). The social media see-saw: Positive and negative influences on adolescents’ affective well-being. New Media & Society, 20(10), 35973623. https://doi.org/10.1177/1461444818755634Google Scholar
Weissman, M. M., Orvaschel, H., & Padian, N. (1980). Children’s symptom and social functioning self-report scales: Comparison of mothers’ and children’s reports. Journal of Nervous Mental Disease, 168(12), 736740. https://doi.org/10.1097/00005053-198012000-00005Google Scholar
Wenninger, H., Krasnova, H., & Buxmann, P. (2019). Understanding the role of social networking sites in the subjective well-being of users: A diary study. European Journal of Information Systems, 28(2), 126148. https://doi.org/10.1080/0960085x.2018.1496883Google Scholar
Wick, M. R., & Keel, P. K. (2020). Posting edited photos of the self: Increasing eating disorder risk or harmless behavior? International Journal of Eating Disorders, 53(6), 864872. https://doi.org/10.1002/eat.23263Google Scholar
Willenberg, L., Wulan, N., Medise, B. E., et al. (2020). Understanding mental health and its determinants from the perspective of adolescents: A qualitative study across diverse social settings in Indonesia. Asian Journal of Psychiatry, 52, Article 102148. https://doi.org/10.1016/j.ajp.2020.102148Google Scholar
Woods, H. C., & Scott, H. (2016). #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51, 4149. https://doi.org/10.1016/j.adolescence.2016.05.008CrossRefGoogle ScholarPubMed
Wright, B., Garside, M., Allgar, V., Hodkinson, R., & Thorpe, H. (2020). A large population-based study of the mental health and wellbeing of children and young people in the North of England. Clinical Child Psychology and Psychiatry, 25(4), 877890. https://doi.org/10.1177/1359104520925873Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×