Introduction

Adolescents represent one important group of individuals who may experience distinct mental wellbeing benefits and risks associated with technology use. Nearly all United States adolescents report smartphone ownership, and almost half describe themselves as constantly connected (Anderson and Jiang 2018). Most of these youth report that they maintain social media accounts, of which YouTube, Instagram, Snapchat, and TikTok are particularly popular (Anderson and Jiang 2018; Richter 2020). These technology usage patterns underscore the importance of research and design approaches to attend to the impacts of technology use on adolescent mental wellbeing, toward promoting benefits and minimizing risks. Such approaches need to address developmental forces relevant to all adolescents and meaningful differences among adolescents based on their identities, contexts, and the ways in which they use and experience technologies.

In recent years, national discourse has given much attention to how technology use may impact adolescent mental-wellbeing. This dialogue has often involved news media stories centered on social media screen time, with implications that any and all social media use is the same and causes negative mental wellbeing outcomes (Newton 2023; Parks 2023). However, this perspective is not aligned with evidence, which has not consistently supported that there are associations between social media screen time and mental wellbeing outcomes (Odgers, Schueller, and Ito 2020). That is not to say there is no evidence of associations between patterns of social media use and mental wellbeing outcomes. For example, a condition called problematic social media use has reliably shown associations with depression and anxiety (Hussain and Griffiths 2018). Thus, discourse with a sole focus on screen time does not lend adequate nuance to understanding social media usage patterns and mental wellbeing outcomes. This narrower focus limits the scope of proposed approaches to support adolescent mental wellbeing to addressing time spent on social media, while overlooking other important features of use which could benefit mental wellbeing or provide important prevention and intervention targets. If the aim is to support adolescent mental wellbeing, research and design approaches must account for diversity of social media use; doing so requires careful attention to social media use measures.

Understanding key categories of social media use requires direct input from youth. Youth perspectives can provide insights into the intersections between social media measures and lived experiences. Notably, the United Nations Convention on the Rights of the Child has described an obligation for researchers to consult youth on matters that affect them (UNICEF 1995). To this end, we collaborated with four youth authors, who are members of our team's youth advisory boards and are between the ages of 17 and 19. Youth authors met and discussed their own experiences and appraisals around interpreting key categories of social media use measures. Each youth author then contributed their perspective on a category of social media measure or considerations for interpretation. Youth authors reviewed this chapter to ensure it represents their views accurately, and all met criteria for authorship and have been included as co-authors. In this way, youth authors offer critical perspectives toward understanding and interpreting social media measures for adolescents.

This chapter reviews three key categories of social media use measures for adolescents: screen time, interactions and activities, and problematic use. For each measure, definitions and conceptual assumptions are provided alongside evidence behind these assumptions and youth perspectives. Implications for approaches to support adolescent mental wellbeing are discussed. Finally, key considerations for the interpretation of social media measures and adolescent mental wellbeing outcomes are described.

Screen Time

Definition

Screen time measures assess the time an individual spends using social media. These measures seek to quantify minutes or hours spent using social media in a given time frame, regardless of the activity carried out on the platform.

Screen time on social media can be assessed using one of five approaches. First, self-report questions may ask about the time, such as minutes or hours, in a time frame such as a day or week, spent using any social media platform (Odgers et al. 2020). This approach may ask open-ended questions or provide multiple choice answers that provide a time range, such as 1-2 or 3-4 hours per week. Second, the frequency of using social media may be examined through questions targeting how many times a person checks or logs into social media in a given time frame (Brailovskaia and Margraf 2018). Third, time spent or frequency of using specific social media platforms, such as Facebook and Tumblr, may be assessed (Brailovskaia and Margraf 2018). The fourth approach does not rely on self-reported data, but uses passive sensing. In this approach, data about technology behaviors are captured through application usage information, such as calling, texting, or GPS (Cornet and Holden 2018). Often, passive sensing involves downloading an application to a smartphone that transmits data from the phone to a research database. In the case of social media research, such data may include time spent using given social media applications. Finally, Ecological Momentary Assessment is an approach that allows real-time data collection of social media use and mental wellbeing information through self-report (Shiffman et al. 2008). Prompts are often delivered multiple times a day over the course of a time period such as a week, commonly via text message, and participants are asked to respond as soon as they are able.

Assumptions

With screen time measures, an hour of posting photos and writing captions would be recorded identically to an hour of scrolling through other users’ photos without adding any content. Thus, a key assumption of screen time measures is that all social media use is similar, regardless of varying features engaged with or patterns of use. When it comes to mental wellbeing outcomes, the quantity, not quality, of social media activities is used as the key predictor.

Evidence

Regarding mental wellbeing outcomes, a review suggests that screen time self-report measures have not been demonstrated to be consistent predictors of mental wellbeing (Odgers et al. 2020). This review argues that findings about associations between screen time self-report and mental wellbeing have been mixed. Many studies have not supported social media screen time as an influence on mental wellbeing, and those providing evidence of such a connection have found very small effect sizes, showing social media to be a relatively minor influence. These observations have led some researchers to recommend moving away from a focus on screen time, and instead attending to how individuals use social media (Orben 2020).

An additional, important consideration regarding evidence around the use of screen time measures is their accuracy and reliability. A significant challenge with screen time self-report measures is recall bias, which occurs when research participants do not remember previous experiences accurately (Porta 2013). A previous study found that self-report of social media use was consistently higher than accurate measures obtained using Ecological Momentary Assessment, in which text messages were used to assess real-time technology use experiences (Moreno, Jelenchick, Koff et al. 2012). Similarly, a systematic review showed that digital media use self-report is only moderately associated with logs used to track digital media use (Parry, Davidson, Sewall et al. 2021). Thus, recall bias may be a significant threat to accurate measurement of social media screen time through self-report, and findings using these measures should be regarded with caution.

One approach that circumvents recall bias commonly associated with screen time self-report measures is through passive sensing, which can collect accurate usage information by directly gathering smartphone usage data. Previous studies of passive sensing have tended to focus on features of general smartphone use or offline behaviors rather than social media use (Choudhary, Thomas, Ellenberger et al. 2022; Fukazawa, Ito, Okimura et al. 2019; Meyerhoff, Liu, Kording et al. 2021; Sarda, Munuswamy, Sarda et al. 2019; Wang, Chen, Chen et al. 2014). Some of these studies have found associations between certain smartphone use patterns and increased self-reported depression or social anxiety symptoms (Choudhary et al. 2022; Fukazawa et al. 2019; Meyerhoff et al. 2021; Sarda et al. 2019), but most of these associations have been weak. Further, some studies have also found associations between certain features of smartphone use and increased mental wellbeing (Choudhary et al. 2022; Meyerhoff et al. 2021). Thus, even though passive sensing measures are likely to be more accurate than screen time self-report, it may be that associations between these measures and mental wellbeing outcomes show similar patterns to screen time self-report.

Previous work using Ecological Momentary Assessment may offer some insight into inconsistent associations between social media screen time and mental wellbeing. One study using this methodology found that almost half of adolescents reported better moods after using social media, while about 10% reported worse moods (Beyens et al. 2020). These findings support that individual adolescents may experience unique mental wellbeing outcomes when using social media, and the authors argue that features of the individual adolescent’s life context are critical to consider.

Youth author perspective: Janice Peng

Challenges with screen time measures include the inaccuracy of self-report as well as the lack of distinction between different interactions and activities on social media. Further, screen time by itself is not a reliable indicator of how social media use affects mental wellbeing. Considering these challenges, one approach may be to disregard screen time measures altogether. On the other hand, although social media interactions and activities are likely important to consider, an adolescent can have certain interactions on social media very seldom or very often. The frequency of these interactions may affect mental wellbeing outcomes. Thus, another approach is to use screen time measures in conjunction with other measures.

Assessing how much time someone spends on different uses of social media would be a useful alternative or addition to assessing general social media screen time. This data could be collected using passive sensing that records the amount of time someone spends on each type of activity. This would provide more accurate data and account for different types of use. One challenge would be to ensure that passive sensing technology can be designed to collect information on different activity types. Another challenge is that this method may collect a great deal of information, and care would need to be taken to ensure a participant's privacy, especially as young people and their guardians may need thorough explanations about how the data is being collected and used. Nevertheless, a key application of screen time may be to assess time spent on specific social media interactions and activities.

Implications

Screen time measures treat all social media use as the same. One reason associations between social media screen time and adolescent mental wellbeing are not consistently supported by evidence may be that they do not capture the wide range of social media activities in which one can engage. Example interactions may include viewing other users’ posts and stories, uploading one’s own photos, or creating accounts without one’s personal information that are hidden from family and friends. Given the degree to which these activities differ from one another, their effects on mood and wellbeing may also be distinct. Indeed, growing evidence suggests that different ways of interacting with social media platforms are associated with different mental wellbeing outcomes (Escobar-Viera, Shensa, Bowman et al. 2018; Moreno, Binger, Minich et al. 2022b), and that the nature of these associations may depend on features of an individual’s identity, such as gender (Allen, Stratman, Kerr et al. 2021). Thus, a focus on screen time measures is likely to generate sweeping guidelines about time spent on social media, without attention to specific activities that may be associated with important benefits or risks for mental wellbeing. Such guidelines are not supported by a strong body of evidence; accordingly, the American Academy of Pediatrics no longer provides guidelines for screen time (AAP Council on Communications and Media 2016).

To be sure, screen time frequency measures, if they are accurate, may be a useful indicator of social media platforms that are gaining popularity, which could be important in guiding future dialogue and research. Another application of screen time measures may be to combine them with interactions and activities measures, so that the frequency of engaging in such activities can be captured. However, screen time measures alone are unlikely to capture adequate nuance in understanding social media use to target strategies for supporting adolescent mental wellbeing.

Interactions and Activities

Definition

Interactions and activities represent a second category of measure of adolescent social media use. This framework emphasizes the type of interactions or activities in which one engages on social media platforms, such as creating posts or viewing content generated by other users.

Assumptions

When interactions and activities measures are used, the many possible ways one can engage with social media are differentiated. For example, creating and uploading a TikTok video would be viewed as a substantially different experience than scrolling through videos uploaded by others. Further, in this framework, an assumption is that adolescents engage with various social media interactions and activities at different rates. Thus, it would be hypothesized that varying manners and patterns of social media use would be associated with differing mental wellbeing benefits and risks.

Evidence

Interactions and activities approaches are supported by such conceptual models as uses and gratifications theory and affordances. Uses and gratifications theory argues that individuals make decisions to interact with media based on their needs and beliefs about what they will gain from doing so (Blumler and Katz 1974). A previous study identified ten uses and gratifications of social media use, including social interaction, information seeking, and entertainment (Whiting and Williams 2013). This model suggests that individual adolescents have unique needs which influence the interactions they have with social media.

Affordances, on the other hand, emphasize elements of a social media platform that could influence how it is used. Affordances are a concept used in fields involved with technology design and refer to design properties that influence the way a user will engage with an object (Zhao, Liu, Tang et al. 2013). In this model, social media features such as video upload options and news feeds, for example, are expected to facilitate different patterns of use. Previous work has identified four affordances associated with social media use (Moreno and D'Angelo 2019), including cultivating and managing 1) identity, 2) social connections, 3) cognitive development and learning, and 4) emotions. Thus, an affordance model suggests that individual social media platforms offer features that can impact how the technology is used. Taken together, usage and gratifications and affordance frameworks support that adolescent social media usage patterns may involve an interplay between the uses and gratifications one seeks and the affordances a social media platform offers.

Interplay between adolescent uses and gratifications and affordances around social media use is supported by a study on the development of the validated Adolescent Digital Technology Interactions and Importance (ADTI) scale (Moreno, Binger, Zhao et al. 2020). This scale includes 18 items reflecting individual technology interactions, which fall into three subscales: 1) technology to bridge online/offline experiences, 2) technology to go outside one’s identity or offline environment, and 3) technology for social connection. This study found differences in the importance adolescents place on each subscale and supported that there are differences between social media interactions and activities. Further, interactions and their associations with mental wellbeing may differ between groups of adolescents. One previous study found differences in associations between importance of technology interactions and certain mental wellbeing outcomes between cisgender and transgender, nonbinary, and gender-diverse youth (Allen et al. 2021). Thus, the development of the ADTI scale provides empirical support that adolescents interact with technology, particularly social media, in differing ways.

Previous work supports that varying ways of interacting with social media are associated with unique mental wellbeing benefits and risks. One interactions and activities measure that has been explored extensively in previous research is active and passive use of social media (Verduyn, Lee, Park et al. 2015). According to this framework, active social media usage involves creating new content, such as posts or comments, that promotes engagement with other users. Passive usage, on the other hand, refers to viewing others’ social media content, but not self-generating content. Several studies have found associations between passive social media usage and negative mental wellbeing outcomes (Escobar-Viera et al. 2018; Frison and Eggermont 2016; Rousseau, Eggermont and Frison 2017; Thorisdottir, Sigurvinsdottir, Asgeirsdottir et al. 2019), possibly due to passive usage fostering less meaningful social connections than active usage (Dienlin and Johannes 2020). However, other work has found a lack of evidence that passive usage causes poor mental wellbeing, suggesting passive usage may instead be the result of poorer mental wellbeing (Aalbers, McNally, Heeren et al. 2019). Thus, previous work has consistently shown associations between passive usage and poorer mental wellbeing, though causal mechanisms behind this relationship remain unclear.

An additional interactions and activities framework that has been associated with mental wellbeing benefits and risks is the multiple self-presentation framework, which argues that social media users may present their real selves (describing oneself accurately), ideal selves (describing an aspirational self), and false selves (intentionally mischaracterizing oneself) on social media platforms. These interactions have been assessed using the Self-Presentation-on-Facebook-Questionnaire (SPFBQ) (Michikyan, Dennis and Subrahmanyam 2015). Previous work shows associations between presentations of the real self and better mental wellbeing as well as between presentations of the ideal or false self and poorer wellbeing (Mun and Kim 2021; Wright, White and Obst 2018). Researchers have hypothesized that these associations may reflect that adolescents who experience more self-doubt spend more time on social media exploring parts of themselves they seek to understand better (Michikyan et al. 2015). Thus, interactions and activities measures, such as multiple self-presentation and active and passive usage frameworks, have shown some social media uses to be associated with positive mental wellbeing outcomes and others with negative mental wellbeing outcomes.

Youth author perspective: Natasha Matta

While interactions and activities approaches such as active and passive and multiple self-presentation framework provide important insight into how adolescents use social media, these social media uses may be more nuanced in real life than currently described in research. Research approaches should acknowledge that interactions and activities on social media are likely to occur on a spectrum. For example, social media use may fall on a spectrum from active to passive use. Commenting on another user’s post, for instance, may be considered more active than simply viewing another person’s post but more passive than creating a post or story on one’s own account or page. Uses that do not fall on either end of the spectrum may have different implications for mental wellbeing risks and outcomes that are overlooked in current research.

There may also be combinations of interactions and activities. An adolescent may represent both their real and ideal selves, and they may engage in commenting on some posts and only viewing others. These activities may hold a different meaning when considered together rather than individually. Users also switch between different interactions and activities rapidly, for example, posting a status update and then passively scrolling through posts on their feed. Additionally, they may switch rapidly between social media apps. It follows that the overall patterns of interactions and activities on social media should be considered, rather than individual uses.

Implications

Interactions and activities frameworks are premised on the notion that there are important differences between ways of using social media. In this framework, guidelines to support mental wellbeing would address beneficial and risky patterns of interacting with social media. This approach is supported by previous research, which provides evidence of importantly different interactions and activities on social media and distinct associations with mental wellbeing benefits and risks. At the same time, a key point is that certain interactions and activities are not mutually exclusive of others and likely occur on a spectrum. Rather than being only active users or real self presenters, individual adolescents likely show patterns of many interactions and activities. Thus, compared to screen time measures, interactions and activities measures offer a more nuanced set of understandings for what forms of technology engagement influences wellbeing.

Problematic Use

Definition

A third category of measures of social media use is problematic use, which includes concerning patterns that may emerge in the relationship between individuals and social media platforms. Such patterns include the interference of social media with daily activities and the experience of distress or withdrawal when social media cannot be used (Andreassen, Pallesen and Griffiths 2017; Elphinston and Noller 2011).

Assumptions

Rather than focusing on the time or activities engaged with on social media, problematic use measures characterize the relationship that can develop between a social media platform and its users. Generally, it is assumed that social media sites have certain characteristics that can facilitate use that may fall on a spectrum from non-problematic to impulsive, compulsive, or addictive use, which in turn can disrupt the user’s daily life. Thus, the development of this concerning relationship with the social media platform has the potential to be associated with mental wellbeing outcomes.

Evidence

Three key scales have been developed to assess problematic use of social media. One approach is the Bergen Facebook Addiction Scale (BFAS), which includes six items representing core elements of addiction (Andreassen, Torsheim, Brunborg et al. 2012). This scale has been adapted to the Bergen Social Media Addiction Scale (BSMAS), following the rise in popularity of additional social media platforms, and broadens the focus to include any social media platform (Andreassen et al. 2017). A similar framework for measuring problematic use of social media is Facebook intrusion, which refers to Facebook use or an attachment to Facebook that interferes with daily activities and causes distress that spills from Facebook into other activities (Elphinston and Noller 2011; Przepiorka and Blachnio 2020).

Across the BFAS, BSMAS, and Facebook intrusion scales, studies have consistently found associations between problematic use of social media and negative mental wellbeing outcomes. Previous research has reliably shown small to moderate, negative associations between problematic social media use and wellbeing indicators (Huang 2022). Research also shows positive relationships between problematic social media use and anxiety, depression, and distress (Huang 2022; Malaeb, Salameh, Barbar et al. 2021; Marino, Gini, Vieno et al. 2018; Shensa, Escobar-Viera, Sidani et al. 2017). Similarly, Facebook intrusion has been linked to depression (Elphinston and Noller 2011; Przepiorka and Blachnio 2020). Thus, problematic social media use may be an important predictor of negative mental wellbeing outcomes for adolescents.

Though problematic social media use may be consistently associated with negative mental wellbeing outcomes, an additional, important factor to consider is the prevalence of adolescents affected by this condition. Previous work estimates that 6-9% of adolescents meet criteria for problematic social media use (van Duin, Heinz, Willems 2021; Paakkari et al. 2021). Thus, it is not likely that most adolescents develop this condition.

Youth author perspective: Armaan Bhasin

Although not all adolescents may screen positive for problematic social media use, it is important to note that one’s relationship with social media occurs on a spectrum between healthy and problematic, and not just a threshold that categorizes social media use as problematic or not. This spectrum may be affected by external factors in the adolescent’s life. For example, problematic use may vary during different times of the year. Social media usage is largely a function of how much free time an individual has. For example, usage might increase during the summer or winter break when adolescents have greater amounts of free time, but then decrease during the school year or during final exams week. It is important to recognize that to some extent, factors such as homework, sports, extracurriculars and academics naturally prevent excessive social media use throughout the year. This limits the amount of problematic usage adolescents could have during certain times of the year which contributes to why problematic social media usage likely falls on a spectrum.

In addition to the time of the year, problematic social media usage is also dependent on the social media platform the adolescent uses. For instance, TikTok is a primarily scrolling-based platform featuring short user-created videos. Viewers are shown a unique “For You” page, which provides videos based on their preferences. These preferences are quickly learned by TikTok’s algorithm. This aspect of TikTok has been described in terms of the app having unique structural elements that make it “addicting” (Petrillo 2021). Thus, individual social media platforms are also important to consider in conjunction with problematic use of social media.

Implications

A problematic social media use framework assumes that social media platforms have characteristics that can facilitate a spectrum of usage patterns between non-problematic, impulsive, compulsive, and addictive use. Evidence supports that some, but not most, adolescents develop this condition, and for them, there are associated negative mental wellbeing outcomes. Thus, prevention and intervention efforts for these adolescents are important..

At the same time, research and intervention design approaches should give careful attention to screening for problematic use of social media. Not all social media use, perhaps not even high-frequency social media use, is likely to meet criteria for problematic social media use. For many adolescents, social media use is not associated with addiction symptoms like withdrawal or disruptions to daily life. At the same time, an adolescent’s relationship to social media may be influenced by external factors such as extracurricular activities as well as differing features of specific social media platforms. It follows that the risk problematic social media use poses for each individual adolescent may change over time. Thus, frequent social media use is not an addiction in and of itself, but changing circumstances may increase or decrease the risk of problematic use.

Interpreting Social Media Measures And Mental Wellbeing Outcomes

This chapter has provided guidelines for uses of three categories of social media measures in research and intervention design keyed to understanding and supporting adolescent mental wellbeing in a tech-saturated world. However, regardless of the measure used, certain factors are important to interpreting studies of social media and adolescent mental wellbeing: the context of study findings and causal inferences afforded by study designs.

Considering findings in context

Examining studies of general adolescent groups may lead to assumptions that all adolescents are equally prone to mental wellbeing outcomes associated with certain social media use measures. However, a growing body of evidence supports that subgroups of adolescents show differing internet and social media use patterns and associations with mental wellbeing outcomes. For example, lesbian, gay, bisexual, and transgender individuals are more likely to report having online friends who are more supportive than in-person friends (Ybarra et al. 2015). Further, for transgender, nonbinary, and gender-diverse youth, but not cisgender youth, higher wellbeing and body image scores are associated with problematic internet use, possibly due to the importance of being perceived as the gender with which one identifies (Allen et al. 2021). Thus, features of identity, and likely other contextual factors, may be related to motivation and usage patterns behind social media use and associated mental wellbeing outcomes. Research and design should acknowledge that social media is not used in a vacuum, but occurs in the context of each adolescent’s unique identities and life circumstances.

Carefully considering causality

When examining associations between social media use measures and mental wellbeing outcomes, many may jump to the conclusion that social media causes mental health outcomes.s. However, it is important to recognize that most studies in this area have employed a cross-sectional approach, an observational study design in which data are collected at one time point. When using such approaches, clear conclusions about whether the time or manner of social media use represents a cause or effect of mental wellbeing outcomes cannot be made. While some social media uses may cause certain mental wellbeing outcomes, it is also possible that individuals with different mental wellbeing states use social media in unique ways. This interpretation would fit with a uses and gratifications framework (Blumler and Katz 1974), in that adolescents with different mental wellbeing states may be expected to have different individual reasons for using social media. Nevertheless, for most studies, causal directions remain unclear, and researchers should be cautious in interpreting relationships between social media use and mental wellbeing.

Youth author perspective: Garv Bhasin

As stated previously, few studies document causal relationships between social media use and wellbeing. One review has argued that for adolescents with depression symptoms, it is unclear whether the usage of social media causes or exacerbates depressive symptoms, or if depressive symptoms cause these individuals to seek out more social media use, creating a harmful cycle (Bozzola et al. 2022). Conversely, we are unsure if adolescents' positive mental wellbeing states are caused by social media habits such as active use or if positive mental wellbeing prompts them to use social media in this way.

An observation I have made through my own interactions with social media and prior research is that negative mental wellbeing would lead to a relatively greater amount of passive social media use whereas positive mental wellbeing would result in more active use of social media. To elaborate, adolescent users who are in a worse mental state would tend to passively scroll through other social media accounts or watch others' content without engaging in the form of comments or posts. By contrast, adolescents who have better mental wellbeing would tend to post more about themselves or their lives on social media while also engaging relatively more with previously posted content in the form of likes or comments. It is important to note here that in both cases adolescent users are not engaging with social media in only one way or the other; rather, they are engaging in either active or passive use a relatively greater amount based on their current mental wellbeing status.

Conclusions

This chapter has described three distinct approaches for measuring adolescent social media use as well as guidelines for interpreting such measures. A critical conclusion is that not all uses and relationships around social media are the same, nor are associated mental wellbeing outcomes. Further, features of adolescents’ identities and experiences may influence their social media usage patterns and mental wellbeing outcomes.

These conclusions have important implications for research and design approaches to supporting adolescent mental wellbeing in a tech-rich world. Strategies to maximize benefits and mitigate risks of social media use should not default to centering on screen time. Instead, multifaceted approaches targeting key features of social media use, particularly interactions and activities and problematic use, should be adopted.

Further, approaches must avoid casting social media use as an uniform experience across adolescents that contributes identical influences on mental wellbeing. Instead, the individuality of adolescent social media users, their personal history, mental wellbeing, and motivations for interacting with technology should be acknowledged. A challenge of this approach is that millions of adolescents use social media, and a focus on the usage patterns of groups, rather than individuals, is essential. Thus, supporting adolescent mental wellbeing requires an ongoing quality improvement process in research and design. This approach should advocate for a continuous cycle of improvement of technologies and other resources to support adolescent mental wellbeing, assessment of their effectiveness, and refinement of these technologies and resources. Doing so will encourage the design of social media and supportive resources to progress ever closer to meeting the needs of all adolescents, while adapting to external circumstances that could change how adolescents use social media.

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