quantitative research about social media addiction

Research trends in social media addiction and problematic social media use: A bibliometric analysis

Affiliations.

• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand.
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand.
• PMID: 36458122
• PMCID: PMC9707397
• DOI: 10.3389/fpsyt.2022.1017506

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013-2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19-25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Keywords: bibliometric analysis; problematic social media use; research trends; social media; social media addiction.

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi.

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• Systematic Review

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Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator

• Original Article
• Open access
• Published: 05 October 2021
• Volume 21 , pages 2037–2051, ( 2023 )

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• Begum Satici   ORCID: orcid.org/0000-0003-2161-782X 1 ,
• Ahmet Rifat Kayis   ORCID: orcid.org/0000-0003-4642-7766 2 &
• Mark D. Griffiths   ORCID: orcid.org/0000-0001-8880-6524 3  

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Social media use has become part of daily life for many people. Earlier research showed that problematic social media use is associated with psychological distress and relationship satisfaction. The aim of the present study was to examine the mediating role of psychological distress in the relationship between social media addiction (SMA) and romantic relationship satisfaction (RS). Participants comprised 334 undergraduates from four mid-sized universities in Turkey who completed an offline survey. The survey included the Relationship Assessment Scale, the Social Media Disorder Scale, and the Depression Anxiety and Stress Scale. According to the results, there were significant correlations between all variables. The results also indicated that depression, anxiety, and stress partially mediated the impact of SMA on RS. Moreover, utilizing the bootstrapping procedure the study found significant associations between SMA and RS via psychological distress. Consequently, reducing social media use may help couples deal with romantic relationship dissatisfaction, thereby mitigating their depression, anxiety, and stress.

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Establishing social relationships is one of the basic needs of human beings (Heaney & Israel, 2008 ). How this basic need is met can vary greatly. In particular, technological developments, such as computers, the Internet, and smartphones have created new ways for people to communicate with each other. One of the most successful new means of communication is through social media. Social media involves many different communication (i.e., social networking) platforms. Among the most popular are platforms in Western countries are Facebook, Twitter, Instagram, and YouTube. These sites, which are accessed via the Internet, provide many opportunities for communication, such as voice and video messaging, photograph and video sharing, and creating profiles, through which individuals can introduce themselves and make connections with others.

The communication opportunities brought about by social networking sites (SNSs) allow for the development of social relationships (Fuchs, 2017 ; Hazar, 2011 ; Valentini, 2015 ). In addition, social media is used for a wider variety of purposes, including obtaining information, communicating, entertainment, playing games, and sharing photos, videos, and music (Griffiths, 2012 ). However, excessive use of social media including SNSs can cause negative effects (Griffiths, 2013 ; van den Eijnden et al., 2016 ). This phenomenon, which is sometimes referred as “social media addiction,” is defined as the irrational and excessive use of social media at a level that negatively affects the daily life of the user (Griffiths, 2012 ). When social media use reaches the level of addiction, it can prevent the establishment of real, face-to-face social relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ). When general characteristics of social media addiction have been examined, it has been found that individuals tend to have restless thoughts concerning the urges and craving to be on social media, lose their self-control over their use of social media, spend excessive amounts of time staying on (or thinking about) social media which in turn lead to negative impacts on their relationships with their families and friends, and compromise their occupation and/or education (Andreassen et al., 2012 ; Griffiths et al., 2014 ). Therefore, examining social media addiction in terms of its effect on human relationships and mental health is an important pursuit.

Theoretical Framework

Social media addiction and relationship satisfaction.

Research into the effects of social media addiction on romantic relationships has increased (Abbasi, 2019a ; Demircioğlu & Köse, 2018 ). The literature suggests that social media addiction negatively affects romantic relationships due to its tendency to create jealousy and suspicion and facilitate deception between married couples and committed partners (Abbasi, 2019b ). Additionally, problematic social media use can hinder the development of face-to-face relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Pollet et al., 2011 ; Young, 2019 ). Therefore, it is possible that some couples’ relationships may become disrupted and that dissatisfaction may be experienced. In some cases, not only has social media use decreased the amount of relationships that individuals have in person, but it has also markedly impaired the quality of the time spent together. Therefore, it can be concluded that some couples may experience relationship dissatisfaction.

Similarly, social media addiction can result in low relationship satisfaction due to the existence of online alternative centers of attraction and investments of time and emotion outside the bilateral relationship in individuals aged between 18 and 73 years (Abbasi, 2019a ). In addition, social media addiction has also been associated with physical and emotional infidelity, romantic separation, decline in the quality of romantic relationships, and relationship dissatisfaction (e.g., Abbasi, 2019a , b ; Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ). Therefore, these aforementioned findings indicate that social media addiction negatively affects relationship satisfaction.

Social Media Addiction and Psychological Distress

One of the most important consequences of social media addiction is the mental health of individuals. When social media use reaches the level of addiction, it can create stress and negatively affect mental health rather than being a method of healthy coping. This occurs because social media addiction triggers social media fatigue and, as a result, individuals may experience anxiety and depression (Dhir et al., 2018 ). Social media users may use social media as a means of diversion in order to cope with stress (van den Eijnden et al., 2016 ). However, social media addicts give a lower priority to hobbies, daily routines, and close relationships (Tutgun-Ünal & Deniz, 2015 ) which in turn lead to problems with daily functioning, completion of tasks, and relationship maintenance. This puts such individuals at risk for experiencing negative physical and psychological health.

In fact, some research has claimed that social media addiction triggers psychological distress factors, such as depression, anxiety (Woods & Scott, 2016 ), and stress (Larcombe et al., 2016 ). In addition, a meta-analysis synthesizing the findings of 13 studies found that social media addiction may increase depression, anxiety, and stress levels (Keles et al., 2020 ). In both meta-analyses and cross-sectional studies, it has been found that social media addiction can increase psychological distress (e.g., Hou et al., 2019 ; Keles et al., 2020 ; Marino et al., 2018 ; Meena et al., 2015 ). In sum, these findings consistently associate social media addiction with psychological distress.

Psychological Distress and Relationship Satisfaction

Individuals experiencing psychological discomfort often have non-functional communication styles characterized by highly negative behaviors, such as criticism, complaining, hostility, defensiveness, and tendency to end relationships. They also experience problems actively listening to others (Fincham et al., 2018 ). In this respect, psychological distress prevents healthy communication in relationships, and a lack of healthy communication may cause conflicts that can embitter psychological distress between couples. Such a situation can continue in a cyclical manner that prevents relationship satisfaction. In romantic relationships, couples are supposed to fulfill their partners’ emotional needs (Willard, 2011 ). When individuals have psychological problems due to social media addiction, they will ignore their partner’s emotional needs because they would be trying to deal with their own problems, which, in turn, may lead to lower relationship satisfaction.

When psychological distress and romantic relationship satisfaction are examined, it can be seen that much psychological distress, such as major depression, panic disorder, social phobia, general anxiety disorder, post-traumatic stress disorder, and mood disorder, positively predict relationship dissatisfaction (Whisman, 1999 ). On the other hand, it can also be seen that individuals who are sensitive to negative affect in romantic relationships and who can successfully stop these emotions early on and cope with their feelings are satisfied with their relationships (Fincham et al., 2018 ).

Couples who have high levels of stress are reported to experience less satisfaction in their relationships (Bodenmann et al., 2007 ). In addition, it is known that depression negatively predicts relationship satisfaction (Cramer, 2004a , b ; Tolpin et al., 2006 ). Therefore, it appears that psychological distress negatively affects relationship satisfaction.

The Present Study

The prevalence of the use of the internet and Internet-related tools has consistently increased year on year (Roser et al., 2020 ). Even though the social media use is widespread and facilitates communication when it is used normally, it can negatively affect daily life when it is used excessively by some individuals. Literature reviews have shown that social media addiction has been mostly studied in East Asian countries like China, Japan, and South Korea (e.g., Bian & Leung, 2015 ; Kwon et al., 2013 ; Tateno et al., 2019 ). In this respect, when the prevalence of social media use among Turkish people and the different cultural context of the present study are considered, the findings would arguably make important contributions to the current literature. Furthermore, the present study appears to be the first to examine the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

Older aged adolescents and emerging adults are inextricably connected with technology in terms of their social media use and stand out as an important risk group in relation to problematic social media use (Griffiths et al., 2014 ). Many young adults closely follow technological developments and often adopt every innovation that arises into their lives without wasting time (Kuyucu, 2017 ). When such use becomes problematic, some individuals experience serious difficulty in maintaining their mental health. For example, cross-sectional studies among adolescents (Woods & Scott, 2016 ) and young adults (Larcombe et al., 2016 ) have found that social media addiction can lead to stress, anxiety, and depression. Moreover, the establishment of close relationships as a young adult is an important stage of emotional and social development (Cashen & Grotevant, 2019 ; Orenstein, & Lewis, 2020 ). Romantic relationship satisfaction may be seen as an important indicator of young people’s ability to engage in intimacy in a healthy manner (Orenstein & Lewis, 2020 ). Therefore, the findings obtained as a result of examining the relationships between social media addiction, psychological distress, and romantic relationship satisfaction among young people will contribute to an understanding of the associations between the psychological and social variables regarding maintenance of their mental health and their success in establishing close relationships.

In previous studies of the variables examined in the present study, even though studies examining the three variables dichotomously have been conducted (e.g., Abbasi, 2019a , b ; Bodenmann et al., 2007 ; Keles et al., 2020 ; Larcombe et al., 2016 ; Whisman, 1999 ), no research examining social media addiction, psychological distress (depression, anxiety and stress), and romantic relationship satisfaction together has been published. In particular, there is no study examining the role of psychological distress mediating between social media addiction and relationship satisfaction. In this respect, the results of the present study may also allow the findings of previous studies (which have been conducted with the aim of identifying the relationship between these variables) to be evaluated from a wider perspective.

Consequently, given the aforementioned theoretical explanations and the research findings, it has been demonstrated that social media addiction appears to induce both psychological distress and a low level of romantic relationship satisfaction (e.g., Demircioğlu & Köse, 2018 ; Woods & Scott, 2016 ). This is due to the deterioration of individuals’ mental health that can arise as a result of social media addiction (Baker & Algorta, 2016 ; Dhir et al., 2018 ), and in contrast to the advantages of developing relationships, it can lead to romantic relationship dissatisfaction (Abbasi, 2019b ; Muise et al., 2009 ). Therefore, when the relationships between social media addiction, psychological distress, and romantic relationship satisfaction are evaluated simultaneously, psychological distress may represent a mediating variable between social media addiction and romantic relationship satisfaction. Consequently, it was hypothesized that psychological distress would mediate the association between social media addiction and relationship satisfaction.

Participants and Procedure

The present cross-sectional study was carried out on a convenience sample of university students from three universities that are located in the west, middle, and east part of Turkey. A total of 350 surveys were originally distributed. Of these, 16 participants were removed because of incomplete data, yielding a final sample of 334 participants aged between 18 and 29 years ( M  = 20.71 years, SD  = 2.18). The participants comprised 214 females (64%) and 120 males (36%), of which 90 were freshmen, 87 were sophomores, 84 were junior students, and 73 were senior students. Participants reported that they were currently in a romantic relationship and reported having an average of 3.21 romantic relationships to date ( SD  = 2.21). Table 1 shows the detailed demographic characteristics of the participants. Written informed consent was obtained from the volunteer participants prior to participation in the study. Research participants were assured of the confidentiality of the collected data. Data collection was carried out through a “paper-and-pencil” survey in the classroom environment. The surveys took less than 15 min to complete.

Relationship Assessment Scale (RAS)

The RAS was designed to assess general relationship satisfaction (Hendrick, 1988 ). Items (e.g., “In general, how satisfied are you with your relationship?”) utilize a seven-point Likert scale ranging from 1 ( low ) to 7 ( high ). The total score ranges from 7 to 49. The higher the score, the higher the relationship satisfaction. Hendrick ( 1988 ) reported very good reliability. The RAS was adapted into Turkish by Curun ( 2001 ) with very good internal consistency. In the present study, the internal consistency of this scale was also good ( α  = 0.80).

Social Media Disorder Scale (SMD)

The SMD was designed to assess overall social media addiction, and the items were developed by adapting the DSM-5 criteria for Internet gaming disorder (van den Eijnden et al., 2016 ). This scale includes nine items (e.g., “… regularly found that you can't think of anything else but the moment that you will be able to use social media again?”) to which participants indicate their level of agreement on a five-point Likert scale ranging from 0 ( never ) to 4 ( always ). The total score ranges from 0 to 36. The higher the score, the higher the risk of social media addiction. The SMD was adapted to Turkish by Savci et al. ( 2018 ) and has very good internal consistency. In the present study, the internal consistency of this scale was also very good ( α  = 0.88).

Depression Anxiety and Stress Scale (DASS-21)

The DASS was designed to assess the level of psychological distress (Henry & Crawford, 2005 ). The scale consists of 21 items that are rated on a four-point Likert scale from 0 ( did not apply to me at all ) to 3 ( applied to me very much or most of the time ) and comprises three sub-scales: depression (seven items; e.g., “I found it difficult to work up the initiative to do things”), anxiety (seven items; e.g., “I felt I was close to panic”), and stress (seven items; “I found myself getting agitated”). The scores range from 0 to 21 for each sub-scale. The DASS-21 subscales’ scores were multiplied by two based on Lovibund and Lovibond’s ( 1995 ) suggestion to the cut-offs (see Appendix 1 ). The DASS-21 was adapted to Turkish by Yilmaz et al. ( 2017 ) with good to very good internal consistencies. In the present study, the internal consistency of the sub-scales were all very good ( α  = 0.89, 0.82, 0.85, respectively).

Statistical Analyses

Pearson correlations, means, and standard deviations were examined as preliminary analyses for all study variables. To examine whether the association between social media addiction and relationship satisfaction was mediated by psychological distress, the mediation model was calculated using the PROCESS macro (model 4), developed by Hayes ( 2018 ). As recommended by Hayes ( 2018 ), all regression/path coefficients are in unstandardized form. A total of 10,000 bootstrap samples were generated and bias corrected 95% confidence intervals calculated.

Written informed consent was obtained from the volunteer participants prior to participation in the study. This research was approved by Artvin Coruh University Scientific Research and Ethical Review Board (REF: E.5375).

Descriptive Statistics

Bivariate Pearson correlations among study variables were investigated (see Table 2 ). As expected, social media addiction was significantly and positively correlated with depression, anxiety, and stress. There was a significant negative correlation between social media addiction and relationship satisfaction.

Results indicated that 156 participants had no depressive symptoms (46.7%), 54 participants had mild depressive symptoms (16.2%), and the remainder had depressive symptoms (16.5% moderate, 9.9% severe, and 10.8% extremely severe). Moreover, 101 participants had no anxiety symptoms (30.2%), 30 participants had mild anxiety symptoms (9.0%), and the remainder had anxiety symptoms (20.4% moderate, 15.6% severe, and 24.9% extremely severe). Finally, 163 participants had no stress symptoms (48.8%), 47 participants had mild depressive symptoms (14.1%), and the remainder had stress symptoms (17.7% moderate, 12.6% severe, and 6.9% extremely severe) (see Appendix 1 ).

Statistical Assumption Tests

Prior to mediation analysis, statistical assumptions were evaluated. Skewness and kurtosis values (> ± 2; George & Mallery, 2003 ) were checked for normality, and there were no violations (see Table 3 ). All reliability coefficients were above Nunnally and Bernstein’s ( 1994 ) 0.70 criterion. Multicollinearity was checked with variance inflated factor (VIF), tolerance, and Durbin-Watson (DW) value. The results showed that VIF ranged from 1.47 to 2.09 and tolerance ranged from 0.48 to 0.87. These findings also showed that there was no multiple linearity problem according to Field’s ( 2013 ) recommendation. Also, the DW value was 1.82 indicating no significant correlations between the residuals.

Mediation Analyses

Applying PROCESS model 4, the analysis assessed whether psychological distress mediated the relationship between social media addiction and relationship satisfaction (see Table 4 ; Fig.  1 ). The results showed a significant total direct effect ( path c ; without mediator) of social media addiction on relationship satisfaction (B =  − 0.36, t (334)  =  − 4.74, p  = 0.001, 95% CI =  − 0.51, − 0.21), significant direct effect ( path c ; with mediator) (B =  − 0.16, t (334)  =  − 2.11, p  = 0.03, 95% CI =  − 0.04, − 0.01), and a significant indirect effect via psychological distress (total B =  − 0.20, 95% CI =  − 0.29, − 0.12).

The mediation model. * p  < .05. ** p  < .001

The results also showed that the social media addiction was associated with higher depression scores (path a 1 ; B = 0.23, p  = 0.001), anxiety scores (path a 2 ; B = 0.23, p  = 0.001), and stress scores (path a 3 ; B = 0.27, p  = 0.001), and these, in turn, were negatively associated with relationship satisfaction (path b 1, b 2, b 3 ; B =  − 0.28, B =  − 0.28, B =  − 0.26, all p values < 0.05, respectively).

In contemporary society, rapidly developing technology has entered human life, but some individuals may have difficulty in adapting to the innovations brought by such technology. Consequently, some individuals may experience psychological and social problems. Social media use, which has markedly increased in the past decade, can cause psychological distress (e.g., Keles et al., 2020 ; Marino et al., 2018 ) and the deterioration of interpersonal relationships (e.g., Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ) among a minority of individuals. In this context, the main purpose of the present study was to evaluate the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

According to the findings, a high level of social media addiction leads to a decrease in relationship satisfaction. Consequently, the first hypothesis was confirmed. A recent study conducted by Abbasi ( 2019b ) found that social media addiction was negatively associated with romantic relationship commitment. In another recent study, it was emphasized that social media addiction results in deception between couples through social media and may lead to the deterioration of relationships as a consequence (Abbasi, 2019a ). In addition, social media addiction not only leads to physical and emotional deception but also appears to negatively impact on the quality of romantic relationships (Demircioğlu & Köse 2018 ; Valenzuela et al., 2014 ). Therefore, the findings obtained in the present study are in line with the findings of previous research.

In the study here, the findings showed that a high level of social media addiction appears to result in psychological distress. Dhir et al. ( 2018 ) argued that social media addiction triggers social media fatigue, leading to anxiety and depression. Similarly, social media addiction has been found to be associated with depression, anxiety (Woods & Scott, 2016 ) and stress (Larcombe et al., 2016 ). In addition, a recent meta-analysis also concluded that social media addiction is closely and positively associated depression, anxiety and stress (Marino et al., 2018 ). Therefore, the findings of the present study are consistent with previous research.

Thirdly, the findings indicate that individuals who experience psychological distress have a low level of satisfaction in their romantic relationships. Whisman ( 1999 ) found that psychological distress positively predicted relationship dissatisfaction. It has also been suggested that couples with high levels of stress experience dissatisfaction in their romantic relationships (Bodenmann et al., 2007 ). In addition, there have also been a number of studies which indicate that the relationship satisfaction of individuals with high levels of depression is low (Cramer, 2004a , b ; Tolpin et al., 2006 ). In this respect, the findings obtained from the present study are similar to the findings of the previous studies.

Within the scope of this study, it was hypothesized that psychological distress would mediate between social media addiction and relationship satisfaction. In this sense, the study showed that social media addiction predicted romantic relationship satisfaction, partially mediated by psychological distress. Consequently, the fourth hypothesis of the research was also confirmed. No previous studies have examined the effect of psychological distress in the relationship between social media addiction and relationship satisfaction. However, there are research findings which provide evidence that social media addiction predicts both psychological distress (e.g., Larcombe et al., 2016 ; Woods & Scott, 2016 ) and relationship dissatisfaction (e.g., Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ) and that psychological distress predicts relationship dissatisfaction (e.g., Bodenmann et al., 2007 ; Whisman, 1999 ). Due to the consideration of a variable’s mediating conditions (Barron & Kenny, 1986 ), it may be asserted that the findings of the previous studies in the literature and the findings of this research are consistent. Furthermore, it has been demonstrated that technological addiction, such as Internet addiction and smartphone addiction, is associated with psychological distress (McNicol & Thorsteinsson, 2017 ; Samaha & Hawi, 2016 ; Young & Rogers, 1998 ). Psychological distress may also predict variables such as closeness in relationships (Manne et al., 2010 ), dating violence (Cascardi, 2016 ), and social support (Robitaille et al., 2012 ) which are based on interpersonal relationships. It is therefore suggested that there is similarity between these findings and the findings of the present study. Consequently, it may be that the results of the studies conducted previously support the findings of this the present research indirectly, if not directly.

In the study here, the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was investigated. However, there could be some other variables that can mediate the relationship between social media addiction and romantic relationship satisfaction. For instance, romantic relationships are considered interpersonal (Knap et al., 2002 ); therefore, it can be assumed that interpersonal relationships and communication skills can be seen as potential mediators of the relationship between social media addiction and romantic relationship satisfaction. Additionally, given that psychological problems are the indicators of poor mental health (American Psychiatric Association, 2013 ), it can be assumed that variables (i.e., other indicators of poor mental health such as burnout, somatization, and hostility) would mediate the relationship between social media addiction and romantic relationship satisfaction. Therefore, future studies should investigate such relationships more closely.

When the role of social media addiction in the development of psychological distress is considered, it is necessary for social media addiction to be included in the process of forming the content of the intervention programs that aim to treat psychological distress. As such, it is interesting that an intervention program aimed at decreasing the level of social media addiction was also found to have a beneficial impact on individuals’ mental health (Hou et al., 2019 ). Likewise, the treatment of couples’ social media usage habits in family and couple therapies may be effective in terms of the efficacy of the therapy, since social media addiction decreases satisfaction in romantic relationships. Moreover, given the mediation relationships in the present research, the results may provide a more holistic viewpoint for mental health professionals which consider all of the three variables (social media addiction, psychological distress, and romantic relationship satisfaction) rather than a focus on only one. In this context, the following suggestions are made: to prevent social media addiction, effective Internet use skills can be taught to couples. In addition, awareness-raising skills such as yoga and meditation could be provided to individuals to protect them from social media addiction and psychological distress.

In terms of the study’s participatory group, it is significant that social media addiction (Kittinger et al., 2012 ; Koc & Gulyagci, 2013 ), psychological distress (Canby et al., 2015 ; Larcombe et al., 2016 ), and relationship satisfaction problems (Bruner et al., 2015 ; Roberts & David, 2016 ) are frequently experienced by university students. Consequently, the findings of the present study may be of particular help to specialists who work in the psychological counseling centers of universities. Within this framework, meetings, conferences, and psycho-educational group activities could be carried out to improve relationship building skills, as well as activities preventing social media addiction and psychological distress.

The present study has some limitations. Firstly, the data comprised self-report scales, which may decrease internal reliability, a limitation which may be prevented through the use of different methods of data collection. Secondly, the generalizability of the findings is limited since the sample was based on convenience sampling. Thirdly, the research design was cross-sectional. This may make it difficult to explain the cause-effect relationship of variables in the study, and therefore, experimental and longitudinal studies are recommended in future research which should examine the relationship between these variables. Finally, only the mediating role of psychological distress was examined in the research. Other possible mediating variables were not examined.

In the present research, the mediation of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was empirically tested. Results showed that social media addiction predicted the partial mediation of depression, anxiety, and stress on romantic relationship satisfaction. In other words, social media addiction apparently increased individuals’ depression, anxiety, and stress levels, and this situation decreased the level of satisfaction in individual’s romantic relationships. In the present study, psychological and social variables were examined simultaneously. Overall, this study suggests that social media addiction may have a meaningful but negative impact on romantic relationship satisfaction via depression, anxiety, and stress.

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Satici, B., Kayis, A.R. & Griffiths, M.D. Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator. Int J Ment Health Addiction 21 , 2037–2051 (2023). https://doi.org/10.1007/s11469-021-00658-0

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Teens and social media: Key findings from Pew Research Center surveys

For the latest survey data on social media and tech use among teens, see “ Teens, Social Media, and Technology 2023 .” 

Today’s teens are navigating a digital landscape unlike the one experienced by their predecessors, particularly when it comes to the pervasive presence of social media. In 2022, Pew Research Center fielded an in-depth survey asking American teens – and their parents – about their experiences with and views toward social media . Here are key findings from the survey:

Pew Research Center conducted this study to better understand American teens’ experiences with social media and their parents’ perception of these experiences. For this analysis, we surveyed 1,316 U.S. teens ages 13 to 17, along with one parent from each teen’s household. The survey was conducted online by Ipsos from April 14 to May 4, 2022.

This research was reviewed and approved by an external institutional review board (IRB), Advarra, which is an independent committee of experts that specializes in helping to protect the rights of research participants.

Ipsos invited panelists who were a parent of at least one teen ages 13 to 17 from its KnowledgePanel , a probability-based web panel recruited primarily through national, random sampling of residential addresses, to take this survey. For some of these questions, parents were asked to think about one teen in their household. (If they had multiple teenage children ages 13 to 17 in the household, one was randomly chosen.) This teen was then asked to answer questions as well. The parent portion of the survey is weighted to be representative of U.S. parents of teens ages 13 to 17 by age, gender, race, ethnicity, household income and other categories. The teen portion of the survey is weighted to be representative of U.S. teens ages 13 to 17 who live with parents by age, gender, race, ethnicity, household income and other categories.

Here are the questions used  for this report, along with responses, and its  methodology .

Majorities of teens report ever using YouTube, TikTok, Instagram and Snapchat. YouTube is the platform most commonly used by teens, with 95% of those ages 13 to 17 saying they have ever used it, according to a Center survey conducted April 14-May 4, 2022, that asked about 10 online platforms. Two-thirds of teens report using TikTok, followed by roughly six-in-ten who say they use Instagram (62%) and Snapchat (59%). Much smaller shares of teens say they have ever used Twitter (23%), Twitch (20%), WhatsApp (17%), Reddit (14%) and Tumblr (5%).

Facebook use among teens dropped from 71% in 2014-15 to 32% in 2022. Twitter and Tumblr also experienced declines in teen users during that span, but Instagram and Snapchat saw notable increases.

TikTok use is more common among Black teens and among teen girls. For example, roughly eight-in-ten Black teens (81%) say they use TikTok, compared with 71% of Hispanic teens and 62% of White teens. And Hispanic teens (29%) are more likely than Black (19%) or White teens (10%) to report using WhatsApp. (There were not enough Asian teens in the sample to analyze separately.)

Teens’ use of certain social media platforms also varies by gender. Teen girls are more likely than teen boys to report using TikTok (73% vs. 60%), Instagram (69% vs. 55%) and Snapchat (64% vs. 54%). Boys are more likely than girls to report using YouTube (97% vs. 92%), Twitch (26% vs. 13%) and Reddit (20% vs. 8%).

Majorities of teens use YouTube and TikTok every day, and some report using these sites almost constantly. About three-quarters of teens (77%) say they use YouTube daily, while a smaller majority of teens (58%) say the same about TikTok. About half of teens use Instagram (50%) or Snapchat (51%) at least once a day, while 19% report daily use of Facebook.

Some teens report using these platforms almost constantly. For example, 19% say they use YouTube almost constantly, while 16% and 15% say the same about TikTok and Snapchat, respectively.

More than half of teens say it would be difficult for them to give up social media. About a third of teens (36%) say they spend too much time on social media, while 55% say they spend about the right amount of time there and just 8% say they spend too little time. Girls are more likely than boys to say they spend too much time on social media (41% vs. 31%).

Teens are relatively divided over whether it would be hard or easy for them to give up social media. Some 54% say it would be very or somewhat hard, while 46% say it would be very or somewhat easy.

Girls are more likely than boys to say it would be difficult for them to give up social media (58% vs. 49%). Older teens are also more likely than younger teens to say this: 58% of those ages 15 to 17 say it would be very or somewhat hard to give up social media, compared with 48% of those ages 13 to 14.

Teens are more likely to say social media has had a negative effect on others than on themselves. Some 32% say social media has had a mostly negative effect on people their age, while 9% say this about social media’s effect on themselves.

Conversely, teens are more likely to say these platforms have had a mostly positive impact on their own life than on those of their peers. About a third of teens (32%) say social media has had a mostly positive effect on them personally, while roughly a quarter (24%) say it has been positive for other people their age.

Still, the largest shares of teens say social media has had neither a positive nor negative effect on themselves (59%) or on other teens (45%). These patterns are consistent across demographic groups.

Teens are more likely to report positive than negative experiences in their social media use. Majorities of teens report experiencing each of the four positive experiences asked about: feeling more connected to what is going on in their friends’ lives (80%), like they have a place where they can show their creative side (71%), like they have people who can support them through tough times (67%), and that they are more accepted (58%).

When it comes to negative experiences, 38% of teens say that what they see on social media makes them feel overwhelmed because of all the drama. Roughly three-in-ten say it makes them feel like their friends are leaving them out of things (31%) or feel pressure to post content that will get lots of comments or likes (29%). And 23% say that what they see on social media makes them feel worse about their own life.

There are several gender differences in the experiences teens report having while on social media. Teen girls are more likely than teen boys to say that what they see on social media makes them feel a lot like they have a place to express their creativity or like they have people who can support them. However, girls also report encountering some of the pressures at higher rates than boys. Some 45% of girls say they feel overwhelmed because of all the drama on social media, compared with 32% of boys. Girls are also more likely than boys to say social media has made them feel like their friends are leaving them out of things (37% vs. 24%) or feel worse about their own life (28% vs. 18%).

When it comes to abuse on social media platforms, many teens think criminal charges or permanent bans would help a lot. Half of teens think criminal charges or permanent bans for users who bully or harass others on social media would help a lot to reduce harassment and bullying on these platforms. 

About four-in-ten teens say it would help a lot if social media companies proactively deleted abusive posts or required social media users to use their real names and pictures. Three-in-ten teens say it would help a lot if school districts monitored students’ social media activity for bullying or harassment.

Some teens – especially older girls – avoid posting certain things on social media because of fear of embarrassment or other reasons. Roughly four-in-ten teens say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (40%) or because it does not align with how they like to represent themselves on these platforms (38%). A third of teens say they avoid posting certain things out of concern for offending others by what they say, while 27% say they avoid posting things because it could hurt their chances when applying for schools or jobs.

These concerns are more prevalent among older teen girls. For example, roughly half of girls ages 15 to 17 say they often or sometimes decide not to post something on social media because they worry people might use it to embarrass them (50%) or because it doesn’t fit with how they’d like to represent themselves on these sites (51%), compared with smaller shares among younger girls and among boys overall.

Many teens do not feel like they are in the driver’s seat when it comes to controlling what information social media companies collect about them. Six-in-ten teens say they think they have little (40%) or no control (20%) over the personal information that social media companies collect about them. Another 26% aren’t sure how much control they have. Just 14% of teens think they have a lot of control.

Despite many feeling a lack of control, teens are largely unconcerned about companies collecting their information. Only 8% are extremely concerned about the amount of personal information that social media companies might have and 13% are very concerned. Still, 44% of teens say they have little or no concern about how much these companies might know about them.

Only around one-in-five teens think their parents are highly worried about their use of social media. Some 22% of teens think their parents are extremely or very worried about them using social media. But a larger share of teens (41%) think their parents are either not at all (16%) or a little worried (25%) about them using social media. About a quarter of teens (27%) fall more in the middle, saying they think their parents are somewhat worried.

Many teens also believe there is a disconnect between parental perceptions of social media and teens’ lived realities. Some 39% of teens say their experiences on social media are better than parents think, and 27% say their experiences are worse. A third of teens say parents’ views are about right.

Nearly half of parents with teens (46%) are highly worried that their child could be exposed to explicit content on social media. Parents of teens are more likely to be extremely or very concerned about this than about social media causing mental health issues like anxiety, depression or lower self-esteem. Some parents also fret about time management problems for their teen stemming from social media use, such as wasting time on these sites (42%) and being distracted from completing homework (38%).

Note: Here are the questions used  for this report, along with responses, and its  methodology .

CORRECTION (May 17, 2023): In a previous version of this post, the percentages of teens using Instagram and Snapchat daily were transposed in the text. The original chart was correct. This change does not substantively affect the analysis.

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A Quantitative Research on the Level of Social Media Addiction Among Young People in Turkey

Internet technology today shows a quick progress, and social networks increase their number of users on each day. Social networking, which is one of the main indicators of the technology era, attracts people of all ages while the virtual world goes beyond the real life via the applications it offers. Especially young people show an intense interest in social media which is an extension of the Internet technology. Social media addiction is increasing both in Turkey and all around the world. This study aims to determine the level of social media addiction in young people in Turkey, and to make suggestions on the prevention of the addiction while stating the current work carried out on the subject in Turkey. Survey type research model is used in the study, and social media addiction is examined in depth to determine causes of the addiction among young people. In this study, the addiction factor of the Social Networking Status Scale is used as a data collection tool to measure social media addiction among young people. The scale has three factors including addiction, ethics and convergence, and it is a reliable and valid scale, as the reliability and validity of the scale had been tested. The study is conducted on 271 students between the ages of 13-19. It has been found that gender (t=0.406; P>0.05) makes no significant difference in social media addiction while the factors of age (F=6.256; P<0.05), daily time spent on the Internet (F=44.036; P<0.05) and daily frequency of visiting social media profiles (F=53.56; P<0.05) make significant differences in addiction level. The results have showed that low addiction level of 14-year group increases with age up to 17 years, and the level decreases in 18-year group. Social media addiction level shows a dramatic increase also in the case of daily time spent on the Internet increases. More frequent daily visits to social media profiles increase the addiction as well. The study also provides suggestions on possible actions to prevent addiction.

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Considering that social media addiction is probably the most recent type of technology addiction, the present study was designed based on the six components suggested by Griffiths (2013). Toward the main purpose of the study, the "Bergen Facebook Addiction Scale" was adapted to social media addiction and translated into Turkish. After the validation process, it was administered to a total of 700 students; of them 397 were high school students and 303 were university students. The data collection instrument included 18 five-point Likert-type items in six categories, along with 5 structured items regarding demographics of the respondents. In addition to the original findings of the present study, similar research on social media addiction in some other countries were examined for comparisons. The results showed that both university students and high school students have a moderate level of addiction to social media. Being a university or high school student does not make any difference on the level of social media addiction. However, significant differences were found regarding gender, duration of use, department at the university, and type of high school. Finally, the results of the study show certain similarities and a few differences with the results of the studies conducted in other countries.

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This study aimed to investigate the level of social media addiction among university students. The sample group comprised a total of 238 participants, 56.7% of whom are female and 43.3% of whom are male, enrolled at Istanbul University-Cerrahpasa Faculty of Sports Sciences. The data were collected using a personal information form and the 5-point Likert type "Social Media Addiction Scale" developed by Tutgun-Ünal and Deniz (2015), including 41 items and four sub-dimensions. Descriptive statistical methods, including percentage and frequency, were employed in the data analysis. The Kolmogorov-Smirnov test was carried out to check whether the data were normally distributed, suggesting a normal distribution. Independent sample t-test for bivariate data and one-way ANOVA test for more than two variables were also performed. The research findings indicated a significant difference between the "Occupation" sub-dimension based on the age of the participants, while no significant difference was observed between gender, grade level, and the level of daily social media use. In this context, social media addiction in young individuals varies according to the sociodemographic characteristics of the individual. As a result, social media addiction can be reduced by determining the demographic characteristics of young individuals.

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Remarking An Analisation

Dr. D I V Y A TYAGI

The purpose of the present investigation was to find out the social media addiction among College students. Social media addiction is one of the most burning problems among young adults, especially among college students. The sample of the present research work consisted of 140 college students from which we randomly selected only (35 boys, 35 girls). For this purpose social media addiction scale students form (smas-sf) developed by Cengiz Sahin was used. The sample of the investigation was randomly selected from Meerut College, Meerut. t-test was used for finding out a significant difference between concerning groups. Other descriptive techniques were also used, which showed that college boys have more social media addiction as compared to their female counterparts. Based on our findings we can also highlight the high, average, and low percentage of social media addiction among college students.1.42% high, 98.5% moderate, 0% low media addiction. Thus, we can explain our findings according to the changeable environment. The paper also suggested some intervention strategies to control the addiction to social media. In this respect, the paper has applied application in emerging adults.

AJIT-e: Online Academic Journal of Information Technology

Assoc. Prof. Dr. Abdulkadir Karacı

Zenodo (CERN European Organization for Nuclear Research)

juweria shaikh

International Journal of Educational Methodology

Ozlem Afacan

The aim of this study was to investigate the social media addiction of high school students in terms of some variables such as age, class, type of school, gender and daily average internet usage period. Survey method was used in the study. "Social Media Addiction Scale" (SMAS) developed by Tutgun-Unal and "Personal Information Form" prepared by the researcher were used as data collection tools. The data were obtained from a total of 596 students studying in three high schools with different academic achievement level in Kirsehir in Turkey. No significant difference was found in terms of gender variable. When the total scores of high school students on Social Media Addiction Scale are examined, it is determined that the students have "low level of addiction". In addition, it was found that there was a significant relationship between high school students' daily average internet usage time and social media addiction.

Duygu Dumanlı Kürkçü

The primary aim of this study is to have information about the frequency of use of the Internet and social media among university students and to determine the relationships between the Internet addiction and social media addiction. Additionally, the other aim of this study is to determine to the relationships between varieties of the Internet and social media; such as sexuality, age and usage time. The sample group consists of 326 students in Istanbul, Turkey: 134 male students and 192 female students. The research data is obtained from a survey based on closed-ended questionnaire and a five-point Likert scale using the Internet addiction scale and social media addiction scale. Collected data has been entered to SPSS programme and this study involves a lot of statistical analysis of data based on cause and effect relation. The classification of the addiction levels are determined by using k-means clustering algorithm. According to the research results, all participants use the Internet and 99.1% of all participants use social media. The more time spending on the Internet by participants, they are getting more addicted to the Internet. This situation is also the same for social media. The 20.2 percent of the sample group can be defined as the Internet addicted, while the 21.2 percent of the sample group can be defined as social media addicted.

International Journal of Social Sciences and Education Research

mehmet ali Gazi

Journal of Education Technology in Health Sciences

Innovative Publication

Abstract Utilizing the technology made our life very easier and brought the globe in our hand which has got both pros and cons. Young generation is more of techno oriented than the values that makes them to be depending on the social medias easily that affects the domains of health. A study was conducted to assess the Social media addiction among the paramedical students. Quantitative research approach with non experimental, descriptive research design was used. Non probability convenient sampling technique was used to select 140 para medical students who fulfills the inclusion criteria. Self administered structured questionnaire was used. Modified social media addiction likert scale was used with 20 items. Findings of the study shows that vast majority (103(74%)) of the students were addicted to the social media. To conclude, it is the high time for the policy-makers to restrict on this and make provision to improve the interaction skills. Keywords: Social media addiction, Social interactions.

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Studies highlight impact of social media use on college student mental health

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When Kyle Palmberg set out to design a research study as the capstone project for his psychology major at St. Mary’s University of M i nnesota in Winona, he knew he wanted his focus to be topical and relevant to college students.

His initial brainstorming centered around the mental health impact of poor sleep quality. 

“I wanted to look at college students specifically, to see the different ways that sleep quality can be harmed and how that can impact your mental health,” he said. As he reviewed the scientific literature, one variable kept appearing. “The topic that kept coming up was social media overuse,” he said. “It is such an important thing to my target demographic of college students.”

Palmberg, 22, grew up surrounded by social media. He’d heard plenty of warnings about the downsides of spending too much time online, and he’d seen many of his peers seemingly anchored to their phones, anxious or untethered if they had to put them down for more than a few minutes at a time.

“I think from my perspective as someone who’s been really interested in psychology as an academic discipline, social media addiction is also something I’ve been aware of personally,” Palmberg said. “I can tell within myself when things can become harmful or easy to misuse. I often see the hints of addictive behaviors in peers and coworkers.”

Palmberg found much of the published research on the topic inspiring, particularly a 2003 study on internet gambling addiction. 

“They were looking at how internet gambling addiction permeates a person’s behavior,” he said. Palmberg hypothesized that there may be behavioral similarities between people addicted to online gambling and those addicted to social media. 

“Social media provides this convenient platform for users to interact with others,” he said. “As users grow addicted, they learn that they can come back to that social platform more and more to get their needs met. The tolerance users have for gratifying that social need grows. Then they have to use social media more and more often to get those benefits.”

The negative impact of a growing dependence on social media is that time spent online takes away from real in-person interactions and reduces the time a person has available for basic personal care needs, like sleep and exercise, Palmberg said. This can ultimately have a negative impact on mental health.

“As a person builds a high tolerance for the use of social media it causes internal and external conflict,” he said. “You know it is wrong but you continue to use it. You relapse and struggle to stop using it.” Palmberg said that social media use can be a form of “mood modification. When a person is feeling down or anxious they can turn to it and feel better at least for a moment. They get a sense of withdrawal if they stop using it. Because of this negative side effect, it causes that relapse.”

Palmberg decided he wanted to survey college students about their social media use and devise a study that looked at connections between the different motivations for that use and potential for addictive behaviors. He ran his idea by his academic advisor, Molly O’Connor, associate professor of psychology at Saint Mary’s, who was intrigued by his topic’s clear connections to student life.

“We often notice social media addiction with our student population,” O’Connor said. She knew that Palmberg wouldn’t have a hard time recruiting study participants, because young people have first-hand experience and interest in the topic. “He’s looking at college students who are particularly vulnerable to that addiction. They are tuned into it and they are using it for coursework, socialization, entertainment, self-documentation.”

O’Connor said she and her colleagues at the university see signs of this addiction among many of their students. 

“They’ll be on their phones during class when they are supposed to pay attention,” she said. “They can’t help themselves from checking when a notification comes through. They say they had trouble sleeping and you’ll ask questions about why and they’ll say they were scrolling on their phone before they went to bed and just couldn’t fall asleep.”

The entertainment-addiction connection

Once his study was given the go-ahead by his advisor and approved by the university for human-subjects research, Palmberg had two months to recruit participants. 

To gather his research subjects, he visited classes and gave a short speech. Afterward, students were given an opportunity to sign up and provide their emails. Palmberg recruited 86 participants this way, and each was asked to fill out an anonymous survey about their social media habits.

Palmberg explained that the main framework of his study was to gain a deeper understanding of why college students use social media and the circumstances when it can become addictive and harmful to their mental health and well-being. He also hypothesized that perceived sleep quality issues would be connected to social media addiction.

After collecting the surveys, Palmberg said, “We essentially threw the data into a big spreadsheet. We worked with it, played with it, analyzed it.” He explained that his analysis focused on motivations for social media use, “including building social connections and self-documentation.”

What Palmberg discovered was that his subjects’ most popular motivation for social media use was for entertainment. While some participants listed other motivations, he said the most “statistically significant” motivation was entertainment.

“Not only was entertainment the most highly endorsed reason to use social media in the study,” Palmberg said, “for college students it was the only motivation we analyzed that was statistically connected to social media addiction and perceived stress. The entertainment motivation was also related to poor sleep quality.”

Mental Health & Addiction

A better way to deliver unexpected news, in her new book, ‘the rock in my throat,’ kao kalia yang shares her struggle with selective mutism, a community-based approach to suicide prevention.

He found connections between a reliance on social media for entertainment and addictive behaviors, like an inability to shut down apps or put a phone away for an extended period of time. “If a person is using social media for entertainment, they are more likely to be addicted to social media than someone who is not using it for entertainment,” Palmberg said.

The structures of popular social media platforms reinforce addictive behaviors, he said. “Current trends in social media lean more toward entertainment platforms like TikTok or Instagram. People are going on there just to pass time,” Palmberg said. These brief and repetitive formats encourage addiction, he said, because the dopamine high they create is short-lived, causing users to keep visiting to get those fleetingly positive feelings. 

O’Connor supports Palmberg’s conclusions. A reliance on social media platforms for entertainment encourages addiction, she said. This is backed up by student behavior.

“My big takeaway was the interest in the entertainment variable was the key predictor of addiction. It’s not necessarily the students that are using it to communicate with each other, but the ones that say, ‘I need to kill time between classes,’ or, ‘I’m bored before bed,’ or, ‘I am trying to relieve stress after working on homework.’” The addictive aspect comes in, O’Connor said, “because users want to be entertained more and more. They are constantly looking for the next thing to talk about with their friends.”

Palmberg said he believes that not all social media use among college students has to be addictive. “It is important for people to view social media as not only something that can be harmful but also something that can be used as a tool. I like to emphasize with my study that it’s not all negative. It is more of an emphasis on moderation. It is possible to use social media responsibly. But just like almost anything, it can be addictive.”

An emphasis on digital well-being

Twice a year, in an effort to get out ahead of digital addiction, students at Gustavus Adolphus College in St. Peter are encouraged to take a deeper look at their social media use and its impact on their mental health. Charlie Potts, the college’s interim dean of students, heads the effort: It’s a clear match with his job and his research interests.

During the semiannual event, known as “Digital Well-Being Week,” Gustavus students learn about the potentially negative impact of social media overuse — as well as strategies for expanding their social networks without the help of technology.

Potts said that event has been held four times so far, and students now tell him they anticipate it. 

“We’ve gotten to the point where we get comments from students saying, ‘It’s that time again,’” he said. Students say they appreciate the information and activities associated with Digital Well-Being Week, Potts continued, and they look forward to a week focused on spending less time with their phones.

“They remember that we put baskets on every table in the dining hall with a little card encouraging them to leave their phones there and instead focus on conversations with others,” he added. “We even include  a card in the basket with conversation starters. Students are excited about it. They know the drill. It is something they like to do that feels good.”

Potts’ own academic research has focused on mental health and belonging. Each fall, he also heads up a campus-wide student survey focused on digital well-being and how to balance phone use with other aspects of mental and physical health.

In the survey, Potts said, “We ask students, ‘How much time do you spend every day on social media? How does it make you feel?’ Students are blown away when they see the number of hours that the average Gustie spends online. The vast majority are in the 4-7 hours a day on their phone range.”

The survey, which uses a motivational style of interviewing to help participants get at the root of why altering their social media behaviors may be valuable to their overall health and well-being, focuses on small changes that might reduce participants’ reliance on technology in favor of face-to-face interaction. 

“We do a lot of conversations with students about strategies they could use,” Potts said. “Things like plugging your phone in across the room while you sleep, leaving it behind while you go to work out at the rec center, subtle changes like that. We also talk about mental health and mindfulness and how…you discern your values about what you are consuming and how that might affect you.”

Though Potts said he has encountered some resistance from students (“You roll with that and help them understand the value of that and think about how they are going to make that change,” he said), he’s also heard a lot of positive student feedback about his survey — and the twice-yearly focus on digital well-being.  

“What we found with our students is they realize deep down that their relationship with their phones and social media was not having a positive impact on their life,” Potts said. “They knew change would be good but they didn’t know how to make change or who to talk to about that or what tools were at their disposal. These options help them understand how to do that.”

Andy Steiner

Andy Steiner is a Twin Cities-based writer and editor. Before becoming a full-time freelancer, she worked as senior editor at Utne Reader and editor of the Minnesota Women’s Press. Email her at  [email protected] .

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• Open access
• Published: 20 April 2024

“Everything is kind of the same except my mind is with me”: exploring cannabis substitution in a sample of adults in early recovery from an opioid or stimulant addiction

• Corinne A. Beaugard 1 , 3 ,
• Alexander Y. Walley 2 &
• Maryann Amodeo 1  

Harm Reduction Journal volume  21 , Article number:  83 ( 2024 ) Cite this article

13 Accesses

Metrics details

Recovery from addiction is frequently equated with abstinence. However, some individuals who resolve an addiction continue to use substances, including via substitution (i.e., increased use of one substance after eliminating/ reducing another). Substitution may play a distinct role during early recovery (≤ 1 year), as this period is marked by dramatic change and adjustment. Cannabis is one of the most used substances and is legal for medical and recreational use in an increasing number of states. Consequently, cannabis an increasingly accessible substitute for substances, like fentanyl, heroin, cocaine and methamphetamine, with higher risk profiles (e.g., associated with risk for withdrawal, overdose, and incarceration).

Fourteen participants reported that they had resolved a primary opioid or stimulant addiction and subsequently increased their cannabis use within the previous 12 months. Using grounded theory, the interviewer explored their experiences of cannabis use during early recovery. Data were analyzed in three stages: line by line coding for all text related to cannabis use and recovery, focused coding, and axial coding to generate a theory about recovery with cannabis substitution. The motivational model of substance use provided sensitizing concepts.

Results & discussion

The final sample included eight men and six women ranging in age from 20 to 50 years old. Three participants resolved an addiction to methamphetamine and the remaining 11, an addiction to opioids. Participants explained that cannabis was appealing because of its less harmful profile (e.g., no overdose risk, safe supply, few side effects). Participants’ primary motives for cannabis use included mitigation of psychiatric symptoms, withdrawal/ cravings, and boredom. While cannabis was effective toward these ends, participants also reported some negative side effects (e.g., decreased productivity, social anxiety). All participants described typical benefits of recovery (e.g., improved self-concept, better relationships) while continuing to use cannabis. Their experiences with and beliefs about substitution suggest it can be an effective strategy for some individuals during early recovery.

Conclusions

Cannabis use may benefit some adults who are reducing their opioid or stimulant use, especially during early recovery. The addiction field’s focus on abstinence has limited our knowledge about non-abstinent recovery. Longitudinal studies are needed to understand the nature of substitution and its impact on recovery over time.

Most addiction treatment settings, mutual aid groups, and research on recovery posit that recovery is built upon a foundation of abstinence from psychoactive drugs, excluding nicotine and prescription medication [ 1 , 2 ]. This operationalization of recovery aligns with the Substance Abuse and Mental Health Services Administration (SAMHSA)’ definition which states, “[Recovery is] a process of change through which individuals improve their health and wellness, live a self-directed life, and strive to reach their full potential” [ 3 ]. And while this definition suggests that recovery incorporates holistic growth, the SAMHSA text later specifies that “abstinence from the use of alcohol, illicit drugs, and non-prescribed medications is the goal for those with addictions” [ 3 ]. This standard orientation toward recovery excludes individuals who resolved their addictions without abstinence, thus limiting the field’s capacity to understand and support this potentially large and heterogenous population. Among individuals who are in non-abstinence recovery, a subset “substitute,” or increase use of one substance following the decreased use or cessation of another. Motives for substitution vary and include the relative availability or cost, side effects, and risks of the original and substitute substances [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].

Second to alcohol and tobacco, cannabis is the most frequently used substance [ 3 ]. Cannabis is perceived to be less harmful than other substances, and consequent to its increasing legalization for medical and recreational purposes, has been viewed more favorably by the public [ 11 , 12 , 13 ]. Research on cannabis substitution suggests it can be an effective strategy to decrease more harmful substance use (e.g., crack cocaine, opioids, alcohol, or prescription drugs) in part because it has less adverse side effects and less withdrawal potential than other drugs [ 7 , 9 , 14 ]. Paradoxically, in clinical samples, cannabis use clustered with more active and severe use of other substances. For example, in one study cannabis substitution was associated with a 27% reduction in odds of abstinence from other drugs or alcohol [ 15 ]. In another, cannabis use was three times higher amongst those who returned to cocaine use; however, cannabis use was not associated with a return to heroin use [ 16 ].

There is little research on cannabis substitution amongst individuals in recovery– likely due to the addiction field’s normative conflation of abstinence and recovery [ 1 , 17 , 18 , 19 , 20 ]. Substitution during recovery, or after resolving an addiction, may function similarly to substitution during an addiction; however, there is no research that examines the experience and function of substitution during recovery. Early recovery, often defined as one year [ 19 , 21 ], is a unique period marked by dramatic change in behavior and lifestyle, and experiences during this period are associated with future recovery outcomes [ 22 , 23 , 24 ]. Because early recovery is distinct in the magnitude of change that occurs across many domains (e.g., professional, family, community, physical and mental health), substitution might be more common or have specific functions during this period [ 19 ].

This study was designed to address the gap in research on substitution among people in recovery. Exploring how people in early recovery from an opioid or stimulant addiction experience cannabis substitution can provide insight on whether increased use of one substance supports recovery from another. The primary aims of this study were: (1) to identify individuals’ motives for cannabis use after resolving an opioid or stimulant addiction (2), to describe individuals’ experiences using cannabis, and (3) to understand whether cannabis substitution and addiction resolution are compatible.

Participants and recruitment

Data for this study were collected from a community sample of people who resolved a stimulant or opioid addiction in the previous 12 months and subsequently increased their cannabis use. Additional eligibility requirements included being at least 18 years old, English language fluency, US residence, and the ability to consent.

For the purposes of recruitment and clarity of construct, “resolved an addiction” was chosen instead of “recovery” so that potential participants did not exclude themselves based on an association between recovery and abstinence. The term “addiction” was used rather than “substance use disorder” so that people could identify with this more common phrase rather than a formal diagnostic term. The authors posted recruitment materials on Facebook and Reddit pages related to addiction and recovery. The materials opened with, “Are you in the first 12 months of resolving an opioid or stimulant addiction?” and stated that the study was designed to, “understand more about non-abstinence recovery for people who resolved an opioid or stimulant addiction and currently use cannabis.” Most participants described themselves as in “recovery,” which is how they will be described in the results. During the phone screening, participants stated which addiction they resolved, their current substance use, and whether their cannabis use increased, decreased, or stayed the same after resolving their addiction (see Fig.  1 ).

Interview Screening Questions

The first author interviewed 14 participants over Zoom. Participants resided across the US, in the Northeast, Southeast, Midwest, and Pacific Northwest regions. The authors did not collect any identifying information about participants and chose participant pseudonyms that reflected each participant’s self-reported racial and ethnic identities. Before the interview, the first author reviewed the consent document with each participant and received verbal consent. Interviews lasted approximately 1 h, and participants received a $30 Amazon electronic gift card upon completion. The Boston University Charles River Campus IRB approved this study.

The interview guide included questions about participants’ substance use routines, experiences that prompted cannabis use, and the effect of cannabis on their recovery. The interview opened with the question, “Since you’ve been in recovery, what substances have you used?” In many cases, participants described their substance use and provided context for this use. Building on their context, probes included questions such as, “What types of things make you want to use cannabis?” or, in response to a specific example of cannabis use, “Can you describe what was going on before you used cannabis?” After participants thoroughly described motives for cannabis use, follow up prompts aimed to understand their experience using cannabis, for example, “How did you feel after you used cannabis?” Of note, prompts reflected the participants’ language about cannabis and their mechanisms of use, such that the phrasing was modified for each participant (e.g., “Can you describe what was going on before you smoked pot?”).

Constructivist grounded theory and the motivational model of substance use

The intent of constructivist grounded theory is to create new theory with the acknowledgment that research is inevitably influenced by researchers’ knowledge about the world and pre-existing theories. Thus, theories can be integrated into this methodology for the purpose of “sensitizing concepts,” which inform the research, rather than direct it. Sensitizing concepts help the researcher find “a place to start inquiry, not to end it” (p. 31) [ 25 ].

The motivational model of substance use is a framework that proposes reasons that people use substances and includes four primary motives: to cope with psychological discomfort (e.g., affect regulation), to be comfortable in social situations, to experience enhancement (e.g., to increase pleasure), and to conform (e.g., to align with peer expectations) [ 26 ]. Coping and enhancement motives are generally associated with more frequent substance use, as well as more severe substance-related problems [ 27 , 28 , 29 ]. During analysis, this theoretical model was used to suggest sensitizing concepts related to substitution motives.

Data analysis: grounded theory

Interviews were recorded and transcribed. In traditional grounded theory research, interviews are conducted and analyzed simultaneously [ 25 ]. This study took a modified grounded theory approach. The first author conducted and analyzed three interviews simultaneously and drafted an initial codebook from these interviews; they analyzed the remaining 11 interviews together. A second coder [MA] independently coded 11 transcripts using the codebook. The two coders discussed discrepancies until consensus was reached.

Following a grounded theory approach, authors coded the interviews in three stages [ 25 ]. The first stage involved line-by-line coding for all text related to participants’ substance use after resolving a primary addiction, experiences using cannabis, and beliefs about the effects of cannabis on their recovery. During initial coding, the motivational model provided sensitizing concepts (i.e., the four motives for substance use) [ 30 ]. During focused coding, the authors identified the salient processes and actions that explained motives for cannabis use, the physical and psychological effects of cannabis, and the role of cannabis use in participants’ lives. Finally, during axial coding, authors identified the relationships across themes to build an explanatory model for cannabis substitution.

Methodological integrity

The interviewer [CAB] had worked in addiction settings, was trained in qualitative methods, and had conducted previous interviews with people in recovery, as well as people with current addictions. During this study, a qualitative scholar provided methodological supervision related to study design, interviewing, and data analysis. Co-authors [AYW and MA], both experts in addiction treatment, offered guidance on the inclusion criteria, recruitment strategies, the interview protocol, and analysis.

Authors engaged with reflexivity by writing memos after each interview and meeting to discuss the interviews and coding to mitigate bias during analysis. Writing after each interview allowed authors to disentangle participants’ construction of the concepts from their impressions and anticipated responses [ 31 ]. As the interviews and analyses progressed, it became apparent that the experience of non-abstinence recovery with cannabis substitution was different from what had been expected. This realization affirmed the importance of this methodology; a different approach (e.g., surveys or more structured interviews), would have limited participants’ ability to shape the preliminary theory of non-abstinence recovery with substitution.

Participants described their experiences of increasing cannabis use after resolving a primary opioid or stimulant addiction (See Table  1 ). Most participants were non-Hispanic White [ 11 ], two participants were Hispanic, and one participant was Black Somali. The sample included eight men and six women ranging in age from 20 to 50 years old. Three participants resolved an addiction to methamphetamine and the remaining 11, an addiction to opioids (primarily fentanyl, reflecting the current drug supply). None of the participants reported their cannabis use was exclusively for medical purposes and only one participant reported access to medical cannabis. The major themes and processes that emerged from interviews included: (1) cannabis is a better alternative: relatively safe, legally accessible, & socially acceptable; (2) cannabis use is motive driven; (3) negative effects of cannabis; and (4) benefits of recovery while using cannabis.

A better alternative: relatively safe, legally accessible, & socially acceptable

All participants believed cannabis was a safe alternative to other drugs. Maya explained that cannabis, even when illicitly procured, was unlikely to be contaminated, making it safter and more reliable than methamphetamine: “Cannabis is pretty safe like as far as adulteration and you know illicit drug use, or whatever. Like, I know what I’m actually putting into my body when I use it, which is a big deal.” Many participants pointed to the relatively lower risk profile of cannabis as one reason for substitution. Sam had previously used synthetic opioids and research chemicals (i.e., unclassified drugs with unpredictable effects) that he purchased online: “All the other drugs, I was doing had serious consequences, and could absolutely kill you during your use. So, I think it was kind of a relief to do something that was safe and kind of fun.” Sam said he, “couldn’t afford to screw up [his] life anymore,” and was relieved that cannabis offered a safer alternative.

Unlike opioids or stimulants, many participants procured cannabis legally. Jessica purchased cannabis from a medical dispensary: “Weed isn’t like a drug. Not like that. I have my prescription card, my medical marijuana card. I went to a doctor about it.” Using cannabis for medical purposes differentiated it from her previous injection opioid use. Terry lived in a state with recreational cannabis and she purchased it from dispensaries: “And marijuana is legal. You know? So, it’s like I consider myself sober as long as I’m not on any illicit street drugs.” Acquiring cannabis legally informed participants’ conceptualization of cannabis as materially different from their previous substance use.

A few participants attributed their beliefs about cannabis to their family of origin’s beliefs about the substance. Russell stated, “Weed was never presented as like a drug to me. People have always smoked weed. My family smoked…It’s not, it’s not looked at like alcohol or even cigarettes.” Terry and Kelly shared similar stories about their families’ beliefs about cannabis. Familial endorsement differentiated it from illicit street drugs, and even from alcohol, as a safe, non-addictive drug that did not interfere with their recovery.

Cannabis use is motive driven

Replacing other drugs.

All participants reported that cannabis helped them avoid using opioids or stimulants. They described this replacement as taking at least three forms: 1) to cope with the cravings for another drug, b) to mimic the effects of another drug, and c) to replace the ritualistic features of other drug use.

Russell explained that cannabis did not prevent cravings, but muted their intensity:

[The cravings are] not completely gone, but they’re tolerable, and I can deal with them… [Using opioids] just doesn’t sound like as good an idea anymore. You know it doesn’t seem like it’s a, it seems like more of a want than a need. You know, like that would be nice if I had some drugs, but I just don’t really feel like going to do that, right now. You know, rather than I need to go get some drugs.

Omar had a similar experience: “Yeah, I’ve pretty much had [cravings] daily and then after [I use] the cannabis, the optimistic sense kind of hits me, and it has been like, ‘Oh I don’t actually need [the opioids].’” Cannabis improved his mood enough so that he could reevaluate his desire to use opioids.

When Jimmy experienced cravings for methamphetamine and used cannabis instead, his cravings were entirely relieved: “It’s good for, for curing cravings. I don’t think about, I honestly, after that initial getting stoned, I don’t think about speed. That’s, that’s a big thing. Like I don’t think, ‘God, I really need to hit right now.’” His infrequent cannabis use meant that he experienced its intoxicating effects more acutely, likely helping him pass through the initial cravings. Jessica used cannabis frequently and believed it prevented the onset of opioid cravings:

It pretty much took [the cravings for opioids] away because I would, I would get high [on cannabis], and I would be relaxed. And I get hungry and [am] able to sleep. And as long as I could do all those things, I’m fine.

The physical effects of cannabis mimicked some of the desired effects of opioids (e.g., relaxation, sleepiness), thus reducing her need to use opioids. Like Jessica, Terry described cannabis as a replacement: “The marijuana feeling is mostly a downer feeling, like benzos and heroin and stuff. It’s basically taken place of other drugs. Know what I mean? It’s like a substitution thing.” Cannabis satisfied her desire for the effects of opioids and benzodiazepines well enough so that she could avoid using those substances.

The final way cannabis helped participants avoid using their primary substance was through behavioral rituals. Ava reflected that ritualizing cannabis use served some of the same purposes of her opioid use:

I would say it helped [my recovery]. Because it was something that I could still kind of ritualize, which was like I said a big part of my opiate use. So, it was something that I could still kind of find a ritual in, which is very calming to me.

Replacing opioid-related rituals with cannabis rituals decreased her desire to use them. Simon also ritualized cannabis use. He typically used opioids before and after his evening shift, which he identified as his two “trigger points:”

[Buying cannabis after work] made it easy. Because there was already that concept of like picking up something at night, which I think a lot of drug addiction at some points is…just like the ritual surrounding it…like exchanging money for goods and services. That little monkey part of my brain was like, ‘Alright, cool. We’re satisfied.’

Simon believed that continuing some of the same drug-related behaviors (e.g., procurement after work, using as a reward at the end of the day), helped him avoid using opioids.

Regulating affect

All but one participant explained that cannabis use helped regulate their mood. Many described disabling anxiety and attempting to manage the symptoms with cannabis. Jimmy was in the first few weeks of recovery from methamphetamine addiction and described emotional lability, extreme fatigue, and disrupted sleep. He smoked cannabis to soften the moments that were “very prickly, like sharp and hard to deal with”:

I do think the weed helps me at least relax my mind enough to say, ‘You know what yeah, okay, this is something we need to take care of. You’re okay, right now, nothing is crashing down on you because of this.’

Many participants shared this desire - to reduce perseveration and anxiety. Ava was diagnosed with bipolar disorder and did not believe her medication reduced her symptoms to a tolerable level. Cannabis dampened some of the remaining symptoms: “My brain is just always very loud. I usually have a lot of thoughts going on at one time, so [cannabis] kind of just slows everything down, makes everything a little bit more manageable for me.” The motivation to reduce psychiatric symptoms with cannabis could be described as self-medication. For example, Maya explained that her anxiety and social phobias prohibited her from going to the grocery store; but when she used cannabis beforehand, she could complete her tasks with less worry:

It’s easier to be in the moment I guess instead of [wondering]… ‘What do [the staff at the grocery store] think? What am I doing this wrong?’ This that, like all these, like freaking out in every direction about how others are perceiving me.

In this case, Maya was describing using cannabis instead of benzodiazepines to manage anxiety; she did so because cannabis had fewer negative consequences. She acknowledged that some of her anxiety and paranoia were due to her continued methamphetamine use: “I mean, yeah, like literally [this panic has] happened, regardless of whether or not I’m on speed. It usually does if I’m on speed.” She also described similar experiences without using methamphetamine. Participants were not always clear whether symptoms were negative drug-induced side effects or endogenous psychiatric conditions. Regardless, they reported that cannabis use mitigated their anxiety and improved their functioning.

Work was a frequent external stressor that provoked anxiety and led to cannabis use. Eric recently started a new job canvassing and found the work challenging. Smoking after work helped him calm down: “[Cannabis] makes me a little bit less crazy. It makes the anxiety and like racing thoughts like drift away and I’m like - it just helps me like relax after like a long like f-cking stressful day.” Jessica recently quit a telemarketing job due to the stress, however while still employed, she reported using cannabis throughout the day to ease her discomfort: “You’re getting yelled at constantly, getting hung up on… It feels much better when you get to go to the car and smoke a bowl. You know, and then you’re a lot more relaxed and it’s easier to deal with the 200 phone calls.” Omar was frequently responsible for family tasks and stated that completing errands for his mother was a major stressor.

And [my mom] can get angry and be very vocal if I make any mistakes. So sometimes I’ll be nervous to complete the job and make sure I don’t make any mistakes. But if I smoke first then I’ll kind of be more into the flow and end up making less mistakes. So, it’s like yeah, this sense of optimism is - comes from a sense of less anxiety. Omar believed that by reducing his anxiety, cannabis allowed him to complete tasks effectively with less distraction.

Avoiding boredom

Participants consumed cannabis when they wanted distraction from boredom or were completing uninteresting tasks. Russell described this as a long-term strategy to motivate him through monotonous tasks: “[I smoke more] if I’m like doing yard work and sh-t like that, or monotonous like physical labor. There’s nothing like being high and having to like clean the house or do the dishes, like it makes it so much easier.” Participants struggled to manage boredom, whether limited to specific tasks or more generalized boredom. Kathryn attended an intensive outpatient program each morning but had few other obligations; she described her discomfort with managing unstructured time: “Smoking [cannabis] helps with that. It makes it not so hard because, just like my brain is so much more clear than it was before that it’s hard to just do like mundane things. So, sitting around and doing nothing is like hard.” Kathryn stated she was offered a job and believed working would lead to reduced cannabis use.

Negative effects of cannabis

Though all participants believed cannabis positively impacted their recovery, many also reported negative side effects. Whereas several people reported cannabis minimized their anxiety, increased social anxiety was the most common negative side effect. Eric explained, “It does kind of take me out of it and make it a little harder for me to connect with people, I think. Like puts you on a different plane of understanding and you get a little anxiety accompanying that.” Sam also experienced increased social anxiety: “With cannabis, the bad effects are, for me, mainly my social anxiety becomes worse. I get too caught up in my own thoughts. Like trains of thought will run on when I don’t want them to.” To prevent this, Sam rarely used cannabis in social situations unless he was with close friends.

Some participants said that cannabis increased their focus and helped them accomplish mundane or monotonous tasks, however others explained it decreased their motivation and productivity. Decreased focus and energy helped some pass the time, but others experienced these effects as counterproductive. Kathryn stated, “And when I’m high I just think, like, I’m not really on my A game. I’m not thinking as like clearly…. And I just feel like I don’t get much done.” She indicated that cannabis’ dulling effects positively affected her recovery because it helped her manage her spare time, simultaneously it negatively affected her recovery because it limited her clarity of mind. The complex, and at times conflicting, side effects of cannabis made its effect unpredictable.

Using cannabis to replace primary drugs was a common reason that participants increased their use during recovery. A few people explained, though, that using cannabis in response to an opioid craving increased their desire to feel the effects of opioids. Eva compared it to ineffectively scratching an itch:

That feeling of I have an itch but it’s not really being scratched. Because it, you know, obviously doesn’t have the same effects as, like an opiate or something like that. But it’s like just barely enough to keep you like from wanting to do anything else, but then that can also be frustrating.

In Marco’s case, this strategy led to opioid use: “One of the times I did relapse was because I thought I was going to feel better, I took a hit [of cannabis], right. And what it actually did was intensified my thinking to where I was like, ‘Oh now I need to calm down, right.’” Marco was the only participant who shared that he used opioids in response to cannabis use. While not common in this sample, return to use is one substantial risk of cannabis substitution.

Benefits of recovery while using cannabis

Despite ongoing challenges related to psychiatric conditions and continued substance use, every participant reported meaningful improvements after resolving their primary addiction and increasing their cannabis use. Many had better, more honest relationships with their families. Ava said that she was able, “to be myself in front of my family and friends, because I’m not hiding anything anymore.” Prior to resolving her addiction, Ava concealed her opioid use. However, her family used cannabis together, and she could join them now without having to hide other substance use. In other cases, participants did not describe using cannabis with their families, but had improved relationships with their families because they were less impaired. Terry was happy to spend more time with her family, “And they want to connect with me more too because I’m not fu-ked up.” Ian echoed a similar experience, “I got to get closer to my mom. And my mother-in-law. So that’s been nice.” Strengthening family intimacy was just one of the benefits of their recovery, even while using cannabis.

Participants also reported relief from the elimination of opioid and stimulant-related consequences. Freeing herself from the powerful hold that opioids had over her, Jessica regained her autonomy:

If I’m going to be dope sick, then I’m not coming. You know what I’m saying? Or I’m going and getting that first. Or, or if I get there, and I have to go get it, I’m going to leave in the middle of family dinner. I’m gonna [sic] go get my drugs… Nothing’s going to stand in the way of me getting, of getting right…the opiates they completely control[ed] my life.

Without urgency to acquire money or drugs, she was more accountable to herself and her family. Kelly shared similar relief that she was rid of the effects of methamphetamine addiction:

[Methamphetamine] just takes over your freakin [sic] life, you forget to eat, you forget to sleep. I’ll be like three days in and not realize I haven’t slept yet, and then you know you start seeing things in the corner of your eyes, because you’re sleep deprived and you’re on this major drug. So yeah, it’s a big difference.

Kelly continued to struggle with her mental health and to moderate her alcohol use. Even so, she was relieved to be rid of methamphetamine-induced deprivation and psychosis: “Everything is kind of the same, except my mind is with me.” The relief from consequences related to opioid and stimulant use was described consistently as impactful to participants’ recovery. Maya was proud of her self-directed change:

I don’t feel any shame whatsoever. I’m actually really proud… I rose to the occasion. Like I made choices, like intentional choices. And followed through on those choices to ensure that I can be responsible and trustworthy.

Maya continued to use methamphetamine, but at a decreased frequency and quantity (i.e., a small amount in the morning), and reaped profound benefits related to improved self-concept and stable employment. Kathryn summarized her growth over the past few months, touching upon many of the themes identified above:

I wake up in the morning and like let my dog out, feed my animals and stuff I could not do before, because I was sick all the time… I read three books which I haven’t read any books and, like the last few years. I made friends, which I didn’t have before. I answer my phone. Less fighting with my husband because I’m not trying to sneak out and go get high. A lot of good things, a lot of little things. I got on depression medication, finally, because I went to the doctor.

For some, cannabis use was directly linked to recovery experiences (e.g., Ava spent time with her family, which involved cannabis use). For the most part, though, cannabis use benefitted participants’ recovery indirectly. They explained that cannabis use helped them reduce or eliminate their primary substance and tolerate experiences without those substances, via replacement, affect regulation, and avoiding boredom; this elimination or reduction facilitated their myriad positive outcomes.

This study identified participants’ motives for, experiences with, and reflections on cannabis use after resolving a primary opioid or stimulant addiction. Participants illustrated cannabis’ host of functional roles. They assessed the risks of cannabis use in comparison to the risks of their previous opioid and methamphetamine use and reasonably concluded that cannabis substitution was substantially less harmful and facilitated progress in their recovery.

Relative risk

In the absence of a safe drug supply, universal healthcare, and access to safe use supplies, individuals with addictions to opioids and/or stimulants face some of the greatest risks for health and social harms related to drug use, risks that are not attributable to cannabis use [ 32 ]. Chronic opioid and methamphetamine use are associated with severe health consequences, including impaired memory and cognition; structural brain changes; increased impulsivity and violent behavior; anxiety, delusions, hallucinations, and psychosis; heart attacks, seizures, liver and kidney damage, and death [ 33 , 34 ]. Some of these changes are permanent, or persist into a period of abstinence [ 35 ]. Additionally, opioid use continues to be a leading cause of drug-related deaths in the U.S., thus, any decrease in opioid use increases survival likelihood [ 36 ].

While cannabis substitution may reduce mortality and morbidity related to opioid or stimulant use, cannabis use is not without acute and long-term risk, especially for youth or pregnant people [ 37 , 38 ]. Acute side-effects include impaired non-verbal learning and memory, attentional control, and motor inhibition, however these side effects generally subside after a period of abstinence [ 37 , 38 ]. However, the changing drug supply may challenge the validity of these findings, as the average THC concentration has increased annually since 1970 [ 39 ]. While research on high potency THC products is nascent, some has found a correlation between high THC and increased likelihood of cannabis use disorder (CUD), increased “dependence,” and increased side effects including memory impairment and paranoia [ 40 , 41 ].

Motivational model and cannabis substitution during recovery

The motivational model was an informative framework in examining participants’ cannabis use. Of the motivational model’s extant motives, “to cope” was the most salient motive for cannabis use during recovery. Cannabis use during recovery supported two types of coping: [ 1 ] to regulate affect; and [ 2 ] to avoid boredom or negative thought patterns. Notably, using cannabis for pleasure or for social purposes was uncommon in this study. Even more than these motives, participants emphasized that cannabis helped them avoid using opioids or methamphetamine. The motivational model does not include a substitution motive and this study suggests that, while similar to the coping motive, substitution is likely a distinct construct. The motivational model’s “to cope” has typically referred to psychological coping with distress of any kind; in contrast, substitution involves physical and psychological dimensions and is exclusively driven by reduced substance use. Additional research may be helpful to determine whether these are different factors.

Motives for use

Substitution.

In line with previous research, participants believed that cannabis use protected them from returning to their primary substance or former pattern of use, in part because cannabis helped them manage cravings [ 32 ]. In studies on the effect of cannabis use on the return to opioid use by individuals taking medication for opioid use disorder (MOUD), cannabis was associated with decreased likelihood of opioid, alcohol, or cocaine use [ 42 , 43 ]. In one study, experiencing euphoria or being “high” was associated with decreased likelihood of any opioid use. In the current study, cannabis intoxication did not change participants’ assessment of cannabis’ effectiveness as a deterrent to opioid or methamphetamine use. For example, some participants benefitted from their cannabis use rituals, which were unrelated to its psychoactive effects.

Affect regulation

Reducing anxiety was the most reported mood-related motive for cannabis use, suggesting some degree of self-medication [ 44 , 45 ]. Many participants in this study reported complex psychiatric disorders and previous trauma, which they aimed to treat with cannabis. In many cases participants described high frequency of use to mitigate these symptoms. Further exploration of self-medication with cannabis use is warranted to discern whether it can be exclusively therapeutic, or whether there are always ancillary motives and/or effects. Other participants experienced increased anxiety, especially social anxiety, after using cannabis. Research on cannabis and anxiety reflects these mixed outcomes. A review on this topic found some evidence that cannabis has anxiolytic effects, though many studies had inverse or null results [ 46 ]. While cannabis is likely to be inadequate to treat patients’ anxiety without additional mental health intervention, many participants in this study indicated skillful use of cannabis by moderating use according to its effects (e.g., only using cannabis with close friends to avoid increased social anxiety).

Boredom, the aversive state due to a monotonous environment and difficulty remaining engaged with the environment, is a natural part of early recovery. Boredom and even the anticipation of boredom are known barriers to entering or staying in recovery [ 47 , 48 , 49 ]. In the general population, people use cannabis to mitigate boredom [ 50 , 51 ] yet there is little research on the relationship between boredom and recovery and how cannabis use interacts with these states. In the present study, participants described the connection between cannabis use and boredom in three ways: first, cannabis helped them accomplish tasks in which they had little interest; second, it ameliorated negative emotional experiences prompted by boredom; and third, it helped them tolerate boredom produced by unstructured time, in many cases due to unemployment.

Unemployment is common in early recovery and increased participation in the workforce often occurs over time in recovery [ 52 , 53 ]. For those in early recovery, engagement with community, work, or hobbies increases recovery capital and diminishes boredom [ 54 ]. Employment interventions in abstinence-based treatment settings have been associated with positive substance use outcomes [ 55 ]. However, many individuals like those in this study do not have clear pathways to access employment, nor are they embedded in peer support communities, which often help people re-enter the workforce. This barrier to services and recovery capital points toward an important area for future intervention development.

Implications for conceptions of recovery

Research increasingly acknowledges that recovery includes both abstinent and non-abstinent paths, paths which may vary by addiction and psychiatric severity, complexity, and chronicity [ 2 , 20 , 56 ]. Yet, there are few treatment or mutual aid settings where non-abstinent individuals can access the recovery resources available to their abstinence-seeking peers, as such settings view cannabis use as incompatible with recovery.

Due to potential medical use, cannabis use may or may not violate the principle of abstinence from non-prescribed psychoactive substances. Taking cannabis to treat a condition (e.g., chronic pain, posttraumatic stress disorder, chemotherapy-induced nausea) may be categorically closer to taking a prescription stimulant for Attention-deficit/hyperactivity disorder (ADHD) than to using cannabis recreationally. However, unlike medication for ADHD, there are no dosing guidelines or maximum dosing thresholds [ 57 ]. Without parameters for use, it remains challenging to classify cannabis use as strictly medical. Further study on the role of medical cannabis use in recovery is warranted to understand whether its use is compatible with the construct of abstinence.

Many participants reported previous treatment or 12-step participation, noting that these settings viewed their goals as incompatible with the settings’ conceptualization of successful recovery [ 56 ]. Upholding the belief that abstinence is the foundation of recovery, as many treatment and mutual aid settings do, discounts the substantial growth and improvement of people who, despite non-abstinence, recover from their addictions [ 2 ]. Equating abstinence with recovery reinforces stigmatizing conceptualizations of this population by differentiating between those who have and have not changed their substance consumption “enough,” or those who do and do not count as “recovered” [ 58 , 59 ]. Without embracing a more inclusive recovery paradigm, individuals like the participants in this study will continue to be perceived as “less well” and will continue to have fewer options for medical and mental health support compared to their abstinence-seeking peers [ 60 ].

Study limitations

Findings should be interpreted in the context of these limitations. First, this preliminary study on cannabis substitution was conducted with a small sample, which may mean that conceptual categories integral to non-abstinence recovery with substitution were missed [ 25 ]. Future qualitative studies on this topic should aim for larger samples and could consider the addition of quantitative measures. Recruiting via social media sites was effective in accessing a hard-to-reach population, but the resultant sample was limited to individuals who were aware of and engaged with these sites. The sample was predominantly White, which possibly reflects the demographics of individuals using Reddit, the recruitment site for most participants. Clinical settings, including primary care and addiction specific clinics, may be useful settings for recruitment in future studies. The interview did not explicitly ask about mental health history and likely missed some participants’ diagnoses and psychiatric medication. Although participants described their substance use history, they did not complete a clinical intake and thus their drug-use severity is unknown. In future work, researchers should collect precise data about mental health and addiction history to improve the understanding of who substitutes with cannabis and under what circumstances. Finally, this study was a single point in time and longitudinal studies are critical to understanding whether substitution and its effects change over time.

This study increases our insight about cannabis substitution in early recovery, documenting its potential roles during this period. At this time, cannabis’ relatively lower- risk profile makes it an effective harm reduction strategy for those in early recovery from an opioid or stimulant addiction [ 7 , 32 ]. Future studies are needed to assess the degree to which this substitution strategy is sustainable over time, as well as the later risks for primary addiction recurrence or development of a cannabis use disorder. Taking a harm reduction approach to drug use and addiction recovery has the potential to positively transform this population’s recovery experiences and willingness to seek support [ 61 , 62 ].

Data availability

No datasets were generated or analysed during the current study.

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I would like to thank Renée Spencer, EDD, MSSW for her methodological training and consistent support, which made this study possible.

Funding for this work was provided by the Boston University School of Social Work grant for doctoral dissertations.

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Beaugard, C.A., Walley, A.Y. & Amodeo, M. “Everything is kind of the same except my mind is with me”: exploring cannabis substitution in a sample of adults in early recovery from an opioid or stimulant addiction. Harm Reduct J 21 , 83 (2024). https://doi.org/10.1186/s12954-024-01002-0

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Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review

Viktor schønning.

1 Department of Health Promotion, Norwegian Institute of Public Health, Bergen, Norway

Gunnhild Johnsen Hjetland

Leif edvard aarø, jens christoffer skogen.

2 Alcohol and Drug Research Western Norway, Stavanger University Hospital, Stavanger, Norway

3 Faculty of Health Sciences, University of Stavanger, Stavanger, Norway

Associated Data

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

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

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

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

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

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

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

• • Which aspects of mental health and well-being have been the focus or foci of research so far?
• • Has the research focused on different research aims across gender, ethnicity, socio-economic status, geographic location? What kind of findings are reported across these groups?
• • Organize and describe the main sources of evidence related to social media that have been used in the studies identified.

Defining Adolescence and Social Media

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

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

Data Sources and Search Strategy

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

Study Selection: Exclusion and Inclusion Criteria

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

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

Flowchart of exclusion process from unsorted results to included studies.

Data Extraction and Organization

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

Visualization of Words From the Titles of the Included Studies

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

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

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

Characteristics of the Included Studies

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

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

Mental Health Foci of Included Studies

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

Social Media Metrics of Included Studies

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

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

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

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

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

Implicit Causation Based on Direction of Association

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

Cyberbullying as a Nexus

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

Strengths and Limitations of Present Study

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

Description of Central Themes and Core Concepts

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

Why the Focus on Poor Mental Health/Pathology?

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

The Metrics of Social Media

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

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

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

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

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

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

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

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

What Is the Gap in the Literature?

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

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

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

Author Contributions

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

Conflict of Interest

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

Acknowledgments

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

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

1 https://rayyan.qcri.org/welcome

Supplementary Material

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

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