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Social media addiction: Its impact, mediation, and intervention

Vol.13, no.1 (2019).

Yubo Hou Dan Xiong Tonglin Jiang Lily Song Qi Wang

https://doi.org/10.5817/CP2019-1-4

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This research examined the relations of social media addiction to college students' mental health and academic performance, investigated the role of self-esteem as a mediator for the relations, and further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes. In Study 1, we used a survey method with a sample of college students ( N = 232) and found that social media addiction was negatively associated with the students' mental health and academic performance and that the relation between social media addiction and mental health was mediated by self-esteem. In Study 2, we developed and tested a two-stage self-help intervention program. We recruited a sample of college students ( N = 38) who met criteria for social media addiction to receive the intervention. Results showed that the intervention was effective in reducing the students’ social media addiction and improving their mental health and academic efficiency. The current studies yielded original findings that contribute to the empirical database on social media addiction and that have important theoretical and practical implications.

Peking University, China

Yubo Hou is an associate professor at Peking University's School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, as well as a core member of the Center for Cultural Psychology at Tsinghua University. His major research interests include organizational behavior, personality and social psychology, social media, and cultural psychology. He is well-known for his cross-cultural research on the thinking styles of Chinese and Western populations. His current work focuses on behavioral problems and Confucian style of coping among Chinese adults, and the influence of social media on psychological wellbeing. Hou holds a BSc in Psychology from Zhejiang University and a Ph.D. in Social Psychology from Peking University.

Southwest University, China, Peking University, China

Dan Xiong, Assistant Professor, Faculty of Psychology, Southwest University

Tonglin Jiang

Peking university, china the university of hong kong, hong kong.

Tonglin Jiang, Ph.D candidate, Department of Psychology, The University of Hong Kong.

Lily Song, Ph.D candidate, Institute of Psychology, Chinese Academy of Science.

Cornell University, The USA

Qi Wang is a professor and department chair in Human Development at Cornell University. Her research integrates developmental, cognitive, and sociocultural perspectives to examine the mechanisms underlying the development of a variety of social-cognitive skills, including autobiographical memory, self, future thinking, and emotion knowledge. She has undertaken extensive studies to examine how cultural beliefs and goals influence social cognitive representations and processes by affecting information processing at the level of the individual and by shaping social practices between individuals. In addition, she has conducted studies to examine the impact of Internet technology as a cultural force unique to our time on cognitive functioning and well-being. A graduate of Peking University, China, Qi Wang earned a Ph.D. in psychology in 2000 at Harvard University. She has received many honors and awards and has over one hundred and fifty publications in scientific journals and in volumes of collected works. Her single-authored book, The Autobiographical Self in Time and Culture (2013, Oxford University Press), is regarded as the definitive work on culture and autobiographical memory.

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Introduction

Human beings have fundamental needs to belong and to relate, for which interpersonal communication is the key (Baumeiste<tar, 1995; Wang, 2013). In recent decades, with the development of information technology, especially with the rapid proliferation of Internet-based social media (e.g., Facebook, WeChat, or Instagram), the ways of interpersonal communication have drastically changed (Smith & Anderson, 2018; Stone, & Wang, 2018). The ubiquitous social media platforms and the easy access to the Internet bring about the potential for social media addiction, namely, the irrational and excessive use of social media to the extent that it interferes with other aspects of daily life (Griffiths, 2000, 2012). Social media addiction has been found to be associated with a host of emotional, relational, health, and performance problems (e.g., Echeburua & de Corral, 2010; Kuss & Griffiths, 2011; Marino, Finos, Vieno, Lenzi, & Spada, 2017; Marino, Gini, Vieno, & Spada, 2018). Understanding the causes, consequences, and remedies of social media addiction is thus of paramount importance. In the current research, we examined the relations of social media addiction to college students' mental health and academic performance and the role of self-esteem as a mediator for the relations (Study 1). We further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes (Study 2).

Social Media Addiction and the Negative Outcomes

Social media addiction can be viewed as one form of Internet addiction, where individuals exhibit a compulsion to use social media to excess (Griffiths, 2000; Starcevic, 2013).  Individuals with social media addiction are often overly concerned about social media and are driven by an uncontrollable urge to log on to and use social media (Andreassen & Pallesen, 2014). Studies have shown that the symptoms of social media addiction can be manifested in mood, cognition, physical and emotional reactions, and interpersonal and psychological problems (Balakrishnan & Shamim, 2013; Błachnio, Przepiorka, Senol-Durak, Durak, & Sherstyuk, 2017; Kuss & Griffiths, 2011; Tang, Chen, Yang, Chung, & Lee, 2016; Zaremohzzabieh, Samah, Omar, Bolong, & Kamarudin, 2014). It has been reported that social media addiction affects approximately 12% of users across social networking sites (Alabi, 2012; Wolniczak et al., 2013; Wu, Cheung, Ku, & Hung, 2013).

Many studies on social media usage and mental health have shown that the prolonged use of social media such as Facebook is positively associated with mental health problems such as stress, anxiety, and depression and negatively associated with long-term well-being (Eraslan-Capan, 2015; Hong, Huang, Lin & Chiu, 2014; Malik & Khan, 2015; Marino et al., 2017; Pantic, 2014; Shakya & Christakis, 2017; Toker & Baturay, 2016). For example, the time spent on social media was positively related to depressive symptoms among high school students in Central Serbia (Pantic, Damjanovic, Todorovic, et al., 2012) and among young adults in the United States (Lin et al., 2016). Furthermore, certain categories of social media use have been shown to be associated with reduced academic performance (Al-Menayes, 2014, 2015; Junco, 2012; Kirschner & Karpinski, 2010; Junco, 2012; Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013; Al-Menayes, 2014, 2015). For example, Lau (2017) found whereas using social media for academic purposes did not predict academic performance indexed by the cumulative grade point average, using social media for nonacademic purposes (video gaming in particular) and social media multitasking negatively predicted academic performance. A large sample (N = 1893) survey conducted in the United States also found that the time students spent on Facebook was negatively associated with their total GPAs (Junco, 2012). Laboratory experiments have provided further evidence for the negative relation between social media use and academic outcomes. For example, Wood et al. (2012) found that multi-tasking via texting, email, MSN, and Facebook had negative effects on real-time learning performance. Jiang, Hou, and Wang (2016) found that the use of Weibo, the Chinese equivalence of Twitter, had negative effects on information comprehension.

Importantly, frequent social media usage does not necessarily indicate social media addiction (Griffiths, 2010) and therefore does not always have negative implications for individuals’ mental health (e.g., Jelenchick, Eickhoff, & Moreno, 2013) or academic performance (Pasek & Hargittai, 2009). A key distinction between normal over-engagement in social media that may be occasionally experienced by many and social media addiction is that the latter is associated with unfavorable consequences when online social networking becomes uncontrollable and compulsive (Andreassen, 2015). Studies investigating social media addiction have mainly focused on Facebook addiction (e.g., Andreassen et al., 2012; Koc & Gulyagci, 2013; Hong et al., 2014). It has been shown that addiction to Facebook is positively associated with depression, anxiety, and insomnia (Bányai et al., 2017; Koc & Gulyagci, 2013; Shensa et al., 2017; Van Rooij, Ferguson, Van de Mheen, & Schoenmakers, 2017) and negatively associated with subjective well-being, subjective vigor, and life satisfaction (Błachnio, Przepiorka, & Pantic, 2016; Hawi & Samaha, 2017; Uysal, Satici, & Akin, 2013). Research has also suggested the negative impact of social media addiction, and Facebook addiction in particular, on academic performance (Huang, 2014; Nida, 2017).

The Role of Self-Esteem

One factor that may underlie the negative effects of social media addiction is self-esteem.  Although viewing or editing one's own online profile enhances self-esteem, according to the Hyperpersonal Model (Amy & Hancock, 2010), social media users are frequently exposed to others’ selective and glorified online self-presentations, which can, in turn, reduce the viewers’ self-esteem (Rosenberg & Egbert, 2011). For example, frequent Facebook users believe that others are happier and more successful than themselves, especially when they do not know well the other users offline (Chou & Edge, 2012). Vogel, Rose, Roberts, & Eckles (2014) suggest that the extent of upward social comparisons on Facebook is greater than the extent of downward social comparisons and that upward social comparisons on social media may diminish self-esteem. Empirical studies have provided support to this proposal. For example, a study by Mehdizadeh (2010) showed that the use of Facebook was correlated with reduced self-esteem, such that individuals who spent a greater amount of time on Facebook per session and who made a greater number of Facebook logins per day had lower self-esteem. Another study found that adolescents’ self-esteem was lowered after receiving negative feedback on social media (Valkenburg, Peter, & Schouten, 2006). Moreover, recent studies have revealed a negative relation between addictive use of social media and self-esteem (e.g., Andreassen et al., 2017; Błachnio, et al., 2016). 

A considerable number of studies have shown that low self-esteem is associated with many psychological dysfunctions such as depression and anxiety (e.g., Orth, Robins, & Roberts, 2008; Orth & Robins, 2013; Sowislo & Orth, 2013). Self-esteem has also been shown to be positively associated with academic performance (e.g., Lane, Lane, & Kyprianou, 2004; Lent, Brown, & Larkin, 1986) and further serve as a protective factor against adversities in aiding academic and emotional resilience (Raskauskas, Rubiano, Offen, & Wayland, 2015). It is possible that social media addiction contributes to lower self-esteem, which, in turn, leads to a decrease in mental health and academic performance. In other words, self-esteem may play a mediating role in the relations of social media addiction to mental health and academic performance.

The Present Study

To further examine the relations of social media addictions to individuals’ mental health and academic performance, we conducted two studies. In Study 1, we investigated the relations of social media addictions to mental health and academic performance in college students and examined the role of self-esteem as a potential mediator for the relations. A survey method was used in which participants reported their addiction to social media, as well as their mental health, academic performance, and self-esteem. Built on the findings of Study 1, we designed an experimental intervention in Study 2 to reduce social media addiction and further promote college students' mental health and academic performance.

In both studies, we used the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2017) to measure social media addiction. Based on the general addiction theory, Andreassen and colleagues (2012) first developed the Bergen Facebook Addiction Scale (BFAS), with six items each describing one dimension of addictive behavior (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse). The scale has good psychometric properties, and the addiction can be scored using a polythetic scoring scheme (i.e., scoring 3 or above on at least four of the six items) or a monothetic scoring scheme (i.e., scoring 3 or above on all six items) (Andreassen et al., 2012). One critique of BFAS is that it is specific to Facebook addiction and thus may not be appropriate for examining addiction to online social networking more generally (Griffiths, 2012). Andreassen and colleagues (2017) later revised BFAS into BSMAS, replacing "Facebook" with "social media." It has been shown to have excellent reliability (Cronbach's alpha = .88) for measuring social media addiction. In addition, BSMAS has been used with non-English populations such as Iranian, Italian, and Hong Kong samples and demonstrated robust psychometric properties (Lin, Broström, Nilsen, Griffiths, & Pakpour, 2017; Monacis, De Palo, Griffiths, & Sinatra, 2017; Yam et al., 2018).

Based on the findings of previous studies (e.g., Jiang et al., 2016; Koc & Gulyagci, 2013; Pantic et al., 2012; Rosen et al., 2011; Valkenburg et al., 2006), we hypothesized that social media addiction would be negatively associated with college students’ mental health and academic performance, and that these relations would be mediated by the students’ self-esteem. We further expected that an intervention to reduce social media addiction would alleviate its negative associations with mental health and academic performance.

Study 1 utilized a survey method to investigate the relations of social media addiction to mental health and academic performance in college students and to examine the role of self-esteem as a potential mediator for the relations.

Participants. The participants were undergraduate students recruited through a social psychology course at Peking University, China. A total of 250 students who enrolled in the course participated in the study for one course credit. Among the students, 18 did not complete the questionnaires and were excluded. The final sample thus included 232 participants (117 males, 115 females; Mean age = 19.18 years, SD age = 1.32).

Procedure and Materials. Participants each completed a set of questionnaires in class. They were told that the questionnaires were unrelated to each other and that they should carefully answer all questions.

Social media addiction. The 6-item Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2017) was used to measure the participants’ addictive use of social media. The items concern experiences occurring over the past year and are rated on 5-point scales ranging from 1 (Very rarely) to 5 (Very often) (e.g., “ How often during the last year have you felt an urge to use social media more and more?” ). Given the characteristics of social networking sites in mainland China, we replaced the examples of social media sites in the original scale, namely “Facebook, Twitter, Instagram and the like,” with those popular in China, "QQ, Weibo, WeChat and the like” in the instruction. A bilingual researcher translated the scale into Chinese, which was then back translated into English by another researcher. The original English version was compared with the back-translated version to resolve any discrepancies between them. The Cronbach's alpha of the Chinese version in the current sample was 0.81. Participants’ ratings were summed across the 6 items to form a social media addiction score, with higher scores indicating greater social media addiction. 

Mental health . Mental health was measured by a 20-item questionnaire adapted by Li and Kam (2002) from the 30-item General Health Questionnaire (GHQ-30; Goldberg, 1972). This questionnaire includes three sub-scales: depression ( Cronbach's α = .65), anxiety ( Cronbach's α = .73), and sense of adequacy ( Cronbach's α = .63). Participants were asked to answer “Yes” or “No” about their feelings in recent weeks (e.g., “I feel that being alive has no meaning,” “I feel unsettled or nervous all day long ,” and “I go happily through daily life” ). The scores for depression and anxiety were reverse-coded. Scores of the three sub-scales were then summed ( Cronbach's α = .80), with higher scores indicating better mental health.

Academic performance. Given that the participants came from diverse majors and different classes, their academic performance was measured by self-reported ranking relative to their respective peers. Participants were asked to rank their academic performance relative to their peers in the past semester as 1) 20% or below; 2) 20 - 40%; 3) 40 - 60%; 4) 60 - 80%; or 5) 80 - 100%.

Self-esteem. The 10-item Chinese version of the Self-esteem Scale ( Cronbach's α = .82; Ji & Yu, 1993) adapted from Rosenberg (1965) was used to measure self-esteem (e.g., “ I feel that I have a number of good qualities ”). Participants answered the questions on 4-point scales ranging from 1 (strongly disagree) to 4 (strongly agree). Higher scores indicated higher levels of self-esteem.

At last, participants were asked to report demographic information including age, gender, only child or non-only child status, and urban or rural residence, and they were fully debriefed and thanked.

Results and Discussion

In the current sample, 41.4% of the participants scored 3 or above on at least four of the six items (the polythetic scoring scheme of BSMAS), and 9.9% scored 3 or above on all six items (the monothetic scoring scheme of BSMAS; Andreassen et al., 2012). Also, 14.7% of the participants could be classified as having social media addiction, whose composite score was above 18 and who scored 3 or above on at least four of the six items. This percentage was close to what was previously reported (12%) in a Chinese sample (Wu et al., 2013). Participants who were only children had poorer academic performance, t (194) = 2.71, p = .007, d = .44, higher levels of self-esteem, t (228) = 2.44, p = .02, d = .38, and lower social media addiction scores, t (228) = -2.58, p = .01, d = -.40, than did those with siblings. Participants who came from cities had higher levels of self-esteem, t (214) = 2.87, p = .005, d = .57, than did those from rural areas. Gender and age were not significantly correlated with any variables.

Following previous studies (Andreassen et al., 2012, 2017; Koc & Gulyagci, 2013; Hong et al., 2014), we treated the social media addiction score as a continuous variable to examine the degree of additive use of social media in relation to mental health and academic performance. Table1 presents the means and standard deviations (SDs) of key variables and the correlations among them. Social media addiction was negatively correlated with mental health, whereby the higher one scored on social media addiction, the poorer mental health he or she had. Social media addiction was also negatively correlated with academic performance as well as self-esteem. Self-esteem, on the other hand, was positively related to mental health. Mental health and academic performance were also positively correlated.

Table 1: Means, SDs and Correlations among Study Variables.

We further conducted partial correlation analyses among the key variables, controlling for demographic variables (i.e., age, gender, only child status, and residence). The pattern of results remained identical. The partial correlations between social media addiction and mental health, academic performance, and self-esteem remained significant, r s(232) =  -.29 ( p <.001), -.15 ( p = .048), and -.20 ( p = .007), respectively. Self-esteem and mental health were also significantly correlated, r (232) = .55, p <.001, so were mental health and academic performance, r (232) = .20, p = .007.

Because self-esteem was not correlated with academic performance, the mediation effect was not tested further for academic performance. To test whether self-esteem played a mediating role in the relations of social media addiction to mental health, we conducted three steps of regression analyses (Wen, Hou, & Zhang, 2005). In the first step, we regressed mental health on demographic variables and social media addiction. Social media addiction uniquely predicted mental health, β = - .29, t (210) = -4.28, p < .001. In the second step, we regressed self-esteem on demographic variables and social media addiction. Social media addiction uniquely predicted self-esteem. β = - .19, t (210) = -2.75, p = .007. In the third step, demographic variables were entered in the first layer, social media addiction was entered in the second layer, and self-esteem was entered in the third layer to predict mental health. After self-esteem was entered, the size of the standard regression coefficient of social media addiction decreased from -.29 to -.19, t (209) = -3.26, p = .001, △R 2 = .26, p < .001. Thus, the relation between social media addiction and mental health was at least partially mediated by self-esteem. The mediating effect of self-esteem is shown in Figure 1.

Figure 1. Mediating effect of self-esteem (Study 1). ** p < .01, p *** < .001.

To corroborate the findings, we further tested the mediating effect of self-esteem using a bootstrapping analysis with 5,000 iterations (Preacher & Hayes, 2008). The 95% confidence interval was [-.1807, -.0215], excluding 0, which indicates that the mediating effect of self-esteem was significant. To explore an alternative pathway, we tested the mediating effect of self-esteem with social media addiction as the dependent variable and mental health as the independent variable. The 95% confidence interval was [-.1422, .0844], including 0, indicating that the reverse mediating effect of self-esteem was not significant. Thus, the results support our hypothesis that social media addiction was associated with reduced mental health through lowering individuals’ self-esteem.

Results from Study 1 confirmed our hypotheses that social media addiction was negatively associated with mental health, consistent with findings from previous studies (e.g., Koc & Gulyagci, 2013). Furthermore, as expected, we found that self-esteem played a mediating role in the relation between social media addiction and mental health, and that the reverse mediating effect was not significant. These findings suggest that the negative association between social media addiction and mental health is at least partially accounted for by reduced self-esteem.

In addition, results from Study 1 also confirmed our prediction that social media addiction was negatively related to academic performance, although the relation was not strong. On the other hand, self-esteem was not significantly associated with academic performance, which differed from previous studies (Lane et al., 2004; Lent et al., 1986; Raskauskas et al., 2015). This might be because there was only one self-report item to measure academic performance, which could be vulnerable to the influence of social desirability concerns. In addition, given that we did not measure the time participants spent on social media, it is unclear how social media use may differ from social media addiction in relation to mental health and academic performance. We addressed these limitations in Study 2.

Study 1 showed that the addictive use of social media was common among college students and that it was negatively associated with mental health and academic performance. One important follow-up question is whether social media addiction can be reduced and thus its negative associations with health and academic outcomes be alleviated. No study that we know of has considered intervention options for social media addiction. We therefore designed an intervention program for social media addiction based on Young's (1999) recommendations for the treatment of Internet addiction, and we conducted an experiment to verify its effectiveness.

To design an intervention program for social media addiction, we referred to previous studies on Internet addiction interventions. Research has shown that metacognitive beliefs about one’s thinking and self-regulation influence problematic Internet use and social media addiction (Casale, Rugai, & Fioravanti, 2018; Caselli et al., 2018; Spada, Langston, Nikĉević, & Moneta, 2008). According to the cognitive-behavioral model, cognitive distortions such as the ruminative cognitive style are the primary cause of excessive Internet use (Davis, 2001). These cognitive distortions can be automatically activated whenever there is a stimulus associated with the Internet. A vicious cycle of cognitive distortions and reinforcement then results in negative outcomes. This model has been widely used in addiction research related to pathological Internet overuse (Larose, Lin, & Eastin, 2003; Liu & Peng, 2009; Turel, Serenko, & Giles, 2011). A number of cognitive-behavioral therapy techniques have been recommended for treating Internet addiction (Young, 2007; Gupta, Arora, & Gupta, 2013). Based on this literature, we believe that the cognitive-behavioral approach will be a helpful way to mitigate the negative associations of social media addiction with health and academic outcomes. It will help individuals with social media addiction to recognize their cognitive distortions and further guide them to reconstruct their thinking and behavior.

In Study 2, we combined cognitive reconstruction, reminder cards, and the diary technique (Young, 1999) into a novel intervention program and designed a 2 by 2 mixed-model experiment to test its effectiveness. In line with findings of previous studies on Internet addiction (e.g., Gupta et al., 2013; Turel et al., 2011; Young, 2007), we predicted that compared with a control group, the experimental group who experienced the intervention would show reduced social media addiction and improved outcomes in mental health and academic efficiency. We included measures of multiple outcome variables to achieve more reliable results, including daily social media use time, self-esteem, sleep quality, mental health, emotional state, learning time, and learning engagement.

Participants. Study 2 was conducted at Peking University, China. Participants who exhibited social media addiction were preselected from a pool of 242 undergraduate students who enrolled in a social psychology course (a different pool from Study 1). The students were asked to complete the 6-item BSMAS (Andreassen et al.,2017). Among them, 43 students scored higher than 18 on the composite score and also scored 3 or above on at least four of the six items. These students were selected to participate in Study 2. They were randomly assigned to either an experimental or a control group and were tested both before (Time 1) and after the intervention (Time 2). The study was thus a 2 x 2 mixed-model design. The 21 participants in the experimental group completed all aspects of the intervention and both tests. Five of the 22 participants in the control group dropped out before the completion. Hence, the final sample included 38 participants (18 males, 18 females, two unreported; M age = 19.71, SD age =1.43).

Procedures and Measures. The intervention program was approved by the Research Ethics Committee of the School of Psychological and Cognitive Sciences at Peking University. Prior to the intervention at Time 1, all participants were informed that the purpose of this study was to investigate social media addiction and they were asked to provide informed consent. Participants then completed a survey, which included the measures of social media addiction, self-esteem, and mental health, same as in Study 1. In addition, participants were asked to report their daily social media use time, indicating the number of hours they spent on social media per day. Participants also reported their sleep quality, rating on a 5-point scale ranging from 1 (very bad) to 5 (very good).

Participants in the experimental group then participated in a one-week intervention program, while those in the control group did not receive any instruction during this time. The intervention included two stages. The first stage involved cognitive reconstruction and took approximately 30 minutes (Young, 1999). Participants visited the lab, where they were asked to reflect on their social media use from five respects: How much time they spent on social media per day and per week? What other meaningful things they could do with that time? What were the benefits of not using social media? Why did they use social media and were there alternative way to achieve the purposes? What were the adverse effects of social media use? Participants wrote down their responses. After the reflection, participants were asked to each list on a card five advantages of reducing the use of social media and five disadvantages of excessive use of social media. They were then asked to take a photo of the card and use it as a lock screen of their phones that would serve as a reminder for themselves. They were also instructed to post the card on their desks during the following week.

The second stage of the intervention took place in the following week, during which participants in the experimental group were asked to keep a daily to record their thoughts, emotions, and behaviors related to social media use, as part of the cognitive-behavioral techniques (Young, 1999). Participants reflected on their daily use of social media every night before going to bed, including what social media they used, how long and how they used the social media, their thoughts and emotions related to their social media use, and the strategies they would like to use to reduce social media use. They were also asked to indicate their emotional state and learning engagement, as well as their expected social media use the next day. To ensure that the participants followed the instruction, daily reminders were sent to them to complete the recording. Participants were further instructed to take a photo of their completed recording and send it to a contact researcher of the lab to confirm its completion. The participants’ responses in the daily reflection task were part of the intervention and were not used in analysis.

After the intervention, at Time 2, all participants completed another survey. The measures included social media addiction, daily social media use time, self-esteem, sleep quality, and mental health, same as those at Time 1. In addition, the participants’ learning engagement in the past week was measured by the 17-item Utrecht Work Engagement Scale-Student (UWES-S, Fang, Shi, & Zhang, 2008). Participants answered the questions (e.g., “My study inspires me ”) on 5-point scales ranging from 1 (strongly disagree) to 5 (strongly agree) ( Cronbach's α = 0.93 for the current sample). A total score was summed, with higher scores indicating higher levels of learning engagement. Participants also reported their daily learning time outside the class in the past week and rated on their emotional state in the past week on a scale ranging from 1 (very bad) to 100 (very good).

Finally, participants in the experimental group provided feedback on the effectiveness of the intervention. They answered 7 questions concerning the various aspects of the intervention (e.g., “ Generally, I think the intervention is effective” ) on 5-point scales from 1 (strongly disagree) to 5 (strongly agree) ( Cronbach's α = 0.81). At last, participants were fully debriefed and thanked.

Across all dependent variables, 2 (Group: Experimental vs. Control) x 2 (Test time: Time 1 vs. Time 2) mixed-model analyses were conducted to examine the effect of intervention. First, the analysis on social media addiction score revealed main effects of group, F (1, 36) = 7.89, p = .008, η p 2 = .18, and test time, F (1, 36) = 33.74, p < .001, η p 2 = .48, qualified by a significant interaction, F (1, 36) = 17.92, p < .001, η p 2 = .33. For participants in the experimental group, there was a significant decrease in social media addiction from Time 1 to Time 2 , changing from 20.62 (higher than 18) to 14.62 (lower than 18), t (20) = 7.17, p < .001, d = 1.97. In contrast, for participants in the control group, there was no significant change in their social media addiction, t (16) = 1.13, p = .28, d = .35. Figure 2 illustrates the interaction effect.

Figure 2. Social media addiction as a function of test time and group (Study 2).

The same analysis was conducted to examine the effect of intervention on daily social media use time, self-esteem, sleep quality, and mental health, respectively. Table 2 presents means and standard deviations for all variables and t-tests within each group. For daily social media use time, there was a main effect of test time, F (1, 36) = 26.54, p < .001, η p 2 = .42, qualified by a Group x Test time interaction, F (1, 36) = 10.47, p = .003, η p 2 = .23. Further t-tests within each group showed that whereas the average daily time participants spent on social media was reduced significantly from Time 1 to Time 2 for both groups, the reduction was larger for the experimental group. There was only a main effect of test time for self-esteem, F (1, 36) = 12.67, p = .001, η p 2 = .26, and sleep quality, F (1, 36) = 9.10, p = .005, η p 2 = .20, whereby self-esteem and sleep quality increased from Time 1 to Time 2. However, further t-tests within each group showed that the improvements were only significant for the experimental group, but not the control group. For mental health, a significant Group x Test time interaction emerged, F (1, 36) = 5.69, p = .02, η p 2 = .14. Whereas mental health scores increased from Time 1 to Time 2 for the experimental group, t (20) = 2.55, p = .02, d =.59, there was no change for the control group, t (16) = -.86, p = .40, d =-.19. Taken together, these results suggest that our intervention effectively reduced social media addiction and improved mental health and other outcomes.

Further analyses of the remaining outcome variables at Time 2 showed that compared with the control group, participants in the experimental group exhibited better learning engagement, t (36) = .2.31, p = .03, d =.77, spent more time on their study outside the class, t (36)= 2.28, p = .03, d = .75, and experienced a better emotional state, t (36) = 2.74, p = .01, d =.86, during the intervention period. In addition, participants in the experimental group reported that the intervention was effective: all participants rated over 3 for the overall intervention; 81% rated over 3 for the first stage of the intervention and 90% for the second. All participants reported that the daily reflections were helpful, and 86% of them were willing to continue to participate in similar studies.

Table 2. Mean and standard deviation of Time 1andTime2's test scores of key variables.

In sum, participants in the experimental group exhibited reduced social media addiction and improved mental health as well as self-esteem and sleep quality after a two-stage intervention, whereas there was no significant change in the control group. The experimental group participants evaluated the intervention to be effective, in line with prior research showing that cognitive reconstruction, the reminder card technique, and daily reflections are effective methods in reducing Internet addiction (Young, 1999). Furthermore, compared with those in the control group, participants who received the intervention spent more time on learning and experienced a higher level of learning engagement and better emotional state. It is noteworthy that although control group participants reported reduced social media use time at Time 2, they did not exhibit reduced social media addiction or significant improvement in any outcome measures. This is consistent with the theoretical notion that the mere social media use time is not equivalent with or sufficient to index social media addiction (Griffiths, 2010; Andreassen, 2015). Together, these findings suggest that our intervention was effective in reducing social media addiction and improving college students’ mental health and learning efficiency.

General discussion

The current studies provided empirical support that social media addiction was negatively associated with college students’ mental health and academic performance (Pantic et al., 2012; Jelenchick et al., 2013). Furthermore, in line with previous findings that social media addiction negatively affects self-esteem (Andreassen et al., 2017; Błachnio, et al., 2016; Chou & Edge, 2012; Vogel et al., 2014) and that low self-esteem is associated with mental disorders (Orth et al., 2008; Orth & Robins, 2013; Sowislo & Orth, 2013), the current research yielded the first empirical finding that self-esteem mediated the relation of social media addiction to mental health. Furthermore, the implementation of an intervention based on the cognitive-behavioral approach (Young, 1999, 2007; Gupta et al., 2013) effectively reduced social media addiction and improved mental health and academic efficiency.

Notably, our results showed that social media addiction was associated with reduced mental health partly through lowering individuals’ self-esteem, and that the reverse mediating effect of self-esteem with mental health as the predictor and social media addiction as the outcome variable was not significant. Nevertheless, it does not rule out the possibility that poor mental health can further contribute to social media addiction. Individuals in poor mental health, including those with low self-worth, may use social media as a compensation for their real-life interpersonal deficiency and further develop excessive dependence on social media (Zywica & Danowski, 2008). Also, individuals in poor mental health often try to use social media to improve their mood and, when this need is not met, their mental condition tends to become worse (Caplan, 2010). Thus, the relation between poor mental health and social media addiction is likely to be bidirectional.

The present studies provided strong support for the relation of social media addiction to academic outcomes by using a variety of measures. Study 1 showed that a self-rank measure of academic performance was negatively associated with social media addiction. This relation was not mediated by self-esteem. Study 2 further showed that an intervention to reduce social media addiction improved learning engagement and increased the time spent on learning outside the class. We speculate that there may be three explanations for the negative relation of social media addiction to academic performance. First, social media addiction may mean more time spent online and less time spent on study. Excessive social media use interrupts students’ time management, which further affects academic performance (Macan et al., 1990). Second, social media addiction may interfere with students’ work by distracting them and making them unable to stay focused. Research has shown that multitasking has negative effects on the performance of specific tasks (Ophir, Nass, & Wagner, 2009). Finally, given that students with social media addiction may be easily distracted, it can be difficult for them to encode and remember what they are learning (Oulasvirta & Saariluoma, 2006).

Our intervention program effectively reduced social media addiction and improved students’ mental health and learning efficiency. This has important practical implications by showing that social media addiction can be mitigated through cognitive reconstruction and the supporting techniques. The stage of cognitive reconstruction helped students realize the negative consequences of their addiction to social media as well as the potential benefits of reducing social media usage. The subsequent application of the reminder card as a lock screen of their phones as well as the daily reflections further reinforced this awareness. These findings suggest that helping college students to gain a better understanding of the adverse effects of social media addiction through cost-efficient self-help interventions can reduce social media addiction and have the potential to improve mental health and academic performance.

The current studies have some limitations. First, participants were recruited through psychology courses at Peking University and the sample sizes were relatively small especially in Study 2, which may limit the generalizability of the findings. Future studies should include more diverse and larger samples to increase external validity. Second, participants in the control group of Study 2 did not receive any instruction during the one-week interval and they could be distracted by things unrelated to the study. Future research should establish more strict control conditions to eliminate any confounding variables. Third, the intervention in Study 2 was limited in length and the post-treatment data were collected only once, right after the intervention ended. It is therefore unclear whether the intervention effects on social media addiction and other outcomes would persist over time. Given that the current intervention program for reducing social media addiction was newly developed, it requires further refinement to improve its effectiveness. In addition, future studies should investigate the bidirectional relation between social media addiction and mental health, using longitudinal approaches to further validate the mediating role of self-esteem and examine other potential mediators such as cognitive distortions for the relations of social media addiction to mental health and other outcomes.

In conclusion, the current research revealed negative associations between social media addiction and college students' mental health and academic performance, and the role of self-esteem as an underlying mechanism for the relation between social media addiction and mental health. A cost-efficient intervention that included cognitive reconstruction, reminder cards, and a week-long diary keeping effectively reduced the addiction to social media and further improved mental health and academic efficiency.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

Copyright © 2019 Yubo Hou, Dan Xiong, Tonglin Jiang, Lily Song, Qi Wang

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• v.9(4); 2022 Apr

Problematic Social Media Use in Adolescents and Young Adults: Systematic Review and Meta-analysis

Holly shannon.

1 Department of Psychiatry, The Royal's Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada

2 Department of Neuroscience, Carleton University, Ottawa, ON, Canada

Paul J Villeneuve

3 School of Mathematics and Statistics, Carleton University, Ottawa, ON, Canada

Kim GC Hellemans

Synthia guimond.

4 Department of Psychoeducation and Psychology, Université du Québec en Outaouais, Gatineau, QC, Canada

Associated Data

Quality assessment rating for each study included in the meta-analysis. Questionnaires used for each study to measure problematic use and the outcome variables. Funnel plots and metaregressions.

Technology is ever evolving, with more and more diverse activities becoming possible on screen-based devices. However, participating in a heavy screen-based lifestyle may come at a cost. Our hypothesis was that problematic social media use increased the prevalence of mental health outcomes.

This study seeks to systematically examine problematic social media use in youth and its association with symptoms of depression, anxiety, and stress.

A systematic search was conducted to identify studies in adolescents and young adults, using the databases Engineering Village, Psycinfo, Pubmed, and Web of Science. A total of 18 studies were identified, with a total of 9269 participants in our review and included in the meta-analysis.

Our metaregression shows moderate but statistically significant correlations between problematic social media use and depression ( r =0.273, P <.001), anxiety ( r =0.348, P <.001), and stress ( r =0.313, P <.001). We did not find evidence of heterogeneity of these summary correlations by age, gender, or year of publication.

Conclusions

This study provides further evidence of the association between problematic social media use and negative mental health among adolescents and young adults and supports future research to focus on the underlying mechanisms of problematic use of social media.

Trial Registration

PROSPERO CRD42021222309; https://tinyurl.com/2p9y4bjx

Introduction

Technology is ever evolving, with more and more diverse activities becoming possible on screen-based devices. With this increasing engagement in the digital world, social networking sites have become an increasingly popular activity, especially among younger populations [ 1 ]. Adolescents and young adults represent a unique population in terms of social media users, as they are the first generations to grow up in a highly digitized society. Social media use is highly normative among young individuals: In 2016, 97.5% of young adults in the United States reported using at least one social media site regularly [ 2 ]. However, participating in a heavy screen-based lifestyle may come at a cost. A wealth of evidence suggests higher levels of social media use are associated with symptoms of anxiety [ 3 - 5 ], symptoms of depression [ 3 , 6 - 8 ], decreased psychological well-being [ 9 ], lower self-esteem [ 3 ], psychological distress [ 10 - 12 ], and loneliness [ 5 ]. A meta-analysis in young adults reports a small correlation between depressive symptoms and adolescent social media use, defined by frequency of use [ 13 ]. However, along with the evidence supporting the negative impacts of social media use, some reports suggest there may exist positive outcomes following use. For example, social media use has also been linked to higher quality of life, social support, well-being, and reduced stress [ 14 , 15 ].

Aside from excessive use of social media, typically defined on the basis of hours of use, the term of problematic use characterizes individuals who experience addiction-like symptoms as a result of their social media use [ 5 ]. Problematic social media use reflects a non–substance related disorder by which detrimental effects occur as a result of preoccupation and compulsion to excessively engage in social media platforms despite negative consequences [ 16 ]. While there exists no official diagnostic term or measurement, Andreassen et al [ 17 ] developed the Facebook Addiction Scale, which measures features of substance use disorder such as salience, tolerance, preoccupation, impaired role performance, loss of control, and withdrawal, to systematically score problematic Facebook use. This scale has been widely used to conceptualize problematic use as a behavioral addiction and has therefore also been modified to measure overall problematic social media use, instead of focusing on Facebook specifically [ 18 ]. Similar to high frequencies of social media use, problematic social media use has also been associated with poor mental health outcomes such as depression, anxiety, decreased well-being, and lower self-esteem [ 1 , 17 , 19 - 22 ]. A recent meta-analysis by Cunningham et al [ 23 ] found that problematic social media use was a stronger predictor of depressive symptoms when compared to the measure of time spent on social networking sites. Therefore, based on previous evidence, problematic social media use may be more imperative to examine than hours spent on social media platforms.

Researchers recognize youth and students as a vulnerable group compared to adults because their increased use of social media is occurring during a time of identity formation, where they are free to explore various life possibilities and develop new values [ 2 ]. Furthermore, their use occurs when critical brain circuits involved in emotion regulation and motivation are continuing to undergo development [ 24 ]. As social media plays a large role in their day-to-day lives, patterns and frequency of use have the potential to become problematic. On this level, youth are more at risk for facing cyberbullying [ 25 ], finding it difficult to disengage from the media and allowing it to interfere with their social relationships [ 26 ]; this in turn puts them at risk for experiencing negative emotional and psychosocial outcomes [ 27 ]. Therefore, younger individuals are a vulnerable group of social media users, and it is important to better understand the outcomes for well-being that are associated with this type of problematic social media use. Yet, the magnitude of impact social media has on adolescents and emerging adults, especially when considering problematic use, remains unclear.

With this background, we systematically examined and summarized, with the most current evidence, the strength of association between problematic social media use and multiple mental health outcomes. Specifically, we considered depressive symptoms, anxiety symptoms, and stress. Our a priori hypothesis was that problematic social media use adversely impacts all mental health outcomes measured. In addition, age, gender, and year of publication were investigated as covariates in the relationship between problematic social media use and all mental health outcome variables.

This meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021222309). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed [ 28 ].

Inclusion and Exclusion Criteria

This systematic review included measures of problematic social media use, with depressive symptoms, anxiety symptoms, and stress as outcome measures, assessed by validated instruments. The studies included were cross-sectional and provided a measure of association between problematic use and at least one of the mental health outcomes. Studies must have included a measure of problematic use from the participants; simply indicating if the participant was a user of social media was not acceptable (eg, grouping users vs nonusers of social media). Social media use was also examined in general, without focusing on specific activities (eg, studies looking at specific screen content or comparisons on social media platforms, etc) or a specific platform (eg, Facebook). Problematic social media use scales must have been validated to specifically measure social media use in terms of addictive use, comprising criteria used when measuring substance use disorders. Studies included were restricted to English language, and ages 12 to 30 years. Studies were excluded if they only measured frequency or problematic use of the internet in general, as social media use specifically must have been measured. Studies were also excluded if social media was being used as a treatment/intervention or in a focus-group setting. Finally, studies were excluded if they only measured social media use in clinical populations.

Literature Search

A systematic literature search was conducted in April 2021 using the databases Engineering Village, PsycInfo, Pubmed, and Web of Science using the terms “social media,” “social networking,” “mental health,” “depression,” “depressive symptoms,” “anxiety,” and “stress.” These search terms were used to quantify social media use in terms of problematic use.

Assessment of Quality

All eligible studies were assessed for quality using an adapted version of the Newcastle-Ottawa quality assessment scale for cross-sectional studies, which was used to score the risk of bias for each study [ 29 ]. All studies were independently rated by HS and KB and given a score out of 10. Conflicts in scoring were resolved by discussion ( Multimedia Appendix 1 ).

Data Extraction

For each study identified as eligible, the following information was extracted; study identification (authors, year of publication, and country conducted), study design (sample size, age range, mean age, gender, and questionnaire used to measure problematic social media use), outcome variables (questionnaire used to measure each outcome and measure of association). See Multimedia Appendix 1 for questionnaires used to measure problematic use and outcome variables for each study included in the meta-analysis.

Statistical Analysis

To quantify the association between problematic social media use and depressive symptoms, anxiety symptoms, and stress, we used the Pearson correlation coefficient ( r ). Problematic use was considered on a continuum, based on the score obtained from the questionnaire used, which measures problematic use as addiction-like tendencies. All data analysis was performed using the statistical software Stata (Stat Corp) [ 30 ]. A random effects model was used, as it does not assume a common effect size across studies. The variance of r was calculated in order to obtain the standard error for each correlation coefficient. The effect size in all groups of analysis had a 95% confidence interval. Publication bias was evaluated by producing a funnel plot, and by performing the Egger test. Age, gender, and year of publication were investigated as covariates by adding mean age, the percentage of male participants reported, and publication year for each study into separate metaregression analyses.

Ethical Considerations

Since meta-analyses do not need Institutional Review Board approval, the authors did not seek ethics approval.

The literature search yielded 2846 articles, with 2410 articles remaining after duplicates were removed ( Figure 1 ). Articles were screened based on titles and abstract to remove any records that were not quantitative, did not assess one of the outcomes, or were longitudinal. After the first screening, 417 (17.30%) articles were considered to be eligible and were then screened based on full text to exclude any remaining records that did not meet the inclusion criteria. Of the remaining articles, 4 were excluded as they were reporting results using dichotomized continuous variables. These studies separated participants into groups based on the scores of their respective scales, and therefore could not be used in our meta-analysis. Additionally, any unpublished data were obtained by contacting the corresponding author. One study included reported statistics distinct to two separate samples; therefore, the two samples were coded independently [ 31 ]. The results from Kim et al [ 32 ] were excluded from the metaregression, as mean age was not reported or received when contacted. The correlation from Giordano et al [ 33 ] with problematic social media use was reported as a combined score of depressive and anxiety symptoms, which therefore could not be included in the meta-analysis. However, all variables were pooled together for the metaregression analyses, so they were included when examining age, gender, and publication year as covariates. Details on the final 18 studies and 9269 total participants included are summarized in Table 1 .

Flow chart of the search process and studies included.

Summary of included studies on the relationship between social media use and outcome variables (note that not all studies measured all three outcomes. Giordano et al [ 33 ] assessed anxiety and depression combined and was therefore only included in the meta-regression analyses).

a N/A: not applicable.

Problematic Social Media Use and Depressive Symptoms

When examining depression as an outcome, 11 studies presented associations between problematic social media use in adolescents and young adults. The Center of Epidemiologic Studies Depression Scale was most commonly used to measure depressive symptoms. The summary metaregression correlation between problematic social media use and depressive symptoms was 0.273 (95% CI 0.215-0.332, P <.001). There was heterogeneity in the measures of association across the studies ( Figure 2 ) with an I 2 =83.2%, Q 2 =59.69, and P <.001. The funnel plot ( Multimedia Appendix 1 ) shows slight asymmetry, suggesting slight publication bias, however Egger’s test for small-study effects was not significant ( P =.35).

Forest plot of depressive symptoms and problematic social media use by year.

Problematic Social Media Use and Anxiety Symptoms

When examining anxiety symptoms as an outcome, 9 studies were identified measuring an association with problematic social media use in adolescents and young adults. The Depression Anxiety Stress Scale was most commonly used to measure anxiety symptoms. The summary metaregression correlation between problematic social media use and anxiety symptoms was 0.348 (95% CI 0.270-0.426, P <.001). There was substantial heterogeneity in the measures of association across the studies ( Figure 3 ) with an I 2 =91.6%, Q 2 =94.75, P <.001. The funnel plot ( Multimedia Appendix 1 ) shows asymmetry, suggesting some publication bias being present; however, the Egger test for small-study effects was not significant ( P =.30).

Forest plot of anxiety symptoms and problematic social media use by year.

Problematic Social Media Use and Stress

Finally, when examining stress as an outcome, only 6 studies were identified measuring an association with problematic social media use in adolescents and young adults. The summary metaregression correlation between problematic social media use and stress was 0.313 (95% CI 0.203-0.423, P <.001). There was heterogeneity in the measures of association across the studies ( Figure 4 ) with an I 2 =92.6%, Q 2 =67.59, P <.001. The funnel plot ( Multimedia Appendix 1 ) shows symmetry, suggesting no publication bias, with no significant bias from the Egger test as well ( P =.79).

Forest plot of stress and problematic social media use by year.

Moderators of Problematic Social Media Use

The metaregression assessing the impact of age as a covariate on the relationship between problematic social media use and all mental health outcomes combined showed that age was not significantly moderating the relationships ( P =.75). When examining gender as a covariate in the relationship between problematic social media use and all mental health outcomes, gender did not significantly moderate the relationship ( P =.25). Finally, year of publication also did not significantly moderate the relationship between problematic social media use and all mental health outcomes when added as a covariate ( P =.09). See Multimedia Appendix 1 for metaregression plots.

Principal Findings

This meta-analysis reports outcome measures of depression, anxiety, and stress in association with problematic social media use, specifically among adolescents and young adults. There is evidence for a significant relationship between problematic social media use in youth and negative mental health outcomes, particularly higher depression and anxiety symptoms, and greater stress. The strongest correlation was observed with anxiety; however, this also presented the most heterogeneity, likely due to the variety of assessments used to quantify symptoms of anxiety in the individual studies.

Although the correlations are moderate, this meta-analysis provides further evidence for the possible harms of problematic social media use. Previous meta-analyses examining time spent on social media and mental health show very small effect sizes, with most correlations being reported below r =0.20 [ 46 - 48 ]. One explanation for previously small correlations observed is the variability of social media content itself and the ways individuals are using or viewing their social media accounts. There has been evidence of multiple variables that can influence the severity of mental health outcomes such as night time–specific use, passive use, the number of social media platforms, motives for using social media, and so on [ 3 , 49 - 52 ]. Problematic social media use is a distinct pattern of use characterized by “addiction-like” symptoms based on behavioral and psychological attributes. It is characterized not only by time spent on social media, but also by measuring the extent of symptoms similar to a substance-related disorder, such as withdrawal, tolerance, and dependence [ 22 ]. Therefore, problematic social media use could represent a more clinically meaningful behavior to direct research, as a stronger relationship is seen with adverse mental health symptoms compared to previous studies investigating time spent on social networking sites or screen time in general [ 23 , 53 , 54 ].

The influence of age is still highly debated with evidence pointing toward younger social media users being more likely to have worse mental health symptoms compared to older users [ 55 ], whereas others have found no significant age effect with time spent on social media [ 56 ]. Cunningham et al [ 23 ] found age did not moderate the relationship between problematic social media use and depression; however, this study was performed in a mainly adult sample. Likewise, in our meta-analysis, age did not significantly moderate the relationship between the outcome variables combined and problematic use. This is likely due to the restricted age range, as the mean age between individual studies were analogous. Higher social media usage, along with developmental vulnerabilities, in adolescents and young adults has been proposed to explain the higher association with worse mental health outcomes compared to adults [ 57 , 58 ]. However, when looking specifically at mental health associated with problematic social media use as a behavior, the severity of reported problematic use symptoms may be more imperative to consider rather than age. Future research could directly compare adolescents to adults to examine if a difference in correlational strength is present, specifically when measuring problematic use.

Gender was examined as a moderator by including the percentage of male participants from each study into a metaregression analysis. Gender did not significantly moderate the relationship between problematic social media use and mental health, suggesting the association between mental health symptoms and problematic use of social media is not different between genders. Studies included in this meta-analysis did not specify if they assessed biological sex. Future research should provide more specific results for each group for both sex and genders to allow future meta-analyses to summarize this information and provide insight into gaps in the current literature on problematic use of social media [ 23 , 51 , 59 ].

Year of publication did not significantly moderate the relationship between problematic social media use and mental health outcomes. Although there are increased rates of social media use in adolescents and young adults over time, this may not be directly pertinent in the strength of the association between mental health and problematic use [ 23 , 60 ]. Year of publication may be more indicative of the prevalence of social media use as it increases with the growing use of technology [ 60 ]. Along with previous data, it is suggested that mental health symptoms associated with problematic social media use do not appear to be worsening over time; however, longitudinal studies exploring this specific aspect are needed.

Strengths and Limitations

This study is not without limitations. The number of studies included in each meta-analysis was limited; therefore, the results are somewhat limited in power. Secondly, the results are based on cross-sectional correlational data. Therefore, a causal relationship cannot be inferred from the direct impact of social media on mental health outcomes of depressive symptoms, anxiety symptoms, or stress. It is possible that there are likely bidirectional effects between poor mental health and social media use [ 61 ]. In addition, the research studies included in the meta-analysis used did not report assessing the presence of a clinical diagnosis; therefore, it is unknown how many participants already had a known or possible clinical psychiatric diagnosis. This could influence the results of the outcome variables being measured, as it is unknown if individuals are more likely to have negative social media experiences or consequences as a result of using social media compared to individuals without a mental health diagnosis. Although the included questionnaires were previously validated, the majority of the research relies on self-report measures, also presenting as a limitation to the results reported.

Future Directions

Overall, there is a lack of research providing evidence on the mental health outcomes of social media use, particularly patterns of problematic use in younger populations. In order to thoroughly understand the direct implications of problematic social media use, longitudinal studies could aid in providing more causational conclusions, as cross-sectional methodology is limited in its ability to draw conclusions beyond correlation [ 62 ]. In addition to a longitudinal design, understanding the biological basis of problematic use could contribute to understanding vulnerability to negative mental health outcomes. Future studies exploring the relationship between problematic social media and mental health outcomes would also benefit from including more detailed information on how participants are using various platforms. Indeed, there are several other scales exploring social media use that explore motivations for and mood associated with use (eg, social media use integration scale), which may provide greater depth of understanding around these associations. Finally, in traditional clinical practices for substance use disorders, treatment is often based on abstinence. For problematic social media use, total abstinence may not be a realistic option in today’s technology-based culture. Therefore, there should also be an increasing focus on identifying healthy ways to use social media in order to avoid the development of problematic use.

The findings from this study provide further evidence of the association between problematic social media and negative mental health outcomes of depression, anxiety, and stress among adolescents and young adults. Although there is a large amount of evidence pointing toward the negative impacts of social media on mental health, there is still a need for further research to provide conclusive results on the causal relationship and how social media can be used without taking a toll on the mental health of users. Considering the omnipresence of social media among youth, more resources should be allocated to better understand the relationship between use and mental health symptoms and to prevent such negative outcomes.

Abbreviations

Multimedia Appendix 1

Conflicts of Interest: None declared.

• Open access
• Published: 31 August 2024

Scrolling through adolescence: unveiling the relationship of the use of social networks and its addictive behavior with psychosocial health

• Caroline Brand 1 ,
• Camila Felin Fochesatto 2 ,
• Anelise Reis Gaya 2 ,
• Felipe Barreto Schuch 3 , 4 , 5 &
• José Francisco López-Gil 6  

Child and Adolescent Psychiatry and Mental Health volume  18 , Article number:  107 ( 2024 ) Cite this article

Metrics details

Understanding the relationship of social network use and addictive behaviors with adolescent psychosocial health is crucial in today’s digital age.

To verify the associations between social network use, messaging applications, and the addictive behaviors to social network with psychosocial health in Spanish adolescents.

A cross-sectional study was developed with 632 adolescents, aged 12 to 17 years from the Region of Murcia, Spain. The assessment of social network use (Facebook, Twitter, Instagram, Snapchat, and TikTok) involved evaluating the frequency of use of each social network individually using a single-item scale with five response options. WhatsApp use (i.e., a messaging application) was evaluated in the same manner. The Short Social Networks Addiction Scale-6 Symptoms was employed to assess potential addictive behaviors to social network use. The psychosocial health was assessed using the Strengths and Difficulties Questionnaire. Generalized linear regression models were conducted, and predictive probabilities of having psychosocial health problems were calculated.

The predicted probability of presenting psychosocial health problems in the medium users and high users of social networks was 19.3% (95% confidence interval [CI] 13.0 to 27.7), and 16.2% (95% CI 10.2 to 24.6) higher compared to low users, respectively. High usage of Instagram, TikTok, Snapchat, and Facebook was associated with increased probabilities of psychosocial health problems, with Facebook showing the highest probabilities, at 31.3% (95% CI 14.8 to 54.2) for medium users and 51.9% (95% CI 26.5 to 76.3) for high users. Additionally, adolescents with addictive behaviors to social network use had from 19.0 to 25.2% probabilities of experiencing psychosocial health problems. Finally, the highest probabilities of having psychosocial health problems were identified in adolescents with high addictive behaviors when using social networks (28.9%; 95% CI 19.3 to 40.8%) and the lowest in those with low addictive behaviors (6.8%; 95% CI 3.3 to 13.6%).

Adolescents who use social networks more frequently and exhibit more addictive behaviors related to their use are more likely to experience psychosocial health problems compared to those who do not. Facebook showed the strongest association, followed by Snapchat, Instagram, and TikTok. Our data also revealed that adolescents exhibit various signs of addictive behaviors to social network use.

Introduction

In recent years, there has been a notable increase in media device usage, with estimates indicating that between 88 and 95% of American adolescents now own smartphones [ 1 ], and almost 90% are online at least several times during the day [ 2 ]. Consequently, the utilization of social networks and messaging applications, such as Instagram, TikTok, and WhatsApp, among adolescents, has become a prominent aspect of contemporary youth culture [ 3 ]. This trend has significantly influenced their social interactions and communication patterns. Social networks offer adolescents opportunities for self-expression, social connection, and information sharing. Similarly, messaging applications like WhatsApp facilitate instant communication and group interactions among peers [ 4 ]. However, alongside the benefits, concerns have emerged regarding the potential impact of excessive social network use and WhatsApp usage on adolescent psychosocial health [ 5 , 6 , 7 ].

The use of social media is a contentious issue, given the presence of mixed evidence regarding its impacts. Mountain cross-sectional evidence has indicated that excessive social network use is associated with higher symptoms of anxiety, depression, hyperactivity, and conduct problems among adolescents [ 7 , 8 , 9 , 10 , 11 , 12 ]. For instance, a study involving 6596 US adolescents revealed that spending more than 3 h per day on social networks is linked to a higher probability of mental health issues [ 8 ]. Also, a longitudinal study revealed an association between problematic social network use and later anxiety symptoms in adolescents aged 13 to 14 years [ 6 ]. However, results are not entirely consistent, demonstrating that more time spent on social media was not significantly related to poorer mental health 2 years later in adolescents [ 13 ]. Also, evidence suggests that the use of Facebook may have more positive than negative effects on mental health [ 14 ].

In this context, it is important to highlight that excessive social network use can lead to behavioral addiction [ 15 ], impacting various aspects of an individual’s life, including academic performance and overall well-being [ 16 , 17 ]. Moreover, the addictive nature of social network use among adolescents raises concerns about the long-term implications on their overall health [ 18 ]. Studies have highlighted an association between excessive screen time and disrupted sleep patterns, insufficient physical activity, and low academic performance [ 19 , 20 , 21 ].

Considering that 4.76 billion people across the globe are using social media, representing 59.4% of the global population [ 22 ], and that 90% of Spanish adolescents utilize social networks, alongside the rising prevalence of mental health issues in this demographic over the past years [ 23 , 24 ], it becomes crucial to examine how these habits might affect psychosocial well-being in this population. By shedding light on the potential risks and consequences of excessive screen time and digital communication, this research seeks to inform public health interventions, educational programs, and policy initiatives aimed at promoting responsible digital citizenship and safeguarding the health and development of young people in the digital age. Therefore, this study aimed to verify the associations between the use of social networks, messaging applications, and the addiction to social networks with psychosocial health in Spanish adolescents.

Study design and sample

This is a secondary cross-sectional study with data from the Eating Healthy and Daily Life Activities (EHDLA) study. This investigation involved a representative group of adolescents aged 12 to 17 years residing in the Valle de Ricote , within the Region of Murcia (Spain). It was conducted across their three secondary schools during the 2021/2022 academic year. The comprehensive methodology of the EHDLA research is documented elsewhere [ 25 ]. The research project received ethical approval from two committees, the Bioethics Committee of the University of Murcia (ID 2218/2018) and the Ethics Committee of the Albacete University Hospital Complex and the Albacete Integrated Care Management (ID 2021–85). Also, the study adhered to the principles outlined in the Helsinki Declaration, prioritizing the safeguarding of participants’ human rights.

For the present study, 632 adolescents (50.8%) with complete information on all variables of interest were included. The following inclusion criteria were considered: fall within the age range of 12 to 17 years and reside in or attend school in Valle de Ricote . Exclusion criteria were as follows: being excused from physical education classes, as assessments and surveys were conducted during these sessions; having any medical condition limiting physical activity or necessitating special care; undergoing any pharmacological treatment; or absence of parental or legal guardian consent.

To take part in this study, parents or legal guardians of the adolescents were required to sign an informed consent form. Furthermore, both parents/legal guardians and their children received an informational document detailing the study’s objectives, assessment instruments, and questionnaires used. Moreover, adolescents were explicitly invited to indicate their willingness to participate in the study.

Measurements

Social network use.

The assessment of social network use (Facebook, Twitter, Instagram, Snapchat, and TikTok) involved evaluating each social network individually using a single-item scale (“Please, indicate the option that you consider most appropriate for yourself regarding the use of each social network of the following:”). Adolescents were asked to indicate their usage level for each social network from five response options: (a) “I never or rarely use them”; (b) “I am a low consumer”; (c) “I am a medium consumer”; (d) “I am a fairly high consumer”; or “I am a very high consumer” [ 26 ]. The responses were transformed into numerical variables ranging from 1 (“I never or rarely use them”) to 5 (“I am a very high consumer”). Subsequently, the scores for each social network were summed to generate a social network use score, ranging from 5 to 25, with higher scores indicating greater social network use [ 27 ]. Given the absence of specific cutoff points for social network use, the social network use score was stratified into tertiles: low social network use (5 to 12 points), moderate social network use (13 to 15 points), and high social network use (16 to 25 points).

The WhatsApp use (i.e., a messaging application) was also assessed using the same single-item scale. However, given that WhatsApp is a messaging application rather than a social network, it was excluded from the social network use score calculation.

For further analyses, the use of each social network and WhatsApp were categorized into: (a) low social network use (“I never or rarely use them” or “I am a low consumer”); (b) medium social network use (“I am a medium consumer”); or (c) high social network use (“I am a fairly high consumer” or “I am a very high consumer).

Addictive behaviors to social network use

The Short Social Networks Addiction Scale-6 Symptoms (SNAddS-6 S) [ 28 ] was employed to assess potential addiction to social networks. This instrument comprises six items capturing behaviors associated with tolerance (i.e., a desire for increased use), salience (i.e., social network use becoming a primary concern), mood modification (i.e., altering mood through social network usage), relapse (i.e., the risk of returning to addiction after controlling use), withdrawal (i.e., experiencing psychological and physical symptoms when unable to use), and conflict (i.e., social network usage interfering with social and daily activities). It features a unifactorial structure and has been previously validated among Spanish adolescents [ 28 ]. To facilitate further analysis, responses for all these behaviors were aggregated (“No” = 0; “Yes” = 1) to derive an overall score for social network addictive behaviors (ranging from 0 to 6 behaviors), with higher scores indicating greater susceptibility to social network addiction. Additionally, the overall addictive-related behaviors to social network use were stratified into tertiles: low addictive behaviors (0 to 1 behavior), moderate addictive behaviors (2 to 3 behaviors), or high addictive behaviors (4 to 6 behaviors).

Psychosocial health problems

The psychosocial health was assessed using the 25-item self-report version of the Strengths and Difficulties Questionnaire (SDQ) [ 29 ]. This tool is utilized for clinical assessment, screening of psychiatric disorders, and epidemiological research. The SDQ comprises five scales: (i) emotional symptoms, (ii) conduct problems, (iii) hyperactivity, (iv) peer problems, and (v) pro-social behavior (reverse scored). Participants respond to the 25 items using a 3-point scale: “certainly true”, “somewhat true”, and “not true”, with scores ranging from 0 to 2 points. To calculate the SDQ score, all the scales are added together except for the prosocial scale, so the score ranges from 0 to 40 points. Additionally, cutoff scores were employed to categorize individuals into three groups: (a) normal (0–15 points); (b) borderline (16–19 points); and (c) abnormal (20–40 points) [ 29 ]. For further analyses, these groups were collapsed into: no psychosocial health problem (“normal” or “borderline”) or psychosocial health problems (“abnormal”).

The adolescents provided self-reported data on their age and sex. Furthermore, socioeconomic status was assessed using the Family Affluence Scale (FAS-III) [ 30 ], comprising six questions with responses graded from 0 to 13 points. The cumulative scores were computed to determine the FAS-III score, with higher values indicating greater socioeconomic status.

The body weight and height of the adolescents were measured, and afterward, body mass index (BMI) was calculated by dividing their weight in kilograms by their height in meters squared.

To collect data on physical activity and sedentary behavior among adolescents, the Spanish version of the Youth Activity Profile (YAP-S) was applied, which was adapted and validated for its implementation among Spanish youth [ 31 ]. Additionally, participants’ sleep patterns were assessed by eliciting their typical weekday and weekend bedtime and wake-up times separately. The mean daily sleep duration for each participant was calculated using the formula: [(average nocturnal sleep duration on weekdays × 5) + (average nocturnal sleep duration on weekends × 2)]/7.

The adherence to the Mediterranean Diet among children and adolescents was evaluated using the Mediterranean Diet Quality Index (KIDMED) [ 32 ]. This index consists of 16 questions regarding the frequency of consumption of healthy foods (e.g., fruits, vegetables) and unhealthy foods (e.g., sweets, pastries), as well as behaviors such as skipping breakfast or eating at fast-food restaurants. The total score ranges from − 4 to 12, with higher scores indicating better adherence to the Mediterranean Diet.

The inclusion of these covariates as adjustments is justified, given their relationship with psychosocial health. The literature demonstrates that adolescents from families with higher socioeconomic status tend to show better indicators of mental health [ 33 ]. Also, studies have shown associations between healthy eating habits, adequate sleep patterns, and regular physical exercise with better mental health among young people [ 33 , 34 , 35 ]. On the other hand, prolonged sedentary behaviors and overweight are often associated with higher odds of psychosocial problems, such as anxiety and depression [ 36 , 37 ].

Statistical analysis

To evaluate the normal distribution of the variables, we utilized visual methods like density and quantile–quantile plots, along with conducting the Shapiro-Wilk test. Therefore, continuous variables are shown as median and interquartile range (IQR), and categorical variables are shown as counts and percentages. Generalized linear regression models with binomial distribution were conducted to calculate the odds ratio (ORs) and their 95% confidence interval (CIs) for the associations between social network use status and psychosocial health problems (Supplementary material 1). Moreover, we calculated the predictive probabilities of having psychosocial health problems based on social network status or addiction to social network status. The models were adjusted for age (in years), sex (boys or girls), socioeconomic status (i.e., FAS-III score), physical activity (i.e., YAP-S physical activity score), sedentary behavior (i.e., YAP-S sedentary behavior score), overall sleep duration (in minutes), BMI (kg/m 2 ), and adherence to the Mediterranean diet (i.e., KIDMED score). These same analyses were conducted for each social network (i.e., Facebook, Twitter, Instagram, Snapchat, and TikTok), a messaging application (i.e., WhatsApp), and for each addictive behavior to social networks (i.e., tolerance, salience, mood modification, relapse, withdrawal, and conflict). We carried out all the statistical analyses using R statistical software (version 4.3.2) (R Core Team, Vienna, Austria) and RStudio (version 2023.09.1 + 494) (Posit, Boston, MA, USA). We considered a p value less than 0.05 to be the threshold for statistical significance.

Predictive probabilities of having psychosocial health problems according to social network use status in adolescents. The data are expressed as predicted probabilities and 95% confidence intervals. Analyses were adjusted for age, sex, socioeconomic status, sleep duration, physical activity, sedentary behavior, body mass index, and adherence to the Mediterranean diet. CI, confidence interval; SN, social network. † According to the Strengths and Difficulties Questionnaire (SDQ) [ 29 ]. ‡ SDQ scores of 17 and above were considered as psychosocial health problems [ 29 ]

Predictive probabilities of having psychosocial health problems for each social network used or for WhatsApp use in adolescents. The data are expressed as predicted probabilities and 95% confidence intervals. Analyses were adjusted for age, sex, socioeconomic status, sleep duration, physical activity, sedentary behavior, body mass index, and adherence to the Mediterranean diet. CI, confidence interval. † According to the Strengths and Difficulties Questionnaire (SDQ) [ 29 ]. ‡ SDQ scores of 17 and above were considered as psychosocial health problems [ 29 ]. a Significant difference from “low social network use” ( p  < 0.05)

Table  1  presents the characteristics of the study participants according to social network use status. The highest proportion of individuals with a normal psychosocial health status (emotional symptoms, conduct problems, hyperactivity, peer problems, and pro-social behavior) was observed among participants with low social networking usage, while the lowest was among those with high social networking usage.

Figure  1 shows the predictive probabilities of having psychosocial health problems according to the social network use status in adolescents. The highest probability of presenting psychosocial health problems was identified in those who were classified as medium users (19.3%; 95% CI 13.0 to 27.7) and high users of social networks (16.2%; 95% CI 10.2 to 24.6). Conversely, the lowest probability of having psychosocial health problems were observed in those categorized as low users (6.6%; 95% CI 3.9 to 11.0). Moreover, significant differences were found between adolescents with low SN use and those with high SN use ( p  = 0.008), as well as with those with medium SN use ( p  < 0.001).

The predictive probabilities of having psychosocial health problems for each social network used or for WhatsApp use in adolescents are shown in Fig.  2 and Supplementary material 2. The probability of having psychosocial health problems was higher for those adolescents with high Instagram use 15.1% (95% CI 10.3 to 21.4), high TikTok use 14.9% (95% CI 10.0 to 21.7), high Snapchat use 43.3% (95% CI 24.3 to 64.5), compared to those with low use of these same social networks. Regarding Facebook, both medium and high users presented a higher probability of having psychosocial health problems compared to low users, 31.2% (95% CI 14.8 to 54.2) and 51.9% (95% CI 26.5 to 76.3), respectively. On the other hand, WhatsApp and Twitter were not significantly associated with psychosocial health problems.

Table  2 presents the predictive probabilities of psychosocial health problems associated with indicators of addictive behaviors to social network use among adolescents. Data indicate that adolescents reporting a desire to increase social network use (tolerance) have a 19.6% (95% CI 12.9 to 28.6) probability of experiencing such problems compared to the ones not reporting this desire. Similarly, those indicating mood alteration through social network usage (mood modification) show a 23.6% (95% CI 16.6 to 32.4) probability of encountering psychosocial health problems. Additionally, adolescents reporting a risk of relapse into addiction after attempting to control usage display a 19.0% (95% CI 12.7 to 27.3) probability of such problems. Those experiencing psychosocial and physical symptoms when unable to use social networks had a 25.0% (95% CI 16.7 to 35.6) probability of encountering psychosocial health issues. Lastly, adolescents reporting interference with social and daily activities due to social network usage present a 25.2% (95% CI 13.1 to 35.5) probability of experiencing psychosocial health problems.

The predictive probabilities of having psychosocial health problems according to the addictive behaviors to social network use status in adolescents are found in Fig.  3 and Supplementary material 2. The highest probabilities of having psychosocial health problems were identified in those with high addictive behaviors to the SN use (28.9%; 95% CI 19.3 to 40.8%). Conversely, the lowest probabilities of having these same problems were observed in adolescents with low addictive behaviors (6.8%; 95% CI 3.3 to 13.6%). Furthermore, significant differences were found between adolescents with high addictive behaviors to SN use and those with low addictive behaviors to SN use ( p  < 0.001), as well as with those with medium addictive behaviors to SN use ( p  = 0.023). Likewise, significant differences were found between adolescents with medium addictive behaviors to SN use and those with low addictive behaviors to SN use ( p  = 0.005).

Predictive probabilities of having psychosocial health problems according to the addictive behaviors to social network use (status)

The key findings of this study revealed that medium users and high users of social media had higher probabilities of experiencing psychosocial health issues at 19.3 and 16.2% compared to lower users, respectively. Regarding the individual social network, Facebook emerged as the most detrimental, with 31.3% probability of psychosocial health problems among medium users and 51.9% among high users, followed by Snapchat users at 43.3%. High usage of Instagram and TikTok usage were also associated with approximately 15% probability of psychosocial health problems. Our data also revealed that adolescents exhibited various signs of social network addiction, such as tolerance, mood alteration, risk of relapse, withdrawal symptoms, and interference with daily activities, having probabilities ranging from 19.0 to 25.2% of experiencing psychosocial health problems. These associations were observed considering the role of several relevant covariates. Finally, we also noticed that the highest probabilities of having psychosocial health problems were identified in adolescents with high addictive behaviors when using social networks and the lowest in those with low addictive behaviors.

To the best of our knowledge, this is the first study to investigate these issues in the adolescent population from Spain, approaching separately the role of different social media platforms. Taken together, these findings underscore a concerning scenario regarding the use of social network and psychosocial health of Spanish adolescents. The results suggest that harm to psychosocial well-being is evident even with moderate use of social networks, indicating that it is not solely high usage that is associated with a risk to adolescents’ health. Previous literature has shown that excessive social network use (more than 3 h a day) is linked to psychosocial distress and internalizing problems among adolescents [ 8 , 38 ]. However, our findings suggest that even moderate use of social networks is associated with worse psychosocial health, suggesting that the impact of social media on psychosocial well-being may be more pronounced than previously thought. On the other hand, our findings indicate that low usage of social networks is associated with a lower probability of experiencing psychosocial health issues, suggesting that abstaining from using social networks may prevent psychosocial health problems.

In this context, it is essential to consider the role of the different social networks, due to their individual characteristics. Each platform offers unique features and content, which could influence how adolescents engage with them and the potential impact on their psychosocial well-being [ 5 ]. We observed that Facebook and Snapchat were the social networks most strongly associated with psychosocial health problems. What is shown by existing literature is that, while it may be beneficial for some aspects like emotional support, self-expression, self-identity, and real-world relationships [ 39 , 40 ], prolonged use of Facebook and Snapchat by adolescents is also associated with symptoms of anxiety and depression, loneliness, sleep problems, body image issues, and bullying [ 39 , 41 , 42 , 43 , 44 ]. These adverse effects could be attributed to tendencies toward comparison and hopelessness, nighttime exposure to screens inhibiting the release of sleep hormones, instant propagation of negative comments, and the perception that by observing others’ “exciting” lives, they are missing out on the chance to live their own [ 45 , 46 ]. On the other hand, the psychological necessities of adolescents may motivate the search for content that will bring relief to their symptoms [ 47 ].

Another aspect identified was the addictive behaviors to social network use among adolescents and their relationship with psychosocial health problems. Each indicator of social media addiction presents unique implications for adolescent well-being. Tolerance, manifested as a desire to increase social media use, signifies a growing dependence on digital platforms for social interaction and validation, potentially leading to escalated addictive behaviors over time [ 48 ]. Similarly, mood alteration through social media use reflects the emotional impact of online interactions, suggesting that the immersive nature of social media platforms could influence adolescents’ mood and overall psychological well-being [ 49 ]. Moreover, the risk of relapse into addiction after attempting to control use underscores the challenges teens face in regulating their online behavior, highlighting the dependence they develop on digital platforms for emotional support, social connection and continuous reward [ 50 ]. Additionally, interference with social and daily activities due to social media use disrupts teenagers’ offline lives, exacerbating feelings of isolation and detachment from real-world interactions, ultimately compromising their overall functioning and well-being [ 51 ]. These findings underscore the multifaceted nature of social media addiction and its profound impact on various dimensions of adolescent psychosocial health.

Several mechanisms could explain the link between social media use and psychosocial health problems. Factors such as idealization of a perfect life, constant comparison, cyberbullying, body image comparisons, and disrupted sleep patterns due to late-night usage all play a role [ 52 , 53 ]. Also, evidence indicates that social media use can lead to dopamine release in the brain, similar to other addictive behaviors. Dopamine is a neurotransmitter associated with pleasure and reward. When we engage in activities that we find pleasurable, such as receiving likes or comments on social media posts, dopamine is released in our brains, reinforcing the behavior and encouraging us to continue engaging in it [ 54 , 55 ]. Another aspect involved is that excessive social media use can lead to dysregulation of dopamine pathways, resulting in individuals feeling compelled to check their social media accounts frequently, even when it interferes with other aspects of their lives such as work, sleep, or social interactions [ 55 ]. It is also speculated that this relationship may be bidirectional, meaning that having symptoms led to higher social media usage [ 47 ].

Regarding addiction to social network behavior predicting psychosocial health problems, the literature presents similar results. Aspects such as attention deficit hyperactivity disorder, anxiety, and depression are associated with social media dependency, demonstrating in some cases that the greater the use of social media, the more severe the symptoms [ 56 , 57 ]. Sümen and Evgin [ 58 ] pointed out that Turkish adolescents who were addicted to social media had a higher risk of developing various mental health issues, such as conduct problems, emotional issues, and issues with peers. Possible mechanisms for this relationship were also indicated, including the barrier that social networks create to establishing personal relationships with family and the environment; the use of social media to relieve undesirable mood states, symptoms of stress and anxiety, and even social media as a mechanism for modulating neuroendocrine responses and the sympathetic nervous system in the face of a stressor event [ 56 , 57 , 58 ].

There are some limitations to consider in this study. First, the use of social network and psychosocial health were based on self-reports by adolescents, which could potentially lead to super estimation or underestimation of data, depending on memory, and may be affected by social desirability. Also, the lack of predefined thresholds for social media usage levels may introduce variability in how participants interpret and report their usage. Additionally, as social media usage patterns evolve rapidly, our data, while collected relatively recently, may not capture the most current trends and newest platforms. Finally, the sample was not representative, which can make it difficult to extrapolate the results. On the other hand, some important strengths of this study must be considered. Our study examined multiple social media platforms and their individual impact, offers crucial insights for developing targeted interventions and prevention strategies. Furthermore, we included in the analyses a large number of covariates, such as age, sex, socioeconomic status, sleep duration, physical activity, sedentary behavior, BMI, and adherence to the Mediterranean diet, thereby enhancing the validity of the present results.

In conclusion, adolescents who use social networks more frequently and exhibit more addictive behaviors related to their use are more likely to experience psychosocial health problems compared to those who do not.Facebook emerged as the most detrimental, followed by Snapchat, Instagram, and TikTok. Our data also revealed that adolescents exhibit various signs of social network addiction and that the greater the addictive behaviors, the greater the psychosocial health problems. Taken together, the findings of the present study have the potential to contribute to a more nuanced understanding of the complex relationship between technology use and adolescent well-being, with implications for both research and practice in the fields of psychology, education, and public health.

Data availability

The data used in this study are available upon request from the corresponding authors. However, given that the participants are minors, privacy and confidentiality must be respected.

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The authors would like to express their gratitude to the Ayuntamiento de Archena , the participants, parents/legal guardians, physical education teachers, schools, and staff who provided information for this study.

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Brand, C., Fochesatto, C.F., Gaya, A.R. et al. Scrolling through adolescence: unveiling the relationship of the use of social networks and its addictive behavior with psychosocial health. Child Adolesc Psychiatry Ment Health 18 , 107 (2024). https://doi.org/10.1186/s13034-024-00805-0

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