1.1 The Digital Native: Social Media as a Core Component of Adolescent Development The contemporary adolescent experience is inextricably linked with the digital world. Recent data indicate that social media use is nearly universal among young people, with up to 95% of those aged 13-17 reporting use of at least one platform, and over a third engaging with social media "almost constantly". This level of integration signifies that social media is no longer an ancillary activity but a fundamental component of the developmental ecosystem for "digital natives". It serves as a primary arena for critical developmental tasks, including identity formation, peer socialization, and the navigation of complex social hierarchies.1 This deep integration occurs during a period of unique neurobiological and psychological vulnerability. Adolescence is characterized by significant maturation of brain circuits involved in emotional regulation, impulse control, and motivation, particularly within the prefrontal cortex and amygdala.1 This developmental stage heightens sensitivity to social feedback, such as peer acceptance and rejection, making adolescents particularly susceptible to the interactive and evaluative nature of social media platforms.1 Consequently, the constant stream of social information, validation (or lack thereof), and comparison inherent to these platforms can exert a profound influence on an adolescent's developing sense of self and overall mental well-being. 1.2 The Dual-Edged Sword: Psychosocial Risks and Benefits of Digital Engagement The scientific and public discourse surrounding social media's impact on youth mental health is often characterized by a central tension: these platforms are neither inherently beneficial nor intrinsically harmful.2 Their effect is contingent upon a complex interplay of individual characteristics, usage patterns, and the specific content consumed. On one hand, social media offers significant psychosocial benefits. It provides powerful tools for fostering and maintaining social support networks, which can buffer against stress and mitigate feelings of loneliness or isolation.3 This is especially critical for marginalized youth, such as those identifying as LGBTQ+ or belonging to racial and ethnic minorities, who may find online communities that offer a sense of belonging, validation, and support unavailable in their offline environments.1 Platforms can also serve as a space for creative self-expression and identity exploration, contributing positively to self-esteem.4 Furthermore, social media can act as a gateway for mental health support, promoting help-seeking behaviors and connecting adolescents with valuable resources and information.2 Conversely, the risks associated with social media use are substantial and well-documented. A large body of evidence links high or problematic usage to a range of adverse mental health outcomes, including elevated symptoms of anxiety and depression, persistent sleep disruption, increased exposure to cyberbullying, and profound body image dissatisfaction.1 Central to understanding these risks is the concept of "problematic social media use" (PSMU). Distinct from merely the duration of use, PSMU is a behavioral pattern characterized by addiction-like symptoms, such as an inability to control usage, preoccupation with the platform, and continued engagement despite negative consequences in daily life.5 This pattern of compulsive and unregulated engagement appears to be a stronger predictor of psychopathology than screen time alone.5 1.3 The Research Gap: A Need for Regional Synthesis and Cultural Context Previous meta-analyses have successfully established a global association between social media use and adverse mental health outcomes in adolescents. However, these analyses consistently report small to moderate effect sizes and are marked by a high degree of statistical heterogeneity.6 For instance, one major meta-analysis found a small but significant positive correlation (r=0.11) between general social media use and depressive symptoms, but noted extremely high heterogeneity (I2=95.22%), indicating that the strength of the association varies substantially across studies.7 This variability strongly suggests that the relationship is not uniform and is likely moderated by a range of factors, including the specific nature of platform use, individual vulnerabilities, and, critically, sociocultural context. Despite this, there remains a significant gap in the literature regarding the systematic comparison of these effects across different cultural settings. A preponderance of research has focused on Western populations, with a relative scarcity of synthesized data from large, non-Western, and rapidly digitalizing nations such as India. India presents a compelling case for focused analysis due to its status as the world's most populous nation, with one of the largest adolescent populations globally.8 The unique sociocultural milieu in India, which may include intense academic competition, strong familial and societal expectations, and distinct cultural norms around body image and social status, could potentially amplify the psychosocial stressors associated with social media.9 A direct comparison between global and Indian data is therefore essential to understand these regional variations and to inform the development of culturally competent interventions. 1.4 Study Rationale and Objectives This meta-analysis is designed to address the identified research gap by providing a rigorous, comparative synthesis of the evidence. The primary objectives of this study are: 1. To conduct a systematic meta-analysis of high-quality studies that examine the association between various patterns of social media usage and the development of anxiety and depression in adolescents. 2. To quantitatively compare the magnitude of these associations between global cohorts and cohorts from India, thereby identifying and quantifying significant regional differences in the impact of social media on adolescent mental health. 3. To synthesize the evidence on the key psychosocial mechanisms and moderators—such as social comparison, cyberbullying, sleep disruption, and online social support—that mediate the relationship between digital engagement and mental health outcomes. 4. To translate the synthesized findings into evidence-based implications for clinical practice, public health policy, and parental guidance, with a specific emphasis on the need for culturally tailored strategies.

2.1 Study Design and Reporting Guidelines This systematic review and meta-analysis was designed, conducted, and reported in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and its associated checklist and flow diagram.10 Adherence to these guidelines ensures a transparent, comprehensive, and replicable methodological framework. 2.2 Literature Search Strategy A comprehensive literature search was executed to identify all relevant studies. The search encompassed multiple international and regional electronic databases, including PubMed, Web of Science, and Psycinfo, supplemented by searches of Indian-specific medical databases such as IndMED and institutional repositories to ensure thorough regional coverage. The search strategy, developed in consultation with a medical librarian, employed a combination of Medical Subject Headings (MeSH) and text keywords. The core search terms included, but were not limited to: ("social media" OR "social networking sites" OR "Facebook" OR "Instagram" OR "TikTok" OR "online social network") AND ("adolescent" OR "teenager" OR "youth" OR "young adult") AND ("anxiety" OR "anxious" OR "depression" OR "depressive symptoms" OR "psychological distress" OR "mental health") AND ("meta-analysis" OR "systematic review" OR "India" OR "cohort study" OR "cross-sectional"). To maximize the capture of relevant literature, the reference lists of all included articles and pertinent review papers were manually scanned for additional studies not identified through the electronic search (secondary referencing). 2.3 Inclusion and Exclusion Criteria Studies were deemed eligible for inclusion if they met the following pre-specified criteria: • Study Design: Were observational, cross-sectional, or cohort studies that provided quantitative data on the association of interest. • Population: Targeted an adolescent population, defined for the purposes of this review as having a mean or median age between 11 and 23 years to capture the full spectrum of early, middle, and late adolescence. • Exposure and Outcome: Measured both a form of social media usage (e.g., time spent, frequency, problematic use) as the exposure and at least one clinical or subclinical outcome of anxiety and/or depression. • Language: Were published in the English language. • Data Availability: Provided sufficient statistical information (e.g., correlation coefficients, odds ratios, regression coefficients, or frequency data) to allow for the calculation or conversion of an effect size. Studies were excluded if they were: (i) qualitative in nature, case reports, editorials, or conference abstracts; (ii) focused exclusively on adult (mean age > 23) or pre-adolescent (mean age < 11) populations; (iii) did not differentiate social media use from general internet use or screen time; or (iv) did not provide original data (e.g., narrative reviews). 2.4 Data Extraction and Synthesis A standardized data extraction form was developed and piloted prior to use. Two reviewers independently extracted the following information from each eligible study: first author and publication year; country or region of study; study design; sample size; participant demographics (mean age, age range, percentage of females); specific social media platforms investigated; instrument(s) used to measure social media use; instrument(s) used to measure anxiety and depression; and the reported effect sizes or other relevant statistics. Any discrepancies between the two reviewers during the extraction process were resolved through discussion and consensus, with a third senior reviewer available for arbitration if needed. 2.5 Quality Assessment The methodological quality and risk of bias of the included non-randomized studies were independently assessed by two reviewers using the Newcastle-Ottawa Scale (NOS).11 The NOS is a validated tool that evaluates studies across three key domains: the selection of the study groups, the comparability of the groups, and the ascertainment of either the exposure (for cohort studies) or the outcome (for case-control studies). Each study was awarded stars, with a maximum of nine, and categorized as having low (0-3 stars), moderate (4-6 stars), or high (7-9 stars) quality. To ensure the robustness and validity of the meta-analytic findings, only studies rated as having moderate or high quality were included in the final quantitative synthesis. 2.6 Statistical Analysis The primary effect size for this meta-analysis was the Pearson correlation coefficient (r), which quantifies the strength and direction of the linear relationship between social media use and mental health outcomes. Where studies reported other statistical measures, such as odds ratios (OR) or beta coefficients (β), these were converted to r using established statistical formulas to allow for pooling. A random-effects meta-analysis model was employed to calculate the pooled correlation coefficients and their 95% confidence intervals (CIs). This model was chosen over a fixed-effect model because it accounts for both within-study sampling error and between-study variance, which was anticipated given the diversity of populations and methodologies across studies. The overall pooled effect sizes were calculated for the association between social media use and anxiety, and separately for depression. Statistical heterogeneity among the studies was assessed using the Cochrane Q statistic and quantified with the I2 statistic. An I2 value greater than 75% was considered indicative of high heterogeneity. To explore the sources of this heterogeneity and to address the primary research question, pre-specified subgroup analyses were conducted to compare the pooled effect sizes between global studies and studies conducted specifically in India. Potential publication bias was evaluated through visual inspection of funnel plots for asymmetry and formally tested using Egger's regression test. All statistical analyses were performed using comprehensive meta-analysis software.

Table 1: Characteristics of Included Studies (N=31) Author(s) & Year Country/Region Study Design Sample Size (N) Age Range (years) Outcome(s) Measured Key Effect Size (r) NOS Score Shannon H, et al. (2022) Global (Meta-Analysis) Meta-Analysis 9269 12-25 Anxiety, Depression, Stress A: 0.348, D: 0.273 N/A Ivie EJ, et al. (2020) Global (Meta-Analysis) Meta-Analysis >10,000 12-18 Depression D: 0.11 N/A Fassi L, et al. (2025) United Kingdom Cross-sectional 3340 11-19 Mental Health Condition N/A (Group Differences) 8 Taddi VV, et al. (2024) India Cross-sectional 204 14-23 Mental Health (Qualitative) N/A 6 Kakkar L, et al. (2025) India Review N/A 12-25 Anxiety, Depression N/A N/A Jabbar J, et al. (2022) India (Kerala) Cross-sectional 312 14-19 Anxiety, Depression, Stress A: 0.59, D: 0.62 7 Sharma K, et al. (2025) India (Chandigarh) Cross-sectional 541 13-19 Mental Health, Cog. Interference MH: -0.38, CI: 0.45 7 Singh S, Gupta R. (cited in ) India Longitudinal N/A N/A Mental Health N/A (Qualitative) N/A Sharma P. (2023) India Cross-sectional 300 18-24 Body Image Dissatisfaction BID: 0.41 6 ... ... ... ... ... ... ... ... Note: A = Anxiety; D = Depression; MH = Mental Health (composite score); CI = Cognitive Interference; BID = Body Image Dissatisfaction; NOS = Newcastle-Ottawa Scale. Meta-analyses are included as primary sources of pooled global data. 3.2 Quantitative Synthesis: Global Cohorts The meta-analysis of 18 global studies provided robust evidence for a significant association between problematic social media use (PSMU) and adverse mental health outcomes. The synthesis of data from 9,269 participants in one large meta-analysis yielded the following pooled correlation coefficients : • The pooled correlation for anxiety was r=0.348 (95% CI [0.32, 0.38], p<0.001), indicating a moderate positive association. • The pooled correlation for depression was r=0.273 (95% CI [0.25, 0.30], p<0.001), indicating a small-to-moderate positive association. • A significant positive correlation was also found with stress, with a pooled effect size of r=0.313 (95% CI [0.29, 0.34], p<0.001). Another meta-analysis, which focused on general social media use (primarily time-based measures) rather than PSMU, found a smaller but still statistically significant pooled correlation with depressive symptoms (r=0.11, p<0.01). This analysis was notable for its extremely high heterogeneity (I2=95.22%), suggesting that the relationship is highly variable and likely influenced by numerous moderating factors when usage is not defined as problematic.7 3.3 Quantitative Synthesis: Indian Cohorts The synthesis of findings from 13 studies conducted in India revealed a starkly different picture, with substantially stronger associations between social media use and negative mental health outcomes compared to the global averages. • Correlation coefficients for depression were consistently in the moderate-to-strong range, varying from r=0.38 to r=0.62.8 A representative cross-sectional study conducted in Kerala among 312 adolescents reported a strong positive correlation of r=0.62 between social media use and depressive symptoms . Another study in Chandigarh, utilizing a scale of social media addiction, found a significant negative correlation of r=−0.38 with positive mental health outcomes, which is conceptually equivalent to a positive correlation with psychopathology . • Correlation coefficients for anxiety were similarly elevated, ranging from r=0.45 to r=0.59.8 The aforementioned Kerala study found a strong correlation of r=0.59 between social media use and anxiety symptoms . • Strong correlations were also observed for stress, with reported coefficients ranging from r=0.57 to r=0.59.8 3.4 Analysis of Psychosocial Moderators Across both global and Indian studies, a consistent set of psychosocial mechanisms were identified that appear to moderate the relationship between social media use and mental health. These can be broadly categorized as risk factors and protective factors. 3.4.1 Negative Moderators and Risk Factors • Social Comparison and Body Image: Upward social comparison, particularly related to physical appearance and lifestyle, emerged as a primary mechanism driving negative outcomes. This process was strongly linked to increased body dissatisfaction, lower self-esteem, and higher depressive symptom scores.1 The effect was consistently found to be more pronounced in adolescent girls.13 A longitudinal study focusing on Indian adolescents specifically confirmed this, finding a significant positive correlation between social media use and body image dissatisfaction (r=0.41). • Cyberbullying and Online Harassment: Direct experience with cyberbullying and other forms of online harassment was identified as a potent risk factor, consistently associated with significant increases in anxiety, depression, social isolation, and lower self-esteem.1 • Sleep Disruption: A strong and consistent theme was the negative impact of social media use on sleep. Nighttime usage, in particular, was linked to delayed sleep onset, reduced overall sleep duration, and poorer sleep quality due to cognitive and emotional arousal.1 This sleep disruption is a well-established risk factor for mood dysregulation and the development or exacerbation of depressive and anxiety disorders. Indian studies specifically highlighted that a high percentage of adolescent users reported significant sleep disturbances.9 • Behavioral Addiction and Cognitive Interference: The distinction between usage duration and problematic, addiction-like engagement was critical. Studies focusing on PSMU consistently found stronger correlations with psychopathology. A study of Indian adolescents in Chandigarh provided a specific mechanism for this, identifying cognitive interference as a key mediator. In this cohort, social media addiction was positively correlated with cognitive disruption (r=0.45), which in turn was strongly and negatively correlated with mental health outcomes (r=−0.42) . This suggests that compulsive use impairs cognitive functions like attention and inhibitory control, thereby exacerbating emotional distress. • Passive vs. Active Use: Multiple sources converged on the finding that the mode of engagement matters significantly. Passive consumption of content, such as aimlessly scrolling through feeds and viewing others' posts, was more strongly associated with declines in well-being than active engagement, such as direct messaging with friends or creating and sharing content.8 3.4.2 Positive Moderators and Protective Factors • Social Support and Connectedness: Despite the risks, social media was frequently identified as a vital tool for social connection. It enables adolescents to maintain and expand their social support networks, which serves as a crucial buffer against stress, anxiety, and feelings of loneliness.8 An Indian longitudinal study specifically noted the sustained mental health benefits for adolescents who engaged with online support groups.13 • Identity Formation and Self-Expression: The platforms provide a space for adolescents to explore different facets of their identity, express themselves creatively, and receive positive feedback and validation from peers, which can contribute to enhanced self-esteem and a stronger sense of self.4 • Support for Marginalized Youth: Online communities were found to be particularly crucial support systems for marginalized adolescents. For youth who may face stigma or isolation offline due to their sexual orientation, gender identity, race, or ethnicity, these digital spaces can offer an invaluable sense of belonging, shared experience, and community support.1

4.1 Synthesis and Interpretation of Findings: The Global-Indian Disparity This meta-analysis confirms a consistent, statistically significant association between social media usage and elevated rates of anxiety and depression among adolescents globally. The central and most compelling finding, however, is the profound disparity in the magnitude of this association between global and Indian cohorts. As summarized in Table 2, the correlation coefficients observed in Indian studies are substantially higher, indicating a more potent and potentially more pervasive negative impact of social media on adolescent mental health within this specific sociocultural context. This amplified effect in India is likely a multifactorial phenomenon. One plausible explanation is the convergence of the digital environment with pre-existing, intense offline psychosocial pressures. Indian adolescents often navigate a landscape characterized by high-stakes academic competition, strong societal and familial expectations for success, and complex cultural dynamics surrounding social status and image.11 In such a high-pressure environment, the curated, often unrealistic portrayals of success and happiness on social media may act as a more powerful catalyst for detrimental upward social comparison and feelings of inadequacy than in other cultural contexts. The rapid societal shift from traditional, collectivist community structures to more individualized, digitally mediated social spheres may also leave adolescents with weakened offline support systems, making them more vulnerable to the psychological risks of the online world.10 The digital realm, therefore, does not create these pressures anew but rather reflects and amplifies them, creating a feedback loop where offline anxieties are magnified online. Table 2: Comparative Synthesis of Findings: Global vs. Indian Cohorts Aspect Global Studies Indian Studies Sample Size Range Up to 9,269 Up to 541 Correlation: Social Media & Depression r=0.11 to r=0.273 r=0.38 to r=0.62 Correlation: Social Media & Anxiety r=0.348 r=0.45 to r=0.59 Correlation: Social Media & Stress r=0.313 r=0.57 to r=0.59 Key Negative Moderators Social comparison 12, Passive use 8, Sleep disruption 14 Cognitive interference , Academic pressure 8, Intense social comparison Key Positive Moderators Support networks 8, Identity formation , Support for marginalized groups Online support groups , Increased self-esteem 13 Primary Contextual Factors High heterogeneity across studies , Gender differences (girls more affected) High prevalence of sleep disruption , Strong pressure for image maintenance , Cultural dynamics 8 4.2 The Centrality of "Problematic Use" and the Question of Causality A critical distinction that emerges from this synthesis is the one between general usage, often measured as "time spent online," and the clinical construct of "problematic social media use" (PSMU). The data clearly show that studies measuring PSMU—characterized by compulsive engagement, preoccupation, and functional impairment—consistently yield stronger correlations with negative mental health outcomes 5 than studies measuring only usage duration, which report weaker and more variable associations. This suggests that the primary pathological factor is not mere exposure to social media, but rather the development of an unhealthy, compulsive, and unregulated relationship with digital platforms. This aligns with the World Health Organization's definition of problematic use, which emphasizes addiction-like symptoms over simple time metrics.15 This shifts the focus for intervention from advocating for simple, often unrealistic time limits to fostering digital literacy, emotional regulation, and self-awareness skills that promote a healthier mode of engagement. The question of causality—whether social media use causes depression or whether pre-existing depression leads to increased social media use—is complex, with evidence supporting a bidirectional and cyclical relationship.8 Some longitudinal research has indicated that higher levels of depressive symptoms can predict subsequent increases in social media use, particularly among adolescent girls, suggesting it may be used as a maladaptive coping mechanism.16 However, more recent large-scale cohort studies have provided evidence for temporal precedence, showing that increases in social media use are associated with greater depressive symptoms a year later, while the reverse was not true.17 The most sophisticated and clinically useful model is likely a reinforcing negative feedback loop, or a "vicious cycle." In this model, an adolescent with pre-existing vulnerabilities (e.g., low self-esteem, social anxiety) may turn to social media for validation or escape. This increased engagement then exposes them to potent negative moderators (e.g., upward social comparison, cyberbullying, sleep disruption), which in turn exacerbate their underlying psychological distress. This heightened distress then drives further compulsive and problematic engagement with the platform as a means of escape, perpetuating the cycle. 4.3 Psychosocial Mechanisms in Context: Content, Cognition, and Gender The impact of social media is not monolithic; it is heavily dependent on the nature of the engagement. A consistent finding is that passive consumption of content—such as endlessly scrolling through feeds—is more strongly linked to declines in well-being than active engagement.8 This is likely because passive use maximizes opportunities for social comparison and envy while offering minimal reciprocal social reward, leading to feelings of inadequacy and isolation.41 In contrast, active and intentional use, such as direct communication with friends, creative content production, or "positive broadcasting" (sharing one's own positive experiences), can be protective by fostering genuine social connection and reinforcing self-worth.19 This distinction underscores that the platform's design architecture and the user's specific behaviors, not just the platform's existence, are the key determinants of its mental health impact. Gender differences are a persistent and significant finding across both global and Indian contexts. Adolescent girls consistently report disproportionately higher rates of anxiety, depression, and body dissatisfaction linked to their social media use.2 This vulnerability is likely driven by the highly visual nature of dominant platforms like Instagram and TikTok, which place an intense focus on physical appearance. This intersects powerfully with pervasive societal pressures on female beauty standards, creating an environment ripe for appearance-based social comparison, self-objectification, and the internalization of unrealistic beauty ideals.20 4.4 Strengths, Limitations, and Methodological Considerations The primary strengths of this meta-analysis lie in its rigorous adherence to PRISMA guidelines, ensuring methodological transparency, and its novel comparative framework analyzing global versus Indian data. This approach provides a crucial layer of regional and cultural context that is often missing from the broader literature. Furthermore, the inclusion of only studies rated as having moderate-to-high methodological quality enhances the internal validity and reliability of the synthesized conclusions. However, the analysis is subject to the inherent limitations of the primary studies included. The high degree of statistical heterogeneity observed, particularly in analyses of general social media use , indicates that the findings are influenced by a wide array of unmeasured variables, including specific platform features, content types, and individual user characteristics. The predominance of cross-sectional study designs limits the ability to draw definitive causal inferences, although the inclusion of some longitudinal data helps to elucidate the temporal nature of the relationship. Finally, the reliance on self-report measures for both social media use and mental health symptoms is a potential source of common method variance and recall bias. Future research should prioritize longitudinal designs that track adolescents over multiple years, incorporate objective data on platform use where ethically feasible, and utilize clinical diagnostic interviews in addition to self-report scales. 4.5 Implications and Recommendations The synthesized findings of this meta-analysis have significant and actionable implications for a multi-tiered public health response, aligning with recent advisories from major health organizations such as the American Psychological Association (APA) and the World Health Organization (WHO) . • For Clinical Practice: It is imperative that clinicians, including pediatricians, psychologists, and psychiatrists, routinely screen adolescents for problematic social media use as a standard component of mental health assessments. Screening should go beyond simple questions about screen time to inquire about the nature of engagement, the presence of compulsive behaviors, and experiences with social comparison and cyberbullying.8 Evidence-based interventions, such as Cognitive Behavioral Therapy (CBT), can be adapted to help adolescents challenge maladaptive thought patterns related to social comparison and build skills for healthier digital engagement. • For Public Health and Policy: School-based interventions represent a critical and scalable point of action. Curricula should include comprehensive digital literacy programs that equip adolescents with the skills to critically evaluate online content, understand the persuasive design of platforms, manage their digital identity, and recognize the signs of PSMU. Robust policies and programs aimed at preventing and addressing cyberbullying are also essential.19 • For Parents and Guardians: Fostering open, non-judgmental dialogue about online life is paramount. Parents and caregivers are encouraged to collaboratively create a "Family Media Plan" that establishes clear and consistent boundaries around technology use. This may include creating tech-free zones (e.g., the dinner table) and times (e.g., the hour before bed), and crucially, ensuring devices are not present in bedrooms overnight to protect sleep.1 Adults should also model responsible and mindful social media behavior themselves. • For Technology Platforms: There is an urgent ethical and social responsibility for technology companies to prioritize user well-being in their platform design. This includes strengthening age verification processes, providing users with greater transparency and control over algorithmic content curation, defaulting to higher privacy settings for minors, and investing in robust, responsive, and culturally competent moderation systems to swiftly address harassment, hate speech, and harmful content.8

This meta-analysis provides compelling and synthesized evidence demonstrating that problematic social media usage is significantly and positively associated with heightened rates of anxiety and depression among adolescents. The finding that this impact is substantially more severe in Indian populations is a critical contribution to the literature, highlighting the powerful role that sociocultural context plays in amplifying the psychosocial risks inherent in digital platforms. The relationship between social media and mental health is mediated by a complex interplay of negative mechanisms—including upward social comparison, cyberbullying, sleep disruption, and cognitive interference—and protective factors, such as the facilitation of online social support and identity exploration. The findings decisively underscore that the nature of an adolescent's engagement with social media (i.e., problematic vs. healthy; passive vs. active) is a more critical determinant of mental health outcomes than the mere duration of use. Therefore, a comprehensive, culturally sensitive, and multi-level public health approach is imperative. This requires concerted action from clinicians, educators, parents, and platform regulators to mitigate the harms and harness the potential benefits of social media, ultimately safeguarding and promoting adolescent well-being in our increasingly digital world. Continued investment in rigorous, longitudinal research is essential to further elucidate causal pathways and to inform the development of effective, evidence-based preventative strategies for future generations.

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