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Social Media Use and Depressive Symptoms Among United States Adolescents

      Abstract

      Purpose

      Depression is increasingly common among US adolescents; the extent to which social media exposure contributes to this increase remains controversial.

      Methods

      We used Monitoring the Future data from 8th and 10th grade students (n = 74,472), 2009–2017, to assess the relationship between daily social media use and depressive symptoms. Self-reported depressive symptom score (range: 4–20) was assessed continuously using a log-transformed outcome and at varying cut scores with logistic regression analyses. First, these outcomes were examined overall, comparing adolescents using social media daily to adolescents who were not. We then estimated predicted depressive symptom scores using 26 predictors in order to establish underlying depression risk. We partitioned students into depression risk quintiles to control for confounding due to underlying depression risk and examine heterogeneity in the association between social media use and depressive symptoms. Sensitivity analyses were used to test the robustness of results with different configurations of the predicted score model, and overall associations were examined in two-year groups to identify differences in effects.

      Results

      For girls, in adjusted risk-stratified analysis, daily social media use was not associated with high (vs. low) depressive symptoms. For boys, results were inconsistent, suggesting a protective effect of daily social media use at some cut scores. Results were consistent across sensitivity analyses, and any potential harmful effects appear to be limited to 2009–2010, limiting the evidence supporting social media as a current risk factor for depressive symptoms.

      Conclusions

      Among US adolescents, daily social media use is not a strong or consistent risk factor for depressive symptoms.

      Keywords

      Implications and Contribution
      By using risk stratification and stronger control of confounding, these rigorous analyses thoroughly examine the association between social media and depressive symptoms in a nationally representative sample of adolescents, finding that contrary to the popular narrative, daily social media use is not a strong or consistent risk factor for depressive symptoms.
      After almost 50 years of stability [
      • Jane Costello E.
      • Erkanli A.
      • Angold A.
      Is there an epidemic of child or adolescent depression?.
      ], recent evidence has indicated unprecedented increases in adolescent depression [
      • Mojtabai R.
      • Olfson M.
      • Han B.
      National trends in the prevalence and treatment of depression in adolescents and young Adults.
      ], depressive symptoms [
      • Keyes K.M.
      • Gary D.
      • O’Malley P.M.
      • et al.
      Recent increases in depressive symptoms among US adolescents: Trends from 1991-2018.
      ], and suicidal behavior [
      Centers for Disease Control and Prevention [CDC]
      ], particularly among girls. There has been widespread speculation that increasing use of smartphones and social media has contributed to these trends. Proponents of this hypothesis note that adolescents are increasingly isolated from face-to-face interaction [
      • Barry C.T.
      • Sidoti C.L.
      • Briggs S.M.
      • et al.
      Adolescent social media use and mental health from adolescent and parent perspectives.
      ,
      • George M.J.
      • Russell M.A.
      • Piontak J.R.
      • et al.
      Concurrent and subsequent associations between daily digital technology use and high-risk adolescents’mental health symptoms.
      ,
      • Twenge J.M.
      • Joiner T.E.
      • Rogers M.L.
      • et al.
      Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time.
      ], experience cyber-bullying [
      • Hamm M.P.
      • Newton A.S.
      • Chisholm A.
      • et al.
      Prevalence and effect of cyberbullying on children and young people: A scoping review of social media studies.
      ], and face challenges to self-esteem and self-worth through curated online images of peers [
      • Appel H.
      • Gerlach A.L.
      • Crusius J.
      The interplay between Facebook use, social comparison, envy, and depression.
      ]. These negative effects of social media affect girls more than boys [
      • Nesi J.
      • Prinstein M.J.
      Using social media for social comparison and feedback-seeking: Gender and popularity moderate associations with depressive symptoms.
      ], plausibly explaining why trends in mental health problems have been more prominent among girls and underscoring the need to examine potential gender-specific effects. On the other hand, social media is often a positive outlet [
      • Odgers C.
      Smartphones are bad for some teens, not all.
      ], and its use may have positive effects on adolescent self-esteem [
      • Steinfield C.
      • Ellison N.B.
      • Lampe C.
      Social capital, self-esteem, and use of online social network sites: A longitudinal analysis.
      ]. Social networking sites help as a resource for self-affirmation [
      • Toma C.L.
      • Hancock J.T.
      Self-affirmation Underlies Facebook Use.
      ] and provide a space for content that is positive or humorous, particularly valuable to adolescents who are depressed [
      • Radovic A.
      • Gmelin T.
      • Stein B.D.
      • et al.
      Depressed adolescents’ positive and negative use of social media.
      ]. Many young people seek out support and advice on social media, particularly those with moderate to severe depressive symptoms, twice as likely to use social media for these emotional resources compared to peers [
      • Rideout V.
      • Fox S.
      Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Hopelab and Well Being Trust.
      ]. Thus, the relationship between social media and mental health remains unresolved.
      Several nationally representative [
      • Twenge J.M.
      • Campbell W.K.
      Media use is linked to lower psychological well-being: Evidence from three datasets.
      ,
      • Twenge J.M.
      • Campbell W.K.
      Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study.
      ] studies and other large samples [
      • Boers E.
      • Afzali M.H.
      • Newton N.
      • et al.
      Association of screen time and depression in adolescence.
      ,
      • Riehm K.E.
      • Feder K.A.
      • Tormohlen K.N.
      • et al.
      Associations between time spent using social media and internalizing and externalizing problems among US youth.
      ] have documented a small association between amount of digital technology use, including social media, and depressive symptoms. Other survey studies [
      • Przybylski A.K.
      • Weinstein N.
      Alarge-scale test of the Goldilocks hypothesis: Quantifying the relations between digital-screen Use and the mental well-being of adolescents.
      ] and detailed time-use diary studies [
      • Orben A.
      • Przybylski A.K.
      Screens, teens, and psychological well-being: Evidence from three time-use-diary studies.
      ] have reported no relationship between social media and depression symptoms. Orben and Przybylski (2019) found that the specification of variables capturing digital technology use, adolescent well-being, and confounders could potentially result in myriad effect sizes, with the most likely association being exceedingly small and explaining only a small portion of well-being variance [
      • Orben A.
      • Przybylski A.K.
      The association between adolescent well-being and digital technology use.
      ]. These divergent findings may be partially due to not accounting for underlying mental health risk, which might influence patterns of social media through reverse causation, and residual confounding. Adolescence marks a period of social changes that may affect health and behavior in a manner related to depression and social media use, such as school attitudes and performance, peer relationships, and family environment. Not accounting for these factors may bias estimates of the effect of social media use on risk of depression.
      Typically, multivariable regression modeling is used to control for confounding. However, parameter estimates can become unstable as the number of covariates increases [
      • Petersen M.L.
      • Porter K.E.
      • Gruber S.
      • et al.
      Diagnosing and responding to violations in the positivity assumption.
      ]. Alternatively, outcome risk scores may be used to avoid this issue. Outcome risk scores are the predicted probability of having the outcome, conditional on a set of well-established predictors, in this case depression [
      • Miettinen O.S.
      Stratification by a multivariate confounder score.
      ]. This provides an estimate of a given adolescent’s underlying depression risk. Using these predicted scores, adolescents with similar predicted depression risks can be compared with one another. This limits the extent to which it could be argued that it is underlying depression risk influencing social media use, rather than the causal direction of interest where social media impacts depressive symptoms. While self-reported symptoms give a direct snapshot of adolescent mental health now, outcome risk scores help to articulate underlying mental health risk, control confounding strongly, and provide an unbiased and transparent estimate of the relationship between social media use and depressive symptoms. Such analyses are particularly important in the context of social media and depression.
      Whether social media use puts teens at risk for depressive symptoms is a critical policy-relevant question, and evidence thus far has been mixed. We draw on nationally representative data, collected from 2009 through 2017, of US 8th and 10th grade students to examine the association between social media use and depressive symptoms among adolescents. These analyses contribute new knowledge by using outcome risk scores to examine underlying risk for depression and stratifying adolescents based on this risk. These analyses introduce strong methods to address confounding in a large, nationally representative sample. This, in conjunction with the ability to account for heterogeneity over time and multiple sensitivity analyses, allows for the rigorous examination of this important and controversial potential risk factor for adolescent depressive symptoms.

      Methods

      The 2009 through 2017 Monitoring the Future (MTF) surveys included an annually conducted nationally representative cross-sectional survey of school-attending adolescents [
      • Miech R.A.
      • Johnston L.D.
      • O’Malley P.M.
      • et al.
      Monitoring the Future national survey results on drug use, 1975-2016: Volume I, secondary school students.
      ]. These data are deidentified and publicly available. Schools were selected under a multistage random sampling design and are invited to participate for two years. Schools that declined participation were replaced with schools with similar geographic location, size, and urbanicity. The overall school participation rates (including replacements of schools that decline to participate) ranged from 91% to 99% for all study years. Student response rates have ranged from 85.0% to 87.3%, and averaged 86.5%, with no systematic trend. Almost all nonresponse was due to absenteeism; less than 1% of students refused to participate. Self-administered questionnaires were given to students. The institutional review boards of University of Michigan and Columbia University approved the study protocol and analytic aims, respectively. Parents were informed of the study and provided the option to decline participation on their child’s behalf. The final analytic sample included 74,472 respondents.
      Students filled out a “core” questionnaire, and then were randomized to a “subform” with unique content. We restricted analysis to the subforms in which key study variables overlapped. There were subforms with both social media and depressive symptom questions for 8th and 10th grade students. The years 2009 through 2017 were selected as these were the years where both social media and depressive symptom items were available and overlapped on subforms.
      Social media use was assessed with the following question: “How often do you do each of the following? Visit social networking Web sites like Facebook, Twitter, Instagram, etc.” For 8th and 10th grade students, social networking items included examples of Myspace and Facebook from 2008 to 2011, and only Facebook from 2012 onward. Response options ranged from “Almost every day” to “Never”, with intermediate options of “At least once a week”, “Once or twice a month” and “A few times a year”. We controlled for survey year in all analyses, accounting for changes in question wording. We dichotomized social media use as daily versus nondaily use. The number of adolescents in these intermediate categories of social media use is quite small relative to those using social media daily, which contributed to our decision to collapse the groups outside of daily social media use. This dichotomization was hypothesis-driven: given how increasingly prevalent and important social media is becoming in adolescents’ lives, the dichotomization of social media use into daily versus nondaily aimed to capture two distinct patterns. Either social media is engrained into an adolescent’s daily life and activities as a personally important means of socializing, or not. Knowing how quickly social media use is increasing among adolescents, daily use has become the norm. Our binary variable, then, distinguishes between adolescents who operate within that norm, and those who do not, regardless of the specific frequency of their nondaily use.
      Four items were used to measure depressive symptoms, 1 (disagree) to 5 (agree) after the stem question “How much do you agree or disagree with each of the following statements”: “Life often seems meaningless”, “The future often seems hopeless”, “It feels good to be alive”, and “I enjoy life as much as anyone.” The latter two questions were reverse coded for analysis. These items are derived from the Bentler Medical and Psychological Functioning Inventory’s depression scale [
      • Newcomb M.D.
      • Huba G.J.
      • Bentler P.M.
      A multidimensional assessment of stressful life events among adolescents: Derivation and correlates.
      ] which exhibits strong reliability (.72) in adolescent samples [
      • Newcomb M.D.
      • Huba G.J.
      • Bentler P.M.
      Life change events among adolescents: An empirical consideration of some methodological issues.
      ]. These items have been used to assess depressive symptoms in previous studies of these data [
      • Maslowsky J.
      • Schulenberg J.E.
      • O’Malley P.M.
      • et al.
      Depressive symptoms, conduct problems, and risk for polysubstance use among adolescents: Results from US national surveys.
      ,
      • Maslowsky J.
      • Schulenberg J.E.
      • Zucker R.A.
      Influence of conduct problems and depressive symptomatology on adolescent substance use: Developmentally proximal versus distal effects.
      ] and have good reliability in this analytic sample (Cronbach’s alpha range: .77 [2009, grade 8] to .85 [2017, grade 10]). Scores were summed to create a total score. Respondents missing data on one of the four items (2.3%) were imputed with the mean value of the other three; respondents missing data on two or more of the four items (11.7%) were excluded from the analysis. Scores ranged from 4 to 20 with a mean of 7.67 (standard deviation = 3.86) and a median of 7.0, indicating that the mean and median scores were close to the “disagree somewhat” response (2 on the response scale).
      Depressive symptom scores were highly right-skewed, did not meet normality assumptions for linear models, and alternative models including negative binomial, cumulative and generalized multinomial did not achieve sufficient model fit and convergence for reliable estimation. Given this, we log-transformed scores to assess the association between social media and continuous depressive symptoms and created dichotomies of depressive symptoms to assess associations with “high”, relative to other adolescents, depressive symptoms. Given that there is no empirically validated clinical cut score, we used a range of potential cut scores, including > 9 symptom score (25.0% of boys; 30.6% of girls), >10 (20.3% of boys; 25.3% of girls), >12 (9.0% of boys; 14.6% of girls), and >15 (3.7% of boys; 6.4% of girls). These correspond to approximately the 75th, 90th, and 95th percentiles, with >9 and >10 serving as the closest cut scores on either side of the 75th percentile.

      Predicted probability of high depressive symptoms

      We created an outcome prediction model that estimated the probability that an adolescent had high depressive symptoms, dichotomized at >10 (i.e., 75th percentile).
      To build the model predicting high depressive symptoms, we considered 97 items from the MTF surveys known to be associated with depressive symptoms. Of these, 26 items with moderate to high correlations (>.10) were retained (see Table A1 for all items). Each individual’s predicted probability of meeting the depressive symptom cutpoint was estimated using logistic regression, which demonstrated adequate prediction (McFadden’s adjusted pseudo-R2 = .55) [
      • McFadden D.
      Conditional Logit analysis of Qualitative Choice behavior.
      ] and excellent accuracy in predicting observed high depressive symptom status (area under curve = .95). We then categorized the predicted probabilities of depression into five groups, based on ascending risk of high depressive symptoms. The thresholds for each category were created using established covariate balancing methods. Within each risk group, none of the covariate means differed between those with and without the outcome. These methods constitute a broader strategy of risk stratification, allowing for more efficient control of observed confounders than regression-based approaches [
      • Cepeda M.S.
      • Boston R.
      • Farrar J.T.
      • et al.
      Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders.
      ,
      • Glynn R.J.
      • Schneeweiss S.
      • Sturmer T.
      Indications for propensity scores and review of their use in pharmacoepidemiology.
      ] and are a better alternative to propensity score estimation when covariates are not highly correlated with the exposure [
      • Arbogast P.G.
      • Ray W.A.
      Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders.
      ]. Prediction models and covariate balancing [
      • Greifer N.
      Covariate balance tables and plots: A guide to the cobalt package.
      ] were implemented in R version 3.5.1. Unadjusted and adjusted mean differences in prediction model covariates are provided in Figure 1.
      Figure thumbnail gr1
      Figure 1Covariate balancing across five subsets. This visualizes the covariates that fed into covariate balancing and the extent to which they vary between groups.
      We included a range of covariates in the depression risk stratification models, adjusting for central sources of confounding including race/ethnicity, grade, parental educational attainment, urbanicity, academic performance, year, and subform of the MTF questionnaire assigned to the student (Tables A2 and A3). The model distinguished between groups with different underlying risks of depressive symptoms. For instance, when self-reported depressive symptom score is dichotomized at the lowest cut score used (>9 symptom score), 10.8% of girls and 9.0% of boys with the lowest depression risk exhibit high depressive symptoms, and 97.7% of girls and 98.1% of boys with the highest depression risk exhibit high depressive symptoms.
      We replicated the predicted depression score at a cut score at the 90th percentile as a sensitivity analysis, to represent very high levels of depressive symptoms. We also conducted a separate sensitivity analysis at the original dichotomization of >10 but removing potential symptoms of depression from the predictive model, specifically self-esteem, self-derogation, and daily social media use, to ensure that the inclusion of these items did not restrict the range of outcomes and bias results toward the null.

      Statistical analysis

      After estimating predicted depression risk scores, we conducted regression analysis to determine the association between daily social media use and depressive symptoms: (1) adjusted and unstratified by depression risk category and (2) adjusted and stratified by depression risk category. For dichotomous outcomes, we used logistic regression. For the analysis of depressive symptom score as a continuous outcome, we used linear regression after log transformation. We also examined the interaction of social media use and year, to examine whether the effects of social media use have changed as it has become more prevalent in this population.

      Results

      Figure 2 shows the trend from 2009 to 2017 in daily social media use among 8th and 10th grade students. The prevalence of daily social media use increased from 61.0% to 89.0% among girls, and from 45.8% to 75.3% among boys.
      Figure thumbnail gr2
      Figure 2Daily use of social network sites over time, by sex, 8th and 10th grade. This plots trends by sex in the proportion of students using social media daily.
      When considering potential confounders, associations were inconsistent in both direction and magnitude. Among girls (Table 1), when considering depressive symptoms continuously, there was a positive association between daily social media use and an increase in mean symptoms (β = .018, 95% confidence interval [CI]: .004, .031) after controlling for potential confounders, but when stratified by depression risk, the association persisted only in the lowest depression risk category (β = .020, 95% CI: .008, .032). When examined by cut score, there were no overall associations between daily social media use and depressive symptoms, and only one (protective) association between daily social media use and depressive symptoms score >10 among those with a middle-low depression risk (odds ratio [OR] = .81, 95% CI: .66–.99). Tests of interaction between predicted risk score and daily social media were null for all cut scores.
      Table 1Association between daily social media exposure and depressive symptoms
      Adjusted for race/ethnicity, grade, urbanicity, parental education, academic performance, year, and form.
      among girls, stratified by risk for depressive symptoms based on existing risk factors, 2009–2017
      Chi-square values from joint tests of interaction effects between predicted risk score category and daily social media use for each depression score cut score: score >9: 4.9794, p = .2894; score >10: 7.5326, p = .1103; score >12: 2.9149, p = .5722; score >15: 8.3081, p = .0809; continuous depression score: 15.49, p = .0038.
      Association between daily social media and depressive symptoms at different cut scores of depression score (odds ratio, 95% CI)
      Depression risk
      Cut scoreLowMiddle-lowModerateMiddle-highHighOverall
      Score > 91.07 (.97, 1.18).85 (.68, 1.06).88 (.65, 1.17)1.04 (.73, 1.49).64 (.29, 1.44)1.03 (.97, 1.09)
      Score > 101.06 (.94, 1.20).81 (.66, .99).89 (.69, 1.14)1.09 (.80, 1.47).74 (.39, 1.38)1.01 (.95, 1.08)
      Score > 12.99 (.78, 1.24).83 (.65, 1.07)1.00 (.79, 1.26)1.13 (.90, 1.41).94 (.64, 1.38)1.02 (.94, 1.10)
      Score > 15--
      Model did not converge.
      1.02 (.53, 1.93)1.40 (.86, 2.27).79 (.61, 1.03)1.12 (.88, 1.43)1.07 (.96, 1.21)
      Linear estimates for log-transformed depressive symptoms (beta, 95% CI)
      .020 (.008, .032)−.029 (−.057, −.002)−.016 (−.046, .014)−.010 (−.021, .042).003 (−.025, .032).018 (.004, .031)
      CI = confidence interval.
      a Adjusted for race/ethnicity, grade, urbanicity, parental education, academic performance, year, and form.
      b Chi-square values from joint tests of interaction effects between predicted risk score category and daily social media use for each depression score cut score: score >9: 4.9794, p = .2894; score >10: 7.5326, p = .1103; score >12: 2.9149, p = .5722; score >15: 8.3081, p = .0809; continuous depression score: 15.49, p = .0038.
      c Model did not converge.
      Among boys (Table 2), while there was evidence of an overall protective effect of daily social media use on depressive symptoms for two out of four symptom cut scores, these associations were largely attenuated within strata of depression risk. Boys in the second lowest depression risk stratum were at increased risk for depressive symptoms >9 (OR = 1.23, 95% CI: 1.02, 1.49), the broadest category of depressive symptoms considered. Similarly, boys with moderate depression risk were at an increased risk of depressive symptoms >12 (OR = 1.42, 95% CI: 1.13, 1.78). Boys in the lowest depression risk category, however, were at decreased risk for depressive symptoms >12 (OR = .77, 95% CI: .62, .96); there was also evidence of a decrease in risk at the highest depression risk category, but small sample size resulted in wider confidence intervals. There was no association between social media use and depressive symptoms when depressive symptoms were considered continuously after controlling for potential confounders, across any risk category. Tests for interaction between daily social media use and depression risk were largely null, and the exception was variation in ORs at a cut score of >12 (chi-square for interaction: 13.01, p < .01).
      Table 2Association between daily social media exposure and depressive symptoms
      Adjusted for race/ethnicity, grade, urbanicity, parental education, academic performance, year, and form.
      among boys, stratified by risk for depressive symptoms based on existing risk factors, 2009–2017
      Chi-square values from joint tests of interaction effects between predicted risk score category and daily social media use for each depression score cut score: score >9: 5.3296, p = .2551; score >10: 1.7221, p = .7867; score >12: 13.0142, p = .0112; score >15: 1.101, p = .8941; continuous depression score: 2.82, p = .589.
      Association between daily social media and depressive symptoms at different cut scores of depression score (odds ratio, 95% CI)
      Depression risk
      Cut scoreLowMiddle-lowModerateMiddle-highHighOverall
      Score > 91.02 (.93, 1.11)1.23 (1.02, 1.49)1.24 (.94, 1.64).77 (.52, 1.12).54 (.18, 1.62).97 (.92, 1.02)
      Score > 101.02 (.91, 1.15)1.09 (.92, 1.30)1.10 (.88, 1.39).89 (.65, 1.21).68 (.29, 1.60).95 (.90, 1.01)
      Score > 12.77 (.62, .96)1.02 (.82, 1.28)1.42 (1.13, 1.78)1.06 (.84, 1.33).66 (.39, 1.12).92 (.84, .99)
      Score > 15.88 (.51, 1.53).76 (.42, 1.38)1.00 (.59, 1.69)1.02 (.74, 1.39).92 (.69, 1.24).86 (.76, .98)
      Linear estimates for log-transformed depressive symptoms (beta, 95% CI)
      .004 (−.006, .015).018 (−.007, .043).018 (−.006, .042)−.003 (−.031, .025)−.012 (−.040, .017)−.005 (−.017, .007)
      CI = confidence interval.
      a Adjusted for race/ethnicity, grade, urbanicity, parental education, academic performance, year, and form.
      b Chi-square values from joint tests of interaction effects between predicted risk score category and daily social media use for each depression score cut score: score >9: 5.3296, p = .2551; score >10: 1.7221, p = .7867; score >12: 13.0142, p = .0112; score >15: 1.101, p = .8941; continuous depression score: 2.82, p = .589.
      As a sensitivity analysis, we replicated the predicted depression risk score at the 90th percentile. Given the small number of adolescents at high levels of depressive symptoms at this cut score, models did not converge for many estimates. However, at lower levels of predicted depression risk, model estimates were in the same direction and general magnitude of model estimates when predicted depression risk was at the 75th percentile.
      As a separate sensitivity analysis, we utilized a predicted risk score based on a model without possible symptoms of depression. The association between daily use of social media and depressive symptoms for any binary depressive outcome was null within every category of predicted risk score for both boys and girls. Among girls, when depressive symptom score was considered continuously within predicted risk categories, daily social media use was associated with increased depressive symptoms among girls in the lowest predicted risk category only (β = .019, 95% CI: .006, .031)
      Finally, we examined the interaction between year and daily social media use. We examined year as both a continuous and a categorical variable in two-year groups up to 2017, and then 2017 as a single year. Among girls, when depressive symptom score was considered continuously, daily social media use was associated with increased depressive symptoms in 2009–2010 (β = .027, 95% CI: .009, .045), but not in other years. Thus, there was no evidence of a consistent association across years; one statistically significant association across all years tested is indicative of an inconsistent and weak signal.

      Discussion

      The extent to which social media use is a risk factor for depressive symptoms among teens is a critical question in the literature, with past results being equivocal. We found that among adolescents in the United States, daily social media use is not a consistent risk factor for depressive symptoms. The most consistent association observed across main and sensitivity analyses indicated that girls who had the lowest risk for depression had increased depressive symptoms with daily social media use, although this was only seen in the log-transformed continuous depressive symptom score outcome, not the binary outcomes. However, it should be noted that girls in this group typically had almost no other risk factors for depressive symptoms. Among boys, daily social media use was not consistently related to increased depressive symptoms. These results run contrary to the popular narrative that social media is significantly harmful to adolescent mental health.
      Findings are consistent with a growing body of evidence demonstrating that social media use is not a risk factor for adolescent depressive symptoms. These results stand in contrast to descriptive analyses, including those previously reported from these data [
      • Twenge J.M.
      • Joiner T.E.
      • Rogers M.L.
      • et al.
      Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time.
      ]. Our results suggest that, in the face of other known risk factors for adolescent depression, the risk conferred by social media use is not meaningful. Among adolescents with the lowest risk of high depressive symptoms, daily social media use was associated with depression risk, although a low number of adolescents were affected given the rarity of developing high depressive symptoms in this group.
      Among boys, there were more associations, albeit small, with social media inversely related to depression. Girls online appear to be more likely than boys to experience negative emotions, given gender-based harassment and bullying [
      • Kowalski R.M.
      • Giumetti G.W.
      • Schroeder A.N.
      • et al.
      Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth.
      ], as well as greater salience of social networks that online engagement can facilitate, creating further isolation during periods of conflict with friends [
      • Ging D.
      • O’Higgins Norman J.
      Cyberbullying, conflict management or just messing? Teenage girls’ understandings and experiences of gender, friendship, and conflict on Facebook in an Irish second-level school.
      ]. The suggestion of increased risk for girls, but not boys, is consistent with gender differences in coping and stressor response in relation to depression. Gender differences in response styles may become evident during adolescence and girls are more likely to use ruminative coping and response styles [
      • Nolen-Hoeksema S.
      Responses to depression and their effects on the duration of depressive episodes.
      ], that include repetitive or frequent negative thinking, and internal versus external attribution of negative experiences [
      • Weiner B.
      An attributional theory of achievement motivation and emotion.
      ], which can increase the risk of depressive symptoms.
      The present study is strengthened by the use of US nationally representative data, which increases generalizability of the findings. Stratifying by year allowed for examination of potential trends in these associations. The analytic approach accounted for potential bias due to unmeasured confounding. Some of the differences in the results of various studies using MTF data may have arisen from improper application of methods that rely on an assumption of normality to non-normal depressive symptom scores, or failure to account for selection and confounding. We found that depressive symptom scores did not meet basic standards for distribution normality and thus did not include estimates of explained variance, as has been provided elsewhere [
      • Twenge J.M.
      • Campbell W.K.
      Media use is linked to lower psychological well-being: Evidence from three datasets.
      ,
      • Orben A.
      • Przybylski A.K.
      The association between adolescent well-being and digital technology use.
      ].
      Nonetheless, several limitations are notable. Daily social media use does not capture the diverse ways in which adolescents use social media, which may be both positive [
      • Odgers C.
      Smartphones are bad for some teens, not all.
      ,
      • Toma C.L.
      • Hancock J.T.
      Self-affirmation Underlies Facebook Use.
      ,
      • Rideout V.
      • Fox S.
      Digital health practices, social media use, and mental well-being among teens and young adults in the U.S. Hopelab and Well Being Trust.
      ] and negative [
      • Barry C.T.
      • Sidoti C.L.
      • Briggs S.M.
      • et al.
      Adolescent social media use and mental health from adolescent and parent perspectives.
      ,
      • George M.J.
      • Russell M.A.
      • Piontak J.R.
      • et al.
      Concurrent and subsequent associations between daily digital technology use and high-risk adolescents’mental health symptoms.
      ,
      • Appel H.
      • Gerlach A.L.
      • Crusius J.
      The interplay between Facebook use, social comparison, envy, and depression.
      ] depending on the social context. We only were able to assess 8th and 10th grade students, and the results may vary among older or younger respondents or youth who are not in school. We potentially underestimated effects of social media on depressive symptoms, as depression is linked to adolescent absenteeism and these students are not represented here [
      • Ingul J.M.
      • Klöckner C.A.
      • Silverman W.K.
      • et al.
      Adolescent school absenteeism: Modelling social and individual risk factors.
      ]. Differences between these absent and present students are uncertain. It is worthwhile to recognize that the role and nature of social media vary over time, although in our sensitivity analyses, any harmful effects were in 2009–2010, so this recognition of heterogeneity over time supported our conclusions that currently social media is not a strong risk factor for depressive symptoms. Depressive symptoms are not diagnostic and therefore of uncertain clinical importance. We also lacked information on whether adolescents had received treatment for mental health problems. However, trends in depressive symptoms were consistent with other national sources with information on depressive episodes and suicide-related behavior [
      • Mojtabai R.
      • Olfson M.
      • Han B.
      National trends in the prevalence and treatment of depression in adolescents and young Adults.
      ,
      Centers for Disease Control and Prevention [CDC]
      ], underscoring that these measures correlate with other meaningful indicators of adolescent mental health. Furthermore, a minority of individuals with depression receive treatment [
      • Hasin D.S.
      • Goodwin R.D.
      • Stinson F.S.
      • et al.
      Epidemiology of major depressive disorder: Results from the national Epidemiologic survey on Alcoholism and related Conditions.
      ], suggesting that treatment is unlikely to have a large impact on the symptom scores in our sample. A strength of our depressive symptom measure is the identical wording and placement of questions across the years of data collection. Given the cross-sectional nature of these data, we were unable to establish temporality needed to rule out reverse causation and estimate incident depression symptoms. Given that the magnitude of model estimates was very low, this is unlikely to be a large concern. Still, many strengths from this study, such as the large sample size, nationally representative sampling, and use of outcome risk scores and stratification to control confounding due to underlying mental health risk, should be applied to future studies with longitudinal designs and more nuanced social media items to strengthen results.

      Conclusion

      Technologies emerge in each generation that change how adolescents interact, including most recently smartphones and social media. Assessing the potential impact of new technologies on adolescent mental health is a critical part of understanding how these tools are used, and in communicating clinical messages to adolescents and parents with a solid empirical foundation. At present, there is not compelling evidence to suggest that social media use meaningfully increases adolescents’ risk of depressive symptoms.

      Acknowledgments

      Author contribution: All authors contributed to the initial draft.

      Supplementary Data

      Funding Sources

      The Monitoring the Future study is supported by the National Institute on Drug Abuse grant R01001411 . Analyses were also funded by R01DA048853 (Keyes).

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