Social Media Use and Mental Health: A Review

An ongoing semi-open-source  literature review posted and curated by Jonathan Haidt (NYU-Stern) and Jean Twenge (San Diego State U). You can cite this document as:

Haidt, J., & Twenge, J. (2019). Social media use and mental health: A review. Unpublished manuscript, New York University.

The review contains comments added by other researchers: Chris Ferguson (Stetson U), Sarah Rose Cavanagh (Assumption College), Tom Hollenstein (Queens U., Canada), Kai Lukoff (U. Washington), Ian Goddard, Ray Aldred (??), Sonia Livingstone (??)

Also see our companion review: Is there an increase in adolescent mood disorders, self-harm, and suicide since 2010 in the USA and UK? A review

See also additional Google docs laying out evidence for trends in mental health and social media use in Australia, Canada, and New Zealand.

First posted: Feb 7, 2019. Last updated: August 22, 2019.

This Google doc is a working document that contains the citations and abstracts of some of the published articles we have found that shed light on a question that is currently being debated in the USA and UK: Does social media use contribute to the recent rise of adolescent  mood disorders (depression and anxiety) and related behaviors (especially self-harm and suicide)?  [See companion review for studies documenting this recent rise.]

This Google Doc is a work in progress. We (Haidt & Twenge) have not done an exhaustive search of citation databases. A Google Scholar search for [“social media” depression] yields 72,000 hits. We begin instead with articles published in or after 2014 that are being cited by scholars on either side of the debate. (We pick 2014 because the increase in adolescent depression and anxiety is not clearly visible until around 2013, and it takes a while for papers to be published.) We invite fellow scholars to point us to studies we have missed, or to note ways in which we are misinterpreting the studies we cite below.

We are not unbiased. Haidt came to the tentative conclusion that there is a causal link, and said so in his book (The Coddling of the American Mind, with Greg Lukianoff.) Twenge said the same thing in her book (iGen). Haidt’s own research (presented in The Righteous Mind) says that we likely to be motivated to find evidence to support the positions we took publicly. Like all people, we suffer from confirmation bias. But we take J.S. Mill seriously, and we know that we need help from critics to improve our thinking and get closer to the truth. If you are a researcher and would like to notify us about other studies, or add comments or counterpoints to this document, please request access to the Google Doc, or contact Haidt directly, and he will set your permissions to add comments to the Google doc. This document is evolving based on feedback. A copy of the original document, as posted on Feb 7, is here. 

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Clickable Table of Contents:

INTRODUCTION        3

CAUTIONS AND CAVEATS        4

QUESTION 1: IS THERE AN ASSOCIATION BETWEEN SOCIAL MEDIA USE AND BAD MENTAL HEALTH OUTCOMES?        6

1.1 STUDIES INDICATING AN ASSOCIATION        6

1.2: STUDIES INDICATING LITTLE OR NO ASSOCIATION        22

QUESTION 2: DOES SOCIAL MEDIA USE AT TIME 1 PREDICT ANYTHING ABOUT MENTAL HEALTH OUTCOMES AT TIME 2?        27

2.1: STUDIES INDICATING EFFECTS AT T2        27

2.2: STUDIES INDICATING LITTLE OR NO EFFECT AT T2        32

QUESTION 3: DO EXPERIMENTS USING RANDOM ASSIGNMENT SHOW A CAUSAL EFFECT OF SOCIAL MEDIA USE ON MENTAL HEALTH OUTCOMES?        35

3.1: STUDIES INDICATING A CAUSAL EFFECT ON MENTAL HEALTH OUTCOMES        36

3.2: STUDIES INDICATING NO CAUSAL EFFECT        41

4.0: MAJOR REVIEW ARTICLES AND DATABASES        41

5. STUDIES SUGGESTED BY COMMENTERS THAT ARE RELEVANT BUT NOT FOCUSED ON THE CENTRAL QUESTION OF SOCIAL MEDIA AND TEENAGERS [e.g., those that focus on screen time and young children]        43

6. DISCUSSION        44

INTRODUCTION

Two studies published in January 2019 suggest that there is little or no association between social media use and harmful mental health outcomes: Orben & Przybylski (2019) and Heffer, Good, et al. (2019). A third study published in January suggested that there is a more substantial link: Kelly, Zilanawala, Booker, & Sacker (2019). These three studies, all published in reputable journals in the same month, are now getting attention from journalists, leaving many parents and policymakers confused about what to believe. We therefore thought it would be useful to gather together in one place the abstracts of the studies that are often referred to in these debates.

We divide the studies into three categories, based on which method they use: 1) cross-sectional correlational studies, 2) time lag or longitudinal studies, and 3) true experiments. Each method answers a different question. Finding answers to the three questions will allow us to address the question everyone cares about: is social media contributing to the recent rise in anxiety, depression, self-harm, and suicide among American and British teenagers? The answers may be too tentative to form the basis of legislation in 2019, but not to form the basis for advice to parents, millions of whom are asking questions like: Should I let my 11-year old child have an Instagram or Snapchat account? If not now, then when? If yes, then should I impose any time limits? These questions are important and in the forefront of many parents’ minds. We’ll offer some suggestions for parents at the end of the document.

We structure this list of abstracts around three questions, each one addressed by a different kind of study. Within each question we present the studies that DO find a relationship in subsection 1, and the studies that DON’T find a relationship in subsection 2. For each study we offer a link to the original publication and we reprint the full abstract with no edits, other than bold-facing some parts. We offer brief comments and show figures from some of the studies.

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CAUTIONS AND CAVEATS

1) We all must beware of the risk of repeating previous moral panics over comics, TV, video games, etc. Whenever a new fad or technology sweeps through the child or teen population, stories get written about how the new trend is harming children. These stories play well in a media environment that thrives on eliciting fear in parents. Later research often shows that there was no detectable harm. (See Moral Combat, by Markey & Ferguson; see this short review of moral panics about media tech, by Vaughn Bell, h/t to Sarah Rose Cavanaugh)

2) We all must be mindful that psychology and other fields are now going through a “replication crisis” as we discover that many -- perhaps half -- of published studies in some disciplines fail to replicate when other researchers try to do so. Much of the problem is due to the fact that researchers have so many degrees of freedom in how they interpret and analyze their data; they can sometimes find statistically significant results that are just random fluctuations in a sea of non-significant results. This is the problem that Orben & Przybylski (2019a) were  responding to. So the studies below may not be as reliable as they seem; no one study is decisive.  (We think that Orben & Przybylski is an advance, but is open to other interpretations too.) Don’t read this document like a tally sheet, awarding victory to whichever side has more studies. It is easier to publish statistically significant results indicating an effect of screens or social media than it is to publish a failure to find such effects, which tend to be left in the “file drawer.” But it is valuable to look through the studies that report effects to see if those effects are similar across studies, or if they are widely divergent.

3) It is important to consult high quality review articles and meta-analyses. [We will add a section of such articles at the end. We think it is helpful to read the details of particular studies in each category first.]

4) Context matters more than hours: What kids are doing with their devices, how they do it, who they are, and above all how it affects their relationships matters more than how much time they spend doing it (see Clark, Algoe & Green, 2017; Waytz & Gray, 2018; and see comment from Sarah Rose Cavanaugh, at right). Unfortunately, nearly all of the published research simply asks teens or parents to estimate the number of hours per day or week that the teen spends in various screen-based activities, and these estimates are often inaccurate. This is one reason why progress is slow; we need better data that will allow us to ask questions that are more nuanced than “does spending a lot of time on social media cause bad mental health outcomes.” But keep this in mind as you consider the many studies that find associations that explain only a tiny amount of the variance. Those tiny numbers don’t mean that the relationship is truly tiny; they mean that the amount of variance we can explain with weak measurement on both ends is tiny. If we had perfect measurement on both ends, the numbers would go up, perhaps by a lot. [Commenter Bradley Riew suggests that we also try to “unbundle” the effects of social media use, distinguishing among at least these 4 negative effects: (a) Sleep deprivation, (b) Cyberbullying, (c)        Negative emotion contagion, (d) Social comparison.]. For research documenting HOW American teens spend their “screen time” see: Rideout (2016) for an analysis using data from 2015. [Is there anything more recent?]

5) Social media use may impose external costs even on those who don’t use it. The hypothesis tested in most of the studies in this review is that there is a dose-response relationship: more hours-per-week causes more harm to the individual who consumes more. But if social media is part of the reason for the rise in teen depression/anxiety that began around 2012 (see our companion review), the causal path need not run through individual users. It may be that a middle-school community changes when many or most of its members get Instagram or Snapchat accounts. Kids may become more cruel, fearful, superficial, gossipy, or appearance-obsessed, and this could make many students more depressed and anxious, even if they do not use social media, or use it only lightly. So the fact that most of the studies below can explain only a small portion of the variance in outcomes does not mean that social media has only tiny effects; the network or groupwide effects could be quite large, and these would not be picked up by studies where the independent variable was how many hours each kid reported spending per week on social media. (See, for example, the beneficial social effects that accrue to most students when schools ban cell phones, as in this Australian high school. We need systematic research on effects of phone bans to evaluate these group-wide effects.)

6) There are benefits to social media use, and digital media more generally, for some kids in some contexts. These benefits are not considered in this review, which is focused on trying to understand whether the arrival of social media can help to explain the sudden sharp rise in depression/anxiety that began in multiple countries around 2012.  A report that tries to tally the costs and benefits is the American Academy of Pediatrics 2016 report: Children and Adolescents and Digital Media [Thanks to Sarah Rose Cavanaugh, and Kai Lukoff, for making this point and suggesting this report]

7) If you want to read essays and articles in the popular press that are skeptical of the link between social media use and mental health outcomes, see:

  • Resnick (2019) Have smartphones really destroyed a generation? We don’t know. In Vox
  • [others?]

8) If you want to read short overviews of the academic literature, see:

  • Twenge (2019). Why increases in adolescent depression may be linked to the technological environment

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QUESTION 1: IS THERE AN ASSOCIATION BETWEEN SOCIAL MEDIA USE AND BAD MENTAL HEALTH OUTCOMES?

This is the most basic question, asking about correlations in real-world data. Is it the case that kids who use social media are doing worse than kids who don’t? What about heavy users, are they doing worse than light to moderate users? What about girls versus boys, or older teens versus younger teens? These questions are typically answered by examining large nationally representative datasets. These correlational studies cannot show causality; we cannot assume that social media use caused any bad outcomes that “go along” with it. Causality could be the reverse process (perhaps depression causes teens to become heavier users of social media), or there could be a third variable that causes both social media use and depression/anxiety. But these studies are common and important first steps in the quest to answer many social science puzzles.

1.1 STUDIES INDICATING AN ASSOCIATION

1.1.1: Przybylski & Weinstein (2017).  A Large-Scale Test of the Goldilocks Hypothesis: Quantifying the Relations Between Digital-Screen Use and the Mental Well-Being of Adolescents. Psychological Science.

ABSTRACT: Although the time adolescents spend with digital technologies has sparked widespread concerns that their use might be negatively associated with mental well-being, these potential deleterious influences have not been rigorously studied. Using a preregistered plan for analyzing data collected from a representative sample of English adolescents (n = 120,115), we obtained evidence that the links between digital-screen time and mental well-being are described by quadratic functions. Further, our results showed that these links vary as a function of when digital technologies are used (i.e., weekday vs. weekend), suggesting that a full understanding of the impact of these recreational activities will require examining their functionality among other daily pursuits. Overall, the evidence indicated that moderate use of digital technology is not intrinsically harmful and may be advantageous in a connected world. The findings inform recommendations for limiting adolescents’ technology use and provide a template for conducting rigorous investigations into the relations between digital technology and children’s and adolescents’ health.

COMMENT: The authors find curvilinear effects in which one or two hours a day is associated with better outcomes than is zero usage, yet after that, outcomes become worse. The lowest “goldilocks point” and sharpest drop is for “using smartphones,” which is the category likely to include most social media use. The highest “goldilocks point” and shallowest drop is for “playing video games,” as you can see here:

Source: Przybylski & Weinstein (2017), p. 207, with labels added.

We agree with the authors’ claim that “moderate use of digital technology is not intrinsically harmful and may be advantageous in a connected world.” However we think the “Goldilocks hypothesis” is true, as it shows up in several other studies listed below. It says that heavy use of smartphones is associated with reduced well-being. We expect that the association would be even larger if the graphs were redrawn just for girls, and if they could be limited to social media use (as opposed to “smart phones” more generally).  

COUNTERPOINT (From Andrew Przybylski, via Twitter, who quoted this text from the paper:) “These analyses indicated that the possible negative effects of excessive screen time found that the average effect size (Cohen’s d) for engagement in excess of the inflection points was –0.18. In other words, these negative slopes accounted for 1.0% or less of the observed variability in the mental well-being of the young people in the sample. Exploratory analyses examining links between individual difference measures in the data set and well-being provide some context to interpret these modest relationships.were less than a third of the size of the positive associations between well-being and eating breakfast regularly (d = 0.54) or getting regular sleep (d = 0.58). Although the coefficients we have reported are statistically significant, it is noteworthy that the size of both the linear and the quadratic relations between screen time and wellbeing were noticeably diminished in half the cases once control factors were accounted for, and that incremental increases in screen time above moderate levels accounted for very little of the variability we observed in mental
Well-being.”

RESPONSE to Przybylski [from Twenge]: The use of percent variance explained may be obscuring practically important associations. In the same dataset used by Przybylski & Weinstein, which he notes explains only 1% of the variance, twice as many heavy smartphone users (vs. light) were low in well-being, as shown here:

Source: Twenge & Campbell (2019). Includes demographic controls.

The same doubling of low well-being appeared for light vs. heavy computer users. For gaming, 73% more heavy users had low well-being compared to light users. Thus, when the data are analyzed by examining low well-being within levels of use rather than percent variance explained, the associations are more than practically important.

Update: 7/12/19, a new tool for exploring the MCS dataset:

One of Orben and Przybylski’s arguments is that the size of the correlation obtained is heavily dependent upon how a researcher defines “technology usage” or “depression.” They call this “researcher degrees of freedom.” We agree that this is a problem. To allow anyone to explore the data themselves, to see how the correlation changes as you change variables, our research assistant Chris Vaccaro created an application that allows users to choose whatever criteria they desire:

https://chrisvacc.shinyapps.io/spec-explorer/ 

The tool allows the user to choose how they define “Technology Use” and items of depressive symptoms. It allows users to choose which questions to include in the analysis, and allows the user to run tens of thousands of different combinations, but our tool allows users to see the  distributions, which are often obscured by linear Rs   and summary statistics.

To use the tool, select one kind of technology use to zoom in on. Then choose which questionnaire items you’d like to examine as the mental health outcome variables. A useful contrast is to switch back and forth between “TV hours” as the tech variable, and “social media hours” as the tech variable. Relationships of mental health outcomes to technology use are much clearer for social media use than for other tech variables. For example for social media, dissatisfaction with one’s appearance rises for girls in a linear fashion: the more time a girl spends per day on social media, the less satisfied she is with how she looks, as you can see in the top line:

But if you select TV hours per day, the line is flat for girls out to 4 hours per day. It’s only the very heavy users who are less satisfied with their appearance:

1.1.2. Twenge, J. M., & Campbell, W. K. (2019). Digital media use is linked to lower psychological well-being: Evidence from three datasets. Psychiatric Quarterly.

ABSTRACT: Adolescents spend a substantial and increasing amount of time using digital media (smartphones, computers, social media, gaming, Internet), but existing studies do not agree on whether time spent on digital media is associated with lower psychological well-being (including happiness, general well-being, and indicators of low well-being such as depression, suicidal ideation, and suicide attempts). Across three large surveys of adolescents in two countries (n = 221,096), light users (<1 h a day) of digital media reported substantially higher psychological well-being than heavy users (5+ hours a day). Datasets initially presented as supporting opposite conclusions produced similar effect sizes when analyzed using the same strategy. Heavy users (vs. light) of digital media were 48% to 171% more likely to be unhappy, to be in low in well-being, or to have suicide risk factors such as depression, suicidal ideation, or past suicide attempts. Heavy users (vs. light) were twice as likely to report having attempted suicide. Light users (rather than non- or moderate users) were highest in well-being, and for most digital media use the largest drop in well-being occurred between moderate use and heavy use. The limitations of using percent variance explained as a gauge of practical impact are discussed.

COMMENT [from Twenge]: As Rosnow and Rosenthal (1989, 2003) and Abelson (1985) showed decades ago, percent variance explained is not a valid measure of practical importance. More recently, Funder and Ozer (in press) described percent variance explained (r squared) as “not merely uninformative; for purposes of evaluating effect size, the practice is actively misleading.”

Thus, some of the discrepancy in conclusions between studies might be due to analysis strategy. Sure enough, when datasets initially presented as supporting different conclusions are analyzed in the same way, the results are the same:

Source: Twenge & Campbell (in press)

The graph above shows smartphone use in the dataset used by Przybylski & Weinstein and time online from the dataset used in 1.1.11 (Twenge et al., 2018, Emotion). Note that both show the “Goldilocks” J-curve effect, with well-being most favorable at low levels of use, with issues increasing after ½ hour online or 2 hours using the smartphone in total. Both also show a doubling of low well-being from light to heavy use.

(A note on data sources: Przybylski & Weinstein made their data available on OSF, and the Monitoring the Future data used by Twenge are publicly available here: https://www.icpsr.umich.edu/icpsrweb/NAHDAP/index.jsp).

1.1.3: Twenge, Joiner, Rogers, & Martin (2018). Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time. Clinical Psychological Science, 6, 3-17.

ABSTRACT: In two nationally representative surveys of U.S. adolescents in grades 8 through 12 (N = 506,820) and national statistics on suicide deaths for those ages 13 to 18, adolescents’ depressive symptoms, suicide-related outcomes, and suicide rates increased between 2010 and 2015, especially among females. Adolescents who spent more time on new media (including social media and electronic devices such as smartphones) were more likely to report mental health issues, and adolescents who spent more time on nonscreen activities (in-person social interaction, sports/exercise, homework, print media, and attending religious services) were less likely. Since 2010, iGen adolescents have spent more time on new media screen activities and less time on nonscreen activities, which may account for the increases in depression and suicide. In contrast, cyclical economic factors such as unemployment and the Dow Jones Index were not linked to depressive symptoms or suicide rates when matched by year.

FIGURE FROM THE PAPER:

Source: Twenge, Joiner, Rogers, & Martin (2018), p. 12.

COMMENT: This study also finds a “Goldilocks” effect -- a J curve. Teens who use “electronic devices” for more than 5 hours per day are much more likely to report suicide-related ideation and other risk factors than kids who use such devices for just one hour per day. (Note that YRBSS does not allow us to break out social media use specifically; just “electronic device use,” as opposed to TV use.)

1.1.4 Kelly, Zilanawala, Booker, & Sacker (2019). Social Media Use and Adolescent Mental Health: Findings From the UK Millennium Cohort Study. EClinicalMedicine (Lancet). 

ABSTRACT: Background: Evidence suggests social media use is associated with mental health in young people but underlying processes are not well understood. This paper i) assesses whether social media use is associated with adolescents' depressive symptoms, and ii) investigates multiple potential explanatory pathways via online harassment, sleep, self-esteem and body image.
Methods:
We used population based data from the UK Millennium Cohort Study on 10,904 14 year olds. Multivariate regression and path models were used to examine associations between social media use and depressive symptoms.
Findings:
The magnitude of association between social media use and depressive symptoms was larger for girls than for boys. Compared with 1–3 h of daily use: 3 to <5 h 26% increase in scores vs 21%; ≥5 h 50% vs 35% for girls and boys respectively. Greater social media use related to online harassment, poor sleep, low self-esteem and poor body image; in turn these related to higher depressive symptom scores. Multiple potential intervening pathways were apparent, for example: greater hours social media use related to body weight dissatisfaction (≥5 h 31% more likely to be dissatisfied), which in turn linked to depressive symptom scores directly (body dissatisfaction 15% higher depressive symptom scores) and indirectly via self-esteem.
Interpretation:
Our findings highlight the potential pitfalls of lengthy social media use for young people's mental health. Findings are highly relevant for the development of guidelines for the safe use of social media and calls on industry to more tightly regulate hours of social media use.

FIGURE CREATED FROM THE PAPER:

Percent of UK adolescents with “clinically relevant depressive symptoms” by hours per weekday of social media use, including controls. Haidt and Twenge created this graph from the data given in Table 2 of Kelly et al. (2019), page 6.

COMMENT: Heavy users have more than double the rate of depressive symptoms as non-users or light users, and the relationship is stronger for girls.

1.1.5 Rosen, Lim, Felt et al. (2014) Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits. Computers in Human Behavior.

ABSTRACT: The American Academy of Pediatrics recommends no screen time for children under the age of 2 and limited screen time for all children. However, no such guidelines have been proposed for preteens and teenagers. Further, research shows that children, preteens, and teenagers are using massive amounts of media and those with more screen time have been shown to have increased obesity, reduced physical activity, and decreased health. This study examined the impact of technology on four areas of ill-being–psychological issues, behavior problems, attention problems and physical health–among children (aged 4–8), preteens (9–12), and teenagers (13–18) by having 1030 parents complete an online, anonymous survey about their own and their child's behaviors. Measures included daily technology use, daily food consumption, daily exercise, and health. Hypothesis 1, which posited that unhealthy eating would predict impaired ill-being, was partially supported, particularly for children and preteens. Hypothesis 2, which posited that reduced physical activity would predict diminished health levels, was partially supported for preteens and supported for teenagers. Hypothesis 3, that increased daily technology use would predict ill-being after factoring out eating habits and physical activity, was supported. For children and preteens, total media consumption predicted illbeing while for preteens specific technology uses, including video gaming and electronic communication, predicted ill-being. For teenagers, nearly every type of technological activity predicted poor health. Practical implications were discussed in terms of setting limits and boundaries on technology use and encouraging healthy eating and physical activity at home and at school.

1.1.6 Lin, Sidani, Shensa et al. (2016) Association between social media use and depression among U.S. young adults. Depression and Anxiety, 33, 323-331.

ABSTRACT: BACKGROUND: Social media (SM) use is increasing among U.S. young adults, and its association with mental well-being remains unclear. This study assessed the association between SM use and depression in a nationally representative sample of young adults.

METHODS: We surveyed 1,787 adults ages 19 to 32 about SM use and depression. Participants were recruited via random digit dialing and address-based sampling. SM use was assessed by self-reported total time per day spent on SM, visits per week, and a global frequency score based on the Pew Internet Research Questionnaire. Depression was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Scale Short Form. Chi-squared tests and ordered logistic regressions were performed with sample weights.

RESULTS: The weighted sample was 50.3% female and 57.5% White. Compared to those in the lowest quartile of total time per day spent on SM, participants in the highest quartile had significantly increased odds of depression (AOR = 1.66, 95% CI = 1.14-2.42) after controlling for all covariates. Compared with those in the lowest quartile, individuals in the highest quartile of SM site visits per week and those with a higher global frequency score had significantly increased odds of depression (AOR = 2.74, 95% CI = 1.86-4.04; AOR = 3.05, 95% CI = 2.03-4.59, respectively). All associations between independent variables and depression had strong, linear, dose-response trends. Results were robust to all sensitivity analyses.

CONCLUSIONS: SM use was significantly associated with increased depression. Given the proliferation of SM, identifying the mechanisms and direction of this association is critical for informing interventions that address SM use and depression.

COMMENT: This study was unusual in finding linear trends, not curves (although Kelly et al, 1.1.4, also found linear effects -- so perhaps linear trends are more common for social media on its own). It is also different from the others in being a survey of millennials (born before 1995) rather than Gen Z. (Participants were mostly in their 20s when surveyed in or around 2015)

1.1.7  Liu, Wu, & Yao (2016). Dose–response association of screen time-based sedentary behaviour in children and adolescents and depression: a meta-analysis of observational studies. British Journal of Sports Medicine.

[Thanks to Ian Goddard for suggesting this article]

ABSTRACT: Background Depression represents a growing public health burden. Understanding how screen time (ST) in juveniles may be associated with risk of depression is critical for the development of prevention and intervention strategies. Findings from studies addressing this question thus far have been inconsistent. Therefore, we conducted a comprehensive systematic review and meta-analysis of data related to this question.
METHODS The meta-analysis was conducted in accordance with the PRISMA guideline. We searched the electronic databases of PubMed, Web of Science and EBSCO systematically (up to 6 May 2015). OR was adopted as the pooled measurement of association between ST and depression risk. Dose–response was estimated by a generalised least squares trend estimation.
RESULTS Twelve cross-sectional studies and four longitudinal studies (including 1 cohort study) involving a total of 127 714 participants were included. Overall, higher ST in preadolescent children and adolescents was significantly associated with a higher risk of depression (OR=1.
12; 95% CI 1.03 to 1.22). Screen type, age, population and reference category acted as significant moderators. Compared with the reference group who had no ST, there was a non-linear dose–response association of ST with a decreasing risk of depression at ST<2 h/day, with the lowest risk being observed for 1 h/day (OR=0.88; 95% CI 0.84 to 0.93).
CONCLUSIONS
 Our meta-analysis suggests that ST in children and adolescents is associated with depression risk in a non-linear dose–response manner.

1.1.8 Kremer, Elshaug, Leslie, Toumbourou, Patton & Williams (2014). Physical activity, leisure-time screen use and depression among children and young adolescents. Journal of Science and Medicine in Sport.

[Thanks to Ian Goddard for suggesting this article]

ABSTRACT: Objectives: Adolescent mental disorders remain a relatively neglected area of research, despite evidence that these conditions affect youth disproportionately. We examined associations between physical activity, leisure-time screen use and depressive symptoms among Australian children and adolescents.
DESIGN: Large cross-sectional observational study.
METHODS: Self-reported physical activity and leisure-time screen behaviours, and depressive symptoms using the Short Mood and Feeling Questionnaire were assessed in 8256 students aged 10–16 years (mean age = 11.5 years, SD = 0.8).
RESULTS: Thirty three percent of the sample reported moderate to high depressive symptoms, with rates higher among females (OR = 1.18; 95% CI: 1.02, 1.36; p = 0.001). Increased opportunities to be active at school outside class (OR = 0.70; 0.58, 0.85; p < 0.001), being active in physical education classes (OR = 0.77; 0.69, 0.86; p < 0.001), greater involvement in sports teams at school (OR = 0.77; 0.67, 0.88; p < 0.001) and outside of school (OR = 0.84; 0.73, 0.96; p = 0.01) were all independently associated with lower odds for depressive symptoms. Meeting recommended guidelines for physical activity (OR = 0.62; 0.44, 0.88; p = 0.007) and, for 12–14 year olds, leisure-time screen use (OR = 0.77; 0.59, 0.99; p = 0.04) were also independently associated with lower odds for depressive symptoms.
CONCLUSIONS:
Higher levels of physical activity among children and young adolescents, and lower levels of leisure-time screen use among young adolescents, are associated with lower depressive symptoms. Longitudinal studies are needed to understand the causal relationships between these variables.

COMMENT: Note the different finding for 12-14 year olds, compared to older teens.

1.1.9. Robinson, Bonnette, Howard, Ceballos, Dailey, Lu, & Grimes (2019). Social comparisons, social media addiction, and social interaction: An examination of specific social media behaviors related to major depressive disorder in a millennial population. Journal of Applied Biobehavioral Research.

[Thanks to Ian Goddard for suggesting this article]

ABSTRACT: Although studies have shown that increases in the frequency of social media use may be associated with increases in depressive symptoms of individuals with depression, the current study aimed to identify specific social media behaviors related to major depressive disorder (MDD). Millennials (N = 504) who actively use Facebook, Twitter, Instagram, and/or Snapchat participated in an online survey assessing major depression and specific social media behaviors. Univariate and multivariate analyses were conducted to identify specific social media behaviors associated with the presence of MDD. The results identified five key social media factors associated with MDD. Individuals who were more likely to compare themselves to others better off than they were (p = 0.005), those who indicated that they would be more bothered by being tagged in unflattering pictures (p = 0.011), and those less likely to post pictures of themselves along with other people (p = 0.015) were more likely to meet the criteria for MDD. Participants following 300 + Twitter accounts were less likely to have MDD (p = 0.041), and those with higher scores on the Social Media Addiction scale were significantly more likely to meet the criteria for MDD (p = 0.031). Participating in negative social media behaviors is associated with a higher likelihood of having MDD. Research and clinical implications are considered.

COMMENT: The associations found in this study are almost certainly partly due to reverse correlation: people who are already depressed/anxious are going to do more social comparison and be more upset about an unflattering photo. But the study is helpful as a reminder that it’s not just total hours that matter, it’s what teens are doing during those hours. Three hours a day may cause real damage for a depressed teen, but may be neutral or positive for a mentally healthy and highly sociable teen.

1.1.10. Faelens, Hoorelbeke, Fried, De Raedt, & Koster (2019). Negative influences of Facebook use through the lens of network analysis. Computers in Human Behavior.

ABSTRACT: Various recent studies suggest a negative association between Facebook use and mental health. Yet, empirical evidence for this association is mixed, raising the question under which conditions Facebook use is related to negative outcomes, such as decreased well-being. Our study addresses this question by investigating the relationship between Facebook use, rumination, depressive, anxiety-, and stress-related symptoms, taking into account potential key variables such as social comparison, contingent self-esteem, and global self-esteem. In a first study, we explored the unique relations between these constructs using state-of-the-art network analysis. Subsequently, we conducted a preregistered replication study. In both studies, social comparison and self-esteem held a central position in the network, connecting social media use with indicators of psychopathology. These findings highlight the prominent role of social comparison and self-esteem in the context of social media use and well-being. Longitudinal and experimental studies will be required to further investigate these relationships.

FIGURE (described as a “graphical abstract”):

1.1.11 Twenge, J.M., Martin, G. N., & Campbell, W. K. (2018). Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion, 18, 765-780.

ABSTRACT: In nationally representative yearly surveys of United States 8th, 10th, and 12th graders 1991–2016 (N = 1.1 million), psychological well-being (measured by self-esteem, life satisfaction, and happiness) suddenly decreased after 2012. Adolescents who spent more time on electronic communication and screens (e.g., social media, the Internet, texting, gaming) and less time on nonscreen activities (e.g., in-person social interaction, sports/exercise, homework, attending religious services) had lower psychological well-being. Adolescents spending a small amount of time on electronic communication were the happiest. Psychological well-being was lower in years when adolescents spent more time on screens and higher in years when they spent more time on nonscreen activities, with changes in activities generally preceding declines in well-being. Cyclical economic indicators such as unemployment were not significantly correlated with well-being, suggesting that the Great Recession was not the cause of the decrease in psychological well-being, which may instead be at least partially due to the rapid adoption of smartphones and the subsequent shift in adolescents’ time use.

FIGURE FROM PAPER:

Source: Twenge, Martin, & Campbell (2018) Figure 5A.

COMMENT: Once again, there’s the “Goldilocks” J-curve -- the fewest are unhappy at low levels of use, and the number unhappy increases from there. For time on the Internet, twice as many heavy users are unhappy as light users.

1.1.12. Primack, Shensa, Sidani, et al. (2017). Social Media Use and Perceived Social Isolation Among Young Adults in the U.S. American Journal of Preventive Medicine.

[h/t Ray Aldred]

Abstract: Introduction. Perceived social isolation (PSI) is associated with substantial morbidity and mortality. Social media platforms, commonly used by young adults, may offer an opportunity to ameliorate social isolation. This study assessed associations between social media use (SMU) and PSI among U.S. young adults.

Methods: Participants were a nationally representative sample of 1,787 U.S. adults aged 19–32 years. They were recruited in October–November 2014 for a cross-sectional survey using a sampling frame that represented 97% of the U.S. population. SMU was assessed using both time and frequency associated with use of 11 social media platforms, including Facebook, Twitter, Google+, YouTube, LinkedIn, Instagram, Pinterest, Tumblr, Vine, Snapchat, and Reddit. PSI was measured using the Patient-Reported Outcomes Measurement Information System scale. In 2015, ordered logistic regression was used to assess associations between SMU and SI while controlling for eight covariates.

Results: In fully adjusted multivariable models that included survey weights, compared with those in the lowest quartile for SMU time, participants in the highest quartile had twice the odds of having greater PSI (AOR=2.0, 95% CI=1.4, 2.8). Similarly, compared with those in the lowest quartile, those in the highest quartile of SMU frequency had more than three times the odds of having greater PSI (AOR=3.4, 95% CI=2.3, 5.1). Associations were linear (p<0.001 for all), and results were robust to all sensitivity analyses.

Conclusions: Young adults with high SMU seem to feel more socially isolated than their counterparts with lower SMU. Future research should focus on determining directionality and elucidating reasons for these associations

[Comment: This study was done with millennials only, in 2014.]

1.1.13. Sampasa-Kanyinga, H., & Lewis, R. F. (2015). Frequent use of social networking sites is associated with poor psychological functioning among children and adolescents. Cyberpsychology, Behavior, and Social Networking, 18, 380–385.

Abstract: Social networking sites (SNSs) have gained substantial popularity among youth in recent years. However, the relationship between the use of these Web-based platforms and mental health problems in children and adolescents is unclear. This study investigated the association between time spent on SNSs and unmet need for mental health support, poor self-rated mental health, and reports of psychological distress and suicidal ideation in a representative sample of middle and high school children in Ottawa, Canada. Data for this study were based on 753 students (55% female; Mage=14.1 years) in grades 7–12 derived from the 2013 Ontario Student Drug Use and Health Survey. Multinomial logistic regression was used to examine the associations between mental health variables and time spent using SNSs. Overall, 25.2% of students reported using SNSs for more than 2 hours every day, 54.3% reported using SNSs for 2 hours or less every day, and 20.5% reported infrequent or no use of SNSs. Students who reported unmet need for mental health support were more likely to report using SNSs for more than 2 hours every day than those with no identified unmet need for mental health support. Daily SNS use of more than 2 hours was also independently associated with poor self-rating of mental health and experiences of high levels of psychological distress and suicidal ideation. The findings suggest that students with poor mental health may be greater users of SNSs. These results indicate an opportunity to enhance the presence of health service providers on SNSs in order to provide support to youth.

Figure:

Figure: Percent with mental health issues by level of use of social media sites, Canadian adolescents

[Graphed by Jean Twenge]

COMMENT: This study finds very large associations between social media use and mental health issues. Trends are linear rather than curvilinear. Compared to non-users, heavy users are 3 times more likely to need mental health services and to experience high psychological distress and 4 times more likely to have seriously considered suicide.

1.1.14 Primack, Bisbey, Shensa et al. (2019). The association between valence of social media experiences and depressive symptoms. Depression and Anxiety.

ABSTRACT: Background: Social media (SM) may confer emotional benefits via connection with others. However, epidemiologic studies suggest that overall SM is paradoxically associated with increased depressive symptoms. To better understand these findings, we examined the association between positive and negative experiences on SM and depressive symptoms.

Methods: We conducted a cross‐sectional survey of 1,179 full‐time students at the University of West Virginia, aged 18 to 30, in August 2016. Independent variables were self‐reported positive and negative experiences on SM. The dependent variable was depressive symptoms as measured using the Patient‐Reported Outcomes Measurement Information System. We used multivariable logistic regression to assess associations between SM experiences and depressive symptoms controlling for sociodemographic factors including age, sex, race/ethnicity, education, relationship status, and living situation.

Results: Of the 1,179 participants, 62% were female, 28% were non‐White, and 51% were single. After controlling for covariates, each 10% increase in positive experiences on SM was associated with a 4% decrease in odds of depressive symptoms, but this was not statistically significant (adjusted odds ratio [AOR] = 0.96; 95% confidence interval [CI] = 0.91–1.002). However, each 10% increase in negative experiences was associated with a 20% increase in odds of depressive symptoms (AOR = 1.20; 95% CI = 1.11–1.31). When both independent variables were included in the same model, the association between negative experiences and depressive symptoms remained significant (AOR = 1.19, 95% CI = 1.10–1.30).

Conclusions: Negative experiences online may have higher potency than positive ones because of negativity bias. Future research should examine temporality to determine if it is also possible that individuals with depressive symptomatology are inclined toward negative interactions.

NOTES: On negativity bias, see this review paper, or see the new book by Tierney and Baumeister, The Power of Bad. Studies across a variety of domains often find that bad events are four or five times more powerful than good events, so if people have three times as many good interactions or social comparisons on social media as bad interactions or social comparisons, the net effect on the person may be negative.

1.1.15 Nesi & Prinstein (2015). Using Social Media for Social Comparison and Feedback-Seeking: Gender and Popularity Moderate Associations with Depressive Symptoms. Journal of Abnormal Child Psychology

ABSTRACT: This study examined specific technology-based behaviors (social comparison and interpersonal feedback seeking) that may interact with offline individual characteristics to predict concurrent depressive symptoms among adolescents. A total of 619 students (57 % female; mean age 14.6) completed self-report questionnaires at 2 time points. Adolescents reported on levels of depressive symptoms at baseline, and 1 year later on depressive symptoms, frequency of technology use (cell phones, Facebook, and Instagram), excessive reassurance-seeking, and technology-based social comparison and feedback-seeking. Adolescents also completed sociometric nominations of popularity. Consistent with hypotheses, technology-based social comparison and feedback seeking were associated with depressive symptoms.

Popularity and gender served as moderators of this effect, such that the association was particularly strong among females and adolescents low in popularity. Associations were found above and beyond the effects of overall frequency of technology use, offline excessive reassurance-seeking, and prior depressive symptoms. Findings highlight the utility of examining the psychological implications of adolescents’ technology use within the framework of existing interpersonal models of adolescent depression and suggest the importance of more nuanced approaches to the study of adolescents’ media use. [H/T Bradley Riew]

[NOTE: this study is not a straight longitudinal study focused on effects at Time 2 of hours spent on social media after Time 1; rather, it examines the more specific associations of depressive symptoms with Technology-Based Social Comparison and Feedback Seeking (SCFS). The more kids do this, the more depressed they are, especially girls, and adolescents who are low in popularity.]

FIGURE:

[Other studies? What have we missed?]

1.2: STUDIES INDICATING LITTLE OR NO ASSOCIATION

1.2.1 Orben & Przybylski (2019). The association between adolescent well-being and digital technology use. Nature Human Behavior.

ABSTRACT: The widespread use of digital technologies by young people has spurred speculation that their regular use negatively impacts psychological well-being. Current empirical evidence supporting this idea is largely based on secondary analyses of large-scale social datasets. Though these datasets provide a valuable resource for highly powered investigations, their many variables and observations are often explored with an analytical flexibility that marks small effects as statistically significant, thereby leading to potential false positives and conflicting results. Here we address these methodological challenges by applying specification curve analysis (SCA) across three large-scale social datasets (total n = 355,358) to rigorously examine correlational evidence for the effects of digital technology on adolescents. The association we find between digital technology use and adolescent well-being is negative but small, explaining at most 0.4% of the variation in well-being. Taking the broader context of the data into account suggests that these effects are too small to warrant policy change.

COMMENT: This study is impressive and important: the authors use a new and powerful statistical technique to conduct tens of thousands of analyses on three very large datasets. (The idea is to eliminate all the decisions researchers make, sometimes post-hoc, about which variables to examine and which covariates to include. Just run every possible combination.) They then report back that the average regression coefficient is negative but tiny, indicating a level of harmfulness so close to zero that it is roughly the same size as they find for “eating potatoes.” But a closer look, driven by our a priori hypothesis that it is heavy use (not light use) by girls (more than boys) of social media (more than any other screen-based activity) shows a larger effect size.

Here is Figure 2 from the paper, showing what happened in each of the 40,000 or so analyses they ran on the Monitoring The Future dataset. Whichever analysis showed the biggest negative effect is plotted on the left side; whichever showed the biggest positive relationship is plotted on the right side. The squiggly line emerges when the authors plot all of the regression coefficients found in all the analyses examining the relationship of a variety of variables related to tech use with a variety of variables related to “adolescent well being.” We have added the green boxes to show which part of the squiggly line reflects nearly all of the analyses done for “technology mean” -- the variable the authors created to capture each adolescent’s total time using technology.

        Figure 2 from Orben & Przybylski (2019), p. 5. Green boxes added.

As you can see, the squiggly line crosses the zero line, meaning that some analyses showed a negative correlation (indicating an association with harm), some a positive correlation (indicating an association with benefits), and many (shown in red) showed a zero correlation, so overall, it’s a wash.

But in Figure 2, you can see a different story if you zoom in on social media:

In the thousands of analyses that used “social media” rather than watching TV, searching the internet, or the average of all tech activities, they nearly always found a statistically significant negative relationship with “adolescent well being”. So for our purposes in this lit review of the effects of social media, the headline number for the MtF dataset should not be β = -.005, which is what is reported in Table 2 for the “complete SCA.” It should be β = -.031, which is what is reported in Table 2 for “social media use only.” That is the average of the regression coefficients within the second green box.  The other two datasets also show much larger effects for social media use (in the MCS) and for “electronic device use” (the closest usage category to social media in the YRBS). With these larger Betas, we are no longer looking at effects the size of “potatoes.” If we could re-run these analyses for girls only, we expect that the average β would increase substantially. If we could re-run the analyses looking for curvilinear effects rather than linear effects, we expect that the predictive power of social media use would increase again. It might well be above “binge drinking,” β = -.045. And if the MtF data on social media use in hours is used instead, and are considered in terms of number affected (as in 1.1.11), effects are larger still.

1.2.2 Berryman, Ferguson, & Negy (2017). Social Media Use and Mental Health among Young Adults. Psychiatric Quarterly.

ABSTRACT:  In recent years many parents, advocates and policy makers have expressed concerns regarding the potential negative impact of social media use. Some studies have indicated that social media use may be tied to negative mental health outcomes, including suicidality, loneliness and decreased empathy. Other studies have not found evidence for harm, or have indicated that social media use may be beneficial for some individuals. The current correlational study examined 467 young adults for their time spent using social media, importance of social media in their lives and tendency to engage in vaguebooking (posting unclear but alarming sounding posts to get attention). Outcomes considered included general mental health symptoms, suicidal ideation, loneliness, social anxiety and decreased empathy. Results indicated that social media use was not predictive of impaired mental health functioning. However, vaguebooking was predictive of suicidal ideation, suggesting this particular behavior could be a warning sign for serious issues. Overall, results from this study suggest that, with the exception of vaguebooking, concerns regarding social media use may be misplaced.

1.2.3 Kardefelt-Winther (2017). How does the time children spend using digital technology impact their mental well-being, social relationships and physical activity? An evidence-focused literature review. Unicef office of research -- discussion paper.

ABSTRACT: Based on an evidence-focused literature review, the first part of this paper examines existing knowledge on how the time children spend using digital technology impacts their well-being across three dimensions; mental/psychological, social and physical. The evidence reviewed here is largely inconclusive with respect to impact on children’s physical activity, but indicates that digital technology seems to be beneficial for children’s social relationships. In terms of impact on children’s mental well-being, the most robust studies suggest that the relationship is U-shaped, where no use and excessive use can have a small negative impact on mental well-being, while moderate use can have a small positive impact. In the second part of the paper, the hypothetical idea of addiction to technology is introduced and scrutinized. This is followed by an overview of the hypothetical idea that digital technology might re-wire or hijack children’s brains; an assumption that is challenged by recent neuroscience evidence. In conclusion, considerable methodological limitations exist across the spectrum of research on the impact of digital technology on child well-being, including the majority of the studies on time use reviewed here, and those studies concerned with clinical or brain impacts. This prompts reconsideration of how research in this area is conducted. Finally, recommendations for strengthening research practices are offered.

1.2.4 Orben & Przybylski (2019). Screens, Teens, and Psychological Well-Being: Evidence From Three Time-Use-Diary Studies. Psychological Science.

ABSTRACT: The notion that digital-screen engagement decreases adolescent well-being has become a recurring feature in public, political, and scientific conversation. The current level of psychological evidence, however, is far removed from the certainty voiced by many commentators. There is little clear-cut evidence that screen time decreases adolescent well-being, and most psychological results are based on single-country, exploratory studies that rely on inaccurate but popular self-report measures of digital-screen engagement. In this study, which encompassed three nationally representative large-scale data sets from Ireland, the United States, and the United Kingdom (N = 17,247 after data exclusions) and included time-use-diary measures of digital-screen engagement, we used both exploratory and confirmatory study designs to introduce methodological and analytical improvements to a growing psychological research area. We found little evidence for substantial negative associations between digital-screen engagement—measured throughout the day or particularly before bedtime—and adolescent well-being.

Comment, from Twenge and Haidt: Our issues here are similar to those we discussed for 1.2.1, including relying solely on linear r for curvilinear data, analyzing boys and girls together, Including control variables that are potential mediators, and considering screen time monolithically without separating social media from less problematic activities such as TV/videos. In addition, 80% of the measures in this study gauged simple participation in the activity (mere use), when it is clear that heavy use is the primary issue, not mere use.

1.2.5 Sewall, Rosen, & Bear (under review).  Examining the accuracy of estimated smartphone use: How well-being and usage level predict discrepancies between estimated and actual use. PsyArXiv Preprints

ABSTRACT: The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated.

[NOTE: THIS STUDY IS UNDER REVIEW. SUGGESTED VIA TWITTER. IT LOOKS IMPORTANT FOR TAKING ADVANTAGE OF THE RECENT INTRODUCTION OF APPLE’S SCREEN TIME FEATURE]

[Other studies? What have we missed?]

* * * * * * * * * * * * * * *

QUESTION 2: DOES SOCIAL MEDIA USE AT TIME 1 PREDICT ANYTHING ABOUT MENTAL HEALTH OUTCOMES AT TIME 2?

2.1: STUDIES INDICATING EFFECTS AT T2

2.1.1 Shakya & Christakis (2017). Association of Facebook Use With Compromised Well-Being: A Longitudinal Study. American Journal of Epidemiology.

ABSTRACT: Face-to-face social interactions enhance well-being. With the ubiquity of social media, important questions have arisen about the impact of online social interactions. In the present study, we assessed the associations of both online and offline social networks with several subjective measures of well-being. We used 3 waves (2013, 2014, and 2015) of data from 5,208 subjects in the nationally representative Gallup Panel Social Network Study survey, including social network measures, in combination with objective measures of Facebook use. We investigated the associations of Facebook activity and real-world social network activity with self-reported physical health, self-reported mental health, self-reported life satisfaction, and body mass index. Our results showed that overall, the use of Facebook was negatively associated with well-being. For example, a 1-standard-deviation increase in “likes clicked” (clicking “like” on someone else's content), “links clicked” (clicking a link to another site or article), or “status updates” (updating one's own Facebook status) was associated with a decrease of 5%–8% of a standard deviation in self-reported mental health. These associations were robust to multivariate cross-sectional analyses, as well as to 2-wave prospective analyses. The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.

2.1.2 Hökby, Hadlaczky, Westerlund et al. (2016). Are Mental Health Effects of Internet Use Attributable to the Web-Based Content or Perceived Consequences of Usage? A Longitudinal Study of European Adolescents. JMIR Mental Health.

ABSTRACT: Background: Adolescents and young adults are among the most frequent Internet users, and accumulating evidence suggests that their Internet behaviors might affect their mental health. Internet use may impact mental health because certain Web-based content could be distressing. It is also possible that excessive use, regardless of content, produces negative consequences, such as neglect of protective offline activities.
Objective: The objective of this study was to assess how mental health is associated with (1) the time spent on the Internet, (2) the time spent on different Web-based activities (social media use, gaming, gambling, pornography use, school work, newsreading, and targeted information searches), and (3) the perceived consequences of engaging in those activities.
Methods: A random sample of 2286 adolescents was recruited from state schools in Estonia, Hungary, Italy, Lithuania, Spain, Sweden, and the United Kingdom. Questionnaire data comprising Internet behaviors and mental health variables were collected and analyzed cross-sectionally and were followed up after 4 months.
Results:
Cross-sectionally, both the time spent on the Internet and the relative time spent on various activities predicted mental health (P<.001), explaining 1.4% and 2.8% variance, respectively. However, the consequences of engaging in those activities were more important predictors, explaining 11.1% variance. Only Web-based gaming, gambling, and targeted searches had mental health effects that were not fully accounted for by perceived consequences. The longitudinal analyses showed that sleep loss due to Internet use (ß=.12, 95% CI=0.05-0.19, P=.001) and withdrawal (negative mood) when Internet could not be accessed (ß=.09, 95% CI=0.03-0.16, P<.01) were the only consequences that had a direct effect on mental health in the long term. Perceived positive consequences of Internet use did not seem to be associated with mental health at all.
Conclusions:
The magnitude of Internet use is negatively associated with mental health in general, but specific Web-based activities differ in how consistently, how much, and in what direction they affect mental health. Consequences of Internet use (especially sleep loss and withdrawal when Internet cannot be accessed) seem to predict mental health outcomes to a greater extent than the specific activities themselves. Interventions aimed at reducing the negative mental health effects of Internet use could target its negative consequences instead of the Internet use itself.

COMMENT: this large study (n=2286) found both correlational and time-lagged effects of heavy internet use. They looked beyond total time spent in various categories and asked subjects to rate the consequences of time spent in each category. Not surprisingly, these self-ratings are much more predictive of mental health outcomes than are the subjects’ estimates of total time spent, so including these ratings in the regression analyses washed out the predictive power of total time. Nonetheless, total time spent online at T1 (other than for schoolwork) predicted bad mental health outcomes at T1, and predicted increased problems at T2. Consistent with other studies, the pathway through disrupted sleep was found to be an important part of the story.

2.1.3 Booker, Kelly, & Sacker (2018). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK. BMC Public Health


ABSTRACT: Background: Adolescents are among the highest consumers of social media while research has shown that their well-being decreases with age. The temporal relationship between social media interaction and well-being is not well established. The aim of this study was to examine whether the changes in social media interaction and two well-being measures are related across ages using parallel growth models.
Methods: Data come from five waves of the youth questionnaire, 10-15 years, of the Understanding Society, the UK Household Longitudinal Study (pooled n = 9859). Social media interaction was assessed through daily frequency of chatting on social websites.
Well-being was measured by happiness with six domains of life and the Strengths and Difficulties Questionnaire.
Results:
Findings suggest gender differences in the relationship between interacting on social media and well-being. There were significant correlations between interacting on social media and well-being intercepts and between social media interaction and well-being slopes among females. Additionally higher social media interaction at age 10 was associated with declines in well-being thereafter for females, but not for males. Results were similar for both measures of well-being.
Conclusions:
High levels of social media interaction in early adolescence have implications for well-being in later adolescence, particularly for females. The lack of an association among males suggests other factors might be associated with their reduction in well-being with age. These findings contribute to the debate on causality and may inform future policy and interventions.

2.1.4 Schmiedeberg & Schroder (2017). Leisure Activities and Life Satisfaction: an Analysis with German Panel Data. Applied Research in Quality of Life.

ABSTRACT: Given the nature of leisure as largely uncoerced and not necessary for survival it seems obvious at a first glance that leisure activities should contribute to happiness. Indeed, recent research has found positive effects of leisure activities on subjective well-being. In this article, we analyze the association between leisure activities and life satisfaction based on longitudinal data from Germany. By applying fixed-effects regression models we are able to rule out potential bias due to unobserved heterogeneity in time-constant variables. We use data from three waves of the German Family Panel (pairfam), a large, randomly sampled longitudinal study of adolescents and adults (aged 15–41 across the observation period), to test the effects of five leisure activities (sports; vacation; meeting with friends; internet use; and TV viewing) on respondents’ life satisfaction. Our results indicate that meeting with friends, doing sports, and going on vacation contributes positively to life satisfaction whereas internet use for personal purposes and TV consumption are negatively related to life satisfaction.

COMMENT: The study does not allow us to look at social media use specifically. The dependent variable is life satisfaction, measured by the question “All in all, how satisfied are you with your life at the moment?” “Personal internet use” was broken down into 3 categories: 0 hours per day, between 0 and 3 hours, and 3 or more hours. [We need to dig deeper to see if the effects applied to adolescents, specifically]

2.1.5 Babic, Smith, Morgan, et al. (2017). Longitudinal associations between changes in screen-time and mental health outcomes in adolescents. Mental Health and Physical Activity.


ABSTRACT: Introduction: The primary aim was to examine longitudinal associations between changes in screen-time and mental health outcomes among adolescents.
Methods: Adolescents (N = 322, 65.5% females, mean age = 14.4 ± 0.6 years) reported screen-time and mental health at two time points over a school year. Multi-level linear regression analyses were conducted after adjusting for covariates.
Results: Changes in total recreational screen-time (β = −0.09 p = 0.048)  and tablet/mobile phone use (β = −0.18, p < 0.001) were negatively associated with physical self-concept. Changes in total recreational screen-time (β = −0.20, p = 0.001) and computer use (β = −0.23, p = 0.003) were negatively associated with psychological well-being. A positive association was found with television/DVD use and psychological difficulties (β = 0.16, p = 0.015). No associations were found for non-recreational screen-time.
Conclusion: Changes in recreational screen-time were associated with changes in a range of mental health outcomes.

[NOTE: this study is about “screen time” in general; it does not tell us about social media effects specifically]

2.1.6 Kim (2017). The impact of online social networking on adolescent psychological well-being (WB): a population-level analysis of Korean school-aged children.

ABSTRACT: This study examines the extent to which online media activities are associated with psychological well-being of adolescents. Data come from the Korean Youth Panel Survey (KYPS), a government-funded multiyear research project. Based on Wave 4 (2007) and Wave 5 (2008) of KYPS, the most recent data available, hierarchical linear models are estimated to probe the psychological effects of time spent online. While holding constant a host of time-lagged control variables at individual (student) and contextual (school) levels, the analysis shows that online social networking is adversely associated with the psychological status of Korean students, measured in terms of self-reported mental problems and suicidal thought. The bulk of previous research on the pros and cons of online social media use is based on cross-sectional data, thereby precluding causal inference. Using longitudinal data, the current research offers more conclusive evidence on the direction of causation.

2.1.7 Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J., … Kross, E. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. Journal of Experimental Psychology.

[h/t Kai Lukoff]

Prior research indicates that Facebook usage predicts declines in subjective well-being over time. How does this come about? We examined this issue in 2 studies using experimental and field methods. In Study 1, cueing people in the laboratory to use Facebook passively (rather than actively) led to declines in affective well-being over time. Study 2 replicated these findings in the field using experience-sampling techniques. It also demonstrated how passive Facebook usage leads to declines in affective well-being: by increasing envy. Critically, the relationship between passive Facebook usage and changes in affective well-being remained significant when controlling for active Facebook use, non-Facebook online social network usage, and direct social interactions, highlighting the specificity of this result. These findings demonstrate that passive Facebook usage undermines affective well-being.

COMMENT (from Kai Lukoff): Study 2 finds that passive use of Facebook at T1 predicts declines in affective wellbeing at T2, but not for active Facebook use. Since people used Facebook passively significantly more than they used it actively, I decided to tentatively include it here under “indicating effects.” Study 1 is an experimental study in a similar vein.  

2.1.8 Boers, Afzali, Newton et al. (2019). Association of Screen Time and Depression in Adolescence. JAMA Pediatrics.

[h/t Ian Goddard]

ABSTRACT: Design, Setting, and Participants:  This secondary analysis used data from a randomized clinical trial assessing the 4-year efficacy of a personality-targeted drug and alcohol prevention intervention. This study assessed screen time and depression throughout 4 years, using an annual survey in a sample of adolescents who entered the seventh grade in 31 schools in the Greater Montreal area. Data were collected from September 2012 to September 2018. Analysis began and ended in December 2018.

Main Outcomes and Measures:  Independent variables were social media, television, video gaming, and computer use. Symptoms of depression was the outcome, measured using the Brief Symptoms Inventory. Exercise and self-esteem were assessed to test displacement and upward social comparison hypothesis.

Results:  A total of 3826 adolescents (1798 girls [47%]; mean [SD] age, 12.7 [0.5] years) were included. In general, depression symptoms increased yearly (year 1 mean [SD], 4.29 [5.10] points; year 4 mean [SD], 5.45 [5.93] points). Multilevel models, which included random intercepts at the school and individual level estimated between-person and within-person associations between screen time and depression. Significant between-person associations showed that for every increased hour spent using social media, adolescents showed a 0.64-unit increase in depressive symptoms (95% CI, 0.32-0.51). Similar between-level associations were reported for computer use (0.69; 95% CI, 0.47-0.91). Significant within-person associations revealed that a further 1-hour increase in social media use in a given year was associated with a further 0.41-unit increase in depressive symptoms in that same year. A similar within-person association was found for television (0.18; 95% CI, 0.09-0.27). Significant between-person and within-person associations between screen time and exercise and self-esteem supported upward social comparison and not displacement hypothesis. Furthermore, a significant interaction between the between-person and within-person associations concerning social media and self-esteem supported reinforcing spirals hypothesis.

Conclusions and Relevance:  Time-varying associations between social media, television, and depression were found, which appeared to be more explained by upward social comparison and reinforcing spirals hypotheses than by the displacement hypothesis. Both screen time modes should be taken into account when developing preventive measures and when advising parents.

2.1.9 Viner, Aswathikutty-Gireesh, et al. (2019) Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: A secondary analysis of longitudinal data. The Lancet Child and Adolescent Health.

[thanks to Ian Goddard]

ABSTRACT:  There is growing concern about the potential associations between social media use and mental health and wellbeing in young people. We explored associations between the frequency of social media use and later mental health and wellbeing in adolescents, and how these effects might be mediated.

Methods: We did secondary analyses of publicly available data from the Our Futures study, a nationally representative, longitudinal study of 12 866 young people from age 13 years to 16 years in England. The exposure considered was the frequency of social media use (from weekly or less to very frequent [multiple times daily]) at wave 1 (participants aged 13–14 years) through wave 3 of the study (participants aged 15–16 years). Outcomes were mental health at wave 2 (with high 12-item General Health Questionnaire [GHQ12] scores [≥3] indicating psychological distress), and wellbeing at wave 3 (life satisfaction, feeling life is worthwhile, happiness, and anxiety, rated from 1 to 10 by participants). Analyses were adjusted for a minimal sufficient confounding structure, and were done separately for boys and girls. Cyberbullying, sleep adequacy, and physical activity were assessed as potential mediators of the effects.

Findings: Very frequent use of social media increased from wave 1 to wave 3: from 34·4% (95% CI 32·4–36·4) to 61·9% (60·3–63·6) in boys, and 51·4% (49·5–53·3) to 75·4% (73·8–76·9) in girls. Very frequent social media use in wave 1 predicted a high GHQ12 score at wave 2 among girls (adjusted odds ratio [OR] 1·31 [95% CI 1·06–1·63], p=0·014; N=4429) and boys (1·67 [1·24–2·26], p=0·0009; N=4379). Persistent very frequent social media use across waves 1 and 2 predicted lower wellbeing among girls only (adjusted ORs 0·86 [0·74–0·99], N=3753, p=0·039 for life satisfaction; 0·80 [0·70–0·92], N=3831, p=0·0013 for happiness; 1·28 [1·11–1·48], N=3745, p=0·0007 for anxiety). Adjustment for cyberbullying, sleep, and physical activity attenuated the associations of social media use with GHQ12 high score (proportion mediated 58·2%), life satisfaction (80·1%), happiness (47·7%), and anxiety (32·4%) in girls, such that these associations (except for anxiety) were no longer significant; however, the association with GHQ12 high score among boys remained significant, being mediated only 12·1% by these factors.

Interpretation: Mental health harms related to very frequent social media use in girls might be due to a combination of exposure to cyberbullying or displacement of sleep or physical activity, whereas other mechanisms appear to be operative in boys. Interventions to promote mental health should include efforts to prevent or increase resilience to cyberbullying and ensure adequate sleep and physical activity in young people.

Note [from Twenge]: Effects were found for both boys and girls, with T1 social media use predicting T2 wellbeing and mental health. But the mediational model including cyberbullying, sleep, and exercise only worked well for girls. That suggests other forces may be at work for boys -- future research should try to figure out what those are. One notable limitation: Social media use was measured from “never” to “more than three times a day,” a measurement that often lacks variance as most teens who use social media do so multiple times a day. Low variance = low effects. Thus, if social media use had been measured in hours per day, effects might have been larger.

2.1.10 Riehm, Feder, Tormohlen et al. (2019). Associations Between Time Spent Using Social Media and Internalizing and Externalizing Problems Among US Youth. JAMA Psychiatry. [h/t Ian Goddard]

Abstract: Objective:  To assess whether time spent using social media per day is prospectively associated with internalizing and externalizing problems among adolescents.

Design, Setting, and Participants:  This longitudinal cohort study of 6595 participants from waves 1 (September 12, 2013, to December 14, 2014), 2 (October 23, 2014, to October 30, 2015), and 3 (October 18, 2015, to October 23, 2016) of the Population Assessment of Tobacco and Health study, a nationally representative cohort study of US adolescents, assessed US adolescents via household interviews using audio computer-assisted self-interviewing. Data analysis was performed from January 14, 2019, to May 22, 2019.

Exposures:  Self-reported time spent on social media during a typical day (none, ≤30 minutes, >30 minutes to ≤3 hours, >3 hours to ≤6 hours, and >6 hours) during wave 2.

Main Outcomes and Measure:  Self-reported past-year internalizing problems alone, externalizing problems alone, and comorbid internalizing and externalizing problems during wave 3 using the Global Appraisal of Individual Needs–Short Screener.

Results:  A total of 6595 adolescents (aged 12-15 years during wave 1; 3400 [51.3%] male) were studied. In unadjusted analyses, spending more than 30 minutes of time on social media, compared with no use, was associated with increased risk of internalizing problems alone (≤30 minutes: relative risk ratio [RRR], 1.30; 95% CI, 0.94-1.78; >30 minutes to ≤3 hours: RRR, 1.89; 95% CI, 1.36-2.64; >3 to ≤6 hours: RRR, 2.47; 95% CI, 1.74-3.49; >6 hours: RRR, 2.83; 95% CI, 1.88-4.26) and comorbid internalizing and externalizing problems (≤30 minutes: RRR, 1.39; 95% CI, 1.06-1.82; >30 minutes to ≤3 hours: RRR, 2.34; 95% CI, 1.83-3.00; >3 to ≤6 hours: RRR, 3.15; 95% CI, 2.43-4.09; >6 hours: RRR, 4.29; 95% CI, 3.22-5.73); associations with externalizing problems were inconsistent. In adjusted analyses, use of social media for more than 3 hours per day compared with no use remained significantly associated with internalizing problems alone (>3 to ≤6 hours: RRR, 1.60; 95% CI, 1.11-2.31; >6 hours: RRR, 1.78; 95% CI, 1.15-2.77) and comorbid internalizing and externalizing problems (>3 to ≤6 hours: RRR, 2.01; 95% CI, 1.51-2.66; >6 hours: RRR, 2.44; 95% CI, 1.73-3.43) but not externalizing problems alone.

Conclusions and Relevance:  Adolescents who spend more than 3 hours per day using social media may be at heightened risk for mental health problems, particularly internalizing problems. Future research should determine whether setting limits on daily social media use, increasing media literacy, and redesigning social media platforms are effective means of reducing the burden of mental health problems in this population.

[NOTE from Haidt: this abstract shows that the study found correlations between social media use in a given wave and internalizing disorders (depression/anxiety) in that same wave, but the abstract and the main text don’t make clear how well social media use at one time predicted changes in internalizing disorders at a later time. But the discussion does begin: “we found that adolescent social media use was prospectively associated with increased risk of comorbid internalizing and externalizing problems as well as internalizing problems alone. This association remained significant after adjusting for demographics, past alcohol and marijuana use, and, most importantly, a history of mental health problems, which mitigates the possibility that reverse causality explains these findings.”]

[NOTE from Twenge: By my reading of the main text, the unadjusted RR’s are social media at Wave 2 predicting mental health problems at Wave 3 (see Figure 1, below).

In addition, the adjusted analyses control for mental health problems reported at Wave 1. Thus, this study shows that social media use at one time predicts mental health problems a year later, even when controlled for previous mental health problems. Thus, it’s good evidence that social media use is associated with future mental health issues apart from past issues.]

[Other studies? What have we missed?]

2.2: STUDIES INDICATING LITTLE OR NO EFFECT AT T2

2.2.1 Heffer, Good, Daly, McDonnell, & Willoughby (2019). The Longitudinal Association Between Social-Media Use and Depressive Symptoms Among Adolescents and Young Adults: An Empirical Reply to Twenge et al. (2018). Clinical Psychological Science

ABSTRACT: Research by Twenge, Joiner, Rogers, and Martin has indicated that there may be an association between social-media use and depressive symptoms among adolescents. However, because of the cross-sectional nature of this work, the
relationship among these variables over time remains unclear. Thus, in this longitudinal study we examined the associations between social-media use and depressive symptoms over time using two samples: 594 adolescents (M age = 12.21) who were surveyed annually for 2 years, and 1,132 undergraduate students (M age = 19.06) who were surveyed annually for 6 years. Results indicate that among both samples,
social-media use did not predict depressive symptoms over time for males or females. However, greater depressive symptoms predicted more frequent social-media use only among adolescent girls. Thus, while it is often assumed that social-media use may lead to depressive symptoms, our results indicate that this assumption may be unwarranted.

FIGURE FROM THE PAPER:

2.2.2 Albers, McNally, Heeren et al. (2018). Social media and depression symptoms: A network perspective. Journal of Experimental Psychology: General

ABSTRACT: Passive social media use (PSMU)—for example, scrolling through social media news feeds—has been associated with depression symptoms. It is unclear, however, if PSMU causes depression symptoms or vice versa. In this study, 125 students reported PSMU, depression symptoms, and stress 7 times daily for 14 days. We used multilevel vector autoregressive time-series models to estimate (a) contemporaneous, (b) temporal, and (c) between-subjects associations among these variables. (a) More time spent on PSMU was associated with higher levels of interest loss, concentration problems, fatigue, and loneliness. (b) Fatigue and loneliness predicted PSMU across time, but PSMU predicted neither depression symptoms nor stress. (c) Mean PSMU levels were positively correlated with several depression symptoms (e.g., depressed mood and feeling inferior), but these associations disappeared when controlling for all other variables. Altogether, we identified complex relations between PSMU and specific depression symptoms that warrant further research into potentially causal relationships.

2.2.3 Ferguson, Munoz, Garza, & Galindo (2013) Concurrent and Prospective Analyses of Peer, Television and Social Media Influences on Body Dissatisfaction, Eating Disorder Symptoms and Life Satisfaction in Adolescent Girls. Journal of Youth and Adolescence.

[Thanks to Chris Ferguson for suggesting this study]

ABSTRACT:  The degree to which media contributes to body dissatisfaction, life satisfaction and eating disorder symptoms in teenage girls continues to be debated. The current study examines television, social media and peer competition influences on body dissatisfaction, eating disorder symptoms and life satisfaction in a sample of 237 mostly Hispanic girls. 101 of these girls were reassessed in a later 6-month follow-up. Neither television exposure to thin ideal media nor social media predicted negative outcomes either concurrently nor prospectively with the exception of a small concurrent correlation between social media use and life satisfaction. Social media use was found to contribute to later peer competition in prospective analysis, however, suggesting potential indirect but not direct effects on body related outcomes. Peer competition proved to be a moderate strong predictor of negative outcomes both concurrently and prospectively. It is concluded that the negative influences of social comparison are focused on peers rather than television or social media exposure.

COMMENT: This study was published one year before our 2014 cutoff, but is included here because it was done by one of the major researchers in this debate, and because it found an interesting indirect effect that could contribute to our understanding of the gender difference found in so many studies. Ferguson’s comment: “It looked as if, for some girls, social media could be one space in which they compete and fail against other girls. But I'd say that's a bit of a more nuanced argument than ‘technology is to blame’.”

2.2.4 Burke, M., & Kraut, R. E. (2016). The Relationship Between Facebook Use and Well-Being Depends on Communication Type and Tie Strength. Journal of Computer-Mediated Communication.

An extensive literature shows that social relationships influence psychological well-being, but the underlying mechanisms remain unclear. We test predictions about online interactions and well-being made by theories of belongingness, relationship maintenance, relational investment, social support, and social comparison. An opt-in panel study of 1,910 Facebook users linked self-reported measures of well-being to counts of respondents' Facebook activities from server logs. Specific uses of the site were associated with improvements in well-being: Receiving targeted, composed communication from strong ties was associated with improvements in well-being while viewing friends' wide-audience broadcasts and receiving one-click feedback were not. These results suggest that people derive benefits from online communication, as long it comes from people they care about and has been tailored for them. [Thanks to Kai Lukoff]

COMMENT (from Kai Lukoff): The key result for the purposes of this review is likely this:

The coefficient for lagged well-being ( beta = .850) shows that well-being was very stable month-to-month. Aggregating over all the communication activities from Table 2, Model A shows that receiving more Facebook communication in general was not associated with changes in well-being (beta = .010, p = .493). Other results: communication from strong ties was associated with improvements in well-being, whereas communication from weak ties had no effect. Note of caution: the lead author works at Facebook.

2.2.5  Orben, Dienlin, & Przybylski (2019). Social media’s enduring effect on adolescent life satisfaction. PNAS.

Abstract: In this study, we used large-scale representative panel data to disentangle the between-person and within-person relations linking adolescent social media use and well-being. We found that social media use is not, in and of itself, a strong predictor of life satisfaction across the adolescent population. Instead, social media effects are nuanced, small at best, reciprocal over time, gender specific, and contingent on analytic methods.

COMMENT [from Twenge and Haidt]: This study focuses on social media and separates boys and girls -- both praiseworthy choices. Yet, it still uses linear r to examine data that is curvilinear and would be better understood by examining the number of people affected. And because only a minority are in the highest category of use, most comparisons focus on movements that don’t matter much for well-being (say, from no use to light use, or light use to moderate use). Even so, the study finds a path from greater social media use to lower well-being that is stronger than the path from lower well-being to greater social media use. That suggests that more of the causation moves from social media use to low well-being rather than the reverse causation argument of low well-being leading to more social media use.

2.2.6 Jensen, George, Russell, & Odgers (2019). Young Adolescents’ Digital Technology Use and Adolescents’ Mental Health Symptoms: Little Evidence of Longitudinal or Daily Linkages. Clinical Psychological Science.

ABSTRACT: This study examines whether 388 adolescents’ digital technology use is associated with mental-health symptoms during early adolescence to midadolescence. Adolescents completed an initial Time 1 (T1) assessment in 2015, followed by a 14-day ecological momentary assessment (EMA) via mobile phone in 2016–2017 that yielded 13,017 total observations over 5,270 study days. Adolescents’ T1 technology use did not predict later mental-health symptoms. Adolescents’ reported mental health was also not worse on days when they reported spending more versus less time on technology. Little was found to support daily quadratic associations (whereby adolescent mental health was worse on days with little or excessive use). Adolescents at higher risk for mental-health problems also exhibited no signs of increased risk for mental-health problems on higher technology use days. Findings from this EMA study do not support the narrative that young adolescents’ digital technology usage is associated with elevated mental-health symptoms.

COMMENT [From Haidt]: This is a large well-done longitudinal study on “digital technology use” among 388 adolescents in North Carolina, using “ecological momentary assessments --three short text surveys a day for two weeks--rather than relying on recall. The general failure to find time-lagged effects makes this study a point in favor of those who say that “screen time” is not necessarily bad for teenagers. However, this study has little to say about the effects of social media. All four of the research questions listed on p. 3 are about “technology use,” which includes watching TV and playing video games, among other activities. The study did include one question on social media use, given only once, at T1 (the longer survey done before the 2 week period began): “How often do you use social networking sites like Facebook or Instagram?,” answered on a seven point scale from 0=”I do not have social media” through 6=”several times a day.” [Note from Twenge: Items like these are problematic because they severely lack variance -- almost all teens who use social media use the sites several times a day, and low variance = low effects. Measurement in hours per day is often necessary for sufficent variance.] But social media use was not assessed in the three “daily technology use” surveys, so this study does not tell us how variations in social media use at any one time (within a person’s own range) are related to subsequent variations in mental health, as assessed by the K6 (Kessler scale) and other measures. The authors do report that social media use self-rated at T1 did not predict mental health problems during the two week assessment period, so that does qualify the study to be in this section of our lit review -- it is a null finding. But the real power of this study was its ability to examine how fluctuations in technology use during the two weeks influenced concurrent and subsequent mental health reports, and that power was not applied to the study of social media use.

[Other studies? What have we missed?]

* * * * * * * * * * * * * * *

QUESTION 3: DO EXPERIMENTS USING RANDOM ASSIGNMENT SHOW A CAUSAL EFFECT OF SOCIAL MEDIA USE ON MENTAL HEALTH OUTCOMES?

True experiments are the gold standard for establishing causality. Only a few studies have randomly assigned participants to reduce or quit social media for a specified period of time.

3.1: STUDIES INDICATING A CAUSAL EFFECT ON MENTAL HEALTH OUTCOMES

3.1.1: Hunt, Marx, Lipson & Young (2018). No More FOMO: Limiting Social Media Decreases Loneliness and Depression. Journal of Social and Clinical Psychology.

ABSTRACT: Introduction: Given the breadth of correlational research linking social media use to worse well-being, we undertook an experimental study to investigate the potential causal role that social media plays in this relationship.
Method: After a week of baseline monitoring, 143 undergraduates at the University of Pennsylvania were randomly assigned to either limit Facebook, Instagram and Snapchat use to 10 minutes, per platform, per day, or to use social media as usual for three weeks.
Results:
The limited use group showed significant reductions in loneliness and depression over three weeks compared to the control group. Both groups showed significant decreases in anxiety and fear of missing out over baseline, suggesting a benefit of increased self-monitoring.
Discussion:
Our findings strongly suggest that limiting social media use to approximately 30 minutes per day may lead to significant improvement in well-being.

FIGURE FROM THE ARTICLE:

Figure 3, from Hunt et al. (2018), p. 762. “Hi” vs. “Lo” refers to participants’ scores on the BDI (Beck Depression Inventory) at the start of the study. CON = control group, EXP = experimental group, assigned to reduce social media use.


COMMENT: This is the best-controlled experimental study yet published, as far as I know. Participants were required to submit screenshots of the battery usage screen on an iPhone that gives the breakdown of time used per app. The experimental group cut its social media use roughly in half. Participants who had high scores on the Beck Depression Inventory at the start of the study experienced large declines in their BDI scores. Those who scored low on the BDI experienced a small but statistically significant drop on their BDI scores.

COUNTERPOINT (from Patrick Markey, Villanova, via Twitter): “This experimental study found that reductions in social media only effected 2 of 7 outcomes examined.  The authors fail to correct for any type-1 error in the study and once that is done only 1 outcome remains significant.” also: “Related to this study, the author failed to find any relation between social media use and any of the SEVEN outcomes at time 1 (before the intervention). NOTE: this study is BOTH correlation and experimental (probably need to include both in a review)”

3.1.2 Sagioglou & Greitemeyer (2014). Facebook's emotional consequences: Why Facebook causes a decrease in mood and why people still use it. Computers in Human Behavior

ABSTRACT: Facebook is the world’s most popular online social network and used by more than one billion people. In three studies, we explored the hypothesis that Facebook activity negatively affects people’s emotional state. A first study shows that the longer people are active on Facebook, the more negative is their mood afterwards. The second study provides causal evidence for this effect by showing that Facebook activity leads to a deterioration of mood compared to two different control conditions. Furthermore, it was demonstrated that this effect is mediated by a feeling of not having done anything meaningful. With such negative outcomes for its users, the question arises as to why so many people continue to use Facebook on a daily basis. A third study suggests that this may be because people commit an affective forecasting error in that they expect to feel better after using Facebook, whereas, in fact, they feel worse.

COMMENT: Study 1 is not impressive, n=123 German speaking Facebook users surveyed after using Facebook. But study 2, the experimental study, is stronger: n=263 Americans, randomly assigned to one of 3 conditions. Note: Kai Lukoff points out that this was done using mTurk, not with teens.

3.1.3: Tromholt (2016). The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being. Cyberpsychology, Behavior, and Social Networking.

ABSTRACT. Most people use Facebook on a daily basis; few are aware of the consequences. Based on a 1-week experiment with 1,095 participants in late 2015 in Denmark, this study provides causal evidence that Facebook use affects our well-being negatively. By comparing the treatment group (participants who took a break from Facebook) with the control group (participants who kept using Facebook), it was demonstrated that taking a break from Facebook has positive effects on the two dimensions of well-being: our life satisfaction increases and our emotions become more positive. Furthermore, it was demonstrated that these effects were significantly greater for heavy Facebook users, passive Facebook users, and users who tend to envy others on Facebook.

COMMENT [from Haidt]: This is a relatively low quality experiment: There were no checks to be sure users complied with instructions, and the experiment ran for just one week. Also, as Sonia Livingston points out, the avg age of participants in the study was 34.

3.1.4 Allcott, Braghieri, Eichmeyer, & Gentzkow (2019, working paper): The Welfare Effects of Social Media. (in press, American Economic Review; available now as an NBER Working Paper).

[Thanks to Florian Kuhn, who suggested this via Twitter]

ABSTRACT: The rise of social media has provoked both optimism about potential societal benefits and concern about harms such as addiction, depression, and political polarization. We present a randomized evaluation of the welfare effects of Facebook, focusing on US users in the run-up to the 2018 midterm election. We measured the willingness-to-accept of 2,743 Facebook users to deactivate their Facebook accounts for four weeks, then randomly assigned a subset to actually do so in a way that we verified. Using a suite of outcomes from both surveys and direct measurement, we show that Facebook deactivation (i) reduced online activity, including other social media, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization;(iii) increased subjective well-being; and (iv) caused a large persistent reduction in Facebook use after the experiment. Deactivation reduced post-experiment valuations of Facebook, but valuations still imply that Facebook generates substantial consumer surplus..

COMMENT: This paper appears to be the largest experimental study yet done. It does not target teens specifically. The authors say that they will conduct a subsequent study with teens. Here is an interview with Gentzkow, about the study, at Recode.

3.1.5 Yuen, E. K., Koterba, E. A., Stasio, M. J., Patrick, R. B., Gangi, C., Ash, P., . . . Mansour, B. (in press). The effects of Facebook on mood in emerging adults. Psychology of Popular Media Culture. http://dx.doi.org/10.1037/ppm0000178

Abstract: Social media usage is on the rise, with the majority of American adults using Facebook. The present study examined how Facebook activity affects mood in a subset of emerging adults, specifically undergraduates attending a private 4-year university. Participants (N = 312) were randomly assigned to one of the following 20-min activities: browse the Internet, passively browse others’ Facebook profiles, actively communicate with others on Facebook via messages/posts, or update their own personal profile on Facebook. Participants also completed questionnaires assessing mood, feelings of envy, and perceived meaningfulness of their time online. The results demonstrated that using Facebook led to significantly worsened mood compared with browsing the Internet, especially when participants passively browsed Facebook. Furthermore, perceptions of meaningfulness, but not feelings of envy, mediated the relationship between online activity and mood. Overall, these findings add to the mounting evidence that social media use may, at times, adversely affect psychological well-being.

Figure:

Figure: Mean for PANAS positive mood pre- and post three types of Facebook use (surveillance: browse and view but do not post messages; communicate: post on others’ timelines, comment on posts, send messages; profile: view, edit, or add content to “about me” or respond to others’ comments on their profile pages) vs. control task (browse the web but do not visit e-mail, SNS, chatrooms, message boards, or dating sites). [graphed by Jean Twenge]

COMMENTS [by Twenge]: It’s impressive that these effects appear after only 20 minutes of use. The effects are moderate in size, about d = .42 for surveillance, d = .31 for communicate, and d = .34 for profile. (Though I’d love to see the variation in the effect -- say, the shift in the % who were very low in positive mood). In addition, those in the Facebook conditions felt less meaning than those who browsed the web doing other things, and this effect was large (d = .71). However, there were no significant effects for negative mood or envy. The mediation by meaningfulness is interesting: Perhaps social media lowers positive mood partially because people feel empty afterward? Or because they realize they have wasted their time and should have been doing something else?

3.1.6 Ozimek & Bierhoff (2019) All my online-friends are better than me – three studies about ability-based comparative social media use, self-esteem, and depressive tendencies. Behaviour & Information Technology

ABSTRACT: We conducted three studies to assess short-term and long-term effects of social comparative SNS use on self-esteem and depressive tendencies. In Study 1 (N = 75) we found in an exposure experiment including two experimental groups and one control group that social comparative internet use decreased participants’ performance-oriented state self-esteem as a short-term effect. In Study 2 and 3(Ns = 809, 145) results of the serial multiple mediator model indicated that passive Facebook use is associated with higher depressive tendencies mediated by a higher ability-related social comparison orientation and lower self-esteem as long-term effect. To obtain more generalisable findings, we transferred the serial multiple mediator model successfully from private to professional SNS use (Study 3).

[Other studies? What have we missed?]

3.2: STUDIES INDICATING NO CAUSAL EFFECT

None found so far.

3.2.1 [Other studies? What have we missed?]

* * * * * * * * * * * * * * *

4.0: MAJOR REVIEW ARTICLES AND DATABASES

4.1 Dickson, Richardson, Kwan et al. (2019). Screen-based activities and children and young
people’s mental health and psychosocial wellbeing: a systematic map of reviews. Department of Health Reviews Facility (UK)

CONCLUSIONS: This systematic map of reviews highlights some key gaps in the field. First, the tendency in primary studies to draw on cross-sectional data with a lack of prospective research designs, prevents reviews from providing a clear indication of nature of any causal relationship between screen-based activities and mental health outcomes. Second, evidence on the factors potentially mediating and/or moderating the relationship between screen-based activities and mental health outcomes was sparse, limiting our understanding of what influences CYP behaviour in this area. Third, few reviews analysed subsets of populations (e.g. specific age groups, gender, mental health status) which could help contextualise the relationship between screen-based activities and mental health and psychosocial outcomes. Lastly, although some reviews included qualitative studies, there is a lack of synthesis of critically appraised evidence about CYP’s experiences of different types of screen-based activities. Future reviews generating evidence of this kind are needed to improve our understanding of the consequences of, and causal mechanisms that explain how and why, the use of screen-based activities may impact mental health and psychosocial outcomes, over time.

4.2. The Harvard Center on Media and Child Health Database of Research “is a digital library free for everyone: researchers, clinicians, educators, parents, policy makers, and anyone interested in reading about the evidence behind the ways media, youth, and health intersect. As of 2014, the database contains over 3500 article citations, and new citations are continually being added. The CMCH Librarian monitors emerging research from academic journals in a variety of disciplines, including medicine, education, sociology, computer science, psychology, gender studies, communication, and more. By drawing from this wide range of academic literature, the database is unique in that it truly is multidisciplinary, ensuring that no relevant evidence is excluded from the database. CMCH Abstract Writers create original structured abstracts for each article, allowing for quick and easy comparisons between studies.”

NOTE [from Haidt]: this database does not seem to have been updated since 2015, making it not very useful for our purposes. Does anyone know of a current database of research articles?

4.3 Education Policy Institute (UK) (2017). Social media and children’s mental health: a review of the evidence.

ABSTRACT: KEY FINDINGS SECTION ON Risks of social media use:

  • The report highlights several risks linked with social media use – including cyber-bullying, concerns about excessive internet use, sharing of private information and harmful content – such as websites that promote self-harm. 34 per cent of UK children have experienced at least one of these risks.
  • Over a third (37.3 per cent) of UK 15 year olds can be classed as ‘extreme internet users’ (6+ hours of use a day) – markedly higher than the average of OECD countries. Young people in the UK are also extensive users of social media sites – 94.8 per cent of 15 year olds in the UK used social media before or after school – slightly above the OECD average.
  • The evidence points towards a correlation between extreme use of social media and harmful effects on young people’s wellbeing. Those classed as ‘extreme internet users’ were more likely to report being bullied (17.8 per cent) than moderate internet users (6.7 per cent).
  • Further evidence points to a link between periods spent on social media and a rise in mental health problems.
  • More research is needed to understand the causal relationship between social networking and mental health and wellbeing problems.

4.4 Lisak (2018). Adverse physiological and psychological effects of screen time on children and adolescents: Literature review and case study. Environmental Research.

ABSTRACT: A growing body of literature is associating excessive and addictive use of digital media with physical, psychological, social and neurological adverse consequences. Research is focusing more on mobile devices use, and studies suggest that duration, content, after-dark-use, media type and the number of devices are key components determining screen time effects. Physical health effects: excessive screen time is associated with poor sleep and risk factors for cardiovascular diseases such as high blood pressure, obesity, low HDL cholesterol, poor stress regulation (high sympathetic arousal and cortisol dysregulation), and Insulin Resistance. Other physical health consequences include impaired vision and reduced bone density. Psychological effects: internalizing and externalizing behavior is related to poor sleep. Depressive symptoms and suicidal are associated to screen time induced poor sleep, digital device night use, and mobile phone dependency. ADHD-related behavior was linked to sleep problems, overall screen time, and violent and fast-paced content which activates dopamine and the reward pathways. Early and prolonged exposure to violent content is also linked to risk for antisocial behavior and decreased prosocial behavior. Psychoneurological effects: addictive screen time use decreases social coping and involves craving behavior which resembles substance dependence behavior. Brain structural changes related to cognitive control and emotional regulation are associated with digital media addictive behavior. A case study of a treatment of an ADHD diagnosed 9-year-old boy suggests screen time induced ADHD-related behavior could be inaccurately diagnosed as ADHD. Screen time reduction is effective in decreasing ADHD-related behavior.

4.5 Yoon, Kleinman, Mertz, & Brannick (2019). Is social network site usage related to depression? A meta-analysis of Facebook–depression relations. Journal of Affective Disorders.

[h/t Ian Goddard]

ABSTRACT: Facebook depression is defined as feeling depressed upon too much exposure to Social networking sites (SNS). Researchers have argued that upward social comparisons made on SNS are the key to the Facebook depression phenomenon. To examine the relations between SNS usage and depression, we conducted 4 separate meta-analyses relating depression to: (1) time spent on SNS, (2) SNS checking frequency, (3) general and (4) upward social comparisons on SNS. We compared the four mean effect sizes in terms of magnitude.

Methods: Our literature search yielded 33 articles with a sample of 15,881 for time spent on SNS, 12 articles with a sample of 8041 for SNS checking frequency, and 5 articles with a sample of 1715 and 2298 for the general and the upward social comparison analyses, respectively.

Results: In both SNS-usage analyses, greater time spent on SNS and frequency of checking SNS were associated with higher levels of depression with a small effect size. Further, higher levels of depression were associated with greater general social comparisons on SNS with a small to medium effect, and greater upward social comparisons on SNS with a medium effect. Both social comparisons on SNS were more strongly related to depression than was time spent on SNS.

Limitations: Limitations include heterogeneity in effect sizes and a small number of samples for social comparison analyses.

Conclusions: Our results are consistent with the notion of ‘Facebook depression phenomenon’ and with the theoretical importance of social comparisons as an explanation.

[Other studies? What have we missed? Are there any literature reviews that FAIL to find evidence of relationships with harmful outcomes?]

* * * * * * * * * * * * * * *

5. STUDIES SUGGESTED BY COMMENTERS THAT ARE RELEVANT BUT NOT FOCUSED ON THE CENTRAL QUESTION OF SOCIAL MEDIA AND TEENAGERS

5.1: STUDIES GENERALLY SUPPORTING CONCERN ABOUT SOCIAL MEDIA

5.1.1. Madigan, Browne, & Racine (2019). Association Between Screen Time and Children’s Performance on a Developmental Screening Test. JAMA Pediatrics.

ABSTRACT: Importance  Excessive screen time is associated with delays in development; however, it is unclear if greater screen time predicts lower performance scores on developmental screening tests or if children with poor developmental performance receive added screen time as a way to modulate challenging behavior.
Objective:  To assess the directional association between screen time and child development in a population of mothers and children.
Design, Setting, and Participants:  This longitudinal cohort study used a 3-wave, cross-lagged panel model in 2441 mothers and children in Calgary, Alberta, Canada, drawn from the All Our Families study. Data were available when children were aged 24, 36, and 60 months. Data were collected between October 20, 2011, and October 6, 2016. Statistical analyses were conducted from July 31 to November 15, 2018.
Exposures:  Media.
Main Outcomes and Measures:  At age 24, 36, and 60 months, children’s screen-time behavior (total hours per week) and developmental outcomes (Ages and Stages Questionnaire, Third Edition) were assessed via maternal report.
Results:  Of the 2441 children included in the analysis, 1169 (47.9%) were boys.
A random-intercepts, cross-lagged panel model revealed that higher levels of screen time at 24 and 36 months were significantly associated with poorer performance on developmental screening tests at 36 months (β, −0.08; 95% CI, −0.13 to −0.02) and 60 months (β, −0.06; 95% CI, −0.13 to −0.02), respectively. These within-person (time-varying) associations statistically controlled for between-person (stable) differences.
Conclusions and Relevance:  
The results of this study support the directional association between screen time and child development. Recommendations include encouraging family media plans, as well as managing screen time, to offset the potential consequences of excess use.

5.1.2 Maras, Flament et al. (2015). Screen time is associated with depression and anxiety in Canadian youth. Preventive Medicine

ABSTRACT: Method: Participants were 2482 English-speaking grade 7 to 12 students. Cross-sectional data collected between 2006 and 2010 as part of the Research on Eating and Adolescent Lifestyles (REAL) study were used. Mental health status was assessed using the Children's Depression Inventory and the Multidimensional Anxiety Scale for Children—10. Screen time (hours/day of TV, video games, and computer) was assessed using the Leisure-Time Sedentary Activities questionnaire.

Results: Linear multiple regressions indicated that after controlling for age, sex, ethnicity, parental education, geographic area, physical activity, and BMI, duration of screen time was associated with severity of depression (β = 0.23, p < 0.001) and anxiety (β = 0.07, p < 0.01). Video game playing (β = 0.13, p < .001) and computer use (β = 0.17, p < 0.001) but not TV viewing were associated with more severe depressive symptoms. Video game playing (β = 0.11, p < 0.001) was associated with severity of anxiety.

Conclusion: Screen time may represent a risk factor or marker of anxiety and depression in adolescents. Future research is needed to determine if reducing screen time aids the prevention and treatment of these psychiatric disorders in youth.

[NOTE: Thanks to Ian Goddard for suggesting this article. It is down here rather than in section 1.1 because it collected data before social media became so popular, and it does not let us examine effects of social media specifically]

5.1.3. Brailovskaia, Rohman, Bierhoff et al. (2019). The relationship between daily stress, social support and Facebook Addiction Disorder. Psychiatry Research.

[h/t Ian Goddard]

ABSTRACT: The present study investigated the links between daily stress, social support, Facebook use, and Facebook Addiction Disorder (FAD). Two varieties of social support were considered, according to the communication channel: offline and online. In a sample of 309 Facebook users (age: M(SD) = 23.76(4.06), range: 18–56), daily stress was positively related to the intensity of Facebook use and to tendencies towards Facebook addiction. The link between daily stress and intensity of Facebook use was negatively moderated by perceived offline social support, indicating that individuals who received low levels of support offline were particularly likely to increase their Facebook use at higher levels of daily stress. Perceived online social support partly mediated the positive relationship between Facebook use intensity and tendencies towards FAD. It is remarkable that Facebook use intensity is systematically related to both positive (i.e., receiving online social support) and negative (i.e., building up FAD) consequences. Thereby, individuals who receive high levels of social support online tend to be at risk for tendencies towards FAD. Thus, while offline social support might protect mental health, online support might influence it negatively. This should be considered when assessing individuals at risk for obsessive Facebook use and when planning interventions to deal with FAD.

5.1.4 Kramer, Guillory, & Hancock (2014). Experimental evidence of massive-scale emotional contagion through social networks. PNAS.

[h/t Ian Goddard]

ABSTRACT: Emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], although the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and nonverbal cues are not strictly necessary for emotional contagion, and that the observation of others’ positive experiences constitutes a positive experience for people.

[COMMENT: the simplest mechanism by which social media may be causing depression and anxiety is simple contagion, combined with the general principle that “bad is stronger than good,” so if everyone shares equal amounts of bad and good stuff, negative moods would be the result across the network]

5.1.5 Marino, Gini, Vieno, & Spada (2018). The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. Journal of Affective Disorders.

[NOTE: this study is about “problematic” facebook behavior, which is, unsurprisingly, strongly related to bad mental health outcomes. It does not tell us about Facebook use overall.]

5.2: STUDIES GENERALLY SUGGESTING THAT SOCIAL MEDIA IS NOT HARMFUL

5.3: STUDIES THAT ARE RELEVANT BUT THAT DON’T POINT IN EITHER DIRECTION

6. DISCUSSION

[Please note that the tentative conclusions listed below are subject to change as we hear from critics]

We believe the evidence reviewed above supports these tentative answers to the three questions:

QUESTION 1: IS THERE AN ASSOCIATION BETWEEN SOCIAL MEDIA USE AND BAD MENTAL HEALTH OUTCOMES?

ANSWER 1: Yes, but with interesting qualifications. All of the studies we listed showed an association between hours of social media use and bad mental health outcomes. However, it also seems to be the case that:

A) there is little evidence of harm for light daily usage (e.g., an hour a day)

B) the relationships are usually curvilinear, with harmful effects usually only becoming visible for heavy users (and for moderate users in some cases). Light users are sometimes in slightly better shape than non-users.

C) In studies that offer a comparison between males and females, the associations with harm are generally larger for females.

D) Mental health problems consistently show stronger associations with social media use than other forms of screen time or device use, including watching TV and movies, and playing video games.

D) We do NOT know, from these studies, whether light daily usage is harmless for pre-teens. The studies mostly involved teens 14 and older, or else they failed to break out younger adolescents. Study 2.1.3 (Booker) looked at pre-teens and found that early heavy social media use, at age 10, was associated with declines in well being in later adolescence, for girls only.

QUESTION 2: DOES SOCIAL MEDIA USE AT TIME 1 PREDICT ANYTHING ABOUT MENTAL HEALTH OUTCOMES AT TIME 2?

ANSWER 2: Yes in six of the studies, no in two of the studies. That doesn’t mean that the “yes” side wins. Answering this question will require a wider set of papers, and a deeper dive into the papers. It is possible, given what we learned in section 1, that changes in average time spent on social media between T1 and T2 only matter when one shifts from being a consistently heavy user to being a consistently light user, or vice versa. Most changes (e.g., from 2 hours a day to a half hour, or vice versa) may indeed have no effect on mental health outcomes measured weeks or months later.

QUESTION 3: DO EXPERIMENTS USING RANDOM ASSIGNMENT SHOW A CAUSAL EFFECT OF SOCIAL MEDIA USE ON MENTAL HEALTH OUTCOMES?

ANSWER 3: So far, yes, but there are very few experiments and the results are sometimes mixed. Hunt, Marx, Lipson & Young (2018) found a beneficial effect on depression--for those who scored in the upper half of the study population on a measure of depression--to reducing daily social media usage to just 10 minutes per platform. However, the study failed to find effects on several other outcomes. Since true experiments with random assignment to condition are the best way to assess causality, we hope that many more such studies are in the works now. Experiments will be the most informative if they 1) focus on limiting social media use, not eliminating it (given the curve in the correlational studies) and 2) last for a week or longer so any “withdrawal” symptoms pass.

IN CONCLUSION: Many studies, using a variety of methods, have found associations between heavy social media use and bad mental health outcomes, particularly for girls. Some of the associations are very small, some are larger (e.g., a doubling of rates of depression as one moves from light to heavy usage in 1.1.4, Kelly et al. 2019; a large decline in depressive symptoms when college students were assigned to reduce social media usage in 3.1.1, Hunt et al. 2018). The recent publication of two papers that find no effect (2.2.1, Heffer et al. 2019), or negligible effects (1.2.1, Orben & Przybylski, 2019) is a normal part of the ongoing scientific debate about the effects of social media on teen mental health. We believe that journalists, legislators, parents, and teens would be making a potentially serious mistake if they interpret these two studies as offering an “all clear” signal for teens to use social media in unlimited quantities, or from an early age. But we welcome feedback from researchers who disagree, and we will post short response essays below. (Please contact Haidt to do so, or just request commenting access to this Google Doc.)

ADVICE FOR PARENTS [to come]

=========== COMMENTS FROM RESEARCHERS AS THEY HAVE REVIEWED THIS DOCUMENT;

From Tom Hollenstein: We must use what we already know about socio-emotional development to understand how these digital contexts do or do not differ from the real world. The developmental achievements, evolutionary and biological adaptations, parenting practices, and social interests of youth have not suddenly and inexorably changed with the presence of a device. The successful digital platforms that have engaged youth the most have been those that facilitate those basic needs and functions. Social support, for example, is a key protective factor. Pre-digital-age experiences can still occur, but now they can transcend time and space - a text message can be a reminder that you are not alone. Positive impacts such as these are going to inexorably cloud  the search for how and when digital contexts might be deleterious. Furthermore, crude measures of total time spent doing anything with peers have been more proximally explained by parental monitoring - in the absence of parental guidance youth will associate with deviant peers, antisocial behavior, social isolation, etc., all of which can also occur online. Hence, developmental models and more precise empirical investigations are the only way to make sense of digital contexts. Without these, any claim about the uniqueness of digital contexts is unfounded. 

============   CRITICAL RESPONSES FROM RESEARCHERS =========

From Patrick Markey (Villanova), via Twitter: “Another major issue with most of this research - almost all of these studies have used self report assessments that have never been validated.  Even more troubling - recent research suggests these self reports are not related to actual screen use.” [He links to this paper:]

Ellis, Davidson, Shaw, Geyer (2018). Do smartphone usage scales predict behaviour?

ABSTRACT: Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite recent methodological advances in portable computing and the ability to record digital traces of behaviour, research concerning smartphone use overwhelmingly relies on self-reported assessments, which have yet to convincingly demonstrate an ability to predict objective behaviour. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviours derived from Apple’s Screen Time application. While correlations between psychometric scales and objective behaviour are generally poor, measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favourably with subsequent behaviour. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviours. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage.

Ellis (2019). Are smartphones really that bad? Improving the psychological measurement of technology-related behaviors. Computers in Human Behavior [Nominated by Patrick Markey, on Twitter]

ABSTRACT: Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. To date, research within psychological science often frames new technology as problematic with overwhelmingly negative consequences. However, this paper argues that the latest generation of psychometric tools, which aim to assess smartphone usage, are unable to capture technology related experiences or behaviors. As a result, many conclusions concerning the psychological impact of technology use remain unsound. Current assessments have also failed to keep pace with new methodological developments and these data-intensive approaches challenge the notion that smartphones and related technologies are inherently problematic. The field should now consider how it might re-position itself conceptually and methodologically given that many ‘addictive’ technologies have long since become intertwined with daily life.

Markey and Przybylski both cited the problem of “outcome switching” as described in Orben & Przybylski (2019),

MORE CRITICS WILL BE ADDED HERE….