Appendix C
Table of Recent Systematic Reviews and Meta-Analyses on the Association Between Social Media and Adolescent Health
Committee and staff conducted three literature searches between January and June 2023, adjusting search terms each time with input from the committee. The terms aimed to find literature reviews, systematic reviews, and meta-analyses with a focus on the health effects of social media exposure on adolescent health that had been published since 2018. In addition, the committee and staff conducted hand searches for relevant landmark reviews or meta-analyses, some of which predate 2018. The results in the table below are not comprehensive but do convey some of the more notable results from the current literature available on the topic.
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
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Alimoradi et al., 2019 | 2007–2018 | n = 23 studies | Cohort, case-control, cross-sectional | Any age, assessed for internet addiction | Internet addiction | Sleep disturbances (presence of sleep problems and sleep duration) | Internet addiction is associated with sleep problems across all 23 studies, but the relationship may be influenced by the tool used to evaluate sleep problems. Children, teenagers, and young adults appear most susceptible to internet addiction as they are still developing. |
No evidence of publication bias. | |||||||
Alonzo et al., 2021 | January 1990 to November 2019 AND forward and backward citation tracing until April 2020 | n = 42 studies | Prospective cohort Cross sectional | Population of interest: ages 16–25 years Studies included: ages 12–30 years | Active social media use | Sleep quality and mental health outcomes | Longitudinal research suggests that sleep problems (sleep disruption and poor quality sleep) may at least partially explain the relationship between excessive social media use and mental health problems. Cross-sectional research is less conclusive. Social media use can contribute to mental health problems both directly and indirectly through sleep disturbance. |
No discussion of publication bias. |
Appel et al., 2020 | Not specified | Well-being n = 4 | Meta-analyses | General, students when relevant to question | Social networking site use | General well-being, academic performance, and narcissism | There is no strong linear association between social networking use and loneliness, self-esteem, life satisfaction, or self-reported depression. |
Academic performance n = 3 | |||||||
Social networking sites may provide a venue to create social capital and relationships—both close and shallow, but there is no strong evidence that social networking promotes well-being. | |||||||
Narcissism n = 3 | |||||||
There is no evidence of a pronounced effect of social networking use on academic performance. | |||||||
There is evidence of small to moderate associations between social networking site use and narcissism. | |||||||
Tests for publication bias not applicable, but topic discussed. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
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Berger et al., 2022 | January 2012 to March 2021 | n = 26 studies | Cohort or cross-sectional; qualitative, quantitative, or mixed methods | LGBTQ youths and adolescents (10–24 years) | Use of social media | Mental health outcomes | The quality of the research is limited, and causation cannot be inferred. Overall, there are both positive and negative aspects for social media use for LGBTQ young people. There is evidence of social media being beneficial for social connection and exploration of identity. Social media is associated with decreased symptoms of mental illness among LGBTQ young people, including decreased feelings of isolation and increased well-being. Heavy social media use can be associated with feelings of loneliness and sensitivity among LGBTQ young people. |
No discussion of publication bias. | |||||||
Brautsch et al., 2023 | January 2010 to April 2021 | n = 42 studies | Cross sectional Longitudinal | 16–25 years | Digital media use | Sleep outcomes | There is an association between general screen use and poor sleep quality and reduced duration of sleep; there is an association between use of social media, mobile phone, computer, and the internet and poor sleep quality and reduced duration of sleep. |
Most studies found an association between digital media use at night or before bed and poor sleep. | |||||||
No discussion of publication bias. | |||||||
Cataldo et al., 2021 | 2006 to July 31, 2020 | n = 44 studies | Studies assessing mental health and psychological disorders | 10–19 years with a profile on a popular social media site | Assessment of psychiatric disorders in developmental ages | Level of psychological well-being or diagnosis of psychiatric disorder | Some evidence of positive associations between having a social media profile and various mental health problems. |
No discussion of publication bias. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Cunningham et al., 2021 | 2012–2018 | n = 62 studies | Any quantitative studies | Interest in children and adolescents but included studies that had adult-only populations | Social media or social networking site use (examines time spent using social networking sites, intensity of use, and problematic use as three distinct constructs) | Depression or depressive symptoms | There is a weak association between time spent using social networking sites and depressive symptoms. |
Evidence of publication bias toward reporting higher effect sizes, effect size adjusted. | |||||||
There is a weak association between intensity of social networking site use and depressive symptoms. | |||||||
No evidence of publication bias. | |||||||
There is a moderate association between problematic use of social networking and depressive symptoms. This effect was not moderated by participants age or gender, by year of study, or by method of recruitment. | |||||||
No evidence of publication bias. |
Keles et al., 2020 | 2011–2018 | n = 13 studies | Cross-sectional (12) Longitudinal (1) | Ages 13–18 years | Measurement of social media use | Depression, anxiety, or psychological distress assessed by validated instruments | Time spent on social media; activity on social media; investment in the experience; and addiction were all correlated with depression, anxiety, and psychological distress. The direction of this relationship is not clear. |
Causality was unclear owing to the cross-sectional study designs and lack of comparator group. | |||||||
No discussion of publication bias. | |||||||
Kuss et al., 2021 | April 2013 to September 2019 | n = 64 studies | Cross-sectional studies with quantitative, qualitative, and mixed methods | Not specified, most samples primarily adolescent or young adult | Internet and internet gaming use Online gambling Online pornography use Social media use | Internet and internet gaming addiction Online gambling addiction Online pornography addiction Social media addiction | Estimates of the prevalence of internet addiction range from 12.6% to more than two-thirds. Risk factors for internet addiction include: psychological distress, mood disorders, suicidality, impulsivity, aggression, and sleep problems. Therapy may be effective at combating internet addiction. |
No discussion of publication bias. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Liu et al., 2016 | n = 58 studies | Empirical studies that used a Pearson correlation (r) or sufficient information from which an effect size for the association between social networking site use and social capital could be derived | Not specified in search | Social networking site use | Bonding and/or bridging of social capital | There is a moderate positive association between use of social networking sites and measures of bridging social capital. No evidence of publication bias. | |
There is small positive association between use of social networking sites and bonding social capital. Mixed evidence of publication bias; effect size was adjusted accordingly. | |||||||
Social networking sites are useful to help people build social capital, but more so with bridging than bonding social capital. |
Lozano-Blasco et al., 2022 | 2017–2020 | n = 20 studies k = 28 samples | Experimental and quantitative studies reporting standardized psychometric evaluations | Adolescents | n/a | Prevalence of internet addiction | Heterogeneity of results is high. |
Internet addiction or problematic use is comorbid with many other problems including obesity, anxiety, depression, stress, and internalizing disorders. | |||||||
There are personality factors that can predispose young people to internet addiction including: “introversion, inhibition, submissiveness, self-evaluation, obsessive-compulsive tendencies, phobic anxiety, hostility, paranoia, borderline personality, hostility, and low self-esteem.” | |||||||
Environmental factors such as family dysfunction, bad family communication, and boredom are associated with risk of addiction to technologies. A good relationship with teachers, positive feelings about one’s school, academic success, and physical activity are protective against internet addiction. | |||||||
Age explains 24% of the variance in internet addiction; gender does not explain the variance in prevalence of internet addiction. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Some studies suggest that internet addiction is the effect of other psychopathologies, not a cause of them. | |||||||
No evidence of publication bias. | |||||||
Mackenzie et al., 2022 | Inception until November 26, 2020 | n = 14 | Studies with a qualitative component with verbatim quotes linking sleep and social media | Studies with at least 80% participants ages 10–24 years | Use of social media, social networks, Twitter, Facebook, smartphones, and screentime | Perceived impact of social media use at bedtime on sleep | Themes of a social motivation for using social media at bedtime, habitual smartphone use, and recognition of the use as a problem emerged from qualitative studies. |
Tests for publication bias not applicable. | |||||||
McComb et al., 2023 | September 2006 to September 2021 | n = 48 studies | Experimental designs with random assignment and a control condition, social comparison through social media was key exposure | General, clinical populations excluded | Upward comparison on any social media platform | Subjective well-being, body image, mental health, and self-esteem | There is a small negative effect of upward social comparison on social media and users’ self-evaluation and emotions. |
There is a small negative effect of upward social comparison on body image, well-being, mental health, and self-esteem. | |||||||
Effects do not vary by age or gender. | |||||||
Mixed evidence of publication bias, effect size adjusted accordingly |
McCrae et al., 2017 | No time period applied to search | n = 11 studies | Cross-sectional, longitudinal | Ages 5–18 years | Social media (defined by authors as websites used primarily for social interaction, e.g., Facebook, WhatsApp, Instagram) | Depression or depressive symptoms | Authors found small correlation between social media use and depressive symptoms in children and adolescents. |
Mixed evidence of publication bias which reduced estimate of random effects. | |||||||
Meier and Reinecke, 2021 | 2010 to 2019 | n = 34 reviews | Systematic reviews and meta-analyses | General, some children and adolescent, some older adults | Computer-mediated communication: social media use, social networking, internet use, mobile phone use | Various mental health outcomes | Reviews suggest a very small negative association between social networking and mental health, even this depends largely on choice of mental health indicators; for applications other than social networking, evidence shows little to no association with mental health outcomes. |
Tests for publication bias not applicable to study design. However the meta-analyses included in the meta-review overall found little evidence of publication bias. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Memon et al., 2018 | No time limit | n = 9 studies | Observational and interven-tional studies | Ages 13–17 years | Use of social networking sites such as Facebook, Instagram, and Snapchat | Deliberate self-harm or suicidality | More time spent on social media is associated with greater self-harming behavior; social media is also an important medium for suicidal youth to seek help. |
No discussion of publication bias. | |||||||
Sedgwick et al., 2019 | Database inception through January 25, 2019 | n = 9 studies | Cross-sectional | Ages 11–18 years | Social networking site usage, problematic internet use, pathological internet use, or hours of noneducational internet use | Suicide attempts | Heterogeneity of exposures and outcomes prevented the combining of results in a meta-analysis, but data suggest problematic use of social media or the internet are associated with suicide risk; the direction of the potential association is not clear. |
No discussion of publication bias. | |||||||
Shannon et al., 2022 | January 2017 to April 2021 | n = 18 studies | Cross-sectional | Ages 12–30 years | Problematic social media use | Depressive symptoms, anxiety symptoms, and stress measured using validated instruments | There is evidence of a moderate correlation between problematic social media use and depression, anxiety, and stress. |
No evidence of publication bias on anxiety and stress outcomes, mixed evidence on depressive outcomes. |
Sina et al., 2022 | 2008 to 2021 | n = 35 studies | Randomized, controlled trials, cross-sectional and longitudinal studies | Ages 2–18 years | Social media exposure | Dietary behaviors | There is an association between social media exposure and unhealthy diet in children and adolescents. |
There was no relationship between exposure to social media and nutrition knowledge. | |||||||
Prolonged smartphone use is associated with unhealthy eating behaviors. | |||||||
No discussion of publication bias. | |||||||
Sohn et al., 2019 | January 1, 2011 to October 15, 2017 | n = 41 | Cohort (3) Cross-sectional (38) | Eligibility criteria included studies of mobile device exposure focusing on children and young people (with a mean population age of no greater than 25) | Problematic smartphone use | Prevalence of mental health issues, including depression, anxiety, stress, and sleep quality | Of the 41 studies included 22 were of poor methodological quality, 19 of moderate quality. |
Estimates of the prevalence of problematic smartphone use among children and adolescents was 23.3% (confidence interval 14.0 to 31.2%). | |||||||
No discussion of publication bias. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Stevens et al., 2021 | 2010–2019 | n = 53 | Studies reporting prevalence of internet gaming disorder or gaming disorder | Age not specified but majority of studies that provided the information had adolescent-only samples | n/a | Global prevalence of gaming disorder | Meta-analysis suggests a prevalence of gaming disorder between 3.05% (confidence interval: 2.38, 3.91 percent), but this figure was adjusted to 1.96% (confidence interval 0.19, 17.12% when considering only studies of higher methodological rigor. |
Choice of screening tool accounted for more than three-quarters of the variance in these results. | |||||||
Males outnumber females by 2.5 to 1 in prevalence of gaming disorder. | |||||||
The global burden of gaming disorder is comparable to obsessive-compulsive disorder and some substance-use disorders. | |||||||
No evidence of publication bias. |
Tang et al., 2021 | 2005 through August 2020 | n = 35 studies | Longitudinal only | Young people (ages 10–24 years) | Any type of screen time: television or video viewing time, computer/internet use, mobile phone use, social media use, and videogame use | Mental health outcomes: depression, anxiety, emotional problems, internalizing problems, etc. | “Some evidence to suggest a very small to small positive association between screen time and subsequent depressive symptoms”; relatively few studies find evidence of association between depressive symptoms and subsequent screen time. “Limited evidence of an association between television or videogames and subsequent depression”; “relatively stronger evidence of associations between mobile phone, computer, or internet use and subsequent depression. Evidence of the association between social media use and subsequent depression is mixed.” |
No discussion of publication bias. | |||||||
Valkenburg et al., 2022 | 2019 to mid-2021 | n = 25 | Systematic reviews, narrative reviews, and meta-analyses | Focused on adolescents | Active, passive, private, and public social media use, including social networking sites, Facebook | Mental health outcomes | Meta-analyses suggest weak association between social media use and higher ill-being and weak associations between social media use and levels of wellbeing, with considerable variability in the associations. Systematic and narrative reviews find small effects and inconsistent findings leaving more room from varying interpretations. |
Test for publication bias not applicable. |
Reference | Time Period of Review | Number of Studies | Study Designs Included | Populations Included (if specified) | Exposure(s) Included | Study Outcome(s) | Results |
---|---|---|---|---|---|---|---|
Yin et al., 2019 | 2005–2016 | n = 63 studies | Any quantitative study reporting a correlation or effect size | General population | Social networking site use excluding addictive or problematic behavior | Wellbeing, life satisfaction, depression, loneliness, anxiety, positive affect, negative affect | Use of social networking sites has small associations with both positive and negative indicators of mental health. These small effects appeared influenced by how social networking is measured, gender, and cultural background. |
No evidence of publication bias. | |||||||
Yoon et al., 2019 | Before February 2018 | Time on social networking n = 33 | Observational studies assessing depression by self-report or clinical interview | General population, including some specific to undergraduates | Time spent on social networking sites, frequency of checking sites, social comparisons | Depression | There is a small, positive correlation between frequency of checking social networking sites and time spent on sites and depressive symptoms, and this effect was not dependent on gender or age. |
Frequency of checking social networking n = 12 | No evidence of publication bias. | ||||||
Social comparison n = 5 | There is a small-to-medium correlation between depressive symptoms and social comparisons on social networking sites. | ||||||
No evidence of publication bias. |
There is a medium correlation between depressive symptoms and upward social comparison. Social comparison, both general and upward, is more correlated with depressive symptoms than social networking usage is. | |||||||
No evidence of publication bias. |
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