Available via license: CC BY 4.0
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=rady20
International Journal of Adolescence and Youth
ISSN: 0267-3843 (Print) 2164-4527 (Online) Journal homepage: https://www.tandfonline.com/loi/rady20
A systematic review: the influence of social media
on depression, anxiety and psychological distress
in adolescents
Betul Keles, Niall McCrae & Annmarie Grealish
To cite this article: Betul Keles, Niall McCrae & Annmarie Grealish (2019): A systematic review:
the influence of social media on depression, anxiety and psychological distress in adolescents,
International Journal of Adolescence and Youth, DOI: 10.1080/02673843.2019.1590851
To link to this article: https://doi.org/10.1080/02673843.2019.1590851
© 2019 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 21 Mar 2019.
Submit your article to this journal
View Crossmark data
A systematic review: the influence of social media on depression,
anxiety and psychological distress in adolescents
Betul Keles , Niall McCrae and Annmarie Grealish
Florence Nightingale Faculty of Nursing and Midwifery, King’s College London, London, UK
ABSTRACT
While becoming inextricable to our daily lives, online social media are blamed
for increasing mental health problems in younger people. This systematic
review synthesized evidence on the influence of social media use on depres-
sion, anxiety and psychological distress in adolescents. A search of PsycINFO,
Medline, Embase, CINAHL and SSCI databases reaped 13 eligible studies, of
which 12 were cross-sectional. Findings were classified into four domains of
social media: time spent, activity, investment and addiction. All domains
correlated with depression, anxiety and psychological distress. However,
there are considerable caveats due to methodological limitations of cross-
sectional design, sampling and measures. Mechanisms of the putative effects
of social media on mental health should be explored further through quali-
tative enquiry and longitudinal cohort studies.
ARTICLE HISTORY
Received 17 January 2019
Accepted 3 March 2019
KEYWORDS
Adolescents; social media;
depression; anxiety;
psychological distress
Introduction
Children and adolescent mental health
The World Health Organization (WHO, 2017)reportedthat10–20% of children and adolescents worldwide
experience mental health problems. It is estimated that 50% of all mental disorders are established by the
age of 14 and 75% by the age of 18 (Kessler et al., 2007;Kim-Cohenetal.,2003). The most common
disorders in children and adolescents are generalizedanxietydisorderanddepression, respectively
(Mental Health Foundation, 2018;Stansfeldetal.,2016). According to the Royal Society for Public
Health, & Young Health Movement (2017), the prevalence of anxiety and depression has increased by
70% in the past 25 years in young people. Depression and anxiety have adverse consequences on
adolescent development, including lower educationalattainment,schooldropout,impairedsocialrela-
tionships, and increased risk of substance abuse, mental health problems and suicide (Copeland, Angold,
Shanahan, & Costello, 2014;Goreetal.,2011;Hetrick,Cox,Witt,Bir,&Merry,2016). Morgan et al. (2017)
reported that the rate of self-harm in the UK has risen by 68% in girls aged 13–16overthelast10years.
Reasons for the apparently growing psychological morbidity in young people are not known conclu-
sively. McCrae (2018) suggests that diagnostic activity has been influenced by educational initiatives to
raise mental health awareness. Undeterred by stigma, many young people feel free to discuss their
psychological difficulties and seek professional help. Another important factor is the ease of sharing
personal experiences in the digital information age (Reid-Chassiakos, Radesky, Christakis, & Moreno, 2016).
Whereas in the past mental health problems were suffered in isolation, today a struggling younger person
can readily find others with similar problems, either through social interaction or support groups.
Alongside increasing awareness and help-seeking behaviour, doctors may be more inclined to diagnose
and treat mental health problems, possibly with the effect of lowering the diagnostic threshold.
CONTACT Betul Keles betul.keles@kcl.ac.uk
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH
https://doi.org/10.1080/02673843.2019.1590851
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/
4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Social media
The term ‘social media’refers to the various internet-based networks that enable users to interact
with others, verbally and visually (Carr & Hayes, 2015). According to the Pew Research Centre
(2015), at least 92% of teenagers are active on social media. Lenhart, Smith, Anderson, Duggan, and
Perrin (2015) identified the 13–17 age group as particularly heavy users of social media users, with
87% having access to a computer, and 58% to a tablet device. Almost three-quarters of adolescents
aged 15 to17 use a smartphone, and 68% of those aged 13 to 14 (Pew Research Centre, 2015).
Impact on mental health
Understanding the impact of social media on adolescents’well-being has become a priority due to
a simultaneous increase in mental health problems (Kim, 2017). Problematic behaviours related to
internet use are often described in psychiatric terminology, such as ‘addiction’.However,some
activity in younger people could be misconstrued as abnormal. For example, young people who
frequently post images of themselves (‘selfies’) may appear narcissistic, but such behaviour has
emerged as a social norm in younger social networks (McCrae, 2018). Nonetheless, warnings have
been issued by psychologists and other experts on how younger people are engaging with social
media and related impairment to personal and social development (Greenfield, 2014;Twenge,2006).
Social media could be regarded as a ‘double-edged sword’. Studies show the benefits of
enabling people to express their thoughts and feelings, and to receive social support (Deters &
Mehl, 2013; Lenhart et al., 2015; Lilley, Ball, & Vernon, 2014;O’Keeffe & Clarke-Pearson, 2011; Rosen,
2011). Research has also indicated a link between social media use and psychological problems.
A systematic review of 11 studies measuring social media use and depressive symptoms in children
and adolescents showed a small but statistically significant relationship (McCrae, Gettings, &
Purssell, 2017). A meta-analysis of 23 studies showed correlation of problematic Facebook use
and psychological distress in adolescent and young adults (Marino, Gini, Vieno, & Spada, 2018).
Other systematic reviews have also found a meaningful relationship between social media use and
depression (Best, Manktelow, & Taylor, 2014; Hoare, Milton, Foster, & Allender, 2016).
The link between social media and mental health problems is not straightforward, with various
contributory factors. A report by the Royal Society for Public Health, & Young Health Movement
(2017) suggested impaired sleep as a mechanism. Internet use is a sedentary behaviour, which in
excess raises the risk of health problems (Iannotti et al., 2009). A meta-analysis by Asare (2015)
showed that sedentary behaviour has a deleterious effect on mental health in young people,
although the direction of this relationship is unclear: people with mental health problems may be
more likely to be less physically active. Multitasking is common on social media, with users having
accounts on multiple platforms. A study by Rosen, Whaling, Rab, Carrier, and Cheever (2013)
showed that online multitasking predicts symptoms of mental disorders. Primack and Escobar-
Viera (2017) found that the number of social media accounts correlated with the level of anxiety,
due to overwhelming demand.
Another principal factor influencing the relationship between social media use and mental health is
social support. According to the report published by the American Academy ofPediatrics, social media
enable adolescent users to strengthen bonds with existing friends and to form new friendships online,
which reduce social isolation and loneliness, and indirectly improve mental health (O’Keeffe & Clarke-
Pearson, 2011). Studies support that those with low social support are more likely to suffer from
mental health problems (e.g. depression, anxiety and psychological distress) compared to those with
high social support from family, friends and neighbours (Klineberg et al., 2006; Maulik, Eaton, &
Bradshaw, 2011). Reviewing 70 studies, Seabrook, Kern, and Rickard (2016) found an inverse correla-
tion between supportive online interaction on social media and both depression and anxiety.
However, as some researchers (e.g. Teo, Choi, & Valenstein, 2013; Vandervoort, 1999) have indicated,
the quality of social support may be more important than quantity.
2B. KELES ET AL.
As explained by social comparison theory (Festinger, 1954), people tend to compare themselves
to others to assess their opinion and abilities. Interestingly, such behaviour is more common in
adolescents than in younger children and adults (Krayer, Ingledew, & Iphofen, 2008; Myers &
Crowther, 2009). The impact of social media on mental health may differ between adolescents
who engage in downward social comparison (comparing themselves to lower performers) and
those who use higher performers as a reference point. A systematic review by Seabrook et al.
(2016) reported a correlation between negative online interaction and both depression and
anxiety. Similarly, Appel, Gerlach, and Crusius (2016) found that passive Facebook use predicts
social comparison and envy, which in turn lead to depression.
Adolescence is the period of personal and social identity formation (Erikson, 1950), and much of
this development is now reliant on social media. Due to their limited capacity for self-regulation
and their vulnerability to peer pressure, adolescents may not evade the potentially adverse effects
of social media use, and consequently, they are at greater risk of developing mental disorder.
However, evidence on the influence of social media on adolescents’psychosocial development
remains at an early stage of development. Much of the research to date has studied young people
of later adolescence and college or university students. Previous systematic reviews included more
studies since they have either focussed on a heterogeneous population including children, ado-
lescents and adults (Baker & Algorta, 2016; Marino et al., 2018; Seabrook et al., 2016) or focussed on
general mental well-being including both clinical outcomes and subjective well-being as the
outcome of interest (Best et al., 2014; Marino et al., 2018).
Current study
This systematic review examined evidence for the influence of social media use on depression,
anxiety and psychological distress in adolescents. The intention was to inform policy and practice
and to indicate further research on this topic.
Method
Protocol and registration
For transparency, the protocol for this review was registered with the International Prospective
Register of Systematic Reviews (Prospero; CRD42018102770). This report follows the guidelines of
the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement
(Moher, Liberati, Tetzlaff, & Altman, 2009a).
Eligibility criteria
For inclusion in this review, studies fulfilled the following eligibility criteria:
●Participants: aged 13 to 18
●Exposure: measurement of social media use
●Outcome: depression, anxiety or psychological distress, assessed by validated instruments
●Studies published in peer-reviewed journals with full text available in English
Studies were excluded if they crossed either boundary of the age range. Studies measuring
exposure to other internet activities such as video-gaming were not included unless social media
use was also measured. Outcomes of substance misuse, eating disorder, well-being, life satisfaction,
self-esteem, body image problems, conduct disorders, loneliness or stress were excluded, unless
the outcomes of interest were also measured by the researchers.
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 3
Search strategy
The databases Medline, Embase, PsychINFO, Cumulative Index to Nursing and Allied Health
(CINAHL) and Social Sciences Citation Index (SSCI) were systematically searched in May 2018.
A set of search terms was created with truncations, Medical Subject Headings (MESH) and
Boolean operators, as shown in Table 1.
Data extraction
All papers from the automated database searches were collated using the Mendeley reference
management software. After duplicates were deleted, screening was conducted to ensure that
studies fulfilled the eligibility criteria. In a three-stage process, papers were screened on title and on
abstract (by BK) and the remaining papers were screened on full text (by BK, NM and AG). Key
information relevant to the research question was systematically extracted and tabulated to aid
comparison and synthesis of the studies. These data comprised authors, publication date, country
of origin, study design and data analysis method, relevant outcome measures, sample size,
demographic data and results. The extraction process was conducted by BK and AG and any
disagreements resolved through discussion with NM.
Assessment of quality
The quality of eligible studies was assessed using the National Institutes of Health Quality
Assessment tool for Observational Cohort and Cross-Sectional Studies (NIH, 2014), which covers
design, selection bias, data collection, confounders, blinding and attrition. An overall rating of
‘good’,‘fair’or ‘poor’was given for each study. All of the studies were independently rated by BK
and AG, and any disagreements were resolved through discussion with NM.
Data analysis
As outcome measures varied across the studies, we were unable to perform meta-analysis. Instead,
narrative synthesis was conducted. This enabled consideration of confounding, mediating and
moderating variables, which are often not given due attention in meta-analysis (Popay et al., 1995).
Each study was described, followed by comparative analysis and synthesis.
Results
The literature search yielded 6598 articles from the five databases. After 1818 duplicates were
removed, screening on title excluded 4206 of the 4780 unique papers. The remaining 574 articles
were screened on abstract, with 475 removed, leaving 99 papers. On reading the full text, 86
papers found to be ineligible, the most common reason being the age range. The PRISMA (Moher,
Liberati, Tetzlaff, & Altman; The PRISMA Group, 2009b)flowchart (Figure 1) provides further detail
on reasons for exclusion. Ultimately a total of 13 papers were eligible for the review.
Table 1. Search terms and linkage (Medline).
Participants exp Adolescent/or adolescen* OR teen* OR youth* OR young OR juvenile OR ‘high school student*’OR
‘secondary school student*’
AND
Exposure social network* OR exp Social Networking/or exp Social Media/OR Facebook OR Instagram OR twitter
AND
Outcomes exp Mental Health/OR ‘psychological well-being’OR exp Mental Disorders/or exp Mood Disorders/or exp
Depression/or affective disorder* or exp Affective Symptoms/or exp Depressive Disorder/OR
psych* OR exp anxiety/or exp anxiety disorders/OR exp Stress, Psychological/or ‘psychological distress*’
4B. KELES ET AL.
Description of studies
The reviewed papers are summarized in Table 2. In design, 12 studies were cross-sectional and only
one was longitudinal. The total sample across the studies was 21,231. Three studies were conducted
in Australia, three in China, and one each in Serbia, USA, Malaysia, Belgium, Thailand and Canada; one
study was conducted in six European counties including Greece, Spain, Poland, the Netherlands,
Romania and Iceland. Participant ages ranged from 13 to 18. Nine studies covered exposure to social
media in general, while four studies (Banjanin, Banjanin, Dimitrijevic, & Pantic, 2015;Dumitrache,
Mitrofan, & Petrov, 2012; Frison & Eggermont, 2016; Hanprathet, Manwong, Khumsri, Yingyeun, &
Phanasathit, 2015) focused specifically on Facebook use. As well as measuring depression, anxiety or
psychological distress, some studies investigated confounding variables (e.g. age and gender) and
mediating and moderating factors (e.g. insomnia, rumination and self-esteem). Ten of the studies
entailed both correlational and regression analyses; one study (Dumitrache et al., 2012)performed
correlational analyses and a t-test for gender differences; Vernon, Modecki, and Barber (2017)used
latent growth modelling to test longitudinal mediation.
Non-relevant records excluded
(n = 4206)
Records screened by abstract (n = 574)
Records identified through database
searching (n = 6598)
Embase = 1764
Medline = 1568
PsychINFO = 935
CINAHL = 1112
SSCI = 1219
Records excluded (n = 475)
Studies included in narrative
synthesis
(n = 13)
Additional studies identified
through the reference lists of
included studies (n = 0)
Full-text articles assessed for eligibility
(n = 99)
Full-text articles excluded (n = 86)
with reaso ns;
Not meeting;
The ‘P’ Criteria (n=48)
The ‘E’ Criteria (n=4)
The ‘O’ Criteria (n=12)
Not original (n=11)
Not peer-reviewed (n=2)
Adolescents with existing MH
problems (n= 2)
Non-English (n = 7)
Records screened by title
(n = 4780)
Records after duplicates removed
(n = 4780)
Figure 1. PRISMA 2009 flow diagram.
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 5
Table 2. Summary of studies.
Study Aim Design Sample size Sample characteristics Outcome measure(s) Findings/results
O’Dea and Campbell (2011) To explore the effect of online
interaction on psychological
distress
Cross-sectional 400 Aged 13–16
54.8% female
The Kessler (K-6) Scale
(Kessler et al., 2003)
Negative correlation
between the time spent
on SM and psychological
distress
Dumitrache et al. (2012) To emphasize the relations
between depression and the
identity outlined on FB
Cross-sectional 76 Aged 16–17
68.4% female
Beck Depression
Inventory (Beck, Ward,
Mendelson, Mock, &
Erbaugh, 1961)
Significant correlations
between depression and
the number of identity
related pieces of
information on FB
Neira and Barber (2014) To investigate the relationship
between social media use
and depressed mood
Cross-sectional 1819 Aged 13–17
55% female
Depressed Mood Scale
(Cronbach’sα= 0.76)
No meaningful relationship
between SM use
frequency & depressed
mood.
A positive association with
investment in SM and
depressed mood
Tsitsika et al. (2014) To investigate the associations
between heavier SNS use and
internalizing behaviours.
Cross-sectional 10,930 Aged 14–17
52.3% female
Youth Self Report (YSR)
problem checklist
(Achenbach & Resorta,
2001)
A positive association
between heavier SM use
(more than 2 h/day) and
internalizing problems
(anxiety and depression).
Hanprathet et al. (2015) To investigate the relationship
between Facebook addiction
and mental health
Cross-sectional 832 Mean age = 16.7 years
SD = 1.0
Grade 10
th
–12
th
63.5% female
The Thai General Health
Questionnaire (GHQ-
28; Goldberg &
Williams, 1988)
A positive association
between Facebook
addiction and depression
Sampasa-Kanyinga and Lewis (2015) To explore the relationship
between SNSs use and
psychological distress
Cross-sectional 753 Mean age = 14.1 years
Grades 7
th
–12
th
48.5% female
The Kessler (K-10) Scale
(Kessler et al., 2002,
Kessler et al., 2003)
The use of SNSs more than 2
hours per day was related
to increased level of
psychological distress.
Banjanin et al. (2015) To investigate potential
relationship between internet
addiction and depression
Cross-sectional 336 Mean age = 18 years
66% female
Center for Epidemiologic
Studies of Depression
Scale for Children
(Faulstich, Carey,
Ruggiero, Enyart, &
Gresham, 1986)
No relationship between
time spent on SM and
depression or between SM
activities and depression.
(Continued)
6B. KELES ET AL.
Table 2. (Continued).
Study Aim Design Sample size Sample characteristics Outcome measure(s) Findings/results
Frison and Eggermont (2016) To provide a deeper
understanding of the
relationships between
different types of Facebook
use, perceived online social
support, and boys’and girls’
depressed mood
Cross-sectional 910 Mean age = 15.44 years
SD = 1.71
51.9% female
Center for
Epidemiological
Studies Depression
Scale for Children
(Faulstich et al., 1986)
Positive correlation between
passive FB use and
depressed mood as well
as between active FB use
and depressed mood
Perceived online social
support mediated this
relationship; and gender
influenced this
association.
Vernon et al. (2017) To examine change in
problematic social
networking investment and
disrupted sleep, in relation to
change in depressed mood
and externalizing behaviour
Cohort 874 Mean age = 14.4 years
Grade 9
th
and 11
th
59% female
Depressed mood scale
(adapted from the
longitudinal Michigan
Study of Adolescent
Life Transitions)
(Barber, Eccles, &
Stone, 2001)
Increased investment in SM
predicted higher
depressed mood in
adolescents, which was
explained by the impact
of higher levels of sleep
disruptions.
Barry, Sidoti, Briggs, Reiter, and
Lindsey (2017)
To determine the relations
between adolescent social
media use and adolescents
psychosocial adjustment
Cross-sectional 226
(113
adolescent-
parent
dyads),
Aged 14–17
45.1% female
N = 7 (6.2% did not report
gender)
DSM checklist V (APA,
2013)
SM activity (#of accounts,
frequency of checking)
was moderately, positively
correlated with anxiety
and depression as
reported by parents.
Li et al. (2017) To examine the mediating
effects of insomnia on the
associations between online
social networking addiction
and depression
Cross-sectional 1015 Grade 7
th
–9
th
41.2% female
Chinese version of the
Center for
Epidemiological
Studies Depression
scale (Chen, Yang, &
Li, 2009)
A significant association
between SM addiction
and depression and
insomnia partially
mediated this
relationship.
Yan et al. (2017) To determine the time spent on
SNSs, and its association with
anxiety
Cross-sectional 2625 13–18 years
46.9% female
Middle School Student
Mental Health Scale to
measure anxiety
(developed by Wang,
2006)
A positive association
between time spent on
SM and the level of
anxiety. More than 2
hours/day and &anxiety
level
Wang et al. (2018) To examine whether rumination
mediated the relation
between SNS addiction and
depression, and whether the
mediating effect was
moderated by self-esteem
Cross-sectional 365 Age 14–18 year
(M
age
= 15.96;
SD = 0.69)
52% female
CES-D scale for
depression
(Radloff,1977)
SM addiction and depression
were positively associated.
Rumination mediated this
relationship and self-
esteem moderated this
mediation
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 7
Quality assessment
The aim was clearly stated for almost all of the studies, but generally, the methodological quality
was poor to fair (Table 3). The cross-sectional design of 12 studies is susceptible to three common
sources of bias: selection, information and confounding bias (Yu & Tse, 2012). Two studies (Barry
et al., 2017; Wang et al., 2018) recruited by convenience sampling, which raises the risk of selection
bias. O’Dea and Campbell (2011) omitted their sampling procedure. Apart from Hanprathet et al.
(2015), the papers did not state or explain their intended sample size, of which two were small
(Barry et al., 2017; Dumitrache et al., 2012). Five studies did not report the participant response rate.
Barry et al. (2017) had a response rate of 33%, which increases the risk of bias and limits general-
izability of the results.
Table 3. Quality assessment.
Authors and year of publication
Quality
rating Quality appraisal findings
O’Dea and Campbell (2011) Poor Cross-sectional design
Demographic information was not clearly defined
Exposure measure poorly defined, and no details about its validity and
reliability reported
Selectivity in reporting findings; researcher emphasized on the outcome of
self-esteem more than psychological distress
Dumitrache et al. (2012) Poor Cross-sectional design
Small sample size < 300, high risk of bias
Measures were not clearly defined, and validity of measures was not reported
Neira and Barber (2014) Good Cross-sectional design
Sample size was not justified
Tsitsika et al. (2014) Fair Cross-sectional design
Sample size was not justified
No standardized instrument to measure exposure was used
Hanprathet et al. (2015) Fair Cross-sectional design
Demographics of participants were not clearly defined
Inconsistent reporting of sample size, different number in the abstract
(972), methods (n = 832), and results (n = 872)
Time period of data collection not clearly reported
Sampasa-Kanyinga and Lewis (2015) Good Cross-sectional design
Sample size was not justified
Banjanin et al. (2015) Poor Cross-sectional design
Demographics of sample was not sufficiently described
Sample size was not justified
Relatively small sample size from one school may not represent a larger
population
Frison and Eggermont (2016) Fair Cross-sectional design
Sample size was not justified
Vernon et al. (2017) Fair Exposure was not measured prior to outcome measure
No sample size justification
Barry et al. (2017) Poor Cross-sectional design
Small sample size < 300, high risk of bias
Lower participation rate than 50% reduces the degree to generalizability of
study findings
Sample size was not jutified
Self-selection bias based on the parents who likely were most interested in
participating in the first place and the adolescents who subsequently did so
Li et al. (2017) Fair Cross-sectional design
Sample size was not justified
Age range of the participants was not provided
Yan et al. (2017) Poor Cross-sectional design
Sample size was not justified
Validity of outcome measure was not reported
Wang et al. (2018) Poor Cross-sectional design
Sample size was not justified
Use of a convenience sample: all participants were recruited from the same
middle school; the representativeness of the sample is limited
8B. KELES ET AL.
Four studies (Dumitrache et al., 2012;O’Dea & Campbell, 2011; Tsitsika et al., 2014) failed to
clearly define the exposure measures and to explicitly report their validity and reliability. Almost all
studies presented a clear definition of the outcome measures, which in most cases were shown as
valid and reliable. Two studies (Dumitrache et al., 2012; Yan et al., 2017) briefly stated the outcome
measures without providing detail on their validity. All studies administered self-report question-
naires, which is a potential source of social desirability bias (Yu & Tse, 2012). Risk of bias and
procedures to reduce this were inadequately considered in most study reports. In the only cohort
study (Vernon et al., 2017), participants were assessed annually over three years, but the research-
ers did not measure exposure at baseline.
Analysis of results
Key findings of the studies were classified into four common domains of exposure to social media:
time spent, activity, investment and addiction. Time spent refers to the amount of time that users
spent on social media. Activity can be defined as the quality and quantity of users’engagement
and interaction with social media sites and other users. Investment refers to the act of putting
effort and time into social media whereas addiction refers to the state of being dependent on
social media. For each domain we discuss the relationship with depression, anxiety and psycholo-
gical distress, with reference to confounding, mediating or moderating variables if measured.
Time spent
The studies produced opposing evidence on the relationship between time spent on social media
and mental health problems. With an Australian sample, O’Dea and Campbell (2011) found an inverse
correlation for psychological distress; no relationship between frequency of social media use and
depressed mood was reported by Neira and Barber (2014) in another study in Australia, and Banjanin
et al. (2015) in Serbia. By contrast, Sampasa-Kanyinga and Lewis (2015) in Canada found that daily
social media use of over two hours was associated with psychological distress. A study of 10,930
adolescents from six European countries by Tsitsika et al. (2014) showed a positive relationship
between heavy social media use and both depression and anxiety. Yan et al. (2017) found that time
spent on social media was associated with anxiety in Chinese adolescents.
Activity
Frison and Eggermont (2016) found that both active and passive use of Facebook, in a sample of Belgian
high school pupils, correlated with an increased frequency of depressed mood. In a study of 113
adolescent-parent dyads, Barry et al. (2017) found that data from parents showed correlation between
adolescents’social media activities (i.e. number of accounts, frequency of checking for messages) and
both anxiety and depression. However, Banjanin et al. (2015)didnotfind any relationship between social
media activities (i.e. number of ‘selfies’) and depression in Serbian high school pupils.
Investment
Dumitrache et al. (2012) found a significant correlation between the number of identity-related
information on Facebook profiles and depressive tendencies in adolescents. The studies by Neira
and Barber (2014) and by Vernon et al. (2017), both using secondary data from the Youth Activity
Participation Study of Western Australia, investigated the relationship between investment in social
media and depressed mood. The cross-sectional study by Neira and Barber (2014) showed that
investment in social media sites was associated with an increased depressed mood. Vernon et al.
(2017) conducted a longitudinal investigation and found an association between problematic social
media investment and depressed mood, with sleep disruption as a mediating variable.
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 9
Addiction
Three studies focused on addictive behaviour. Hanprathet et al. (2015) found a significant associa-
tion between Facebook addiction and depression among 972 high school pupils in affluent districts
in Thailand. A study of Chinese secondary school students by Li et al. (2017) showed a mediating
influence of insomnia on the statistically significant relationship between social media addiction
and depression. In another study in China, Wang et al. (2018) found that social networking sites
addiction was positively associated with depression; rumination mediated the relationship
between social networking sites addiction and depression while self-esteem moderated this
mediating effect. In other words, low self-esteem compounded the impact of addiction on
depression through rumination.
Confounding factors
Four studies measured the effect of gender in the relationship between social media-related
variables and mental health outcomes. Neira and Barber (2014) found that social media might
have negative aspects for female youth while being a positive leisure activity for male youth. Frison
and Eggermont (2016) found that girls who passively use Facebook and boys who actively use
Facebook in a public setting were more likely to be affected by the negative impacts of Facebook.
Banjanin et al. (2015) did not find any significant effect of gender in the relationship between
depression and time spent on social media. Similarly, Barry et al. (2017) did not find any change in
the analysis when controlling for gender in the relationship between social media use and
depression as well as between social media use and anxiety.
Two studies measured the effect of age. Tsitsika et al. (2014) found a significant effect of age in the
relationship between heavy social media use and negative internalizing symptoms (anxious/depressed,
withdrawn/depressed), with younger heavier social media users being more likely to experience
internalizing symptoms compared to older heavier users. Banjanin et al. (2015) did not find any
significant age effect in the relationship between depression and time spent on social media.
Discussion
This systematic review examined the evidence for a putative relationship between social media use
and mental health problems in adolescents. In the 13 studies, depression was the most commonly
measured outcome. The prominent risk factors for depression, anxiety and psychological distress
emerging from this review comprised time spent on social media, activities such as repeated
checking for messages, personal investment, and addictive or problematic use.
Although results of the studies were not entirely consistent, this review found a general
correlation between social media use and mental health problems. However, most authors noted
that the observed relationship is too complex for straightforward statements. Few studies were
designed to explore this complexity although some assessed the effect of mediating and moder-
ating factors. Insomnia and other sleep-related factors were most frequently reported as mediators
of the relationship between social media use and depressed mood (Li et al., 2017; Vernon et al.,
2017). Perceived social support (Frison & Eggermont, 2016) and rumination (Wang et al., 2018) were
other mediating factors reported in the studies. Researchers suggested further investigation of
these factors, and other factors such as personal traits (O’Dea & Campbell, 2011), socio-cultural
factors that influence the roles of and expectations from adolescents in family and society,
environmental factors which may affect development of adolescents and social skills (Tsitsika
et al., 2014), motivations for social media use (Barry et al., 2017;O’Dea & Campbell, 2011), social
comparison and peer feedback (Neira & Barber, 2014), self-esteem (Banjanin et al., 2015), contextual
factors, lack of physical activity, and cyberbullying (Sampasa-Kanyinga & Lewis, 2015).
Other important findings of this review suggest that particular attitudes or behaviours (e.g.
social comparison, active or passive use of social media, motives for social media use) may have
10 B. KELES ET AL.
a greater influence on the symptoms of depression, anxiety and psychological distress than the
frequency of social media use or the number of online friends. Although there is evidence of
a relationship between time spent on social media and depression as well as social media-related
activities and depression, contrary findings have also emerged. For example, Banjanin et al. (2015)
found no relationship between the amount of time spent on social media and depression, or
between social media-related activities such as the number of online friends and the number of
‘selfies’and depression. Similarly, Neira and Barber (2014) found that while higher investment in
social media (e.g. active social media use) predicted adolescents’depressive symptoms, no relation-
ship was found between the frequency of social media use and depressed mood. Such mixed
findings might be explained by confounders, mediators and moderators as discussed above.
This systematic review also sheds light on the influence of age and sex. Although some studies
found that these variables had no effect on the relationship between social media use and mental
health problems, other studies showed that girls and younger adolescents are more prone to
depression and anxiety. Further investigation is needed to assess the effects of age and gender.
Limitations
Although the results of this systematic review contributed to the existing literature in a way of
providing considerable evidence for the mental health impact of social media use by focussing on
not only the symptoms of depression but also other related outcomes including anxiety and
psychological distress among adolescents who are at higher risk of developing anxiety and
depression. Several limitations in the evidence emerged from included studies and review process
have been identified. First, 12 out of 13 studies did not answer the review question since they were
cross-sectional and unable to determine a causal relationship between the variables of interest.
Looking evidence emerged from cross-sectional studies, it is not possible to decide whether social
media use causes depression, anxiety and psychological distress, or whether those with depression,
anxiety and psychological distress are more likely to spend more time on social media; have
addictive and problematic social media use behaviour; have negative interaction with social
media; and invest on social media. Only one longitudinal study (Vernon et al., 2017) investigated
the causal relationship between problematic social media use and change in depressed mood, but
this study has also limited to show evidence whether social media use causes depressed mood in
adolescents. The study did not use a control and a comparison group to differentiate those who
exposed to social media sites and those who not. Therefore, it is difficult to determine whether
a change in depressed mood was more in those who exposed to social media more compared to
those who less or not.
Second, small sample size and the use of convenience sampling in some studies limited the
representativeness of and generalizability to a larger adolescent population. Third, all studies
included in this review used self-report measures which may not provide reliable outcomes
because of some sources of risk of bias. Participants may show positive self-presentation by
over- or under-reporting their social media-related behaviours and some mental health-related
items, which may directly or indirectly lead to social desirability bias, information bias and report-
ing bias. Another identified limitation was that some studies made an investigation towards only
Facebook use over other social media sites, which also causes a significant bias and limits the
generalizability of findings to other social media sites. Finally, despite the fact that the proposed
relationship between social media-related variables, depression, anxiety and psychological distress
is complex, few studies investigated mediating factors that may contribute or exacerbate this
relationship. Further investigations are needed to explain the underlying factors that help deter-
mine why social media has negative impact on some adolescents’mental health whereas it has no
or positive affect on others’mental health.
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 11
Conclusion
The impact of social media use on incidence of depression, anxiety and psychological distress
among adolescents, as examined by this review, is likely to be multifactorial. It is important to
distinguish between the terms used for the relationship. It is fair to say that there is an ‘association’
between social media use and mental health problems, on the basis that this means a socially
constructed reality. But this is not necessarily scientifically valid. Objective researchers investigate
correlations rather than accepting socially assumed truths. Correlation is statistical, not phenom-
enal. Thirdly, there is causation, which requires directional evidence. The latter has not been
adequately investigated in this topic, and we must, therefore, state that the relationship is correla-
tional but not conclusively causative.
Key findings of included studies were classified into four categories of exposure to social media:
time spent; activity; investment; and addiction. All these categories were found as correlated with
depression, anxiety and psychological distress, with an acknowledgement for the complexity of
these relationships. Although there are studies which investigated mediating and moderating
factors that may contribute or exacerbate the proposed relationship, there are still several under-
explored mediators and moderators, which may explain the direction of this relationship. We also
identified gaps in literature in terms of methods, study design and sampling. Causality was unclear
due to the cross-sectional study design used in almost all studies and the lack of comparison group
in the cohort study. Also, the number of quantitative studies in literature is substantially higher
than qualitative studies. Through this systematic review, we hope we contribute to the existing
literature in the way of addressing the gaps and highlighting the importance of the phenomenon
of the mental health impact of social media use on adolescents.
Acknowledgments
The authors would like to thank Dr Sorina Daniela Dumitrache who supplied us the full-text of their study which was not
available online. The authors also declare that there is no conflict of interest regarding the publication of this article.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Betul Keles is a Ph.D. student at King's College London. She is interested in child and adolescent mental health.
Dr. Niall McCrae is a lecturer at King's College London, with research interests in mental healthcare of older people,
role development in mental health nursing, and the history of psychiatric institutions and their nursing workforce.
Author of almost 100 articles in professional journals and bulletins, Dr McCrae has also written two books: The Moon
and Madness (Imprint Academic, 2011) and The Story of Nursing in British Mental Hospitals: Echoes from the
Corridors, co-authored with Professor Peter Nolan (Routledge, 2016). He is also a substantial contributor to the
leading textbook: The Art and Science of Mental Health Nursing, Edited by Ian Norman and Iain Ryrie (2018).
Dr. Annmarie Grealish is a lecturer in mental health nursing at the University of Limerick, with research interests in
empowerment, wellbeing, and early interventions for young people with mental health problems. She has 20 years of
extensive experience in clinical practice and education in mental health. She has worked in a number of clinical
settings, including specialist Child and Adolescent Mental Health Services (CAMHS) in NHS Lothian. She also under-
took research funded by the Scottish Executive in 2001 evaluating and implementing Telehealth in CAMHS whilst
working as a CBT and IPT practitioner.
ORCID
Betul Keles http://orcid.org/0000-0001-7724-9953
12 B. KELES ET AL.
References
Achenbach, T. M., & Resorta, L. A. (2001). Manual for ASEBA school-age forms and profiles (pp. 99–107). Burlington, VT:
University of Vermont, Research Center for Children, Youth, & Families. doi:10.1080/713932693
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA:
American Psychiatric Publishing.
Appel, H., Gerlach, A. L., & Crusius, J. (2016). The interplay between Facebook use, social comparison, envy, and
depression. Current Opinion in Psychology,9,44–49.
Asare, M. (2015). Sedentary behaviour and mental health in children and adolescents: A meta-analysis. Journal of Child
and Adolescent Behavior,3, 259.
Baker, D. A., & Algorta, G. P. (2016). The relationship between online social networking and depression: A systematic
review of quantitative studies. Cyberpsychology, Behavior, and Social Networking,19(11), 638–648.
Banjanin, N., Banjanin, N., Dimitrijevic, I., & Pantic, I. (2015). Relationship between internet use and depression: Focus
on physiological mood oscillations, social networking and online addictive behavior. Computers in Human Behavior,
43, 308–312.
Barber, B. L., Eccles, J. S., & Stone, M. R. (2001). Whatever happened to the jock, the brain, and the princess? Young adult
pathways linked to adolescent activity involvement and social identity. Journal of Adolescent Research,16(5), 429–455.
Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., & Lindsey, R. A. (2017). Adolescent social media use and mental
health from adolescent and parent perspectives. Journal of Adolescence,61,1–11.
Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives
of General Psychiatry,4, 561–571.
Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing:
A systematic narrative review. Children and Youth Services Review,41,27–36.
Carr, C. T., & Hayes, R. A. (2015). Social media: defining, developing, and divining. Atlantic Journal of Communication,23
(1), 46–65.
Chen, Z. Y., Yang, X. D., & Li, X. Y. (2009). Psychometric features of CES-D in Chinese adolescents. Chinese Journal of
Clinical Psychology,17(4), 443–448.
Copeland, W. E., Angold, A., Shanahan, L., & Costello, E. J. (2014). Longitudinal patterns of anxiety from childhood to
adulthood: The great smoky mountains study. Journal of the American Academy of Child & Adolescent Psychiatry,53
(1), 21–33.
Deters, F. G., & Mehl, M. R. (2013). Does posting facebook status updates increase or decrease loneliness? An online
social networking experiment. Social Psychological and Personality Science,4(5), 579–586.
Dumitrache, S. D., Mitrofan, L., & Petrov, Z. (2012). Self-image and depressive tendencies among adolescent Facebook
users. Revista De Psihologie,58, 285–295.
Erikson, E. H. (1950). Childhood and society. New York, NY, US: W W Norton & Co.
Faulstich, M. E., Carey, M. P., Ruggiero, L., Enyart, P., & Gresham, F. (1986). Assessment of depression in childhood and
adolescence: An evaluation of the center for epidemiological studies depression scale for children (CES-DC).
American Journal of Psychiatry,143(8), 1024–1027.
Festinger, L. (1954). A theory of social comparison processes. Human Relations,7, 117–140.
Frison, E., & Eggermont, S. (2016). Exploring the relationships between different types of facebook use, perceived
online social support, and adolescents’depressed mood. Social Science Computer Review,34(2), 153–171.
Goldberg, D. P., & Williams, P. (1988). A user’s guide to the general health questionnaire: Windsor, berks: Retrieved
from http://trove.nla.gov.au/work/18049508?q&versionId=21182576
Gore, F. M., Bloem, P. J., Patton, G. C., Ferguson, J., Jospeh, V., Coffey, C., . . . Mathers, C. D. (2011). Global burden of
disease in young people aged 10–24 years: A systematic analysis. Lancet,377, 2093–2102.
Greenfield, S. (2014). Mind change: How digital technologies are leaving their mark on our brains. London: Rider.
Hanprathet, N., Manwong, M., Khumsri, J., Yingyeun, R., & Phanasathit, M. (2015). Facebook addiction and its relationship
with mental health among Thai high school students. Journal of the Medical Association of Thailand,98, S81–S90.
Hetrick, S. E., Cox, G. R., Witt, K. G., Bir, J. J., & Merry, S. N. (2016). Cognitive behavioural therapy (CBT), third-wave CBT
and interpersonal therapy (IPT) based interventions for preventing depression in children and adolescents.
Cochrane Database of Systematic Reviews (Online),9,8.
Hoare, E., Milton, K., Foster, C., & Allender, S. (2016). The associations between sedentary behaviour and mental health
among adolescents: A systematic review. International Journal of Behavioral Nutrition and Physical Activity. BioMed
Central Ltd. doi: 10.1186/s12966-016-0432-4.
Iannotti, R. J., Janssen, I., Haug, E., Kololo, H., Annaheim, B., Borraccino, A., . . . Roberts, C. (2009). Interrelationships of
adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. International
Journal of Public Health,54((SUPPL), 2.
Kessler, R. C., Amminger, G. P., Aguilar-Gaxiola, S., Alonso, J., Lee, S., & Üstün, T. B. (2007). Age of onset of mental
disorders: A review of recent literature. Current Opinion in Psychiatry. doi:10.1109/CCECE.2006.277836
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 13
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand, S. L. T., & Zaslavsky, A. M. (2002). Short
screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychological
Medicine,32(6), 959–976.
Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., . . . Zaslavsky, A. M. (2003). Screening for
serious mental illness in the general population. Archives of General Psychiatry,60(2), 184–189.
Kim, H. H. (2017).Theimpactofonlinesocialnetworkingonadolescent psychological well-being (WB): A population-level
analysis of Korean schoolaged children. International Journal of Adolescence and Youth,22(3), 364–376.
Kim-Cohen, J., Caspi, A., Moffitt, T. E., Harrington, H., Milne, B. J., & Poulton, R. (2003). Prior juvenile diagnoses in adults
with mental disorder developmental follow-back of a prospective-longitudinal cohort. Archives of General
Psychiatry,60(7), 709–717.
Klineberg, E., Clark, C., Bhui, K. S., Haines, M. M., Viner, R. M., Head, J., & Stansfeld, S. A. (2006). Social support, ethnicity
and mental health in adolescents. Social Psychiatry and Psychiatric Epidemiology,41(9), 755–760.
Krayer, A., Ingledew, D. K., & Iphofen, R. (2008). Social comparison and body image in adolescence: A grounded theory
approach. Health Education Research,23(5), 892–903.
Lenhart, A., Smith, A., Anderson, M., Duggan, M., & Perrin, A. (2015). Teens, technology and friendships. Retrieved from
http://www.pewinternet.org/2015/08/06/teens-technology-and-friendships/
Li, J.-B., Lau, J. T. F., Mo, P. K. H., Su, X.-F., Tang, J., Qin, Z.-G., & Gross, D. L. (2017). Insomnia partially mediated the
association between problematic Internet use and depression among secondary school students in China. Journal
of Behavioral Addictions,6(4), 554–563.
Lilley, C., Ball, R., & Vernon, H. (2014). The experiences of 11–16 year olds on social networking sites. NSPCC. Retrieved
from https://www.nspcc.org.uk/globalassets/documents/research-reports/experiences-11-16-year-olds-social-
networking-sites-report.pdf
Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018). The associations between problematic Facebook use, psycho-
logical distress and well-being among adolescents and young adults: A systematic review and meta-analysis.
Journal of Affective Disorders,226,274–281. Elsevier B.V
Maulik, P., Eaton, W., & Bradshaw, C. (2011). The effect of social networks and social support on mental health services
use, following a life event, among the Baltimore epidemiologic catchment area cohort. The Journal of Behavioral
Health Services & Research,38(1), 2950.
McCrae, N. (2018). The weaponising of mental health. Journal of Advanced Nursing. doi:10.1111/jan.13878
McCrae, N., Gettings, S., & Purssell, E. (2017). Social media and depressive symptoms in childhood and adolescence:
A systematic review. Adolescent Research Review. doi:10.1007/s40894-017-0053-4
Mental Health Foundation. (2018). Children and young people. Retrieved from https://www.mentalhealth.org.uk/a-to-
z/c/children-and-young-people
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009a). Preferred reporting items for systematic reviews and
meta-analyses: The PRISMA statement. PLoS Medicine,6(7), e1000097.
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G.; The PRISMA Group. (2009b). PRISMA 2009 Flow Diagram. PLoS
Medicine,6(7), e1000097.
Morgan, C., Webb, R. T., Carr, M. J., Kontopantelis, E., Green, J., Chew-Graham, C. A., . .. Ashcroft, D. M. (2017). Incidence,
clinical management, and mortality risk following self harm among children and adolescents: Cohort study in
primary care. BMJ (Online),359. doi:10.1136/bmj.j4351
Myers, T. A., & Crowther, J. H. (2009). Social comparison as a predictor of body dissatisfaction: A meta-analytic review.
Journal of Abnormal Psychology,118(4), 683–698.
National Institutes of Health. (2014). Quality assessment tool for observational cohort and cross-sectional studies.
Retrieved 02 August 2018, from https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools
Neira, B. C. J., & Barber, B. L. (2014). Social networking site use: Linked to adolescents’social self-concept, self-esteem,
and depressed mood. Australian Journal of Psychology,66(1), 56–64.
O’Dea, B., & Campbell, A. (2011). Online social networking amongst teens: Friend or foe? Annual Review of
CyberTherapy and Telemedicine,9(1), 108–112.
O’Keeffe, G., & Clarke-Pearson, K.; Council on Communications and Media. (2011). The impact of social media on
children, adolescents and families. Pediatrics,124, 800–804.
Pew Research Centre (2015). Teens, social media & technology overview 2015. Retrieved from http://www.pewinter
net.org/2015/04/09/teens-social-media-technology-2015/
Popay, J., Roberts, H., Sowden, A., Petticrew, M., Arai, L., Rodgers, M., . . . Duffy, S. (1995). Guidance on the conduct of
narrative synthesis in systematic reviews. In J. Popay. (Ed.), A product from the ESRC methods programme (Vol. 22,
pp. 211–219). London: ESRC. Biostats 536 (ESRC
Primack, B. A., & Escobar-Viera, C. G. (2017). Social media as it interfaces with psychosocial development and mental
illness in transitional age youth. Child and Adolescent Psychiatric Clinics of North America,26(2), 217–233.
doi:10.1016/j.chc.2016.12.007
Radloff,L.S.(1977). The CES-D scale: A self report depression scale for research in the general population. Applied
Psychological Measurements,1, 385–401.
14 B. KELES ET AL.
Reid-Chassiakos, Y., Radesky, J., Christakis, D., & Moreno, M. A. (2016). From the American Academy of Pediatrics.
Children and adolescents and digital media. Council on Communications and Media. Pediatrics,138,5.
Rosen, L. D. (2011). Social networking’s good and bad impacts on kids. Washington, DC: American Psychological
Association. Retrieved from http://www.apa.org/news/press/releases/2011/08/social-kids.aspx
Rosen, L. D., Whaling, K., Rab, S., Carrier, L. M., & Cheever, N. A. (2013). Is Facebook creating “iDisorders”? The link
between clinical symptoms of psychiatric disorders and technology use, attitudes and anxiety. Computers in Human
Behavior,29, 1243–1254.
Royal Society for Public Health, & Young Health Movement. (2017). StatusOfMind social media and young people’s
mental health and wellbeing. Retrieved from https://www.rsph.org.uk/uploads/assets/uploaded/62be270a-a55f-
4719-ad668c2ec7a74c2a.pdf
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
(7), 380–385.
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: A systematic
review. JMIR Mental Health,3(4), e50.
Stansfeld, S., Clark, C., Bebbington, P., King, M., Jenkins, R., & Hinchliffe, S. (2016). Chapter 2: Common mental
disorders. In S. McManus, P. Bebbington, R. Jenkins, & T. Brugha (Eds.), Mental health and wellbeing in England:
Adult psychiatric morbidity survey 2014 (pp. 37–68). Leeds: NHS Digital.
Teo, A., Choi, H., & Valenstein, M. (2013). Social relationships and depression: Ten-year follow-up from a nationally
representative study. PloS one,8(4), e62396.
Tsitsika, A. K., Tzavela, E. C., Janikian, M., Ólafsson, K., Iordache, A., Schoenmakers, T. M., . .. Richardson, C. (2014). Online
social networking in adolescence: Patterns of use in six European countries and links with psychosocial functioning.
Journal of Adolescent Health,55(1), 141–147.
Twenge, J. (2006). Generation me: Why we expect more from technology and less from each other. New York: Basic Books.
Vandervoort, D. (1999). Quality of social support in mental and physical health. Current Psychology,18(2), 205.
Retrieved from: https://link.springer.com/content/pdf/10.1007%2Fs12144-999-1029-8.pdf
Vernon, L., Modecki, K. L., & Barber, B. L. (2017). Tracking effects of problematic social networking on adolescent
psychopathology: The mediating role of sleep disruptions. Journal of Clinical Child and Adolescent Psychology,46(2),
269–283.
Wang, P., Wang, X., Wu, Y., Xie, X., Wang, X., Zhao, F., . .. Lei, L. (2018). Social networking sites addiction and adolescent
depression: A moderated mediation model of rumination and self-esteem. Personality and Individual Differences,
127, 162–167.
Wang, Y. L. (2006). A summary of the researches about the factors of family influence on the children necessary to be
brought up by other people. Progress in Modern Biomedical,6,3.
World Health Organization. (2017). Maternal, newborn, child and adolescent health. Retrieved from http://www.who.
int/maternal_child_adolescent/topics/adolescence/mental_health/en/
Yan, H., Zhang, R., Oniffrey, T. M., Chen, G., Wang, Y., Wu, Y., . . . Moore, J. B. (2017). Associations among screen time
and unhealthy behaviors, academic performance, and well-being in Chinese adolescents. International Journal of
Environmental Research and Public Health,14(6). doi:10.3390/ijerph14060596
Yu, I. T. S., & Tse, S. L. A. (2012). Workshop 6 - Sources of bias in cross-sectional studies; Summary on sources of bias for
different study designs. Hong Kong Medical Journal. doi:10.2478/v10313-012-0001-z
INTERNATIONAL JOURNAL OF ADOLESCENCE AND YOUTH 15
