Loneliness and Social Media Use Among Adolescents with Psychiatric Disorders

Abstract

Studies have demonstrated that a prolonged feeling of loneliness is a major risk factor for psychopathology among children and adolescents. The purpose of this study was to evaluate the association between patterns of social media use with loneliness and psychopathology among 65 adolescents who were diagnosed with psychiatric disorders and treated at a psychiatric outpatient clinic in Israel. Social capital (online and offline) was negatively associated with loneliness. There was no association between loneliness and patterns of social media use, age, gender, psychiatric diagnosis, or disease severity. Our findings indicate that both online and offline social capital are associated with loneliness, and highlight the importance of studying the effect of peer online social support in alleviating loneliness.

Introduction

Adolescents' social interactions have changed considerably in recent years, as young people spend more time communicating virtually, on the expense of in-person interactions.¹ Most social network sites (SNS) serve as platforms for instant messaging and sharing content about one's personal live. According to the co-construction theory, adolescents' online social lives are in continuum with their offline social world, allowing them to explore issues of identity and sexuality.²

According to the online engagement theory,³ social media enables constant communication with friends, and allows users to feel connectedness with others through shared experiences. Social media can have a positive effect either by providing an extra resource of social interaction for people who already enjoy good social relationships,⁴ or by serving as an alternative networking channel for people who suffer from poor peer relationships.⁵ In contrast, SNS have quickly emerged as a potential explanation for the increase in depressive symptoms and suicidal behaviors among adolescents in recent years,⁶ although evidence is inconclusive.⁷

Loneliness is a psychological construct that represents perceived lack of satisfying social relationships, which is accompanied by discomfort and distress.⁸ Loneliness peaks at early adolescence,⁹ and associates with current and future risk for psychiatric morbidities.^10–12

It is argued that SNS use might worsen social isolation and loneliness,¹³ especially among individuals who are already experiencing loneliness in their “offline” lives,¹⁴ a common situation among adolescents with psychiatric disorders.¹⁵ Adolescents with externalizing disorders, (i.e., attention-deficit/hyperactivity disorder, oppositional defiant disorder, and conduct disorder), are characterized by poor inhibitory control and impulsive behaviours.¹² When using SNS, these individuals tend to share inappropriate personal content and engage in self-presentation, which in turn can evoke negative feedback from other users.¹³ Teenagers with internalizing disorders, (i.e., depression, anxiety, and stress-related disorders) tend to suffer from poor peer relationships, low self-esteem, and difficulty initiating social interactions,¹⁶ and are characterized by passive SNS use (e.g., reading social media feeds without posting), which is associated with lower well-being.¹⁷

Several facets of online social media use have been previously studied and found to be associated with loneliness among adolescents. One of these is social capital, a term that reflects specific benefits that ensues from one's social networks, including feelings of trust, reciprocity, and cooperation.¹⁸ Online social capital is defined as accessibility to online social relationships that promote trust and belongingness,¹⁹ and is categorized into two subscales: (a) bonding social capital, which refers to social connections within the individual's group or community (i.e., in-group)²⁰ and (b) bridging social capital, which relates to connections between individuals who are dissimilar with respect to socioeconomic and other major characteristics (i.e., out-group).²¹

Passive SNS use exposes the person to positive representations of other people's lives, promotes comparison with others' positive social status, and leads to lower self-esteem and depression, especially among those who are less popular.²² Active social media use refers to both targeted one-on-one exchanges (i.e., directed communication) and nontargeted exchanges,²³ and is associated with “offline” social inclusion.²⁴

Social media addiction is a type of behavioral addiction that bears resemblance to substance use,^25,26 and is characterized by the individual's inability to control social media use that interferes with other life tasks.^27,28

In this study, we aim to explore the association between patterns of SNS use, loneliness, and psychopathology among adolescents diagnosed with psychiatric disorders. We examine the association between loneliness and the following: (a) online and offline social capital, (b) type of SNS use (passive vs. active), (c) hours spent on SNS, and (d) social media addiction. Another exploratory open research question is whether social media use is associated with the type and severity of psychopathology.

Method

Participants

The participants of this study were psychiatric patients between the ages 10 and 20, who receive treatment at the Child & Adolescent psychiatric Outpatient Clinic at Sheba Medical Center in Israel. Patients with psychotic disorders, autism spectrum disorder, intellectual disability or lack of SNS use were excluded from the study. The study protocol was approved by the Institutional Review Board (IRB-8260-21-SMC). Informed consent and ascent were obtained from parents and underage patients, respectively.

Assessment

Psychiatric diagnoses were established after a thorough clinical assessment of the patients' medical file by a senior child and adolescent psychiatrist (authors M.S.L. and L.B.-M.). Diagnoses were then categorized as internalizing or externalizing disorders. Evaluations were based on the Diagnostic and Statistical Manual of Mental Disorders version 5 (DSM-5).²⁹ Illness severity was assessed by the Clinical Global Impression-symptom severity scale (CGI-S).³⁰

Loneliness was assessed by the University of California, Los Angeles (UCLA) loneliness scale,³¹ a 20-item scale designed to measure subjective feelings of loneliness as well as feelings of social isolation. Participants rate each item on a 4-point Likert scale ranging from 0 (“I never feel this way”) to 3 (“I often feel this way”).³²

Assessment of active and passive SNS use was based on the Multidimensional Scale of Facebook Use,³³ which was modified to refer to a variety of other social platforms and translated to Hebrew. Time spent on social media was measured by a single item inquiring to the number of hours per day on SNS. Participants rated their answer on a 5-point Likert scale, ranging from 1 (less than an hour) to 5 (7 hours or more). Offline social capital was assessed by the Social Capital Questionnaire-Adolescent Students, a 12-item questionnaire that is designed to measure one's social capital on a 3-point Likert scale ranging from 1 (disagree) to 3 (agree).³⁴

Online social capital was assessed by adapting seven items that examined online bridging social capital³⁵ and five items that measure online bonding social capital.³⁶ Relevant items from each subscale were selected and minor changes were made to the rewording of items. Social media addiction was measured by the Bergen Social Media Addiction Scale,³⁷ which contains six items reflecting core addiction elements (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse).³⁸ Each item refers to experiences within a time frame of 12 months, and is answered on a 5-point Likert scale ranging from 1 (very rarely) to 5 (very often). Internal consistency ranged from acceptable (Cronbach's α = 0.76) to excellent (α = 0.94), with the exception of screen addiction that demonstrated questionable reliability (α = 0.66).

Procedure

Upon arrival at the clinic, patients were given a tablet that included a link to a secured survey platform (Redcap). In cases of reading or comprehension difficulties, the questions were read aloud by research assistants and the patient verbally indicated their response.

Statistical analysis

Descriptive statistics were used to characterize study participants according to the following: age, gender, psychiatric diagnosis, and illness severity. A hierarchical linear regression model was used to determine association among predetermined variables and UCLA loneliness ratings. The first step was aimed to assess the relations between loneliness and sociodemographic factors (age, gender [male = reference group, coded as 0]) and clinical characteristics (main diagnosis, i.e., externalizing [coded as 0] vs. internalizing [coded as 1], and comorbidity [externalizing+internalizing vs. only one of the two]). In the second step, we introduced our measures of interest: offline social capital, online social capital (categorized by bonding and bridging), screen addiction, Internet use (categorized by active and passive use), and time spent online.

To keep the recommended 10:1 subjects per variable ratio in variable selection,³⁹ before introducing the second step we removed from the model variables that were not significantly associated with loneliness, and corrected for multiple comparisons using Benjamini and Hochberg's False Discovery Rate (FDR) correction.⁴⁰ We present all p values as adjusted values after controlling for false discoveries (adjusted p). To avoid multicollinearity, we detrended independent variables from one another and used their residuals in the final model.^41,42

No missing data were observed. We used the standard α = 0.05 chance of a type-I error. Analysis was conducted with the “stats” package in R.

Results

From a total of 100 patients, 14 declined and 20 were not eligible due to lack of social media use. A total of 66 patients participated in the study (mean age ± standard deviation = 14.88 ± 2.09, 71.2 percent girls) (Table 1).

Table 1.

Demographic and Clinical Characteristics of the Study Sample

Age years, mean (SD), range	14.88 (2.09), [10–20]
Males/females, n (%)	19/47, (28.8%/71.2%)
Primary diagnosis, n (%)
Depressive episode/adjustment disorder	22 (32.8)
Social anxiety disorder	5 (7.5)
Other anxiety disorder	16 (23.9)
ADHD/ODD	17 (25.4)
OCD	5 (7.5)
CGI-S, n (%)
Mildly ill	9 (13.4)
Moderately ill	40 (59.7)
Extremely ill	22.4 (5)
Severely ill	1 (1.5)
UCLA score, mean (SD), range
Total	42.78, (12.80), [20–74]
Loneliness level, n (%)
Low	20 (29.9)
Medium	24 (35.8)
Medium-high	19 (28.78)
High	3 (4.5)
Social capital score, mean (SD), range
Online social capital	35.18, (8.89), [14–55]
Offline social capital	25.46, (4.67), [14–33]

ADHD, attention-deficit/hyperactivity disorder; CGI-S, Clinical Global Impression-symptom severity scale; OCD, obsessive compulsive disorder; ODD, oppositional defiant disorder; SD, standard deviation; UCLA, University of California, Los Angeles.

Loneliness was inversely associated with offline social capital (standardized β = −0.41, 95% confidence interval [CI] = −0.65 to −0.10, adjusted p = 0.005) and online bonding (β = −0.36, 95% CI = −0.62 to −0.10, p = 0.024) but not with online bridging (p = 0.19), screen addiction, passive Internet use, or time spent on SNS. Of note, active Internet use had a trend level effect before applying FDR correction (β = −0.26, 95% CI = −0.56 to 0.04, unadjusted p = 0.09, adjusted p = 0.19). None of the sociodemographic (age and gender) and clinical variables (having an internalizing disorder or comorbidity) that were introduced in the first step significantly associated with loneliness. The first step accounted for only 2 percent (adjusted R² = 0.02, p = 0.27) of the variance in loneliness scores. However, the second step accounted for 21 percent of the variance (adjusted R² = 0.21), and significantly improved the model's predictability (p = 0.004) (Table 2).

Table 2.

Hierarchical Linear Regression with University of California, Los Angeles Loneliness Scale as the Dependent Variable

Variable	Unstandardized B (95% CI)	Standardized β (95% CI)	t	p
Step I—sociodemographic properties and psychiatric diagnosis (adjusted R² = 0.02, df = 62)
^aAge	0.40 (−1.21 to 2.02)	0.07 (−0.20 to 0.33)	0.50	0.62
Gender (female)	4.88 (−2.05 to 11.82)	0.38 (−0.16 to 0.93)	1.41	0.16
Internalizing disorders	5.37 (−3.93 to 14.68)	0.42 (−0.31 to 1.15)	1.15	0.25
Internalizing+externalizing comorbidity	−0.54 (−7.35 to 6.28)	−0.04 (−0.58 to 0.49)	−0.16	0.88
^bStep II—psychosocial and Internet-related measures (adjusted R² = 0.21, df = 59)
Offline social capital	−1.21 (−1.89 to −0.53)	−0.41 (−0.65 to −0.10)	−3.55	0.005
Online social capital—bonding	−0.97 (−1.66 to −0.28)	−0.36 (−0.62 to −0.10)	−2.81	0.024
Online social capital—bridging	−0.77 (−1.72 to 0.18)	−0.23 (−0.52 to 0.05)	−1.63	0.19
Screen addiction	0.13 (−0.63 to 0.89)	0.04 (−0.21 to 0.29)	0.33	0.74
Active Internet use	−1.01 (−2.20 to 0.18)	−0.26 (−0.56 to 0.04)	−1.70	0.19
Passive Internet use	−0.47 (−1.65 to 0.71)	−0.11 (−0.38 to 0.17)	−0.79	0.43
Time spent on SNS	−0.07 (−0.23 to 0.09)	−0.11 (−0.35 to 0.14)	−0.88	0.38

Age, gender (0 = male, 1 = female), internalizing disorders (0 = no, 1 = yes) and internalizing + externalizing comorbidity (0 = no, 1 = yes) were modeled as binary variables.

Social capital (offline and online), screen addiction, Internet use (active and passive) and time spent online were modeled as continuous variables.

CI, confidence interval; SNS, social network sites.

A sensitivity power analysis using G*Power 3.1.9.4 indicated that to achieve a significant regression model given our sample size and an estimated power of 80 percent, an effect size of f² = 0.12, equivalent to R² = 0.11, was required. This indicates that the study had enough power to determine the significance of the observed effect (R² = 0.21).

Discussion

In this study, we assessed the association between patterns of SNS use and loneliness among adolescents who are treated at a psychiatric outpatient clinic. We found a negative association between social capital and loneliness. Offline social capital had higher association with loneliness than online social capital. Our findings are compatible with previous research,^43–45 and emphasize the role of online social capital that is gained by SNS use in supporting offline social interactions.⁴⁶

Among adolescents who suffer from psychiatric disorders, fear of stigmatization is a prevailing reason for self-isolation,⁴⁷ social withdrawal, and social distancing.^48–50 Therefore, peer support and strong social networks are especially important in this population. Social activity through SNS can assist with these issues, as studies have found that young people often turn to SNS to find information,⁵¹ support,^52,53 or even utilize them to “bounce back” after social rejection.⁵⁴ Adolescents' risk behavior online often mirrors their vulnerability in their offline lives.^55,56 Through digital technologies, clinicians can now connect with adolescents through another platform for communication, and use social media data to identify risk for mental illness.^57–59 We did not find an association between the time spent on SNS and loneliness.

This is in accordance with data from recent years,^60,61 which suggest that the type of SNS usage (e.g., active vs. passive use), and not its frequency, should be the focus of attention.⁶² However, we did not find an association between the type of SNS use and loneliness. This can be explained by lack of further stratification to interactive use, which is the specific type of interaction that was found to be negatively associated to loneliness.⁶³ We did not find an association between social media addiction and loneliness. A possible explanation is that SNS allows adolescents to create virtual spaces that serve their need to belong, form new social ties, and strengthen existing ones,⁶⁴ thus increasing social capital, which in turn decreases loneliness by neutralizing the positive effect that excessive SNS use has on loneliness. However, this postulation requires further investigation.

Age, gender, type of psychopathology, or its severity were not associated with loneliness. This might be due to the small sample size and the specific type of population studied, the high rate of comorbidity between internalizing and externalizing disorders in our sample,⁶⁵ or the pre-existed positive association between the existence of any psychiatric disorder and loneliness.⁶⁶

To the best of our knowledge, this is the first study to examine the association between social media use and loneliness among adolescents with psychiatric disorders. The study has several limitations that should be noted. The sample size was modest, and a larger sample may lead to more significant results; we lacked a control group of typically developed adolescents that would have allowed us to better understand the specificities of SNS use and its relation to loneliness among adolescents with psychiatric disorders. It is our hope that this study will serve as an initial basis for larger comparative studies, which will elaborate on the role assumed by the individual in their social community and its effect on the experience of loneliness.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

References

Twenge

, Campbell

. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Preventive Medicine Reports, 2018; 12:271–283.

Subrahmanyam

, Smahel

, Greenfield

. Connecting developmental constructions to the Internet: identity presentation and sexual exploration in online teen chat rooms. Developmental Psychology, 2006; 42:395–406.

Valkenburg

, Peter

. Online communication and adolescent well-being: testing the stimulation versus the displacement hypothesis. Journal of Computer-Mediated Communication, 2007; 12:1169–1182.

Liu-2014-Self-disclosure-on-SNS.pdf. https://prsg.wiscweb.wisc.edu/wp-content/uploads/sites/1209/2019/09/Liu-2014-Self-disclosure-on-SNS.pdf (accessed Feb. 22, 2022).

Frontiers | The Facebook Paradox: Effects of Facebooking on Individuals' Social Relationships and Psychological Well-Being | Psychology. https://www.frontiersin.org/articles/10.3389/fpsyg.2017.00087/full (accessed Feb. 22, 2022).

Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions—Odgers—2020. Journal of Child Psychology and Psychiatry, Wiley Online Library. https://acamh.onlinelibrary.wiley.com/doi/full/10.1111/jcpp.13190?casa_token=cVqqazPmcEQAAAAA%3A0-7wckmSjf37U1PvDfWaCyY3KELYsnPQXOog0lfKNG7D4lsZ0R3Cfth7vSNyE_QdRBK2-wLiE7YdGQ (accessed Mar. 1, 2022).

Odgers

, Jensen

. Annual Research Review: adolescent mental health in the digital age: facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 2020; 61:336–348.

Qualter

, Vanhalst

, Harris

, et al. Loneliness across the life span. Perspectives on Psychological Science, 2015; 10:250–264.

Qualter

, Brown

, Rotenberg

, et al. Trajectories of loneliness during childhood and adolescence: predictors and health outcomes. Journal of Adolescence, 2013; 36:1283–1293.

10.

Lasgaard

, Goossens

, Bramsen

, et al. Different sources of loneliness are associated with different forms of psychopathology in adolescence. Journal of Research in Personality, 2011; 45:233–237.

11.

Stednitz

, Epkins

. Girls' and mothers' social anxiety, social skills, and loneliness: associations after accounting for depressive symptoms. Journal of Clinical Child & Adolescent Psychology, 2006; 35:148–154.

12.

Vanhalst

, Klimstra

, Luyckx

, et al. The interplay of loneliness and depressive symptoms across adolescence: exploring the role of personality traits. Journal of Youth and Adolescence, 2012; 41:776–787.

13.

Huang

. Time spent on social network sites and psychological well-being: a meta-analysis. Cyberpsychology, Behavior, and Social Networking, 2017; 20:346–354.

14.

Underwood

, Ehrenreich

. The power and the pain of adolescents' digital communication: cyber victimization and the perils of lurking. The American Psychologist, 2017; 72:144–158.

15.

Chen

, Cohen

, Johnson

, et al. Psychiatric disorders during adolescence and relationships with peers from age 17 to age 27. Social Psychiatry and Psychiatric Epidemiology, 2009; 44:223–230.

16.

Seabrook

, Kern

, Rickard

. Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health, 2016; 3:e50.

17.

Appel

, Gerlach

, Crusius

. The interplay between Facebook use, social comparison, envy, and depression. Current Opinion in Psychology, 2016; 9:44–49.

18.

Sander

. Social capital and new urbanism: leading a civic horse to water?. National Civic Review, 2002; 91:213–234.

19.

Paige

, Stellefson

, Chaney

, et al. Examining the relationship between online social capital and eHealth literacy: Implications for instagram use for chronic disease prevention among college students. American Journal of Health Education, 2017; 48:264–277.

20.

Coffé

, Geys

. Toward an empirical characterization of bridging and bonding social capital. Nonprofit and Voluntary Sector Quarterly, 2007; 36:121–139.

21.

Villalonga-Olives

, Adams

, Kawachi

. The development of a bridging social capital questionnaire for use in population health research. SSM Population Health, 2016; 2:613–622.

22.

Nesi

, Prinstein

. Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 2015; 43:1427–1438.

23.

Burke

. (2012) Reading, Writing, Relationships: The Impact of Social Network Sites on Relationships and Well-Being. http://search.proquest.com/docview/1030427751/abstract/7B3080F00789402DPQ/1 (accessed Feb. 25, 2021).

24.

Deters

, Matthias

. Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social Psychological and Personality Science, 2013; 4:579–586.

25.

Meshi

, Elizarova

, Bender

, et al. Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task. Journal of Behavioral Addictions, 2019; 8:169–173.

26.

Kuss and Griffiths. (2017) Social Networking Sites and Addiction Ten Lessons.pdf. https://sheu.org.uk/sheux/EH/eh353mg.pdf (accessed Mar. 15, 2022).

27.

Monacis

, Palo

V de

, Griffiths

, et al. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. Journal of Behavioral Addictions, 2017; 6:178–186.

28.

Kim

, LaRose

, Peng

. Loneliness as the cause and the effect of problematic internet use: the relationship between internet use and psychological well-being. CyberPsychology & Behavior, 2009; 12:451–455.

29.

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders. 5th ed. American Psychiatric Association. [Epub ahead of print]; DOI: 10.1176/appi.books.978089042, 5596.

30.

Busner

, Targum

. The Clinical Global Impressions Scale. Psychiatry (Edgmont), 2007; 4:28–37.

31.

Mahon

, Yarcheski

. Validation of the revised UCLA loneliness scale for adolescents. Research in Nursing & Health, 1995; 18:263–270.

32.

Self_Measures_for_Loneliness_and_Interpersonal_Problems_UCLA_LONELINESS.pdf. https://fetzer.org/sites/default/files/images/stories/pdf/selfmeasures/Self_Measures_for_Loneliness_and_Interpersonal_Problems_UCLA_LONELINESS.pdf (accessed Mar. 18, 2022).

33.

Frison

, Eggermont

. Toward an integrated and differential approach to the relationships between loneliness, different types of Facebook use, and adolescents' depressed mood. Communication Research, 2020; 47:701–728.

34.

Paiva

PCP

, Paiva

HN de

, Filho PM de

, et al. Development and validation of a Social Capital Questionnaire for Adolescent Students (SCQ-AS). PLoS One, 2014; 9:e103785.

35.

Ellison

, Steinfield

, Lampe

. The benefits of facebook “friends”: social capital and college students' use of online social network sites. Journal of Computer-Mediated Communication, 2007; 12:1143–1168.

36.

Williams

. On and off the ’net: scales for social capital in an online era. Journal of Computer-Mediated Communication, 2006; 11:593–628.

37.

Duradoni

, Innocenti

, Guazzini

. Well-being and social media: a systematic review of bergen addiction scales. Future Internet, 2020; 12:24.

38.

Griffiths. 2005. A ‘components' model of addiction within a biopsyc.pdf. http://addictionpsychologist.org/wp-content/uploads/2019/03/griffiths-2005-addiction-components.pdf (accessed Mar. 18, 2022).

39.

Austin

, Steyerberg

. The number of subjects per variable required in linear regression analyses. Journal of Clinical Epidemiology, 2015; 68:627–636.

40.

Benjamini

, Hochberg

. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological), 1995; 57:289–300.

41.

McCoach

, Rifenbark

, Newton

, Li

, Kooken

, Yomtov

, Gambino

, Bellara

, 2018. Does the package matter? A comparison of five common multilevel modeling software packages. https://journals-sagepub-com-s.web.bisu.edu.cn/doi/10.3102/1076998618776348 (accessed Mar. 18, 2022).

42.

The Development of Somatic Symptom Disorder in Children: Psychological Characteristics and Psychiatric Comorbidity | Elsevier Enhanced Reader. [Epub ahead of print]; DOI: 10.1016/j.jaclp.2021.10.008.

43.

Cheng

, Wang

, Sigerson

, et al. Do the socially rich get richer? A nuanced perspective on social network site use and online social capital accrual. Psychological Bulletin, 20190516; 145:734.

44.

McPherson

, Kerr

, McGee

, et al. The association between social capital and mental health and behavioural problems in children and adolescents: an integrative systematic review. BMC Psychology, 2014; 2:7.

45.

Kaye

, Kowert

, Quinn

. The role of social identity and online social capital on psychosocial outcomes in MMO players. Computers in Human Behavior, 2017; 74:215–223.

46.

Yau

, Reich

. Are the qualities of adolescents' offline friendships present in digital interactions? Adolescent Research Review 2018; 3:339–355.

47.

Jorm

, Wright

. Influences on young people's stigmatising attitudes towards peers with mental disorders: national survey of young Australians and their parents. The British Journal of Psychiatry, 2008; 192:144–149.

48.

Achterbergh

, Pitman

, Birken

, et al. The experience of loneliness among young people with depression: a qualitative meta-synthesis of the literature. BMC Psychiatry, 2020; 20:415.

49.

Granek

. What's love got to do with it? The relational nature of depressive experiences. Journal of Humanistic Psychology, 2006; 46:191–208.

50.

Loades

, Chatburn

, Higson-Sweeney

, et al. Rapid systematic review: the impact of social isolation and loneliness on the mental health of children and adolescents in the context of COVID-19. Journal of the American Academy of Child and Adolescent Psychiatry, 2020; 59:1218–1239.e3.

51.

Kauer

, Mangan

, Sanci

. Do online mental health services improve help-seeking for young people? A systematic review. Journal of Medical Internet Research, 2014; 16:e66.

52.

Toma

, Hancock

. Self-affirmation underlies facebook use. Personality & Social Psychology Bulletin, 2013; 39. [Epub ahead of print]; DOI: 10.1177/0146167212474694.

53.

Rideout et al. Digital Health Practices, Social Media Use, and Me.pdf. https://digitalcommons.psjhealth.org/cgi/viewcontent.cgi?article=2092&context=publications (accessed Mar. 6, 2022).

54.

Gross. (2009) Logging on, bouncing back An experimental investi.pdf. https://www.cdmc.ucla.edu/wp-content/uploads/sites/170/2018/05/Sheva-dissertation.pdf (accessed Mar. 6, 2022).

55.

Kowalski

, Giumetti

, Schroeder

, et al. Bullying in the digital age: a critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 2014; 140:1073–1137.

56.

Odgers

. Smartphones are bad for some adolescents, not all. Nature, 2018; 554:432–434.

57.

Choudhury

, Gamon

, Counts

, et al. Predicting depression via social media. Proceedings of the International AAAI Conference on Web and Social Media, 2013; 7:128–137.

58.

Lind

, Byrne

, Wicks

, et al. The Effortless Assessment of Risk States (EARS) Tool: an interpersonal approach to mobile sensing. JMIR Mental Health, 2018; 5:e10334.

59.

Mohr

, Zhang

, Schueller

. Personal sensing: understanding mental health using ubiquitous sensors and machine learning. Annual Review of Clinical Psychology, 2017; 13:23–47.

60.

Przybylski

, Weinstein

. A large-scale test of the goldilocks hypothesis: quantifying the relations between digital-screen use and the mental well-being of adolescents. Psychological Science, 2017; 28:204–215.

61.

Odgers

, Jensen

. Annual research review: adolescent mental health in the digital age: facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 2020; 61:336–348.

62.

Nesi

, Miller

, Prinstein

. Adolescents' depressive symptoms and subsequent technology-based interpersonal behaviors: a multi-wave study. Journal of Applied Develomental Psychology, 2017; 51:12–19.

63.

Yang

. Instagram use, loneliness, and social comparison orientation: interact and browse on social media, but don't compare. Cyberpsychology, Behavior, and Social Networking, 2016; 19:703–708.

64.

boyd danah. (2014) It's complicated: the social lives of networked teens. Yale University Press.

65.

Youngstrom

, Findling

, Calabrese

. Who are the comorbid adolescents? Agreement between psychiatric diagnosis, youth, parent, and teacher report. Journal of Abnormal Child Psychology, 2003; 31:231–245.

66.

Christiansen

, Qualter

, Friis

, et al. Associations of loneliness and social isolation with physical and mental health among adolescents and young adults. Perspectives in Public Health, 2021; 141:226–236.