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. 2024 Feb 8;26(3):104–119. doi: 10.1007/s11920-024-01481-9

The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review

Danny J Yu 1, Yun Kwok Wing 1, Tim M H Li 1,✉, Ngan Yin Chan 1,✉
PMCID: PMC10948475  PMID: 38329569

Abstract

Purpose of Review

Social media use (SMU) and other internet-based technologies are ubiquitous in today’s interconnected society, with young people being among the commonest users. Previous literature tends to support that SMU is associated with poor sleep and mental health issues in youth, despite some conflicting findings. In this scoping review, we summarized relevant studies published within the past 3 years, highlighted the impacts of SMU on sleep and mental health in youth, while also examined the possible underlying mechanisms involved. Future direction and intervention on rational use of SMU was discussed.

Recent Findings

Both cross-sectional and longitudinal cohort studies demonstrated the negative impacts of SMU on sleep and mental health, with preliminary evidence indicating potential benefits especially during the COVID period at which social restriction was common. However, the limited longitudinal research has hindered the establishment of directionality and causality in the association among SMU, sleep, and mental health.

Summary

Recent studies have made advances with a more comprehensive understanding of the impact of SMU on sleep and mental health in youth, which is of public health importance and will contribute to improving sleep and mental health outcomes while promoting rational and beneficial SMU. Future research should include the implementation of cohort studies with representative samples to investigate the directionality and causality of the complex relationships among SMU, sleep, and mental health; the use of validated questionnaires and objective measurements; and the design of randomized controlled interventional trials to reduce overall and problematic SMU that will ultimately enhance sleep and mental health outcomes in youth.

Keywords: Social media use, Sleep, Mental health, Youth, Adolescent

Introduction

Youth population, which typically refers to individuals between the ages of 15 and 24, experience substantial changes in their neurobiology, physical development, behavior, and emotions, making it a vulnerable stage for the development of both sleep and mental health problems [1–3]. In Hong Kong, approximately 64.5% of adolescents sleep less than 8 h during weekdays [4] and 29.2% have reported insomnia symptoms [5]. Both cross-sectional and longitudinal studies have demonstrated that sleep loss and disturbances in youth lead to significant personal distress, increase risk of psychiatric illnesses, and risky behaviors such as drug abuse and dangerous driving [5, 6]. In addition to sleep disturbances, mental health problems are highly prevalent among the youth population. Evidence suggested that nearly 75% of psychiatric illnesses have their age onset during adolescence [7, 8].

There are multiple risk factors that commonly contribute to sleep and mental health problems, including being female, heavy school workload, physical inactivity, and worse general health [9]. Recently, a growing number of studies indicate that social media use (SMU) is associated with both sleep and mental health problems in youth [10•]. In particular, identity development and peer acceptance during adolescence are important developmental needs, at which social media may apparently serve as a convenient means to meet these needs. A previous study reported that over 80% of adolescents (16–19 years) use electronic devices near bedtime [11]. On the other hand, excessive SMU can have detrimental health effects [12, 13], and contribute to various negative repercussions such as cyberbullying [14], gender stereotypes [15], self-objectification [16], and exposure to inappropriate content, such as unsolicited violent and sexual contents [17]. The effect becomes more prominent in young people who are considered as digital native [18]. Nevertheless, SMU also comes with some potential benefits [19] such as increased self-esteem [20], increased social capital [21], identity presentation and sexual exploration [22], and social support [23].

Despite the emerging evidence supporting the link among SMU, sleep, and mental health, the relationship and directionality are complex and inconsistent. For example, two recent studies did not find significant associations among SMU, sleep, and mental health [24, 25•]. Nonetheless, a US study reported that greater SMU was significantly associated with sleep disturbances [26], and some also reported bidirectional relationship at which poor sleepers tend to use electronic devices as a sleep aid [27]. In general, it is believed that youth are at a higher risk of experiencing the negative impacts of SMU as they are more susceptible to peer pressure and fear of missing out (FOMO). FOMO refers to the perception of missing out on enjoyable experiences, followed up with a compulsive behavior to maintain these social connections with others to avoid being excluded from those experiences [28–30]. Hence, understanding the association and directionality among SMU, sleep, and mental health is crucial for developing public health strategies on how to cultivate healthy SMU habits and develop effective interventions targeting inappropriate and excessive SMU.

This scoping review summarized recent studies on SMU, sleep, and mental health in youth (Fig. 1) and explored the potential underlying mechanisms of how SMU affects sleep and mental health in youth (Fig. 2). Finally, we have put forward several potential avenues for future research and recommendations in this area. Search terms including #adolescent, #social media, #sleep, and #mental health were used to search for relevant studies that were published between January 2020 and July 2023 in MEDLINE. A summary of the study attributes, such as the authors, the country/region where the study was conducted, study design, the number of participants, sample age range, characteristics, as well as the measures used to assess SMU, sleep, and mental health are listed in Table 1.

Fig. 1.

Fig. 1

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Structure of the scoping review

Fig. 2.

Fig. 2

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Potential pathways of social media use on sleep and mental health

Table 1.

Summary of studies on social media use (SMU), sleep, and mental health

Author Country/region Study design Sample size Sample characteristics SMU measures Sleep and mental health measures Main findings
Age Female %
Barthorpe et al. UK Cross-sectional 4032 13–15 55.2 SMU (time use diary)

Self-harm (single question)

Depressive symptoms (short mood and feelings questionnaire, SMFQ)

Self-esteem (5-item shortened Rosenberg scale)

 A greater amount of time spent on social media was associated with increased risk of self-harm and depression and lower levels of self-esteem in females
Bailey et al. Australia Cross-sectional 371 16–25 70.4 SMU (self-report questionnaire)

Mental health (Depression Anxiety Stress Scales (DASS-21))

Diagnosis of mental health problem (single question)

Impact of COVID-19 on mental health (single question)

High levels of daily SMU were associated with poorer mental health

Young people use social media to seek support for suicidal thoughts and self-harm
Berfeld and Bulck US Cross-sectional 410 12–18 54.3

Affordances of social media (self-report questionnaire)

Nighttime SMU (self-report questionnaire)

Problematic SMU (the Bergen Facebook Addiction Scale)

Adverse SMU (self-report questionnaire)

Sleep displacement (sleep diary)

Pre-sleep arousal (the Pre-Sleep Arousal Scale)

Sleep quality (the Pittsburgh Sleep Quality Index, PSQI)

Use of Snapchat but not Instagram predicted a later bedtime

Social media affordances were associated with at least one sleep parameter

Being the second most viewed content, viewing post of celebrities had no relationship with sleep
Brailovskaia et al. Russia Cross-sectional 1123 18–29 100

SMU (time tracked by specific applications, and self-report questionnaire)

Problematic SMU (the brief version of the Bergen Social Media Addiction Scale, BSMAS)

Daily stress (the Brief Daily Stressors Screening Tool, BDSS-Tool)

Depression and anxiety symptoms (the Depression Anxiety Stress Scales 21 (DASS-21))

Positive mental health (the unidimensional Positive Mental Health Scale, PMH-Scale)

Lifetime suicide-related outcomes (the Suicidal Behaviors Questionnaire-Revised, SBQ-R)

 SMU was associated with low mental health, and increased suicide-related outcomes
Buda et al. Lithuania Cross-sectional (representative sample) 4191 13.9 49.1 Problematic SMU (the nine-item Social Media Disorder Scale) Sleep problems (difficulties in getting to sleep, and tiredness in the morning) (self-report questionnaire) Increased SMU is associated with worse sleep quality
Burnell et al. US Cross-sectional 388 13.37 50 Daily digital technology use (total amount of messages sent and duration spent online and on phone)

Subjective sleep parameters (sleep duration, sleep quality) (sleep diary)

Objective sleep parameters (Jawbone wearable device)

 Greater non-academic digital technology use was associated with shorter subjective and objective sleep duration, and later bedtime
Chao et al. China Cross-sectional 1346 14.97 51.8

Short video platform use (self-report questionnaire)

Short video addiction (the short version of smartphone addiction scale, SAS-SV)

Sleep quality and duration (self-report questionnaire)

Social anxiety (the social interaction anxiety scale, SIAS-6)

Psychological distress (the 21-item Depression Anxiety Stress Scale, DASS-21)

Loneliness (the UCLA loneliness scale, ULS-4)

 Significant association between short video use, sleep quality, and mental health conditions was observed
Charmaraman et al. US Cross-sectional 772 11–15 N/A SMU (self-report questionnaire) Sleep (sleep duration and bedtime) (self-report questionnaire) Self-report SMU behaviours were associated with negative sleep outcomes
Fardouly et al. Australia Cross-sectional 528 10–12 49.1 SMU (self-report questionnaire)

Body satisfaction (the Body Esteem Scale for Adolescents and Adults)

Eating pathology (the Children’s Eating Attitude Test)

Depressive symptoms (the Short Mood and Feelings Questionnaire)

Social anxiety (the Spence Children’s Anxiety Scale)

Users of YouTube, Instagram, and Snapchat reported more body image concerns and eating pathology than non‐users

Social media appearance comparisons are associated with negative mental health among preadolescents
Friebel et al. UK Cross-sectional 101 19.7 N/A

Emotional investment in social media (the Social Media Use Integration Scale)

SMU in the last hour before sleep

Sleep quality (the Pittsburgh Sleep Quality Index, PSQI)

Objective sleep parameters (actigraphy)

Depressive symptoms (the 10-item Centre for the Epidemiologic Studies of Depression Short Form, CESD-10)

Bedtime SMU may not be detrimental to sleep in youth

Youth with increased levels of depressive symptoms are at higher risk of experiencing negative impacts of bedtime SMU
Gaya et al. Spain Cross-sectional 1101 12–17 54.2 SMU (self-report questionnaire, and ecological momentary assessment) Sleep duration and sleep-related problems (self-report questionnaire, the BEARS scale A relationship between cell phones, playing video games, and use of social networks and sleep problems
Gumport et al. US Cross-sectional 176 10–18 58 SMU (self-report questionnaire and ecological momentary assessment

Objective sleep parameters (total sleep time, bedtime, and sleep onset latency) (actigraphy)

Emotional health (Ecological Momentary Assessment)

 Technology use was associated with increased sleep onset latency, but better emotional health
Hamilton et al. US Cross-sectional 93 12–17 100 SMU (self-report questionnaire)

Perceived sleep (sleep quality and sleep duration) (self-report questionnaire)

Depressive symptoms (6-item Patient Health Questionnaire)

Anxiety symptoms (Generalized Anxiety Disorder-7, GAD-7)

 During COVID-19 pandemic, higher levels of SMU and lower levels of physical activity are associated with later sleep timing
Hasan Beyari Saudi Arabia Cross-sectional 385 18–24 44.7 SMU (self-report questionnaire) Mental health (self-report questionnaire) Social media has a significant negative impact on the mental health of Saudi Arabian youth
Henzel and Håkansson Sweden Cross-sectional 182 16–24 N/A

Social media addiction (the Bergen Social Media Addiction Scale, BSMAS; the Problematic and Risky Internet Use Screening Scale-3, PRIUSS)

SMU over the past 12 months (self-report questionnaire)

Mental health (the Kessler Psychological Distress Scale, Kessler-6)

Addictive disorder (the Gaming Addiction Scale, GAS; The Problem Gambling Severity Index, PGSI)

 Bidirectional relationship was observed between problematic SMU and psychological distress, as well as alcohol abuse
Khan et al. Europe and North America Cross-sectional (representative sample) 195,668 13.6 51 Self-report discretional time spent watching TV, electronic gaming, and computer use Difficulty in falling sleep (self-report questionnaire) Higher levels of recreational screen use were associated with sleep-onset difficulties
Kortesoja et al. Finland Cross-sectional 1084 15–20 45.7

Evening and late-night SMU (self-report questionnaire)

SMU after lights out (self-report questionnaire)

Sleep quality (the Pittsburgh Sleep Quality Index, PSQI)

Chronotype (the Morningness-Eveningness Questionnaire)

Bedtime, wake-up time, and sleep duration (self-report questionnaire)

Insufficient sleep (self-report questionnaire)

 Youth with evening chronotype are at greater risks of late-night media use, which negatively affect sleep
Li et al. Hong Kong Cross-sectional 3455 14.9 55.7 Electronic media use (self-report questionnaire)

Sleep (the Insomnia Severity Index, ISI)

Chronotype preference (the reduced Horne and Östberg Morningness and Eveningness Questionnaire, rMEQ)

Sleep deprivation

Social jetlag

Emotional difficulties (the Strengths and Difficulties Questionnaire, SDQ)

 Bedtime electronic media use of computers, electronic game consoles, and phones contributed to increased risks of sleep, circadian problems and mental health issues in adolescents
Liu et al. China Cross-sectional 4852 13.8 51.5 Problematic internet use (the 26-item Chinese Internet Addiction Scale-Revised) Sleep quality (the Chinese version of the 8-item Athens Insomnia Scale) Problematic internet use was associated with higher level of insomnia severity
Michelle O’Reilly UK Cross-sectional 54 11–18 44.4 N/A Mental health (focus group discussion) SMU confers negative impacts on mental health of adolescents, although there was acknowledgement of potential benefits
Nagata et al. US

Cross-sectional

(Representative sample)

 11,878 12.0 48.8

Bedtime screen usage (self-report questionnaire)

Overall screen usage (self-report questionnaire)

Sleep disturbance (self-report questionnaire)

Presence of sleep disorders (26-item parent-report questionnaire)

 Engaging in electronic devices use was associated with overall sleep disturbance, sleep onset and daytime sleepiness
Nissim et al. Europe and North America

Cross-sectional

(Representative sample)

 86,542 13–15 50.9

Intense SMU (EU Kids Online Survey)

Problematic SMU (the Social Media Disorder Scale)

 Sleep duration (self-report questionnaire) Across 18 countries, both intense and problematic SMU were associated with shorter sleep duration, later bedtimes and greater social jetlag
Tao and Fisher US Cross-sectional 407 15–18 82.3

Hours of SMU (self-report questionnaire)

Social media intergroup contact (the Developmental Intergroup Contact Measure, DICM)

Social media racial justice civic engagement (the Online Civic Engagement Behaviour Construct)

Social media racial discrimination (the Online Victimization Scale)

Depressive symptoms (the 20-item Center for Epidemiologic Studies Depression Scale, CES-D)

Anxiety symptoms (the 7-item Generalized Anxiety Disorder, GAD-7)

Alcohol use disorder (Alcohol Use Disorders Identification Test (AUDIT))

Illicit drug use and problems (self-report questionnaire)

 SMU and racial discrimination are associated with poor mental health and drug use problem
Valero et al. UK Cross-sectional 2232 18.0 51.1

Problematic digital technology use

(the Compulsive Internet Use Scale)

Sleep quality (the Pittsburgh Sleep Quality Index, PSQI)

Loneliness (the UCLA Loneliness Scale, version 3)

Depression and anxiety symptoms (structured clinical interview)

 Problematic use of technology was associated with poor sleep quality, after controlling for loneliness, depressive and anxiety symptoms, neighbourhood disorder, sex, and maternal insomnia
Woods and Scoot UK Cross-sectional 467 11–17 N/A Overall and night-time-specific SMU (self-report questionnaire)

Anxiety and depression (the Hospital Anxiety and Depression Scale (HADS))

Self-esteem (the Rosenberg Self-Esteem Scale, RSES)

Emotional investment in social media (the Social Media Use Integration Scale)

Sleep quality (the Pittsburgh Sleep Quality Index (PSQI))

 Overall and night-time-specific SMU were associated with lower self-esteem and increased anxiety and depression
Xie et al. Macau Cross-sectional 212 15–24 51.9 Screen-based electronic media device usage (self-report questionnaire) Sleep quality (the Pittsburgh Sleep Quality Index (PSQI)) A J-shape relationship was observed between TV, computer, mobile phone use and sleep quality
Beeres et al. Sweden Cohort 3501 14–15 51.5 SMU (self-report questionnaire) Mental health (Strength and Difficulties Questionnaire (SDQ)) Higher use of social media was associated with more symptoms of mental health problems. No longitudinal association was found
Eijnden et al. Netherlands Cohort 2021 13.9 45.4

Frequency of SMU (self-report questionnaire)

Problematic SMU (the Social Media Disorder (SMD))

Sleep quality (the 5-item Groningen Sleep Quality Scale (GSKS))

Bedtime (self-report questionnaire)

 The intensity of SMU and problematic SMU delay bedtime of adolescents
Lee et al. US Cohort 5114 12–14 48.7 SMU (self-report questionnaire) Mental health (the Global Appraisal of Individual Needs-Short Screener (GAIN-)SS) Frequent SMU negatively affects mental health in this recent representative sample of US adolescents
Maksniemi et al. Finland Cohort 426 13–19 65.7 Active SMU (the Socio-Digital Participation Inventory)

Bedtime on schooldays (self-report questionnaire)

Emotional exhaustion (the School Burnout Inventory)

No clear pattern between active SMU and bedtime across adolescence

Active SMU was associated with emotional exhaustion
Plackett et al. UK Cohort (representative sample) 3228 10–15 51.4 SMU (self-report questionnaire) Mental health (the Strengths and Difficulties Questionnaire (SDQ)) SMU was not longitudinally associated with poorer mental health 2 years later
Richardson et al. Australia Cohort 528 10–12 49

Time spent using technology (self-report questionnaire)

Parental control of technology (self-report questionnaire)

Chronotype preference (the 10-item Children’s Morningness-Eveningness Scale (MESC))

School night sleep duration (self-report questionnaire)

Daytime sleepiness (the 8 item Paediatric Daytime Sleepiness Scale (PDSS))

This current study suggested a bidirectional relationship between technology use and sleep in adolescents

Parental restriction of technology use may not predict prospective changes in adolescent technology use, nor sleep, over time
Zhou, et al. China Cohort 2329 18.4 63.1 SMU (self-report questionnaire) Mental health (self-report questionnaire) Problematic internet use was reciprocally associated with mental health issues
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Overview of SMU

SMU refers to the act of engaging with online platforms specifically designed for social interaction, whereas electronic media use (or digital use, digital media, internet use, screen time) is a broader term that encompasses various forms of media delivered electronically, including but not limited to social media. In this scoping review, we focus on SMU.

Over the past decade, subjective measures have been the primary tool to investigate individual perceptions, opinions, or personal experiences of SMU. For example, a self-report scale was developed to assess compulsive use of social media and its severity [31]. Besides, there are several platform-specific scales for social media addiction features such as salience, mood modification, tolerance, withdrawal, conflict, and relapse. The Bergen Facebook Addiction Scale, for instance, focuses specifically on addiction to Facebook [32•], while the Bergen Social Media Addiction Scale has emerged to examine a broader scope, including social media platforms beyond Facebook [33•, 34]. Indeed, more researchers used general metrics to measure SMU across multiple platforms collectively such as the Social Media Disorder Scale which measures aspects of social media addiction features, such as preoccupation with social media, excessive time spent, withdrawal symptoms, and negative consequences [35–37]. Other SMU experiences are also captured including social comparison on social media and negative experiences such as bullying, FOMO, and extensive negative feedback [32•].

In addition, the duration and timing of SMU also have significant implications for sleep and mental health, as excessive or inappropriate use of social media at certain timing, for example at bedtime, can potentially contribute to negative biopsychosocial effects. The Socio-Digital Participation Inventory includes four items to measure the frequency of SMU on a seven-point frequency scale (1 = never, 2 = a couple of times a year, 3 = monthly, 4 = weekly, 5 = daily, 6 = multiple times a day, 7 = all the time) [25•]. The total time spent on SMU (in daytime and night-time) are usually captured by questionnaires and social media time use diary [38•]. In view of the limitation of self-reported measures, there has been a shift towards incorporating more objective measures in addition to subjective self-report scales. Increasing number of studies used time tracker via specific apps (installed on participants’ devices used for online activity) to reduce recall bias [33•]. Other objective features, such as the number of followers, likes, comments, shares, bookmarks, and total interactions, which can be retrieved from various social media platforms [39•] were also used to reflect social medica engagement. In addition, the content (e.g., educational vs non-educational) posted, read, and shared on social media platforms plays a significant role in shaping user experiences, engagement levels, and the overall impact of SMU. It is worth to note that no included studies attempted to measure multi-device SMU as it can be challenging due to the wide range of devices that people use to access social media platforms. Traditional research methods often rely on self-reporting, surveys, or tracking software installed on specific devices, which may not capture the full extent of multi-device usage. Some individuals may switch between multiple devices throughout the day, making it challenging to track their overall social media engagement accurately.

Overview of Recent Studies

Study Characteristics

A total of 33 studies were included in this scoping review, with 26 of them were cross-sectional in nature, indicating a snapshot overview of the relationship between SMU, sleep, and mental health. Moreover, only a few studies utilized representative samples [35, 36, 40, 41], as outlined in Table 1. It is also worth noting that the sample sizes varied significantly across studies, ranging from 54 to 195,668 participants.

Measurement of SMU

In terms of the measurement of SMU, all studies used either self-developed questionnaires (e.g., “in a typical school week, how often do you check social media?” and “on a normal weekday, how many hours you spend on social medias, write blogs/read each people blogs, or chat online?”) or validated self-report questionnaires (e.g., the 26-item Chinese Internet Addiction Scale-Revised and the Online Civic Engagement Behavior Construct). Different dimensions of SMU were measured such as overall and night-time SMU, problematic SMU, emotional investment in social media, racial discrimination, and racial justice civic engagement on social media. Only a limited number of studies incorporated more reliable measurements such as ecological momentary assessment [42] and total message count [43].

Measurement of Sleep and Mental Health

In terms of sleep outcome assessment, most of these studies employed subjective instruments such as sleep diary [32•], self-developed self-report questionnaires (e.g., “How many hours did you sleep over the past week?”) [43], and validated self-report questionnaires (e.g., the Pittsburgh Sleep Quality Index and the Insomnia Severity Index) [44, 45•] to measure different sleep outcomes including sleep quality, sleep duration, sleep displacement, bedtime, and sleep-onset latency. In addition to these subjective instruments, 3 studies have utilized objective devices such as actigraphy and other wearable devices to capture objective sleep data [43, 46, 47]. While for mental health aspects, depression and anxiety are the main outcomes. Most of the recent studies utilized standardized questionnaires such as the Short Mood and Feelings Questionnaire, the Depression Anxiety Stress Scales 21, and the Suicidal Behaviors Questionnaire-Revised to measure the symptoms of depression and anxiety.

Synthesis of Recent Findings

SMU and Sleep

Both longitudinal and cross-sectional studies tend to support the association between SMU and sleep disturbances (Table 1). A total of 17 cross-sectional studies observed an association between SMU and various sleep parameters in youth. Of these studies, a total of 16 reported a significant association between different dimensions of SMU (internet addition, duration of screen use, inappropriate time use (near bedtime), with one additionally measure parent control of technology) and poor sleep outcomes (both subjectively and objectively measured sleep parameters, such as bedtime, sleep-onset latency, sleep duration, and sleep quality) [31, 32•, 35, 36, 40, 42–44, 45•, 47–53]. Nevertheless, a study of 101 undergraduate students did not find that bedtime SMU was detrimental to sleep [46]. However, in the subgroup analysis, the authors found that youth with increased levels of depressive symptoms are at higher risk of experiencing negative impacts of bedtime SMU on sleep [46].

Among the three cohort studies, two indicated that higher levels of SMU predicted later bedtime and shorter sleep duration in youth after 1–2 years of follow-up [37, 54]. These studies revealed that both frequent and problematic use of SMU could result in later bedtime [37, 54]. In addition, Richardson and colleagues further found that SMU predicted greater daytime sleepiness in adolescence [54]. In addition, adolescents with evening chronotype preference and shorter sleep duration were found to have longer usage of social media, suggesting a potential bidirectional relationship between SMU and sleep duration [54]. Another cohort study conducted by Maksniemi and colleagues did not find a significant association between SMU and bedtime among 426 youth aged between 13 and 19 [25•]. Interestingly, subgroup analyses indicated that significant associations were only observed in early adolescence (at age 13 and 14), but not in middle (at age 14 and 15) nor late adolescence (at age 17 and 18) [25•]. This finding highlights the importance of considering the developmental stages of youth in order to unravel the complex relationship between SMU and sleep [55].

SMU and Mental Health

A total of 9 cross-sectional studies examined the relationship between SMU and mental health [33•, 34, 38•, 39•, 56•, 57–60]. A greater amount of time spent on social media was associated with an increased risk of depression, self-harm, and lower self-esteem. On the other hand, adolescents who exhibited mental health issues tended to spend more time on social media platforms, suggesting a potential bidirectional relationship between SMU and mental health. However, it is important to point out that despite appealing hypotheses, actual effect size estimates of SMU on various mental health outcomes (e.g., self-esteem, life satisfaction, depression, and loneliness) were of small-to-medium magnitude as reported in previous meta-analytic studies, ranging from − 0.11 to − 0.32 [61•, 62].

Four longitudinal cohort studies reported mixed findings between SMU and mental health [41, 63, 64•, 65]. Two cohort studies conducted in the USA and China reported that frequent and problematic SMU were significantly associated subsequent mental health issues [64•, 65]. Interestingly, the authors identified substantial sex differences in the mental health trajectories, with only girls showing a deteriorating linear trend (β = 0.23, p < 0.05) [64•]. On the contrary, the other two longitudinal studies conducted in Sweden and UK reported that although frequent SMU was associated with increased levels of mental problems at a single timepoint, there was no longitudinal association [41, 63], which suggests that SMU may be only an indicator for mental health instead of a risk factor.

SMU, Sleep, and Mental Health

A total of 7 studies measured both sleep and mental health outcomes [31, 45•, 46–48, 50, 59]. Five of these studies reported significant associations among SMU, sleep, and mental health outcome [31, 45•, 46, 50, 59]. It was reported that SMU was significantly associated with poor sleep quality and increased mental health issues [31, 45•, 46, 50, 59], and sleep was found to mediate the negative impacts of SMU on mental health and emotional symptoms in adolescents [45•]. Poor sleep was also shown to be significantly associated with mental health outcomes [31, 50, 59]. Furthermore, adolescents with higher level of depressive symptoms were at higher risk of experiencing negative impacts of bedtime SMU on sleep outcomes [46]. Indeed, these findings preliminarily unveiled the complex interplay among SMU, sleep, and mental health.

Nevertheless, it is essential to highlight that recent research has also recognized the positive impacts of SMU on mental health, particularly in the context of the COVID-19 pandemic, at which physical social interactions were significantly disrupted [56•, 58]. Adolescents in Australia and UK were found to use social media as an active coping strategy to relieve external stressors (e.g., exam pressure), to seek support for suicidal ideation or self-harm behavior, and to support others via social media [56•, 58].

Discussion

This scoping review synthesized recent publications from the past 3 years that investigated the impact of SMU on sleep and/or mental health outcomes in youth. The majority of the studies provide supporting evidence for an association between SMU, poor sleep quality, and adverse mental health outcomes. Problematic SMU or addiction, as well as the duration of SMU, were identified as the most prevalent aspects of social media examined in the included studies. Sleep duration, bedtime, and insomnia emerged as the most commonly assessed sleep problems, while depression and anxiety were the most frequently measured mental health outcomes. However, it is important to note that despite the significant associations identified among these variables, the directionality of the relationship remains unclear in view of inconsistent findings across studies.

Underlying Mechanism Between SMU and Sleep

Numerous mechanisms have been proposed to elucidate the relationship between social/digital media usage and sleep quantity and quality [66]. Hyperarousal, a core mechanism in explaining insomnia [67], plays a role in explaining how night-time SMU disrupts sleep. Active engagement in media activities can directly induce physiological and psychological arousal, leading to longer sleep onset latency [68]. This effect is particularly noticeable when individuals actively engage in interactive digital media, such as social messaging and social media, as opposed to passive media consumption like television viewing [69•], likely due to the heightened arousal associated with interactive activities [70]. Interestingly, a study found that engaging in phone conversations near bedtime was associated with longer sleep duration, while the use of social media and texting displayed a negative association [71]. It has been hypothesized that conversing with a friend may positively influence emotional well-being, thereby promoting sleep [72]. However, social networking, despite its potential for fostering friendships, may also trigger FOMO and social media stress. In addition to the psychological arousal induced by electronic media usage, the light emitted from device screens is another hypothesis explaining the detrimental effects of digital media on sleep. Specifically, the light emitted by electronic devices, especially blue light (at a wavelength of 480 nm), has a significant impact on the suppression of melatonin, a hormone that promotes sleep [73]. Moreover, a recent study indicated that high-risk adolescents whose parents with bipolar affective disorder have lower level of nocturnal melatonin secretion. It might be possible that adolescents with certain risk factors (even without psychopathologies) may be particularly hypersensitive and vulnerable to light suppression of melatonin secretion [74], which are considered as high-risk group that require early intervention. Furthermore, it is plausible that an interaction or interplay might exist between arousal and light exposure, and the combination of these conditions could potentially heighten the risk of sleep disturbances. Additionally, the direct displacement of sleep resulting from engagement in social media activities may also lead to shorter sleep duration. Although initial evidence suggests a negative impact of both content and light emitted by electronic devices on sleep, the precise underlying mechanism remains poorly established. Last but not least, some preliminary studies have also investigated other sleep- and circadian-related factors such as chronotype preference and daytime sleepiness in mediating and/or moderating the relationship between SMU and sleep [32•, 44, 54], albeit the findings have been inconclusive. Future studies are warranted to thoroughly explore the role of these factors in the interplay between SMU and sleep.

Underlying Mechanism Between SMU and Mental Health

It has been suggested both behavioral and cognitive factors mediate the impact of SMU on mental health. Among the behavioral factors, sleep has been identified as one of the notable mediators of the association [31, 44, 45•, 46, 50, 59, 75]. Physiologically, prolonged SMU before bedtime delays sleep onset, reduces sleep duration, and mediates the association between eveningness and sleep as well as daytime sleepiness [44], which have been identified as risk factors for mental illness [76–78]. This complex interplay between sleep and mental health has also been documented in interventional studies. Our previous clinical trial demonstrated that a brief insomnia prevention program, adapted from cognitive-behavioral therapy for insomnia, significantly decreased the severity of depressive symptoms in adolescents at 12-month follow-up [79], suggesting the potential mediating role of sleep in mental health. While for the cognitive factors, FOMO has been recognized as a possible mediator. In particular, Elhai et al. reported that FOMO mediated relationship between anxiety and smartphone use frequency, as well as problematic SMU [80]. Besides FOMO, recent literatures have also identified several other cognitive factors that mediate the relationship between SMU and mental health, such as self-esteem [38•], body satisfaction [57], and emotional investment [59].

In addition to these behavioral and cognitive factors, cyberbullying is also one of the important mediators of SMU and mental health in youth [81]. Cyberbullying has become a prevalent phenomenon worldwide, with victimization rates in children and adolescents ranging from 14 to 57.5%, and lifetime perpetration rates ranging from 6.0 to 46.3% [82, 83]. Previous meta-analytical study demonstrated that cyberbullying significantly increased the risks of developing depression, self-harm, suicidal attempts, and ideation [84]. Moreover, over the COVID-19 pandemic, stressors associated with disasters have also been reported to potentially exacerbate the negative effects of SMU on mental health, thereby increasing the risk for mental health issues [85, 86].

In summary, there have been significant developments in recent years in understanding the magnitude and mechanisms that underlie the association between SMU and mental health. However, most of the studies employed a cross-sectional design, which prevented from a thorough understanding of the causality. Experimental and interventional studies are warranted to better comprehend the underlying mechanisms, establish causality, and improve the negative outcomes.

Future Direction

There are several potential avenues for future investigation. Firstly, prospective cohort studies using representative samples are needed to elucidate the magnitude and directionality of relationships among SMU, sleep, and mental health, which is of clinical practice implication for precision intervention. To capture the varying dynamics among SMU, sleep, and mental health across different age groups of adolescents (early and late adolescents), it is recommended that prospective studies may need to have a follow-up period that will better cover the entirety of adolescence period [41]. Secondly, the lack of consistency in the methodologies employed by different studies measuring SMU has been a major contributing factor to the conflicting findings found in the current literature. It is imperative to use validated questionnaires to measure the SMU. More importantly, objective measurements (e.g., screen time monitors on smartphones [87], ecological momentary assessments [88], and wearable devices [89]) should also be incorporated. Apart from timing, it is equally important to capture the content, and number of devices that subjects engage with. Thirdly, future research should consider conducting randomized controlled trials at different levels (e.g., individual, school, and family) to reduce overall and problematic SMU and to ultimately improve sleep and mental health outcomes in youth. The design of the intervention may benchmark to existing guidelines such as the American Academy of Paediatrics recommendations 2016 on media use [90], and the WHO guidelines on physical activity and sedentary behavior [91], in which both guidelines recommend limiting the amount of recreational screen time, and avoiding SMU 1 h before bedtime. Intervention formats may consider psychoeducation, cognitive and behavioral techniques, and motivational interviewing. Finally, priority may be given to conduct observational and interventional studies on SMU in vulnerable populations, such as youth experiencing mood or sleep problems, as well as those who are high-risk offspring of parents with sleep and mood disorders, as these populations are more susceptible to experience significant negative impacts from inappropriate and excessive SMU [92].

Conclusion

Despite the heterogeneity observed in the recent studies, both cross-sectional and cohort studies highlight the impact of SMU on poor sleep and mental health, albeit there are some inconsistent findings. Research has progressed from focusing solely on “screen time” to exploring the social, emotional, and cognitive dimensions of SMU. When measuring sleep outcomes, researchers have investigated the sleep duration and quality and also consider factors such as chronotype and pre-sleep arousal, which will enable a better understanding of how social media impacts sleep in a broader context. Similar advancements have also been made in the field of SMU-related mental health research. Recognizing the interconnections among SMU, sleep, and mental health is crucial for public health and will contribute to improving sleep and mental health outcomes while promoting rational SMU. Future studies should evaluate the effectiveness of interventions on reducing SMU, with ultimate goal to improve sleep and mental health.

Acknowledgements

We would like to thank Mr. Lin, Colin Qin Li, and Mr. Sin, Calvin Chun Hei for their help during the literature review.

Author Contribution

All authors conceived the study and formulated the research question. Drs. Yu, Li, and Chan drafted the initial version of the manuscript. All authors revised the manuscript for important intellectual content and approved the final version of the manuscript.

Data Availability

Since this review article solely relies on published articles and does not include individual participant data, therefore no data sharing is available.

Declarations

Ethics Approval

This review article does not require medical ethical approval.

Conflict of Interest

The authors declare no competing interests.

Human and Animal Rights and Informed Consent

This review article does not require medical ethical approval or human informed consent.

Footnotes

Publisher's Note

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Contributor Information

Tim M. H. Li, Email: manholi@cuhk.edu.hk

Ngan Yin Chan, Email: rachel.chan@cuhk.edu.hk.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  • 1.Nations U. Secretary General’s Report to the General Assembly, (A/36/215). 1981. United Nations New York, NY, USA.
  • 2.Blair RJR. The neurobiology of psychopathic traits in youths. Nat Rev Neurosci. 2013;14(11):786–799. doi: 10.1038/nrn3577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Pichardo AW, et al. Integrating models of long-term athletic development to maximize the physical development of youth. Int J Sports Sci Coach. 2018;13(6):1189–1199. doi: 10.1177/1747954118785503. [DOI] [Google Scholar]
  • 4.Wing YK, et al. A school-based sleep education program for adolescents: A cluster randomized trial. Pediatrics. 2015;135(3):e635–e643. doi: 10.1542/peds.2014-2419. [DOI] [PubMed] [Google Scholar]
  • 5.Chen SJ, et al. The trajectories and associations of eveningness and insomnia with daytime sleepiness, depression and suicidal ideation in adolescents: A 3-year longitudinal study. J Affect Disord. 2021;294:533–542. doi: 10.1016/j.jad.2021.07.033. [DOI] [PubMed] [Google Scholar]
  • 6.Roane BM, Taylor DJ. Adolescent insomnia as a risk factor for early adult depression and substance abuse. Sleep. 2008;31(10):1351–1356. [PMC free article] [PubMed] [Google Scholar]
  • 7.Merikangas KR, et al. Lifetime prevalence of mental disorders in U.S.adolescents: Results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry, 2010. 49(10): p. 980–9. [DOI] [PMC free article] [PubMed]
  • 8.Kessler RC, et al. Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593–602. doi: 10.1001/archpsyc.62.6.593. [DOI] [PubMed] [Google Scholar]
  • 9.Grandner MA, et al. Social and behavioral determinants of perceived insufficient sleep. Front Neurol. 2015;6:112. doi: 10.3389/fneur.2015.00112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Alonzo R, et al. Interplay between social media use, sleep quality, and mental health in youth: A systematic review. Sleep Med Rev. 2021;56:101414. doi: 10.1016/j.smrv.2020.101414. [DOI] [PubMed] [Google Scholar]
  • 11.Hysing M, et al. Sleep and use of electronic devices in adolescence: Results from a large population-based study. BMJ Open. 2015;5(1):e006748. doi: 10.1136/bmjopen-2014-006748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Levenson JC, et al. Social media use before bed and sleep disturbance among young adults in the United States: A nationally representative study. Sleep, 2017. 40(9): p. zsx113. [DOI] [PMC free article] [PubMed]
  • 13.Viner RM, et al. Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: A secondary analysis of longitudinal data. The Lancet Child & Adolescent Health. 2019;3(10):685–696. doi: 10.1016/S2352-4642(19)30186-5. [DOI] [PubMed] [Google Scholar]
  • 14.Craig W, et al. Social media use and cyber-bullying: A cross-national analysis of young people in 42 countries. J Adolesc Health. 2020;66(6):S100–S108. doi: 10.1016/j.jadohealth.2020.03.006. [DOI] [PubMed] [Google Scholar]
  • 15.Jungblut M, Haim M. Visual gender stereotyping in campaign communication: Evidence on female and male candidate imagery in 28 countries. Commun Res. 2023;50(5):561–583. doi: 10.1177/00936502211023333. [DOI] [Google Scholar]
  • 16.Feltman CE, Szymanski DM. Instagram use and self-objectification: The roles of internalization, comparison, appearance commentary, and feminism. Sex Roles. 2018;78:311–324. doi: 10.1007/s11199-017-0796-1. [DOI] [Google Scholar]
  • 17.Moreno MA, et al. Display of health risk behaviors on MySpace by adolescents: Prevalence and associations. Arch Pediatr Adolesc Med. 2009;163(1):27–34. doi: 10.1001/archpediatrics.2008.528. [DOI] [PubMed] [Google Scholar]
  • 18.Yonker LM, et al. “Friending” teens: Systematic review of social media in adolescent and young adult health care. J Med Internet Res. 2015;17(1):e3692. doi: 10.2196/jmir.3692. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Uhls YT, Ellison NB, Subrahmanyam K. Benefits and costs of social media in adolescence. Pediatrics, 2017;140(Supplement_2): p. S67-S70. [DOI] [PubMed]
  • 20.Cingel DP, Carter MC, Krause H-V. Social media and self-esteem. Curr Opin Psychol. 2022;45:101304. doi: 10.1016/j.copsyc.2022.101304. [DOI] [PubMed] [Google Scholar]
  • 21.Ellison NB, Steinfield C, Lampe C. Connection strategies: Social capital implications of Facebook-enabled communication practices. New Media Soc. 2011;13(6):873–892. doi: 10.1177/1461444810385389. [DOI] [Google Scholar]
  • 22.Subrahmanyam K, Smahel D, Greenfield P. Connecting developmental constructions to the internet: Identity presentation and sexual exploration in online teen chat rooms. Dev Psychol. 2006;42(3):395. doi: 10.1037/0012-1649.42.3.395. [DOI] [PubMed] [Google Scholar]
  • 23.Utz S, Breuer J. The relationship between use of social network sites, online social support, and well-being. J media psychol. 2017. [DOI] [PMC free article] [PubMed]
  • 24.Boer M, et al. Social media use intensity, social media use problems, and mental health among adolescents: Investigating directionality and mediating processes. Comput Hum Behav. 2021;116:106645. doi: 10.1016/j.chb.2020.106645. [DOI] [Google Scholar]
  • 25.Maksniemi E, et al. Intraindividual associations between active social media use, exhaustion, and bedtime vary according to age—a longitudinal study across adolescence. J Adolesc. 2022;94(3):401–414. doi: 10.1002/jad.12033. [DOI] [PubMed] [Google Scholar]
  • 26.Levenson JC, et al. The association between social media use and sleep disturbance among young adults. Prev Med. 2016;85:36–41. doi: 10.1016/j.ypmed.2016.01.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Exelmans L, Van den Bulck J. The use of media as a sleep aid in adults. Behav Sleep Med. 2016;14(2):121–133. doi: 10.1080/15402002.2014.963582. [DOI] [PubMed] [Google Scholar]
  • 28.Fabris MA, et al. Investigating the links between fear of missing out, social media addiction, and emotional symptoms in adolescence: The role of stress associated with neglect and negative reactions on social media. Addict Behav. 2020;106:106364. doi: 10.1016/j.addbeh.2020.106364. [DOI] [PubMed] [Google Scholar]
  • 29.Vannucci A, et al. Social media use and risky behaviors in adolescents: A meta-analysis. J Adolesc. 2020;79:258–274. doi: 10.1016/j.adolescence.2020.01.014. [DOI] [PubMed] [Google Scholar]
  • 30.Gupta M, Sharma A. Fear of missing out: A brief overview of origin, theoretical underpinnings and relationship with mental health. World journal of clinical cases. 2021;9(19):4881. doi: 10.12998/wjcc.v9.i19.4881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Madrid-Valero, J.J., et al. Problematic technology use and sleep quality in young adulthood: Novel insights from a nationally representative twin study. Sleep. 2023:zsad038. [DOI] [PMC free article] [PubMed]
  • 32.Bergfeld NS, Van den Bulck J. It's not all about the likes: Social media affordances with nighttime, problematic, and adverse use as predictors of adolescent sleep indicators. Sleep Health. 2021;7(5):548–555. doi: 10.1016/j.sleh.2021.05.009. [DOI] [PubMed] [Google Scholar]
  • 33.Brailovskaia J, et al. Social media use, mental health, and suicide-related outcomes in Russian women: A cross-sectional comparison between two age groups. Womens Health. 2022;18:17455057221141292. doi: 10.1177/17455057221141292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Henzel V, Håkansson A. Hooked on virtual social life. Problematic social media use and associations with mental distress and addictive disorders. PloS one, 2021;16(4):p. e0248406. [DOI] [PMC free article] [PubMed]
  • 35.Boniel-Nissim M, et al. Adolescent use of social media and associations with sleep patterns across 18 European and North American countries. Sleep Health. 2023;9(3):314–321. doi: 10.1016/j.sleh.2023.01.005. [DOI] [PubMed] [Google Scholar]
  • 36.Buda G, et al. Possible effects of social media use on adolescent health behaviors and perceptions. Psychol Rep. 2021;124(3):1031–1048. doi: 10.1177/0033294120922481. [DOI] [PubMed] [Google Scholar]
  • 37.van den Eijnden RJ, et al. Social media use and adolescents’ sleep: A longitudinal study on the protective role of parental rules regarding internet use before sleep. Int J Environ Res Public Health. 2021;18(3):1346. doi: 10.3390/ijerph18031346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Barthorpe A, et al. Is social media screen time really associated with poor adolescent mental health? A time use diary study. J Affect Disord. 2020;274:864–870. doi: 10.1016/j.jad.2020.05.106. [DOI] [PubMed] [Google Scholar]
  • 39.Beyari H. The relationship between social media and the increase in mental health problems. Int J Environ Res Public Health. 2023;20(3):2383. doi: 10.3390/ijerph20032383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Khan A, et al. Associations between adolescent sleep difficulties and active versus passive screen time across 38 countries. J Affect Disord. 2023;320:298–304. doi: 10.1016/j.jad.2022.09.137. [DOI] [PubMed] [Google Scholar]
  • 41.Plackett R, Sheringham J, Dykxhoorn J. The longitudinal impact of social media use on UK adolescents' mental health: Longitudinal observational study. J Med Internet Res. 2023;25:e43213. doi: 10.2196/43213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Gaya AR, et al. Electronic device and social network use and sleep outcomes among adolescents: The EHDLA study. BMC Public Health. 2023;23(1):1–11. doi: 10.1186/s12889-023-15579-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Burnell K, et al. Associations between adolescents’ daily digital technology use and sleep. J Adolesc Health. 2022;70(3):450–456. doi: 10.1016/j.jadohealth.2021.09.033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Kortesoja L, et al. Late-night digital media use in relation to chronotype, sleep and tiredness on school days in adolescence. J Youth Adolesc. 2023;52(2):419–433. doi: 10.1007/s10964-022-01703-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Li TM, et al. The associations of electronic media use with sleep and circadian problems, social, emotional and behavioral difficulties in adolescents. Front Psych. 2022;13:892583. doi: 10.3389/fpsyt.2022.892583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Das-Friebel A, et al. Bedtime social media use, sleep, and affective wellbeing in young adults: An experience sampling study. J Child Psychol Psychiatry. 2020;61(10):1138–1149. doi: 10.1111/jcpp.13326. [DOI] [PubMed] [Google Scholar]
  • 47.Gumport NB, et al. The impact of television, electronic games, and social technology use on sleep and health in adolescents with an evening circadian preference. J Youth Adolesc. 2021;50(12):2351–2362. doi: 10.1007/s10964-021-01429-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Chao M, et al. TikTok use and psychosocial factors among adolescents: Comparisons of non-users, moderate users, and addictive users. Psychiatry Res. 2023;325:115247. doi: 10.1016/j.psychres.2023.115247. [DOI] [PubMed] [Google Scholar]
  • 49.Charmaraman L, et al. Quantity, content, and context matter: Associations among social technology use and sleep habits in early adolescents. J Adolesc Health. 2021;69(1):162–165. doi: 10.1016/j.jadohealth.2020.09.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Hamilton JL, et al. Daily and average associations of physical activity, social media use, and sleep among adolescent girls during the COVID-19 pandemic. J Sleep Res. 2023;32(1):e13611. doi: 10.1111/jsr.13611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Nagata JM, et al. Bedtime screen use behaviors and sleep outcomes: Findings from the Adolescent Brain Cognitive Development (ABCD) Study. Sleep Health. 2023. [DOI] [PMC free article] [PubMed]
  • 52.Xie YJ, et al. Relationships between the usage of televisions, computers, and mobile phones and the quality of sleep in a Chinese population: Community-based cross-sectional study. J Med Internet Res. 2020;22(7):e18095. doi: 10.2196/18095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Liu S, et al. Problematic Internet use and academic engagement during the COVID-19 lockdown: The indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. J Affect Disord. 2022;309:9–18. doi: 10.1016/j.jad.2022.04.043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Richardson C, et al. A longitudinal investigation of sleep and technology use in early adolescence: Does parental control of technology use protect adolescent sleep? Sleep Med. 2021;84:368–379. doi: 10.1016/j.sleep.2021.06.003. [DOI] [PubMed] [Google Scholar]
  • 55.Valkenburg PM, Peter J. The differential susceptibility to media effects model. J Commun. 2013;63(2):221–243. doi: 10.1111/jcom.12024. [DOI] [Google Scholar]
  • 56.Bailey E, et al. The mental health and social media use of young Australians during the COVID-19 pandemic. Int J Environ Res Public Health. 2022;19(3):1077. doi: 10.3390/ijerph19031077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Fardouly J, et al. The use of social media by Australian preadolescents and its links with mental health. J Clin Psychol. 2020;76(7):1304–1326. doi: 10.1002/jclp.22936. [DOI] [PubMed] [Google Scholar]
  • 58.O’reilly M. Social media and adolescent mental health: The good, the bad and the ugly. J Ment Health. 2020;29(2):200–206. doi: 10.1080/09638237.2020.1714007. [DOI] [PubMed] [Google Scholar]
  • 59.Woods HC, Scott H. # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. J Adolesc. 2016;51:41–49. doi: 10.1016/j.adolescence.2016.05.008. [DOI] [PubMed] [Google Scholar]
  • 60.Tao X, Fisher CB. Fisher, Exposure to social media racial discrimination and mental health among adolescents of color. J youth adolesc. 2022: p. 1–15. [DOI] [PMC free article] [PubMed]
  • 61.Huang C. A meta-analysis of the problematic social media use and mental health. Int J Soc Psychiatry. 2022;68(1):12–33. doi: 10.1177/0020764020978434. [DOI] [PubMed] [Google Scholar]
  • 62.Ivie EJ, et al. A meta-analysis of the association between adolescent social media use and depressive symptoms. J Affect Disord. 2020;275:165–174. doi: 10.1016/j.jad.2020.06.014. [DOI] [PubMed] [Google Scholar]
  • 63.Beeres DT, et al. Social media and mental health among early adolescents in Sweden: A longitudinal study with 2-year follow-up (KUPOL study) J Adolesc Health. 2021;68(5):953–960. doi: 10.1016/j.jadohealth.2020.07.042. [DOI] [PubMed] [Google Scholar]
  • 64.Lee S, et al. Frequent social media use and its prospective association with mental health problems in a representative panel sample of US adolescents. J Adolesc Health. 2022;70(5):796–803. doi: 10.1016/j.jadohealth.2021.11.029. [DOI] [PubMed] [Google Scholar]
  • 65.Zhou N, et al. A four-wave, cross-lagged model of problematic internet use and mental health among Chinese college students: Disaggregation of within-person and between-person effects. Dev Psychol. 2020;56(5):1009. doi: 10.1037/dev0000907. [DOI] [PubMed] [Google Scholar]
  • 66.Cain N, Gradisar M. Electronic media use and sleep in school-aged children and adolescents: A review. Sleep Med. 2010;11(8):735–742. doi: 10.1016/j.sleep.2010.02.006. [DOI] [PubMed] [Google Scholar]
  • 67.Spiegelhalder K, Riemann D. Hyperarousal and Insomnia. Sleep Med Clin. 2013;8(3):299–307. doi: 10.1016/j.jsmc.2013.04.008. [DOI] [Google Scholar]
  • 68.LeBourgeois MK, et al. Digital media and sleep in childhood and adolescence. Pediatrics. 2017;140(Suppl 2):S92–S96. doi: 10.1542/peds.2016-1758J. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.• Brautsch LA, et al. Digital media use and sleep in late adolescence and young adulthood: A systematic review. Sleep Med Rev. 2022:101742. A narrative synthesis summarized the impact of digital media use on eight sleep outcomes. [DOI] [PubMed]
  • 70.Zhang J, et al. Emergence of sex differences in insomnia symptoms in adolescents: A large-scale school-based study. Sleep. 2016;39(8):1563–1570. doi: 10.5665/sleep.6022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Tavernier R, et al. Adolescents' technology and face-to-face time use predict objective sleep outcomes. Sleep Health. 2017;3(4):276–283. doi: 10.1016/j.sleh.2017.04.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Güroğlu B. The power of friendship: The developmental significance of friendships from a neuroscience perspective. Child Development Perspectives. 2022;16(2):110–117. doi: 10.1111/cdep.12450. [DOI] [Google Scholar]
  • 73.West KE, et al. Blue light from light-emitting diodes elicits a dose-dependent suppression of melatonin in humans. J appl physiol. 2011. [DOI] [PubMed]
  • 74.Feng H, et al. Dim light melatonin patterns in unaffected offspring of parents with bipolar disorder: A case-control high-risk study. J Affect Disord. 2022;315:42–47. doi: 10.1016/j.jad.2022.07.029. [DOI] [PubMed] [Google Scholar]
  • 75.Adams SK, Kisler TS. Sleep quality as a mediator between technology-related sleep quality, depression, and anxiety. Cyberpsychol Behav Soc Netw. 2013;16(1):25–30. doi: 10.1089/cyber.2012.0157. [DOI] [PubMed] [Google Scholar]
  • 76.Merikanto I, et al. Evening-types show highest increase of sleep and mental health problems during the COVID-19 pandemic—multinational study on 19 267 adults. Sleep. 2022. 45(2): p. zsab216. [DOI] [PMC free article] [PubMed]
  • 77.Zhang J, et al. Sleep patterns and mental health correlates in US adolescents. J Pediatr. 2017;182:137–143. doi: 10.1016/j.jpeds.2016.11.007. [DOI] [PubMed] [Google Scholar]
  • 78.Li Y, et al. The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nature Aging. 2022;2(5):425–437. doi: 10.1038/s43587-022-00210-2. [DOI] [PubMed] [Google Scholar]
  • 79.Chan NY, et al. A prevention program for insomnia in at-risk adolescents: a randomized controlled study. Pediatrics. 2021;147(3). [DOI] [PubMed]
  • 80.Elhai JD, et al. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addict Behav. 2020;101:105962. doi: 10.1016/j.addbeh.2019.04.020. [DOI] [PubMed] [Google Scholar]
  • 81.Gan X, et al. Positive youth development attributes and cyberbullying victimization among Chinese middle school students: A longitudinal moderated mediation model involving internet gaming disorder and depression. PLoS ONE. 2023;18(6):e0287729. doi: 10.1371/journal.pone.0287729. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Kowalski RM, Limber SP, McCord A. A developmental approach to cyberbullying: Prevalence and protective factors. Aggress Violent Beh. 2019;45:20–32. doi: 10.1016/j.avb.2018.02.009. [DOI] [Google Scholar]
  • 83.Zhu C, et al. Cyberbullying among adolescents and children: A comprehensive review of the global situation, risk factors, and preventive measures. Front Public Health. 2021;9:634909. doi: 10.3389/fpubh.2021.634909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Li C, et al. Traditional bullying and cyberbullying in the digital age and its associated mental health problems in children and adolescents: A meta-analysis. Eur Child Adolesc Psychiatry. 2022: p. 1–15. [DOI] [PMC free article] [PubMed]
  • 85.Zhao N, Zhou G. Social media use and mental health during the COVID-19 pandemic: Moderator role of disaster stressor and mediator role of negative affect. Appl Psychol Health Well Being. 2020;12(4):1019–1038. doi: 10.1111/aphw.12226. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Valdez D, et al. Social media insights into US mental health during the COVID-19 pandemic: Longitudinal analysis of Twitter data. J Med Internet Res. 2020;22(12):e21418. doi: 10.2196/21418. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Eirich R, et al. Association of screen time with internalizing and externalizing behavior problems in children 12 years or younger: A systematic review and meta-analysis. JAMA psychiatry. 2022. [DOI] [PMC free article] [PubMed]
  • 88.Fried EI, Papanikolaou F, Epskamp S. Mental health and social contact during the COVID-19 pandemic: An ecological momentary assessment study. Clinical Psychological Science. 2022;10(2):340–354. doi: 10.1177/21677026211017839. [DOI] [Google Scholar]
  • 89.Liguori C, et al. The evolving role of quantitative actigraphy in clinical sleep medicine. Sleep Med Rev. 2023:p. 101762. [DOI] [PubMed]
  • 90.Guram S, Heinz P. Media use in children: American Academy of Pediatrics recommendations 2016. Archives of Disease in Childhood-Education and Practice. 2018;103(2):99–101. doi: 10.1136/archdischild-2017-312969. [DOI] [PubMed] [Google Scholar]
  • 91.World Health Organization. WHO guidelines on physical activity and sedentary behaviour. 2020. [PubMed]
  • 92.Marttila E, Koivula A, Räsänen P. Does excessive social media use decrease subjective well-being? A longitudinal analysis of the relationship between problematic use, loneliness and life satisfaction. Telematics Inform. 2021;59:101556. doi: 10.1016/j.tele.2020.101556. [DOI] [Google Scholar]

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Since this review article solely relies on published articles and does not include individual participant data, therefore no data sharing is available.

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