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Adolescent use of social media and associations with sleep patterns across 18 European and North American countries

• Meyran Boniel-Nissim, PhD Meyran Boniel-Nissim Affiliations Department of Educational Counselling, The Max Stern Academic College of Emek Yezreel, Emek Yezreel, Israel Search for articles by this author
• Jorma Tynjälä, PhD Jorma Tynjälä Affiliations Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland Search for articles by this author
• Inese Gobiņa, PhD Inese Gobiņa Affiliations Department of Public Health and Epidemiology, Institute of Public Health, Riga Stradins University, Riga, Latvia Search for articles by this author
• Jana Furstova, MSc Jana Furstova Affiliations Olomouc University Social Health Institute, Palacky University Olomouc, Olomouc, Czech Republic Search for articles by this author
• Regina J.J.M. van den Eijnden, PhD Regina J.J.M. van den Eijnden Affiliations Interdisciplinary Social Science, Utrecht University, Utrecht, The Netherlands Search for articles by this author
• Claudia Marino, PhD Claudia Marino Affiliations Department of Developmental and Social Psychology, University of Padova, Padova, Italy Search for articles by this author
• Helena Jeriček Klanšček, PhD Helena Jeriček Klanšček Affiliations National Institute of Public Health, Ljubljana, Slovenia Search for articles by this author
• Solvita Klavina-Makrecka, MSc Solvita Klavina-Makrecka Affiliations Department of Public Health and Epidemiology, Riga Stradins University, Riga, Latvia Search for articles by this author
• Anita Villeruša, MD Anita Villeruša Affiliations Department of Public Health and Epidemiology, Institute of Public Health, Riga Stradins University, Riga, Latvia Search for articles by this author
• Henri Lahti, MSc Henri Lahti Affiliations Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland Search for articles by this author
• Alessio Vieno, PhD Alessio Vieno Affiliations Department of Developmental and Social Psychology, University of Padova, Padova, Italy Search for articles by this author
• Suzy L. Wong, PhD Suzy L. Wong Affiliations Public Health Agency of Canada, Ottawa, Canada Search for articles by this author
• Jari Villberg, MSc Jari Villberg Affiliations Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland Search for articles by this author
• Joanna Inchley, PhD Joanna Inchley Affiliations MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK Search for articles by this author

Design, setting, and participants

Measurements, conclusions.

• Social media
• Adolescents
• International survey

Introduction

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Data and participants

Social media use.

Mascheroni G, Ólafsson K. Net children go mobile: risks and opportunities. 2022. Available at: http://eprints.lse.ac.uk/55798/1/Net_Children_Go_Mobile_Risks_and_Opportunities_Full_Findings_Report.pdf . Accessed Jan 31, 2022.

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Sleep patterns

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Sociodemographic variables

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Statistical analysis

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SMU and sleep duration

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SMU and bedtime

SMU and social jetlag by country

SMU and sleep patterns by age

• Daly-Cano M
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Study strengths and limitations

Declaration of conflict of interest, disclosures, appendix. supplementary materials.

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DOI: https://doi.org/10.1016/j.sleh.2023.01.005

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

• Open access
• Published: 08 February 2024
• Volume 26 , pages 104–119, ( 2024 )

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• Danny J. Yu 1 ,
• Yun Kwok Wing 1 ,
• Tim M. H. Li 1 &
• Ngan Yin Chan 1  

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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.

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.

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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 , 2 , 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 , 29 , 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 .

Structure of the scoping review

Potential pathways of social media use on sleep and mental health

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 , 36 , 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 , 43 , 44 , 45 •, 47 , 48 , 49 , 50 , 51 , 52 , 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 , 58 , 59 , 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 , 47 , 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 ].

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 , 77 , 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 ].

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.

Data Availability

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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Acknowledgements

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

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Danny J. Yu, Yun Kwok Wing, Tim M. H. Li & Ngan Yin Chan

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Yu, D.J., Wing, Y.K., Li, T.M.H. et al. The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review. Curr Psychiatry Rep 26 , 104–119 (2024). https://doi.org/10.1007/s11920-024-01481-9

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Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study

Affiliations.

• 1 School of Psychology, University of Glasgow, Glasgow, UK [email protected].
• 2 School of Psychology, University of Glasgow, Glasgow, UK.
• PMID: 31641035
• PMCID: PMC6830469
• DOI: 10.1136/bmjopen-2019-031161

Objectives: This study examines associations between social media use and multiple sleep parameters in a large representative adolescent sample, controlling for a wide range of covariates.

Design: The authors used cross-sectional data from the Millennium Cohort Study, a large nationally representative UK birth cohort study.

Participants: Data from 11 872 adolescents (aged 13-15 years) were used in analyses.

Methods: Six self-reported sleep parameters captured sleep timing and quality: sleep onset and wake times (on school days and free days), sleep onset latency (time taken to fall asleep) and trouble falling back asleep after nighttime awakening. Binomial logistic regressions investigated associations between daily social media use and each sleep parameter, controlling for a range of relevant covariates.

Results: Average social media use was 1 to <3 hours per day (31.6%, n=3720). 33.7% were classed as low users (<1 hour; n=3986); 13.9% were high users (3 to <5 hours; n=1602) and 20.8% were very high users (5+ hours; n=2203). Girls reported spending more time on social media than boys. Overall, heavier social media use was associated with poorer sleep patterns, controlling for covariates. For example, very high social media users were more likely than comparable average users to report late sleep onset (OR 2.14, 95% CI 1.83 to 2.50) and wake times (OR 1.97, 95% CI 1.32 to 2.93) on school days and trouble falling back asleep after nighttime awakening (OR 1.36, 95% CI 1.10 to 1.66).

Conclusions: This study provides a normative profile of UK adolescent social media use and sleep. Results indicate statistically and practically significant associations between social media use and sleep patterns, particularly late sleep onset. Sleep education and interventions can focus on supporting young people to balance online interactions with an appropriate sleep schedule that allows sufficient sleep on school nights.

Keywords: adolescents; delayed bedtime; screen time; sleep; sleep quality; social media use.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ.

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Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

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THE ASSOCIATION BETWEEN SOCIAL MEDIA USAGE AND SLEEP DISTURBANCE AMONG YOUNG ADULTS-AN ANALYSIS

Insufficient sleep is highly prevalent among young adults, and it is associated with daytime sleepiness and a range of poor health outcomes. Social media use which has increased rapidly in recent years, has been positively associated with disturbed sleep among young adults. Social Media use has also been associated with factors linked to disturbed sleep—such as higher levels of anxiety and depression—among adolescents. Screen media are commonly used by youth and young adults, and their use has been associated with important sleep-related outcomes such as shorter sleep duration, later sleep timing, and poorer sleep quality. This research paper aims to analyze the association between social media and sleep disruption among young adults. It is also opined to examine the relation between the volume and frequency of social media use and different sleep problems.

Related Papers

Scholars Journal of Applied Medical Sciences (SJAMS)

GÜLDEN AYNACI

Social media (SM) is becoming increasingly important in the lives of young people and very little is known about its relationship with sleep disturbance. The sample of the study consisted of 204 students from a university in Turkey. The relationship between SM use and sleep disturbance in students was evaluated. Social Media Use Integration Scale (SMUIS) and Pittsburgh Sleep Quality Index (PSQI) was used. Sleep quality scale values of males were better than females. Evaluation of SMUIS demonstrated that females used SM more than males. With increasing income level sleep quality increased but SM usage level decreased. The sleep quality level of students who used SM for a long period of time was lower than that of short-term users. Sleep quality of the subjects who used SM less frequent during the day was better than frequent users. As the time spent on SM sites increased, the PSQI values were observed to be adversely affected. The use of SM can take the place of sleep directly; it may take a long time to stop it before sleeping. Our study demonstrated that sleep quality tended to reduce in participants who used SM for longer duration and spend more time for SM. Although sleep quality tended to decrease with increasing SM use in our study no significant relationship could be found between the scales. But there was a trend for decreasing sleep quality with increasing SM use. The adverse effects of SM use on sleep is controversial. More controlled use of SM and its positive effects on young people should be supported. SM may affect social relations including friendships and present different learning options. Planning the duration and times of the day SM is used can prevent negative effects of SM use on sleep quality of young people.

International Journal of Multidisciplinary Research and Growth Evaluation

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This research investigates the intricate relationship between social media addiction and sleep quality among university students, with a particular focus on both male and female participants. Employing a correlational survey design, the study, conducted in the Bathinda region, garnered data from 100 students (55 males and 45 females) aged 18 to 29. The data collection process utilized Google Forms, incorporating questionnaires designed to assess social media addiction and sleep quality. The discerned findings underscore a noteworthy negative correlation between social media addiction and sleep quality among the student population. This correlation is consistently observed across male and female participants, with a marginally higher correlation coefficient identified in the female cohort. The study unequivocally rejects the null hypotheses, lending support to the alternate hypotheses, thereby positing that social media addiction adversely impacts sleep quality. The strengths of this research lie in its comprehensive exploration of social media usage, encompassing diverse platforms, rationales for usage, employed devices, and the temporal dynamics of usage preceding bedtime. Despite these merits, the study is not without limitations, notably the confinement of the sample to a single center, limiting the broader applicability of its findings. Additionally, the reliance on self-report measures introduces susceptibility to response bias. Future research endeavours stand to benefit from the integration of more robust data collection tools, such as the Polysomnography technique, and a broader, more diverse sample size, thereby enhancing the study&#39;s generalizability and relevance. In summary, this study furnishes valuable insights into the nuanced interplay between social media addiction and sleep quality among university students. As society grapples with the multifaceted implications of escalating social media usage, understanding its ramifications on sleep emerges as an imperative facet for promoting holistic well-being.

Heather Woods

ObjectivesThis study examines associations between social media use and multiple sleep parameters in a large representative adolescent sample, controlling for a wide range of covariates.DesignThe authors used cross-sectional data from the Millennium Cohort Study, a large nationally representative UK birth cohort study.ParticipantsData from 11 872 adolescents (aged 13–15 years) were used in analyses.MethodsSix self-reported sleep parameters captured sleep timing and quality: sleep onset and wake times (on school days and free days), sleep onset latency (time taken to fall asleep) and trouble falling back asleep after nighttime awakening. Binomial logistic regressions investigated associations between daily social media use and each sleep parameter, controlling for a range of relevant covariates.ResultsAverage social media use was 1 to &lt;3 hours per day (31.6%, n=3720). 33.7% were classed as low users (&lt;1 hour; n=3986); 13.9% were high users (3 to &lt;5 hours; n=1602) and 20.8% w...

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Background : Social media has become a part of human daily life, including students. The high intensity of social media usage can affect various aspects of life, one of which is the quality of sleep. The high intensity of social media usage is thought to be related with poor sleep quality. This study analyzes the relationship between the intensity of social media usage and sleep quality. Objective : To know the relationship between the intensity of social media usage with sleep quality in dental students. Met hod : This research was an observational analytic study with a cross-sectional design. The sample was students of the Dentistry Study Program at Faculty of Medicine, University of Diponegoro (n = 79). The intensity of social media usage was measured using the Social Network Time Use Scale and sleep quality was measured using the Pittsburgh Sleep Quality Index. Measurement of dependent and independent variables was done once at a time. Result : Among respondents, 34,2% were repo...

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Crisis situations affect our behaviors and social surroundings including religious orientations. The area of the study is an area of the world that experienced a major situational crisis influence. To test that whether the ‘affect’ influenced the responses of college and school students of the area of the study as compared with other areas randomly selected (n = 270) students, including male (n = 135) and(n = 135) female belonging to various Schools and College of a city district were tested to find that how do their responses on social media addiction and related sleep quality resemble or are different from the reported responses of the subjects reported by the other researchers belonging to the areas those never experienced the ‘situational affect’ as subjects of the present study experienced? Bergen Social Media Addiction Scale and Sleep Quality Assessment Scale used. The SPSS analysis revealed that social media addiction was significantly and negatively correlated with sleep qua...

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• Volume 9, Issue 9
• Social media use and adolescent sleep patterns: cross-sectional findings from the UK millennium cohort study
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• Holly Scott ,
• Stephany M Biello ,
• Heather Cleland Woods
• School of Psychology , University of Glasgow , Glasgow , UK
• Correspondence to Holly Scott; h.scott.1{at}research.gla.ac.uk

Objectives This study examines associations between social media use and multiple sleep parameters in a large representative adolescent sample, controlling for a wide range of covariates.

Design The authors used cross-sectional data from the Millennium Cohort Study, a large nationally representative UK birth cohort study.

Participants Data from 11 872 adolescents (aged 13–15 years) were used in analyses.

Methods Six self-reported sleep parameters captured sleep timing and quality: sleep onset and wake times (on school days and free days), sleep onset latency (time taken to fall asleep) and trouble falling back asleep after nighttime awakening. Binomial logistic regressions investigated associations between daily social media use and each sleep parameter, controlling for a range of relevant covariates.

Results Average social media use was 1 to <3 hours per day (31.6%, n=3720). 33.7% were classed as low users (<1 hour; n=3986); 13.9% were high users (3 to <5 hours; n=1602) and 20.8% were very high users (5+ hours; n=2203). Girls reported spending more time on social media than boys. Overall, heavier social media use was associated with poorer sleep patterns, controlling for covariates. For example, very high social media users were more likely than comparable average users to report late sleep onset (OR 2.14, 95% CI 1.83 to 2.50) and wake times (OR 1.97, 95% CI 1.32 to 2.93) on school days and trouble falling back asleep after nighttime awakening (OR 1.36, 95% CI 1.10 to 1.66).

Conclusions This study provides a normative profile of UK adolescent social media use and sleep. Results indicate statistically and practically significant associations between social media use and sleep patterns, particularly late sleep onset. Sleep education and interventions can focus on supporting young people to balance online interactions with an appropriate sleep schedule that allows sufficient sleep on school nights.

• social media use
• adolescents
• delayed bedtime
• sleep quality
• screen time

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjopen-2019-031161

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Strengths and limitations of this study

Provides a current normative profile of social media use and sleep in UK adolescents.

Moves beyond generic ‘screen time’ to examine social media specifically.

Uses data from a large representative sample, including comprehensive covariates.

Uses self-reported measures of social media use and sleep patterns.

Measures only duration of social media use, rather than content and context.

Introduction

There is significant current attention towards the possible impact of screen time and social media on our adolescents’ health. However, the lack of empirical evidence to support policy and practice development in this area has been consistently voiced by clinicians and researchers. For example, at the UK House of Commons Science and Technology Committee inquiry into the impact of social media and screen use on young people’s health use in adolescence, the Royal College of Paediatrics and Child Health (RCPCH) urged the UK government as a matter of priority to develop guidance for health practitioners along the same lines as the American Academy of Paediatrics (AAP) but importantly based on UK data. 1 2 They also argue along with other researchers that there is a need to refocus away from correlations between generic terms such as ‘screentime’ and poor well-being, towards meaningfully quantifying how various types of technology use impact on different areas of child and adolescent health and well-being. This study presents UK data that provide a nationally representative profile of current adolescent social media use and takes a data-driven approach to quantify sleep patterns for high and very high users relative to average users.

This study focuses on sleep, which—despite often being overlooked in public health messages and education interventions 3 4 —is increasingly recognised as a key component of wider health and well-being. 5 Adolescent sleep is an important public health issue, as insufficient sleep is highly prevalent in this age group and has implications for mental health, obesity, academic performance and safety. 6 With the majority of adolescents reporting insufficient sleep to function properly or to meet recommended guidelines, 7 8 there is growing concern that social media may be a contributing factor for today’s teenagers. For example, the potential for 24/7 social media interactions may exacerbate the existing conflict of early school start times with naturally delayed adolescent rhythms and other social and educational demands. 6 9 10 As a highly relevant issue for paediatric practice, there is a clear need for UK evidence to inform and update decision-making in medical practice and policy to address this current issue in adolescent sleep.

This study responds to this need, presenting large-scale UK data on adolescent sleep and social media use, while addressing a number of existing gaps in available international evidence. In addition to providing much-needed UK evidence, the current approach also addresses the need for evidence that (1) examines social media specifically, rather than generic screentime; (2) isolates effects for a range of sleep parameters by accounting for an extensive range of covariates and (3) frames these effects within the context of current adolescent social media norms to provide meaningful comparisons. The current approach to ensure that this evidence can meaningfully inform policy and practice by addressing each of these needs is discussed further below.

First, it is important for available evidence to examine social media individually, rather than aggregating these interactions and other media use under the umbrella term ‘screentime’. A recent large-scale US study indicated a significant but modest effect for overall screentime and sleep and called for future research to examine effects for specific technologies. 11 In particular, the interactive nature of social media presents uniquely relevant issues for adolescent sleep compared with other forms of screentime or traditional media. 7 12 Although facilitated by screens, social media interactions are underpinned by the same drivers as any social interaction, with a desire for inclusion and belonging mixing with concerns over violating social expectations or etiquette. 13 These concerns can make it difficult to disengage from social media at bedtime, with some adolescents identifying this as a cause of delayed sleep onset and daytime tiredness. 13 These unique social and emotional aspects of online interactions underline the importance of examining social media use specifically, in relation to adolescent sleep outcomes.

Second, to meaningfully inform an evidence-based response to social media use, research must examine multiple sleep parameters and isolate these effects by controlling for an extensive range of relevant covariates. This is crucial to assess the practical significance of underlying direct effects 11 and to identify which aspects of adolescent sleep merit attention to social media use in practice and policy. Available research on social media use and sleep is often left questioning whether reported effects could be explained by other individual factors: for example, if more anxious, depressed or sedentary adolescents may tend to both use social media more and report poorer sleep. 12 14 Individual studies that have controlled for specific groups of covariates generally suggest that associations do persist. 15 16 However, there remains a need for large-scale evidence that addresses a wide range of covariates simultaneously, to more robustly establish which dimensions of sleep have a direct association with social media use and which reflect another underlying issue (eg, anxious or depressive symptoms).

This type of evidence is required to invest time and resources effectively, by identifying which sleep complaints may benefit from directly addressing social media use. For example, a range of sleep complaints from insufficient sleep to problems initiating or maintaining sleep have been examined in relation to social media use. In terms of sleep duration, time spent using social media may displace sleep directly or displace other daytime activities (such as homework) that are then delayed and disrupt nighttime routines. 9 17 Social media use may also impact on the quality of sleep via increased arousal, not simply through light exposure, 18 but particularly via cognitive and social activity. 12 14 19 Given these different potential mechanisms, it is therefore important to examine social media use in relation to a range of sleep parameters, to identify which of these links have the most practical significance after accounting for relevant factors.

Third, evidence should frame these effects within the context of current norms for adolescent social media use. Research to date has tended to focus on problematic or ‘addicted’ social media users 10 20 or to compare outcomes for the highest users against the lowest users. 15 17 21 In contrast, first establishing what constitutes typical use and then comparing outcomes for relatively higher or lower users against this reference point can support more meaningful conclusions. This data-driven approach avoids imposing arbitrary or quickly outdated cut-offs, taking into account recent rapid increases in social media use. 22 Comparing sleep patterns for higher users against average users can better support practical and realistic discussions on best practice that consider the context of current adolescent social media norms.

This study targets these existing gaps in available international evidence, while providing much-needed large-scale UK evidence. It examines associations between social media use and multiple sleep parameters in a large, nationally representative adolescent sample: the UK Millennium Cohort Study. 23 It first investigates current norms in adolescent social media use to establish the average level of daily use and the prevalence of comparatively high use. It then examines which sleep parameters are associated with social media use, isolating these effects by controlling for extensive covariates and quantifying effects for higher users relative to average users. This aims to provide rigorous and meaningful evidence to inform practice and policy to support healthy adolescent sleep and social media use.

Participants

The UK Millennium Cohort Study is a nationally representative, multidisciplinary survey which aims to explore the influence of family context on child and adolescent development and outcomes. The survey covers a broad range of domains, such as parenting, housing, poverty and health. It consists of a random two-stage sample drawn from all live UK births in the 12-month period starting 1 September 2000 in England and Wales and 1 December 2000 in Scotland and Northern Ireland, identified through the Child Benefit register. 23 The clustered sample is drawn from a disproportionately stratified sample of electoral wards (local areas) to provide adequate representation of areas with higher concentrations of minority ethnic and disadvantaged families. Parents completed the first survey sweep in 2001 when their child was aged 9 months, with 18 818 cohort members. Children also completed surveys from age 7 (sweep 4) onwards. The most recent survey (sweep 6 at around age 14) gathered self-report data from 11 872 cohort members, including questions on their typical social media use and sleep patterns. Parents were required to give written consent to complete the parent survey and for the interviewer to invite the cohort member to participate in the young person survey. Cohort members then also had to give verbal consent to complete the young person survey, which was self-completed on the interviewer’s tablet.

The current analyses make use of available data from the UK Millennium Cohort Study, which measured social media use and sleep using single-item self-report questions. Although not validated questionnaire measures, these survey questions do provide a snapshot of the subjective experience of sleep and social media use in this large representative sample, capturing a range of sleep habits and the typical time spent using social media each day.

Social media use

Participants indicated how much time they spent using social media on a typical weekday, choosing from eight response categories (ranging from 0 hours to 7+ hours) to answer the following question: ‘On a normal week day during term time, how many hours do you spend on social networking or messaging sites or Apps on the internet such as Facebook, Twitter and WhatsApp?’

Sleep parameters

Participants reported typical sleep habits through six single items (each with five or six response categories) that assessed: sleep onset and wake times (on school days and free days, separately), sleep onset latency (time taken to fall asleep) and trouble falling back asleep after nighttime awakening. The online supplementary materials provide a full list of items and response categories.

Supplemental material

In addition, the following relevant covariates (identified based on literature) had available data in the UK Millennium Cohort Study: demographics (ethnic minority status, Organisation for Economic Cooperation and Development equivalised weekly family income); family composition (number of siblings in household, the presence of both parents, age of primary parent/carer responder); psychosocial adjustment (using the parent-report Strengths and Difficulties Questionnaire) 24 ; depressive symptoms (using the Short Mood and Feelings Questionnaire) 25 ; self-esteem (using a shortened and adapted version of the Rosenberg Self-Esteem Scale) 26 and general health (single item), social support (three items) and physical activity (single item).

Data analysis

Since the aim of the study was to compare sleep outcomes for high and low users vs average users, based on the distribution we initially collapsed responses into three categories: under 1 hour for ‘low’ users (33.7%), 1 to <3 hours for ‘average’ users (31.6%) and 3 hours or more for ‘high’ users (34.7%). Given the broad range covered by this ‘high’ user category (including responses of 3 to <5 hours, 5 to <7 hours and 7+ hours), and with sufficient numbers, we separated this into ‘high’ (3 to <5 hours; 13.9%) and ‘very high’ users (5+ hours; 20.8%) to allow more detailed exploration.

We collapsed responses for each sleep measure into binary outcomes. For poor sleep quality, these outcomes were: sleep onset latency over 30 min (commonly used to indicate poor sleep quality) 27 28 and difficulty falling asleep following nighttime awakenings at least ‘a good bit of the time’. For late sleep onset and wake times, we took a data-driven approach to identify meaningful cut-off points, which were defined as later than average (including responses in categories later than the median response category).The table 1 summarises the resulting criteria for each sleep outcome and associated prevalence rates.

• View inline

Social media use and sleep outcomes: criteria and prevalence

Separate binomial logistic regression models predicted ORs of each sleep outcome for low, high and very high social media users, compared with average users. We ran models that controlled only for exact age and sex, followed by models that further controlled for measures of demographics, family characteristics, psychological well-being and health (see the Materials section for full details). All analyses allowed for the complex survey design (with its clustered, stratified sample) and used longitudinal weights to account for non-random longitudinal attrition from the sample, using the ‘survey’ and ‘srvyr’ packages in R. 29–31

Multiple imputation was performed to account for missing data, reducing bias and increasing power. 32 The overall missing data rate was 2.8%, ranging from 0.0% to 6.0% for individual measures, with most measures below 5%. We make the assumption that data are missing at random (ie, that patterns of missingness can be explained by other variables available in the data). 32 All variables, including covariates, were used in the imputation model, which was run using R package ‘mice’. 33 Estimates were combined across 10 imputed data sets (each produced through 10 iterations). Results were similar for analyses on multiply imputed and complete case data, so only multiply imputed analysis is presented here.

The median time spent using social media on a typical day was 1 to <3 hours (32% of adolescents); however, 21% used social media for at least 5 hours. Girls tended to use social media more than boys (see table 1 ).

Median sleep onset times were 22:00–23:00 on school days (with 26% falling asleep later than this) and between 23:00 and midnight on free days (with 34% falling asleep later; see table 1 ). Median wake times were 07:00–08:00 on school days (with only 4% waking later than this) and 10:00–11:00 on free days (with 22% waking later). Boys were more likely to fall asleep late on free days and wake up late on school days. In measures of poor sleep quality, 34% typically took longer than 30 min to fall asleep and 21% reported difficulties falling asleep following nighttime awakenings at least ‘a good bit of the time’. Girls were more likely to have long sleep onset latency and trouble falling back asleep after nighttime awakening.

Separate binomial logistic regression models explored whether odds of each sleep outcome differed for low, high and very high social media users, compared with average users (1 to <3 hours). First, models controlled for exact age and sex (see table 2 ). Very high social media use (5+ hours) was associated with higher odds of all six sleep outcomes. High social media use (3 to <5 hours) was associated with higher odds of all outcomes except for late rise times on free days. Low social media use (<1 hour) was associated with lower odds of late sleep onset on school days and free days and late wake times on free days.

Binomial logistic regressions (adjusting only for age and sex)

Further modelling then controlled for a more comprehensive set of covariates (see table 3 , with note detailing list of covariates). High social media use was no longer significantly associated with long sleep onset latency or frequent nighttime awakenings. Very high social media was no longer significantly associated with long sleep onset latency; however, its association with frequent nighttime awakenings remained significant but smaller. Patterns of significant associations for late sleep onset and wake times remained unchanged, although effect sizes were reduced, particularly for very high social media use.

Binomial logistic regressions (with further adjustments for covariates)

For ease of interpretation, we also transformed the resulting adjusted ORs from these covariate models into adjusted relative risks 34 (see table 4 ). These summarise differences in probabilities, as opposed to odds, and can be interpreted more intuitively. For example, the adjusted relative risk of 1.68 indicates that an adolescent with very high social media use is 68% more likely to fall asleep after 11pm on school nights than a comparable adolescent (controlling for covariates) with average social media use.

RR (from covariate-adjusted models)

This study aimed to address calls from those working in policy and practice to establish a UK data-driven profile of current adolescent daily social media use and to examine links to a key component of wider adolescent health and well-being using multiple sleep parameters while accounting for a wide range of covariates, using data from a large nationally representative sample of UK adolescents. The results highlighted a wide range of reported daily social media use, with tertiles defining low, average and high use on a typical school day as <1 hour, 1 to <3 hours and 3+hours, respectively. This indicates generally heavier social media use compared with young adults 21 and provides a current normative profile for UK adolescents. One in five adolescents were classed as very high users, spending 5+ hours using social media on a typical school day, whereas two-thirds of the sample used social media for less than 3 hours. This provides a data-driven profile of use to support decision-making, rather than relying on assumptions around prevalence of high use. In line with previous studies, girls tended to spend more time on social media than boys 35 36 and report poorer sleep quality. 37 38 This reinforces the importance of controlling for gender when examining these associations and highlights the need for continued work to explore the sleep implications of how adolescent boys and girls spend their time on social media (with previous evidence of gender differences in preferred platforms, motivations and self-presentation). 35 36 39

In terms of sleep timing, social media use remained significantly associated with late sleep onset and wake times after controlling for covariates, with the strongest effect for sleep onset. Very high social media users were roughly 70% more likely than comparable average users to fall asleep later than average, that is, after 23:00 on school days and after midnight on free days. Low social media users were least likely to fall asleep late, indicating that unlike mental well-being, optimal outcomes for sleep are associated with minimal—not moderate—use. 40 These findings are consistent with the idea that social media displaces sleep: either directly or indirectly. 9 17 Direct sleep displacement may be particularly likely on school days, especially for very high users, since limited social media access during school hours means that at least part of this daily time on social media is likely to take place close to bedtime. Bedtime social media use can delay sleep onset, 14 with some adolescents reporting difficulties disengaging from social media to sleep. 13 A similar process could also indirectly delay sleep onset, if other daytime activities (eg, homework) are delayed due to a sense of urgency to check and respond to social media notifications. This link to later sleep onset is a particular concern on school days, as late school day bedtimes longitudinally predict poorer academic and emotional outcomes. 41 While the survey question aimed to measure sleep onset time by asking what time participants ‘go to sleep’, some participants may have reported the time that they get into bed, in which case actual sleep onset would be even further delayed. 42

Social media use was also associated with later wake times on school days (for both high and very high users) and on free days (for very high users). This overall pattern of later sleep timing among heavier social media users could be driven partly by circadian factors, if adolescents with a natural preference for later sleep timing use social media to fill time in the late evening until they feel sleepy. This possibility merits further investigation. Alternatively, this later sleep timing could suggest that heavier social media users may compensate for later sleep onset with later wake times that still allow sufficient sleep. This compensation may be possible on free days, with flexible rise times. However, on school days only 4% of adolescents reported late wake times (after 08:00), as fixed rise times mean that later sleep onset effectively equates to shorter sleep opportunity on school days. 6 41 Consequently, these slightly later rise times are unlikely to fully compensate for delayed sleep onset on school days and suggest sleep restriction in a population where sleep need is high. 6 Across the sample, this observed pattern of later sleep onset and rise times on free days compared with school days is consistent with well-established delays to the circadian rhythm during this developmental period, 43 44 with growing pressure on policymakers to delay school start times to better align with adolescent body clocks. 45

Delayed sleep onset is therefore a key issue to target in relation to adolescents’ social media use. The current cross-sectional study cannot establish causality; however, some adolescents do report delaying bedtimes as a result of social media use. 13 46 Adolescent sleep interventions should therefore consider assessing the impact of social media use on sleep schedules as standard. Further research can explore adolescents’ motivations for prioritising social media over other needs, including sleep, 13 and identify factors that lead some individuals to struggle with this more than others. This can inform efforts to effectively support young people to balance online interactions - and the benefits they can offer 40 47 48 - with an appropriate and consistent sleep schedule across the week, particularly to allow sufficient sleep on school nights. By helping to combat insufficient sleep, this can have a positive impact on adolescent physical and mental health, daytime functioning and academic performance, addressing a significant health and educational burden. 6

In terms of sleep quality, very high social media users were more likely to experience nighttime awakenings than comparable average users, whereas the effect for long sleep onset latency was fully explained by covariates. Previous studies have found a significant association between social media use and measures of sleep disturbance (including long sleep onset latency and difficulty falling asleep) when controlling for: age and sex 19 ; sociodemographic measures 21 and sleep hygiene behaviours. 15 The current more extensive set of covariates also included measures of psychological well-being (depression and psychosocial adjustment), which were strong predictors of long sleep onset latency and have been shown to be linked to generic screentime in previous work. 40 Therefore, considering previous and current findings together, this suggests that although adolescents who spend more time on social media do tend to take longer to fall asleep, both these behaviours could reflect underlying aspects of well-being, with depression and anxiety linked to both poor sleep quality and social media use. 16 This is consistent with evidence that sleep onset latency and presleep cognitive arousal is predicted by underlying concerns about potentially missing out, rather than social media behaviour itself. 14 Since the purpose of this study was to isolate and quantify associations between social media use and sleep, the current approach of including well-being measures as covariates provided this insight into which sleep associations do and do not persist independent of well-being and other covariates. However, future studies can specifically examine in more detail which aspects of mental health and well-being may mediate or moderate these associations. Given the increasing recognition of sleep and mental health as two inextricably linked aspects of health, 49 the current findings lay the foundation for more complex model testing to examine the likely bidirectional and interactive effects between social media use, sleep, mental health and other associated measures, such as school performance. Applying this approach to longitudinal and experimental data will be particularly valuable to elucidate these complex mechanisms and to build a more holistic and balanced understanding of social media’s links to both positive and negative aspects of health and well-being.

In contrast, the association between social media use and nighttime awakenings was only partly explained by covariates, with very high social media users still 28% more likely to have frequent difficulties with nighttime awakenings than comparable average users. Social media notification alerts may disrupt sleep during the night, particularly if users then respond by re-engaging with social media. Adolescents who use social media more also tend to have a stronger emotional connection to platforms and experience more fear of missing out. 14 50 Therefore, it is possible that very high users are more likely to remain vigilant for incoming social media alerts or to respond to these during the night, increasing arousal and contributing to difficulties falling asleep again. Further research can focus on this type of specific social media behaviours during the night, to examine whether they explain the link between higher overall use and nighttime awakenings. If incoming alerts are indeed mostly responsible, interventions can promote simple practical steps such as setting ‘do not disturb’ periods on social media apps.

Limitations

These findings should be considered within the limitations of the current study. Given the broad scope of the UK Millennium Cohort Study, sleep and social media use were measured using individual questions rather than validated multi-item questionnaires. This limits the current analyses to a single measure of social media use—defined as the amount of time spent using social media on a typical day—which does not capture the different experiences of individual users, 51 for example, in terms of content, context, timing and emotional engagement. Future research should carefully consider a range of measures to provide a more holistic view of adolescents’ experiences of using social media, particularly since evidence highlights the importance of emotional and cognitive aspects of social media use for sleep. 14 16 To support future research, there is a clear need to establish validated measurement tools that move beyond hours per day to capture these more nuanced aspects of social media engagement. This is a key area for future development, as available tools limit the scope of potential research questions, conclusions and recommendations. Improved measurement tools moving forward can enhance understanding of the mechanisms linking social media use and sleep, as well as providing a more balanced view of both positive and negative impacts of social media experiences.

These analyses make use of six available reported sleep parameters from this representative cohort, which do allow a rounded picture of timing and quality, but future research would benefit from including validated measures of sleep duration and quality, as well as circadian preference. The current self-reported sleep measures offer valuable insight into adolescents’ subjective experience of sleep, which is one important component of sleep, but this can diverge from verifiable objective measures of sleep parameters provided by other methods, such as the gold-standard polysomnography. 52 53 In particular, sleep state misperception in both good and poor sleepers can result in poor self-report estimates of sleep onset latency, 53 although these differences in sleep onset latency estimates from self-report and polysomnography tend to be small. 54 The current analyses therefore contribute one part of the picture, with a continued need to triangulate insight from multiple methodologies (both subjective and objective) to build a more nuanced, holistic understanding of adolescent social media use and sleep. 52 55

Furthermore, this study presents cross-sectional data, which precludes conclusions of causality. Cross-sectional analyses are prevalent in this research area, with calls for longitudinal and experimental work to enrich current understanding. 56 57 Recent studies on general technology use have supported bidirectional links between higher technology use and shorter sleep in adolescents, 58 with evidence that restricting phone access can advance bedtimes and extend sleep opportunity. 59 Restricting adolescent social media use is likely to be especially challenging, as this is a developmental period of increasing autonomy during which peer interactions, belonging and acceptance are highly valued. 2 Therefore, an important avenue for future research is establishing how best to support young people to balance these rewarding online social interactions with an appropriate and consistent sleep schedule, to optimise associated health and school outcomes.

Finally, we note that research in this area is constantly contending with rapidly evolving social media platforms and associated norms and expectations for online interactions. This can be particularly challenging for this type of large national cohort data, which in this case provides a snapshot of UK adolescents’ social media use in 2015.

Conclusions

This study provides robust evidence on associations between social media use and sleep outcomes, controlling for an extensive range of covariates, in a large nationally representative sample of UK adolescents. It provides a normative profile of adolescent social media use and sleep in the UK, which can be used as a baseline to support evidence-based decision-making policy and practice rather than relying on assumptions around prevalence of high social media use. The findings indicate statistically and practically significant associations between social media use and sleep patterns, particularly late sleep onset. Future research should explore the context and experience of time using social media to inform more meaningful discussions around best practice and updating sleep education and interventions to meet the needs of today’s society. Interventions should focus on addressing delayed sleep onset, by supporting young people to balance online social interactions with an appropriate and consistent sleep schedule that allows sufficient sleep on school nights, with benefits for health and educational outcomes.

Acknowledgments

The authors would like to thank A Przybylski and J Lewsey for helpful discussions when planning methodology.

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Supplementary materials

Press Release

Presented at This work presents secondary analysis of data deposited in the UK Data Service. It is therefore not possible for the authors to share the current findings directly with original study participants.

Contributors HS designed the study and carried out data analysis. HS, HCW and SMB interpreted the findings. HS drafted the manuscript in consultation with HCW. HCW and SMB revised the manuscript for important intellectual content. All authors approve the submitted manuscript and agree to be accountable for all aspects of the work.

Funding This study was funded by an Economic and Social Research Council +3 PhD studentship for HS (grant number ES/J500136/1). The Millennium Cohort Study was funded by the Economic and Social Research Council.

Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Patient consent for publication Not required.

Ethics approval The Millennium Cohort Study Sweep 6 was approved by the London Multicentre Research Ethics Committee (13/LO/1786).

Provenance and peer review Not commissioned; externally peer reviewed.

Data availability statement Data are available in a public, open access repository.

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• v.16(2); 2021 Apr

Social Media Use and Sleep Disturbance among Adolescents: A Cross-Sectional Study

Azar pirdehghan.

1 School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.

Edris Khezmeh

2 Department of Community and Preventive Medicine, School of Public Health and Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran.

Soheila Panahi

3 Department of Psychiatry, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.

Objective: Recently, social media use has become prevalent in the daily lives of many adolescents. This study was performed to address adolescents’ sleep quality and depression in relation to social media use.

Method : This cross-sectional cluster-sampling study was directed on 576 high school students in 2019 in Hamadan, Iran. Three standard self-reported questionnaires were used for recording sleep patterns (Pittsburgh Sleep Questionnaire Index (PSQI)), depression (Beck), and Electronic Media Use. Data was analyzed using SPSS. P-values less than 0.05 were considered as being significant.

Results: Among the adolescents 290 (50.3%) were female and the age median was 17. The average time of all Smart devices used was 7.5±4.4 hours per day. Among all students 62.3 % (359) said that they had their cell phone on in their bedroom when they sleep. In boys, the amount of social media use was significantly more than girls and poor sleep quality had a statically significant relationship with social media use (P-Value = 0.02). Additionally, there was a reverse correlation between the average use of electronic devices and sleep duration (Spearman’s rho = 0.17; P-Value = 0.03), and a direct correlation between the average use in social media and depression (Spearman’s rho = 0.171; P-Value < 0.001).

Conclusion: In this important age group a high level of electronic devices use and its relationship with sleep quality, daily dysfunction, sleep duration and depression is worthy of issue awareness among health managers, parents and teachers for providing interventional programs, based on standard updated guidelines, in order to reduce the problem and familiarize adolescents and their parents, at home or school, with restrictions on using devices to view and participate in social media.

Electronic Media Device Use (EMDU) such as computers, smartphones, television, and video games has recently become prevalent in the lives of adolescents all over the world ( 1 ). Extensive use of handheld Smart devices is increasing but based on several studies the use has been different; from 50% to more than 90 % ( 2 - 5 ).

This irregular electronic device use has been associated with several harmful outcomes including a higher body mass index (BMI) ( 6 ), neck or shoulder pain ( 7 ), symptoms of unclear vision and eye strain ( 6 ), reduced daytime functioning ( 8 ), and parent-child relationship problems ( 9 ).

Sleep disturbances are considered among the main ten caution signs of adolescent suicide ( 10 ) and improving troubled sleep may be a protective factor in the prevention of mental problems specially, depression ( 11 , 12 ).

Previous research has demonstrated that sleep disturbance might be an important risk factor in several mental health problems in adolescents ( 13 , 14 15 ). Limitation in device use, up to two hours a day, was recommended by the American Academy of Pediatrics ( 16 ).

Several studies have demonstrated that use of the abovementioned electronic devices causes sleep damage by keeping late bedtimes, limiting the sleep hours, and sleep quality disturbances ( 8 , 17 - 20 ).

Jiewen Yang et al., in their study for finding the association between problematic Internet use and sleep in adolescents, found that problematic Internet users were at a higher risk (2.41) of sleep disturbance, and they recommended that improving the sleep patterns of adolescents and Internet use was needed ( 21 ).

In another study, Hiu Yan Wong and colleagues suggested that both severities of Internet gaming disorder and social media addiction are associated with more psychological distress and poorer sleep quality ( 22 ).

In Iran, Poorolajal and colleagues assessed 4,261 university students and found that one-third of medical students suffered from problematic Internet use which was associated with poor general health (OR=12.1) and risk of suicidal behavior (OR = 2.7) ( 23 ).In a study by Azizi performed on 360 university students, it was declared that social networking addiction of the students was at a moderate level and there was a negative correlation between the overall use of social networks and academic performance of the students (r = − 0. 210, p < 0.01) ( 24 ).

Considering the high prevalence of social media use and the importance of sleep patterns and the effect on the physical and psychological health in adolescents, including insufficient studies on this important age group in Iran, our study was designed to address the adolescent-sleep-quality-relationship with social media use in order to plan interventional programs for reducing the problem, and to familiarize adolescents and their parents, at home or in school, with restrictions on using devices to enter social media environments based on standard updated guidelines.

Materials and Methods

Procedure and Sample

Details about the method was already mentioned in previous article ( 25 ) as they were in parallel with each other in a common survey.

The sample size was calculated at 720 individuals out of 6,830 high school students. Considering that p = 0.25 for prevalence of sleep disturbance ( 26 ) (d = 0.1, z = 1.96, cluster effect = 1.5, and attrition rate = 20%) the following sample size formula was used:

We applied a self-reported questionnaire consisting of 4 parts. The first part of the questionnaire was included in the demographic questions. In the second part we applied the Farsi version of the Pittsburgh Sleep Questionnaire Index (PSQI). The standard self-reported valid and reliable (α: 0.83) questionnaire for recording sleep quality ( 27 ) and the Farsi language version is available. The questionnaire, itself, has optimal psychometric properties for assessment of subjective sleep quality in clinical and research settings. Cronbach's alpha coefficient was shown at 0.77 in previous research ( 27 ) and questions could be answered by a 3-level scale (never, sometimes; 2 or 3 times in a week, often; more than 3 times in a week). A standard instruction was used for determining the score of sleep quality in different subscales.

The instrument for recording the amount of an electronic device used in bed before sleep on a regular school night was applied similar to what Lemola and Hysing had used in previous research ( 2 , 8 ). Cronbach’s alpha was 0.70. Media use in bed was checked using four items: how often participants play video games, watch TV, talk on the cell phone, and spend time online or surfing the Internet before going to sleep. Answers were classified ranging from 1 (never), 5 (most of the time) to always (5–7 days per week). A higher sum score represents more electronic media consumption before going to sleep.

For the Electronic Media Use during a day, students were asked to indicate how many minutes and hours they [ 1 ] watch TV, [ 2 ] play video games, and [ 3 ] spend time online during weekdays ( 2 ).

Beck Depression’s Inventory questionnaire was used in for measuring depression as a valid and reliable questionnaire. The internal consistency was demonstrated at 0.9 and the retest reliability ranged from 0.73 to 0.96 ( 28 ).The educational status was defined based on semester average score: ≥ 17 was considered fine, 14-16 moderate, and < 14 was evaluated as poor.

Ethical Consideration

All necessary ethical consideration was mentioned in previous article ( 25 ).

The study commenced after approval from the institute’s ethical committee (ID: IR.UMSHA.REC.1397.978).

Statistical Analysis

The data were entered into SPSS. Analysis strategy was explained formerly ( 25 ).

The mean age of the 576 assessed students was 16.53 ± 0.69 years (Min- Max: 15-19); 286 (49.7%) were boys where 132 (22.9%) were studying mathematics, 319 (55.4%) were in experimental fields, and the rest were in humanities.

Bedtime was 1a.m. and after in 34.8 % (201) of the students, sleep latency was more than 15 minutes in 36.6% (218), and sleep duration was 6 hours or less in 26.3% (152).

The average of all devices used was 7.5±4.4 hours per day. In boys, the time spent using social media was significantly more than girls, and computer games in girls was statistically more than boys ( Table 1 ).

Average Use of Electronic Devices in High School Students

Results obtained about ‘before bedtime electronic devices use’ showed that watching TV was more frequent in girls (P-Value < 0.001) and overindulgence of watching TV was significantly related to severe, and very severe, daily dysfunction (P-Value = 0.002). However, watching TV was less in depressed adolescents (P-Value = 0.026). Playing computer games was more frequent in girls and adolescents who have been studying in the mathematics field (P-Value < 0.001), which could increase moderate and severe daily dysfunction (P-Value = 0.025). Sending SMS text messages or talking on the cell phone, just before bedtime, were more prevalent in girls and students studying in the humanities. Finally, sleep disorder and moderate to severe daily dysfunction were significantly related to more frequent use of the Internet or social media. Details about ‘before bedtime electronic devices use’ and related variables have been shown in ( Table 2 ).

Before Bedtime Watching TV or Playing Computer Games and Related Variables in High School Students

To avoid crowding the table, frequency of variables that didn’t have a significant relationship with electronic device use were not shown.

Among all students, only 34 persons (5.9%) reported that they did not have a cell phone, and 62.3% (359) said that they did have a cell phone and that it was on in their room while they were sleeping ( Figure 1 ).

Mobile Status while Sleeping in High School Students

In the analysis for determining the relationship between the amount of social media use and sleep quality, and total sleep disorder, results showed that in adolescents with poor sleep quality their average social media use was 36 minutes more than the others. Poor sleep quality had a statistically significant relationship with a high amount of social media use (P-Value = 0.02) ( Table 3 ). In addition, results showed that the higher the average hours use of social media, the greater overall score of sleep disorder was seen (P-Value < 0.001) ( Table 4 ). The reverse correlation between average use of electronic devices and sleep duration (Spearman’s rho = -0.17; P-Value = 0.03) ( Figure 2 ) and a direct correlation between average use of social media and depression (Spearman’s rho = 0.171; P-Value < 0.001) ( Figure 3 ) was reported.

Relationship between Amount of Social Media Use and Sleep Quality and Total Sleep Disorder in High School Students

Before Bedtime Mobile Use and Related Variables in High School Students

Reverse Correlation between Average Use of Electronic Devices (TV, Mobile and Computer Games) and Sleep Duration in High School Students

Direct Correlation between Average Use of Social Media Use and Depression in High School Students

This study on 576 high school students in 10 th to 12 th grades, assessed the relationship between electronic devices use and sleep pattern in adolescents.

The average use of devices (TV, cell phone, and computer games) was more than 7 hours a day and especially, for social media use, was more than 2 hours per day; longer than recommended by the American Academy of Pediatrics, and which has been accepted internationally ( 16 ).

Nearly a third of the adolescents’ sleep duration was 6 hours or less and it was correlated with the amount of time Smart devices were used. There is strong evidence linking poor sleep, including shorter sleep duration ( 29 ), daily dysfunction ( 30 ), and longer sleep latency ( 31 ), with general Smart devices and Internet use. Therefore, in this important age group high levels of electronic devices use is worthy of assessment among health managers, parents and teachers.

Several studies have investigated the effect of Internet use and computer games in adolescents. For instance, in a research, 10.8% of the adolescents were moderate or severely addicted to the Internet and the risk of Internet overuse and addiction was higher in boys including adolescents who experienced recent stressful events ( 32 ). Mikiko Tokiya et al. indicated that sleep disturbance in more than half of the students in their research and a significant relationship between insufficient sleep and Internet addiction was shown in Japanese adolescents. That study found a higher percentage of sleep disturbances in private high school students and adolescents with depressed moods among other related variables ( 33 ).

Durkee et al. in another study, for finding the relationship between Pathological Internet Use (PIU) and Risk-Behaviors among European adolescents, showed that adolescents with poor sleep pattern and risky behaviors showed the strongest associations with pathological Internet use, tobacco use, poor nutrition and physical inactivity. In the study, poor sleeping habits were considered as the strongest factors related to PIU. Moreover, the prevalence of maladaptive Internet users (MIU) was significantly higher among females, whereas PIU was significantly higher in males ( 34 ).

In a study performed by Gholamian et al. it was reported that nearly one third of high school students are mild to severely addicted to the Internet in Iran and anxiety, depression, and stress among the Internet addicted was significantly higher than among the normal Internet users ( 15 ).

The results of these studies in different cultures can confirm the effect of EMDU on sleep patterns similar to the present research. Whereas, there were some differences in instruments for measuring the dependency of students to EMDU, different grades of the students, and various assessed variables.

Among all students involved in the present study more than 60% reported that they have a cell phone and it is on, in their room, while they are sleeping. The other studies have shown different results based on location and grade of students. In Lenhart et al. findings show 86% of the adolescents fell asleep with their phones, under their pillows, or in their hands ( 35 ). In a survey conducted by Haug et al. over 85% of the adolescents used at least one Smart device every day, and more than half of them used them more than 2 h per day ( 36 ), and its use for leisure was higher in use than studying. Although the reasons for use (studying or leisure) was not specified in our study, using for leisure can be considered a negative side effect of device use which should attract the specific attention for behavior control by adolescents’ health officials.

In a recent study, there was particular attention paid to the consequences of excessive social media use on depression besides sleep disturbance ( 25 ). The significant relationship between excessive social media use and poor sleep quality, daily dysfunction, sleep disorder and depression was shown in the findings. One study indicated that social media use at night and emotional interests in social media are two important elements in relation to adolescent sleep and wellbeing ( 37 ). Hallmarks in social media use such as sleep interruptions from incoming text messages ( 30 ), the pressure of being available, and feeling stressed and guilty in missing a new message, content or call ( 38 ), has been mentioned as reasons for depression referred to in previous studies. Age vulnerability of adolescents for anxiety and depression ( 39 , 40 ), besides the interfering digital screen exposure at bedtime with melatonin production ( 41 ), and the stress of availability ( 42 ) in social media use can be considered as the most important reasons for increasing risk of depression by excessive use of social media.

The average use of electronic devices (TV, cell phone and computer games) was more than 7, and especially for social media, was more than 2 hours per day. This high level of social media use was positively associated with sleep quality, daily dysfunction, sleep duration and depression. Additionally, more than 60% of students said that they have a cell phone and it is on, in their room, while they are sleeping. In boys, the amount of using social media was significantly more than girls and watching TV was more frequent in girls and was significantly related with severe, and very severe, daily dysfunction. Computer games were more frequent in girls, and it could increase moderate and severe daily dysfunction. Sending SMS text messages or talking on the cell phone just before bedtime was more prevalent in girls.

There were, however, some limitations in the study. The importance of sleep health and electronic devices, especially social media use, in adolescents as an important age group and the high number of the sample size were advantages of our study. The limitations will need to be considered in future research. First, this is a cross-sectional study and lower sleep quality is not necessarily the consequence of using social media, so we cannot demonstrate causality in our interpretation. Second, our sample was limited to adolescents in specific grades (10 th to 12 th grade high school students), so results may not be generalized to all adolescents. Third, for determining the consequence of excessive electronic devices and social media use we cannot rely on such a weak correlation despite statistical significance. As a result, more empowered studies are recommended in the future. The next recall bias might be considered in the study because the students, who had sleep problems, probably remembered their excessive use of electronic devices and social media use, more than others. Finally, identifying reasons for using social media such as leisure time or learning are needed in future studies, and interventional programs for reducing the social media use and modifying sleep problems must be considered in adolescents health care packages.

The present study investigated associations between electronic devices and social media use and two important health outcomes (i.e. sleep quality and depression) among Iranian students in Hamedan, Iran. The results showed a high level of social media use which was positively associated with sleep quality, daily dysfunction, sleep duration and depression. Additionally, more than half of the students said that they have cell phones and it is on in their room while they are sleeping. Hence, in this important age group it is worthy of concern for health officials, parents and teachers to provide interventional programs in order to reduce the problem and familiarize adolescents and their parents, at home or school, with restrictions when using devices to view social media sites based on present standard updated guidelines.

Acknowledgment

We gratefully acknowledge all managers and staff in the Central Department of Education, and all managers of the participating high schools in Hamadan Province that helped in the collection of all the data.

Conflict of Interest

Screen Time and Sleep—It’s Different for Adults

By Mary Grace Descourouez, MS, NBC-HWC

Many of us have heard that looking at our phones or iPads at night can keep us awake due to light exposure, however, research shows this may be true for children, but there is not sufficient evidence to support this claim for adults .

“Young children have a greater sensitivity to light because more light gets to the retina of a child than an adult,” says Jamie Zeitzer, PhD, Co-Director of the Stanford Center for Sleep and Circadian Sciences. “Since adults have more opacities in their eyes and smaller pupils than children, less light passes through adult eyes, so there’s less of an effect on melatonin production.”

Melatonin is a hormone that makes us feel sleepy and is released when the eyes perceive darkness. Conversely, when we see natural light in the morning, we feel more awake because light hitting our eyes stops the production of melatonin.

Given this logic, it would seem reasonable that looking at our screens (smart phones, computers, iPads, etc.) at night could delay melatonin production and inhibit our ability to fall asleep, but Dr. Zeitzer says this is not the case.

While darkness enables melatonin production, suppressing melatonin production works by the brain comparing the amount of light we receive during at night with how much we received during the day. It’s the shift from light to dark that cues the release of melatonin, which is why we start to feel sleepy after the sun goes down.

Since natural sunlight emits 10,000-100,000 lux of light and phone screens emit 25-50 lux under usual conditions at night, Dr. Zeitzer says the light from our screens doesn’t have much of an impact on the melatonin cueing process.

“There just isn’t that much light coming from your phone,” says Dr. Zeitzer. “As long as you go outside during the day and get exposed to the intensity of natural light then the amount of light from a screen in the evening most likely won’t halt the production of melatonin.”

If it’s Not Light, What Keeps us Up at Night?

Rather than light exposure, Dr. Zeitzer believes that what is keeping us awake is what we are watching on our screens. Millions of Americans stay awake at night scrolling on social media looking at page after page of emotionally activating content and writing posts that lead to likes, comments, and followers. Others stay up to play games on their phones or computers, all of which stimulate the dopamine reward system in the brain, which is the basis of addictive behaviors .

“In the past, when a television show ended, you turned off the TV and went to sleep because there was nothing else to do,” says Dr. Zeitzer. “But now you could watch Netflix, look at apps or play computer games all night because this entertainment has been commodified to engaged with it for as long as possible; it’s optimized to never stop playing and this is causing sleep deprivation.”

When watching screens before bed, Dr. Zeitzer recommends that we not only avoid content that could be distressing, but also content that could stir excitement within us.

“In order to fall asleep, we need to reduce stimuli exposure and calm our mind and body,” says Dr. Zeitzer. “Even if you’re watching something positive, if it stirs excitement, the brain will release dopamine, and over time we can develop a dopamine addiction, making staying awake playing games or on social media much more fun that going to sleep.”

Lastly, Dr. Zeitzer says that he can’t make a general statement that nighttime screen use negatively affects everyone’s sleep. For some, their addiction to games or apps could make falling asleep a challenge, while others may watch soothing nature videos on their phones to help them relax and fall asleep. Therefore, Dr. Zeitzer suggests that you take note of how screens are impacting your sleep health by asking yourself these questions:

• Is the content of your screen time making you feel distressed or excited? If yes, then you should not look at screens for about an hour before bedtime to calm the mind and body and prepare for sleep.
• Also, do you engage with screens throughout the night when you could be sleeping? If so, you may have a dopamine addiction that is making screen time activities more enjoyable than sleep.

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