CHILD AND ADOLESCENT SLEEP TaggedH1Cyberbullying and Sleep Disturbance Among Early Adolescents in the U.S. TaggedEnd TaggedPJason M. Nagata, MD, MSc; Joanne H. Yang, BA; Gurbinder Singh, BS; Orsolya Kiss, PhD; Kyle T. Ganson, PhD, MSW; Alexander Testa, PhD; Dylan B. Jackson, PhD; Fiona C. Baker, PhD TaggedEnd From the TaggedPDivision of Adolescent and Young Adult Medicine, Department of Pediatrics (JM Nagata, JH Yang, and G Singh), University of California, San Francisco; Center for Health Sciences (O Kiss, FC Baker), SRI International, Menlo Park, Calif; Factor-Inwentash Faculty of Social Work (KTGanson), University of Toronto, Toronto, Ontario, Canada; Department of Management, Policy and Community Health (A Testa), University of Texas Health Science Center at Houston; Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health (DB Jackson), Johns Hopkins University, Baltimore, Md; and School of Physiology (FC Baker), University of the Witwatersrand, Parktown, Johannesburg, South Africa TaggedEndThe authors have no conflict to declare. Address correspondence to Jason M Nagata, MD, MSc, Department of Pediatrics, University of California, 550 16th St, 4th Floor, Box 0503, San Francisco, CA 94143. (e-mail: jason.nagata@ucsf.edu). Received for publication September 3, 2022; accepted December 17, 2022. A © P N TAGGEDPABSTRACT OBJECTIVE: To determine the association between cyberbully- ing (victimization and perpetration) and sleep disturbance among a demographically diverse sample of 10−14-year-old early adolescents. METHODS: We analyzed cross-sectional data from the Adoles- cent Brain Cognitive Development (ABCD) Study (Year 2, 2018−2020) of early adolescents (10−14 years) in the US. Modified Poisson regression analyses examined the associa- tion between cyberbullying and self-reported and caregiver- reported sleep disturbance measures. RESULTS: In a sample of 9,443 adolescents (mean age 12.0 years, 47.9% female, 47.8% white), 5.1% reported cyber- bullying victimization, and 0.5% reported cyberbullying per- petration in the past 12 months. Cyberbullying victimization in the past 12 months was associated with adolescent-reported trouble falling/staying asleep (risk ratio [RR] 1.87, 95% confi- dence interval [CI] 1.57, 2.21) and caregiver-reported overall CADEMIC PEDIATRICS 2022 The Author(s). Published by Elsevier Inc. on behalf of Academic ediatric Association. This is an open access article under the CC BY- C-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) 1220 sleep disturbance of the adolescent (RR: 1.16 95% CI 1.00, 1.33), in models adjusting for sociodemographic factors and screen time. Cyberbullying perpetration in the past 12 months was associated with trouble falling/staying asleep (RR 1.95, 95% CI 1.21, 3.15) and caregiver-reported overall sleep distur- bance of the adolescent (RR: 1.49, 95% CI 1.00, 2.22). CONCLUSIONS: Cyberbullying victimization and perpetration are associated with sleep disturbance in early adolescence. Digital media education and counseling for adolescents, parents, teachers, and clinicians could focus on guidance to prevent cyberbullying and support healthy sleep behavior for early adolescents. TaggedEndTAGGEDPKEYWORDS: adolescent; cyberbullying; screen time; sleep; sleep disturbance TaggedEnd ACADEMIC PEDIATRICS 2023;23:1220−1225 TAGGEDPWHAT’S NEW In a demographically diverse, contemporary sample of 10-14-year-old early adolescents in the United States, cyberbullying victimization was associated with trou- ble falling/staying asleep and sleep disturbance. Cyberbullying perpetration was also associated with trouble falling/staying asleep and sleep disturbance. TaggedEndTAGGEDPSCREEN USE AMONG children and adolescents has increased and transformed over the past few years with new social media and digital technology devices and plat- forms (eg, smart phones, gaming consoles, tablets), which has led to more potential exposure to cyberbullying vic- timization and perpetration.1 Cyberbullying is the willful and repeated harm by a perpetrator to a victim through the use of computers, cell phones, or other electronic devi- ces.2 Cyberbullying is recognized as a serious public health issue affecting children and adolescents, and there is a critical need to understand health consequences of cyberbullying.3 More screen usage has been shown to be associated with poorer sleep outcomes,4 yet there is a rela- tive lack of studies examining the potential relationship between cyberbullying and sleep. TaggedEnd TaggedPTraditional bullying has been shown to be associated with poor sleep, and poor sleep may increase the risk for criminal activities and psychiatric disorders.5 One study of a cohort of Portuguese students aged 11 to 16 years reported that tra- ditional bullying is associated with higher insomnia, espe- cially among the victims of bullying.5 Similarly, prior studies have found associations between cyberbullying and sleep problems among adolescents in Finland,6 Canada,7 Volume 23, Number 6 August 2023 http://crossmark.crossref.org/dialog/?doi=10.1016/j.acap.2022.12.007&domain=pdf mailto:jason.nagata@ucsf.edu http://creativecommons.org/licenses/by-nc-nd/4.0/ TAGGEDENDACADEMIC PEDIATRICS ADOLESCENT CYBERBULLYING AND SLEEP DISTURBANCE 1221 and from a single high school in the northeastern US.8 There is however a paucity of data focusing on early adolescence, a critical developmental period when cyberbullying behav- iors may develop. For instance, the age of permissible use for most social media platforms is 13 years, although robust age verification is not required, and social media use gener- ally increases from early to late adolescence.9 Furthermore, there is a need to investigate this relationship at a national level in the United States.TaggedEnd TaggedPThe current study aimed to investigate associations between contemporary cyberbullying behaviors (victimiza- tion and perpetration) and sleep disturbance across a nation- ally demographically diverse sample of early adolescents aged 10−14 years old in the United States. We hypothesized that increased cyberbullying victimization and perpetration would be associated with sleep problems.TaggedEnd TAGGEDH1METHODS TAGGEDEND TaggedPCross-sectional data from 2-year follow-up of the Ado- lescent Brain Cognitive Development (ABCD) study (4.0 release) were analyzed. The ABCD study is a longitudinal study (baseline 2016−2018) of health, brain, and cogni- tive development in 11,875 children from 21 recruitment sites across the United States. Study participants, recruit- ment, protocol, and measures have previously been described in detail.10 Participants were predominantly 11 −12 years old (range 10−14 years) during the 2-year fol- low-up, which was conducted between 2018 and 2020. We excluded participants with missing cyberbullying or sleep data, leaving 9443 adolescents in this analysis (Appendix A). Institutional review board (IRB) approval was received from the University of California, San Diego and the respective IRBs of each study site. Written assent was obtained from participants, and written informed con- sent was obtained from their caregivers. TaggedEnd TAGGEDH2MEASURES TAGGEDEND TAGGEDPPREDICTORS TAGGEDEND TaggedPCyberbullying Questionnaire. Adolescents completed a self-reported questionnaire to capture cyberbullying (vic- timization and perpetration) based on the validated Cyber- bullying Scale.3,11,12 Cyberbullying victimization was assessed with the question, “Have you ever been cyber- bullied, where someone was trying on purpose to harm you or be mean to you online, in texts, or group texts, or on social media (like Instagram or Snapchat)?” Cyberbul- lying perpetration was assessed with the question, “Have you ever cyberbullied someone, where you purposefully tried to harm another person or be mean to them online, in texts or group texts, or on social media (like Instagram or Snapchat)?” For both cyberbullying victimization and perpetration, participants were also asked if this occurred in their lifetime, as well as in the past 12 months. TaggedEnd T AGGEDPOUTCOMES TAGGEDEND TaggedPKiddie Schedule for Affective Disorders and Schizo- phrenia (KSADS) DSM-5 Sleep Outcomes. Adolescents were asked “In the past two weeks, how often did you have trouble falling asleep or staying asleep when you were tired and wanted to sleep?” adapted from the KSADS DSM-5 survey,13 a psychiatric diagnostic assess- ment tool for school-aged children. Responses were given on a 5-point Likert type scale, which were dichotomized into two categories (those having a problem at least sev- eral days in the past 2 weeks versus those having a prob- lem rarely or never). TaggedEnd TaggedPSleep Disturbance Scale for Children (SDSC). A 26- item measure was administered to the caregivers of the adolescent to assess for overall sleep disturbance and sleep problems including disorders of initiating and main- taining sleep, sleep breathing disorders, disorders of arousal/nightmares, sleep-wake transition disorders, disor- ders of excessive somnolence, and sleep hyperidrosis. Responses to each item were given on a 5-point Likert scale ranging from 1 (never) to 5 (daily). A cutoff of 39 was used to indicate that a child had more sleep distur- bance.14 Cronbach’s alpha for the SDSC was 0.83 in this sample indicating good internal consistency. TaggedEnd TAGGEDPCONFOUNDERSTAGGEDEND TaggedPSex (female, male), race and ethnicity (White, Latino/ Hispanic, Black, Asian, Native American, other), and study site (n = 21) were recorded at baseline. Age (years), household income (greater or less than 75,000 US dollars based on the approximate median US household income), and highest parent education (high school or less vs. col- lege or more) were recorded at Year 2 by the caregiver. Total recreational screen time was based on the sum of adolescents’ self-reported hours of eight different screen modalities on a typical weekday and weekend at Year 2.15 Total daily screen use was calculated as the weighted sum ([weekday average x 5] + [weekend average x 2])/7. Potential confounders for the association between cyber- bullying and sleep outcomes were selected based on pre- vious literature.6−8 TaggedEnd TAGGEDH2STATISTICAL ANALYSES TAGGEDEND TaggedPData analysis was performed in 2022 using Stata 15.1 (StataCorp, College Station, TX). Multiple modi- fied Poisson regression analyses using robust standard errors were conducted to calculate risk ratios (RR) estimating associations between cyberbullying victimi- zation and perpetration (exposure variables) and sleep problems and disturbance (outcome variables). For each analysis, we report three models: Model 1: unad- justed; Model 2: adjusted for sociodemographic varia- bles; Model 3: adjusted for sociodemographic variables and screen time. We selected the modified Poisson regression approach using robust standard errors for the main analysis, as it has shown to be a reliable approach to estimate relative risk compared to logistic regression.16 Propensity weights were applied to yield representative estimates based on the Ameri- can Community Survey from the US Census.17 TaggedEnd TaggedEndTable 1. Sociodemographic and Cyberbullying Characteristics of Adolescent Brain Cognitive Development (ABCD) Study Participants (N = 9,443) Sociodemographic Characteristics Mean (SD)/% Age (years), Year 2 12.00 (0.66) Sex, baseline (%) Female 47.9% Male 52.1% Race and ethnicity, baseline (%) White 52.2% Latino/Hispanic 17.2% Black 20.2% Asian 6.0% Native American 3.5% Other 0.9% Household income, Year 2 (%) Less than $75,000 37.7% $75,000 and greater 62.3% Parents’ highest education, Year 2 (%) High school education or less 13.5% College education or more 86.5% Total recreational screen time (hours per day), Year 2* 7.26 (7.59) Cyberbullying, Year 2 Cyberbullying victimization, past 12 months (%) No 94.9% Yes 5.1% Cyberbullying perpetration, past 12 months (%) No 99.5% Yes 0.47% Sleep Outcomes, Year 2 Trouble falling/staying asleep in past two weeks† (%) No 84.8% Yes 15.2% Overall sleep disturbance (%), Year 2‡ (%) No 73.5% Yes 26.6% ABCD propensity weights were applied based on the American Community Survey from the US Census. SD indicates standard deviation. *Weighted sum for weekdays and weekends. †Adolescent-reported sleep problems at least several times in the past 2 weeks. ‡Caregiver-reported score of >39 on the Sleep Disturbance Scale. T AGGEDEND1222 NAGATA ET AL ACADEMIC PEDIATRICS TAGGEDH1RESULTSTAGGEDEND TaggedPIn a population of 9443 early adolescents (mean age 12.0 years, 47.9% female, 47.8% white), 5.1% had experi- enced cyberbullying victimization in the past 12 months (Table 1). Overall, 0.5% of early adolescents had experi- enced cyberbullying perpetration in the past 12 months. Nearly one-sixth (15.2%) of the adolescents admitted to trou- ble falling or staying asleep at least several times in the past 2 TaggedEndTable 2. Total Recreational Screen Time Comparisons by Cyberbullying Total Re Cyberbullying Cyberbullying victimization, past 12 months No Yes Cyberbullying perpetration, past 12 months No Yes ABCD propensity weights were applied based on the American Comm SD indicates standard deviation. *P from independent samples t-test. weeks, and 26.6% had caregiver-reported overall sleep dis- turbance. The correlation between the caregiver- and adoles- cent-reported sleep measures was very weak (r = 0.09, P < .001). Total recreational screen time was higher among cyberbullying victims compared to non-victims and cyber- bullying perpetrators vs. non-perpetrators (Table 2).TaggedEnd TaggedPTable 3 shows the associations between cyberbullying and sleep outcomes. Cyberbullying victimization in the Victimization and Perpetration creational Screen Time (Hours per Day) P* Mean (SD) 6.91 (7.42) <.001 10.21 (8.31) 7.08 (7.49) <.001 12.98 (10.20) unity Survey from the US Census. TaggedEndTable 3. Associations Between Cyberbullying Items and Sleep Disturbance Outcomes in the Adolescent Brain Cognitive Development (ABCD) Study (n = 9443) Trouble Falling or Staying Asleep in Past Two Weeks, Adolescent Report Overall Sleep Disturbance, Caregiver Report RR RR Model 1: Unadjusted Cyberbullying victimization, last 12 months 1.98 (1.69, 2.32) 1.21 (1.06, 1.38) Cyberbullying perpetration, last 12 months 1.94 (1.21, 3.11) 1.31 (1.17, 1.45) Model 2: Adjusted for sociodemographics* Cyberbullying victimization, last 12 months 1.97 (1.67, 2.33) 1.20 (1.04, 1.38) Cyberbullying perpetration, last 12 months 2.21 (1.36, 3.59) 1.55 (1.06, 2.28) Model 3: Adjusted for sociodemographics and screen time† Cyberbullying victimization, last 12 months 1.87 (1.57, 2.21) 1.16 (1.00, 1.33) Total recreational screen time 1.02 (1.01, 1.03) 1.01 (1.01, 1.01) Cyberbullying perpetration, last 12 months 1.95 (1.21, 3.15) 1.49 (1.00, 2.22) Total recreational screen time 1.02 (1.01, 1.03) 1.01 (1.01, 1.01) RR indicates risk ratio. Models represent the abbreviated output from Poisson regression models transformed to risk ratios. Propensity weights from the Adoles- cent Brain Cognitive Development Study were applied based on the American Community Survey from the US Census. *Model 2 adjusted for age, sex, race and ethnicity, household income, parent education, and study site. †Model 3 adjusted for age, sex, race and ethnicity, household income, parent education, study site, and total recreational screen time. TAGGEDENDACADEMIC PEDIATRICS ADOLESCENT CYBERBULLYING AND SLEEP DISTURBANCE 1223 past 12 months was associated with adolescent-reported trouble falling or staying asleep and caregiver-reported sleep disturbance of the adolescent in all models, whether unadjusted (Model 1), adjusted for sociodemographic fac- tors (Model 2), or adjusted for sociodemographic factors and screen time (Model 3). In models adjusted for socio- demographic factors and screen time (Model 3), cyberbul- lying victimization was associated with a 1.87 (95% CI 1.57, 2.21) higher risk for trouble falling/staying asleep and a 1.16 (95% CI 1.00, 1.33) greater risk of overall sleep disturbance. TaggedEnd TaggedPCyberbullying perpetration in the past 12 months was associated with adolescent-reported trouble falling or staying asleep and caregiver-reported sleep disturbance of the adolescent in unadjusted models (Model 1), models adjusted for sociodemographic factors (Model 2), and models adjusted for sociodemographic factors and screen time (Model 3). In models adjusted for sociodemographic factors and screen time, cyberbullying perpetration was associated with trouble falling/staying asleep (RR 1.95, 95% CI 1.21, 3.15) and overall sleep disturbance (RR 1.49, 95% CI 1.00, 2.22). TaggedEnd TAGGEDH1DISCUSSION TAGGEDEND TaggedPIn this demographically diverse, contemporary sample of 10-14-year-old early adolescents in the United States, we found that participants who experienced cyberbullying victimization and perpetration reported at least several days of trouble falling/staying asleep in the past 2 weeks. Based on the caregiver’s report, cyberbullying victimiza- tion and perpetration were also associated with adolescent sleep disturbance, but findings were attenuated when adjusting for screen time.TaggedEnd TaggedPOur results confirm prior literature demonstrating a relationship between cyberbullying and sleep distur- bance,6−8 but build upon those findings by analyzing a demographically diverse national sample from the United States and focusing on early adolescence, a critical devel- opmental period when exposure to cyberbullying may first occur. There could be multiple reasons why cyberbullying victimization is associated with poor sleep, including psy- chological effects of cyberbullying, such as anxiety, depression, stress, and self-esteem deterioration, all of which may be associated with poor sleep.18,19 Further- more, as we also show in this dataset, cyberbullying vic- tims spend more time online or on screens,3 which could further exacerbate sleep disturbance. Although we still found effects when considering total screen use in the models, they were slightly attenuated, implying that spending more time on screens contributed to the associa- tion. More time spent on screens especially in the late evening before bedtime can be engaging and could delay sleep onset.20 Also, blue-light-induced suppression of melatonin, a hormone that regulates circadian rhythms, could cause phase-shifting in the circadian clock, leading to sleep disturbances and increased sleep latency.21 TaggedEnd TaggedPCyberbullying perpetration was similarly associated with greater trouble falling asleep and staying asleep, which could be linked through similar mechanisms including depression, anxiety, stress, and greater screen use.3,18,19 In addition, cyberbullying perpetrators may experience counterfactual emotions such as shame, regret, and guilt, which would lead to sleep disturbance.22 Schmidt and colleagues document that those counterfac- tual emotions are preferentially processed in the bedtime window, which may result in emotional arousal leading to sleep interference. TaggedEnd TaggedPOverall, there were stronger associations between cyberbullying and adolescent-reported trouble falling or staying asleep than with the caregiver-reported sleep dis- turbance scale. Caregivers may not be as attuned to the adolescents’ subjective experiences of difficulty falling or staying asleep. Also, the Sleep Disturbance Scale14 meas- ures several sleep disturbance domains compared to the single-item question asked of the adolescent, which may TAGGEDEND1224 NAGATA ET AL ACADEMIC PEDIATRICS account for the low correlation between the two measures. Cyberbullying may be less related to some of the sleep disorders measured in the Sleep Disturbance Scale, such as disorders of excessive somnolence or sleep breathing disorders, which may additionally account for the weaker associations with this measure. TaggedEnd TaggedPThere are several strengths and limitations worth not- ing. The large, diverse, and population-based sample is a major strength, which gives the study greater external validity. To our knowledge, no other research has evalu- ated cyberbullying and sleep outcomes in a national US sample focused on early adolescents. The limitations include the cross-sectional study design precluding causal relationships, and residual confounders may exist. Due to sleep behaviors being asked about retrospectively, the sample is also vulnerable to recall bias. However, we included sleep measures from both adolescents and their caregivers. Due to the small sample sizes of cyberbullying perpetrators, we were unable to analyze participants who experienced both perpetration and victimization. Given that a higher proportion of racial/ethnic minority, low- income, and low parent education adolescents were excluded from the analysis, selection bias may affect results and generalizability. TaggedEnd TaggedPThis study represents an advancement in our under- standing of the potential health consequences of cyberbul- lying among early adolescents, focusing on sleep disturbance. Our findings could inform adolescents’ adap- tation and implementation of digital technology and cyberbullying guidance. The American Academy of Pedi- atrics advocates for a family media use plan,23 which could incorporate guidance on family discussions on cyberbullying, including supporting adolescents at risk for cyberbullying victimization and the sleep consequen- ces of cyberbullying. Caregivers can also monitor their child’s screen use, regulate hours of use, and implement rules regarding screen use in the bedroom at bedtime as part of the family media use plan. Pediatricians may con- sider assessing for cyberbullying and sleep disturbances and provide support and guidance for early adolescents24 as appropriate in this important period for development and intervention. Future research could investigate mech- anisms linking cyberbullying to sleep disturbances and develop guidance and interventions to reduce cyberbully- ing, especially around bedtime. TaggedEnd TAGGEDH1ACKNOWLEDGMENTS TAGGEDEND TaggedPThe authors thank Anthony Kung and Ananya Rupanagunta for edito- rial assistance. The ABCD Study was supported by the National Insti- tutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of sup- porters is available at https://abcdstudy.org/federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. TaggedEnd TaggedPFunding: J.M.N. was supported by the National Institutes of Health (K08HL159350) and the Doris Duke Charitable Foundation (2022056). TaggedEnd TaggedPRole of Funder Sponsor: The funders had no role in the study analy- sis, decision to publish the study, or the preparation of the manuscript. TaggedEnd TaggedPAuthor Contributions: Jason Nagata − analysis, Joanne Yang − data analysis, Gurbinder Singh − data analysis, Orsyola Kiss, Kyle Ganson, Alexander Testa, Dylan Jackson − writing-critical revisions, Fiona Baker − data acquisition, methods, writing-critical revisions, All authors approve of the final submitted version.TaggedEnd TAGGEDH1SUPPLEMENTARY DATA TAGGEDEND TaggedPSupplementary data related to this article can be found online at https://doi.org/10.1016/j.acap.2022.12.007. 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T aggedEnd https://doi.org/10.1111/J.1365-2869.1996.00251.X https://doi.org/10.1111/J.1365-2869.1996.00251.X https://doi.org/10.1016/j.dcn.2018.03.008 https://doi.org/10.1016/j.dcn.2018.03.008 https://doi.org/10.1093/aje/kwh090 https://doi.org/10.1093/aje/kwh090 https://doi.org/10.1101/2020.02.10.942011 https://doi.org/10.1101/2020.02.10.942011 https://doi.org/10.3109/07420528.2013.877475 https://doi.org/10.3109/07420528.2013.877475 https://doi.org/10.5607/EN.2012.21.4.141 https://doi.org/10.5665/SLEEP.1152 https://doi.org/10.5665/SLEEP.1152 https://doi.org/10.2147/NSS.S253375 https://doi.org/10.2147/NSS.S253375 https://doi.org/10.3389/FPSYG.2018.01288 https://doi.org/10.3389/FPSYG.2018.01288 https://doi.org/10.1542/peds.2016-2593 https://doi.org/10.1542/peds.2016-2593 https://doi.org/10.1016/J.ACAP.2020.10.011 https://doi.org/10.1016/J.ACAP.2020.10.011 Cyberbullying and Sleep Disturbance Among Early Adolescents in the U.S. Methods Measures Predictors Outcomes Confounders Statistical Analyses Results Discussion Acknowledgments Supplementary Data References