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Social Epidemiology of Early Adolescent Cyberbullying in the United States

Open AccessPublished:July 12, 2022DOI:https://doi.org/10.1016/j.acap.2022.07.003

      What's New

      • In a demographically diverse, contemporary sample of 11-12-year-old early adolescents in the U.S., 9.6% reported a lifetime prevalence of cyberbullying victimization and 1.1% reported lifetime cyberbullying perpetration. Girls, sexual minorities, and adolescents from low-income households reported higher cyberbullying victimization.

      Abstract

      Objective

      To determine the prevalence and sociodemographic correlates of cyberbullying victimization and perpetration among a racially/ethnically and socioeconomically diverse population-based sample of 11-12-year-old early adolescents.

      Methods

      We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (Year 2; N=9,429). Multiple logistic regression analyses were used to estimate associations between sociodemographic factors (sex, race/ethnicity, sexual orientation, country of birth, household income, parental education) and adolescent-reported cyberbullying victimization and perpetration.

      Results

      In the overall sample, lifetime prevalence of cyberbullying victimization was 9.6%, with 65.8% occurring in the past 12 months, while lifetime prevalence of cyberbullying perpetration was 1.1%, with 59.8% occurring the past 12 months. Boys reported higher odds of cyberbullying perpetration (AOR 1.71, 95% CI 1.01-2.92) but lower odds of cyberbullying victimization (AOR 0.80, 95% CI 0.68-0.94) than girls. Sexual minorities reported 2.83 higher odds of cyberbullying victimization (95% CI 1.69-4.75) than non-sexual minorities. Lower household income was associated with 1.64 (95% CI 1.34-2.00) higher odds of cyberbullying victimization than higher household income, however household income was not associated with cyberbullying perpetration. Total screen time, particularly on the internet and social media, was associated with both cyberbullying victimization and perpetration.

      Conclusions

      Nearly one in ten early adolescents reported cyberbullying victimization. Pediatricians, parents, teachers, and online platforms can provide education to support victims and prevent perpetration for early adolescents at the highest risk of cyberbullying.

      Keywords

      Introduction

      Screen use among children and adolescents has dramatically increased and transformed over the past few years with new social media and other platforms (e.g., smart phones, gaming consoles, tablets),
      • Nagata JM
      • Cortez CA
      • Cattle CJ
      • et al.
      Screen time use among us adolescents during the COVID-19 pandemic: Findings from the Adolescent Brain Cognitive Development (ABCD) study.
      ,
      • Twenge JM
      • Martin GN
      • Spitzberg BH.
      Trends in U.S. Adolescents’ Media Use, 1976-2016: The Rise of Digital Media, the Decline of TV, and the (Near) Demise of Print.
      leading to more potential exposure to cyberbullying victimization and perpetration. Generally, cyberbullying is the willful and repeated harm by a perpetrator to a victim through the use of computers, cell phones, or other electronic devices.
      • Englander E
      • Donnerstein E
      • Kowalski R
      • Lin CA
      • Defining Cyberbullying Parti K.
      Cyberbullying perpetration is identified as an intention to inflict harm in a repetitive and focused manner upon a less powerful individual.
      • Englander E
      • Donnerstein E
      • Kowalski R
      • Lin CA
      • Defining Cyberbullying Parti K.
      Compared to in-person bullying, cyberbullying can allow users to maintain anonymity, occur outside of educational vicinities, and be more challenging to escape.
      • Englander E
      • Donnerstein E
      • Kowalski R
      • Lin CA
      • Defining Cyberbullying Parti K.
      Cyberbullying is recognized as a serious public health issue affecting children and adolescents, but its prevalence and sociodemographic associations may be changing given recent increases in adolescent screen use and exposures to new forms of digital technologies.
      • Aboujaoude E
      • Savage MW
      • Starcevic V
      • Salame WO.
      Cyberbullying: Review of an Old Problem Gone Viral.
      Recognizing the contemporary prevalence of cyberbullying behaviors and associated sociodemographic factors is crucial for implementing preventive measures against downstream consequences such as anxiety and depression, loneliness, and suicidal ideation.
      • Nixon CL.
      Current perspectives: the impact of cyberbullying on adolescent health.
      Children from lower socioeconomic backgrounds or racial/ethnic minority groups have demonstrated higher screen time exposure that might facilitate cyberbullying.
      • Soares S
      • Brochado S
      • Barros H
      • Fraga S.
      Does Cyberbullying Prevalence Among Adolescents Relate With Country Socioeconomic and Development Indicators? An Ecological Study of 31 Countries.
      Among a sample of middle school students in the Los Angeles Unified School District surveyed in 2012, 6.6% reported cyberbullying victimization and 5% reported cyberbullying perpetration.
      • Rice E
      • Petering R
      • Rhoades H
      • et al.
      Cyberbullying perpetration and victimization among middle-school students.
      Cyberbullying perpetrators and victims were more likely to report at least 3 hours of internet use per day.
      • Rice E
      • Petering R
      • Rhoades H
      • et al.
      Cyberbullying perpetration and victimization among middle-school students.
      Students who texted more than 50 times per day were also more likely to report cyberbullying victimization. Sexual minority adolescents reported double the cyberbullying victimization rates than their non-sexual minority adolescent peers in Los Angeles
      • Rice E
      • Petering R
      • Rhoades H
      • et al.
      Cyberbullying perpetration and victimization among middle-school students.
      and Boston.
      • Schneider SK
      • O'donnell L
      • Stueve A
      • Coulter RWS
      Cyberbullying, school bullying, and psychological distress: A regional census of high school students.
      However, the reported percentage of cyberbullying among sexual minority youth has ranged widely, from 10.5% to 71.3%.
      • Abreu RL
      • Kenny MC.
      Cyberbullying and LGBTQ Youth: A Systematic Literature Review and Recommendations for Prevention and Intervention.
      Findings on sex differences in cyberbullying have been mixed and may depend on age.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      ,
      • Guo S.
      A meta-analysis of the predictors of cyberbullying perpetration and victimization.
      One meta-analysis showed that early to mid-adolescent girls were more likely, whereas late-adolescent girls were less likely, to report cyberbullying (victimization or perpetration) than their male counterparts.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      This finding is supported in a study on traditional bullying across late childhood and early adolescence, where rates of bullying were more persistent in girls than in boys, but also declined across the transition from primary school to secondary school.
      • Fujikawa S
      • Mundy LK
      • Canterford L
      • Moreno-Betancur M
      • Patton GC.
      Bullying Across Late Childhood and Early Adolescence: A Prospective Cohort of Students Assessed Annually From Grades 3 to 8.
      With respect to race and ethnicity, a prior study of White and Black respondents observed similar cyberbullying victimization and perpetration behaviors.
      • Kowalski RM
      • Dillon E
      • Macbeth J
      • Franchi M
      • Bush M.
      Racial differences in cyberbullying from the perspective of victims and perpetrators.
      Greater screen use is also associated with more cyberbullying since cyberbullying requires access to an electronic device.
      • Carter JM
      • Wilson FL.
      Cyberbullying: a 21st Century Health Care Phenomenon.
      ,
      • Li Q.
      Bullying in the new playground: Research into cyberbullying and cyber victimisation.
      However, there is a paucity of data on contemporary cyberbullying prevalence, also considering multiple sociodemographic characteristics, in US early adolescents, when cyberbullying behaviors may begin to develop.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      Early adolescence is a critical period of development carrying high potential for interventions that target screen behaviors associated with cyberbullying behaviors.
      • Ng ED
      • Chua JYX
      • Shorey S.
      The Effectiveness of Educational Interventions on Traditional Bullying and Cyberbullying Among Adolescents: A Systematic Review and Meta-Analysis.
      The purpose of the current study was to investigate contemporary cyberbullying behaviors (victimization and perpetration) characterized across a national population-based and demographically diverse sample of US early adolescents aged 10-14 years-old. We considered potential differences in cyberbullying behaviors by sex, sexual orientation, race/ethnicity, and socioeconomic status. We also investigated associations between cyberbullying behaviors and usage of different screen time modalities.

      Methods

      We conducted a secondary cross-sectional analysis of data from the 2-year follow-up of the Adolescent Brain Cognitive Development (ABCD) study (4.0 release). The ABCD study is a longitudinal study (baseline 2016-2018) of health, brain, and cognitive development in 11,875 children from 21 recruitment sites across the U.S. The ABCD study participants, recruitment, protocol, and measures have previously been described in detail.
      • Barch DM
      • Albaugh MD
      • Avenevoli S
      • et al.
      Demographic, physical and mental health assessments in the Adolescent Brain and Cognitive Development study: Rationale and description.
      Participants were predominantly 11-12 years old (range 10-14 years) during the 2-year follow-up, which was conducted between 2018-2020. We omitted study participants with missing data for cyberbullying or sociodemographic variables (Supplemental Appendix). After omitting participants with missing data, 9,429 children remained in the analytic sample. Institutional review board (IRB) approval was received from the University of California, San Diego (UCSD) and the respective IRBs of each study site. Written assent was obtained from participants, and written informed consent was obtained from their caregivers.

      Measures and Study Variables

      Dependent Variables

      Cyberbullying Questionnaire. Adolescents completed a self-reported questionnaire to capture cyberbullying (victimization and perpetration) based on the validated Cyberbullying Scale.
      • Stewart RW
      • Drescher CF
      • Maack DJ
      • Ebesutani C
      • Young J.
      The Development and Psychometric Investigation of the Cyberbullying Scale.
      Cyberbullying victimization was assessed with the question, “Have you ever been cyberbullied, 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)?” Cyberbullying 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.

      Independent Variables

      Parents reported participants’ sex at birth (male or female), race/ethnicity (Non-Latino/Hispanic White, Non-Latino/Hispanic Black, Native American, Latino/Hispanic, Asian, or Other), and country of birth (born in U.S. or outside U.S.) at baseline. Additionally, parents reported highest parent education and household income at Year 2. Highest parent education was classified as high school or lower versus college or higher. Household income was grouped into two categories reflecting the U.S. median household income: less than $75,000 and $75,000 or more.
      • Semega J
      • Kollar M
      • Creamer J
      • Mohanty A.
      Participants reported their own sexual orientation (“Are you gay or bisexual?”; yes, maybe, no, don't understand the question) at Year 2. Responses “yes” and “maybe” were grouped together to represent sexual minority youth.
      Screen use for the following modalities was determined using adolescents’ self-reported hours of use on a typical weekday and weekend: multi-player gaming, single-player gaming, texting, social media, video chatting, browsing the internet, and watching/streaming movies, videos, or TV.
      • Bagot KS
      • Matthews SA
      • Mason M
      • et al.
      Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health.
      Total typical daily screen use was calculated as the weighted sum ([weekday average x 5] + [weekend average x 2])/7.

      Statistical Analyses

      Data analyses were performed in 2022 using Stata 15.1 (StataCorp). Multiple logistic regression analyses were conducted to estimate cross-sectional associations between sociodemographic factors (both models included sex, race/ethnicity, sexual orientation, country of birth, household income, parents’ highest education) as independent variables and lifetime cyberbullying victimization or perpetration as outcomes, controlling for study site (n = 21). We additionally used multiple logistic regression analyses to estimate associations between screen time and lifetime cyberbullying victimization or perpetration in unadjusted and adjusted models. Both adjusted models controlled for sex, race/ethnicity, sexual orientation, country of birth, household income, parents’ highest education, and study site. Propensity weights were applied to match key sociodemographic variables in the ABCD Study to the American Community Survey from the U.S. Census.
      • Heeringa S
      • Berglund P.
      A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data.

      Results

      Table 1 describes sociodemographic characteristics of the 9,429 participants included. The analytic sample was approximately balanced according to sex (48.6% female) and was racially and ethnically diverse (43.8% racial/ethnic minority). Lifetime prevalence of cyberbullying victimization was 9.6%, with 6.3% reporting victimization in the past 12 months. Lifetime prevalence of cyberbullying perpetration was 1.1%, with 0.7% reporting perpetration in the past 12 months.
      Table 1Sociodemographic and cyberbullying characteristics of Adolescent Brain Cognitive Development (ABCD) Study participants at the Year 2 (2018-2020) visit (N=9,429)
      Sociodemographic characteristicsMean (SD) / %
      Age (years)12.0 (0.7)
      Sex (%)
      Female48.6%
      Male51.4%
      Race/ethnicity (%)
      White56.2%
      Latino / Hispanic19.0%
      Black15.2%
      Asian5.3%
      Native American3.1%
      Other1.2%
      Sexual minority status (%)
      Yes / maybe1.5%
      No73.5%
      Don't understand the question25.1%
      U.S.-born (%)
      Yes96.3%
      No3.7%
      Household income (%)
      Less than $75,00052.6%
      $75,000 and greater47.4%
      Parents' highest education (%)
      College education or more16.7%
      High school education or less83.3%
      Total daily screen time (hours)5.9 (5.2)
      Television shows/movies1.3 (1.2)
      Videos (YouTube)1.1 (1.3)
      Video games (single player)0.8 (1.1)
      Video games (multi player)0.9 (1.3)
      Texting0.5 (0.9)
      Video chat0.3 (0.7)
      Social media0.5 (1.0)
      Internet0.3 (0.4)
      Cyberbullying
      Victimization
      Lifetime prevalence9.6%
      Within last 12 months6.3%
      Perpetration
      Lifetime prevalence1.1%
      Within last 12 months0.7%
      ABCD propensity weights were applied to yield nationally representative estimates based on the American Community Survey from the US Census. SD = standard deviation
      Table 2 shows sociodemographic associations with lifetime cyberbullying victimization and perpetration. Boys reported higher odds of cyberbullying perpetration (adjusted odds ratio (AOR) 1.71, 95% confidence interval (CI) 1.01-2.92) but lower odds of cyberbullying victimization (AOR 0.80, 95% CI 0.68-0.94) than girls. There were no significant differences in cyberbullying victimization by race/ethnicity. Native American adolescents reported 4.39 higher odds of cyberbullying perpetration (95% CI 1.32-14.57) than White adolescents. Sexual minority adolescents reported 2.83 higher odds of cyberbullying victimization (95% CI 1.69-4.75) than heterosexual adolescents. Lower household income was associated with 1.64 (95% CI 1.34-2.00) higher odds of cyberbullying victimization than higher household income.
      Table 2Sociodemographic associations with lifetime cyberbullying victimization and perpetration in the Adolescent Brain Cognitive Development (ABCD) Study
      Cyberbullying victimizationCyberbullying perpetration
      Sociodemographic characteristicsOR (95% CI)pOR (95% CI)p
      Sex
      Femalereferencereference
      Male0.80 (0.68 - 0.94)0.0061.71 (1.01 - 2.92)0.048
      Race/ethnicity
      Whitereferencereference
      Latino / Hispanic0.84 (0.63 - 1.12)0.2341.02 (0.38 - 2.72)0.966
      Black0.91 (0.71 - 1.17)0.4591.60 (0.78 - 3.31)0.203
      Asian0.65 (0.39 - 1.07)0.0881.54 (0.36 - 6.52)0.560
      Native American1.49 (0.96 - 2.32)0.0784.39 (1.32 - 14.57)0.016
      Other0.65 (0.25 - 1.71)0.3863.21 (0.57 - 17.97)0.185
      Sexual minority
      Noreferencereference
      Yes / maybe2.83 (1.69 - 4.75)<0.0011.04 (0.14 - 7.72)0.969
      Don't understand the question0.79 (0.65 - 0.97)0.0270.64 (0.29 - 1.39)0.256
      Country of birth (adolescent)
      United Statesreferencereference
      Outside United States0.81 (0.47 - 1.39)0.4430.36 (0.06 - 2.04)0.250
      Household income
      $75,000 and greaterreferencereference
      Less than $75,0001.64 (1.34 - 2.00)<0.0011.90 (0.95 - 3.82)0.070
      Parents' highest education
      College education or morereferencereference
      High school education or less1.11 (0.87 - 1.43)0.4071.14 (0.52 - 2.48)0.744
      Bold indicates p<0.05. ABCD propensity weights were applied to yield nationally representative estimates based on the American Community Survey from the US Census.
      All models (victimization and perpetration) include sex, race/ethnicity, sexual orientation, country of birth, household income, parent education, and site.
      Table 3 shows unadjusted and adjusted associations among screen time and cyberbullying victimization and perpetration. Each additional hour of total screen time was associated with 1.09 (95% CI 1.07-1.10) higher odds cyberbullying victimization and 1.10 (95% CI 1.06-1.45) higher odds of cyberbullying perpetration in adjusted models. The specific screen modalities most strongly associated with cyberbullying victimization and perpetration were the internet and social media.
      Table 3Unadjusted and adjusted associations between screen time and cyberbullying in the Adolescent Brain Cognitive Development (ABCD) Study
      Screen timeCyberbullying victimization, unadjustedCyberbullying victimization, adjustedaCyberbullying perpetration, unadjustedCyberbullying perpetration, adjusteda
      Odds Ratio (95% CI)pOdds Ratio (95% CI)pOdds Ratio (95% CI)pOdds Ratio (95% CI)p
      Total screen time1.08 (1.07-1.09)<0.0011.09 (1.07-1.10)<0.0011.11 (1.08-1.14)<0.0011.10 (1.06-1.45)<0.001
      Television shows/movies1.24 (1.17-1.31)<0.0011.22 (1.15-1.30)<0.0011.23 (1.05-1.44)0.0101.16 (0.98-1.38)0.087
      Videos (YouTube)1.25 (1.19-1.31)<0.0011.23 (1.16-1.30)<0.0011.29 (1.13-1.48)<0.0011.20 (1.02-1.42)0.029
      Video games (single player)1.15 (1.08-1.22)<0.0011.16 (1.08-1.24)<0.0011.27 (1.09-1.47)0.0021.17 (.097-1.41)0.095
      Video games (multi player)1.23 (1.17-1.30)<0.0011.28 (1.21-1.36)<0.0011.40 (1.24-1.58)<0.0011.36 (1.17-1.59)<0.001
      Texting1.37 (1.28-1.46)<0.0011.33 (1.24-1.44)<0.0011.46 (1.23-1.73)<0.0011.39 (1.12-1.72)0.003
      Video chat1.36 (1.25-1.48)<0.0011.34 (1.21-1.48)<0.0011.42 (1.16-1.76)0.0011.28 (0.98-1.65)0.067
      Social media1.45 (1.37-1.54)<0.0011.45 (1.35-1.55)<0.0011.64 (1.43-1.89)<0.0011.62 (1.34-1.95)<0.001
      Internet1.86 (1.63-2.12)<0.0011.75 (1.51-2.02)<0.0011.97 (1.39-2.80)<.0011.71 (1.14-2.57)0.009
      Bold indicates p<0.05. ABCD propensity weights were applied to yield estimates based on the American Community Survey from the US Census.
      Adjusted models include sex, race/ethnicity, sexual orientation, country of birth, household income, parent education, and study site.

      Discussion

      In a demographically diverse, contemporary sample of 11- and 12-year-old early adolescents in the United States, we found that 9.6% reported a lifetime prevalence of cyberbullying victimization, and 1.1% reported lifetime cyberbullying perpetration.
      We found sex differences in cyberbullying victimization and perpetration in this early adolescent sample, with girls reporting more cyberbullying victimization than boys, consistent with a prior meta-analysis.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      In contrast, boys reported more cyberbullying perpetration than girls, which is consistent with gender differences in general bullying,
      • Carbone-Lopez K
      • Esbensen FA
      • Brick BT.
      Correlates and consequences of peer victimization: Gender differences in direct and indirect forms of bullying.
      but opposite findings of the prior cyberbullying meta-analysis although these differences may be due to sampling, age, or technology use differences.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      While speculative, males’ higher prevalence of cyberbullying perpetration may partially be explained by greater aggression or materialism, a cluster of goals and values focused on possessions, wealth, image, and status.
      • Barlett C
      • Coyne SM.
      A meta-analysis of sex differences in cyber-bullying behavior: the moderating role of age.
      ,
      • Wang P
      • Wang X
      • Lei L.
      Gender Differences Between Student–Student Relationship and Cyberbullying Perpetration: An Evolutionary Perspective.
      Verbal anger and aggression are explanatory factors for traditional and cyberbullying perpetration such that perpetration is associated with increased aggression.
      • Escortell R
      • Aparisi D
      • Martínez-Monteagudo MC
      • Delgado B.
      Personality Traits and Aggression as Explanatory Variables of Cyberbullying in Spanish Preadolescents.
      Conversely, less aggression makes adolescents easier targets for bullying because it guarantees more anonymity for the bullying perpetrator.
      • Escortell R
      • Aparisi D
      • Martínez-Monteagudo MC
      • Delgado B.
      Personality Traits and Aggression as Explanatory Variables of Cyberbullying in Spanish Preadolescents.
      One study found that materialism was associated with cyberbullying in boys but not girls.
      • Wang P
      • Wang X
      • Lei L.
      Gender Differences Between Student–Student Relationship and Cyberbullying Perpetration: An Evolutionary Perspective.
      The higher rates of victimization among sexual minorities are consistent with prior studies showing that sexual minority youth are at increased risk of victimization through cyber and non-cyberbullying,
      • Schneider SK
      • O'donnell L
      • Stueve A
      • Coulter RWS
      Cyberbullying, school bullying, and psychological distress: A regional census of high school students.
      ,
      • Abreu RL
      • Kenny MC.
      Cyberbullying and LGBTQ Youth: A Systematic Literature Review and Recommendations for Prevention and Intervention.
      ,
      • Birkett M
      • Espelage DL
      • Koenig B.
      LGB and Questioning Students in Schools: The Moderating Effects of Homophobic Bullying and School Climate on Negative Outcomes.
      ,
      • Toomey RB
      • Russell ST.
      The Role of Sexual Orientation in School-Based Victimization: A Meta-Analysis.
      although it is worth noting that 25% of respondents did not understand the question about sexual orientation. Future research in the ABCD Study could track this relationship as the participants progress across adolescence. Furthermore, cyberbullying victimization in sexual minority youth is associated with higher mental health problems; parental support can protect against mental health problems while non-supportive parents may exacerbate harms.
      • Desmet A
      • Rodelli M
      • Walrave M
      • Portzky G
      • Dumon E
      • Soenens B.
      The Moderating Role of Parenting Dimensions in the Association between Traditional or Cyberbullying Victimization and Mental Health among Adolescents of Different Sexual Orientation.
      We did not find significant differences in cyberbullying victimization by race/ethnicity in this early adolescent sample, indicating that they are susceptible to cyberbullying victimization regardless of their race and ethnicity. The finding that Native American early adolescents reported higher rates of cyberbullying perpetration compared to White early adolescents is based on a relatively small sample of Native American early adolescents who reported cyberbullying perpetration and may not be representative of this population. Our preliminary finding requires further research, particularly qualitative exploration of cyberbullying experiences among understudied and underserved Native American adolescents, as well as replication, as we are unaware of prior studies reporting this finding.
      We found that more screen time was associated with cyberbullying victimization and perpetration, and this was expected given that cyberbullying requires use of an electronic device.
      • Carter JM
      • Wilson FL.
      Cyberbullying: a 21st Century Health Care Phenomenon.
      ,
      • Li Q.
      Bullying in the new playground: Research into cyberbullying and cyber victimisation.
      The internet and social media had the strongest associations with cyberbullying and may be future targets for interventions to prevent cyberbullying.
      Overall, fewer early adolescents reported cyberbullying perpetration than victimization. Cyberbullying perpetration could be concentrated among a smaller group of early adolescents, or participants may be less likely to admit to perpetration due to social desirability bias. Similar reporting patterns are seen in intimate partner violence where participants are three times more likely to report being a victim than a perpetrator.
      • Tillyer MS
      • Wright EM.
      Reasons for cyberbullying perpetration include intrinsic and extrinsic factors.
      • Varjas K
      • Talley J
      • Meyers J
      • Parris L
      • Cutts H.
      High School Students’ Perceptions of Motivations for Cyberbullying: An Exploratory Study.
      Intrinsic factors include a redirection of feelings, instigation, boredom, anonymity/disinhibition, and consolation, while extrinsic factors include a lack of consequences, perceived target differences, and a lack of confrontation.
      • Varjas K
      • Talley J
      • Meyers J
      • Parris L
      • Cutts H.
      High School Students’ Perceptions of Motivations for Cyberbullying: An Exploratory Study.
      There are several limitations and strengths of this study worth noting. The data are cross-sectional and differences in sex, race/ethnicity, or socioeconomic status do not reflect causality but could be proxies of other underlying factors. Due to measures being self-reported, there is potential for recall, reporting, and social desirability bias. The effects of some sociodemographic factors were low. The potential for selection bias may be represented by a greater proportion of ethnic/racial minorities and parents with lower education excluded from the analysis. The strengths of this study are derived from the large, diverse, contemporary, and national sample.
      Our findings have significant clinical, policy, and public health implications, particularly to inform the adaptation and implementation of digital technology guidance for adolescents. This research may further inform targeted screen-related guidance for educators, clinicians, and parents. The American Academy of Pediatrics advocates for a Family Media Use Plan,
      • Hill D
      • Ameenuddin N
      • Chassiakos YR
      • et al.
      Media use in school-aged children and adolescents.
      which could incorporate guidance on family discussions on cyberbullying including supporting adolescents at risk for cyberbullying victimization and the consequences of cyberbullying perpetration. Studies show that parental intervention is critical in adolescence; therefore, informing and educating parents on the warning signs of cyberbullying perpetration or victimization could be helpful. Furthermore, school and community-level efforts to engage families may incorporate tailoring of culturally sensitive messages that address teaching the youth skills in communication and social empathy, coping with cyberbullying, and digital citizenship.
      • Hutson E
      • Kelly S
      • Militello LK.
      Systematic Review of Cyberbullying Interventions for Youth and Parents With Implications for Evidence-Based Practice.
      One meta-analysis found that cyberbullying programs were more effective when delivered by technology-savvy content experts compared to teachers.
      • Ng ED
      • Chua JYX
      • Shorey S.
      The Effectiveness of Educational Interventions on Traditional Bullying and Cyberbullying Among Adolescents: A Systematic Review and Meta-Analysis.
      Although the intervention used a trained psychologist as the content expert,
      • Schoeps K
      • Villanueva L
      • Prado-Gascó VJ
      Montoya-Castilla I. Development of emotional skills in adolescents to prevent cyberbullying and improve subjective well-being.
      future research could examine the role of pediatricians or other healthcare providers. Pediatricians can consider assessing for cyberbullying and provide support and anticipatory guidance for early adolescents as appropriate in this highly potentiated period for intervention.
      • Fujikawa S
      • Mundy LK
      • Canterford L
      • Moreno-Betancur M
      • Patton GC.
      Bullying Across Late Childhood and Early Adolescence: A Prospective Cohort of Students Assessed Annually From Grades 3 to 8.
      However, it is important for pediatricians to note that adolescents may avoid the term “cyberbullying” due to its association with suicidality and severe depression and may instead describe their experiences as “online conflict.”
      • Ranney ML
      • Pittman SK
      • Riese A
      • et al.
      What Counts?: A Qualitative Study of Adolescents’ Lived Experience With Online Victimization and Cyberbullying.
      This study represents an advance in our understanding of cyberbullying prevalence among early adolescents, and how these behaviors are associated with sociodemographic factors. Greater knowledge on the sociodemographic and behavioral risk factors of cyberbullying perpetration and victimization suggest that a wide range of social marginalizing factors correlate with victimization, which requires additional attention. Such efforts can strengthen and inform future individualized early adolescent-focused interventions across numerous technological platforms. Comprehension of the social epidemiology of cyberbullying behavior is crucial, especially given the unprecedented rise of technology usage during the COVID-19 pandemic.
      • Nagata JM
      • Cortez CA
      • Cattle CJ
      • et al.
      Screen time use among us adolescents during the COVID-19 pandemic: Findings from the Adolescent Brain Cognitive Development (ABCD) study.
      ,
      • Trompeter N
      • Jackson E
      • Sheanoda V
      • Luo A
      • Allison K
      • Bussey K

      Funding/Support

      J.M.N. was supported by the American Heart Association Career Development Award ( CDA34760281 ) and the National Institutes of Health ( K08HL159350 ). S.B.M. was supported by the National Institutes of Health ( K23 MH115184 ). K.B.D. is supported by the National Institutes of Health ( K24DK103992 ). The ABCD Study was supported by the National Institutes 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 supporters is available at https://abcdstudy.org/nihcollaborators. 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.

      Role of Funder Sponsor

      The funders had no role in the study analysis, decision to publish the study, or the preparation of the manuscript.

      Conflicts of Interest

      The authors have no conflict to declare

      Acknowledgements

      None

      Appendix. Supplementary Data

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