Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-19T16:07:14.570Z Has data issue: false hasContentIssue false
  • English
  • Français

Cyber-victimisation and mental health in young people: a co-twin control study

Published online by Cambridge University Press:  04 May 2020

Jessie R. Baldwin Open the ORCID record for Jessie R. Baldwin [Opens in a new window] ,
Ziada Ayorech ,
Fruhling V. Rijsdijk ,
Tabea Schoeler  and
Jean-Baptiste Pingault
Jessie R. Baldwin*
Affiliation:
Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Ziada Ayorech
Affiliation:
Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
Fruhling V. Rijsdijk
Affiliation:
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
Tabea Schoeler
Affiliation:
Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK
Jean-Baptiste Pingault
Affiliation:
Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
*
Author for correspondence: Jessie R. Baldwin, E-mail: j.baldwin@ucl.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

The rise of social media use in young people has sparked concern about the impact of cyber-victimisation on mental health. Although cyber-victimisation is associated with mental health problems, it is not known whether such associations reflect genetic and environmental confounding.

Methods

We used the co-twin control design to test the direct association between cyber-victimisation and multiple domains of mental health in young people. Participants were 7708 twins drawn from the Twins Early Development Study, a UK-based population cohort followed from birth to age 22.

Results

Monozygotic twins exposed to greater levels of cyber-victimisation had more symptoms of internalising, externalising and psychotic disorders than their less victimised co-twins at age 22, even after accounting for face-to-face peer victimisation and prior mental health. However, effect sizes from the most stringent monozygotic co-twin control analyses were decreased by two thirds from associations at the individual level [pooled β across all mental health problems = 0.06 (95% CI 0.03–0.10) v. 0.17 (95% CI 0.15–0.19) in individual-level analyses].

Conclusions

Cyber-victimisation has a small direct association with multiple mental health problems in young people. However, a large part of the association between cyber-victimisation and mental health is due to pre-existing genetic and environmental vulnerabilities and co-occurring face-to-face victimisation. Therefore, preventative interventions should target cyber-victimisation in conjunction with pre-existing mental health vulnerabilities and other forms of victimisation.

Keywords

Co-twin control designcyber-bullyingcyber-victimisationmental healthtwin study
Type
Original Article
Information
Psychological Medicine , Volume 51 , Issue 15 , November 2021 , pp. 2620 - 2630
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Andreasen, N. C. (1989). The Scale for the Assessment of Negative Symptoms (SANS): Conceptual and theoretical foundations. British Journal of Psychiatry, 155(S7), 49–52.CrossRefGoogle Scholar
Angold, A., Costello, E. J., Messer, S. C., Pickles, A., Winder, F., & Silver, D. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 6(11), 237–249.Google Scholar
Arseneault, L., Milne, B. J., Taylor, A., Adams, F., Delgado, K., Caspi, A., & Moffitt, T. E. (2008). Being bullied as an environmentally mediated contributing factor to children's internalizing problems: A study of twins discordant for victimization. Archives of Pediatrics & Adolescent Medicine, 162(2), 145–150.CrossRefGoogle ScholarPubMed
Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (1992). The alcohol use disorders identification test: Guidelines for use in primary health care. Geneva: World Health Organisation.Google Scholar
Baldwin, J. R., Arseneault, A., Caspi, A., Moffitt, T. E., Fisher, H. L., Odgers, C. L., … Danese, A. (2019). Adolescent victimization and self-injurious thoughts and behaviors: A genetically sensitive cohort study. Journal of the American Academy of Child and Adolescent Psychiatry, 58(5), 506–513.CrossRefGoogle ScholarPubMed
Bell, V., Halligan, P. W., & Ellis, H. D. (2005). The Cardiff Anomalous Perceptions Scale (CAPS): A new validated measure of anomalous perceptual experience. Schizophrenia Bulletin, 32(2), 366–377.CrossRefGoogle ScholarPubMed
Betts, L. R., Houston, J. E., & Steer, O. L. (2015). Development of the multidimensional peer victimization scale-revised (MPVS-R) and the multidimensional peer bullying scale (MPVS-RB). Journal of Genetic Psychology, 176(2), 93–109.CrossRefGoogle Scholar
Boker, S., Neale, M., Maes, H., Wilde, M., Spiegel, M., Brick, T., … Bates, T. (2011). Openmx: An open source extended structural equation modeling framework. Psychometrika, 76(2), 306–317.CrossRefGoogle ScholarPubMed
Borenstein, M. (2009). Effect sizes for continuous data. In Cooper, H., Hedges, L. V., & Valentine, J. C. (Ed.), The handbook of research synthesis and meta analysis (pp. 279–293). New York: Russell Sage Foundation.Google Scholar
Carlin, J. B., Gurrin, L. C., Sterne, J. A., Morley, R., & Dwyer, T. (2005). Regression models for twin studies: A critical review. International Journal of Epidemiology, 34(5), 1089–1099.CrossRefGoogle ScholarPubMed
Chen, Q., Lo, C. K., Zhu, Y., Cheung, A., Chan, K. L., & Ip, P. (2018). Family poly-victimization and cyberbullying among adolescents in a Chinese school sample. Child Abuse & Neglect, 77, 180–187.CrossRefGoogle Scholar
Connors, C. (1997). Conners’ rating scales – revised technical manual. New York: Multi-Health Systems: Inc.Google Scholar
Craske, M., Wittchen, U., Bogels, S., Stein, M., Andrews, G., & Lebeu, R.. (2013). Severity measure for generalized anxiety disorder–adult. Retrieved from: https://www.psychiatry.org/FileLibrary/Psychiatrists/Practice/DSM/APA_DSM5_Severity-Measure-For-Generalized-Anxiety-Disorder-Adult.pdfGoogle Scholar
Del Re, A. C. (2013). ‘compute. es’: Compute effect sizes. R package version 0.2-2. Retrieved from: https://cran.r-project.org/web/packages/compute.es/compute.es.pdf.Google Scholar
Del Re, A., & Hoyt, W. T. (2014). MAd-package: Meta-analysis with mean differences. Retrieved from: https://cran.r-project.org/web/packages/MAd/MAd.pdf.Google Scholar
Dinkler, L., Lundström, S., Gajwani, R., Lichtenstein, P., Gillberg, C., & Minnis, H. (2017). Maltreatment-associated neurodevelopmental disorders: A co-twin control analysis. Journal of Child Psychology and Psychiatry, 58(6), 691–701.CrossRefGoogle ScholarPubMed
Edwards, V. J., Holden, G. W., Felitti, V. J., & Anda, R. F. (2003). Relationship between multiple forms of childhood maltreatment and adult mental health in community respondents: Results from the adverse childhood experiences study. American Journal of Psychiatry, 160(8), 1453–1460.CrossRefGoogle ScholarPubMed
Eley, T. C., Bolton, D., O'connor, T. G., Perrin, S., Smith, P., & Plomin, R. (2003). A twin study of anxiety-related behaviours in pre-school children. Journal of Child Psychology and Psychiatry, 44(7), 945–960.CrossRefGoogle ScholarPubMed
Fenigstein, A., & Vanable, P. A. (1992). Paranoia and self-consciousness. Journal of Personality and Social Psychology, 62(1), 129.CrossRefGoogle ScholarPubMed
Fisher, H. L., Caspi, A., Moffitt, T. E., Wertz, J., Gray, R., Newbury, J., … Mill, J. (2015). Measuring adolescents' exposure to victimization: The Environmental Risk (E-Risk) Longitudinal Twin Study. Development and Psychopathology, 27(4pt2), 1399–1416.CrossRefGoogle ScholarPubMed
Frisell, T., Öberg, S., Kuja-Halkola, R., & Sjölander, A. (2012). Sibling comparison designs: Bias from non-shored confounders and measurement error. Epidemiology, 23(5), 713–720.CrossRefGoogle ScholarPubMed
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2(2), 156–168.CrossRefGoogle Scholar
Gámez-Guadix, M., Orue, I., Smith, P. K., & Calvete, E. (2013). Longitudinal and reciprocal relations of cyberbullying with depression, substance use, and problematic internet use among adolescents. Journal of Adolescent Health, 53(4), 446–452.CrossRefGoogle ScholarPubMed
Garner, D. (1991). Eating disorder inventory-2. Odessa, FL: Psychological Assessment Resources: Inc.Google Scholar
Gini, G., Card, N. A., & Pozzoli, T. (2018). A meta-analysis of the differential relations of traditional and cyber-victimization with internalizing problems. Aggressive Behavior, 44(2), 185–198.CrossRefGoogle ScholarPubMed
Goodman, R. (1997). The strengths and difficulties questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38(5), 581–586.CrossRefGoogle ScholarPubMed
Kendler, K. S., Bulik, C. M., Silberg, J., Hettema, J. M., Myers, J., & Prescott, C. A. J. A. O. G. P. (2000). Childhood sexual abuse and adult psychiatric and substance use disorders in women: An epidemiological and cotwin control analysis. JAMA Psychiatry, 57(10), 953–959.Google ScholarPubMed
Kim, S., Colwell, S. R., Kata, A., Boyle, M. H., & Georgiades, K. (2018). Cyberbullying victimization and adolescent mental health: Evidence of differential effects by sex and mental health problem type. Journal of Youth and Adolescence, 47(3), 661–672.CrossRefGoogle ScholarPubMed
Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattanner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying research among youth. Psychological Bulletin, 140(4), 1073.CrossRefGoogle ScholarPubMed
Legleye, S., Karila, L., Beck, F., & Reynaud, M. (2007). Validation of the CAST, a general population Cannabis Abuse Screening Test. Journal of Substance Use, 12(4), 233–242.CrossRefGoogle Scholar
Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22.CrossRefGoogle Scholar
Madge, N., Hewitt, A., Hawton, K., Wilde, E. J. D., Corcoran, P., Fekete, S., … Ystgaard, M. (2008). Deliberate self-harm within an international community sample of young people: Comparative findings from the Child & Adolescent Self-harm in Europe (CASE) Study. Journal of Child Psychology and Psychiatry, 49(6), 667–677.CrossRefGoogle ScholarPubMed
McGue, M., Osler, M., & Christensen, K. (2010). Causal inference and observational research: The utility of twins. Perspectives on Psychological Science, 5(5), 546–556.CrossRefGoogle Scholar
Modecki, K. L., Minchin, J., Harbaugh, A. G., Guerra, N. G., & Runions, K. C. (2014). Bullying prevalence across contexts: A meta-analysis measuring cyber and traditional bullying. Journal of Adolescent Health, 55(5), 602–611.CrossRefGoogle ScholarPubMed
Muthén, L. K., & Muthén, B. (2009). Regression analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation modeling for categorical, censored, and count outcomes. In. Los Angeles: Mplus Short Courses.Google Scholar
Office for National Statistics. (2017). Social networking by age group, 2011 to 2017. Retrieved from https://www.ons.gov.uk/peoplepopulationandcommunity/householdcharacteristics/homeinternetandsocialmediausage/adhocs/007401socialnetworkingbyagegroup2011to2017.Google Scholar
Orben, A., & Przybylski, A. K. (2019). The association between adolescent well-being and digital technology use. Nature Human Behaviour, 3(2), 173.CrossRefGoogle ScholarPubMed
Perret, L. C., Orri, M., Boivin, M., Ouellet-Morin, I., Denault, A. S., Côté, S. M., … Geoffroy, M. C. (2020). Cybervictimization in adolescence and its association with subsequent suicidal ideation/attempt beyond face-to-face victimization: A longitudinal population-based study. Journal of Child Psychology and Psychiatry. doi: 10.1111/jcpp.13158.CrossRefGoogle ScholarPubMed
Pingault, J.-B., & Schoeler, T. (2017). Assessing the consequences of cyberbullying on mental health. Nature Human Behaviour, 1(11), 775.CrossRefGoogle ScholarPubMed
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.CrossRefGoogle ScholarPubMed
Przybylski, A. K., & Bowes, L. (2017). Cyberbullying and adolescent well-being in England: A population-based cross-sectional study. The Lancet Child & Adolescent Health, 1(1), 19–26.CrossRefGoogle ScholarPubMed
R Core Team. (2019). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.Google Scholar
Rijsdijk, F. V., & Sham, P. C. (2002). Analytic approaches to twin data using structural equation models. Briefings in Bioinformatics, 3(2), 119–133.CrossRefGoogle ScholarPubMed
Rimfeld, K., Malanchini, M., Spargo, T., Spickernell, G., Selzam, S., McMillan, A., … Plomin, R. (2019). Twins Early Development Study: A genetically sensitive investigation into behavioural and cognitive development from infancy to emerging adulthood. Twin Research and Human Genetics, 22(6), 1–6.CrossRefGoogle ScholarPubMed
Ronald, A., Sieradzka, D., Cardno, A. G., Haworth, C. M., McGuire, P., & Freeman, D. (2013). Characterization of psychotic experiences in adolescence using the specific psychotic experiences questionnaire: Findings from a study of 5000 16-year-old twins. Schizophrenia Bulletin, 40(4), 868–877.CrossRefGoogle ScholarPubMed
Rose, C. A., & Tynes, B. M. (2015). Longitudinal associations between cybervictimization and mental health among US adolescents. Journal of Adolescent Health, 57(3), 305–312.CrossRefGoogle Scholar
Schaefer, J. D., Moffitt, T. E., Arseneault, L., Danese, A., Fisher, H. L., Houts, R., … Caspi, A. (2017). Adolescent victimization and early-adult psychopathology: Approaching causal inference using a longitudinal twin study to rule out non-causal explanations. Clinical Psychological Science, 6(3), 352–371. doi: 10.1177/2167702617741381CrossRefGoogle Scholar
Schoeler, T., Choi, S. W., Dudbridge, F., Baldwin, J., Duncan, L., Cecil, C. M., … Pingault, J.-B. (2019). Multi–polygenic score approach to identifying individual vulnerabilities associated with the risk of exposure to bullying. JAMA Psychiatry, 76(7), 730–738.CrossRefGoogle ScholarPubMed
Schoeler, T., Duncan, L., Cecil, C. M., Ploubidis, G. B., & Pingault, J.-B. (2018). Quasi-experimental evidence on short-and long-term consequences of bullying victimization: A meta-analysis. Psychological Bulletin, 144(12), 1229.CrossRefGoogle ScholarPubMed
Shakoor, S., McGuire, P., Cardno, A. G., Freeman, D., Plomin, R., & Ronald, A. (2014). A shared genetic propensity underlies experiences of bullying victimization in late childhood and self-rated paranoid thinking in adolescence. Schizophrenia Bulletin, 41(3), 754–763.CrossRefGoogle ScholarPubMed
Silberg, J. L., Copeland, W., Linker, J., Moore, A. A., Roberson-Nay, R., & York, T. P. (2016). Psychiatric outcomes of bullying victimization: A study of discordant monozygotic twins. Psychological Medicine, 46(9), 1875–1883.CrossRefGoogle ScholarPubMed
Silva, Y. N., Hall, D. L., & Rich, C. (2018). Bullyblocker: Toward an interdisciplinary approach to identify cyberbullying. Social Network Analysis Mining, 8(1), 18.CrossRefGoogle Scholar
Singham, T., Viding, E., Schoeler, T., Arseneault, L., Ronald, A., Cecil, C. M., … Pingault, J. B. (2017). Concurrent and longitudinal contribution of exposure to bullying in childhood to mental health: The role of vulnerability and resilience. JAMA Psychiatry, 74(11), 1112–1119.CrossRefGoogle ScholarPubMed
Smith, D. J., & McVie, S. (2003). Theory and method in the Edinburgh study of youth transitions and crime. British Journal of Criminology, 43(1), 169–195.CrossRefGoogle Scholar
Vitaro, F., Brendgen, M., & Arseneault, L. (2009). The discordant MZ-twin method: One step closer to the holy grail of causality. International Journal of Behavioral Development, 33(4), 376–382.CrossRefGoogle Scholar
Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, S. (2014). The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies. International Journal of Surgery, 12(12), 1495–1499.CrossRefGoogle ScholarPubMed
Wolke, D., Lee, K., & Guy, A. (2017). Cyberbullying: A storm in a teacup? European Child and Adolescent Psychiatry, 26(8), 899–908.CrossRefGoogle ScholarPubMed
Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42(1), 121–130.CrossRefGoogle ScholarPubMed
Supplementary material: File

Baldwin et al. supplementary material

Baldwin et al. supplementary material

Download Baldwin et al. supplementary material(File)
File 1.3 MB