Table 1.
Results from studies carried out on cyberbullying correlated with excessive Internet use
| Reference | Study design | N | Country | Time frame | Age range | Measures of cyberbullying and IUD | Findings |
|---|---|---|---|---|---|---|---|
| Floros et al. [35•] | Cohort, two waves, 2 years apart | 2017 | Greece | First wave 4/2008 (T1) second wave 4/2010 (T2) | Age range 12–19, mean age 15.06, (SD = 0.05) | Ad hoc items on cyberbullying victimization and perpetration. Online Cognitions Scale (OCS) | The impulsiveness subscale of OCS was a significant predictor of cyberbullying perpetration |
| Gámez-Guadix et al. [36••] | Cohort. two waves, six months apart | 845 | Spain | First wave 4/2011 (T1) second wave 5–6/2012 (T2) | Mean age 15.2, SD = 1.2 at T1 | Victimization subscale of the Cyberbullying Questionnaire (CBQ). Generalized Problematic Internet Use Scale 2 (GPIUS2) | Cyberbullying victimization at T1 predicted depressive symptoms and problematic Internet use at T2 |
| Jung et al. [37] | Cross-sectional | 4531 | South Korea | 2011 | 11–14, 94.2% between 11 and 12 | Ad hoc questionnaire on cyberbullying experiences. Internet Addiction Proneness Scale for Youth-Short Form (KS), | Being a victim, perpetrator, and victim-perpetrator significantly increased the likelihood of the presence of problematic internet use (adjusted OR: 2.36, 1.66, and 2.38, respectively) |
| Yang et al. [38•] | Cohort, two waves, 2 years apart | 1173 | South Korea | First wave 2004 (T1) second wave 5–6 2006 (T2) | 13–14 years at T1 | Ad hoc measures of cyberbullying and computer online use time | There was no relationship between computer online use time and cyberbullying |
| Chang et al. [39] | Cross-sectional | 1808 | Taiwan | 2013 | 12–14 years | Ad hoc items on cyberbullying victimization/perpetration. Chen Internet Addiction Scale (CIAS), | Adolescent Internet addiction was associated with cyberbullying victimization/perpetration, smoking, consumption of alcohol, and depression |
| Rassmussen et al. [40] | Cross-sectional | 2100 | Denmark | 2009 | Limited to ages 11, 13, and 15 | Ad hoc items on perceived problems related to computer gaming and computer use, single items on bullying and being bullied | Children perceiving problems with computer gaming showed higher prevalence of both being bullied and having bullied others. Boys perceiving problems with internet use had the same issues but the relationship was not statistically significant for girls |
| Gámez-Guadix et al. [41••] | Cohort. two waves, six months apart | 888 | Spain | First wave 11–12/2011 (T1) second wave 5–6/2012 (T2) | Mean age 15.42, SD = 1.01 at T1 | Perpetration subscale of the CBQ. GPIUS2 | IUD at T1 predicted an increase in the perpetration of cyberbullying and meeting strangers online at T2 |
| Yu et al. [42] | Cross-sectional | 8480 | Taiwan | 2014 | Unspecified, high-school students | Ad hoc selection of five items on cyberbullying and four items on internet addiction picked by previous research | IUD has a significant moderating effect on the relationships among cyber bullying, cyber pornography, and physical and mental health of individuals |
| Šincek et al. [43] | Cross-sectional | 1150 | Croatia | Fall 2015 | Age range 11–21, mean age 14.77, SD = 2.259 | Cyberbullying Inventory (CBI), Problematic Online Gaming (POGQ) | Potentially problematic gamers (those who played games for more than five hours per day) experienced and committed more violence both face-to-face and via the Internet |
| Tsimtsiou et al. [44] | Cross-sectional | 5590 | Greece | 2013–2014 | Age range 12–18, mean age 14.77, (SD = 2.259) |
Ad hoc items on cyberbullying victimization and perpetration. Young’s Internet addiction test (YIAT) |
The odds of IUD increased with online hours, Internet café visits, chatrooms usage, and engagement in cyberbullying. Cyberbullying victims were more likely to be older, female, Facebook and chatrooms users, while perpetrators were more likely to be male, older Internet users and fans of pornographic sites |
| Machimbarrena et al. [45] | Cross-sectional | 3213 | Spain | 12/2017–4/2018 | Age range 11–21, mean age 13.92 (SD = 1.44) | Victimization Scale of the CBQ. GPIUS2 | Multiple correlations between IUD, cyberbullying victimization, cyber dating abuse victimization, grooming, and sexting were reported |
| Zsila et al. [46] | Cross-sectional | 6237 | Hungary | 2015 | Age range 15–22, mean age 16.62 (SD = 0.95) | Four ad hoc items on cyberbullying perpetration and victimization. Problematic Internet Use Questionnaire (PIUQ-6) | IUD was related to an increased risk of victimization in both traditional bullying and cyberbullying |
| Brighi et al. [47] | Cross-sectional | 3602 | Italy | 2014–2015 | Age range 11–20, mean age 14.64 (SD = 1.7) |
Cyberbullying scale from the European Cyberbullying Intervention Project Questionnaire (ECIPQ). Ad hoc scale of five items on problematic Internet use |
There is a common pathway to IUD and cyberbullying from reduced parental monitoring and emotional symptoms via increased time spent online |
| Gansner et al. [48] | Cross-sectional | 205 | US | 2012–2016 | Age range 12–20 | A single yes/no item on cyberbullying, three items on IUD | IUD severity correlated with being cyberbullied and sexting in psychiatrically hospitalized adolescents |
| Handono et al. [49] | Cross-sectional | 210 | Indonesia | 2018 | Age range 15–24 |
Ad hoc list of 24 cyberbullying indicators on a five-point Likert scale for frequency. Problematic and Risky Internet Use Screening Scale (PRIUSS) |
Time spent online, IUD, and attitude toward cyberbullying, had a positive and high correlation with cyberbullying behavior |
| Lee et al. [50] | Cross-sectional | 1678 | South Korea | 2016 | Mean age 18.6 (SD = 0.5) |
Three items on victims, witnesses and bully-victims of cybersexual harassment and bullying. Short-form Korean Scale for Internet Addiction (K-scale) for adolescents |
Cybersexual harassment and cybervictimization along with IUD predicted the levels of stress associated with psychotic-like experiences |
| Şimşek et al. [51] | Cross-sectional | 2422 | Turkey | 2017 | Mean age 16.23 (SD = 1.11) | Cyber Victimization and Cyberbullying Scale. YIAT | Cyber-victimization and cyberbullying were related to Internet usage characteristics and IUD |
| Zhai et al. [52] | Cross-sectional | 2758 | China | 2012 | Mean age 13.53 (SD = 1.06) | Ad hoc seven-item questionnaire on adolescent exposure to aggression from peers, Deviant Peer Affiliation Questionnaire. Ten items from YIAT | Peer victimization was positively associated with IUD, Deviant peer affiliation (DPA) partially mediated the link between peer victimization and IUD, and family functioning moderated the association between peer victimization and DPA |
| Arpaci et al. [53] | Cross-sectional | 665 | Turkey | 2016 | Age range 17–19, mean age 17.94 (SD = 1.12) | Ad hoc 23-item scale on cyberbullying. YIAT | IUD had a significant direct effect on cyberbullying (effect size 0.39) as well as an intervening effect on the relationship between vertical individualism and cyberbullying |
| Chao et al. [54] | Cross-sectional | 5211 | Taiwan | 2018 | Age range 16–19, mean age 17.31 (SD = 0.95) | Ad hoc selection of three items for cyberbullying, ad hoc scale of six items for IUD | Cyberbullying was correlated with IUD, with the correlation moderated by community bond |
| Lee et al. [55] | Cross-sectional | 500 | Taiwan | 2015–2016 | Age range 20–25 | School Bullying Experience Questionnaire (C-SBEQ). CIAS, Smartphone Addiction Inventory (SPAI) | Victims had more severe IUD and problematic smartphone use than non-victims. Victims of multi-type bullying had more severe IUD than victims of single-type bullying. Prolonged victimization was significantly associated with IUD and PSU |
| Li et al. [56] | Cross-sectional, same survey as Lee et al. [55] | 500 | Taiwan | 2015–2016 | Age range 20–25 | C-SBEQ. CIAS, SPAI | The results previously reported on Lee et al.[55] were mediated by the severity of emotional symptoms |
| Lin et al. [57] | Cross-sectional | 1854 | China | Undisclosed | Mean age 15 (SD = 1.11) |
Cyber victimization was measured using a single question. YIAT |
IUD mediated the relationship between cyber-victimization and psychological and physical symptoms |
| Méndez et al. [58] | Cross-sectional | 810 | Spain | 2019 | Age range 12–16, mean age 13.99 (SD = 1.32) | Psychometric Properties of School Violence Questionnaire-Revised. Questionnaire of Experiences Related to Internet (CERI) | Increased levels of IUD were corelated with higher levels of bullying perpetration of all types, incl. cyberbullying |
| Wachs et al. [59] | Cross-sectional | 1442 | Germany, Netherlands, US | Undisclosed | Age range 12–17, mean age 14.17 (SD = 1.38) | Ad hoc four-item, cyberbullying victimization scale, ad hoc four-item cyber-harassment scale. Internet-related experiences questionnaire | Cyberbullying victimization and IUD were directly and indirectly associated via alexithymia |
| Feijóo et al. [60] | Cross-sectional | 3188 | Spain | 2019 | Age range 12–17, mean age 14.44 (SD = 1.67) |
European Cyberbullying Intervention Project Questionnaire (ECIPQ), Escala de Uso Problemático de Internet en adolescentes |
Probability of IUD increased incrementally from bullied, to bullies and bully-victims |
| Li et al. [61] | Cross-sectional | 2843 | China | 2018 | Age range 12–17, mean age 13.97 (SD = 0.84) | The nine-item Cyberbullying Questionnaire. A short 12-item version of YIAT | Being victimized was associated with IUD and cyberbullying with the association mediated by depression, especially for girls, while the association was also mediated by anxiety in boys |
| Liu et al. [62••] | Two-wave longitudinal design (T1, T2) | 879 | China | T1 2018, T2 8 months later | Age range 12–17, mean age 13.51 (SD = 1.17) |
Revised Cyber Bullying Inventory-Cyberbullying Subscale (RCBPI-CS Adolescents) Problematic Internet Use Scale (APIUS) |
The experience of cyberbullying victimization was positively related to IUD through the mediating variables of mindfulness and depression |
| Machimbarrena et al. [63] | Cross-sectional | 25,341 | Spain | 2019 | Age range 10–18, mean age 14.6 (SD = 1.68 years) | Cyberbullying Triangulation Questionnaire (CTQ). GPIUS2 |
Participants who presented severe IUD are the ones who obtained higher scores in cybervictimization and cyberaggression, particularly in the case of cyberbully victims |
| Samara et al. [64] | Cross-sectional | 1613 | UK | 2016–2017 | Age range 10–16 | Ad hoc scale with items on cyberbullying and cybervictimization. Ad hoc 15-item scale on IUD | There was a significant positive correlation between IUD and substance abuse, which is mediated by traditional bullying, cyber bullying, and victimization |
| Tamarit et al. [65] | Cross-sectional | 1763 | Spain | Undisclosed | Age range 12–16, mean age 14.56 (SD = 1.16) | Thirteen-item Sexting Scale, ten-item Sextortion scale, thirteen-item Grooming Scale. Risk of addiction to social media and the internet for adolescents’ scale (ERA-RSI) | IUD predicts online sexual victimization, while body self-esteem and sexting mediate the relationship |
| Yudes et al. [66] | Cross-sectional | 2039 | Spain | 2018 | Age range 12–18, mean age 14.57 (SD = 1.58) | ECIPQ, YIAT | Cyberbullying perpetration was positively associated with IUD and negatively with emotional intelligence. Emotional intelligence moderated the relation between IUD and cyberbullying perpetration in boys, especially at lower levels |