Excessive internet use in European adolescents: What determines differences in severity?



This study investigated the differences between non-excessive, moderately excessive, and highly excessive internet use among adolescents. These differences were explored in terms of personal characteristics, psychological difficulties, environmental factors, and manner of internet use.


A representative sample was investigated, consisting of 18,709 adolescents aged 11–16 and their parents, from 25 European countries. Excessive internet use was measured using a five item scale covering following factors: salience, conflict, tolerance, withdrawal symptoms, and relapse and reinstatement. The main data analysis utilised multinomial and binary logistic regression models.


The vast majority of respondents reported no signs of excessive internet use. Moderately excessive users (4.4 %) reported higher emotional and behavioural difficulties, but also more sophisticated digital skills and a broader range of online activities. The highly excessive users (1.4 %) differed from the non-excessive and moderately excessive users in their preference for online games and in having more difficulties with self-control.


Adolescents who struggle with attention and self-control and who are inclined toward online gaming may be especially vulnerable to the otherwise uncommon phenomenon of excessive internet use.

This is a preview of subscription content, access via your institution.


  1. American Psychiatric Association (2013) Fifth edition of Statistical and Mental Disorders. American Psychiatric Publishing, Arlington

    Google Scholar 

  2. Beard KW (2011) Working with adolescents addicted to the Internet. In: Young KS, de Abreau CN (eds) Internet addiction: a handbook and guide of evaluation and treatment. Wiley, Hoboken, pp 173–190

    Google Scholar 

  3. Bioulac S, Arfi L, Bouvard MP (2008) Attention deficit/hyperactivity disorder and video games: a comparative study of hyperactive and control children. Eur Psychiatry 23:134–141. doi:10.1016/j.eurpsy.2007.11.002

    Article  PubMed  Google Scholar 

  4. Charlton JP (2002) A factor-analytic investigation of computer ‘addiction’ and engagement. Brit J Psychol 93:329–344. doi:10.1348/000712602760146242

    Article  PubMed  Google Scholar 

  5. Clark LA, Watson D (1995) Constructing validity: basic issues in objective scale development. Psychol Assessment 7:309–319

    Article  Google Scholar 

  6. Cortina JM (1993) What is coefficient alpha? An examination of theory and applications. J Appl Psychol 78:98–104

    Article  Google Scholar 

  7. Dreier M, Wölfling K, Beutel ME (2014) Internetsucht bei Jugendlichen; Internet addiction in youth. Monatsschr Kinderheilkd 162:496–602. doi:10.1007/s00112-013-3069-2

    Article  Google Scholar 

  8. Durkee T, Kaess M, Carli V, Parzer P, Wasserman C, Floderus B, Apter A, Balazs J, Barzilay S, Bobes J, Brunner R, Corcoran P, Cosman D, Cottler P, Despalins R, Graber N, Guillemin F, Harinq C, Kahn JP, Mandelli L, Marusic D, Mészáros G, Musa GJ, Postuvan V, Resch F, Saiz PA, Sisask M, Varnik A, Sarchiapone M, Hoven CW, Wasserman D (2012) Prevalence of pathological internet use among adolescents in Europe: demographic and social factors. Addiction 107:2210–2222. doi:10.1111/j.1360-0443.2012.03946.x

    Article  PubMed  Google Scholar 

  9. Geurro VM, Johnson RA (1982) Use of the Box-Cox transformation with binary response models. Biometrika 69:309–314

    Article  Google Scholar 

  10. Griffiths MD (2005) A ‘components’ model of addiction within a biopsychosocial framework. J Subst Use 10:191–197. doi:10.1080/14659890500114359

    Article  Google Scholar 

  11. Holstein BE, Pedersen TP, Bendtsen P, Madsen KR, Meilstrup CR, Nielsen L, Rasmussen M (2014) Perceived problems with computer gaming and internet use among adolescents: measurement tool for non-clinical survey studies. BMC Public Health 14:361. doi:10.1186/1471-2458-14-361

    Article  PubMed Central  PubMed  Google Scholar 

  12. Johansson A, Götestam KG (2004) Internet addiction: characteristics of a questionnaire and prevalence in Norwegian youth (12–18 years). Scand J Psychol 45:223–229. doi:10.1111/j.1467-9450.2004.00398.x

    Article  PubMed  Google Scholar 

  13. Kalmus V, Siibak A, Blinka L (2014) Internet and child well-being. In: Ben-Arieh A, Casas F, Frønes I, Korbin JE (eds) Handbook of child well-being: theories, methods and policies in global perspective. Springer, Dordrecht, pp 2093–2133

    Google Scholar 

  14. Kalmus V, Blinka L, Ólafsson K (in press) Does it matter what mama says: evaluating the role of parental mediation in European adolescents’ excessive Internet use. Child Soc. doi:10.1111/chso.12020

  15. King DL, Haagsma MC, Delfabbro PH, Gradisar M, Griffiths MD (2013) Toward a consensus definition of pathological video-gaming: a systematic review of psychometric assessment tools. Clin Psychol Rev 33:331–342. doi:10.1016/j.cpr.2013.01.002

    Article  PubMed  Google Scholar 

  16. Ko CH, Yen JY, Yen CF, Lin HC, Yang MJ (2007) Factors predictive for incidence and remission of Internet addiction in young adolescents: a prospective study. Cyberpsychol Behav 10:545–551. doi:10.1089/cpb.2007.9992

    Article  PubMed  Google Scholar 

  17. Ko CH, Yen JY, Yen CF, Chen CS, Chen CC (2012) The association between Internet addiction and psychiatric disorder: a review of the literature. Eur Psychiat 27:1–8. doi:10.1016/j.eurpsy.2010.04.011

    Article  CAS  Google Scholar 

  18. Kuss D, Griffiths MD (2011) Addiction Social networking and Addiction–a review of the psychological literature. Int J Environ Res Public Health 8:3528–3552. doi:10.3390/ijerph8093528

    Article  PubMed Central  PubMed  Google Scholar 

  19. Livingstone S, Haddon L, Görzig A, Ólafsson K (2011) Technical Report and User Guide: The 2010 EU Kids Online Survey.LSE, London: EU Kids Online. Retrieved October 14, 2013, from http://eprints.lse.ac.uk/45270/1/__Libfile_repository_Content_Livingstone%2C%20S_Technical%20Report%20and%20User%20Guide%20EU%20Kids%20Online%28author%29.pdf

  20. Menard S (1995) Applied logistic regression analysis. Sage, Thousand Oaks

    Google Scholar 

  21. Myers R (1990) Classical and modern regressions with applications, 2nd edn. Duxburyvan, Boston

    Google Scholar 

  22. Nuutinen T, Roos E, Ray C, Villberg J, Välimaa R, Rasmussen M, Holstein B, Godeau E, Beck F, Léger D, Tynjälä J (2014) Computer use, sleep duration and health symptoms: a cross-sectional study of 15-years olds in three countries. Int J Public Health 59:619–628. doi:10.1007/s00038-014-0561-y

    Article  PubMed  Google Scholar 

  23. Paus-Hasenbrink I, Ponte C, Dürager A, Bauwens J (2012) Understanding digital inequality: the interplay between parental socialisation and children’s development. In: Livingstone S, Haddon L, Görzig A (eds) Children, risk and safety on the internet: research and policy challenges in comparative perspective. The Policy Press, Bristol, pp 257–272

    Google Scholar 

  24. Reinecke L, Vorderer P (2013) Well-being and media use. In Donsbach W (ed) The International Encyclopedia of Communication. Wiley. Retrieved October 14, 2013, from http://www.communicationencyclopedia.com/public/

  25. Schwarzer R, Jerusalem M (1995) Generalized Self-Efficacy scale. In: Weinman J, Wright S, Johnston M (eds) Measures in health psychology: a user’s portfolio. Causal and control beliefs, NFER-NELSON, Windsor, pp 35–37

    Google Scholar 

  26. Sonck N, Kuiper E, de Haan J (2012) Digital skills in the context of media literacy. In: Livingstone S, Haddon L, Görzig A (eds) Children, risk and safety on the internet: research and policy challenges in comparative perspective. The Policy Press, Bristol, pp 87–98

    Google Scholar 

  27. Spada MM (2014) An overview of problematic Internet use. Addict Behav 39:3–6. doi:10.1016/j.addbeh.2013.09.007

    Article  PubMed  Google Scholar 

  28. Tsitsika, A et al (2013) Internet use and internet addictive behaviour among European adolescents: A cross-sectional study. National and Kapodistrian University of Athens (NKUA), Athens: EU NET ADB. Retrieved from www.eunetadb.eu

  29. van de Vijver FJR, Leung K (2011) Equivalence and bias: a review of concepts, models, and data analytic procedures. In: Matsumoto D, van de Vijver FJR (eds) Cross-cultural Research Methods in Psychology. Cambridge University Press, New York, pp 17–45

    Google Scholar 

  30. van den Eijnden RJ, Meerkerk GJ, Vermulst AA, Spijkerman R, Engels RC (2008) Online communication, compulsive Internet use, and psychological well-being among adolescents: a longitudinal study. Dev Psychol 44:655–665. doi:10.1037/0012-1649.44.3.655

    Article  PubMed  Google Scholar 

  31. Weinstein A, Lejoyeux M (2010) Internet Addiction or Excessive Internet Use. Am J Drug Alcohol Ab 36:277–283. doi:10.3109/00952990.2010.491880

    Article  Google Scholar 

Download references


This article draws on the work of the EU Kids Online network funded by the European Commission (DG Information Society) Safer Internet plus Programme (project code SIP-KEP-321803); see www.eukidsonline.net. The authors also acknowledge the support of the VITOVIN project (CZ.1.07/2.3.00/20.0184), which is co-financed by the European Social Fund and the state budget of Czech Republic, and also the project of Czech Science Foundation (P407/12/1831) and Estonian Research Council (ETF8527).

Author information



Corresponding author

Correspondence to Lukas Blinka.

Additional information

This article is part of the special issue “Communication Technology, Media Use and the Health of Our Kids”.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blinka, L., Škařupová, K., Ševčíková, A. et al. Excessive internet use in European adolescents: What determines differences in severity?. Int J Public Health 60, 249–256 (2015). https://doi.org/10.1007/s00038-014-0635-x

Download citation


  • Adolescents
  • Excessive internet use
  • Internet addiction