When addiction symptoms and life problems diverge: a latent class analysis of problematic gaming in a representative multinational sample of European adolescents

Abstract

The proposed diagnosis of Internet gaming disorder (IGD) in DSM-5 has been criticized for “borrowing” criteria related to substance addiction, as this might result in misclassifying highly involved gamers as having a disorder. In this paper, we took a person-centered statistical approach to group adolescent gamers by levels of addiction-related symptoms and gaming-related problems, compared these groups to traditional scale scores for IGD, and checked how groups were related to psychosocial well-being using a preregistered analysis plan. We performed latent class analysis and regression with items from IGD and psychosocial well-being scales in a representative sample of 7865 adolescent European gamers. Symptoms and problems matched in only two groups: an IGD class (2.2%) having a high level of symptoms and problems and a Normative class (63.5%) having low levels of symptoms and problems. We also identified two classes comprising 30.9% of our sample that would be misclassified based on their report of gaming-related problems: an Engaged class (7.3%) that seemed to correspond to the engaged gamers described in previous literature, and a Concerned class (23.6%) reporting few symptoms but moderate to high levels of problems. Our findings suggest that a reformulation of IGD is needed. Treating Engaged gamers as having IGD when their poor well-being might not be gaming related may delay appropriate treatment, while Concerned gamers may need help to reduce gaming but would not be identified as such. Additional work to describe the phenomenology of these two groups would help refine diagnosis, prevention and treatment for IGD.

This is a preview of subscription content, log in to check access.

Fig. 1

References

  1. 1.

    American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. American Psychiatric Association, Arlington

    Google Scholar 

  2. 2.

    Pontes HM, Griffiths MD (2015) Measuring DSM-5 internet gaming disorder: development and validation of a short psychometric scale. Comput Hum Behav 45:137–143

    Article  Google Scholar 

  3. 3.

    Pontes HM, Kiraly O, Demetrovics Z, Griffiths MD (2014) The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: the development of the IGD-20 Test. PloS One 9. https://doi.org/10.1371/journal.pone.0110137

  4. 4.

    Lemmens JS, Valkenburg PM, Gentile DA (2015) The Internet gaming disorder scale. Psychol Assess. https://doi.org/10.1037/pas0000062

    PubMed  Google Scholar 

  5. 5.

    Rehbein F, Kliem S, Baier D et al (2015) Prevalence of Internet Gaming Disorder in German adolescents: diagnostic contribution of the nine DSM-5 criteria in a statewide representative sample. Addict Abingdon Engl. https://doi.org/10.1111/add.12849

    Google Scholar 

  6. 6.

    Petry NM, Rehbein F, Gentile DA et al (2014) An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addict Abingdon Engl. https://doi.org/10.1111/add.12457

    Google Scholar 

  7. 7.

    Griffiths MD, Van Rooij AJ, Kardefelt-Winther D et al (2016) Working towards an international consensus on criteria for assessing internet gaming disorder: a critical commentary on Petry et al. (2014). Addict Abingdon Engl 111:167–175. https://doi.org/10.1111/add.13057

    Article  Google Scholar 

  8. 8.

    Kardefelt-Winther D (2016) Conceptualizing Internet use disorders: addiction or coping process? Psychiatry Clin Neurosci. https://doi.org/10.1111/pcn.12413

    PubMed  Google Scholar 

  9. 9.

    Kardefelt-Winther D (2017) Making the case for hypothesis-driven theory testing in the study of Internet Gaming Disorder. Addict Behav 64:234–237. https://doi.org/10.1016/j.addbeh.2015.09.012

    Article  PubMed  Google Scholar 

  10. 10.

    Charlton JP, Danforth IDW (2007) Distinguishing addiction and high engagement in the context of online game playing. Comput Hum Behav 23:1531–1548

    Article  Google Scholar 

  11. 11.

    Kardefelt-Winther D, Heeren A, Schimmenti A et al (2017) How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction 112(10):1709–1715. https://doi.org/10.1111/add.13763

    Article  PubMed  Google Scholar 

  12. 12.

    Van Rooij AJ, Prause N (2014) A critical review of “Internet addiction” criteria with suggestions for the future. J Behav Addict 3:203–213. https://doi.org/10.1556/JBA.3.2014.4.1

    Article  PubMed  Google Scholar 

  13. 13.

    Billieux J, Schimmenti A, Khazaal Y et al (2015) Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. J Behav Addict 4:119–123. https://doi.org/10.1556/2006.4.2015.009

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Wakefield JC (2015) DSM-5 substance use disorder: how conceptual missteps weakened the foundations of the addictive disorders field. Acta Psychiatr Scand 132:327–334. https://doi.org/10.1111/acps.12446

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Wood RTA (2008) Problems with the concept of video game “addiction”: some case study examples. Int J Ment Health Addict 6:169–178

    Article  Google Scholar 

  16. 16.

    Kardefelt-Winther D, Heeren A, Schimmenti A, et al (2016) How can we conceptualize behavioral addictions without pathologizing common behaviors? Addiction in press

  17. 17.

    Cuthbert BN (2014) The RDoC framework: facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry 13:28–35. https://doi.org/10.1002/wps.20087

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Eaton W, Mojtabai R, Stuart EA et al (2012) Assessment of distress, disorder, impairment, and need in the population. In: Eaton W (ed) Public Mental Health, 1st edn. Oxford University Press, USA

    Google Scholar 

  19. 19.

    Kessler RC, Merikangas KR, Berglund P et al (2003) Mild disorders should not be eliminated from the DSM-V. Arch Gen Psychiatry 60:1117–1122. https://doi.org/10.1001/archpsyc.60.11.1117

    Article  PubMed  Google Scholar 

  20. 20.

    Regier DA, Narrow WE, Rae DS (2004) For DSM-V, it’s the “disorder threshold,” stupid. Arch Gen Psychiatry 61:1051. https://doi.org/10.1001/archpsyc.61.10.1051-a (author reply 1051–1052)

  21. 21.

    Kessler RC, Merikangas KR, Berglund P et al (2004) For DSM-V, it’s the “disorder threshold”, stupid—reply. Arch Gen Psychiatry 61:1051–1052. https://doi.org/10.1001/archpsyc.61.10.1051-b

    Article  Google Scholar 

  22. 22.

    Üstün B, Kennedy C (2009) What is “functional impairment”? Disentangling disability from clinical significance. World Psychiatry 8:82–85. https://doi.org/10.1002/j.2051-5545.2009.tb00219.x

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Ferguson CJ, Coulson M, Barnett J (2011) A meta-analysis of pathological gaming prevalence and comorbidity with mental health, academic and social problems. J Psychiatr Res 45:1573–1578. https://doi.org/10.1016/j.jpsychires.2011.09.005

    Article  PubMed  Google Scholar 

  24. 24.

    King DL, Delfabbro PH (2014) Is preoccupation an oversimplification? A call to examine cognitive factors underlying internet gaming disorder. Addict Abingdon Engl 109:1566–1567. https://doi.org/10.1111/add.12547

    Article  Google Scholar 

  25. 25.

    Kardefelt Winther D (2014) A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Hum Behav 31:351–354. https://doi.org/10.1016/j.chb.2013.10.059

    Article  Google Scholar 

  26. 26.

    Kardefelt Winther D (2015) A critical account of DSM-5 criteria for Internet gaming disorder. Addict Res Theory 23:93–98. https://doi.org/10.3109/16066359.2014.935350

    Article  Google Scholar 

  27. 27.

    Paul CA (2011) Optimizing play: how theorycraft changes gameplay and design. Game Stud 11. http://gamestudies.org/1102/about

  28. 28.

    Colder Carras M (2016) Fostering Rationality in Games and Health Research: theorycrafting and the phenomenology of psychiatric disorder. In: Foster. Ration. Games Health Res. http://froghrblog.blogspot.com/2016/02/theorycrafting-and-phenomenology-of.html. Accessed 5 Jul 2016

  29. 29.

    Müller KW, Janikian M, Dreier M et al (2014) Regular gaming behavior and internet gaming disorder in European adolescents: results from a cross-national representative survey of prevalence, predictors, and psychopathological correlates. Eur Child Adolesc Psychiatry. https://doi.org/10.1007/s00787-014-0611-2

    Google Scholar 

  30. 30.

    Tsitsika A, Janikian M, Schoenmakers TM et al (2014) Internet addictive behavior in adolescence: a cross-sectional study in seven European countries. Cyberpsychol Behav Soc Netw 17:528–535. https://doi.org/10.1089/cyber.2013.0382

    Article  PubMed  Google Scholar 

  31. 31.

    Wölfling K, Müller KW, Beutel M (2011) Reliability and validity of the Scale for the Assessment of Pathological Computer-Gaming (CSV-S). Psychother Psychosom Med Psychol 61:216–224. https://doi.org/10.1055/s-0030-1263145

    Article  PubMed  Google Scholar 

  32. 32.

    Müller KW, Beutel ME, Wölfling K (2014) A contribution to the clinical characterization of Internet addiction in a sample of treatment seekers: validity of assessment, severity of psychopathology and type of co-morbidity. Compr Psychiatry 55:770–777. https://doi.org/10.1016/j.comppsych.2014.01.010

    Article  PubMed  Google Scholar 

  33. 33.

    Achenbach T (1991) Manual for the Youth Self-Report and 1991 Profile. University of Vermont, Department of Psychiatry., Burlington, VT

  34. 34.

    Achenbach TM, Becker A, Döpfner M et al (2008) Multicultural assessment of child and adolescent psychopathology with ASEBA and SDQ instruments: research findings, applications, and future directions. J Child Psychol Psychiatry 49:251–275. https://doi.org/10.1111/j.1469-7610.2007.01867.x

    Article  PubMed  Google Scholar 

  35. 35.

    Young KS (1998) Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1:237–244. https://doi.org/10.1089/cpb.1998.1.237

    Article  Google Scholar 

  36. 36.

    Asparouhov T, Muthén B (2014) Auxiliary variables in mixture modeling: using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model (Mplus Web Notes No. 21, version 2). Retrieved from https://www.statmodel.com/download/asparouhov_muthen_2014.pdf

  37. 37.

    StataCorp (2013) Stata statistical software: Release 13. StataCorp LP, College Station

    Google Scholar 

  38. 38.

    Muthén BO, Muthén LK (1998) Mplus. version 7.3

  39. 39.

    Kraemer HC (2011) Moderators and mediators: towards the genetic and environmental bases of psychiatric disorders. In: Tsuang MT, Tohen M, Jones PB (eds) Textbook in psychiatric epidemiology, 3rd edn. Wiley, West Sussex, pp 87–97

    Google Scholar 

  40. 40.

    Siontis GCM, Ioannidis JPA (2011) Risk factors and interventions with statistically significant tiny effects. Int J Epidemiol 40:1292–1307. https://doi.org/10.1093/ije/dyr099

    Article  PubMed  Google Scholar 

  41. 41.

    Ferguson CJ (2009) An effect size primer: a guide for clinicians and researchers. Prof Psychol Res Pract 40:532

    Article  Google Scholar 

  42. 42.

    Lewis G, Tsuang MT, Tohen M, Jones PB (2011) Introduction to epidemiologic research methods. In: Tsuang MT, Tohen M, Jones PB (eds) Textbook in psychiatric epidemiology, 3rd edn. Wiley, West Sussex, United Kingdom, pp 1–7

    Google Scholar 

  43. 43.

    Olivier J, May WL, Bell ML (2017) Relative effect sizes for measures of risk. Commun StatTheory Methods 46:6774–6781. https://doi.org/10.1080/03610926.2015.1134575

    Article  Google Scholar 

  44. 44.

    Tsitsika A, Tzavela EC, Schoenmakers TM et al (2013) Internet use and internet addictive behaviour among European adolescents: a cross-sectional study. EU NET ADB. http://youth-health.gr/media/2016/03/eu-net-adb-quantitative-report-d6-2-r-june-2013_2.pdf

  45. 45.

    Faulkner G, Irving H, Adlaf EM, Turner N (2015) Subtypes of adolescent video gamers: a latent class analysis. Int J Ment Health Addict 13(1):1–18

    Article  Google Scholar 

  46. 46.

    Van Rooij AJ, Schoenmakers TM, Vermulst AA et al (2011) Online video game addiction: identification of addicted adolescent gamers. Addict Abingdon Engl 106:205–212. https://doi.org/10.1111/j.1360-0443.2010.03104.x

    Article  Google Scholar 

  47. 47.

    Ahmadi J, Amiri A, Ghanizadeh A et al (2014) Prevalence of addiction to the internet, computer games, DVD, and video and its relationship to anxiety and depression in a sample of iranian high school students. Iran J Psychiatry Behav Sci 8(2):75–80

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Desai RA, Krishnan-Sarin S, Cavallo D, Potenza MN (2010) Video-gaming among high school students: health correlates, gender differences, and problematic gaming. Pediatrics 126:e1414–1424. https://doi.org/10.1542/peds.2009-2706

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Jeong EJ, Kim DH (2011) Social activities, self-efficacy, game attitudes, and game addiction. Cyberpsychol Behav Soc Netw 14:213–221. https://doi.org/10.1089/cyber.2009.0289

    Article  PubMed  Google Scholar 

  50. 50.

    Johansson A, Gotestam KG (2004) Problems with computer games without monetary reward: similarity to pathological gambling. Psychol Rep 95:641–650. https://doi.org/10.2466/pr0.95.2.641-650

    Article  PubMed  Google Scholar 

  51. 51.

    Colder Carras M, Van Rooij AJ, Van de Mheen D et al (2017) Video gaming in a hyperconnected world: a cross-sectional study of heavy gaming, problematic gaming symptoms, and online socializing in adolescents. Comput Hum Behav 68:472–479. https://doi.org/10.1016/j.chb.2016.11.060

    Article  Google Scholar 

  52. 52.

    Kowert R, Oldmeadow JA (2015) Playing for social comfort: online video game play as a social accommodator for the insecurely attached. Comput Hum Behav 53:556–566. https://doi.org/10.1016/j.chb.2014.05.004

    Article  Google Scholar 

  53. 53.

    Tzavela EC, Karakitsou C, Dreier M et al (2015) Processes discriminating adaptive and maladaptive internet use among European adolescents highly engaged online. J Adolesc 40:34–47. https://doi.org/10.1016/j.adolescence.2014.12.003

    Article  PubMed  Google Scholar 

  54. 54.

    Colder Carras M, Labrique A, Foster AM, Lange A, Carras M (2016) Crowdsourcing phenomenology for internet gaming disorder. Presented at the American Psychopathology Association, New York, NY

  55. 55.

    Aarseth E, Bean AM, Boonen H et al (2016) Scholars’ open debate paper on the World Health Organization ICD-11 Gaming Disorder proposal. J Behav Addict 6:267–270. https://doi.org/10.1556/2006.5.2016.088

    Article  PubMed  Google Scholar 

  56. 56.

    Scharkow M, Festl R, Quandt T (2014) Longitudinal patterns of problematic computer game use among adolescents and adults-a 2-year panel study. Addict Abingdon Engl. https://doi.org/10.1111/add.12662

    Google Scholar 

  57. 57.

    Mößle T, Rehbein F (2013) Predictors of problematic video game usage in childhood and adolescence. Sucht Z Für Wiss Prax 59:153–164

    Article  Google Scholar 

  58. 58.

    Colder Carras M, Porter AM, van Rooij AJ et al (2017) Gamers’ insights into the phenomenology of normal gaming and game “addiction”: a mixed methods study. Comput Hum Behav. https://doi.org/10.1016/j.chb.2017.10.029

    Google Scholar 

  59. 59.

    Mihara S, Higuchi S (2017) Cross-sectional and longitudinal epidemiological studies of internet gaming disorder: a systematic review of the literature. Psychiatry Clin Neurosci 71:425–444. https://doi.org/10.1111/pcn.12532

    Article  PubMed  Google Scholar 

  60. 60.

    Keyes KM, Galea S (2017) Commentary: the limits of risk factors revisited: is it time for a causal architecture approach? Epidemiol Camb Mass 28:1–5. https://doi.org/10.1097/EDE.0000000000000578

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the EU NET ADB study for sharing data. This research was supported by the National Institute of Mental Health Training Grant 5T32MH014592-39.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Michelle Colder Carras.

Ethics declarations

Conflict of interest

On behalf of both authors, the corresponding author states that there is no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 1412 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Colder Carras, M., Kardefelt-Winther, D. When addiction symptoms and life problems diverge: a latent class analysis of problematic gaming in a representative multinational sample of European adolescents. Eur Child Adolesc Psychiatry 27, 513–525 (2018). https://doi.org/10.1007/s00787-018-1108-1

Download citation

Keywords

  • Internet gaming disorder
  • Video games
  • Problematic gaming
  • Hazardous gaming
  • Adolescence