Gaming Disorder (GD) was recently recognised by the World Health Organization as a behavioural addiction in the eleventh edition of the International Classification of Diseases (ICD-11), while the apparently synonymous Internet Gaming Disorder (IGD) is not recognised diagnostically, but was included in the Diagnostic and Statistical Manual (DSM-5) to foster research in the area .
A comparison of both systems in Mexico found that prevalence estimates of the DSM were almost twice as high as the ICD . Similarly, Jo, Bhang found that while all ICD-11 cases were found by the DSM-5, not all DSM-5 cases were found by the ICD-11. This could suggest that the current DSM-5 criteria are too inclusive, or that the ICD-11 criteria are not sensitive enough. We have focused this study on the DSM-5 criteria, since evidence has shown the measure to have robust psychometric properties . In addition, Aarseth, Bean highlighted a number of concerns with the inclusion of GD in the ICD-11.
Previous studies on gaming have been inconsistent in classification, and results on prevalence, course, treatment, and biomarkers have been inconclusive . Many researchers believe that gaming can become problematic [8, 9], while some are cautious  and do not regard IGD as a genuine behavioural addiction. Some of the concerns highlighted by  around gaming in the ICD-11 are relevant to the IGD, and these suggest that the introduction of gaming in any diagnostic manual is premature. In fact, Przybylski and Weinstein suggested that disordered gaming may actually be a symptom of a different underlying issue.
Latent class analyses help researchers to determine the number and type of classes a potential disorder may be split into, however the results are generally a function of the sample characteristics, and so may not be representative of ‘definite’ classes. Despite this, we can examine the classes found across several studies and see that research on problem gambling typically reports a three- [12-14] or four-class pattern [15, 16], with increasing severity between classes. Similarly, substance use has been found to fit a three-class [17-20], or four-class model [21, 22], categorised by severity. Interestingly, Deleuze, Rochat investigated both behavioural and substance addiction and found three theoretical subgroups. These included addiction-prone individuals, at-risk users, and not-prone individuals. They noted that although only a small sample of participants reported gaming, it was associated with loss of control and negative outcomes over half of the time.
Previous research into IGD has found a similar three-class model [5, 24], with Peeters, Koning suggesting that the DSM-5 criteria could be helpful in identifying what they called ‘problematic’ gamers. However, they note that a strict cut-off point could lead to false positives. In contrast, Myrseth and Notelaers found a five-class model using the Gaming Addiction Scale-Adolescents. Despite this, Deleuze, Nuyens determined in their study that a two-class system was more able to distinguish between ‘problematic’ and ‘regular’ gamers. This dichotomous outcome hints at gaming being different to established addiction disorders and suggests a need for more research into how gaming compares to formally recognised addictions.
The listed studies either used a small sample, did not include adults, or used non-DSM criteria. Although Clement reported that most gamers in the UK during 2019 were young adults (16-24), a significant number were older. In fact, 52% aged 25-34 were identified as gamers, 36% aged 35-44, and 40% aged 45-54. This would suggest that including a range of ages in gaming analysis could be beneficial.