The Gaming Problem: A Latent Class Analysis of DSM-5 Criteria For Internet Gaming Disorder In A Non-Clinical Sample

Background: We aimed to test whether suggested DSM-5 criteria for Internet Gaming Disorder (IGD) share a similar latent structure to formally recognised addiction. Methods: To do this we used latent class analysis on a dichotomous measure of IGD. The data was collected from a convenient general population sample (500) and a targeted gaming forum sample (236). Results: We found a four or six-class model to be most appropriate, ranging from ‘casual/non-gamer’ to ‘potentially disordered’ with increasing symptom severity. The majority of ‘potentially disordered’ gamers (5+ criteria) were found to be 18-30 years old, and no ‘potentially disordered’ gamers were over 42. Conclusion: The results suggest that gaming may share a similar latent structure to established addictions, with adolescents and young adults being more at risk. Studies replicating these results would be benecial, with further emphasis on a critical evaluation of the criteria and symptom cut-off point.

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 [24] 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 [25] found a veclass model using the Gaming Addiction Scale-Adolescents. Despite this, Deleuze, Nuyens [26] 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 [27] reported that most gamers in the UK during 2019 were young adults (16-24), a signi cant number were older. In fact, 52% aged 25-34 were identi ed 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 bene cial.

Design
Using data collected from a cross-sectional online survey we conducted latent class analysis of DSM-5 criteria for IGD. Data was collected from a sample of adults (18+) to provide evidence towards whether IGD has a similar class structure to established addictions.

Participants
Five-hundred participants from the general population were recruited using convenience sampling through proli c.com in return for £7.50 (US$10.02). There were 244 females, 250 males, and six selected the option 'other'. The average age was 29.67 years (sd = 10.04). A further 236 participants were recruited from online gaming forums (Discord and Reddit). Eighty-two were female, 139 were male, seven selected 'other', and ve did not answer. The average sample age was 25.41 years (sd = 6.52).

Procedure
Participants from the general population completed an IGD checklist (here referred to as IGD-9) that listed the nine DSM-5 symptom criteria as dichotomous yes/no questions. The survey was hosted at Qualtrics.com as part of a preregistered study [28] that gained ethical approval from the Aston University ethics committee. The targeted gamer sample also completed the IGD-9 at Qualtrics.com in a study approved by Aston University.

Statistical Analysis
We conducted latent class analysis on the samples separately using poLCA in R [29] , and then combined samples to examine IGD distribution across non-gamers, casual gamers, and dedicated gamers as a whole.
Following this, we analysed the relationship between age and gaming using regression and descriptive statistics.

Results
Latent class analysis of the separate samples (Additional File 1) suggested a two-, four-, or ve-class model in the general population, and a two-, four-or six-class model in the gaming sample. The lowest Bayesian-Information Criteria (BIC), Akaike Information Criteria (AIC), and Likelihood ratio (LR) indicated different models, suggesting high model uncertainty.
We then compared the distribution of participant responses ( Figure 1) and found left-skewed results for both samples, with a more normal distribution in the gamers. This suggests that a large number of the general population were casual/non-gamers, while most of the gaming sample scored 2-3 checklist items.
Interestingly, participants scoring 5+ were similar in both samples, suggesting an equal share of potential candidates for diagnosis.
We then repeated analysis in the combined sample, testing model t up to six classes since the BIC was consistently larger ( Table 1). The three-and ve-class models failed to reach signi cance, whereas the two-, four-, and six-class models were signi cant. The lowest BIC indicated a four-class model, however the lowest AIC and LR suggested six-classes. We therefore analysed both in more detail ( Table 2).   (1) with low likelihood of symptoms, and 'potentially disordered' class with high likelihood of all symptoms (4/6) was present in both models.
In the four-class model we also found a group who are more likely than not to be preoccupied with gaming and use games to escape (2: 'mild gamer'), and a group who are additionally likely to be unable to stop and have lost interest in other hobbies (3: 'at-risk'). Similarly, the six-class model included class 2 'mild gamers', and 'at-risk' gamers as class 4. In addition, we found class 3 'moderate gamers' who are likely to be preoccupied, gaming to escape, and have withdrawal, and class 5 'borderline' gamers who are likely to be  (Table 3).  Age related to IGD Score (F 1,735 = 68.373, R 2 = .085, p = .000), and accounted for 9% of symptom variation.

Discussion
The criteria for IGD appears to have a four-or six-class structure ranging from 'casual/non-gamers' to 'potentially disordered' with increasing severity, suggesting that IGD may be presenting in a similar manner to established addictions. A four-class model was identi ed in both the combined and separate sample analysis; however, a six-class model may offer more nuance.
We additionally found that most potentially disordered gamers were under 30 years old, and none were over 42. Additionally, mean IGD scores continued to decrease with age, reaching as low as 0.26 in those over 51.
Lemmens, Valkenburg [5] also found that 31-40 year olds scored signi cantly lower than young adults and adolescents, which may suggest that adolescents and young adults are more at risk. Despite this, gaming is a new activity, with the rst home consoles introduced in the 1970s. Contemporary gaming is very different from these simple arcade-style games, and Olson, O'Brien [30] reported that younger adults were more likely to use new technology, speci cally computer/video games than older adults. Since the apparent addictive nature of gaming has only emerged recently it is therefore possible that future studies will nd more potentially disordered gamers among older participants who have had more exposure to 'modern' videogaming from a young age.
In exploring the current DSM-5 symptom criteria, relationship issues were less than 50% likely in all classes except model-six 'potentially disordered' gamers (57%), suggesting it may not be an appropriate criterion. However, without additional information on relationships we cannot test this result. Similarly, lying, and increased involvement were both less than 50% likely for low-moderate classes, but at least 70% likely in 'borderline' or 'potentially disordered' gamers. These may therefore be signs of maladaptive gaming. In contrast, preoccupation and gaming to escape were over 50% likely in all but the 'casual/non-gamer' class and therefore may be facets of gaming generally rather than an indication of potentially disordered use.
Withdrawal symptoms were found to be 100% likely in the 'moderate gamer' and 'potentially disordered' class (six-class), suggesting a group of non-clinical gamers who experience withdrawal. Despite this, Kaptsis, King [31] found the evidence on withdrawal in behavioural addiction was underdeveloped, and symptoms were reported in less than 50 participants across ve studies. They noted that withdrawal in IGD can be mistaken for reactions to imposed deprivation, and many studies did not specify the expected withdrawal symptoms proposed by the DSM-5. Further to this, Orford, Morison [32] reported that emotional withdrawal in gambling did not signi cantly contribute to maintaining the addiction, while Rosenthal and Lesieur [33] found that some abstaining gamblers experienced symptoms which did not correlate with substance abuse withdrawal.
Studies relying on a participant's understanding of withdrawal therefore may not accurately re ect potential symptoms.
Future research into IGD should continue to build evidence on whether gaming is addictive, with an emphasis on critically evaluating the suggested criteria. Additionally, research comparing online and o ine play, and various game types, may help to explain the different ndings between studies. Subtle differences may arise as the social bene ts of online multiplayer are likely to be signi cantly different from local multiplayer.
Similarly, while most online games involve multiplayer competitive elements, o ine gaming is often singleplayer storylines.

Conclusion
This study found that a four-or six-latent class model was most appropriate with classes increasing in severity, suggesting a similar structure to established addiction disorders. However, the current diagnostic cut-off of ve criteria appears to be too inclusive, and relationship issues, preoccupation, and gaming to escape may not be appropriate criteria for diagnosis. This suggests that IGD in the DSM-5 may need revisions following further research, to accurately identify individuals with a potential addiction to gaming.