

Online Video game Addiction:

Identification of Addicted Adolescent Gamers









Studies have consistently demonstrated the existence of a small subgroup of video gamers that is

seemingly ‘addicted’ to games (Gentile, 2009; Grüsser et al., 2007; Lemmens, Valkenburg, & Peter, 2009).

Although video game addiction is not a new phenomenon (Keepers, 1990), the introduction of an online

component in the current generation of games has probably increased the size and scope of the problem.

This online component in gaming led to the initiation of (private and public) treatment programs targeting

gaming addiction (Lin-Liu, 2006; Sharples, 2009; Telegraph (UK), 2009). Consequently, there is increasing

focus on online games when studying video game addiction (Hussain & Griffiths, 2009a; Peters & Malesky,

2008; Van den Eijnden et al., 2010; Wood, 2008b).

Both Korean and Western researchers specifically report that Massive Multiplayer Online Role Playing

Games (MMORPGs) are the main culprit in cases of online video game addiction (Chappell et al., 2006;

Council on Science and Public Health, 2007; M. S Lee et al., 2007). In a MMORPG the player develops one

or more characters (avatars) over time in a persistent virtual world. Examples include World of Warcraft,

Age of Conan, and Runescape. Typically, higher levels require players to cooperate to achieve goals.

Moreover, MMORPGs can not be completed: due to the regular introduction of new content it is practically

impossible to finish all assignments. This places a considerable burden on the players’ time as they are

required to continue playing to ‘keep up’ with the game. Research among a sample of World of Warcraft

players identified a group of 10% that played an average of 63 hours per week and showed considerable

negative symptoms (Longman et al., 2009). Grüsser et al. sampled readers of an online gaming magazine

in an online survey and found that 12% of those gamers fulfilled diagnostic criteria of addiction concerning

their gaming behavior (Grüsser et al., 2007).

These findings demonstrate the existence of a small subgroup of online gamers that can potentially be

classified as ‘online video game addicts’. This group is likely to have various psychological and social

problems, as game overuse can be severely disruptive to school, work, and ‘real-life’ social contacts

(Grüsser et al., 2007; Chappell et al., 2006; Wan & Chiou, 2006). Drawing parallels to the internet addiction

literature, we hypothesize that this ‘flight from reality’ may be associated with negative self-esteem,

depressive mood, social anxiety, and/or loneliness (Caplan, 2007, 2003; Ha et al., 2007; H. K. Kim & K. E.

Davis, 2009). However, the relationship between psychosocial health and online games is potentially more

complicated, as social and psychological benefits from playing online games have also been reported

(Longman et al., 2009; Lim & Roselyn Lee, 2009; C. C. Wang & C. H. Wang, 2008). Moreover, effects might

differ based on the psychological profile of the gamer, i.e. there may be a group of addicted heavy gamers

that suffer as a result of their unbalanced lifestyle, and another group of heavy gamers that benefit from

having multiple social environments. Given the former, and the fact that the vast majority of gamers does

not report addictive tendencies (Gentile, 2009), we hypothesize that a second group of heavy gamers is

likely to exist. These non-addicted heavy gamers will probably not show negative psychosocial outcomes

or addictive symptoms, or perhaps to a lesser extent.





Unfortunately, there is no consensus on an operational definition of video game addiction (Wood, 2008b,

2008a; Blaszczynski, 2008; N. E. Turner, 2008). Despite the ongoing debate on diagnosis and definition,

several methods are used to increase our understanding of game addiction. Researchers construct new

scales to measure game addiction (Gentile, 2009; Lemmens et al., 2009), avoid using standardized scales

altogether (Grüsser et al., 2007), or approach the specific group of online games indirectly through more

established measures of internet addiction (C. H. Ko et al., 2009; Van den Eijnden et al., 2010). Estimates

of the size of the group of ‘addicted gamers’ are subsequently made by applying various cut-off points to

scales measuring symptoms of video game addiction or internet addiction (Gentile, 2009; Lemmens et al.,

2009; Griffiths & Hunt, 1998). This results in a wide variety of estimates, depending on the selected cut-off

points and composition of the sample. In the absence of consensus on a definition, the absence of a gold

standard with which to compare results, and the lack of clinical studies using these instruments, these

efforts are speculative at best.

The present study contributes to the debate on video game addiction by applying a different approach.

It seeks to provide empirical, data-driven evidence for the assumed subgroup of addicted online video

gamers, using two large-scale samples from the Dutch ‘Monitor Study Internet and Youth’. Results provide

a basis for data-based scale validation and cut-off scores. Identification of this group will be done through

a combination of two indirect measures: game addiction severity and time spent on online gaming.

In the present study, internet addiction is thought to be an appropriate measure of online game addiction

severity for several reasons. Firstly, previous work by our group (utilizing an earlier Monitor Study sample),

established cross-sectional and longitudinal relationships between online gaming and internet addiction,

referred to as Compulsive Internet Use (CIU) (Van den Eijnden et al., 2010). Secondly, the latter study

found low correlations between various internet activities and online video gaming among adolescents

(Van Rooij et al., 2010), in line with its immersive nature (Yee, 2006), thus confirming that online gaming is

a monolithic activity for adolescents (these findings were replicated for the samples utilized in the present

study). In combination with the inclusion of a measure of time spent on online gaming, this reduces the

risk of misidentification (i.e. erroneously measuring addiction to various other applications). Consequently,

the combination of a high score on CIU with many hours of online gaming per week is hypothesized to

identify addicted online gamers. Note that we choose to utilize the term ‘addiction’ for the sake of consistency

with other studies: the group is more precisely defined as heavy online gamers that score high on criteria

for non-substance addiction. These criteria are theorized to be applicable to online behavior (Gentile,

2009; Lemmens et al., 2009), also: see Measures (CIUS).

From this, several research questions emerge. Can the two hypothesized groups of heavy online gamers

(addicted and non-addicted) be identified using a data-driven approach? If so, how large are these groups?

Finally, the present study explores the psychosocial correlates for the addicted versus the non addicted