Antonius van Rooij (IVO Addiction Research Institute), Daria Kuss (Briminghan City University), Mark Griffiths (Nottingham Trent University), Gillian Shorter (University of Ulster), Tim Schoenmakers (IVO Addiction Research Institute) and Dike van de Mheen (IVO Addiction Research Institute) have published an article in the Journal of Behavioral Addictions regarding the occurrence of problematic video game use and substance use among Dutch adolescents.
Aims: The current study explored the nature of problematic (addictive) video gaming (PVG) and the association with game type, psychosocial health, and substance use. Methods: Data were collected using a paper and pencil survey in the classroom setting. Three samples were aggregated to achieve a total sample of 8478 unique adolescents. Scales included measures of game use, game type, the Video game Addiction Test (VAT), depressive mood, negative self-esteem, loneliness, social anxiety, education performance, and use of cannabis, alcohol and nicotine (smoking). Results: Findings confirmed problematic gaming is most common amongst adolescent gamers who play multiplayer online games. Boys (60%) were more likely to play online games than girls (14%) and problematic gamers were more likely to be boys (5%) than girls (1%). High problematic gamers showed higher scores on depressive mood, loneliness, social anxiety, negative self-esteem, and self-reported lower school performance. Nicotine, alcohol, and cannabis using boys were almost twice more likely to report high PVG than non-users. Conclusions: It appears that online gaming in general is not necessarily associated with problems. However, problematic gamers do seem to play online games more often, and a small subgroup of gamers – specifically boys – showed lower psychosocial functioning and lower grades. Moreover, associations with alcohol, nicotine, and cannabis use are found. It would appear that problematic gaming is an undesirable problem for a small subgroup of gamers. The findings encourage further exploration of the role of psychoactive substance use in problematic gaming.
I should start writing my end of year blogging report, I’ve collected data regarding the type of videogames studies published this year.
I use ‘video game addiction’ as a general term for other terms used in the literature, such as compulsive, excessive, and problematic, and ‘addiction’ was self-explanatory to the public, but the area experts would disagree due to important details. Clinical validity and whether there is a necessity for a diagnosis of videogame addiction is still undetermined and requires additional research data. Much of the data come from surveys and each survey may differ according to which questionnaires used to operationalize and define addiction. These surveys suggest that the prevalence of videogame addiction range from 0.6% to 11.9% with 3.1% being the probable accurate estimate.
The authors, therefore, opted to refer their current research towards ‘problematic video gaming’ as it is geared towards surveying adolescents. The author defined problematic video gaming as addictive-like behavior that include experience a loss of control over the behavior, conflicts with the self and with others, preoccupation with gaming, utilization of games for purposes of coping/mood modification, and withdrawal symptoms.
The authors observed some interesting findings from past studies. Problematic video gaming seemed to be related more often with online gaming, associated with psychosocial problems, such as depression, loneliness, social anxiety among others, and poor school performance. The authors reasoned that problematic video gaming can be viewed as a risky behavior as similar to gambling being a risky behavior. This riskiness can lead to overlapping with other addictions, a co-occurrence of problematic behaviors or comorbidity. The authors suggest that problematic video gaming might be associated with co-occurring substance use, such as alcohol, cannabis and smoking. Thus, the authors included these interesting findings into their surveys.
Participants: 8478 Dutch adolescents who were surveyed for a larger and ongoing national survey. These adolescents were surveyed at their schools and their schools were randomly selected. The dataset is aggregated from three years worth of survey data, from 2009 to 2011. The authors specifically examined the data from a cross-sectional manner, this means that adolescents who were surveyed again in the surveys, their ‘second-time’ data were removed from the data analysis. they could have examined it from a longitudinal manner, but I don’t know reasons.
Videogame use: Respondents were asked how much time they spent on three types of videogames: multiplayer, offline and causal/browser games. The time spent on gaming is based on two items, days spent per week and average number of hours spent per day. For their data analysis, they categorized respondents into users vs. non-users for each type of games.
Video game Addiction Test: 14-items answered on a 5-point scale. Example items include: “how often do you find it difficult to stop gaming?” For their data analysis, they categorized respondents into the low group where the average rating is from 0 to 2 and the ‘high’ group where the average rating is 3 to 4.
Psychoactive substance use: Respondents reported how often consumed alcohol, cigarettes and cannabis during the weekdays or weekends in the past month. For their data analysis, they categorized respondents into use vs. non-use.
Self-esteem: The Rosenberg self-esteem scale, 10 items answered on a 4-point scale.
Loneliness: The Loneliness scale, 10 items answered on a 5-point scale.
Depressive mood: The Depressive Mood List, 6 items answered on a 5-point scale.
Social Anxiety: The Revised Social Anxiety Scale for Children, 6 items answered on 5-point scale and the Social Avoidance and Distress in general inventory, a 4 items answered on 5-point scale.
Educational performance: Respondents self-reported how well they think they are doing in school on a 7-point scale.
There is a great deal of information, so I reduced it to what I think is important. For full details, please contact the authors for the article. The data analysis only includes respondents who played video games.
|Offline gamers||Online gamers|
|M (SD)||M (SD)|
|Video game addiction||0.37 (0.49)||0.84 (0.68)|
|Depressive mood||2.23 (0.71)||2.16 (0.70)|
|Loneliness||1.57 (0.50)||1.64 (0.51)|
|Social anxiety general||1.67 (0.70)||1.71 (0.71)|
|Social anxiety new situations||2.21 (0.78)||2.27 (0.78)|
|Negative self-esteem||1.74 (0.55)||1.68 (0.52)|
|School performance||5.40 (1.20)||5.15 (1.23)|
Differences between offline and online gamers are all significant.
Fortunately, individuals with high levels of problematic video gaming are small, estimated from 0.6% to 11.9% based on past studies. Hence, the authors will need to account the huge sample difference between the low (n = 5789) vs. high group (n = 204, 174 boys and 30 girls) on the video game addiction test in their sample. Therefore, they conducted non-parametric tests, specifically examining the relative risk between the low vs. high video game addiction groups.
Taken from Wikipedia, relative risk is the ratio of probability of something occurring in an exposed group (the high group) compared to the non-exposure group (the low group). For example, whether the ratio probability that an addicted gamer smokes is higher or smaller as compared to a non-addicted gamer smokes. Here is one finding, boys were 4.42 times were more likely to be online gamers than girls.
The following table shows the relative risk between the low and high video game addiction groups. The authors separated boys and girls in their analyses. The numbers are the relative risks for those in the high video game addiction group with the low addiction group as a reference point.
They compared low and high groups in boys and girls. The authors did not compared between boys and girls.
|Low M(SD)||High M(SD)||Low M(SD)||High M(SD)|
|Hours online gaming||6.90 (11.52)||22.62 (19.78)||0.96 (3.98)||13.92 (19.38)|
|Hours causal gaming||1.84 (4.19)||4.16 (9.80)||1.73 (4.08)||1.89 (2.58)|
|Hours offline gaming||5.09 (8.60)||11.23 (15.64)||2.22 (5.11)||13.29 (17.70)|
|Depressive mood||2.03 (0.64)||2.70 (0.82)||2.31 (0.72)||3.24 (0.79)|
|Loneliness||1.61 (0.48)||1.97 (0.73)||1.58 (0.51)||2.06 (0.71)|
|Social anxiety general||1.65 (0.65)||2.15 (0.97)||1.73 (0.75)||2.68 (1.14)|
|Social anxiety new situations||2.16 (0.73)||2.60 (0.94)||2.43 (0.81)||2.89 (0.95)|
|Negative self-esteem||1.60 (0.47)||1.98 (0.62)||1.81 (0.58)||2.34 (0.72)|
|School performance||5.20 (1.21)||4.69 (1.46)||5.52 (1.12)||4.63 (1.45)|
The take home message is that those with high scores on the video game addiction were more likely to spend time playing videogames, in particular in online multiplayer than their low scoring counterpart. However, it is not just online gaming because respondents reported playing multiple types of games, like offline and casual gaming. An important reminder is that 3.4% of the survey sample scored high on the video game addiction test, which is a comparable prevalence rate to past studies. Problematic video gaming is indeed a risky behavior as there is relative risk for alcohol, smoking and cannabis use co-occurring. Furthermore, it is associated with lower school performance, self-esteem, increased depressive mood, loneliness, and social anxiety.
The authors suggested further examining the mechanisms and aspects of online gaming that increases the addictive potential. Although, they analyzed the data cross-sectionally thus they are unable to establish causality, they noted that from previous studies, that the causality between problematic video gaming and psychosocial problems (i.e. depressive mood, social anxiety, self-esteem, loneliness) might be two-ways. It is possible that socially anxious individuals prefer online social interactions and by extension online gaming. Conversely, problematic video gaming adversely affect school performance or social competence.
The authors noted some limitations. They acknowledged that dividing videogames into three broad categories (i.e. online, offline, casual) entailed a loss of details, not knowing the genres. Although, I think we can speculate which genres mainly fall under which categories, specifically MMOs being online and quite often associated with problematic gaming. The categorization of respondents into the low and high group on the video game addiction test makes some practical sense and makes a very clear distinction, but the authors noted that this dividing could be debated. I suppose their cutoff score is quite conservative. Another limitation is that this study was done in the Netherlands and may lack generalizability to other countries. I’d like to bring a limitation is that I have yet seen a study of a similar scale in the United States, is there anyone willing to fund a nationally-representative survey? There are many experts in the States, but the funding is not there I am aware of.