| Literature DB >> 27156376 |
Filip Nuyens1, Jory Deleuze1, Pierre Maurage1, Mark D Griffiths2, Daria J Kuss2, Joël Billieux1.
Abstract
Background and aims Multiplayer Online Battle Arena (MOBA) games have become the most popular type of video games played worldwide, superseding the playing of Massively Multiplayer Online Role-Playing Games and First-Person Shooter games. However, empirical studies focusing on the use and abuse of MOBA games are still very limited, particularly regarding impulsivity, which is an indicator of addictive states but has not yet been explored in MOBA games. In this context, the objective of the present study is to explore the associations between impulsivity and symptoms of addictive use of MOBA games in a sample of highly involved League of Legends (LoL, currently the most popular MOBA game) gamers. Methods Thirty-six LoL gamers were recruited and completed both experimental (Single Key Impulsivity Paradigm) and self-reported impulsivity assessments (s-UPPS-P Impulsive Behavior Scale, Barratt Impulsiveness Scale), in addition to an assessment of problematic video game use (Problematic Online Gaming Questionnaire). Results Results showed links between impulsivity-related constructs and signs of excessive MOBA game involvement. Findings indicated that impaired ability to postpone rewards in an experimental laboratory task was strongly related to problematic patterns of MOBA game involvement. Although less consistent, several associations were also found between self-reported impulsivity traits and signs of excessive MOBA game involvement. Conclusions Despite these results are preliminary and based upon a small (self-selected) sample, the present study highlights potential psychological factors related to the addictive use of MOBA games.Entities:
Keywords: Internet Gaming Disorder; Internet addiction; Multiplayer Online Battle Arena; delay discounting; impulsivity; videogame addiction
Mesh:
Year: 2016 PMID: 27156376 PMCID: PMC5387787 DOI: 10.1556/2006.5.2016.028
Source DB: PubMed Journal: J Behav Addict ISSN: 2062-5871 Impact factor: 6.756
Self-report questionnaires used in the study
| Questionnaire | Scale | Scale description | Cronbach’s alpha (α) | Average score | Standard deviation |
| Problematic Online Game Questionnaire | Preoccupation | Constant thoughts about gaming experiences | .66 | 5.81 | 1.35 |
| Overuse | Spending too much time on the game | .79 | 8.06 | 2.67 | |
| Immersion | Losing track of time when playing the game | .61 | 13.36 | 2.68 | |
| Social isolation | Neglect other activities in favor of gaming | .60 | 5.69 | 1.89 | |
| Interpersonal conflicts | Negative consequences in social life due to gaming | .71 | 4.58 | 2.20 | |
| Withdrawal | Feeling reckless when unable to play the game | .70 | 9.11 | 2.76 | |
| Total score | Problematic use of online video game | .77 | 46.61 | 9.35 | |
| Short version of the UPPS-P | Negative urgency | Proneness to act rashly in negative emotional contexts | .77 | 9.19 | 2.35 |
| Positive urgency | Proneness to act rashly in positive emotional contexts | .67 | 11.75 | 2.09 | |
| Lack of premeditation | Difficulties to foresee the possible consequences of the action | .89 | 7.69 | 2.29 | |
| Lack of perseverance | Difficulties to stay focused on an annoying or complicated task | .94 | 8.00 | 3.18 | |
| Sensation seeking | Openness to new experiences and preferences for risky activities | .81 | 11.22 | 2.94 | |
| Barratt Impulsiveness Scale (11th version) | Motor impulsiveness | Acting on the spur of the moment | .53 | 20.60 | 3.35 |
| Cognitive impulsiveness | Difficulties to stay focused on a task | .56 | 17.11 | 2.97 | |
| Non-planning impulsiveness | Difficulties to take on a complicated task | .72 | 23.86 | 5.05 |
Figure 1.This figure illustrates the algorithm that was used to compute the amount of money earned for a click. This algorithm was developed to ensure that each participant earned between 5 (if he/she clicks approximately every second during each block) and 10 Euros (if he/she clicks only once per block) after completion of the task. The algorithm implies an exponential growing of the rewards earned when participants successfully delay their clicks. In this formula, “S” refers to the seconds waited before clicking.
Progression through the blocks regarding the time waited between each click
| Blocks ( | Waiting time ( | Waiting time ( | Difference ( | Cohen’s | |
| 1–2 | 14.28 s | 30.19 s | 15.91 s | 0.874 | .000 |
| 2–3 | 30.19 s | 38.87 s | 8.68 s | 0.365 | .000 |
| 3–4 | 38.87 s | 41.08 s | 2.21 s | 0.092 | .484 |
Correlations between self-report measures and behavioral task
| Variables | Preoccupation | Overuse | Immersion | Isolation | Conflicts | Withdrawal | Total POGQ | Hours/week | SKIP12 | SKIP34 |
| Negative urgency | .237 | .057 | .310 | .148 | .092 | .124 | .226 | .009 | −.115 | .065 |
| Positive urgency | .013 | −.197 | .139 | −.258 | .009 | .213 | −.002 | .055 | .112 | −.303 |
| Lack of premeditation | .147 | .040 | −.014 | −.154 | .263 | .105 | .084 | .034 | −.034 | −.038 |
| Lack of perseverance | .320 | .145 | .244 | .000 | −.037 | .123 | .187 | .035 | −.135 | −.042 |
| Sensation seeking | −.104 | .100 | −.065 | −.192 | .179 | .197 | .052 | .026 | −.223 | −.074 |
| Motor impulsivity | .130 | .136 | .128 | −.192 | .204 | .168 | .148 | .026 | −.184 | −.371 |
| Cognitive impulsivity | .405 | .208 | .331 | −.014 | .263 | .315 | .358 | .110 | −.243 | −.321 |
| Non-planning impulsivity | .181 | .100 | .141 | −.037 | .202 | .255 | .205 | .115 | .040 | −.032 |
| SKIP12 | −.487 | −.483 | −.370 | −.086 | −.436 | −.184 | −.478 | −.243 | / | / |
| SKIP34 | −.087 | −.145 | −.246 | .182 | −.069 | .057 | −.084 | −.175 | / | / |
Note. Pairwise treatment of missing data. SKIP12 = mean waiting time during the first half of the task; SKIP34 = mean waiting time during the second half of the task.
p < .08.
*p < .05. **p < .01.