Problematic gaming behavior and the personality traits of video gamers: A cross-sectional survey.

Journal Pre-proof Problematic Gaming Behavior and the Personality Traits of Video Gamers: A Cross-Sectional Survey.

Jan Dieris-Hirche, Magdalena Pape, Bert Theodor te Wildt, Aram Kehyayan, Maren Esch, Salam Aicha, Stephan Herpertz, Laura Bottel PII:

S0747-5632(20)30028-5

DOI:

https://doi.org/10.1016/j.chb.2020.106272

Reference:

CHB 106272

To appear in:

Computers in Human Behavior

Received Date:

14 July 2019

Accepted Date:

19 January 2020

Please cite this article as: Jan Dieris-Hirche, Magdalena Pape, Bert Theodor te Wildt, Aram Kehyayan, Maren Esch, Salam Aicha, Stephan Herpertz, Laura Bottel, Problematic Gaming Behavior and the Personality Traits of Video Gamers: A Cross-Sectional Survey., Computers in Human Behavior (2020), https://doi.org/10.1016/j.chb.2020.106272

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE Problematic Gaming Behavior and the Personality Traits of Video Gamers: A CrossSectional Survey. Jan Dieris-Hirche1, Magdalena Pape1, Bert Theodor te Wildt2,1, Aram Kehyayan1, Maren Esch1, Salam Aicha1, Stephan Herpertz1, Laura Bottel1

1Department

of Psychosomatic Medicine and Psychotherapy, LWL-University Clinic Bochum, Ruhr-University Bochum, Germany 2Psychosomatic

Hospital Diessen Monastery, Germany

Author Note Corresponding Author: Dr. Jan Dieris-Hirche Department of Psychosomatic Medicine and Psychotherapy, LWL-University Clinic Bochum of Ruhr-University Bochum, Alexandrinenstr. 1-3, 44791 Bochum, Germany Email: [email protected] Phone: +40-234-5077-3531 Funding: None Conflict of Interest: The authors declare that they have no conflicts of interest. Authors' contributions: JDH, LB, MP conceived the study, JDH, MP, LB, SA, AK, ME acquired the data, JDH did the analysis and drafted the manuscript. BtW, LB, MP and SH gave critical input and contributed to the interpretation of results and writing of the manuscript from draft to submission. All authors read and approved the final manuscript.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

1

Problematic Gaming Behavior and the Personality Traits of Video Gamers: A CrossSectional Survey.

1. Introduction In 2013, internet gaming disorder (IGD) was included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a condition warranting further research (American Psychiatric Association, 2013). Because of growing scientific evidence attesting to its existence, online gaming disorder (OGD) was recently introduced as a new diagnosis in the upcoming International Classification of Diseases, 11th Revision (ICD-11), in the section “Disorders Due to Addictive Behaviors” (World Health Organization, 2018). Other types of internet-use disorder (IUD), e.g. internet addiction (IA), pathological pornography use, or problematical social media use, are classified in a subsection called “Other Specified Disorders Due to Addictive Behaviors” (World Health Organization, 2018). According to ICD-11, OGD is characterized by a persistent or recurrent pattern (lasting at least 12 months) of gaming behavior that is characterized by (1) impaired control regarding its onset, intensity, duration, frequency, termination, and context; (2) increased priority given to video gaming to the extent that gaming takes precedence over daily activities and life interests; and (3) escalation and continuation despite the occurrence of negative consequences. Furthermore, the pathological behavior pattern must be sufficiently severe and cause significant impairment in personal, social, educational, occupational, or other relevant areas of functioning (World Health Organization, 2018). A number of empirical studies conducted among natural populations between 1998 and 2016 indicated IGD prevalence rates of 0.7% to 15.6%. The average prevalence during this time period was 4.7% (Feng, Ramo, Chan, &

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

2

Bourgeois, 2017). The broader condition, IA, has shown similarly high prevalence rates between 3% and 6% (Cheng & Li, 2014). IGD and IA are additionally significantly associated with other comorbid psychiatric disorders, especially depression, social anxiety, ADHD, obsessivecompulsive symptoms, lower life satisfaction, and higher suicidality (Bielefeld et al., 2017; Carli et al., 2013; Dieris-Hirche et al., 2017; González-Bueso et al., 2018; Ko, Yen, Yen, Chen, & Chen, 2012; Lachmann, Sariyska, Kannen, Stavrou, & Montag, 2017; Steinbüchel et al., 2018). Much research in recent years has focused on the treatment of IA and IGD. Cognitive behavioral therapy (CBT) has been suggested most frequently for the treatment of IA and IGD as a way of addressing motivational changing, analyzing dysfunctional cognitions, improving social and interpersonal deficits, and establishing alternative real-life behaviors (King, Delfabbro, Griffiths, & Gradisar, 2012). A preliminary meta-analysis including 16 CBT intervention studies reported large effect sizes (g = 1.48; 95% CI [0.84-2.13]) on IA symptoms (Winkler, Dörsing, Rief, Shen, & Glombiewski, 2013). Methodological shortcomings of the studies have been pointed out, however, owing to a lack of randomization, insufficient control groups, insufficient information on recruitment and samples, and a lack of manualized treatments (King et al., 2017). Nevertheless, a recently published, methodologically well-conducted randomized clinical trial (RCT) also reported a large effect size (d = 1.19) for manualized CBT on IA symptoms (Wölfling et al., 2019). In recent years, scholars have developed many approaches to IGD and IA. Many empirical findings on neurobiological mechanisms support the idea that behavioral addiction underlies IGD and IA (Fauth-Bühler & Mann, 2017; Kuss, Pontes, & Griffiths, 2018; Zhang & Brand, 2018). In addition, different theoretical models on the development and persistence of IUD have been proposed, with several overlapping components such as vulnerability factors,

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

3

certain cognitive processes, motivation-seeking, craving, and decision-making (Brand et al., 2019; Brand, Young, Laier, Wölfling, & Potenza, 2016; Davis, 2001; Dong & Potenza, 2014). The following chapter 1.1 and 1.2 presents the current scientific model for IGD disease development (I-PACE), and the important role of personality characteristics and traits as predisposing factors in IGD. The I-PACE model serves as the scientific basis for our study concept and the hypotheses. Chapter 1.3 introduce the relevance of game genres in the development of IGD; chapter 1.4 presents the current knowledge about gender-specific differences in video game use and IGD. Finally, the study objectives and hypotheses are presented in 1.5.

1.1 The “Interaction of Person-Affect-Cognition-Execution (I-PACE) Model” of Specific Internet-Use Disorder To study IUDs, it is important to understand what predisposes gamers to pathological behaviors. The “Interaction of Person-Affect-Cognition-Execution (I-PACE) model”, first introduced in 2016, is a theoretical framework for the processes underlying the development and persistence of specific IUDs, such as IGD, IA, pathological pornography use, and problematical social media use (Brand et al., 2016). It was recently updated and generalized to other kinds of addictive behaviors, such as pathological gambling, compulsive sexual behavior, and shopping (Brand et al., 2019). In the I-PACE model, IGD is considered a consequence of interactions between predisposing factors called a person’s core characteristics, mediators (e.g. cognitive and affective responses to video games), moderators (e.g. IGD-related cognitive bias and coping styles), and reduced executive function, such as a lack of inhibition. In the early stages of addiction, problematic gaming behavior might be related to gratification processes, while in the

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

4

later stages it might serve a compensating function to balance out negative experiences, such as loneliness, conflicts with parents, and feelings of being misunderstood (Brand et al., 2019, 2016). Predisposing factors (P-component) relevant to the I-PACE model have been discussed more generally, and they are categorized as (1) biopsychological constitutions (e.g. stress vulnerability and genetic factors); (2) psychopathologies (e.g. depression, social anxiety, and ADHD); (3) social cognition (e.g. loneliness and a lack of perceived social support); (4) motives for using games; and (5) personality characteristics, such as high impulsivity, low self-esteem, and low conscientiousness (Brand et al., 2016). Research on the specific predisposing factors for IGD is growing, but more research is needed to confirm current tentative findings.

1.2 Personality Characteristics and IUDs Personality traits and temperamental characteristics are considered important factors in the development and persistence of IUDs (Brand et al., 2016; Mihara & Higuchi, 2017; Munno et al., 2017). Most consistent associations have been reported between IUD features and low self-esteem; high levels of impulsivity, neuroticism, and shyness; low levels of conscientiousness; and procrastination (Ebeling-Witte, Frank, & Lester, 2007; Floros, Siomos, Stogiannidou, Giouzepas, & Garyfallos, 2014; Koo & Kwon, 2014; Müller, Beutel, Egloff, & Wölfling, 2014; Wang, Ho, Chan, & Tse, 2015; Wartberg, Kriston, Zieglmeier, Lincoln, & Kammerl, 2019). In the case of the broader concept of IA, a meta-analytical review including 12 studies (12,019 participants) reported that all Big Five personality traits have a meaningful association with IA behavior: the authors found that neuroticism is positively associated with IA, whereas openness, conscientiousness, extraversion, and agreeableness show a negative association (Kayiş et al., 2016). Studies specifically focusing on IGD have shown that

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

5

personality traits are potential factors associated with an individual’s preference for gaming (Seong, Hong, Kim, Kim, & Han, 2019). Furthermore, several personality traits, such as introversion, submissiveness, self-devaluation, psychoticism, detachment, interpersonal sensibility, phobic anxiety, obsessive-compulsive tendencies, and hostility have been found to be highly associated with IGD behavior (Laier, Wegmann, & Brand, 2018; Torres-Rodríguez, Griffiths, Carbonell, & Oberst, 2018).

1.3 Role of Game Genres in IGD A recent systematic review discusses a significant number of studies which indicate a significant link between addictive gaming behavior and specific game genres, in particular massively multiplayer online role-playing games (MMORPGs) and first-person shooter games (FPS) (Rumpf, 2017). Comparing different game genres, 14 studies have shown increased addictive behavior and time spent playing connected to MMORPGs, and 5 studies have indicated increased IGD behavior associated with FPS games (Rumpf, 2017). Several specific factors have been described as potential addictive components in MMORPGs and FPS games, such as social interaction, altering reward mechanisms, unpredictability, negative consequences of absence (Rehbein, Florian; Mößle, Thomas; Jukschat, 2010; Rumpf, 2017), micropayment components (Dreier et al., 2017), and pay-to-win systems, e.g. loot boxes (Li, Mills, & Nower, 2019; Zendle & Cairns, 2018).

1.4 Gender-Specific Factors The number of female video game players has increased over the past two decades, and they currently make up almost half of the gaming population (ESA, 2019); however, most

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

6

research on IDG has focused on men (Lopez-Fernandez, Williams, Griffiths, & Kuss, 2019). Scholars have also discussed whether IDG and IA might be predominantly male disorders (Kuss & Griffiths, 2015). Further research has highlighted that female gamers typically play more casual games and play them for shorter periods than male gamers do (Griffiths, Lewis, & Griffiths, 2011; McLean & Griffiths, 2018). On the other hand, recent studies examining IGD have reported a more gender-balanced prevalence of IGD but have noted different gaming preferences and motives for women and men (Laconi, Pirès, & Chabrol, 2017; Lopez-Fernandez, 2018). Male video gamers appear to prefer e.g. MMORPGs while female video gamers prefer casual games (Laconi et al., 2017). In contrast to earlier discussions, a recent neuroimaging study using a structural magnetic resonance imaging technique suggested that female gamers might be even more vulnerable to IGD than male ones (Z. Wang et al., 2019). In summary, problematic gaming behavior and IGD among female gamers has rarely been addressed and requires further empirical research (Lopez-Fernandez et al., 2019).

1.5 Objectives and Hypotheses Although previous research and psychological models of development and persistence of IUD have suggested a meaningful association between specific personality traits and IUD characteristics, many of these studies have been conducted either among non-specific collectives (e.g. college students), using unspecified online surveys, or on small clinical samples of IGD patients. Additionally, many studies have been performed using the broader concept of IA rather than specifically focusing on IGD. Further research is necessary to collect evidence about individuals’ core characteristics as a predisposing factor and to increase our knowledge of IGD (Brand et al., 2019). We therefore conducted a cross-sectional study among a large sample of

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

7

video gamers, focusing on explicit problematic video gaming behavior and its relation to personality characteristics. Our main research interest was to assess potential personality-related predictors for problematic gaming behavior among a large sample of ardent video gamers. For this purpose, we conducted a survey at the world’s largest trade fair for video games in 2018. Building on the above-mentioned theoretical considerations, we developed several hypotheses. Hypothesis H1. Video gamers with problematic gaming behavior show higher levels of neuroticism and lower levels of openness, conscientiousness, extraversion, and agreeableness than gamers with unproblematic gaming behavior. Based on the above-mentioned evidence for the high psychological burden and high psychiatric co-morbidity associated with IA and IGD, we expected that our study would confirm this association between problematic gaming behavior and psychological burden: Hypothesis H2. Video gamers with problematic gaming behavior present more symptoms of depression, lower levels of self-efficacy, and lower general life satisfaction than gamers with unproblematic gaming behavior. As mentioned above, the choice of game genre seems to be associated with the risk of becoming addicted to video gaming. Especially MMORPGs and FPS games are associated with addictive behavior and increased gaming time (Rumpf, 2017). This led to the following hypothesis: Hypothesis H3. Video gamers who prefer MMORPGs or FPS games show higher levels of problematic gaming behavior and depression symptoms; more time spent playing; and lower levels of life satisfaction and self-efficacy than video gamers preferring other genres. Additionally, MMORPG and FPS gamers show higher levels of neuroticism and lower levels of openness, conscientiousness, extraversion, and agreeableness.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

8

There is an ongoing discussion about the role of female video gamers and gender differences in gaming preferences, problematic gaming behavior, and time spent playing (LopezFernandez et al., 2019). Despite inconsistent findings regarding problematic gaming behavior, female gamers appear to prefer different game genres and show different gaming behavior than male gamers. Based on this, we formulated the following hypothesis: Hypothesis H4. Men are more likely to show problematic gaming symptoms, spend more time gaming, and prefer MMROPGs and FPS games than women. Finally, we examined predictors for problematic gaming behavior among the group of gamers: Hypothesis H5. Personality traits, depression symptoms, and time spent playing are risk factors for problematic game behavior.

2. Methods 2.1 Participants The study included a sample of 820 video gamers between the ages 12 and 66 (M = 25.25, SD = 10.31). All the participants reported that they played video games on a regular basis. In total, 217 female gamers (26.5%) and 597 male gamers (72.8%) participated in the study, while 6 gamers indicated the gender “other/diverse/transgender” (0.7%). Most gamers (n = 517 or 63.0%) were students at a university or still attended school or vocational training (n = 255 or 31.3%). Twenty-seven gamers (3.3%) were employed, no-one was unemployed (0%), and 21 gamers (2.6%) selected the answer option “other”. When asked about their highest level of education, more than half of the sample (n = 452 or 55.1%) indicated a high educational level

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

9

(12–13 years of schooling), 299 gamers (36.5%) indicated a moderate educational level (10–12 years of schooling), and 69 gamers (8.4%) reported a low educational level (9 years or fewer of schooling).

2.2 Study Design and Process The study was conducted as a retrospective cross-sectional study. To identify a large sample of video gamers who play video games on a regular basis, the survey was carried out at the world’s largest trade fair for video games, which took place in August 2018 in Cologne, Germany. Trade fair visitors were invited to participate in the survey and received information about the study. Informed consent was obtained from all participants. For underage participants, the consent of accompanying parents was obtained. All the participants confirmed the voluntary nature of their participation as well as authorized data processing. The survey was conducted digitally using tablet computers in a waiting area next to the researchers’ stand in a large trade fair hall. The participants received brief feedback about their gaming behavior (problematic or non-problematic) and a small giveaway (a rubber duck, a small tangram game, or a frisbee). A total of 864 visitors took part in the survey. Only the participants who indicated general use of video games were included in the data analysis. As a result, 44 participants identified as nongamers (e.g. parents accompanying their children) were excluded. The study followed the tenets of the Helsinki Declaration.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

10

2.3 Measures Participants’ characteristics, including gender (“woman,” “man,” “other/diverse”), age, employment status, and level of education, were assessed. Furthermore, time spent playing video games (hours/day) was surveyed both for weekdays and for weekend. Favorite game genres were surveyed using the following categories: massively multiplayer online role-playing games (MMORPGs), first-person shooter games (FPS), strategy/simulation games, jump ‘n run games, sport games, beat ‘em up games, adventure games, and “other.” Psychological variables were measured using the standardized questionnaires discussed in the following subsections.

2.3.1 Problematic gaming behavior. The Short Compulsive Internet Use Scale (Short CIUS) was used to assess the extent of problematic gaming behavior (Besser et al., 2017; Bischof, Bischof, Besser, & Rumpf, 2016). The 5-item Short CIUS is an efficient version of the 14-item CIUS (Meerkerk, Van Den Eijnden, Vermulst, & Garretsen, 2009) and covers both harmful and abusive use as well as pathologies of an internet-related addiction according to the DSM-5 IGD criteria. A distinction between problematic and addictive usage is not possible due to the limited number of questions. The scale measures the major symptoms of addiction: loss of control, disregard of everyday duties, and usage to escape or relieve a negative mood. The total score ranges between 0 and 20, and the creators of the scale propose two different cut-off points along the scale regarding sensitivity and specificity. Using a cut-off ≥ 7, the test has a sensitivity of 95% and a specificity of 87%; for a higher degree of specificity, a cut-off ≥ 9 can be used, yielding a sensitivity of 78% and a specificity of 96%. To avoid overestimation, the cut-off  9 was chosen for our study. We used a gaming-adapted version by adding an instruction noting that all questions explicitly referred to

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

11

gaming behavior instead of general internet use. Cronbach's Alpha for internal consistency of  = .77 indicates good reliability among the German general population (Bischof et al., 2016).

2.3.2 Personality traits. The Big Five Inventory-10 (BFI-10) scale was used to measure extraversion, agreeableness, conscientiousness, neuroticism, and openness (Rammstedt & John, 2007; Rammstedt, Kemper, Klein, Beierlein, & Kovaleva, 2013). This scale was developed based on the 44-item Big Five Inventory and is designed for contexts in which respondents’ time is severely limited. The BFI-10 consists of 10 items, 2 for each of the 5 main personality dimensions according to the 5-factor model. Each dimension is captured by an item that has a positive and a negative pole. The participants used a 5-point scale ranging from “disagree” (1) to “fully agree” (5). The values on the 2 items are each aggregated to yield a scale value that indicates a personality dimension. In previous testing of the instrument, mean test-retest correlations between r = .72 (US sample) and r = .78 (German sample) have suggested acceptable levels of stability (Rammstedt & John, 2007); the items’ internal consistencies are considerably smaller than the test-retest correlations. Cronbach’s Alpha ranges between  = .43 for agreeableness and  = .72 for extraversion (Thalmayer, Saucier, & Eigenhuis, 2011).

2.3.3 General self-efficacy. The German General Self-Efficacy Short Scale (ASKU) is a self-assessment tool for the effective measurement of subjective competence expectations (Beierlein, Kovaleva, Kemper, & Rammstedt, 2012). ASKU contains 3 items that can be answered on a 5-point scale. The values on the 3 items are aggregated to a scale value (ranging from 3 to 15 points) indicating the extent

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

12

of general self-efficacy expectations. The reliability of ASKU is determined using the Mc Donald`s Omega coefficient and varies across the 3 items between .81 and .86. This corresponds to a sufficient level of reliability for group examinations (Beierlein et al., 2012).

2.3.4 Depression symptoms. The Patient Health Questionnaire-2 (PHQ-2) is a two-question diagnostic test to screen for major depression (Löwe, Kroenke, & Gräfe, 2005). It is a very short version of the PHQ-9 that is the depression module of the German Patient Health Questionnaire PHQ-D (Löwe, Spitzer, Zipfel, & Herzog, 2002). The PHQ-2 focuses on the two main criteria of major depression according to DSM-IV: loss of interest and depressed mood. The total score ranges from 0 to 6. With a cut-off value of ≥ 3, the diagnosis of major depression has been reported to have a sensitivity of 87% and a specificity of 78%. Internal consistency according to Cronbach’s Alpha has been reported, with the value  = .83 (Löwe et al., 2005).

2.3.5 Global life satisfaction. The Life Satisfaction-1 short scale (L-1) was developed to allow for a simple measurement of the characteristics related to general life satisfaction in social science examinations (Beierlein, Kovaleva, László, Kemper, & Rammstedt, 2014). The L-1 consists of a modification of the scale established in the Socio-Economic Panel (SOEP), and it measures one’s general life satisfaction with only one item. The answer format of the L-1 consists of a unipolar, 11-point scale ranging from “not at all satisfied” (0) to “completely satisfied” (10). The reliability of L-1 has been estimated using the retest method and has been reported as r = .67, with an average repeat interval of 6 weeks (Beierlein et al., 2014).

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

13

2.4 Statistical Analyses Descriptive statistical analyses used in this study include frequency, median, mean, standard deviation, range of scores, and 95% confidence interval. For all statistical tests, the option “pairwise exclusion of missing data” was used. To compare the different metric scales for the quantitative variables, z-standardized scores were used. The Kolmogorov-Smirnov test was used to test the normality of the distribution. Chi-squared tests were used for dichotomic and nominal variables. Independent-sample t-tests (2-sided) were conducted to compare problematic gamers with unproblematic gamers regarding continuous variables. One-way between-groups analysis of variance with post-hoc comparison (Tukey’s HSD test) was used to compare continuous variables across 3 or more groups, such as game genres. Correlation analyses were conducted to explore relationships among pairs of variables. Pearson product-moment correlation coefficient r was calculated following the Cohen guidelines: 0.10–0.29 = weak, 0.30– 0.49 = moderate, 0.50–1.0 = strong correlation (Cohens, 1988). The comparison effect size was calculated using Cohen’s d coefficient for different group sizes: |d| > 0.20 was considered to be a small effect, |d| > 0.50 moderate, and |d| > 0.80 large (Cohens, 1988). Cramér’s V or φ was calculated for the Chi-squared test: V, φ = 0.1 indicated a small effect; V, φ = 0.3 a medium effect; and V, φ = 0.5 a large effect (Cohens, 1988). Direct logistic regression was used to investigate the impact of the variables age, gender, life satisfaction, self-efficacy, depressiveness, Big Five personality traits, gaming time, and game genre on the likelihood that gamers would report problematic gaming behavior. All analyses were conducted with IBM SPSS Statistics for Macintosh, version 25 (IBM Corp., Armonk, N.Y., USA).

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

14

3. Results 3.1 Gaming Behavior and Prevalence of Problematic Gaming The average time spent playing per week among the group of 820 gamers was 23.57 hours (SD = 16.11). The time spent ranged between 0.70 and 90.00 hours per week. Gamers indicated more time spent playing on the weekends (M = 4.46 hrs./d, SD = 3.23) than on weekdays (M = 2.93 hrs./d, SD = 2.39). The majority of gamers (n = 788 or 96.1%) reported video game use both on weekdays and on the weekend. Twenty gamers (2.4%) reported gaming only on weekends, and 12 (1.5%) gamers indicated playing only on weekdays. A total of 252 (30.7%) gamers reported MMORPGs as their favorite game genre, followed by FPS (n = 220 or 26.8%), strategy/simulation (n = 108 or 13.2%), jump ‘n run (n = 74 or 9.0%), sports (n = 68 or 8.3%), beat ‘em up (n = 21 or 2.6%), adventure (n = 9 or 1.1%), and other (n = 68 or 8.3%). Overall, 238 of 820 gamers (29.0%) exceeded the cut-off value (Short CIUS ≥ 9) indicating problematic gaming behavior in terms of IGD. The distribution of the Short CIUS scores was right-skewed (skewness = 0.56, kurtosis = 0.27) and therefore not normally distributed (D(820) = 0.085, p ≤ .001). Male gamers (n = 185 or 31.0%) were significantly more often affected by problematic gaming behavior than female ones (n = 51 or 23.5%), with X2 (1) = 4.33 and p < .037. In addition, gamers showing problematic gaming behavior rising to the level of IGD (M = 23.06 yrs., SD = 9.33) were significantly younger than gamers with unproblematic gaming behavior (M = 26.14 yrs., SD = 10.56) (t(818) = 3.91, p = .001, |d| = 0.30). Gaming time (hours/week) was significantly and considerably greater (by about 10 hours per week) among those showing problematic gaming behavior (M = 30.35, SD = 16.03) compared with other gamers (M = 20.80, SD = 15.31) (t(817) = -7.96, p ≤ .001, |d| = 0.61). Furthermore, gamers

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

15

showing problematic gaming behavior preferred to play MMORPGs (33.6%) and FPS games (34.5%) more frequently and listed those as their favorite game genres, while gamers with unproblematic gaming behavior preferred these genres less frequently (MMORPGs 29.6% and FPS 23.7%), while all other genres were preferred less frequently in the group of problematic gamers (X2 (7) = 26.43, p ≤ .001). A description of the mean values and standard deviations for all the instruments utilized and gaming behavior for gamers with and without problematic gaming behavior is shown in Table 1.

<>

3.2 Problematic Gaming and Personality Traits Significant differences were found between gamers with and without problematic gaming behavior in terms of the four scales for the Big Five personality traits and the self-efficacy scale (Figure 1). In particular, gamers engaging in problematic gaming behavior (M = 3.02, SD = 0.83) indicated significantly lower levels of conscientiousness than other gamers (M = 3.45, SD = 0.78) (t(818) = 6.80, p ≤ .001, |d| = 0.54). Moreover, gamers showing problematic gaming behavior reported significantly lower levels of extraversion (M = 3.29, SD = 0.97 vs. M = 3.51, SD = 1.00, t(818) = 2.85, p = .004, |d| = 0.22), higher levels of neuroticism (M = 2.86, SD = 0.85 vs. M = 2.50, SD = 0.87, t(818) = -5.54, p ≤ .001, |d| = 0.40), lower levels of openness (M = 3.56, SD = 1.01 vs. M = 3.71, SD = 0.92, t(818) = 2.14, p = .032, |d| = 0.15), and lower levels of selfefficacy (M = 3.85, SD = 0.70 vs. M = 4.14, SD = 0.63, t(404.9) = 5.38, p ≤ .001, |d| = 0.44) than those engaging in unproblematic gaming behavior (Table 1). No significant difference was found in agreeableness (t(818) = 1.03, p = .301).

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

16

<>

3.3 Problematic Gaming, Depression and Life Satisfaction An independent-sample t-test was conducted to compare the depression symptoms (PHQ2) and general life satisfaction (L-1) of gamers with and without problematic gaming behavior, using the Short CIUS cut-off ≥ 9. Gamers engaging in problematic gaming behavior reported significantly higher levels of depression symptoms (M = 1.91, SD = 1.52) than gamers without such behavior (M = 1.21, SD = 1.32) (t(818) = -6.56, p ≤ .001, |d| = 0.50). About each third gamer indicating problematic gaming behavior (74 of 238 or 31.1%) exceeded the PHQ-2 cut-off ≥ 3, indicating major depression, while only 16.7% (97 of 582) of other gamers did (X2 (1) = 21.29, p ≤ .001). Moreover, general life satisfaction was significantly decreased among those showing problematic gaming behavior (M = 7.28, SD = 2.29) compared with gamers engaging in unproblematic gaming behavior (M = 7.75, SD = 1.80) (t(362) = 2.79, p = .005, |d| = 0.24). The correlations between all the utilized instruments, including personality traits, depressive mood, life satisfaction, and gaming behavior, are shown in Table 2.

<>

3.4 Problematic Gaming and Game Genres To study the connections between preferred game genres, gaming behavior, personality traits, depressive mood, and life satisfaction, we divided the genres into three groups: (1) MMORPGs (n = 252); (2) FPS games (n = 220); and (3) all other genres (n = 348). A one-way

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

17

ANOVA was conducted to compare the effect of game genre on personality traits, depressive mood, life satisfaction, and gaming behavior. In terms of gaming behavior, MMORPGs genre played on average 28.60 hours per week (SD = 16.55), with an average Short CIUS score of 7.09 (SD =3.60); FPS gamers had an average gaming time of 24.75 hours per week (SD = 14.81) and an average Short CIUS score of 7.24 (SD = 4.35). The mean time spent playing by gamers preferring other genres was 19.18 hours per week (SD = 15.40) and the average Short CIUS score was 5.54 (SD = 3.88). In other words, the effect of game genre on time spent playing, F(2,816) = 27.42, p ≤ .001, and on problematic gaming behavior, F(2,817) = 17.14, p ≤ .001, was significant. The effect of game genre on personality traits, depressive mood, self-efficacy, and life satisfaction is presented in Figure 2. Firstly, gamers who prefer MMORPGs or FPS games presented higher rates of time spent playing per week (M = 28.60 hrs., SD = 16.55, and M = 24.75 hrs., SD = 14.81, respectively) and more problematic gaming behavior (M = 7.09, SD = 3.60, and M = 7.24, SD = 4.35, respectively) than players of other genres (M = 5.54, SD = 3.88) (p < .001). Furthermore, players of both MMORPGs and FPS games showed significantly lower levels of conscientiousness (M = 3.18, SD = 0.83, and M = 3.25, SD = 0.80, respectively) and agreeableness (M = 3.03, SD = 0.88, and M = 3.12, SD = 0.80, respectively) than gamers playing other genres (p < .001 and p = .023).

<>

3.5 Predictors of Problematic Gaming Behavior Direct logistic regression was performed to assess the predictive value of independent variables (age, gender, time spent playing, favorite game genre, depression symptoms, Big Five

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

18

personality traits, self-efficacy, and life satisfaction) on problematic gaming behavior (Short CIUS score ≥ 9). The full model containing all the predictors was statistically significant (X2 (13, N = 819) = 165.89, p ≤ .001), indicating that the model was able to distinguish between participants who did or did not report problematic gaming behavior. The model explained between 18.3% (Cox & Snell’s R2) and 26.2% (Nagelkerke’s R2) of the variance in gaming behavior and correctly classified 74.5% of cases. As shown in Table 3, only four independent variables (depressive symptoms, neuroticism, conscientiousness, and hours spent playing per week) were significant. The higher the level conscientiousness, the less likely the gamer is to report problematic gaming behavior. The higher the levels of depressive symptoms, neuroticism, and the hours spent playing, the more likely a player is to show problematic gaming behavior.

<>

3.6 Gender The effects of gender on gaming behavior were evaluated for 217 female and 597 male gamers. Six gamers who reported their gender identity as “other/diverse/transgender” were not considered due to the small number of cases. Female (M = 25.88, SD = 10.80) and male (M = 25.08, SD = 10.14) gamers did not differ in age (t(812) = 1.03, p = .30, |d| = 0.08). In contrast, the frequencies of educational levels were significantly different (X2 (2, N = 814) = 10.7, p = .005); women were more frequently highly educated. Overall, 63.6% of female gamers reported completing high-level education, whereas only 51.9% of male gamers did. Work situations, however, did not differ by gender (X2 (3, N = 814) = 0.5, p = .901). Concerning gaming behavior, there was a significant relationship between gender and one’s favorite game genre (X2 (7, N =

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

19

814) = 83.7, p ≤ .001). About one-third of male gamers (32.3%) reported FPS games as their preferred genre, while only 11.5% of female gamers did. In contrast, simulation/strategy games (21.2% vs. 10.4%) and jump ‘n run games (17.5% vs. 5.9%) were preferred more often by female gamers. MMORPGs were preferred equally by men (31.2%) and women (30.0%). Male gamers reported more time spent playing (M = 25.04 hrs./week, SD = 16.06) and increased Short CIUS scores (M = 6.80, SD = 4.01) in comparison to female gamers (M = 19.35 hrs./week, SD = 15.27; M = 5.56, SD = 3.89), both p ≤ .001. Finally, the prevalence of problematic gaming behavior (Short CIUS ≥ 9) was significantly higher among male gamers (31.0%) than female ones (23.5%) (X2(1, N = 814) = 4.3, p ≤ .037). A detailed description of gender-specific mean values and standard deviations for all the instruments used to measure gaming behavior, personality traits, depressive mood, and life satisfaction is shown in Table 4.

<>

4. Discussion The present study examined the relationship between personality traits and problematic computer gaming behaviors among a large group of video gamers who play games on a regular basis. The particular strength of this study is its large homogenic sample of 820 gamers with an explicit interest in frequent gaming and different game genres. It was found that video gamers reporting problematic gaming behavior showed significant differences across four of five personality scales of the Big Five model. The largest effects were found for conscientiousness: video gamers engaging in problematic game use showed lower levels of conscientiousness at the level of a medium effect size. The conscientiousness scale measures characteristics such as a

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

20

sense of duty, pursuit of achievement, discipline, and prudence (Fehr, 2006). Our results confirmed a previous study conducted on general internet addiction, with the exception of agreeableness where no difference was found in our study (Kayiş et al., 2016). Additionally and from a clinical point of view, patients with IGD often present deficits in endurance and energy as well as strong tendencies to procrastinate, which might be related to their low levels of conscientiousness (Pechler, 2018). Video gamers in our study showed substantial differences in neuroticism, with higher levels of it associated with problematic gaming behavior. This supports recent findings on general internet addiction and IGD (Kayiş et al., 2016; Laier et al., 2018). Subjects with high levels of neuroticism are characterized by increased nervousness, irritability, moodiness, insecurity, somatization, sadness, and melancholy (Fehr, 2006), and positive associations have been found between neuroticism and depression and loss of quality of life, both among the general population as well as among smartphone-addicted patients (Gao, Xiang, Zhang, Zhang, & Mei, 2017; Navrady et al., 2017). Our findings thus support previous studies identifying depression and social phobias as the most common psychiatric co-morbidity in IGD and problematic gaming behavior (Carli et al., 2013; González-Bueso et al., 2018). Minor differences were found regarding the Big Five scales for openness and extraversion in relation to problematic gaming behavior. Hypothesis 1 can thus be mostly confirmed. An important question for the construction of a disease model, however, is causality. Our study presented low levels of conscientiousness, high levels of neuroticism, depression symptoms, and increased time spent playing as predictors for problematic gaming. So far, only few studies have discussed whether chronic IGD or internet addiction involving excessive consumption might cause changes in personality traits (e.g. impulsivity) or executive control that could lead to less control over behavior and increased cravings (Dong et al., 2018). While the

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

21

Big Five traits were formerly considered as relatively stable human basic traits related to genetic predispositions, fluctuating in adolescence and reaching relative stability in adulthood, recent perspectives have suggested that the assumption of stability in adulthood is not tenable. More specifically, recent studies have discussed natural changes to personality traits through life experiences, social changes, relevant life events, but also through psychotherapeutic interventions (Huang, 2010; Piedmont, 2001; Soldz & Vaillant, 1999; Soto, John, Gosling, & Potter, 2011; Spinhoven, Huijbers, Ormel, & Speckens, 2017). As expected, gamers reporting problematic gaming behavior presented more symptoms of depression as well as reduced self-efficacy and general life satisfaction. This confirms Hypothesis 2. Our results confirm previous studies concerning a valid link between IGD and increased psychological burden. In addition, the group of gamers who played video games on a regular basis showed a high prevalence (31.0% of men, 23.5% of women) of problematic gaming behavior despite using a more conservative cut-off for the Short CIUS questionnaire. By comparison, a representative study from Germany reported a considerably lower prevalence of problematic gaming (13.6% in the group of participants aged 12–24, N = 2937) among a similarly aged group of participants (Rumpf, Meyer, Kreuzer, & John, 2011). The measures of prevalence should be comparable due to the fact that both studies used similar versions of the same CIUS questionnaire. In other words, video gamers who play games on a regular basis have a substantially higher risk of problematic gaming rising to the level of IGD. Moreover, our findings verified previous studies, which have identified spending much time gaming as a significant factor for the development and persistence of IGD (Mihara & Higuchi, 2017). Two game genres, MMORPGs and FPS games, were significantly related to problematic gaming behavior, considerably increased time spent playing, depression, and significantly lower

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

22

life satisfaction. Hypothesis 3 is thus confirmed. Our study provided additional support for a need to identify video games with particular risky, addictive factors to enhance consumers’ knowledge (Albertini et al., n.d.). Gamers who preferred MMORPs and FPS games also scored significantly lower on conscientiousness. Again, the question of causality is relevant but hardly answerable because of the cross-sectional design of our study. A bidirectional causality might be possible: On one hand, excessive and chronic use of video gaming may result in an decrease in conscientiousness; on the other hand, gamers with low levels of conscientiousness may primary choose MMORPGs and FPS games due to their stimuli and the reactions they evoke. Further information about causality is necessary for understanding the development of IGD as well as identifying treatment strategies. Gender differences regarding IGD have been discussed concerning game genres, gaming patters, and gaming time (Laconi et al., 2017; Lopez-Fernandez et al., 2019; McLean & Griffiths, 2018). The results of our study confirmed these postulated differences, especially when it comes to of favorite game genres, IGD symptoms, and gaming time (Rumpf, 2017). Therefore, Hypothesis 4 is confirmed. Female gamers reported significantly lower levels of preference for FPS games than male gamers but showed similar preferences in their use of MMORPGs. In contrast to these reported effects of gender in our study, some representative studies have repeatedly found no gender differences in the prevalence of internet addiction and IGD (Laconi et al., 2017; Lopez-Fernandez, 2018), although the majority of IGD patients in clinical samples are men (Kuss & Griffiths, 2015). A convincing explanation for this discrepancy is still missing (Scherer, Mader, Beutel, Wölfling, & Müller, 2019). Nevertheless, our findings illustrate the high prevalence of problematic gaming among video gamers playing on a regular basis whether

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

23

male (31.0%) or female (23.5%). The effect of gender should be focused on in further studies to clarify its relevance for the development of specific treatment interventions. Furthermore, female gamers differed from male ones in the case of three of the five Big Five personality traits, with the greatest differences seen in neuroticism, openness, and conscientiousness. Representative surveys conducted among healthy samples, however, have reported gender differences in Big Five personality traits in general. Therefore, the gender differences in personality traits might not be related to problematic gaming itself (Schmitt, Realo, Voracek, & Allik, 2008). Natural changes in personality traits over time were discussed above, and a secondary analysis of personality traits sorted by age would be interesting (Soldz & Vaillant, 1999; Soto et al., 2011). Nevertheless, our study found neuroticism and conscientiousness to be important predictors of problematic gaming (see Hypothesis 5), and the effects of gender should be considered as relevant mediators in the treatment of problematic gaming and IGD.

4.1 Limitations and Directions for Future Research Causality between personality traits and problematic gaming is an important research question that is addressed in this study as well. Due to the cross-sectional design of the present study, however, conclusions about causality are limited. Longitudinal prospective studies in childhood and adolescence are necessary to detect the onset of video gaming and the development of potential problematic gaming behavior. Only few prospective cohort studies have been conducted in schools until now, since there are extensive and costly (an overview is given in Mihara & Higuchi, 2017). Alternatively, a randomized controlled trial with exposure to controlled doses of more or less addictive games (e.g. MMORPGs vs. sports games) could

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

24

examine the causality between short-term alteration of personality traits and psychological wellbeing after excessive gaming. A similar study has recently been conducted on aggressiveness and the use of FPS games (Kühn et al., 2019). We used very short questionnaires to enhance people’s willingness to participate in the study and to increase the sample size. Although the brevity of the survey might reduce the validity and reliability of the measurements, only well-standardized and established questionnaires were used. To the extent possible, conservative cut-offs were chosen to minimize the risk of overestimation. A diagnosis of problematic gaming behavior was made only by the questionnaire, which might be another limitation. Due to the shortness of the used questionnaire, there was no possibility to differentiate between problematic use and addiction. Further studies should therefore use standardized clinical interviews for diagnosing hazardous and addictive gaming behavior according to ICD-11 and DSM-5 criteria, which have recently been developed and published (Kai W Müller & Wölfling, 2017). There is evidence that psychotherapy in general might influence and alter Big Five personality traits at least temporarily (Piedmont, 2001; Spinhoven et al., 2017). There is, however, a lack of scientific knowledge on specific treatment options to improve neuroticism and conscientiousness in patients with IGD to reduce pathological gaming and addictive behavior. Further IDG treatment studies should focus on treatment options affecting personality traits in patients with IGD.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

25

5. Conclusion Our study found that personality traits, especially conscientiousness and neuroticism, as well as gender-specific gaming patterns might be important core factors in the development and persistence of IGD based on the current I-PACE model (Brand et al., 2016). Furthermore, this study illustrated that the use of particular MMORPGs and FPS games on a regular basis might increase the risk of problematic gaming behavior. A requirement to declare the addictive effects of these games should be discussed.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

26

References Albertini, V., Dreier, M., Groppler, A., Kiepe, K., Lindenberg, T., Müller, K. W., … ZorrWerner, M. (n.d.). Position des Fachverbands Medienabhängigkeit e.V. zur Einbeziehung von entwicklungsbeeinträchtigenden Bindungskriterien bei der Altersfreigabe von Computerspielen zur Prävention und Verhinderung einer Medienabhängigkeit. Retrieved from http://www.biu-online.de/de/fakten/marktzahlen/online-und-browserAmerican Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. Diagnostic and Statistical Manual of Mental Disorders, 5th Edition. https://doi.org/10.1176/appi.books.9780890425596.744053 Beierlein, C., Kovaleva, A., Kemper, C. J., & Rammstedt, B. (2012). Ein Messinstrument zur Erfassung subjektiver Kompetenzerwartungen Allgemeine Selbstwirksamkeit Kurzskala (ASKU) 2012|17. Retrieved from https://www.psycharchives.org/bitstream/20.500.12034/431/1/PT_9006490_ASKUManual_2012.PDF Beierlein, C., Kovaleva, A., László, Z., Kemper, C. J., & Rammstedt, B. (2014). Eine SingleItem-Skala zur Erfassung der Allgemeinen Lebenszufriedenheit: Die Kurzskala Lebenszufriedenheit-1 (L-1). Retrieved from https://www.gesis.org/fileadmin/kurzskalen/working_papers/L1_WorkingPapers_201433.pdf Besser, B., Rumpf, H.-J., Bischof, A., Meerkerk, G.-J., Higuchi, S., & Bischof, G. (2017). Internet-Related Disorders: Development of the Short Compulsive Internet Use Scale. Cyberpsychology, Behavior and Social Networking, 20(11), 709–717. https://doi.org/10.1089/cyber.2017.0260

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

27

Bielefeld, M., Drews, M., Putzig, I., Bottel, L., Steinbüchel, T., Dieris-Hirche, J., … Theodor Te Wildt, B. (2017). Comorbidity of Internet use disorder and attention deficit hyperactivity disorder: Two adult case-control studies. Journal of Behavioral Addictions, 6(4), 490–504. https://doi.org/10.1556/2006.6.2017.073 Bischof, G., Bischof, A., Besser, B., & Rumpf, H.-J. (2016). Entwicklung eines Kurzscreenings (PIEK) Abschlussbericht An das Bundesministerium für Gesundheit. Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., & Potenza, M. N. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience and Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032 Brand, M., Young, K. S., Laier, C., Wölfling, K., & Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-CognitionExecution (I-PACE) model. Neuroscience and Biobehavioral Reviews, 71, 252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033 Carli, V., Durkee, T., Wasserman, D., Hadlaczky, G., Despalins, R., Kramarz, E., … Kaess, M. (2013). The association between pathological internet use and comorbid psychopathology: a systematic review. Psychopathology, 46(1), 1–13. https://doi.org/10.1159/000337971 Cheng, C., & Li, A. Y. (2014). Internet addiction prevalence and quality of (real) life: a metaanalysis of 31 nations across seven world regions. Cyberpsychology, Behavior and Social Networking, 17(12), 755–760. https://doi.org/10.1089/cyber.2014.0317 Cohens, J. (1988). Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale,

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

28

New Jersey: Erlbaum. Davis, R. A. (2001). Cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17(2), 187–195. https://doi.org/10.1016/S0747-5632(00)00041-8 Dieris-Hirche, J., Bottel, L., Bielefeld, M., Steinbüchel, T., Kehyayan, A., Dieris, B., & te Wildt, B. (2017). Media use and Internet addiction in adult depression: A case-control study. Computers in Human Behavior, 68, 96–103. https://doi.org/10.1016/j.chb.2016.11.016 Dong, G., & Potenza, M. N. (2014). A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. Journal of Psychiatric Research, 58, 7– 11. https://doi.org/10.1016/j.jpsychires.2014.07.005 Dong, G., Zheng, H., Liu, X., Wang, Y., Du, X., & Potenza, M. N. (2018). Gender-related differences in cue-elicited cravings in Internet gaming disorder: The effects of deprivation. Journal of Behavioral Addictions, 7(4), 953–964. https://doi.org/10.1556/2006.7.2018.118 Dreier, M., Wölfling, K., Duven, E., Giralt, S., Beutel, M. E., & Müller, K. W. (2017). Free-toplay: About addicted Whales, at risk Dolphins and healthy Minnows. Monetarization design and Internet Gaming Disorder. Addictive Behaviors, 64, 328–333. https://doi.org/10.1016/j.addbeh.2016.03.008 Ebeling-Witte, S., Frank, M. L., & Lester, D. (2007). Shyness, Internet use, and personality. Cyberpsychology & Behavior : The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 10(5), 713–716. https://doi.org/10.1089/cpb.2007.9964 ESA, E. S. A. (2019). 2019 Essential Facts About the Computer and Video Game Industry Entertainment Software Association. Retrieved October 11, 2019, from https://www.theesa.com/esa-research/2019-essential-facts-about-the-computer-and-videogame-industry/

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

29

Fauth-Bühler, M., & Mann, K. (2017, January 1). Neurobiological correlates of internet gaming disorder: Similarities to pathological gambling. Addictive Behaviors. Elsevier Ltd. https://doi.org/10.1016/j.addbeh.2015.11.004 Fehr, T. (2006). Big Five: die fünf grundlegenden Dimensionen der Persönlichkeit und ihre 30 Facetten. Persönlichkeitsmodelle Und Persönlichkeitstests, 15, 113–135. Retrieved from https://books.google.de/books?hl=de&lr=&id=Gzlib3P5UQMC&oi=fnd&pg=PA113&ots= m1l0t-QifP&sig=K2754txjbpRX7OPUMFVzd-YB_Bk Feng, W., Ramo, D. E., Chan, S. R., & Bourgeois, J. A. (2017). Internet gaming disorder: Trends in prevalence 1998-2016. Addictive Behaviors, 75, 17–24. https://doi.org/10.1016/j.addbeh.2017.06.010 Floros, G., Siomos, K., Stogiannidou, A., Giouzepas, I., & Garyfallos, G. (2014). The relationship between personality, defense styles, internet addiction disorder, and psychopathology in college students. Cyberpsychology, Behavior and Social Networking, 17(10), 672–676. https://doi.org/10.1089/cyber.2014.0182 Gao, T., Xiang, Y. T., Zhang, H., Zhang, Z., & Mei, S. (2017). Neuroticism and quality of life: Multiple mediating effects of smartphone addiction and depression. Psychiatry Research, 258, 457–461. https://doi.org/10.1016/j.psychres.2017.08.074 González-Bueso, V., Santamaría, J. J., Fernández, D., Merino, L., Montero, E., & Ribas, J. (2018). Association between Internet Gaming Disorder or Pathological Video-Game Use and Comorbid Psychopathology: A Comprehensive Review. International Journal of Environmental Research and Public Health, 15(4). https://doi.org/10.3390/ijerph15040668 Griffiths, M., Lewis, A., & Griffiths, M. D. (2011). Confronting gender representation: A qualitative study of the experiences and motivations of female casual-gamers. Retrieved

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

30

from http://www.raco.cat/index.php/Aloma Huang, C. (2010). Mean-level change in self-esteem from childhood through adulthood: Metaanalysis of longitudinal studies. Review of General Psychology, 14(3), 251–260. https://doi.org/10.1037/a0020543 Kayiş, A. R., Satici, S. A., Yilmaz, M. F., Şimşek, D., Ceyhan, E., & Bakioǧlu, F. (2016). Big five-personality trait and internet addiction: A meta-analytic review. Computers in Human Behavior, 63, 35–40. https://doi.org/10.1016/j.chb.2016.05.012 King, D. L., Delfabbro, P. H., Griffiths, M. D., & Gradisar, M. (2012). Cognitive-Behavioral Approaches to Outpatient Treatment of Internet Addiction in Children and Adolescents. Journal of Clinical Psychology, 68(11), 1185–1195. https://doi.org/10.1002/jclp.21918 King, D. L., Delfabbro, P. H., Wu, A. M. S., Doh, Y. Y., Kuss, D. J., Pallesen, S., … Sakuma, H. (2017). Treatment of Internet gaming disorder: An international systematic review and CONSORT evaluation. Clinical Psychology Review, 54, 123–133. https://doi.org/10.1016/j.cpr.2017.04.002 Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. S., & Chen, C. C. (2012). The association between Internet addiction and psychiatric disorder: a review of the literature. European Psychiatry : The Journal of the Association of European Psychiatrists, 27(1), 1–8. https://doi.org/10.1016/j.eurpsy.2010.04.011 Koo, H. J., & Kwon, J.-H. (2014). Risk and protective factors of internet addiction: a metaanalysis of empirical studies in Korea. Yonsei Medical Journal, 55(6), 1691–1711. https://doi.org/10.3349/ymj.2014.55.6.1691 Kühn, S., Kugler, D. T., Schmalen, K., Weichenberger, M., Witt, C., & Gallinat, J. (2019). Does playing violent video games cause aggression? A longitudinal intervention study. Molecular

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

31

Psychiatry, 24(8), 1220–1234. https://doi.org/10.1038/s41380-018-0031-7 Kuss, D. J., & Griffiths, M. D. (2015). Internet Addiction in Psychotherapy. Internet Addiction in Psychotherapy. Palgrave Macmillan UK. https://doi.org/10.1057/9781137465078 Kuss, D. J., Pontes, H. M., & Griffiths, M. D. (2018, May 8). Neurobiological correlates in internet gaming disorder: A systematic literature review. Frontiers in Psychiatry. Frontiers Media S.A. https://doi.org/10.3389/fpsyt.2018.00166 Lachmann, B., Sariyska, R., Kannen, C., Stavrou, M., & Montag, C. (2017). Commuting, LifeSatisfaction and Internet Addiction. International Journal of Environmental Research and Public Health, 14(10). https://doi.org/10.3390/ijerph14101176 Laconi, S., Pirès, S., & Chabrol, H. (2017). Internet gaming disorder, motives, game genres and psychopathology. Computers in Human Behavior, 75, 652–659. https://doi.org/10.1016/j.chb.2017.06.012 Laier, C., Wegmann, E., & Brand, M. (2018). Personality and cognition in gamers: Avoidance expectancies mediate the relationship between maladaptive personality traits and symptoms of Internet-gaming disorder. Frontiers in Psychiatry, 9(JUL). https://doi.org/10.3389/fpsyt.2018.00304 Li, W., Mills, D., & Nower, L. (2019). The relationship of loot box purchases to problem video gaming and problem gambling. Addictive Behaviors, 97, 27–34. https://doi.org/10.1016/j.addbeh.2019.05.016 Lopez-Fernandez, O. (2018). Generalised Versus Specific Internet Use-Related Addiction Problems: A Mixed Methods Study on Internet, Gaming, and Social Networking Behaviours. International Journal of Environmental Research and Public Health, 15(12). https://doi.org/10.3390/ijerph15122913

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

32

Lopez-Fernandez, O., Williams, A. J., Griffiths, M. D., & Kuss, D. J. (2019). Female Gaming, Gaming Addiction, and the Role of Women Within Gaming Culture: A Narrative Literature Review. Frontiers in Psychiatry, 10, 454. https://doi.org/10.3389/fpsyt.2019.00454 Löwe, B., Kroenke, K., & Gräfe, K. (2005). Detecting and monitoring depression with a twoitem questionnaire (PHQ-2). Journal of Psychosomatic Research, 58(2), 163–171. https://doi.org/10.1016/j.jpsychores.2004.09.006 Löwe, B., Spitzer, R. L., Zipfel, S., & Herzog, W. (2002). Gesundheitsfragebogen für Patienten (PHQ-D). Komplettversion und Kurzform. 2. Auflage. (Vol. 9). Karlsruhe. Retrieved from https://www.psycharchives.org/bitstream/20.500.12034/431/1/PT_9006490_ASKUManual_2012.PDF McLean, L., & Griffiths, M. D. (2018). Female Gamers’ Experience of Online Harassment and Social Support in Online Gaming: A Qualitative Study. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-018-9962-0 Meerkerk, G.-J., Van Den Eijnden, R. J. J. M., Vermulst, A. A., & Garretsen, H. F. L. (2009). The Compulsive Internet Use Scale (CIUS): some psychometric properties. Cyberpsychology & Behavior : The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 12(1), 1–6. https://doi.org/10.1089/cpb.2008.0181 Mihara, S., & Higuchi, S. (2017, July 1). Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry and Clinical Neurosciences. Blackwell Publishing. https://doi.org/10.1111/pcn.12532 Müller, K. W., Beutel, M. E., Egloff, B., & Wölfling, K. (2014). Investigating risk factors for Internet gaming disorder: a comparison of patients with addictive gaming, pathological gamblers and healthy controls regarding the big five personality traits. European Addiction

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

33

Research, 20(3), 129–136. https://doi.org/10.1159/000355832 Müller, Kai W, & Wölfling, K. (2017). AICA-SKI:IBS Strukturiertes klinisches Interview zu Internetbezogenen Störungen * Handbuch. Mainz. Munno, D., Cappellin, F., Saroldi, M., Bechon, E., Guglielmucci, F., Passera, R., & Zullo, G. (2017). Internet Addiction Disorder: Personality characteristics and risk of pathological overuse in adolescents. Psychiatry Research, 248, 1–5. https://doi.org/10.1016/j.psychres.2016.11.008 Navrady, L. B., Ritchie, S. J., Chan, S. W. Y., Kerr, D. M., Adams, M. J., Hawkins, E. H., … McIntosh, A. M. (2017). Intelligence and neuroticism in relation to depression and psychological distress: Evidence from two large population cohorts. European Psychiatry, 43, 58–65. https://doi.org/10.1016/j.eurpsy.2016.12.012 Pechler, S. (2018). Internet Gaming Disorder – Depression – Prokrastination. Nervenheilkunde, 37(09), 582–586. https://doi.org/10.1055/s-0038-1670567 Piedmont, R. L. (2001). Cracking the plaster cast: Big five personality change during intensive outpatient counseling. Journal of Research in Personality, 35(4), 500–520. https://doi.org/10.1006/jrpe.2001.2326 Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41(1), 203–212. https://doi.org/10.1016/j.jrp.2006.02.001 Rammstedt, B., Kemper, C. J., Klein, M. C., Beierlein, C., & Kovaleva, A. (2013). Eine kurze Skala zur Messung der fünf Dimensionen der Persönlichkeit A Short Scale for Assessing the Big Five Dimensions of Personality. Jg, 7(2), 233–249. https://doi.org/10.12758/mda.2013.013

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

34

Rehbein, Florian; Mößle, Thomas; Jukschat, N. (2010). Zum Suchtpotential von Computerspielen: Onlinerollenspiele wie” World of Warcraft” bergen ein erhöhtes Abhängigkeitsrisiko und erfordern Konsequenzen in den Bereichen Jugendmedienschutz und Prävention. Retrieved October 4, 2019, from https://www.researchgate.net/publication/236630472_Zum_Suchtpotential_von_Computers pielen_Onlinerollenspiele_wie_World_of_Warcraft_bergen_ein_erhohtes_Abhangigkeitsris iko_und_erfordern_Konsequenzen_in_den_Bereichen_Jugendmedienschutz_und_Praventio n Rumpf, H.-J. (2017). Expertise “Suchtfördernde Faktoren von Computer-und Internetspielen.” Lübeck. Rumpf, H.-J., Meyer, C., Kreuzer, A., & John, U. (2011). Prävalenz der Internetabhängigkeit (PINTA) Bericht an das Bundesministerium für Gesundheit. Scherer, L., Mader, L., Beutel, M., Wölfling, K., & Müller, K. (2019). Geschlechtsspezifische Aspekte der Internetsucht. https://doi.org/10.1055/s-0039-1696222 Schmitt, D. P., Realo, A., Voracek, M., & Allik, J. (2008). Why Can’t a Man Be More Like a Woman? Sex Differences in Big Five Personality Traits Across 55 Cultures. Journal of Personality and Social Psychology, 94(1), 168–182. https://doi.org/10.1037/00223514.94.1.168 Seong, W., Hong, J. S., Kim, S., Kim, S. M., & Han, D. H. (2019). Personality and Psychological Factors of Problematic Internet Gamers Seeking Hospital Treatment. Frontiers in Psychiatry, 10, 583. https://doi.org/10.3389/fpsyt.2019.00583 Soldz, S., & Vaillant, G. E. (1999). The Big Five Personality Traits and the Life Course: A 45Year Longitudinal Study. Journal of Research in Personality, 33(2), 208–232.

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

35

https://doi.org/10.1006/jrpe.1999.2243 Soto, C. J., John, O. P., Gosling, S. D., & Potter, J. (2011). Age Differences in Personality Traits From 10 to 65: Big Five Domains and Facets in a Large Cross-Sectional Sample. Journal of Personality and Social Psychology, 100(2), 330–348. https://doi.org/10.1037/a0021717 Spinhoven, P., Huijbers, M. J., Ormel, J., & Speckens, A. E. M. (2017). Improvement of mindfulness skills during Mindfulness-Based Cognitive Therapy predicts long-term reductions of neuroticism in persons with recurrent depression in remission. Journal of Affective Disorders, 213, 112–117. https://doi.org/10.1016/j.jad.2017.02.011 Steinbüchel, T. A., Herpertz, S., Külpmann, I., Kehyayan, A., Dieris-Hirche, J., & Te Wildt, B. T. (2018). [Internet Addiction, Suicidality and Non-Suicidal Self-Harming Behavior - A Systematic Review]. Psychotherapie, Psychosomatik, Medizinische Psychologie, 68(11), 451–461. https://doi.org/10.1055/s-0043-120448 Thalmayer, A. G., Saucier, G., & Eigenhuis, A. (2011). Comparative Validity of Brief to Medium-Length Big Five and Big Six Personality Questionnaires. Psychological Assessment, 23(4), 995–1009. https://doi.org/10.1037/a0024165 Torres-Rodríguez, A., Griffiths, M. D., Carbonell, X., & Oberst, U. (2018). Internet gaming disorder in adolescence: Psychological characteristics of a clinical sample. Journal of Behavioral Addictions, 7(3), 707–718. https://doi.org/10.1556/2006.7.2018.75 Wang, C.-W., Ho, R. T. H., Chan, C. L. W., & Tse, S. (2015). Exploring personality characteristics of Chinese adolescents with internet-related addictive behaviors: trait differences for gaming addiction and social networking addiction. Addictive Behaviors, 42, 32–35. https://doi.org/10.1016/j.addbeh.2014.10.039 Wang, Z., Hu, Y., Zheng, H., Yuan, K., Du, X., & Dong, G. (2019). Females are more

Journal Pre-proof Running Head: PROBLEMATIC GAMING BEHAVIOR AND THE

36

vulnerable to Internet gaming disorder than males: Evidence from cortical thickness abnormalities. Psychiatry Research. Neuroimaging, 283, 145–153. https://doi.org/10.1016/j.pscychresns.2018.11.001 Wartberg, L., Kriston, L., Zieglmeier, M., Lincoln, T., & Kammerl, R. (2019). A longitudinal study on psychosocial causes and consequences of Internet gaming disorder in adolescence. Psychological Medicine, 49(2), 287–294. https://doi.org/10.1017/S003329171800082X Winkler, A., Dörsing, B., Rief, W., Shen, Y., & Glombiewski, J. A. (2013). Treatment of internet addiction: a meta-analysis. Clinical Psychology Review, 33(2), 317–329. https://doi.org/10.1016/j.cpr.2012.12.005 Wölfling, K., Müller, K. W., Dreier, M., Ruckes, C., Deuster, O., Batra, A., … Beutel, M. E. (2019). Efficacy of Short-term Treatment of Internet and Computer Game Addiction: A Randomized Clinical Trial. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2019.1676 World Health Organization. (2018). WHO releases new International Classification of Diseases (ICD 11). Retrieved September 20, 2019, from https://www.who.int/news-room/detail/1806-2018-who-releases-new-international-classification-of-diseases-(icd-11) Zendle, D., & Cairns, P. (2018). Video game loot boxes are linked to problem gambling: Results of a large-scale survey. PLoS ONE, 13(11). https://doi.org/10.1371/journal.pone.0206767 Zhang, J.-T., & Brand, M. (2018). Editorial: Neural Mechanisms Underlying Internet Gaming Disorder. Frontiers in Psychiatry, 9, 404. https://doi.org/10.3389/fpsyt.2018.00404

Journal Pre-proof

CRediT statement

Jan Dieris-Hirche: Conceptualization, Methodology, Writing - Original Draft, Project administration, Formal analysis, Resources, Investigation / Acquired the data Magdalena Pape: Conceptualization, Writing - Review & Editing, Investigation / Acquired the data Laura Bottel: Conceptualization, Writing - Review & Editing, Investigation / Acquired the data Stephan Herpertz: Writing - Review & Editing Bert te Wildt: Writing - Review & Editing Salam Aicha: Investigation / Acquired the data Maren Esch: Investigation / Acquired the data ______________________ Authors' contributions: JDH, LB, MP conceived the study, JDH, MP, LB, SA, AK, ME acquired the data, JDH did the analysis and drafted the manuscript. BtW, LB, MP and SH gave critical input and contributed to the interpretation of results and writing of the manuscript from draft to submission. All authors read and approved the final manuscript.

Journal Pre-proof Figure 1:

Video Gamers, N = 820 Short CIUS < 9 (n = 582)

Short CIUS ≥ 9 (n = 238) Life satisfaction (L-1), p = .005 0.4 0.3

Depression (PHQ-2), p < .001

0.2 0.1

Agreeableness (BFI_A), p = .301

0 -0.1 -0.2 -0.3 -0.4 self-efficacy (ASKU), p < .001

-0.5

Extraversion (BFI_E), p = .004

Conscientiousness (BFI_C), p < .001

Openness (BFI_O), p = .032

Neuroticism (BFI_N), p < .001

Figure 1. Radar-chart of gaming behavior, Big Five personality traits, depression symptoms, self-efficacy, and life-satisfaction (z-standardized scores). Differences between problematic and unproblematic gaming behavior. PHQ-2 = Patient Health Questionnaire – 2 item version; BFI = Big Five Inventory; L-1 = Life satisfaction; ASKU = Allgemeine Selbstwirksamkeit Kurzskala.

Journal Pre-proof

Game Genres, N = 820 MMORPGs (n = 252)

Gaming time h/week, p < .001

Ego-shooter (n = 220) Life satisfaction (L-1), p = .038 0.4 0.3 0.2

Other genres (n = 348)

Agreeableness (BFI_A), p = .023

0.1 0 Depression (PHQ-2), p = .429

-0.1 -0.2

Conscientiousness (BFI_C), p < .001

-0.3 -0.4 Problematic gaming (short CIUS), p < .001

Openness (BFI_O), p = .099

Self-efficacy (ASKU), p = .377

Neuroticism (BFI_N), p = .598 Extraversion (BFI_E), p = .163

Figure 2. Radar-chart of gaming behavior, Big Five personality traits, depression symptoms, and life-satisfaction (z-standardized scores). Differences between game genres. PHQ-2 = Patient Health Questionnaire – 2 item version; BFI = Big Five Inventory; L-1 = Life satisfaction; ASKU = Allgemeine Selbstwirksamkeit Kurzskala.

Journal Pre-proof

Manuscript No.: CHB-D-19-01826: Title: Problematic Gaming Behavior and the Personality Traits of Video Gamers: A cross-sectional survey. Highlights

-

Problematic video gaming behavior is associated with specific personality traits.

-

MMORPGs and FPS games are associated with increased time spent playing and an increased risk of problematic gaming.

-

Depression symptoms, extensive time spent playing, and personality traits can predict problematic gaming.

Journal Pre-proof

Table 1

Characteristics, Gaming Behavior, Personality Traits, Depressive Mood, and Life Satisfaction: Problematic (Short CIUS Cut-off < 9) vs. Unproblematic (Short CIUS Cut-off ≥ 9) Gamers Gaming behavior, N = 820 Variable

Unproblematic

Problematic

(n =582, 71%)

(n = 238, 29%)

Statistics (df)

p

effect size

26.1 (10.5)

23.0 (9.3)

t(818) = 10.21

< .001

|d| = 0.08

X2 (2) = 4.38

.112

V = 0.07

X2 (2) = 4.16

.125

V = 0.07

X2 (3) = 15.62

< .001

V = 0.14

Age (M, SD) Gender (n, %) Women

166 (28.5%)

51 (21.4%)

Men

412 (70.8%)

185 (77.7%)

4 (0.7%)

2 (0.8%)

Others / diverse Level of education (n, %) Low (≤ 9 yrs.)

42 (7.2%)

27 (11.3%)

Moderate (10 - 11 yrs.)

211 (36.3%)

88 (25.7%)

High (≥ 12 yrs.)

329 (56.5%)

123 (40.1%)

Employment status School / vocational training Student

159 (27.3%)

96 (40.3%)

389 (66.8%)

128 (53.8%)

17 (2.9%)

10 (4.2%)

0 (0%)

0 (0%)

17 (2.9%)

4 (1.7%)

On weekdays

2.60 (2.23)

3.72 (2.56)

t(817) = 3.86

< .001

|d| = 0.43

On the weekends

3.88 (3.03)

5.86 (3.21)

t(818) = 7.82

< .001

|d| = 0.64

20.80 (15.31)

30.35 (16.02)

t(817) = 0.80

< .001

|d| = 0.61

4.45 (2.43)

11.42 (2.54)

t(818) = 2.19

< .001

|d| = 2.83

Total Score (M, SD)

1.21 (1.32)

1.91 (1.52)

t(818) = 361

< .001

|d| = 0.50

Cuf-off ≥ 3 (n, %)

97 (16.7%)

74 (31.1%)

X2 (1) = 21.29

< .001

φ = 0.16

Extraversion

3.51 (1.00)

3.29 (0.97)

t(818) = 2.85

.004

|d| = 0.22

Conscientiousness

3.45 (0.78)

3.02 (0.83)

t(818) = 6.98

< .001

|d| = 0.54

Openness

3.71 (0.92)

3.56 (1.01)

t(818) = 2.14

.032

|d| = 0.15

Neuroticism

2,50 (0.87)

2.86 (0.85)

t(818) = -5.54

< .001

|d| = 0.41

Agreeableness

3.16 (0.84)

3.09 (0.85)

t(818) = 1.03

.301

|d| = 0.08

ASKU (M, SD)

4.14 (0.63)

3.85 (0.70)

t(818) = 5.61

< .001

|d| = 0.44

L-1 (M, SD)

7.74 (1.80)

7.28 (2.29)

t(818) = 3.08

.002

|d| = 0.23

Employed Unemployed Ohters time spent playing (hours / day)

Per week Short CIUS (M, SD) PHQ-2

BFI-10 (M, SD)

Journal Pre-proof

Note. Short CIUS = short Compulsive Internet Use Scale; PHQ-2 = Patient Health Questionnaire - 2 item version; BFI-10 = Big Five Inventory - 10 items version; ASKU = Allgemeine Selbstwirksamkeit Kurzskala; L-1 = Life Satisfaction - 1 item version.

Journal Pre-proof

Table 2:

Pearson's Correlation Coefficients between Variables, N=820 Gamers. 1

2

3

4

5

6

7

8

9

1

Age (y)

2

Short CIUS

-.191**

3

time spent playing (h/week)

-.215**

.353**

4

L-1

-.020

-.100**

-.063

5

PHQ-2

-.044

.228**

.067

-.380**

6

ASKU

.105**

-.214**

-.149**

.426**

-.261**

7

BFI_A

-.002

-.043ns

-0.059ns

0.031ns

-0.005ns

-0.036ns

8

BFI_C

0.266**

-.304**

-.154**

.178**

-.146**

.253**

.044

9

BFI_O

.020

-.064

-.015

-.089*

.055

.046

-.004

.110**

10

BFI_N

-.073*

.171**

.026

-.333**

.238**

-.333**

.021

-.100**

.000

11

BFI_E

.071*

-.128**

-.116**

.273**

-.224**

.242**

-.018

.142**

.080*

10

-.251**

Note. PHQ-2 = Patient Health Questionnaire - 2 item, Short CIUS = Short Compulsive Internet Use Scale, L-1 = Life Satisfaction-1 short scale, BFI10 = Big Five Inventory, BFI_E = Big Five Inventory scale "extraversion", BFI_C = Big Five Inventory scale "conscientiousness", BFI_O = Big Five Inventory scale "openness", BFI_A = Big Five Inventory scale "agreeableness", ASKU = Allgemeine Selbstwirksamkeit Kurzskala (General SelfEfficacy Short Scale), * = p ≤ .05, ** = p ≤ .01

Journal Pre-proof

Table 3 Logistic Regression for the Likelihood of Problematic Gaming Behavior. B S.E. Wald

df

p

OR

95%-CI

Age Gender Life satisfaction Self-efficacy Depression symptoms Agreeableness Conscientiousness Openness Neuroticism Extraversion Gaming hours / week Genre Genre1 Genre2 Constant

1 1 1 1 1 1 1 1 1 1 1 2 1 1 1

.402 .185 .439 .175 < .001 .554 < .001 .118 < .001 .568 < .001 .078 .085 .702 .317

0.99 1.34 1.04 0.82 1.34 0.94 0.62 0.86 1.55 1.05 1.13

[0.97, 1.01] [0.87, 2.07] [0.941, 1.15] [0.62, 1.09] [1.18, 1.52] [0.77, 1.15 [0.49, 0.78] [0.72, 1.04] [1.24, 1.94] [0.88, 1.26] [1.09, 1.17]

1.45 0.92 0.37

[0.95, 2.27] [0.60, 1.41]

-0.008 0.295 0.039 -0.199 0.295 -0.061 -0.475 -0.146 0.439 0.053 0.119

0.01 0.222 0.051 0.146 0.064 0.103 0.116 0.093 0.113 0.092 0.018

0.383 -0.083 -1.004

0.223 0.216 1.003

0.703 1.759 0.599 1.842 21.221 0.351 16.818 2.447 15.076 0.326 42.413 5.101 2.964 0.147 1.002

Note. B = regression coefficient, S.E. = standard error, Wald = Wald Test coefficient, df = degrees of freedom, p = statistical significance, OR = odds ratio

Journal Pre-proof

Table 4

Gender Differences Regarding Gaming Behavior, Personality Traits, Depression Mood, and Life Satisfaction: Female vs. Male Gamers

Variable

Women

Men

(n = 217)

(n = 597)

M

SD

M

SD

t(812)

p

|d|

25.88

10.88

25.03

10.14

1.03

.31

0.08

2.36

2.30

3.11

2.36

-4.02

< .001

0.32

On the weekends

3.76

3.10

4.72

3.25

-3.73

< .001

0.29

Per week

19.35

15.27

25.04

16.06

-4.52

< .001

0.36

Short CIUS

5.56

3.89

6.80

4.01

-3.94

< .001

0.31

PHQ-2

1.58

1.40

1.34

1.42

2.15

.032

0.17

Extraversion

3.41

1.02

3.46

0.98

-.68

.50

0.05

Conscientiousness

3.49

0.83

3.27

0.81

3.29

< .001

0.27

Openness

3.93

0.97

3.57

0.93

4.91

< .001

0.38

Neuroticism

2.97

0.87

2.47

0.84

7.40

< .001

0.59

Agreeableness

3.21

0.79

3.11

0.87

1.60

.11

0.12

ASKU

3.97

0.72

4.09

0.64

-2.18

.03

0.18

L-1

7.08

2.16

7.82

1.84

-4.44

< .001

0.38

Age (years) time spent playing (hrs./day) On weekdays

BFI-10

Note. Short CIUS = short Compulsive Internet Use Scale; PHQ-2 = Patient Health Questionnaire - 2 item version; BFI-10 = Big Five Inventory - 10 items version; ASKU = Allgemeine Selbstwirksamkeit Kurzskala; L-1 = Life Satisfaction - 1 item version.