RISK FACTORS IN NTERNET AND NON INTERNET GAMBLING AND VIDEO GAME PLAYING AMONG UNIVERSITY STUDENTS

Transcription

1 RISK FACTORS IN NTERNET AND NON INTERNET GAMBLING AND VIDEO GAME PLAYING AMONG UNIVERSITY STUDENTS Tsvetelina Mihaylova, MA candidate and Sylvia Kairouz, PhD Concordia University, Montreal, Canada September 17, th European Conference on Gambling and Policy Issues

2 BACKGROUND Gamblers and video game players similarities and differences (Griffiths & Wood, 2005) Gambling and video game playing on the Internet linked to problems/addiction (Ng, 2005; Welte et al., 2004) Associated problems could include psychological distress, poor academic performance (Gupta & Derevensky, 1998; Griffiths, 2000, 2008) (Problem) gambling linked to alcohol use & illicit drug use & dependence (LaBrie et al., 2003, Engwall et al., 2004) Video game playing linked to alcohol use (Greenberg et al., 1999)

3 BACKGROUND Need to look at the role of personality traits in differentiating gambling and video game playing, such as propensity to risk taking (Steel & Blaszynski, 1998) Risk taking can take two forms: (Eysenck & Eysenck, 1991) 1) Impulsivity 2) Venturesomeness Problem gambling marked by high impulsivity and low venturesomeness (Moore & Ohtsuka, 1997)

4 AIMS OF THE STUDY 1) To examine differences in personality traits (i.e. impulsivity and venturesomeness) in Internet and non Internet gamblers and video game players, which may predispose them to risky behaviours and problems 2) To compare the four groups on associated problems, such as psychological distress, and poor academic performance 3) To compare the four groups on co occurring risk behaviours such as alcohol and substance (mis)use

5 DATA ENHJEU (Enquête sur les habitudes de jeu des étudiants universitaires) Survey on gambling habits among university students (2008) Stratified design with three universities & three affiliated schools in Montreal Undergraduate full time students sample (N=2,139); Age (M=22.6, range 17 51) Response rate 41% 66.5% responded via web Survey language: French 46.5%

6 SAMPLE CHARACTERISTICS Study sample (N=1,118) Gamblers: Gender (316 Male, 634 Female); Age (M=22.6, SD=3.9) Vide0 gamers: Gender (107 Male, 61 Female); Age (M=22.4, SD=3.7) Offline videogamers Online videogamers Offline gamblers Online gamblers Gender (Male) % Age (SD=2.59) (SD=4.37) (SD=4.01) (SD=2.99) Place of birth (in Canada) % Marital status (Married or de facto union) %

7 PROCEDURE Comparison of four groups: Video game players:* Internet : played video games online (incl. MMOGs/MMORPGs) Non Internet: played video games offline (Arcade, Private residence, etc.) Gamblers: * Internet: gambled on any of 10 gambling activities online (i.e., on Internet) Non Internet: gambled on any of 10 gambling activities offline (i.e., in casinos, Bars, at school/work, etc.) *Past 12 month time frame employed

8 MEASURES & ANALYSES Personality traits/dispositions (measured by the Eysenck Impulsiveness Scale)(Eysenck et al., 1984) Impulsivity Venturesomeness Associated problems Psychological distress (measured by the General Health Questionnaire (GHQ)) (a cut off point of 4+ was applied) (Goldberg, 1988) Academic performance (measured by GPA) Co occurring risk behaviours (Alcohol and substance (mis)use) Hazardous & harmful drinking AUDIT8+ (Babor et al., 2001) Cannabis use (past 12 months) Illicit drug use (past 12 months) Analyses Linear and binomial logistic regressions

9 * ANOVA: F(3, 1096) = 2.43, p=0.064 Personality Dispositions: Impulsivity

10 * ANOVA: F(3, 1096) = 2.46, p=0.061 Personality Dispositions: Venturesomeness

11 * 2=24.088, (p=0.000) Associated Problems: Psychological distress

12 * ANOVA: F(3, 921) = 0.56, p=0.639 Associated Problems: Academic performance

13 * χ2=18.845, (p=0.000) Co-occurring Risk Behaviours: Harmful drinking

14 * χ2=33.452, (p=0.000) Co-occurring Risk Behaviours: Cannabis use

15 * χ2=15.547, (p=0.001) Co-occurring Risk Behaviours: Illicit drug use

16 Personality Dispositions Table 1. Linear regression predicting personality dispositions among Internet and non Internet video gamers and gamblers (N=1,118) MAIN EFFECTS INTERACTION EFFECT PLAYING STATUS PLAYING ENVIRONMENT Gamblers Internet (ref. Video gamers) (ref. non Internet) PERSONALITY DISPOSITIONS Impulsivity 0.38** 0.14 ns Venturesomeness 0.40* 0.42* ns

17 Associated Problems: Psychological Distress Table 2. Logistic regression predicting elevated level of psychological distress among Internet and non Internet video gamers and gamblers (N=1,118) MAIN EFFECTS INTERACTION EFFECT PLAYING STATUS PLAYING ENVIRONMENT Gamblers (ref. Video gamers) Internet (ref. non Internet) ASSOCIATED PROBLEMS OR OR OR Psychological Distress (ref. No) ns

18 RESULTS: Associated Problems (Academic Performance) Table 3. Logistic regression predicting academic performance among Internet and non Internet video gamers and gamblers (N=1,118) MAIN EFFECTS INTERACTION EFFECT PLAYING STATUS PLAYING ENVIRONMENT Gamblers (ref. Video gamers) Internet (ref. non Internet) ASSOCIATED PROBLEMS Academic performance (GPA) ns

19 RESULTS: Co-occurring Risk Behaviours Table 4. Playing status and playing environment as predictors of co occurring risk behaviours among Internet and non Internet video gamers and gamblers (N=1,118) PLAYING STATUS Gamblers (ref. Videogamers) MAIN EFFECTS PLAYING ENVIRONMENT Internet (ref. non Internet) INTERACTION EFFECT CO OCCURRING RISK BEHAVIOURS OR OR OR Audit 8+ (ref. No) 3.64*** 1.61 ns Cannabis Use (ref. No) 4.18*** 2.28*** ns Illicit Drug Use (ref. No) 3.32*** 2.91*** ns

20 Personality dispositions: DISCUSSION: FINDINGS Impulsivity and venturesomeness lowest among offline video gamers, and highest among online gamblers Gamblers scored higher on impulsivity & venturesomeness Internet players scored higher on venturesomeness Associated problems: No significant differences emerged between the four groups on psychological distress and academic performance Co occurring alcohol and substance use: Lowest risk among offline video gamers, highest risk among online gamblers Gamblers more likely to engage in hazardous drinking, cannabis and illicit drug use Internet players more likely to endorse cannabis and illicit drug use

21 CONCLUSION Gamblers and online players display risk taking personality dispositions, more so than video gamers and offline players Gambling and the Internet are more attractive for risk loving individuals? Risk loving tendencies do not necessarily result in problems associated with the behaviour Yet, co occurrence of other risk behaviours may point to increased vulnerability of gamblers and Internet players Need for innovative preventive services, targeting this at risk population and addressing this vulnerability

22 LIMITATIONS/CAVEATS Problem gambling and video gaming addiction not assessed Still, not clear if: individuals with certain personality traits are drawn to the activity and the medium Or if it is the structural features of the activity and the medium which unlock and exacerbate risk taking predispositions so that some individuals develop problems Self report nature of data may alter some realities Risk taking tendencies may be part of a transitory experimentation period characteristic of young adults

23 ACKNOWLEDGEMENTS This project was realized with funding provided by Fonds de recherche sur la société et la culture (FQRSC) to Sylvia Kairouz. The analyses were conducted while Tsvetelina Mihaylova was supported by graduate scholarships through the Social Sciences and Humanities Research Council (SSRHC) and Fonds de recherce sur la société et la culture (FQRSC). We are also particularly grateful to Dr. Louise Nadeau and Marc Antoine Coté Marcil for their insights during the various phases of the project. Lastly, we thank all the universities which gave us access to our participants and made this study possible.

24 Thank you! Contact: Dr. Sylvia Kairouz Tsvetelina Mihaylova Lifestyle and Addiction Research Lab Concordia University 2080 Mackay, X 304 Montreal, QC, Canada H3G 2J1 Phone: (514) extension skairouz@alcor.concordia.ca lina.mihaylova@gmail.com