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International Journal of Mental Health and Addiction

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Open Access 01-02-2022 | Original Article

The Physical, Emotional, and Identity User-Avatar Association with Disordered Gaming: A Pilot Study

Authors: Vasileios Stavropoulos, Emma Dumble, Sandra Cokorilo, Mark D. Griffiths, Halley M. Pontes

Published in: International Journal of Mental Health and Addiction | Issue 1/2022

Abstract

Internet gaming disorder (IGD) is a rapidly expanding psychopathological manifestation necessitating further research and clinical attention. Although recent research has investigated relationships between user-avatar and excessive gaming, little is known about the interplay between IGD and avatar self-presence and its dimensions (i.e., the physical, emotional, and identity bond developed between the user and the in-game character). The aim of the present pilot study was twofold: (i) to investigate the associations between physical, emotional, and identity aspects of self-presence associate and IGD severity, and (ii) to assess IGD variations longitudinally in relation to the three dimensions of self-presence (i.e., proto-self-presence, core-self-presence, and extended-self-presence). The sample comprised 125 young adults aged between 18 and 29 years who underwent either (i) three offline measurements (1 month apart, over 3 months) or (ii) a cross-sectional online measurement. Regression and latent growth analysis indicated that the initial intensity of the physical, emotional, and identity self-presence aspects associated with IGD severity, but not to its longitudinal change. Overall, young adult gamers may exhibit higher IGD risk and severity when the experience of physical, emotional, and identity bonding with their in-game character is pronounced. The implications surrounding treatment and preventative policy recommendations are further discussed.

To examine potential differences between the Internet and FtF respondents related to their sociodemographic and Internet behaviors, independent sample t tests and chi-square analyses were conducted. The results yielded non-significant variations considering participants’ gender (X² = .21, df = 1, p = .89), chronological age (t = −.54, df = 120, p = .59) and history of Internet use (t = 2.35, df = 122, p = .06). Accordingly, Internet and FtF responses (i.e., TP1) were merged for the cross-sectional results.

The attrition of the longitudinal data collection across the three waves during 3months in the FtF measurements was examined. The measurements’ frequency per respondent was situated within a range of 1–3 (M_average = 2.57). T1 involved 61 respondents, while T2 involved 56 participants (8.20% attrition) and T3 included a total of 43 respondents (29.51% attrition). Attrition across all three waves was deemed non-problematic according to Missing Completely At Random test (MCAR X² = 1715.79, p = 1.00; Little 1988). Therefore, maximum likelihood imputation (five times) of missing values was conducted (Newman 2003).

The present research belongs to a broader project examining the interactions of gamer and game-related influences in the emergence of IGD behaviors among young adults, MMO gamers. Instruments used in the data include the following: (1) Internet Gaming Disorder 9- Short Form (Pontes and Griffiths 2015); (2) Beck Depression Inventory II

(Beck et al. 1996); (3) Beck Anxiety Inventory (Beck and Steer 1990); (4) Hikikomori-Social Withdrawal Scale (Teo et al. 2015); (5) Attention Deficit Hyperactivity Self-Report Scale (Kessler et al. 2005); (6); Ten Item Personality Inventory (Gosling et al. 2003); (7) The Balanced Family Cohesion Scale (Olson 2000); (8) Presence Questionnaire (Faiola et al. 2013); (9) Online Flow Questionnaire (Chen et al. 2000) (10) Self-Presence Questionnaire (Ratan and Hasler 2009; Ratan and Dawson 2016; (11) The Gaming-Contingent Self-Worth Scale (Beard and Wickham 2016); and (12) Demographic and Internet Use Questions. The above sequence of self-reported scales was administered for Internet and FtF respondents, while a fitness tracker (Fitbit flex) was additionally employed to monitor physical activity in the FtF collection mode. Different parts of the data have been used to answer different research aims in the four previously published studies by Burleigh et al. 2018; Adams, Stavropoulos, Bulreigh, Liew and Griffiths 2018; Liew, Stavropoulos, Burleigh, Adams and Griffiths 2018; Stavropoulos, Burleigh, Beard, Gomez and Griffiths 2018b).

Abiding to the ethical permission granted, the research advertisements (a) described that respondents were requested to address the survey at three occasions, 1 month apart each; (b) provided email correspondence to be contacted when necessary; and (c) clearly defined the data collection modes and stages (FtF and Internet). Young adult MMO gamers willing to be enrolled first accessed the Plain Language Information Statement (PLIS). This clarified and indicated that the survey was voluntary and that respondents could interrupt their participation at any point. Gamers who chose to take part were first requested to confirm their informed consent.

If the chi-square is insignificant, it indicates a good model fit because the observed and the model produced covariance matrices do not significantly differ. Nevertheless, this indicator is dismissed in sample sizes exceeding 200 and when multivariate normality is absent.

The RMSEA reflects the square root of the average regarding the residual differences between the observed and the model produced covariance matrices. Zero suggests an excellent fit. In general, RMSEA rates lower than .08, and in an optimum case, lower than .05 are considered sufficient (Hu and Bentler 1999).

The CFI compares the calculated model with the null model. In particular, the CFI describes the level at which the calculated mode provides a better solution than the null model. The closer CFI is to 1, the better the fit, with values in the area above .90 indicating sufficient fit. It is noted that CFI is less sensitive to sample size variations (Marsh et al. 1988).

The TLI is associated to the ratio of the chi-square for the model of reference and the null model and their associated df values that are then subtracted from each other and their difference is finally divided by the null model chi-square minus 1. As with CFI, TLI values in the range between .90 and .95 are considered sufficient, while they tend not to fluctuate due to sample size (Marsh et al. 1988).

The AIC constitutes an information theory–derived fit indicator, which is employed when maximum likelihood estimation is conducted. Similar to the chi-square, the AIC describes the rate of difference between the observed and model-related covariance matrices with lower values indicating better fit (Hu and Bentler 1999).

The BIC, as the AIC, derives from information theory and refers to the log of a Bayes factor of the calculated model in contrast to the saturated model, while penalizing against more complex models and smaller samples (Burham and Anderson 2004).

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Title: The Physical, Emotional, and Identity User-Avatar Association with Disordered Gaming: A Pilot Study
Authors: Vasileios Stavropoulos
Emma Dumble
Sandra Cokorilo
Mark D. Griffiths
Halley M. Pontes
Publication date: 01-02-2022
Publisher: Springer US
Published in: International Journal of Mental Health and Addiction / Issue 1/2022
Print ISSN: 1557-1874
Electronic ISSN: 1557-1882
DOI: https://doi.org/10.1007/s11469-019-00136-8

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The Physical, Emotional, and Identity User-Avatar Association with Disordered Gaming: A Pilot Study

Abstract

Keynote webinar | Spotlight on medication adherence

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