ABSTRACT
Background: Video gaming revenues have increased dramatically during the COVID-19 pandemic. Trending social games and hyper-casual games are attracting new audiences that require further study. While engaging in video games and Internet-related behaviors inherently may help promote social connection and alleviate stress during the pandemic, a small proportion of individuals develop problematic habits that interfere with daily functioning. Therefore, the aim of this study is to examine the impact of COVID-19 lockdowns on the number of helpline calls for gaming disorder and problematic Internet use in the province of Ontario, Canada. Methods: Helpline calls were collected from a provincial mental health & addiction treatment service hotline from January 2019 to December 2021. This free and confidential service is for people who experience problems with alcohol, drugs, mental illness, and behavioral disorders. Growth modeling will be employed to examine the links between the number of calls received, the number of COVID-19 cases reported province-wide and the accumulated lockdown days across the different months. Results: The associations between the linear, quadratic and cubic growth/change curve factors of the number of calls received in relation to the progress of the pandemic will be reported for time variant, time-invariant and parallel growth moderators. Conclusions: Helpline calls are expected to increase during lockdowns and decrease when restrictions are lifted. This study serves to inform preventive measures that should be considered with the implementation of lockdown during a pandemic to prevent problematic forms of gaming or Internet use.
ABSTRACT
Symposium summary: The present symposium integrates novel findings from Australia, Canada, Germany, the UK and the US in a broad look at measurement, prevention, and help-seeking for gaming disorder in the context of other addictions and the COVID-19 pandemic. The empirical evidence presented is based on survey, epidemiological, and clinical data and aims to enhance the understanding of: a) the positioning of gaming disorder (GD) in relation to other addictive behaviors;b) the utility of machine learning approaches on analyzing electroencephalography (EEG) data to identify GD;c) the effectiveness of a CBT-based intervention in preventing GD and;d) the links between addiction help seeking and lockdowns during the COVID-19 pandemic. The first presentation presents the results of the registered trial from Germany and examines the effectiveness of school-based, 4-session cognitive behavioral therapy prevention intervention (PROTECT) for GD. The second contribution uses advanced growth modelling on call data from an addictions helpline in the province of Ontario, Canada to illustrate the temporal associations between GD-and Internet-related help-seeking requests and lockdowns during the COVID-19 pandemic. The third presentation is from the UK and uses machine learning to generate the EEG footprint of GD and contrast this with recreational gaming. Finally, the fourth and fifth contributions from Australia and the USA use variable-and person-centered approaches to highlight the positioning of GD in relation to other addictions and to introduce novel profiles of cross-addictive comorbidities. Findings will be discussed in the light of the emerging GD literature around identification, prevention, and treatment for GD.
ABSTRACT
Background: 'Cross-addiction' involves a person substituting one form of addictive behaviour for another. While various cross-addiction presentations have been described throughout the literature (drugs to alcohol, alcohol to smoking, gambling to sex), minimal research has explored whether different types of cross-addiction risk exist. We examined the co-occurrence of substance and behavioral addictions and whether addiction profiles differed in COVID-19-related anxiety. Methods: We recruited 968 participants through social media and online forums in the latter half of 2020. We assessed 10 addictive behaviours and COVID-19-related anxiety with validated instruments. We used Latent Class/Profiling analyses with mean scores on addictive behavior scales to identify different risk profiles and compared proportions who met cut-off scores for each disorder between classes. We conducted t-tests to examine differences in COVID-19 anxiety between classes. Results: Two distinct risk profiles were identified, Cross-Addiction Low-Risk (57.4%) and Cross-Addiction High-Risk (42.6%), with the high-risk profile having higher average scores across all addictive behaviours and more participants who met the diagnostic cut-off for multiple addictions. No individuals in the low-risk profile met cut-off scores for alcohol, drug, or gambling disorders. T-test showed significantly higher levels of COVID-19 anxiety in the high-risk compared to the low-risk profile Discussion & Conclusions: Profiles of those at-risk for addictions do not distinguish between type of addiction so much as severity of multiple addictions and addictions having more severe health impacts. Identification of individuals at risk for multiple addictions has important implications for targeting prevention, treatment, and further research.