Examining Problem Gaming During COVID19 Subgroups and Mental

Examining Problem Gaming During COVID-19: Subgroups and Mental Health Correlates 1 2 3 1 Emma V. Ritchie , Karli K. Rapinda , Hyoun S. (Andrew) Kim , Matthew T. Keough 1 York University 2 University of Manitoba 3 Ryerson University Figures Background & Objective • The COVID-19 pandemic has resulted in unprecedented levels of stress and isolation. Reports also indicate that people are playing more video games during this time. Table 1 Summary of Model Fit for Latent Trajectory Models Class # 1 2 3 4 5 6 • The stress-vulnerability model (Zubin & Spring, 1977) suggests that vulnerable individuals may develop psychopathology in times of elevated stress. • As such, there is concern that people vulnerable to excessive gaming may be at risk of developing disordered gaming behaviours during the pandemic. Method • A total of 332 participants (60. 80% male; 69. 28% Caucasian) completed three waves of surveys from April 2020 to November 2020. • Participants all reported gaming at least once in the three months prior to March 2020, when the pandemic began. • Measures completed included: • The Internet Gaming Disorder Scale (IGD; Pontes et al. , 2015) to assess gaming severity • The Alcohol Use Disorders Identification Test (AUDIT) to assess hazardous alcohol use • The Patient Health Questionnaire-9 (PHQ-9; Kroenke et al. , 2001) to assess depression • The General Anxiety Disorder-7 (GAD-7; Spitzer et al. , 2006) scale to assess anxiety • As well as a question assessing time spent gaming SSA BIC 4752. 571 4624. 11 4579. 302 4556. 095 4542. 926 4529. 009 Entropy N/A 0. 92 0. 902 0. 894 0. 879 0. 88 Smallest group (%) 100% 15% 6. 90% 5. 40% 2. 70% 2. 10% LMRp NA 0. 0024 0. 1292 0. 6304 0. 1165 0. 5527 Note. SSA BIC = Sample Size Adjusted Bayesian Information Criterion; LMRp = Lo-Mendell-Rubin likelihood ratio test p values, where p < 0. 05 indicates statistical significance. Bolded values represent the model that was retained based on both fit indices and accuracy of classification. Internet Gaming Disorder Scale Scores • The objective of the current study was to assess the trajectory of problem video gaming symptoms throughout the pandemic. We also assessed anxiety, depression, and hazardous alcohol use to determine whether other measures of mental health changed over the course of the pandemic. Results Class 1 Observed IGD Class 1 Estimated IGD Class 2 Observed IGD 30 25 Latent Trajectory Analysis • Six latent trajectory classes were tested; the fit of these models is displayed in Table 1. • The LMRp value was significant for the model with two classes, but not for any other classes, indicating that this model fit the data best. • Class 1 gamers (low-risk gamers) were characterized by low scores on the IGD that remained stable across the three waves. • Class 2 gamers (high-risk gamers) were characterized by high scores on the IGD that remained stable across the three waves. T-tests • AUDIT: High-risk gamers scored significantly higher (M = 8. 30) compared to low-risk gamers (M = 5. 26), d = 0. 59. • PHQ-9: High-risk gamers scored significantly higher (M = 10. 42) compared to low-risk gamers (M = 7. 34), d = 0. 59. • GAD-7: High-risk gamers scored significantly higher (M = 10. 42) compared to low-risk gamers (M = 7. 34), d = 0. 63. • Time spent gaming: High-risk gamers spent significantly more time gaming (M = 18. 07) compared to low-risk gamers (M = 30. 52), d = 0. 81. Discussion 20 • Our findings are somewhat consistent with the stress-vulnerability model as some individuals maintained high levels of problem gaming during COVID-19. However, gaming remained stable over time, indicating that individuals who did not have problems gaming at the start of the pandemic did not develop them as the pandemic continued. 15 10 • The high-risk gamers in our study were also at risk for other mental health and addiction issues, which may reflect maladaptive coping strategies to manage pandemic-related distress. 5 0 T 1 T 2 T 3 Time Point Figure 1. Scores on the IGD across the three waves for the two groups identified by the latent trajectory analysis. • Overall, our suggests that problems related to gaming remained stable over time. References Kroenke, K. , Spitzer, R. L. , & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606 -613. Pontes, H. M. , & Griffiths, M. D. (2015). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137 -143. https: //doi. org/10. 1016/j. chb. 2014. 12. 006 Spitzer, R. L. , Kroenke, K. , Williams, J. B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092 -1097. https: //doi. org/10. 1001/archinte. 166. 1092 Zubin, J. , & Spring, B. (1977). Vulnerability: A new view of schizophrenia. Journal of Abnormal Psychology, 86, 103 -126.