ANALYZING THE DETERMINANTS, PSYCHOLOGICAL CORRELATES, AND INTERVENTION STRATEGIES FOR ONLINE GAMING ADDICTION AMONG AVIATOR BET USERS IN NAIROBI COUNTY, KENYA

Authors: Fredrick Kiplangat Sigei*, Susan Kimotho, PhD & Priscilla Gachigi, PhD

ABSTRACT

The rapid proliferation of digital gaming and betting platforms has fundamentally transformed patterns of leisure, economic participation, and behavioral health across the globe, with particularly acute implications for emerging digital economies in sub-Saharan Africa. The small but growing niche occupied by hybridal types of gaming-gambling games, like the Aviator Bet service, has combined real-time wagering systems with the high frequency nature of gaming in ways that increase the susceptibility to compulsive and addictive behavior. The overlap of efforts to provide a mobile financial infrastructure, most prominently M-Pesa, and a greater Sony number of more advanced online betting technologies has provided a space in which younger adult online gambling addicts are uniquely predisposed to form behavioral addiction patterns. The paper explores the predictors, mental factors, and treatment consequences of online gaming addiction between the users of Aviator Bet in the Nairobi County, Kenya with the two-fold aim of defining the extent of the issue, and analyzing evidence-based interventions.To obtain convergent qualitative and quantitative data, a parallel mixed-methods approach was used that combined quantitative survey data that was gathered on 248 participants who are adults and qualitative information obtained via the in-depth interview. The study sample was selected using stratified sampling in the different sub-counties in Nairobi, which is a diverse city and peri-urban area. The high prevalence of moderate to severe online gaming addiction was found as quantitative results, where 62.5% of the participants fit the criteria of engaging in a way that was considered to be clinically significant. Regression analysis proved the severity of addiction as a significant predictor of psychological distress (0.48, p <.001) and significant positive correlation was identified between addiction severity and anxiety (r =.52), depression (r =.49) and perceived stress (r =.46). Social outcomes were also evident with high addiction levels showing the existence of financial strain (  0.41, p <.01), interpersonal conflict ( ${-0.37, p <.01).It is the qualitative data that complemented these quantitative trends by pointing to cognitive distortions such as illusions of control and gambler-type fallacy thinking, maladaptive coping styles, vulnerability of socioeconomic status and peer normalization as primary psychosocial influences in perpetuating and maintaining compulsive involvement. Individuals experiencing the Cognitive Behavioral Therapy (CBT) intervention groups had statistically and clinically significant mean skin scores for both addiction severity (ΔM = -1.32, p <.01) and the level of psychological distress (ΔM = -1.15, p <.01) which supports the validity of the structured psychological therapy in this group.

Substantive contributions to the sparse empirical research on hybrid gaming-gambling addiction in sub-Saharan Africa are also made in this research. It expands theoretical knowledge by combining Cognitive Behavioral Theory and Addiction Syndrome Model within a locally relevant context, and offers an evidence base on CBT-based intervention programming, specific to the Kenyan context of digital betting. Discussed are policy and clinical implications.

Keywords: online gaming addiction; hybrid gaming-gambling; Aviator Bet; cognitive behavioral therapy; behavioral addiction; psychological distress; Kenya; sub-Saharan Africa

REFERENCES

  1. Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160
  2. Beck, A. T. (2011). Cognitive therapy: Basics and beyond (2nd ed.). Guilford Press.
  3. Bhui, K., & Morgan, N. (2007). Effective psychotherapy for Black and minority ethnic groups. Advances in Psychiatric Treatment, 13(3), 187–195. https://doi.org/10.1192/apt.bp.106.002295
  4. Billieux, J., Schimmenti, A., Khazaal, Y., Maurage, P., & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119–123. https://doi.org/10.1556/2006.4.2015.009
  5. Blaszczynski, A., & Nower, L. (2002). A pathways model of problem and pathological gambling. Addiction, 97(5), 487–499. https://doi.org/10.1046/j.1360-0443.2002.00015.x
  6. Brand, M., Wegmann, E., Stark, R., Muller, A., Wolfling, 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 & Biobehavioral Reviews, 104, 1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032
  7. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  8. Carver, C. S., & Connor-Smith, J. (2010). Personality and coping. Annual Review of Psychology, 61, 679–704. https://doi.org/10.1146/annurev.psych.093008.100352
  9. Clark, L., Lawrence, A. J., Astley-Jones, F., & Gray, N. (2009). Gambling near-misses enhance motivation to gamble and recruit win-related brain circuitry. Neuron, 61(3), 481–490. https://doi.org/10.1016/j.neuron.2008.12.031
  10. Cowlishaw, S., Merkouris, S., Dowling, N., Anderson, C., Jackson, A., & Thomas, S. (2012). Psychological therapies for pathological and problem gambling. Cochrane Database of Systematic Reviews, 11, CD008937. https://doi.org/10.1002/14651858.CD008937.pub2
  11. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.
  12. Delfabbro, P., & King, D. L. (2015). On the utility of DSM-5 criteria for Internet gaming disorder. Journal of Gambling Studies, 31(3), 1139–1145. https://doi.org/10.1007/s10899-014-9498-x
  13. Derevensky, J. L., & Gainsbury, S. M. (2016). Social casino gaming and adolescents: Should we be concerned and is regulation in sight? International Journal of Law and Information Technology, 24(1), 1–19. https://doi.org/10.1093/ijlit/eav009
  14. Griffiths, M. D. (1993). Fruit machine gambling: The importance of structural characteristics. Journal of Gambling Studies, 9(2), 101–120. https://doi.org/10.1007/BF01014863
  15. Griffiths, M. D. (2017). The psychosocial impact of professional gambling, professional video gaming & eSports. Casino & Gaming International, 28, 59–63.
  16. King, D. L., Delfabbro, P. H., Doh, Y. Y., Wu, A. M. S., Kuss, D. J., Pallesen, S., … & Billieux, J. (2020). Policy and prevention approaches for disordered and hazardous gaming and internet use: An international perspective. Prevention Science, 21(5), 649–660. https://doi.org/10.1007/s11121-020-01092-z
  17. Király, O., Potenza, M. N., Stein, D. J., King, D. L., Hodgins, D. C., Saunders, J. B., … & Demetrovics, Z. (2020). Preventing problematic internet use during the COVID-19 pandemic: Consensus guidance. Comprehensive Psychiatry, 100, 152180. https://doi.org/10.1016/j.comppsych.2020.152180
  18. Király, O., Tóth, D., Urbán, R., Demetrovics, Z., & Maraz, A. (2022). Intense video gaming is not essentially problematic. Psychology of Addictive Behaviors, 31(7), 807–817. https://doi.org/10.1037/adb0000316
  19. Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. The Lancet Psychiatry, 3(8), 760–773. https://doi.org/10.1016/S2215-0366(16)00104-8
  20. Kuss, D. J., & Griffiths, M. D. (2012). Internet and gaming addiction: A systematic literature review of neuroimaging studies. Brain Sciences, 2(3), 347–374. https://doi.org/10.3390/brainsci2030347
  21. Langer, E. J. (1975). The illusion of control. Journal of Personality and Social Psychology, 32(2), 311–328. https://doi.org/10.1037/0022-3514.32.2.311
  22. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144(9), 1184–1188. https://doi.org/10.1176/ajp.144.9.1184
  23. Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Psychology Foundation.
  24. Merkouris, S. S., Thomas, A. C., Browning, C. J., & Dowling, N. A. (2016). Predictors of outcomes of psychological treatments for disordered gambling: A systematic review. Clinical Psychology Review, 48, 7–31. https://doi.org/10.1016/j.cpr.2016.06.007
  25. Mihara, S., & Higuchi, S. (2017). Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry and Clinical Neurosciences, 71(7), 425–444. https://doi.org/10.1111/pcn.12532
  26. Ndung’u, N. (2018). The M-Pesa technological revolution for financial services in Africa: A platform for financial inclusion. In D. Lee & R. Deng (Eds.), Handbook of blockchain, digital finance, and inclusion (Vol. 1, pp. 421–436). Academic Press.
  27. Otieno, B. A., Mutisya, B., & Opiyo, R. (2023). Determinants of online sports betting among youth in Nairobi, Kenya. African Journal of Health Sciences, 36(1), 14–27.
  28. Patton, M. Q. (2015). Qualitative research and evaluation methods (4th ed.). SAGE Publications.
  29. Petry, N. M., Ammerman, Y., Bohl, J., Doersch, A., Gay, H., Kadden, R., … & Steinberg, K. (2006). Cognitive-behavioral therapy for pathological gamblers. Journal of Consulting and Clinical Psychology, 74(3), 555–567. https://doi.org/10.1037/0022-006X.74.3.555
  30. 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
  31. Przybylski, A. K., Weinstein, N., & Murayama, K. (2017). Internet gaming disorder: Investigating the clinical relevance of a new phenomenon. American Journal of Psychiatry, 174(3), 230–236. https://doi.org/10.1176/appi.ajp.2016.16020224
  32. Shaffer, H. J., LaPlante, D. A., LaBrie, R. A., Kidman, R. C., Donato, A. N., & Stanton, M. V. (2012). Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12(6), 367–374. https://doi.org/10.1080/10673220490905705
  33. Sharpe, L. (2002). A reformulated cognitive-behavioral model of problem gambling: A biopsychosocial perspective. Clinical Psychology Review, 22(1), 1–25. https://doi.org/10.1016/S0272-7358(00)00087-8
  34. Skinner, B. F. (1938). The behavior of organisms: An experimental analysis. Appleton-Century-Crofts.
  35. World Bank. (2022). Kenya economic update: Aiming high – Seizing the potential to achieve a middle income Kenya. World Bank Group.
  36. World Health Organization. (2019). International classification of diseases for mortality and morbidity statistics (11th revision). https://icd.who.int/
  37. Young, K. S. (2011). CBT-IA: The first treatment model for Internet addiction. Journal of Cognitive Psychotherapy, 25(4), 304–312. https://doi.org/10.1891/0889-8391.25.4.304