Skip to main content
Log in

Predictors of Relapse in Problem Gambling: A Prospective Cohort Study

  • Original Paper
  • Published:
Journal of Gambling Studies Aims and scope Submit manuscript

Abstract

To explore the variation of predictors of relapse in treatment and support seeking gamblers. A prospective cohort study with 158 treatment and support seeking problem gamblers in South Australia. Key measures were selected using a consensus process with international experts in problem gambling and related addictions. The outcome measures were Victorian Gambling Screen (VGS) and behaviours related to gambling. Potential predictors were gambling related cognitions and urge, emotional disturbance, social support, sensation seeking traits, and levels of work and social functioning. Mean age of participants was 44 years (SD = 12.92 years) and 85 (54 %) were male. Median time for participants enrolment in the study was 8.38 months (IQR = 2.57 months). Patterns of completed measures for points in time included 116 (73.4 %) with at least a 3 month follow-up. Using generalised mixed-effects regression models we found gambling related urge was significantly associated with relapse in problem gambling as measured by VGS (OR 1.29; 95 % CI 1.12–1.49) and gambling behaviours (OR 1.16; 95 % CI 1.06–1.27). Gambling related cognitions were also significantly associated with VGS (OR 1.06; 95 % CI 1.01–1.12). There is consistent association between urge to gamble and relapse in problem gambling but estimates for other potential predictors may have been attenuated because of methodological limitations. This study also highlighted the challenges presented from a cohort study of treatment and support seeking problem gamblers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Anton, R. F. (1999). What is craving? Alcohol Research and Health, 23(3), 165–173.

    CAS  PubMed  Google Scholar 

  • Ashrafioun, L., Kostek, J., & Ziegelmeyer, E. (2013). Assessing post-cue exposure craving and its association with amount wagered in an optional betting task. Journal of Behavioral Addictions, 1–5.

  • Battersby, M., Pols, R., Oakes, J., Smith, D., Mclaughlin, K., & Baigent, M. (2010). The Definition and Predictors of Relapse in Problem Gambling: From: http://www.gamblingresearch.org.au/home/research/gra+research+reports/the+definition+and+predictors+of+relapse+in+problem+gambling+(2010).

  • Becona, E. (1996). Prevalence surveys of problem and pathological gambling in Europe: The cases of Germany, Holland and Spain. Journal of Gambling Studies, 12(2), 179–192.

    Article  CAS  Google Scholar 

  • Bondolfi, G., Osiek, C., & Ferrero, F. (2000). Prevalence estimates of pathological gambling in Switzerland. Acta Psychiatrica Scandinavica, 101(6), 473–475.

    Article  CAS  PubMed  Google Scholar 

  • Brandon, T. H., Vidrine, J. I., & Litvin, E. B. (2007). Relapse and relapse prevention. Annual Review of Clinical Psychology, 3(1), 257–284.

    Article  PubMed  Google Scholar 

  • Daughters, S. B., Lejuez, C. W., Strong, D. R., Brown, R. A., Breen, R. B., & Lesieur, H. R. (2005). The relationship among negative affect, distress tolerance, and length of gambling abstinence attempt. Journal of Gambling Studies, 21(4), 363–378.

    Article  PubMed  Google Scholar 

  • Delfabbro, P. (2009). Australasian Gambling Review: Independent Gambling Authority of South Australia.

  • Dillman, D. A. (2007). Mail and internet surveys: The total design method (2nd ed.). New York: Wiley.

    Google Scholar 

  • Donovan, D. M. (1996). Marlatt’s classification of relapse precipitants: Is the Emperor still wearing clothes? Addiction, 91(12s1), 131–138.

    Article  Google Scholar 

  • Donovan, D., & Witkiewitz, K. (2012). Relapse prevention: From radical idea to common practice. Addiction research & theory, 20(3), 204–217.

    Article  Google Scholar 

  • Echeburua, E., Fernandez-Montalvo, J., & Baez, C. (2001). Predictors of therapeutic failure in slot-machine pathological gamblers following behavioural treatment. Behavioural and Cognitive Psychotherapy, 29, 379–383.

    Article  Google Scholar 

  • Edwards, P., Roberts, I., Clarke, M., DiGuiseppi, C., Pratap, S., Wentz, R., et al. (2002). Increasing response rates to postal questionnaires: Systematic review. British Medical Journal, 324(7347), 1183.

    Article  PubMed Central  PubMed  Google Scholar 

  • Ferris, J., & Wynne, H. (2001). The Canadian problem gambling index. Final Report: Canadian Centre on Substance Abuse.

  • Gooding, P., & Tarrier, N. (2009). A systematic review and meta-analysis of cognitive-behavioural interventions to reduce problem gambling: Hedging our bets? Behaviour Research and Therapy, 47(7), 592–607.

    Article  PubMed  Google Scholar 

  • Goudriaan, A. E., Oosterlaan, J., De Beurs, E., & Van Den Brink, W. (2007). The role of self-reported impulsivity and reward sensitivity versus neurocognitive measures of disinhibition and decision-making in the prediction of relapse in pathological gamblers. Psychological Medicine, 38, 41–50.

    PubMed  Google Scholar 

  • Government of South Australia: Consumer and Business Services. (2012). Gaming Machines Act 1992, Annual Report 20112012.

  • Hedeker, D., & Mermelstein, R. (1996). Application of random-effects regression models in relapse research. Addiction, 91(supplement), S211–S229.

    Article  PubMed  Google Scholar 

  • Hodgins, D. C. (2009). Randomized trial of brief motivational treatments for pathological gamblers: More is not necessarily better. Journal of Consulting and Clinical Psychology, 77(5), 950.

    Article  PubMed  Google Scholar 

  • Hodgins, D. C., Currie, S. R., el-Guebaly, N., & Diskin, K. M. (2007). Does providing extended relapse prevention bibliotherapy to problem gamblers improve outcome? Journal of Gambling Studies, 23(1), 41–54.

    Article  PubMed  Google Scholar 

  • Hodgins, D. C., & el-Guebaly, N. (2004). Retrospective and prospective reports of precipitants to relapse in pathological gambling. Journal of Consulting and Clinical Psychology, 72(1), 72–80.

    Article  PubMed  Google Scholar 

  • Hosmer, D., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York: Wiley.

    Book  Google Scholar 

  • Ledgerwood, D. M., & Petry, N. M. (2006). What do we know about relapse in pathological gambling? Clinical Psychology Review, 26(2), 216–228.

    Article  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Long, J., & Freese, J. (2006). Regression models for categorical dependent variables using Stata. College Station, Texas: Stata Corporation.

    Google Scholar 

  • Lorains, F. K., Cowlishaw, S., & Thomas, S. A. (2011). Prevalence of comorbid disorders in problem and pathological gambling: Systematic review and meta-analysis of population surveys. Addiction, 106(3), 490–498.

    Article  PubMed  Google Scholar 

  • Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the depression anxiety stress scales. Sydney: Psychology Foundation.

    Google Scholar 

  • Marlatt, G. A., & Gordon, J. R. (1985). Relapse prevention: Maintenance strategies in the treatment of addictive behaviors. New York: Guilford.

    Google Scholar 

  • McMillen, J., & Wenzel, M. (2006). Measuring problem gambling: Assessment of three prevalence screens. International Gambling Studies, 6(2), 147–174.

    Article  Google Scholar 

  • Mullen, P. M. (2003). Delphi: Myths and reality. Journal of Health Organization and Management, 17(1), 37–52.

    Article  PubMed  Google Scholar 

  • Mundt, J. C., Marks, I. M., Shear, M. K., & Greist, J. M. (2002). The Work and Social Adjustment Scale: A simple measure of impairment in functioning. The British Journal of Psychiatry, 180(5), 461–464.

    Article  PubMed  Google Scholar 

  • National Research Council. (2010). The prevention and treatment of missing data in clinical trials. Panel on handling missing data in clinical trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

  • Oakes, J. E., Pols, R. G., Battersby, M. W., Lawn, S. J., Pulvirenti, M., & Smith, D. P. (2011). A focus group study of predictors of relapse in electronic gaming machine problem gambling, part 2: Factors that ‘pull’ the gambler away from relapse. Journal of Gambling Studies,. doi:10.1007/s10899-011-9267-8.

    Google Scholar 

  • Oei, T. P. S., & Gordon, L. M. (2008). Psychosocial factors related to gambling abstinence and relapse in members of gamblers anonymous. Journal of Gambling Studies, 24(1), 91–105.

    Article  PubMed  Google Scholar 

  • Raylu, N., & Oei, T. P. (2002). Pathological gambling: A comprehensive review. Clinical Psychology Review, 22(7), 1009–1061.

    Article  PubMed  Google Scholar 

  • Raylu, N., & Oei, T. (2004a). The Gambling Related Cognitions Scale (GRCS): Development, confirmatory factor validation and psychometric properties. Addiction, 99(6), 757–769.

    Article  PubMed  Google Scholar 

  • Raylu, N., & Oei, T. (2004b). The Gambling Urge Scale: Development, confirmatory factor validation, and psychometric properties. Psychology of Addictive Behaviors, 18(2), 100–105.

    Article  PubMed  Google Scholar 

  • Reinert, D., & Allen, J. (2002). The alcohol use disorders identification test (AUDIT): A review of recent research. Alcoholism, Clinical and Experimental Research, 26(2), 272–279.

    Article  PubMed  Google Scholar 

  • Roth, M. (2003). Validation of the Arnett Inventory of Sensation Seeking (AISS): Efficiency to predict the willingness towards occupational chance, and affection by social desirability. Personality and Individual Differences, 35, 1307–1314.

    Article  Google Scholar 

  • Shaffer, H. J., & Hall, M. N. (2001). Updating and refining prevalence estimates of disordered gambling behaviour in the United States and Canada. Canadian Journal of Public Health, 92(3), 168–172.

    CAS  PubMed  Google Scholar 

  • Sharpe, L. (2002). A reformulated cognitive–behavioral model of problem gambling: A biopsychosocial perspective. Clinical Psychology Review, 22(1), 1–25.

    Article  PubMed  Google Scholar 

  • Shiffman, S. (1989). Conceptual issues in the study of relapse. In M. Gossop (Ed.), Relapse and addictive behavior. London: Routledge.

    Google Scholar 

  • Skrondal, A., & Rabe-Hesketh, S. (2003). Multilevel logistic regression for polytomous data and rankings. Psychometrika, 68(2), 267–287.

    Article  Google Scholar 

  • Smith, D. P., Pols, R. G., Battersby, M. W., & Harvey, P. W. (2013). The Gambling Urge Scale: Reliability and validity in a clinical population. Addiction research & theory, 21(2), 113–122.

    Article  Google Scholar 

  • Spielberger, C. D., Gorsuch, R. L., Lushene, R. E., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the state-trait anxiety inventory. Palo Alto: Consulting Psychologists Press.

    Google Scholar 

  • StataCorp. (2008). Stata Statistical Software: Release 10.0. College Station, Texas: Stata Corporation.

  • Tolchard, B., & Battersby, M. (2010). The Victorian Gambling screen: Reliability and validity in a clinical population. Journal of Gambling Studies, 26, 623–638.

    Article  CAS  PubMed  Google Scholar 

  • van Holst, R., van den Brink, W., Veltman, D., & Goudriaan, A. (2010). Brain imaging studies in pathological gambling. Current Psychiatry Reports, 12(5), 418–425.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wardle, H., Sproston, K., Orford, J., Erens, B., Griffiths, M. D., Constantine, R., et al. (2007). The British gambling prevalence survey. London: The Stationery Office.

    Google Scholar 

  • White, I. R., Kalaitzaki, E., & Thompson, S. G. (2011). Allowing for missing outcome data and incomplete uptake of randomised interventions, with application to an Internet-based alcohol trial. Statistics in Medicine, 30(27), 3192–3207.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wilde, B., Goudriaan, A., Sabbe, B., Hulstijn, W., & Dom, G. (2013). Relapse in pathological gamblers: A pilot study on the predictive value of different impulsivity measures. Journal of Behavioral Addictions, 2(1), 23–30.

    Article  Google Scholar 

  • Witkiewitz, K., & Marlatt, G. A. (2004). Relapse prevention for alcohol and drug problems: That was Zen, this is Tao. American Psychologist, 59(4), 224–235.

    Article  PubMed  Google Scholar 

  • Witkiewitz, K., & Marlatt, G. A. (2007). Modeling the complexity of post-treatment drinking: It’s a rocky road to relapse. Clinical Psychology Review, 27(6), 724–738.

    Article  PubMed Central  PubMed  Google Scholar 

  • Wong, I. L. K., & Ernest, M. T. S. (2003). Prevalence estimates of problem and pathological gambling in Hong Kong. The American Journal of Psychiatry, 160(7), 1353.

    Article  PubMed  Google Scholar 

  • Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(11), 30–41.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the assistance of Statewide Gambling Therapy Service, Gambling Helpline, Relationships Australia South Australia, Pokies Anonymous, and Offenders Aid and Rehabilitation Service in recruitment and ongoing support for the follow-up of participants. This project was funded by Gambling Research Australia (Grant No. 084/06).

Conflict of interest

The authors have no other conflicts of interest to declare.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David P. Smith.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Smith, D.P., Battersby, M.W., Pols, R.G. et al. Predictors of Relapse in Problem Gambling: A Prospective Cohort Study. J Gambl Stud 31, 299–313 (2015). https://doi.org/10.1007/s10899-013-9408-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10899-013-9408-3

Keywords

Navigation