Recovery Capital and Symptom Improvement in Gambling Disorder: Correlations with Spirituality and Stressful Life Events in Younger but Not Older Adults

Abstract

Although age-related differences have been reported in gambling disorder, prior studies have not examined how age may influence recovery in gambling disorder. Recovery may be influenced by positive factors (e.g., spirituality and recovery capital) and negative factors (e.g., depression, anxiety, and stressful life events). The current study examined associations between these positive and negative factors and gambling disorder DSM-5 symptom improvement in younger and older adults. Younger (less than 55 years of age; n = 86) and older (55 years or older; n = 54) adults, with lifetime gambling disorder treated currently or within the past 5 years in five treatment centers in Israel were assessed using structured scales on past-year and lifetime DSM-5 gambling disorder, intrinsic spirituality, recovery capital, anxiety, depression and stressful life-events. Among younger adults, recovery capital and intrinsic spirituality were associated with gambling disorder symptom improvement. Among older adults, only recovery capital was associated with gambling disorder symptom improvement. Correlations between recovery capital and spirituality (z = 2.34, p = 0.02) and recovery capital and stressful life events (z = 2.29, p = 0.02) were stronger in younger than in older adults. Recovery capital is an important resource that should be considered across older and younger adults with gambling disorder. Spirituality and stressful life events may operate differently across age groups in gambling disorder. Future studies should investigate whether the findings may extend to other groups and the extent to which promoting recovery capital should be integrated into treatments for gambling disorder.

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

References

  1. Abbott, M., Romild, U., & Volberg, R. (2018). The prevalence, incidence, and gender and age-specific incidence of problem gambling: Results of the Swedish longitudinal gambling study (Swelogs). Addiction, 113(4), 699–707.

    Article  Google Scholar 

  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Washington, DC: American Psychiatric Publishing.

    Google Scholar 

  3. Best, D., Honor, S., Karpusheff, J., Loudon, L., Hall, R., Groshkova, T., et al. (2012). Well-being and recovery functioning among substance users engaged in posttreatment recovery support groups. Alcoholism Treatment Quarterly, 30(4), 397–406.

    Article  Google Scholar 

  4. Burns, J., & Marks, D. (2013). Can recovery capital predict addiction problem severity? Alcoholism Treatment Quarterly, 31(3), 303–320.

    Article  Google Scholar 

  5. Cloud, W., & Granfield, R. (2008). Conceptualizing recovery capital: Expansion of a theoretical construct. Substance Use and Misuse, 43(12–13), 1971–1986.

    Article  Google Scholar 

  6. Clyde, M. A. (2018). BAS: Bayesian variable selection and model averaging using Bayesian adaptive sampling. R Package Version 1.4.9. CRAN Comprehensive R Archive Network [Computer software]. https://doi.org/10.5281/zenodo.1212636.

  7. Clyde, M. A., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80–101. https://doi.org/10.1198/jcgs.2010.09049.

    Article  Google Scholar 

  8. Dawson, D. A., Grant, B. F., & Ruan, W. J. (2005). The association between stress and drinking: Modifying effects of gender and vulnerability. Alcohol and Alcoholism, 40(5), 453–460.

    Article  Google Scholar 

  9. Fragoso, T. M., Bertoli, W., & Louzada, F. (2018). Bayesian model averaging: A systematic review and conceptual classification. International Statistical Review, 86(1), 1–28. https://doi.org/10.1111/insr.12243.

    Article  Google Scholar 

  10. Gavriel-Fried, B. (2018). The crucial role of recovery capital in individuals with a gambling disorder. Journal of Behavioral Addictions, 7(3), 792–799.

    Article  Google Scholar 

  11. Gavriel-Fried, B., Moretta, T., & Potenza, M. N. (2019a). Associations between recovery capital, spirituality, and DSM–5 symptom improvement in gambling disorder. Psychology of Addictive Behaviors. https://doi.org/10.1037/adb0000492.

    Article  PubMed  Google Scholar 

  12. Gavriel-Fried, B., Moretta, T., & Potenza, M. N. (2019b). Modeling intrinsic spirituality in gambling disorder. Addiction Research & Theory. https://doi.org/10.1080/16066359.2019.1622002.

  13. Gonzalez-Ibanez, A., Mora, M., Gutierrez-Maldonado, J., Ariza, A., & Lourido-Ferreira, M. (2005). Pathological gambling and age: Differences in personality, psychopathology, and response to treatment variables. Addictive Behaviors, 30(2), 383–388.

    CAS  Article  Google Scholar 

  14. Granero, R., Penelo, E., Stinchfield, R., Fernandez-Aranda, F., Savvidou, L. G., Fröberg, F., et al. (2014). Is pathological gambling moderated by age? Journal of Gambling Studies, 30(2), 475–492.

    PubMed  Google Scholar 

  15. Hennessy, E. A. (2017). Recovery capital: A systematic review of the literature. Addiction Research & Theory, 25(5), 349–360.

    Article  Google Scholar 

  16. Hodge, D. R. (2003). The intrinsic spirituality scale: A new six-item instrument for assessing the salience of spirituality as a motivational construct. Journal of Social Service Research, 30(1), 41–61.

    Article  Google Scholar 

  17. Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401.

    Article  Google Scholar 

  18. Jorm, A. F. (2000). Does old age reduce the risk of anxiety and depression? A review of epidemiological studies across the adult life span. Psychological Medicine, 30(1), 11–22.

    CAS  Article  Google Scholar 

  19. Kausch, O. (2004). Pathological gambling among elderly veterans. Journal of Geriatric Psychiatry and Neurology, 17(1), 13–19.

    Article  Google Scholar 

  20. Kelly, J. F., & Hoeppner, B. (2015). A biaxial formulation of the recovery construct. Addiction Research & Theory, 23(1), 5–9.

    Article  Google Scholar 

  21. Krentzman, A. R. (2013). Review of the application of positive psychology to substance use, addiction, and recovery research. Psychology of Addictive Behaviors, 27(1), 151.

    Article  Google Scholar 

  22. Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32(9), 509–515.

    Article  Google Scholar 

  23. Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Cambridge: Academic Press.

    Google Scholar 

  24. Moberg, D. O. (2005). Research in spirituality, religion, and aging. Journal of Gerontological Social Work, 45(1–2), 11–40.

    Article  Google Scholar 

  25. Morey, R. D., & Rouder, J. N. (2018). BayesFactor: Computation of Bayes factors for common designs [Computer software]. Retrieved from https://CRAN.R-project.org/package=BayesFactor.

  26. Potenza, M. N., Steinberg, M. A., Wu, R., Rounsaville, B. J., & O’Malley, S. S. (2006). Characteristics of older adult problem gamblers calling a gambling helpline. Journal of Gambling Studies, 22(2), 241–254.

    Article  Google Scholar 

  27. Ramseyer, G. C. (1979). Testing the difference between dependent correlations using the Fisher Z. The Journal of Experimental Education, 47(4), 307–310.

    Article  Google Scholar 

  28. Schönbrodt, F. D., Wagenmakers, E.-J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322.

    Article  Google Scholar 

  29. Spitzer, R. L., Kroenke, K., Williams, J. B., & Löwe, B. (2006). A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine, 166(10), 1092–1097.

    Article  Google Scholar 

  30. Sterling, R., Slusher, C., & Weinstein, S. (2008). Measuring recovery capital and determining its relationship to outcome in an alcohol dependent sample. The American Journal of Drug and Alcohol Abuse, 34(5), 603–610.

    Article  Google Scholar 

  31. Streiner, D. L., Cairney, J., & Veldhuizen, S. (2006). The epidemiology of psychological problems in the elderly. The Canadian Journal of Psychiatry, 51(3), 185–191.

    Article  Google Scholar 

  32. Team, R. C. (2018). R: A language and environment for statistical computing: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/.

  33. Vilsaint, C. L., Kelly, J. F., Bergman, B. G., Groshkova, T., Best, D., & White, W. (2017). Development and validation of a brief assessment of recovery capital (BARC-10) for alcohol and drug use disorder. Drug and Alcohol Dependence, 177, 71–76.

    Article  Google Scholar 

  34. Wagenmakers, E. J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., et al. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35–57. https://doi.org/10.3758/s13423-017-1343-3.

    Article  Google Scholar 

  35. Welte, J. W., Barnes, G. M., Tidwell, M.-C. O., & Hoffman, J. H. (2011). Gambling and problem gambling across the lifespan. Journal of Gambling Studies, 27(1), 49–61.

    Article  Google Scholar 

  36. West, R. (2016). Using Bayesian analysis for hypothesis testing in addiction science. Addiction, 111(1), 3–4. https://doi.org/10.1111/add.13053.

    Article  PubMed  Google Scholar 

Download references

Funding

This study was supported by a seed Grant awarded to Belle Gavriel-Fried by the National Center for Responsible Gaming (NCRG) in 2017. Marc N. Potenza’s involvement was supported by the National Center for Responsible Gaming, the Connecticut Council on Problem Gambling, and the Connecticut Department of Mental Health and Addiction Services.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Belle Gavriel-Fried.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Belle Gavriel-Fried: Visiting scholar at Yale University (2018–2019).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gavriel-Fried, B., Moretta, T. & Potenza, M.N. Recovery Capital and Symptom Improvement in Gambling Disorder: Correlations with Spirituality and Stressful Life Events in Younger but Not Older Adults. J Gambl Stud 36, 1379–1390 (2020). https://doi.org/10.1007/s10899-019-09905-5

Download citation

Keywords

  • Age differences
  • Recovery capital
  • Spirituality
  • Gambling disorder
  • Symptom improvement