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

  • Belle Gavriel-FriedEmail author
  • Tania Moretta
  • Marc N. Potenza
Original Paper


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.


Age differences Recovery capital Spirituality Gambling disorder Symptom improvement 



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.


  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.CrossRefGoogle Scholar
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5 ® ). Washington, DC: American Psychiatric Publishing.CrossRefGoogle 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.CrossRefGoogle Scholar
  4. Burns, J., & Marks, D. (2013). Can recovery capital predict addiction problem severity? Alcoholism Treatment Quarterly, 31(3), 303–320.CrossRefGoogle Scholar
  5. Cloud, W., & Granfield, R. (2008). Conceptualizing recovery capital: Expansion of a theoretical construct. Substance Use and Misuse, 43(12–13), 1971–1986.CrossRefGoogle 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].
  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. 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.CrossRefGoogle 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. 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.CrossRefGoogle 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. Scholar
  12. Gavriel-Fried, B., Moretta, T., & Potenza, M. N. (2019b). Modeling intrinsic spirituality in gambling disorder. Addiction Research & Theory.
  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.CrossRefGoogle 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.PubMedGoogle Scholar
  15. Hennessy, E. A. (2017). Recovery capital: A systematic review of the literature. Addiction Research & Theory, 25(5), 349–360.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  19. Kausch, O. (2004). Pathological gambling among elderly veterans. Journal of Geriatric Psychiatry and Neurology, 17(1), 13–19.CrossRefGoogle Scholar
  20. Kelly, J. F., & Hoeppner, B. (2015). A biaxial formulation of the recovery construct. Addiction Research & Theory, 23(1), 5–9.CrossRefGoogle 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.CrossRefGoogle Scholar
  22. Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: A new depression diagnostic and severity measure. Psychiatric Annals, 32(9), 509–515.CrossRefGoogle 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.CrossRefGoogle Scholar
  25. Morey, R. D., & Rouder, J. N. (2018). BayesFactor: Computation of Bayes factors for common designs [Computer software]. Retrieved from
  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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle 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.CrossRefGoogle Scholar
  32. Team, R. C. (2018). R: A language and environment for statistical computing: R Foundation for Statistical Computing. Retrieved from
  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.CrossRefGoogle 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. 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.CrossRefGoogle Scholar
  36. West, R. (2016). Using Bayesian analysis for hypothesis testing in addiction science. Addiction, 111(1), 3–4. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Bob Shapell School of Social WorkTel Aviv UniversityTel AvivIsrael
  2. 2.Department of General PsychologyUniversity of PadovaPaduaItaly
  3. 3.Departments of Psychiatry, Child Study, and NeuroscienceYale School of MedicineNew HavenUSA
  4. 4.Connecticut Council on Problem GamblingClintonUSA
  5. 5.Connecticut Mental Health Center, USANew HavenUSA

Personalised recommendations