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01-09-2016 | Original Paper

Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior

Authors: David Forsström, Hugo Hesser, Per Carlbring

Published in: Journal of Gambling Studies | Issue 3/2016

Abstract

Gambling is a common pastime around the world. Most gamblers can engage in gambling activities without negative consequences, but some run the risk of developing an excessive gambling pattern. Excessive gambling has severe negative economic and psychological consequences, which makes the development of responsible gambling strategies vital to protecting individuals from these risks. One such strategy is responsible gambling (RG) tools. These tools track an individual’s gambling history and supplies personalized feedback and might be one way to decrease excessive gambling behavior. However, research is lacking in this area and little is known about the usage of these tools. The aim of this article is to describe user behavior and to investigate if there are different subclasses of users by conducting a latent class analysis. The user behaviour of 9528 online gamblers who voluntarily used a RG tool was analysed. Number of visits to the site, self-tests made, and advice used were the observed variables included in the latent class analysis. Descriptive statistics show that overall the functions of the tool had a high initial usage and a low repeated usage. Latent class analysis yielded five distinct classes of users: self-testers, multi-function users, advice users, site visitors, and non-users. Multinomial regression revealed that classes were associated with different risk levels of excessive gambling. The self-testers and multi-function users used the tool to a higher extent and were found to have a greater risk of excessive gambling than the other classes.

Adami, N., Benini, S., Boschetti, A., Canini, L., Maione, F., & Temporin, M. (2013). Markers of unsustainable gambling for early detection of at-risk online gamblers. International Gambling Studies, 13(2), 188–204. doi:10.1080/14459795.2012.754919.CrossRef

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. doi:10.1109/tac.1974.1100705.CrossRef

Andersen, R. M. (1995). Revisiting the behavioral model and access to medical care: Does it matter? Journal of Health and Social Behavior, 36(1), 1. doi:10.2307/2137284.CrossRefPubMed

Auer, M., & Griffiths, M. D. (2012). Voluntary limit setting and player choice in most intense online gamblers: An empirical study of gambling behaviour. Journal of Gambling Studies, 29(4), 647–660. doi:10.1007/s10899-012-9332-y.CrossRef

Auer, M. M., & Griffiths, M. D. (2015). The use of personalized behavioral feedback for online gamblers: An empirical study. Frontiers in Psychology. doi:10.3389/fpsyg.2015.01406.

Blaszczynski, A., Collins, P., Fong, D., Ladouceur, R., Nower, L., Shaffer, H., & Venisse, J.-L. (2011). Responsible gambling: General principles and minimal requirements. Journal of Gambling Studies, 27(4), 565–573. doi:10.1007/s10899-010-9214-0.CrossRefPubMed

Blaszczynski, A., Ladouceur, R., & Shaffer, H. J. (2004). A science-based framework for responsible gambling: The Reno model. Journal of Gambling Studies, 20(3), 301–317. doi:10.1023/b:jogs.0000040281.49444.e2.CrossRefPubMed

Braverman, J., & Shaffer, H. J. (2012). How do gamblers start gambling: Identifying behavioural markers for high-risk internet gambling. European Journal of Public Health, 22(2), 273–278. doi:10.1093/eurpub/ckp232.

Broda, A., LaPlante, D. A., Nelson, S. E., LaBrie, R. A., Bosworth, L. B., & Shaffer, H. J. (2008). Virtual harm reduction efforts for Internet gambling: Effects of deposit limits on actual Internet sports gambling behavior. Harm Reduction Journal, 5(1), 27. doi:10.1186/1477-7517-5-27.CrossRefPubMedPubMedCentral

Carragher, N., & McWilliams, L. A. (2011). A latent class analysis of DSM-IV criteria for pathological gambling: Results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychiatry Research, 187(1–2), 185–192. doi:10.1016/j.psychres.2010.12.022.CrossRefPubMed

Croudace, T. J. (2003). Developmental typology of trajectories to nighttime bladder control: Epidemiologic application of longitudinal latent class analysis. American Journal of Epidemiology, 157(9), 834–842. doi:10.1093/aje/kwg049.CrossRefPubMed

Cunningham, J., Cordingley, J., Hodgins, D., & Toneatto, T. (2011). Beliefs about gambling problems and recovery: Results from a general population telephone survey. Journal of Gambling Studies, 27(4), 625–631. doi:10.1007/s10899-010-9231-z.CrossRefPubMed

Donkin, L., & Glozier, N. (2012). Motivators and motivations to persist with online psychological interventions: A qualitative study of treatment completers. Journal of Medical Internet Research, 14(3).

Dufour, M., Brunelle, N., & Roy, É. (2013). Are poker players all the same? Latent class analysis. Journal of Gambling Studies. doi:10.1007/s10899-013-9429-y.

Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), e11. doi:10.2196/jmir.7.1.e11.CrossRefPubMedPubMedCentral

Gainsbury, S., Hing, N., & Suhonen, N. (2013a). Professional help-seeking for gambling problems: Awareness, barriers and motivators for treatment. Journal of Gambling Studies, 30(2), 503–519. doi:10.1007/s10899-013-9373-x.CrossRef

Gainsbury, S., Parke, J., & Suhonen, N. (2013b). Consumer attitudes towards Internet gambling: Perceptions of responsible gambling policies, consumer protection, and regulation of online gambling sites. Computers in Human Behavior, 29(1), 235–245. doi:10.1016/j.chb.2012.08.010.CrossRef

Goldstein, A. L., Faulkner, B., Cunningham, R. M., Zimmerman, M. A., Chermack, S., & Walton, M. A. (2012). A latent class analysis of adolescent gambling: Application of resilience theory. International Journal of Mental Health and Addiction, 11(1), 13–30. doi:10.1007/s11469-012-9396-z.CrossRef

Griffiths, M. D., Wood, R. T. A., & Parke, J. (2009). Social responsibility tools in online gambling: A survey of attitudes and behavior among Internet gamblers. CyberPsychology & Behavior, 12(4), 413–421. doi:10.1089/cpb.2009.0062.CrossRef

Haefeli, J., Lischer, S., & Haeusler, J. (2014). Communications-based early detection of gambling-related problems in online gambling. International Gambling Studies, 15(1), 23–38. doi:10.1080/14459795.2014.980297.CrossRef

Haefeli, J., Lischer, S., & Schwarz, J. (2011). Early detection items and responsible gambling features for online gambling. International Gambling Studies, 11(3), 273–288. doi:10.1080/14459795.2011.604643.CrossRef

Hagenaars, J. A., & McCutcheon, A. L. (2002). Applied latent class analysis. Cambridge: Cambridge University Press.CrossRef

Health, S. I. O. P. (2012). Spel om pengar och spelproblem i Sverige 2009/2010 [Gambling and gambling problems in Sweden 2009/2010]. Retrieved from: http://www.folkhalsomyndigheten.se/pagefiles/12792/R2012-04-Spel-om-pengar-och-spelproblem-i-Sverige-2009-2010.pdf.

Hodgins, D. C., & El-Guebaly, N. (2000). Natural and treatment-assisted recovery from gambling problems: A comparison of resolved and active gamblers. Addiction, 95(5), 777–789. doi:10.1046/j.1360-0443.2000.95577713.x.CrossRefPubMed

Holtgraves, T. (2009). Evaluating the problem gambling severity index. Journal of Gambling Studies, 25(1), 105–120.CrossRefPubMed

Jolley, W., Lee, A., Mizerski, R., & Sadeque, S. (2013). Permission email messages significantly increase gambler retention. Journal of Business Research, 66(9), 1617–1622. doi:10.1016/j.jbusres.2012.12.006.CrossRef

Kalischuk, R. G., Nowatzki, N., Cardwell, K., Klein, K., & Solowoniuk, J. (2006). Problem gambling and its impact on families: A literature review. International Gambling Studies, 6(1), 31–60. doi:10.1080/14459790600644176.CrossRef

Kourgiantakis, T., Saint-Jacques, M.-C., & Tremblay, J. (2013). Problem gambling and families: A systematic review. Journal of Social Work Practice in the Addictions, 13(4), 353–372. doi:10.1080/1533256X.2013.838130.CrossRef

Ladouceur, R., Blaszczynski, A., & Lalande, D. R. (2012). Pre-commitment in gambling: A review of the empirical evidence. International Gambling Studies, 12(2), 215–230. doi:10.1080/14459795.2012.658078.CrossRef

Lloyd, J., Doll, H., Hawton, K., Dutton, W. H., Geddes, J. R., Goodwin, G. M., & Rogers, R. D. (2010). Internet gamblers: A latent class analysis of their behaviours and health experiences. Journal of Gambling Studies, 26(3), 387–399. doi:10.1007/s10899-010-9188-y.CrossRefPubMed

Martens, M. P., Arterberry, B. J., Takamatsu, S. K., Masters, J., & Dude, K. (2015). The efficacy of a personalized feedback-only intervention for at-risk college gamblers. Journal of Consulting and Clinical Psychology, 83(3), 494–499. doi:10.1037/a0038843.CrossRefPubMed

McBride, O., Adamson, G., & Shevlin, M. (2010). A latent class analysis of DSM-IV pathological gambling criteria in a nationally representative British sample. Psychiatry Research, 178(2), 401–407. doi:10.1016/j.psychres.2009.11.010.CrossRefPubMed

McCutcheon, A. L. (1987). Latent class analysis. Newbury Park, Calif: Sage Publications.

Miller, W., & Rollnick, S. (2002). Motivational interviewing: Preparing people for change. New York, NY: Guilford.

Muthén, L. K., & Muthén, B. O. (1998-2011). Mplus user guide, 6th edition. Los Angeles, CA: Muthén & Muthén.

Neighbors, C., Rodriguez, L. M., Rinker, D. V., Gonzales, R. G., Agana, M., Tackett, J. L., & Foster, D. W. (2015). Efficacy of personalized normative feedback as a brief intervention for college student gambling: A randomized controlled trial. Journal of Consulting and Clinical Psychology, 83(3), 500–511. doi:10.1037/a0039125.CrossRefPubMedPubMedCentral

Nelson, S. E., LaPlante, D. A., Peller, A. J., Schumann, A., LaBrie, R. A., & Shaffer, H. J. (2008). Real limits in the virtual world: Self-limiting behavior of Internet gamblers. Journal of Gambling Studies, 24(4), 463–477. doi:10.1007/s10899-008-9106-8.CrossRefPubMed

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007a). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. doi:10.1080/10705510701575396.CrossRef

Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007b). Subtypes, severity, and structural stability of peer victimization: What does latent class analysis say? Child Development, 78(6), 1706–1722. doi:10.1111/j.1467-8624.2007.01097.x.CrossRefPubMed

Pears, K. C., Kim, H. K., & Fisher, P. A. (2008). Psychosocial and cognitive functioning of children with specific profiles of maltreatment. Child Abuse and Neglect, 32(10), 958–971. doi:10.1016/j.chiabu.2007.12.009.CrossRefPubMedPubMedCentral

Philander, K. S. (2013). Identifying high-risk online gamblers: A comparison of data mining procedures. International Gambling Studies, 14(1), 53–63. doi:10.1080/14459795.2013.841721.CrossRef

Prochaska, J. O., DiClemente, C. C., & Norcross, J. C. (1993). In Search of how people change: Applications to addictive behaviors. Journal of Addictions Nursing, 5(1), 2–16. doi:10.3109/10884609309149692.CrossRef

Rachlin, H., Safin, V., Arfer, K. B., & Yen, M. (2015). The attraction of gambling. Journal of the Experimental Analysis of Behavior, 103(1), 260–266. doi:10.1002/jeab.113.CrossRefPubMed

Ramaswamy, V., Desarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103–124. doi:10.1287/mksc.12.1.103.CrossRef

Rossini-Dib, D., Fuentes, D., & Tavares, H. (2015). A naturalistic study of recovering gamblers: What gets better and when they get better. Psychiatry Research, 227(1), 17–26. doi:10.1016/j.psychres.2015.03.003.CrossRefPubMed

Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. doi:10.1214/aos/1176344136.CrossRef

Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343. doi:10.1007/bf02294360.CrossRef

Shaffer, H. J., Peller, A. J., LaPlante, D. A., Nelson, S. E., & LaBrie, R. A. (2010). Toward a paradigm shift in Internet gambling research: From opinion and self-report to actual behavior. Addiction Research & Theory, 18(3), 270–283. doi:10.3109/16066350902777974.CrossRef

Slutske, W. S. (2006). Natural recovery and treatment-seeking in pathological gambling: Results of two US National Surveys. American Journal of Psychiatry, 163(2), 297–302. doi:10.1176/appi.ajp.163.2.297.CrossRefPubMed

Studer, J., Baggio, S., Mohler-Kuo, M., Simon, O., Daeppen, J.-B., & Gmel, G. (2015). Latent class analysis of gambling activities in a sample of young swiss men: Association with gambling problems, substance use outcomes, personality traits and coping strategies. Journal of Gambling Studies. doi:10.1007/s10899-015-9547-9.

Suurvali, H., Cordingley, J., Hodgins, D., & Cunningham, J. (2009). Barriers to seeking help for gambling problems: A review of the empirical literature. Journal of Gambling Studies, 25(3), 407–424. doi:10.1007/s10899-009-9129-9.CrossRefPubMed

Suurvali, H., Hodgins, D., Toneatto, T., & Cunningham, J. (2008). Treatment seeking among ontario problem gamblers: Results of a population survey. PS, 59(11), 1343–1346. doi:10.1176/ps.2008.59.11.1343.CrossRef

Tackett, J. L., Rodriguez, L. M., Rinker, D. V., & Neighbors, C. (2014). A personality-based latent class analysis of emerging adult gamblers. Journal of Gambling Studies. doi:10.1007/s10899-014-9500-3.

Toneatto, T., Cunningham, J., Hodgins, D., Adams, M., Turner, N., & Koski-Jannes, A. (2008). Recovery from problem gambling without formal treatment. Addiction Research & Theory, 16(2), 111–120. doi:10.1080/16066350801923638.CrossRef

Vaughn, M. G., DeLisi, M., Gunter, T., Fu, Q., Beaver, K. M., Perron, B. E., & Howard, M. O. (2011). The severe 5%: A latent class analysis of the externalizing behavior spectrum in the United States. Journal of Criminal Justice, 39(1), 75–80. doi:10.1016/j.jcrimjus.2010.12.001.CrossRefPubMedPubMedCentral

Walker, D., Litvin, S., Sobel, R., & St-Pierre, R. (2014). Setting win limits: An alternative approach to “Responsible Gambling”? Journal of Gambling Studies, 1–22. doi:10.1007/s10899-014-9453-6.

Wangberg, S. C., Bergmo, T. S., & Johnsen, J.-A. K. (2008). Adherence in Internet-based interventions. Patient Preference and Adherence, 2, 57.PubMedPubMedCentral

Wanner, M., Martin-Diener, E., Bauer, G., Braun-Fahrländer, C., & Martin, B. W. (2010). Comparison of trial participants and open access users of a web-based physical activity intervention regarding adherence, attrition, and repeated participation. Journal of Medical Internet Research, 12(1), e3. doi:10.2196/jmir.1361.CrossRefPubMedCentral

Wohl, M. A., Santesso, D., & Harrigan, K. (2013). Reducing erroneous cognition and the frequency of exceeding limits among slots players: A short (3-minute) educational animation facilitates responsible gambling. International Journal of Mental Health and Addiction, 11(4), 409–423. doi:10.1007/s11469-012-9424-z.CrossRef

Wood, R. T. A., Shorter, G. W., & Griffiths, M. D. (2014). Rating the suitability of responsible gambling features for specific game types: A resource for optimizing responsible gambling strategy. International Journal of Mental Health and Addiction, 12(1), 94–112. doi:10.1007/s11469-013-9473-y.

Wood, R. T. A., & Wohl, M. J. A. (2015). Assessing the effectiveness of a responsible gambling behavioural feedback tool for reducing the gambling expenditure of at-risk players. International Gambling Studies, 15(2), 1–16. doi:10.1080/14459795.2015.1049191.CrossRef

Title: Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior
Authors: David Forsström
Hugo Hesser
Per Carlbring
Publication date: 01-09-2016
Publisher: Springer US
Published in: Journal of Gambling Studies / Issue 3/2016
Electronic ISSN: 1573-3602
DOI: https://doi.org/10.1007/s10899-015-9590-6

At a glance: The STEP trials

Springer Medicine

Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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