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Relations between delay discounting and low to moderate gambling, cannabis, and alcohol problems among university students
Behavioural Processes 88 (2011) 202–205
Contents lists available at SciVerse ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Relations between delay discounting and low to moderate gambling, cannabis, and alcohol problems among university students Jonathan N. Stea ∗ , David C. Hodgins, Michael J. Lambert Department of Psychology, Program in Clinical Psychology, University of Calgary, Canada
a r t i c l e
i n f o
Article history: Received 11 June 2011 Received in revised form 8 September 2011 Accepted 9 September 2011 Keywords: Alcohol Cannabis Delay discounting Gambling University students
a b s t r a c t Research has generally demonstrated that the discounting of delayed rewards is associated with severity of addictive behaviour. Less clear, however, is the relative strength of the relation for specific addictive behaviours. University students (N = 218) completed a computerized delay discounting task for hypothetical monetary rewards, and gambling, cannabis, and alcohol problem severity was assessed. A multiple regression analysis revealed that while the overall model was significant, only gambling problem severity accounted for delay discounting scores above and beyond cannabis and alcohol problem severity. The results support the hypothesis that delay discounting of hypothetical monetary rewards is more associated with gambling than other addictive behaviour problems, including substance use problems. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Delay discounting is thought to be at least one component integral to the broader construct of impulsivity, whereby it describes the extent to which smaller and immediate rewards are preferred over larger and delayed rewards (Ainslie, 1975; Bickel and Marsch, 2001). Numerous studies have found that in general, individuals with substance use – including cigarette (e.g., Bickel et al., 1999), heroin (e.g., Madden et al., 1997), and alcohol (e.g., Bjork et al., 2004; Dom et al., 2006; Mitchell et al., 2005; Petry, 2001a; Vuchinich and Simpson, 1998) – and gambling (e.g., Alessi and Petry, 2003; Dixon et al., 2003; MacKillop et al., 2006; Petry, 2001b; Petry and Casarella, 1999) problems discount delayed rewards more rapidly than controls (for reviews, see Yi et al., 2010; Petry and Madden, 2010). Thus, the relation between delay discounting and addictive behaviours appears to be robust, with only some apparent inconsistencies (e.g., Holt et al., 2003; Johnson et al., 2010; Kirby and Petry, 2004), which are likely due to methodological differences between studies (e.g., variability in the types, severity, and comorbidity of addictive behaviours across samples). It is has been suggested that not only might delay discounting rates vary as a function of preferred addictive behaviour (Kirby and Petry, 2004), but that moderately high rates of delay discounting
∗ Corresponding author at: Department of Psychology, University of Calgary, Addictive Behaviours Laboratory, Administration 240, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. Tel.: +1 403 210 9500; fax: +1 403 210 9500. E-mail address: [email protected] (J.N. Stea). 0376-6357/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2011.09.002
may be a risk factor for developing a problem with either substances or gambling, with still higher rates of delay discounting placing an individual at risk for developing multiple impulse control problems (Petry and Madden, 2010). In other words, higher dispositional levels of delay discounting might confer a greater vulnerability to the development of multiple addictive behaviour problems. While this dimensionalized vulnerability model of delay discounting is indeed plausible and consistent with research demonstrating that additive effects of pathological gambling and substance use disorders predict rates of delay discounting (Petry, 2001b; Petry and Casarella, 1999), it might also be the case that delay discounting of monetary rewards is a qualitatively stronger predictor of gambling than other addictive behaviour problems, including substance use problems; this latter view is consistent with several studies (e.g., Alessi and Petry, 2003; Businelle et al., 2010; Ledgerwood et al., 2009). To this end, our study is the first to directly test whether delay discounting can be incrementally predicted by gambling, cannabis, and alcohol problem severity, which is an important step needed to enhance our models of delay discounting and to unravel the complex relations of delay discounting to particular addictive behaviour problems more generally. Moreover, to our knowledge, our study is only the second to examine the relations between cannabis problems and delay discounting. For the most part, the literature suggests that higher levels of both gambling and alcohol problems are related to higher rates of delay discounting. In addition, while there has been a demonstrated trend towards higher discounting of hypothetical monetary rewards among currently dependent cannabis users as compared to formerly dependent cannabis users and matched controls, it is thought that the effect size of this
J.N. Stea et al. / Behavioural Processes 88 (2011) 202–205
relation appears to be smaller than for other addictive behaviour problems (Johnson et al., 2010). Thus, using a multiple regression approach and given our sample of university students – who typically display non-clinical levels of addictive behaviour problems – we hypothesized that the presence of higher levels of gambling, cannabis, and alcohol problems combined would predict higher levels of delay discounting, but only gambling and alcohol problems would uniquely predict delay discounting; with gambling problems being the stronger predictor.
2. Method 2.1. Participants Participants were 218 undergraduate students at the University of Calgary who participated in the study for a psychology course credit. Recruitment was based on the aims of a separate study that sought to recruit three groups of participants whom (1) gambled four or more times per month, (2) used cannabis four or more times per week, and (3) did not gamble or use cannabis in the past sixty days, respectively (Stea, 2010); by virtue of these criteria, only the third group was mutually exclusive to the other groups. This recruitment strategy, with no other exclusion criteria, yielded a sample of participants with sufficient variability in gambling, cannabis, and alcohol problem severity to test the hypotheses of the present study.
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2.3.2. Alcohol, Smoking, and Substance Involvement Screening Test, Cannabis Section (ASSIST; Ali et al., 2002) The cannabis section from the ASSIST is a 6-item measure that was used to assess problems associated with cannabis during the past three-months. The total score can categorize individuals into moderate- and high-risk for problems and dependence. The ASSIST has demonstrated excellent psychometric properties (Humeniuk et al., 2008), and was viewed as an appropriate measure of cannabis problem severity for the current sample of non-clinical university students in light of its recent use among the general population in the Canadian Addiction Survey (Adlaf et al., 2005). In the current sample, the Cronbach’s alpha coefficient was ˛ = .86, which shows good internal reliability. 2.3.3. Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) The 10-item AUDIT questionnaire was used to assess current alcohol problems. Total AUDIT scores can categorize individuals into low-risk drinking or abstinence, medium-risk drinking, highrisk drinking, and risk for alcohol dependence (Babor et al., 2001). The AUDIT has demonstrated favourable psychometric properties (Allen et al., 1997), whereby it has been shown to be both reliable and valid for indicating harmful alcohol use among college students (Fleming et al., 1991; Kokotailo et al., 2004). In the current sample, the Cronbach’s alpha coefficient was ˛ = .80, which demonstrates good internal reliability. 3. Results
2.2. Delay discounting procedure A computerized delay discounting procedure (Holt et al., 2003) was used, which instructed participants to choose between a hypothetical reward that was available immediately or a reward that was delayed by a specified period of time. The delayed reward was always $1000 and was available after one of seven delays (i.e., 1, 7, 30, 90, 180, 365, or 730 days). At each of the seven delays, six questions were presented sequentially on the computer screen as a block. Importantly, the amount of the immediate reward was adjusted across questions within each block such that after the first question was answered, the amount of the immediate reward was adjusted based on the participant’s response to the previous question. The size of this adjustment decreased with successive choices in order to rapidly converge on the participant’s indifference point at each delay. Each adjustment was half the difference between the immediate and delayed rewards from the previous question. Degree of delay discounting was established by calculating the area under the curve (AUC) (using Microsoft Excel), which is an appropriate measure of discounting that obviates several interpretative and statistical difficulties that are presented by using curve-fitting parameters; calculation details for AUC can be found in Myerson et al. (2001). Smaller AUC values reflect higher degrees of delay discounting. 2.3. Measures 2.3.1. Problem Gambling Severity Index (PGSI) from the Canadian Problem Gambling Index (CPGI; Ferris and Wynne, 2001) Designed to measure problematic gambling behaviour in the general population, the PGSI is comprised of 9-items and the total score can categorize individuals as non-problem, low-risk, moderate-risk, and problem gamblers. The PGSI has good concurrent validity with other measures of gambling problems and has good internal reliability as well as adequate test–retest reliability (Ferris and Wynne, 2001). In the current sample, the Cronbach’s alpha coefficient was ˛ = .86 (Cronbach, 1951).
3.1. Demographic and descriptive information All analyses were conducted using SPSS 16.0. Demographic and descriptive statistics are presented in Table 1. Due to the fact that participants were recruited based on frequency of gambling and cannabis use, relatively high rates of low to moderate gambling and cannabis problem severity levels were observed, including 23.9% of participants qualifying as moderate-risk gamblers, 6.0% of participants qualifying as problem gamblers; and 29.8% of participants falling into the category of moderate-risk cannabis users, with an additional 3.2% qualifying as high-risk cannabis users. In addition, 27.8% of participants were categorized as medium-risk problem drinkers, and 8.8% could be placed into the high-risk problem drinking category, with 0.5% falling into the higher-risk category. 3.2. Data analyses Square root transformations were conducted on the ASSIST and PGSI scores, which improved normality of their distributions. The data were then analyzed via simultaneous (i.e., forced entry) multiple regression using these two variables in addition to total AUDIT scores and Employment as predictors of AUC; Employment (employed = 1; not employed = 2) was the only demographic variable found to be significantly associated with AUC scores and it was therefore included in the regression analysis. Assumptions of linearity and homoscedasticity were met and no outliers were identified in the data using a p < .001 criterion for Mahalanobis distance. Table 2 displays the Pearson intercorrelations among the variables and the regression table depicting the unstandardized and standardized beta values for each predictor. Complete data were available for 215 participants (the AUDIT was incomplete for two participants and the AUC score was not calculated for one participant due to a computer malfunction). The overall regression model consisting of all four predictors significantly predicted AUC scores, R = .27, R2 = .07, F (4, 210) = 4.05, p < .01. As shown in Table 2, only PGSI scores and Employment accounted for significant incremental variance in AUC scores. Specifically, PGSI scores and Employment
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J.N. Stea et al. / Behavioural Processes 88 (2011) 202–205
Table 1 Demographic and descriptive statistics (N = 218). Variable
M (SD)/Mdn (IQR)/Frequency (%)
Age (M, SD)a Gender (% male) Marital status (% single) Area of residence (% urban)d Employment (% employed) Ethnicity Caucasian Asian Other PGSI score (Mdn, IQR)e PGSI category Low-risk Moderate-risk Problem gambling ASSIST score (Mdn, IQR)f ASSIST category Low-risk Moderate-risk High-risk AUDIT score (M, SD)b AUDIT categoryb Low-risk Medium-risk High-risk Higher-risk AUC score (M, SD)c
21.3 (4.4) 80 (36.7%) 201 (92.2%) 205 (96.2%) 132 (60.6%)
Table 2 Pearson intercorrelations and regression table depicting the unstandardized and standardized beta values for the PGSI, ASSIST, and AUDIT scores on AUC scores. Measure
1
2
3
4
5
1. AUC
–
−.21** n = 217 –
−.11 n = 217 .11 n = 218 –
−.11 n = 215 .24** n = 216 .48** n = 216 –
.14* n = 217 −.10 n = 218 .02 n = 218 .02 n = 218 –
2. PGSI 3. ASSIST
94 (43.1%) 87 (39.9%) 37 (17.0%) 0.0 (3.0) 153 (70.1%) 52 (23.9%) 13 (6.0%) 0.0 (8.0) 146 (67.0%) 65 (29.8%) 7 (3.2%) 6.7 (5.2) 136 (63.0%) 60 (27.8%) 19 (8.8%) 1 (0.5%) .32 (.14)
Note: PGSI scores are interpreted using the following categories: Low-risk (0–2); Moderate-risk (3–7); Problem gambling (≥8). ASSIST scores are interpreted using the following categories: Low-risk (0–3); Moderate-risk (4–26); High-risk (≥27). AUDIT scores are interpreted using the following categories: Low-risk (0–7); Medium-risk (8–15); High-risk (16–19); Higher-risk (≥20). ASSIST = cannabis section from the Alcohol, Smoking, and Substance Involvement Screening Test; AUC = Area Under the Curve; AUDIT = Alcohol Use Disorder Identification Test; PGSI = Problem Gambling Severity Index. a M (SD) represents age in years. b N = 216. c N = 217. d N = 213. e Mdn (IQR) is presented for non-normally distributed data. Square root transformations were conducted on the PGSI scores to improve normality of the distribution as evidenced by the following change in Kurtosis and Skewness values: PGSI Kurtosis = 7.6, z-value = 23.2; Square root of PGSI Kurtosis = −1.0, z-value = −0.3. PGSI Skewness = 2.5, z-value = 15.1; Square root of PGSI Skewness = 0.9, z-value = 5.4. f Mdn (IQR) is presented for non-normally distributed data. Square root transformations were conducted on the ASSIST scores to improve normality of the distribution as evidenced by the following change in Kurtosis and Skewness values: ASSIST Kurtosis = 1.2, z-value = 3.7; Square root of ASSIST Kurtosis = −0.7, z-value = −2.3. ASSIST Skewness = 1.5, z-value = 9.3; Square root of ASSIST Skewness = 0.9, z-value = 5.2.
accounted for 3.0% and 2.0% of unique variance in AUC scores, respectively, as indicated by their the semi-partial correlations with AUC scores (−.17 and .15, respectively). 4. Discussion This study provides evidence that low to moderate gambling problems among university students are more strongly associated with delay discounting than low to moderate levels of cannabis and alcohol problems. The results support Johnson et al.’s (2010) contention that the effect size of the association between cannabis problems and delay discounting may be smaller than that of other addictive behaviours and delay discounting, and they support the hypothesis that the relation between gambling problems and delay discounting might be qualitatively stronger than the relation between other substance problems and delay discounting. One explanation for this qualitative difference could be that money acts as a more rewarding reinforcer for individuals with gambling problems, which would be in keeping with the notion that among individuals with addictive behaviour problems, delay discounting
4. AUDIT 5. Employment Variable
B
SE (B)
ˇ
Constant PGSI ASSIST AUDIT Employment
.30 −0.02 −0.01 0.00 0.04
0.03 0.01 0.01 0.00 0.02
−.20* −.08 −.03 −.14*
Note 1: PGSI and ASSIST scores were square root transformed in order to improve normality of the distribution. Note 2: R2 = .07 (p < .01, n = 215). Note 3: Employment is a categorical variable coded as 1 = employed, 2 = not employed. ASSIST = cannabis section from the Alcohol, Smoking, and Substance Involvement Screening Test; AUC = Area Under the Curve; AUDIT = Alcohol Use Disorder Identification Test; PGSI = Problem Gambling Severity Index. * p < .05, ** p < .01. Intercorrelations are two-tailed.
might be stronger for rewards that are specifically related to an individual’s preferred addictive behaviour. Indeed, this explanation is supported by research demonstrating that heroin (Madden et al., 1997, 1999), cigarettes (Bickel et al., 1999), cannabis (Johnson et al., 2010), and alcohol (Petry, 2001a) are all discounted more rapidly than money among individuals with problems in those respective substance use domains; and by research demonstrating that erotica is less rewarding than money for non-erotica users compared to erotica users (Lawyer, 2008). It is also possible, given the results of Odum and Rainaud (2003) – which found that participants with no self-reported eating disorders or gambling or alcohol problems discounted hypothetical food and alcohol more steeply than hypothetical money – that a primary or consumable reinforcer is discounted more steeply than a conditioned or non-consumable reinforcer (e.g., money) among individuals without gambling problems; and that individuals with gambling problems regard the value of money to be as rewarding as the value of a primary or consumable reinforcer. The results, however, did not support the hypothesis that irrespective of gambling and cannabis problem severity, higher levels of alcohol problem severity would predict higher levels of delay discounting. One possible explanation – which is consistent with some studies (e.g., Fernie et al., 2010; Kirby and Petry, 2004; Pryor and MacKillop, 2009) – is that the low to moderate alcohol problem severity levels observed in the present sample were not sufficient to reliably predict levels of delay discounting. The results also indicated that employment was the only significant and unique demographic predictor of delay discounting, whereby employed students tended to demonstrate higher levels of delay discounting. One possible explanation is that the students who were employed in our sample viewed themselves as more in need of money than the non-employed students, which might have affected their preference for smaller, more immediate rewards. While this study is unique in that was the first to test whether delay discounting could be incrementally predicted by gambling, cannabis, and alcohol problem severity, and was the second to examine the relations between cannabis problems and delay discounting, it is limited insofar as it utilized a non-clinical sample and restricted level of problem severity – namely, university students
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and low to moderate problem severity. In order to further test the hypothesis that delay discounting might be a qualitatively stronger predictor of gambling relative to other addictive behaviour problems, future studies should include problem severity measures for a variety of addictive behaviour problems, among both clinical and non-clinical samples, with and without comorbid addictive behaviour problems. More broadly, the cross-sectional nature of both the present study and past studies in this area warrants confirmation and extension of the findings in the context of longitudinal designs. Ultimately, a further understanding of the nature of delay discounting and its influence on the decision to engage in particular addictive behaviours, especially gambling, might aid in the design of addictive behaviour-specific treatment efforts. Acknowledgments We thank Dr. N. Will Shead for his helpful insights into the area of delay discounting and its relation to addictive behaviours, as well as for providing us with the AUC calculator program used in the present study. We also thank Cayla Martin for her exquisite skill in helping to conduct the study. This research was financially supported by graduate level scholarships granted to Jonathan N. Stea from the Alberta Gaming Research Institute, the Social Sciences and Humanities Research Council of Canada, the University of Calgary, and the Government of Alberta. Additional financial support was provided by an Alberta Gaming Research Institute grant held by Dr. David C. Hodgins. References Adlaf, E.M., Begin, P., Sawka, E. (Eds.), 2005. Canadian Addiction Survey (CAS): A National Survey of Canadians’ Use of Alcohol and Other Drugs: Prevalence of Use and Related Harms: Detailed Report, Ottawa. Ainslie, G., 1975. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol. Bull. 82, 463–496. Alessi, S., Petry, N., 2003. Pathological gambling severity is associated with impulsivity in a delay discounting procedure. Behav. Process. 64, 345–354. Ali, R., Awwad, E., Babor, T., Bradley, F., Butau, T., Farrell, M., Formigoni, M.L.O., Isralowitz, R., Boerngen de Lacerda, R., Marsden, J., McRee, B., Monteiro, M., Pal, H., Rubio-Stipec, M., Vendetti, J., 2002. The Alcohol Smoking and Substance Involvement Screening Test (ASSIST): development, reliability and feasibility. Addiction 97, 1183–1194. Allen, J.P., Litten, R.Z., Fertig, J.B., Babor, T., 1997. A review of research on the Alcohol Use Disorders Identification Test (AUDIT). Alcohol. Clin. Exp. Res. 21, 613–619. Babor, T.F., Higgins-Biddle, J.C., Saunders, J.B., Monteiro, M.G., 2001. The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care, second ed. World Health Organization, Geneva. Bickel, W.K., Marsch, L.A., 2001. Toward a behavioral economic understanding of drug dependence: delay discounting processes. Addiction 96, 73–86. Bickel, W.K., Odum, A.L., Madden, G.J., 1999. Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology 146, 447–454. Bjork, J.M., Hommer, D.W., Grant, S.J., Danube, C., 2004. Impulsivity in abstinent alcohol-dependent patients: relation to control subjects and type 1-/type 2-like traits. Alcohol 34, 133–150. Businelle, M.S., McVay, M.A., Kendzor, D., Copeland, A., 2010. A comparison of delay discounting among smokers, substance abusers, and non-dependent controls. Drug Alcohol Depend. 112, 247–250. Cronbach, L.J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334.
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Contents lists available at SciVerse ScienceDirect
Behavioural Processes journal homepage: www.elsevier.com/locate/behavproc
Relations between delay discounting and low to moderate gambling, cannabis, and alcohol problems among university students Jonathan N. Stea ∗ , David C. Hodgins, Michael J. Lambert Department of Psychology, Program in Clinical Psychology, University of Calgary, Canada
a r t i c l e
i n f o
Article history: Received 11 June 2011 Received in revised form 8 September 2011 Accepted 9 September 2011 Keywords: Alcohol Cannabis Delay discounting Gambling University students
a b s t r a c t Research has generally demonstrated that the discounting of delayed rewards is associated with severity of addictive behaviour. Less clear, however, is the relative strength of the relation for specific addictive behaviours. University students (N = 218) completed a computerized delay discounting task for hypothetical monetary rewards, and gambling, cannabis, and alcohol problem severity was assessed. A multiple regression analysis revealed that while the overall model was significant, only gambling problem severity accounted for delay discounting scores above and beyond cannabis and alcohol problem severity. The results support the hypothesis that delay discounting of hypothetical monetary rewards is more associated with gambling than other addictive behaviour problems, including substance use problems. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Delay discounting is thought to be at least one component integral to the broader construct of impulsivity, whereby it describes the extent to which smaller and immediate rewards are preferred over larger and delayed rewards (Ainslie, 1975; Bickel and Marsch, 2001). Numerous studies have found that in general, individuals with substance use – including cigarette (e.g., Bickel et al., 1999), heroin (e.g., Madden et al., 1997), and alcohol (e.g., Bjork et al., 2004; Dom et al., 2006; Mitchell et al., 2005; Petry, 2001a; Vuchinich and Simpson, 1998) – and gambling (e.g., Alessi and Petry, 2003; Dixon et al., 2003; MacKillop et al., 2006; Petry, 2001b; Petry and Casarella, 1999) problems discount delayed rewards more rapidly than controls (for reviews, see Yi et al., 2010; Petry and Madden, 2010). Thus, the relation between delay discounting and addictive behaviours appears to be robust, with only some apparent inconsistencies (e.g., Holt et al., 2003; Johnson et al., 2010; Kirby and Petry, 2004), which are likely due to methodological differences between studies (e.g., variability in the types, severity, and comorbidity of addictive behaviours across samples). It is has been suggested that not only might delay discounting rates vary as a function of preferred addictive behaviour (Kirby and Petry, 2004), but that moderately high rates of delay discounting
∗ Corresponding author at: Department of Psychology, University of Calgary, Addictive Behaviours Laboratory, Administration 240, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4. Tel.: +1 403 210 9500; fax: +1 403 210 9500. E-mail address: [email protected] (J.N. Stea). 0376-6357/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.beproc.2011.09.002
may be a risk factor for developing a problem with either substances or gambling, with still higher rates of delay discounting placing an individual at risk for developing multiple impulse control problems (Petry and Madden, 2010). In other words, higher dispositional levels of delay discounting might confer a greater vulnerability to the development of multiple addictive behaviour problems. While this dimensionalized vulnerability model of delay discounting is indeed plausible and consistent with research demonstrating that additive effects of pathological gambling and substance use disorders predict rates of delay discounting (Petry, 2001b; Petry and Casarella, 1999), it might also be the case that delay discounting of monetary rewards is a qualitatively stronger predictor of gambling than other addictive behaviour problems, including substance use problems; this latter view is consistent with several studies (e.g., Alessi and Petry, 2003; Businelle et al., 2010; Ledgerwood et al., 2009). To this end, our study is the first to directly test whether delay discounting can be incrementally predicted by gambling, cannabis, and alcohol problem severity, which is an important step needed to enhance our models of delay discounting and to unravel the complex relations of delay discounting to particular addictive behaviour problems more generally. Moreover, to our knowledge, our study is only the second to examine the relations between cannabis problems and delay discounting. For the most part, the literature suggests that higher levels of both gambling and alcohol problems are related to higher rates of delay discounting. In addition, while there has been a demonstrated trend towards higher discounting of hypothetical monetary rewards among currently dependent cannabis users as compared to formerly dependent cannabis users and matched controls, it is thought that the effect size of this
J.N. Stea et al. / Behavioural Processes 88 (2011) 202–205
relation appears to be smaller than for other addictive behaviour problems (Johnson et al., 2010). Thus, using a multiple regression approach and given our sample of university students – who typically display non-clinical levels of addictive behaviour problems – we hypothesized that the presence of higher levels of gambling, cannabis, and alcohol problems combined would predict higher levels of delay discounting, but only gambling and alcohol problems would uniquely predict delay discounting; with gambling problems being the stronger predictor.
2. Method 2.1. Participants Participants were 218 undergraduate students at the University of Calgary who participated in the study for a psychology course credit. Recruitment was based on the aims of a separate study that sought to recruit three groups of participants whom (1) gambled four or more times per month, (2) used cannabis four or more times per week, and (3) did not gamble or use cannabis in the past sixty days, respectively (Stea, 2010); by virtue of these criteria, only the third group was mutually exclusive to the other groups. This recruitment strategy, with no other exclusion criteria, yielded a sample of participants with sufficient variability in gambling, cannabis, and alcohol problem severity to test the hypotheses of the present study.
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2.3.2. Alcohol, Smoking, and Substance Involvement Screening Test, Cannabis Section (ASSIST; Ali et al., 2002) The cannabis section from the ASSIST is a 6-item measure that was used to assess problems associated with cannabis during the past three-months. The total score can categorize individuals into moderate- and high-risk for problems and dependence. The ASSIST has demonstrated excellent psychometric properties (Humeniuk et al., 2008), and was viewed as an appropriate measure of cannabis problem severity for the current sample of non-clinical university students in light of its recent use among the general population in the Canadian Addiction Survey (Adlaf et al., 2005). In the current sample, the Cronbach’s alpha coefficient was ˛ = .86, which shows good internal reliability. 2.3.3. Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) The 10-item AUDIT questionnaire was used to assess current alcohol problems. Total AUDIT scores can categorize individuals into low-risk drinking or abstinence, medium-risk drinking, highrisk drinking, and risk for alcohol dependence (Babor et al., 2001). The AUDIT has demonstrated favourable psychometric properties (Allen et al., 1997), whereby it has been shown to be both reliable and valid for indicating harmful alcohol use among college students (Fleming et al., 1991; Kokotailo et al., 2004). In the current sample, the Cronbach’s alpha coefficient was ˛ = .80, which demonstrates good internal reliability. 3. Results
2.2. Delay discounting procedure A computerized delay discounting procedure (Holt et al., 2003) was used, which instructed participants to choose between a hypothetical reward that was available immediately or a reward that was delayed by a specified period of time. The delayed reward was always $1000 and was available after one of seven delays (i.e., 1, 7, 30, 90, 180, 365, or 730 days). At each of the seven delays, six questions were presented sequentially on the computer screen as a block. Importantly, the amount of the immediate reward was adjusted across questions within each block such that after the first question was answered, the amount of the immediate reward was adjusted based on the participant’s response to the previous question. The size of this adjustment decreased with successive choices in order to rapidly converge on the participant’s indifference point at each delay. Each adjustment was half the difference between the immediate and delayed rewards from the previous question. Degree of delay discounting was established by calculating the area under the curve (AUC) (using Microsoft Excel), which is an appropriate measure of discounting that obviates several interpretative and statistical difficulties that are presented by using curve-fitting parameters; calculation details for AUC can be found in Myerson et al. (2001). Smaller AUC values reflect higher degrees of delay discounting. 2.3. Measures 2.3.1. Problem Gambling Severity Index (PGSI) from the Canadian Problem Gambling Index (CPGI; Ferris and Wynne, 2001) Designed to measure problematic gambling behaviour in the general population, the PGSI is comprised of 9-items and the total score can categorize individuals as non-problem, low-risk, moderate-risk, and problem gamblers. The PGSI has good concurrent validity with other measures of gambling problems and has good internal reliability as well as adequate test–retest reliability (Ferris and Wynne, 2001). In the current sample, the Cronbach’s alpha coefficient was ˛ = .86 (Cronbach, 1951).
3.1. Demographic and descriptive information All analyses were conducted using SPSS 16.0. Demographic and descriptive statistics are presented in Table 1. Due to the fact that participants were recruited based on frequency of gambling and cannabis use, relatively high rates of low to moderate gambling and cannabis problem severity levels were observed, including 23.9% of participants qualifying as moderate-risk gamblers, 6.0% of participants qualifying as problem gamblers; and 29.8% of participants falling into the category of moderate-risk cannabis users, with an additional 3.2% qualifying as high-risk cannabis users. In addition, 27.8% of participants were categorized as medium-risk problem drinkers, and 8.8% could be placed into the high-risk problem drinking category, with 0.5% falling into the higher-risk category. 3.2. Data analyses Square root transformations were conducted on the ASSIST and PGSI scores, which improved normality of their distributions. The data were then analyzed via simultaneous (i.e., forced entry) multiple regression using these two variables in addition to total AUDIT scores and Employment as predictors of AUC; Employment (employed = 1; not employed = 2) was the only demographic variable found to be significantly associated with AUC scores and it was therefore included in the regression analysis. Assumptions of linearity and homoscedasticity were met and no outliers were identified in the data using a p < .001 criterion for Mahalanobis distance. Table 2 displays the Pearson intercorrelations among the variables and the regression table depicting the unstandardized and standardized beta values for each predictor. Complete data were available for 215 participants (the AUDIT was incomplete for two participants and the AUC score was not calculated for one participant due to a computer malfunction). The overall regression model consisting of all four predictors significantly predicted AUC scores, R = .27, R2 = .07, F (4, 210) = 4.05, p < .01. As shown in Table 2, only PGSI scores and Employment accounted for significant incremental variance in AUC scores. Specifically, PGSI scores and Employment
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Table 1 Demographic and descriptive statistics (N = 218). Variable
M (SD)/Mdn (IQR)/Frequency (%)
Age (M, SD)a Gender (% male) Marital status (% single) Area of residence (% urban)d Employment (% employed) Ethnicity Caucasian Asian Other PGSI score (Mdn, IQR)e PGSI category Low-risk Moderate-risk Problem gambling ASSIST score (Mdn, IQR)f ASSIST category Low-risk Moderate-risk High-risk AUDIT score (M, SD)b AUDIT categoryb Low-risk Medium-risk High-risk Higher-risk AUC score (M, SD)c
21.3 (4.4) 80 (36.7%) 201 (92.2%) 205 (96.2%) 132 (60.6%)
Table 2 Pearson intercorrelations and regression table depicting the unstandardized and standardized beta values for the PGSI, ASSIST, and AUDIT scores on AUC scores. Measure
1
2
3
4
5
1. AUC
–
−.21** n = 217 –
−.11 n = 217 .11 n = 218 –
−.11 n = 215 .24** n = 216 .48** n = 216 –
.14* n = 217 −.10 n = 218 .02 n = 218 .02 n = 218 –
2. PGSI 3. ASSIST
94 (43.1%) 87 (39.9%) 37 (17.0%) 0.0 (3.0) 153 (70.1%) 52 (23.9%) 13 (6.0%) 0.0 (8.0) 146 (67.0%) 65 (29.8%) 7 (3.2%) 6.7 (5.2) 136 (63.0%) 60 (27.8%) 19 (8.8%) 1 (0.5%) .32 (.14)
Note: PGSI scores are interpreted using the following categories: Low-risk (0–2); Moderate-risk (3–7); Problem gambling (≥8). ASSIST scores are interpreted using the following categories: Low-risk (0–3); Moderate-risk (4–26); High-risk (≥27). AUDIT scores are interpreted using the following categories: Low-risk (0–7); Medium-risk (8–15); High-risk (16–19); Higher-risk (≥20). ASSIST = cannabis section from the Alcohol, Smoking, and Substance Involvement Screening Test; AUC = Area Under the Curve; AUDIT = Alcohol Use Disorder Identification Test; PGSI = Problem Gambling Severity Index. a M (SD) represents age in years. b N = 216. c N = 217. d N = 213. e Mdn (IQR) is presented for non-normally distributed data. Square root transformations were conducted on the PGSI scores to improve normality of the distribution as evidenced by the following change in Kurtosis and Skewness values: PGSI Kurtosis = 7.6, z-value = 23.2; Square root of PGSI Kurtosis = −1.0, z-value = −0.3. PGSI Skewness = 2.5, z-value = 15.1; Square root of PGSI Skewness = 0.9, z-value = 5.4. f Mdn (IQR) is presented for non-normally distributed data. Square root transformations were conducted on the ASSIST scores to improve normality of the distribution as evidenced by the following change in Kurtosis and Skewness values: ASSIST Kurtosis = 1.2, z-value = 3.7; Square root of ASSIST Kurtosis = −0.7, z-value = −2.3. ASSIST Skewness = 1.5, z-value = 9.3; Square root of ASSIST Skewness = 0.9, z-value = 5.2.
accounted for 3.0% and 2.0% of unique variance in AUC scores, respectively, as indicated by their the semi-partial correlations with AUC scores (−.17 and .15, respectively). 4. Discussion This study provides evidence that low to moderate gambling problems among university students are more strongly associated with delay discounting than low to moderate levels of cannabis and alcohol problems. The results support Johnson et al.’s (2010) contention that the effect size of the association between cannabis problems and delay discounting may be smaller than that of other addictive behaviours and delay discounting, and they support the hypothesis that the relation between gambling problems and delay discounting might be qualitatively stronger than the relation between other substance problems and delay discounting. One explanation for this qualitative difference could be that money acts as a more rewarding reinforcer for individuals with gambling problems, which would be in keeping with the notion that among individuals with addictive behaviour problems, delay discounting
4. AUDIT 5. Employment Variable
B
SE (B)
ˇ
Constant PGSI ASSIST AUDIT Employment
.30 −0.02 −0.01 0.00 0.04
0.03 0.01 0.01 0.00 0.02
−.20* −.08 −.03 −.14*
Note 1: PGSI and ASSIST scores were square root transformed in order to improve normality of the distribution. Note 2: R2 = .07 (p < .01, n = 215). Note 3: Employment is a categorical variable coded as 1 = employed, 2 = not employed. ASSIST = cannabis section from the Alcohol, Smoking, and Substance Involvement Screening Test; AUC = Area Under the Curve; AUDIT = Alcohol Use Disorder Identification Test; PGSI = Problem Gambling Severity Index. * p < .05, ** p < .01. Intercorrelations are two-tailed.
might be stronger for rewards that are specifically related to an individual’s preferred addictive behaviour. Indeed, this explanation is supported by research demonstrating that heroin (Madden et al., 1997, 1999), cigarettes (Bickel et al., 1999), cannabis (Johnson et al., 2010), and alcohol (Petry, 2001a) are all discounted more rapidly than money among individuals with problems in those respective substance use domains; and by research demonstrating that erotica is less rewarding than money for non-erotica users compared to erotica users (Lawyer, 2008). It is also possible, given the results of Odum and Rainaud (2003) – which found that participants with no self-reported eating disorders or gambling or alcohol problems discounted hypothetical food and alcohol more steeply than hypothetical money – that a primary or consumable reinforcer is discounted more steeply than a conditioned or non-consumable reinforcer (e.g., money) among individuals without gambling problems; and that individuals with gambling problems regard the value of money to be as rewarding as the value of a primary or consumable reinforcer. The results, however, did not support the hypothesis that irrespective of gambling and cannabis problem severity, higher levels of alcohol problem severity would predict higher levels of delay discounting. One possible explanation – which is consistent with some studies (e.g., Fernie et al., 2010; Kirby and Petry, 2004; Pryor and MacKillop, 2009) – is that the low to moderate alcohol problem severity levels observed in the present sample were not sufficient to reliably predict levels of delay discounting. The results also indicated that employment was the only significant and unique demographic predictor of delay discounting, whereby employed students tended to demonstrate higher levels of delay discounting. One possible explanation is that the students who were employed in our sample viewed themselves as more in need of money than the non-employed students, which might have affected their preference for smaller, more immediate rewards. While this study is unique in that was the first to test whether delay discounting could be incrementally predicted by gambling, cannabis, and alcohol problem severity, and was the second to examine the relations between cannabis problems and delay discounting, it is limited insofar as it utilized a non-clinical sample and restricted level of problem severity – namely, university students
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and low to moderate problem severity. In order to further test the hypothesis that delay discounting might be a qualitatively stronger predictor of gambling relative to other addictive behaviour problems, future studies should include problem severity measures for a variety of addictive behaviour problems, among both clinical and non-clinical samples, with and without comorbid addictive behaviour problems. More broadly, the cross-sectional nature of both the present study and past studies in this area warrants confirmation and extension of the findings in the context of longitudinal designs. Ultimately, a further understanding of the nature of delay discounting and its influence on the decision to engage in particular addictive behaviours, especially gambling, might aid in the design of addictive behaviour-specific treatment efforts. Acknowledgments We thank Dr. N. Will Shead for his helpful insights into the area of delay discounting and its relation to addictive behaviours, as well as for providing us with the AUC calculator program used in the present study. We also thank Cayla Martin for her exquisite skill in helping to conduct the study. This research was financially supported by graduate level scholarships granted to Jonathan N. Stea from the Alberta Gaming Research Institute, the Social Sciences and Humanities Research Council of Canada, the University of Calgary, and the Government of Alberta. Additional financial support was provided by an Alberta Gaming Research Institute grant held by Dr. David C. Hodgins. References Adlaf, E.M., Begin, P., Sawka, E. (Eds.), 2005. Canadian Addiction Survey (CAS): A National Survey of Canadians’ Use of Alcohol and Other Drugs: Prevalence of Use and Related Harms: Detailed Report, Ottawa. Ainslie, G., 1975. Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol. Bull. 82, 463–496. Alessi, S., Petry, N., 2003. Pathological gambling severity is associated with impulsivity in a delay discounting procedure. Behav. Process. 64, 345–354. Ali, R., Awwad, E., Babor, T., Bradley, F., Butau, T., Farrell, M., Formigoni, M.L.O., Isralowitz, R., Boerngen de Lacerda, R., Marsden, J., McRee, B., Monteiro, M., Pal, H., Rubio-Stipec, M., Vendetti, J., 2002. The Alcohol Smoking and Substance Involvement Screening Test (ASSIST): development, reliability and feasibility. Addiction 97, 1183–1194. Allen, J.P., Litten, R.Z., Fertig, J.B., Babor, T., 1997. A review of research on the Alcohol Use Disorders Identification Test (AUDIT). Alcohol. Clin. Exp. Res. 21, 613–619. Babor, T.F., Higgins-Biddle, J.C., Saunders, J.B., Monteiro, M.G., 2001. The Alcohol Use Disorders Identification Test: Guidelines for Use in Primary Health Care, second ed. World Health Organization, Geneva. Bickel, W.K., Marsch, L.A., 2001. Toward a behavioral economic understanding of drug dependence: delay discounting processes. Addiction 96, 73–86. Bickel, W.K., Odum, A.L., Madden, G.J., 1999. Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology 146, 447–454. Bjork, J.M., Hommer, D.W., Grant, S.J., Danube, C., 2004. Impulsivity in abstinent alcohol-dependent patients: relation to control subjects and type 1-/type 2-like traits. Alcohol 34, 133–150. Businelle, M.S., McVay, M.A., Kendzor, D., Copeland, A., 2010. A comparison of delay discounting among smokers, substance abusers, and non-dependent controls. Drug Alcohol Depend. 112, 247–250. Cronbach, L.J., 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16, 297–334.
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