Delay discounting in adults receiving treatment for marijuana dependence.

Over four million Americans meet criteria for marijuana use disorders, and one million Americans received treatment for marijuana use in 2010 (Substance Abuse & Mental Health Services Administration, 2011). Although there are now several empirically validated therapies for marijuana use disorders (Denis, Lavie, Fatseas, & Auriacombe, 2008), abstinence rates reported for existing interventions are modest. One potential strategy for improving marijuana abstinence involves identifying individual characteristics that predict poor treatment response and tailoring interventions to more effectively address these characteristics. Delay discounting is one individual characteristic that may predispose substance users to poor treatment response. Delay discounting is an index of impulsive decision-making and reflects individuals’ preference for smaller immediate rewards relative to larger delayed rewards (Ainslie, 1975; Kirby, 1997; Logue, 1988). Delay discounting has substantial relevance for understanding addictive behavior, as it may explain individuals’ preference for immediate transient effects of drug use at the cost of future benefits from abstinence, as well as self-control failure and ambivalence related to abstinence. A recent meta-analysis of discounting and addictive behavior demonstrated that high rates of discounting characterize individuals with addictive behaviors, particularly individuals who meet criteria for an addictive disorder (MacKillop et al., 2011). However, few studies have examined discounting in marijuana users. One study of adults with current marijuana dependence, former marijuana dependence, or no history of regular marijuana use observed that discounting showed a nonsignificant trend toward being higher in individuals with current marijuana dependence compared with the other two groups; thus, the effect size of discounting may be smaller for marijuana than for other drugs (Johnson et al., 2010). One explanation for the relationship of delay discounting to poor treatment response may be the concurrent relationship of delay discounting with other characteristics that themselves impact response to treatment. For example, characteristics that predict poor abstinence outcomes or treatment retention include greater severity of substance use (Adamson, Sellman, & Frampton, 2009; Foulds et al., 2006); polysubstance use (see reviews by Agrawal, Budney, & Lynskey, 2012 and Peters, Budney, & Carroll, 2012; Poling, Kosten, & Sofuoglu, 2007; Toneatto, Sobell, Sobell, & Kozlowski, 1995); and higher self-reported impulsivity (Patkar et al., 2004), all of which are related to more impulsive discounting (de Wit, Flory, Acheson, McCloskey, & Manuck, 2007; Kirby & Petry, 2004; Kirby, Petry, & Bickel, 1999; Mitchell, Fields, D’Esposito, & Boettiger, 2005; Petry, 2001; Petry & Casarella, 1999; Sweitzer, Donny, Dierker, Flory, & Manuck, 2008; cf., Crean, de Wit, & Richards, 2000; Dom, De Wilde, Hulstijn, & Sabbe, 2007; Dom, De Wilde, Hulstijn, van den Brink, & Sabbe, 2006; Mitchell, 1999). Readiness to change is commonly assessed in treatment outcome studies because of its association with treatment response (DiClemente & Hughes, 1990; McKay & Weiss, 2001), yet to our knowledge, its relationship to delay discounting has not been described. Although most studies of delay discounting are cross-sectional in nature, data from randomized clinical trials of addiction treatments that longitudinally evaluate correlates of discounting are now emerging. Generally, higher pretreatment delay discounting (i.e., more impulsive decision-making) is associated with poorer abstinence outcomes in tobacco smokers (Krishnan-Sarin et al., 2007; MacKillop & Kahler, 2009; Sheffer et al., 2012; Yoon et al., 2007) and cocaine users (Washio et al., 2011). Other indices of impulsive decision-making have associations with abstinence outcomes in alcohol drinkers (Tucker, Roth, Vignolo, & Westfall, 2009; Tucker, Vuchinich, Black, & Rippens, 2006; Tucker, Vuchinich, & Rippens, 2002) and opiate users (Passetti et al., 2011; Passetti, Clark, Mehta, Joyce, & King, 2008). Only one study has evaluated how pretreatment discounting is associated with abstinence outcomes in marijuana users (Stanger et al., 2012). In this study, adolescent marijuana users completed four different discounting tasks: two with hypothetical monetary rewards with magnitudes of $100 and $1,000 as the reinforcer, and two with amounts of marijuana subjectively equivalent to $100 and $1,000 as the reinforcer. Discounting of $1,000 of money was related to several abstinence outcomes; however, discounting of the smaller magnitude of money and of either amount of marijuana were not. Evaluating the relationship of delay discounting to established clinical indicators and to abstinence outcomes is important for understanding shared and unique contributions of delay discounting to treatment response. The emerging clinical trials literature also suggests that discounting appears stable over time (Audrain-McGovern et al., 2009; Beck & Triplett, 2009; Kirby, 2009; Takahashi, Furukawa, Miyakawa, Maesato, & Higuchi, 2007). Thus, some assert that discounting may be considered a personality trait (Odum, 2011). Arguing against the stability of discounting over time, however, are studies showing that short-term deprivation from substance use increases, and psychosocial treatment for substance use decreases, impulsive responding. For example, among smokers, 13 hours of deprivation from nicotine was associated with an increase in discounting for cigarettes (Field, Santarcangelo, Sumnall, Goudie, & Cole, 2006), and five days of reductions in nicotine use were related to increases in discounting of cigarettes and monetary rewards (Yi et al., 2008). Similarly, opioid deprivation was associated with increases in discounting of both heroin and monetary rewards among opioid-dependent adults (Giordano et al., 2002). On the other hand, receiving buprenorphine plus either contingency management or standard counseling was related to reductions in discounting among opioid-dependent individuals (Landes, Christiansen, & Bickel, in press), and receiving 12-step and relapse prevention treatment was associated with significant decreases in performance on the Balloon Analogue Risk Task, a measure of risk-taking propensity, but not in discounting (Aklin, Tull, Kahler, & Lejuez, 2009). Given conflicting findings on the malleability of discounting over time, further research appears warranted, and research on specific types of substance use treatment that are associated with changes in discounting would be especially beneficial. For example, contingency management appears to be an especially effective treatment for substance users with higher discounting due to its provision of relatively immediate rewards (Marlowe, Festinger, Dugosh, Arabia, & Kirby, 2008; Petry, 2002) and, thus, may also have therapeutic effects on discounting. This report describes a secondary analysis of data on delay discounting among adults in a randomized clinical trial of psychosocial treatments for marijuana dependence (Carroll et al., 2012) and aims to address three hypotheses. First, we hypothesized that pretreatment delay discounting would correlate with indicators of treatment response; specifically, we hypothesized that higher discounting (i.e., more impulsive responding) would correlate with greater severity of marijuana use, higher self-reported impulsivity, greater tobacco use, and lower readiness to change marijuana use. Second, we hypothesized that higher pretreatment delay discounting would be associated with poorer treatment abstinence and retention across study treatments. Third, we investigated whether the specific treatments provided in this trial [contingency management (CM) and cognitive–behavioral therapy (CBT)] were associated with changes in discounting over time. Because CM has been associated with improvement in discounting in opioid users (Landes et al., in press), we hypothesized that CM would be associated with improvement in discounting in these marijuana users. To our knowledge, no study has yet evaluated the impact of CBT on discounting; thus, we had no specific hypotheses about the direction of the effect of CBT on discounting over time.