al., 2014).
The second domain of research in this area has focused on state-based influences on demand. To date, the main focus of this work has been alcohol demand, with a relatively smaller number of studies on tobacco demand (Acker and MacKillop, 2013; MacKillop et al., 2012). In the context of alcohol demand, one finding that has been replicated across a number of studies is that the relative value of alcohol is increased by alcohol-related environmental cues. Specifically, MacKillop, O’Hagen et al. (2010) observed significant increases in several indices of alcohol demand in heavy drinkers following a laboratory alcohol cue exposure. These findings were replicated in a subsequent study (Amlung et al., 2012) that also demonstrated close correspondence between estimated demand on an alcohol purchase task and actual alcohol consumption during a laboratory self-administration protocol.
In another set of studies, negative affect and stress inductions significantly increased alcohol demand in heavy drinkers (Amlung and MacKillop, 2014;
Owens, Ray, and MacKillop, 2015). Finally, Amlung et al. (2015) recently demonstrated that demand for alcohol is dynamically increased by acute alcohol consumption, which may contribute to loss of control over drinking that can occur following intoxication. These findings suggest that state-based indices of demand may complement existing measures of alcohol motivation, such as subjective craving. Importantly, no studies have examined state-based influences on demand for illicit drugs, which is an important target for future research.
Predicting treatment response
High rates of relapse following treatment for alcohol and other drug use disorders have been well documented. For instance, two large systematic reviews of relapse rates following alcohol treatment found that between 66–
79 percent of individuals with alcohol use disorders experience at least one relapse following treatment (Miller et al., 2001; Moyer and Finney, 2002).
The high percentage of individuals who return to problematic use of alcohol and drugs following treatment underscores the importance of identifying risk factors for relapse and improving current treatment approaches. An increasing number of studies have examined whether DD and demand can be used to predict treatment response.
Several studies have shown that impulsive DD is associated with poor addiction treatment outcomes, though much of this work has focused on nicotine dependence. One study found that steeper DD was associated with a shorter number of days to first lapse following smoking cessation treatment (MacKillop and Kahler, 2009). Individuals who failed to maintain abstinence at two- and eight-week follow-up visits were also significantly more impulsive at baseline compared to those who were successful. Similar results have been reported in other studies of smokers (Dallery and Raiff, 2007;
Krishnan-Sarin et al., 2007; Sheffer et al., 2012; Yoon et al., 2007). To date, two studies have examined whether DD predicts treatment outcomes in drug abusers. Washio et al. (2011) found that steeper DD is associated with worse treatment response following outpatient treatment for cocaine dependence. In another study, Stanger et al. (2012) found that impulsive DD at baseline prospectively predicted treatment outcomes in adolescents with marijuana use disorder.
The literature examining whether level of demand is associated with treatment outcomes is limited to a handful of studies focused on alcohol and tobacco use. MacKillop and Murphy (2007) found that indices of alcohol demand predicted post-treatment drinking behavior in college student drinkers who completed a brief alcohol intervention. Murphy et al. (2015) examined whether baseline performance on an alcohol purchase task predicted drinking levels and alcohol problems at 1 and 6 months following brief alcohol interventions in college drinkers. Elevated alcohol demand predicted increased drinks per week and greater alcohol problems at 1-month follow up. Furthermore, greater reductions in demand immediately following
the intervention were associated with better outcomes at 1 month, but not at 6 months. In the case of nicotine dependence, elevated demand for cigarettes was associated with fewer abstinent carbon monoxide readings among smokers receiving brief advice and nicotine patch smoking-cessation treatment in a residential substance use treatment setting (MacKillop et al., 2015). However, the indices of demand did not predict abstinence at 1- and 3- month follow-ups.
Taken together, these studies indicate that behavioral economics measures of DD and substance demand (at least for alcohol and tobacco) may have clinical utility as predictors of risk for relapse following treatment. For DD, this has several potentially important clinical applications. First, impulsive DD may serve as a target for novel treatments for addiction, as described below. Second, assessment of DD may highlight individuals who are at particularly high risk for relapse, and may inform enhanced and individualized treatments for these individuals. Third, impulsive DD could indicate a positive prognosis and suggest a lower risk for relapse. Ultimately, these possibilities are largely speculative and are worthwhile pursuits for future research. The clinical applications for demand are less clear, given the relatively limited research conducted to date. While demand has been shown to predict early response to substance use treatment, its long-term predictive power has been mixed. Demand measures may also have clinical utility in identifying abuse liability of novel tobacco products (see Chapter 3 by Bickel et al. in this book).
Well-established reinforcement-based interventions for addiction
Clinical applications of behavioral economics often focus on increasing the value of abstinence and reducing the value of substance use by altering an individual’s reinforcement contingencies. One well-established reinforcement-based treatment is the community reinforcement approach (CRA; Hunt and Azrin, 1973). This involves working with the individual to rearrange their vocational, social, recreational, and familial contingencies in order to restore healthy forms of reinforcement that are distinct from substance use (Smith et al., 2001). The CRA is designed to break the vicious cycle commonly seen in substance use disorders whereby heavy substance
use leads to negative outcomes (e.g., loss of occupation or personal relationships), which in turn leads to reduced reinforcement from activities other than substance use. A systematic review of research on CRA found strong support for its efficacy for cocaine and alcohol use disorders, but somewhat limited efficacy for opioid dependence (Roozen et al., 2004). In addition, two recent randomized trials have provided initial support for implementing CRA as an internet-based treatment (Campbell et al., 2014;
Christensen et al., 2014). A final adaptation of the CRA that specifically engages family members (the community reinforcement approach family treatment; CRAFT) has been shown to be effective in alcohol (e.g., Dutcher et al., 2009) and other substance use disorders (e.g., Brigham et al., 2014).
The second well-established form of reinforcement-based treatment is contingency management (CM). The CM approach differs from CRA in that it focuses more directly on reinforcing pro-treatment outcomes using incentives (Stitzer and Petry, 2006). Generally speaking, CM involves providing tangible rewards to reinforce positive behaviors such as abstinence (National Institute on Drug Abuse, 2012). In the context of alcohol use disorders, this might include providing a cash reward or voucher that can be redeemed for food items, movie passes, etc. each time an individual has a negative biochemical screen for recent alcohol use. Therefore, the CM approach aims to shift reinforcement away from alcohol or drug use toward behaviors that are consistent with a drug-free lifestyle. This approach has been shown to be effective in treating alcohol and other substance use disorders, and it has been implemented successfully by trained community providers (Petry et al., 2012; Petry et al., 2000; Prendergast et al., 2006).
A number of randomized clinical trials have demonstrated the efficacy of a number of variants of CM, including internet delivery and with the use of non-monetary and non-voucher-based reinforcement (e.g., Prendergast et al., 2006; Schumacher et al., 2007). By comparison, the number of randomized controlled trials of the CRA approach remains limited. Additional research is needed to verify the efficacy of CRA across a range of mediums (e.g., in person or internet-delivered; family involvement), types of addictive disorders, and populations. Furthermore, the underlying mechanisms of action of both of these interventions have yet to be fully characterized (e.g., does a change in DD or substance demand mediate the relationship between CRA and CM and positive outcomes?).
Novel interventions to reduce demand or impulsive discounting in addiction
Recent research has begun to explore novel behavioral-economics-based interventions that seek to directly alter individuals’ level of DD or increase substance-free reinforcement. A variety of experimental manipulations have been shown to increase the salience of delayed rewards, thereby decreasing DD (for a review, see Koffarnus et al., 2013). For example, episodic future thinking has been shown to significantly reduce impulsive DD compared to a control imagery condition (Daniel et al., 2013; Daniel et al., 2015; Peters and Buchel, 2010). Furthermore, a study of individuals with alcohol use disorder suggest that episodic future thinking reduced DD rates and reduced hypothetical alcohol demand (Snider et al., 2016). Finally, two studies have shown that having participants interact with age-progressed computer- generated images of themselves reduced DD and risky long-term health- related decisions (Hershfield et al., 2011; Kaplan et al., 2015). While shifting mental orientation farther into the future successfully altered DD in these studies, whether the effect on DD is long-lasting remains unclear.
A second strategy for altering DD relies on working memory training as a way to strengthen the brain’s executive system which is thought to be hypoactive in addicted individuals (Bickel et al., 2007). Specifically, Bickel et al. (2011) randomly assigned a sample of treatment-seeking stimulant addicts to receive either a working memory training regimen or control training of similar duration. On average, the active training group showed a 50% reduction in DD rates following the working memory training. If replicated, these results could indicate a novel strategy for reducing impulsive DD; however, as with the future forecasting techniques, additional studies are needed to determine whether these effects have any influence on substance- related variables.
An emerging line of research has explored the effectiveness of pairing traditional brief motivational interventions (BMIs) with behavioral- economics-based supplements (for a review, see Murphy et al., 2007). To date, the behavioral economics supplement that has shown the most promise is the Substance Free Activity Session (SFAS), developed by Murphy and colleagues (Murphy, Dennhardt et al., 2012; Murphy, Skidmore et al., 2012).
The SFAS supplement consists of a 50-minute individual session that is
rooted in a motivational interviewing framework. The supplement attempts to increase engagement in constructive, alcohol-free activities, draw attention to the potentially negative relationship between alcohol use and long-term goal accomplishment, and increase the salience of delayed rewards that are personally relevant to the college student population (e.g., academic and career success). The efficacy of the SFAS supplement has been evaluated in two studies, including an initial pilot study (Murphy, Skidmore et al., 2012) and a subsequent randomized controlled trial (Murphy, Dennhardt et al., 2012). Both studies reported significant improvement in alcohol-related outcomes (i.e., alcohol problems, heavy drinking) in the combined BMI and supplement group compared to the BMI alone group. A more recent randomized controlled trial provided initial evidence of the efficacy of an abbreviated SFAS in college student marijuana users (Yurasek et al., 2015).
Further research is needed to evaluate the ideal duration and timing of these supplements, and to evaluate their utility for other substances.