Regulation of cocaine craving by cognitive strategies in an online sample of cocaine users.

Abstract

Emphasis on the negative consequences of drug use is a critical component of cognitive-behavioral therapy (CBT) skills to regulate craving. Despite the relative success of CBT for treating substance use disorders, effective human laboratory models of CBT are lacking. Recent reports have indicated that the regulation of craving (ROC) task provides a valid model of craving regulation for nicotine, alcohol, and methamphetamine use. The present study examined ROC in an online sample of regular cocaine users (n = 44) recruited from Amazon.com's Mechanical Turk. In the ROC task, cognitive regulation strategies were manipulated by instructing participants to think about either the positive or negative consequences of consuming cocaine. Participants were then presented with cocaine images while engaging in each cognitive regulation strategy and asked to report current craving that was then compared to neutral look conditions. Food images served as a control. A cocaine purchase task was also completed to assess economic demand for cocaine and its relationship with cocaine craving. The use of negative appraisal strategies that model those used in CBT significantly attenuated craving for cocaine. Cocaine craving was also stimulus-specific, with greater smoked cocaine craving reported by individuals with a history of smoked cocaine use. This online extension of the ROC task provides converging evidence for its use as a model of CBT cocaine-craving regulation. Futures studies can use this model to examine the mechanisms underlying the effectiveness of CBT for cocaine use and the relationship between craving regulation and drug-use behavior. (PsycINFO Database Record

Differential sensitivity to learning from positive and negative outcomes in cocaine users

“…Crowdsourcing technology has recently gained popularity in the field of substance-use research (e.g., Koffarnus et al, 2015; Rass et al, 2015; Strickland and Stoops, 2015; Strickland et al, in press) and carries several benefits relative to laboratory research. For example, crowdsourcing allows for improved demographic and geographic variability in study samples, provides access to hard-to-reach populations, and is generally less expensive than laboratory-based studies.…”

“…In the second version, cocaine-related images were used. Cocaine-related stimuli were images of the drug (e.g., crack rocks) and/or drug paraphernalia (e.g., crack pipes) and were selected from a set of images used in other cognitive-behavioral tasks in our laboratory (Strickland et al, in press). Half of the images depicted smoked cocaine and half of the images depicted intranasal cocaine.…”

Feasibility and Acceptability of the ‘HABIT’ Group Programme for Comorbid Bipolar and Alcohol and Substance use Disorders

“…For example, functioning and quality of life in both BD and ASUD can be impaired by residual mood, anxiety and sleep symptoms (Judd et al, 2003;Gerra et al, 2014;Wang et al, 2014). These symptoms can be partially ameliorated by pharmacotherapy, but it is increasingly clear that psychological interventions, especially cognitive, behavioural or self-regulation approaches, may be more efficient (McMurrich et al, 2012;Naqvi et al, 2015;Strickland, Reynolds, & Stoops, 2016). Also, symptoms of BD and ASUD may all be precipitated or maintained by dysfunctional cognitive emotional regulation (CER), particularly a negative ruminative response style (Gruber et al, 2011;Caselli et al, 2013;de Silveira & Kauer-Sant'Anna, 2015;Grierson et al, 2016).…”

Using crowdsourcing to examine behavioral economic measures of alcohol value and proportionate alcohol reinforcement.

“…In recent years, MTurk and other crowdsourcing sites have been used to examine behavioral economic variables in a variety of addiction samples. While many of these studies have focused on delay discounting preferences (Bickel et al, 2014; Jarmolowicz, Bickel, Carter, Franck, & Mueller, 2012; Johnson, Herrmann, & Johnson, 2015; VanderBroek, Acker, Palmer, de Wit, & MacKillop, 2015), there is also a growing literature using MTurk to investigate behavioral economic demand for commodities such as cigarettes (Koffarnus, Franck, Stein, & Bickel, 2015) and cocaine (Strickland, Reynolds, & Stoops, 2016). A recent MTurk study by Kaplan and colleagues investigated the construct validity of the APT by demonstrating changes in alcohol demand associated with differing time constraints in the APT vignette (i.e., how long the participant should imagine being at a party to watch a band) in a moderately sized sample ( n = 179; Kaplan et al, 2017).…”