Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand

Abstract

Few studies have simultaneously evaluated delay discounting and behavioral economic demand to determine their unique contribution to drug use. A recent study in cannabis users found that monetary delay discounting uniquely predicted cannabis dependence symptoms, whereas cannabis demand uniquely predicted use frequency. This study sought to replicate and extend this research by evaluating delay discounting and behavioral economic demand measures for multiple commodities and including a use quantity measure. Amazon.com’s Mechanical Turk was used to sample individuals reporting recent cannabis use (n = 64) and controls (n = 72). Participants completed measures of monetary delay discounting as well as alcohol and cannabis delay discounting and demand. Cannabis users and controls did not differ on monetary delay discounting or alcohol delay discounting and demand. Among cannabis users, regression analyses indicated that cannabis delay discounting uniquely predicted use severity, whereas cannabis demand uniquely predicted use frequency and quantity. These effects remained significant after controlling for other delay discounting and demand measures. This research replicates previous outcomes relating delay discounting and demand with cannabis use and extends them by accounting for the contribution of multiple commodities. This research also demonstrates the ability of online crowdsourcing methods to complement traditional human laboratory techniques.

Feasibility, acceptability, and validity of crowdsourcing for collecting longitudinal alcohol use data

“…This emerging literature has also replicated common effects described in the addiction laboratory (e.g., higher discounting rates among tobacco cigarette smokers relative to nonsmokers) or demonstrated correspondence in behaviors between in-person and online samples, further supporting the validity of the approach (e.g., Jarmolowicz, Bickel, Carter, Franck, & Mueller, 2012;Johnson et al, 2015;Morris et al, 2017;Strickland, Bolin, Lile, Rush, & Stoops, 2016;Strickland, Lile, & Stoops, 2017).…”

“…Alcohol was selected given extensive existing longitudinal research evaluating alcohol and the clinical acceptance of alcohol use self‐report as a primary outcome. A growing body of research has evaluated illicit substance use on mTurk (e.g., cannabis, cocaine, opioids) and generally revealed findings consistent with biologically verified in‐person research (e.g., Dunn, Barrett, Herrmann et al, 2016, Dunn, Barrett, Yepez‐Laubach et al 2016; Peters et al, ; Strickland et al, ; Strickland et al, ). These prior studies suggest that recruitment of other substance‐using populations should not prove problematic and that the methods proposed here would effectively translate.…”

“…Data were also examined for inconsistent responding (e.g., reporting smoking on the screening survey, but not in the baseline survey). We have successfully used these or similar data quality checks in previous research on mTurk (e.g., Strickland et al, ; Strickland & Stoops, ), as have other investigators in the behavioral and addiction sciences (e.g., Chavarria, Allan, Moltisanti, & Taylor, ; Donaldson, Siegel, & Crano, ; Johnson et al, ; Peters et al, ). Data quality checks were not included in the weekly surveys, which could be considered a limitation of the design (although also see discussion of overuse of attention checks in Chandler & Shapiro, ).…”

The Demand Curve Analyzer: Behavioral economic software for applied research

“…Indeed, enduring challenges such as alcohol use and sexual risk (Lemley, Fleming, & Jarmolowicz, 2017;MacKillop et al, 2015), food choice and obesity (Epstein, Stein, Paluch, MacKillop, & Bickel, 2018;Rasmussen et al, 2016), including applications to nutrition education (Guthrie, 2017) and improving the USA SNAP program (Ammerman, Hartman, & DeMarco, 2017) may be informed by such work. Further, innovative practical and policy approaches for emerging health issues such as nonmedical use of prescription drugs (Pickover, Messina, Correia, Garza, & Murphy, 2016), indoor tanning , cannabis use (Strickland, Lile, & Stoops, 2017), and substitution between conventional cigarettes and e-cigarettes (Snider, Cummings, & Bickel, 2017) may well arise from this work.…”

Examining interrelationships between the Cigarette Purchase Task and delay discounting among pregnant women

“…To date, the available empirical findings are equivocal at this early stage of investigation. Although both demand and DD are related to dependence severity (Aston, Metrik, Amlung, Kahler, & MacKillop, ; MacKillop et al, ; MacKillop & Tidey, ; Strickland, Lile, & Stroops, ), several researchers have reported weak or no relationship between demand, DD, and their primary outcomes (Amlung et al, ; Aston et al, ; Teeters and Murphy, ). Although smoking cessation outcomes and associated markers have been predicted using the CPT (e.g., Higgins et al, ; MacKillop et al, ; Secades‐Villa, Pericot‐Valverde, & Weidberg, ) and DD (Sheffer et al, ; Stanger et al, ), we know of no studies examining their combined utility in predicting smoking cessation or associated markers, particularly in pregnant women.…”