Justin C Strickland1, Olga A Vsevolozhskaya2, William W Stoops3,4,5,6. 1. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD. 2. Department of Biostatistics, University of Kentucky College of Public Health, Lexington, KY. 3. Department of Behavioral Science, University of Kentucky College of Medicine, Lexington, KY. 4. Center on Drug and Alcohol Research, University of Kentucky College of Medicine, Lexington, KY. 5. Department of Psychology, University of Kentucky College of Arts and Sciences, Lexington, KY. 6. Department of Psychiatry, University of Kentucky College of Medicine, Lexington, KY.
Abstract
INTRODUCTION: Behavioral economic demand provides a multidimensional understanding of reinforcement. Commodity purchase tasks are an efficient method for measuring demand in human participants. One challenge in translating these procedures to electronic nicotine delivery systems (ENDS or e-cigarettes) is defining commodity units given the lack of standardization in the e-cigarette marketplace. AIMS AND METHODS: The purpose of this study was to directly compare methods of operationalizinge-cigarette purchases, puffs, cartridges, and mLs liquid, using a within-subject design. Participants (N = 132) reporting past week e-cigarette use were recruited using crowdsourcing. Purchase tasks were completed operationalizing e-cigarette units as puffs or cartridges at baseline and puffs or mLs liquid at a 3-month follow-up. RESULTS: Bivariate associations supported convergent and discriminant validity with the largest effect size correlations for intensity and elasticity observed for the puff version. Interaction models suggested that product preferences moderated the relationship between time-to-first use and cartridge demand with larger effect size correlations among persons reporting a preference for JUULs, but weaker relationships among persons reporting other device preferences. Puff intensity (rxx = .61) and elasticity (rxx = .62) showed good test-retest reliability for participants reporting stable consumption, but poor test-retest reliability for individuals with changed consumption levels (intensity rxx = -.08; elasticity rxx = -.10). CONCLUSIONS: This study highlights the relevance of commodity definitions in the e-cigarette purchase task. Puffs as an experimental commodity may provide flexibility for studying e-cigarette demand in heterogenous or unknown populations, whereas more tailored or personalized approaches like cartridge or mL-based tasks will likely be helpful when studying known subgroups. IMPLICATIONS: The commodity purchase task procedure is widely used for understanding cigarette and e-cigarette demand in nicotine dependence research. This study evaluates the importance of operational definitions of e-cigarette commodities in the purchase task (ie, puffs, cartridges, or mLs liquid). Puffs may provide a more flexible commodity unit when evaluating e-cigarette demand in general or heterogenous populations, whereas device-specific units may prove more valuable when studying populations with consistent and known product use.
INTRODUCTION: Behavioral economic demand provides a multidimensional understanding of reinforcement. Commodity purchase tasks are an efficient method for measuring demand in humanparticipants. One challenge in translating these procedures to electronic nicotine delivery systems (ENDS or e-cigarettes) is defining commodity units given the lack of standardization in the e-cigarette marketplace. AIMS AND METHODS: The purpose of this study was to directly compare methods of operationalizinge-cigarette purchases, puffs, cartridges, and mLs liquid, using a within-subject design. Participants (N = 132) reporting past week e-cigarette use were recruited using crowdsourcing. Purchase tasks were completed operationalizing e-cigarette units as puffs or cartridges at baseline and puffs or mLs liquid at a 3-month follow-up. RESULTS: Bivariate associations supported convergent and discriminant validity with the largest effect size correlations for intensity and elasticity observed for the puff version. Interaction models suggested that product preferences moderated the relationship between time-to-first use and cartridge demand with larger effect size correlations among persons reporting a preference for JUULs, but weaker relationships among persons reporting other device preferences. Puff intensity (rxx = .61) and elasticity (rxx = .62) showed good test-retest reliability for participants reporting stable consumption, but poor test-retest reliability for individuals with changed consumption levels (intensity rxx = -.08; elasticity rxx = -.10). CONCLUSIONS: This study highlights the relevance of commodity definitions in the e-cigarette purchase task. Puffs as an experimental commodity may provide flexibility for studying e-cigarette demand in heterogenous or unknown populations, whereas more tailored or personalized approaches like cartridge or mL-based tasks will likely be helpful when studying known subgroups. IMPLICATIONS: The commodity purchase task procedure is widely used for understanding cigarette and e-cigarette demand in nicotine dependence research. This study evaluates the importance of operational definitions of e-cigarette commodities in the purchase task (ie, puffs, cartridges, or mLs liquid). Puffs may provide a more flexible commodity unit when evaluating e-cigarette demand in general or heterogenous populations, whereas device-specific units may prove more valuable when studying populations with consistent and known product use.
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