Quality of life (QOL) and delay discounting (preference for smaller, immediate rewards) are significantly associated with substance use status, severity, and treatment outcomes. Associations between delay discounting and QOL among individuals in recovery from substance use have not been investigated. In this 2-study investigation, using data collected from The International Quit & Recovery Registry, we examined the association between QOL, discounting rates, and remission status among individuals in recovery from SUD. Study 1 (N = 166) investigated the relationship between delay discounting and QOL among individuals in recovery from SUD. Study 2 (N = 282) aimed to validate and extend the results of Study 1 by assessing the association between the remission status, delay discounting, and QOL among individuals in recovery from alcohol use disorder (AUD). In both studies, delay discounting was a significant predictor of QOL domains of physical health, psychological, and environment even after controlling for age, gender, race, ethnicity, education, and days since last use. In Study 2, a mediation analysis using Hayes's methods revealed that the association between the remission status and QOL domains of physical health, psychological and environment were partially mediated by the discounting rates. The current study expands the generality of delay discounting and indicates that discounting rates predict QOL and remission status among individuals in recovery from substance use disorders. This finding corroborates the recent characterizations of delay discounting as a candidate behavioral marker of addiction and may help identify subgroups that require special treatment or unique interventions to overcome their addiction. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Crowdsourced methods of data collection such as Amazon Mechanical Turk (MTurk) have been widely adopted in addiction science. Recent reports suggest an increase in poor quality data on MTurk, posing a challenge to the validity of findings. However, empirical investigations of data quality in addiction-related samples are lacking. In this study of individuals with alcohol use disorder (AUD), we compared poor quality delay discounting data to randomly generated data. A reanalysis of prior published delay discounting data was conducted comparing included, excluded, and randomly generated data samples. Nonsystematic criteria were implemented as a measure of data quality. The excluded data was statistically different from the included sample but did not differ from randomly generated data on multiple metrics. Moreover, a response bias was identified in the excluded data. This study provides empirical evidence that poor quality delay discounting data in an AUD sample is not statistically different from randomly generated data, suggesting data quality concerns on MTurk persist in addiction samples. These findings support the use of rigorous methods of a priori defined criteria to remove poor quality data post hoc. Additionally, it highlights that the use of nonsystematic delay discounting criteria to remove poor quality data is rigorous and not simply a way of removing data that does not conform to an expected theoretical model. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Banning vaping products may have unintended outcomes, such as increased demand for illegal products. This study experimentally examined the effects of a vaping ban and a flavored vaping ban on the probability of purchasing illicit vaping products, and factors affecting purchasing from a hypothetical illegal marketplace. A crowdsourced sample of exclusive cigarette smokers, exclusive e-cigarette users, and frequent dual users (n = 150) completed hypothetical purchasing trials in an Experimental Tobacco Marketplace under three conditions (no ban, vaping ban, and flavored vaping ban). Participants chose to purchase in a hypothetical legal experimental tobacco marketplace (LETM) or illegal experimental tobacco marketplace (IETM). Vaping products were available in each marketplace depending on the condition. Other tobacco products were always available in the LETM. A hypothetical illicit purchase task with five fine amounts assessed the effect of monetary penalties. Participants from all groups were more likely to purchase from the IETM when product availability in the LETM was more restricted, with e-cigarette users being most affected. The likelihood of purchasing illegal products was systematically decreased as monetary penalties associated with the IETM increased, with e-cigarette users showing greater persistence in defending their illicit purchases. Restricting vaping products from the marketplace may shift preference towards purchasing vaping products in the illegal marketplace. Nevertheless, penalties imposed on consumer’s behavior might be effective in preventing illicit trade. The IETM is a methodological extension that supports the utility and flexibility of the ETM as a framework for understanding the impact of different tobacco regulatory policies. This study suggests that limiting or banning vaping products as a possible strategy to reduce the adverse effects of vaping products could result in some tobacco-users seeking banned products from illegal sources. Monetary fines were shown to reduce illegal purchases. Therefore, policymakers should consider implementing strategies that may mitigate illegal purchases.

Until pharmacological measures are effective at containing the COVID-19 outbreak, adopting protective behaviors is paramount. In this work, we aim at informing interventions to limit the spread of the contagion and prepare against any future outbreaks by developing a behavioral framework to interpret and prescribe both the individual and large-scale uptake of non-pharmaceutical measures. First, we analyze the barriers and facilitators to adherence to protective behaviors according to a three-term contingency by exploring potential gaps in terms of setting stimuli, motivating operations, delayed consequences, and positive or negative consequences. We explore their roles in the likelihood of individual compliance to protective behaviors, taking physical distancing as an example of functional analysis. Second, we interpret contagion control as the cumulative effect of large-scale adherence to protective behaviors. We explore the interrelations between societal problems caused or amplified by similar behaviors presented by many individuals and the coordination of agents or agencies aiming at promoting large-scale behavioral change. Then, we highlight the potential of developing a behavioral vaccine, and practical steps for applying it to promote sustainable cultural change that may protect against health, social, and economic losses in future outbreaks.

Crowdsourcing platforms allow researchers to quickly recruit and collect behavioral economic measures in substance-using populations, such as cigarette smokers. Despite the broad utility and flexibility, data quality issues have been an object of concern. In two separate studies recruiting cigarette smokers, we sought to investigate the association between a practical quality control measure (accuracy on an instruction quiz), on internal consistency of number of cigarettes smoked per day and purchasing patterns of tobacco products in an Experimental Tobacco Marketplace (ETM; Study 1), and in a Cigarette Purchase Task (CPT; Study 2). Participants (N=312 in Study 1; N=119 in Study 2) were recruited from Amazon Mechanical Turk. Both studies included task instructions, a quiz, a purchase task, cigarette usage and dependence questions, and demographics. The results show that participants who answered all instruction items correctly: 1) reported the number of cigarettes per day more consistently (partial η² = 0.11, p<0.001, Study 1; partial η² = 0.09, p=0.016, Study 2) 2) demonstrated increased model fit among the cigarette demand curves (partial η² = 0.23, p<0.001, Study 1; partial η² = 0.08, p=0.002, Study 2), and purchased tobacco products in the ETM more consistently with their current usage. We conclude that instruction quizzes before purchase tasks may be useful for researchers evaluating demand data. Instruction quizzes with multiple items may allow researchers to choose the level of data quality appropriate for their studies.