Literature DB >> 30789298

Using behavioral economic variables to predict future alcohol use in a crowdsourced sample.

Justin C Strickland1, Joseph L Alcorn2, William W Stoops1,3.   

Abstract

BACKGROUND: Theoretical perspectives at the intersection of behavioral economics and operant theory have resulted in numerous advances for addiction science. Three mechanisms (i.e. behavioral economic demand [consumption-price relationships], delay discounting [reinforcer devaluation with delay], and proportionate alcohol-related reinforcement [relative reinforcement attributable to alcohol]) are proposed to contribute to problematic alcohol use. Limited research has evaluated the unique contribution of these mechanisms for predicting future alcohol consumption. AIM: The purpose of this study was to evaluate the predictive relationship between these mechanisms and self-reported alcohol use frequency, quantity, and severity.
METHODS: Participants (n=223) sampled from the crowdsourcing website Amazon Mechanical Turk completed a survey containing behavioral economic measures. Weekly reports of daily alcohol use were then collected for 18 weeks. Unadjusted and adjusted models determined the association between behavioral economic variables and alcohol use. RESULTS/OUTCOMES: Behavioral economic measures were associated with alcohol and soda use at baseline in a stimulus-selective manner (e.g. alcohol demand associated with alcohol, but not soda, variables). Predictive models adjusted for Alcohol Use Disorder Identification Test scores indicated that increased proportionate alcohol-related reinforcement and behavioral economic demand were uniquely and incrementally associated with frequency (e.g. adjusted odds ratio (AOR)=5.54 for proportionate alcohol-related reinforcement, p<0.05) and quantity-severity measures (e.g. AOR=7.58 for alcohol demand intensity, p<0.001), respectively. Test-retest reliability was generally acceptable (rxx=0.42-0.76) with the exception of proportionate alcohol-related reinforcement (rxx=0.29). CONCLUSION/
INTERPRETATION: These findings indicate the unique, predictive, and incremental validity of behavioral economic measures for evaluating future alcohol consumption, supporting their continued use in addiction science research.

Entities:  

Keywords:  Demand; Mechanical Turk; R-ratio; discounting

Year:  2019        PMID: 30789298     DOI: 10.1177/0269881119827800

Source DB:  PubMed          Journal:  J Psychopharmacol        ISSN: 0269-8811            Impact factor:   4.153


  10 in total

1.  Can initial experiences with drugs predict future drug abuse risks?

Authors:  Neil B Varshneya; Kelly E Dunn; Caitlyn J Grubb; Sandra I Okobi; Andrew S Huhn; Cecilia L Bergeria
Journal:  Exp Clin Psychopharmacol       Date:  2022-03-10       Impact factor: 3.492

2.  E-Cigarette Demand: Impact of Commodity Definitions and Test-Retest Reliability.

Authors:  Justin C Strickland; Olga A Vsevolozhskaya; William W Stoops
Journal:  Nicotine Tob Res       Date:  2021-02-16       Impact factor: 4.244

3.  Behavioral economics and the aggregate versus proximal impact of sociality on heavy drinking.

Authors:  Samuel F Acuff; William W Stoops; Justin C Strickland
Journal:  Drug Alcohol Depend       Date:  2021-01-11       Impact factor: 4.492

4.  Behavioral economic indicators of risky drinking among community-dwelling emerging adults.

Authors:  Jalie A Tucker; Katie Lindstrom; Susan D Chandler; Joseph P Bacon; JeeWon Cheong
Journal:  Psychol Addict Behav       Date:  2021-02-25

Review 5.  Concurrent validity of the Alcohol Purchase Task for measuring the reinforcing efficacy of alcohol: an updated systematic review and meta-analysis.

Authors:  Victor Martínez-Loredo; Alba González-Roz; Roberto Secades-Villa; José R Fernández-Hermida; James MacKillop
Journal:  Addiction       Date:  2021-01-15       Impact factor: 7.256

6.  Behavioral economic methods predict future COVID-19 vaccination.

Authors:  Justin C Strickland; Derek D Reed; Lauren Dayton; Matthew W Johnson; Carl Latkin; Lindsay P Schwartz; Steven R Hursh
Journal:  Transl Behav Med       Date:  2022-08-25       Impact factor: 3.626

7.  Behavioral economic interactions between cannabis and alcohol purchasing: Associations with disordered use.

Authors:  Sean B Dolan; Tory R Spindle; Ryan Vandrey; Matthew W Johnson
Journal:  Exp Clin Psychopharmacol       Date:  2020-10-01       Impact factor: 3.157

8.  (Non-) impact of task experience on behavioral economic decision-making.

Authors:  Justin C Strickland; B Levi Bolin; Katherine R Marks
Journal:  Exp Clin Psychopharmacol       Date:  2021-02-22       Impact factor: 3.492

9.  Comparison of delay discounting of different outcomes in cigarette smokers, smokeless tobacco users, e-cigarette users, and non-tobacco users.

Authors:  William Brady DeHart; Jonathan E Friedel; Meredith Berry; Charles C J Frye; Ann Galizio; Amy L Odum
Journal:  J Exp Anal Behav       Date:  2020-08-27       Impact factor: 2.215

10.  Behavioral economic methods to inform infectious disease response: Prevention, testing, and vaccination in the COVID-19 pandemic.

Authors:  Justin C Strickland; Derek D Reed; Steven R Hursh; Lindsay P Schwartz; Rachel N S Foster; Brett W Gelino; Robert S LeComte; Fernanda S Oda; Allyson R Salzer; Tadd D Schneider; Lauren Dayton; Carl Latkin; Matthew W Johnson
Journal:  PLoS One       Date:  2022-01-19       Impact factor: 3.240

  10 in total

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