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.
AbbeyAZawackiTBuckPOet al. (2001) Alcohol and sexual assault. Alcohol Res Health25: 43–51.
2.
AcuffSFMurphyJG (2017) Further examination of the temporal stability of alcohol demand. Behav Processes141: 33–41.
3.
AcuffSFSoltisKEDennhardtAAet al. (2018) Evaluating behavioral economic models of heavy drinking among college students. Alcohol Clin Exp Res42: 1304–1314.
4.
American Psychiatric Association (1994) Diagnostic and statistical manual of mental disorders. 4th ed.Arlington, VA: American Psychiatric Publishing, Inc.
5.
AnokhinAPGolosheykinSMulliganRC (2015) Long-term test-retest reliability of delayed reward discounting in adolescents. Behav Processes111: 55–59.
6.
AstonERFarrisSGMacKillopJet al. (2017) Latent factor structure of a behavioral economic marijuana demand curve. Psychopharmacology (Berl)234: 2421–2429.
7.
AstonERMetrikJAmlungMet al. (2016) Interrelationships between marijuana demand and discounting of delayed rewards: Convergence in behavioral economic methods. Drug Alcohol Depend169: 141–147.
8.
BakerFJohnsonMWBickelWK (2003) Delay discounting in current and never-before cigarette smokers: Similarities and differences across commodity, sign, and magnitude. J Abnorm Psychol112: 382–392.
9.
BardoMTNeisewanderJLKellyTH (2013) Individual differences and social influences on the neurobehavioral pharmacology of abused drugs. Pharmacol Rev65: 255–290.
10.
BatesDMaechlerMBolkerBet al. (2015) Fitting linear mixed effects models using lme4. J Stat Softw67: 1–48.
11.
BeckRCTriplettMF (2009) Test-retest reliability of a group-administered paper-pencil measure of delay discounting. Exp Clin Psychopharmacol17: 345–355.
12.
BentzleyBSJhouTCAston-JonesG (2014) Economic demand predicts addiction-like behavior and therapeutic efficacy of oxytocin in the rat. Proc Natl Aca Sci111: 11822–11827.
BertholetNMurphyJGDaeppenJet al. (2015) The alcohol purchase task in young men from the general population. Drug Alcohol Depend146C: 39–44.
15.
BickelWKJarmolowiczDPMuellerETet al. (2012) Excessive discounting of delayed reinforcers as a trans-disease process contributing to addiction and other disease-related vulnerabilities: Emerging evidence. Pharmacol Therapeut134: 287–297.
16.
BickelWKJohnsonMWKoffarnusMNet al. (2014) The behavioral economics of substance use disorders: Reinforcement pathologies and their repair. Annu Rev Clin Psychol10: 641–677.
17.
BickelWKMoodyLHigginsST (2016) Some current dimensions of the behavioral economics of health-related behavior change. Prev Med92: 16–23.
18.
BickelWKSniderSEQuisenberryAJet al. (2017) Reinforcer pathology: The behavioral economics of abuse liability testing. Clin Pharmacol Ther101: 185–187.
19.
BidwellLCMacKillopJMurphyJGet al. (2012) Latent factor structure of a behavioral economic cigarette demand curve in adolescent smokers. Addict Behav37: 1257–1263.
20.
BoyntonMHRichmanLS (2014) An online daily diary study of alcohol use using Amazon’s Mechanical Turk. Drug Alcohol Rev33: 456–461.
21.
BrodyGHYuTMacKillopJet al. (2014) Catecholamine levels and delay discounting forecast drug use among African American youths. Addiction109: 1112–1118.
22.
BrooksMEKristensenKvan BenthemKJet al. (2017) Modeling zero-inflated count data with glmmTMB. bioRxiv 132753. DOI: https://doi.org/10.1101/132753.
23.
Center for Behavioral Health Statistics and Quality (2018) 2017 National Survey on Drug Use and Health: Detailed Tables. Rockville, MD: Substance Abuse and Mental Health Services Administration.
24.
ChandlerJShapiroD (2016) Conducting clinical research using crowdsourced convenience samples. Annu Rev Clin Psychol12: 53–81.
25.
ChaseHWMackillopJHogarthL (2013) Isolating behavioural economic indices of demand in relation to nicotine dependence. Psychopharmacology (Berl)226: 371–380.
26.
CollinsRLMorsheimerETShiffmanSet al. (1998) Ecological momentary assessment in a behavioral drinking moderation training program. Exp Clin Psychopharmacol6: 306–315.
27.
CorreiaCJSimonsJCareyKBet al. (1998) Predicting drug use: Application of behavioral theories of choice. Addict Behav23: 705–709.
28.
CoxDJDalleryJ (2016) Effects of delay and probability combinations on discounting in humans. Behav Processes131: 15–23.
29.
CunninghamJAGodinhoAKushnirV (2017) Using Mechanical Turk to recruit participants for internet intervention research: Experience from recruitment for four trials targeting hazardous alcohol consumption. BMC Med Res Methodol17: e156.
30.
de Meneses-GayaCZuardiAWLoureiroSRet al. (2009) Alcohol Use Disorders Identification Test (AUDIT): An updated systematic review of psychometric properties. Psychol Neurosci2: 83–97.
31.
DennhardtAAYurasekAMMurphyJG (2015) Change in delay discounting and substance reward value following a brief alcohol and drug use intervention. J Exp Anal Behav103: 125–140.
ElmanIKrauseSBreiterHCet al. (2000) The validity of self-reported drug use in non-treatment seeking individuals with cocaine dependence: Correlation with biochemical assays. Am J Addict9: 216–221.
34.
EpsteinLHSteinJSPaluchRAet al. (2018) Binary components of food reinforcement: Amplitude and persistence. Appetite120: 67–74.
35.
FarreMde la TorreRGonzalezMLet al. (1997) Cocaine and alcohol interactions in humans: Neuroendocrine effects and cocaethylene metabolism. J Pharmacol Exp Ther283: 164–176.
FewLRAckerJMurphyCet al. (2012) Temporal stability of a cigarette purchase task. Nicotine Tob Res14: 761–765.
38.
ForanHMO’LearyKD (2008) Alcohol and intimate partner violence: A meta-analytic review. Clin Psychol Rev28: 1222–1234.
39.
FremouwWJBrownJPJr. (1980) The reactivity of addictive behaviors to self-monitoring: A functional analysis. Addict Behav5: 209–217.
40.
GrayJCMacKillopJ (2014) Interrelationships among individual differences in alcohol demand, impulsivity, and alcohol misuse. Psychol Addict Behav28: 282–287.
41.
HagmanBT (2017) Development and psychometric analysis of the Brief DSM–5 Alcohol Use Disorder Diagnostic Assessment: Towards effective diagnosis in college students. Psychol Addict Behav31: 797–806.
42.
HallgrenKAGreenfieldBLLaddBO (2016) Psychometric properties of the adolescent reinforcement survey schedule-alcohol use version with college student drinkers. Subst Use Misuse51: 812–822.
43.
HarrisonLHughesA (1997) Introduction–the validity of self-reported drug use: Improving the accuracy of survey estimates. NIDA Res Monogr167: 1–16.
44.
HuffCTingleyD (2015) “Who are these people?” Evaluating the demographic characteristics and political preferences of MTurk survey respondents. Res Politics2: e2053168015604648.
45.
JacobsEABickelWK (1999) Modeling drug consumption in the clinic using simulation procedures: Demand for heroin and cigarettes in opioid-dependent outpatients. Exp Clin Psychopharmacol7: 412–426.
46.
JohnsonMWBrunerNR (2012) The Sexual Discounting Task: HIV risk behavior and the discounting of delayed sexual rewards in cocaine dependence. Drug Alcohol Depend123: 15–21.
47.
JohnsonMWJohnsonPSRassOet al. (2017) Behavioral economic substitutability of e-cigarettes, tobacco cigarettes, and nicotine gum. J Psychopharmacol31: 851–860.
48.
JohnsonPSHerrmannESJohnsonMW (2015) Opportunity costs of reward delays and the discounting of hypothetical money and cigarettes. J Exp Anal Behav103: 87–107.
Kampov-PolevoyABGarbuttJCJanowskyDS (1999) Association between preference for sweets and excessive alcohol intake: A review of animal and human studies. Alcohol Alcohol34: 386–395.
51.
KaplanBAFosterRNReedDDet al. (2018) Understanding alcohol motivation using the Alcohol Purchase Task: A methodological systematic review. Drug Alcohol Depend191: 117–140.
52.
KimHSHodginsDC (2017) Reliability and validity of data obtained from alcohol, cannabis, and gambling populations on Amazon’s Mechanical Turk. Psychol Addict Behav31: 85–94.
53.
KiselicaAMWebberTABornovalovaMA (2016) Validity of the alcohol purchase task: A meta-analysis. Addiction111: 806–816.
54.
KoffarnusMNBickelWK (2014) A 5-trial adjusting delay discounting task: Accurate discount rates in less than one minute. Exp Clin Psychopharmacol22: 222–228.
55.
KoffarnusMNFranckCTSteinJSet al. (2015) A modified exponential behavioral economic demand model to better describe consumption data. Exp Clin Psychopharmacol23: 504–512.
56.
KoffarnusMNJarmolowiczDPMuellerETet al. (2013) Changing delay discounting in the light of the competing neurobehavioral decision systems theory: A review. J Exp Anal Behav99: 32–57.
57.
KokkeviARichardsonCPalermouBet al. (1997) Reliability of drug dependents’ self-reports. Drug Alcohol Depend45: 55–61.
58.
KoobGFRocioMCarreraAet al. (1998). Substance dependence as a compulsive behavior. J Psychopharmacol12: 39–48.
59.
KranzlerHRSandstromKAVan KirkJ (2001) Sweet taste preference as a risk factor for alcohol dependence. Am J Psychiatry158: 813–815.
60.
LakensD (2013) Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front Psychol4: e863.
61.
LittMDCooneyNLMorseP (1998) Ecological momentary assessment (EMA) with treated alcoholics: Methodological problems and potential solutions. Health Psychol17: 48–52.
62.
MacKillopJ (2016) The behavioral economics and neuroeconomics of alcohol use disorders. Alcohol Clin Exp Res40: 672–685.
63.
MacKillopJMirandaRJrMontiPMet al. (2010) Alcohol demand, delayed reward discounting, and craving in relation to drinking and alcohol use disorders. J Abnorm Psychol119: 106–114.
64.
MacKillopJMurphyJG (2007) A behavioral economic measure of demand for alcohol predicts brief intervention outcomes. Drug Alcohol Depend89: 227–233.
65.
MacKillopJMurphyJGTideyJWet al. (2009) Latent structure of facets of alcohol reinforcement from a behavioral economic demand curve. Psychopharmacology (Berl)203: 33–40.
66.
MatusiewiczAKCarterAELandesRDet al. (2013) Statistical equivalence and test-retest reliability of delay and probability discounting using real and hypothetical rewards. Behav Processes100: 116–122.
67.
MorrisVAmlungMKaplanBAet al. (2017) Using crowdsourcing to examine behavioral economic measures of alcohol value and proportionate alcohol reinforcement. Exp Clin Psychopharmacol25: 314–321.
68.
MurphyCMOwensMMSweetLHet al. (2016) The substitutability of cigarettes and food: A behavioral economic comparison in normal weight and overweight or obese smokers. Psychol Addict Behav30: 857–867.
69.
MurphyJGCorreiaCJColbySMet al. (2005) Using behavioral theories of choice to predict drinking outcomes following a brief intervention. Exp Clin Psychopharmacol13: 93–101.
MurphyJGMacKillopJ (2006) Relative reinforcing efficacy of alcohol among college student drinkers. Exp Clin Psychopharmacol14: 219–227.
72.
MurphyJGMacKillopJSkidmoreJRet al. (2009) Reliability and validity of a demand curve measure of alcohol reinforcement. Exp Clin Psychopharmacol17: 396–404.
73.
NapperLEFisherDGJohnsonMEet al. (2010) The reliability and validity of drug users’ self reports of amphetamine use among primarily heroin and cocaine users. Addict Behav35: 350–354.
74.
NeckaEACacioppoSNormanGJet al. (2016) Measuring the prevalence of problematic respondent behaviors among MTurk, campus, and community participants. PloS One11: e0157732.
75.
OdumAL (2011) Delay discounting: I’m a k, you’re a k. J Exp Anal Behav96: 427–439.
76.
OhmuraYTakahashiTKitamuraNet al. (2006) Three-month stability of delay and probability discounting measures. Exp Clin Psychopharmacol14: 318–328.
77.
PeerEVosgerauJAcquistiAet al. (2014) Reputation as a sufficient condition for data quality on Amazon Mechanical Turk. Behav Res Methods46: 1023–1031.
78.
PerryJLCarrollME (2008) The role of impulsive behavior in drug abuse. Psychopharmacol200: 1–26.
79.
PetersENRosenberryZRSchauerGLet al. (2017) Marijuana and tobacco cigarettes: Estimating their behavioral economic relationship using purchasing tasks. Exp Clin Psychopharmacol25: 208–215.
80.
QuigleyBMLeonardKE (2000) Alcohol and the continuation of early marital aggression. Alcohol Clin Exp Res24: 1003–1010.
RasmussenEBLawyerSRReillyW (2010) Percent body fat is related to delay and probability discounting for food in humans. Behav Processes83: 23–30.
83.
ReedDDBecirevicAAtchleyPet al. (2016) Validation of a novel delay discounting of text messaging questionnaire. Psychol Rec66: 253–261.
84.
ReedDDNiilekselaCRKaplanBA (2013) Behavioral economics: A tutorial for behavior analysts in practice. Behav Anal Pract6: 34–54.
85.
RehmJMathersCPopovaSet al. (2009) Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet373: 2223–2233.
86.
ReinertDFAllenJP (2007) The alcohol use disorders identification test: An update of research findings. Alcohol Clin Exp Res31: 185–199.
87.
SacksJJGonzalesKRBoucheryEEet al. (2015) 2010 National and state costs of excessive alcohol consumption. Am J Prev Med49: e73–79.
88.
SaundersJBAaslandOGBaborTFet al. (1993) Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction88: 791–804.
89.
SheehanDVLecrubierYSheehanKHet al. (1998) The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry59(Suppl 20): 22–33.
90.
SimpsonCAVuchinichRE (2000) Reliability of a measure of temporal discounting. Psychol Rec50: 3–16.
91.
SingerJDWillettJB (2003) Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. New York, NY: Oxford University Press.
92.
SmithAEMartensMPMurphyJGet al. (2010) Reinforcing efficacy moderates the relationship between impulsivity-related traits and alcohol use. Exp Clin Psychopharmacol18: 521–529.
93.
SniderSECummingsKMBickelWK (2017) Behavioral economic substitution between conventional cigarettes and e-cigarettes differs as a function of the frequency of e-cigarette use. Drug Alcohol Depend177: 14–22.
94.
SobellMBBogardisJSchullerRet al. (1989) Is self-monitoring of alcohol consumption reactive?Behav Assess11: 447–458.
95.
StairsDJBardoMT (2009) Neurobehavioral effects of environmental enrichment and drug abuse vulnerability. Pharmacol Biochem Behav92: 377–382.
96.
SteinJSKoffarnusMNSniderSEet al. (2015) Identification and management of nonsystematic purchase task data: Toward best practice. Exp Clin Psychopharmacol23: 377–386.
97.
StricklandJCBolinBLLileJAet al. (2016) Differential sensitivity to learning from positive and negative outcomes in cocaine users. Drug Alcohol Depend166: 61–68.
98.
StricklandJCLileJAStoopsWW (2017) Unique prediction of cannabis use severity and behaviors by delay discounting and behavioral economic demand. Behav Processes140: 33–40.
99.
StricklandJCStoopsWW (2017) Stimulus selectivity of drug purchase tasks: A preliminary study evaluating alcohol and cigarette demand. Exp Clin Psychopharmacol25: 198–207.
100.
StricklandJCStoopsWW (2018a) Evaluating autonomy, beneficence, and justice with substance-using populations: Implications for clinical research participation. Psychol Addict Behav32: 552–563.
101.
StricklandJCStoopsWW (2018b) Feasibility, acceptability, and validity of crowdsourcing for collecting longitudinal alcohol use data. J Exp Anal Behav110: 136–153.
102.
StricklandJCStoopsWW (2019) The use of crowdsourcing in addiction science research: Amazon Mechanical Turk. Exp Clin Psychopharmacol27: 1–18.
TurnerCFKuLRogersSMet al. (1998) Adolescent sexual behavior, drug use, and violence: Increased reporting with computer survey technology. Science280: 867–873.
105.
UchalikDC (1979) A comparison of questionnaire and self-monitored reports of alcohol intake in a nonalcoholic population. Addict Behav4: 409–413.
106.
U.S. Department of Health and Human Services, National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism (2016) Helping Patients Who Drink Too Much: A Clinician’s Guide, Updated 2005 Edition. NIH Publication No. 07–3769. Bethesda, MD: NIH. Available at: https://pubs.niaaa.nih.gov/publications/practitioner/cliniciansguide2005/guide.pdf (accessed 6 June 2018).
107.
WeaferJBaggottMJde WitH (2013) Test-retest reliability of behavioral measures of impulsive choice, impulsive action, and inattention. Exp Clin Psychopharmacol21: 475–481.
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