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

DeHart, William Brady; Friedel, Jonathan E.; Berry, Meredith S.; Frye, Charles C. J.; Galizio, Ann; Odum, Amy L.

doi:10.1002/jeab.623

Cited by 17 publications

(13 citation statements)

References 50 publications

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“…We administered a monetary delay discounting task in which participants were required to complete a total of 70 choices between an immediate reward and a delayed reward (see Figure 1A ). In accordance with previous studies ( Reynolds and Schiffbauer, 2004 ; Yi et al, 2017 ; DeHart et al, 2020 ), the delayed reward was always CNY100 presented at one of five delays (7, 15, 30, 60, 120 days). Delay periods were presented in blocks of 14 trials per delay.…”

Section: Methodssupporting

confidence: 62%

Episodic future thinking predicts differences in delay discounting: The mediating role of hippocampal structure

Guo

Zhao

et al. 2022

Front. Psychol.

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A growing body of evidence suggests that engagement in episodic future thinking (EFT) could reduce delay discounting rates. However, little is known about whether individual differences in the ability of EFT are associated with differences in delay discounting in young adults. In the present study, this association was tested in healthy college students (n = 106, 19.98 ± 1.56 years), and the neural basis underlying this association was also examined using voxel-based morphometry (VBM) method. Behavioral analysis indicated that individual differences in EFT ability can significantly negatively predict discounting rates. VBM analysis first revealed that the EFT score positively correlated with gray matter volume (GMV) of a cluster in hippocampus, while negatively correlated with GMV of a cluster in rostral anterior cingulate cortex. We also found the GMV of a cluster in the mPFC was positively correlated with delay discounting. ROI analysis further revealed that individual differences in delay discounting could be reliably predicted by the GMV in the hippocampus and mPFC. The final mediation analysis showed that the GMV of the hippocampus plays a significant mediating role in the association between EFT and delay discounting, and the indirect effect of the hippocampal GMV accounts for 33.2% of the total effect. Our results suggest that individuals’ EFT ability may be an important determinant of differences in delay discounting, and highlight the hippocampal structure as a neural biomarker for explaining the association between EFT ability and delay discounting.

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Section: Methodssupporting

confidence: 62%

Episodic future thinking predicts differences in delay discounting: The mediating role of hippocampal structure

Guo

Zhao

et al. 2022

Front. Psychol.

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“…Behavioral outcomes are devalued as a function of the delay until they are experienced (for review, see Odum, 2011 ). This process, known as delay discounting, is reliably associated with cigarette smoking (for meta-analysis, see MacKillop et al, 2011 ; Amlung et al, 2016 ) and other tobacco use (e.g., Stein et al, 2018a ; DeHart et al, 2020 ). For example, high discounting rates for delayed monetary gains are cross-sectionally associated with smoking status (e.g., Mitchell, 1999 ) and longitudinally predict both smoking initiation ( Audrain-McGovern et al, 2009 ) and relapse following smoking cessation treatment (e.g., Yoon et al, 2007 ; Sheffer et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Choice Bundling Increases Valuation of Delayed Losses More Than Gains in Cigarette Smokers

Stein

Brown

Tegge

et al. 2022

Front. Behav. Neurosci.

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Choice bundling, in which a single choice produces a series of repeating consequences over time, increases valuation of delayed monetary and non-monetary gains. Interventions derived from this manipulation may be an effective method for mitigating the elevated delay discounting rates observed in cigarette smokers. No prior work, however, has investigated whether the effects of choice bundling generalize to reward losses. In the present study, an online panel of cigarette smokers (N = 302), recruited using survey firms Ipsos and InnovateMR, completed assessments for either monetary gains or losses (randomly assigned). In Step 1, participants completed a delay-discounting task to establish Effective Delay 50 (ED50), or the delay required for an outcome to lose half of its value. In Step 2, participants completed three conditions of an adjusting-amount task, choosing between a smaller, sooner (SS) adjusting amount and a larger, later (LL) fixed amount. The bundle size (i.e., number of consequences) was manipulated across conditions, where a single choice produced either 1 (control), 3, or 9 consequences over time (ascending/descending order counterbalanced). The delay to the first LL amount in each condition, as well as the intervals between all additional SS and LL amounts (where applicable), were set to individual participants’ ED50 values from Step 1 to control for differences in discounting of gains and losses. Results from Step 1 showed significantly higher ED50 values (i.e., less discounting) for losses compared to gains (p < 0.001). Results from Step 2 showed that choice bundling significantly increased valuation of both LL gains and losses (p < 0.001), although effects were significantly greater for losses (p < 0.01). Sensitivity analyses replicated these conclusions. Future research should examine the potential clinical utility of choice bundling, such as development of motivational interventions that emphasize both the bundled health gains associated with smoking cessation and the health losses associated with continued smoking.

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“…Regarding substance use specifically, high rates of delay discounting relate to important aspects including: differentiating individuals dependent on substances from healthy controls (Businelle et al, 2010;Finn & Hall, 2004;Madden et al, 1997), substance use initiation (Audrain-McGovern et al, 2009), severity of use (Albein-Urios et al, 2012;MacKillop et al, 2010;Reynolds et al, 2004;Vuchinich & Simpson, 1998), and substance use treatment outcomes (Dallery & Raiff, 2007;Washio et al, 2011;Yoon et al, 2007). These findings are consistent across different substances of abuse, including alcohol (Amlung et al, 2017;MacKillop et al, 2010;Moody et al, 2017;Phung et al, 2019) and tobacco (Amlung et al, 2017;Barlow et al, 2017;Bickel et al, 2008;DeHart et al, 2020).…”

mentioning

confidence: 96%

Utility of the 5-trial adjusting delay task in screening for high delay discounting rates: A cohort study of individuals reporting harmful alcohol and tobacco use.

Dwyer¹,

Craft²,

Tegge³

et al. 2023

Experimental and Clinical Psychopharmacology

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High delay discounting (DD) rates are associated with several health conditions, including addiction. Investigators interested in the modulation of DD rates may be interested in screening for individuals with high DD rates within substance-using samples. The adjusting delay discounting task (ADT) is a brief DD rate measure suitable for screening purposes, but how performance on this task is related to more granular DD measures (e.g., adjusting amount task; AAT) in individuals who use substances is unknown. This study investigated the relationship between DD rates measured by the ADT and AAT to assess the utility of the ADT in screening for high discounting rates in individuals who use tobacco and alcohol. Participants (N = 488) were screened for high discounting rates, ln(k) ≥ −4.3118, using the ADT. Subsequently, participants completed the AAT. Linear regressions were conducted to understand the relationship between the ADT and AAT DD rates. Separate analyses between the total, high-quality and low-quality samples were conducted. Significant relationships between the ADT and AAT were found for the total (p < .001), high-quality (p < .001), and low-quality samples (p < .001). Although the ADT overestimated DD rates on the AAT by 2.49, 2.29, and 2.97 units in the total, high-quality, and low-quality samples, the ADT accurately identified individuals with high discounting rates. Specifically, 71% of the total sample, 80% of the high-quality sample, and 66% of the low-quality sample maintained a high DD rate on the AAT. These results demonstrate that ADT can be used as a quick, accurate screening tool to identify high discounting rates in individuals who use substances.

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

Cited by 17 publications

References 50 publications

Episodic future thinking predicts differences in delay discounting: The mediating role of hippocampal structure

Episodic future thinking predicts differences in delay discounting: The mediating role of hippocampal structure

Choice Bundling Increases Valuation of Delayed Losses More Than Gains in Cigarette Smokers

Utility of the 5-trial adjusting delay task in screening for high delay discounting rates: A cohort study of individuals reporting harmful alcohol and tobacco use.

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