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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: Drug Alcohol Depend. 2021 Jan 11;221:108522. doi: 10.1016/j.drugalcdep.2021.108522

Effects of Acute Distress and Tobacco Cues on Tobacco Demand

Elizabeth R Aston b, Jacqueline E Smith b, Angelo M DiBello b,c, Samantha G Farris a
PMCID: PMC8026530  NIHMSID: NIHMS1661922  PMID: 33582490

Abstract

Introduction:

Cigarette demand, or relative value, can be assessed via analysis of performance on a hypothetical behavioral economic cigarette purchase task (CPT). Substance purchase tasks are highly amenable to manipulation, namely, external stimuli, instructional changes, or acute stressors. In this regard, the current secondary analysis evaluates the role a novel, computerized stress induction paradigm, the Contextual-Frustration Intolerance Typing Task (C-FiTT), plays in eliciting varying levels of stress and resulting demand.

Method:

Daily smokers (n = 484) completed a computerized internet-based distress provocation task wherein they were randomly assigned to one of five distress conditions: combination of task difficulty (low or high difficulty) with neutral or withdrawal cues, and a neutral control group. Tobacco demand was assessed immediately following the distress task using the hypothetical CPT.

Results:

The C-FiTT distress-induction task significantly increased key cigarette demand indices, including price at maximum expenditure (Pmax) and first price where consumption was suppressed to zero (breakpoint). Moreover, demand increased with severity of C-FiTT condition, with the high-difficulty condition resulting in significantly higher breakpoint and Pmax, compared to other conditions. C-FiTT condition was not related to a significant increase in Omax, intensity, or elasticity.

Discussion:

The novel C-FiTT paradigm produced comparable effects on tobacco demand relative to in vivo withdrawal induction, indicating that C-FiTT is a viable procedure by which to influence demand. Reduction of internal and external stressors may be effective in lowering motivation for tobacco. These results highlight the importance of state distress in tobacco demand, and offer a potential avenue for intervention.

Keywords: cigarette purchase task, distress, tobacco demand, stress

1.0. Introduction

The reinforcer pathology model of addiction (Bickel et al., 2014, 2011) is a contemporary behavioral economic theory developed to understand the initiation and maintenance of substance use. One core process implicated in the reinforcer pathology model is persistently elevated value attributed to a given reinforcer such as a drug. This elevated value, or demand, is a construct that quantifies an organism’s subjective value of a reinforcer, gauged as persistence in consumption amidst increasing cost and effort (Hursh et al., 2005). Demand can be assessed using substance purchase tasks via examination of hypothetical or actual substance consumption across a range of prices (Aston and Cassidy, 2019). These tasks generate five distinct demand indices that generally reflect amplitude of demand, including demand intensity (i.e., amount of drug consumed at zero cost) and Omax (i.e., peak expenditure for drug), and indices that reflect persistence of demand, namely Pmax (i.e., price at maximum expenditure for drug), breakpoint (i.e., cost whereby drug consumption is suppressed to zero), and elasticity of demand (i.e., the degree to which consumption decreases with increasing price).

Tobacco demand is a critical mechanism for understanding persistent smoking behavior (Zvorsky et al., 2019) and tobacco control efforts (Tidey, 2016). Tobacco demand indices, measured by the cigarette purchase task (CPT), can be used to differentiate between heavier and lighter smokers (Higgins et al., 2017). Moreover, they are associated with cigarette dependence and smoking rate (Bidwell et al., 2012; MacKillop et al., 2008; Murphy et al., 2011; O’Connor et al., 2016), topographical aspects of smoking (Farris et al., 2017a), and predict cigarette self-administration behavior (Bergeria et al., 2019; Chase et al., 2013). Tobacco demand indices are also sensitive to changes in environmental contexts like tobacco excise taxes (Acuff et al., 2020; Grace et al., 2015) and individual differences, which has aided in the understanding of nicotine reinforcement in vulnerable sub-groups of smokers like adolescents (Bidwell et al., 2012; Murphy et al., 2011), pregnant women (Higgins et al., 2017), economically-disadvantaged smokers (Bergeria et al., 2019), and smokers with psychiatric symptoms or disorders (Bergeria et al., 2019; Dahne et al., 2017; Farris et al., 2017b; MacKillop and Tidey, 2011; Secades-Villa et al., 2018).

With regard to smoking cessation, elevated demand indices are related to lower motivation and intent for quitting (Bidwell et al., 2012; Murphy et al., 2011; O’Connor et al., 2016), lower likelihood of making a quit attempt (Higgins et al., 2017), and predict poorer abstinence outcomes (Mackillop et al., 2016; Murphy et al., 2017; Secades-Villa et al., 2016). There has been some initial evidence in rodent studies that certain pharmacological interventions, like varenicline, may aid in acutely attenuating demand (e.g., increasing demand elasticity), though this has yet to be demonstrated consistently in human studies (McClure et al., 2013). It is possible that the presence of nicotine withdrawal contributes to the maintenance of elevated tobacco demand through craving or affective distress and ultimately undermines an individual’s ability to abstain from smoking (Schlienz et al., 2014). Indeed, experimental evidence indicates that acute withdrawal produces significant increases in breakpoint and Pmax (i.e., price at which demand becomes elastic, or sensitive to increases in price) (MacKillop et al., 2012). Moreover, exposure to tobacco cues versus neutral cues produces lower elasticity (i.e., less sensitive to pricing) and higher Pmax (Acker &MacKillop, 2013; MacKillop et al., 2012). The acute influence of drug cues on demand has also been observed (Acuff et al., 2019), including with alcohol (MacKillop et al., 2010; Amlung et al., 2012) and marijuana (Metrik et al., 2016) purchases tasks. Of note, heightened negative affect and increased smoking urges followed withdrawal cues in the development of the Contextual-Frustration Intolerance Typing Task (C-FiTT), the parent study from which this secondary analysis follows (Farris et al., 2018). Further, heightened negative affect following laboratory-induced distress has also been shown to heighten tobacco demand (Dahne et al., 2017), which has been documented in alcohol demand research as well (Owens & MacKillop, 2015). Thus, there is initial evidence that withdrawal-relevant distress can serve to amplify demand, which may be a key mechanistic pathway in which smoking is maintained.

To extend this line of inquiry, the current study was designed to evaluate the nature of tobacco demand in adult daily cigarette smokers following exposure to a novel withdrawal cue and distress induction task, the Contextual-Frustration Intolerance Typing Task (C-FiTT) (Farris et al., 2018). C-FiTT is a computerized task that involves exposure to withdrawal-related or neutral cues in the context of a low- or high-difficulty persistence task that produces gradients in severity of negative affect. Based on the existing literature, it was hypothesized that the C-FiTT task would produce greater hypothetical tobacco demand following exposure to withdrawal cues compared to neutral cues, especially when paired with the high-difficulty persistence task compared to low-difficulty.

2.0. Material and Methods

2.1. Participants

Participants were daily smokers recruited from all 50 US states for an anonymous online study on smoking and health through Qualtrics Panels service. This study is a secondary analysis that investigated tobacco demand after exposure to a novel frustration task. Data were collected in 2015, and participants were eligible on the basis of: (a) being a daily smoker for ≥1year, (b) smoking ≥5 cigarettes/day, and (c) use of combustible cigarettes as their primary tobacco product; and were excluded if they had reduced their smoking rate by more than half in the past month. Complete study details for the parent trial are presented elsewhere (Farris et al., 2018).

2.2. Measures

2.2.1. Descriptive measures.

Demographic characteristics were assessed via self-report, including biological sex (male = 0; female = 1) and annual household income (ordinal scale: 1 = $0–$4,999; 2 = $5,000–$9,999; 3 = $10,000–$14,999; 4 = $15,000–$24,999; 5 = $25,000–$34,999; 6 = $35,000–$49,999; 7 = $50,000–$74,999; 8 = ≥ $75,000). Items from the Smoking History Questionnaire (SHQ; Brown et al., 2002) were used to describe the sample in terms of smoking characteristics. Two additional items were used to assess same-day smoking (cigarettes smoked today and minutes since last cigarette). The Fagerström Test for Cigarette Dependence (FTCD; Fagerström, 2012) was used to assess level of cigarette dependence, with higher scores reflecting higher dependence (possible range 0–10).

2.2.2. Contextual-Frustration Intolerance Typing Task (C-FiTT).

C-FiTT is a computerized behavioral frustration task that requires re-typing a passage while adhering to specific instructions, in which various contextual states can be introduced such as level of difficulty and thematic passage content (Farris et al., 2018). Individuals are visually presented with a passage of typed text and instructed to retype the 57-word passage without making any mistakes. Participants are told the goal is to see how well they “pay attention to details.” Individuals are required to correctly retype the passage to complete the task while following the retyping instructions, which varies by difficulty level manipulation (e.g., omitting certain letters while re-typing). If an incorrectly re-typed response are submitted, an error message is displayed prompting individuals to try again. Individuals are also informed that they have the option to self-terminate the task at any point by typing “I quit” (Farris et al., 2018). C-FiTT was effective in producing medium-sized increases in negative affect and small-sized increases in smoking urges in the parent study. The minimum and maximum task participation time was set post hoc at 10 and 600 seconds, respectively, to ensure the task served as an acute perturbation of distress (Farris et al., 2018). Four attentional check items were embedded in the survey. The initial validity of C-FiTT has been documented among smokers within the context of tobacco withdrawal cues (i.e., neutral versus withdrawal thematic typing content; for complete detail on text cues, please see supplementary data in parent article) at low and high levels of task difficulty (Farris et al., 2018).

2.2.3. Pre-C-FITT measures.

The Questionnaire of Smoking Urges-Brief (QSU-B; Cox et al., 2001) is a 10-item self-report measure of smoking urges that was completed pre-post C-FiTT. Items are rated on 0–100 scale and summed such that higher ratings indicate greater intensity of smoking urges (possible range 0–1000). The pre-task QSU-B was used as a model covariate to adjust for baseline craving as an indicator of satiation.

2.2.4. The Cigarette Purchase Task (CPT) (MacKillop et al., 2008).

The CPT is a validated behavioral economic hypothetical purchase task based on progressive-ratio operant schedules wherein respondents self-report their cigarette consumption under various levels of price (MacKillop et al., 2008; Murphy et al., 2011). A state version of the CPT (Hitsman et al., 2008) was used in the current study. Participants were provided with the following task instructions: “Imagine that you could smoke RIGHT NOW. The following questions ask how many cigarettes you would consume if they cost various amounts of money. Assume the available cigarettes are your favorite brand. Assume that you have the same income/savings that you have now and NO ACCESS to any cigarettes or nicotine products other than those offered at these prices. In addition, assume that you would consume the cigarettes that you request at this time. You cannot save or stockpile cigarettes for a later date. Be sure to consider each price increment carefully.” The CPT was completed immediately following the C-FiTT task, and was administered on the computer with each of the 22 questions/prices presented one at a time. Each trial read: “How many cigarettes would you smoke RIGHT NOW if they were: Free [$0/pack], 1¢ each [20¢/pack], 5¢ each [$1/pack], 10¢ each [$2/pack], 20¢ each [$4/pack], 30¢ each [$6/pack], 40¢ each [$8/pack], 50¢ each [$10/pack], 60¢ each [$12/pack], 70¢ each [$14/pack], 80¢ each [$16/pack], 90¢ each [$18/pack], $1 each [$20/pack], $2 each [$40/pack], $3 each [$60/pack], $4 each [$80/pack], $5 each [$100/pack], $6 each [$120/pack], $7 each [$140/pack], $8 each [$160/pack], $9 each [$180/pack], $10 each [$200/pack]?”

2.3. Procedures

The online survey involved an initial baseline assessment, which was followed by the experimental phase in which a 2×2+1 design was used to randomize participants to one of four C-FiTT versions that crossed: typing difficulty (low versus high difficulty) with cue exposure (neutral versus withdrawal text). The combination of these conditions plus a neutral control resulted in five conditions: (1) neutral cues + basic re-typing (control condition); (2) neutral cues + low-difficulty; (3) neutral cues + high-difficulty; (4) withdrawal cues + low-difficulty; and (5) withdrawal cues + high-difficulty. Smoking urges were assessed before and after the C-FiTT task, and the hypothetical CPT was completed after C-FiTT.

All participants were shown a written debriefing statement at the completion of the study. Participants received compensation in the form of Qualtrics credits, which are used to purchase gift cards or other items through the Qualtrics Panels portal. Researchers are not informed of the specific number of credits that participants receive. All study procedures were approved by the Institutional Review Board where the study took place.

2.4. Data Analytic Procedures

Calculations of demand indices were obtained using the following methods. Price elasticity values were generated by fitting individual curves in GraphPad Prism using an exponentiated demand equation (Koffarnus et al., 2015), Q=Q0×10k(e−αQ0C−1), where Q = quantity consumed, Q0 = derived intensity, k = a constant across individuals that denotes the range of the dependent variable (tobacco cigarettes), C = the cost of the commodity, and α = elasticity or the rate constant determining the rate of decline in consumption based on increases in price (i.e., essential value). The appropriate k value was determined by subtracting the log10-transformed average consumption at the highest price ($10.00) from the log10-transformed average consumption at the lowest price used in curve fitting ($0.01). The k value used in analyses was 1.772. An R2 value was generated to reflect percentage of variance accounted for by the demand equation (i.e., the adequacy of the fit of the model to the data).

Data cleaning procedures were completed consistent with standard recommendations (Stein et al., 2015). Raw CPT data were examined for outliers using standard scores, with a criterion of Z = 3.29 to retain maximum data. A small number of outliers were detected (1.1%). The outliers were determined to be legitimate high-magnitude values and were recoded as one unit higher than the next lowest non-outlying value between subjects within a single price (Tabachnick and Fidell, 2000). Observed values for intensity, Omax, Pmax, and breakpoint were estimated by directly examining CPT performance. Elasticity of demand was empirically derived using values generated from the exponentiated demand curve model.

All data were examined for distribution normality using histograms. All tobacco demand variables were non-normally distributed. A square root transformation was used for intensity, Pmax, and breakpoint, a log10 transformation was used for Omax, and a cube root transformation was used for elasticity. These transformations were successful in normalizing the data. All transformations improved the distribution substantially.

Primary analyses examined the demand indices. Analysis of covariance (ANCOVA) was used to test differences in demand indices by C-FiTT condition. Income (coded 0 = < $25,000; 1 = ≥ $25,000), pre-C-FiTT smoking urges, and task persistence time were entered as model covariates. Significance was defined as α < .05 for descriptive analyses. Demand curve modeling was conducted using GraphPad Prism 8, and all other analyses were conducted using SPSS 25.0. As a secondary analysis, the research question was not pre-registered, and the results should be considered exploratory.

3.0. Results

Of the original 550 cases, 14 cases showed evidence of inconsistent responding across prices on the CPT (i.e., had ≥3 reversals), 46 cases exhibited constant demand (i.e., purchased same number at every price) suggesting low effort, 4 cases demonstrated demand that was too low (i.e., purchased only at one price) resulting in invalid elasticity values, and 2 cases were excluded for reporting extreme purchases (i.e., > 100 cigarettes) reflecting biological implausibility. Thus, a total of 66 cases were excluded from analyses (12% of cases). The modified exponentiated demand equation provided an excellent fit to the CPT data (R2 = .986) and a good fit to the individual data (median R2 = .842, interquartile range = .780 – .909). Excluded versus included cases did not significantly differ by sex, age, income, C-FiTT condition, C-FiTT persistence time, or pre-task smoking urges; however, excluded cases had lower FTCD scores compared to included cases (M±SD, 4.5±2.0 vs. 5.6±1.9, t[548] = −3.85, p < .001).

3.1. Sample Characteristics

Participants (n = 484; M±SD age = 44.5±13.5; 52.92% female) identified race as white (90.3%), black/African-American (3.9%), Asian (1.9%), American Indian/Alaska Native (1.4%), or other (2.5%); 5.8% identified ethnicity as Hispanic. Half of the sample was married (51.0%), employed full time (48.8%), and approximately two-thirds completed at least some college (69.8%). Annual household income was reported as $0–$4,999 (1.0%), $5,000–$9,999 (1.7%), $10,000–$14,999 (5.2%), $15,000–$24,999 (11.6%), $25,000–$34,999 (15.9%), $35,000–$49,999 (30.6%), $50,000–$74,999 (20.9%), and ≥$75,000 (13.2%). The sample reported smoking an average of 17.6±8.5 cigarettes/day, daily smoking for 26.0±14.1 years, and had moderate levels of cigarette dependence (FTCD: 5.6±1.9). There were no significant differences between C-FiTT condition in terms of any baseline characteristics.

3.2. Differences in Tobacco Demand by C-FiTT Condition

The adjusted untransformed indices of CPT, as well as significant differences between all conditions, are presented in Table 1 by C-FITT condition. Covarying for income, smoking urges, and task persistence time, results indicated a significant main effect of C-FiTT condition on breakpoint (F(4, 476) = 3.00, p = .018). In the context of neutral cues, C-FiTT produced significantly higher breakpoint following low-difficulty (M = $2.30) versus high-difficulty (M = $1.50) task (p = .021). The opposite pattern was observed for smokers exposed to withdrawal cues: here, C-FiTT produced higher breakpoint following high-difficulty (M = $2.77) versus low-difficulty (M = $2.04) task (p = .055). Moreover, the high-difficulty C-FITT produced significantly higher breakpoint following withdrawal cues ($2.77) compared to neutral cues ($1.50) (p = .001).

Table 1.

CPT indices by C-FiTT Condition

Condition 1 (n = 106) Condition 2 (n = 103) Condition 3 (n = 93) Condition 4 (n = 87) Condition 5 (n = 95) F value Sig
M (SEM) M (SEM) M (SEM) M (SEM) M (SEM)
Intensity 6.28 (1.01) 7.71 (1.04) 7.42 (1.08) 6.30 (1.13) 6.67 (1.08) 0.766 .548
Omax 3.81 (0.68) 4.59 (0.70) 2.78 (0.73) 4.27 (0.76) 5.25 (0.73) 1.122 .346
Pmax 1.81 (0.26) 2.02 (0.27)a 1.34 (0.28)a,b 1.89 (0.29) 2.43 (0.28)b 2.365 .052
Breakpoint 1.89 (0.27)a 2.30 (0.28)b 1.50 (0.29)b,c 2.04 (0.30)d 2.77 (0.29)a,c,d 3.001 .018
Elasticity 0.54 (0.09) 0.34 (0.09) 0.41 (0.09) 0.44 (0.10) 0.33 (0.09) 1.116 .348
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Note: Paired superscript letters within rows denote presence of group differences (e.g., the statistically significant difference between Condition 1 and 5 on Breakpoint is represented by “a”); Non-transformed adjusted means are presented controlling income and smoking urges; Condition 1 = Neutral control; Condition 2 = Neutral cues+low-difficulty C-FiTT; Condition 3 = Neutral cues+high-difficulty C-FiTT; Condition 4 = Withdrawal cues+low-difficulty C-FiTT; Condition 5 = Withdrawal cues+high-difficulty C-FiTT.

Results also revealed a significant main effect of C-FiTT condition on Pmax (F(4, 476) = 2.37, p = .05). Specifically, in the presence of neutral cues, C-FiTT produced significantly higher Pmax following low-difficulty (M = $2.02) versus high-difficulty (M = $1.34) task (p = .026). Additionally, the high-difficulty C-FiTT produced significantly higher Pmax in the context of withdrawal cues (M = $2.43) compared to neutral cues (M = $1.34; p = .003).

There was a non-significant main effect of C-FiTT condition on Omax, although the high-difficulty C-FiTT produced roughly twice as high Omax in the context of withdrawal (M = $5.25) versus neutral content (M = $2.78) at a trend level (p = .065). There were no significant main effects of C-FiTT condition on demand intensity or elasticity. Table 2 presents specific pairwise comparisons between conditions, with the exception of Condition 1 (i.e., the control condition) across all of the outcomes. Findings are largely consistent with pairwise comparisons presented across all five Conditions shown in Table 1. Figure 1 displays prototypical consumption and expenditure curves, as well as mean consumption and expenditure curves for all five C-FiTT conditions. Data were analyzed using a high-density range of prices via the GraphPad Prism 8.4.3 template for rendering work and demand functions for exponentiated demand plots (KU Applied Behavioral Economics Laboratory). Data were transformed and the solved consumption values (from the best fit curve) were subsequently multiplied by the high-density range of prices (interpolated). As depicted, there is a clear pattern of increasing and persistent expenditures as price increases in response to the high-difficulty C-FiTT with withdrawal cues.

Table 2.

CPT indices by C-FiTT Condition

Condition 2 (n = 103) Condition 3 (n = 93) Condition 4 (n = 87) Condition 5 (n = 95) F value Sig
M (SEM) M (SEM) M (SEM) M (SEM)
Intensity 7.76 (1.03) 7.38 (1.08) 6.34 (1.12) 6.63 (1.08) 0.646 .586
Omax 4.56 (0.74) 2.79 (0.74) 4.24 (0.77) 5.26 (0.75) 1.422 .236
Pmax 2.00 (0.27)a 1.34 (0.28)a,b 1.89 (0.29) 2.44 (0.28)b 3.111 .026
Breakpoint 2.29 (0.29)a 1.50 (0.30)a,b 2.02 (0.31) 2.77 (0.30)a,b 3.747 .011
Elasticity 0.33 (0.07) 0.41 (0.07) 0.43 (0.08) 0.34 (0.07) 1.253 .290
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Note: Paired superscript letters within rows denote presence of group differences (e.g., the statistically significant difference between Condition 2 and 5 on Breakpoint is represented by “a”); Non-transformed adjusted means are presented controlling income and smoking urges; Condition 2 = Neutral cues+low-difficulty C-FiTT; Condition 3 = Neutral cues+high-difficulty C-FiTT; Condition 4 = Withdrawal cues+low-difficulty C-FiTT; Condition 5 = Withdrawal cues+high-difficulty C-FiTT.

Figure 1.

Figure 1.

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Prototypical consumption and expenditure curves are displayed in panels a and b, respectively. Mean consumption and expenditure curves for all five study conditions are displayed in panels c and d, respectively. Data were analyzed using a high-density range of prices via the GraphPad Prism 8.4.3 template for rendering work and demand functions for exponentiated demand plots (KU Applied Behavioral Economics Laboratory). Mean Consumption by C-FiTT Condition. Note: Condition 1 = Neutral control; Condition 2 = Neutral cues+low-difficulty C-FiTT; Condition 3 = Neutral cues+high-difficulty C-FiTT; Condition 4 = Withdrawal cues+low-difficulty C-FiTT; Condition 5 = Withdrawal cues+high-difficulty C-FiTT.

3.3. Post hoc analyses

Post hoc ANOVA analyses, using Fisher’s Least Significant Difference (LSD) test to identify specific differences among the means of the five conditions, were conducted at the price-point level to evaluate changes in hypothetical expenditure across increasing price points by C-FiTT condition (Figure 2). Results indicated that there were significant differences between conditions when cigarettes were $0.90 each (F(4,479 = 2.50, p = .042), $8.00 each (F(4,479 = 2.42, p = .048), $9.00 each (F(4,479 = 2.41, p = .049), and $10.00 each (F(4,479 = 2.71, p = .030). Specifically, at higher price points (e.g., $10/cigarette), the high-difficulty with withdrawal cues condition produced significantly higher expenditure (M = $2.63) relative to the attentional control condition (M = $1.13; p = .046), low-difficulty with neutral cues condition (M = $0.58, p = .007), and low-difficulty with withdrawal cues (M = $0.32, p = .003), and high-difficulty with neutral cues at a trend level (M = $1.26, p = .084).

Figure 2.

Figure 2.

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Price-level mean consumption (top panel) and price-level mean expenditure (bottom panel) by C-FiTT Condition. Note: Condition 1 = Neutral control; Condition 2 = Neutral cues+low-difficulty C-FiTT; Condition 3 = Neutral cues+high-difficulty C-FiTT; Condition 4 = Withdrawal cues+low-difficulty C-FiTT; Condition 5 = Withdrawal cues+high-difficulty C-FiTT.

4.0. Discussion

Among daily smokers, tobacco demand indices reflecting persistence of demand were influenced by exposure to the C-FiTT distress-induction task, namely, Pmax and breakpoint. Of note, the combination of the exposure to withdrawal cues and high-difficulty task resulted in significantly higher breakpoint and Pmax, compared to other task conditions. This finding was consistent with the hypothesis that the high-difficulty procedure and withdrawal cues would result in heightened demand for tobacco. The heightened Pmax and breakpoint mirror previous findings documenting the acute effects of withdrawal and tobacco cues on demand indices (MacKillop et al., 2012). Thus, the current “virtual” withdrawal exposure paradigm involving reading about nicotine withdrawal sensations produced comparable effects on tobacco demand relative to in vivo withdrawal induction, caused by unwrapping and lighting a cigarette. This suggests that C-FiTT could be a viable and economic alternative means of demand manipulation.

Unexpectedly, compared to the high-difficulty condition, the low-difficulty C-FiTT condition produced significantly higher persistence of demand, specifically in the indices Pmax and breakpoint. In other words, those with the less difficult typing procedure were willing to spend more money per cigarette and their purchasing was suppressed to zero at a higher price point. In examining the role of stress induction paradigms, the ambiguity of previous findings indicates the possibility that heightened demand may, in part, be a result of heightened negative affect in both the high and low-difficulty stress conditions (Dahne et al., 2017). Little work has focused on the appropriateness of the comparison condition, which leaves room for misinterpretation if low-difficulty conditions are not equivalent to participants’ neutral state. In this group of heavy daily smokers, it is possible that the low-difficulty condition may have produced feelings of boredom, a potential motivator of tobacco use (e.g., Berg et al., 2012) rather than the intended neutral affective state. Indeed, one prior study found that a social-stress induction versus ‘neutral’ comparison did not elicit differences in demand. Instead, affective reactivity to the task, regardless of condition, was a predictor of demand (Dahne et al., 2017). Boredom and similar concepts may be an area of future study that would illuminate nuanced differences in the role affective states play in tobacco demand. Although the C-FiTT, a motor-oriented frustration induction task, produces greater increases in negative affect in the high-difficulty compared to low-difficulty condition (Farris et al., 2018), the degree to which a smokers responds with negative affect to C-FiTT may be one area of explanation for tobacco demand increases; though more work is needed to further explore this hypothesis. Another notable finding is that the high-difficulty C-FiTT in the context of withdrawal cues produced persistent purchasing despite high costs compared to all other conditions: specifically when cigarettes were priced at $0.90, $8.00, $9.00, and $10.00 each. These findings suggest that cigarette smokers may continue to purchase tobacco at high costs if both negative affect and withdrawal cues are present.

This is the first study to examine acute distress across varying smoking-related contexts in terms of tobacco demand. This paper serves to elucidate the underlying mechanisms of demand, but there are several limitations to address. First, as a web-based study, it was not possible to monitor the participants as they completed the task. Self-reports could have been biased, but this method of data collection is most convenient for the participants and bolsters the number able to participate. Second, while all participants reported being a current daily smoker, we were unable to confirm this biologically. Third, demand was not measured prior to the C-FiTT task, however, urges were assessed prior to the task and controlled for in analyses, which mitigates this limitation. Fourth, this was a predominantly white sample (90.3%) indicating that these results may not be generalizable to the greater population of smokers. Subsequent research must be conducted with a more diverse sample of smokers. Finally, we excluded data from the CPT where participants displayed ≥ 3 reversals. Reversals may have occurred from misunderstanding directions, impaired cognitive functioning, or may be an artifact based on administration of the CPT: items were presented one at a time rather than all at once.

This study aimed to build upon the existing literature by evaluating the acute effect of negative affect and withdrawal-related cue-exposure on tobacco demand, which has translational implications for understanding the nature of affective aspects of the smoking abstinence/cessation experience. While withdrawal cues emerged as a consistent predictor of increased demand, particularly in the context of negative affect, more work is needed to understand the nature of affective states with respect to demand, in the absence of withdrawal or tobacco cues. Importantly, subsequent research should examine the duration of changes in state tobacco demand following a manipulation or induction. Studies on contextual variables that impact state demand over a period of time are critical components to understanding mechanisms underlying tobacco use, cessation, and relapse.

Highlights.

  • This study examined acute distress across smoking-related contexts

  • The impact of acute distress on tobacco demand was subsequently assessed

  • Withdrawal cues emerged as a consistent predictor of increased demand

  • Virtual withdrawal exposure produced comparable effects to in vivo induction

  • C-FiTT may be a viable and economic alternative means of demand manipulation

Author Disclosures

Funding for this study was provided by Qualtrics Behavioral Research Grant to Samantha G. Farris. Qualtrics had no role in the study design, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. Data analysis and manuscript preparation was supported in part by a grant to Elizabeth R. Aston from the National Institute on Drug Abuse (K01DA039311). The funding agencies had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Footnotes

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Conflict of Interest

No conflict declared.

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