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. Author manuscript; available in PMC: 2019 Feb 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2018 Jan 11;26(1):65–76. doi: 10.1037/pha0000170

Effects of Experimental Pain Induction on Alcohol Urge, Intention to Consume Alcohol, and Alcohol Demand

Dezarie Moskal 1, Stephen A Maisto 1, Martin De Vita 1, Joseph W Ditre 1
PMCID: PMC5794517  NIHMSID: NIHMS924399  PMID: 29323505

Abstract

Research suggests one determinant of alcohol consumption may be physical pain, but there is no empirical evidence that pain has a causal effect on drinking. Therefore, the primary aim of this study was to test experimental pain as a determinant of several alcohol consumption proxies: self-reported urge to drink, intention to consume alcohol, and alcohol demand. This study also was designed to test negative affect as a mediator of the effects of pain on alcohol use proxies. We hypothesized that participants randomized to experimental pain induction (vs. no pain) would report greater urge, intention, and alcohol demand, and that these effects would be mediated by increased negative affect. Participants were healthy undergraduates who were hazardous drinkers (N = 61). Experimental pain was induced using a novel capsaicin-heat model intended to approximate key features of clinical pain. Results indicated that participants in the pain condition subsequently endorsed greater urge and intention to drink. Furthermore, these effects were mediated by pain-induced negative affect. We observed no effect of pain on alcohol demand. This is the first study to demonstrate a causal effect of acute pain on urge and intention to drink. Given the close association between alcohol consumption, urge and intention to drink, these findings suggest that pain may influence alcohol consumption, which can have implications for individuals with co-occurring pain and Alcohol Use Disorder (AUD). Specifically, individuals with co-occurring pain and AUD may drink to alleviate pain-related negative affect. Therefore, improving pain-coping skills may enhance pain-management abilities, subsequently reducing coping-motivated drinking.

Keywords: alcohol, pain, urge, intention, negative affect

Negative reinforcement models of alcohol use posit that individuals frequently consume alcohol to alleviate aversive physical or emotional states (Baker, Piper, McCarthy, Majeskie, & Fiore, 2004; Cappell & Herman, 1972; Khantzian, 1985; Sher & Levenson, 1982). Pain is a multidimensional, subjective experience that consists of sensory-physiological, motivational-affective, and cognitive-evaluative components (McMahon, Koltzenburg, Tracey, & Turk, 2013). Commonly defined as “an unpleasant sensory and emotional experience associated with actual or potential tissue damage” (International Association for the Study of Pain (IASP), 1994), pain is a virtually universal experience that is often classified as acute or chronic (i.e. persistent pain lasting at least three months; Nahin, 2015; VanDenKerkhof, Peters, & Bruce, 2013). Chronic and acute pain are highly prevalent (e.g., >30 % of Americans adults with chronic pain) aversive states that have been associated with alcohol use (Johannes, Le, Zhou, Johnston, & Dworkin, 2010; Larson et al., 2007; Price & Harkins, 1992). As such, both the experience and anticipation of pain have been shown to share neural substrates with the experience of aversive psychological states (Ploghaus et al., 1999), and there is evidence of a moderate to large correlation between negative affect and physical pain (Logan, 2003; Ruiz-Aranda, Salguero, & Fernandez-Berrocal, 2011).

Large population-based and clinical studies support an association between pain and alcohol use. For example, findings from one study indicated that individuals who endorsed chronic back/neck pain (vs. no chronic pain) had 60% greater odds of also meeting DSM-IV criteria for alcohol abuse/dependence (Demyttenaere et al., 2007). There is also evidence of positive associations between pain severity and increased risk for alcohol use and AUD (Edlund, Sullivan, Han, & Booth, 2013; Lawton & Simpson, 2009). Furthermore, pain has been associated with alcohol use among adolescents, young adults, and older adults along a continuum of pain severity levels and durations (i.e., brief instances of pain to severe chronic pain conditions; Bastardo, 2011; Edlund et al., 2013; Heaps, Davis, Smith, & Straker, 2011; Tsui et al., 2014). Lastly, several prospective studies have suggested that physical pain is a significant predictor of alcohol use (i.e., both heavy alcohol use and any alcohol use) and relapse to drinking after a period of abstinence, even after controlling for variables known to be associated with alcohol use (Caldeiro et al., 2008; Larson et al., 2007; Witkiewitz, Vowles, et al., 2015).

Conceptualization of the Physical Pain-Alcohol Use Relation

Zale, Maisto, and Ditre (2015) proposed a reciprocal model of pain and alcohol, in which the experience of pain may serve as a situational motivator of alcohol consumption, partly as a function of pain-induced negative affect (i.e. negative affect may mediate the effects of situational pain on alcohol use). Consistent with conceptualizations of pain as a determinant of alcohol consumption, alcohol has been shown to produce acute pain-inhibitory effects (Thompson, Oram, Correll, Tsermentseli, & Stubbs, 2017) that aligns with both negative reinforcement and self-medication models of substance use (Baker et al., 2004; Khantzian, 1985). Indeed, drinkers have reported consuming alcohol to self-medicate physical pain (Aira, Hartikainen, & Sulkava, 2008; Brennan, Schutte, & Moos, 2005; Goebel et al., 2011; Riley & King, 2009), and there is prospective evidence that negative affect may mediate relations between self-reported pain and alcohol consumption (Witkiewitz, McCallion, et al., 2015). Specifically, in one study, negative affect as measured by the Depression and Anxiety subscales of the Brief Symptom Inventory was examined as a mediator of the relation between self-reported pain and alcohol use. In this study, pain scores predicted drinking outcomes, and the relation was significantly mediated by negative affect.

Current support for the effects of pain on alcohol consumption, as summarized earlier, is derived from either observational correlational studies (e.g., Edlund et al., 2013) or prospective work (e.g., Caldeiro et al., 2008), which do not allow causal inferences. Therefore, experimental studies examining the proximal effects of pain on alcohol use are warranted. Furthermore, although Zale et al. (2015) proposed a causal mechanism linking pain, negative affect, and motivation to consume alcohol, human experimental studies have not tested pain-related negative affect as a mediator of the relation between pain and alcohol use. Thus, the purpose of this study was to test the effects of experimentally-induced pain on proximal antecedents of alcohol consumption, as proxies for ad lib alcohol consumption, and to investigate pain-induced negative affect as a mechanism of action. It was hypothesized that participants randomized to experimental pain induction (vs. no pain induction) would show greater increases in alcohol urge, intention to use alcohol, and alcohol demand. It also was hypothesized that increased negative affect as a result of experimentally-induced pain would mediate the effects of pain on alcohol urge, intention to use alcohol, and alcohol demand.

Method

Participants

Participants were recruited from a larger pool of university undergraduate students and completed pre-screening for the following study inclusion criteria: between the ages of 18 and 35 (which is defined as young adulthood, Schultz & Schultz, 2016); English speaking; and hazardous drinker (as defined by scoring 5+ and 7+ for females and males, respectively, on the Alcohol Use Disorders Identification Test – Consumption; AUDIT-C; Bush, Kivlahan, McDonell, Fihn, & Bradley, 1998). These cut-offs are consistent with research studying a similar population (C. E. Campbell, 2015; DeMartini & Carey, 2012). Those who drank less frequently (i.e., abstainers, light, and infrequent drinkers) were excluded from the study to create a more homogeneous sample and to reduce the potential for floor effects of the outcome variables that are expected with less frequent alcohol users. Exclusion criteria were as follows: currently using pain medication (as per self-report); currently experiencing physical pain; and chili pepper allergies (due to contraindication with the capsaicin pain induction procedure). Individuals who signed up for the study were invited to participate in a one-session laboratory study (occurring after 12 PM), and asked to refrain from alcohol and other drug use for 24 hours prior to the appointment. Upon arrival to the laboratory, informed consent was obtained, and pre-screening inclusion/exclusion criteria were confirmed.

A total of 77 undergraduate students attended an experimental study session, and 66 were randomized. Reasons that individuals were not randomized included equipment malfunction (n = 3), ineligibility based on the AUDIT-C (n = 7), and withdrawing prior to randomization due to other commitments (n = 1). Of the 66 individuals who were randomized, 61 were included in the analyses (n = 4 failed manipulation check, n = 1 withdrew because of pain discomfort).

Research Design

This study followed a two-group, between-subjects single-blind repeated measures experimental design. Participants were randomly assigned to either pain- or no-pain induction conditions using block randomization based on gender and the order that each individual entered the study. Current pain, state negative affect, urge to drink, intention to use alcohol, and alcohol demand were evaluated both at baseline and following the experimental manipulation.

Experimental Pain Model

A novel capsaicin-heat model was used to safely deliver a prolonged stimulus (15 minutes) to evoke a moderate level of pain that would be more analogous to clinical pain (Carr et al., 2013; Wang et al., 2016). A clinically significant level of pain has been defined as a pain intensity rating of greater than or equal to 4 out of 10 (Belfer et al., 2017; Ferreira-Valente, Pais-Ribeiro, & Jensen, 2011; Lalonde et al., 2014). There is also evidence that a moderate or suprathreshold level of pain provides a closer approximation of clinical pain compared to other indices of experimental pain reactivity (e.g., threshold, tolerance) as measured by the association between pain ratings and clinical pain response (Valencia, Fillingim, & George, 2011).

Contact-heat pain

Contact-heat pain was induced using the Conditioned Pain Modulation (CPM) system (Q-Sense-CPM, Medoc Ltd, Ramat Yishai, Israel). The computerized Medoc Q-Sense-CPM system has two thermodes with an active area of 30 × 30 mm and a temperature range from 20 °C to a safety limit of 50 °C. Heat is produced using a heating foil and a Peltier element; the perception of heat pain in humans is thought to be mediated by activity in Aδ and C fibers (for reviews, see Reddy, Naidu, Rani, & Rao, 2012; Schepers & Ringkamp, 2009). Research has found contact-heat pain to produce ratings of moderate pain (Dirks, Petersen, & Dahl, 2003; Jensen & Petersen, 2006). That contact-heat pain can be evoked via a computer-controlled thermode exhibiting high levels of heat enables a standardized administration across participants. The heat limit during the test-of-limits protocol (described in the measures section) was set to the Medoc Q-Sense-CPM system safety limit of 50 °C. Based on thorough examination of the literature, a temperature of 44 °C was set as the maximum allowable heat during the 15-minute experimental pain induction to safeguard against tissue damage that can occur at high temperatures over a longer duration (Kilminster, 1974).

Capsaicin

Capsaicin is a derivative of chili peppers that is available commercially and can be mixed in a base compound of ethyl alcohol to form a solution. Various concentrations of capsaicin (e.g., .01% - 10%) have been used in previous human research studies (e.g., Anderson, Sheth, Bencherif, Frost, & Campbell, 2002; Dirks et al., 2003). In the present study, pilot testing showed that 8% capsaicin (compounded with ethyl alcohol for topical application) in combination with an individualized level of moderate heat (described in measures section) was sufficient to meet the stated goal (i.e. safely incur prolonged clinically significant pain). When applied topically, capsaicin stimulates transient receptor potential vanilloid (TRPV1) receptors on Aδ and C fiber nociceptors and causes a painful burning sensation similar to that experienced in clinical pain conditions, such as neuropathy (Lotsch et al., 2015). Capsaicin also sensitizes the skin to heat, and therefore a lower level of thermal heat can be administered and perceived as more painful over a longer period without incurring harm (Schmelz, 2009). The capsaicin-heat combination also creates a longer-lasting stimulus than contact-heat alone (Mohr et al., 2008). Capsaicin has been applied safely, both alone, and in combination with contact-heat in several previous studies (e.g., C. M. Campbell et al., 2009; Madsen, Johnsen, Fuglsang-Frederiksen, Jensen, & Finnerup, 2012; Magerl, Fuchs, Meyer, & Treede, 2001).

Procedures

The Syracuse University Institutional Review Board approved all study procedures. After the consenting and screening procedures, participants completed demographic characteristic questionnaires, baseline measures of negative affect and the dependent variables (i.e., APT, intent to use alcohol, urge to drink). Second, participants provided pain ratings (threshold, tolerance, P80) and then were randomized to either the pain or no-pain induction condition as per the randomization scheme. Participants were in a seated position and instructed to refrain from moving during the experimental procedures. Approximately 5 minutes after the test-of-limits procedure, either the 8% capsaicin (pain condition) or water (control condition) solution was applied to the participant’s non-dominant vulvar forearm using a circular 2.5 cm2 spot bandage. Fourth, an individualized safe level of heat (P80) or room temperature (32 °C), for the pain and control condition, respectively, was applied directly on top of the bandage via the computer-controlled thermode. Participants in the pain condition whose P80 exceeded the upper heat safety limit for a 15-minute exposure period (n = 18) received the maximum allowable level of heat during the experimental pain induction at 44 °C.

Participants were instructed to close their eyes and focus on the sensations until they heard a tone sound on the computer. After five minutes of the experimental pain/no-pain induction, the tone sounded and participants began completing, in order, the outcome measures of pain intensity, negative affect, urge to drink, intent to use alcohol and alcohol demand. Results of pilot testing indicated that at five minutes peak pain intensity was achieved. Once the participant completed the post-manipulation measures, but not before 15 minutes elapsed, the temperature of the thermode decreased to room temperature, and a research assistant removed the thermode and the capsaicin bandage. Finally, participants were debriefed and awarded course credit for their participation.

Measures

Screening

A screening questionnaire was administered to assess current acute or chronic pain conditions, current use of pain medications (in the last week), and allergies to peppers, whose consumption is contraindicated for capsaicin application. Participants were also asked their age and to self-report whether they spoke and read English well. The AUDIT-C (Bush et al., 1998) was used to identify hazardous drinkers for inclusion in this study. The AUDIT-C measures patterns of alcohol consumption over the past year. Specifically, the AUDIT-C consists of three items on a 5-point scale (0-4) that assess past-year drinking frequency, typical quantity, and frequency of heavy drinking, respectively. To help participants in providing accurate reports of the number of standard drinks consumed, they were provided with a definition and figure of a “standard drink” (i.e., a 12 oz. beer, 5 oz. glass of wine, or 1.5 oz. of hard liquor/distilled spirits; NIAAA, 2005).

Participant Characteristics

Demographics

A demographic questionnaire was used to collect information on gender, age, race, ethnicity, income, and class status.

Alcohol Use

In addition to the AUDIT-C, the National Institute on Alcohol Abuse and Alcoholism’s (NIAAA, 2003) recommended set of three alcohol consumption questions was included to gather more nuanced information regarding participants’ drinking patterns. This measure assesses frequency and quantity of alcohol consumption during the past year. Participants reported the frequency of any alcohol use and binge drinking (5+/4+ for males/females within a two-hour period) via categorical responses ranging from “every day” to “1-2 times in the past year.” Categorical ranges of drinking quantities were also provided for drinks per drinking day (e.g., 7 to 8). Consistent with previous work which has provided reliable estimates (Gallagher, Hudepohl, & Parrott, 2010; Leeman, Corbin, Fucito, Urwin, & O’Malley, 2013), frequencies were converted to weekly estimates (e.g., every day = 7), and an average was taken of each range of alcohol consumption quantities (e.g., 7 to 8 drinks = 7.5).

Pain Ratings

The intensity of the heat-capsaicin model was calibrated to each participant to reduce individual differences related to pain sensitivity. Each person underwent a test-of-limits protocol, in which they were asked to indicate (via computerized remote) when ascending heat reached a pain intensity of 8/10 (i.e., Pain-80/P80 rating) in three separate trials. During these trials, the heat stimuli began at 32 °C (baseline) and increased at a rate of 1 °C per second. After a P80 rating was reached, the temperature was automatically recorded and then returned to baseline at a rate of 2 °C per second. Individualized pain ratings were determined by averaging the temperatures recorded during the three trials. The individualized pain rating was then used to determine the level of heat to be administered during the pain procedure. Of note, however, the level of pain experienced throughout the pain induction was not assumed to be 8/10 (as reported for P80) and therefore was not used as a substitute for collecting pain intensity ratings during the experimental pain induction. Similar individualized methods have been shown to be less susceptible to floor and ceiling effects than non-individualized methods (e.g., using a fixed temperature; Granot, Granovsky, Sprecher, Nir, & Yarnitsky, 2006).

Pain Intensity

Pain intensity was assessed using the Numerical Rating Scale (NRS). The NRS is an 11-point scale from 0 (anchored at no pain) located at the far left to 10 (anchored at pain as bad as you can imagine) located at the far right. Participants were asked to click the number on the computer screen that reflects their level of pain intensity (0-10 as described previously) at that moment. The NRS has shown strong reliability and internal consistency in previous research (Price, Bush, Long, & Harkins, 1994).

Dependent Variables

Researchers have included proximal predictors of alcohol consumption, such as craving and demand, as outcome measures in clinical trials, and craving has been the target of many treatment interventions (Murphy et al., 2015; Oslin, Leong, Lynch, & et al., 2015). Although alcohol use is not always preceded by increased urge, intent, or demand (Kavanagh et al., 2013; Shiffman, 1987), these variables often are also highly correlated with alcohol use (Flannery, Poole, Gallop, & Volpicelli, 2003; Heinz et al., 2016; Ooteman, Koeter, Vserheul, Schippers, & van den Brink, 2006). Further, a benefit of examining proximal predictors of alcohol consumption over ad lib alcohol consumption is the ability to explore important alcohol-related relationships independent of factors that may constrain alcohol use, such as cost, availability, and legality. Therefore, urge, intent, and demand were selected as proxies of alcohol consumption.

Urge to Drink

A one-item question on a 10-point Likert scale asked participants to indicate the strength of their urge to drink at that moment. Participants indicated their urge from 1 (“absolutely no urge”) to 10 (“very strong urge”). Similar single-item measures have demonstrated both reliability and validity in assessing an individual’s urge to drink alcohol (Monti et al., 1993; Rohsenow & Monti, 1999).

Intent to Use Alcohol

The intent to use alcohol dimension of the Alcohol Craving Questionnaire (ACQ-NOW; Singleton, Henningfield, & Tiffany, 1995) measured current endorsement of planning or behavioral actions to consume alcohol, regardless of availability. Each item was rated on a 7-point scale from strongly disagree to strongly agree. Specified items were first reverse scored, then the raw scores for each factor were summed. Subscales of this measure have demonstrated high internal consistency (Connolly, Coffey, Baschnagel, Drobes, & Saladin, 2009). In the current sample, the intent to use alcohol scale demonstrated good internal consistency (α = .87).

Alcohol Demand

Alcohol demand was defined as the relative reinforcing efficacy of alcohol (MacKillop et al., 2009). The Alcohol Purchase Task (APT) was used to measure the demand or reinforcing value of alcohol. This task is an assessment of self-reported hypothetical alcohol consumption and financial expenditure across a range of prices. Like previous research, 24 beverage prices ranging from $0 - $15 were randomly presented (MacKillop et al., 2009; Murphy, MacKillop, Skidmore, & Pederson, 2009). The APT yields five indices: Intensity (i.e., level of consumption when drinks are free), Breakpoint (i.e., price at which consumption is completely suppressed), Omax (i.e., maximum alcohol expenditure value), Pmax (i.e., the price at which demand becomes elastic), and Elasticity (i.e., α; the aggregated slope of the demand curve). Larger values reflect a greater sensitivity to increasing drink prices. The APT demonstrates good reliability and validity (MacKillop et al., 2010; Murphy et al., 2009) and corresponds with decisions made with actual money and alcohol (M. T. Amlung, Acker, Stojek, Murphy, & MacKillop, 2012).

Mediating Variables

Negative Affect

State negative affect was measured using the 10 items from the negative affect scale of the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988). The PANAS consists of two dimensions of emotional experience labeled positive affect (i.e., interested, alert, strong) and negative affect (i.e., distressed, upset, irritable). Participants were asked to what extent they felt a certain way “right now (that is, at the present moment).” Items were rated on a 5-point scale from very slightly or not at all (1) to extremely (5). Scores on the scale were summed, resulting in a total score for the negative affect scale. The PANAS negative affect scale demonstrated good internal consistency in the current sample (α = .84-.86). Also, in previous research the PANAS demonstrated acceptable test-retest reliability in a college-aged sample (Watson et al., 1988).

Overview of the Data Analyses

All analyses were conducted using the Statistical Package for Social Sciences (SPSS) versions 22 and 23 (IBM, 2012), GraphPad Prism 7.01 (GraphPad Inc., San Diego, CA), and the calculator provided by the Institute for Behavioral Resources (to estimate elasticity; www.ibrinc.org/centers/bec/BEC_demand.html). The criterion for statistical significance was an alpha level of 0.05.

Preliminary analyses

Prior to analyses, the skewness and kurtosis of variable distributions were examined for normality. Variables were also examined for the presence of univariate outliers. Consistent with recommendations of Tabachnick and Fidell (2006), log10 transformations were performed as appropriate for variables that were significantly non-normal as defined by a z-score for skewness or kurtosis in excess of 3.29. Following transformations, a total of three outliers, values ≥ 3.29 SDs above the mean, were found in the measure of elasticity and were increased to one unit greater than the highest non-outlier value (Tabachnick & Fidell, 2006).

Descriptive statistics for all variables and Cronbach alpha coefficients for relevant measures were computed. In addition, t-test and Chi-square analyses were conducted to test for differences in participant characteristic by condition to determine if randomization was successful of if there was a need to control for demographic variables in later analyses. To determine if the experimental pain induction procedures were effective, participants in the pain condition and the no-pain condition were compared on reported level of pain intensity, controlling for baseline levels of pain, using a hierarchical regression analysis.

APT Demand Indices

To permit the use of logarithmic transformations in the calculation of elasticity, zero values were replaced with arbitrarily low non-zero values (i.e., $0.001) as has been done in other studies (Jacobs & Bickel, 1999; MacKillop et al., 2010). APT data were also examined for evidence of low effort (e.g., inconsistent responding across prices; >3 contradictions at any given price level (M. Amlung & Mackillop, 2012; Gray & MacKillop, 2014). Seven participants showed evidence of low effort on the APT and were excluded from subsequent APT analyses (total APT n = 54). Of the five demand indices, Omax and Intensity are most consistently correlated with alcohol use (Acker, Amlung, Stojek, Murphy, & MacKillop, 2012; Bertholet, Murphy, Daeppen, Gmel, & Gaume, 2015; Kiselica, Webber, & Bornovalova, 2016). Therefore, to reduce the potential for Type I error, and to examine variables most closely related to alcohol use, Omax and Intensity were the demand indices analyzed as dependent variables.

Primary analyses

Primary Aim 1 Analyses

Hierarchical multiple regression analyses were conducted to examine the effects of pain on the proxies of alcohol use (i.e., urge to drink, intent to use alcohol, and demand). Separate regression models were tested for each of the outcome measures. Typical pattern of past-year alcohol consumption (binge drinking days per week) and the baseline level of the respective proxy of alcohol were entered first in the model as covariates. Binge drinking days per week was included as a covariate in the models because it is well established that patterns of alcohol consumption are theoretically and empirically related to proxies of alcohol consumption (Drummond, 2001; Ooteman et al., 2006). Indeed, past-year binge drinking patterns was highly correlated with intent to use alcohol and urge to drink (r = .28, .38, ps < .05; Table 1). Moreover, controlling for typical patterns of alcohol consumption removes variance that is attributed to individual baseline differences and results in a purer test of the hypothesized effects of pain on urge to drink and intention to use alcohol. Binge drinking days per week was selected to represent the pattern of alcohol consumption, because it was most highly correlated with the outcome measures. The next variable entered into the model was the experimental pain induction condition (dummy coded as either no-pain [0] or pain [1]).

Table 1.

Bivariate Correlations among Select Study Variables

  r
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.
1. Condition −
2. BDD Per Week −.11 −
3. Intent†‡ .14 .31* −
4. Urge†‡ .14 .28* .66*** −
5. Intensity‡ −.07 .25 .50*** .41** −
6. Breakpoint‡ .01 .20 .56*** .45** .68*** −
7. Omax†‡ −.07 .26 .59*** .44** .77*** .85*** −
8. Pmax‡ .08 .10 .46** .34* .49*** .83*** .81*** −
9. Elasticity†‡ −.08 .04 .36** .25 .27 .42** .51*** .37** −
10. State NA†‡ .36** −.12 .15 .38** .03 .21 .13 .21 .16 −
11. Income −.04 .12 .21 .20 −.02 .07 −.04 −.04 .01 −.03 −
12. Discretionary −.13 .17 .31* .20 .03 .18 .15 .12 .11 −.05 .17 −
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Note. N =53-61 due to missing data. Condition = Experimental Condition; BDD = Binge drinking days; Elasticity= inverse of elasticity where higher scores reflect greater price insensitivity; NA = Negative Affect; Income = Total family income at permanent residence; Discretionary = Monthly Discretionary Income.

‡

Indicates variables that were measured after the experimental manipulation

†

Indicates variable was log10 transformed prior to analyses.

*

p < .05.

**

p < .01.

***

p < .001.

Primary Aim 2 Analyses

Negative affect as a mediator (M) of the relation between pain (X) and proxies of alcohol use (Y) was examined by performing mediation analyses using the PROCESS macro in SPSS (Hayes, 2013; Preacher & Hayes, 2008). The PROCESS macro estimates path a (effect of X on Y), path b (effect of M on Y controlling for X), path c (indirect effect of X on Y through M), and path c’ (direct effect of X on Y). The conceptual path model tested in this study is shown in Figure 1.

Figure 1.

Figure 1

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Path model for the effect of pain on alcohol urge, intent to use alcohol, and alcohol demand with negative affect as a mediator. Alcohol demand indices tested in this study included intensity and Omax. Unstandardized estimates and standard error, b (SE), shown for urge to drink and intention to use alcohol, respectively. Neither alcohol demand index met criteria for mediation (data not shown). Confidence intervals shown are the lower and upper bound of a 95% bootstrapped confidence interval of the indirect/mediating effects based on 10,000 resamples.

*p < .05.

**p < .001.

Condition was specified as the independent variable, state negative affect (post-experimental induction) as the mediator, and, in separate models, each proxy of alcohol use as the dependent variable. Pattern of alcohol use and the baseline levels of the respective proxy of alcohol use and state negative affect were entered as covariates. The statistical significance of indirect effects was assessed using 10,000 resamples and bias-corrected CI. The mediating and indirect effect were considered to be significant if zero is not within the 95% CI.

Results

Attrition Analyses

As noted earlier, of the 70 individuals who were eligible, 61 completed the experimental study and were retained in the analyses. Individuals who were eligible but who were not randomized or retained in the analyses did not differ from those who were eligible and included in the analyses with respect to demographic factors (ps > .05).

Manipulation Check

The experimental manipulation did not lead to the intended effect in three participants. One participant who was in the control condition reported pain (6/10) and two participants in the pain condition reported pain intensity below the clinical pain threshold of greater than 4/10 (1/10 and 2/10). Therefore, to enhance interpretability of findings related to the effects of physical pain, these three participants were excluded from the primary analyses. The remaining 61 individuals randomized to the pain and no-pain conditions were compared on their reported level of pain intensity after the experimental manipulation while controlling for their baseline levels of pain. Results indicated that individuals in the pain (vs control) condition reported significantly greater pain intensity (b = 6.35, p = <.001) and the experimental pain manipulation was highly effective in producing clinical levels of pain (Mpain intensity = 7.04, SD = 1.45 vs. Mpain intensity= 0.70, SD = 0.77).

Descriptive Results

Participants were predominantly White, hazardous drinkers (N = 61; M age = 18.7; 49.2% female). On average, participants reported having 2.61 (1.21) drinking days per week and consuming 7.44 (3.33) drinks per drinking day. Of the drinking days, participants reported binge drinking (5+/4+ for males/females within a two-hour period) on 1.77 (1.30) days per week. Participant characteristics are shown in Table 2; no significant differences were found in any of the baseline variables between participants in the pain and control experimental conditions. Descriptive results are summarized in Table 3. Bivariate correlation coefficients for key study variables are shown in Table 1. Binge drinking days per week was positively correlated with urge to drink and intent to consume alcohol (p < .05). Income was not correlated with any of the APT indices, and therefore was not included as a covariate in subsequent APT analyses (p > .05).

Table 2.

Characteristics of Participants in the Experimental Study, by Condition

Characteristic Overall
n = 61
N (%)/M (SD)		 Pain
n = 28
N (%)/M (SD)		 Control
n = 33
N (%)/M (SD)		 p-value†
Gender (male) 31 (50.8%) 14 (50.0%) 17 (51.5%) .906
Age 18.70 (0.82) 18.82 (0.86) 18.61 (0.79) .313
Race (White) 53 (86.9%) 22 (78.6%) 31 (93.9%) .127
Hispanic 5 (8.2%) 24 (85.7%) 32 (97.0%) .170
English first Language 57 (93.4%) 26 (92.9%) 31 (93.9%) .865
Class Status .511
 Freshman 39 (63.9%) 17 (60.7%) 22 (66.7%)
 Sophomore 17 (27.9%) 9 (32.1%) 8 (24.2%)
 Junior 4 (6.6%) 1 (3.6%) 3 (9.1%)
 Senior 1 (1.6%) 1 (3.6%) 0 (0%)
Household Income .853
 Less than $10,000 2 (3.3%) 1 (3.6%) 1 (3.0%)
 $10,000 – 25,000 1 (1.6 %) 1 (3.6%) 0 (0%)
 $25,000 – 50,000 6 (9.8%) 2 (7.1%) 4 (12.1%)
 $50,000 – 75,000 7 (11.5%) 4 (14.3%) 3 (9.1%)
 $75,000 – 100,000 9 (14.8%) 4 (14.3%) 5 (15.2)
 More than $100,000 36 (59%) 16 (57.1%) 20 (60.6%)
Discretionary Income ($)a 538.08 (953.07) 397.04 (569.86) 653.48 (1174.90) .304
AUDIT-C Total 7.92 (1.58) 7.61 (0.31) 8.18 (1.49) .160
Drinking Days/Week 2.61 (1.21) 2.63 (1.29) 2.60 (1.15) .917
Drinks Per Drinking Day 7.44 (3.33) 7.00 (3.74) 7.82 (2.94) .343
Binge Drinking Days/Week 1.77 (1.30) 1.62 (1.17) 1.89 (1.41) .420
P-80 (°C) 44.56 (2.39) 44.52 (2.40) 44.59 (2.42) .915
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Note. AUDIT-C = Alcohol Use Disorder Identification Test- Consumption; P-80 = individualized pain rating in which participant reported 80/100 pain intensity.

a

one participant’s data was not included in this analysis, either due to missing data or improbability (i.e., one person reported a monthly discretionary income of $100,000).

†

= chi-square (categorical) or t-test (continuous) inferential difference test between pain and control group. Statistics were computed using untransformed data. Sample included individuals who completed pre- and post-test measures and who reported pain ratings consistent with experimental condition (i.e., 3 participants were removed; 1 participant in the control condition reported pain [6/10] and 2 participants in the pain condition reported no pain [1-2/10]).

Table 3.

Effects of Experimental Pain Manipulation on Pain, Negative Affect, Alcohol Urge, Intention, and Alcohol Demand

Variable Pain
(n = 28)		 Control
(n = 33)
Pre
M (SD) 		Post
M (SD) 		Pre
M (SD) 		Post
M (SD) 		b (SE) t p ΔR2
Pain Levels (Intensity) 0.14 (0.45) 7.04 (1.45) 0.21 (0.49) 0.70 (0.77) 6.35 (.29) 21.58 <.001 .89
State Negative Affecta 12.93 (3.85) 17.93 (7.12) 14.58 (4.83) 13.73 (4.00) .14 (.03) 4.74 <.001 .21
Proxies of Alcohol Use
 Intent to Use Alcohola 17.36 (10.00) 22.14 (10.36) 20.06 (9.59) 19.70 (10.91) .12 (.05) 2.61 .01 .07
 Urge to Drink Alcohola 1.43 (2.03) 1.54 (1.97) 1.82 (2.04) 1.00 (1.71) .14 (.07) 2.11 .04 .05
 Alcohol Purchase Task
  Intensity 4.81 (3.67) 5.08 (3.59) 5.96 (3.55) 5.61 (4.16) .62 (.55) 1.13 .26 .01
  Omaxa 13.81 (17.87) 12.41 (12.23) 17.75 (19.62) 15.59 (17.15) .04 (.07) .56 .58 <.01
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Note.

a

Variable was Log10 transformed prior to regression analyses. b = unstandardized coefficient. Statistical comparison presented represents the effects of experimental condition on the respective measure, controlling for pre-experimental manipulation values. Means and Standard Deviations were computed using untransformed data. Pre and post refer to pre- and post-experimental manipulation. N = 54 – 61.

Primary Study Results

It was hypothesized that pain condition would predict increases in proxies of alcohol consumption. Consistent with hypotheses, results of the hierarchical linear regression revealed that a significant proportion of the total variation in alcohol urge and intent to use alcohol (post-experimental manipulation) was predicted by experimental condition, after controlling for baseline levels of alcohol use (binge drinking days per week) and urge and intent to use alcohol, respectively, (b = 0.16, p < .05; b = 0.12, p < .05). After the experimental manipulation, individuals in the pain group reported on average 1.44 units greater urge to drink and 1.32 units greater intent to consume alcohol, as compared to individuals in the control group (calculated by taking the antilog10 of b = .16 and .12, respectively; note that condition is dummy coded to assist with interpretation: no-pain [0] or pain [1]). Multiple R2 revealed that approximately 36% and 14% of the variation in urge and intent to use alcohol, respectively, was predicted by experimental condition. Contrary to hypotheses, experimental condition did not predict variation in alcohol demand, either by Omax or Intensity (b’s = 0.05, 0.56, p’s = .33, .44; see Table 3; Figure 2).

Figure 2.

Figure 2

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Consumption across price levels by condition and timepoint. Pre- and post- refer to pre- and post- experimental manipulation. Drinks are reported in standard drink units. N = 54

The second hypothesis was that state negative affect (post-experimental manipulation) would mediate the effect of condition on increases in proxies of alcohol use. Also, consistent with hypotheses, results indicated that experimental condition significantly predicted state negative affect (path a1: b = .13, p <.001; path a2: b = .12, p <.001) and urge to drink and intent to use alcohol (path c1: b = .16, p = .016; path c2: b = .12, p = .011). State negative affect also significantly predicted urge to drink and intent to use alcohol after controlling for the effect of condition (path b1; b = .75, p = .01; path b2; b = .49, p = .02; See Figure 1). A test of indirect effects of pain condition on urge to drink and intent to use alcohol via state negative affect post-experimetal induction was also significant (path ab1: b = .10, 95% CI = .01-.19; path ab2: b = .06, 95% CI = .00-.13), indicating that state negative affect mediated the relation between pain condition and urge to drink and intent to use alcohol. As mentioned previously, condition did not significantly predict alcohol demand (Omax and Intensity). Therefore, state negative affect was not tested as a mediator of this relation.

Discussion

This is the first study to examine the influence of acute pain on proximal determinants of alcohol consumption. Results confirmed that participants who underwent experimental pain induction (vs. no pain induction) subsequently reported greater urge to drink and greater intention to consume alcohol. These effects were further shown to be mediated by pain-induced negative affect. Although significant correlations in the hypothesized direction were observed between alcohol demand and both urge to drink and intention to use alcohol, the effects of experimental pain induction on alcohol demand did not reach statistical significance.

Regarding the null finding for alcohol demand, the APT may have had limited sensitivity due to the population, setting, and interpretation of instructions. Specifically, the concept of alcohol demand may not be well-developed in young adult undergraduate students (Gallet, 2007). Younger (vs. older) individuals are likely less experienced with alcohol and therefore have had less time to develop demand for alcohol. Imagining a hypothetical situation, especially when in pain, also may have tended to be difficult for this study’s participants and have resulted in inaccurate hypothetical estimations. Indeed, pain has been shown to interrupt cognitive functioning (Eccleston & Crombez, 1999), and participants in the pain condition reported being moderately distracted by the sensations (M = 5.35, SD = 2.76 on a 0 to 10 scale). Additionally, participants may have interpreted the APT instructions literally and restricted the alcohol they were willing to consume due to the implications of drinking within an academic setting. Further, anticipated drinking consequences may have been particularly salient because many participants were under the legal drinking age (M = 18.70, SD = 0.82). Future studies may consider revising the instructions to explicitly indicate that the study setting would not impose additional consequences.

The current study builds on previous work (e.g., Brennan et al., 2005; Witkiewitz, McCallion, et al., 2015) to provide causal evidence that acute pain may be a critical determinant of urge to drink and intention to use alcohol, which are known to be highly correlated with alcohol consumption (Heinz et al., 2016; Ooteman et al., 2006). These findings are consistent with a conceptual model of pain and alcohol use (Zale et al., 2015) as well as the negative reinforcement and self-medication models of substance use (e.g., Khantzian, 1985). The data are also consistent with previous research showing that experimentally induced pain can influence smoking urge and behavior (Ditre & Brandon, 2008). In the present study, negative affect accounted for 62% and 50% of the variance in increased urge to drink intention to use alcohol, respectively. Thus, a portion of variance remains unexplained, and future research may benefit from exploring additional factors that may account for this variance (e.g., lack of alternative coping strategies, positive reinforcement; Zale et al., 2015).

Although this study is only a first indication of the causal effects of acute pain on proxies of alcohol use, together with previous research, these findings suggest that pain and pain-induced negative affect may be of critical importance in determining alcohol use and treating AUD. This importance is further supported by research showing that individuals who reported pain have an altered SUD presentation with more severe medical and psychiatric problems, which are undoubtedly costlier (Trafton, Oliva, Horst, Minkel, & Humphreys, 2004). Accordingly, some recent work has forged ahead and integrates the treatment of co-occurring pain and alcohol use using a combination of cognitive behavioral therapy and acceptance and commitment therapy, with promising results (Ilgen et al., 2016).

Limitations

Several limitations of this study should be considered when interpreting its findings. First, although experimental pain models attempt to approximate characteristics of clinical pain, experimental pain is not equivalent to clinical pain (Edens & Gil, 1995; Rainville, Feine, Bushnell, & Duncan, 1992). Accordingly, there is a substantial difference between the transient negative affect induced by experimental pain models, relative to negative affect resulting from persistently painful conditions. For instance, non-experimental pain has been shown to increase anxiety and compromise quality of life and mobility which have implications for employment, social relationships, and self-identity (Institute of Medicine, 2011). However, experimental pain induction methods have been used as an analog for clinical pain and have been applied to advance the understanding of other pain relationships, such as the effects of pain on smoking (Ditre & Brandon, 2008; Ditre, Heckman, Butts, & Brandon, 2010; Parkerson & Asmundson, 2016) and the effects of pain on decision-making (Koppel et al., 2017).

Second, these results are based on data collected from healthy undergraduate students who were hazardous drinkers. Recruiting a homogeneous sample of healthy participants allows for a degree of experimental control that is not necessarily available when working with individuals experiencing clinical pain whose pain may vary in duration, severity, and locale. Accordingly, it is important for future studies to determine if the present findings extend to more diverse populations, such as same-aged, non-college students living in the community, older adults, low-level drinkers, and individuals with co-occurring pain conditions.

Third, although participants were asked to report on their use of pain medications, which may have altered their response to the pain model, pain medications were not operationally defined for the respondent. Participants were also not excluded on the basis of current use of non-pain-related medication for which alcohol intake is contraindicated (e.g., benzodiazepines, antihistamines, etc.). Although participants reported drinking alcohol on average more than twice per week and consuming greater than 8 drinks per drinking day, it is possible that current use of contraindicated medication may have restricted their urge, intention to use alcohol, and alcohol demand.

Lastly, the current study used a between-subjects study design. Thus, although participants were randomized to each condition and several participant characteristics were examined for potential covariates, it is possible that some other between-group factor could have influenced the results. Future research should consider using a within-subject design to address this limitation and strengthen the interpretation of study results.

Directions for Future Research

Future research may benefit from extending the current findings to both clinical pain and alcohol consumption directly. Actual alcohol use can be examined within the laboratory using an alcohol self-administration procedure such as an ad-libitum taste test in which the guise of rating beverages on taste is used to measure the amount of available alcohol consumed (Marlatt, Demming, & Reid, 1973). Also, relations between in-vivo alcohol consumption and in-vivo clinical pain can be examined using ecological momentary assessment (EMA) methods (e.g., smart phone surveys). EMA methods offer the benefits of having high external validity and providing detailed information that is critical to understanding dynamic associations (Shiffman & Stone, 1998). As such, EMA may be particularly relevant in the study of pain and alcohol use, because the relation is theorized to be bidirectional (Zale et al., 2015) and may vary by context.

The cumulative empirical evidence on this topic highlights the need for future research investigating whether acute pain is also a causal determinant of other substance use. Although some experimental studies have been conducted with regards to tobacco smoking to support a causal relation (Ditre & Brandon, 2008; Parkerson & Asmundson, 2016), the effects of experimental pain on other substance (e.g., opioids, marijuana) have not been tested in human research participants. For example, observational studies and animal research suggest that pain may motivate opioid use (Griffin et al., 2016; Hipólito et al., 2015). In addition, recent increases in rates of opioid use and related problems suggest that this is a public health concern and a particularly important area for future study (Rudd, Aleshire, Zibbell, & Matthew Gladden, 2016; Vowles et al., 2015).

Lastly, there is evidence that different pain modalities can produce effects that mimic aspects of different pain conditions (Rainville et al., 1992; Staahl, Olesen, Andresen, Arendt-Nielsen, & Drewes, 2009). Research that replicates the current findings with other experimental pain induction modalities (e.g., cold pressor, ischemic muscle pain) may be helpful to determine the qualities of pain that relate to alcohol outcomes. Similar results using other modalities would also strengthen evidence from the present study that physical pain is a determinant of alcohol use.

Conclusions

This study provides the first experimental evidence that acute pain can be a potent antecedent of the urge to drink and intention to consume alcohol. Further, the significant mediation effect of negative affect corroborates previous research that stresses the importance of pain and pain-related negative affective states in alcohol consumption (e.g., Witkiewitz, McCallion, et al., 2015). The finding that hazardous drinkers experienced increased urge and intention to use alcohol in response to acute pain suggests that pain may influence alcohol consumption.

Granting that the present study by itself is limited in its ability to generate clinical implications because of the acute nature of the pain model, current and previous research findings suggest pain-related negative affect is driving the pain-alcohol relation. Together, these results may have implications for individuals with co-occurring pain and AUD. Specifically, these results raise the possibility that individuals with co-occurring pain may consume alcohol to alleviate pain-related negative affect. Therefore, in the treatment of alcohol use among individuals with pain conditions, tailored intervention that addresses negative affect and improves pain-coping skills may be indicated. Such a tailored approach may enhance pain-management abilities, subsequently preventing or reducing coping-motivated alcohol consumption.

Public Health Significance.

This study showed that acute pain caused increases in urge to drink and intention to consume alcohol as a function of increased pain-related negative affect. Findings suggest that pain may be a potent antecedent of alcohol consumption, which is important given the high prevalence of pain conditions. Prevention and intervention approaches that enhance pain-coping skills may prevent or reduce coping-motivated alcohol consumption among individuals with co-occurring alcohol and clinical pain conditions.

Acknowledgments

We gratefully acknowledge Dr. Aesoon Park and Dr. Sarah Woolf-King for their contributions to the current research. All authors contributed in a significant way to the manuscript and have read and approved the final manuscript. This work was supported by a Syracuse University Department of Psychology Master’s Thesis Award to Dezarie Moskal, and 2K05 AA16928 and R01 AA024844 from the National Institute on Alcohol Abuse and Alcoholism. The funding sources had no role other than financial support. This paper is based on an analysis of data collected for Ms. Moskal’s master’s thesis. Data reported in this study have not been included in any previous publication. A subset of these analyses was presented at the 40th Annual Research Society on Alcoholism conference, 2017, June, Denver, Colorado.

Footnotes

Disclosures: The authors have no conflicts of interest to report.

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