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. Author manuscript; available in PMC: 2021 Aug 1.
Published in final edited form as: Psychol Addict Behav. 2020 Mar 5;34(5):620–627. doi: 10.1037/adb0000565

Disrupting the path to craving: Acting without awareness mediates the link between negative affect and craving.

Matthew Enkema 1, Kevin A Hallgren 2, Elizabeth C Neilson 3, Sarah Bowen 4, Elizabeth R Bird 5, Mary E Larimer 6
PMCID: PMC7394723  NIHMSID: NIHMS1553544  PMID: 32134279

Abstract

Low treatment utilization, relapse, and chronicity are characteristic of substance use disorders (SUDs). Craving is an important predictor of relapse. Individuals with a SUD report using various coping strategies in response to negative affect, two of which are investigated in the current study: suppression and acting with awareness. Suppression is typically understood to be avoidance of unwanted experience through turning attention away from unwanted stimuli. Acting with awareness (AWA) is a facet of mindfulness, which includes turning toward experience with kindness and curiosity, even when the experience is unwanted. Evidence suggests mindfulness may reduce craving, while suppression has been associated with increased craving. In the current study, participants (N = 210) had recently completed inpatient or intensive outpatient SUD treatment followed by an RCT of aftercare. Participants completed measures within two weeks following the completion of the aftercare intervention. Negative affect and severity of dependence were both positively associated with craving. Structural equation modeling (SEM) evaluated AWA and suppression as partial mediators of the link between negative affect and craving. Suppression was positively associated, and AWA was negatively associated with craving. Mediation analyses revealed the indirect effect of negative affect on craving through AWA was significant but the path through suppression was not. These findings suggest that AWA may inhibit the development of craving from negative affect, but further research is needed. Future research should investigate the path from negative affect to craving with greater temporal resolution to evaluate how these phenomena function with increased ecological validity.

Keywords: Addiction, Negative Affect, Craving, Mindfulness, Suppression

Despite the existence of several efficacious psychotherapies and medications for substance use disorders (SUD), their effect sizes on substance use outcomes are typically only small to moderate (Mouaffak et al., 2017; Dutra et al., 2008). Targeting the mechanisms that maintain substance use, such as craving, negative affect, and strategies people use to cope with craving and negative affect, may improve treatment efficacy. For example, experiencing craving after SUD treatment predicts a three-fold increase in risk for relapse (T. M. Moore et al., 2014), indicating that helping patients cope with cravings is particularly important for improving post-treatment outcomes.

Suppression and acceptance are two opposing coping strategies that people can use in response to craving and negative affect (Hone-Blanchet, Wensing, & Fecteau, 2014; T. M. Moore et al., 2014; Murphy & MacKillop, 2014; Litvin, Kovacs, Hayes, & Brandon, 2012; Bowen & Marlatt, 2009). Many SUD treatments teach coping strategies encourage suppression (i.e., avoidance) of unwanted craving and negative affect (Murphy & MacKillop, 2014; Rogojanski, Vettese, & Antony, 2011; Gross, 1998). These coping strategies can provide momentary relief: for example, in moments of temptation to use substances or when experiencing acute anxiety, suppression can momentarily reduce craving and help individuals avoid substance use (Litvin et al., 2012; Murphy & MacKillop, 2014; Rogojanski et al., 2011). However, research also shows that suppression may increase the salience of the same stimuli that individuals attempt to avoided (Aldao & Nolen-Hoeksema, 2013; Aldao, Nolen-Hoeksema, & Schweizer, 2010; Klein, 2007; Abramowitz, Tolin, & Street, 2001), which may lead to rebound effects (Sayers & Sayette, 2013), including increases in negative affect and craving (Garland, Roberts-Lewis, Kelley, Tronnier, & Hanley, 2014; Witkiewitz et al., 2014).

Alternatively, mindfulness-based treatment models, including Mindfulness-Based Relapse Prevention (MBRP; Bowen, Chawla, & Marlatt, 2011; Witkiewitz et al., 2005), encourage coping with negative affect and craving by acting with awareness, which could reduce the likelihood of suppression and associated rebound effects (Harris, Stewart, & Stanton, 2017; Ostafin, Marlatt, & Greenwald, 2008). A substantial body of research indicates that mindfulness-based interventions are efficacious for treating SUDs (Creswell, 2017; Goldberg et al., 2018). Unfortunately, evidence suggests that higher levels of depression and anxiety symptoms are associated with lower levels of trait mindfulness (Garland et al., 2014; Baer, 2006), and also lower levels of acting with awareness (Brown & Ryan, 2003). Similarly, ecological momentary assessment research has found momentary negative affect to be associated with reduced mindfulness and acting with awareness (Blanke & Brose, 2017; Chapman, Rosenthal, Dixon-Gordon, Turner, & Kuppens, 2017; Felsman, Verduyn, Ayduk, & Kross, 2017; Gotink et al., 2016; R. C. Moore, Depp, Wetherell, & Lenze, 2016; Shoham, Goldstein, Oren, Spivak, & Bernstein, 2017). This pattern of findings indicates that greater distress may inhibit the deployment of mindfulness. By contrast, higher levels of mindfulness may prevent the escalation of thought patterns that lead to craving and relapse (Garland et al., 2014; Marlatt, 2003; Teasdale et al., 2000; Witkiewitz et al., 2014). However, it is not clear whether reductions in craving are explained, at least in part, by higher levels of acting with awareness and/or decreases in suppression (Bowen et al., 2014; Witkiewitz et al., 2014; Bowen & Marlatt, 2009).

The current study examined whether suppression and acting with awareness mediated the link between negative affect and craving. The purpose of the study was to test two specific hypotheses based on the literature. Firstly, to test the theory undergirding mindfulness-based treatments, our hypothesis was that higher negative affect would be associated with lower levels of acting with awareness and higher levels of craving. Secondly, to test the theorized rebound effect, our hypothesis was that higher negative affect would be associated with higher levels of suppression and higher levels of craving.

Material and Methods

Participants

Recruitment was carried out at a private nonprofit agency that provided a continuum of care for alcohol and drug use disorders in both inpatient and outpatient settings. During the period of the parent study (Bowen et al., 2014), 57% of the outpatient and 2% of inpatient clients were legally mandated to substance abuse treatment, and 19% of outpatient, and 75% of inpatient clients were homeless. Roughly 55% of clients at the agency completed treatment as recommended.

Inclusion criteria in the parent study (Bowen et al., 2014) were age 18 years or older, fluent in English, having completed intensive outpatient or inpatient treatment prior to enrollment, able to attend treatment sessions, had agreed to random assignment and follow up assessment, and medical clearance for participation. Exclusion criteria included psychosis, dementia, suicidality, imminent danger to others, or previous MBRP trial participation. All participants (N=210, see Table 1) completed an 8-week aftercare intervention after being assigned to one of three conditions: treatment as usual (i.e., weekly group-based treatment based on twelve-step principles), relapse prevention (RP), or MBRP (Bowen et al., 2014).

Table 1.

Means, standard deviations, and zero-order correlations.

Variable % M SD
Age 39.22 11.14
Gender
 Male 72.2
 Female 27.3
 Transgender 0.5
Race/Ethnicity
 Latino 8.2
 African American 23.1
 Asian 0.5
 Native American 7.7
 Native Hawaiian/Pacific 1.0
Islander
 White 48.6
 Other 2.0
 Mixed 9.1
Abstinent 90.0
Substance of choice
 Alcohol 12.4
 Cocaine 1.4
 Opiates 1.0
 Methamphetamine 1.4
 Marijuana 0.5
 Polysubstance 83.3
Zero order correlations
M SD 1 2 3 4
1. Negative affect 12.41 11.02
2. Severity of dependence 3.59 4.01 .37
3. Acting with awareness 3.43 0.72 −.45 −.20
4. Suppression 16.67 4.81 .48 .27 −.45
5. Craving 1.08 1.13 .43 .30 −.36 .32
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Note. N=210. Negative affect measured with BDI (Beck Depression Inventory), Severity of dependence measured with SDS (Severity of Dependence Scale), Acting with awareness measured with subscale of the same name from FFMQ (Five Facet Mindfulness Questionnaire), Craving measured with PACS (Penn Alcohol Craving Scale). All variables were measured at post-intervention time point.

Measures

Craving was measured using the Penn Alcohol Craving Scale (PACS) and was adapted to include both alcohol and drug craving. The PACS (Cronbach’s α = .91) is a five-item 7-level self-report measure assessing frequency, intensity, and duration of craving over the past week (Flannery, Poole, Gallop, & Volpicelli, 2003; Flannery, Volpicelli, & Pettinati, 1999). The PACS has been adapted for other drug use in previous studies (Bowen et al., 2009), and has demonstrated strong predictive validity (Enkema & Bowen, 2017).

Severity of Dependence was assessed using the Severity of Dependence Scale (SDS), a five-item scale with response values ranging from 0–3. The SDS (Cronbach’s α = .88) assesses degree of substance dependence, and is validated across a range of substances, and is typically related to dependence severity and correlated with craving (Gossop et al., 1995). Severity of dependence is a widely replicated predictor of treatment and relapse outcomes, such that greater severity of dependence is associated with worse treatment outcomes and higher likelihood of relapse (Adamson, Sellman, & Frampton, 2009)

Negative Affect was assessed using items from the Beck Depression Inventory (BDI) and the Beck Anxiety Inventory. The BDI and the BAI are two separate 21-item 4-level scales, with each item ranging from 0–3, which is then summed. The BDI (Cronbach’s α = .93) is a widely used, well-validated measurement of depression (Storch, Roberti, & Roth, 2004), and has been used previously as a measure of negative affect (Shahar & Herr, 2011; Witkiewitz & Villarroel, 2009; Garamoni et al., 1991). The BAI (Cronbach’s α = .96) is also widely used and well validated in the measurement of anxiety (Beck, Epstein, Brown, & Steer, 1988) and has been used previously as a measure of negative affect (Glasner et al., 2016).

Acting with Awareness (AWA) was measured using the Five Facet Mindfulness Questionnaire (FFMQ), which consists of 39-items on a 5-point Likert scale, with each item ranging from 1–5, and then mean scored. The FFMQ assesses five facets of mindfulness: acting with awareness (AWA), observing, describing, non-judgment of inner experiences, and non-reactivity to inner experience. The scale is the consolidated product of decades of scale development research (Baer, 2006). Recent findings indicate that analysis using the full scale is not as reliable as the subscales, which have demonstrated the most reliable results (Bergomi, Tschacher, & Kupper, 2013). Research has shown AWA to measure aspects of mindfulness that are most strongly associated with craving and substance use disorders (Garland et al., 2014; Levin, Dalrymple, & Zimmerman, 2014). AWA also appears to be the most modifiable subscale in response to mindfulness interventions (Bowen et al., 2009). Thus, only the AWA subscale was used in the current analyses (Cronbach’s α = .85). An example of an item from the AWA subscale is, “I find it difficult to stay focused on what’s happening in the present,” which is reverse scored.

Suppression was assessed using the White Bear Suppression Inventory (WBSI), a 15-item scale, with each item ranging from 1–5, which is then summed. While the full WBSI is a well-established assessment tool with satisfactory reliability and validity (Wegner & Zanakos, 1994), recent investigations into the factor structure of the scale have identified two subscales within the full WBSI: intrusion and suppression (Schmidt et al., 2009). Drawing from a review of this more recent analysis (Schmidt et al., 2009), we modified our suppression measure to only include the 5 items that have reliably been associated with suppression across all subscale reviews (Cronbach’s α = .88). An example of an item from the Suppression subscale of the WBSI is, “I often do things to distract myself from my thoughts.”

Procedures

Participants were recruited near the end of their inpatient or outpatient substance abuse treatment episode using flyers and referrals from agency or research staff. Interested and qualified participants contacted research staff by telephone, provided verbal consent for screening, and completed a 30- to 45-minute telephone eligibility screen. Eligible participants provided informed consent and completed an on-site computerized baseline assessment, with research staff available to assist or answer questions. Following completion of the assessment, participants were randomly assigned using a computerized random number generator to 8 weeks of MBRP, RP, or continuation of their existing TAU (treatment as usual), the results of which are presented elsewhere (Bowen et al., 2014). Post-intervention follow-up was completed in-person using the same computerized assessment format.

Data analytic plan

The current study was designed to test specific mechanisms hypothesized to underlie craving (Bowen & Enkema, 2014; Witkiewitz et al., 2014; Witkiewitz & Marlatt, 2007). To test these hypotheses, a structural equation model (SEM) was fit using latent variables comprised of parcels (i.e., multiple items combined into a smaller number of indicators) to reduce the number of item indicators for long instruments (e.g., the 21-item BDI and BAI), which in turn reduces estimation errors and improves multivariate normality (Bandalos, 2008; Little, Rhemtulla, Gibson, & Schoemann, 2013). All variables were measured at the post-intervention point, which was after the 8-week aftercare intervention period. The structural equation model was tested using lavaan (Rosseel, 2012) in R (R Core Team, 2013).

The model was constructed to test associations based upon theory regarding the associations between study variables. Model fit indices (i.e., Chi-square, CFI, TLI, RMSEA, and SRMR) were evaluated based on established standards (West, Taylor, & Wu, 2012), and described in the Results section. The model was tested with and without grouping by condition (MBRP, RP, or TAU) and compared using a Chi-square difference test. Results indicated that there was not a significant difference between the models when accounting for conditition (p = 0.11), thus all analyses are reported with data collapsed across conditions. The same test was run grouping by gender (Male, Female), and results indicated there was not a significant difference (p = 0.26). Transgender participants were excluded from the gender comparison analysis due to the small sample size (n = 1). Due to the lack of variability, abstinence status was not included in the model despite the fact that abstinence status may have influenced the observed results.

Craving was a latent variable with two parcels. Parcel 1 contained items 1–3, and parcel 2 contained items 4 and 5. Severity of dependence was a latent variable with two parcels. Parcel 1 contained items 1–3, and parcel 2 contained items 4 and 5. Negative affect was a latent variable with two parcels containing an equal number of items from each of the scales. Parcel 1 contained items 1–10 from the BAI and BDI, and parcel 2 contained items 11–21 from both scales. Acting with awareness was a latent variable with four parcels containing two items each. Parcel 1 contained items 5 and 8, parcel 2 contained items 13 and 18, parcel 3 contained items 23 and 28, and parcel 4 contained items 34 and 38. Suppression was a latent variable with two parcels. Parcel 1 contained items 1, 10, and 11, and parcel 2 contained items 13 and 14.

The Monte Carlo simulation method for estimating confidence intervals of the indirect effects was used because it has been demonstrated to be an improvement over the Sobel Test or bootstrapping (Preacher & Selig, 2012). The Monte Carlo method produces a sampling distribution of a composite statistic by using point estimates of constituent statistics and the asymptotic covariance matrix of the constituent statistics. The sampling distribution of the composite statistics is then used to generate confidence intervals for the indirect effects.

Missing data

A portion of the full sample did not complete any of the post-intervention survey, or some portion of the post-intervention survey items. Specifically, 210 of 286 participants completed at least some portion of the post-intervention survey. Missingness was investigated to evaluate how non-completion affected the internal validity of the study (Snijders & Bosker, 2011). There were no study variables observed at baseline that predicted missingness on the post-intervention survey. Missingness was therefore treated as missing-at-random. To account for missingness of some portion of the survey items the lavaan package in R provides full-information maximum likelihood estimation using all available data. Using maximum likelihood to calculate estimates when data is missing at random has been cited as an effective method of reducing bias (Hallgren & Witkiewitz, 2013).

Results

The model converged normally after 155 iterations (see Figure 1). Fit indices showed that the hypothesized model had good fit; χ2 (df = 46, N = 210) = 84.4, p < .001, CFI = .970, TLI = .957, RMSEA = .063 (95% CI [.041, .084]), SRMR = .058. While the Chi-square was significant, the CFI and TLI both being above the typical minimum threshold (.95) suggested that the model fit the data well. The RMSEA was below .08, which indicates adequate fit (West et al., 2012). Additionally, the RMSEA confidence interval was within accepted bounds; lower bound of the confidence interval for the RMSEA was below .05, and the upper bound did not exceed .10. Each of the factor loadings also exceeded .50, which indicated that the item parcels were a good fit with the latent factors.

Figure 1.

Figure 1.

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Structural equation model with estimates

Note. Asterisks represent p values for coefficients. * > .05, ** > .01, and *** > .001.

Each observed and latent variable also has a residual variance term that isn’t displayed in the figure to increase readability.

The direct effect of negative affect on craving was positive and significant (β = .179, p = .032), controlling for severity of dependence, suppression, and AWA (Figure 1). The path from negative affect to AWA was significant in the expected direction (β = −.471, p < .001), as was the path from AWA to craving (β = −.229 p = .009). The dual negative paths indicated that higher negative affect was associated with less acting with awareness, and less AWA was associated with more craving. The indirect effect of negative affect on craving through AWA was also significant (β = .108, 95% CI [0.015, 0.112], p = .014), and positive. In sum, results of the direct and indirect paths indicated that acting without awareness mediated the link between negative affect and craving.

As hypothesized, more negative affect was associated with more suppression (β = .466, p < .001). However, the path from suppression to craving was not significant (β = .149, p = .086) and the indirect effect of negative affect on craving through suppression also was not significant (β = .069, 95% CI [−0.006, 0.089], p = .098).

Discussion

We found support for the hypothesis that negative affect is associated with craving and there is an indirect path through AWA following intensive treatment and aftercare. In this sample, negative affect was associated with being less mindful, and being less mindful was associated with having more craving. Results from the current study are consistent with the growing body of research suggesting that mindfulness may protect individuals from cognitive and behavioral patterns that lead to increased craving (T. M. Moore et al., 2014; Garland, Gaylord, Boettiger, & Howard, 2010). For individuals in recovery attempting to maintain sobriety, low levels of acting with awareness may facilitate a fertile path from negative affect to craving.

Although negative affect and suppression were related in the current study, the path from suppression to craving was not significant. Some empirical investigations have found that suppression is associated with increased craving (Garland, Brown, & Howard, 2016). However, suppression strategies have also been demonstrated to produce adaptive behavioral outcomes for heavy drinkers (Murphy & MacKillop, 2014) and tobacco users (Litvin et al., 2012; Rogojanski et al., 2011) during cue exposures. In the current sample, results did not indicate that there was a path to craving from negative affect through suppression. Together, our findings and those of previous studies suggest that the use of suppression could be either adaptive, maladaptive, or unrelated to the link between negative affect and craving, likely depending on the circumstances in which the skill is being used and who is using it.

Craving has come to be recognized as a fruitful target of intervention for substance use disorder treatment due to the risk of relapse associated with increases in craving. While negative affect is related to craving, results from the current study suggest that training that increases acting with awareness may enhance treatment effects and reduce the risk of relapse related to negative affect. Treatments that train participants to practice mindfulness may enhance the effect of interventions to reduce craving, and therefore, relapse. Indeed, mindfulness-based relapse prevention programs have shown strong efficacy and effectiveness in a variety of populations (Bowen et al., 2014; Amaro, 2014; Amaro, Spear, Vallejo, Conron, & Black, 2014), and even brief mindfulness interventions are related to decreased use of substances and decreased likelihood of relapse (Vinci et al., 2014). Results from the present study suggest that changes in acting with awareness may help explain reductions in relapse reported in trials of mindfulness-based interventions.

Future research, especially research using ecological momentary assessment, is warranted to investigate the use of interventions to increase acting with awareness in order to reduce the risk of craving and relapse related to negative affect.

Strengths

The present study has two clear strengths. First, despite evidence that craving is predictive of relapse, limited research has examined mechanisms that trigger cravings. Second, there is a dearth of advanced statistical methods investigating craving. To the best of our knowledge, the majority of research has utilized bivariate correlations or linear regression methods to examine craving, and even then often only as a secondary or exploratory outcome (Brewer, Elwafi, & Davis, 2014; Garland et al., 2014). The current analysis focused on investigating craving as an outcome and tested a full theoretical model to investigate craving using structural equation modeling and the Monte Carlo method for assessing mediation in a clinical sample.

Limitations

The present study has three important limitations. First, there were a variety of drug type preferences reported by participants, but the majority of participants were alcohol users. Evidence indicates that predictors of craving vary depending on drug type (Serre, Fatseas, Denis, Swendsen, & Auriacombe, 2018). Drug type may be an important factor for future consideration to further clarify the associations between negative affect, acting with awareness, suppression, and craving. Second, the cross-sectional nature of the current study, and the non-randomization to suppression or AWA limit the conclusions that can be made regarding causality. Using ecological momentary assessment, or other intensive longitudinal methods, and randomizing to either suppression or AWA would increase internal validity. Third, the lack of variability in abstinence status (90% abstinent) prevented the current analysis from including this important variable in the model, which limits the generalizability of findings. Finally, negative affect is a construct with some variability in how it is operationalized, and the current study is limited to negative affect assessed using a combined score drawn from separate measures of anxiety and depression (Olsson, Cooper, Nugent, & Reid, 2016).

Conclusions and clinical implications

The current study adds to the literature on craving, relapse prevention, and coping by examining a more comprehensive model of risk factors for craving. Findings highlight the protective role of acting with awareness in the important relationship between negative affect and craving. Clinicians and treatment researchers who work with people experiencing symptoms of a severe substance use disorder may infer from these findings that increasing peoples’ behavioral repertoire in coping with negative affect, specifically their ability to act with awareness when experiencing something unpleasant, may help reduce the quantity, frequency, and severity of craving.

Acknowledgments

Funding: This work was funded by the National Institutes of Health [T32AA007455, F31DA042503, K01AA024796, R01DA025764, F31AA024352]

Footnotes

Declaration of Interest: None.

Contributor Information

Matthew Enkema, University of Washington.

Kevin A. Hallgren, University of Washington

Elizabeth C. Neilson, Morehead State University

Sarah Bowen, Pacific University.

Elizabeth R. Bird, University of Washington

Mary E. Larimer, University of Washington

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