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Negative affect intensity influences drinking to cope through facets of emotion dysregulation
Personality and Individual Differences 59 (2014) 96–101
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Negative affect intensity influences drinking to cope through facets of emotion dysregulation Jennifer C. Veilleux ⇑, Kayla D. Skinner, Elizabeth D. Reese, Jennifer A. Shaver University of Arkansas, United States
a r t i c l e
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Article history: Received 22 July 2013 Received in revised form 9 November 2013 Accepted 20 November 2013 Available online 12 December 2013 Keywords: Drinking motives Alcohol Emotion Affect Emotion regulation
a b s t r a c t Of all the motives for drinking, drinking to cope is the strongest predictor of problematic alcohol use, particularly for adolescent and college-age drinkers, with limited work assessing predictors of drinking to cope for community adult samples. At least for young adults, drinking to cope is associated with heightened negative affect and may serve as a substitute for more adaptive emotion regulation strategies. The current study tested an indirect relationship between negative affect intensity, or the propensity to experience strong negative affect, and drinking to cope via difficulties in emotion regulation, using a multiple mediator model. The model was tested using bootstrapping estimates of indirect effects in a combined sample of 566 college students (Mage = 19.75%, 40.8% women) and 104 non-college student adults (Mage = 35.40%, 36.5% women). Results revealed that negative affect intensity indirectly predicted drinking to cope through lack of emotional clarity and limited emotional strategies, with no moderation by sample. Results indicate that problems in clearly identifying specific emotional experiences appear to be important in predicting drinking to cope for people who experience intense negative emotions, suggesting that treatment and prevention efforts focused on teaching emotional clarity and/or learning multiple regulation strategies may be important in reducing coping-motivated drinking. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction A substantial percentage of college students (20%) engage in heavy alcohol consumption (National Institute on Alcohol Abuse, 2012), including binge drinking and general alcohol misuse. Moreover, research examining why individuals consume alcohol (Cooper, Frone, Russell, & Mudar, 1995; Crutzen, Kuntsche, & Schelleman-Offermans, 2013) indicates that drinking to cope with negative emotions is the drinking motive most predictive of problematic drinking (Merrill & Thomas, 2013; Read, Wood, Kahler, Maddock, & Palfai, 2003), at least for adolescents and young adults (Kuntsche, Knibbe, Gmel, & Engels, 2005). Despite the fact that hazardous drinking occurs across all age groups and populations (Thun et al., 1997), most available research on drinking patterns focuses primarily on college students or alcohol dependent samples (for recent exceptions, see Crutzen et al., 2013; Kim & Joen, 2012). Past research has examined personality and emotional factors that predict alcohol misuse and drinking to cope. For example, neuroticism, an index of the tendency to experience negative emotions, is a risk factor for the onset and maintenance of alcohol use disorders (Kilbey, Downey, & Breslau, 1998), and predicts both ⇑ Corresponding author. Address: Department of Psychological Science, 216 Memorial Hall, University of Arkansas, Fayetteville, AR 72701, United States. Tel.: +1 (479) 575 5329; fax: +1 (479) 575 3219. E-mail address: [email protected] (J.C. Veilleux). 0191-8869/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2013.11.012
drinking problems and drinking to cope (Cooper, Agocha, & Sheldon, 2000). Coping motives mediate the relationship between neuroticism and drinking problems for young adults (Mezquita, Stewart, & Ruipérez, 2010; Stewart, Loughlin, & Rhyno, 2001; Theakston, Stewart, Dawson, Knowlden-Loewen, & Lehman, 2004), suggesting that young adults low in emotional stability are prone to drinking as a way to cope with heightened negative emotions. However, the generalizability of personality and emotional predictors of drinking motives for community adult samples is heretofore unknown. Investigations focusing broadly on neuroticism may obscure the role of specific aspects of emotional dysfunction in predicting alcohol related outcomes, highlighted by research that suggests only some facets of neuroticism (e.g. self-consciousness, impulsivity) are predictive of drinking problems (Ruiz, Pincus, & Dickinson, 2003). Moreover, some aspects of affective dysfunction are not captured by neuroticism measures, including affect intensity, or the temperamental propensity to experience strong reactions to emotional events. Defined as stable individual differences in the strength of emotional experience (Larsen & Diener, 1987), affect intensity incorporates both positive and negative subjective experiences to typical life events. Negative intensity, or the strength of experienced negative affect, and negative reactivity, or an individual’s response or reaction to negative emotional events, appear to be most indicative of psychopathology. For example, higher affect
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intensity and reactivity are associated with borderline personality disorder (Gratz, Tull, Baruch, Bornovalova, & Lejuez, 2008), and compared to non-substance using controls, substance abusing individuals report higher negative intensity and reactivity (Thorberg & Lyvers, 2006). Experiencing more frequent and/or more intense negative emotions is not inherently problematic; individuals who experience strong emotions and know how to regulate them may not encounter negative consequences. Research has demonstrated that maladaptive emotion regulation strategies such as emotion inhibition mediate the relationship between affect intensity and psychological distress (Lynch, Robins, Morse, & Krause, 2001) and psychopathology (Gratz et al., 2008). In the substance use realm, difficulties in emotion regulation have been linked to both substance abuse (Cheetham, Allen, Yucel, & Lubman, 2010; Kashdan, Ferssizidis, Collins, & Muraven, 2010) and to coping motives (Bonn-Miller, Vujanovic, & Zvolensky, 2008). It may be that people with more frequent and more intense negative emotions abuse substances (e.g. drink alcohol) because they lack the skills to regulate these emotions otherwise (Cooper et al., 1995; Merrill & Thomas, 2013), and thus learn to use substances as a coping strategy (e.g. drink to cope). Taken together, these literatures suggest that characteristic responses to negative emotions may predict drinking to cope, as stronger reactions to negative stimuli or more intense negative feelings may make drinking to alleviate negative emotions even more appealing. In addition, it may be that affect intensity influences drinking to cope indirectly via emotion dysregulation, which can be conceptualized as the inability to effectively tolerate and access adaptive strategies to modulate the intensity and/or duration of emotional responses (Gratz, Rosenthal, Tull, Lejuez, & Gunderson, 2006). Although emotion dysregulation is thought to be a multidimensional construct (Gratz & Roemer, 2004), minimal literature has explored which facets are predictive of drinking to cope. Identifying particular areas of dysregulation that are most predictive of problematic drinking may be valuable in both treatment and prevention efforts. Evidence suggests that non-acceptance of emotional responses predicts coping-related marijuana use (Bonn-Miller et al., 2008; Shaver, Veilleux, & Ham, in press), differentiation among emotions appears to serve as a protective factor for alcohol use (Kashdan et al., 2010), and drinking to cope may stem from lack of appropriate alterative coping strategies (Cooper et al., 1995; Merrill & Thomas, 2013). Thus, we predicted that non-acceptance, lack of emotional clarity, and lack of regulation strategies would be likely mediator candidates for the relationship between affect intensity and drinking to cope. As drinking to cope has been studied primarily in college students, it will be valuable to study the relationships between affect intensity, emotion regulation and drinking to cope in multiple samples to test for generalizability of the proposed relationships. Specifically, we wondered if the proposed mediation of affective factors predicting drinking to cope would differ between samples (e.g. moderated mediation). We had no specific predictions about how the samples would differ considering the paucity of research on correlates of drinking motives in community adult samples.
2. Method 2.1. Participants and procedure There were two samples used in the current study. College students (N = 566, Mage = 19.75%, 40.8% female, 85.9% Caucasian) were recruited through a psychology subject pool at a large mid-south university. Adult non-college participants were recruited through Amazon Mechanical Turk, a web-based service composed of
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‘‘workers’’ who complete online tasks for small amounts of money. For this study, workers were restricted to those living in the U.S., and any participants who indicated current enrollment in college (N = 16) were excluded to ensure sample independence (N = 104 participants, Mage = 35.40%, 36.5% female, 82.7% Caucasian). Participants in both samples completed self-report measures online. All participants were required to be current drinkers, identified by reporting drinking frequency of at least monthly via the first item of the Alcohol Use Disorders Identification Test (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). 2.2. Measures 2.2.1. Drinking The Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) is a 10-item questionnaire designed to identify recent hazardous and harmful drinking. Internal consistency (a) in the current study was .82. The Drinking Motives Questionnaire—Revised (DMQ-R; Cooper, 1994) is a 20-item self-report measure designed to quantify reasons for drinking alcohol. The DMQ-R includes four subscales: coping, social (e.g., drinking to obtain social rewards), enhancement (e.g., drinking to enhance positive mood), and conformity motives for drinking (e.g., drinking to avoid social rejection). All subscales had adequate internal consistency: coping (a = .84), social (a = .91), enhancement (a = .87), and conformity (a = .85). 2.2.2. Affect intensity The Affect Intensity Measure (AIM; Larsen, Diener, & Emmons, 1986) measures temperamental responsivity to emotions. The Negative Intensity subscale (a = .77) measures the tendency to have intense negative emotional experiences, whereas the Negative Reactivity (a = .74) subscale assesses the tendency to respond strongly to emotional stimuli (Bryant, Yarnold, & Grimm, 1996). 2.2.3. Emotion dysregulation The Difficulties with Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) is a 36-item measure designed to assess clinically relevant difficulties in six skills theoretically needed for effective emotion regulation, with higher scores indicative of greater difficulties. The skills include: Non-acceptance of emotional responses (a = .88), difficulties engaging in Goal-directed behavior (a = .86), Impulsivity (a = .86), limited emotional Awareness (a = .79), limited access to emotion regulation Strategies (a = .91) and lack of emotional Clarity (a = .78). 2.3. Statistical analyses Mediation analyses were conducted in SPSS via Hayes’ (2013) macro PROCESS that models moderation, mediation and/or moderated mediation. The mediation procedure allows for multiple parallel mediators, assessment of potential moderators of model pathways, and assessment of direct, indirect, and conditional (e.g. moderated mediation) effects. The procedure provides unstandardized regression coefficients and estimates indirect effects using 95% bootstrapped confidence intervals (using 10000 bootstrapped samples), where confidence intervals that do not include zero are considered significant effects. This method is thought to be superior to traditional regression approaches to mediation (e.g., Baron & Kenny, 1986), because the bootstrapping method does not require the data to adhere to assumptions of normality, and it maximizes power with smaller samples (Hayes, 2009, 2013). In the current study, each affect intensity factor (negative intensity and negative reactivity) was evaluated as a central predictor while controlling for the other factor, with the same random number seed entered into the macro to ensure the same bootstrapped samples
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were used for each model. In addition, the other drinking motives were entered into the model as covariates. The six facets of emotion dysregulation were evaluated as multiple parallel mediators, with drinking to cope as the outcome variable. We also tested for moderated mediation, to evaluate if sample differentially influenced any of the model pathways, using conditional process procedures outlined by Hayes (2013). 3. Results 3.1. Descriptive statistics Means, standard deviations and correlations comparing study variables are reported in Table 1. Independent samples t-tests revealed that college students had greater hazardous drinking (t(631) = 4.75, p < .001) and increased difficulty with emotional clarity (t(668) = 2.65, p < .01), but lower negative reactivity (t(641) = 1.99, p < .05) compared to non-college adults. No other differences between samples were statistically significant. Correlations revealed that for both samples, drinking to cope was associated with greater hazardous drinking and higher negative intensity. Negative reactivity was not associated with drinking to cope in the non-college sample, and the magnitude of the correlation was small (.10) in the college sample. Drinking to cope was positively associated with all facets of emotion dysregulation other than Awareness (for both samples) and lack of Goal-directed behavior (non-college sample only). Likewise, for both samples, negative intensity was positively correlated with all facets of emotion dysregulation except Awareness. 3.2. Mediation analyses When controlling for negative intensity, negative reactivity did not predict drinking to cope either directly (direct effect = .04, SE = .05, p = .44) or indirectly via facets of emotion dysregulation (total indirect effect = .05, SE = .03, all confidence intervals included 0). Thus, remaining analyses focused exclusively on the effect of negative intensity on drinking to cope. The model predicted 49.7% of the variability in drinking to cope, F(11, 606) = 54.40, p < .00. As displayed in Fig. 1, negative intensity was predictive of all six subscales of the DERS (see Fig. 1 for unstandardized regression coefficients and standard errors). Of the six facets of emotion dysregulation, only limited Strategies and lack of Clarity predicted drinking to cope. With all variables in the model, the direct effect of negative intensity on drinking
to cope was marginally significant (B = .08, SE = .05, p = .05). Finally, as depicted in Table 2, negative intensity indirectly influenced drinking to cope through emotion dysregulation. Specifically, higher negative intensity predicted drinking to cope by way of limited Strategies for emotion regulation and lack of Clarity. Finally, to test for model invariance, we repeated all of the above analyses using sample as a moderating factor to ascertain if the relationships among drinking motives, affect intensity and difficulties with emotion regulation varied between college students and non-college adults. We found no evidence of moderation for any model paths, nor any moderated indirect effects, suggesting that college students and non-college adults did not substantially differ in terms of how affect intensity (either reactivity or intensity) and emotion dysregulation influenced drinking to cope.
4. Discussion This study contributes to our understanding of the relationship between affect intensity and drinking to cope in several ways. First, we replicated the finding that higher drinking to cope is associated with increased hazardous drinking in college students, (Kuntsche et al., 2005; Merrill & Thomas, 2013), and found a similar relationship in non-college adults. With few exceptions (e.g., Crutzen et al., 2013; Kim & Joen, 2012), including recent validation of the drinking motives measures adult samples (Crutzen & Kuntsche, 2013; Gilson et al., 2013), drinking motives have been studied primarily in adolescent and young adult samples (Kuntsche et al., 2005), with a particular focus on college students (Read et al., 2003). Thus, the inclusion of a non-college, adult sample is a strength of the current study, particularly as the pattern of results did not differ across samples. Second, we found the expected significant zero-order relationships among affect intensity, drinking to cope, and difficulties with emotion regulation. Specifically, stronger negative intensity was associated with greater deficits in emotion regulation, and with greater drinking to cope. Third, we found that the propensity to experience intense negative emotions indirectly predicted drinking to cope via deficits in emotional Clarity. These results dovetail with studies suggesting that individuals who regularly experience heightened negative affect and are poor at labeling specific negative emotions drink more (Kashdan et al., 2010), as well as evidence that alexithymic individuals may be prone to alcohol dependence (Thorberg, Young, Sullivan, & Lyvers, 2009) because they are poor at identifying specific emotions. These results suggest that treatment or prevention efforts focused on helping people
Table 1 Descriptive statistics and correlations among main study variables by sample.
1 2 3 4 5 6 7 8 9 10
AUDIT DMQR coping AIM negative intensity AIM negative reactivity DERS (lack of) strategies DERS non-acceptance DERS (lack of) goals DERS impulsivity DERS (lack of) awareness DERS (lack of) clarity
1
2
–
.47** – .27** .10* .46** .39** .26** .39** .07 .37**
.41** .07 .11* .19** .13** .13** .26** .14** .19**
3
4
.13 .30**
.02 .13 .66**
– .55** .43** .37** .33** .43** .01 .23**
– .15** .12** .28** .10* .21** .02
5
6
.13 .29** .54** .35** – .76** .51** .77** .11* .52**
.17 .30** .38** .19 .70** – .44** .68** .10* .51**
7
8
.02 .19 .40** .27** .59** .48** – .50** .00 .27**
.17 .26** .46** .27** .75** .58** .50** – .16** .50**
9
10
.02 .11 .05 .12 .34** .28** .23* .31** – .57**
.21* .36** .38** .21* .55** .54** .35** .58** .64** –
Mean (SD) College sample
Online sample
8.75 2.08 3.04 3.64 2.10 2.10 2.86 1.89 2.56 2.16
5.70 1.97 3.04 3.83 2.06 1.98 2.79 1.78 2.46 1.95
(5.90) (.91) (.84) (.84) (.87) (87) (57) (.77) (.77) (.71)
(5.99) (.98) (.93) (.94) (.79) (.79) (.60) (.78) (.77) (.72)
t-Test 4.75** 1.10 .03 1.99* .37 1.28 1.23 1.36 1.26 2.65*
College students reported below the diagonal, non-college adults above the diagonal. AUDIT = Alcohol Use Disorders Identification Test; DMQR = Drinking Motives Questionnaire Revised; AIM = Affect Intensity Measure; DERS = Difficulties with Emotion Regulation Scale. * p < .05. ** p < .01.
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(Lack of) Strategies
Non-Acceptance
(Lack of ) Goals Negative Intensity
Impulsivity
.02(.07)
Drinking to Cope
(Lack of) Awareness (Lack of) Clarity
-.11(.05)** Fig. 1. DERS facets as parallel multiple mediator of the negative intensity and drinking to cope relationship, controlling for negative reactivity and other drinking motives.
Table 2 Indirect effects of negative affect intensity on drinking to cope through facets of the DERS. Indirect regression coefficient (SE)
BC 95% bootstrapped CI Lower
(lack of) Strategies Non-acceptance (Lack of) goal directed behavior Impulsivity (lack of) Awareness (lack of) Clarity Total indirect effect of all mediators
*
.13 (.03) .001 (.02) .01 (.01) .001 (.03) .001 (.001) .03 (.01)* .16 (.04)*
.07 .03 .03 .05 .001 .01 .11
Upper .19 .04 .01 .07 .01 .07 .21
* Confidence intervals do not include 0. BC = bias corrected. CI = Confidence interval; DERS = Difficulties with Emotion Regulation.
clarify and differentiate among their feelings might particularly help those with a propensity for experiencing intense negative emotions, and thus potentially reduce drinking as a coping mechanism (Kashdan et al., 2010). In addition, we found that negative affect intensity indirectly influenced drinking to cope via lack of Strategies for emotion regulation. Lack of Clarity and limited strategies were likewise the two facets of emotion dysregulation to predict binge eating (Whiteside et al., 2007), another behavior thought to serve as a maladaptive coping strategy. Without effective regulation strategies, individuals who experience emotions intensely are likely to become easily overwhelmed by their emotional experience. As negative emotions are thought to narrow attention and reduce breadth of thinking (Friedman & Förster, 2010), particularly for motivationally intense negative emotions (Gable & Harmon-Jones, 2010), individuals may reach for any nearby available strategy to alleviate distress. These results suggest that those who drink and are prone to emotional intensity may benefit from over-learning multiple regulation strategies so that other options come readily to mind during distressing situations. Surprisingly, we found no effect of negative reactivity on drinking to cope, either directly or indirectly via emotion dysregulation. This may be due to the significant correlations between negative reactivity and negative intensity (see Table 1), whereby negative
reactivity is not a unique predictor above and beyond intensity. Indeed, an alternative measure of emotional reactivity not included in the current study (Nock, Wedig, Holmberg, & Hooley, 2008) emphasizes intensity as well as responsiveness to emotional situations and emotional volatility, suggesting that these constructs are closely related, where intensity may represent the stronger and more distinct component. The results of the current study should be considered in light of limitations to the study design. First, all data were collected online, which raises questions of generalizability to non-internet collection methods, although experts indicate similar results in data collected via lab and the internet (Birnbaum, 2004), and studies have found that mTurk provides samples of at least equal quality to traditional college student convenience samples (Buhrmester, Kwang, & Gosling, 2011). The cross-sectional nature of the data collection limits our ability to test prospective or causal relations among variables. However, as aptly described by Hayes (2013), the lack of experimental or prospective data does not diminish the validity of testing moderation and mediation relationships in simultaneously collected correlational data, as no model (including experimental designs) will fully or accurately model the relationships between variables, and these kind of analyses and models can provide the necessary background for designing more tightly controlled causal studies. These constructs are individual difference measures, and thus do not necessarily speak to the dynamic processes that may occur for people in emotional states. Alternate data collection processes, such as ecological momentary assessment, are required to see if people in intense negative states show reduction in emotional clarity or restricted access to emotion regulation strategies in the moment. Moreover, experimental laboratory designs are ideal to test whether or not coping-motivated drinking or alcohol consumption will decrease if someone in an intense negative emotional state is taught to clarify the specific emotion they are experiencing or is given access to alternative coping strategies. These prospective, dynamic designs would extend the current study by providing clear and concrete treatment strategies for use in clinical practice. Ultimately, although testing the model in a prospective design will yield important information about the relationship between these constructs, it may be that individuals prone to experiencing
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strong or intense negative emotions are qualitatively different from other individuals, such that individual difference factors may moderate dynamic processes. In short, it may be that biologically-based propensities to experience negative affect intensity or reactivity are instrumental in determining momentary contextual responses to emotionally evocative situations (Linehan, 1993). Moreover, although theoretically affect intensity is conceptualized as a temperament factor (Linehan, 1993), emotion dysregulation may reinforce and increase affect intensity over time, which may iteratively influence the effect of these variables on drinking to cope. All of these are questions ripe for future research. Finally, as with any individual difference study, important variables may have been neglected from analyses; we only accounted for roughly half of the variability in drinking to cope, which suggests additional research is needed to determine other predictive factors. Other affective variables known to influence both drinking and drinking to cope include anxiety sensitivity (Howell, Leyro, Hogan, Buckner, & Zvolensky, 2010; Stewart, Zvolensky, & Eifert, 2002), distress tolerance (Howell et al., 2010), alexithymia (Stewart et al., 2002; Thorberg et al., 2009) and cognitive expectancies regarding emotion dysregulation (Thorberg & Lyvers, 2006). Future studies may wish to examine the contribution of these variables in addition to affect intensity and emotion dysregulation. In conclusion, the current study provides a novel test of the relationship between temperamental affective factors, difficulties in emotion regulation and drinking to cope among both college and non-college adults using bootstrapping analyses of indirect effects (Preacher & Hayes, 2008) in a multiple mediator model. We found evidence for a direct effect of negative intensity on drinking to cope, such that higher negative intensity predicts greater coping-motivated drinking, and that this relation is partially mediated by problems with emotional Clarity and limited regulation Strategies. Importantly, these results were not moderated by sample, suggesting similar patterns of relations among college and noncollege adults. References Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). AUDIT— The alcohol use disorders identification test—Guidelines for use in primary care (2nd ed.). Geneva, Switzerland: World Health Organization. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Birnbaum, M. H. (2004). Human research and data collection via the internet. Annual Reviews of Psychology, 55, 803–832. http://dx.doi.org/10.1146/ annurev.psych.55.090902.141601. Bonn-Miller, M., Vujanovic, A. A., & Zvolensky, M. J. (2008). Emotion dysregulation: Association with coping-oriented marijuana use motives among current marijuana users. Substance Use & Misuse, 43, 1656–1668. Bryant, F. B., Yarnold, P. R., & Grimm, L. G. (1996). Toward a measurement model of the affect intensity measure: A three-factor structure. Journal of Research in Personality, 30(2), 223–247. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5. http://dx.doi.org/10.1177/1745691610393980. Cheetham, A., Allen, N. B., Yucel, M., & Lubman, D. I. (2010). The role of affective dysregulation in drug addiction. Clinical Psychology Review, 30, 621–634. http:// dx.doi.org/10.1016/j.cpr.2010.04.005. Cooper, M. L. (1994). Motivations for alcohol use among adolescents: Development and validation of a four factor model. Psychological Assessment, 6(2), 117–128. Cooper, M. L., Agocha, V. B., & Sheldon, M. S. (2000). A motivational perspective on risky behaviors: The role of personality and affect regulatory processes. Journal of Personality, 68, 1059–1088. http://dx.doi.org/10.1111/1467-6494.00126. Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P. (1995). Drinking to regulate positive and negative emotions: A motivational model of alcohol use. Journal of Personality and Social Psychology, 6(5), 990–1005. Crutzen, R., & Kuntsche, E. (2013). Validation of the four-dimensional structure of drinking motives among adults. European Addiction Research, 19, 222–226. http://dx.doi.org/10.1159/000345457. Crutzen, R., Kuntsche, E., & Schelleman-Offermans, K. (2013). Drinking motives and drinking behavior over time: A full cross-lagged panel study among adults.
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Negative affect intensity influences drinking to cope through facets of emotion dysregulation Jennifer C. Veilleux ⇑, Kayla D. Skinner, Elizabeth D. Reese, Jennifer A. Shaver University of Arkansas, United States
a r t i c l e
i n f o
Article history: Received 22 July 2013 Received in revised form 9 November 2013 Accepted 20 November 2013 Available online 12 December 2013 Keywords: Drinking motives Alcohol Emotion Affect Emotion regulation
a b s t r a c t Of all the motives for drinking, drinking to cope is the strongest predictor of problematic alcohol use, particularly for adolescent and college-age drinkers, with limited work assessing predictors of drinking to cope for community adult samples. At least for young adults, drinking to cope is associated with heightened negative affect and may serve as a substitute for more adaptive emotion regulation strategies. The current study tested an indirect relationship between negative affect intensity, or the propensity to experience strong negative affect, and drinking to cope via difficulties in emotion regulation, using a multiple mediator model. The model was tested using bootstrapping estimates of indirect effects in a combined sample of 566 college students (Mage = 19.75%, 40.8% women) and 104 non-college student adults (Mage = 35.40%, 36.5% women). Results revealed that negative affect intensity indirectly predicted drinking to cope through lack of emotional clarity and limited emotional strategies, with no moderation by sample. Results indicate that problems in clearly identifying specific emotional experiences appear to be important in predicting drinking to cope for people who experience intense negative emotions, suggesting that treatment and prevention efforts focused on teaching emotional clarity and/or learning multiple regulation strategies may be important in reducing coping-motivated drinking. Ó 2013 Elsevier Ltd. All rights reserved.
1. Introduction A substantial percentage of college students (20%) engage in heavy alcohol consumption (National Institute on Alcohol Abuse, 2012), including binge drinking and general alcohol misuse. Moreover, research examining why individuals consume alcohol (Cooper, Frone, Russell, & Mudar, 1995; Crutzen, Kuntsche, & Schelleman-Offermans, 2013) indicates that drinking to cope with negative emotions is the drinking motive most predictive of problematic drinking (Merrill & Thomas, 2013; Read, Wood, Kahler, Maddock, & Palfai, 2003), at least for adolescents and young adults (Kuntsche, Knibbe, Gmel, & Engels, 2005). Despite the fact that hazardous drinking occurs across all age groups and populations (Thun et al., 1997), most available research on drinking patterns focuses primarily on college students or alcohol dependent samples (for recent exceptions, see Crutzen et al., 2013; Kim & Joen, 2012). Past research has examined personality and emotional factors that predict alcohol misuse and drinking to cope. For example, neuroticism, an index of the tendency to experience negative emotions, is a risk factor for the onset and maintenance of alcohol use disorders (Kilbey, Downey, & Breslau, 1998), and predicts both ⇑ Corresponding author. Address: Department of Psychological Science, 216 Memorial Hall, University of Arkansas, Fayetteville, AR 72701, United States. Tel.: +1 (479) 575 5329; fax: +1 (479) 575 3219. E-mail address: [email protected] (J.C. Veilleux). 0191-8869/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.paid.2013.11.012
drinking problems and drinking to cope (Cooper, Agocha, & Sheldon, 2000). Coping motives mediate the relationship between neuroticism and drinking problems for young adults (Mezquita, Stewart, & Ruipérez, 2010; Stewart, Loughlin, & Rhyno, 2001; Theakston, Stewart, Dawson, Knowlden-Loewen, & Lehman, 2004), suggesting that young adults low in emotional stability are prone to drinking as a way to cope with heightened negative emotions. However, the generalizability of personality and emotional predictors of drinking motives for community adult samples is heretofore unknown. Investigations focusing broadly on neuroticism may obscure the role of specific aspects of emotional dysfunction in predicting alcohol related outcomes, highlighted by research that suggests only some facets of neuroticism (e.g. self-consciousness, impulsivity) are predictive of drinking problems (Ruiz, Pincus, & Dickinson, 2003). Moreover, some aspects of affective dysfunction are not captured by neuroticism measures, including affect intensity, or the temperamental propensity to experience strong reactions to emotional events. Defined as stable individual differences in the strength of emotional experience (Larsen & Diener, 1987), affect intensity incorporates both positive and negative subjective experiences to typical life events. Negative intensity, or the strength of experienced negative affect, and negative reactivity, or an individual’s response or reaction to negative emotional events, appear to be most indicative of psychopathology. For example, higher affect
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intensity and reactivity are associated with borderline personality disorder (Gratz, Tull, Baruch, Bornovalova, & Lejuez, 2008), and compared to non-substance using controls, substance abusing individuals report higher negative intensity and reactivity (Thorberg & Lyvers, 2006). Experiencing more frequent and/or more intense negative emotions is not inherently problematic; individuals who experience strong emotions and know how to regulate them may not encounter negative consequences. Research has demonstrated that maladaptive emotion regulation strategies such as emotion inhibition mediate the relationship between affect intensity and psychological distress (Lynch, Robins, Morse, & Krause, 2001) and psychopathology (Gratz et al., 2008). In the substance use realm, difficulties in emotion regulation have been linked to both substance abuse (Cheetham, Allen, Yucel, & Lubman, 2010; Kashdan, Ferssizidis, Collins, & Muraven, 2010) and to coping motives (Bonn-Miller, Vujanovic, & Zvolensky, 2008). It may be that people with more frequent and more intense negative emotions abuse substances (e.g. drink alcohol) because they lack the skills to regulate these emotions otherwise (Cooper et al., 1995; Merrill & Thomas, 2013), and thus learn to use substances as a coping strategy (e.g. drink to cope). Taken together, these literatures suggest that characteristic responses to negative emotions may predict drinking to cope, as stronger reactions to negative stimuli or more intense negative feelings may make drinking to alleviate negative emotions even more appealing. In addition, it may be that affect intensity influences drinking to cope indirectly via emotion dysregulation, which can be conceptualized as the inability to effectively tolerate and access adaptive strategies to modulate the intensity and/or duration of emotional responses (Gratz, Rosenthal, Tull, Lejuez, & Gunderson, 2006). Although emotion dysregulation is thought to be a multidimensional construct (Gratz & Roemer, 2004), minimal literature has explored which facets are predictive of drinking to cope. Identifying particular areas of dysregulation that are most predictive of problematic drinking may be valuable in both treatment and prevention efforts. Evidence suggests that non-acceptance of emotional responses predicts coping-related marijuana use (Bonn-Miller et al., 2008; Shaver, Veilleux, & Ham, in press), differentiation among emotions appears to serve as a protective factor for alcohol use (Kashdan et al., 2010), and drinking to cope may stem from lack of appropriate alterative coping strategies (Cooper et al., 1995; Merrill & Thomas, 2013). Thus, we predicted that non-acceptance, lack of emotional clarity, and lack of regulation strategies would be likely mediator candidates for the relationship between affect intensity and drinking to cope. As drinking to cope has been studied primarily in college students, it will be valuable to study the relationships between affect intensity, emotion regulation and drinking to cope in multiple samples to test for generalizability of the proposed relationships. Specifically, we wondered if the proposed mediation of affective factors predicting drinking to cope would differ between samples (e.g. moderated mediation). We had no specific predictions about how the samples would differ considering the paucity of research on correlates of drinking motives in community adult samples.
2. Method 2.1. Participants and procedure There were two samples used in the current study. College students (N = 566, Mage = 19.75%, 40.8% female, 85.9% Caucasian) were recruited through a psychology subject pool at a large mid-south university. Adult non-college participants were recruited through Amazon Mechanical Turk, a web-based service composed of
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‘‘workers’’ who complete online tasks for small amounts of money. For this study, workers were restricted to those living in the U.S., and any participants who indicated current enrollment in college (N = 16) were excluded to ensure sample independence (N = 104 participants, Mage = 35.40%, 36.5% female, 82.7% Caucasian). Participants in both samples completed self-report measures online. All participants were required to be current drinkers, identified by reporting drinking frequency of at least monthly via the first item of the Alcohol Use Disorders Identification Test (Babor, Higgins-Biddle, Saunders, & Monteiro, 2001). 2.2. Measures 2.2.1. Drinking The Alcohol Use Disorders Identification Test (AUDIT; Babor et al., 2001) is a 10-item questionnaire designed to identify recent hazardous and harmful drinking. Internal consistency (a) in the current study was .82. The Drinking Motives Questionnaire—Revised (DMQ-R; Cooper, 1994) is a 20-item self-report measure designed to quantify reasons for drinking alcohol. The DMQ-R includes four subscales: coping, social (e.g., drinking to obtain social rewards), enhancement (e.g., drinking to enhance positive mood), and conformity motives for drinking (e.g., drinking to avoid social rejection). All subscales had adequate internal consistency: coping (a = .84), social (a = .91), enhancement (a = .87), and conformity (a = .85). 2.2.2. Affect intensity The Affect Intensity Measure (AIM; Larsen, Diener, & Emmons, 1986) measures temperamental responsivity to emotions. The Negative Intensity subscale (a = .77) measures the tendency to have intense negative emotional experiences, whereas the Negative Reactivity (a = .74) subscale assesses the tendency to respond strongly to emotional stimuli (Bryant, Yarnold, & Grimm, 1996). 2.2.3. Emotion dysregulation The Difficulties with Emotion Regulation Scale (DERS; Gratz & Roemer, 2004) is a 36-item measure designed to assess clinically relevant difficulties in six skills theoretically needed for effective emotion regulation, with higher scores indicative of greater difficulties. The skills include: Non-acceptance of emotional responses (a = .88), difficulties engaging in Goal-directed behavior (a = .86), Impulsivity (a = .86), limited emotional Awareness (a = .79), limited access to emotion regulation Strategies (a = .91) and lack of emotional Clarity (a = .78). 2.3. Statistical analyses Mediation analyses were conducted in SPSS via Hayes’ (2013) macro PROCESS that models moderation, mediation and/or moderated mediation. The mediation procedure allows for multiple parallel mediators, assessment of potential moderators of model pathways, and assessment of direct, indirect, and conditional (e.g. moderated mediation) effects. The procedure provides unstandardized regression coefficients and estimates indirect effects using 95% bootstrapped confidence intervals (using 10000 bootstrapped samples), where confidence intervals that do not include zero are considered significant effects. This method is thought to be superior to traditional regression approaches to mediation (e.g., Baron & Kenny, 1986), because the bootstrapping method does not require the data to adhere to assumptions of normality, and it maximizes power with smaller samples (Hayes, 2009, 2013). In the current study, each affect intensity factor (negative intensity and negative reactivity) was evaluated as a central predictor while controlling for the other factor, with the same random number seed entered into the macro to ensure the same bootstrapped samples
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were used for each model. In addition, the other drinking motives were entered into the model as covariates. The six facets of emotion dysregulation were evaluated as multiple parallel mediators, with drinking to cope as the outcome variable. We also tested for moderated mediation, to evaluate if sample differentially influenced any of the model pathways, using conditional process procedures outlined by Hayes (2013). 3. Results 3.1. Descriptive statistics Means, standard deviations and correlations comparing study variables are reported in Table 1. Independent samples t-tests revealed that college students had greater hazardous drinking (t(631) = 4.75, p < .001) and increased difficulty with emotional clarity (t(668) = 2.65, p < .01), but lower negative reactivity (t(641) = 1.99, p < .05) compared to non-college adults. No other differences between samples were statistically significant. Correlations revealed that for both samples, drinking to cope was associated with greater hazardous drinking and higher negative intensity. Negative reactivity was not associated with drinking to cope in the non-college sample, and the magnitude of the correlation was small (.10) in the college sample. Drinking to cope was positively associated with all facets of emotion dysregulation other than Awareness (for both samples) and lack of Goal-directed behavior (non-college sample only). Likewise, for both samples, negative intensity was positively correlated with all facets of emotion dysregulation except Awareness. 3.2. Mediation analyses When controlling for negative intensity, negative reactivity did not predict drinking to cope either directly (direct effect = .04, SE = .05, p = .44) or indirectly via facets of emotion dysregulation (total indirect effect = .05, SE = .03, all confidence intervals included 0). Thus, remaining analyses focused exclusively on the effect of negative intensity on drinking to cope. The model predicted 49.7% of the variability in drinking to cope, F(11, 606) = 54.40, p < .00. As displayed in Fig. 1, negative intensity was predictive of all six subscales of the DERS (see Fig. 1 for unstandardized regression coefficients and standard errors). Of the six facets of emotion dysregulation, only limited Strategies and lack of Clarity predicted drinking to cope. With all variables in the model, the direct effect of negative intensity on drinking
to cope was marginally significant (B = .08, SE = .05, p = .05). Finally, as depicted in Table 2, negative intensity indirectly influenced drinking to cope through emotion dysregulation. Specifically, higher negative intensity predicted drinking to cope by way of limited Strategies for emotion regulation and lack of Clarity. Finally, to test for model invariance, we repeated all of the above analyses using sample as a moderating factor to ascertain if the relationships among drinking motives, affect intensity and difficulties with emotion regulation varied between college students and non-college adults. We found no evidence of moderation for any model paths, nor any moderated indirect effects, suggesting that college students and non-college adults did not substantially differ in terms of how affect intensity (either reactivity or intensity) and emotion dysregulation influenced drinking to cope.
4. Discussion This study contributes to our understanding of the relationship between affect intensity and drinking to cope in several ways. First, we replicated the finding that higher drinking to cope is associated with increased hazardous drinking in college students, (Kuntsche et al., 2005; Merrill & Thomas, 2013), and found a similar relationship in non-college adults. With few exceptions (e.g., Crutzen et al., 2013; Kim & Joen, 2012), including recent validation of the drinking motives measures adult samples (Crutzen & Kuntsche, 2013; Gilson et al., 2013), drinking motives have been studied primarily in adolescent and young adult samples (Kuntsche et al., 2005), with a particular focus on college students (Read et al., 2003). Thus, the inclusion of a non-college, adult sample is a strength of the current study, particularly as the pattern of results did not differ across samples. Second, we found the expected significant zero-order relationships among affect intensity, drinking to cope, and difficulties with emotion regulation. Specifically, stronger negative intensity was associated with greater deficits in emotion regulation, and with greater drinking to cope. Third, we found that the propensity to experience intense negative emotions indirectly predicted drinking to cope via deficits in emotional Clarity. These results dovetail with studies suggesting that individuals who regularly experience heightened negative affect and are poor at labeling specific negative emotions drink more (Kashdan et al., 2010), as well as evidence that alexithymic individuals may be prone to alcohol dependence (Thorberg, Young, Sullivan, & Lyvers, 2009) because they are poor at identifying specific emotions. These results suggest that treatment or prevention efforts focused on helping people
Table 1 Descriptive statistics and correlations among main study variables by sample.
1 2 3 4 5 6 7 8 9 10
AUDIT DMQR coping AIM negative intensity AIM negative reactivity DERS (lack of) strategies DERS non-acceptance DERS (lack of) goals DERS impulsivity DERS (lack of) awareness DERS (lack of) clarity
1
2
–
.47** – .27** .10* .46** .39** .26** .39** .07 .37**
.41** .07 .11* .19** .13** .13** .26** .14** .19**
3
4
.13 .30**
.02 .13 .66**
– .55** .43** .37** .33** .43** .01 .23**
– .15** .12** .28** .10* .21** .02
5
6
.13 .29** .54** .35** – .76** .51** .77** .11* .52**
.17 .30** .38** .19 .70** – .44** .68** .10* .51**
7
8
.02 .19 .40** .27** .59** .48** – .50** .00 .27**
.17 .26** .46** .27** .75** .58** .50** – .16** .50**
9
10
.02 .11 .05 .12 .34** .28** .23* .31** – .57**
.21* .36** .38** .21* .55** .54** .35** .58** .64** –
Mean (SD) College sample
Online sample
8.75 2.08 3.04 3.64 2.10 2.10 2.86 1.89 2.56 2.16
5.70 1.97 3.04 3.83 2.06 1.98 2.79 1.78 2.46 1.95
(5.90) (.91) (.84) (.84) (.87) (87) (57) (.77) (.77) (.71)
(5.99) (.98) (.93) (.94) (.79) (.79) (.60) (.78) (.77) (.72)
t-Test 4.75** 1.10 .03 1.99* .37 1.28 1.23 1.36 1.26 2.65*
College students reported below the diagonal, non-college adults above the diagonal. AUDIT = Alcohol Use Disorders Identification Test; DMQR = Drinking Motives Questionnaire Revised; AIM = Affect Intensity Measure; DERS = Difficulties with Emotion Regulation Scale. * p < .05. ** p < .01.
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(Lack of) Strategies
Non-Acceptance
(Lack of ) Goals Negative Intensity
Impulsivity
.02(.07)
Drinking to Cope
(Lack of) Awareness (Lack of) Clarity
-.11(.05)** Fig. 1. DERS facets as parallel multiple mediator of the negative intensity and drinking to cope relationship, controlling for negative reactivity and other drinking motives.
Table 2 Indirect effects of negative affect intensity on drinking to cope through facets of the DERS. Indirect regression coefficient (SE)
BC 95% bootstrapped CI Lower
(lack of) Strategies Non-acceptance (Lack of) goal directed behavior Impulsivity (lack of) Awareness (lack of) Clarity Total indirect effect of all mediators
*
.13 (.03) .001 (.02) .01 (.01) .001 (.03) .001 (.001) .03 (.01)* .16 (.04)*
.07 .03 .03 .05 .001 .01 .11
Upper .19 .04 .01 .07 .01 .07 .21
* Confidence intervals do not include 0. BC = bias corrected. CI = Confidence interval; DERS = Difficulties with Emotion Regulation.
clarify and differentiate among their feelings might particularly help those with a propensity for experiencing intense negative emotions, and thus potentially reduce drinking as a coping mechanism (Kashdan et al., 2010). In addition, we found that negative affect intensity indirectly influenced drinking to cope via lack of Strategies for emotion regulation. Lack of Clarity and limited strategies were likewise the two facets of emotion dysregulation to predict binge eating (Whiteside et al., 2007), another behavior thought to serve as a maladaptive coping strategy. Without effective regulation strategies, individuals who experience emotions intensely are likely to become easily overwhelmed by their emotional experience. As negative emotions are thought to narrow attention and reduce breadth of thinking (Friedman & Förster, 2010), particularly for motivationally intense negative emotions (Gable & Harmon-Jones, 2010), individuals may reach for any nearby available strategy to alleviate distress. These results suggest that those who drink and are prone to emotional intensity may benefit from over-learning multiple regulation strategies so that other options come readily to mind during distressing situations. Surprisingly, we found no effect of negative reactivity on drinking to cope, either directly or indirectly via emotion dysregulation. This may be due to the significant correlations between negative reactivity and negative intensity (see Table 1), whereby negative
reactivity is not a unique predictor above and beyond intensity. Indeed, an alternative measure of emotional reactivity not included in the current study (Nock, Wedig, Holmberg, & Hooley, 2008) emphasizes intensity as well as responsiveness to emotional situations and emotional volatility, suggesting that these constructs are closely related, where intensity may represent the stronger and more distinct component. The results of the current study should be considered in light of limitations to the study design. First, all data were collected online, which raises questions of generalizability to non-internet collection methods, although experts indicate similar results in data collected via lab and the internet (Birnbaum, 2004), and studies have found that mTurk provides samples of at least equal quality to traditional college student convenience samples (Buhrmester, Kwang, & Gosling, 2011). The cross-sectional nature of the data collection limits our ability to test prospective or causal relations among variables. However, as aptly described by Hayes (2013), the lack of experimental or prospective data does not diminish the validity of testing moderation and mediation relationships in simultaneously collected correlational data, as no model (including experimental designs) will fully or accurately model the relationships between variables, and these kind of analyses and models can provide the necessary background for designing more tightly controlled causal studies. These constructs are individual difference measures, and thus do not necessarily speak to the dynamic processes that may occur for people in emotional states. Alternate data collection processes, such as ecological momentary assessment, are required to see if people in intense negative states show reduction in emotional clarity or restricted access to emotion regulation strategies in the moment. Moreover, experimental laboratory designs are ideal to test whether or not coping-motivated drinking or alcohol consumption will decrease if someone in an intense negative emotional state is taught to clarify the specific emotion they are experiencing or is given access to alternative coping strategies. These prospective, dynamic designs would extend the current study by providing clear and concrete treatment strategies for use in clinical practice. Ultimately, although testing the model in a prospective design will yield important information about the relationship between these constructs, it may be that individuals prone to experiencing
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strong or intense negative emotions are qualitatively different from other individuals, such that individual difference factors may moderate dynamic processes. In short, it may be that biologically-based propensities to experience negative affect intensity or reactivity are instrumental in determining momentary contextual responses to emotionally evocative situations (Linehan, 1993). Moreover, although theoretically affect intensity is conceptualized as a temperament factor (Linehan, 1993), emotion dysregulation may reinforce and increase affect intensity over time, which may iteratively influence the effect of these variables on drinking to cope. All of these are questions ripe for future research. Finally, as with any individual difference study, important variables may have been neglected from analyses; we only accounted for roughly half of the variability in drinking to cope, which suggests additional research is needed to determine other predictive factors. Other affective variables known to influence both drinking and drinking to cope include anxiety sensitivity (Howell, Leyro, Hogan, Buckner, & Zvolensky, 2010; Stewart, Zvolensky, & Eifert, 2002), distress tolerance (Howell et al., 2010), alexithymia (Stewart et al., 2002; Thorberg et al., 2009) and cognitive expectancies regarding emotion dysregulation (Thorberg & Lyvers, 2006). Future studies may wish to examine the contribution of these variables in addition to affect intensity and emotion dysregulation. In conclusion, the current study provides a novel test of the relationship between temperamental affective factors, difficulties in emotion regulation and drinking to cope among both college and non-college adults using bootstrapping analyses of indirect effects (Preacher & Hayes, 2008) in a multiple mediator model. We found evidence for a direct effect of negative intensity on drinking to cope, such that higher negative intensity predicts greater coping-motivated drinking, and that this relation is partially mediated by problems with emotional Clarity and limited regulation Strategies. Importantly, these results were not moderated by sample, suggesting similar patterns of relations among college and noncollege adults. References Babor, T. F., Higgins-Biddle, J. C., Saunders, J. B., & Monteiro, M. G. (2001). AUDIT— The alcohol use disorders identification test—Guidelines for use in primary care (2nd ed.). Geneva, Switzerland: World Health Organization. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Birnbaum, M. H. (2004). Human research and data collection via the internet. Annual Reviews of Psychology, 55, 803–832. http://dx.doi.org/10.1146/ annurev.psych.55.090902.141601. Bonn-Miller, M., Vujanovic, A. A., & Zvolensky, M. J. (2008). Emotion dysregulation: Association with coping-oriented marijuana use motives among current marijuana users. Substance Use & Misuse, 43, 1656–1668. Bryant, F. B., Yarnold, P. R., & Grimm, L. G. (1996). Toward a measurement model of the affect intensity measure: A three-factor structure. Journal of Research in Personality, 30(2), 223–247. Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6, 3–5. http://dx.doi.org/10.1177/1745691610393980. Cheetham, A., Allen, N. B., Yucel, M., & Lubman, D. I. (2010). The role of affective dysregulation in drug addiction. Clinical Psychology Review, 30, 621–634. http:// dx.doi.org/10.1016/j.cpr.2010.04.005. Cooper, M. L. (1994). Motivations for alcohol use among adolescents: Development and validation of a four factor model. Psychological Assessment, 6(2), 117–128. Cooper, M. L., Agocha, V. B., & Sheldon, M. S. (2000). A motivational perspective on risky behaviors: The role of personality and affect regulatory processes. Journal of Personality, 68, 1059–1088. http://dx.doi.org/10.1111/1467-6494.00126. Cooper, M. L., Frone, M. R., Russell, M., & Mudar, P. (1995). Drinking to regulate positive and negative emotions: A motivational model of alcohol use. Journal of Personality and Social Psychology, 6(5), 990–1005. Crutzen, R., & Kuntsche, E. (2013). Validation of the four-dimensional structure of drinking motives among adults. European Addiction Research, 19, 222–226. http://dx.doi.org/10.1159/000345457. Crutzen, R., Kuntsche, E., & Schelleman-Offermans, K. (2013). Drinking motives and drinking behavior over time: A full cross-lagged panel study among adults.
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