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. Author manuscript; available in PMC: 2020 Aug 6.
Published in final edited form as: Cognit Ther Res. 2012 May 5;37(2):363–367. doi: 10.1007/s10608-012-9463-6

An Examination of the Association of Distress Intolerance and Emotion Regulation with Avoidance

R Kathryn McHugh 1,2, Elizabeth K Reynolds 3, Teresa M Leyro 4, Michael W Otto 5
PMCID: PMC7410348  NIHMSID: NIHMS1031938  PMID: 32773909

Abstract

Distress intolerance is an important motivator of maladaptive avoidance-based coping strategies. The selection of such avoidance behaviors is also influenced by one’s access to alternative emotion regulatory strategies. However, little research has examined the relative contributions of these vulnerability factors to avoidance. This study examined whether distress intolerance and access to emotion regulation strategies were uniquely (additively or interactively) associated with self-reported avoidance. Two samples—an unselected sample (n = 300) and a clinical sample (n = 100)—comprised of patients seeking treatment for unipolar mood and/or anxiety disorders were administered measures of distress intolerance, emotion regulation, and avoidance. Results of linear regression analyses indicated that distress intolerance and access to emotion regulation strategies were uniquely and additively associated with avoidance. Implications for the prevention and treatment of psychological disorders are discussed.

Keywords: Distress intolerance, Distress tolerance, Emotion regulation, Avoidance, Anxiety disorders

Introduction

The perceived inability to tolerate negative somatic and emotional states (distress intolerance) is associated with maladaptive avoidance behaviors, such as substance use (Brown et al. 2009), procrastination (Harrington 2005), risk taking (MacPherson et al. 2010), and avoidance of trauma cues (Vujanovic et al. 2011a). Distress intolerance (DI) may motivate these behaviors that serve as strong, proximal reinforcers to quickly remove distressing states that are interpreted as dangerous or unmanageable. The selection of avoidance strategies for coping is also influenced by the degree to which one has alternative strategies for distress reduction. Indeed, the presence of available strategies for regulating negative emotions (emotion regulation repertoire; ERR) is associated with less anxiety and depression as well as fewer maladaptive avoidance behaviors such as substance use and self-injury (Gonzalez et al. 2009; Gratz and Tull 2010). Hence, the selection of maladaptive avoidance strategies should depend on the degree of DI (e.g., the motivation for quick action should distress occur) as well as the range of emotion regulation skills that can take the place of maladaptive avoidance. As such, the aim of the current study was to examine the nature of the association between these processes and avoidance.

Although both emotion regulation (Bradley et al. 2011) and DI (Leyro et al. 2011; Simons and Gaher 2005) have demonstrated discriminant validity from negative affect and distress, few studies have specifically attempted to distinguish DI from emotion regulation or to examine whether these vulnerability factors contribute uniquely to avoidance behaviors. Although there are many operational definitions of emotion regulation in the literature, here we refer to explicit emotion regulation skills. This skill repertoire (ERR) is thus differentiated from the perception of distress as something that is intolerable (DI). Studies have found that DI and ERR incrementally predict coping motives for alcohol use (Vujanovic et al. 2011b); however, these associations have yet to be examined relative to avoidance specifically. In particular, studies are needed to examine whether DI and ERR act in either an additive or interactive fashion to confer a greater likelihood of engaging in avoidance behaviors. In other words, it is currently unclear whether these constructs would independently be associated with avoidance or whether they would exhibit a multiplicative influence on avoidance (e.g., someone with both vulnerability factors exhibits more avoidance than would be expected by the combined contribution of each individual factor).

In this study, we examined the association between DI and ERR and experiential avoidance in a secondary analysis in two large samples. Experiential avoidance refers to the trait-level tendency to avoid a wide range of uncomfortable experiences and thus captures a broad range of avoidance experiences and behaviors and allows for assessment of the heterogeneous ways in which avoidance may manifest. Self-report of experiential avoidance is associated with behavioral avoidance (Cochrane et al. 2007) and is linked to a range of poor outcomes, such as heightened response to fear provocation (Feldner et al. 2003). Although it is likely that the negative reinforcement afforded by avoidance may yield greater DI or prevent learning of alternative regulation strategies, we examined avoidance as an outcome given the perspective that avoidance may be a common consequence of both the interpretation of distress as intolerable and the absence of skills for regulating distress, thus yielding a strong action tendency to remove the distress as quickly as possible. Specifically, we examined (1) whether DI and ERR were positively associated with avoidance and (2) whether these variables provided incremental (additive or interactive) prediction of avoidance when considered together in both a clinical and unselected sample. We examined these relationships in both clinical and non-clinical samples due to the presumed importance of these variables for both the development of psychological disorders and the maintenance of disorders once developed (e.g., Leyro et al. 2010). We hypothesized that these risk factors would be uniquely and incrementally associated with avoidance in both samples.

Methods

Participants

Two samples were recruited for related studies of the measurement of DI. An unselected community sample consisted of adults who responded to web and print advertisements for a web-based study on stress. Of those who responded, 300 (75 % female) completed all study procedures. The mean age of the sample was 35.6 years (SD = 13.9) and the majority of the sample self-reported race as White (82 %) and non-Hispanic (95 %). A college-level education or higher was reported by 72 % of the sample. The second sample consisted of participants recruited from an outpatient clinic providing treatment for anxiety and mood disorders. In this sample, 100 participants (62 % female) completed all study procedures. The mean age of the sample was 31.4 years (SD = 11.7) and the majority of the sample self-reported race as White (90 %) and non-Hispanic (92 %). The sample was highly educated with 68 % reporting at least a college education. All participants had received a principle diagnosis of a unipolar mood or anxiety disorder based on a semi-structured clinical interview. All diagnoses were confirmed in a consensus meeting under the supervision of a licensed psychologist. See McHugh and Otto (in press) for more details on sample composition and inclusion/exclusion criteria.

Procedures

All study procedures were conducted using web-based data collection software (SurveyMonkey). Interested participants were provided with an identification number and instructions for accessing the study website. Informed consent was administered within the program, followed by a battery of self-report measures. Upon completion of procedures, participants were entered into a raffle for monetary prizes. All study procedures received Institutional Review Board approval.

Measures

The Acceptance and Action Questionnaire (AAQ; Hayes et al. 2004) is a 9 item self-report measure of experiential avoidance (sample item: When I feel depressed or anxious, I am unable to take care of my responsibilities). Items are rated on a 7-point Likert-type scale (1 = never true, 7 = always true) and scores range from 9 to 63. The AAQ has demonstrated adequate retest and internal consistency reliability in large clinical and non-clinical samples (Hayes et al. 2004) and decreases in AAQ scores are associated with treatment response (Hayes et al. 2010). The AAQ has also demonstrated strong concordance with behavioral and electrophysiological response to emotional stimuli (Cochrane et al. 2007) and has been associated with a range of negative outcomes, such as greater reactivity to fear exposure (Feldner et al. 2003) and depressive and anxiety symptoms related to health (Andrew and Dulin 2007). Internal consistency for the AAQ in both samples was adequate (α = .71 and .68).

The Distress Intolerance Index (DII; McHugh and Otto in press) is a 10 item self-report measure of DI that was developed based on a refinement of three measures of DI, the Anxiety Sensitivity Index (Peterson and Reiss 1992), the Distress Tolerance Scale (Simons and Gaher 2005) and the Frustration Discomfort Scale (Harrington 2005) (sample item: I can’t handle feeling distressed or upset). Items maintained the scales from the original measures and standardized scores were used to calculate a total score. The DII has demonstrated strong internal consistency reliability (α = .92; McHugh and Otto in press) and construct validity relative to behavioral measures of DI in clinical and healthy samples (McHugh and Otto 2011). The current samples evidenced strong internal consistency (α = .91 and .92). Possible scores reflect a sum of the 10 standardized item scores.

The Difficulties in Emotion Regulation Scale (DERS; Gratz and Roemer 2004) is a 36-item self-report measure of emotion regulation. The DERS consists of several sub-scales evaluating different domains of emotion regulation. Items are rated on a Likert scale (1 = almost never to 5 = almost always). In this study the “access to emotion regulation strategies” scale (sample item: When I’m upset, I believe that there is nothing I can do to make myself feel better.) was used as a measure of ERR (higher scores on the DERS subscale reflect lower access to emotion regulation strategies). The DERS has demonstrated strong internal consistency and retest reliability and good construct validity (Gratz and Roemer 2004). The current samples evidenced strong internal consistency (α = .91 and .90). Scores on this subscale range from 1 to 5.

Data Analysis

Variables of interest were screened for skewness and univariate outliers. First, the two samples (clinical and unselected) were compared with respect to all three measures to evaluate whether these purported risk factors were higher in the clinical sample. Second, to determine the extent of the association between the study variables, correlation coefficients were calculated. Third, a linear regression was conducted regressing avoidance onto DI and ERR. Finally, to examine whether associations between DI, ERR, and avoidance were better characterized as additive or interactive, a DI X ERR interaction term was calculated and added in a second step of the regression models.

Results

No evidence of skewness or outlier values was identified. As indicated in Table 1, the clinical sample exhibited significantly greater scores on DI (t[398] = 7.16, p<.001), ERR (t[398] = 7.19, p< .001), and avoidance (t[398] = 8.96, p<.001) than the unselected sample. Correlations between age, gender, ERR, DI, and avoidance in both samples are presented in Table 2.

Table 1.

Descriptive statistics for study variables

Unselected sample Clinical sample
Mean SD Range Mean SD Range
DII −0.02 2.55 −5.4 to 8.5 0.09 2.73 −6.5 to 5.9
DERS 1.88 0.8 1–5 2.61 0.90 1–4.8
AAQ 31.83 7.41 17–56 39.28 6.53 21–54
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DII Distress Intolerance Index, DERS Difficulties with Emotion Regulation Scale strategies subscale, AAQ Acceptance and Action Questionnaire. DII scores are standardized values

Table 2.

Correlations among study variables

Age Sex DII DERS AAQ
Unselected sample (n = 300)
 Age 1.00
 Sex 0.02 1.00
 DII −0.17* −0.11 1.00
 DERS −0.24** −0.02 0.76** 1.00
 AAQ −0.09 −0.05 0.67** 0.71** 1.00
Clinical sample (n = 100)
 Age 1.00
 Sex .15 1.00
 DII −.05 −.02 1.00
 DERS −.19 −.03 .64** 1.00
 AAQ −.17 −.02 .69** .70** 1.00
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DII Distress Intolerance Index, DERS Difficulties with Emotion Regulation Scale strategies subscale, AAQ Acceptance and Action Questionnaire

*

p<.01,

**

p<.001

In Sample 1 (unselected), both DI (beta = .29, t = 5.29, p<.001) and ERR (beta = .50, t = 9.28, p<.001) were significantly associated with avoidance, predicting 55 % of the variance in avoidance. Similar results were found in Sample 2 (clinical), with significant, unique associations between DI (beta = .47, t = 5.81, p<.001) and ERR (beta = .40, t = 4.86, p<.001) and avoidance, with the model predicting 62 % of the variance in avoidance. The interaction term was added in a second step to both models. The interaction term was not significant in either the unselected (beta = –.05, t = −0.61, p = .54) or clinical samples (beta = −.04, t = −0.28, p = .78) and did not yield a significant improvement in model fit (R change 2<.01) . Examination of collinearity suggested no problems with multicollinearity in either sample.

Discussion

In two large samples, DI and deficits in the availability of emotion regulation strategies (ERR) were significantly and incrementally associated with experiential avoidance. This result provides further evidence that DI is distinct from ERR and suggests that both intolerance of distress and access to emotion regulation strategies contribute to avoidance. This effect appeared to be additive (i.e., greater avoidance as each risk factor increases) and not interactive(i.e., different associations between each risk factor and avoidance contingent on the value of the other factor).

Despite the presence of significantly elevated scores in the clinical sample relative to the unselected sample, the relationship among variables (i.e., the prediction of avoidance offered by the consideration of ERR and DI) remained the same. The consistency of these findings across both an outpatient sample (with current unipolar mood or anxiety disorders) and an unselected sample provides additional evidence for the utility of these constructs across a range of psychological functioning. One implication of this finding is that DI and ERR may serve as risk factors for both disorder onset and maintenance. By attending to both ERR and DI, clinicians may be able to refine interventions for both prevention and treatment of maladaptive avoidance responses. That is, if ERR and DI both contribute to avoidance tendencies, then each of these patterns can be targeted individually as part of emotional resiliency training. Both DI and ERR are modifiable with behavioral treatment (e.g., Bornovalova et al. in press; Linehan et al. 2006), and may mediate broader clinical change (Hinton et al. 2009; Neacsiu et al. 2010; Smits et al. 2004).

There are several limitations to the current study. First, this study relied exclusively on self-report measures and was cross-sectional in nature. Studies using behavioral indices of avoidance and examining prospective associations between DI, ERR and avoidance are needed to better understand the nature and temporal sequencing of these relationships. In considering the developmental origins of DI, repeated avoidance of distress inducing scenarios or avoidance of distress in the moment (e.g., escape, numbing), over time, could result in deficient development of an emotion regulation repertoire. Thus, when distress is encountered, few adaptive regulatory skills are available. Similarly, ERR is likely impacted by a range of interactive processes, such as genetic predisposition and parental attachment, that may be better understood within a developmental framework. Thus, research on the interactive nature of these variables developmentally is needed to further understand how they impact the development and maintenance of avoidance behaviors and psychological disorders. Second, all data were collected via a web-based system and thus the generalizability of these results to samples with lower levels of computer literacy is necessary. Replication in samples that differ with respect to racial/ethnic composition, education, and psychological disorders is needed to determine the generalizability of these results. Third, the internal consistency reliability of the AAQ was somewhat low, particularly in the clinical sample, and thus replication of these results using alternative measures is needed. Additionally, the scales used were highly correlated and the associations among these variables (e.g., potential higher-order relationships) have not been fully elucidated, highlighting the need for further research in this area. Finally, given evidence that self-report and behavioral measures of DI are not well-correlated, results may differ with use of behavioral measures of DI that measure behavioral persistence in the context of distress.

Both DI and emotion regulation have received much attention in the literature as important transdiagnostic factors. The importance of these risk factors is underscored by their ability to be modified with treatment and thus to serve as targets for both prevention and treatment interventions. The results of the current study provide further support for the conceptualization of DI and ERR as risk factors relevant across a range of psychological functioning. These findings further suggest that DI and ERR have an additive effect on avoidance, and thus targeting both processes may be of particular importance to the reduction of maladaptive avoidance patterns.

Contributor Information

R. Kathryn McHugh, Division of Alcohol and Drug Abuse, McLean Hospital, Proctor House 3 MS 222, Belmont, MA 02478, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.

Elizabeth K. Reynolds, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Teresa M. Leyro, Department of Psychiatry, University of California, San Francisco, CA, USA

Michael W. Otto, Department of Psychology, Boston University, Boston, MA, USA

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