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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: J Psychopathol Behav Assess. 2019 Dec 10;42:93–100. doi: 10.1007/s10862-019-09773-8

Emotion Regulation Flexibility in Generalized Anxiety Disorder

Kristina Conroy 1, Joshua E Curtiss 2, Abigail L Barthel 2, Rebecca Lubin 3, Sarah Wieman 4, Eric Bui 4, Naomi M Simon 3, Stefan G Hofmann 2
PMCID: PMC7357573  NIHMSID: NIHMS1546217  PMID: 32661451

Abstract

Background:

People differ in their affective styles, which refers to habitual use of emotion regulation (ER) strategies. Previous research has shown that mental health is associated with an individual’s adaptive flexibility of emotion regulation strategies rather than any one particular ER strategy.

Methods:

The present study employed a person-centered approach using latent profile analyses to distinguish patients with generalized anxiety disorder based on their responses on an affective styles measure.

Results:

Results of the latent profile analysis supported a three-class solution. Class 1 (26% of participants) identified individuals with the lowest scores of each affective style; class 2 (10%) included individuals with the highest scores of each style; and class 3 (64%) consisted of individuals who scored in the mid-range of each affective style. Greater ER flexibility was associated with better emotional functioning and quality of life.

Conclusions:

Patients with GAD differ in ER flexibility. The vast majority of patients appear to have only moderate or low ER flexibility. Those individuals with high ER flexibility show a greater quality of life and less emotional distress.

Keywords: Emotion Regulation, Anxiety, Generalized Anxiety Disorder, Latent Profile Analysis

Introduction

Generalized Anxiety Disorder (GAD) is a highly prevalent and impairing disorder characterized by excessive, uncontrollable worries about one or more areas such as: work, school, finances, daily tasks, interpersonal relationships, or the health of oneself or others. Those who experience GAD report significant physical symptoms that co-occur with their worrying. These symptoms may include muscle tension, restlessness, trouble sleeping, irritability or feeling on edge, fatigue, and difficulty concentrating. Lifetime risk for GAD is 9%, with 12-month prevalence rates for adults in the United States at just under 3% (Kessler et al., 2012). Furthermore, women are twice as likely to be diagnosed with GAD than men (Seedat et al., 2009; Vesga-Lopez et al., 2008). Given its chronicity and infrequent remission (Bruce et al., 2005; Yonkers et al., 2003), GAD is associated with significant distress and impairment and poor quality of life (Comer et al., 2011). GAD is also commonly comorbid with other anxiety and depressive disorders (Brown et al., 2001; Curtiss & Klemanski, 2015; Curtiss & Klemanski, 2016; Grant et al., 2005; Hunt et al., 2002; Brawman-Mintzer et al., 1993); however, research shows that GAD is associated with disability and distress irrespective of these comorbidities (Kessler et al., 2002a; Kessler et al., 2002b).

Efforts to explore the etiology and maintenance of GAD and other anxiety disorders have pointed to significant deficits in the understanding and use of emotion regulation (ER) strategies (Cisler et al., 2010; Mennin et al., 2005; Aldao & Nolen-Hoeksema, 2010). Furthermore, both inter- and intrapersonal differences in ER have been associated with a variety of clinical outcomes such as anxiety and depression severity, quality of life, as well as risk for other forms of psychopathology such as substance use disorders, post-traumatic stress disorder (PTSD), and borderline personality disorder (BPD; Comer et al., 2011; Marroquín, 2011; Dingle et al., 2018; Dixon-Gordon et al., 2018; Cloitre et al., 2005; Aldao & Nolen-Hoeksema, 2010; Barthel et al., 2018; Hofmann et al., 2016).

In mood and anxiety disorders specifically, Hofmann and colleagues (2012) proposed an emotion dysregulation model such that a trigger circumstance interacts with one’s predisposition to evoke positive or negative affect. One’s affective response depends on the individual’s affective style, with anxiety disorders being characterized by negative affect dysregulation in addition to low positive affect (Hofmann et al., 2012). According to this model, treatment of mood and anxiety disorders should target emotion dysregulation, increase positive affect while lowering negative affect, and educate patients on the use of adaptive affective styles (Hofmann et al., 2012; Mennin et al., 2005). There is also evidence to suggest that people with mood and anxiety disorders, including GAD, tend to improperly engage or employ interpersonal ER strategies which further maintain symptoms and distress (Hofmann, 2014; for review, see Barthel et al., 2018). It has further been suggested that mental health difficulties are associated with a deficiency in adaptive flexibility of ER strategies rather than any particular ER strategy (Aldao & Nolen-Hoeksema, 2012; Gupta & Bonanno, 2011; Kashdan & Rottenberg, 2010; Westphal et al., 2010) because this flexibility enables a person to recognize and adapt ER strategies to specific situational demands. In GAD, inflexibility may be expressed as excessive worrying and deficits in emotional processing (McLaughlin et al., 2007; Mennin et al., 2007). As such, intra- and interpersonal ER models highlight the inefficiency and deficits in employing adaptive skills to manage emotions in GAD, which can cause and maintain anxiety and worry behaviors.

Given the literature on the vast individual differences in affective styles and ER, especially in anxiety disorders, it is optimal to explore these nuanced differences according to an idiographic, person-centered approach. GAD is a particularly suitable candidate for person-centered analyses given its broad and variable symptoms and associated treatment targets (Craske & Waters, 2005). Latent Profile Analysis (LPA) allows for the identification of underlying subgroups within a cohort according to specified parameters, and is thus ideal for capturing the heterogeneity of ER. To our knowledge, this is the first study to examine ER profiles in adults with GAD using person-centered analyses. Furthermore, understanding subgroups of GAD based on ER profiles has the potential to yield important clinically relevant guidance for precision medicine and individualized treatment approaches. Despite its suitability to elucidate individual differences, LPA has been employed relatively infrequently within the ER literature. Turpyn and colleagues (2015) used LPA to differentiate adolescents’ emotion expression, experiences, and physiological attributes. Results revealed four distinct ER profiles: 1) suppression; 2) low reactivity; 3) high reactivity; and 4) moderate expression and reactivity. Each of these profiles were differentially associated with symptoms of anxiety, depression, or conduct disorder symptoms (Turpyn et al., 2015). Thus, there is precedence for using LPA to characterize ER differences as well as to relate these nuances to distal outcomes relevant to psychopathology and behavior.

The present study focused on two aims: 1) to identify subgroup flexibility profiles of affective styles in a clinical sample of patients with GAD; and 2) to examine whether these profiles differentially relate to anxiety and depression symptom severity and quality of life. Given the diverse affective symptomology found within GAD, we hypothesized that meaningful subgroups of individuals with different affective styles as defined by their adaptive flexibility of ER strategies. Further, we hypothesized that these classes will be differentially associated with symptoms of anxiety, depression, and quality of life. In response to the shared vulnerability of emotion dysregulation across GAD and major depressive disorder, differential prediction between anxiety and depressive severity was of particular interest (Curtiss & Klemanski, 2016). Quality of life was included to capture relevant differences in impairment outside of symptomology associated with affective styles profiles.

Methods

Participants and Procedures

Participants were adult patients (n=203) at baseline in a clinical trial (R01AT007257; NCT01912287). The study was approved by the Institutional Review Boards at three academic institutions (Boston University, Massachusetts General Hospital and NYU Langone Health), and recruitment and enrollment occurred in the greater Boston area. Informed consent was obtained from all individual participants included in the study. Participants were the first 203 eligible study subjects who completed baseline assessments for the trial. All data were collected prior to randomization, and thus, present analyses were unaffected by treatment condition. They were between the ages of 18 and 80 (M age=32.04, SD= 14.07) and the majority were women (69.5%). Reported racial backgrounds were White/Caucasian (76.4%), Black or African American (5.4%), Asian (7.9%), Native Hawaiian or Other Pacific Islander (0.5%), “Other” (5.4%), and more than one race (2.5%); further, 12.8% of participants identified as Hispanic/Latino.

Eligibility criteria for the clinical trial included a primary diagnosis of GAD based on DSM-5 criteria by the use of the Structured Clinical Interview for DSM-IV (SCID)1 at the Massachusetts General Hospital site and the Anxiety and Related Disorders Interview Schedule for DSM-5 (ADIS-5) at the Boston University site (First et al., 2002; Brown & Barlow, 2014). The diagnostic assessments used reflect minor difference in routine screening procedures at each site. Both interviews have been shown to have adequate validity in the assessment of GAD (Dugas & Charette, 2018). Comorbid substance use disorder, post-traumatic stress disorder, eating disorder, organic mental disorder, or significant suicidal ideation were exclusionary. Additionally, psychotic disorder, bipolar disorder, or developmental disorders were excluded. Participants with current pharmacotherapy were required to remain on a stabilized dose for a minimum of 6 weeks before baseline assessment. Furthermore, participants were required to have limited experience with Cognitive Behavioral Therapy (CBT) and Yoga. Eligible participants completed a battery of self-report questionnaires at baseline.

Measures

Affective Styles

The 20-item Affective Styles Questionnaire (ASQ) has been validated for measuring individual differences in ER in clinical and nonclinical samples (Hofmann & Kashdan, 2010; Totzeck et al., 2018). It has 3 subscales that align with the three affective styles: 1) concealing, suppressing negative emotions; 2) tolerating, bearing through negative emotions; and 3) adjusting, flexibly regulating emotions to respond adaptively to contextual factors. The subscales have demonstrated good internal consistency with Cronbach’s alphas .84, .82, and .68 for concealing, adjusting, and tolerating, respectively (Hofmann & Kashdan, 2010).

Anxiety and Depression Severity

Anxiety severity was measured using the 21-item Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988). Participants rated their responses on a 4-point Likert scale. This instrument has shown high internal consistency (Cronbach’s alpha= .92) and test-retest reliability (.75) in psychiatric populations (Beck, 1988). Depression severity was measured using the 21-item Beck Depression Inventory- Version II (BDI-II) (Beck, Steer, & Brown, 1996). The BDI-II has been shown to have good internal consistency (Cronbach’s alpha= .92) and test-retest reliability (.93).

Quality of Life

Quality of life (QoL) was measured using the 26-item Quality of Life Scale (WHOQOL-BREF), which demonstrates good psychometric properties (WHOQOL Group, 1998). The WHOQOL-BREF assesses QoL across four domains: environmental (Cronbach’s alpha= .80), psychological (Cronbach’s alpha= .75), physical (Cronbach’s alpha= .82), and social (Cronbach’s alpha= .66). The subscales had poor to good internal consistency in the current study. Each item is rated on a 5-point Likert scale with higher ratings indicating higher quality of life.

Data Analysis

Statistical analyses were conducted using MPlus Version 8.2 (Muthen & Muthen, 1998–2018) using Robust Maximum Likelihood estimation. Classes were added iteratively, and model testing was conducted by comparing a k-class model against a k-1 class model. Model fit was determined based on several indicators. The Lo-Mendell-Rubin Adjusted Likelihood Ratio Test (LRT) was used to ascertain the number of latent profiles based on the pattern of means of the three subscales on the ASQ in our sample (Lo et al., 2001; Vuong, 1989). Information criteria such as Akaike’s Information Criterion (AIC; Akaike, 1973, 1974), Bayesian information criterion (BIC, Schwarz, 1978), and the Adjusted BIC (Sclove, 1987) were considered with lower values indicating superior model fit. Lastly, entropy values provided information on the proportion of participants who were correctly classified in each iteration (Clark & Muthén, 2009), with entropy levels above 80% being considered as excellent. In separate analyses, we used the Mplus auxiliary BCH command (Dziak et al. 2016) to test the equality of means across latent classes on distal outcomes (anxiety and depression symptom severity and quality of life).

Results

The bivariate correlations and descriptive statistics for all variables are shown in Table 1. Of note, all three of the subscales of the ASQ were positively associated with each other, and these correlations were moderate in magnitude.

Table 1.

Descriptive Statistics and Bivariate Correlations

Variable M(SD) 1 2 3 4 5 6 7 8 9
1. ASQ Conceal 22.33 (8.50) -
2. ASQ Adjust 15.14 (6.18) .56** -
3. ASQ Tolerate 12.90 (5.04) .55** .77** -
4. WHOQOL Physical Health 23.48 (6.41) .32** .49** .55** -
5. WHOQOL Psychological 15.96 (6.28) .21** .35** .41** .66** -
6. WHOQOL Social Relationships 8.55 (4.28) .28** .49** .41** .60** .50** -
7. WHOQOL Environment 27.04 (7.83) .37** .44** .55** .74** .68** .59** -
8. BDI-II 15.89 (10.61) .14* −.10 −.05 −.27** −.31** −0.25** −.15** -
9. BAI 15.91 (10.14) .23** .06 .14 .04 .02 .07 .10 .38** -
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N=203.

*

p<0.05,

**

p<0.01

Latent Profile Analysis

Results of the analysis supported a three-class solution (AIC = 3510.057, BIC = 3556.092, Adjusted BIC = 3511.740, Entropy = 0.736), which exhibited the best balance of good fit indices and parsimony (i.e., having fewer interpretable classes). None of the test values for the LRTs were statistically significant. However, the LRT comparing the 3-class to the 2-class solution was marginally significant (p = .056). Regarding the information criteria, the 5-class solution exhibited the lowest AIC and BIC values; however, these classes were not interpretable and some classes contained a very small percentage of the sample. In light of this, the 3-class solution was preferred, as it contained the second lowest AIC and BIC values and had interpretable class solutions. Table 2 provides the fit statistics for the class solutions 1 through 5.

Table 2.

Goodness of fit statistics from Latent Profile Analysis

Number of Classes
Fit Statistics 1 2 3 4 5
AIC 3609.007 3546.623 3510.057 3511.238 3507.782
BIC 3628.737 3579.506 3556.092 3570.426 3580.124
Adjusted BIC 3609.729 3547.826 3511.74 3513.402 3510.428
Entropy ----------- 0.752 0.736 0.646 0.751
LRT Value ----------- 67.207 42.555 6.511 8.147
LRT P-Value ---------- 0.1565 0.0562 0.5302 0.2759
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Note. AIC= Akaike Information Criterion, BIC= Bayesian Information Criterion.

The Entropy value of the 3-class solution was satisfactory and indicated that 73.6% of the participants were correctly classified into the 3 classes. Class 1 (n = 51) consisted of 26% of the participants and contained individuals with the lowest scores of each of the three subscales (labeled as “low ER flexibility”). Class 3 (n = 127) consisted of 64% of the participants and contained individuals who scored in the mid-range of each of the three ASQ subscales (labeled as “moderate ER flexibility”). Class 2 (n = 20) consisted of 10% of the participants and contained individuals with the highest scores of each subscale (labeled as “high ER flexibility”). Figure 1 shows the subscale means across the 3 classes.

Figure 1.

Figure 1.

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Three Latent Classes by Patterns of Means on Affective Styles Note. This figure displays the three latent classes by patterns of means on affective style subscales. The three classes have been labeled Low ER flexibility (n=51), High ER flexibility (n=20), and Moderate ER flexibility (n=127) based on their respective patterns of means.

Class Associations with Anxiety, Depression, and Quality of Life

Based on this 3-class solution, we assessed whether different capacities of ER were differentially associated with distal outcome variables. Findings revealed that classes differed significantly in anxiety severity, depression severity, and QoL. Specifically, lower ER flexibility (Class 1) was associated with the greatest anxiety severity (χ2= 11.026, p=.004), greatest depression severity (χ2=7.133, p=.028), and poorest QoL in the environmental (χ2= 8.400, p=.015), psychological (χ2= 13.956, p=0.001), and social (χ2=7.931, p=.019) domains. In contrast, higher ER capacity (Class 2) was associated with the least severe symptoms and best QoL. Notably, QoL in the physical domain did not follow the same pattern; moderate ER flexibility (Class 3) was associated with the highest physical QoL and lower ER flexibility (Class 1) was associated with the lowest physical QoL (χ2=8.123, p=.017). Table 3 depicts the distal outcomes across the 3 classes.

Table 3.

Mean comparisons between latent classes on anxiety, depression, and quality of life outcomes

Raw Scores
Class 1: Low ER flexibility (n=51) Class 3: Moderate ER flexibility (n=127) Class 2: High ER flexibility (n=20)
Anxiety symptoms (BAI) 17.596a 16.851a 10.022b
Depression symptoms (BDI) 19.710a 15.010b 11.604b
Quality of Life – Environment (WHOQOL-BREF) 25.828a 28.919b 28.945ab
Quality of Life - Social Relationships (WHOQOL-BREF) 8.065a 9.326b 10.629b
Quality of Life – Psychological (WHOQOL-BREF) 15.108a 17.662b 18.976b
Quality of Life - Physical Health (WHOQOL-BREF) 22.297a 24.946b 24.812ab
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Note. Means sharing a subscript in a row indicate that the means are not significantly different from each other.

Discussion

The current study found three distinct classes of individuals based on the affective style subscales of the ASQ. As predicted, the profiles did not distinguish between the original three affective styles (concealing, tolerating, and adjusting) but rather classified as high, low, or moderate on ER flexibility as a whole. This is also consistent with the pattern of moderate, positive associations between the three affective styles, suggesting that there might be a broad tendency to use any given ER strategy irrespective of whether it is adaptive or maladaptive. Furthermore, one out of every four participants was classified in the low ER flexibility profile, and the vast majority of participants (90%) fell between the low and moderate ER flexibility groups. In short, almost all participants with GAD had at least moderate difficulty with ER flexibility. Consistent with variable-centered analyses on the relationship between ER flexibility and emotional health (Aldao & Nolen-Hoeksema, 2012; Gupta & Bonanno, 2011; Kashdan & Rottenberg, 2010; Westphal et al., 2010), we found that low and moderate ER flexibility was associated with more anxiety and depression symptom severity and lower quality of life compared to the high ER flexibility group. This finding should be interpreted in light of the psychometric characteristics of the instruments; specifically, the quality of life measure had modest internal consistency in the current study. Nevertheless, this is the first study to extend these findings to a sample of adults with GAD, implying that in the context of GAD, individuals with a greater repertoire of ER strategies (higher levels of adaptive and maladaptive strategies) seem better able to shift responses based on contextual demands (Aldao et al., 2015).

These findings have several important implications for the advancement of ER research. Whereas previous work has dwelled upon the examination of singular ER strategies (e.g., appraisal and suppression) and their association with various psychosocial outcomes (Gross, 2015; Aldao et al., 2010), more recently researchers have devoted attention to ER flexibility, which seeks to evaluate how individuals employ a range of strategies based on specific environmental demands (Aldao et al., 2015; Bonanno & Burton, 2013). This more nuanced approach encourages researchers to consider how the adaptiveness of ER strategies may vary based on context (Gross, 2015). In particular, concealment of negative emotion might be associated with positive psychosocial outcomes when paired with other ER strategies. Our findings are consistent with Kashdan and Rottenberg’s (2010) contention that emotional flexibility is a crucial determinant in mental health. The results are also consistent with the work conducted by Bonanno and colleagues, which has suggested reconsidering the maladaptive label prescribed to suppression of emotions, showing that flexible suppression predicted long-term adjustment (Bonanno et al., 2004) and protected against high levels of cumulative life stress (Westphal et al., 2010).

Lastly, this study expands upon recent efforts to personalize treatments and maximize efficacy for individuals with GAD (Fischer, 2015; Schneider, Arch, & Wolitsky-Taylor, 2015). Careful screening of patients’ unique ER strategies prior to the initiation of treatment has the potential to guide clinicians in helping their clients develop a more diverse repertoire of strategies. Routine outcome monitoring may equip clinicians to be more flexible in fostering discussion around which strategy may be the most adaptive based on contextual demands and client goals. However, more work is needed to develop scalable tools for clinicians that increase the feasibility of implementing this idiographic approach in routine care.

Several limitations merit mention. First, due to the cross-sectional nature of the current design, we cannot draw causal inferences between ER flexibility, quality of life, and anxiety and depression symptoms. The distal outcomes referred to in this study are conceptual in nature and do not yield any information on the temporal nature of these associations. More work is needed to examine how profile memberships shift over time, particularly in the context of treatment. Second, these results require replication in larger samples and across other emotional disorders. We do not suggest that the three latent classes that emerged in the current study will persist across diagnostic categories. However, the current study adds utility within the realm of GAD. Third, the present study relies on self-report data; future work should utilize a multi-method assessment of emotion by incorporating psychophysiological, behavioral, and neuroimaging data (Bradley & Lang, 2000; Buhle et al., 2014).

Taken together, future research may benefit from micro-analysis of ER strategies to capture moment-to-moment variability in ER strategies, and more broadly, should emphasize the evaluation of context (situational demands and goals) in delineating how specific ER strategies may be adaptively utilized or sequenced (Aldao et al., 2014). Despite these limitations, the present study elucidates the utility of evaluating patterns of ER strategies rather than restricting investigation to associations between specific ER strategies and clinical outcomes. The results suggest that ER flexibility is associated with greater emotional health and quality of life in individuals with GAD, and thus support the measurement of ER flexibility in adults with GAD and across other clinical samples. Moreover, these results suggest the importance of incorporating strategies to promote ER flexibility in psychotherapy in order to improve outcomes.

Funding:

This work was supported by the National Institute of Health/National Center for Complementary and Integrative Health (R01AT007257: PIs Simon and Hofmann).

Footnotes

Conflict of Interest: Dr. Hofmann receives support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition – Special Initiative, and the Department of the Army for work unrelated to the studies reported in this article. He receives compensation for his work as editor from SpringerNature and the Association for Psychological Science, and as an advisor from the Palo Alto Health Sciences and for his work as a Subject Matter Expert from John Wiley & Sons, Inc. and SilverCloud Health, Inc. He also receives royalties and payments for his editorial work from various publishers. Dr. Simon receives funding from the Patient Centered Outcomes Research Institute, Department of Defense, NIH, Highland Street Foundation, American Foundation for Suicide Prevention, and Janssen. She also receives compensation for her work as a speaker for the Massachusetts General Hospital Psychiatry Academy and her role as a consultant with Axovant Sciences, Springworks, Praxis Therapeutics, and Aptinyx. Her spouse has an equity stake in G1 Therapeutics. Dr. Bui receives royalties from Springer Nature for a textbook on Grief Reactions. Dr. Bui receives grant funding from the Elizabeth Dole Foundation, Patient Centered Outcomes Research Institute, NIH, and the Department of Defense.

All other authors declare they have no conflicts of interest.

1

Diagnostic criteria of GAD is identical in the SCID-IV and SCID-5.

Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee (this study was approved by the Institutional Review Boards at three academic institutions: Boston University, Massachusetts General Hospital and New York University Langone Health) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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