Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms,Positive Reappraisal,and Repetitive Negative Thinking

Abstract

Frequent repetitive negative thinking and infrequent positive reappraisal use are theorized to increase risk for depression and anxiety. Yet, research has studied these regulatory strategies at the disorder level, ignoring the clinical heterogeneity and differential relations among their individual symptoms. In this study, we examined the associations among repetitive negative thinking, positive reappraisal, and individual symptoms of depression and anxiety disorders. Models of regularized partial-correlation networks were estimated using cross-sectional data from 468 participants. Results showed that repetitive negative thinking and positive reappraisal were differentially related to affective, cognitive, and somatic symptoms of depression and anxiety. Moreover, repetitive negative thinking was more central than positive reappraisal with stronger connections to individual symptoms. Finally, repetitive negative thinking was more important than positive reappraisal in connecting clusters of depression and anxiety symptoms. These findings cast light on potential pathways through which repetitive negative thinking and positive reappraisal may operate within depression and anxiety.

Keywords

depression anxiety emotion regulation repetitive negative thinking positive reappraisal network analysis open data

Depression and anxiety disorders are among the most prevalent, comorbid, and burdensome mental illnesses (Baxter, Scott, Vos, & Whiteford, 2013; Kessler & Bromet, 2013). It is therefore of paramount importance to identify the etiological and maintaining factors for these disorders to improve existing intervention strategies. Theories of depression and anxiety disorders have implicated emotion regulation difficulties in the onset and maintenance of these disorders (Hofmann, Sawyer, Fang, & Asnaani, 2012; Joormann, 2010; Mennin, Heimberg, Turk, & Fresco, 2006; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). Emotion regulation refers to a range of processes that influence the frequency, intensity, and duration of emotional experiences (Gross, 2014). In depression and anxiety disorders, difficulties occur in the use of specific emotion-regulation strategies to reduce the effect of negative emotional experiences (Campbell-Sills, Ellard, & Barlow, 2014; Dryman & Heimberg, 2018; Liu & Thompson, 2017).

Two regulatory strategies that play a prominent role in depression and anxiety disorders are the increased use of repetitive negative thinking and decreased use of positive reappraisal (Aldao, Nolen-Hoeksema, & Schweizer, 2010; Drost, van der Does, van Hemert, Penninx, & Spinhoven, 2014; Kivity & Huppert, 2018). Repetitive negative thinking refers to a transdiagnostic process of excessive thinking about negative topics that is passive and/or difficult to control (Mahoney, McEvoy, & Moulds, 2012; Watkins, 2008). Engaging in repetitive negative thinking is related to heightened emotional reactivity (Ruscio, Seitchik, Gentes, Jones, & Hallion, 2011), enhanced negative memory retrieval (Nolen-Hoeksema et al., 2008), and impaired stress recovery (Watkins, 2008). Positive reappraisal has been defined as cognitively reframing the meaning of a distressing event in a less negative or more positive manner to minimize its emotional impact (Garnefski & Kraaij, 2007; Gross, 2014). Decreased positive reappraisal use may prevent individuals with elevated depression or anxiety levels from obtaining beneficial outcomes associated with this strategy, such as increased positive and decreased negative emotions (Gross & John, 2003), more benign interpretations of ambiguity (Everaert et al., 2017), and better stress recovery (Jamieson, Nock, & Mendes, 2012). In sum, extensive research indicates that the use of repetitive negative thinking and less frequent use of positive reappraisal are associated with critical affective, cognitive, and somatic aspects of depression and anxiety disorders.

To date, the pathways through which repetitive negative thinking and positive reappraisal are related to individual symptoms of depression and anxiety remain elusive. Prior research has generally studied problematic emotion regulation at the disorder level. That is, studies have compared the use of repetitive negative thinking and/or positive reappraisal across different diagnostic groups (D’Avanzato, Joormann, Siemer, & Gotlib, 2013) or examined individual differences in the use of these emotion-regulation strategies in relation to total scores on self-report measures of depression or anxiety symptoms (Garnefski, Kraaij, & Spinhoven, 2001). However, this dominant focus on the disorder level may be problematic because depression and different anxiety disorders are heterogeneous syndromes characterized by diverse affective, cognitive, and somatic symptoms (Fried & Nesse, 2015; Nandi, Beard, & Galea, 2009; Zimmerman, Ellison, Young, Chelminski, & Dalrymple, 2015).

Neglecting the symptomatic heterogeneity of these disorders is an important limitation because it may conceal differential associations between clinically different symptoms (e.g., sad mood vs. suicidal ideation or fear of worst happening vs. numbness) and the use of emotion-regulation strategies (Gross & Jazaieri, 2014). This shortcoming seems highly plausible in light of prior research showing that individual symptoms of depression are differentially related to functional impairments (Fried & Nesse, 2015), adverse life events (Keller, Neale, & Kendler, 2007), as well as cognitive and biological risk factors (Beevers et al., 2019; Marchetti et al., 2018; Santos, Fried, Asafu-Adjei, & Ruiz, 2017). Knowledge of whether repetitive negative thinking and positive reappraisal are (uniquely) associated with individual symptoms may provide insight into whether variation in the use of these emotion-regulation strategies may be related to variation in the clinical presentation of anxiety and depression disorders. To gain such a fine-grained understanding of the mechanisms in depression and anxiety, researchers should adopt a symptom-level approach considering common affective, cognitive, and somatic symptoms of these disorders (Gross & Jazaieri, 2014; Jones, Heeren, & McNally, 2017; Wichers, 2014).

A promising approach to revealing complex relations among individual symptoms of mental disorders and their risk factors is the network approach. According to this perspective, mental disorders do not reflect a latent common cause but arise from complex reciprocal influences between their constituting symptoms (Borsboom, 2017; Borsboom & Cramer, 2013). Recently, research has expanded symptom networks to integrate cognitive and biological factors that are hypothesized to play a causal role in mental disorders (Bernstein, Heeren, & McNally, 2017; Heeren & McNally, 2016; Jones, Heeren, & McNally, 2017). The expanded network models aim to map the causal structure of risk factor–symptom relations in mental disorders. In such networks, symptoms and risk factors are represented by nodes, and their pairwise interactions are represented by edges connecting the nodes.

Applying the network approach to depression and anxiety, researchers have revealed critical differences in the overall importance or connectivity of its constituting symptoms (Bos et al., 2018; Bryant et al., 2017; McNally et al., 2015). For example, loss of interest/pleasure and sad mood emerged as highly connected or central nodes in the depressive-symptom network (Boschloo, van Borkulo, Borsboom, & Schoevers, 2016; Fried, Epskamp, Nesse, Tuerlinckx, & Borsboom, 2016), and worry-related symptoms and problems to relax were central nodes in the generalized-anxiety-disorder network (Beard et al., 2016). Moreover, network studies on the comorbidity between depression and anxiety disorders have found that symptoms of these disorders were strongly interconnected (Borsboom, Cramer, Waldorp, van der Maas, & Borsboom, 2010; Heeren, Jones, & McNally, 2018; McNally, Mair, Mugno, & Riemann, 2017). For example, of the connections between symptoms of depression and generalized anxiety disorder, a study found that guilty feelings were related to worry-related symptoms and sadness was related to nervousness (Beard et al., 2016). By elucidating the connectivity of symptoms within and across disorders, these network studies are casting new light on the structure and comorbidity of depression and anxiety disorders.

In the present study, we sought to extend prior work by uncovering pathways through which emotion-regulation strategy use connects common affective, cognitive, and somatic symptoms that may characterize depression and various anxiety disorders. To this end, we adopted an expanded network approach to model repetitive negative thinking and positive reappraisal within separate and combined symptom networks of depression and anxiety (see Jones, Heeren, & McNally, 2017). There were three aims: (a) to specify differential associations among repetitive negative thinking, positive reappraisal, and individual symptoms of depression and anxiety disorders; (b) to determine the relative importance or connectivity of repetitive negative thinking and positive reappraisal within separate symptom networks to understand their role in the coherence of depression and anxiety symptoms; and (c) to examine whether positive reappraisal and/or repetitive negative thinking act as “bridges” connecting the clusters of depression and anxiety symptoms to understand their role in the co-occurrence of depression and anxiety symptoms. In addressing these objectives, we sought to keep with the Research Domain Criteria (Insel et al., 2010) by considering varying degrees of repetitive negative thinking and positive reappraisal along the continuum of symptom severity. In this way, we attempted to improve the understanding of mental health and illness in terms of complex relations between individual differences in repetitive negative thinking, positive reappraisal, as well as symptom severity of depression and anxiety.

Method

Participants and sampling strategy

We used a dimensional approach that considered individual differences in repetitive negative thinking and positive reappraisal along a full range of normal to abnormal symptom severity levels of depression and anxiety (see Research Domain Criteria; Insel et al., 2010). Therefore, there were no eligibility requirements for participants in this study. Four hundred sixty-eight participants (for demographics, see Table S1 in the Supplemental Material available online) were recruited through Amazon’s Mechanical Turk (MTurk). MTurk provides an online crowdsourcing platform with access to large and diverse samples suitable for clinical research collecting mental-health data (Chandler & Shapiro, 2016).

Participation in the study was restricted to MTurk users who were 18 years or older and resided in the United States. The MTurk workers were recruited between October 2016 and February 2017 to participate in a 20-wave longitudinal study on emotion regulation and mental health. This article presents data from the first wave of data collection. All participants gave informed consent in accordance with the institutional review board at Yale University. Participants were remunerated per survey, up to a total of $15.20 for completing all waves of data collection.

Data quality requirements

Following recommendations for research using crowdsourced samples (Chandler & Shapiro, 2016), only MTurk workers with a history of providing good-quality responses (i.e., an acceptance ratio of ≥ 95%) were allowed to participate. To further ensure high data quality, we presented three questions during the survey to discriminate attentive from inattentive MTurk workers. For example, one validation question read: “Thank you for your work in this survey so far. To show that you are a human, please refuse to answer this question: How many fingers does a typical person have on each hand?” Respondents were then given four response options (e.g., five, six, ten, and three) which they had to leave blank. These questions were presented at irregular intervals, and participants were required to correctly answer all questions. Data from participants who failed to meet this requirement were not considered in the analyses. Finally, the data were screened for repeating GPS coordinates to ensure that responses were unique and minimize the possibility that random responses from nonhuman entities (e.g., bots) contributed to the results. Previous researchers that have used these requirements have demonstrated that MTurk data are comparable with those collected in the laboratory (Chandler & Shapiro, 2016).

Procedure and measures

Widely used questionnaires were used to assess individual differences in anxiety and depression symptoms as well as the use of repetitive negative thinking and positive reappraisal as emotion-regulation strategies in response to negative events or affect. In light of the study’s objectives, symptom questionnaires were selected that measure common cognitive, affective, and somatic components of depression and anxiety, given that the emotion-regulation strategies under investigation have been shown to be related to abnormalities in each of these domains (see introductory section). All questionnaires were presented in randomized order. Participants were instructed to complete the questionnaires with reference to the past week to standardize the temporal orientation across all questionnaires and waves of data collection.

Depression symptoms

The Beck Depression Inventory–II (BDI-II; Beck, Steer, & Brown, 1996) is the most frequently used self-report instrument to measure depressive symptom severity. On 21 items, respondents indicate the degree to which they have experienced a certain symptom on a 4-point scale from 0 to 3. The cognitive, affective, and somatic symptoms assessed by the BDI-II align with the criteria of major depression from the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013). The BDI-II has been shown to have the largest overlap in symptoms with other common depression measures (Fried, 2017). The psychometric properties of the BDI-II have been extensively supported in both nonclinical and clinical adult samples (Erford, Johnson, & Bardoshi, 2016; Joiner, Walker, Pettit, Perez, & Cukrowicz, 2005).

Anxiety symptoms

The Beck Anxiety Inventory (BAI; Beck, Epstein, Brown, & Steer, 1988; Steer & Beck, 1997) is a widely used 21-item self-report measure of the severity of common affective, cognitive, and somatic symptoms of anxiety (Clark & Watson, 1991). The BAI has been designed to have limited overlap with symptoms measured by the BDI-II (Beck et al., 1988) and was selected for this study to avoid inflated correlations with BDI-II symptoms as a result of symptom overlap. Research has shown that the BAI can be used as an anxiety-symptom-severity indicator in patients with different anxiety disorders, including social phobia, panic disorder, agoraphobia, and generalized anxiety disorder (Muntingh et al., 2011). On each item of the BAI, respondents indicate the degree to which they have experienced a certain symptom on a 4-point scale from 0 (not at all) to 3 (severely). The psychometric properties of the BAI have been extensively documented in adult patient and community samples (Bardhoshi, Duncan, & Erford, 2016).

Repetitive negative thinking

The repetitive-negative-thinking subscale of the Repetitive Thinking Questionnaire (RTQ; McEvoy, Mahoney, & Moulds, 2010) is a transdiagnostic measure of perseverative negative thinking. The RTQ was developed by modifying items from commonly used measures of worry, rumination, and postevent processing to remove diagnosis-specific content (Mahoney et al., 2012). The 27 items of the repetitive-negative-thinking subscale are scored on a 5-point scale from 1 (not true at all) to 5 (very true) in reference to a recent-past distressing situation. Example items are: “My thoughts overwhelmed me” and “I had thoughts or images asking ‘Why do I always react this way?’” Psychometric research evaluating the repetitive-negative-thinking subscale in nonclinical and clinical samples has demonstrated that the subscale has a good to excellent high internal consistency; αs ranged from .88 to .93 in these sample types (Mahoney et al., 2012; McEvoy et al., 2010). Supporting the convergent validity, the repetitive-negative-thinking subscale converges with measures of depression and anxiety as well as other related constructs of negative emotions, metacognitive beliefs, cognitive avoidance, and thought suppression (McEvoy et al., 2010). Providing evidence for the divergent validity, the repetitive-negative-thinking subscale shows divergence with measures of extraversion and alcohol use (Mahoney et al., 2012). The reliability of the repetitive-negative-thinking subscale of the RTQ in this study was excellent (Cronbach’s α = .97 and McDonald’s total ω = .97).

Positive reappraisal

The use of positive reappraisal¹ was measured using the reappraisal subscale of the Cognitive Emotion Regulation Questionnaire (CERQ; Garnefski et al., 2001), which is one of the most widely used questionnaires to assess use of emotion-regulation strategies. The four-item reappraisal subscale specifically measures the use of positive reappraisal in response to negative events. The positive-reappraisal subscale does not include diagnosis-specific content. Example items are “I think I can learn something from the situation” and “I think that I can become a stronger person as a result of what has happened.” On each of the four items, respondents rate the extent to which they engage in positive reappraisal using a 5-point scale from 1 (almost never) to 5 (almost always). The positive-reappraisal subscale of the CERQ has good to excellent internal consistency ranging from .82 to .85 (Garnefski et al., 2001; Ireland, Clough, & Day, 2017) and an acceptable test–retest reliability coefficient of .57 for a 14-month time interval (Garnefski et al., 2001). In support of the convergent and divergent validity in both nonclinical and clinical samples, the positive-reappraisal subscale converges with measures of anxiety and depression (Garnefski & Kraaij, 2006, 2007; Garnefski et al., 2001) and diverges with certain problems in emotion regulation such as the limited access to emotion-regulation strategies (Ireland et al., 2017). In this study, the reliability of the positive-reappraisal subscale of the CERQ was excellent (Cronbach’s α = .90; McDonald’s total ω = .93).

Network analysis

To address the first two study aims, we specified a network modeling emotion-regulation strategy–symptom relations separately for depression and anxiety symptoms. The emotion-regulation–depression-symptoms (ER-DEP) network consisted of the repetitive-negative-thinking subscale score, the positive-reappraisal subscale score, and the individual BDI-II symptom scores as nodes and their edges (i.e., connections among the nodes). Moreover, the emotion-regulation–anxiety symptoms (ER-ANX) network consisted of the repetitive-negative-thinking subscale sore, the positive-reappraisal-subscale score, and the individual BAI symptom scores as nodes and their edges.

To address the third study aim, we specified a network combining both depression and anxiety symptoms. This network will be referred to as the emotion regulation–depression and anxiety co-occurrence symptom (ER-COO) network and included as nodes the repetitive-negative-thinking subscale score, the positive-reappraisal-subscale score, and symptoms of the BDI-II and BAI and their edges.

For each network, the conceptual overlap between regulatory strategies and individual symptoms was examined. To this end, we used the goldbricker function of the package networktools (Jones, 2017) for the R Software environment (Version 3.5.2; R Core Team, 2018) to identify strongly correlated item pairs (r ≥ .70) that had less than 20% unique correlations with other items (see also Bernstein et al., 2019). Note that the algorithm did not identify potentially redundant nodes in the ER-DEP, ER-ANX, or ER-COO networks.

Network estimation and inference

The networks were estimated via Gaussian graphical models (GGM) using the R package qgraph (Epskamp, Cramer, Waldorp, Schmittmann, & Borsboom, 2012). In a GGM, the edges represent pairwise relations between two nodes while controlling for all other nodes in the network. The GGMs were estimated on the basis of nonparametric Spearman rho correlation matrices. The GGMs were regularized using a graphical least absolute shrinkage and selection operator (glasso) algorithm. This regularization procedure shrinks all edges and sets small edges to zero to return parsimonious networks (Friedman, Hastie, & Tibshirani, 2008). This powerful method avoids estimating false-positive edges and provides insight into strong relations in the data set (Epskamp, Kruis, & Marsman, 2016). The GGM tuning parameter was set to the conservative value of 0.5 to increase the specificity of the estimated networks (Epskamp & Fried, 2016). This method enables examination of unique relations among repetitive negative thinking, positive reappraisal, and individual symptoms in the networks. In the visualized networks, blue edges represent positive relations, and orange edges represent negative relations between the network nodes. Thicker edges indicate stronger associations between the nodes.

The relative importance of repetitive negative thinking and positive reappraisal within the ER-DEP and ER-ANX networks was examined using the (one-step) expected influence metric (Robinaugh, Millner, & McNally, 2016) in the networktools (Jones, 2017) R package. This metric is more appropriate than traditional centrality metrics (e.g., strength centrality) in cases in which networks contain both positive and negative edges (Robinaugh et al., 2016). Expected influence is defined as the sum of all edges extending from a given node (maintaining the sign of each edge). Higher expected influence values indicate greater importance in the network.

The role of positive reappraisal and repetitive negative thinking as potential nodes connecting clusters of depression and anxiety symptoms in the ER-COO network was examined using the metric (one-step) bridge expected influence (Heeren et al., 2018; Jones, Ma, & McNally, 2017) in the networktools R package (Jones, 2017). Bridge expected influence is defined as the sum of all edges that exist between a given node and the nodes in the other clusters. In line with prior work (Jones, Ma, & McNally, 2017), the clusters of interconnected nodes were defined a priori so that the symptom clusters corresponded with depression and anxiety symptoms. The ER-COO network was separated into three clusters: a cluster of the emotion-regulation strategies (i.e., repetitive negative thinking and positive reappraisal), a cluster of depression symptoms (i.e., the 21 BDI-II items), and a cluster of anxiety symptoms (i.e., the 21 BAI items). In this way, the bridge expected influence values for positive reappraisal or repetitive negative thinking reflected their connectivity with the clusters of depression and anxiety symptoms.

It is possible that unequal variances of positive reappraisal, repetitive negative thinking, as well as the BDI-II and BAI items affect the centrality of the nodes, thereby influencing the observed network structure (Terluin, de Boer, & de Vet, 2016). Therefore, correlations between bridge expected influence values and standard deviations of the individual nodes were examined. The expected influence values were not significantly related to the nodes’ standard deviations for the ER-DEP (ρ = .17, p = .431) and ER-ANX (ρ = –.05, p = .806) networks. Furthermore, the bridge expected influence values were not significantly related to the nodes’ standard deviations in the ER-COO network (ρ = .14, p = .358). This finding indicates that differential variability of the nodes in the estimated networks did not drive their centrality.

Network stability

The robustness of the network estimates was examined using the R package bootnet (Epskamp, Borsboom, & Fried, 2018). The stability of the edge weights was examined by constructing a 95% confidence interval (CI) around each edge using nonparametric bootstrapping with 1,000 samples and computing bootstrapped difference tests for edge weights. Furthermore, the stability of the centrality metrics was examined using case-dropping subset, bootstrapping with 1,000 samples, and by computing bootstrapped difference tests for expected influence and bridge expected influence values. This method draws samples from subsets from the original data and reestimates the centrality metric for each subset. Correlation stability (CS) coefficients were calculated to quantify the stability of the (bridge) expected influence metrics. The coefficient should not be below 0.25 and preferably should be above 0.5 (Epskamp et al., 2018). For both the ER-DEP and ER-ANX networks, the CS coefficient of expected influence was 0.75. For the ER-COO network, the CS coefficient of bridge expected influence was 0.59. Note that all CS coefficients were above the recommended threshold for stable estimation (Epskamp et al., 2018). The results of the stability analyses for the ER-DEP, ER-ANX, and ER-COO networks are provided in Supplement S3 in the Supplemental Material.

To further investigate the stability of the estimated centrality metrics, we examined the correlations between the centrality metrics of the networks estimated using the Wave 1 data and networks using the data from four subsequent waves that were part of this 20-wave longitudinal study (the waves were separated by 1-week time intervals). For the ER-DEP network, the expected influence values at Wave 1 correlated .932 with the values at Wave 2, .907 with Wave 3, .900 with Wave 4, and .921 with Wave 5. For the ER-ANX network, the expected influence values at Wave 1 correlated .885 with the values at Wave 2, .946 with Wave 3, .916 with Wave 4, and .946 with Wave 5. Finally, for the ER-COO network, the bridge expected influence values at Wave 1 correlated .944 with the values at Wave 2, .941 with Wave 3, .889 with Wave 4, and .904 with Wave 5. These correlations suggest stability of the relative importance of the individual nodes in the different networks over the course of 5 weeks.

Results

Sample characteristics

Participants’ BDI-II and BAI scores represented almost the full range of symptom severity. On the BDI-II (M = 15.50, SD = 12.62), a total of 234 respondents reported minimal (range = 0–13), 78 reported mild depressive symptoms (range = 14–19), 79 reported moderate depressive symptoms (range = 20–28), and 77 reported severe depressive symptoms (range = 29–58). On the BAI (M = 12.51, SD = 10.44), a total of 221 respondents reported minimal anxiety symptoms (range = 0–9), 84 reported mild anxiety symptoms (range = 10–15), 101 reported moderate (range = 16–24) anxiety symptoms, and 62 reported severe anxiety symptoms (range = 25–52) The means and standard deviations for each of the symptom items of the BDI-II and BAI are provided in Table S2 in the Supplemental Material. Table S2 shows that there was sufficient and comparable variability in the item scores of both the BDI-II and BAI, as in results of prior research (Bos et al., 2018).

Furthermore, the scores on the repetitive-negative-thinking subscale of the RTQ (M = 70.90, SD = 26.66, range = 27–135) and the positive-reappraisal subscale of the CERQ (M = 14.23, SD = 4.10, range = 4–20) covered the full range of emotion-regulation-strategy use. Together, the distributional characteristics of the variables allowed the present investigation to estimate the strength of the associations among individual differences in repetitive negative thinking, positive reappraisal, and individual symptoms of depression and anxiety.

The ER-DEP network

The glasso ER-DEP network structure is depicted in Figure 1a. Various edges between emotion-regulation strategies and depressive symptoms survived the conservative regularization procedure. Repetitive negative thinking was most strongly related to guilty feelings (BDI-II Item 5), changes in appetite (BDI-II Item 18), agitation (BDI-II Item 11), self-criticalness (BDI-II Item 8), and sadness (BDI-II Item 1). Positive reappraisal was most strongly and negatively related to pessimism (BDI-II Item 2). Repetitive negative thinking and positive reappraisal were only weakly related.

Fig. 1.

Emotion regulation–depression symptoms network. (a) Blue lines represent positive associations, and orange lines represent negative associations. The thickness of the edges reflects the magnitude of the association. (b) Centrality plot depicting the one-step expected influence of each node in the network (raw coefficients are plotted). RNT = repetitive negative thinking; RPL = positive reappraisal; BDI-II = Beck Depression Inventory–II; BDI-II-1 = sadness; BDI-II-2 = pessimism; BDI-II-3 = past failure; BDI-II-4 = loss of pleasure; BDI-II-5 = guilty feelings; BDI-II-6 = punishment feelings; BDI-II-7 = self-dislike; BDI-II-8 = self-criticalness; BDI-II-9 = suicidal thoughts or wishes; BDI-II-10 = crying; BDI-II-11 = agitation; BDI-II-12 = loss of interest; BDI-II-13 = indecisiveness; BDI-II-14 = worthlessness; BDI-II-15 = loss of energy; BDI-II-16 = changes in sleeping pattern; BDI-II-17 = irritability; BDI-II-18 = changes in appetite; BDI-II-19 = concentration difficulty; BDI-II-20 = tiredness/fatigue; BDI-II-21 = loss of interest in sex.

To examine the overall connectivity or centrality of the individual nodes, we computed expected-influence values for all nodes in the network (see Fig. 1b). Among the most central nodes in the network were worthlessness (BDI-II Item 14), loss of pleasure (BDI-II Item 4), repetitive negative thinking, self-dislike (BDI-II Item 7), loss of interest (BDI-II Item 12), and sadness (BDI-II Item 1). Positive reappraisal had the lowest value on expected influence of all variables in the network. The centrality-difference test (see Fig. S10 in the Supplemental Material) suggested that the expected influence value for repetitive negative thinking was significantly greater than the value for positive reappraisal. This result indicates that repetitive negative thinking was significantly more connected to depressive symptoms than positive reappraisal in the ER-COO network.

The ER-ANX network

Figure 2a presents the glasso ER-ANX network structure. Several edges between emotion-regulation strategies and anxiety symptoms survived the glasso procedure. The strongest edges between repetitive negative thinking and anxiety symptoms were found for fear of losing control (BAI Item 14), fear of worst happening (BAI Item 5), unable to relax (BAI Item 4), and nervous (BAI Item 10). Positive reappraisal was negatively related to fear of worst happening BAI Item 5). Repetitive negative thinking and positive reappraisal were not related.

Fig. 2.

Emotion regulation–anxiety symptoms network. (a) Blue lines represent positive associations, and orange lines represent negative associations. The thickness of the edges reflects the magnitude of the association. (b) Centrality plot depicting the one-step expected influence of each node in the network (raw coefficients are plotted). RNT = repetitive negative thinking; RPL = positive reappraisal; BAI = Beck Anxiety Inventory; BAI-1 = numbness or tingling; BAI-2 = feeling hot; BAI-3 = wobbliness in legs; BAI-4 = unable to relax; BAI-5 = fear of worst happening; BAI-6 = dizzy or lightheaded; BAI-7 = heart pounding/racing; BAI-8 = unsteady; BAI-9 = terrified or afraid; BAI-10 = nervous; BAI-11 = feeling of choking; BAI-12 = hands trembling; BAI-13 = shaky/unsteady; BAI-14 = fear of losing control; BAI-15 = difficulty in breathing; BAI-16 = fear of dying; BAI-17 = scared; BAI-18 = indigestion; BAI-19 = faint/lightheaded; BAI-20 = face flushed; BAI-21 = hot/cold sweats.

The expected influence values for the nodes in the ER-ANX network (see Fig. 2b) were inspected to examine their relative importance. The five most central nodes in the network were: shaky/unsteady (BAI Item 13), fear of worst happening (BAI Item 5), terrified or afraid (BAI Item 9), difficulty in breathing (BAI Item 15), and faint/lightheaded (BAI Item 19). Repetitive negative thinking had a significantly higher expected influence value than positive reappraisal (see Figure S11 in Supplemental Material), suggesting that repetitive negative thinking was significantly more connected to anxiety symptoms than positive reappraisal.

The ER-COO network

Figure 3a depicts the network structure and Figure 3b the centrality plot for the ER-COO network. The values of the bridge expected influence metric were examined for repetitive negative thinking and positive reappraisal. Repetitive negative thinking had the highest value of bridge expected influence of all nodes in the ER-COO network. By contrast, positive reappraisal has the lowest value of all nodes in the network. The difference test (see Figs. S12 in Supplemental Material) showed that the bridge expected influence value for repetitive negative thinking was significantly higher than the value for positive reappraisal.

Fig. 3.

Emotion regulation–depression and anxiety co-occurrence symptom network. (a) Blue lines represent positive associations, and orange lines represent negative associations. The thickness of the edges reflects the magnitude of the association. (b) Centrality plot depicting the one-step expected influence of each node in the network (raw coefficients are plotted). RNT = repetitive negative thinking; RPL = positive reappraisal; BDI-II = Beck Depression Inventory–II; BDI-II-1 = sadness; BDI-II-2 = pessimism; BDI-II-3 = past failure; BDI-II-4 = loss of pleasure; BDI-II-5 = guilty feelings; BDI-II-6 = punishment feelings; BDI-II-7 = self-dislike; BDI-II-8 = self-criticalness; BDI-II-9 = suicidal thoughts or wishes; BDI-II-10 = crying; BDI-II-11 = agitation; BDI-II-12 = loss of interest; BDI-II-13 = indecisiveness; BDI-II-14 = worthlessness; BDI-II-15 = loss of energy; BDI-II-16 = changes in sleeping pattern; BDI-II-17 = irritability; BDI-II-18 = changes in appetite; BDI-II-19 = concentration difficulty; BDI-II-20 = tiredness/fatigue; BDI-II-21 = loss of interest in sex; BAI = Beck Anxiety Inventory; BAI-1 = numbness or tingling; BAI-2 = feeling hot; BAI-3 = wobbliness in legs; BAI-4 = unable to relax; BAI-5 = fear of worst happening; BAI-6 = dizzy or lightheaded; BAI-7 = heart pounding/racing; BAI-8 = unsteady; BAI-9 = terrified or afraid; BAI-10 = nervous; BAI-11 = feeling of choking; BAI-12 = hands trembling; BAI-13 = shaky/unsteady; BAI-14 = fear of losing control; BAI-15 = difficulty in breathing; BAI-16 = fear of dying; BAI-17 = scared; BAI-18 = indigestion; BAI-19 = faint/lightheaded; BAI-20 = face flushed; BAI-21 = hot/cold sweats.

Inspecting the strongest edges, repetitive negative thinking connected the depressive symptoms of guilty feelings (BDI-II Item 5), changes in appetite (BDI-II Item 18), and self-criticalness (BDI-II Item 8) with anxiety symptoms of fear of losing control (BAI Item 14), fear of worst happening (BAI Item 5), and nervousness (BAI Item 10). Positive reappraisal was (negatively) related to pessimism (BDI-II Item 2) and weakly (negatively) related to fear of worst happening (BAI Item 5).

Discussion

Employing network analysis, we aimed to reveal the pathways by which repetitive negative thinking and positive reappraisal connect various affective, cognitive, and somatic symptoms that may characterize depression and anxiety disorders. Inspecting associations between emotion-regulation strategies and individual symptoms, it was observed that repetitive negative thinking and positive reappraisal were differentially related to individual depression and anxiety symptoms (see first study aim). Regarding depression symptoms, repetitive negative thinking was positively related to guilty feelings, changes in appetite, agitation, self-criticalness, and sadness. Positive reappraisal was negatively related to pessimism. Regarding anxiety symptoms, repetitive negative thinking was positively related to fear of losing control, fear of worst happening, unable to relax, and nervousness. Positive reappraisal was negatively related to fear of worst happening. The absence of uniform connections with individual symptoms suggests that repetitive negative thinking and positive reappraisal may not function as a central mechanism that is equally important to all symptoms of depression and anxiety. Instead, the role of repetitive negative thinking and positive reappraisal in depression and anxiety may be confined to specific affective, cognitive, and/or somatic aspects of these disorders. Through their relation with specific symptoms, individual differences in the use of repetitive negative thinking and positive reappraisal may be associated with variation in the clinical presentation of depression and anxiety disorders. Indeed, this finding adds to emerging research showing that psychosocial, cognitive, and biological risk factors differ considerably for individual symptoms (Fried, Nesse, Zivin, Guille, & Sen, 2014; Keller et al., 2007; Marchetti et al., 2018; Santos et al., 2017).

Of the specific regulatory strategy–symptom relations, the negative partial correlations between positive reappraisal and symptoms of pessimism and fear of worst happening are notable. This finding suggests that decreased use of positive reappraisal is related to more negative views and expectations about the future, which is a known risk factor for suicide and other mental disorders (Roepke & Seligman, 2016). Moreover, our findings also revealed intriguing relations between repetitive negative thinking and the symptoms guilty feelings and self-criticalness. Whereas most research focused on the role of repetitive negative thinking in negative emotions such as sadness (Nolen-Hoeksema et al., 2008; Watkins, 2008), its relations with guilt and self-criticalness remain underexplored. Recent research has linked self-criticism to greater negative thinking in response to a laboratory-based stressor (Bernstein et al., 2017). Likewise, it seems plausible that feelings of guilt may fuel and characterize the content of repetitive negative thinking. These findings warrant further research exploring the etiological significance of these pathways.

Determining their relative importance in separate symptom networks, we found that repetitive negative thinking had significantly higher value on expected influence compared with positive reappraisal (see second study aim). This suggests that repetitive negative thinking was more strongly interconnected to symptoms of depression and anxiety than positive reappraisal. Thus, individuals who frequently engage in repetitive negative thinking also experience a larger number of depression and anxiety symptoms. By contrast, individuals who are using positive reappraisal less frequently do not necessarily experience a wide range of depression and anxiety symptoms. This difference in connectivity with individual symptoms may account for the stronger relation between total scores of depression/anxiety (which capture the shared variance of all symptoms) and forms of repetitive negative thinking versus positive reappraisal (Aldao et al., 2010). Furthermore, it is of note that repetitive negative thinking was among the most important nodes in the depression (ER-DEP) network but not in the anxiety (ER-ANX) symptom network. This finding suggests that repetitive negative thinking may be a particularly relevant mechanism in understanding the coherence among depression symptoms and the clinical presentation of depression as a disorder. Although these findings suggest that repetitive negative thinking and positive reappraisal differ in their connectivity with individual symptoms, it should be emphasized that this does not mean that positive reappraisal is unimportant. Positive reappraisal was connected to clinically important symptoms in the separate networks of depression and anxiety symptoms.

In examining whether regulatory strategies act as bridges connecting depression and anxiety symptoms (see third study aim), we found that repetitive negative thinking had a higher bridge expected influence value than positive reappraisal. This finding indicates that repetitive negative thinking was relatively more important than positive reappraisal in connecting clusters of depression and anxiety symptoms. In particular, repetitive negative thinking connected several symptoms of depression (e.g., guilty feelings, changes in appetite, and self-criticalness) and anxiety (fear of losing control, fear of worst happening, nervousness). In contrast, positive reappraisal was (negatively) related to the depression symptom pessimism and weakly related to fear of worst happening. These findings suggest that repetitive negative thinking could be particularly important as a mechanism explaining the high co-occurrence among depression and anxiety symptoms.

Together, our findings suggest that repetitive negative thinking and positive reappraisal explain the relations between individual symptoms of depression and anxiety. Consequently, this study elucidates potential pathways through which these regulatory strategies cause and/or are caused by symptoms of depression and anxiety disorders. Indeed, these novel findings highlight how including hypothesized risk factors may enrich symptom networks to gain a precise understanding of processes operating in mental disorders and their comorbid forms (Bernstein et al., 2017; Heeren & McNally, 2016; Jones, Heeren, & McNally, 2017).

Understanding how putative risk factors such as repetitive negative thinking and positive reappraisal are related to individual symptoms of depression and anxiety may provide important clues for treatment. Indeed, targeting those risk factors that are strongly connected to individual symptoms of a disorder and/or clusters of symptoms belonging to multiple disorders holds potential to effectively treat (comorbid) mental disorders. Our results suggest that interventions focusing on repetitive negative thinking could be effective at reducing various symptoms of depression and anxiety as well as their co-occurring forms. If causally linked, reducing repetitive negative thinking may result in improvements in affective symptoms (e.g., sadness, guilty feelings, fear of losing control/worst happening), cognitive symptoms (e.g., self-criticalness), and somatic symptoms (e.g., changes in appetite, agitation, unable to relax, nervousness). Note that increasing the use of positive reappraisal may still be important (e.g., in terms of suicide risk reduction). If patients display symptoms related to negative expectations about the future (e.g., pessimism, fear of the worst happening), then treatments may adopt a symptom-focused strategy and increase positive reappraisal use (e.g., through cognitive restructuring) to obtain relief in these specific symptoms. Again, caution about these clinical implications is required because longitudinal research has yet to establish the temporal relations among repetitive negative thinking, positive reappraisal, and individual symptoms to understand the importance of the regulatory strategies as part of a causal system of interacting depression and anxiety symptoms (Rodebaugh et al., 2018).

Several limitations of the current study point to future directions. First, the cross-sectional data used to construct the emotion-regulation strategy–symptom networks preclude claims about causality. Therefore, the present study cannot rule out whether repetitive negative thinking and/or positive reappraisal influence are influenced by symptoms of depression and anxiety. Intensive longitudinal data with repeated assessments of emotion-regulation strategies and symptoms are better suited to clarify the temporal precedence of emotion-regulation strategies and symptoms of anxiety and depression.

Second, we recruited a general population sample of individuals reporting a variety of symptom severity levels, which may limit the generalizability of the findings to clinical samples. Yet our dimensional approach is particularly well suited to cast light on varying degrees of problems in emotion regulation along the continuum of symptom severity. Indeed, a considerable portion of the participants reported severe levels of depression and anxiety symptoms. This approach complies with the Research Domain Criteria (Insel et al., 2010) and may help us to better understand the heterogeneous nature of conventional diagnostic categories (Fried & Cramer, 2017). Note that we did not explore differences in network structure between low and high symptom levels because using the conventional cutoffs for the BDI-II and BAI restricts the variability in the network variables, which may affect the network structure. Therefore, future work should replicate the present findings in clinical samples of depression and anxiety disorders.

Third, the analyses used data from single-item, self-report measures of depression and anxiety symptoms. It is possible that these measures imperfectly capture the clinical phenomena. Future network studies should use multiple items and methods to measure each symptom. In this respect, some research suggests that symptom networks based on (single-item) self-report versus clinician-report data may be highly similar (Moshier et al., 2018). This research challenges the notion that network methods produce unreliable results due to estimations consisting primarily of measurement error.

Fourth, this study was limited by its focus on two prominent emotion-regulation strategies in depression and anxiety disorders: repetitive negative thinking and positive reappraisal. Other emotion-regulation strategies (Aldao et al., 2010) as well as related factors, such as information-processing biases (Everaert et al., 2017) and personality variables (Stanton, Rozek, Stasik-O’Brien, Ellickson-Larew, & Watson, 2016), may be important in understanding the connections between symptoms of depression. Therefore, future studies could integrate a broader set of variables in symptom networks of depression and anxiety.

Finally, it is possible that the observed associations among emotion-regulation strategies and psychopathology symptoms are specific to the depression and anxiety questionnaires we used. Self-report instruments of depression and anxiety often differ in the set of symptoms that are measured. This difference restricts the relations that can be observed in network models. However, the questionnaires we used are widely used self-report measures that were carefully selected on the basis of their psychometric properties and the variety in common cognitive, affective, and somatic symptoms assessed. Therefore, the current study contributes to knowledge of how emotion-regulation strategy use may be related to common symptoms of depression and anxiety. The findings may serve as an impetus for future studies that use other questionnaires of depression and anxiety to determine the robustness of these initial observations.

Despite these limitations, this study advances the understanding of the complex relations among repetitive negative thinking, positive reappraisal, and symptoms of depression and anxiety in important ways. Using network analysis, we observed that (a) repetitive negative thinking and positive reappraisal were differentially related to individual depression and anxiety symptoms, (b) repetitive negative thinking was more strongly connected to symptoms of depression and anxiety than positive reappraisal, and (c) repetitive negative thinking was relatively more important than positive reappraisal in connecting clusters of depression and anxiety symptoms. Collectively, the results provide cues to the pathways through which repetitive negative thinking and positive reappraisal may influence symptoms of depression and anxiety.

Supplemental Material

Everaert_Open_Practices_Disclosure – Supplemental material for Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking

Supplemental material, Everaert_Open_Practices_Disclosure for Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking by Jonas Everaert and Jutta Joormann in Clinical Psychological Science

Supplemental Material

Everaert_Supplemental_Material – Supplemental material for Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking

Supplemental material, Everaert_Supplemental_Material for Emotion Regulation Difficulties Related to Depression and Anxiety: A Network Approach to Model Relations Among Symptoms, Positive Reappraisal, and Repetitive Negative Thinking by Jonas Everaert and Jutta Joormann in Clinical Psychological Science

Footnotes

Action Editor

Christopher G. Beevers served as action editor for this article.

Author Contributions

J. Everaert and J. Joormann developed the study concept and contributed to the study design. Testing and data collection were performed by J. Everaert. J. Everaert performed the data analysis and interpretation under the supervision of J. Joormann. J. Everaert drafted the manuscript, and J. Joormann provided critical revisions. Both authors approved the final manuscript for submission.

ORCID iD

Jonas Everaert

Declaration of Conflicting Interests

The author(s) declared that there were no conflicts of interest with respect to the authorship or the publication of this article.

Supplemental Material

Additional supporting information can be found at

Open Practices

All data have been made publicly available via Open Science Framework and can be accessed at https://osf.io/677jr/. The complete Open Practices Disclosure for this article can be found at https://journals-sagepub-com.web.bisu.edu.cn/doi/suppl/10.1177/2167702619859342. This article has received the badge for Open Data. More information about the Open Practices badges can be found at .

Notes

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