Reexamining trait rumination as a system of repetitive negative thoughts: A network analysis

Highlights

• Rumination could be conceptualized as a system of interacting elements.

• Highly central nodes were largely passive, self-critical processes.

• Bridge nodes could explain how reflection could lead to more problematic brooding.

Abstract

Background and objectives

Rumination is strongly associated with risk, maintenance, and worsening of depressive and related symptoms, and it predicts poor treatment response and relapse. More work is needed to clarify the nature and malleability of rumination. We propose reexamining trait rumination as a system of interacting components (“nodes”).

Methods

A regularized partial correlation network was first computed to estimate the functional relations among items from the Ruminative Responses Scale (RRS) (N = 403). We then tested whether items constitute multiple distinguishable sub-networks or communities, and if so, if particular items function as “bridges” connecting them.

Results

RRS items were not interchangeable, with network components varying widely in their centrality. We identified three communities of nodes and the nodes bridging these communities.

Limitations

Data were derived from a heterogeneous community sample and include items from a single measure. Thus, results should not be interpreted as definitive, but instead as hypothesis-generating and highlighting the utility of rethinking the conceptualization and measurement of rumination.

Conclusions

Of the larger set of cognitive patterns forming the rumination construct, the high centrality nodes were largely passive and self-critical processes. Community detection analyses identified a sub-network largely comprising items from the RRS that have traditionally been labeled reflective pondering and adaptive; however, strong bridge nodes were also from this community. This implies that in isolation or at low levels such processes may not be problematic, but that their persistence or intensification could be associated with the activation of more maladaptive processes.

Introduction

Everyone feels down sometimes. However, not everyone responds to such feelings in the same way. For example, some people tend to ruminate with repetitive, negative, and self-focused thoughts like “why am I so sad?” and “why can't I handle things better?” (Nolen-Hoeksema & Morrow, 1991). This cognitive-affective response style is strongly associated with risk, maintenance, and worsening of depressive symptoms (Nolen-Hoeksema, 2000, 1991; Nolen-Hoeksema, Wisco, & Lyubomirsky, 2008). Chronic rumination increases the salience of negative events and impairs problem-solving, thus exacerbating and prolonging negative mood states (Donaldson, Lam, & Mathews, 2007; Joormann & Gotlib, 2010; Nolen-Hoeksema et al., 2008). Longitudinal, prospective, and experimental data converge: getting stuck in this mental habit of “self-critical moody pondering”, as it has been called, and getting stuck often, is a problem (Raes & Hermans, 2008; Watkins & Nolen-Hoeksema, 2014). And notably, frequent negative rumination and its consequences are not unique to depression, but are evident in anxiety disorders and related psychopathology as well (McLaughlin, Aldao, Wisco, & Hilt, 2014; McLaughlin & Nolen-Hoeksema, 2011).

Broadly considered as a core transdiagnostic feature of psychological disorders (e.g. Harvey, Watkins, Mansell, & Shafran, 2004; McLaughlin & Nolen-Hoeksema, 2011), rumination is a plausible target of treatment (Watkins, 2015). And there is preliminary evidence that rumination-focused therapies, such as mindfulness-based cognitive therapy (van Aalderen et al., 2012) and cognitive-behavioral approaches (Watkins et al., 2011), may improve treatment outcomes for depression. However, the results of such targeted interventions have been few and inconsistent, and in general, high rates of rumination predict slower treatment response, lower rates of recovery, and higher rates of relapse (Ciesla & Roberts, 2002; Schmaling, Dimidjian, Katon, & Sullivan, 2002). Given that rumination is so relevant for understanding emotional disorders and developing interventions, more work is needed to clarify the nature and malleability of rumination itself.

One limitation to extant efforts could be the conceptualization and subsequent measurement of rumination as a unitary construct. In reality, rumination is complex and multifaceted. For example, the typical definition includes components of perseveration, passivity, negativity, and self-focus. Furthermore, rumination about a negative experience can feel like a productive strategy for introspection and resolution (Papageorgiou & Wells, 2001). There have been important strides toward analyzing rumination to understand its function better. Factor analyses and other approaches have distinguished between adaptive versus maladaptive types of rumination or perseverative thinking, such as differentiating between abstract-evaluative and concrete repetitive thought (Watkins, 2008), or brooding and reflective pondering (Treynor, Gonzalez, & Nolen-Hoeksema, 2003). For example, abstract-evaluative thoughts (e.g. fixation on high-level, “why” aspects of one’ situation) and brooding (e.g. “What am I doing to deserve this?”) are more frequent and persistent in individuals with a history of major depression, those experiencing current symptoms, and those who eventually experience an episode than those without psychopathology. Such thinking patterns can increase the salience of negative thoughts and memories, delay problem solving or instrumental behaviors, and reduce cognitive and attentional flexibility (Nolen-Hoeksema et al., 2008; Watkins, 2008). In contrast, the links between mental health and more concrete thoughts (e.g. low-level, “how” details of one's situation) and reflection (e.g. analyzing one's thoughts) are less clear. Although there is evidence that such forms of self-reflection can be adaptive and benefit problem-solving, and that such thinking patterns are not elevated in individuals in remission from depressive episodes and are associated with less depression over time, they are associated with concurrent depressive symptoms and negative memory biases (Joormann, Dkane, & Gotlib, 2006; Nolen-Hoeksema et al., 2008). These models usefully categorize a person's general response style, the types of thoughts that are generally associated with mood symptoms, and the types of thoughts that typically have direct negative versus neutral or positive consequences.

However, in real life, people rarely fall into just one pattern of negative repetitive thinking. Instead, people exhibit different combinations of reflective, brooding, abstract, and concrete thoughts, for instance. Indeed, existing models fail to clarify why one person's pattern or combination of adaptive and maladaptive thoughts leaves them vulnerable to frequent, problematic rumination and associated mood symptoms, whereas another person's pattern of thinking does not. Although categorizing individual thoughts alone can be beneficial (e.g. adaptive or maladaptive, reflective or brooding, and abstract or concrete), such labels may become blurry once we consider how they all interact as a system.

We therefore propose reconsidering trait rumination from a network perspective. Currently, rumination and its sub-types are measured as single sum scores from self-report scales. As a result, component processes (e.g. items) or entire scales are treated as interchangeable and reflective of an underlying, latent response style. In some ways, this is perplexing as it is easy to generate examples of how component processes might interact (e.g., the more one criticizes oneself for failures, the more likely one is to brood about how sad one feels) and how these processes might be more or less influential on one's mood and circular thinking. Furthermore, some ostensibly neutral or even adaptive thinking styles from one factor, like reflection, may become problematic in relation to others. A network approach to reconceptualizing rumination and its components allows for these possibilities. The feature distinguishing this computational approach from factor analyses is that it characterizes rumination as a system of interacting components that do not have to have a common, underlying cause (Schmittmann et al., 2013). Accordingly, we can look for clues as to why some people get stuck in a pattern of ruminative responses and to what specific targets are ripe for intervention.

To do so, we used the items of the Ruminative Responses Scale (RRS; Nolen-Hoeksema & Morrow, 1991), the most common instrument for measuring trait rumination. There are important limitations to this approach. First, this is only one measure and thus results may vary with another scale. Second, some items do share conceptual overlap (e.g., going away to think about one's feelings and thinking about why one is feeling that way) as is typical of indices intended to reflect a conjectured latent, common cause. However, this intention notwithstanding such items need not measure the same process. Thus, although we take data-driven steps (described below) to reduce items that do appear to be measuring single processes, we still do not argue that the resulting network models completely capture the trait rumination construct. Nor do we argue that each item necessarily reflects a completely unique aspect of rumination. In fact, achieving these aims would likely require a rigorous, iterative process of including various self-report, behavioral, and other variables to devise a parsimonious and comprehensive measure for this purpose. We do argue that there is value in examining all items simultaneously as conceptual overlap is not synonymous with fungibility and simple sum scores likely occlude meaningful differences and interactions between components. The network approach is a fresh, data-driven way to gain new perspectives on how component processes or items cluster and relate to one another (individually and as sub-networks). Results can drive new hypotheses to be experimentally tested regarding different components of the rumination construct that might be most influential initiating or maintaining problematic response styles. The goal is to explore whether past approaches to quantifying rumination could be missing informative elements.

We have three primary aims: (1) explore whether and how RRS items interrelate in different ways, (2) test whether within this larger network RRS items constitute distinguishable communities (sub-networks) of processes, and (3) if so, are there particular items that function as “bridges,” i.e. processes that connect or are shared by communities. These analyses can help to highlight especially potent component processes that may foster broader vulnerability for problematic emotional and cognitive responses. Additionally, community detection and bridge analyses could highlight thought patterns that make a person more likely to tip from adaptive reflection and introspection to maladaptive brooding. Overall, this new lens on rumination is exploratory. Each analysis has its strengths and limitations and is intended to provide new hypotheses about plausible causal connections between components of rumination.