The dynamic interplay between negative and positive emotions in daily life predicts response to treatment in depression: A momentary assessment study

Wichers, Marieke; Lothmann, Claudia; Simons, Claudia; Nicolson, Nancy A.; Peeters, Frenk

doi:10.1111/j.2044-8260.2011.02021.x

Cited by 73 publications

(75 citation statements)

References 42 publications

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“…S1A). This is of course an overly simple representation of the mood system, but consistent with the empirical observations that samevalenced emotions tend to augment and opposite-valenced emotions tend to blunt each other (16,40), and that this dynamic interplay has relevance for the course of depression (41). Also on theoretical grounds, it stands to reason that emotions that show large overlap in terms of their underlying components (such as appraisals; see ref.…”

Section: Resultsmentioning

confidence: 92%

Critical slowing down as early warning for the onset and termination of depression

Leemput

Wichers

Cramer

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

610

577

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About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression. D epression is one of the main mental health hazards of our time. It can be viewed as a continuum with an absence of depressive symptoms at the low endpoint and severe and debilitating complaints at the high end (1). (Throughout this manuscript, the term "depression" refers to this continuum of depressive symptoms.) The diagnosis major depressive disorder (MDD) defines individuals at the high end of this continuum. Approximately 10-20% (2) of the general population will experience at least one episode of MDD during their lives, but even subclinical levels of depression may considerably reduce quality of life and work productivity (3). Depressive symptoms are therefore associated with substantial personal and societal costs (4,5). The onset of MDD in an individual can be quite abrupt, and similarly rapid shifts from depression into a remitted state, so-called sudden gains, are common (6). However, despite the high prevalence and associated societal costs of depression, we have little insight into how such critical transitions from health to depression (and vice versa) in individuals might be foreseen. Traditionally, the broad array of correlated symptoms found in depressed people (e.g., depressed mood, insomnia, fatigue, concentration problems, loss of interest, suicidal ideation, etc.) was thought to stem from some common cause, much as a lung tumor is the common cause of symptoms such as shortness of breath, chest pain, and coughing up blood. Recently, however, this common-cause view has been challenged (7-9). The alternative view is that the correlated symptoms should be regarded as the result of interactions of components of a complex dynamical system (7,(10)(11)(12). Consequently, new models of the etiology of depression involve a network of interactions between components, such as emotions, cognitions, and behaviors (8,9). This implies, for instance, that a person may become depressed through a causal chain of feelings and experiences, such as the following: stress → negative emotions → sleep problems → anhedonia (9, 13-15). However, the network view also implies that there can be positive feedback mechanisms between symptoms, such...

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Section: Resultsmentioning

confidence: 92%

Critical slowing down as early warning for the onset and termination of depression

Leemput

Wichers

Cramer

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

610

577

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“…Furthermore, individuals may be more or less sensitive to these factors. While it is possible to include measurements of such factors in our level 1 model (e.g., Suls et al, 1998), and to model an individual's sensitivity to such a factor (e.g., Wichers et al, 2009;Wichers, Lothmann, Simons, Nicolson, & Peeters, 2012;Wichers et al, 2010;, there will always be additional factors that influence the process but that were not observed and therefore cannot be modeled explicitly. These effects are absorbed into the innovation term, and thus influence the innovation variance parameter σ 2 i .…”

Section: Level 1: Within-personmentioning

confidence: 99%

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance

Jongerling

Laurenceau

Hamaker

2015

Multivariate Behavioral Research

105

106

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In this article we consider a multilevel first-order autoregressive [AR(1)] model with random intercepts, random autoregression, and random innovation variance (i.e., the level 1 residual variance). Including random innovation variance is an important extension of the multilevel AR(1) model for two reasons. First, between-person differences in innovation variance are important from a substantive point of view, in that they capture differences in sensitivity and/or exposure to unmeasured internal and external factors that influence the process. Second, using simulation methods we show that modeling the innovation variance as fixed across individuals, when it should be modeled as a random effect, leads to biased parameter estimates. Additionally, we use simulation methods to compare maximum likelihood estimation to Bayesian estimation of the multilevel AR(1) model and investigate the trade-off between the number of individuals and the number of time points. We provide an empirical illustration by applying the extended multilevel AR(1) model to daily positive affect ratings from 89 married women over the course of 42 consecutive days.

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“…Peeters et al (2010) and Wichers et al (2012) used AA indices of emotional reactivity to predict improvement and remission in major depression. In the Peeters et al (2010) study, MDD patients received pharmacotherapy with supportive psychotherapy and were followed and assessed for response status and depression level over the next 18 months.…”

Section: The Use Of Ambulatory Assessment In Clinical Psychologymentioning

confidence: 99%

Ambulatory Assessment

Trull¹,

Ebner‐Priemer

2013

Annu. Rev. Clin. Psychol.

628

467

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Ambulatory assessment (AA) covers a wide range of assessment methods to study people in their natural environment, including self-report, observational, and biological/physiological/behavioral. AA methods minimize retrospective biases while gathering ecologically valid data from patients’ everyday life in real time or near real time. Here, we report on the major characteristics of AA, and we provide examples of applications of AA in clinical psychology (a) to investigate mechanisms and dynamics of symptoms, (b) to predict the future recurrence or onset of symptoms, (c) to monitor treatment effects, (d) to predict treatment success, (e) to prevent relapse, and (f) as interventions. In addition, we present and discuss the most pressing and compelling future AA applications: technological developments (the smartphone), improved ecological validity of laboratory results by combined lab-field studies, and investigating gene-environment interactions. We conclude with a discussion of acceptability, compliance, privacy, and ethical issues.

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The dynamic interplay between negative and positive emotions in daily life predicts response to treatment in depression: A momentary assessment study

Abstract: Subtle individual differences in daily life emotional dynamics predict future treatment outcome in depression.

Cited by 73 publications

References 42 publications

Critical slowing down as early warning for the onset and termination of depression

Critical slowing down as early warning for the onset and termination of depression

A Multilevel AR(1) Model: Allowing for Inter-Individual Differences in Trait-Scores, Inertia, and Innovation Variance

Ambulatory Assessment

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