Not only how good or bad people feel on average, but also how their feelings fluctuate across time is crucial for psychological health. The last 2 decades have witnessed a surge in research linking various patterns of short-term emotional change to adaptive or maladaptive psychological functioning, often with conflicting results. A meta-analysis was performed to identify consistent relationships between patterns of short-term emotion dynamics-including patterns reflecting emotional variability (measured in terms of within-person standard deviation of emotions across time), emotional instability (measured in terms of the magnitude of consecutive emotional changes), and emotional inertia of emotions over time (measured in terms of autocorrelation)-and relatively stable indicators of psychological well-being or psychopathology. We determined how such relationships are moderated by the type of emotional change, type of psychological well-being or psychopathology involved, valence of the emotion, and methodological factors. A total of 793 effect sizes were identified from 79 articles (N ϭ 11,381) and were subjected to a 3-level meta-analysis. The results confirmed that overall, low psychological well-being co-occurs with more variable (overall ˆϭ Ϫ.178), unstable (overall ˆϭ Ϫ.205), but also more inert (overall ˆϭ Ϫ.151) emotions. These effect sizes were stronger when involving negative compared with positive emotions. Moreover, the results provided evidence for consistency across different types of psychological well-being and psychopathology in their relation with these dynamical patterns, although specificity was also observed. The findings demonstrate that psychological flourishing is characterized by specific patterns of emotional fluctuations across time, and provide insight into what constitutes optimal and suboptimal emotional functioning.Keywords: psychological well-being, psychopathology, emotional variability, emotional instability, emotional inertia A fundamental feature of our emotions and feelings is that they change over time. The patterns of emotional fluctuations reflect how people deal with changes in the environment and how they regulate their emotions (Larsen, 2000), and both contribute importantly to their psychological well-being. Indeed, a surge of research focusing on the time dynamic patterns of emotional experience has shown that, next to how people usually feel or how they feel on average, the patterns with which people's emotional experiences change over time provide unique information that is relevant for psychological well-being. Here we present a meta-analysis of studies investigating the relation between on the one hand short-term dynamical patterns of emotions and on the other hand stable forms of psychological well-being and psychopathology, to identify the patterns of emotional change associated with general and specific forms of psychological health.We define psychological well-being as a broad construct that involves either or both the presence of positive indicators of psychologi...

This paper examines the concept of emotional inertia to capture a fundamental property of the emotion dynamics that may characterize psychological maladjustment. Emotional inertia simply refers to the degree to which emotional states are resistant to change. As psychological maladjustment has been associated with both emotional underreactivity and ineffective emotion regulation skills, we hypothesized that its overall emotion dynamics would be characterized by high levels of inertia. Using different methods, we provide evidence from two naturalistic studies that the emotional fluctuations of individuals suffering from low self-esteem (Study 1) and depression (Study 2) are indeed characterized by higher levels of emotional inertia in both positive and negative emotions than those of their counterparts. We discuss the usefulness of the concept of emotional inertia as a hallmark feature of maladaptive emotion dynamics. Keywordsemotion; psychological adjustment; emotional inertia; emotional variability Feelings change. Our emotional lives are characterized by ups and downs, changes and fluctuations following the ebb and flow of daily life. Studying the patterns and characteristics of these changes gives researchers insight into the dynamics of emotions and how people regulate their emotions, for better or for worse. In this paper we examine the concept of "emotional inertia" as a fundamental feature of the dynamics of emotional experience, and study its relationship to psychological maladjustment. Emotional variability and adjustmentOne of the most pervasive findings in the study of emotion dynamics is that high levels of emotional variability are associated with maladaptive psychological functioning. Individuals who display large emotional variability over time (expressed as for instance the standard deviation of repeated emotion assessments across time) are characterized by higher levels of depression, neuroticism, and lower self-esteem, among others (e.g., Eid & Diener, 1999; Kuppens, Van Mechelen, Nezlek, Dossche, & Timmermans, 2008).This line of research could be interpreted as showing that psychological maladjustment is characterized by greater emotional reactivity (Kuppens, et al., 2008 with the demands and threats in the environment (Frijda, 2007;Izard, 2009). Anger motivates antagonism and the removal of the object of frustration, fear motivates avoiding or fleeing a threatening environment, happiness signals that things are going well and promotes further approach. In sum, the adaptive value of emotions lies in their capacity to be mobilized in response to (internal or external) events. It therefore seems contradictory that emotional reactivity per se would be maladaptive. Experiencing changing emotions should be generally functional and adaptive (depending on how attuned these emotional changes are to environmental contingencies), and lack of emotional responsiveness may be a sign that emotional responses have become decoupled from environmental or psychological demands, and thus, may be indicati...

In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.

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...