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Anxiety and Depression from a Dynamic Systems Perspective


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Anxiety and depression disorders are the biggest mental health hazards of our time and in many ways closely related. The first anxiety disorder episodes emerge during childhood, while the first depression episodes more typically emerge in adolescence. Such early episodes are highly predictive for lifespan developments. This chapter reviews literature on dynamic system perspectives on anxiety and depression across scales of temporal resolution, from affect and highly contextualized emotion episodes to more persistent moods that evaluate the world as a whole, and the personality traits anxiety and depression that capture thematic recurrences of feelings, thoughts and behavior along the lifespan and how people talk about themselves. These various processes are intimately connected via their self-organizing and dynamic nature and circular causality, which demonstrates how dynamic system perspectives can help us to understand anxiety and depression across the lifespan.
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Bertus F. Jeronimus1,2*
1 Department of Developmental Psychology, Faculty of Social and Behavioural sciences,
Groningen, University of Groningen, The Netherlands.
2 Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), Department of
Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The
Please cite this work as: Jeronimus, B.F. (2019). Dynamic system perspectives on Anxiety
and Depression. In book: Psychosocial Development in Adolescence: Insights from the
Dynamic Systems Approach (Editors: Kunnen, E.S., de Ruiter, N.M.P., Jeronimus, B.F., van
der Gaag, M.A.), chapter 7. London: Routledge Psychology. This author version does not
exactly replicate the final version published in the book. It is not the copy of record.
Anxiety and depression disorders are the biggest mental health hazards of our time and in many
ways closely related. The first anxiety disorder episodes emerge during childhood, while the
first depression episodes more typically emerge in adolescence. Such early episodes are highly
predictive for lifespan developments. This chapter reviews literature on dynamic system
perspectives on anxiety and depression across scales of temporal resolution, from affect and
highly contextualized emotion episodes to more persistent moods that evaluate the world as a
whole, and the personality traits anxiety and depression that capture thematic recurrences of
feelings, thoughts and behavior along the lifespan and how people talk about themselves. These
various processes are intimately connected via their self-organizing and dynamic nature and
circular causality, which demonstrates how dynamic system perspectives can help us to
understand anxiety and depression across the lifespan.
“Sherrington (…) thought that the brain worked like a telegraph system. Freud often
compared the brain to hydraulic and electro-magnetic systems. Leibniz compared it to
a mill, and I am told the ancient Greeks thought the brain functions like a catapult. At
present, obviously, the metaphor is the digital computer.”
Searle (1984, p. 44).
Anxiety and depression disorders are the biggest mental health hazards of our time, and first
episodes often emerge during childhood and adolescence (Merikangas et al., 2010; Rutter et al.,
2011). Children can respond with anxiety to separation, animals, thunder, social situations,
dentists, and dreams, among others, and many adolescents experience spells of depressed mood,
as these are common and transient distress experiences in response to the major biological,
psychological and social transitions that are outlined throughout this book (cf. Rutter et al.,
2011; Revonsuo, 2000). At the end of adolescence, about 25% of the population experienced
an anxiety or depression disorder, which is defined by significant suffering and functional
impairment (APA, 2013; Beesdo et al., 2009; Merikangas et al., 2010; Rutter et al., 2011).
Many children and early adolescents suffer in silence because their mood disorders go
unnoticed (Zahn-Waxler et al., 2000), and many more suffer from high anxiety and depression
symptom levels. Childhood disorder episodes are often indicative of lifespan developments, as
75% recur during adolescence, up to an average of nine separate episodes over their lifespan
(Burcusa & Iacono, 2007; Rutter et al., 2011). Some studies suggests that the majority of the
youth who have ever been clinically depressed will be in an episode in any given year over the
remainder of their lives (Kessler & Wang, 2009), although others paint a less gloomy future
(Bos et al., 2018; Rottenberg et al., 2018). Nonetheless, most adolescents with a depression also
report current or past anxiety disorders, while about a third of the adolescents with anxiety
disorders also experience depression (Brady, 1992; Davis et al., 2015).
A central goal of psychiatric research is to better understand anxiety and depression
states (Kendler et al., 2011). This chapter illustrates the use of dynamic system perspectives to
understand anxiety and depression as experiences that emerge from a system of constituent
affective, emotion, and mood components that synchronize across scales of resolution and
function as one integrated process that evolves within each of us. In doing so this chapter
illustrates how dynamic system perspectives can provide new insights into anxiety and
depression, connect various literatures, and point at new angles for future research.
Nested systems
Anxiety and depression can be defined as emotions that span minutes to days, as mood episodes
that can persist over weeks to months, and as part of our personalities, which change along the
lifespan, and describe how people navigate the world, define who they are, and talk about
themselves. Dynamic system perspectives can increase our understanding of anxiety and
depression as multi-component and dynamic processes that continuously change over time and
are inherently connected via their self-organizing and dynamic nature and circular causality.
Moods and personality traits are thought to emerge from interactions between highly variable
“microscopic” emotion components (as outlined below), after which these more integrated
macroscopic system levels start to structure and constrain our moment-to-moment component
experiences top-down (see Table 7.1), resulting in circular causality and increasingly complex
and stable affect systems (e.g., Fajkowska, 2015; Granic, 2005; Kendler et al., 2011; Van der
Stel, 2009; Van Geert & Steenbeek, 2005; Wichers et al., 2018; Witherington, 2007).
The functional unity of this multiscale system is often illustrated with the metaphor of
emotions as the weather of our lives (e.g., sadness as rain clouds, a cold breeze of fear, and
sunshine as joy), and mood and personality as our internal climates (e.g., Liljenström & Svedin,
2005; Ochsner et al., 2012; Russell, 2017). It may be difficult to forecast daily weather and
emotions over more than ten days, as these trajectories are inherently chaotic (i.e., irregular and
extremely sensitive to small baseline deviations), but more climatic or “average weather” such
as seasons are rather stable, predictable, orderly, and relatively enduring (e.g., Boeing, 2016;
Liljenström et al., 2005). The personality traits Anxiety and Depression can be seen as macro-
level attractor basins that calibrate our internal dynamics resulting in less environmental input
necessary to move toward specific micro real-time emotional states of anxiety and depression
(Jeronimus, 2015; Revelle & Condon, 2017; Van der Stel, 2009). Below we zoom into each of
these component levels and processes in detail.
Affect and emotions: the micro level
Affect is a continuous hedonic “commentary” on our current state of affairs that naturally
infuses our perception as if it were a “sixth sense” that is integrated with other sensory
processing and is typically experienced as a property of people or objects to help us navigate
our world (Barrett, 2017, 2018; Kahneman & Egan, 2011; Panksepp & Biven, 2012; Schwarz,
2010; Slovic et al., 2007). Affects are internal subjective states that are only known to those
who have them. Anxiety and depression as emotions refer to discrete snapshots of this
continuous stream of affective experience, when a specific context becomes imbued with
meaning to instigate a specific action.
Emotions can be defined as (a) intense and transient subjective states that last for
seconds up to 72 hours at most (Panksepp & Biven, 2012; Sonnemans & Frijda, 1994; Verduyn
et al., 2011); which (b) “emerge” in response to specific events or stimuli (extrinsic or intrinsic
to the organism); and (c) can be categorized according to their affective valence (from
unpleasant to pleasant) and emotional arousal or bodily activation (Anderson & Adolphs, 2014;
Yik et al., 2011; Kuppens et al., 2010; Panksepp & Biven, 2012). Anxiety and depression are
both unpleasant affects, and low positive affect is relatively specific for depression and social
anxiety, with anxiety being marked by high physiological arousal and depression by low arousal
(Russell, 2003; Watson et al., 2005).
The classical emotion perspective postulated that children are born with abilities to
distinguish the primary emotions (e.g., anxiety/fear, depression/sadness, happiness, anger, and
disgust) that cover most of our transactions with the world (Darwin, 1872; Russell, 1990). These
emotions can be recognized via facial expression, skin color, vocal timbre, gestures, and odor,
among others (Darwin, 1972; Ekman, 2008; De Waal, 2019; Panksepp et al., 2012). In the
classical perspective anxiety and depression were understood as hardwired prototypical
adaptive scripts that become activated by a particular class of stimuli (e.g., a threat, obstacle, or
loss) to orchestrate psychological changes in feelings and thoughts and activate coordinated
thought-action repertoires and goal-oriented behavior, which we evolved to respond quickly
and adequately to changes in the environment that might affect our well-being (Damasio et al.,
2000; Ortony & Turner, 1990; Panksepp et al., 2012; Plutchik, 2001; Roseman et al., 1994;
Russell, 2017).
The prototypical script for anxiety becomes activated when a threat (stimulus) signals
the detection of an ongoing source of danger (cognition) and evokes anxiety or fear (affect) as
an impulse to prepare, flee, hide, counterattack, or scream (action) to protect ourselves and
reach safety (goal), which may well save our lives. Similarly, instances of depression start with
the loss of a valued object or support (stimulus) which indicates abandonment (cognition) and
evokes sadness (affect) and an impulse to cry (action) in order to reattach with this lost object
(goal), or to save energy and tread carefully until conditions improve, and our needs are met.
More complex emotions such as shame or guilt were thought to originate as mixtures of these
five basic emotions (Plutchik, 2001; Russell, 1990; 2017; Shaver et al., 1987).
A more dynamically oriented constructivist perspective postulates that anxiety and
depression have no single physical form and must be understood as spectra (like colours) or
categories of instances (families of functionally related states) that differ in many degrees,
qualities, and intensities (see Table 7.2), but share their template like chairs or cookies and
thus cluster near one another in property space (Barrett, 2017, 2018; Kendler et al., 2011; Posner
et al., 2005; Russell, 2003; Salzman & Fusi, 2010). Instances of anxiety and depression differ
between people and within people at different instances and along the lifespan, because each
episode is constructed from learned assemblies of bits and pieces of previous experience (i.e.,
statistical regularities) which form prototypical scripts or cognitive tools to communicate about
feelings and to anticipate future events and to deal with them (Barrett, 2017, 2018; Russel,
1990, 2001). Recent emotion theories group the thousands semantic emotion terms we use over
up to 27 emotion families including anxiety (nervous), fear (afraid, horror), horror (shock,
scared), and depression (sadness), see Table 7.2 (e.g., Cowen et al., 2017; Verduyn et al., 2015;
Watson et al., 1988).
Today most theorists argue that the emotions anxiety and depression emerge from the
synchronization of various interoceptive, perceptual, cognitive, and motor components that
mutually amplify one another until they converge at a dominant emotion state that best fits the
specific situational instance in which one finds oneself, and from which a subjective
understanding of the situation emerges to potentiate a particular remedy to deal with it
(Adolphs, 2003; Anderson & Adolphs, 2014; Barrett, 2018; Damasio et al., 2000; Lewis, 2000;
Scherer, 2009; Tooby & Cosmides, 2008). Subsequently, these anxiety and depression states
influence the way people screen, categorize, and interpret information, decide what is important
and valuable, and stimulate rumination, which are all feedback loops that direct the dynamic
system towards a stable emotion configuration or attractor (see Chapter 1). Such feedback
Table 7.1. Hierarchical temporal organization of levels for analysis of distress experiences
Level of organization
Feeling - thoughts
Affect - cognitions
Anxiety, Sadness, Fear, Anger
Daily - diurnal
Examples provided in Table 7.3
Anxiety - depression
Disorder states
Negative affectivity
Trait anxiety - trait depression
Cultural scrips and norms of reaction
Forces that maintain heritable variation
Note: MDD = major depressive disorder; GAD = Generalized Anxiety Disorder. The micro and meso level are often covered in cross-sectional
perspectives (e.g., a point prevalence or frequency at a specific point in time) whereas the macrolevel covers lifespan perspectives and lifetime
loops enable the emotion state to unfold over time periods far beyond the presence of the stimuli
that caused the emotion and the component processes themselves (called “hysteresis”), a non-
linearity that is typical for complex systems (e.g., Hollenstein, 2015; Verduyn et al., 2015; Van
der Maas & Molenaar, 1992).
This complex emotion system is favored by natural selection because it can reconfigure
itself into a multitude of different states while dissimilar representations can give rise to emotion
instances of the same category (such as anxiety or depression) in different contexts (Barrett,
2017; Kuppens et al., 2010). Anxiety and depression are thus understood as self-organizing
dynamic processes that combine affect, motivation, evaluation, attention, learning, memory,
wanting, and so on, to construct emotional experiences that give meaning to context. Because
every instance of anxiety or depression is constructed, variability is the norm, which enables
individuals to respond flexibly, and to establish unique individual-environment relationships (
Hollenstein, 2015; Scherer, 2009; Tooby & Cosmides, 2008; Kuppens et al., 2009).
Emotions are multifaceted phenomena that can differ markedly across individuals and
cultures, which illustrates their developmental malleability, and each individual should
therefore be pictured as a dynamic system with an unique architecture and resulting dynamics
over time (Barrett, 2018; Fisher et al., 2018; Fogel et al., 1992; Thompson, 1994).
Consequently, two people can experience an identical situation quite differently, and converge
to different emotion states (e.g., sad versus anxious) as a consequence of their personal history
and situational understanding, including whether the event was unexpected, controllable, can
be coped with, was one’s own fault, and so on (Barrett, 2017, 2018; Jeronimus et al., 2017,
2019; Stanton, 2012). Individual differences in emotional clarity or the extent into which one
can identify, label, and characterize emotions, can explain why some adolescents recognize and
experience anxiety and depression as strongly differentiated and discrete emotion states, while
others experience either both together or none (Bailen et al., 2019; Erbas et al., 2014; Fisher et
al., 2018; Gohm & Clore, 2000; Mathews et al., 2016).
Adolescents learn to construct and differentiate increasingly fine-tuned feelings as
specific emotion categories to summarize experiences more efficiently and precisely, thus
increasing emotional granularity or “emotional intelligence” (see Table 7.2; Barrett, 2018;
Erbas et al., 2014; Nook et al., 2018; Russell, 1990). Adolescents with heightened interoception
and low emotional clarity (i.e., difficulties in attributing these inner sensations to specific
emotions) or low granularity (i.e., little emotion differentiation) more often feel unhappy, report
social problems, and develop anxiety and depression disorders (Barrett, 2018; Demiralp et al.,
2012; Erbas et al., 2014; Kashdan & Farmer, 2014; Mathews et al., 2016; Palser et al., 2018;
Sendzik et al., 2017). Being able to put a feeling into words (such as sad or afraid) can already
decrease the subjective intensity of the experience (Lieberman et al., 2011).
An improved understanding of how adolescents construct anxiety and depression and
accept these experiences can result in strategies to intervene in this construction process, to
buffer adolescents against all kinds of social and mental adversity, as emotions can be
deconstructed into their experiential components to be re-categorized, which is thought to
influence how adolescents perceive their reality (Barrett, 2018; Brackett et al., 2012; Kashdan
et al., 2015; Sendzik et al., 2017). Now that we have defined anxiety and depression as
prototypical emotion categories (i.e., populations of diverse instances) that emerge in response
to specific contexts to give the ebb and flow of life meaning, we zoom into their dynamics.
Table 7.2. Anxiety and depression as emotions and personality traits
Instances / definitions
Tense, apprehension, worry, distress, dread, unease,
distressed, frustrated, nervousness, etc.
Alarm, fright, terror, panic, hysteria, horror, shock,
mortification, scared, etc.
Shock, scared
Gloomy, sorrow, grief, despair, hopeless, misery,
melancholy, bored, droopy, tired, sleepy
Disappointment, displeasure, dismay
Guilt, shame, embarrassment, regret, remorse
Neglect, loneliness, isolation, defeat, rejection,
humiliation, insecurity, homesickness, etc.
Tendency to perceive the world as threatening, to be prone to
experience unpleasant and disturbing emotions in reaction to
various types of stress (emotional instability), and to select
oneself into situations that foster negative affect.
Characteristic level of free floating anxiety and fear proneness
Tendency to experience feelings of sadness, guilt, loneliness,
and hopelessness
Note: William James (1890) already emphasized the tremendous variability in the human emotions that people
refer to with the same emotion word. Source: The depicted emotion instances were derived from Barrett (2018),
Cowen et al. (2017), Russell (1980), and Shaver et al. (1987). The description of neuroticism was derived from
Jeronimus (2015) and the definition of the facet traits anxiety and depression from Costa and McCrae (2006).
Dynamic processes
One of the most challenging topics in the study of anxiety and depression is to understand the
processes underlying the temporal dynamics of emotion (Hollenstein, 2015; Houben et al.,
2015; Kuppens et al., 2010; Lewis, 2005; Reitsema et al., 2019). The duration of emotions can
help to distinguish closely related experiences such as shame versus guilt or fear versus anxiety
(Panksepp & Biven, 2012). For example, when studying 27 emotions, in most people sadness
lasted the longest (median 48 hours), while anxiety and guilt (median 4 hours) and fear (median
1 hour) and shame (median 30 minutes) were among the shortest emotion episodes (Verduyn
et al., 2015). The most studied emotion dynamics in adolescents include the frequency with
which they experience anxiety or depression over a protracted period of time, the average
magnitude of emotion over time (intensity), the range of fluctuations (variability), the
magnitude of these fluctuations from moment to moment (instability), the temporal dependency
or persistence of emotion states (inertia), whether anxiety or depression increase (augment) or
decrease (blunt) one another over time, and adolescents’ differentiation (granularity) and clarity
of emotions (see Bailen et al., 2019 and Reitsema et al., 2019 for reviews).
Adolescents show substantial individual differences in the dynamics of anxiety and depression,
but the average frequencies and intensities peak in late adolescence, especially in girls, followed
by a decrease over adulthood (Bailen et al., 2019; Carstensen et al., 2000; Reitsema et al., 2019;
see for adults Fisher et al., 2017, 2018; Houben et al., 2015). Adolescents who report more
frequent, intense, persistent, and variable negative emotions (especially sadness, nervousness
and anger) and decreased positive emotions and energy are most vulnerable for the development
of episodes of anxious or depressed mood (Bailen et al., 2019; Kuppens et al., 2012; Neumann
et al., 2011; Silk et al., 2003; Morgan et al., 2017).
Today there is a scarcity of studies of real-time (moment-to-moment) dynamics of affect
and emotion components and regulation processes in single individuals across and within time
under natural conditions in daily life (Hollenstein, 2015; Houben et al., 2015; Wichers et al.,
2015, 2018), or how these emotional experiences change over adolescence (see for reviews:
Bailen et al., 2019; Reitsema et al., 2019). Momentary assessment studies (EMA) of emotions
typically apply non-adjacent sampling intervals and 3 to 10 assessments per day for 7 to 30 days
(Houben et al., 2015; Kuppens & Verduyn, 2017; Reitsema et al., 2019; Trull et al., 2015; Van
der Krieke et al., 2015); although some studies assessed daily anxiety symptoms over 236 days
(Hoenders et al., 2012) or daily depression symptoms over 239 days (Wichers et al., 2016), and
there are studies in which a group completed 500 measures per participant over 4 months
(Wichers et al., 2018). Researchers suggest that minimal 25 to 30 assessments per subject may
be recommended for multilevel models (Bolger et al., 2012; Maas & Hox, 2005; which still mix
within- and between-person effects), whereas for subject-specific dynamic models of
interacting variables by means of vector autoregressive (VAR) modeling at least 44-50
assessments are required (Box et al., 2015; Fisher et al, 2018; Van der Krieke et al., 2016) up
to 100 assessments to model changes in the conventional dynamic processes themselves
(Bringmann et al., 2017; Wichers et al., 2018) and switches in state-space models (Hamaker &
Grasman, 2012).
Promising future avenues to improve our understanding of anxiety and depression as
emotion states are challenging studies that (a) examine various dynamic processes in single
adolescents and changes therein over adolescence (Bailen et al., 2019; Reitsema et al., 2019;
Koval et al., 2013; Krone et al., 2018) and (b) capture the stream of affective experience in real
time to follow their emergence and unfolding via the synchronization of various emotion
components (time-shifting contemporaneous relations), which requires high-frequency
sampling. Furthermore, dynamic system perspectives can also help to zoom into instances in
which emotions fail to function properly, such as disruptions in affective valence or emotional
intensity, and problems with emotional clarity and differentiation (APA, 2013; Sendzik et al.,
2017). Examples of emotion intensity disturbances include being over- or under-reactive to
emotional situations, such as extremely low levels of positive affect in depressed adolescents,
or a swing towards extreme high positive affect intensities during manic-depressive episodes
(Birmaher, 2013; Rutter et al., 2011). Finally, future studies may use multimethod assessments
to capture adolescent experience, and compare and combine different indices for emotion
dynamics (Bos et al., 2018; Houben et al., 2015; Koval et al., 2013; Reitsema et al., 2019).
The emotion literature suggests that (a) anxiety and depression are constructions of the
world rather than reactions to it, (b) that changes in emotion dynamics can be indicative for the
development of a mood disorder, and (c) may serve as an early warning signal, and these ideas
shall be explored in more depth below.
Mood episodes (meso level)
Anxious and depressed mood spells describe sustained negative background affects that color
as generalized emotions but are much lower in arousal and wider in scope. Whereas emotions
are highly contextualized, moods evaluate the world as a whole, and often lack a specific start
and stop (Barrett, 2018; Beedie et al., 2005; Horwitz & Wakefield, 2007). Moods can be defined
as internal subjective states perpetuated by extended cognitive configurations that narrow
attention towards ongoing internal processes (see Table 7.3) and a restricted set of interactions
and understandings of the surrounding world (APA, 2013; Gotlib & Joormann, 2010; Isen,
1990). In short, depressed adolescents are characterized by negative views of themselves, the
world, and their future, and uncontrollable self-critical cognitions, which impede their ability
to generate positive affect (Gotlib & Joorman, 2010; Rutter et al., 2011). Mood states typically
last for weeks to months and have a relatively constant quality even though the state itself is
dynamic (Judd et al., 1998; Larsen, 1987), and this progression from normal affect fluctuations
to entrenchment is what characterizes mood as a developmental process (De Zwart et al., 2018;
Lewis, 2000; Rutter et al., 2011).
Mood episodes are characterized by the emergence of a metacognitive or recursive
consciousness (a higher-order mental structure) as individuals can become aware of their
internal state as a mode of perception that underlies cognitive decoupling or “head-and-heart
splits” (Damasio et al., 2000; Schooler et al., 2011). People disengage their attention from
perception (called perceptual decoupling), notice the current contents of consciousness (i.e.
meta-awareness), and realize that their feelings do not match their cognitive evaluation of the
situation, thus that something goes awry. Key examples are anhedonia and guilt (see Table 7.3).
Anhedonia refers to being unable to feel pleasure in situations or after events that one recognizes
as normally pleasurable activities, which is typical for childhood depression (see APA, 2013;
Russell, 2017). Depressed adolescents often report guilt, a self-conscious emotion characterized
by tension, regret, and remorse about a particular (in)action that comprises one’s moral or
personal standards (i.e. not living up to ideal self) while one could have done otherwise (APA,
2013; Roseman et al., 1994; Rutter et al., 2011). These higher-order mental structures illustrate
the complexity of mood states as they emerge from various nonlinear dynamic interactions
between affective and cognitive elements.
Anxious and depressed syndromes refer to flexible and dynamic systems of constitutive
heterogeneous elements (co-occurring feelings, thoughts, and actions, see Table 7.3 and APA,
2013), thus moods are formative dimensional constructs similar to the constructivist perspective
on emotions (e.g., Borsboom, 2008, 2017; Constantini et al., 2019; Fried & Nesse, 2015; Gotlib
& Joorman, 2010; Heeren et al., 2018; Kendler et al., 2011, 2012; Wichers et al., 2018). There
may not even be a classification for anxiety or depression etched in the structure of the world
and both may be merely concepts (Kendler et al., 2011, 2015). The idea of anxiety and
depression as populations of instances and dynamic perspectives on their development can
explain how different causes may eventually yield similar symptom clusters while similar
causes do not necessarily result in similar outcomes. In a constantly changing world such stable
(or inert) anxious or depressed mood states must be maintained with feedback loops that affect
the emotional state space by steeping the slopes towards these attractors that previously were
passed easily, including cognitive emotion regulation strategies and behaviors (Aldao et al.,
2010; Fogel et al., 1992; Granic, 2005; Lewis, 2000; Mathews et al., 2016; Thompson et al.,
1994), affective reactivity (e.g., Booij et al., 2018), and feelings of control (e.g., Hovenkamp-
Hermelink et al., 2019), often in response to stressful experiences (e.g., Jeronimus et al., 2013).
The synchronization of mood elements is typically studied using networks of symptoms
(or “nodes”) and their interactions (or “connections,” see Borsboom 2017; Fried et al., 2017;
Marsman et al., 2018; McNally, 2016; Wichers, 2013). The interactions in these systems are
described with node centrality indexes, especially connection strength, closeness, and
betweenness (Bringmann et al., 2018; McNally, 2016; Opsahl ea, 2010). Strength indicates the
direct influence of each symptom or node on the network (via partial correlations), and can be
subdivided over instrength (activated by many other symptoms) and outstrength (activates
many other symptoms), and node interconnectedness can also be expressed in terms of shared
variance with their neighbors (Haslbeck & Fried, 2017). Closeness indicates the shortest
distance between each node to identify direct and indirect influences. Betweenness indicates
the number of times a node traverses the shortest path between two other nodes. Nodes with
high betweenness are “gate keepers” that connect less central nodes or “bridge” different
substructures within the psychological network (see Table 7.3), and because reciprocal
symptom connections within and between anxiety and depression are often equivalent (Cramer
et al., 2010; Fisher et al., 2017), these experiences frequently co-occur.
Recovery time is a network metric that quantifies emotion regulation efficiency
(Thompson, 1994; Yang et al., 2018), and the networks of people with higher overall depression
require longer recovery time after socioemotional processes (Yang et al., 2018), also after
adjustment for recent life events. High or increasing connectivity between symptoms of affect
and cognition may indicate emotional inflexibility or “inertia, which is typically understood
as a more fragile system state in which sudden transitions towards other symptom states
becomes more likely (Scheffer et al., 2012; Wichers et al., 2018). Adolescents often report
sudden changes in symptom levels of anxiety and depression (Rutter et al., 2011), and such
transitions are a hallmark of complex systems in which tension may rise gradually (as resilience
levels may diminish slowly or even without notice) after which minor contextual disturbances
can push the system over a “tipping point” towards another basin of attraction (Callahan et al.,
1990; Scheffer et al., 2012; Van der Maas et al., 1992; Wichers et al., 2018).
The concept of an attractor basin can reconcile the common experience of mood states
as emerging out of the blue (Hayes et al., 2007) with studies that indicate developmental
trajectories for mood symptoms, although discontinuous and non-stationary (Myin-Germeys et
al., 2009; Wichers et al., 2018). This dynamic bimodality of normal versus disorder system
states is intuitive to many scholars and clinicians and has derived some empirical support (e.g.,
Cramer et al., 2016; Hosenfeld et al., 2015; Van de Leemput et al., 2013). One double-blind
study using intensive self-monitoring of one man over 239 days (1474 observations) confirms
the presence of a sudden shift in the severity of depressive symptoms (Wichers et al., 2016) and
a study over 236 days showed a sharp increase in anxiety symptoms (Hoenders et al., 2012),
but such longitudinal evidence remains both scarce and essential (Bos & De Jonge, 2014). The
possibility of discrete shifts between normal versus anxious or depressed states is supported by
brain stimulation studies (Panksepp & Biven, 2012) and rapid shifts from states of anxiety with
marked dysphoria to normal states of mind after the anxiety-eliciting situation - such as a
separation, animal, object, social situation or thunder - is eliminated (Rutter et al., 2011).
Mood symptoms typically develop, activate, and synchronize over time until the system
state becomes self-sustaining and unusually intense or persistent (Myin-Germeys et al., 2009;
Wichers et al., 2018). For example, insomnia may drive fatigue and concentration problems,
while anhedonia may lead to guilt, which may lead to low self-worth, and so on (e.g., Borsboom
et al., 2017; Kendler et al., 2011; Wichers et al., 2013). Moods themselves are defined in terms
of symptoms that persist over weeks (APA, 2013, macro level) while the underlying processes
probably reflect everyday hour-to-hour fluctuations (micro level) as outlined above. In a study
of 104 depressed patients by Bos et al. (2017; assessed ten times a day for five days) sadness
was most sensitive to the other symptoms (highest instrength), whereas anhedonia had the most
influence on other symptoms (outstrength). Daily level changes in positive mood, hopelessness,
anger, and irritability most often activated other symptoms in anxious and depressed patients
(Fisher et al., 2017), and thus not depressed mood, anhedonia, and worry, the putative cardinal
symptoms (see Table 7.3). When studied on a weekly resolution, however, all symptoms of
depression showed connections over time (either direct or indirectly), with anhedonia as the
most central item (Bringmann et al., 2015). These examples illustrate that dynamic relationships
differ across timescales (Dormann & Griffin, 2015; Hamaker & Wichers, 2017; Schiepek et al.,
2016). Real-time changes in affect and rapidly cycling emotions (which require high-frequency
assessments) must therefore be connected with changes in mood symptoms and negative
cognitions (about the self and future), which mandate comparatively low-frequency
measurements over weeks and months. Therapeutic processes, for example, cover both micro
processes such as interactions within each session and more long-term sequential regularities
that occur between consecutive sessions (see Chapter 4; Molenaar, 2010). Studies increasingly
combine measurements across multiple “bursts” of intensive experience sampling over one year
(Yang et al., 2018) which may help improve our understanding of mood in adolescence
(Maciejewski et al., 2015).
A major application of dynamic system perspectives in contemporary mood research is
the quest for generic early warning signals that may indicate upcoming transitions into a mood
disorder episode, as such signals would enable for preventive strategies. Most attention goes to
critical slowing down, enhanced symptom variability, and increasing autocorrelation as signals
for transitions between such qualitatively different system states (Nelson et al., 2017; Wichers
et al., 2015, 2016, 2018), but also frequency distributions, varying complexity, or other dynamic
features are used (Molenaar, 2010; Schiepek et al., 2016).
Table 7.3. Definitions of distress states according to the Diagnostic and Statistical manual of Mental Disorders (DSM-5)
Criteria for MDD
Depressed mood, i.e. feeling sad, empty, or hopeless
Diminished interest or pleasure in virtually all activities
Weight loss or gain or decreased/increased appetite
Sleep disturbance
Insomnia or hypersomnia
Psychomotor agitation or retardation (lethargy)
Loss of energy
Feelings of worthlessness or excessive or inappropriate guilt
Problems with thinking/concentration or indecisiveness
Irritable mood most of the time (childhood specific)
Suicidal ideation
Preoccupation with death, suicidal ideation (plans) or attempt
Social withdrawal
Apprehensive expectation
Muscle tension
Note: X = core symptoms; MDD = major depressive disorder; GAD = generalized anxiety disorder; PTSD = post-traumatic stress disorder.
Four major distress disorders can be identified in the DSM-5 (APA, 2013) on the basis of multiple experiences of which many are shared as
indicated in gray (example derived from Lahey et al., 2017, table 1; but also see Kotov et al., 2017; Zahn-Waxler et al., 2000). Children can show
several anxiety disorders, including separation anxiety, panic disorder, agoraphobia, specific phobia, social phobia, obsessive-compulsive
disorder, generalized anxiety disorder (GAD), and post-traumatic stress disorder (PTSD).
At the group-level there is some evidence for increases in autocorrelation (Van de Leemput et
al., 2013) and higher system entropy before people experience a transition into a depressive
state (Lanata et al., 2015). One individual-level study provided evidence for rising
autocorrelations and variance before the onset of a depressed episode (Wichers et al., 2016).
Studies of therapeutic contexts showed that increasing emotional fluctuations can indicate
qualitative change (Lichtwarck-Aschoff et al., 2012; Schiepek, 2016).
Definite proof of generic early warning signals for the onset of anxiety or depression
episodes requires researchers to monitor many individuals with high-frequency assessments
over a long period in advance of the transition under study and because such data has been
collected with compliance rates of 80% and up (see Hoenders et al., 2012; Wichers et al., 2016,
2018; Schiepek et al., 2016), I expect studies that show whether the idea of warning signals has
substance in the near future, which may result in the implementation of prevention strategies.
At the group-level the timing of such discrete transitions in depressive symptom severity is
inconsistent (De Zwart et al., 2018) which indicates substantial individual variability and
sudden changes; This is illustrated in Figure 7.1, which shows the recovery trajectory of 267
participants over 140 weeks in terms of their depressive symptoms (a 50% symptom reduction
after 30 weeks) and demonstrates how uninformative group-level indicators can be for
individual patterns. The idea of non-linear shifts in symptom severity combined with large
individual variability favors knowledge on how symptoms evolve at the level of the individual
in daily life to elucidate mechanisms, because the underlying causes are probably individual-
specific (Fisher et al., 2018; Wichers et al., 2013; Yang et al., 2018). People who have been
through an episode of psychosis or bipolar disorder often identified their individualized early
warning signals for an upcoming future episode, which illustrates how dynamic system
applications can lay hidden in routine care practices (see Chapter 4 for an elaboration).
Paradoxically, dynamic system perspectives also predict inherent limitations to the
application of warning signals. Mood symptoms are connected via many feedback and
multiplicative processes within individuals, which makes our development nonlinear and
dynamic, as it depends on our current system state. As previously outlined with the weather
metaphor, individual dynamics, emergent processes, thresholds of instability, and sudden
transitions limit the predictability of developmental processes, and some futures shall therefore
remain unknowable with precision, if only because tiny changes can compound to substantial
outcome differences (“the butterfly effect,see Chapter 4; Boeing, 2016; Granic, 2005). Finally,
the question remains as to whether mood disorder symptoms (Table 7.3) are defined at the right
level of granularity to successfully identify the components of the system (as there may be
hundreds of variables) while state transitions (if they exist) are likely to be governed by only a
few (Borsboom, 2017; Van der Maas et al., 1992).
Promising future avenues to study anxious and depressed moods include the integration
of complex dynamic processes across levels of resolution via simultaneous top-down and
bottom-up approaches (Forbes et al., 2016; Witherington, 2007) and models that integrate
within and between person estimates (Adolf et al., 2014; Ernst et al., 2019; Fisher et al., 2018).
Figure 7.1. A group-level trajectory of depressive symptoms over 140 weeks (above) and
the associated person-level trajectories (below) in 267 participants.
Note. The trajectory of depressive symptoms over 140 weeks as assessed with the composite international
diagnostic interview (CIDI) based on the DSM-IV criteria, to study remission rates (≤5 symptoms) and group-
level changes over time. Source: Conradi et al. (2007). This figure was generously provided by Peter de Jonge
and Elske Bos.
Researchers increasingly estimate person-specific complex dynamic systems, which they
subsequently aggregate into group-level clusters with more comparable dynamics and response
synchronization processes (Beltz et al., 2016; Ernst et al., 2019; Yang et al., 2018), to inform
etiological theories. Studies of mood dynamics often use regression-based methods to unravel
autoregressive effects (how symptoms affect themselves) and cross-regressive effects (how
symptoms affect each other) over time via VAR models using all lag-1 relations, which can
handle unique direct effects, but not shared changes (i.e., shared variance is removed) or stable
predictors (see Bulteel et al., 2016 and Molenaar, 2018, for a discussion of limitations, and
Bringmann et al., 2013, for multilevel models, e.g. person-mean centering requires the
assumption of stationarity).
Researchers also questioned the validity of using centrality indices to study mood
symptom networks (Bringmann et al., 2018; Forbes et al., 2017; Hallquist et al., 2019),
including betweenness and closeness centrality as measures of node importance (Bringmann et
al., 2018), and struggled with statistical equivalent network and latent variable models despite
their marked conceptual differences (Markman et al., 2018; Molenaar et al., 2007, 2010), which
suggests that other and more advanced methodologies are required. Future studies might apply
more advanced dynamic system techniques (examples in this book) and dynamic cluster models
(Ernst et al., 2019) and more diverse and multimethod measures (e.g, observers, language
analyses, interviews), as the almost exclusive usage of self-report measures of adolescents’
anxiety and depression goes along with some threats to validity (Eisenberg et al., 2010), and
should be interpreted with caution.
Personality (macro level)
Without pattern recognition our observations remain unfocused and random. Humans are highly
variable and complex, but also show shared patterns in feelings, thoughts, and behaviors that
persist over time and across situations. The “dynamic organization within the individual of those
psychophysical traits that determine his unique adjustments to his environment” pertains to our
personality (Allport, 1937). A large part of what makes each of us unique is our emotional life
and idiographic history (Kuppens et al., 2009; McAdams, 2015). Most individual differences
in these patterned characteristics can be summarized in terms of Five Factors or broad “trait
families” (John et al., 2008), namely, Neuroticism, Extraversion, Conscientiousness,
Agreeableness, and Openness to new experiences (the Big Five). Nowadays many theorists
assume that personality “emerges out of the connectivity structure that exists between the
various components of personality” (Cramer et al., 2012, p.414, also see Baumert et al., 2017;
Constantini et al., 2019; Mõttus & Allerhand, 2017; Ormel et al., 2017), a perspective similar
to the constructivist perspectives on emotion and mood, but with different content and over
longer time scales (see Table 7.1).
The neuroticism factor taps into the organization and functioning of negative emotion
systems (APA, 2013, p.679; Ormel et al., 2013; Shackman et al., 2016), and refers to a dynamic
macro-level structure of synchronized preferences, goals, values, concepts, motives, and
narratives, which evolves in each of us while we develop along the lifespan (e.g., Back et al.,
2011; Cramer et al., 2012; DeYoung, 2015; Fajkowska, 2015; Fleeson et al., 2015;
Jayawickreme et al., 2019; Jeronimus, 2015; Lewis, 2000; McAdams, 2015; Mischel & Shoda,
1995; Mõttus et al., 2018; Ormel et al., 2017; Tackett et al, 2012; Wrzus & Roberts, 2017)
Prominent members of the neuroticism family are the facet traits Anxiety and Depression (see
Table 7.2 for definitions) and Vulnerability (or general susceptibility to stress), which capture
the frequency and intensity with which adolescents experience the emotions anxiety and
depression over time and in different situations (Costa & McCrae, 2006), as well as current
mood symptom levels (Kotov et al., 2010; Riese et al., 2015). The neuroticism trait family is a
vulnerability for the later development of full-blown anxiety and depression disorders
(Jeronimus et al., 2016). A key difference between mood episodes and the facet traits of Anxiety
and Depression is that the former are deemed to indicate intra-individual deviations from our
particular personal normality, whereas the latter refers to more permanent individual differences
in personality dispositions (Jeronimus et al., 2013, 2016; Riese et al., 2015).
Neuroticism can be understood as an umbrella term or macro-level “attractor basin” for
negative affect - the rather stable climatic “average weather” over adolescence - but this
personality landscape changes over the lifespan, which can help explain why adolescence is
marked by high intensity and variable emotion states of anxiety and depression. Most
adolescents experience temporary decreases in conscientiousness (self-regulation) and
agreeableness (warmth, friendliness and tact) and increase in neuroticism
(Anxiety/Depression), a pattern that reverses from early adulthood onwards (see Roberts &
Wood, 2006 for a meta-analysis and Soto et al., 2011 for a cross-sectional study of 1 million
participants). High neuroticism (or negative emotional reactivity and distress), and low
conscientiousness (or poor effortful control and emotion regulation) and low extraversion (or
low positive emotionality) underlie individual differences in sensitivity to reward and
punishment and predict levels of anxiety and depression over adolescence (Watson et al., 2005;
Rutter et al., 2011).
Emotion regulation refers to adolescents’ abilities to regulate emotion dynamics back to
their “normal” system state and the regulatory processes that are installed to keep their core
affect in check (“attractor strength”; see Thompson, 1994; Kuppens et al., 2010), and
dysfunctional patterns play a crucial role in the development and persistence of depression and
anxiety (Jeronimus et al., 2016; Sendzik et al., 2017; Snyder et al., 2015). Most therapies
therefore aim to re-establish cognitive control over emotions, via exposure, re-interpretation (of
the way a situation is construed), or suppression (Gross, 2002), and clinicians increasingly focus
on neuroticism as a target for therapy (Barlow et al., 2014; Roberts et al., 2017).
The various dynamic accounts of personality have a long history but recent accounts include CB5 theory
(DeYoung, 2015), Mixed Model of Personality (Ormel et al., 2017), PERSOC (Back et al., 2011), and TESSERA
(Wrzus & Roberts, 2017), and Whole Trait Theory (Jayawickreme et al., 2019), among others.
High neuroticism is associated with the neurotic cascade(Suls et al., 2005) which
describes a set of processes that can help explain the recurrence of states of anxiety and
depression: (a) heightened reactivity, exposure, and negative appraisals to signs of threat and
negative emotion in the social world (Ormel et al., 2013; Shackman et al., 2016); (b) being
exposed to more negative events (Jeronimus et al., 2013, 2014); (c) the tendency to appraise
objectively neutral or positive events in negative terms (Laceulle et al., 2015; Shackman et al.,
2016); (d) mood spill-over, whereby negative feelings in one area of life spill over into others
(Jeronimus, 2015); and (e) excessive rumination, worries, and intolerance of uncertainty, which
propel feelings of sadness and anxiety and decrease feelings of cheerfulness (Bringmann et al.,
2013; Hong & Cheung, 2015; Nolen-Hoeksema, 2000). High neuroticism and
Anxiety/Depression are also marked by lower emotional granularity and clarity (Barrett, 2018;
Carstensen et al., 2000; Luminet et al., 1999).
Next to the neurotic cascade, high neuroticism scores are also reflected in the way
adolescents think and talk about themselves and construct their life narratives, as people vary
in their emotional tone, themes, and complexity, which are fundamental aspects of our
personality. High-neurotic people report more negatively toned biographical scenes, with more
sadness and distress (but not more expressions of fear/anxiety), more negative inferences about
themselves or others, more contamination sequences (i.e., good scenes that end poorly for them,
which are associated with depression), less agency, and lower recall of specific positive
memories (e.g., McAdams, 2015; Singer et al., 2013). Narrative identities subsume tasks, goals,
projects, tactics, defences, values, and other developmental, motivational, and/or strategic
concerns that contextualize individual lives in time, context, and social roles (McAdams, 2010,
2015). Such meaning-making processes are hallmark of the neuroticism system and treatment
strategies (Hong & Cheung, 2015; Roberts et al., 2017) may influence how adolescents
experience reality (Laceulle et al., 2015).
Dynamic perspectives
Personality traits like neuroticism are often understood as dynamic equilibria (i.e., “attractor
basin” or “set-point”) around which micro- and meso-level elements fluctuate in response to
life experiences (Fleeson & Jajawickreme, 2015; Fleeson & Law, 2015; Jeronimus et al., 2013;
Ormel et al., 2017). Also the attractor landscape itself changes under influence of experiences,
as neuroticism and anxiety/depression basins often become less deep after we take on new social
roles such as worker or parent (Bleidorn et al., 2013; Roberts et al., 2006; Mund et al., 2018),
but deepen in response to stressful life events (especially social stress and conflict) that can be
characterized as unpredictable, uncontrollable, unexpected, undesirable, and non-normative
from a life history perspective (Jeronimus, 2015). The components of the dynamic system
deviate from their balanced configuration (in level or contingencies) until the old or a new
homeostatic equilibrium is found.
Rapid increases in the facets anxiety and depression after stressful experiences (in terms
of weeks and months) can be followed by slower external adaptation processes in our personal
environment (in terms of years and decades) after which the affect landscape gravitates slowly
back to the levels to which one was accustomed, due to substitution in resources, identity,
habits, social support and social interaction although these processes can also propel feedback
loops that prevent regression towards the previous equilibrium, after which the person stabilizes
in a new attractor (Jeronimus, 2015, p.260; Ormel et al., 2017). These hypotheses about
developmental processes can be tested in detail when both the individual and the changing
context are frequently and persistently sampled, but to the best of my knowledge, such high
resolution data is currently unavailable.
People are thought to function in a relatively fixed region of a potentially large
behavioral space, in balance with their environment, resulting in stable personality states
(Cramer et al., 2012, p.416; Ormel et al., 2017) based on a Pareto-optimal allocation of one’s
energy budget, when no additional internal or external change can be made without increasing
the costs somewhere else (e.g., Jeronimus, 2015, p.256). However, continuous small changes
in our environment require adaptations to maintain this Pareto balance, and such fluctuations
play out at the micro level. Future studies may unravel the relationship between differences in
neuroticism and Anxiety or Depression facets and processes underlying dynamic fluctuations
at the individual level. Whether the variation is over days or years, it is important to comprehend
what it means for a person to vary from him or herself, and what it means for persons to vary
from one another (Adolfs et al., 2014; Breiman, 2011; Fisher et al., 2018; Mroczek et al., 2003;
Rose, 2016).
Anxiety and depression emerge from dynamic interactions between various affective, cognitive,
and behavioral elements, which synchronize as emotions (micro), moods (macro), and
personality traits (meso). In terms of emotions, the dynamic systems perspective supported an
understanding of anxiety and depression as constructions of the world (rather than reactions to
it), and suggests that changes in emotion dynamics can serve as warning signals for the
development of a mood disorder, which could be highly valuable for prevention strategies.
Although multiple studies support the existence of rapid transitions between normal and mood
states, there is a dearth of literature that covers such transitions at the individual level.
Dynamic constructivist perspectives and evidence for connections between various
anxiety and depression symptoms provided the field with a new explanation for the frequent
co-occurrence of anxiety and depression states. Neuroticism was identified as a broad meso-
level attractor basin that is stabilized via “the neurotic cascade” and describes our inner climate,
which makes some adolescents more prone to experience negative emotions including anxiety
and depression, and more vulnerable to develop anxiety and depression disorders. The young
age of onset for most anxiety and depression problems suggest that it would be cost-effective
to influence these meso-level processes before adolescents let symptoms of anxiety and/or
depression cascade into other spheres of functioning and the attractor landscape “stabilizes.
Promising future avenues to improve our understanding of states of anxiety and
depression include challenging studies that test various dynamic processes in single adolescents
and examine changes therein over time. These studies ideally connect processes across multiple
time scales, from real-time affective experiences, to the emergence of emotions and of emotion
dysfunctions (which requires high-frequency sampling), and the development of symptoms of
anxiety and depression and mood problems (which should be assessed in terms of weeks and
months) and tendencies and personal narratives (in terms of years and decades). Only studies
that capture all three levels (emotion, mood, personality) can illustrate how various emotion
components synchronize and enable for the emergence of the macro and meso levels, which in
turn influence and constrain the emotions and moods we experience a circular causality that
is the hallmark of the complex dynamic system that evolves within each of us.
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... By adopting such a systemic approach, we can enhance our understanding of anxiety and depression as multi-component and dynamic processes that evolve continuously over time. These conditions are intrinsically interconnected due to their self-organizing and dynamic nature, exhibiting circular causality (Jeronimus, 2019). In this study, we adopted systemic dynamic model of depression and anxiety as a theoretical framework. ...
... This approach has provided support for understanding the causes of illnesses and associated trends as well as for the design of prevention, treatment, and policy interventions (Homer et al., 2016;Thompson et al., 2015). According to this model, depression (Jeronimus, 2019;Wittenborn et al., 2016) and anxiety (Jeronimus, 2019;Morris et al., 2010) can both be considered systemic syndromes. Ruscio et al. (2015) proposed that rumination, psychological distress, and depression are closely interconnected. ...
... This approach has provided support for understanding the causes of illnesses and associated trends as well as for the design of prevention, treatment, and policy interventions (Homer et al., 2016;Thompson et al., 2015). According to this model, depression (Jeronimus, 2019;Wittenborn et al., 2016) and anxiety (Jeronimus, 2019;Morris et al., 2010) can both be considered systemic syndromes. Ruscio et al. (2015) proposed that rumination, psychological distress, and depression are closely interconnected. ...
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This study aims to examine whether psychological distress mediates the association between rumination and symptoms of depression-anxiety, and whether such a mediating role is moderated by the ability to inhibit irrelevant negative information (a moderated-mediation model). On-line questionnaires comprising the Ruminative Response Scale (RRS), Depression, Anxiety, and Stress Scale (DASS-21), and Negative Affective Priming (NAP) Task as a measure of inhibitory control (IC) on negative information were administered to 181 participants (M = 21.57 years old, 80.1% females). The results of the analyses showed (1) a significant negative association between psychological distress and the performance of inhibitory control on negative information, (2) a partial mediating role of psychological distress in the relationship between rumination and symptoms of depression and anxiety, and (3) that the mediating role was moderated by inhibitory control performance. The stronger the inhibitory control, the weaker a relationship between rumination and psychological distress, which is associated with the reduction in the mediating role of psychological distress on the symptoms of depression and anxiety. The implications of our findings will be discussed by considering the systemic dynamic Model for understanding depression and anxiety.
... The phenomenon of comorbidity is related to an increase in severity, poorer treatment results, increased health system costs, and higher suicide rates [9,10], underlining the urgent need to study, understand, and address this issue. Depression and anxiety are among the most prevalent comorbid disorders, especially in child and adolescent populations [11][12][13][14][15]; this is alarming because each disorder is independently associated with substantial functional impairment and future mental health problems. Together, they represent a far greater threat to health (e.g., functional impairment, substance abuse, and poorer response to treatment) [16][17][18][19][20]. ...
... Both depressed and anxious children may demonstrate social deficits (e.g., low social skills and social status) and, as a result, do not receive positive social reinforcement; and they have more problems coping with negative life events and high stress. Such symptoms also point to children's negative academic cognitions (i.e., poor beliefs about their important role in academic competence and ability to control academic outcomes) and poor academic performance (14). Consistent with hypothesis e, the interpersonal symptoms show higher scores on the bridge centrality measures based on their capacity to activate other symptoms in the network. ...
... Symptoms of "worries" and "nervous/tense" (associated with anxiety) as well as "feels depressed" and "feels sad" (associated with depression) stand out in terms of their impact on the connectivity of the network. This is congruent with the main diagnostic criteria of both depression (depressed mood) and anxiety (tension/nervousness) [46,80] and with existing literature on both disorders across development (ages [5][6][7][8][9][10][11][12][13][14], which identify feeling "anxious/ fearful" and "unhappy/sad" as the most central symptoms [39]. Again, most of these items are related to the negative affect, in line with the tripartite model. ...
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The combination of depression and anxiety is among the most prevalent comorbidities of disorders leading to substantial functional impairment in children and adolescents. The network perspective offers a new paradigm for understanding and measuring psychological constructs and their comorbidity. The present study aims to apply network analysis to explore the comorbidity between depression and anxiety symptoms. Specifically, the study examines bridge symptoms, comorbidity, and shortest pathway networks and estimates the impact of the symptoms in the network’s connectivity and structure. The findings show that “feeling lonely” and “feeling unloved” are identified as the most central bridge symptoms. The shortest path network suggests that the role of a mixed anxiety-depressive symptomatology, and specific and non-specific symptoms of clinical criteria, such as “worries,” “feels depressed,” “fears school,” and “talks about suicide” could serve as a warning for comorbidity.
... These effects were observed when talking about sensory experiences (topic 2) and were most pronounced when participants talked about their socio-emotional life (topic 3). Arguably, these self-referencing topics promoted the emergence of critical states in the participants, and more neuroticism made such critical transitions more likely, in line with a heightened sensitivity to environmental demands and more rapid mood changes (e.g., Jeronimus, 2019). Contrarily, low neuroticism would predict more complex, predictable, and stable dynamics of body motion (high emotional stability, see H2b). ...
... Neuroticism would optimize organismic security against perceived threats or uncertainty (i.e., psychological entropy, DeYoung, 2013). Phenomenologically, this could be observed in experiencing uncertainty as threatening and eliciting anxiety and defensive responses; however, this configuration makes individuals better adapted to threatening situations, in opposition to emotionally stable individuals who are less likely to experience uncertainty as threatening (Hovhannisyan & Vervaeke, 2022;Jeronimus, 2019). Highly neurotic (i.e. ...
... In part, this renewed interest in dynamical systems is due to the rise in studies revealing that changes in psychopathology are often nonlinear (Delignières et al., 2004;Fisher et al., 2011;Hamaker & Wichers, 2017;Hayes et al., 2007;Helmich et al., 2020;Hosenfeld et al., 2015;Molenaar & Campbell, 2009). In the dynamical systems framework, people are viewed as complex systems of interacting components (e.g., behaviors, cognitions, and emotions) in which the dynamics of those components can be studied to anticipate impending changes (Boker et al., 2016;Fried & Robinaugh, 2020;Jeronimus, 2019). Particularly, the phenomenon of "critical slowing down" may lead to early-warning signals in the temporal dynamics of the system variables before a critical transition from one dynamically stable state to another (e.g., from a depressed to nondepressed state). ...
... To be able to capture and calculate early-warning signals in affect observations before shifts in depressive symptoms, we need frequent momentary assessments over a period in which clinical change is likely. High-resolution time-series data (Cabrieto et al., 2018;Dablander et al., 2022;Dakos, Carpenter, et al., 2012;Liu et al., 2015) that capture moment-to-moment variations at a time scale that is short enough to cover the full range of fluctuations in the state of the system (Hamaker et al., 2015;Haslbeck & Ryan, 2022;Jeronimus, 2019;Kuppens et al., 2010;van Der Bolt et al., 2021), and over a long-enough period to capture the entire state change, is required to detect change in the system dynamics. Therefore, we collected intensive longitudinal data from 41 individuals who were starting treatment for depressive complaints as they entered the study and were thus likely to show symptom improvement. ...
Full-text available
Drawing on dynamical systems theory, we investigated whether within-persons-detected early-warning signals in momentary affect preceded critical transitions toward lower levels of depressive symptoms during therapy. Participants were 41 depressed individuals who were starting psychological treatment. Positive and negative affect (high and low arousal) were measured 5 times a day using ecological momentary assessments over 4 months ( M = 522 observations per individual). Depressive symptoms were assessed weekly over 6 months. Within-persons rising autocorrelation was found for 89% of individuals with transitions in at least one variable (vs. 62.5% for individuals without transitions) and in a consistently higher proportion of the separate variables (~44% across affect measures) than for individuals without transitions (~27%). Rising variance was found for few individuals, both preceding transitions (~11%) and for individuals without transitions (~12%). Part of our sample showed critical slowing down, but early-warning signals may have limited value as a personalized prediction method.
... In part, this renewed interest in dynamical systems is due the rise in studies revealing that changes in psychopathology are often nonlinear [4][5][6][7][8][9][10]. In the dynamical systems framework, people are viewed as complex systems of interacting components (e.g., behaviors, cognitions and emotions) in which the dynamics of those components can be studied to anticipate impending changes [11][12][13]. Particularly, the phenomenon of 'critical slowing down' may lead to early warning signals (EWS) in the temporal dynamics of the system variables before a critical transition from one dynamically stable state to another (e.g., from a depressed to non-depressed state). When a system critically slows, it destabilizes and gradually becomes less resilient to external shocks (e.g., daily events), which results in more extreme fluctuations (rising variance) and a longer time to return to the equilibrium position (rising autocorrelation). ...
... To be able to capture and calculate EWS in affect observations before shifts in depressive symptoms, we need frequent momentary assessments over a period in which clinical change is likely. High resolution time series data [24,[59][60][61] that captures moment-to-moment variations at a time scale that is short enough to cover the full range of fluctuations in the state of the system [12,[62][63][64][65], and over a long enough period to capture the entire state change, is required to detect change in the system dynamics. ...
Full-text available
Drawing on dynamical systems theory, this study investigated whether within-person detected early warning signals (EWS) in momentary affect preceded critical transitions towards lower levels of depressive symptoms during therapy. Participants were 41 depressed individuals who were starting psychological treatment. Positive and negative affect (high and low arousal) were measured five times a day using ecological momentary assessments over four months (M=521 observations per individual). Depressive symptoms were assessed weekly over six months. Within-person rising autocorrelation was found for 89% of individuals with transitions in at least one variable (62.5% in the no-transition group), and in a consistently higher proportion of the separate variables (~44% across affect measures) than for individuals without transitions (~27%). Rising variance was found for few individuals, both preceding transitions (~11%) and for individuals without a transition (~12%). Part of our sample showed critical slowing down, but EWS may have limited value as a personalized prediction method.
... Eörs Szathmáry once said that linguists "would rather use each other's toothbrushes than their terminology, " and I fear the same is true of psychologists. Several researchers have already commented on the fact that there are a large set of metrics that fall under the umbrella of "affective dynamics" (e.g., Trull et al., 2015;Dejonckheere et al., 2019;Jeronimus, 2019;Reitsema et al., 2021) and that there may be more effective ways to integrate them into a common framework (Hoemann et al., 2020a). A proliferation of terms is not necessarily a bad thing. ...
Full-text available
A growing body of research identifies emotion differentiation—the ability to specifically identify one’s emotions—as a key skill for well-being. High emotion differentiation is associated with healthier and more effective regulation of one’s emotions, and low emotion differentiation has been documented in several forms of psychopathology. However, the lion’s share of this research has focused on adult samples, even though approximately 50% of mental disorders onset before age 18. This review curates what we know about the development of emotion differentiation and its implications for youth mental health. I first review published studies investigating how emotion differentiation develops across childhood and adolescence, as well as studies testing relations between emotion differentiation and mental health in youth samples. Emerging evidence suggests that emotion differentiation actually falls across childhood and adolescence, a counterintuitive pattern that merits further investigation. Additionally, several studies find relations between emotion differentiation and youth mental health, but some instability in results emerged. I then identify open questions that limit our current understanding of emotion differentiation, including (i) lack of clarity as to the valid measurement of emotion differentiation, (ii) potential third variables that could explain relations between emotion differentiation and mental-health (e.g., mean negative affect, IQ, personality, and circularity with outcomes), and (iii) lack of clear mechanistic models regarding the development of emotion differentiation and how it facilitates well-being. I conclude with a discussion of future directions that can address open questions and work toward interventions that treat (or even prevent) psychopathology.
... Eörs Szathmáry once said that linguists "would rather use each other's toothbrushes than their terminology," and I fear the same is true of psychologists. Several researchers have already commented on the fact that there are a large set of metrics that fall under the umbrella of "affective dynamics" (e.g., Dejonckheere et al., 2019;Jeronimus, 2019;Reitsema et al., 2021;Trull et al., 2015) and that there may be more effective ways to integrate them into a common framework (Hoemann, Nielson, et al., 2020). A proliferation of terms is not necessarily a bad thing. ...
Full-text available
A growing body of research identifies emotion differentiation—the ability to specifically identify one’s emotions—as a key skill for well-being. High emotion differentiation is associated with healthier and more effective regulation of one’s emotions, and low emotion differentiation has been documented in several forms of psychopathology. However, the lion’s share of this research has focused on adult samples, even though approximately 50% of mental disorders onset before age 18. This review curates what we know about the development of emotion differentiation and its implications for youth mental health. I first review published studies investigating how emotion differentiation develops across childhood and adolescence, and studies testing relations between emotion differentiation and mental health in youth samples. Emerging evidence suggests that emotion differentiation actually falls across childhood and adolescence, a counterintuitive pattern that merits further investigation. Similarly, although several studies find relations between emotion differentiation and youth mental health, instability in results suggests that more data are needed for a firm conclusion to be drawn. I then identify open questions that currently limit our understanding of emotion differentiation, including (i) lack of clarity as to the valid measurement of emotion differentiation, (ii) potential third variables that could explain relations between emotion differentiation and mental-health (e.g., mean negative affect, IQ, personality, and circularity with outcomes), and (iii) lack of clear mechanistic models regarding the development of emotion differentiation and how it facilitates well-being. I conclude with a discussion of future directions that can address open questions and work towards interventions that treat (or even prevent) psychopathology.
Full-text available
Now in its fourth edition, the acclaimed Oxford Textbook of Psychopathology aims for both depth and breadth, with a focus on adult disorders and special attention given to personality disorders. It provides an unparalleled guide for professionals and students alike. Esteemed editors Robert F. Krueger and Paul H. Blaney selected the most eminent researchers in abnormal psychology to provide thorough coverage and to discuss notable issues in the various pathologies which are their expertise. This fourth edition of the Oxford Textbook of Psychopathology is fully updated and also reflects alternative, emerging perspectives in the field (e.g., the National Institute of Mental Health’s Research Domain Criteria Initiative [RDoC, the Hierarchical Taxonomy of Psychopathology [HiTOP]). The Textbook exposes readers to exceptional scholarship, the history and philosophy of psychopathology, the logic of the best approaches to current disorders, and an expert outlook on what researchers and mental health professionals will be facing in the years to come. This volume will be useful for all mental health workers, including clinical psychologists, psychiatrists, and social workers, and as a textbook focused on understanding psychopathology in depth for anyone wishing to be up to date on the latest developments in the field.
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Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.
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Empirical evidence is mounting that monitoring momentary experiences for the presence of early warning signals (EWS) may allow for personalized predictions of meaningful symptom shifts in psychopathology. Studies aiming to detect EWS require intensive longitudinal measurement designs that center on individuals undergoing change. We recommend that researchers: (a) define criteria for relevant symptom shifts a priori to allow specific hypothesis testing; (b) balance the observation period length and high-frequency measurements with participant burden by testing ambitious designs with pilot studies; (c) choose variables that are meaningful to their patient group and facilitate replication by others. Thoroughly considered designs are necessary to assess the promise of EWS as a clinical tool to detect, prevent or encourage impending symptom changes in psychopathology.
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Theories on children and adolescents emotion dynamics were reviewed using data from 102 ecological momentary assessment studies with 19.928 participants and 689 estimates. We examined age-graded differences in emotional intensity, variability, instability, inertia, differentiation, and augmentation/blunting. Outcomes included positive versus negative affect scales, discrete emotions (anger, sadness, anxiety, and happiness), and we compared samples of youth with or without mental or physiological problems. Multi-level models showed more variable positive affect and sadness in adolescents compared to children, and more intense negative affect. Our additional descriptive review suggests a decrease in instability of positive and negative emotions from early to late adolescence. Mental health problems were associated with more variable and less intense positive affect, and more intense anxiety and heightened sadness variability. These results suggest systematic changes in emotion dynamics throughout childhood and adolescence, but the supporting literature proved to be limited, fragmented, and based on heterogeneous concepts and methodology.
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Studying emotion dynamics through time series models is becoming increasingly popular in the social sciences. Across individuals, dynamics can be rather heterogeneous. To enable comparisons and generalizations of dynamics across groups of individuals, one needs sophisticated tools that express the essential similarities and differences. A way to proceed is to identify subgroups of people who are characterized by qualitatively similar emotion dynamics through dynamic clustering. So far, these methods assume equal generating processes for individuals per cluster. To avoid this overly restrictive assumption we outline a probabilistic clustering approach based on a mixture model that clusters on individuals’ vector autoregressive (VAR) coefficients. We evaluate the performance of the method and compare it to a non-probabilistic method in a simulation study. The usefulness of the methods is illustrated using 366 ecological momentary assessment time series with external measures of depression and anxiety.
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We thank Adolf and Fried (2019) for their insightful commentary on our paper (Fisher et al., 2018). We agree, in principle, that group-to-individual generalizability lies along a continuum. Some intraindividual and interindividual statistical estimates may be ergodic, sharing equivalent values across all statistical moments. Under these conditions, inferences from cross-sectional data could be applied to individuals. On the other end of this continuum, intra- and interindividual estimates are orthogonal, rendering them unrelated and nontransferable.
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Over recent years, it has become clear that group-based approaches cannot directly be used to understand individual adolescent development. For that reason, interest in Dynamic Systems Theory, or DST, has increased rapidly. Psychosocial Development in Adolescence: Insights from the Dynamic Systems Approach covers state-of-the-art insights into adolescent development that have resulted from adopting a dynamic systems approach. The first chapter of the book provides a basic introduction into dynamic systems principles and explains their consequences for the study of development. Subsequently, different experts discuss why and how we can apply a dynamic systems approach to the study of the adolescent transition period and psychological interventions. Various examples of the application of a dynamic systems approach are showcased, ranging from basic to more advanced techniques, as well as the insights they have generated. These applications cover a variety of fundamental topics in adolescent development, ranging from the development of identity, morality, sexuality, and peer-networks, to more applied topics such as psychological interventions, educational dropout, and talent development. This book will be invaluable to both beginner and expert level students and researchers interested in a dynamic systems approach and in the insights that it has yielded for adolescent development.
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Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists and, therefore, have limited utility for advancing clinical theory and nomenclature.
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How can depression be associated with both instability and inertia of affect? Koval et al. (2013, Emotion, 13, 1132) showed that this paradox can be solved by accounting for the statistical overlap between measures of affect dynamics. Nevertheless, these measures are still often studied in isolation. The present study is a replication of the Koval et al. study. We used experience sampling data (three times a day, 1 month) of 462 participants from the general population and a subsample thereof (N = 100) selected to reflect a uniform range of depressive symptoms. Dynamics measures were calculated for momentary negative affect scores. When adjusting for the overlap among affect dynamics measures, depression was associated with ‘dispersion’ (SD) but not ‘instability’ (RMSSD) or ‘inertia’ (AR) of negative affect. The association between dispersion and depression became non‐significant when mean levels of negative affect were adjusted for. These findings substantiate the evidence that the presumed association between depression and instability is largely accounted for by the SD, while the association between dispersion and depression may largely reflect mean levels of affect. Depression may thus not be related to higher instability per se, which would be in line with theories on the adaptive function of moment‐to‐moment fluctuations in affect.
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Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a psychological network context. In this paper we critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness and closeness centrality. We show that problems with centrality indices mentioned in the social network literature also apply to the psychological networks. Assumptions underlying centrality indices, such as presence of a flow and shortest paths, may not correspond with a general theory of how psychological variables relate to one another. Furthermore, the assumptions of node distinctiveness and node exchangeability may not hold in psychological networks. We conclude that, for psychological networks, especially betweenness and closeness centrality seem unsuitable as measures of node importance. We therefore suggest three ways forward: (1) using centrality measures that are tailored to the psychological network context, (2) reconsidering existing measures of importance used in statistical models underlying psychological networks, and (3) leaving the whole idea of node centrality behind. Foremost, we argue that one has to make explicit what is meant with being central and what assumptions the centrality measure of choice entails to make sure that there is a match between the process under study and the centrality measure that is used. General summary: In clinical psychology, networks of symptoms or affect states are increasingly used to study psychopathology. Such psychopathological networks are often further analyzed with centrality measures that indicate which symptoms or affect states are structurally important. We argue that the use of these centrality measures, which originally stem from social networks, is problematic in psychological networks, and propose several alternative ways forward.
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Socioemotional processes engaged in daily life may afford and/or constrain individuals’ emotion regulation in ways that affect psychological health. Recent findings from experience sampling studies suggest that persistence of negative emotions (emotion inertia), the strength of relations among an individual’s negative emotions (density of the emotion network), and cycles of negative/aggressive interpersonal transactions are related to psychological health. Using multiple bursts of intensive experience sampling data obtained from 150 persons over one year, person-specific analysis, and impulse response analysis, this study quantifies the complex and interconnected socioemotional processes that surround individuals’ daily social interactions and on-going regulation of negative emotion in terms of recovery time. We also examine how this measure of regulatory inefficiency is related to interindividual differences and intraindividual change in level of depressive symptoms. Individuals with longer recovery times had higher overall level of depressive symptoms. Also, during periods where recovery time of sadness was longer than usual, individuals’ depressive symptoms were also higher than usual, particularly among individuals who experienced higher overall level of stressful life events. The findings and analysis highlight the utility of a person-specific network approach to study emotion regulation, how regulatory processes change over time, and potentially how planned changes in the configuration of individuals’ systems may contribute to psychological health.
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People can experience disasters vicariously (indirectly) via conversation, social media, radio, and television, even when not directly involved in a disaster. This study examined whether vicarious exposure to the MH17-airplane crash in Ukraine, with 196 Dutch victims, elicited affective and somatic responses in Dutch adults about 2,600 km away, who happened to participate in an ongoing diary study. Participants (n = 141) filled out a diary three times a day for 30 days on their smartphones. Within-person changes in positive affect (PA) and negative affect (NA) and somatic symptoms after the crash were studied. Additionally, we tested whether between-person differences in response could be explained by age, baseline personality (NEO-FFI-3), and media exposure. The MH17 crash elicited a small within-person decrease in PA and an increase in NA and somatic symptoms. This response waned after 3 days and returned to baseline at day four. The decrease in PA was larger in more extraverted participants but smaller in those higher on neuroticism or conscientiousness. The NA response was smaller in elderly. Personality did not seem to moderate theNAand somatic response, and neither did media exposure. Dutch participants showed small acute somatic and affective responses up till 3 days to a disaster that they had not directly witnessed. Vicariously experienced disasters can thus elicit affective-visceral responses indicative of acute stress reactions. Personality and age explained some of the individual differences in this reaction.