Complex affect dynamics add limited information to the prediction of psychological well-being

Dejonckheere, Egon; Mestdagh, Merijn; Houben, Marlies; Rutten, Isa; Sels, Luc; Kuppens, Peter; Tuerlinckx, Francis

doi:10.1038/s41562-019-0555-0

Cited by 362 publications

(528 citation statements)

References 74 publications

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“…For instance, in our symptom severity models, we did not consistently find significant associations between mean levels of affect and symptom experiences. We might expect, given findings by studies such as Dejonckheere et al (2019), that such effects would emerge. The present study utilized specific symptom measures rather than more general measures of self-reported well-being, so it is possible that this lack of associations with mean levels is due to the fact that specific symptoms are less associated with mean levels of affect than with broader measures of "psychological health", but it is also possible that this is a function of low statistical power.…”

Section: Limitationsmentioning

confidence: 95%

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity

Bosley

Soyster

Fisher

2019

JPOR

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Studies of affect dynamics in psychopathology often focus on the prediction of broad constructs like subjective well-being and psychological health. Less is known about how fluctuation in affect over time relates to specific symptom measures (e.g., anxiety or depression), or how these domains change in treatment. A clinical sample of 32 adults with mood and anxiety disorders (13 generalized anxiety, 5 major depression, 14 comorbid) completed four daily assessments of positive (PA) and negative affect (NA) for 30 days prior to receiving cognitive behavioral treatment. Anxiety and depression symptom severity were assessed pre-and post-treatment. We calculated three metrics of affect dynamics for each person's PA and NA time series: (1) variability (experiencing emotional extremes, the standard deviation of a person's PA or NA vector); (2) instability (magnitude of point-to-point change in emotion, the vector's mean squared successive difference); and (3) inertia (the extent to which emotions self-perpetuate over time, the lag-1 autocorrelation of the vector). Multiple regression models were run to test dynamics of positive and negative affect as between-subjects predictors of symptom severity and pre-to-posttreatment change in symptoms. Findings suggest NA dynamics are unrelated to depression symptom severity or treatment response, but we observed a specific effect of NA instability (MSSD) on both severity and response of anxiety symptoms. All PA dynamics were unrelated to anxiety or depression symptom severity. However, variability, instability, and inertia of PA were all found to relate to treatment response for both anxiety and depression symptoms. Taken together, our results suggest that affect dynamics have some specificity in their relationship to clinically relevant phenomena such as symptom severity and treatment outcomes at the between-subjects level of analysis.

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Section: Limitationsmentioning

confidence: 95%

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity

Bosley

Soyster

Fisher

2019

JPOR

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“…). Of course, it is possible to estimate any other person-specific parameter, such as weighted means, median, person-standard deviations (Jones, Brown, Serfass, & Sherman, 2017), or a number of other parameters (Dejonckheere et al, 2019;Zimmermann et al, 2019), each of which technically needs to be validated in their own right, and based on theoretical assumptions. The focus on average state scores in the current study reflects its popularity as an estimate of a person characteristic (see Table 1).…”

Section: Current Practicesmentioning

confidence: 99%

Assessing Personality States: What to Consider when Constructing Personality State Measures

Horstmann¹,

Ziegler²

2020

Preprint

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Repeated assessments of personality states in daily diary or experience sampling studies have become a more and more common tool in the psychologist's toolbox. However, and contrary to the widely available literature on personality traits, no best practices for the development of personality state measures exist, and personality state measures have been developed in many different ways. To address this, we first define what a personality state is and discuss important components. Based on this, we define what a personality state measure is and suggest a general guideline for the development of such measures. Following the ABC of test construction can then guide the strategy for obtaining validity and reliability evidence: (A) What is the construct being measured? (B) What is the intended purpose of the measure? And (C)What is the targeted population of persons and situations? We then conclude with an example by developing an initial item pool for the assessment of conscientiousness personality states.

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“…These current efforts to describe personality via dynamic system parameters are, however, short on explaining how individual and dyadic differences in these parameters develop and what they exactly reflect. Partly as a consequence of this, it is currently also unclear how much different of these dynamic indices relate to each other (and how much they should) and whether they provide incremental information above and beyond state averages (e.g., Dejonckheere et al, 2019). As explained in detail above, intra-individual (as well as intra-dyadic) variability parameters are, strictly speaking, not psychological entities themselves but epiphenomena reflecting the strength and/or stability of specific interaction state contingencies.…”

Section: Personality and Social Interaction 49mentioning

confidence: 99%

Social Interaction Processes and Personality

Back¹

2020

Preprint

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Social interactions are one of the most relevant contexts of our lives and they are intimately connected to the conceptualization, dynamics, development, and consequences of personality. In this chapter, I will first analyze the way social interactions unfold via interaction states of all interaction partners and describe how people differ in social interaction processes. Following the PERSOC model, I will argue that these individual differences are a key window to understanding the nature of some of the most popular personality traits (e.g., extraversion, dominance, shyness, agreeableness, narcissism), as well as their effects on and development in social relationships. Empirical research on individual differences in interaction state levels, contingencies, and fluctuations is summarized. In closing, I describe a couple of current limitations, and outline perspectives for understanding and assessing personality traits as dynamic social interaction systems.

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Complex affect dynamics add limited information to the prediction of psychological well-being

Cited by 362 publications

References 74 publications

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity

Affect Dynamics as Predictors of Symptom Severity and Treatment Response in Mood and Anxiety Disorders: Evidence for Specificity

Assessing Personality States: What to Consider when Constructing Personality State Measures

Social Interaction Processes and Personality

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