Disentangling the Role of Affect in the Evolution of Depressive Complaints Using Complex Dynamical Networks

Gabriela Lunansky, Ria H.A. Hoekstra, Tessa F. Blanken

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Many studies have found that depressive complaints are associated with the regulation of affect while facing stress. Individuals inclined towards the experience of negative affect are more vulnerable to developing depressive complaints, while frequent experiences of positive affect buffer the development of such complaints. To better understand the dynamic mechanisms between affect and depression in detail, this paper investigates how different evaluations of depressive complaints over a prolonged period of stress relate to fluctuations in affect. We included assessments of affect (Positive and Negative Affect Scale) and depressive complaints (Patient Health Questionnaire) in 228 participants who completed at least 20 assessments spanning between 9-14 weeks. We (i) explored affect trajectories for different evolutions of depressive complaints, (ii) estimated longitudinal multilevel network models to examine the direct interplay between affect and depressive complaints in detail, and (iii) investigated how person-specific network density relates to changes in depressive complaints over time. When separating affect trajectories based on depressive complaints, we identified that individuals consistently experiencing depressive complaints (PHQ > 4) report higher negative affect levels than positive affect. Contrary, individuals consistently reporting no depressive complaints (PHQ ≤4) showed the opposite pattern. Furthermore, the longitudinal networks included many and strong relations between the affects and depressive complaints variables. Lastly, we found a strong correlation between the density of person-specific networks and their change (aggravation or alleviation) in depressive complaints. We conclude that affect fluctuations and evolutions of depressive complaints are directly related both within- and across individuals over time.

Original languageEnglish
Article number74841
JournalCollabra: Psychology
Volume9
Issue number1
DOIs
Publication statusPublished - 11 May 2023

Keywords

  • affect
  • depressive complaints
  • dynamical networks
  • longitudinal analysis
  • psychopathology mechanisms

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