Major depressive disorder as a nonlinear dynamic system: bimodality in the frequency distribution of depressive symptoms over time

Bettina Hosenfeld, Elisabeth H. Bos, Klaas J. Wardenaar, Henk Jan Conradi, Han L. J. van der Maas, Ingmar Visser, Peter de Jonge*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Background: A defining characteristic of Major Depressive Disorder (MDD) is its episodic course, which might indicate that MDD is a nonlinear dynamic phenomenon with two discrete states. We investigated this hypothesis using the symptom time series of individual patients.

Methods: In 178 primary care patients with MDD, the presence of the nine DSM-IV symptoms of depression was recorded weekly for two years. For each patient, the time-series plots as well as the frequency distributions of the symptoms over 104 weeks were inspected. Furthermore, two indicators of bimodality were obtained: the bimodality coefficient (BC) and the fit of a 1- and a 2-state Hidden Markov Model (HMM).

Results: In 66 % of the sample, high bimodality coefficients (BC > .55) were found. These corresponded to relatively sudden jumps in the symptom curves and to highly skewed or bimodal frequency distributions. The results of the HMM analyses classified 90 % of the symptom distributions as bimodal.

Conclusions: A two-state pattern can be used to describe the course of depression symptoms in many patients. The BC seems useful in differentiating between subgroups of MDD patients based on their life course data.

Original languageEnglish
Article number222
Number of pages9
JournalBMC Psychiatry
Volume15
Issue number1
DOIs
Publication statusPublished - 18-Sep-2015

Keywords

  • HIDDEN MARKOV-MODELS
  • ENHANCED TREATMENT
  • PRIMARY-CARE
  • RECURRENCE
  • INVENTORY
  • REMISSION
  • EPISODES
  • ONSET

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