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Accepted for/Published in: JMIR Mental Health

Date Submitted: Jun 20, 2023
Date Accepted: Feb 14, 2024

The final, peer-reviewed published version of this preprint can be found here:

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

Siepe BS, Sander C, Schultze M, Kliem A, Ludwig S, Hegerl U, Reich H

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

JMIR Ment Health 2024;11:e50136

DOI: 10.2196/50136

PMID: 38635978

PMCID: 11066753

Temporal dynamics of depressive symptomatology: Applying time-varying network models to data from patients with depressive disorders

  • Björn Sebastian Siepe; 
  • Christian Sander; 
  • Martin Schultze; 
  • Andreas Kliem; 
  • Sascha Ludwig; 
  • Ulrich Hegerl; 
  • Hanna Reich

ABSTRACT

Background:

As depression is highly heterogenous, a growing number of studies investigate person-specific associations of depressive symptoms in longitudinal data. However, most studies in this area of research conceptualize symptom interrelations to be static and time-invariant, which may miss important temporal features of the disorder.

Objective:

To shed a light on the dynamical nature of depression, we used a recently developed technique to investigate if and how associations between depressive symptoms change over time.

Methods:

In daily data (mean length: 274 days) of 20 participants with depression, we modeled idiographic associations between depressive symptoms, rumination, sleep, and quantity/quality of social contacts as dynamical networks using time-varying vector autoregressive models.

Results:

Resulting models showed marked inter- & intraindividual differences. For some participants, associations between variables changed in the span of some weeks, whereas they stayed stable over months for others. Our results further indicated nonstationarity in all participants.

Conclusions:

Idiographic symptom networks can provide insights into the temporal course of mental disorders and open new avenues of research for the study of the development and stability of psychopathological processes.


 Citation

Please cite as:

Siepe BS, Sander C, Schultze M, Kliem A, Ludwig S, Hegerl U, Reich H

Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

JMIR Ment Health 2024;11:e50136

DOI: 10.2196/50136

PMID: 38635978

PMCID: 11066753

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