TY  - JOUR
AU  - Siepe, Björn Sebastian
AU  - Sander, Christian
AU  - Schultze, Martin
AU  - Kliem, Andreas
AU  - Ludwig, Sascha
AU  - Hegerl, Ulrich
AU  - Reich, Hanna
PY  - 2024
DA  - 2024/4/18
TI  - Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data
JO  - JMIR Ment Health
SP  - e50136
VL  - 11
KW  - depression
KW  - time series analysis
KW  - network analysis
KW  - experience sampling
KW  - idiography
KW  - time varying
KW  - mobile phone
AB  - Background: As depression is highly heterogenous, an increasing 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 lead to important temporal features of the disorder being missed. Objective: To reveal the dynamic nature of depression, we aimed to use a recently developed technique to investigate whether and how associations among depressive symptoms change over time. Methods: Using daily data (mean length 274, SD 82 d) of 20 participants with depression, we modeled idiographic associations among depressive symptoms, rumination, sleep, and quantity and quality of social contacts as dynamic networks using time-varying vector autoregressive models. Results: The resulting models showed marked interindividual and intraindividual differences. For some participants, associations among 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. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2024/1/e50136
UR  - https://doi.org/10.2196/50136
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38635978
DO  - 10.2196/50136
ID  - info:doi/10.2196/50136
ER  -