ArticleJMIR mental health2024
Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data.
Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- Gender-specific temporal dynamics of depressive symptoms and emotional resilience in adolescents: a cross-lagged network and simulation-based intervention study.Child and adolescent psychiatry and mental health · 2026Article
- Distinguishing Chronic and Non-Chronic Depression: A Clinical Profile and Symptom Networks Approach.Psychotherapy and psychosomatics · 2026Article
- An investigation into in-sample and out-of-sample model selection for nonstationary autoregressive models.The British journal of mathematical and statistical psychology · 2026Article
- An Adaptation of Perceived Causal Networks for Children and Adolescents (PECAN-CA): An Evaluation of its Reliability and Feasibility.Child psychiatry and human development · 2025Article
- Links between self-monitoring data collected through smartphones and smartwatches and the individual disease trajectories of adult patients with depressive disorders: Study protocol of a one-year observational trial.Contemporary clinical trials communications · 2025Article
- Depressive symptom trajectories and incident metabolic syndrome in middle-aged and older adults: A longitudinal analysis of the ELSA study.Frontiers in psychiatry · 2025Article
- Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach.Cognitive therapy and research · 2024Article
- Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.Journal of psychopathology and clinical science · 2024Article
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7 authors.
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Abstract
backgroundAs 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.
objectiveTo 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.
methodsUsing 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.
resultsThe 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.
conclusionsIdiographic 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.
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