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This paper is in the following e-collection/theme issue:

Depression and Mood Disorders; Suicide Prevention (1770) New Methods (1189) mHealth for Data Collection and Research (1273) Longitudinal and Cohort Studies in Public Health (444) Secondary Use of Clinical Data for Research and Surveillance (499) Digital Biomarkers and Digital Phenotyping (329) Mental Health Surveillance and Epidemiology (105)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50136, first published .
Time-Varying Network Models for the Temporal Dynamics of Depressive Symptomatology in Patients With Depressive Disorders: Secondary Analysis of Longitudinal Observational Data

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

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

Authors of this article:

Björn Sebastian Siepe1 Author Orcid Image ;   Christian Sander2, 3 Author Orcid Image ;   Martin Schultze4 Author Orcid Image ;   Andreas Kliem5 Author Orcid Image ;   Sascha Ludwig6 Author Orcid Image ;   Ulrich Hegerl7, 8 Author Orcid Image ;   Hanna Reich2, 8 Author Orcid Image
Article Authors Cited by (8) Tweetations (3) Metrics

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  1. Westhoff M, Berg M, Reif A, Rief W, Hofmann S. Major Problems in Clinical Psychological Science and How to Address them. Introducing a Multimodal Dynamical Network Approach. Cognitive Therapy and Research 2024;48(5):791 View
  2. Reich H, Schreynemackers S, Amin R, Ludwig S, Zippelius J, Leimhofer J, Dunker T, Schriewer E, Carell A, Weber Y, Hegerl U. 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 2025;45:101492 View
  3. Vogel F, Fromm C, Reichert J, Schwenck C, Rupp S. An Adaptation of Perceived Causal Networks for Children and Adolescents (PECAN-CA): An Evaluation of its Reliability and Feasibility. Child Psychiatry & Human Development 2025 View
  4. Chen X, Wu J, Wang Y, He Y, Ye H, Liu J. Depressive symptom trajectories and incident metabolic syndrome in middle-aged and older adults: A longitudinal analysis of the ELSA study. Frontiers in Psychiatry 2025;16 View
  5. Vogel F, Delvert A, Rupp S, Reichert J. Heterogeneity of Symptomatology in Socially Anxious Adolescents: Are Case Conceptualizations Based on Perceived Causal Networks (PECAN) Able to Capture Personalized Symptoms and their Relations?. Cognitive Therapy and Research 2025 View
  6. Seewald A, Schummer S, Franz S, Franz M, Rief W. Networks for treatment selection in psychotherapy: providing a manual for process-based perceived causal networks. Cognitive Behaviour Therapy 2025:1 View
  7. Andreoli G, Rafanelli C, Hofmann S, Casu G. A Systematic Scoping Review of Fully Idiographic Network Analysis in Mental Health. Cognitive Therapy and Research 2025 View
  8. Zhang Y, Ernst A, Lafit G, Eiling W, Bringmann L. An investigation into in‐sample and out‐of‐sample model selection for nonstationary autoregressive models. British Journal of Mathematical and Statistical Psychology 2026;79(2):409 View