A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BackgroundAlthough major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory...

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Bibliographic Details
Main Authors: Claire R. van Genugten, Josien Schuurmans, Adriaan W. Hoogendoorn, Ricardo Araya, Gerhard Andersson, Rosa M. Baños, Thomas Berger, Cristina Botella, Arlinda Cerga Pashoja, Roman Cieslak, David D. Ebert, Azucena García-Palacios, Jean-Baptiste Hazo, Rocío Herrero, Jérôme Holtzmann, Lise Kemmeren, Annet Kleiboer, Tobias Krieger, Anna Rogala, Ingrid Titzler, Naira Topooco, Johannes H. Smit, Heleen Riper
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-03-01
Series:Frontiers in Psychiatry
Subjects:
depression
ecological momentary assessment
mood dynamics
mood instability
heterogeneity
cluster analysis
Online Access:https://www.frontiersin.org/articles/10.3389/fpsyt.2022.755809/full

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https://www.frontiersin.org/articles/10.3389/fpsyt.2022.755809/full

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