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

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 TitzlerNaira Topooco, Johannes H. Smit, Heleen Riper

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Background: Although 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 processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare. Methods: Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1–10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period. Results: Overall, four profiles were identified, which we labeled as: (1) “very negative and least variable mood” (n = 14) (2) “negative and moderate variable mood” (n = 204), (3) “positive and moderate variable mood” (n = 41), and (4) “negative and highest variable mood” (n = 28). The degree of emotional inertia was virtually identical across the profiles. Conclusions: The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.

Original languageEnglish
Article number755809
Pages (from-to)1-12
Number of pages12
JournalFrontiers in Psychiatry
Volume13
Issue numberMarch
Early online date17 Mar 2022
DOIs
Publication statusPublished - Mar 2022

Bibliographical note

Funding Information:
The European COMPARative Effectiveness research on blended depression treatment versus treatment-as-usual (E-COMPARED) project, was funded by the European Commission FP7-Health-2013-Innovation-1 program, Grant Agreement Number: 603098-2.

Publisher Copyright:
Copyright © 2022 van Genugten, Schuurmans, Hoogendoorn, Araya, Andersson, Baños, Berger, Botella, Cerga Pashoja, Cieslak, Ebert, García-Palacios, Hazo, Herrero, Holtzmann, Kemmeren, Kleiboer, Krieger, Rogala, Titzler, Topooco, Smit and Riper.

Keywords

  • cluster analysis
  • depression
  • ecological momentary assessment
  • heterogeneity
  • mood dynamics
  • mood instability

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