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Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration
BACKGROUND: Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377528/ https://www.ncbi.nlm.nih.gov/pubmed/34458105 http://dx.doi.org/10.1016/j.invent.2021.100437 |
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author | van Genugten, Claire R. Schuurmans, Josien van Ballegooijen, Wouter Hoogendoorn, Adriaan W. Smit, Jan H. Riper, Heleen |
author_facet | van Genugten, Claire R. Schuurmans, Josien van Ballegooijen, Wouter Hoogendoorn, Adriaan W. Smit, Jan H. Riper, Heleen |
author_sort | van Genugten, Claire R. |
collection | PubMed |
description | BACKGROUND: Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons. METHODS: After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1–10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants. RESULTS: Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively). CONCLUSIONS: The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons. |
format | Online Article Text |
id | pubmed-8377528 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-83775282021-08-26 Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration van Genugten, Claire R. Schuurmans, Josien van Ballegooijen, Wouter Hoogendoorn, Adriaan W. Smit, Jan H. Riper, Heleen Internet Interv Full length Article BACKGROUND: Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons. METHODS: After completing baseline measures, mildly-to-moderately depressed persons (n = 37) were prompted to rate their current mood (1–10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants. RESULTS: Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 (n = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 (n = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively). CONCLUSIONS: The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons. Elsevier 2021-07-27 /pmc/articles/PMC8377528/ /pubmed/34458105 http://dx.doi.org/10.1016/j.invent.2021.100437 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Full length Article van Genugten, Claire R. Schuurmans, Josien van Ballegooijen, Wouter Hoogendoorn, Adriaan W. Smit, Jan H. Riper, Heleen Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title | Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title_full | Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title_fullStr | Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title_full_unstemmed | Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title_short | Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
title_sort | discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration |
topic | Full length Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8377528/ https://www.ncbi.nlm.nih.gov/pubmed/34458105 http://dx.doi.org/10.1016/j.invent.2021.100437 |
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