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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Internet Interventions |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214782921000774 |
| _version_ | 1818572804555014144 |
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| author | Claire R. van Genugten Josien Schuurmans Wouter van Ballegooijen Adriaan W. Hoogendoorn Jan H. Smit Heleen Riper |
| author_facet | Claire R. van Genugten Josien Schuurmans Wouter van Ballegooijen Adriaan W. Hoogendoorn Jan H. Smit Heleen Riper |
| author_sort | Claire R. van Genugten |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-15T00:02:32Z |
| format | Article |
| id | doaj.art-6f238ef439474be78c54935be355839b |
| institution | Directory Open Access Journal |
| issn | 2214-7829 |
| language | English |
| last_indexed | 2024-12-15T00:02:32Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Internet Interventions |
| spelling | doaj.art-6f238ef439474be78c54935be355839b2022-12-21T22:42:51ZengElsevierInternet Interventions2214-78292021-12-0126100437Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first explorationClaire R. van Genugten0Josien Schuurmans1Wouter van Ballegooijen2Adriaan W. Hoogendoorn3Jan H. Smit4Heleen Riper5Department of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands; Corresponding author at: Department of Research and Innovation, GGZ inGeest, Specialized Mental Health Care. Oldenaller 1, 1081HJ Amsterdam, the Netherlands.Department of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the NetherlandsDepartment of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands; Department of Clinical, Neuro and Developmental Psychology, Clinical Psychology Section, Vrije Universiteit Amsterdam and Amsterdam Public Health Research Institute, Amsterdam, the NetherlandsDepartment of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the NetherlandsDepartment of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the NetherlandsDepartment of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands; Department of Clinical, Neuro and Developmental Psychology, Clinical Psychology Section, Vrije Universiteit Amsterdam and Amsterdam Public Health Research Institute, Amsterdam, the Netherlands; Institute of Telepsychiatry, University of Southern Denmark, Odense, DenmarkBackground: 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.http://www.sciencedirect.com/science/article/pii/S2214782921000774DepressionEcological momentary assessmentMood dynamicsMood instabilityHeterogeneityCluster analysis |
| spellingShingle | Claire R. van Genugten Josien Schuurmans Wouter van Ballegooijen Adriaan W. Hoogendoorn Jan H. Smit Heleen Riper Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration Internet Interventions Depression Ecological momentary assessment Mood dynamics Mood instability Heterogeneity Cluster analysis |
| 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 | Depression Ecological momentary assessment Mood dynamics Mood instability Heterogeneity Cluster analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2214782921000774 |
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