Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration

C.R. van Genugten; J. Schuurmans; W. van Ballegooijen; A.W. Hoogendoorn; J.H. Smit; H. Riper

doi:10.1016/j.invent.2021.100437

Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration

C.R. van Genugten
, J. Schuurmans
, W. van Ballegooijen
, A.W. Hoogendoorn
, J.H. Smit
, H. Riper

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

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.

Original language	English
Article number	100437
Pages (from-to)	1-7
Number of pages	7
Journal	Internet Interventions
Volume	26
Early online date	27 Jul 2021
DOIs	https://doi.org/10.1016/j.invent.2021.100437
Publication status	Published - Dec 2021
Externally published	Yes

Bibliographical note

Funding

Jeroen Ruwaard was involved in setting up and conducting the MoodMonitor study. He passed away on 16 July 2019. This study is funded by 2 sources. As part of the European Comparative Effectiveness (E-COMPARED) project the study has received financial support from the European Commission FP7-Health-2013-Innovation-1 program, grant agreement no. 603098. The second financial source is internal funding from the Department of Clinical, Neuro and Developmental Psychology of VU University and the Department of Research and Innovation of GGZ inGeest, Specialized Mental Health Care, both in Amsterdam, the Netherlands. The funders had no rule in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Funders	Funder number
European Commission FP7-Health-2013-Innovation-1 program
E-COMPARED
European Comparative Effectiveness
European Commission
Department of Research and Innovation
Department of Clinical, Neuro and Developmental Psychology of VU University
Seventh Framework Programme	603098

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title = "Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration",

abstract = "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.",

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AU - Smit, J.H.

AU - Riper, H.

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Discovering different profiles in the dynamics of depression based on real–time monitoring of mood: a first exploration

Abstract

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