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

Claire R van Genugten; Josien Schuurmans; Wouter van Ballegooijen; Adriaan W Hoogendoorn; Jan H Smit; Heleen 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

Internet Interv. 2021 Jul 27:26:100437. doi: 10.1016/j.invent.2021.100437. eCollection 2021 Dec.

Authors

Claire R van Genugten^{1

2}, Josien Schuurmans^{1

2}, Wouter van Ballegooijen^{1

2

3}, Adriaan W Hoogendoorn^{1

2}, Jan H Smit^{1

2}, Heleen Riper^{1

2

3

4}

Affiliations

¹ Department of Research and Innovation, GGZ inGeest, Specialized Mental Health Care, Amsterdam, the Netherlands.
² Department of Psychiatry, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands.
³ Department of Clinical, Neuro and Developmental Psychology, Clinical Psychology Section, Vrije Universiteit Amsterdam and Amsterdam Public Health Research Institute, Amsterdam, the Netherlands.
⁴ Institute of Telepsychiatry, University of Southern Denmark, Odense, Denmark.

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.

Keywords: AC, autocorrelation; AIC, Akaike information criterion; BIC, Bayesian information criterion; BLRT, bootstrapped likelihood ratio test; CES-D, Center for Epidemiological Studies Depression Scale; Cluster analysis; DSM-5, Diagnostic manual of mental disorders, 5th edition; Depression; EMA, ecological momentary assessment; Ecological momentary assessment; Heterogeneity; IQR, interquartile range; LMRA-LRT, Lo-Mendell-Rubin adjusted likelihood ratio test; LPA, latent profile analysis; M, mean; Mdn, median; Mood dynamics; Mood instability; PHQ-9, Patient Health Questionnaire; SD, Standard deviation; VAS, Visual analogue scale.