Self-reported and actimetry-based cluster analysis of mood rhythmicity profiles in adolescents with and at risk for Major Depressive Disorder

Greater self-perceived rhythmicity of mood-related symptoms and behaviors has been associated with depressive symptoms in the general public. We aimed to evaluate differences in adolescents at risk for or with a diagnosis of major depressive disorder (MDD) regarding perception of symptom rhythmicity and actimetry parameters. In this cross-sectional study, 96 adolescents were stratified into three groups based on either a diagnosis of MDD or on a composite score for the risk of developing depression: MDD, high risk (HR), and low risk (LR). Participants completed questionnaires regarding depressive symptoms (Mood and Feelings Questionnaire for adolescents) and self-perceived mood rhythmicity (Mood Rhythm Instrument for Youth - MRhI-Y). Actimetry data were collected for 10 continuous days and Non-Parametric Circadian Rhythm Analyses were performed. The MDD group reported higher MRhI-Y total scores, particularly in affective symptoms compared to both other groups. In spite of actimetry variables that did not correlate with MRhI-Y total scores, cluster analysis using MRhI-Y and actimetry revealed three distinct profiles corresponding to all groups. Identifying rhythmicity in mood-related behaviors in adolescents may help distinguish different groups at-risk for MDD and in a current depressive episode. Understanding these patterns could inform early interventions, potentially preventing the onset of the disorder in susceptible individuals.

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Publication Details

Journal
Chronobiology International
Published
2025-05-04
DOI
https://doi.org/10.1080/07420528.2025.2496345
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00

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article

Self-reported and actimetry-based cluster analysis of mood rhythmicity profiles in adolescents with and at risk for Major Depressive Disorder

M. Mota, Pedro H. Manfro, Rogério Boff Borges, Jader Piccin et al.
Chronobiology International
Mental Health Research Topics
article

Self-reported and actimetry-based cluster analysis of mood rhythmicity profiles in adolescents with and at risk for Major Depressive Disorder

M. Mota, Pedro H. Manfro, Rogério Boff Borges, Jader Piccin, Adile Nexha, María Paz Loayza Hidalgo, Guilherme Rodriguez Amando, Rivka Pereira, André Comiran Tonon, Christian Kieling, Nicóli Bertuol Xavier, Lígia M. N. Souza, Fernanda Rohrsetzer
article en

Abstract

Greater self-perceived rhythmicity of mood-related symptoms and behaviors has been associated with depressive symptoms in the general public. We aimed to evaluate differences in adolescents at risk for or with a diagnosis of major depressive disorder (MDD) regarding perception of symptom rhythmicity and actimetry parameters. In this cross-sectional study, 96 adolescents were stratified into three groups based on either a diagnosis of MDD or on a composite score for the risk of developing depression: MDD, high risk (HR), and low risk (LR). Participants completed questionnaires regarding depressive symptoms (Mood and Feelings Questionnaire for adolescents) and self-perceived mood rhythmicity (Mood Rhythm Instrument for Youth - MRhI-Y). Actimetry data were collected for 10 continuous days and Non-Parametric Circadian Rhythm Analyses were performed. The MDD group reported higher MRhI-Y total scores, particularly in affective symptoms compared to both other groups. In spite of actimetry variables that did not correlate with MRhI-Y total scores, cluster analysis using MRhI-Y and actimetry revealed three distinct profiles corresponding to all groups. Identifying rhythmicity in mood-related behaviors in adolescents may help distinguish different groups at-risk for MDD and in a current depressive episode. Understanding these patterns could inform early interventions, potentially preventing the onset of the disorder in susceptible individuals.

Chronobiology InternationalVol. 42(5)
Universidade Federal do Rio Grande do Sul (BR), Hospital de Clínicas de Porto Alegre (BR), McMaster University (CA)
Global Challenges Research Fund, Royal Academy of Engineering, Academy of Medical Sciences, Medical Research Council, National Institute of Mental Health
Openalex Percentile: Top 91%
Mental Health Research Topics
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