Self-reported and actimetry-based cluster analysis of mood rhythmicity profiles in adolescents with and at risk for Major Depressive Disorder
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".