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

Greater within- and between-day instability is associated with worse anxiety and depression symptoms

BACKGROUND

Depression and anxiety affect hundreds of millions of people worldwide, and their prevalence increased during the COVID-19 pandemic as social schedules were disrupted. This study explores the associations between anxiety and depression and within- and between-day instability of affective, somatic, and cognitive symptoms during the early pandemic stages.

METHODS

Participants (n = 153, ages 18-77, 72 % female) reported daily levels of affective (anxiety/sadness), somatic (appetite/sleepiness), and cognitive (concentration/energy) symptoms for 14-44 days at five timepoints: 0, 3, 6, 9, and 12 h after awakening. At the end of the study, participants completed validated scales for anxiety (GAD-7) and depression (PHQ-9). Symptom instability was assessed using the Absolute Real Variability (ARV) index. Regression models examined within-day instability (WD-I) and between-day instability (BD-I) with GAD-7 and PHQ-9 scores as outcomes.

RESULTS

Greater instability (both WD-I and BD-I) of affective symptoms correlated with elevated GAD-7 and PHQ-9 scores. For somatic and cognitive symptoms, greater BD-I was associated with higher scores.

LIMITATIONS

The study used retrospective daily data, which could benefit from real-time assessments for improved accuracy.

CONCLUSIONS

This study provides empirical evidence of a connection between greater anxiety and depression severity and increased instability in daily mood and physiological symptoms. The findings underscore the importance of consistent symptom monitoring to understand overall mental health trajectories. Additionally, it highlights the role of daily routines in stabilizing the circadian system, potentially regulating physiological and psychological processes and reducing symptom instability.

Zero-shot personalization of speech foundation models for depressed mood monitoring

The monitoring of depressed mood plays an important role as a diagnostic tool in psychotherapy. An automated analysis of speech can provide a non-invasive measurement of a patient's affective state. While speech has been shown to be a useful biomarker for depression, existing approaches mostly build population-level models that aim to predict each individual's diagnosis as a (mostly) static property. Because of inter-individual differences in symptomatology and mood regulation behaviors, these approaches are ill-suited to detect smaller temporal variations in depressed mood. We address this issue by introducing a zero-shot personalization of large speech foundation models. Compared with other personalization strategies, our work does not require labeled speech samples for enrollment. Instead, the approach makes use of adapters conditioned on subject-specific metadata. On a longitudinal dataset, we show that the method improves performance compared with a set of suitable baselines. Finally, applying our personalization strategy improves individual-level fairness.

Self-perceived rhythmicity in affective and cognitive functions is related to psychiatric symptoms in adolescents

The objective of this study was to evaluate the relationship between self-perceived rhythms measured using the Mood Rhythm Instrument for adolescents (MRhI-Y) and depressive and psychiatric symptoms measured with the Children's Depressive Instrument (CDI) and the Strengths and Difficulties Questionnaire (SDQ). In this study, 186 adolescents were recruited in Rio Grande do Sul, Brazil. We performed a Spearman correlation analysis to evaluate the relationships between quantitative variables. All variables that had a statistically significant correlation were included in ANOVA multiple regression models. The dependent variables in the multiple regression analyses were CDI score and total and emotional scores on the SDQ. We found that only Cognitive self-perceived rhythmicity contributed significantly to the first multiple regression with CDI as the outcome variable. The second regression with SDQ Emotional score as the outcome variable showed that female sex, age, and self-perceived affective rhythmicity contributed significantly to the model. The third regression with SDQ total score as the outcome variable showed that chronotype, self-perceived cognitive symptoms, and affective rhythmicity contributed significantly to the model. In conclusion, we found that lower self-perceived rhythmicity in cognitive factors and higher self-perceived rhythmicity in affective factors were related to presence and intensity of psychiatric and depressive symptoms.

A Data-Driven Clustering Method for Discovering Profiles in the Dynamics of Major Depressive Disorder Using a Smartphone-Based Ecological Momentary Assessment of Mood

BACKGROUND

Although major depressive disorder (MDD) is characterized by a pervasive negative mood, research indicates that the mood of depressed patients is rarely entirely stagnant. It is often dynamic, distinguished by highs and lows, and it is highly responsive to external and internal regulatory processes. Mood dynamics can be defined as a combination of mood variability (the magnitude of the mood changes) and emotional inertia (the speed of mood shifts). The purpose of this study is to explore various distinctive profiles in real-time monitored mood dynamics among MDD patients in routine mental healthcare.

METHODS

Ecological momentary assessment (EMA) data were collected as part of the cross-European E-COMPARED trial, in which approximately half of the patients were randomly assigned to receive the blended Cognitive Behavioral Therapy (bCBT). In this study a subsample of the bCBT group was included (n = 287). As part of bCBT, patients were prompted to rate their current mood (on a 1-10 scale) using a smartphone-based EMA application. During the first week of treatment, the patients were prompted to rate their mood on three separate occasions during the day. Latent profile analyses were subsequently applied to identify distinct profiles based on average mood, mood variability, and emotional inertia across the monitoring period.

RESULTS

Overall, four profiles were identified, which we labeled as: (1) "very negative and least variable mood" (n = 14) (2) "negative and moderate variable mood" (n = 204), (3) "positive and moderate variable mood" (n = 41), and (4) "negative and highest variable mood" (n = 28). The degree of emotional inertia was virtually identical across the profiles.

CONCLUSIONS

The real-time monitoring conducted in the present study provides some preliminary indications of different patterns of both average mood and mood variability among MDD patients in treatment in mental health settings. Such varying patterns were not found for emotional inertia.