Individual and common patterns in the order of symptom improvement during outpatient treatment for major depression

Childhood trauma, psychache, and depression among university students: a moderated mediation model

Background

Childhood trauma is a potential threat to depression and can have a lifelong impact on the mental health of university students. Our study aimed to construct a moderated mediation model to explore the relationship between childhood trauma, psychache, ambivalence over emotional expression, physical activity, and depression in university students.

Methods

A cross-sectional study was conducted in three universities in China, recruiting 476 university students using self-report questionnaires. The moderated mediation model was examined using the SPSS PROCESS model 21.

Results

Ambivalence over emotional expression (F=12.843), childhood trauma (F=117.639), and psychache (F=581.594) all had a significant positive effect on depression (p<0.001), explaining 2.9%, 21.7%, and 56.8% of the variance, respectively. On the chain of influence between childhood trauma and depression, the mediating effect of psychache, the moderating effect of ambivalence over emotional expression, and the moderating effect of physical activity are all significant the overall indirect effect value of the three is 0.287, accounting for 61.59% of the total effect.

Conclusion

This study investigated the relationship between childhood trauma, ambivalence over emotional expression, psychache, physical activity, and depression in university students. Future interventions should focus on developing good emotional expression among university students, increasing opportunities for physical activity, and reducing psychache to reduce depression.

Mapping network connection and direction between anxiety and depression symptoms across the early, middle, and late adolescents: Insights from a large Chinese sample

Depression and anxiety are two mental disorders prevailing among adolescents. However, issues regarding the trajectory of depression and anxiety are still controversial on both the disease and symptom dimensions. The novel method of network analysis was used to provide insight into the symptom dimension. 20,544 adolescents (female = 10,743, 52.3%) aged between 14 and 24 years (age mean ± sd = 16.9 ± 2.94) were divided into three subgroups according to age so that the course of depression and anxiety could be traced. Network analysis and the Bayesian network model were used in the current study. The results indicated that uncontrollable worry - excessive worry was the most significant edge for all adolescents, whereas concentration - motor had the highest edge weights for early adolescents, and anhedonia - energy was the most critical pairwise symptom for middle and late adolescents. Irritability can bridge anxiety and depression in the early and middle stages of adolescence, while suicide plays a bridging role in the early and late stages of adolescence. Restlessness and guilt can bridge anxiety and depression in middle- and late-stage adolescents, and feeling afraid plays a unique role in middle-stage adolescents. Except for sad mood, which can trigger middle adolescents' anxiety and depression, the other three subgroups were mainly triggered by nervousness. In addition, all results in our current study were shown to be stable and accurate. In treatment, targeting central and triggering symptoms at different stages of adolescence may be critical to alleviating the comorbidity of anxiety and depression.

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.

Electroconvulsive therapy improves somatic symptoms before mood in patients with depression: A directed network analysis

BACKGROUND

The recent network perspective of depression conceptualizes depression as a dynamic network of causally related symptoms, that contrasts with the traditional view of depression as a discrete latent entity that causes all symptoms. Electroconvulsive therapy (ECT) is an effective treatment for severe depression, but little is known about the temporal trajectories of symptom improvement during a course of ECT.

OBJECTIVE

To gain insight into the dynamics of depressive symptoms in individuals treated with ECT.

METHODS

The Quick Inventory of Depressive Symptomatology (QIDS) was used to assess symptoms twice a week in 68 participants with a unipolar or bipolar depression treated with ECT, with an average of 12 assessments per participant. Dynamic time warping (DTW) was used to analyze individual time series data, which were subsequently aggregated to calculate a directed symptom network and the in- and out-strength for each symptom.

RESULTS

Participants had a mean age of 49.6 (SD = 12.8) and 60% were female. Somatic symptoms (e.g., decreased weight) and suicidal ideation showed the highest out-strength values, indicating that their improvement tended to precede improvements in mood symptoms, which showed high in-strength. Sad mood had the highest in-strength, and thus appeared to be the last symptom to improve during ECT treatment (p < 0.001).

CONCLUSION

This study addresses a gap in the existing literature on ECT, by first analysing the temporal trajectories of symptoms within individual patients and subsequently aggregating them to the group level. The results show that somatic symptoms tend to improve before mood symptoms during ECT.

Building an Early Warning System for Depression: Rationale, Objectives, and Methods of the WARN-D Study

Background

Depression is common, debilitating, often chronic, and affects young people disproportionately. Given that only 50% of patients improve under initial treatment, experts agree that prevention is the most effective way to change depression's global disease burden. The biggest barrier to successful prevention is to identify individuals at risk for depression in the near future. To close this gap, this protocol paper introduces the WARN-D study, our effort to build a personalized early warning system for depression.

Method

To develop the system, we follow around 2,000 students over 2 years. Stage 1 comprises an extensive baseline assessment in which we collect a broad set of predictors for depression. Stage 2 lasts 3 months and zooms into participants' daily experiences that may predict depression; we use smartwatches to collect digital phenotype data such as sleep and activity, and we use a smartphone app to query participants about their experiences 4 times a day and once every Sunday. In Stage 3, we follow participants for 21 months, assessing transdiagnostic outcomes (including stress, functional impairment, anxiety, and depression) as well as additional predictors for future depression every 3 months. Collected data will be utilized to build a personalized prediction model for depression onset.

Discussion

Overall, WARN-D will function similarly to a weather forecast, with the core difference that one can only seek shelter from a thunderstorm and clean up afterwards, while depression may be successfully prevented before it occurs.

Levels of depressed mood and low interest for two years after response to cognitive therapy for recurrent depression

BACKGROUND

Major depressive disorder (MDD) involves depressed mood (high negative affect, predominantly) and low interest/pleasure (low positive affect). In past research, negative affect has improved more than positive affect during acute-phase antidepressant medication or cognitive therapy (CT). We extended this literature by differentiating depressed mood and two dimensions of low interest (general and sexual), assessing persistence of symptom differences after acute-phase CT response, and testing whether continuation treatment acted differently on depressed mood versus low interest.

METHODS

We analyzed data from two randomized controlled trials. Patients with recurrent MDD first received acute-phase CT. Then, responders were randomized to 8-month continuation treatments and assessed for 16-24 additional months.

RESULTS

Depressed mood and low general interest improved more than low sexual interest during acute-phase CT. Among responders, these symptom differences persisted for at least 2 years and were not changed by continuation CT or antidepressant medication.

LIMITATIONS

Generalization of findings to other patient populations and treatments is uncertain. Depressed mood and low interest scales were constructed from standard symptom measures and overlapped empirically.

CONCLUSIONS

Less improvement during CT, and persistent low sexual interest despite continuation treatment, highlights the need for MDD treatments more effectively targeting this positive affective symptom.