Psychosocial determinants of recovery in depression

Goal Attainment Scaling for Depression: Validation of the Japanese GAS-D Tool in Patients with Major Depressive Disorder

Purpose

Goal attainment scaling (GAS) has been proposed as a person-centric, semi-quantitative measure that assimilates achievement of individually set goals into a single standardized "goal attainment score" that can be compared at the population level. We aimed to examine the reliability and validity of the Japanese version of the GAS for depression (GAS-D) tool in assessing goal attainment in people living with major depressive disorder (MDD).

Patients and Methods

This was a prespecified analysis of a prospective, 24-week, multicenter, observational cohort study of employed Japanese outpatients with MDD initiating treatment with vortioxetine according to the Japanese label (JRCT1031210200). Participants were assessed using the Japanese version of the GAS-D and other clinical rating scales at baseline and Weeks 8, 12 and 24.

Results

Goal attainment was significantly associated with symptom severity as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS) scale, confirming convergent validity. In particular, GAS-D scores were significantly related to MADRS total score at Weeks 12 and 24, indicating that improvements in overall symptom severity with vortioxetine treatment were likely to be reflected in the achievement of individualized treatment goals. With an intraclass correlation coefficient of 0.67 (95% CI 0.45-0.82), the GAS-D also showed moderate test-retest reliability between Weeks 8 and 12 while proving independent of demographic characteristics.

Conclusion

The results of this open-label study support the use of the GAS-D as a valid and sensitive outcome measure in the assessment of treatment response in MDD.

Long-term effects of chronic stress models in adult mice

Neuropsychiatric disorders, such as major depression, anxiety disorders, and post-traumatic stress disorder, tend to be long-term conditions in whose development and maintenance stress are central pathogenic factors. Translational mouse models are widely used in neuropsychiatric research, exploiting social and non-social stressors to investigate the mechanisms underlying their detrimental effects. However, most studies focus on the short-term consequences of chronic stress, whereas only a few are interested in the long-term course. This is counterintuitive given the human conditions that preclinical models are designed to mimic. In this review, we have summarized the limited work to date on long-term effects of chronic stress in mice models. First, the different models are presented and a definition of short- vs. long-term sequelae is proposed. On this basis, behavioral, endocrine, and vegetative effects are addressed before examining data on cellular and molecular alterations in the brain. Finally, future directions for research on the long-term effects of stress are discussed.

Conversational agents and the making of mental health recovery

Background

Artificial intelligence (AI) is said to be “transforming mental health”. AI-based technologies and technique are now considered to have uses in almost every domain of mental health care: including decision-making, assessment and healthcare management. What remains underexplored is whether/how mental health recovery is situated within these discussions and practices.

Method

Taking conversational agents as our point of departure, we explore the ways official online materials explain and make sense of chatbots, their imagined functionality and value for (potential) users. We focus on three chatbots for mental health: Woebot, Wysa and Tess.

Findings

“Recovery” is largely missing as an overt focus across materials. However, analysis does reveal themes that speak to the struggles over practice, expertise and evidence that the concept of recovery articulates. We discuss these under the headings “troubled clinical responsibility”, “extended virtue of (technological) self-care” and “altered ontologies and psychopathologies of time”.

Conclusions

Ultimately, we argue that alongside more traditional forms of recovery, chatbots may be shaped by, and shaping, an increasingly individualised form of a “personal recovery imperative”.

Difficult-to-treat depression: A clinical and research roadmap for when remission is elusive

OBJECTIVES

The report considers the pros and cons of the most commonly used conceptual model that forms the basis for most clinical practice guidelines for depression. This model promotes the attainment of sustained symptom remission as the treatment goal based on its well-established prognostic and functional importance. Sustained remission is very unlikely, however, after multiple treatment attempts. Our current model propels many clinicians to continue to change or add treatments despite little chance for remission or full functional restoration and despite the increasing risk of more adverse events from polypharmacy. An alternative 'difficult-to-treat depression' model is presented and considered. It accepts that the treatment aims for some depressed patients may shift to optimal symptom control rather than remission. When difficult-to-treat depression is suspected, the many treatable causes of persistent depression must be assessed and addressed (given the importance of remission when attainable) before difficult-to-treat depression can be ascribed. The clinical and research implications of the difficult-to-treat depression model are discussed.

CONCLUSION

Suspected difficult-to-treat depression provides a practical basis for considering when to conduct a comprehensive evaluation. Once difficult-to-treat depression is confirmed, treatment may better focus on optimal disease management (symptom control and functional improvement).

Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder

Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS), and frontoparietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients (⩾2 MDD episodes) and 41 healthy controls (HCs) were recruited. We used experience sampling methodology to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional magnetic resonance imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS, and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared with HCs, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared with HCs, are less stable in their negative mood and these dynamics are related to differences in information processing within- and between-specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real life are represented in the brain and provide insights into the vulnerability profile of MDD.