Predicting optimal interventions for clinical depression: Moderators of outcomes in a positive psychological intervention vs. cognitive-behavioral therapy

Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study

BACKGROUND

Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems.

METHODS

This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203).

RESULTS

Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU.

LIMITATIONS

Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables.

CONCLUSIONS

Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.

The Model for Sustainable Mental Health: Future Directions for Integrating Positive Psychology Into Mental Health Care

This position paper proposes a model for systematic integration of positive psychology interventions (PPIs) in mental healthcare. On the one hand, PPIs can contribute to the decrease of dysfunctional processes underlying mental illness. This evidence is at the core of the new domains of positive clinical psychology and positive psychiatry. On the other hand, a growing number of studies demonstrate that mental health is not merely the absence of mental illness. Mental wellbeing represents a related but separate dimension of mental health. Mental wellbeing reduces the risk of future incidence of mental illness and is highly valued by people receiving psychological treatment as an important aspect of personal and complete recovery and personal growth. This makes mental wellbeing a vital outcome of mental healthcare. PPIs can directly increase mental wellbeing. The model of sustainable mental health is presented integrating the science of positive psychology and mental wellbeing into mental healthcare. This heuristic model can guide both practitioners and researchers in developing, implementing, and evaluating a more balanced, both complaint- and strength-oriented, treatment approach. The role of gratitude interventions is discussed as an example of applying the model. Also, three potential modalities for implementing PPIs as positive psychotherapy in treatment are as: positive psychotherapy as primary treatment, as combinatorial treatment, and as intervention for personal recovery of people with severe or persistent mental disorder. Finally, we argue that longitudinal studies are needed to substantiate the model and the processes involved.

Disentangling the mediating role of modifying interpretation bias on emotional distress using a novel cognitive bias modification program

BACKGROUND

Negative interpretation bias is a potential risk factor for emotional disorders. In this study, we tested a clinically inspired 4-session online Cognitive Bias Modification-Interpretation (CBM-I_Clin) program to modify negative interpretation biases.

METHODS

We randomized one hundred and twenty-one volunteer young adults (Mean age = 21.6 years, SD = 3.5; 85 % women) with varying levels of emotional distress to either an experimental or waitlist control group. Mediation analyses were used to disentangle the associations between the intervention, changes in interpretation biases (assessed by both a self-report and an experimental task), and changes in measures of cognitive vulnerability and symptoms of depression and anxiety.

RESULTS

The results showed that the CBM-I_Clin could change negative interpretation biases. Also, it had a direct effect on the change in negative memory bias, an indirect effect on the change in depression symptoms via the change in interpretation bias, and both direct and indirect effects on the change in self-reported dysfunctional attitudes.

LIMITATIONS

The study included a non-clinical sample of participants and it did not control for some potential confounding factors (e.g., attentional disorders). Furthermore, participants' engagement during the sessions at home was not supervised.

CONCLUSIONS

The CBM-I_Clin is a potential tool to prevent and intervene in emotional disorders in young adults and could complement other traditional CBM procedures or clinical interventions.

Moderators and predictors of treatment outcome in transdiagnostic group cognitive-behavioral therapy for primary care patients with emotional disorders

OBJECTIVE

Transdiagnostic group cognitive behavior therapy (TD-GCBT) has shown to be efficacious in the treatment of emotional disorders in primary care. However, little is known about possible moderators or predictors of treatment outcome. We aimed to explore the potential predictors and moderators of outcome in a large multicentre randomized controlled trial comparing TD-GCBT plus treatment as usual (TAU) to TAU alone.

METHOD

Putative demographic and baseline clinical variables were examined using the PROCESS macro as potential predictors/moderators of depressive and anxiety symptoms at posttreatment and 1-year follow-up.

RESULTS

Analyses were based on a study completer sample of 1061 participants randomized to TD-CBT + TAU (n = 527) or TAU alone (n = 534), with 631 participants assessed at the posttreatment evaluation and 388 at the 1-year follow-up. Individuals working or with a partner among sociodemographic variables, and higher baseline comorbidities and more severity of symptoms among clinical variables obtained more benefits from adding TDCBT to TAU. Those taking medication before treatment obtained less benefits from the TD-GCBT than those without prescribed antidepressant medications, after controlling for baseline severity of symptoms. Overall, the moderating effect of clinical (but not sociodemographic) variables remained at 1-year follow-up.

CONCLUSION

Findings support largely the generalization of the TD-GCBT for emotional disorders in primary care to a variety of sociodemographic and clinical groups. However, TD-GCBT seems to work to a greater extent for those individuals with a more severe clinical profile. Providing TD-GCBT before prescribing antidepressant medication and while people are still working may enhance the effects of adding this psychological treatment to TAU in primary care.

Predicting optimal treatment outcomes using the Personalized Advantage Index for patients with persistent somatic symptoms

Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.