Determining prognostic variables of treatment outcome in obsessive-compulsive disorder: effectiveness and its predictors in routine clinical care.

The objectives of this study were to investigate the naturalistic effectiveness of routine inpatient treatment for patients with obsessive-compulsive disorder (OCD) and to identify predictors of treatment outcome. A routinely collected data set of 1,596 OCD inpatients (M = 33.9 years, SD = 11.7; 60.4% female) having received evidence-based psychotherapy based on the cognitive-behavioral therapy (CBT) in five German psychotherapeutic clinics was analyzed. Effect sizes (Hedges' g) were calculated for several outcome variables to determine effectiveness. Predictor analyses were performed on a subsample (N = 514; M = 34.3 years, SD = 12.2; 60.3% female). For this purpose, the number of potential predictors was reduced using factor analysis, followed by multiple regression analysis to identify robust predictors. Effect sizes of various outcome variables could be classified as large (g = 1.34 of OCD-symptom change). Predictors of changes in OCD and depressive symptoms were symptom severity at admission and general psychopathological distress. In addition, patients with higher social support and more washing compulsions benefited more from treatment. Subgroup analyses showed a distinct predictor profile of changes in compulsions and obsessions. The results indicate that an evidence-based psychotherapy program for OCD can be effectively implemented in routine inpatient care. In addition to well-established predictors, social support, and washing compulsions in particular were identified as important positive predictors. Specific predictor profiles for changes in obsessions and compulsions are discussed.

Predictors of outcome during inpatient psychotherapy for posttraumatic stress disorder: a single-treatment, multi-site, practice-based study.

Objective: The aims of this study were to determine the effectiveness of a routine clinical care treatment and to identify predictors of treatment outcome in PTSD inpatients. Methods: A routinely collected data set of 612 PTSD inpatients (M = 42.3 years [SD = 11.6], 75.7% female) having received trauma-focused psychotherapy was analyzed. Primary outcome was the clinical symptom severity change score, secondary outcomes were assessed using functional, anxiety, and depression change scores. Hedges g-corrected pre-post effect sizes (ES) were computed for all outcomes. Elastic net regulation as a data-driven, stability-based machine-learning approach was used to build stable clinical prediction models. Results: Hedges g ES indicated medium to large effects on all outcomes. The results of the predictor analyses suggested that a combined predictor model with sociodemographic, clinical, and psychometric variables contribute to predicting different treatment outcomes. Across the clinical and functional outcome, psychoticism, total number of diagnoses, and bronchial asthma consistently showed a stable negative predictive relationship to treatment outcome. Conclusion: Trauma-focused psychotherapy could effectively be implemented in a routine inpatient setting. Some important prognostic variables could be identified. If the proposed models of predictors are replicated, they may help personalize treatment for patients receiving routine clinical care.

Testing Bayesian models of belief updating in the context of depressive symptomatology.

OBJECTIVES: Predictive processing approaches to belief updating in depression propose that depression is related to more negative and more precise priors. Also, belief updating is assumed be negatively biased in comparison to normative Bayesian updating. There is a lack of efficient methods to mathematically model belief updating in depression. METHODS: We validated a novel performance belief updating paradigm in a nonclinical sample (N = 133). Participants repeatedly participated in a non-self-related emotion recognition task and received false feedback. Effects of the feedback manipulation and differences in depressive symptoms on belief updating were analysed in Bayesian multilevel analyses. RESULTS: Beliefs were successfully manipulated through the feedback provided. Depressive symptoms were associated with more negative updating than normative Bayesian updating but results were influenced by few cases. No evidence of biased change in beliefs or overly precise priors was found. Depressive symptoms were associated with more negative updating of generalised performance beliefs. CONCLUSIONS: There was cautious support for negatively biased belief updating associated with depressive symptoms, especially for generalised beliefs. The content of the task may not be self-relevant enough to cause strong biases. Further explication of Bayesian models of depression and replication in clinical samples is needed.

Oversampled and undersolved: Depressive rumination from an active inference perspective.

Rumination is a widely recognized cognitive deviation in depression. Despite the recognition, researchers have struggled to explain why patients cannot disengage from the process, although it depresses their mood and fails to lead to effective problem-solving. We rethink rumination as repetitive but unsuccessful problem-solving attempts. Appealing to an active inference account, we suggest that adaptive problem-solving is based on the generation, evaluation, and performance of candidate policies that increase an organism's knowledge of its environment. We argue that the problem-solving process is distorted during rumination. Specifically, rumination is understood as engaging in excessive yet unsuccessful oversampling of policy candidates that do not resolve uncertainty. Because candidates are sampled from policies that were selected in states resembling one's current state, "bad" starting points (e.g., depressed mood, physical inactivity) make the problem-solving process vulnerable for generating a ruminative "halting problem". This problem leads to high opportunity costs, learned helplessness and diminished overt behavior. Besides reviewing evidence for the conceptual paths of this model, we discuss its neurophysiological correlates and point towards clinical implications.

Fostering compliance with physical distancing by interactive feedback in the context of the COVID-19 pandemic: A web-based randomized controlled trial.

Background: To slow down the spread of COVID-19, the observance of basic hygiene measures, and physical distancing is recommended. Initial findings suggest that physical distancing in particular can prevent the spread of COVID-19. Objectives: To investigate how information to prevent the spread of infectious diseases should be presented to increase willingness to comply with preventive measures. Methods: In a preregistered online experiment, 817 subjects were presented with either interactively controllable graphics on the spread of COVID-19 and information that enable them to recognize how much the spread of COVID-19 is reduced by physical distancing (experimental group) or text-based information about quantitative evidence (control group). It was hypothesized that participants receiving interactive information on the prevention of COVID-19 infections show a significantly higher willingness to comply with future containment measures than participants reading the text-based information. Explorative analyses were conducted to examine whether other factors influence compliance. Results: As predicted, we found a small effect (d = 0.22, 95% CI: 0.11; 0.23, p < .001) for the tested intervention. The exploratory analysis suggests a decline in compliance later in the study (r = -0.10, 95% CI: -0.15; -0.07). Another significant predictor of change in compliance was health-related anxiety, but the effect was trivial. Conclusions: When presented interactively, information on how the own behavior can help prevent infectious diseases can lead to slightly stronger changes in attitude towards behavioral prevention measures than just text-based information. Given the scalability of this simple internet-based intervention, it could play a role in fostering compliance during a pandemic within universal prevention strategies. Future work on the predictive validity of self-reported compliance and the real-world effects on the intervention is needed.

Inpatient psychotherapy for depression in a large routine clinical care sample: A Bayesian approach to examining clinical outcomes and predictors of change.

BACKGROUND: A routinely collected dataset was analyzed (1) to determine the naturalistic effectiveness of inpatient psychotherapy for depression in routine psychotherapeutic care, and (2) to identify potential predictors of change. METHODS: In a sample of 22,681 inpatients with depression, pre-post and pre-follow-up effect sizes were computed for various outcome variables. To build a probabilistic model of predictors of change, an independent component analysis generated components from demographic and clinical data, and Bayesian EFA extracted factors from the available pre-test, post-test and follow-up questionnaires in a subsample (N = 6377). To select the best-fitted model, the BIC of different path models were compared. A Bayesian path analysis was performed to identify the most important factors to predict changes. RESULTS: Effect sizes were large for the primary outcome and moderate for various secondary outcomes. Almost all pretreatment factors exerted significant influences on different baseline factors. Several factors were found to be resistant to change during treatment: suicidality, agoraphobia, life dissatisfaction, physical disability and pain. The strongest cross-loadings were observed from suicidality on negative cognitions, from agoraphobia on anxiety, and from physical disability on perceived disability. LIMITATIONS: No causal conclusions can be drawn directly from our results as we only used cross-lagged panel data without control group. CONCLUSIONS: The results indicate large effects of inpatient psychotherapy for depression in routine clinical care. The direct influence of pretreatment factors decreased over the course of treatment. However, some factors appeared stable and difficult to treat, which might hinder treatment outcome. Findings of different predictors of change are discussed.

Cognitive Change and Relaxation as Key Mechanisms of Treatment Outcome in Chronic Pain: Evidence From Routine Care.

Despite effective treatment approaches within the cognitive behavioral framework general treatment effects for chronic pain are rather small to very small. Translation from efficacy trials to naturalistic settings is questionable. There is an urgent need to improve the effectiveness of well-established treatments, such as cognitive-behavior therapy (CBT) and the investigation of mechanisms of change is a promising opportunity. We performed secondary data analysis from routine data of 1,440 chronic pain patients. Patients received CBT in a multidisciplinary setting in two inpatient clinics. Effect sizes and reliable change indices were computed for pain-related disability and depression. The associations between changes in the use of different pain coping skills (cognitive restructuring, activity despite pain, relaxation techniques and mental distraction) and changes in clinical outcomes were analyzed in structural equation models. Pre-post effect sizes range from g = 0.47 (disability) to g = 0.89 (depression). Changes in the use of cognitive restructuring, relaxation and to a lesser degree mental distraction were associated with changes in disability and depression. Effects from randomized trials can be translated to naturalistic settings. The results complement experimental research on mechanisms of change in the treatment of chronic pain and indicate an important role of cognitive change and relaxation as mechanisms of change. Our findings cautiously suggest that clinicians should optimize these processes in chronic pain patients to reduce their physical and emotional disability.

Drawing the borderline: Predicting treatment outcomes in patients with borderline personality disorder.

BACKGROUND: A routinely collected big data set was analyzed to determine the effectiveness of naturalistic inpatient treatment and to identify predictors of treatment outcome and discontinuation. METHODS: The sample included 878 patients with borderline personality disorder who received non-manualized dialectic behavioral therapy in a psychosomatic clinic. Effect sizes (Hedge's g) were calculated to determine effectiveness. A bootstrap-enhanced regularized regression with 91 potential predictors was used to identify stable predictors of residualized symptom- and functional change and treatment discontinuation. Results were validated in a holdout sample and repeated cross validation. RESULTS: Effect sizes were small to medium (g = 0.28-0.51). Positive symptom-related outcome was predicted by low affect regulation skills and no previous outpatient psychotherapy. Lower age, absence of work disability, high emotional and physical role limitations and low bodily pain were associated with greater improvement in functional outcome. Higher education and comorbid recurrent depressive disorder were the main predictors of treatment completion. The predictive quality of the models varied, with the best being found for symptom-related outcome (R2 = 18%). CONCLUSION: While the exploratory process of variable selection replicates previous findings, the validation results suggest that tailoring treatment to the individual patient might not be based solely on sociodemographic, clinical and psychological baseline data.

Attachment and social support mediate the association between childhood maltreatment and depressive symptoms.

OBJECTIVE: To examine attachment insecurity and low social support as potential mediators of the association between childhood maltreatment (CM) types and depression severity in patients with a lifetime history of major depressive disorders (MDD). METHOD: Participants with an acute or remitted MDD (N = 580) completed questionnaires about CM (Childhood Trauma Questionnaire), attachment (Relationship Scales Questionnaire), social support (Social Support Questionnaire), and depression severity (Beck Depression Inventory). Mediation and path models with CM types as independent variables, attachment avoidance and anxiety as mediators and depression severity as dependent variable were calculated. In addition, a sequential mediation model with attachment insecurity and social support as mediators of the association between CM and depression was tested. RESULTS: Attachment avoidance and anxiety partially mediated the effect of CM on depression. In the path model including the different CM types, there were significant indirect effects of emotional abuse on depression via attachment anxiety and of emotional neglect on depression via attachment avoidance. Results also supported the hypothesized sequential mediation via attachment insecurity and social support. LIMITATIONS: A cross-sectional design with a retrospective self-report measure of CM was used and the developmental timing of exposure to CM was not considered. CONCLUSION: Our findings suggest that the effect of emotional abuse and emotional neglect on depression is partially mediated by attachment avoidance and anxiety. Further, the results support the hypothesis of a sequential mediation via attachment insecurity and social support. Accordingly, attachment insecurity is discussed as a target of psychotherapy for patients with MDD and CM.