Mediation of social anxiety and depression during internet-delivered treatment for social anxiety disorder.

Many individuals with social anxiety disorder (SAD) have depressive symptoms that meet criteria for major depressive disorder (MDD). In our study, we examined the temporal relationship between symptoms of social anxiety and symptoms of depression during the course of an 11-week internet-delivered cognitive behavioral treatment (ICBT) for SAD (n = 170). Specifically, we investigated whether weekly changes in social anxiety mediated changes in depression, changes in depression mediated changes in anxiety, both or neither. In addition, we compared individuals with SAD and MDD (n = 50) and individuals with SAD and no MDD (n = 120) to examine the role of MDD as a moderator of the social anxiety-depression relationship. Lower-level mediational modeling revealed that changes in social anxiety symptoms mediated changes in depression symptoms to a greater extent than vice versa. In addition, mediation among individuals with SAD and MDD was significantly greater compared to individuals with SAD and no MDD. Our findings suggest that ICBT is effective in treating individuals with SAD regardless of comorbid depression, and that focusing ICBT interventions on social anxiety can lead to significant reductions in depression among individuals with SAD.

Using machine-learning to predict sudden gains in treatment for major depressive disorder.

OBJECTIVE: Sudden gains during psychotherapy have been found to consistently predict treatment outcome but evidence on predictors of sudden gains has been equivocal. To address this gap, the present study utilized three machine learning algorithms to predict sudden gains during treatment for major depressive disorder. METHOD: We examined predictors of sudden gains in two large samples of individuals receiving treatment in a partial hospital setting (n = 726 and n = 788; total N = 1514). Predictors included age, gender, marital status, education, employment status, previous hospitalization, comorbid diagnoses, and pretreatment measures of depressive and generalized anxiety symptoms. We used three machine learning models: a Random Forest model, a Random Forest model with an adaptive boosting meta-algorithm, and a Support Vector Machine model. RESULTS: In both samples, sudden gains were identified and found to significantly predict outcome. However, none of the machine learning algorithms was able to identify robust predictors of sudden gains. Thus, even though some models achieved fair prediction of sudden gains in the training subset, prediction in the test subset was poor. CONCLUSIONS: Despite the use of a large sample and three machine-learning models, we were unable to identify robust demographic and pretreatment clinical predictors of sudden gains. Implications for clinical decision making and future studies are discussed.

A Revised Theory of Sudden Gains in Psychological Treatments.

Sudden gains were first defined and quantified by Tang and DeRubeis (1999) and were found to predict treatment outcome in cognitive therapy for depression. Since that seminal paper, over 100 examinations of sudden gains have been published and sudden gains have been found to be ubiquitous in psychological treatments and to consistently predict better treatment outcomes across a multitude of disorders and contexts (see Shalom & Aderka, 2020 for a review). The research on sudden gains has seen considerable growth over the past 20 years. However, the theory behind sudden gains (which addresses processes leading to sudden gains, and processes resulting from sudden gains) has never been revised. Based on the empirical research which has accrued over the last 20 years, we present an empirically-based revision of the theory of sudden gains. The revised theory addresses both predictors of sudden gains and processes that may lead to sudden gains, as well as the consequences of sudden gains and the processes by which sudden gains can affect outcome. We also present a number of hypotheses that can be derived from the theory as well as the status of empirical evidence supporting these hypotheses. Research and clinical implications are discussed.

Predicting sudden gains before treatment begins: An examination of pretreatment intraindividual variability in symptoms.

Objective: Sudden gains during psychotherapy have been found to be predictive of positive treatment outcomes. Previous attempts at predicting occurrence of sudden gains have yielded equivocal findings. Recently, intraindividual variability in symptoms during treatment was suggested as a trans-therapeutic and trans-diagnostic predictor of sudden gains. The goal of the present study was to examine this predictor in Internet-delivered treatment for social anxiety disorder (SAD) and to examine whether this predictor predicts sudden gains when measured before treatment begins. Method: We examined data from a preregistered randomized controlled trial (RCT) of Internet-delivered cognitive-behavioral therapy (CBT) for SAD (n = 101). We measured variability in symptoms both within-treatment and before treatment (i.e. during waitlist). Results: Intraindividual variability in symptoms significantly predicted sudden gains both when measured before treatment or within-treatment and correctly classified 84% and 83% of individuals to sudden gains versus non-sudden gains status, respectively. Conclusions: Intraindividual variability in symptoms can predict sudden gains in Internet-delivered treatment for SAD, thus supporting its trans-diagnostic and trans-therapeutic nature. Predicting sudden gains before treatment begins has implications for treatment planning and clinical decision making as well as for personalized tailoring of interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A meta-analysis of sudden gains in psychotherapy: Outcome and moderators.

Sudden gains have been shown to be a common phenomenon and an important predictor of outcome in psychotherapy (Tang & DeRubeis, 1999). The present meta-analysis examined the growing literature on sudden gains in psychotherapy, their effects on outcome, and moderators of these effects. We searched PsycINFO, PubMed, ProQuest Dissertations & Theses, Scholar and Web of Science databases for articles between January 1st 1999 and May 31st 2019 and included 50 studies (n = 6355) in the meta-analysis. Sudden gains were found to significantly predict outcome at post-treatment (g = 0.68) and follow up (g = 0.61) above and beyond treatments, disorders and settings. Studies using Tang and DeRubies's (1999) criteria for identification of sudden gains had smaller effect sizes compared to studies using altered criteria. Pretreatment severity levels did not significantly predict the effects of sudden gains and neither did treatment setting (randomized controlled trials vs. naturalistic settings). Finally, number of sessions and sudden gains' reversal rates were both negatively associated with the effect sizes of sudden gains. These findings suggest that sudden gains are a ubiquitous phenomenon in psychotherapy but their effects may be moderated by a number of factors. Research and clinical implications are discussed.

Intraindividual variability in symptoms consistently predicts sudden gains: An examination of three independent datasets.

OBJECTIVE: Sudden gains are robust predictors of outcome in psychotherapy. However, previous attempts at predicting sudden gains have yielded inconclusive findings. The aim of the present study was to examine a novel, transdiagnostic, transtherapeutic predictor of sudden gains that would replicate in different settings and populations. Specifically, we examined intraindividual variability in symptoms. METHOD: We examined data from a randomized controlled trial (RCT) of prolonged exposure therapy for posttraumatic stress disorder (PTSD) in children and adolescents (n = 63), an RCT of cognitive and behavioral therapies for obsessive-compulsive disorder (OCD) in adults (n = 91), and psychodynamic therapy delivered under routine clinical conditions in a naturalistic setting for diverse disorders (n = 106). In all 3 data sets, we examined whether a measure of variability in symptoms occurring during the first sessions could predict sudden gains. RESULTS: Variability in symptoms was found to be independent of total change during treatment. Variability in symptoms significantly predicted sudden gains in all 3 data sets and correctly classified 81.0%, 69.2%, and 76.9% of individuals to sudden gain or nonsudden gain status, respectively. CONCLUSIONS: The present study represents the first examination of variability in symptoms as a predictor of sudden gains. Findings indicated that sudden gains are significantly predicted by intraindividual variability in symptoms, in diverse settings, contexts, and populations. Advantages of this predictor, as well as clinical and research implications are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Combining Face-to-face Therapy with Computerized Techniques: A therapists' Attitudes Survey.

Therapists' attitudes towards the use of computerized therapies have been the focus of numerous studies. Nevertheless, little is known about therapists' perception of a combined model that uses computerized methods as adjuncts to face-to-face (FTF) therapy. Current study surveyed 87 Israeli therapists' attitudes towards such combined model. Results show that more than half of therapists find it as potentially more effective than regular therapy, better for providing feedback and maintaining continuity of care. More than a third of the therapists found it may elicit better patient satisfaction and engagement. With respect to different modalities, therapists indicated that e-mail correspondence are better suited for combining with FTF treatments. Theoretical orientation had little effect on practitioners' attitudes. Finally, more than half of the therapists showed willingness to undergo training for a combined model. Compared to previously researched computerized interventions, current study indicates more positive attitudes and acceptability among therapists towards an integrative model.