Background: While growing research confirms the efficacy of transdiagnostic cognitive behavioral psychotherapy (TD-CBT) by examining group-level changes from pre to post-treatment, less is known about inter-individual differences and dynamic changes during therapy. Understanding different symptom trajectories and their associations with treatment outcome would allow early detection of non-responders and treatment adjustments.Methods: Patients with anxiety disorders reported on demographics, clinical history, treatment motivation, and expectations before randomization to TD-CBT or wait-list. We analyzed data from the treatment group who provided symptom ratings every session and post-treatment follow-up (N = 64, 71.9% female, age: M = 32.67, SD = 11.91). Using latent class growth analysis (LCGA), we identified symptom trajectory classes and tested whether these could be predicted from baseline data. We also examined associations between classes and treatment outcome, using multilevel modeling and considering reliable change indices. Results: LCGA revealed four distinct trajectory classes: low severity - improved (32.81%), high severity - improved (29.69%), high severity-stagnant (18.75%), and low severity - worsened (18.75%). Only number of comorbidities emerged as a significant predictor of class, with patients in the high severity - stagnant group having more comorbidities. They also showed higher symptom severity at baseline and follow-ups compared to other classes, despite clear improvement.Conclusions: During TD-CBT, latent classes of symptom change can be distinguished and point to malleable factors already present at pre-treatment. Particularly patients with high initial burden and a higher number of comorbidities may require monitoring and adaptive treatment strategies, which should be explored in future studies.

Internet-based treatments for social anxiety disorder (SAD) have been widely studied in unguided and therapistguided formats and, more recently, in group-guided formats that include guided online discussion forums. Little is known about what participants communicate and which processes unfold within these forum groups. This mixed-methods study analyzed communication in therapist-guided small-group forums integrated into an internet-based cognitive behavioral self-help program for SAD. Data stemmed from a three-arm randomized controlled trial and were limited to the forum arm. A qualitative content analysis of 511 forum posts from 60 participants identified 18 main categories. Communication was dominated by therapist input, interactional group processes, symptom-related exchanges, and relational content, indicating that the forum functioned as a shared therapeutic space linked to the cognitivebehavioral intervention. Exploratory associations between category frequencies and treatment outcomes were examined for completers (n = 45). Higher frequencies of messages reflecting motivation (r = 0.41), alliance (r = 0.34), and observing positive consequences (r = 0.32) were associated with greater symptom reduction on the Social Phobia Scale, but not on the Social Interaction Anxiety Scale. Program adherence (completed modules) was significantly associated with 10 of 18 categories, primarily motivational/relational processes, responses to self-disclosure, difficulties, and evaluations of alternative behaviors. Overall, therapist-guided online group forums extend the therapeutic learning environment by providing a social context in which motivational, relational, and treatment processes co-occur and are linked to outcome and adherence. Given the correlational and exploratory design, these associations should be interpreted cautiously and may represent mechanisms of change, markers of improvement, or both.

OBJECTIVE:Social anxiety disorder (SAD) is one of the most common mental disorders, with the majority of those affected not receiving primary care or psychotherapy. Internet-based treatments may be an alternative for individuals with SAD. We hypothesize that interpersonal characteristics differ between SAD patients in face-to-face (ftf) and online treatments, and that these factors predict treatment outcomes. METHODS:The sample consisted of 539 patients with SAD from four different online treatment studies (N = 376) and one university outpatient clinic in Europe (N = 163) who received integrative CBT. Interpersonal problems were assessed at baseline and symptom severity at baseline and treatment termination. RESULTS:Results showed similar interpersonal patterns of being nonassertive and socially inhibited in both treatment groups at baseline, with patients in online treatment being more severely affected than those in ftf therapy. Interpersonal problems of communion were predictive of outcome in both treatments. Low communion reflects interpersonal coldness, distance, and difficulty forming close bonds, whereas high communion reflects excessive dependence, submissiveness, and overinvolvement with others. Patients with interpersonal problems of low communion benefited more from ftf therapy, while patients with interpersonal problems of high communion benefited more from online therapy. CONCLUSION:These findings underscore the importance of considering interpersonal problems when planning and evaluating treatment for SAD. In the future, it may be possible to predict if a particular SAD patient benefits more from online versus ftf treatment from their baseline interpersonal problems and recommend the suitable treatment.

Self-efficacy is a key construct in behavioral science affecting mental health and psychopathology. Here, we expand on previously demonstrated between-persons self-efficacy effects. We prompted 66 patients five times daily for 14 days before starting cognitive behavioral therapy (CBT) to provide avoidance, hope, and perceived psychophysiological-arousal ratings. Multilevel logistic regression analyses confirmed self-efficacy's significant effects on avoidance in daily life (odds ratio [OR] = 0.53, 95% confidence interval [CI] = [0.34, 0.84], p = .008) and interaction effects with anxiety in predicting perceived psychophysiological arousal (OR = 0.79, 95% CI = [0.62, 1.00], p = .046) and hope (OR = 1.21, 95% CI = [1.03, 1.42], p = .02). More self-efficacious patients also reported greater anxiety-symptom reduction early in treatment. Our findings assign a key role to self-efficacy for daily anxiety-symptom experiences and for early CBT success. Self-efficacy interventions delivered in patients' daily lives could help improve treatment outcome.

Internet-based interventions show promise for meeting the increasing demand for psychological support, yet the mechanisms driving their effectiveness remain unclear. This study examines the roles of empowerment and working alliance in reducing social anxiety disorder (SAD) and improving adherence in a clinician-guided group (GT) compared to a clinician-guided individual treatment (IT). A total of 120 individuals meeting the SAD diagnostic criteria were randomized into one of the two active conditions. Both empowerment and working alliance were repeatedly assessed, and their effects on social anxiety and adherence (measured by completed exercises) were analyzed through t-tests, correlations, repeated measures ANOVA, and mediation models. Results revealed no significant differences between GT and IT in empowerment or alliance, although both improved throughout the intervention. GT demonstrated early advantages in alliance, while IT showed slightly better adherence and a stronger connection between empowerment and adherence. No mediation effects were observed. This study is among the first to indicate that online interventions can enhance empowerment. However, neither group nor individual treatment proved superior in enhancing empowerment or alliance. This could be due to power limitations; therefore, results should be interpreted as tendencies. Further research is needed to clarify how these factors help reduce symptoms of social anxiety.

Transdiagnostic cognitive behavioural psychotherapy (TD-CBT) may facilitate the treatment of emotional disorders. Here we investigate short- and long-term efficacy of TD-CBT for emotional disorders in individual, group and internet-based settings in randomized controlled trials (PROSPERO CRD42019141512). Two independent reviewers screened results from PubMed, MEDLINE, PsycINFO, Google Scholar, medRxiv and OSF Preprints published between January 2000 and June 2023, selected studies for inclusion, extracted data and evaluated risk of bias (Cochrane risk-of-bias tool 2.0). Absolute efficacy from pre- to posttreatment and relative efficacy between TD-CBT and control treatments were investigated with random-effects models. Of 56 identified studies, 53 (6,705 participants) were included in the meta-analysis. TD-CBT had larger effects on depression ( g = 0.74 , 95% CI = 0.57–0.92, P < 0.001) and anxiety ( g = 0.77, 95% CI = 0.56–0.97, P < 0.001) than did controls. Across treatment formats, TD-CBT was superior to waitlist and treatment-as-usual. TD-CBT showed comparable effects to disorder-specific CBT and was superior to other active treatments for depression but not for anxiety. Different treatment formats showed comparable effects. TD-CBT was superior to controls at 3, 6 and 12 months but not at 24 months follow-up. Studies were heterogeneous in design and methodological quality. This review and meta-analysis strengthens the evidence for TD-CBT as an efficacious treatment for emotional disorders in different settings.

Background Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices. Objective This study aims to establish predictive features that forecast responses to transdiagnostic CBT in anxiety disorders and to investigate key mechanisms underlying treatment responses. Methods This study is a 2-armed randomized controlled clinical trial. We include patients with anxiety disorders who are randomized to either a transdiagnostic CBT group or a waitlist (referred to as WAIT). We index key features to predict responses prior to starting treatment using subjective self-report questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing to derive a multimodal feature set for predictive modeling. Additional assessments take place weekly at mid- and posttreatment and at 6- and 12-month follow-ups to index anxiety and depression symptom severity. We aim to include 150 patients, randomized to CBT versus WAIT at a 3:1 ratio. The data set will be subject to full feature and important features selected by minimal redundancy and maximal relevance feature selection and then fed into machine leaning models, including eXtreme gradient boosting, pattern recognition network, and k-nearest neighbors to forecast treatment response. The performance of the developed models will be evaluated. In addition to predictive modeling, we will test specific mechanistic hypotheses (eg, association between self-efficacy, daily symptoms obtained using ecological momentary assessments, and treatment response) to elucidate mechanisms underlying treatment response. Results The trial is now completed. It was approved by the Cantonal Ethics Committee, Zurich. The results will be disseminated through publications in scientific peer-reviewed journals and conference presentations. Conclusions The aim of this trial is to improve current CBT treatment by precise forecasting of treatment response and by understanding and potentially augmenting underpinning mechanisms and personalizing treatment. Trial Registration ClinicalTrials.gov NCT03945617; https://clinicaltrials.gov/ct2/show/results/NCT03945617 International Registered Report Identifier (IRRID) DERR1-10.2196/42547

Negative emotions and associated avoidance behaviors are core symptoms of anxiety. Current treatments aim to resolve dysfunctional coupling between them. However, precise interactions between emotions and avoidance in patients' everyday lives and changes from pre- to post-treatment remain unclear. We analyzed data from a randomized controlled trial where patients with anxiety disorders underwent 16 sessions of cognitive behavioral therapy (CBT). Fifty-six patients (68 % female, age: M = 33.31, SD = 12.45) completed ecological momentary assessments five times a day on 14 consecutive days before and after treatment, rating negative emotions and avoidance behaviors experienced within the past 30 min. We computed multilevel vector autoregressive models to investigate contemporaneous and time-lagged associations between anxiety, depression, anger, and avoidance behaviors within patients, separately at pre- and post-treatment. We examined pre-post changes in network density and avoidance centrality, and related these metrics to changes in symptom severity. Network density significantly decreased from pre- to post-treatment, indicating that after therapy, mutual interactions between negative emotions and avoidance were attenuated. Specifically, contemporaneous associations between anxiety and avoidance observed before CBT were no longer significant at post-treatment. Effects of negative emotions on avoidance assessed at a later time point (avoidance instrength) decreased, but not significantly. Reduction in avoidance instrength positively correlated with reduction in depressive symptom severity, meaning that as patients improved, they were less likely to avoid situations after experiencing negative emotions. Our results elucidate mechanisms of successful CBT observed in patients' daily lives and may help improve and personalize CBT to increase its effectiveness.

BACKGROUND:The Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) are widely used self-report questionnaires to assess symptoms of social anxiety. While SPS measures social performance anxiety, SIAS measures social interaction anxiety. They are mostly reported simultaneously, but there have not been consistent results of the joint factor structure and therefore no consistent recommendations on how to use and evaluate the questionnaires. This study aimed (1) to evaluate the underlying joint factor structure of the SPS and SIAS and (2) to test whether SPS and SIAS are reliable scales to assess two different aspects of social anxiety.METHODS:The one-factor, two-factor, and bifactor models were tested in a clinical sample recruited from the community and diagnosed with a social anxiety disorder. Exploratory and confirmatory factor analyses were conducted, bifactor-specific indices were calculated, and the content of the less fitting items was examined.RESULTS:Confirmatory factor analyses showed that the best-fitting model was the bifactor model with a reduced set of items. The bifactor-specific indices showed that the factor structure cannot be considered unidimensional and that SPS and SIAS are reliable subscales. A closer examination of the less fitting item content and implications for future studies are discussed.CONCLUSIONS:In conclusion, SPS and SIAS can be reported together as an overall score of social anxiety and are separately reliable measures to assess different aspects of social anxiety.TRIAL REGISTRATION:This is a secondary analysis of data from two trials registered under ISRCTN75894275 and ISRCTN10627379.

Background: This paper describes the study protocol for our clinical trial “Optimizing Outcomes in Psychotherapy for Anxiety Disorders (OPTIMAX)” funded by the Swiss National Science Foundation (10001C_169827). The study aims to establish predictive features for forecasting response to cognitive behavior therapy (CBT) and to investigate mechanisms underlying treatment response. Methods: OPTIMAX comprises a monocentric, randomized-controlled clinical trial. We employ the Unified Treatment Protocol (UP, Barlow, 2017), an established transdiagnostic CBT protocol for treating emotional disorders, to treat patients with anxiety disorders. We use psychological questionnaires, experimental tasks, biological samples, ecological momentary assessments, activity tracking, and smartphone-based passive sensing data in order to derive a multimodal feature set for predictive modeling. We obtain assessments at different time points including baseline, mid-, and post-treatment as well as 6 and 12 months after treatment completion. Anxiety and depression symptom severity are indexed weekly during treatment. We aim to include 150 patients, randomized to CBT versus WAIT group in a 3:1 ratio. Machine learning (e.g., support vector machines, random forest) and linear regression modeling will be employed to establish predictive accuracy in forecasting treatment response. In addition to predictive modelling, we test mechanistic hypotheses, e.g., on the association between self-efficacy, dynamic symptom changes and treatment response, to elucidate mechanisms underlying treatment response. Discussion: The aim of the current trial is to improve current CBT treatment, such as the transdiagnostic unified treatment protocol employed here, by precise forecasting of treatment response and by understanding and, in the future, augmenting underpinning mechanisms and personalizing treatment. Registration: This study has been registered on clinicaltrials.gov (NCT03945617, 10 of May 2019, https://clinicaltrials.gov/ct2/show/NCT03945617)

Self-efficacy is a key construct in behavioral science with significant impact on mental health and wellbeing. A growing body of work has shown that perceptions of self-efficacy can be increased through recall of autobiographical episodes (AEs) of mastery ("self-efficacy memories") in experimental settings. Doing so contributes to improvements in clinically relevant processes, such as emotion regulation and problem solving. Here we examine whether the recall of self-efficacy AEs contributes to more adaptive appraisals for personally experienced negative memories. Seventy-five healthy individuals each identified an idiosyncratic personal negative memory that was screened for emotional attributes. Participants were then asked to either recall self-efficacy (SE, n = 25) or positive (POS, n = 25) autobiographical episodes. We investigated induction effects on subsequent reappraisals of the personal negative memories. The SE induction was associated with significant reductions in distress, and subjective physiological responses as compared to the POS induction. No significant induction effects emerged in autonomic regulation. These findings suggest that recalling self-efficacy episodes may promote adaptive self-appraisals for negative memories, which in turn may contribute to recovery from stressful events and, with further research, may prove to be a useful adjunctive strategy for treatments such as CBT. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

ZUSAMMENFASSUNG Einleitung Eine wachsende Zahl psychologischer Behandlungsangebote erfolgt über das Smartphone, bzw. über Apps. Viele der wissenschaftlich geprüften Apps basieren auf den Prinzipien der kognitiven Verhaltenstherapie (KVT), Goldstandard zur Behandlung vieler psychischer Problematiken. Ziel dieses Reviews war es, den Status Quo der Forschung zu Stand-Alone Smartphone-basierten Apps zusammenzufassen, welche auf diesen therapeutischen Ansätzen basieren und mittels App-basierten Ecological Momentary Interventions (EMIs) durchgeführt werden. Methode Eine systematische Literaturrecherche in MEDLINE, PsycINFO, Embase und PubMed identifizierte 26 zwischen 2007 und 2020 publizierte, peer-reviewte Studien, in denen Durchführbarkeit und/oder Wirksamkeit und/oder Effektivität von KVT-basierten EMIs sowohl in Studiendesigns mit inter- als auch intraindividuellen Vergleichen, sowohl bei gesunden als auch klinischen Stichproben untersucht wurden. Gemischte Interventionen (blended interventions), z. B. App-basierte Behandlungen in Kombination mit klassischer Psychotherapie wurden nicht mitberücksichtigt. Ergebnisse KVT-basierte EMIs wurden von Teilnehmern angenommen, verbesserten das Wohlbefinden der Nutzer signifikant und reduzierten Symptome psychischer Störungen. Stand-Alone EMIs wurden von den Teilnehmenden als hilfreich (M = 70,8 %) und bezüglich der Outcomes als zufriedenstellend (M = 72,6 %) eingeschätzt. Schlussfolgerung In Summe zeigten die Studien, dass EMIs dazu beitragen können, die psychische Gesundheit zu verbessern und damit Individuen in ihrem täglichen Leben zu unterstützen. Sie bieten somit eine unmittelbar verfügbare, skalierbare und evidenzbasierte Form der Unterstützung psychischer Gesundheit. Diese Charakteristiken sind nicht zuletzt relevant im Kontext der Bemühungen, die individuellen und ökonomischen Kosten psychischer Erkrankungen zu reduzieren, wie auch im Kontext globaler Pandemien.

Transdiagnostic treatments span a heterogeneous group of interventions that target a wider range of disorders and can be applied to treat several disorders simultaneously. Several meta-analyses have highlighted the evidence base of these novel therapies. However, these meta-analyses adopt different definitions of transdiagnostic treatments, and the growing field of transdiagnostic therapies has become increasingly difficult to grasp. The current narrative review proposes a distinction of "one size fits all" unified and "my size fits me" individualized approaches within transdiagnostic therapies. Unified treatments are applied as "broadband" interventions to a range of disorders without tailoring to the individual, while individualized treatments are tailored to the specific problem presentation of the individual, e.g., by selecting modules within modular treatments. The underlying theoretical foundation and relevant empirical evidence for these different transdiagnostic approaches are examined. Advantages and limitations of the transdiagnostic treatments as well as future developments are discussed.

BACKGROUND:A growing number of psychological interventions are delivered via smartphones with the aim of increasing the efficacy and effectiveness of these treatments and providing scalable access to interventions for improving mental health. Most of the scientifically tested apps are based on cognitive behavioral therapy (CBT) principles, which are considered the gold standard for the treatment of most mental health problems.OBJECTIVE:This review investigates standalone smartphone-based ecological momentary interventions (EMIs) built on principles derived from CBT that aim to improve mental health.METHODS:We searched the MEDLINE, PsycINFO, EMBASE, and PubMed databases for peer-reviewed studies published between January 1, 2007, and January 15, 2020. We included studies focusing on standalone app-based approaches to improve mental health and their feasibility, efficacy, or effectiveness. Both within- and between-group designs and studies with both healthy and clinical samples were included. Blended interventions, for example, app-based treatments in combination with psychotherapy, were not included. Selected studies were evaluated in terms of their design, that is, choice of the control condition, sample characteristics, EMI content, EMI delivery characteristics, feasibility, efficacy, and effectiveness. The latter was defined in terms of improvement in the primary outcomes used in the studies.RESULTS:A total of 26 studies were selected. The results show that EMIs based on CBT principles can be successfully delivered, significantly increase well-being among users, and reduce mental health symptoms. Standalone EMIs were rated as helpful (mean 70.8%, SD 15.3; n=4 studies) and satisfying for users (mean 72.6%, SD 17.2; n=7 studies).CONCLUSIONS:Study quality was heterogeneous, and feasibility was often not reported in the reviewed studies, thus limiting the conclusions that can be drawn from the existing data. Together, the studies show that EMIs may help increase mental health and thus support individuals in their daily lives. Such EMIs provide readily available, scalable, and evidence-based mental health support. These characteristics appear crucial in the context of a global crisis such as the COVID-19 pandemic but may also help reduce personal and economic costs of mental health impairment beyond this situation or in the context of potential future pandemics.

The Unified Protocol (UP) as a transdiagnostic intervention has primarily been applied in the treatment of anxiety disorders and in face-to-face-settings. The current study investigated the efficacy of a 10-week internet-based adaptation of the UP for anxiety, depressive, and somatic symptom disorders. N=129 participants were randomized to treatment or waitlist control. Linear mixed effect models revealed significant treatment effects for symptom distress, satisfaction with life, positive/negative affect and markers of anxiety, depression, and somatic symptom burden (within-group Hedges’ g = 0.32-1.38 and between-group g = 0.20-1.11). Treatment gains were maintained at 1- and 6-month-follow-up. Subgroup analyses showed comparable effects in participants with anxiety and depressive disorders. The results strengthen the application of the UP as an internet-based treatment for alleviating symptom distress across emotional disorders. More research on the applicability for single disorders and the mechanisms underlying the effects is needed.

BackgroundSocial anxiety disorder (SAD) is highly prevalent among university students, but the majority of affected students remain untreated. Internet- and mobile-based self-help interventions (IMIs) may be a promising strategy to address this unmet need. This study aims to investigate the efficacy and cost-effectiveness of an unguided internet-based treatment for SAD among university students. The intervention is optimized for the treatment of university students and includes one module targeting fear of positive evaluations that is a neglected aspect of SAD treatment.MethodsThe study is a two arm randomized controlled trial in which 200 university students with a primary diagnosis of SAD will be assigned randomly to either a wait-list control group (WLC) or the intervention group (IG). The intervention consists of 9 sessions of an internet-based cognitive-behavioral treatment, which also includes a module on fear of positive evaluation (FPE). Guidance is delivered only on the basis of standardized automatic messages, consisting of positive reinforcements for session completion, reminders, and motivational messages in response to non-adherence. All participants will additionally have full access to treatment as usual. Diagnostic status will be assessed through Structured Clinical Interviews for DSM Disorders (SCID). Assessments will be completed at baseline, 10weeks and 6-month follow-up. The primary outcome will be SAD symptoms at post-treatment, assessed via the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Secondary outcomes will include diagnostic status, depression, quality of life and fear of positive evaluation. Cost-effectiveness and cost-utility analyses will be evaluated from a societal and health provider perspective.DiscussionResults of this study will contribute to growing evidence for the efficacy and cost-effectiveness of unguided IMIs for the treatment of SAD in university students. Consequently, this trial may provide valuable information for policy makers and clinicians regarding the allocation of limited treatment resources to such interventions.Trial registrationDRKS00011424 (German Clinical Trials Register (DRKS)) Registered 14/12/2016.

OBJECTIVES:Internet- and mobile-based interventions (IMIs) offer the opportunity to deliver mental health treatments on a large scale. This randomized controlled trial evaluated the efficacy of an unguided IMI (StudiCare SAD) for university students with social anxiety disorder (SAD). METHODS:University students (N = 200) diagnosed with SAD were randomly assigned to an IMI or a waitlist control group (WLC) with full access to treatment as usual. StudiCare SAD consists of nine sessions. The primary outcome was SAD symptoms at posttreatment (10 weeks), assessed via the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Secondary outcomes included depression, quality of life, fear of positive evaluation, general psychopathology, and interpersonal problems. RESULTS:Results indicated moderate to large effect sizes in favor of StudiCare SAD compared with WLC for SAD at posttest for the primary outcomes (SPS: d = 0.76; SIAS: d = 0.55, p < 0.001). Effects on all secondary outcomes were significant and in favor of the intervention group. CONCLUSION:StudiCare SAD has proven effective in reducing SAD symptoms in university students. Providing IMIs may be a promising way to reach university students with SAD at an early stage with an effective treatment.

Increased levels of self-criticism and a lack of self-compassion have been associated with the development and maintenance of a range of psychological disorders. In the current study, we tested the efficacy of an online version of a compassion-focused intervention, mindfulness-based compassionate living (MBCL), with guidance on request. A total of 122 self-referred participants with increased levels of self-criticism were randomly assigned to care as usual (CAU) or the intervention group (CAU + online intervention). Primary endpoints were self-reported depressive, anxiety and distress symptoms (DASS-21) and self-compassion (SCS) at 8 weeks. Secondary endpoints were self-criticism, mindfulness, satisfaction with life, fear of self-compassion, self-esteem, and existential shame. At posttreatment, the intervention group showed significant changes with medium to large effect sizes compared to the control group regarding primary outcomes (Cohen's d: 0.79 [DASS] and -1.21 [SCS]) and secondary outcomes (Cohen's ds: between 0.40 and 0.94 in favor of the intervention group). The effects in the intervention group were maintained at 6-months postrandomization. Adherence measures (number of completed modules, self-reported number of completed exercises per week) predicted postintervention scores for self-compassion but not for depressive, anxiety, and distress symptoms in the intervention group. The current study shows the efficacy of an online intervention with a transdiagnostic intervention target on a broad range of measures, including depressive and anxiety symptoms and self-compassion.

Objective: Internet-based cognitive–behavioral treatments (ICBT) have shown promise for various mental disorders, including social anxiety disorder (SAD). Most of these treatments have been delivered on desktop computers. However, the use of smartphones is becoming ubiquitous and could extend the reach of ICBT into users’ everyday life. Only a few studies have empirically examined the efficacy of ICBT delivered through a smartphone app and there is no published study on mobile app delivered ICBT for SAD. This three-arm randomized-controlled trial (RCT) is the first to compare the efficacy of guided ICBT for smartphones (app) and conventional computers (PC) with a wait list control group (WL). Method: A total of 150 individuals meeting the diagnostic criteria for SAD were randomly assigned to one of the three conditions. Primary endpoints were self-report measures and diagnostic status of SAD. Results: After 12 weeks of treatment, both active conditions showed superior outcome on the composite of all SAD measures (PC vs. WL: d = 0.74; App vs. WL: d = 0.89) and promising diagnostic response rates (NNTPC = 3.33; NNTApp = 6.00) compared to the WL. No significant between-groups effects were found between the two active conditions on the composite score (Cohen’s d = 0.07). Treatment gains were maintained at 3-month follow-up. Program use was more evenly spread throughout the day in the mobile condition, indicating an integration of the program into daily routines. Conclusions: ICBT can be delivered effectively using smartphones.

Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.