Investigating Relationships Among Self-Efficacy, Mood, and Anxiety Using Digital Technologies: Randomized Controlled Trial.

BACKGROUND Digital tools assessing momentary parameters and offering interventions in people's daily lives play an increasingly important role in mental health research and treatment. Ecological momentary assessment (EMA) makes it possible to assess transient mental health states and their parameters. Ecological momentary interventions (EMIs) offer mental health interventions that fit well into individuals' daily lives and routines. Self-efficacy is a transdiagnostic construct that is commonly associated with positive mental health outcomes. OBJECTIVE The aim of our study assessing mood, specific self-efficacy, and other parameters using EMA was 2-fold. First, we wanted to determine the effects of daily assessed moods and dissatisfaction with social contacts as well as the effects of baseline variables, such as depression, on specific self-efficacy in the training group (TG). Second, we aimed to explore which variables influenced both groups' positive and negative moods during the 7-day study period. METHODS In this randomized controlled trial, we applied digital self-efficacy training (EMI) to 93 university students with elevated self-reported stress levels and daily collected different parameters, such as mood, dissatisfaction with social contacts, and specific self-efficacy, using EMA. Participants were randomized to either the TG, where they completed the self-efficacy training combined with EMA, or the control group, where they completed EMA only. RESULTS In total, 93 university students participated in the trial. Positive momentary mood was associated with higher specific self-efficacy in the evening of the same day (b=0.15, SE 0.05, P=.005). Higher self-efficacy at baseline was associated with reduced negative mood during study participation (b=-0.61, SE 0.30, P=.04), while we could not determine an effect on positive mood. Baseline depression severity was significantly associated with lower specific self-efficacy over the week of the training (b=-0.92, SE 0.35, P=.004). Associations between higher baseline anxiety with higher mean negative mood (state anxiety: b=0.78, SE 0.38, P=.04; trait anxiety: b=0.73, SE 0.33, P=.03) and lower mean positive mood (b=-0.64, SE 0.28, P=.02) during study participation were found. Emotional flexibility was significantly enhanced in the TG. Additionally, dissatisfaction with social contacts was associated with both a decreased positive mood (b=-0.56, SE 0.15, P<.001) and an increased negative mood (b=0.45, SE 0.12, P<.001). CONCLUSIONS This study showed several significant associations between mood and self-efficacy as well as those between mood and anxiety in students with elevated stress levels, for example, suggesting that improving mood in people with low mood could enhance the effects of digital self-efficacy training. In addition, engaging in 1-week self-efficacy training was associated with increased emotional flexibility. Future work is needed to replicate and investigate the training's effects in other groups and settings. TRIAL REGISTRATION ClinicalTrials.gov NCT05617248; https://clinicaltrials.gov/study/NCT05617248

Investigating Relationships Among Self-Efficacy, Mood, and Anxiety Using Digital Technologies: Randomized Controlled Trial

Background Digital tools assessing momentary parameters and offering interventions in people’s daily lives play an increasingly important role in mental health research and treatment. Ecological momentary assessment (EMA) makes it possible to assess transient mental health states and their parameters. Ecological momentary interventions (EMIs) offer mental health interventions that fit well into individuals’ daily lives and routines. Self-efficacy is a transdiagnostic construct that is commonly associated with positive mental health outcomes. Objective The aim of our study assessing mood, specific self-efficacy, and other parameters using EMA was 2-fold. First, we wanted to determine the effects of daily assessed moods and dissatisfaction with social contacts as well as the effects of baseline variables, such as depression, on specific self-efficacy in the training group (TG). Second, we aimed to explore which variables influenced both groups’ positive and negative moods during the 7-day study period. Methods In this randomized controlled trial, we applied digital self-efficacy training (EMI) to 93 university students with elevated self-reported stress levels and daily collected different parameters, such as mood, dissatisfaction with social contacts, and specific self-efficacy, using EMA. Participants were randomized to either the TG, where they completed the self-efficacy training combined with EMA, or the control group, where they completed EMA only. Results In total, 93 university students participated in the trial. Positive momentary mood was associated with higher specific self-efficacy in the evening of the same day (b=0.15, SE 0.05, P=.005). Higher self-efficacy at baseline was associated with reduced negative mood during study participation (b=–0.61, SE 0.30, P=.04), while we could not determine an effect on positive mood. Baseline depression severity was significantly associated with lower specific self-efficacy over the week of the training (b=–0.92, SE 0.35, P=.004). Associations between higher baseline anxiety with higher mean negative mood (state anxiety: b=0.78, SE 0.38, P=.04; trait anxiety: b=0.73, SE 0.33, P=.03) and lower mean positive mood (b=–0.64, SE 0.28, P=.02) during study participation were found. Emotional flexibility was significantly enhanced in the TG. Additionally, dissatisfaction with social contacts was associated with both a decreased positive mood (b=–0.56, SE 0.15, P<.001) and an increased negative mood (b=0.45, SE 0.12, P<.001). Conclusions This study showed several significant associations between mood and self-efficacy as well as those between mood and anxiety in students with elevated stress levels, for example, suggesting that improving mood in people with low mood could enhance the effects of digital self-efficacy training. In addition, engaging in 1-week self-efficacy training was associated with increased emotional flexibility. Future work is needed to replicate and investigate the training’s effects in other groups and settings. Trial Registration ClinicalTrials.gov NCT05617248; https://clinicaltrials.gov/study/NCT0561724

Optimizing Outcomes in Psychotherapy for Anxiety Disorders Using Smartphone-Based and Passive Sensing Features: Protocol for a Randomized Controlled Trial

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) DERR1-10.2196/4254

Recalling autobiographical self-efficacy episodes boosts reappraisal-effects on negative emotional memories

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

A systematic review and meta-analysis of transdiagnostic cognitive behavioural therapies for emotional disorders.

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

Effects of a digital self-efficacy training in stressed university students: a randomized controlled trial

Objective: Self-efficacy is associated with positive mental health outcomes. We developed and tested a digital self-efficacy training for daily recall of autobiographical self-efficacy memories (e.g., memories of successfully overcoming a personal challenge). Method: In this randomized controlled trial, we investigated the effects of the week-long digital self-efficacy training on key mental health outcomes, including anxiety, stress, and hopelessness, and on self-efficacy in 93 university students (mean age 23.3 years, SD: 3.49) with elevated self-reported stress levels. Participants completed either the self-efficacy training combined with ecological momentary assessment (EMA) (training group) or EMA only (control group). Results: We found significantly reduced hopelessness (d = 0.29) and trait anxiety (d = 0.79) in the training group compared to the control group at post-assessment (one day post intervention). Effects on ratings of self-efficacy at post-assessment were also significant when controlling for baseline self-efficacy. Conclusions: This stand-alone digital self-efficacy training was significantly associated with a number of positive effects on outcomes compared to a control condition, including reduced hopelessness, trait anxiety, and increased self-efficacy. Future work is needed to replicate and investigate long-term effects of the training and explore its implementation in clinical populations

Short- and Long-term Efficacy of Transdiagnostic Cognitive Behavioral Therapies for Emotional Disorders: Systematic Review and Meta-Analysis

Transdiagnostic cognitive-behavioral psychotherapy (TD-CBT) may facilitate treatment of emotional disorders. 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 January 2023, selected studies for inclusion, extracted data and evaluated risk of bias (Cochrane risk-of-bias tool 2.0). Absolute efficacy from pre- to post-treatment and relative efficacy between TD-CBT and control treatments were investigated with random-effects models. Of 56 identified studies, 53 (6610 participants) were included in the meta-analysis. TD-CBT had large effects on depression (d = 1.15, 95%CI = 1.03 - 1.28) and anxiety (d = 1.08, 95%CI = 0.95 - 1.21), from pre- to post treatment. Across treatment formats, TD-CBT was superior to waitlist (depression: g = 1.31, 95%CI = 0.88 - 1.74; anxiety: g = 1.22, 95%CI = 0.82 - 1.63) and treatment-as-usual (depression: g = 0.87, 95%CI = 0.63 - 1.11; anxiety: g = 0.94, 95%CI = 0.57 - 1.30). TD-CBT showed comparable effects to disorder-specific treatments, with indications of TD-CBT’s superiority (depression: g = 0.16, 95%CI = 0.01 - 0.32; anxiety: g = 0.15, 95%CI = 0.04 - 0.26). Large effects were reported for Internet-based TD-CBT for up to 2 years. 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

Self-efficacy effects on symptom experiences in daily life and early treatment success in anxiety patients

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

Optimizing Outcomes in Psychotherapy for Anxiety Disorders (OPTIMAX) Protocol– A Randomized Controlled Trial on Efficacy and Response Prediction in a Transdiagnostic Psychotherapy Treatment for Anxiety Disorders

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