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Context: Antidepressant medications represent the best established treatment for major depressive disorder, but there is little evidence that they have a specific pharmacological effect relative to pill placebo for patients with less severe depression.Objective: To estimate the relative benefit of medication vs placebo across a wide range of initial symptom severity in patients diagnosed with depression.Data Sources: PubMed, PsycINFO, and the Cochrane Library databases were searched from January 1980 through March 2009, along with references from meta-analyses and reviews.Study Selection: Randomized placebo-controlled trials of antidepressants approved by the Food and Drug Administration in the treatment of major or minor depressive disorder were selected. Studies were included if their authors provided the requisite original data, they comprised adult outpatients, they included a medication vs placebo comparison for at least 6 weeks, they did not exclude patients on the basis of a placebo washout period, and they used the Hamilton Depression Rating Scale (HDRS). Data from 6 studies (718 patients) were included.Data Extraction: Individual patient-level data were obtained from study authors.Results: Medication vs placebo differences varied substantially as a function of baseline severity. Among patients with HDRS scores below 23, Cohen d effect sizes for the difference between medication and placebo were estimated to be less than 0.20 (a standard definition of a small effect). Estimates of the magnitude of the superiority of medication over placebo increased with increases in baseline depression severity and crossed the threshold defined by the National Institute for Clinical Excellence for a clinically significant difference at a baseline HDRS score of 25.Conclusions: The magnitude of benefit of antidepressant medication compared with placebo increases with severity of depression symptoms and may be minimal or nonexistent, on average, in patients with mild or moderate symptoms. For patients with very severe depression, the benefit of medications over placebo is substantial. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) such as Open AI's GPT-4 (which power ChatGPT) and Google's Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy., (© 2024. The Author(s).)

Competing Interests: Declaration of competing interest Michael Barkham has co-produced outcome measures used in routine outcome monitoring and also jointly published a text on routine outcome monitoring but receives no financial payment for these activities. Christopher G. Beevers has received funding for his research from the National Institutes of Health, Brain and Behavior Foundation, Aiberry Inc., and other private, not-for-profit foundations. He has received income from the Association for Psychological Science for his editorial work and from Orexo, Inc. for serving on a Scientific Advisory Board related to digital therapeutics. Dr. Beevers' financial disclosures have been reviewed and approved by the University of Texas at Austin in accordance with its conflict-of-interest policies. Tim Dalgleish has received payment for consultancy work with Princeton Biopartners, Boehringer Ingelheim and Felix Inc. He delivers paid workshops on psychological therapies. He receives royalties for books on affect and psychological disorders. Nicholas C. Jacobson declares the following conflicts of interest. He has edited a book that includes discussion of this topic from which he receives royalties. Additionally, he has accepted paid speaking invitations from various organizations. Dr. Jacobson confirms that these interests do not influence the research, interpretation, or conclusions presented in this manuscript. Daniel R. Karlin is an employee, officer, and shareholder of MindMed; shareholder and director at Sonara Health; trustee at Trudeau Institute; shareholder and consultant to NightWare; consultant to Otsuka, Tempus, Limitless Ventures, Recovery Delivered, and 4YouandMe. Cheri A. Levinson has a financial interest in BeWell and Awaken Digital Solutions. Lorenzo Lorenzo-Luaces has received consulting fees from Syra Health who had no involvement in the current research. Stephen M. Schueller serves on the Scientific Advisory Board for Headspace for which he receives compensation and has received consulting payments from Boehringer Ingelheim, K Health (Trusst), and Otsuka Pharmaceuticals for unrelated work. Greg J. Siegle receives royalty payments on a patent regarding a vibroacoustic intervention for psychiatric conditions, licensed to Apollo Neurosciences. Wolfgang Lutz and Eva-Lotta Brakemeier initiated and chair the Interest Group "Evidence-Based Personalized Psychotherapy Research (ePePsy)" of the German Psychological Association.

The current conceptualization of anxiety in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)-which includes 11 anxiety disorders plus additional anxiety-related conditions-does not align with accumulating evidence that anxiety is transdiagnostic and dimensional in nature. Transdiagnostic dimensional anxiety models have been proposed, yet they measure anxiety at either a very broad (e.g., "anxiety") or very narrow (e.g., "performance anxiety") level, overlooking intermediate properties of anxiety that cut across DSM disorders. Using indicators from a well-validated semistructured interview of anxiety-related disorders, we constructed intermediate-level transdiagnostic dimensions representing the intensity, avoidance, pervasiveness, and onset of anxiety. We captured these content-agnostic dimensions in a sample representing varying levels and forms of anxiety (N = 268), including individuals with generalized anxiety disorder, social anxiety disorder, panic disorder, agoraphobia, specific phobia, separation anxiety disorder, posttraumatic stress disorder, and obsessive-compulsive disorder (n = 205) and individuals with no psychopathology (n = 63). In preregistered analyses, our dimensional anxiety model showed noninferiority to DSM-5 diagnoses in predicting concurrent and prospective measures of anxiety-related impairment, anxiety vulnerabilities, comorbid depression, and suicidal ideation. These results held regardless of whether the dimensions were combined into a single composite or retained as separate components. Our transdiagnostic dimensional model offers meaningful gains in parsimony over DSM, with no loss of predictive power. This project provides a methodological framework for the empirical evaluation of other transdiagnostic dimensional models of psychopathology that have been proposed as alternatives to the DSM. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: Mental health problems cause significant distress and impairment in adolescents worldwide. One-fifth of the world's adolescents live in India, and much remains to be known about their mental health and wellbeing., Aim: In this preregistered study, we aimed to estimate the rates of depressive and anxiety symptoms, examine their relationship with indicators of wellbeing, and identify correlates of mental health among Indian adolescents., Methods: We administered self-report measures of depressive symptoms (PHQ-9), anxiety symptoms (GAD-7), wellbeing (WEMWBS), and happiness (SHS) to 1,213 Indian adolescents (52.0% male; M age = 14.11, SD age = 1.48)., Results: Findings from the PHQ-9 ( M = 8.08, SD = 5.01) and GAD-7 ( M = 7.42, SD = 4.78) indicated high levels of depressive symptoms and anxiety symptoms. Thirty seven percent of the sample scored above the clinical cutoff for depressive symptoms, and 30.6% scored above the cutoff for anxiety symptoms. Although measures of mental health symptoms (PHQ-9 and GAD-7) were associated with measures of wellbeing and happiness (WEMWBS and SHS), these associations were only modest (Correlation < 0.45). Female students reported higher symptoms (and worse wellbeing) compared to male students, and older students reported higher symptoms (and worse wellbeing and happiness) compared to younger students., Conclusion: This study highlights the high prevalence of depressive symptoms and anxiety symptoms among Indian high school students. Symptom measures correlated only modestly with measures of wellbeing and happiness, suggesting that wellbeing and happiness reflect more than the absence of internalizing symptoms. Future research is needed to identify effective and appropriate ways to promote mental health and wellness among Indian students., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Indian Journal of Psychiatry.)

Background: This study aimed to develop, validate and compare the performance of models predicting post-treatment outcomes for depressed adults based on pre-treatment data., Methods: Individual patient data from all six eligible randomised controlled trials were used to develop ( k = 3, n = 1722) and test ( k = 3, n = 918) nine models. Predictors included depressive and anxiety symptoms, social support, life events and alcohol use. Weighted sum scores were developed using coefficient weights derived from network centrality statistics (models 1-3) and factor loadings from a confirmatory factor analysis (model 4). Unweighted sum score models were tested using elastic net regularised (ENR) and ordinary least squares (OLS) regression (models 5 and 6). Individual items were then included in ENR and OLS (models 7 and 8). All models were compared to one another and to a null model (mean post-baseline Beck Depression Inventory Second Edition (BDI-II) score in the training data: model 9). Primary outcome: BDI-II scores at 3-4 months., Results: Models 1-7 all outperformed the null model and model 8. Model performance was very similar across models 1-6, meaning that differential weights applied to the baseline sum scores had little impact., Conclusions: Any of the modelling techniques (models 1-7) could be used to inform prognostic predictions for depressed adults with differences in the proportions of patients reaching remission based on the predicted severity of depressive symptoms post-treatment. However, the majority of variance in prognosis remained unexplained. It may be necessary to include a broader range of biopsychosocial variables to better adjudicate between competing models, and to derive models with greater clinical utility for treatment-seeking adults with depression.

Depression is highly recurrent, even following successful pharmacological and/or psychological intervention. We aimed to develop clinical prediction models to inform adults with recurrent depression choosing between antidepressant medication (ADM) maintenance or switching to Mindfulness-Based Cognitive Therapy (MBCT). Using data from the PREVENT trial ( N =424), we constructed prognostic models using elastic net regression that combined demographic, clinical and psychological factors to predict relapse at 24 months under ADM or MBCT. Only the ADM model (discrimination performance: AUC=.68) predicted relapse better than baseline depression severity (AUC=.54; one-tailed DeLong's test: z =2.8, p =.003). Individuals with the poorest ADM prognoses who switched to MBCT had better outcomes compared to those who maintained ADM (48% vs. 70% relapse, respectively; superior survival times [ z =-2.7, p =.008]). For individuals with moderate-to-good ADM prognosis, both treatments resulted in similar likelihood of relapse. If replicated, the results suggest that predictive modeling can inform clinical decision-making around relapse prevention in recurrent depression.

Background and Objectives: Research has shown that state emotion can affect emotion regulation strategies in healthy samples. Emotion regulation plays an important role in depression. We hypothesized that for depressed individuals, experiencing anxiety or anger affects emotion regulation strategy use differently than experiencing sadness., Design and Methods: Individuals diagnosed with chronic or recurrent depression ( N  = 386) responded to vignettes of hypothetical stressors and reported their state emotions and emotion regulation strategies in a thought-listing procedure. We modeled the effect of reporting anger or anxiety compared to sadness on the use of seven emotion regulation strategies: avoidance, distraction, other-blame, problem-solving, rumination, self-blame, and social support., Results: Compared to sadness, anger was associated with a greater likelihood of using other-blame, and a lower likelihood of using avoidance, rumination, or self-blame. Compared to sadness, anxiety was associated with a greater likelihood of using self-blame. Responses with anger or anxiety did not significantly differ from sadness in coder-rated adaptiveness., Conclusions: Differences in observed emotion regulation strategy use were correlated with the discrete emotions experienced, and not overall adaptiveness of a response. These results highlight the importance of assessing for emotion type when studying emotion regulation strategy use in depression.

One goal of clinical psychological science is to help people with problems that matter to them. However, little is known about which kinds of symptoms are viewed as most important, particularly among individuals in non-western settings. We examined the extent to which young adults in India rated individual symptoms of depression and anxiety as important, concerning, and undesirable. Participants were college students at Indian Universities (n = 283). They received a measure of depressive symptoms (Patient Health Questionnaire-9) and anxiety symptoms (Generalized Anxiety Disorder Screener-7). For each of the 16 symptoms, they provided three judgments relating to the extent to which they found the symptom important, harmful, and undesirable. These judgments were averaged to form a "subjective importance rating" (SIR) for each symptom. Anxiety symptoms were viewed as more important than depressive symptoms (d = 0.34), and nonsomatic symptoms were viewed as more important than somatic symptoms (d = 0.83). Females rated symptoms as more important than males (d = 0.32), and individuals with higher self-reported symptoms rated symptoms as more important. Sad mood, suicidal ideation, and controlling worries were rated as the most important symptoms, whereas concentration problems, appetite problems, and psychomotor problems were rated as the least important. Overall, some symptoms are viewed as more important and concerning than others. We discuss how this understanding can affect our conceptualization, assessment, and treatment of mental disorders around the world., (Copyright © 2022. Published by Elsevier Ltd.)

Importance: Socioeconomic factors are associated with the prevalence of depression, but their associations with prognosis are unknown. Understanding this association would aid in the clinical management of depression., Objective: To determine whether employment status, financial strain, housing status, and educational attainment inform prognosis for adults treated for depression in primary care, independent of treatment and after accounting for clinical prognostic factors., Data Sources: The Embase, International Pharmaceutical Abstracts, MEDLINE, PsycINFO, and Cochrane (CENTRAL) databases were searched from database inception to October 8, 2021., Study Selection: Inclusion criteria were as follows: randomized clinical trials that used the Revised Clinical Interview Schedule (CIS-R; the most common comprehensive screening and diagnostic measure of depressive and anxiety symptoms in primary care randomized clinical trials), measured socioeconomic factors at baseline, and sampled patients with unipolar depression who sought treatment for depression from general physicians/practitioners or who scored 12 or more points on the CIS-R. Exclusion criteria included patients with depression secondary to a personality or psychotic disorder or neurologic condition, studies of bipolar or psychotic depression, studies that included children or adolescents, and feasibility studies. Studies were independently assessed against inclusion and exclusion criteria by 2 reviewers., Data Extraction and Synthesis: Data were extracted and cleaned by data managers for each included study, further cleaned by multiple reviewers, and cross-checked by study chief investigators. Risk of bias and quality were assessed using the Quality in Prognosis Studies (QUIPS) and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) tools, respectively. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses-Individual Participant Data (PRISMA-IPD) reporting guidelines., Main Outcomes and Measures: Depressive symptoms at 3 to 4 months after baseline., Results: This systematic review and individual patient data meta-analysis identified 9 eligible studies that provided individual patient data for 4864 patients (mean [SD] age, 42.5 (14.0) years; 3279 women [67.4%]). The 2-stage random-effects meta-analysis end point depressive symptom scale scores were 28% (95% CI, 20%-36%) higher for unemployed patients than for employed patients and 18% (95% CI, 6%-30%) lower for patients who were homeowners than for patients living with family or friends, in hostels, or homeless, which were equivalent to 4.2 points (95% CI, 3.6-6.2 points) and 2.9 points (95% CI, 1.1-4.9 points) on the Beck Depression Inventory II, respectively. Financial strain and educational attainment were associated with prognosis independent of treatment, but unlike employment and housing status, there was little evidence of associations after adjusting for clinical prognostic factors., Conclusions and Relevance: Results of this systematic review and meta-analysis revealed that unemployment was associated with a poor prognosis whereas home ownership was associated with improved prognosis. These differences were clinically important and independent of the type of treatment received. Interventions that address employment or housing difficulties could improve outcomes for patients with depression.

Within mental health, approaches to determine whether a patient experienced "meaningful" change from treatment have predominantly involved imposing thresholds on three types of metrics derived from assessments of symptom severity: end score (posttreatment score), absolute change (pre- minus posttreatment score), and proportion of change. However, none of these approaches have considered input from the consumer. This study examined correspondences between various reductions from pre- to posttreatment symptom severity levels and patients' judgments of satisfaction with change. Former or currently depressed patients were asked to provide judgments of their satisfaction reflected in vignettes that used descriptions from the Hamilton Rating Scale for Depression. Judgments from 108 female participants were fit using four metrics: end score, absolute change, proportion of change, and the combination of end score and absolute change. Akaike information criteria (AICs) and Akaike weights were used to determine the best-fitting model. Cutoffs were calculated for the five levels of satisfaction with change. Proportion of change best accounted for variation in the patients' ratings. For "slightly … ," "somewhat … ," "moderately … ," and "very … ," the proportions of reduction that corresponded with each of these ratings of satisfaction were, respectively: 17%, 39%, 62%, and 84%. Our a priori level of satisfaction (between "somewhat" and "moderately") corresponded to a 50% reduction in pretreatment severity. This study may provide services some insight into their female patients' satisfaction with change from treatment for depression using only the proportion of reduction from pretreatment severity. A similar procedure could be applied to other diagnostic groups, as well as other constructs that attend to the patient's perspective., (Copyright © 2021. Published by Elsevier Ltd.)

The mental health of college students is increasingly viewed as an important public health priority. However, there has been little attention paid specifically to college students' perspectives on factors that contribute to mental health challenges or on potential initiatives that could address them. Even less research has focused on students in low- and middle-income countries. In an effort to better understand how to improve mental health and wellness on college campuses, we administered an open-ended survey to 141 Indian college students (M age = 19.47, 65% female). We asked the students to identify: (a) issues that contribute to mental health problems among college students, (b) potential initiatives or strategies that could be used to improve mental health and wellness, and (c) topics that students would like to learn about in a course about mental health and wellness. Applying thematic analysis, we identified academic stressors (e.g., pressure to succeed, competitiveness) and social stressors (e.g., lack of community, party culture and substance abuse) that students reported as contributors to mental health problems. Students also described mental health promotion strategies that could be implemented by faculty members (e.g., providing academic accommodations for students with mental health concerns), the student body (e.g., establishing peer counseling groups), and individual students (e.g., checking in with others). Finally, they identified topics that they would like to learn about in mental health and wellness courses (e.g., how to identify mental health concerns, how to support friends). By raising several potential targets for mental health and wellness interventions for Indian college students, our study illustrates how open-ended surveys can be a useful and feasible way to solicit input from stakeholders in low- and middle-income countries. Future research will be needed to assess the effectiveness and feasibility of mental health promotion strategies, including those proposed by students., (Copyright © 2021. Published by Elsevier Ltd.)

Objective: An implicit assumption in the use of depressive severity measures to assess change during treatment, such as the Hamilton Rating Scale for Depression (HRSD), is that reductions from pre- to post-treatment that are equal to each other are of equal value. However, stakeholders' valuations of changes might depart substantially from this assumption. Method: Vignettes were constructed that reflected the six possible 1, 2, and 3-point reductions on five cognitive and four somatic symptoms derived from the HRSD. Former or currently depressed patients provided judgments of the importance of the symptom reductions. Mean importance ratings were modeled using symptom category and the pre/post-treatment combination. Differences were explored using the Tukey method. Results: Results indicated that mean ratings, from most to least important, were: Anxiety, Suicide, Depressed Mood, Work, and Guilt (the cognitive symptoms) followed by Somatic, Sleep, Appetite & Weight, and Retardation (the somatic symptoms). Participants valued reductions that resulted in posttreatment scores of zero more than expected, given the magnitude of the reductions. Conclusions: The value of reductions in symptoms captured by the HRSD, as judged by patients, appears to differ as a function of symptom category and the post-treatment score. Similar patterns might characterize other measures of depression severity.

Objective: To investigate associations between major life events and prognosis independent of treatment type: (1) after adjusting for clinical prognostic factors and socio-demographics; (2) amongst patients with depressive episodes at least six-months long; and (3) patients with a first life-time depressive episode., Methods: Six RCTs of adults seeking treatment for depression in primary care met eligibility criteria, individual patient data (IPD) were collated from all six (n = 2858). Participants were randomized to any treatment and completed the same baseline assessment of life events, demographics and clinical prognostic factors. Two-stage random effects meta-analyses were conducted., Results: Reporting any major life events was associated with poorer prognosis regardless of treatment type. Controlling for baseline clinical factors, socio-demographics and social support resulted in minimal residual evidence of associations between life events and treatment prognosis. However, removing factors that might mediate the relationships between life events and outcomes reporting: arguments/disputes, problem debt, violent crime, losing one's job, and three or more life events were associated with considerably worse prognoses (percentage difference in 3-4 months depressive symptoms compared to no reported life events =30.3%(95%CI: 18.4-43.3))., Conclusions: Assessing for clinical prognostic factors, social support, and socio-demographics is likely to be more informative for prognosis than assessing self-reported recent major life events. However, clinicians might find it useful to ask about such events, and if they are still affecting the patient, consider interventions to tackle problems related to those events (e.g. employment support, mediation, or debt advice). Further investigations of the efficacy of such interventions will be important., (Copyright © 2021. Published by Elsevier B.V.)

Importance: Depression is a major cause of disability worldwide. Although empirically supported treatments are available, there is scarce evidence on how to effectively personalize psychological treatment selection., Objective: To compare the clinical effectiveness and cost-effectiveness of 2 treatment selection strategies: stepped care and stratified care., Design, Setting, and Participants: This multisite, cluster randomized clinical trial recruited participants from the English National Health Service from July 5, 2018, to February 1, 2019. Thirty clinicians working across 4 psychological therapy services were randomly assigned to provide stratified (n = 15) or stepped (n = 15) care. In stepped care, patients sequentially access low-intensity guided self-help followed by high-intensity psychotherapy. In stratified care, patients are matched with either low- or high-intensity treatments at initial assessment. Data were analyzed from May 18, 2020, to October 13, 2021, using intention-to-treat principles., Interventions: All clinicians used the same interview schedule to conduct initial assessments with patients seeking psychological treatment for common mental disorders, but those in the stratified care group received a personalized treatment recommendation for each patient generated by a machine learning algorithm. Eligible patients received either stratified or stepped care (ie, treatment as usual)., Main Outcomes and Measures: The preregistered outcome was posttreatment reliable and clinically significant improvement (RCSI) of depression symptoms (measured using the 9-item Patient Health Questionnaire). The RCSI outcome was compared between groups using logistic regression adjusted for baseline severity. Cost-effectiveness analyses compared incremental costs and health outcomes of the 2 treatment pathways., Results: A total of 951 patients were included (618 women among 950 with data available [65.1%]; mean [SD] age, 38.27 [14.53] years). The proportion of cases of RCSI was significantly higher in the stratified care arm compared with the stepped care arm (264 of 505 [52.3%] vs 134 of 297 [45.1%]; odds ratio, 1.40 [95% CI, 1.04-1.87]; P = .03). Stratified care was associated with a higher mean additional cost per patient (£104.5 [95% CI, £67.5-£141.6] [$139.83 (95% CI, $90.32-$189.48)]; P < .001) because more patients accessed high-intensity treatments (332 of 583 [56.9%] vs 107 of 368 [29.1%]; χ2 = 70.51; P < .001), but this additional cost resulted in an approximately 7% increase in the probability of RCSI., Conclusions and Relevance: In this cluster randomized clinical trial of adults with common mental disorders, stratified care was efficacious and cost-effective for the treatment of depression symptoms compared with stepped care. Stratified care can improve depression treatment outcomes at a modest additional cost., Trial Registration: isrctn.org Identifier: ISRCTN11106183.

Objective: The Personalized Advantage Index (PAI) is a method to guide treatment selection by investigating which of two or more treatments is optimal for a given individual. Recently, it was shown that, on average, twice-weekly sessions of psychotherapy for depression lead to better outcomes compared to once-weekly sessions. The present study applied the PAI method to assess if subgroups of patients may have a differential response to psychotherapy frequency., Method: Data came from a clinical trial (n = 200) randomizing depressed patients into different session frequencies: weekly sessions versus twice-weekly sessions. Machine-learning techniques were used to select pretreatment variables and develop a multivariable prediction model that calculated each patient's PAI. Differences in observed depression post-treatment scores (Beck Depression Inventory-II [BDI-II]) were tested between patients that received their PAI-indicated versus non-indicated session frequency. Between-group effect sizes (Cohen's d) were reported., Results: We identified prognostic indicators generally associated with lower post-treatment BDI-II regardless of treatment assignment. In addition, we identified specific demographic and psychometric features associated with differential response to weekly- versus twice-weekly therapy sessions. Observed post-treatment BDI-II scores were significantly different between individuals receiving the PAI-indicated versus non-indicated session frequency (d = .37)., Conclusions: Although a higher session frequency is more effective on average, different session frequencies seem beneficial for different patients. Future studies should externally validate these findings before they can be generalized to other settings. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Background: Subgrouping methods have the potential to support treatment decision making for patients with depression. Such approaches have not been used to study the continued course of depression or likelihood of relapse following treatment., Method: Data from individual participants of seven randomised controlled trials were analysed. Latent profile analysis was used to identify subgroups based on baseline characteristics. Associations between profiles and odds of both continued chronic depression and relapse up to one year post-treatment were explored. Differences in outcomes were investigated within profiles for those treated with antidepressants, psychological therapy, and usual care., Results: Seven profiles were identified; profiles with higher symptom severity and long durations of both anxiety and depression at baseline were at higher risk of relapse and of chronic depression. Members of profile five (likely long durations of depression and anxiety, moderately-severe symptoms, and past antidepressant use) appeared to have better outcomes with psychological therapies: antidepressants vs. psychological therapies (OR (95% CI) for relapse = 2.92 (1.24-6.87), chronic course = 2.27 (1.27-4.06)) and usual care vs. psychological therapies (relapse = 2.51 (1.16-5.40), chronic course = 1.98 (1.16-3.37))., Conclusions: Profiles at greater risk of poor outcomes could benefit from more intensive treatment and frequent monitoring. Patients in profile five may benefit more from psychological therapies than other treatments.

Background: Network analyses have been applied to understand the relationships between individual symptoms of depression and anxiety. However, little is known about which symptoms are most strongly associated with "positive" indicators of mental health, such as happiness. Furthermore, few studies have examined symptom networks in participants from low- and middle-income countries., Methods: To address these gaps, we applied network analyses in a sample of Indian adolescents (Study 1; n=1080) and replicated these analyses in a pre-registered study with Kenyan adolescents (Study 2; n=2176). Participants from both samples completed the same measures of depressive symptoms, anxiety symptoms, and happiness., Results: Feeling sad and feeling like a failure had the strongest (negative) associations with happiness items. These two symptoms, as well as worrying and feeling nervous, had the strongest associations with other symptoms of depression and anxiety. Symptoms of depression and anxiety formed a single cluster, which was distinct from a cluster of happiness items. Main findings were consistent across the two samples, suggesting a cross-culturally robust pattern., Limitations: We used cross-sectional data, and we administered scales assessing a limited subset of symptoms and happiness items., Conclusions: Our findings support the idea that some symptoms of depression and anxiety are more strongly associated with happiness. These findings contribute to a body of literature emphasizing the advantages of symptom-level analyses. We discuss how efforts to understand associations between individual symptoms and "positive" mental health indicators, like happiness, could have theoretical and practical implications for clinical psychological science., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Objective: To evaluate the costs and cost-effectiveness of Shamiri-Digital , an online single-session intervention (SSI) for depression among Kenyan adolescents. Method: Data were drawn from a randomized clinical trial with n = 103 Kenyan high school students (64% female, M age = 15.5). All students were eligible to participate, regardless of baseline depression symptomatology. We estimated delivery costs in 2020 U.S. dollars from multiple perspectives. To account for uncertainty, we performed sensitivity analyses with different cost assumptions and definitions of effectiveness. Using number needed to treat (NNT) estimates, we also evaluated the cost required to achieve a clinically meaningful reduction in depressive symptoms. Results: In the base-case (the most realistic cost estimate), it costs U.S. $3.57 per student to deliver Shamiri-Digital . Depending on the definition of clinically meaningful improvement, 7.1-9.7 students needed to receive the intervention for one student to experience a clinically meaningful improvement, which translated to a cost of U.S. $25.35 to U.S. $34.62 per student. Under a worst-case scenario (i.e., assuming the highest treatment cost and the strictest effectiveness definition), the cost to achieve clinically meaningful improvement was U.S. $92.05 per student. Conclusions: Shamiri-Digital is a low-cost intervention for reducing depression symptomatology. The public health benefit of empirically supported SSIs is especially important in low-income countries, where funding for mental health care is most limited. Future research can compare the cost-effectiveness of online SSIs to higher-cost treatments and estimate the robustness of Shamiri-Digital 's effects over a longer time horizon. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Background: No evidence-based therapy for borderline personality disorder (BPD) exhibits a clear superiority. However, BPD is highly heterogeneous, and different patients may specifically benefit from the interventions of a particular treatment., Methods: From a randomized trial comparing a year of dialectical behavior therapy (DBT) to general psychiatric management (GPM) for BPD, long-term (2-year-post) outcome data and patient baseline variables (n = 156) were used to examine individual and combined patient-level moderators of differential treatment response. A two-step bootstrapped and partially cross-validated moderator identification process was employed for 20 baseline variables. For identified moderators, 10-fold bootstrapped cross-validated models estimated response to each therapy, and long-term outcomes were compared for patients randomized to their model-predicted optimal v. non-optimal treatment., Results: Significant moderators surviving the two-step process included psychiatric symptom severity, BPD impulsivity symptoms (both GPM > DBT), dependent personality traits, childhood emotional abuse, and social adjustment (all DBT > GPM). Patients randomized to their model-predicted optimal treatment had significantly better long-term outcomes (d = 0.36, p = 0.028), especially if the model had a relatively stronger (top 60%) prediction for that patient (d = 0.61, p = 0.004). Among patients with a stronger prediction, this advantage held even when applying a conservative statistical check (d = 0.46, p = 0.043)., Conclusions: Patient characteristics influence the degree to which they respond to two treatments for BPD. Combining information from multiple moderators may help inform providers and patients as to which treatment is the most likely to lead to long-term symptom relief. Further research on personalized medicine in BPD is needed.

PTSD treatment guidelines recommend several treatments with extensive empirical support, including Prolonged Exposure (PE), a trauma-focused treatment and Present-Centered Therapy (PCT), a non-trauma-focused therapy. Research to inform treatment selection has yielded inconsistent findings with single prognostic variables that are difficult to integrate into clinical decision-making. We examined whether a combination of prognostic factors can predict different benefits in a trauma-focused vs. a non-trauma-focused psychotherapy. We applied a multi-method variable selection procedure and developed a prognostic index (PI) with a sample of 267 female veterans and active-duty service members (mean age 45; SD = 9.37; 53% White) with current PTSD who began treatment in a randomized clinical trial comparing PE and PCT. We conducted linear regressions predicting outcomes (Clinician-Administered PTSD Scale score) with treatment condition, the PI, and the interaction between the PI and treatment condition. The interaction between treatment type and PI moderated treatment response, moderated post-treatment symptom severity, b = 0.30, SE b  = 0.15 [95% CI: 0.01, 0.60], p = .049. For the 64% of participants with the best prognoses, PE resulted in better post-treatment outcomes; for the remainder, there was no difference. Use of a PI may lead to optimized patient outcomes and greater confidence when selecting trauma-focused treatments., (Copyright © 2021. Published by Elsevier Ltd.)

Background: Mental health problems are the leading cause of disability among adolescents worldwide, yet access to treatment is limited. Brief digital interventions have been shown to improve youth mental health, but little is known about which digital interventions are most effective., Aims: To evaluate the effectiveness of two digital single-session interventions (Shamiri-Digital and Digital-CBT (cognitive-behavioural therapy)) among Kenyan adolescents., Methods: We will perform a school-based comparative effectiveness randomised controlled trial. Approximately 926 Kenyan adolescents will be randomly assigned to one of three conditions: Shamiri-Digital (focused on gratitude, growth mindsets and values), Digital-CBT (focused on behavioural activation, cognitive restructuring and problem solving) or a study-skills control condition (focused on note-taking and essay writing skills). The primary outcomes include depressive symptoms (measured by the Patient Health Questionnaire-8), anxiety symptoms (Generalized Anxiety Disorder Screener-7) and subjective well-being (Short Warwick-Edinburgh Mental Well-being Scale). The secondary outcomes include acceptability, appropriateness, primary control and secondary control. Acceptability and appropriateness will be measured immediately post-intervention; other outcomes will be measured 2 weeks, 4 weeks and 12 weeks post-intervention., Results: We hypothesise that adolescents assigned to Shamiri-Digital and adolescents assigned to Digital-CBT will experience greater improvements (assessed via hierarchical linear models) than those assigned to the control group. We will also compare Shamiri-Digital with Digital-CBT, although we do not have a preplanned hypothesis., Conclusions: Our findings will help us evaluate two digital single-session interventions with different theoretical foundations. If effective, such interventions could be disseminated to reduce the public health burden of common mental health problems., Trial Registration Number: PACTR202011691886690., Competing Interests: Competing interests: TLO, AW and JRW are affiliated with the Shamiri Institute., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Aims: To determine whether age, gender and marital status are associated with prognosis for adults with depression who sought treatment in primary care., Methods: Medline, Embase, PsycINFO and Cochrane Central were searched from inception to 1st December 2020 for randomised controlled trials (RCTs) of adults seeking treatment for depression from their general practitioners, that used the Revised Clinical Interview Schedule so that there was uniformity in the measurement of clinical prognostic factors, and that reported on age, gender and marital status. Individual participant data were gathered from all nine eligible RCTs (N = 4864). Two-stage random-effects meta-analyses were conducted to ascertain the independent association between: (i) age, (ii) gender and (iii) marital status, and depressive symptoms at 3-4, 6-8, and 9-12 months post-baseline and remission at 3-4 months. Risk of bias was evaluated using QUIPS and quality was assessed using GRADE. PROSPERO registration: CRD42019129512. Pre-registered protocol https://osf.io/e5zup/., Results: There was no evidence of an association between age and prognosis before or after adjusting for depressive 'disorder characteristics' that are associated with prognosis (symptom severity, durations of depression and anxiety, comorbid panic disorderand a history of antidepressant treatment). Difference in mean depressive symptom score at 3-4 months post-baseline per-5-year increase in age = 0(95% CI: -0.02 to 0.02). There was no evidence for a difference in prognoses for men and women at 3-4 months or 9-12 months post-baseline, but men had worse prognoses at 6-8 months (percentage difference in depressive symptoms for men compared to women: 15.08% (95% CI: 4.82 to 26.35)). However, this was largely driven by a single study that contributed data at 6-8 months and not the other time points. Further, there was little evidence for an association after adjusting for depressive 'disorder characteristics' and employment status (12.23% (-1.69 to 28.12)). Participants that were either single (percentage difference in depressive symptoms for single participants: 9.25% (95% CI: 2.78 to 16.13) or no longer married (8.02% (95% CI: 1.31 to 15.18)) had worse prognoses than those that were married, even after adjusting for depressive 'disorder characteristics' and all available confounders., Conclusion: Clinicians and researchers will continue to routinely record age and gender, but despite their importance for incidence and prevalence of depression, they appear to offer little information regarding prognosis. Patients that are single or no longer married may be expected to have slightly worse prognoses than those that are married. Ensuring this is recorded routinely alongside depressive 'disorder characteristics' in clinic may be important.

We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement . Our software, "Personalized Treatment Evaluator" (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method's promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kapelner, Bleich, Levine, Cohen, DeRubeis and Berk.)

Background: Depression is commonly perceived as a single underlying disease with a number of potential treatment options. However, patients with major depression differ dramatically in their symptom presentation and comorbidities, e.g. with anxiety disorders. There are also large variations in treatment outcomes and associations of some anxiety comorbidities with poorer prognoses, but limited understanding as to why, and little information to inform the clinical management of depression. There is a need to improve our understanding of depression, incorporating anxiety comorbidity, and consider the association of a wide range of symptoms with treatment outcomes., Method: Individual patient data from six RCTs of depressed patients (total n = 2858) were used to estimate the differential impact symptoms have on outcomes at three post intervention time points using individual items and sum scores. Symptom networks (graphical Gaussian model) were estimated to explore the functional relations among symptoms of depression and anxiety and compare networks for treatment remitters and those with persistent symptoms to identify potential prognostic indicators., Results: Item-level prediction performed similarly to sum scores when predicting outcomes at 3 to 4 months and 6 to 8 months, but outperformed sum scores for 9 to 12 months. Pessimism emerged as the most important predictive symptom (relative to all other symptoms), across these time points. In the network structure at study entry, symptoms clustered into physical symptoms, cognitive symptoms, and anxiety symptoms. Sadness, pessimism, and indecision acted as bridges between communities, with sadness and failure/worthlessness being the most central (i.e. interconnected) symptoms. Connectivity of networks at study entry did not differ for future remitters vs. those with persistent symptoms., Conclusion: The relative importance of specific symptoms in association with outcomes and the interactions within the network highlight the value of transdiagnostic assessment and formulation of symptoms to both treatment and prognosis. We discuss the potential for complementary statistical approaches to improve our understanding of psychopathology.

Objective: To examine the frequency of evidence-based treatment elements in popular smartphone apps for eating disorders (EDs), and to characterize the extent to which real-world users encounter different elements., Method: We searched the Apple App Store and Google Play Store for apps offering treatment or support to individuals with EDs. Then, we created a codebook of 47 elements found in evidence-based treatments for EDs. We examined the presence or absence of each element within each ED app. We also acquired estimates of the monthly active users (MAU) of each app., Results: The ED apps (n = 28) included a median of nine elements of empirically supported treatments (mean = 9.46, SD = 6.28). Four apps accounted for 96% of all MAU. MAU-adjusted analyses revealed that several elements are reaching more users than raw frequency tallies would suggest. For example, assessments were included in 32% of apps, but 84% of users used an app with assessments. Similar trends were found for cognitive restructuring (21% of apps, 56% of MAU), activity scheduling (39%, 57%), and self-monitoring (14%, 46%). Problem solving, exposure, and relapse prevention strategies, elements that are prominent in face-to-face empirically supported treatments, were rarely included in the apps., Discussion: Evidence-based content is commonly included in ED apps, with certain elements reaching more users than others. Additionally, the top four apps are responsible for nearly all active users. We recommend that ED clinicians and researchers familiarize themselves with these apps-those that patients are most likely to encounter., (© 2021 Wiley Periodicals LLC.)

Background: This study aimed to investigate general factors associated with prognosis regardless of the type of treatment received, for adults with depression in primary care., Methods: We searched Medline, Embase, PsycINFO and Cochrane Central (inception to 12/01/2020) for RCTs that included the most commonly used comprehensive measure of depressive and anxiety disorder symptoms and diagnoses, in primary care depression RCTs (the Revised Clinical Interview Schedule: CIS-R). Two-stage random-effects meta-analyses were conducted., Results: Twelve (n = 6024) of thirteen eligible studies (n = 6175) provided individual patient data. There was a 31% (95%CI: 25 to 37) difference in depressive symptoms at 3-4 months per standard deviation increase in baseline depressive symptoms. Four additional factors: the duration of anxiety; duration of depression; comorbid panic disorder; and a history of antidepressant treatment were also independently associated with poorer prognosis. There was evidence that the difference in prognosis when these factors were combined could be of clinical importance. Adding these variables improved the amount of variance explained in 3-4 month depressive symptoms from 16% using depressive symptom severity alone to 27%. Risk of bias (assessed with QUIPS) was low in all studies and quality (assessed with GRADE) was high. Sensitivity analyses did not alter our conclusions., Conclusions: When adults seek treatment for depression clinicians should routinely assess for the duration of anxiety, duration of depression, comorbid panic disorder, and a history of antidepressant treatment alongside depressive symptom severity. This could provide clinicians and patients with useful and desired information to elucidate prognosis and aid the clinical management of depression.

The COVID-19 outbreak has simultaneously increased the need for mental health services and decreased their availability. Brief online self-help interventions that can be completed in a single session could be especially helpful in improving access to care during the crisis. However, little is known about the uptake, acceptability, and perceived utility of these interventions outside of clinical trials in which participants are compensated. Here, we describe the development, deployment, acceptability ratings, and pre-post effects of a single-session intervention, the Common Elements Toolbox (COMET), adapted for the COVID-19 crisis to support graduate and professional students. Participants ( n = 263), who were not compensated, were randomly assigned to two of three modules: behavioral activation, cognitive restructuring, and gratitude. Over 1 week, 263 individuals began and 189 individuals (72%) completed the intervention. Participants reported that the intervention modules were acceptable (93% endorsing), helpful (88%), engaging (86%), applicable to their lives (87%), and could help them manage COVID-related challenges (88%). Participants reported pre- to post-program improvements in secondary control (i.e., the belief that one can control their reactions to objective events; d av = 0.36, d z = 0.50, p < 0.001) and in the perceived negative impact of the COVID-19 crisis on their quality of life ( d av = 0.22, d z = 0.25, p < 0.001). On average, differences in their perceived ability to handle lifestyle changes resulting from the pandemic were positive, but small and at the level of a non-significant trend ( d av = 0.13, d z = 0.14, p = 0.066). Our results highlight the acceptability and utility of an online intervention for supporting individuals through the COVID-19 crisis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Wasil, Taylor, Franzen, Steinberg and DeRubeis.)

Background: Research on which therapy processes are crucial for whom is necessary to optimize treatment outcomes for major depressive disorder (MDD) but may be impeded by a lack of variation in therapy quality and because of individual differences in therapy response. The present study used a dataset with large variation in therapy quality to investigate the impact of therapy quality on change in therapy processes and outcome. Machine-learning techniques were used to explore individual differences in these relationships., Method: Data come from a multicenter trial that randomized patients with MDD into weekly versus twice weekly sessions of cognitive behavioral therapy (CBT) or interpersonal psychotherapy (IPT). Correlations between quality of therapy, change in therapy processes and depressive symptoms were calculated. Using elastic net regression, a prognostic model was developed that investigated individual differences., Results: There were no significant correlations between therapy quality and change in therapy process variables or between therapy quality and therapy outcome, except between therapy quality and dysfunctional thinking and IPT skills. Improvement in therapy processes played a larger role in patients whose therapy outcome was poorly predicted by their baseline characteristics., Conclusions: In this study, in which therapy quality varied considerably, we found that therapy quality was not related to change in therapy processes or outcome. In addition, this study provides a first demonstration of proposed methods to identify individual responses to change in therapy processes. Results suggested that the importance of certain therapy processes might differ between patients. Future research into the relation between therapy quality, change in therapy processes and outcome should take into account the variation of therapy quality, focus on improved measurement of therapy quality and use a combination of machine learning techniques and experimental lab studies to determine to which extent and for whom therapy quality and change in therapy processes matter., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: The frequency and clinical impact of Sudden Gains -large symptom improvements during a single between-session interval-in psychotherapy for depression have been well established. However, there have been relatively few efforts to identify the processes that lead to sudden gains. Aim: To explore therapy processes associated with sudden gains in cognitive therapy for depression by examining changes in the sessions surrounding the gains, and the session preceding the gain in particular. Methods: Using ratings of video-recordings ( n = 36), we assessed the content, frequency and magnitude of within-session cognitive-, behavioral-, and interpersonal change, as well as the quality of the therapeutic alliance in the session prior to the gain (pre-gain session), the session after the gain (post-gain session) and a control session. After that, we contrasted scores in the pre-gain session with those in the control session. In addition, we examined changes that occurred between the pre- and post-gain session (between-session changes) and explored patients' attributions of change. Results: Although not statistically significant, within-session changes were more frequent and stronger in the pre-gain session compared to the control session. The largest difference between the pre-gain and control session was found in the behavioral domain, and reached the level of trend-significance. There were more, and more impactful between-session changes in the interval during which the gain occurred as compared to a control interval. Exploratory analysis of attributions of change revealed eight subcategories, all corresponding with the cognitive-, behavioral- and interpersonal- domain. The quality of the therapeutic alliance was high and almost identical in all sessions. Conclusion: In spite of its small sample size, our study provides relevant descriptive information about potential precipitants of, themes related to, and attributions given for sudden gains. Furthermore, our study provides clear suggestions for future research. A better understanding of session content in the sessions surrounding sudden gains may provide insight into the mechanisms of change in psychotherapy, hereby suggesting treatment-enhancing strategies. We encourage researchers to conduct research that could clarify the nature of these mechanisms, and believe the methods used in this study could serve as a framework for further work in this area., Competing Interests: TT was employed by Empirically Validated Investment Strategies Co. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Lemmens, DeRubeis, Tang, Schulte-Strathaus and Huibers.)

Background: The COVID-19 crisis has introduced a variety of stressors, while simultaneously decreasing the availability of strategies to cope with stress. In this context, it could be useful to understand issues that people find most concerning and ways in which they cope with stress. In this study, we explored these questions with a sample of graduate and professional students., Method: Using open-ended assessments, we asked participants ( n = 305) to identify their biggest challenge or concern ("top problem"), their most effective way of handling stress ("effective strategy"), and their most common way of handling stress ("common strategy"). We applied thematic analysis and evaluated whether participants' strategies corresponded with evidence-based practices (EBPs)., Results: Participants frequently reported top problems relating to productivity (27% of sample), physical health (26%), and emotional health (14%). Distraction was the most frequently classified common strategy (43%), whereas behavioral activation was the most frequently identified effective strategy (50%). Participants who reported a common strategy classified as an EBP reported lower depressive and anxiety symptoms. In contrast, there was no evidence of an association between symptom levels and whether or not participants' effective strategy was an EBP. Participants who reported the same strategy as both their common and effective strategy (29%) reported lower depressive symptoms than those whose common and effective strategies were different., Conclusion: Our findings highlight stressors that students are experiencing and ways they are coping during the COVID-19 crisis. We discuss how these findings can inform mental health promotion efforts and future research on coping with stressors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer JB declared a shared affiliation with one of the authors, SG, to the handling editor at time of review., (Copyright © 2021 Wasil, Franzen, Gillespie, Steinberg, Malhotra and DeRubeis.)

Background: Psychotherapy for depression and antidepressant medications have both been associated with decreases in suicidal ideation. Studies have not examined whether adding psychotherapy to antidepressant medications further reduces suicidal ideation relative to medications alone in adults., Methods: Participants (N = 452) were randomized to 7 months of treatment with antidepressant medications or combined treatment with both medications and cognitive therapy for depression. We examined change in the suicide items from the Beck Depression Inventory (BDI) and Hamilton Depression Rating Scale (HDRS) across treatment using Bayesian generalized linear mixed models for non-continuous outcomes., Results: Suicidal ideation decreased across treatment. When measured with the BDI, participants receiving both cognitive therapy and antidepressant medications showed 17% greater reductions in suicidal ideation relative to those receiving medications alone; this effect remained significant when controlling for depression severity. While the same pattern was observed when suicidal ideation was measured with the HDRS, the effect was smaller (7%) and not statistically significant. When BDI and HDRS scores were combined, participants receiving both therapy and medications showed 9% greater reductions in suicidal ideation relative to those receiving medications alone; this effect was marginally significant when controlling for depression severity., Limitations: This is a secondary analysis of a randomized clinical trial designed to treat depression, in which suicidal ideation was assessed using single-item measures., Conclusions: Adding cognitive therapy to antidepressant medications may reduce suicidal ideation to a greater extent than medications alone. Pending replication, combination treatment may be preferred for individuals with suicidal ideation., Trial Registration: clinicaltrials.gov Identifier: NCT00057577., (Published by Elsevier B.V.)

Background: The therapeutic alliance is considered an important causal agent of psychotherapy efficacy. However, studies in cognitive behavioral therapy (CBT) for depression have suggested that alliance might be more of a consequence rather than a cause of depressive symptom change, while adherence to CBT specific techniques was found to be associated with subsequent depression change. We aimed to add to this body of literature by assessing the temporal associations of both therapeutic alliance and manual adherence with depressive symptom change in a relatively large sample of depressed adult outpatients over the full course of CBT. Methods: Adults with a major depressive episode ( n = 98) participating in a randomized clinical trial were offered 22 weeks of CBT and rated the Penn Helping Alliance Questionnaire (HAq-I) at weeks 5 and 22. Therapists rated their adherence to the CBT manual after each session and observers assessed the Hamilton Depression Rating Scale scores at weeks 0, 5, 10, and 22. Linear mixed model analyses were used to assess the associations of alliance and adherence with prior and subsequent depression change. Results: HAq-I Relationship and manual adherence ratings were not significantly associated with prior nor with subsequent depression change ( p > 0.14). Prior depression change was associated with the HAq-I subscale Perceived helpfulness at the end of treatment ( r = 0.30, CI = 0.03-0.56, p = 0.03). Conclusion: We were not able to replicate prior depression change in CBT for depression to be associated with improved quality of the therapeutic alliance when using a more "pure" measure of the therapeutic relationship. Limitations of this study include the subjective alliance and adherence assessments. Our findings indicate the need to appropriately distinguish between the perceived helpfulness and the relationship factors when examining therapeutic alliance., Competing Interests: FD and JD receive fees from Springer Media. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Don, Driessen, Peen, Spijker, DeRubeis, Blankers and Dekker.)

Background: Psychotherapies for depression are equally effective on average, but individual responses vary widely. Outcomes can be improved by optimizing treatment selection using multivariate prediction models. A promising approach is the Personalized Advantage Index (PAI) that predicts the optimal treatment for a given individual and the magnitude of the advantage. The current study aimed to extend the PAI to long-term depression outcomes after acute-phase psychotherapy., Methods: Data come from a randomized trial comparing cognitive therapy (CT, n = 76) and interpersonal psychotherapy (IPT, n = 75) for major depressive disorder (MDD). Primary outcome was depression severity, as assessed by the BDI-II, during 17-month follow-up. First, predictors and moderators were selected from 38 pre-treatment variables using a two-step machine learning approach. Second, predictors and moderators were combined into a final model, from which PAI predictions were computed with cross-validation. Long-term PAI predictions were then compared to actual follow-up outcomes and post-treatment PAI predictions., Results: One predictor (parental alcohol abuse) and two moderators (recent life events; childhood maltreatment) were identified. Individuals assigned to their PAI-indicated treatment had lower follow-up depression severity compared to those assigned to their PAI-non-indicated treatment. This difference was significant in two subsets of the overall sample: those whose PAI score was in the upper 60%, and those whose PAI indicated CT, irrespective of magnitude. Long-term predictions did not overlap substantially with predictions for acute benefit., Conclusions: If replicated, long-term PAI predictions could enhance precision medicine by selecting the optimal treatment for a given depressed individual over the long term.

Objective: Optimizing treatment selection may improve treatment outcomes in depression. A promising approach is the Personalized Advantage Index (PAI), which predicts the optimal treatment for a given individual. To determine the generalizability of the PAI, models needs to be externally validated, which has rarely been done. Method: PAI models were developed within each of two independent trials, with substantial between-study differences, that both compared CBT and IPT for depression (STEPd: n  = 151 and FreqMech: n  = 200). Subsequently, both PAI models were tested in the other dataset. Results: In the STEPd study, post-treatment depression was significantly different between individuals assigned to their PAI-indicated treatment versus those assigned to their non-indicated treatment ( d  = .57). In the FreqMech study, post-treatment depression was not significantly different between patients receiving their indicated treatment versus those receiving their non-indicated treatment ( d  = .20). Cross-trial predictions indicated that post-treatment depression was not significantly different between those receiving their indicated treatment and those receiving their non-indicated treatment ( d  = .16 and d  = .27). Sensitivity analyses indicated that cross-trial prediction based on only overlapping variables didn't improve the results. Conclusion: External validation of the PAI has modest results and emphasizes between-study differences and many other challenges.

Objective: To assess the dissemination of evidence-based content within smartphone apps for depression and anxiety by developing and applying user-adjusted analysis -a method for weighting app content based on each app's number of active users., Method: We searched the Apple App Store and Google Play Store and identified 27 apps within the top search hits, which real-world users are most likely to encounter. We developed a codebook of evidence-based treatment elements by reviewing past research on empirically supported treatments. We coded the apps to develop an initial tally of the frequency of treatment elements within the mental health (MH) apps. We then developed and applied user-adjusted analysis to refine the tallies based on each app's number of monthly active users., Results: The 2 most popular apps were responsible for 90% of monthly active users, and user-adjusted analysis markedly altered conclusions of prior reports based on tallies alone. For example, mindfulness was present in 37% of apps but reached 96% of monthly active users, cognitive restructuring was present in 22% but reached only 2%, and exposure was present in 7% but reached only 0.0004%., Conclusions: The potential impact of MH apps on mental health may be best evaluated via assessment that combines tallies of evidence-based content with data on the content users are actually accessing. Given wide variation in the popularity of MH apps, findings weighted by usage data may differ markedly from findings based on raw tallies alone. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Considerable evidence from clinical trials supports the efficacy of cognitive therapy (CT) of depression. Less is known about outcomes when provided in other contexts, such as when provided by student therapists. We conducted a non-randomized comparison of student therapists vs. clinical trial therapists on change in depressive symptoms, dropout, change in CT skills, and therapeutic alliance among 100 clients with moderate to severe depression. Treatment manual and duration were the same. Clients treated by student therapists had largely comparable outcomes on depressive symptom change, therapeutic alliance, and CT skills. Results supported non-inferiority of student therapists on change in depressive symptoms, but non-inferiority was not supported when using an interviewer evaluated measure of depression. Evidence of non-inferiority was also obtained for client CT skills and therapeutic alliance. In fact, conventional superiority analyses indicated student therapists outperformed clinical trial therapists on alliance and CT skills. The rate of dropout among student therapists (30%) was numerically higher than among clinical trial therapists (17%) and our results did not support non-inferiority on dropout. CT provided by student therapists can achieve outcomes similar to those in a clinical trial, but more research about dropout is needed., Competing Interests: Declaration of competing interest None., (Published by Elsevier Ltd.)

Background : Pre-treatment severity is a key indicator of prognosis for those with depression. Knowledge is limited on how best to encompass severity of disorders. A number of non-severity related factors such as social support and life events are also indicators of prognosis. It is not clear whether this holds true after adjusting for pre-treatment severity as a) a depressive symptom scale score, and b) a broader construct encompassing symptom severity and related indicators: "disorder severity". In order to investigate this, data from the individual participants of clinical trials which have measured a breadth of "disorder severity" related factors are needed. Aims : 1) To assess the association between outcomes for adults seeking treatment for depression and the severity of depression pre-treatment, considered both as i) depressive symptom severity only and ii) "disorder severity" which includes depressive symptom severity and comorbid anxiety, chronicity, history of depression, history of previous treatment, functional impairment and health-related quality of life. 2) To determine whether i) social support, ii) life events, iii) alcohol misuse, and iv) demographic factors (sex, age, ethnicity, marital status, employment status, level of educational attainment, and financial wellbeing) are prognostic indicators of outcomes, independent of baseline "disorder severity" and the type of treatment received. Methods : Databases were searched for randomised clinical trials (RCTs) that recruited adults seeking treatment for depression from their general practitioners and used the same diagnostic and screening instrument to measure severity at baseline - the Revised Clinical Interview Schedule; outcome measures could differ between studies. Chief investigators of all studies meeting inclusion criteria were contacted and individual patient data (IPD) were requested. Conclusions : In total 15 RCTs met inclusion criteria. The Dep-GP database will include the 6271 participants from the 13 studies that provided IPD. This protocol outlines how these data will be analysed. Registration : PROSPERO CRD42019129512 (01/04/2019)., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Buckman JEJ et al.)

Background: Many evidence-based prevention and intervention approaches for youth mental health exist, but most are time-consuming and expensive to deliver. Innovative approaches are needed to develop and disseminate scalable mental health interventions, especially for youth in low- and middle-income countries. Computerized single-session interventions (SSIs) may be able to overcome many of these implementation barriers, especially if deployed in school settings., Methods: Guided by this logic, we designed three computerized SSIs (based on behavioral activation, gratitude, and growth mindset exercises) for delivery in Indian high schools. Then, we conducted focus groups with Indian adolescents and school officials to inform adaptations of the interventions. Finally, we designed a Hybrid Type 1 effectiveness-implementation study to evaluate the interventions., Results: In this paper, we present the rationale for selecting the interventions, the results of the focus groups, and the design of a Hybrid Type 1 effectiveness-implementation study. Our initial focus group data showed that the interventions were considered acceptable, feasible, and appropriate by both educators and students. We also describe feedback on the interventions which we incorporated to refine the interventions prior to our randomized controlled trial., Discussion: Computerized SSIs may offer innovative, scalable ways to disseminate evidence-based interventions. By identifying which interventions are most acceptable and effective in this highly scalable format, we may understand which SSIs are best suited for continued development, testing, and, ultimately, widespread dissemination., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Importance: Antidepressant medication (ADM) maintenance treatment is associated with the prevention of depressive recurrence in patients with major depressive disorder (MDD), but whether cognitive behavioral therapy (CBT) treatment is associated with recurrence prevention remains unclear., Objective: To determine the effects of combining CBT with ADM on the prevention of depressive recurrence when ADMs are withdrawn or maintained after recovery in patients with MDD., Design, Setting, and Participants: A total of 292 adult outpatients with chronic or recurrent MDD who participated in the second phase of a 2-phase trial. Participants had recovered in the first phase of the trial receiving ADM, either alone or in combination with CBT. The trial was conducted in research clinics in 3 university medical centers in the United States. Patients in phase 2 were randomized to receive maintenance of or withdrawal from ADM and were followed up for 3 years. The first and last patients entered phase 2 in August 2003 and October 2009, respectively. The last patient completed phase 2 in August 2012. Data were analyzed from December 2013 to December 2018., Interventions: Maintenance of or withdrawal from treatment with ADM., Main Outcomes and Measures: Recurrence of an MDD episode using longitudinal interval follow-up evaluations; sustained recovery across both phases., Results: A total of 292 participants (171 women, 121 men; mean [SD] age 45.1 [12.9] years) were included in analyses of depressive recurrence. Maintenance ADM yielded lower rates of recurrence compared with ADM withdrawal regardless of whether patients had achieved recovery in phase 1 with ADM alone (48.5% vs 74.8%; z = -3.16; P = .002; number needed to treat [NNT], 2.8; 95% CI, 1.8-7.0) or ADM plus CBT (48.5% vs 76.7%; z = -3.49; P < .001; NNT, 2.7; 95% CI, 1.9-5.9). Sustained recovery rates differed as a function of phase 2 condition, with maintenance ADM superior to ADM withdrawal (z = 2.90; P = .004; OR, 2.54; 95% CI, 1.37-4.84; NNT, 2.3; 95% CI, 1.5-6.4). Phase 1 condition was not associated with differential rates of sustained recovery (ADM alone vs ADM plus CBT; z = 0.22; P = .83; OR, 1.08; 95% CI, 0.52-2.11; NNT, 26.0; 95% CI, number needed to harm 3.2 to NNT 2.8), nor was there a significant interaction of phase 1 condition and phase 2 condition (z = 0.30; P = .77; OR, 1.14; 95% CI, 0.49-2.88)., Conclusions and Relevance: Maintenance ADM treatment, but not previous exposure to CBT, was associated with reduced rates of depressive recurrence. In previous studies, when CBT has been provided without ADM, CBT has shown a preventive effect on depressive relapse. Whether CBT also has a preventive effect on depressive recurrence, or if adding ADM interferes with any such preventive effect, remains unclear., Trial Registration: ClinicalTrial.gov identifier: NCT00057577.

Objective: Although the effectiveness of interpersonal psychotherapy (IPT) and cognitive therapy (CT) for major depression has been established, little is known about how and for whom they work and how they compare in the long term. The latter is especially relevant for IPT because research on its long-term effects has been limited. This overview paper summarizes findings from a Dutch randomized controlled trial on the effects and mechanisms of change of IPT versus CT for major depression., Methods: Adult outpatients with depression (N=182) were randomly assigned to CT (N=76), IPT (N=75), or a 2-month waitlist control group followed by patient's treatment of choice (N=31). The primary outcome was depression severity. Other outcomes were quality of life, social and general psychological functioning, and scores on various mechanism measures. Interventions were compared at the end of treatment and up to 17 months follow-up., Results: On average, IPT and CT were both superior to waitlist, and their outcomes did not differ significantly from one another. However, the pathway through which change occurred appeared to differ. For a majority of participants, one of the interventions was predicted to be more beneficial than the other. No support for the theoretical models of change was found., Conclusions: Outcomes of IPT and CT did not appear to differ significantly. IPT may have an enduring effect not different from that of CT. The field would benefit from further refinement of study methods to disentangle mechanisms of change and from advances in the field of personalized medicine (i.e., person-specific analyses and treatment selection methods).

Two core features of depression include depressed mood (heightened distress) and anhedonia (reduced pleasure). Despite their centrality to depression, studies have not examined their contribution to treatment outcomes in a randomized clinical trial providing mainstream treatments like antidepressant medications (ADM) and cognitive therapy (CT). We used baseline distress and anhedonia derived from a factor analysis of the Mood and Anxiety Symptom Questionnaire to predict remission and recovery in 433 individuals with recurrent/chronic major depressive disorder. Patients were provided with only ADM or both ADM and CT. Overall, higher baseline distress and anhedonia predicted longer times to remission within one year and recovery within three years. When controlling for treatment condition, distress improved prediction of outcomes over and above anhedonia, while anhedonia did not improve prediction of outcomes over and above distress. Interactions with treatment condition demonstrated that individuals with higher distress and anhedonia benefited from receiving CT in addition to ADM, whereas there was no added benefit of CT for individuals with lower distress and anhedonia. Assessing distress and anhedonia prior to treatment may help select patients who will benefit most from CT in addition to ADM. For the treatments and outcome measures tested, utilizing distress to guide treatment planning may yield the greatest benefit. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00057577., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In panic-focused psychodynamic psychotherapy (PFPP), exploration and interpretation of avoided and conflicted emotions and fantasies surrounding anxiety are thought to promote panic-specific reflective functioning (PSRF), which drives panic disorder improvements. Patient emotional expression within a session may be a marker of engaged processing and experiencing of affectively charged material. Degree of in-session expressed emotion, indicating both verbal and nonverbal emotions, was examined across three early therapy sessions for prediction of subsequent outcomes. We further investigated whether personality disorder traits, theorized to relate to constricted (obsessive-compulsive) or heightened (borderline) emotions, moderated this relationship. Emotional expression in Sessions 2, 5, and 10 of a 24-session PFPP protocol was assessed by blinded observers in 44 patients randomized to PFPP in a two-site randomized controlled trial of psychotherapies for panic disorder. Robust regressions were conducted to examine the relationship between average emotional expression across the measured sessions and symptom and PSRF changes subsequent to the sampled sessions, as well as moderation by personality disorder criteria, controlling for early outcomes. Higher levels of emotional expression across the early sessions predicted greater subsequent symptom and PSRF improvement. Elevations in expression of grief/sadness drove the symptomatic finding. Patients meeting more borderline criteria experienced a smaller and potentially negative relationship between emotional expression and symptom improvement. Emotional expression in PFPP may be an indicator of positive therapy process for patients without comorbid borderline personality traits, predicting prospective improvements in both a key mediator (PSRF) and symptoms. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In evidence-based mental health practice, decisions must often be made for which there is little or no empirical basis. A common example of this is when there are multiple empirically supported interventions for a person with a given diagnosis, where the aim is to recommend the treatment most likely to be effective for that person. Data obtained from randomized clinical trials allow for the identification of patient characteristics that could be used to match patients to treatments. Historically, researchers have focused on individual moderators, single variables that interact statistically with treatment type, but these have rarely proved powerful enough to inform treatment decisions. Recently, researchers have begun to explore ways in which the use of multivariable algorithms might improve clinical decision-making. Common pitfalls have been identified, including the use of methods that provide overoptimistic estimates of the gains that can be expected from the applications of an algorithm in a clinical setting. It is too early to tell if these efforts will pay off and, if so, how much their use can increase the efficiency and effectiveness of mental health systems. It behooves the field to continue to learn and develop the most powerful methods that can produce generalizable knowledge that will advance the aims of precision mental health., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Identifying differences in the clinical response to specific interventions is an important challenge in the field of Clinical Psychology. This is especially true in the treatment of depression where many treatments appear to have comparable outcomes. In a controlled trial, we compared a positive psychology group intervention, the Integrative Positive Psychological Intervention for Depression (IPPI-D; n = 62) to a cognitive-behavioral therapy group intervention (CBT; n = 66) for depression. No statistically or clinically-significant differences between the treatments were found, but a slight advantage was observed, on average, for IPPI-D. The aim of the present study was to identify and combine moderators of the differential efficacy of these two psychological interventions for clinical depression. For this purpose, a secondary analysis using the Personalized Advantage Index (PAI) was performed to identify the intervention predicted to produce the better outcome for each patient. Six of the 21 potential moderators were found to predict differential efficacy between the treatments. IPPI-D was predicted to be the optimal treatment for 73% of the sample. Baseline features that characterized these individuals were: mental and physical comorbidity, prior antidepressant medication, higher levels of negative thoughts, and higher personal growth. The 27% who were predicted to achieve better outcomes in CBT than in IPPI-D tended to have these baseline features: no comorbidities, no prior antidepressant medication, lower levels of negative thoughts, and lower personal growth., (Copyright © 2019 Elsevier Inc. All rights reserved.)