A systematic review of predictors and moderators of treatment outcomes in internet- and mobile-based interventions for depression.

This systematic review aimed to synthesize evidence on predictors and moderators of treatment outcomes in internet- and mobile-based interventions (IMIs) for depression, informing personalized care. A systematic search across PubMed, PsycInfo, and Cochrane yielded 33,002 results. Two reviewers independently performed screening, data extraction, risk of bias assessment, and methodological quality evaluation. Fifty-eight single studies (m = 466 analyses) focusing on baseline-predictors (59.7 %, m = 278), process-predictors (16.5 %, m = 77), and moderators (21.9 %, m = 102), and six individual patient data meta-analyses (m = 93) were included. Only 24.0 % (m = 112/466) of analyses in single studies and 15.1 % (m = 14/93) in individual patient data meta-analyses were significant. Evidence from single studies was rated as insufficient for all variable categories with only 2 out of 40 categories showing >50 % significant results. Baseline depression severity had the strongest predictive value with higher scores linked to better outcomes followed by variables indicative for the course-of-change. Other frequently analyzed and potentially relevant variables with significant results were adherence, age, educational level, ethnicity, relationship status, treatment history, and behavioral variables. More high quality quantitative studies with sufficient power are essential to validate and expand findings, identifying predictors and moderators specifically relevant in IMIs to explain differential treatment effects.

How to distinguish promotion, prevention and treatment trials in public mental health? Study protocol for the development of the VErona-LUgano Tool (VELUT).

INTRODUCTION: Promoting mental health, preventing and treating mental disorders are critically important in public health, and many randomised controlled trials (RCTs) evaluate intervention strategies for these objectives. However, distinguishing promotion from prevention and from treatment RCTs is challenging. A tool to place studies along the promotion-to-treatment continuum in mental health research does not exist, leaving it to researchers and policymakers to decide on how to classify individual RCTs, which hinders evidence synthesis. METHODS AND ANALYSIS: We present a protocol for the development of a new tool to assist researchers in distinguishing RCTs along the promotion-to-treatment continuum. We will establish a Tool Development Group, and use the Population, Intervention, Comparison and Outcome framework to define constructs. We will generate, define, categorise and reduce the items in the tool using qualitative methods, including cognitive interviews and a Delphi exercise. Psychometric evaluation-including unidimensionality, local independence, monotonicity and item homogeneity-will include data collection, scoring, internal consistency checks and factor analysis of the tool's indicators for available RCTs. We will use standard Cohen's kappa statistics to assess the reliability of the tool. ETHICS AND DISSEMINATION: This study involves data collection from the already published literature. However, this protocol has been approved by the ethics committee of the Università della Svizzera Italiana (CE 2024 04). The results of the present project will be disseminated in peer-reviewed journals and at international and national scientific meetings. Training materials for the application of the tool will also be developed and disseminated to the scientific community. The tool and all related implementation materials will be published on a website and will be freely accessible to the public.

Does outcome expectancy predict outcomes in online depression prevention? Secondary analysis of randomised-controlled trials.

BACKGROUND: Evidence shows that online interventions could prevent depression. However, to improve the effectiveness of preventive online interventions in individuals with subthreshold depression, it is worthwhile to study factors influencing intervention outcomes. Outcome expectancy has been shown to predict treatment outcomes in psychotherapy for depression. However, little is known about whether this also applies to depression prevention. The aim of this study was to investigate the role of participants' outcome expectancy in an online depression prevention intervention. METHODS: A secondary data analysis was conducted using data from two randomised-controlled trials (N = 304). Multilevel modelling was used to explore the effect of outcome expectancy on depressive symptoms and close-to-symptom-free status postintervention (6-7 weeks) and at follow-up (3-6 months). In a subsample (n = 102), Cox regression was applied to assess the effect on depression onset within 12 months. Explorative analyses included baseline characteristics as possible moderators. Outcome expectancy did not predict posttreatment outcomes or the onset of depression. RESULTS: Small effects were observed at follow-up for depressive symptoms (ß = -.39, 95% confidence interval [CI]: [-0.75, -0.03], p = .032, padjusted = .130) and close-to-symptom-free status (relative risk = 1.06, 95% CI: [1.01, 1.11], p = .013, padjusted = 0.064), but statistical significance was not maintained when controlling for multiple testing. Moderator analyses indicated that expectancy could be more influential for females and individuals with higher initial symptom severity. CONCLUSION: More thoroughly designed, predictive studies targeting outcome expectancy are necessary to assess the full impact of the construct for effective depression prevention. PATIENT OR PUBLIC CONTRIBUTION: This secondary analysis did not involve patients, service users, care-givers, people with lived experience or members of the public. However, the findings incorporate the expectations of participants using the preventive online intervention, and these exploratory findings may inform the future involvement of participants in the design of indicated depression prevention interventions for adults. CLINICAL TRIAL REGISTRATION: Original studies: DRKS00004709, DRKS00005973; secondary analysis: osf.io/9xj6a.

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression: A Systematic Review and Meta-Analysis.

Importance: While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary. Objective: To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness. Data Sources: A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials. Study Selection: Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design. Data Extraction and Synthesis: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted. Main Outcomes and Measures: Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs. Results: The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (ß̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (ß̂ = -0.24, 95% CrI, -0.03 to -0.46). Conclusions and Relevance: In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

Planetary Health and Mental Health Nexus: Least Understood and Embraced in Policy Decisions.

Planetary health influences mental health and a better management of climate, biodiversity and pollution has co-benefits of improving mental health outcomes. The recognition and treatment of mental health, however, has been marginalized within environmental and climate change sciences and a greater understanding of the complex underlying processes and societal costs is required to appropriately manage and motivate policy responses. The paper provides seven recommendations underscoring that public policy developers and implementors need to be aware of the combined costs of inaction - that might accrue from neglecting mental health and environmental sciences- two areas that have been historically marginalized. Improved methodologies in conducting studies on the nature and mental health nexus are needed. The trajectories and models of adaptation and mitigation of climate change and environmental damage can be strengthened through adoption of mental and behavioral sciences approach.

Depression prevalence of the Geriatric Depression Scale-15 was compared to Structured Clinical Interview for DSM using individual participant data meta-analysis.

Depression questionnaire cutoffs are calibrated for screening accuracy and not to assess prevalence, but the Geriatric Depression Scale (GDS-15) is often used to estimate diagnostic prevalence among older adults, most commonly with scores of ≥ 5. We conducted an individual participant data meta-analysis to compare depression prevalence based on GDS-15 ≥ 5 to Structured Clinical Interview for Diagnostic and Statistical Manual (SCID) diagnoses and assessed whether an alternative cutoff could be more accurate. We used generalized linear mixed models to estimate prevalence. Data from 14 studies (3602 participants, 434 SCID major depression) were included. Pooled GDS-15 ≥ 5 prevalence was 34.2% (95% confidence interval [CI] 27.5-41.6%), and pooled SCID prevalence was 14.8% (95% CI 10.0-21.5%; difference of 17.6%, 95% CI 11.6-23.6%). GDS-15 ≥ 8 provided the closest estimate to SCID with mean difference of - 0.3% (95% prediction interval - 17.0-16.5%). Prevalence estimate differences were not associated with study or participant characteristics. In sum, GDS-15 ≥ 5 substantially overestimated depression prevalence. A cutoff of ≥ 8 was accurate overall, but heterogeneity was too high for implementation in practice. Validated diagnostic interviews should be used to estimate major depression prevalence among older adults.

Individual patient data meta-analysis estimates the minimal detectable change of the Geriatric Depression Scale-15.

OBJECTIVES: To use individual participant data meta-analysis (IPDMA) to estimate the minimal detectable change (MDC) of the Geriatric Depression Scale-15 (GDS-15) and to examine whether MDC may differ based on participant characteristics and study-level variables. STUDY DESIGN AND SETTING: This was a secondary analysis of data from an IPDMA on the depression screening accuracy of the GDS. Datasets from studies published in any language were eligible for the present study if they included GDS-15 scores for participants aged 60 or older. MDC of the GDS-15 was estimated via random-effects meta-analysis using 2.77 (MDC95) and 1.41 (MDC67) standard errors of measurement. Subgroup analyses were used to evaluate differences in MDC by participant age and sex. Meta-regression was conducted to assess for differences based on study-level variables, including mean age, proportion male, proportion with major depression, and recruitment setting. RESULTS: 5876 participants (mean age 76 years, 40% male, 11% with major depression) from 21 studies were included. The MDC95 was 3.81 points (95% confidence interval [CI] 3.59, 4.04), and MDC67 was 1.95 (95% CI 1.83, 2.03). The difference in MDC95 was 0.26 points (95% CI 0.04, 0.48) between ≥80-year-olds and <80-year-olds; MDC95 was similar for females and males (0.05, 95% CI -0.12, 0.22). The MDC95 increased by 0.29 points (95% CI 0.17, 0.41) per 10% increase in proportion of participants with major depression; mean age had a small association (0.04 points, 95% CI 0.00 to 0.09) with MDC95, but sex and recruitment setting were not significantly associated. CONCLUSION: The MDC95 was 3.81 points and MDC67 was 1.95 points. MDC95 increased with the proportion of participants with major depression. Results can be used to evaluate individual changes in depression symptoms and as a threshold for assessing minimal clinical important difference estimates.

Use of Machine Learning Algorithms Based on Text, Audio, and Video Data in the Prediction of Anxiety and Posttraumatic Stress in General and Clinical Populations: A Systematic Review.

Research in machine learning (ML) algorithms using natural behavior (i.e., text, audio, and video data) suggests that these techniques could contribute to personalization in psychology and psychiatry. However, a systematic review of the current state of the art is missing. Moreover, individual studies often target ML experts who may overlook potential clinical implications of their findings. In a narrative accessible to mental health professionals, we present a systematic review conducted in 5 psychology and 2 computer science databases. We included 128 studies that assessed the predictive power of ML algorithms using text, audio, and/or video data in the prediction of anxiety and posttraumatic stress disorder. Most studies (n = 87) were aimed at predicting anxiety, while the remainder (n = 41) focused on posttraumatic stress disorder. They were mostly published since 2019 in computer science journals and tested algorithms using text (n = 72) as opposed to audio or video. Studies focused mainly on general populations (n = 92) and less on laboratory experiments (n = 23) or clinical populations (n = 13). Methodological quality varied, as did reported metrics of the predictive power, hampering comparison across studies. Two-thirds of studies, which focused on both disorders, reported acceptable to very good predictive power (including high-quality studies only). The results of 33 studies were uninterpretable, mainly due to missing information. Research into ML algorithms using natural behavior is in its infancy but shows potential to contribute to diagnostics of mental disorders, such as anxiety and posttraumatic stress disorder, in the future if standardization of methods, reporting of results, and research in clinical populations are improved.

Population mental health science: Guiding principles and initial agenda.

A recent American Psychological Association Summit provided an urgent call to transform psychological science and practice away from a solely individual-level focus to become accountable for population-level impact on health and mental health. A population focus ensures the mental health of all children, adolescents, and adults and the elimination of inequities across groups. Science must guide three components of this transformation. First, effective individual-level interventions must be scaled up to the population level using principles from implementation science, investing in novel intervention delivery systems (e.g., online, mobile application, text, interactive voice response, and machine learning-based), harnessing the strength of diverse providers, and forging culturally informed adaptations. Second, policy-driven community-level interventions must be innovated and tested, such as public efforts to promote physical activity, public policies to support families in early life, and regulation of corporal punishment in schools. Third, transformation is needed to create a new system of universal primary care for mental health, based on models such as Family Connects, Triple P, PROmoting School-community-university Partnerships to Enhance Resilience, Communities That Care, and the Early Childhood Collaborative of the Pittsburgh Study. This new system must incorporate valid measurement, universal screening, and a community-based infrastructure for service delivery. Addressing tasks ahead, including scientific creativity and discovery, rigorous evaluation, and community accountability, will lead to a comprehensive strategic plan to shape the emergent field of public mental health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Web-Based, Human-Guided, or Computer-Guided Transdiagnostic Cognitive Behavioral Therapy in University Students With Anxiety and Depression: Randomized Controlled Trial.

BACKGROUND: Internet-based cognitive behavioral interventions (iCBTs) are efficacious treatments for depression and anxiety. However, it is unknown whether adding human guidance is feasible and beneficial within a large educational setting. OBJECTIVE: This study aims to potentially demonstrate the superiority of 2 variants of a transdiagnostic iCBT program (human-guided and computer-guided iCBT) over care as usual (CAU) in a large sample of university students and the superiority of human-guided iCBT over computer-guided iCBT. METHODS: A total of 801 students with elevated levels of anxiety, depression, or both from a large university in the Netherlands were recruited as participants and randomized to 1 of 3 conditions: human-guided iCBT, computer-guided iCBT, and CAU. The primary outcome measures were depression (Patient Health Questionnaire) and anxiety (Generalized Anxiety Disorder scale). Secondary outcomes included substance use-related problems (Alcohol Use Disorder Identification Test and Drug Abuse Screening Test-10 items). Linear mixed models were used to estimate the effects of time, treatment group, and their interactions (slopes). The primary research question was whether the 3 conditions differed in improvement over 3 time points (baseline, midtreatment, and after treatment) in terms of depression and anxiety symptoms. Results were analyzed according to the intention-to-treat principle using multiple imputation. Patients were followed exploratively from baseline to 6 and 12 months. RESULTS: In both short-term and long-term analyses, the slopes for the 3 conditions did not differ significantly in terms of depression and anxiety, although both web-based interventions were marginally more efficacious than CAU over 6 months (P values between .02 and .03). All groups showed significant improvement over time (P<.001). For the secondary outcomes, only significant improvements over time (across and not between groups) were found for drug use (P<.001). Significant differences were found in terms of adherence, indicating that participants in the human-guided condition did more sessions than those in the computer-guided condition (P=.002). CONCLUSIONS: The transdiagnostic iCBT program offers a practical, feasible, and efficacious alternative to usual care to tackle mental health problems in a large university setting. There is no indication that human guidance should be preferred over technological guidance. The potential preference of human support also depends on the scale of implementation and cost-effectiveness, which need to be addressed in future trials. TRIAL REGISTRATION: International Clinical Trials Registry Platform NL7328/NTR7544; https://trialsearch.who.int/Trial2.aspx?TrialID=NL-OMON26795.

Changes in the prevalence of mental health problems during the first year of the pandemic: a systematic review and dose-response meta-analysis.

AIM: To describe the pattern of the prevalence of mental health problems during the first year of the COVID-19 pandemic and examine the impact of containment measures on these trends. METHODS: We identified articles published until 30 August 2021 that reported the prevalence of mental health problems in the general population at two or more time points. A crowd of 114 reviewers extracted data on prevalence, study and participant characteristics. We collected information on the number of days since the first SARS-CoV-2 infection in the study country, the stringency of containment measures and the number of cases and deaths. We synthesised changes in prevalence during the pandemic using a random-effects model. We used dose-response meta-analysis to evaluate the trajectory of the changes in mental health problems. RESULTS: We included 41 studies for 7 mental health conditions. The average odds of symptoms increased during the pandemic (mean OR ranging from 1.23 to 2.08). Heterogeneity was very large and could not be explained by differences in participants or study characteristics. Average odds of psychological distress, depression and anxiety increased during the first 2 months of the pandemic, with increased stringency of the measures, reported infections and deaths. The confidence in the evidence was low to very low. CONCLUSIONS: We observed an initial increase in the average risk of psychological distress, depression-related and anxiety-related problems during the first 2 months of the pandemic. However, large heterogeneity suggests that different populations had different responses to the challenges imposed by the pandemic.

Evaluating the psychometric structure of the Hamilton Rating Scale for Depression pre- and post-treatment in antidepressant randomised trials: Secondary analysis of 6843 individual participants from 20 trials.

BACKGROUND: The 17-item Hamilton Rating Scale for Depression (HRSD-17) is the most popular depression measure in antidepressant clinical trials. Prior evidence indicates poor replicability and inconsistent factorial structure. This has not been studied in pooled randomised trial data, nor has a psychometrically optimal model been developed. AIMS: To examine the psychometric properties of the HRSD-17 for pre-treatment and post-treatment clinical trial data in a large pooled database of antidepressant randomised controlled trial participants, and to determine an optimal abbreviated version. METHOD: Data for 6843 participants were obtained from the data repository Vivli.org and randomly split into groups for exploratory (n = 3421) and confirmatory (n = 3422) factor analysis. Invariance methods were used to assess potential sex differences. RESULTS: The HRSD-17 was psychometrically sub-optimal and non-invariant for all models. High item variances and low variance explained suggested redundancy in each model. EFA failed at baseline and produced four item models for outcome groups (five for placebo-outcome), which were metric but not scalar invariant. CONCLUSIONS: In antidepressant trial data, the HRSD-17 was psychometrically inadequate and scores were not sex invariant. Neither full nor abbreviated HRSD models are suitable for use in clinical trial settings and the HRSD's status as the gold standard should be reconsidered.

The effectiveness of self-guided interventions in adults with depressive symptoms: a systematic review and meta-analysis.

BACKGROUND: Despite promising scalability and accessibility, evidence on the efficacy of self-guided interventions for adult depression is inconclusive. This study investigated their effectiveness and acceptability, considering diverse delivery formats and support levels. METHODS: We systematically searched PubMed, PsycINFO, Embase, and Cochrane Library until 1st January 2024. Included were randomised controlled trials comparing self-guided interventions with a control condition for adult depression. Two independent researchers extracted data. Effect sizes were pooled using random-effects models, with post-intervention depressive severity compared with control conditions as the primary outcome. Study validity was evaluated using Cochrane Risk of Bias 2.0. This study was pre-registered with OSF (https://osf.io/rd43v). FINDINGS: We identified 92 studies (111 interventions vs. control comparisons) with 16,706 participants (mean age: 18.78-74.41 years). Compared to controls, self-guided interventions were moderately effective at post-assessment (g = 0.53, 95% CI: 0.45-0.61; I2 = 79.17%) and six to twelve months post-randomisation follow-up (g = 0.32, 95% CI: 0.16-0.48; I2 = 79.19%). Trials with initial human screening (g = 0.59) and interventions delivered in computer programs (g = 1.04) had the significantly largest effect sizes. No differences in treatment effects were observed across support levels, therapy types, commercial availability, or the presence of online discussion forums. Self-guided interventions were less acceptable than control conditions (RR = 0.92, p < 0.001). Most studies showed a moderate to high risk of bias (n = 80). INTERPRETATION: Existing trials on self-guided interventions are at high risk of bias, potentially overestimating treatment effects. Despite lower acceptability compared to controls, self-guided interventions are moderately effective in treating adult depression, regardless of support levels and online discussion features. FUNDING: None.

Comparison of Patient Health Questionnaire-9, Edinburgh Postnatal Depression Scale and Hospital Anxiety and Depression - Depression subscale scores by administration mode: An individual participant data differential item functioning meta-analysis.

Administration mode of patient-reported outcome measures (PROMs) may influence responses. We assessed if Patient Health Questionnaire-9 (PHQ-9), Edinburgh Postnatal Depression Scale (EPDS) and Hospital Anxiety and Depression Scale - Depression subscale (HADS-D) item responses and scores were associated with administration mode. We compared (1) self-administration versus interview-administration; within self-administration (2) research or medical setting versus private; and (3) pen-and-paper versus electronic; and within interview-administration (4) in-person versus phone. We analysed individual participant data meta-analysis datasets with item-level data for the PHQ-9 (N = 34,529), EPDS (N = 16,813), and HADS-D (N = 16,768). We used multiple indicator multiple cause models to assess differential item functioning (DIF) by administration mode. We found statistically significant DIF for most items on all measures due to large samples, but influence on total scores was negligible. In 10 comparisons conducted across the PHQ-9, EPDS, and HADS-D, Pearson's correlations and intraclass correlation coefficients between latent depression symptom scores from models that did or did not account for DIF were between 0.995 and 1.000. Total PHQ-9, EPDS, and HADS-D scores did not differ materially across administration modes. Researcher and clinicians who evaluate depression symptoms with these questionnaires can select administration methods based on patient preferences, feasibility, or cost.

Cost-Effectiveness of Digital Mental Health Versus Usual Care During Humanitarian Crises in Lebanon: Pragmatic Randomized Trial.

BACKGROUND: There is evidence from meta-analyses and systematic reviews that digital mental health interventions for depression, anxiety, and stress-related disorders tend to be cost-effective. However, no such evidence exists for guided digital mental health care in low and middle-income countries (LMICs) facing humanitarian crises, where the needs are highest. Step-by-Step (SbS), a digital mental health intervention for depression, anxiety, and stress-related disorders, proved to be effective for Lebanese citizens and war-affected Syrians residing in Lebanon. Assessing the cost-effectiveness of SbS is crucial because Lebanon's overstretched health care system must prioritize cost-effective treatment options in the face of continuing humanitarian and economic crises. OBJECTIVE: This study aims to assess the cost-effectiveness of SbS in a randomized comparison with enhanced usual care (EUC). METHODS: The cost-effectiveness analysis was conducted alongside a pragmatic randomized controlled trial in 2 parallel groups comparing SbS (n=614) with EUC (n=635). The primary outcome was cost (in US $ for the reference year 2019) per treatment response of depressive symptoms, defined as >50% reduction of depressive symptoms measured using the Patient Health Questionnaire (PHQ). The secondary outcome was cost per remission of depressive symptoms, defined as a PHQ score <5 at last follow-up (5 months post baseline). The evaluation was conducted first from the health care perspective then from the societal perspective. RESULTS: Taking the health care perspective, SbS had an 80% probability to be regarded as cost-effective compared with EUC when there is a willingness to pay US $220 per additional treatment response or US $840 per additional remission. Taking the wider societal perspective, SbS had a >75% probability to be cost-saving while gaining response or remission. CONCLUSIONS: To our knowledge, this study is the first cost-effectiveness analysis based on a large randomized controlled trial (n=1249) of a guided digital mental health intervention in an LMIC. From the principal findings, 2 implications flowed, from the (1) health care perspective and (2) wider societal perspective. First, our findings suggest that SbS is associated with greater health benefits, albeit for higher costs than EUC. It is up to decision makers in health care to decide if they find the balance between additional health gains and additional health care costs acceptable. Second, as seen from the wider societal perspective, there is a substantial likelihood that SbS is not costing more than EUC but is associated with cost-savings as SBS participants become more productive, thus offsetting their health care costs. This finding may suggest to policy makers that it is in the interest of both population health and the wider Lebanese economy to implement SbS on a wide scale. In brief, SbS may offer a scalable, potentially cost-saving response to humanitarian emergencies in an LMIC. TRIAL REGISTRATION: ClinicalTrials.gov NCT03720769; https://clinicaltrials.gov/ct2/show/NCT03720769. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21585.

Amount and frequency of psychotherapy as predictors of treatment outcome for adult depression: A meta-regression analysis.

BACKGROUND: It is not clear whether the amount and frequency of psychotherapy is associated with treatment effects for adult depression. We investigated whether a number of indicators of the amount and frequency of psychotherapy were related to the treatment effects in randomized controlled trials (RCTs) comparing individual, face-to-face psychotherapy to a control group (e.g., care-as-usual [CAU] or waitlist condition [WL]). METHODS: Four databases were systematically searched, and meta-regression analyses conducted to assess the relationship between the effect size (Hedges' g) of the treatment and number of sessions, duration of psychotherapy, total contact time with the therapist, and number of sessions per week. RESULTS: We included 176 studies (210 comparisons) with 15,158 participants. We did not find a relationship between the effect size, and number of sessions, or total contact time. There was a small negative association between duration of treatment and the effect size, an additional week of treatment was related to a 0.014 decrease in the effect size. In addition, there was a strong association between number of sessions per week and the effect size, an increase from one to two sessions per week was related to an effect size higher by 0.596. Both associations were no longer significant when controlling for characteristics of studies. LIMITATIONS: The current findings are correlational, future research should thus address this question in an RCT. CONCLUSIONS: We should deliver brief therapies, and thus shorten waiting lists. More sessions, if necessary, should be delivered with higher frequency and over a shorter period of time.

The efficacy of psychotherapy for social anxiety disorder, a systematic review and meta-analysis.

BACKGROUND: Given the growth in research examining the effects of psychotherapy on social anxiety disorder (SAD), an up-to-date comprehensive meta-analysis in this field is needed. METHODS: We selected studies from a database of randomized trials (RCTs) on psychotherapies for anxiety disorders (last updated search of PubMed, PsycINFO, Embase, and Cochrane (CENTRAL): 1 January 2024) We included RCTs comparing psychotherapy to a control condition for adults with SAD and conducted random effects meta-analyses to examine the efficacy of psychotherapy compared to control conditions at post-treatment. RESULTS: Sixty-six RCTs were included with 5560 participants and 98 comparisons between psychotherapy and control groups. Psychotherapy was effective in reducing SAD symptoms, with a large effect size (g = 0.88; 95 % CI: 0.76 to 1.0; I2 = 74 %; 95 % CI: 69 to 79, NNT = 3.8). Effects remained robust across sensitivity analyses. However, there was evidence for significant risk of bias in the included trials. The multivariable meta-regression indicated significant differences in treatment delivery formats, type of recruitment strategy, target group, and number of sessions. CONCLUSION: Psychotherapy is an effective treatment for SAD, with moderate to large effect sizes across all treatment types and formats. Future research is needed to determine the long-term effects.

Comparative effectiveness of remote digital gamified and group CBT skills training interventions for anxiety and depression among college students: Results of a three-arm randomised controlled trial.

Digital interventions can enhance access to healthcare in under-resourced settings. However, guided digital interventions may be costly for low- and middle-income countries, despite their effectiveness. In this randomised control trial, we evaluated the effectiveness of two digital interventions designed to address this issue: (1) a Cognitive Behavioral Therapy Skills Training (CST) intervention that increased scalability by using remote online group administration; and (2) the SuperBetter gamified self-guided CBT skills training app, which uses other participants rather than paid staff as guides. The study was implemented among anxious and/or depressed South African undergraduates (n = 371) randomised with equal allocation to Remote Group CST, SuperBetter, or a MoodFlow mood monitoring control. Symptoms were assessed with the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Intention-to-treat analysis found effect sizes at the high end of prior digital intervention trials, including significantly higher adjusted risk differences (ARD; primary outcome) in joint anxiety/depression remission at 3-months and 6-months for Remote Group CST (ARD = 23.3-18.9%, p = 0.001-0.035) and SuperBetter (ARD = 12.7-22.2%, p = 0.047-0.006) than MoodFlow and mean combined PHQ-9/GAD-7 scores (secondary outcome) significantly lower for Remote Group CST and SuperBetter than MoodFlow. These results illustrate how innovative delivery methods can increase the scalability of standard one-on-one guided digital interventions. PREREGISTRATION INTERNATIONAL STANDARD RANDOMISED CONTROLLED TRIAL NUMBER (ISRTCN) SUBMISSION #: 47,089,643.