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Background Digital mental health interventions for smartphones, such as the World Health Organization (WHO) Step-by-Step (SbS) program, are potentially scalable solutions to improve access to mental health and psychosocial support in refugee populations. Our study objective was to evaluate the effectiveness of SbS as self-guided intervention with optional message-based contact-on-demand (COD) support on reducing psychological distress, functional impairment, symptoms of posttraumatic stress disorder (PTSD), and self-identified problems in a sample of Syrian refugees residing in Egypt. Methods and findings We conducted a 2-arm pragmatic randomized controlled trial. A total of 538 Syrians residing in Egypt with elevated levels of psychological distress (Kessler Psychological Distress Scale; K10 > 15) and reduced psychosocial functioning (WHODAS 2.0 > 16) were randomized into SbS + CAU (N = 266) or CAU only (N = 272). Primary outcomes were psychological distress (Hopkins Symptom Checklist 25) and impaired functioning (WHO Disability Assessment Schedule 2.0) at 3-month follow-up. Secondary outcomes were symptoms of PTSD (PTSD Checklist for DSM-5 short form, PCL-5 short) and self-identified problems (Psychological Outcomes Profiles Scale, PSYCHLOPS). Intention-to-treat (ITT) analyses showed significant but small effects of condition on psychological distress (mean difference: -0.15; 95% CI: -0.28, -0.02; p = .02) and functioning (mean difference: -2.04; 95% CI: -3.87, -0.22; p = .02) at 3-month follow-up. There were no significant differences between groups on symptoms of PTSD and self-identified problems. Remission rates did not differ between conditions on any of the outcomes. COD was used by 9.4% of participants for a median of 1 contact per person. The main limitations are high intervention dropout and low utilization of COD support. Conclusions The trial provides a real-world implementation case, showing small positive effects of a digital, potentially scalable and self-guided mental health intervention for Syrian refugees in Egypt in reducing psychological distress and improving overall functioning. Further user-centered adaptations are required to improve adherence and effectiveness while maintaining scalability. Trial registration German Register for Clinical Studies DRKS00023505., Author(s): Sebastian Burchert 1,*, Mhd Salem Alkneme 1, Ammar Alsaod 1, Pim Cuijpers 2,3, Eva Heim 4, Jonas Hessling 1, Nadine Hosny 4,5, Marit Sijbrandij 2, Edith van't Hof 6, [...]

BackgroundIn recent years, armed conflicts in the Middle East have resulted in high rates of exposure to traumatic events. Despite the increasing demand of mental health care provision, ongoing violence limits conventional approaches of mental health care provision. Internet-based interventions for posttraumatic stress disorder (PTSD) have proved feasible and effective in Western countries, but their applicability and efficacy in war and conflict regions remains unknown. ObjectiveThis study investigated the efficacy of a cognitive behavioral Internet-based intervention for war-traumatized Arab patients, with focus on Iraq. MethodsA total of 159 individuals with PTSD participated in a parallel group randomized trial. Participants were randomly allocated by a computer-generated sequence to a treatment group (n=79) or a waiting list control group (n=80). The treatment group received 2 weekly 45-minute cognitive behavioral interventions via Internet over a 5-week period (10 sessions in total). The primary outcome was recovery from posttraumatic stress symptoms. ResultsPosttraumatic stress symptoms were significantly reduced from baseline to posttreatment (intention-to-treat analysis) in the treatment group relative to the control group (F1,157=44.29, P

Internet‐based interventions have proven to be effective for the treatment of depression in different samples, but evidence from the Middle East and North Africa (MENA) region is scarce. The aim of this study was to investigate the acceptance and efficacy of an internet‐based cognitive behavioural writing intervention for Arabic‐speaking participants with depression living in the MENA region. A total of 259 participants (167 female, age in years: M = 25.58, SD = 6.39) with depressive symptoms indicative of clinical relevance were randomly allocated to a treatment group (TG; nTG = 128) or a waitlist control group (WG; nWG = 131). The TG received an internet‐based intervention over a 6‐week period. The primary outcome was depressive symptoms, and secondary outcomes were anxiety and quality of life (QoL). T‐tests with change scores from pre‐ to post‐treatment were used for data analyses. Intention‐to‐treat (ITT) as well as completer analyses were calculated. The ITT analysis revealed significant differences between the TG and WG in depression (T257 = −4.89, p < 0.001, d = 0.70) and QoL (T257 = 3.39, p < 0.001, d = 0.47). Significant differences regarding anxiety symptoms (T257 = 3.25, p < 0.05, d = 0.53) were identified for the completer sample. The general dropout rate was 39.9%. The results indicate the feasibility and efficacy of an internet‐based cognitive behavioural writing intervention in adults from Arabic‐speaking countries. The development and implementation of such interventions can be used to improve access to psychological help and adequate treatment. [ABSTRACT FROM AUTHOR]

Qualitative investigations that openly explore changes and facilitators of changes from the patient’s perspective might offer valuable insights on impacts of therapy and helpful and hindering aspects. Our aim for this study was to explore the perspective of patients on a transdiagnostic Internet-based intervention to understand (1) which changes (positive as well as negative effects) responders and non-responders experienced, and (2) which aspects of the intervention they found helpful or hindering in facilitating those changes. We interviewed 21 patients that showed response or non-response to treatment using the Change Interview Schedule following a 10-week Internet-based intervention based on the Unified Protocol. Interviews were analyzed using qualitative content analysis. Both responders and non-responders reported positive changes, with few negative changes mentioned. Across both groups, increased positive affect was reported most frequently (81%). Both groups reported helpful factors, with guidance mentioned most frequently across both responders and non-responders (85.7%). Mainly, aspects of the specific framework were perceived as hindering (e.g. lack of personalization) (66.7%). Overall, patients reported mostly positive impacts, even if they did not respond to treatment. Results highlighted that what patients find helpful or hindering is individual. [ABSTRACT FROM AUTHOR]

Objective: Depressive symptom dynamics, including change trajectories and symptom variability, have been related to therapy outcomes. However, such dynamics have often been examined separately and related to outcomes of interest using two-step analyses, which are characterized by several limitations. Here, we show how to overcome these limitations using location-scale models in a dynamic structural equation modeling framework. Method: We introduce location-scale modeling in an accessible manner to pave the way for its use in research integrating within-person dynamics and intervention-related change in psychopathology, and we illustrate this modeling approach in a large-scale internet-based intervention for depression (N = 1,656). Using eight data points sampled across about 8 weeks, we predicted improvement across the intervention (50% symptom reduction) as a function of early change and symptom variability. Results: Early symptom change was associated with a more likely improvement across therapy. Variability of symptoms beyond change trajectories during the intervention was associated with less likely improvement. Conclusions: Location-scale models, and dynamic structural equation modeling more generally, are well suited to modeling how patterns of symptom change during psychotherapy are related to important (e.g., therapy) outcomes. Our illustrative application of location-scale modeling showed that symptom variability was associated with less overall improvement in depressive symptoms. However, this finding requires replication with more intensive sampling of symptoms before final conclusions can be drawn on when and how to distinguish maladaptive from adaptive variability during psychotherapy. What is the public health significance of this article?: During psychotherapy, multiple mechanisms of change should work simultaneously and in concert. As this study highlights, it is important to examine multiple types of symptom dynamics during psychotherapy simultaneously to understand the processes and ultimately the causes of therapeutic change. [ABSTRACT FROM AUTHOR]

Author(s): Pim Cuijpers, Eva Heim, Jinane Abi Ramia, Sebastian Burchert, Kenneth Carswell, Ilja Cornelisz, Christine Knaevelsrud, Philip Noun, Chris van Klaveren, Edith van't Hof, Edwina Zoghbi, Mark van Ommeren, Rabih [...]

Background: In recent years, a growing body of evidence has demonstrated the efficacy of non-pharmacological interventions for schizophrenia spectrum disorders (SSD) including positive symptoms such as auditory hallucinations (AH). However, clinical trials predominantly examine general treatment effects for positive symptoms. Therefore, previous research is lacking in comprehensive and clear evidence about psychological and psychosocial approaches that are primarily tailored to treat AH. To overcome this knowledge gap in the current literature, we will conduct a systematic review and meta-analysis to assess the efficacy of clearly targeted psychological and psychosocial interventions for AH in persons with SSD. Methods and analysis: This study protocol has been developed according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols. We will include all randomized controlled trials analyzing the efficacy of targeted psychological and psychosocial interventions especially aimed at treating AH in SSD. We will include studies on adult patients with SSD experiencing AH. The primary outcome will be the change on a published rating scale measuring AH. Secondary outcomes will be delusions, overall symptoms, negative symptoms, depression, social functioning, quality of life, and acceptability (drop-out). We will search relevant databases and the reference lists of included literature. The study selection process will be conducted by two independent reviewers. We will conduct a random-effect meta-analysis to consider heterogeneity across studies. Analyses will be carried out by software packages in R. The risk of bias in each study will be evaluated using the Cochrane Risk of Bias tool. Assessment of heterogeneity and sensitivity analysis will be conducted. Discussion: The proposed study will augment the existing evidence by providing an overview of effective treatment approaches and their overall efficacy at treating AH in SSD. These findings will complement existing evidence that may impact future treatment implementations in clinical practice by addressing effective strategies to treat AH and therefore improve outcomes for the addressed population. Ethics and dissemination: No ethical issues are foreseen. We will publish the results from this study in peer-reviewed journals and at relevant scientific conferences. Trial registration: PROSPERO registration number:CRD42023475704. [ABSTRACT FROM AUTHOR]

Background: Menstrual cycle regularity is an important marker of reproductive health and associated with physiological and psychological illnesses, as well as experiencing stress. We hypothesized that individuals with irregular menstrual cycles report higher depressive symptom severity, after controlling for stress occurrence. Methods: The hypothesis was examined through two measurement approaches: a cross-sectional and a prospective, longitudinal study. In the cross-sectional study, participants (n = 394) reported depressive symptoms and their overall menstrual cycle regularity. In the longitudinal study, participants (n = 77) completed questionnaires on depressive symptoms and stress during the mid-follicular and periovulatory phase of one menstrual cycle. Depressive symptoms were compared between participants with regular and irregular cycles through a Welch t test and an ANCOVA. Results: Participants with irregular menstrual cycles reported more depressive symptoms in the cross-sectional analysis. Similarly, in the longitudinal analysis, the group with a current irregular menstrual cycle reported more depressive symptoms after controlling for stress occurrence. When including only complete data sets without multiple imputation (n = 52), the direction of the effects remained but did not reach statistical significance. Conclusions: The results indicate an association between depressive symptoms and menstrual cycle irregularity. Limitations were that although we investigated the menstrual cycle prospectively, it would have been more precise to include two or more cycles and daily sex hormone measurements. Further limitations were the suboptimal statistical power and the data collection during the COVID pandemic. We give recommendations on how to incorporate the association of depressive symptoms and cycle irregularity in future study designs on women's mental health. [ABSTRACT FROM AUTHOR]

Background: Internet-based interventions produce comparable effectiveness rates as face-to-face therapy in treating depression. Still, more than half of patients do not respond to treatment. Machine learning (ML) methods could help to overcome these low response rates by predicting therapy outcomes on an individual level and tailoring treatment accordingly. Few studies implemented ML algorithms in internet-based depression treatment using baseline self-report data, but differing results hinder inferences on clinical practicability. This work compares algorithms using features gathered at baseline or early in treatment in their capability to predict non-response to a 6-week online program targeting depression. Methods: Our training and test sample encompassed 1270 and 318 individuals, respectively. We trained random forest algorithms on self-report and process features gathered at baseline and after 2 weeks of treatment. Non-responders were defined as participants not fulfilling the criteria for reliable and clinically significant change on PHQ-9 post-treatment. Our benchmark models were logistic regressions trained on baseline PHQ-9 sum or PHQ-9 early change, using 100 iterations of randomly sampled 80/20 train-test-splits. Results: Best performances were reached by our models involving early treatment characteristics (recall: 0.75–0.76; AUC: 0.71–0.77). Therapeutic alliance and early symptom change constituted the most important predictors. Models trained on baseline data were not significantly better than our benchmark. Conclusions: Fair accuracies were only attainable by involving information from early treatment stages. In-treatment adaptation, instead of a priori selection, might constitute a more feasible approach for improving response when relying on easily accessible self-report features. Implementation trials are needed to determine clinical usefulness. [ABSTRACT FROM AUTHOR]