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Introduction Digital cognitive behavioral therapy (i-CBT) interventions for the treatment of depression have been extensively studied and shown to be effective in the reduction of depressive symptoms. However, little is known about their effects on suicidal thoughts and behaviors (STB). Information on the impact of digital interventions on STB are essential for patients’ safety because most digital interventions are self-help interventions without direct support options in case of a suicidal crisis. Therefore, we aim to conduct a meta-analysis of individual participant data (IPDMA) to investigate the effects of i-CBT interventions for depression on STB and to explore potential effect moderators. Methods Data will be retrieved from an established and annually updated IPD database of randomized controlled trials investigating the effectiveness of i-CBT interventions for depression in adults and adolescents. We will conduct a one-stage and a two-stage IPDMA on the effects of these interventions on STB. All types of control conditions are eligible. STB can be measured using specific scales (e.g., Beck scale suicide, BSS) or single items from depression scales (e.g., item 9 of the PHQ-9) or standardized clinical interviews. Multilevel linear regression will be used for specific scales, and multilevel logistic regression will be used for treatment response or deterioration, operationalized as a change in score by at least one quartile from baseline. Exploratory moderator analyses will be conducted at participant, study, and intervention level. Two independent reviewers will assess the risk of bias using the Cochrane Risk of Bias Tool 2. Conclusion This IPDMA will harness the available data to assess the effects (response and deterioration) of i-CBT interventions for depression interventions on STB. Information about changes in STB is essential to estimate patients’ safety when engaging in digital treatment formats. Trial registration We will pre-register this study with the open science framework after article acceptance to ensure consistency between online registration and the published trial protocol.

In this project, we aim to investigate the effects of digital cognitive behavioral therapy for depression on suicidal thoughts and behavior by performing a systematic review and meta-analysis of individual participant data. We will explore potential effect modifiers.

Just-In-Time Adaptive Interventions (JITAIs) intend to encourage change by delivering appropriate support, at the right time, at the right place, and only when needed. JITAIs use mobile technologies (e.g., geolocations, mobile questionnaires) to monitor individual’s behaviors and to determine when and which intervention to provide. By taking advantage of new technologies to anticipate and capture a person’s needs at a particular moment, JITAIs have the potential to intervene in ways that traditional interventions cannot. JITAIs have been studied, mainly in areas such as physical activity, weight loss, and smoking cessation. In the domain of mental health, JITAIs are of potential interest because symptoms are often dynamic, dependent on contextual factors, and highly variable within individuals. Although JITAIs have been regarded as a promising ‘future direction’ for interventions in mental health disorders, it is understudied compared with other areas.

Stress is a major problem among university students around the globe as they have to cope with numerous stress factors and transitional life events. Stress prevalence varies across countries; however studies suggest that approximately half of the student body experiences significant levels of stress (Regehr, Glancy, & Pitts, 2013). Even though stress is an advantageous stimulation for human thriving (Alzahem, Van Der Molen, Alaujan, Schmidt, & Zamakhshary, 2011), ongoing high levels of stress may lead to negative outcomes, such as psychological distress, anxiety, depression, physical illness, substance abuse, and impaired work-related and academic performance (Alzahem et al., 2011; Misra & Castillo, 2004; Storrie, Ahern, & Tuckett, 2010). Due to potential burden and negative consequences stress could bring, effective interventions for helping university students alleviate their stress are needed. A variety of interventions have been created to reduce stress level in university students for example psychoeducation, relaxation training, cognitive behavioral therapy (CBT), social support, coping skills training, and mindfulness training (Yusufov, Nicoloro-Santabarbara, Grey, Moyer, & Lobel, 2018). Various meta-analyses and systematic reviews have evaluated such stress reduction interventions both in non-student and student samples. In these studies stress Management Interventions (SMIs) have been found to be effective reducing stress among student population (Davies, Morriss, & Glazebrook, 2014; Regehr et al., 2013; Yusufov et al., 2018), and increasing students’ quality of life (Ribeiro et al., 2018). However, most of the studies have been conducted in Western countries while the potential benefit of SMIs are less well-established for students in Low- and Middle-Income Countries (LMICs). It is also important to examine whether SMIs are as effective among university students in LMICs and this will overcome a current knowledge gap. Studies also show that students from LMIC report additional stressors such as being sole financial providers for their family, living in a war zone or isolated area, inadequate health resources, lack of water, and poor study conditions (Awino & Agolla, 2008; Ogba et al., 2019). These conditions may provide unique stressors for university students from LMIC compared to higher income countries which in turn may influence the impact and effectiveness of treatment. The present study investigates available evidence for the effect of stress management interventions on stress symptoms among university students in LMICs. A quantitative evaluation will follow if we find sufficient studies.

The aim of this study is to investigate the following research questions: 1. What is the overall effectiveness of any psychotherapy in reducing suicidal thoughts and behavior? 2. Which participant characteristics moderate the overall effects of psychotherapy? 3. Which intervention characteristics moderate their effectiveness? In addition, the analyses will be performed in subgroups of interest: different types of psychotherapy and specific psychiatric patient groups. In addition, the analyses will be performed in subgroups of interest (e.g. different types of psychotherapy and specific psychiatric patient groups). This study will be conducted in collaboration with the Australian National Health and Medical Research Council Centre for Research Excellence for Suicide Prevention (CRESP) and the Black Dog Institute and Australian National University, Australia. Two researchers will run systematic search using the databases PubMed, Embase, PsycINFO, Web of Science, Scopus, and The Cochrane Central Register of Controlled Trials. Index and free terms, such as ‘suicide’, ‘self-kill’, ‘self-poison’, ‘RCT’, ‘randomised’, ‘clinical trials’, and ‘psychotherapy’ will be used. Studies of interest will also be extracted from the reference lists of relevant articles. Study extraction and selection will be carried out in the Covidence software. We are studying suicidal thoughts and behavior, i.e. outcomes of suicidal ideation and/or suicidal thoughts, further suicide attempt, death by suicide where applicable, or self-harm leading to hospitalization. We will exclude non-suicidal self-injury (i.e. clearly without the intention to die), as this has been shown to be a distinct phenomenon. We also exclude self harm that did not result in hospitalization. The control conditions to be studied are waiting list, treatment as usual (medication use is allowed), placebo, psychological placebo (supportive therapy), sham version of the intervention. Studies will be excluded when they measure suicidal ideation with only one or few item(s) from a scale developed for a different construct, such as depression or only report on non-suicidal self-injury (i.e. clearly without the intention to die) as this has been shown to be a distinct phenomenon), they were conducted in an inpatient setting, and the participants (or at least the majority of them) were under 18 years old. The authors of eligible studies will be contacted to obtain the IPD of baseline and follow-up measurements.The main analyses will be conducted with post-treatment outcomes.If enough data is available on follow-up measures, additional analyses will be performed for these outcomes.An email requesting the raw data will be sent to the corresponding authors of the articles.If no author has responded 9 weeks after the first email request or if the contacted authors indicate that they do not have access to the data, data will be considered unavailable.When requested, we will also draft a written agreement on data sharing, management, and use.The data will be stored at the server of the VU and is owned by SURE-NET. The IPD-MA will be performed in R package ipdmeta in one stage.Analyses will be performed according to the intention-to-treat principle. Multiple imputations will be conducted within studies under the missing at random assumption. This method will generate 100 imputed datasets using data available across studies (if available, predictors for imputation will be at least baseline suicidality scores, age, sex, and psychiatric disorder).In addition, we will run analyses with complete cases only to examine the differences in results between imputed and complete cases.A multilevel linear regression analysis will be used to assess the effect of the intervention on standardized suicidal ideation scores post-treatment, while controlling for baseline suicidal ideation.Patient-level data will be treated as level 1, and study-level data as level 2.In case of a cluster RCT design, we will add the cluster as a third level. We will use a random effects model to estimate summary intervention effects and to capture unobserved heterogeneity between study populations and study efficacy, respectively.Hedges’ g will be calculated as an effect size measure.A multilevel logistic regression analysis will be used to assess the efficacy of the intervention on dichotomous variables (suicide attempt and death by suicide) post-treatment. RRs and the corresponding 95% confidence intervals will be calculated using the Kenward-Roger variance estimator.Again, a random effects model will be used.We will investigate the moderating effects of patient and intervention characteristics by adding the selected moderators as well as the moderator*intervention interaction terms to the multilevel (logistic) regression analyses, separating within- and across-study interaction effects to avoid ecological bias by centering the patient-level moderator about its mean.Each potential moderator will be added separately to the model.The moderators that show a statistically significant effect will then be tested simultaneously in one model to investigate which moderators have an independent effect on treatment outcome. The above analyses will additionally be performed in subgroups of psychiatric disorder and type of psychotherapy. For types of psychotherapy, subgroup analyses will likely be performed for CBT, DBT, problem-solving therapy (PST), CT, mindfulness-based therapy, and collaborative assessment and management of suicidality (CAMS), as these are the mostly used psychotherapies for suicidality. Regarding psychiatric patient groups, the following subgroups will likely be analyzed: (borderline) personality disorder, depression, substance abuse, post-traumatic stress disorder, psychosis, and sleep disorder. Subgroup analyses will also be performed for different suicidality scales and comparison groups to investigate whether effect sizes differ substantially when using a different scale or control condition. Other subgroup analyses may be performed depending on which factors reach statistical significance in the moderator analyses.

ObjectiveTo examine whether the outcomes of a chat-based suicide-prevention helpline could be improved by training counselors in motivational interviewing (MI).MethodsIn a pre- and post-test design, visitors of a chat-based suicide prevention helpline received either the Five-Phase Model (treatment as usual [TAU]) or MI. They completed a pre- and post-chat questionnaire on several suicide-related risk factors. Linear mixed modeling was used to estimate the effect of the condition. Furthermore, the treatment proficiency of newly trained counselors was assessed using MI-Scope.ResultsA total of 756 visitors and 55 counselors were included in this study. The visitors showed an improvement in suicidal ideation and psychological risk factors after a chat conversation. However, there were no significant differences between the MI and TAU conditions (β = 0.03, 95% CI [−0.23–0.30], p = 0.80). The treatment integrity indices showed that the counselors mostly used MI-consistent techniques but were unable to strategically employ these techniques to evoke enough change talk.ConclusionsMI and TAU led to comparable outcomes in a chat-based suicide prevention helpline. The effectiveness of MI might improve by intensifying or improving the training of counselors, keeping the process of engaging more concise or offering visitors multiple sessions of MI.