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Background: Temstem is a smartphone app developed with and for clinical voice hearing individuals with the aim to reduce their voice hearing distress and improve social functioning., Methods: A randomized controlled trial with adult outpatients suffering from distressing and frequent auditory verbal hallucinations (AVH) was conducted. Participants were randomized to unguided 'Temstem+AVH monitoring' or unguided 'AVH monitoring only' (control condition). Assessments were performed at baseline, post-intervention (week 5-6), and follow-up (week 9-10). Primary outcomes were voice hearing distress and social functioning, as measured with Experience Sampling Method (ESM), consisting of multiple daily questionnaires during six days. In addition, voices and mood were self-monitored with help of a daily reflective questionnaire. Analyses were linear regression models (intention-to-treat)., Results: 44 Participants were allocated to Temstem and 45 to the control condition. No significant differences between the groups were found on both primary outcomes., Conclusion: Our results do not support the effectiveness of stand-alone use of Temstem versus symptom monitoring on voice hearing distress or social functioning in voice hearing individuals. In order to potentially improve effectiveness of an mHealth tool in a population of people with frequent and distressing voices, we recommend to involve persons with lived experience in all stages of development and research; to thoroughly test the (technological) usability before performing an RCT; to test whether guidance of a therapist is needed to optimize effectiveness; and to provide prompts to remind the user to actually use the tool., Competing Interests: None., (© 2024 The Authors.)

Background: Temstem is a mobile application developed in cooperation with voice-hearing persons to help them cope with distressing voices. After psychoeducation about voice hearing, Temstem offers two functions: Silencing is a mode designed to inhibit voice activity through the processing of incompatible language; the Challenging mode introduces dual tasking (as used in eye movement desensitisation and reprocessing) designed to reduce the emotionality and vividness of a voice memory. Two different language games, Lingo Tapper and Word Link, are provided, containing both functions. This study aimed to explore the momentary effects of Temstem on voice-hearing distress, emotionality and vividness in a naturalistic sample of voice-hearing app users., Method: Temstem is freely available in the Netherlands. We collected data through the app from 1048 individual users who had given informed consent for the study. We assessed changes in pre- and post-session scores on distress, emotionality and vividness, and we evaluated differences in outcomes between the games and whether effects remained stable over multiple sessions., Results: Users had been hearing voices for an average of 4.95 years; 79 % had been informed about Temstem by a mental health therapist or coach. After a Silencing session, voice-hearing distress was reduced, t (958) = 27.12, p  < .001, d  = 0.49; the degree of reduction remained stable after repeated use, F (1, 7905.57) = 1.91, p  = .167. After a Challenging session, emotionality, t (651) = 23.16, p  < .001, d  = 0.74, and the vividness of voice memories were reduced, t (651) = 22.20, p  < .001, d  = 0.71; both diminished slightly with frequent use, F (1, 2222.86) = 7.21, p  < .05; F (1, 2289.92) = 4.25, p  < .05. In comparison with Lingo Tapper, larger reductions were seen for a Word Link game: emotionality t (226) = 2.88, p  < .005, d  = 0.21; vividness t (226) = 2.29, p  < .05, d  = 0.17., Discussion: In this heterogeneous sample of voice-hearing individuals, Temstem appeared to be a promising coping tool; momentary voice-hearing distress and the emotionality and vividness of voice statements were reduced after a Temstem session. Despite important limitations and the need for more research, naturalistic studies of user app data may yield interesting and generalisable findings., Competing Interests: None., (© 2022 The Authors.)

Background: Personality disorders (PDs) form a substantial part of the mental health disease burden. Effective therapies to treat PDs exist, but they are time-consuming, costly, and difficult to scale up. Delivery through the internet could facilitate the scalability of effective treatment methods., Objective: This review summarizes existing evidence on internet-delivered psychotherapy for personality disorders., Methods: Because few randomized controlled trials (RCTs) have been carried out, we conducted a scoping review. We performed a systematic literature search in PubMed, Embase, MEDLINE, CINAHL, PsycInfo, and Cochrane. Studies were selected if they conveyed research findings on internet-delivered PD interventions., Results: Eleven studies were included. The majority ( n  = 8) focused specifically on borderline personality disorder (BPD) and the other three on PD in general. The most frequently used form of intervention ( n  = 7) was the addition of a mobile app to a conventional evidence-based face-to-face treatment such as dialectical behavioral therapy (DBT). Most interventions ( n  = 8) were still in the development and piloting phase; only two RCTs were found. Usability and patient satisfaction were moderate to high in all studies. Three studies demonstrated significant decreases in borderline personality disorder symptoms.The majority of the studies found were pilot or feasibility studies, most involving mobile apps offered in addition to face-to-face treatment. The add-ons were rated feasible, acceptable, and useful by patients. Reported challenges involved technical difficulties such as programming errors and bugs. Only 45% of the included studies reported on changes in PD symptoms, all showing reduction of symptoms and absence of adverse effects., Conclusions: This scoping review found that internet interventions for PD are still under-researched, although initial outcomes show promise. The outcomes also encourage future research in terms of developing internet interventions as an add-on to existing treatments, as well as working toward the creation and testing of more encompassing internet-delivered treatments for PD., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Background: Although depression is typically characterized by a persistent depressed mood, mood dynamics do seem to vary across a depressed population. Heterogeneity of mood variability (magnitude of changes) and emotional inertia (speed at which mood shifts) is seen in clinical practice. However, studies investigating the heterogeneity of these mood dynamics are still scarce. The aim of the present study is to explore different distinctive profiles in real-time monitored mood dynamics among depressed persons., Methods: After completing baseline measures, mildly-to-moderately depressed persons ( n  = 37) were prompted to rate their current mood (1-10 scale) on their smartphones, 3 times a day for 7 consecutive days. Latent profile analyses were applied to identify profiles based on average mood, variability of mood and emotional inertia as reported by the participants., Results: Two profiles were identified in this sample. The overwhelming majority of the sample belonged to profile 1 ( n  = 31). Persons in profile 1 were characterized by a mood just above the cutoff for positive mood (M = 6.27), with smaller mood shifts (lower variability [SD = 1.05]) than those in profile 2 ( n  = 6), who displayed an overall negative mood (M = 4.72) and larger mood shifts (higher variability [SD = 1.95]) but at similar speed (emotional inertia) (AC = 0.19, AC = 0.26, respectively)., Conclusions: The present study provides preliminary indications for patterns of average mood and mood variability, but not emotional inertia, among mildly-to-moderately depressed persons., Competing Interests: None., (© 2021 The Authors.)

Blended cognitive-behavioural therapy (bCBT) combines face-to-face CBT (FtFCBT) and Internet-based CBT (iCBT) into one integrated treatment protocol, opening up new ways to deliver therapy, increase cost-effectiveness and resolve scarcity of therapist availability. When traditional therapy is transformed into a new format, there is a need to evaluate whether principles of the new protocol are consistently applied. This study aimed to explore therapist fidelity to bCBT protocols for anxiety disorders in specialised mental health care and to assess whether fidelity is related to patient characteristics. Adult patients ( N  = 44) received bCBT within a randomised controlled trial. Ratio of FtF to online sessions, session frequency and therapist adherence to instructions were assessed. Overall therapist fidelity with regard to ratio of blending, session frequency and instructions was high. Correlations were found between patients' share of online sessions and both session frequency ( r  = 0.373, p  = .013), as well as patient computer experience ( r  = 0.314, p  = .038). Adherence to instructions in FtF sessions was based on a subset of patients ( n  = 23) and should therefore be interpreted with caution. The blended approach was generally delivered as intended, indicating that the format is feasible in specialised mental health., Competing Interests: The authors report no conflicts of interest related to this publication., (© 2021 The Authors.)

Since the emergence of psychological interventions delivered via the Internet they have differed in numerous ways. The wealth of formats, methods, and technological solutions has led to increased availability and cost-effectiveness of clinical care, however, it has simultaneously generated a multitude of terms. With this paper, we first aim to establish whether a terminology issue exists in the field of Internet-delivered psychological interventions. If so, we aim to determine its implications for research, education, and practice. Furthermore, we intend to discuss solutions to mitigate the problem; in particular, we propose the concept of a common glossary. We invited 23 experts in the field of Internet-delivered interventions to respond to four questions, and employed the Delphi method to facilitate a discussion. We found that experts overwhelmingly agreed that there were terminological challenges, and that it had significant consequences for conducting research, treating patients, educating students, and informing the general public about Internet-delivered interventions. A cautious agreement has been reached that formulating a common glossary would be beneficial for the field to address the terminology issue. We end with recommendations for the possible formats of the glossary and means to disseminate it in a way that maximizes the probability of broad acceptance for a variety of stakeholders., (© 2020 The Authors.)

Background: Mental health symptoms are common among college and university students and these can affect their academic performance. E-mental health interventions have proven effective in addressing mental health complaints but their effect on academic performance has not been synthesized yet., Objectives: To synthesize the evidence from randomized controlled trials for the effectiveness of e-mental health interventions on academic performance in college and university students compared to inactive controls., Data Sources and Eligibility Criteria: We searched six databases (PubMed, Cochrane library, CINAHL, ERIC, PsycINFO, Web of Science) during the period January 2000 until September 2019 for randomized controlled trials that reported on e-mental health interventions (guided or unguided) for college and university students and measured academic performance (e.g. grade point average)., Study Appraisal and Synthesis Methods: Study and participant characteristics and the academic performance measures at post-intervention were extracted. The latter were pooled and Hedges' g was calculated as the effect size. Heterogeneity and publication bias were investigated., Results: Six studies containing 2428 participants were included in the meta-analysis. These focussed on either mood and anxiety or alcohol and tobacco use. The pooling of data resulted in a small but non-significant effect of g  = 0.26 (95% CI, -0.00, 0.52; p  = .05) on academic performance, favouring e-mental health interventions over inactive controls. Interventions had positive effects on depression ( g  = -0.24) and anxiety ( g  = -0.2). Heterogeneity was high., Discussion: Despite the small and non-significant effect, our meta-analysis points to a promising direction for the effectiveness of e-mental health interventions on academic performance. Yet, these results must be interpreted with caution, as heterogeneity was high and few studies on the effectiveness of e-mental health interventions for students reported academic performance measures., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Ebert has served as a consultant to/on the scientific advisory boards of Sanofi, Novartis, Minddistrict, Lantern, Schoen Kliniken, Ideamed and German health insurance companies (BARMER, Techniker Krankenkasse) and a number of federal chambers for psychotherapy. He is also stakeholder of the Institute for health training online (GET.ON), which aims to implement scientific findings related to digital health interventions into routine care., (© 2020 The Authors.)

Background: Panic disorder with and without agoraphobia (PD) is a common psychological disorder. Internet-based interventions have the potential to offer highly scalable low-threshold evidence-based care to people suffering from PD. GET.ON Panic is a newly developed internet-based intervention addressing symptoms of PD. In order to transfer the training into the daily life of the individuals, we integrated mobile components in the training and created a so-called hybrid online training. The development and beta-testing of such a training requires a novel interdisciplinary approach between IT specialists and psychologists. From this point of view, we would like to share our experiences in this exploratory paper., Methods: This initial feasibility study ( N  = 10) offers, on the one hand, a brief overview of the interdisciplinary development phase of the mobile application and on the other hand, provides first insights into the usage, usability and acceptance of this mobile application using qualitative interview data as well quantitative measures of 8 completing participants. For these reasons, we used a pre-posttest design without a control group. Furthermore, we present initial clinical outcomes of the intervention on e.g. panic symptom severity, depressive symptoms as well additional anxiety measures. Finally, we end with implications for further research in the relatively new field of mobile mental health., Results: Overall, usability, user satisfaction, motivational value and technology acceptance of the app were perceived as high. The usage of app components was diverse: The use of interoceptive exposure exercises and daily summaries on anxiety and mood was highest while using in-vivo exposure exercises and monitoring panic symptoms was perceived as difficult. Furthermore, participants showed after the training less clinical symptoms as at baseline-assessment., Discussion: The current feasibility study contributes to an in-depth understanding of the potential of mobile technology in e-mental health. Overall, the GET.ON Panic app appears to be an acceptable and motivational part of a CBT-based hybrid online training for PD that has the potential to promote training success. After some suggested adjustments have been made, the efficacy should be investigated in a randomized controlled trial., Competing Interests: DL, DE and BF are shareholders, LE is an employee of the transfer institute (GET.ON Institute) that currently commercializes the GET.ON Panic training. However, during developing and evaluating the app and the hybrid online training GET.ON Panic the company was not yet incorporated., (© 2019 The Authors.)

Background: There is an increasing prevalence of mental health problems among university student populations. In Indonesia, treatment rates are low among university students because of the high cost of treatment, stigma and the limited availability of mental care services. Internet-based interventions have been found to be effective in treating anxiety and depression in Western countries. However, little is known about the effectiveness of Internet-based interventions in low- and middle-income countries (LMICs) such as Indonesia. In this paper, we describe how we culturally adapted an Internet-based intervention to meet the needs of Indonesian students with depression and anxiety based on the theoretical framework of Barrera et al. (2013)., Method: We culturally adapted a Western Internet-based student intervention for depression and anxiety. This intervention consists of 8 guided online sessions. 50 students from Universitas Gadjah Mada Yogyakarta who have mild to moderate depression or anxiety as assessed with the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), will participate in a feasibility study in which we will test participants' satisfaction, system usability and uptake. Secondary outcomes include assessment of participants' depression, anxiety and quality of life. Outcomes will be measured pre and post intervention., Discussion: The present paper presents the protocol of a pilot study aimed at assessing the feasibility and acceptability of a culturally adapted intervention for Indonesian university students with depression and anxiety. The results from the feasibility study will further guide the development of the intervention and may inform the protocol of a future randomized controlled trial (RCT) examining the effectiveness of the Internet-based intervention.

In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

Background: It remains difficult to predict and prevent suicidal behaviour, despite growing understanding of the aetiology of suicidality. Clinical guidelines recommend that health care professionals develop a safety plan in collaboration with their high-risk patients, to lower the imminent risk of suicidal behaviour. Mobile health applications provide new opportunities for safety planning, and enable daily self-monitoring of suicide-related symptoms that may enhance safety planning. This paper presents the rationale and protocol of the Continuous Assessment for Suicide Prevention And Research (CASPAR) study. The aim of the study is two-fold: to evaluate the feasibility of mobile safety planning and daily mobile self-monitoring in routine care treatment for suicidal patients, and to conduct fundamental research on suicidal processes., Methods: The study is an adaptive single cohort design among 80 adult outpatients or day-care patients, with the main diagnosis of major depressive disorder or dysthymia, who have an increased risk for suicidal behaviours. There are three measurement points, at baseline, at 1 and 3 months after baseline. Patients are instructed to use their mobile safety plan when necessary and monitor their suicidal symptoms daily. Both these apps will be used in treatment with their clinician., Conclusion: The results from this study will provide insight into the feasibility of mobile safety planning and self-monitoring in treatment of suicidal patients. Furthermore, knowledge of the suicidal process will be enhanced, especially regarding the transition from suicidal ideation to behaviour.The study protocol is currently under revision for medical ethics approval by the medical ethics board of the Vrije Universiteit Medical centre Amsterdam (METc number 2017.512/NL62795.029.17).

Background: Depression and anxiety are highly prevalent and often co-occur. Several studies indicate the potential of disorder-specific psychological interventions for the prevention of each of these disorders. To treat comorbidity, transdiagnostic treatment concepts seem to be a promising approach, however, evidence for transdiagnostic concepts of prevention remains inconclusive. Internet- and mobile-based interventions (IMIs) may be an effective means to deliver psychological interventions on a large scale for the prevention of common mental disorders (CMDs) such as depression and anxiety. IMIs have been shown to be effective in treating CMDs, e.g. in reducing symptoms of depression and anxiety. However, there is a lack of studies examining the efficacy of interventions reducing the incidence of CMDs. Moreover, the comparative cost-effectiveness of guided versus unguided IMIs for the prevention of depression and anxiety has not been studied yet. Hence, this study aims at investigating the (cost-) effectiveness of guided and unguided internet- and mobile-based transdiagnostic individually tailored indicated prevention of depression and anxiety., Methods: A multi-country three-armed randomized controlled trial will be conducted to compare a guided and unguided intervention to treatment as usual (TAU). Both active conditions are based on the same intervention, ICare Prevent , and differ only with regard to guidance format. Altogether, 954 individuals with subclinical symptoms of depression (CES-D ≥ 16) and anxiety (GAD-7 ≥ 5) who do not have a full-blown disorder will be recruited in Germany, Switzerland, Spain and the Netherlands, and randomized to one of three conditions (guided intervention, unguided intervention, or TAU). The TAU arm will receive access to the training after a 12-month waiting period. The primary outcome will be time to CMD onset (any depression/anxiety disorder) within a follow-up period of 12 months after baseline. Secondary outcomes will include disorder-specific symptom severity (depression/anxiety) assessed by diagnostic raters blinded to intervention condition at post-intervention, self-reports, acceptability, health related quality of life, and psychosocial variables associated with developing a CMD. Assessments will take place at baseline, mid-intervention (5 weeks into the intervention), post-intervention (8 weeks after randomization) and follow-up (6 and 12 months after randomization). Data will be analyzed on an intention-to-treat basis and per protocol. Cost-effectiveness will be evaluated from a public health and a societal perspective, including both direct and indirect costs., Discussion: The present study will further enhance the evidence-base for transdiagnostic preventive interventions and provide valuable information about optimal trade-off between treatment outcome and costs., Trial Registration: German Clinical Trial Registration (DRKS - http://www.drks.de/drks&#95;web/): DRKS00011099.

Introduction: Clinical trials of blended Internet-based treatments deliver a wealth of data from various sources, such as self-report questionnaires, diagnostic interviews, treatment platform log files and Ecological Momentary Assessments (EMA). Mining these complex data for clinically relevant patterns is a daunting task for which no definitive best method exists. In this paper, we explore the expressive power of the multi-relational Inductive Logic Programming (ILP) data mining approach, using combined trial data of the EU E-COMPARED depression trial., Methods: We explored the capability of ILP to handle and combine (implicit) multiple relationships in the E-COMPARED data. This data set has the following features that favor ILP analysis: 1) Time reasoning is involved; 2) there is a reasonable amount of explicit useful relations to be analyzed; 3) ILP is capable of building comprehensible models that might be perceived as putative explanations by domain experts; 4) both numerical and statistical models may coexist within ILP models if necessary. In our analyses, we focused on scores of the PHQ-8 self-report questionnaire (which taps depressive symptom severity), and on EMA of mood and various other clinically relevant factors. Both measures were administered during treatment, which lasted between 9 to 16 weeks., Results: E-COMPARED trial data revealed different individual improvement patterns: PHQ-8 scores suggested that some individuals improved quickly during the first weeks of the treatment, while others improved at a (much) slower pace, or not at all. Combining self-reported Ecological Momentary Assessments (EMA), PHQ-8 scores and log data about the usage of the ICT4D platform in the context of blended care, we set out to unveil possible causes for these different trajectories., Discussion: This work complements other studies into alternative data mining approaches to E-COMPARED trial data analysis, which are all aimed to identify clinically meaningful predictors of system use and treatment outcome. Strengths and limitations of the ILP approach given this objective will be discussed.

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

Background: Mental health problems are common and place a burden on the individual as well as on societal resources. Despite the existence of evidence-based treatments, access to treatment is often prevented or delayed due to insufficient health care resources. Effective internet-based self-help interventions have the potential to reduce the risk for mental health problems, to successfully bridge waiting time for face-to-face treatment and to address inequities in access. However, little is known about the cost-effectiveness of such interventions. This paper describes the study protocol for the economic evaluation of the studies that form the ICare programme of internet-based interventions for the prevention and treatment of a range of mental health problems., Methods: An overarching work package within the ICare programme was developed to assess the cost-effectiveness of the internet-based interventions alongside the clinical trials. There are two underlying tasks in the ICare economic evaluation. First, to develop schedules that generate equivalent and comparable information on use of services and supports across seven countries taking part in clinical trials of different interventions and second, to estimate unit costs for each service and support used. From these data the cost per person will be estimated by multiplying each participant's use of each service by the unit cost for that service. Additionally, productivity losses will be estimated. This individual level of cost data matches the level of outcome data used in the clinical trials. Following the analyses of service use and costs data, joint analysis of costs and outcomes will be undertaken to provide findings on the relative cost-effectiveness of the interventions, taking both a public sector and a societal perspective. These analyses use a well-established framework, the Production of Welfare approach, and standard methods and techniques underpinned by economic theory., Discussion/conclusion: Existing research tends to support the effectiveness of internet-based interventions, but there is little information on their cost-effectiveness compared to 'treatment as usual'. The economic evaluation of ICare interventions will add considerably to this evidence base.

Introduction: Blended therapies (BT) combine face-to-face (f2f) sessions with internet- and mobile-based interventions (IMIs). However, the use of blended interventions in routine care is still rare and depends on the acceptance of key health care professionals such as the therapists. Little is yet known about the therapists' perspective on and experiences with blended approaches. The aim of this pilot study was to identify barriers and facilitators, as perceived by psychotherapists, for implementing a blended therapy for depression., Methods: Semi-structured expert interviews were conducted with five therapists, who were part of the German study arm of the FP7-project E-Compared (www.e-compared.eu). All patients ( N  = 173) were treated in the context of a registered RCT (DRKS00006866) in which the clinical and cost-effectiveness of BT for depression, consisting of ten internet- and mobile-based cognitive behavioral therapy modules and six f2f sessions, was compared to the treatment usually provided by general practitioners. To identify barriers and facilitators an interview guide based on the theoretical domains framework (TDF) was developed. The interviews were audio-recorded, transcribed verbatim and analyzed using a qualitative content analysis by two independent coders., Results: The results revealed 29 barriers and 33 facilitators, which are hindering or enabling factors on the levels of 'implementation in the health care system', 'therapy', 'therapists' and 'patients'. Key barriers stated by all therapists were 'Limited customizability and autonomy of decisions concerning blending the therapy' (number of statements, k  = 44); 'Disease-related contraindications for BT' ( k  = 25); 'Negative affect was caused by burden through technical problems' ( k  = 18); 'Limited number of f2f sessions hindered the therapy process'; and 'Establishment of therapeutic alliance was burdened by technical issues' (each k  = 15). Key facilitators stated by all therapists were: 'Patients' interest, willingness and motivation to participate' ( k  = 22); 'Patients' access to online content between f2f sessions and after therapy end' ( k  = 20); 'Preset structure of IMI-part guided the treatment course of BT' ( k  = 18); and 'Effective help with BT in a short time frame' ( k  = 15), as well as 'Reduction of the treatment gap' ( k  = 13)., Discussion: Therapists supported the implementation of BT for depression. Results indicated the consideration of a wide range of determinants: among others, the possibility of individualizing the treatment; the autonomy of decision making in respect to the ratio and number of online and f2f sessions; the necessity of providing training; the need to develop a concept of embedding BT in the health care system and funding the additional effort; and the use of sophisticated technical solutions.

Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA) but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place. In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE)). We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425 days of EMA data) who were diagnosed with a major depressive disorder according to DSM-IV. Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11). The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant. Short term mood prediction remains a difficult task, but from this research we can conclude that sophisticated machine learning algorithms/setups can result in accurate performance. For future work, we want to use more data from the mobile phone to improve predictive performance of short term mood.

In this paper we introduce a new Android library, called ULTEMAT, for the delivery of ecological momentary assessments (EMAs) on mobile devices and we present its use in the MoodBuster app developed in the H2020 E-COMPARED project. We discuss context-aware, or event-based, triggers for the presentation of EMAs and discuss the potential they have to improve the effectiveness of mobile provision of mental health interventions as they allow for the delivery of assessments to the patients when and where these are most appropriate. Following this, we present the abilities of ULTEMAT to use such context-aware triggers to schedule EMAs and we discuss how a similar approach can be used for Ecological Momentary Interventions (EMIs).

Background: The integration of digital treatments into national mental health services is on the agenda in the European Union. The E-COMPARED consortium conducted a survey aimed at exploring stakeholders' knowledge, acceptance and expectations of digital treatments for depression, and at identifying factors that might influence their opinions when considering the implementation of these approaches., Method: An online survey was conducted in eight European countries: France, Germany, Netherlands, Poland, Spain, Sweden, Switzerland and The United Kingdom. Organisations representing government bodies, care providers, service-users, funding/insurance bodies, technical developers and researchers were invited to participate in the survey. The participating countries and organisations reflect the diversity in health care infrastructures and e-health implementation across Europe., Results: A total of 764 organisations were invited to the survey during the period March-June 2014, with 175 of these organisations participating in our survey. The participating stakeholders reported moderate knowledge of digital treatments and considered cost-effectiveness to be the primary incentive for integration into care services. Low feasibility of delivery within existing care services was considered to be a primary barrier. Digital treatments were regarded more suitable for milder forms of depression. Stakeholders showed greater acceptability towards blended treatment (the integration of face-to-face and internet sessions within the same treatment protocol) compared to standalone internet treatments. Organisations in countries with developed e-health solutions reported greater knowledge and acceptability of digital treatments., Conclusion: Mental health stakeholders in Europe are aware of the potential benefits of digital interventions. However, there are variations between countries and stakeholders in terms of level of knowledge about such interventions and their feasibility within routine care services. The high acceptance of blended treatments is an interesting finding that indicates a gradual integration of technology into clinical practice may fit the attitudes and needs of stakeholders. The potential of the blended treatment approach, in terms of enhancing acceptance of digital treatment while retaining the benefit of cost-effectiveness in delivery, should be further explored., Funding: The E-COMPARED project has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603098.

Introduction: Although older adults are just as likely to benefit from e-mental health as their younger counterparts, there are virtually no applications specifically designed to accommodate the needs of older adults with recurrent depression or bipolar disorder. Recurrent mood disorders constitute a large and rising proportion of the global disease in older populations, indicating a need for more e-mental health applications targeting this group. This paper describes the theoretical background and methodology of a study examining the feasibility of a tablet-based self-management platform for older adults with recurrent mood disorders. The eCare@Home platform was designed to 1) improve patients' awareness and knowledge of recurrent mood disorders and their treatment, 2) promote self-management through the use of a simple daily monitoring tool, and 3) facilitate online contact with their clinician through videoconferencing., Methods: The design involves a single-group four-month pilot study, with measurements at baseline (T0), and at weeks 8 and 16 (T1 and T2). The target group consists of older outpatients (aged 60 or above) who are undergoing treatment for recurrent depressive or bipolar disorder (N = 50), and their clinicians (N = 10). Primary feasibility endpoints will be system acceptability, system usability, and client satisfaction with the platform. In addition, qualitative data from semi-structured interviews in N = 10 patients and N = 5 clinicians will be gathered to provide more insight into user experiences and evaluations of the platform's added value., Discussion: To the best of our knowledge, this is the first study to evaluate the feasibility and acceptability of a tablet-based e-mental health platform for older adults with severe mood disorders. If tablet-based support for this group is shown to be feasible, the intention is to proceed with the design of a large-scale process and outcome evaluation. The strengths and limitations of the methodology used are addressed in this article. Trial R egistration: registration is pending.

Ample studies have demonstrated that internet-based cognitive behavioural therapy (iCBT) for anxiety disorders is effective and acceptable in controlled settings. Studies assessing the clinical effectiveness of iCBT for anxiety disorders among routine care populations are, however, not as numerous. The purpose of this study was to assess the effectiveness of iCBT among anxiety patients, who were on a waiting list for intensive outpatient treatment, in a specialised routine care clinic. A randomised controlled pilot trial was conducted. Recruited patients were on a waiting list and had a primary diagnosis of either social phobia or panic disorder. Participants were randomised into either receiving iCBT with minimal therapist contact (received access to the programme FearFighter® (FF) and received support from a clinician via telephone) or no treatment (stayed on the waiting list). The primary outcome was self-reported symptomatic change of anxiety on Beck Anxiety Inventory (BAI). The secondary outcomes were comorbid depression measured on Beck Depression Inventory (BDI-II) and quality of life measured with the EuroQol one-item visual-analogue scale (EQ-vas). All results were analysed by intention-to-treat analyses using a mixed-effects approach. N  = 158 patients were assessed for eligibility of which N  = 67 met all eligibility inclusion criteria, signed informed consent forms, and were randomised. Post-treatment assessment was completed by N  = 47 (70%). In the intervention group, N  = 11 (31%) completed all modules of FF. No significant differences of change of symptomatic levels were found between the intervention and control group for anxiety (BAI: mean diff. = 2.42; 95% CI - 1.03 to 5.86; p  = 0.17; d  = 0.06) or for depression (BDI-II: mean diff. 1.87; 95% CI - 2.25 to 6.00; p  = 0.37; d  = 0.02). A large and significant effect was found in self-reported quality of life in favour of the experimental group (EQ-vas: mean diff. - 20.88; 95% CI - 30.64 to - 11.11; p  < 0.001; d  = 0.81). This study was not able to document statistically significant clinical effect of iCBT with minimal therapist contact compared to a waiting list control group in a specialised anxiety clinic in routine care. However, a large and significant effect was seen on self-reported quality of life. Although these results offer an interesting perspective on iCBT in specialised care, they should be interpreted with caution, due to the limitations of the study. A large scale fully powered RCT is recommended.

Background: Blended care combines face-to-face treatment with web-based components in mental health care settings. Blended treatment could potentially improve active patient participation, by letting patients work though part of the protocol autonomously. Further, blended treatment might lower the costs of mental health care, by reducing treatment duration and/or therapist contact. However, knowledge on blended care for depression is still limited., Objectives: To develop a blended cognitive behavioural treatment (bCBT) for depressed patients in an outpatient specialized mental health care centre and to conduct a preliminary evaluation of this bCBT protocol., Method: A bCBT protocol was developed, taking recommendations into account from depressed patients (n = 3) and therapists and experts in the field of e-health (n = 18). Next, an initial evaluation of integrated high-intensive bCBT was conducted with depressed patients (n = 9) in specialized mental health care. Patients' clinical profiles were established based on pre-treatment diagnostic information and patient self-reports on clinical measures. Patient treatment adherence rates were explored, together with patient ratings of credibility and expectancy (CEQ) before treatment, and system usability (SUS) and treatment satisfaction after treatment (CSQ-8). During and after treatment, the blended treatment protocol was evaluated in supervision sessions with the participating therapists (n = 7)., Results: Seven out of nine patients started bCBT, of whom five completed ≥ 90% of treatment. System usability was evaluated as being above average (range 63 to 85), and patients were mostly to very satisfied with bCBT (range 16 to 32). Patients reported improvements in depression, health-related quality of life and anxiety. We observed that therapists evaluated the highly structured blended treatment as a helpful tool in providing evidence-based treatment to this complex patient group., Discussion: Although no conclusions can be drawn based on the current study, our observations suggest that a blended CBT approach might shorten treatment duration and has the potential to be a valuable treatment option for patients with severe depression in specialized mental health care settings. Further exploration of the effectiveness of our bCBT protocol by means of a randomized controlled trial is warranted.