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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. Keywords: M-mental health, Panic disorder, Agoraphobia, Feasibility study, Thematic analyses, Cognitive behavior therapy

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. Keywords: Prediction, Therapy success, E-health, Depression, Classification

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.

Self-esteem is a crucial factor for an individual’s well-being and mental health. Low self-esteem is associated with depression and anxiety. Data about self-esteem is oftentimes collected in Internet-based interventions through Ecological Momentary Assessments and is usually provided on an ordinal scale. We applied models for ordinal outcomes in order to predict the self-esteem of 130 patients based on diary data of an online depression treatment and thereby illustrated a path of how to analyze EMA data in Internet-based interventions. Specifically, we analyzed the relationship between mood, worries, sleep, enjoyed activities, social contact, and the self-esteem of patients. We explored several ordinal models with varying degrees of heterogeneity and estimated them using Bayesian statistics. Thereby, we demonstrated how accounting for patient-heterogeneity influences the prediction performance of self-esteem. Our results show that models that allow for more heterogeneity performed better regarding various performance measures. We also found that higher mood levels and enjoyed activities are associated with higher self-esteem. Sleep, social contact, and worries were significant predictors for only some individuals. Patient-individual parameters enable us to better understand the relationships between the variables on a patient-individual level. The analysis of relationships between self-esteem and other psychological factors on an individual level can therefore lead to valuable information for therapists and practitioners.