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In the study, predictive models for predicting therapy outcome are created using the dataset from E-COMPARED project, which belongs to the so-called type 3 models that use data from the intervention and preintervention phases to predict treatment outcomes, which can help to adapt intervention to maximize treatment. The predictive models aim to classify patients into two groups, improved and nonimproved. Since it is important to determine whether the models contribute to improvement of treatment, research questions that can contribute to the usage of type 3 models are established. The study focuses on the following three questions: (1) How accurately can the therapy outcome be predicted by various machine learning algorithms? Answering this question can let the people concerned obtain information about the reliability of contemporary predictive models. In addition, if the predictive power of the models is good, it is more likely to be used to assist therapists’ decisions. (2) Which kind of data is more important in predicting the therapy outcome? The answer to this question can show which dataset should be considered first to make better predictive models. Therefore, it can be helpful for researchers who want to make predictive models in the future and eventually help to facilitate personalized therapy. (3) What are the features with strong predictive power? The answer to this question can affect the people concerned, especially therapists. Therapists can use the most influential features revealed to adjust and improve future treatments.
Internet- and mobile technologies are increasingly used to deliver mental health care. E-Mental Health is promising for the prevention and treatment of mental disorders. However, while E-Mental Health was shown to be an effective treatment tool, fewer studies investigated the prevention of mental health problems with E-Mental Health approaches. In a series of three studies, this dissertation examines internet- and mobile-based approaches for the early monitoring and supporting of mental health. First, a pilot study investigates the use of smartphone data as collected by daily self-reports and sensor information for the self-monitoring of bipolar disorder symptoms. It was found that some, but not all smartphone measurements predicted clinical symptoms of mania and depression, indicating that smartphones could be used as an earlywarning system for patients with bipolar disorder. Second, a randomized controlled trial evaluates the effectiveness of an internet-based intervention among persons with depression and sickness absence. The intervention was found to be effective in reducing depressive symptoms compared to a control group, suggesting that the internet can provide effective support for people with sickness absence due to depression. Third, a study protocol proposes to combine self-monitoring with a mobile intervention to support mental health in daily life. Supportive self-monitoring will be evaluated in a fully mobile randomized controlled trial among a sample of smartphone users with psychological distress. If supportive self-monitoring on the basis of a smartphone application is effective, it could be widely distributed to monitor and support mental health on a population level. Finally, the contribution of the presented studies to current research topics in E-Mental Health is discussed.