In the Netherlands, approximately 1 million children (0-25 years) have a chronic disease. Above and beyond the ever-present challenges of growing up with an illness, these children have 40% chance to develop psychological problems, including depression, anxiety and loneliness. Throughout their life, this translates into decreased well-being and reduced social participation and generates additional costs for society. Early prevention of psychological problems is thus key to break this vicious cycle. Therefore, eHealth applications are promising. However, scientific knowledge is missing and validated tools are not yet available for this group and involved health care professionals. Our mission is to make scientifically validated eHealth tools that allow personalized and trans-diagnostic prevention of psychological problems widely available for this highly vulnerable group of chronically ill children and future adults, through an accessible, user-friendly, safe, and sustainable platform. To succeed in this mission, we present an iterative learning cycle approach in two four-year phases during which we gather the insights, and develop, evaluate, and implement the much needed eHealth tools: I. Development: Distil and validate the theoretical and game-design factors that make eHealth effective for chronically ill children. II. Evaluation: Evaluate trans-diagnostic and personalized eHealth tools for chronically ill children, using and developing state-of-the-art methods. III. Implementation: Study and remove the barriers that currently hinder implementation and uptake, and threaten availability of eHealth applications for chronically ill children. Our eHealth junior consortium includes (applied) researchers, pediatricians, psychiatrists, psychologists, patient organizations, knowledge centers, game designers, industrial designers, insurance companies, and business professionals. We will collaborate with the end-users (children, families, and professionals) in order to achieve both international scientific breakthroughs and optimal clinical and societal impact. Knowledge utilization is a crucial part of our project.

Recent technical developments present new ways to track and reflect upon behaviours related to a healthier lifestyle. For example, individuals can use their smart phones or other interactive devices to measure and alter physical activity or dietary choices. In the proposed project, a recently developed wearable camera is used to gather information on factors that could trigger one?s alcohol use. By doing so, we will set the first step in testing the extent to which lifelogging (the process of tracking personal data generated by own behavioural activities) can be used as input for tailor-made applications to reduce alcohol use. Exposure to alcohol-related cues (e.g., alcohol marketing) has been associated with individual?s drinking levels (i.e., cue-reactivity) in controlled lab settings. Little is known about how cue-reactivity affects individual?s drinking in every-day life contexts. The current project examines the potential value of a wearable automatic camera that unobtrusively captures a picture every 30 seconds, to assess cue-reactivity outside the laboratory. The use of this innovative product makes it possible to follow the user throughout the day and potentially capture the cues that could trigger alcohol consumption. The aim is to examine whether we could establish correlations between alcohol-related cues and subsequent drinking behaviour in ecological-valid settings, providing evidence on the effectiveness of wearable technology to assess cue-reactivity. If cue-reactivity is established, this information will lay foundation for larger collaborative research grants in which wearable technology is used to track and alter one?s substance use related behaviours.

Title: Perfect Fit: Targeting key risk factors for cardiovascular disease in at-risk individuals using a personalized and adaptive approach Summary: Smoking and insufficient physical activity are key risk factors for cardiovascular disease. Perfect Fit uses big data science techniques to develop highly personalized and adaptive virtual coaching for individuals at risk of cardiovascular disease. This will help them to live healthily and prevent disease.

Depression is the leading cause of disability. Treatment of depression is currently performed on a trial-and-error basis, with only 30-50% of patients that benefit. With this project, we aim to develop biomarkers with which we can provide each patient with the right treatment from the start. We will test the improvement in treatment success and evaluate the reduction in treatment costs. This project may lead to a drastic change in the way patients with depression are treated, which is expected to shorten treatment trajectories and lower the burden for patients and costs for society.

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