The quantified self in health

Health problems and their accompanying costs (healthcare, social, economic) are an ever increasing problem. The extremely high demands on the current care system necessitate innovative approaches to health management. Sensor technology and smartphone applications offer novel opportunities to monitor and quantify physiological and psychological states over prolonged periods of time in the natural context of everyday life. This allows the generation of valuable insights into the temporal dynamics in health (across days, weeks, months, and years) and the antecedents of disturbances in health. Moreover, these technologies enable better diagnostics, facilitatethe development of preventive measures and user-tailored interventions to promote health.

In this course, we will focus on health in context, methodologies for monitoring, modeling, predicting, and promoting health in everyday-life situations, and the ethical implications of these technologies. The course combines lectures with assignments to provide students with advanced knowledge on - and hands-on experiences with - quantified self approaches. These approaches involve using quantitative methods to gain self-knowledge through numbers by means of self-tracking, utilizing mobile sensors or self-reports. We will specifically apply these methods to the health domain.

At the end of the course, students should be able to:

• Evaluate Quantified Self approach and data collection methodologies
• Explain health concepts frequently studied with the Quantified Self approach
• Interpret and apply statistical analyses for experience sampling data on a conceptual level and apply these analyses in Quantified Self research  Develop a proposal to apply Quantified Self methods in research
• Evaluate and apply (basic) Machine Learning (ML) analyses on Quantified Self data
• Write a critical assessment of Quantified Self research designs and outcomes, including ethical considerations

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Collection 2
• Completed Pre-Master

• Selection of scientific articles and book chapters