Technologies for everyday health: a quantified self approach

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. Digital health technologies, like sensor technology and smartphone applications, offer novel opportunities to monitor and quantify physiological states and lived experiences 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 and facilitate the 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 using digital health technologies 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 insights into health status utilizing mobile sensors or self-reports and inform just-in-time adaptive interventions to support everyday health.

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

• Evaluate opportunities and challenges related to digital health technologies for longitudinal health monitoring and management in real life , including ethical considerations
• Explain the use and principles of ambulatory monitoring of psychophysiological signals in the realm of everyday life
• Interpret and apply time series analyses for experience sampling data and sensor data on a conceptual level and apply these analyses in research using digital health technologies
• Develop a proposal to apply Quantified Self methods in research
• Evaluate and apply (basic) Machine Learning (ML) analyses on data  collected using digital health technologies in context

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