Behavioral Health Informatics, Lectures

When passing this course, the student:

• Understands the differences in practical requirements for data analysis in preventive care vs. specialised healthcare.
• Understands and can objectively evaluate the merits of different devices for measuring behaviour during daily living.
• Is familiar with the main methods to quantize: sleep quality, cognitive load and stress, food intake, and physical activity.
• Can assess performance of low-cost measurements in different scenarios (risk assessment, lifestyle coaching, chronic disease management)
• Understands the concept of adherence to interventions and is able to apply methods to assess it in practice.
• Has basic knowledge of behaviour change technologies and motivational tools.
• Understand the possibilities and limitations of using AI, ML and advanced data analysis methods for data-driven lifestyle change recommendations
• Has knowledge of the technical considerations to take into account for integrating wellness/behaviour data with traditional healthcare databases.  
• The student has
  gained knowledge about common methods to model human behavioural patterns and
  to create personalised profiles.

Traditional lectures + Exercises gaining bonus points. Final exam at the end of the course.

General scale, 0-5