Topic outline

  • General

    Welcome to the Behavioral Health Informatics course!

    The field of Behavioral Health Informatics concentrates on the fact that health-related behaviors (e.g., physical acticivity, food intake, sleep quality, social interaction) are among the most significant determinants of health and quality of life. Improving health behavior is an effective way to improve health outcomes and reduce risks that are related to many chronic diseases. Advances in technology, for example in the form of wearable devices, advanced data analytics and mobile phone apps and social media, may provide the tools to act upon risk and thus help contain healthcare costs.
    Behavioral health informatics has the potential to optimize interventions through monitoring, assessing, and modeling behavior in support of providing tailored and timely actions.

    However, there are many questions that relate to practical implementation and actual usage. How reliable is data that we measure during daily life, how can we use it to assess complex health concepts, how do we measure performance and cost-effectiveness, how do we motivate the end-user?

    This course aims to address these issues.

    When passing this course you :

    - Understand the differences in practical requirements for data analysis in preventive care vs. specialised healthcare.
    - Understand and can objectively evaluate the merits of different devices for measuring behaviour during daily living.
    - Are 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 (e.g. risk assessment, lifestyle coaching, chronic disease management)
    - Understand the concept of adherence to interventions and is able to apply methods to assess it in practice.
    - Have 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
    - Have knowledge of the technical considerations to take into account for integrating wellness/behaviour data with traditional healthcare databases.

    Contact information:
    Lecturer and main course responsible: Mark van Gils (mark.vangils@tuni.fi)
    Teaching Assistant: Liya Merzon (liya.merzon@aalto.fi)