Data Science for Healthy Lifestyles

Data science plays an increasing role in healthy lifestyle research. Big data, AI, machine learning and data science techniques are gaining importance both for a better understanding of health behaviors and their contextual factors, and for the development of interventions that take into account individual variability (i.e. personalized interventions strategies). Precision health (i.e., prevention strategies that take individual variability into account) has been greatly expanded in the past years resulting in a shift from group level data towards individual data. This shift requires new research approaches and advanced data analytics such as data science techniques.

Data science can be used to generate and investigate relevant research questions on causes and consequences of consumption and lifestyle variables. In this course students learn about the opportunities and challenges for data science in health research. Practical examples will be used to illustrate this.

In this course the focus will be on data science approaches applied in the field of consumption and healthy lifestyles. Course topics (topics may be included or replaced according to recent developments in the field) include:

• real-life data acquisition (real time, real-world context);

• high dimensional data (e.g. continuously and automatically data acquisition via sensors);

• environmental exposures (GIS, GPS tracking);

• use of data science in innovative eHealth applications, such as interventions using embodied conversational agents, virtual reality or augmented reality;

• just-in time-adaptive interventions;

• machine learning and self-learning algorithms to optimize and personalize interventions strategies;

• data science techniques (e.g. data mining, prediction models) for a better understanding (and prediction) of health behaviors and its determinants.

The assessment of this course is based on a group assignment (75%) and a portfolio including individual assignments or small-group assignments (25%). For the group assignment, students will work in small groups on an assignment resulting in a scientific report, including a relevant research question, the description and/or use of appropriate data science techniques to solve the research question, and a discussion.

The use of generative artificial intelligence to create ready-made content in assignments is considered fraud unless this use of AI is explicitly permitted by the examiner in the instructions for the assignment.

After successful completion of this course students are expected to be able to:

• Identify relevant data sources in the field of healthy lifestyles
• Apply data science techniques with the aim to better understand healthy lifestyle behaviors
• Apply data science techniques with the aim to develop, advance and evaluate personalized (behavior change) interventions
• Understand the role of data science in the field of precision Health (i.e. to deliver timely and targeted prevention)
• Apply appropriate data science techniques based on the research question and data type
• Interpret, visualize and communicate results from data science techniques to a multidisciplinary data science team

Assumed Knowledge:
INF34306 Data Science Concepts, HNH37006 Data Science for Health: Principles and MAT32806 Statistics for Data Scientists.