Perfect Fit

Targeting key risk factors for cardiovascular disease in at-risk individuals using a personalized and adaptive approach

life sciences and ehealth

Perfect Fit

Perfect Fit

Principal Investigator
Prof. dr. N.H. Chavannes

Targeting key risk factors for cardiovascular disease in at-risk individuals using a personalized and adaptive approach

Smoking tobacco and physical inactivity are key preventable risk factors of cardiovascular disease (CVD). Perfect Fit

combines big-data science, sensor technology, and personalized real-time feedback for individuals to achieve and maintain
abstinence from smoking and sufficient physical activity (PA; in gyms/daily life), prevent CVD, facilitate wellbeing and reduce
healthcare costs. The approach is designed to serve disadvantaged groups, where smoking and physical inactivity are most
prevalent. Once developed, the approach can be extended to include other risk factors and systems.
The project develops tailored, evidence-based, near real-time computer coaching for quitting smoking and enhancing PA.
For every individual, a personal model is designed which generates personalized recommendations based on high-quality
existing and newly collected data, and adapts to changing circumstances/progress (similar to a TomTom navigation system),
using machine learning techniques and incorporating domain-specific expert knowledge (e.g. health behaviour change
strategies). Virtual coaches (VCs) communicate advice in a motivating way that fits individuals’ persuasive communication
styles.
Key objectives are to examine how different types of (senso-)data can be used to provide personalized advice; which
adaptivity is needed to create a robust, safe, and effective interaction between individuals and machine (VC); how advanced
data science methods can be developed and embedded in current smoking cessation and PA coaching practice; and how
measurement modalities, feedback and communication affect individuals’ smoking status and PA. The project addresses
challenges regarding small and big-data quality and usability; linkage of data sources; making data meaningful for
individuals; and responsible data science.

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