The complex relationship between people and their environment greatly affects health, lifestyle, and health disparities. This project focuses on combining health data with My Life Kit’s Vioscore™ to find health and environmental indicators. UMCG-partners are skilled at analysing large datasets that connect health, behaviour, and device monitoring information. They specialize in understanding physical activity, diet, and interactions between individuals and their environment, and provide personalized interventions for healthy aging and lifestyle changes. MyLifeKit introduces an AI-powered platform, that provides an easy-to-use interface for identifying trends and patterns in combination with a subset of datapoints of individual and environment data.

Globally, preventable lifestyle related chronic diseases such as type 2 diabetes and cardiovascular disorders, are expected to increase by 500 million cases from 2020 to 2030, resulting in an annual economic burden of around US$27 billion. In the Netherlands, in 2017, more than 21 healthy life years were lost due to poor dietary factors for every 1,000 persons, and more than 3 years per 1,000 persons were lost due to low physical activity. At regional and local scales (neighbourhoods or municipalities), risk factors and behaviours also cluster spatially, leading to health inequalities. Understanding and changing lifestyle and health habits requires more than just individual efforts. It involves the physical environment, societal structures, and policies that influence behaviour.

This project aims to develop a predictive mapping method by analysing and combining LifeLines-cohort data with environmental (GIS) data and My Life Kit’s VioScore™. It will analyse the interactions between individuals and their environment to pinpoint areas or clusters with a higher likelihood of high-risk inhabitants in the northern region of the Netherlands.

Leveraging AI, it identifies trends and patterns, paving the way for tailored interventions strategies addressing lifestyle habits and health disparities in targeted clusters, addressing critical health inequities through precise strategies.