In this project, we explore the input (food intake and personal health status) for generating personalized dietary advice and the format of the advice to ensure more long-lasting compliance. We focus on: 1) non-invasive biomarkers related to health status; 2) tools that measure food intake and aim to change eating behavior 3) personalized formats of advice based on socio-psychological factors and 4) the development of a digital model in which algorithms generate the personalized advice.
Despite various public health campaigns, EU citizens have low compliance with dietary recommendations and guidelines, resulting in well documented poor health situations and unhealthy aging. It is now believed that a more personal approach can effectively stimulate and motivate people to comply with a healthy diet that meets their individual needs and thereby contribute to a long-lasting behavioral change. By combining data on personal preferences, personal eating patterns and habits, measures of personal health status as well as understanding of motivational goals and habits, tailored personal dietary advice can be generated that is likely to increase compliance as compared to generic dietary advices.