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Optimising functioning and self-efficacy within different stages of life

World-wide the number of people with overweight-related chronic metabolic diseases is increasing. This urgently calls for the development of smart (predictive, preventive, and personalized) healthcare tools to improve personal wellbeing and to reduce the pressure on the healthcare system. Therefore, the aim of this project is to develop a digital twin that provides personal dietary and lifestyle advice.

Most of the chronic metabolic diseases such as type 2 Diabetes Mellitus and cardiovascular disease are related to life-style such as nutrition. Although dietary interventions are effective, nutritional advice is still given at a population-level via general nutritional guidelines reliant on the group mean (one-size-fits-all). Yet, dietary advice using a personalized approach will empower people to take responsibility, provide them with better opportunities and motivate them to take an active role in their own health.

Personal advice

Experts from Wageningen University & Research (Human Nutrition and Health), Eindhoven University of Technology (Computational Biology) and Utrecht University (Human-Centered Computing Group) will join forces in this project to develop a digital twin that provides personal dietary and lifestyle advice. This advice will be based on a person’s phenotype (characteristics), preferences, values and on a continuous flow of information from non-invasive wearables including daily life glucose, physical activity, heart rate and blood pressure assessment.

Artificial Intelligence (AI) in combination with mechanistic knowledge based computational modelling will be used to analyse this continuous flow of real-time data in combination with the other personal characteristics to provide personal dietary and lifestyle advice.

Key objectives

The key objectives of this project are:

  • Connecting the already developed prediction model for post-meal response with data of the newly performed study.
  • Connecting the glucose data to meal intake using the fully dietary controlled intervention study and making predictions about personalized dietary advice.
  • Combining the personalized dietary advice with a meal/ recipe database that also includes personal behaviour, preferences and values.
  • Performing a field study to test and compare the personalized dietary advices, as created with the digital twin, with a population based dietary advice.
  • Investigating the psychosocial and ethical aspects of the use of the digital twin.

Longer term

Their ultimate goal is to build a digital twin that will provide automatedly generated personalized nutritional advice that incorporates a real-time feedback mechanism from a sensor, such as the continuous glucose monitoring sensors and physical activity trackers, continuously improving the personal dietary advice. This low-cost smart tool will be used by consumers and patients in their own environment and gives them a feeling of self-control, which is supported by science, and gives healthcare providers a unique look into their patient’s personal situation which helps to further personalize therapy.

In 2022 this project team received seed funding from the working group Artificial Intelligence for optimal preparation of the project in 2023 (writing a grant proposal and building a consortium).


Lydia Afman