myPIPPI - Personalized and functional food Intervention Proactive Preventive Individual care
Reference number | |
Coordinator | RISE Research Institutes of Sweden AB - RISE AB |
Funding from Vinnova | SEK 479 010 |
Project duration | November 2021 - February 2022 |
Status | Completed |
Venture | Preparation projects for international application within health |
Call | Preparation projects for international application within health 2021 |
Important results from the project
myPIPPI will create an ecosystem through research partners, patient organizations, care providers and companies. The project will develop and design a digital platform to a) increase knowledge and prevention of overweight / obesity, b) The digital platform will be accessible for 16-25 year old to create tools that support obesity prevention and healthy lifestyle. myPIPPI platform will offer digital solutions related to i) nutritional intake; ii) physical activity; iii) mental health. iv) social; v) economic;. The goal is also to improve work ability.
Expected long term effects
The result of myPIPPI is to design, develop and validate new digital services that can be integrated with data from food producers (food parameters), the biological system (patient data) and lifestyle variables (activities). These prototypes will then be tested in 4 different EU countries and will allow us to create a proof of concept that utilizes the high levels of skills available at all myPIPPI´s partners to prevent overweight / obesity and increase work capacity by 12 months.
Approach and implementation
We will implement partners capabilities in machine learning ML including: (a) grouping overweight/obese patients into distinct categories which generate individual treatment strategies, (b) prediction of outcomes and trajectories in the population, (c) supporting the extraction of important data from clinical databases, (d) prediction and prevention of comorbidities for overweight/obese patients. In essence, this will create the ability to individualize an AI-based model which gradually learns the requirements for successful measures to positively influence obesity.