Aplicaciones anidadas

titulo-portions-v

 

PORTIONS-V

 

Aplicaciones anidadas

Aplicaciones anidadas

titulo-portions-v

Comprehensive intervention for dietary habit change based on portion control: Optimization plus technological validation and clinical trial.

Funding Entity: department of University, Innovation and Digital Transformation; Service of research and development; Government of Navarra.

principalresearcher : The consortium is led by the research center in Nutrition (PI, Eva Almirón) and has the partnership of the Public University of Navarra (co-IP, Arantxa Villanueva).

reference letter: PC24-PORTIONS-V-007-002.

FOTO-PORTIONS-V

texto-portions-v

The PORTIONS-V aims to explore the impact of the prolonged use of a set of theoretical and practical strategies aimed at improving diet and general health in the adult population with overweight and obesity. The project will include a randomized controlled trial (RCT) with 180 participants and mechanistic studies to evaluate the effects of a comprehensive weight loss program focused on portion control. The intervention components have been developed specifically for people living with overweight and obesity based on four preceding projects (PORTIONS-1 to 4, 2018-2024) and include educational guides, optimized dietary tools, and technologies to facilitate self-management. PORTIONS-V will also examine changes in the gut microbiome and in the activation of neural networks involved in eating behaviour and the sensations of pleasure experienced when eating, to better characterize the role of the gut-brain axis, the regulatory centre of appetite, during weight  loss.

In addition, PORTIONS-V will validate a prototype for a remote eye-tracking device against a commercial model and explore possible adaptations for its application in the home environment. These data can be combined with the data obtained in the RCT regarding brain activity, eating behaviour and metabolic markers to allow a functional analysis of the processes involved in the control/inhibition of food intake. It is expected that the integrated set of all these data can be used for the development of predictive algorithms for eating behaviour patterns using AI.