Personalized nutrition (PN) represents an approach aimed at providing tailored dietary recommendations, products, or services designed to support both the prevention and treatment of diet-related conditions, while improving individual health outcomes. This is achieved by leveraging genetic, phenotypic, medical, nutritional, and other relevant data. However, current PN models have demonstrated limited scientific effectiveness in improving dietary habits or reducing nutrition-related diseases. Moreover, personalized nutrition remains largely confined to a narrow segment of the population, failing to deliver significant impact at the broader public health level.
To address these challenges, it is essential to integrate traditional biomedical and dietary assessment methods with cutting-edge psycho-behavioral, digital, and diagnostic approaches. Such comprehensive data integration holds significant promise for overcoming existing limitations in the field of PN.
This integrated approach allows not only the formulation of personalized goals (“what should be achieved”), but also the dynamic customization of behavior change processes (“how to facilitate change”). In this context, we present and explore the concept of Adaptive Personalized Nutrition Advice Systems, which synthesizes data across three key domains:
Biomedical/health phenotyping,
Stable and dynamic behavioral signatures,
Food environment data.
In this framework, nutritional goals and behavior modification strategies are no longer based solely on static information but are continuously adapted in real time and specific contexts, using individualized data streams.
To successfully implement this model, advanced digital tools (such as wearable sensors) and artificial intelligence–driven methods will be critical. Ultimately, the integration of both well-established and emerging static and dynamic assessment paradigms offers substantial potential to shift personalized nutrition from an elite-oriented service to a scalable, inclusive solution—capable of delivering meaningful health benefits across entire populations.
Source SCIENCE DIRECT.