Perspective: A Conceptual Framework for Adaptive Personalized Nutrition Advice Systems (APNASs)

Area of Science:

• Nutritional Science
• Public Health
• Behavioral Science

Background:

• Personalized nutrition (PN) uses individual data, like genetics, for tailored advice.
• Current PN shows limited efficacy and effectiveness in scientific studies.
• PN faces public health criticism for potential health inequalities.

Purpose of the Study:

• To propose adaptive personalized nutrition advice systems (APNASs).
• To enhance PN by tailoring advice to individual needs, capacities, and receptivity in real-life settings.
• To broaden PN goals beyond biomedical targets to include individual preferences and sustainable choices.

Main Methods:

• Developing APNASs integrating real-time monitoring and "just-in-time" behavioral change support.
• Incorporating individual goal preferences and constraints (e.g., economic resources).
• Facilitating participatory dialogue between individuals and experts for goal setting and adaptation.

Main Results:

• APNASs offer tailored, timely information in real-world food environments.
• Digital nutrition ecosystems enable continuous monitoring and support.
• This framework can address diverse individual and population needs.

Conclusions:

• APNASs represent a novel framework for personalized nutrition.
• This approach has the potential to improve PN efficacy and equity.
• APNASs can better target diverse populations and their specific needs.