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

Abstract

Nearly all approaches to personalized nutrition (PN) use information such as the gene variants of individuals to deliver advice that is more beneficial than a generic "1-size-fits-all" recommendation. Despite great enthusiasm and the increased availability of commercial services, thus far, scientific studies have only revealed small to negligible effects on the efficacy and effectiveness of personalized dietary recommendations, even when using genetic or other individual information. In addition, from a public health perspective, scholars are critical of PN because it primarily targets socially privileged groups rather than the general population, thereby potentially widening health inequality. Therefore, in this perspective, we propose to extend current PN approaches by creating adaptive personalized nutrition advice systems (APNASs) that are tailored to the type and timing of personalized advice for individual needs, capacities, and receptivity in real-life food environments. These systems encompass a broadening of current PN goals (i.e., what should be achieved) to incorporate "individual goal preferences" beyond currently advocated biomedical targets (e.g., making sustainable food choices). Moreover, they cover the "personalization processes of behavior change" by providing in situ, "just-in-time" information in real-life environments (how and when to change), which accounts for individual capacities and constraints (e.g., economic resources). Finally, they are concerned with a "participatory dialog between individuals and experts" (e.g., actual or virtual dieticians, nutritionists, and advisors) when setting goals and deriving measures of adaption. Within this framework, emerging digital nutrition ecosystems enable continuous, real-time monitoring, advice, and support in food environments from exposure to consumption. We present this vision of a novel PN framework along with scenarios and arguments that describe its potential to efficiently address individual and population needs and target groups that would benefit most from its implementation.

Keywords: advice; behavior change; digital ecosystem; dynamic system; food environment; framework; just-in-time adaptive intervention; personalized nutrition; public health.

FIGURE 1

Mechanisms that underlie the value of personalization.

FIGURE 2

Causes of cases (A) and incidence (B), and current personalized nutrition concepts (C). PN, personalized nutrition.

FIGURE 3

Schematics of the action gap between individual dietary behaviors and goals as defined by national agencies or with personalized approaches and the required behavioral changes.

FIGURE 4

Event sequence in the food environment [31,70].

FIGURE 5

Framework for just-in-time adaptive interventions that lead to favorable outcomes (adapted from [83]).

FIGURE 6

Adaptive personalized nutrition advice systems and their key features. JITAI, just-in-time adaptive intervention.

FIGURE 7

Example of an adaptive personalized nutrition advice system (APNAS) that addresses behavioral acts in time and in situ by providing a vision of the new quality in a built virtual reality. AI, artificial intelligence.