TY  - JOUR
A1  - Linseisen, Jakob
A1  - Renner, Britta
A1  - Gedrich, Kurt
A1  - Wirsam, Jan
A1  - Holzapfel, Christina
A1  - Lorkowski, Stefan
A1  - Watzl, Bernhard
A1  - Daniel, Hannelore
A1  - Leitzmann, Michael
T1  - Data in personalized nutrition: bridging biomedical, psycho-behavioral, and food environment approaches for population-wide impact
T2  - Advances in Nutrition
N2  - Personalized Nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products or services to support both prevention and treatment of nutrition-related conditions and improve individual health using genetic, phenotypic, medical, nutritional, and other pertinent information. However, current approaches have yielded limited scientific success in improving diets or in mitigating diet-related conditions. In addition, PN currently caters to a specific subgroup of the population rather than having a widespread impact on diet and health at a population level. Addressing these challenges requires integrating traditional biomedical and dietary assessment methods with psycho-behavioral, and novel digital and diagnostic methods for comprehensive data collection, which holds considerable promise in alleviating present PN shortcomings. This comprehensive approach not only allows for deriving personalized goals (“what should be achieved”) but also customizing behavioral change processes (“how to bring about change”). We herein outline and discuss the concept of “Adaptive Personalized Nutrition Advice Systems” (APNASs), which blends data from three assessment domains: 1) biomedical/health phenotyping; 2) stable and dynamic behavioral signatures; and 3) food environment data. Personalized goals and behavior change processes are envisaged to no longer be based solely on static data but will adapt dynamically in-time and in-situ based on individual-specific data. To successfully integrate biomedical, behavioral and environmental data for personalized dietary guidance, advanced digital tools (e.g., sensors) and artificial intelligence (AI)-based methods will be essential. In conclusion, the integration of both established and novel static and dynamic assessment paradigms holds great potential for transitioning PN from its current focus on elite nutrition to a widely accessible tool that delivers meaningful health benefits to the general population.
Y1  - 2025
UR  - https://opus.bibliothek.uni-augsburg.de/opus4/frontdoor/index/index/docId/118537
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:384-opus4-1185374
SN  - 2161-8313
N1  - Published on behalf of the Working Group "Personalized Nutrition" of the German Nutrition Society. Please see publisher's website for further details.
VL  - 16
IS  - 7
SP  - 100377
PB  - Elsevier BV
CY  - Amsterdam
ER  -