A hierarchy for personalised nutrition platform inputs
When compared to other approaches for promoting health and reducing the risk for chronic disease, a personalised approach, utilizing data-driven assessments and advice with relevant support to the individual, appears more effective. Data gathered from dietary assessments, wearable technology, testing of the genome and microbiome, and relevant blood biomarkers can be translated into scientifically sound and practical dietary recommendations. However, the strengths and limitations of each of these inputs must inform the algorithms of personalised nutrition platforms. As the number of commercial personalised nutrition services and systems is growing, it is worthwhile considering a science-based hierarchy of the elements of these programs.
It has been well-documented that adherence to sound diet and lifestyle guidelines can substantially reduce the risk of developing diet-related metabolic diseases (1-3). However, adherence to recommended regimens for diet, physical activity, and other lifestyle behaviors is difficult, despite the evidence demonstrating that compliance is essential to achieve long-term benefits from any intervention (4, 5). The emergence of personalised nutrition has the potential to improve the precision and effectiveness of lifestyle-based health promotion and disease management (6-8). This approach also allows for the identification of individual barriers and the incorporation of personal preferences into recommendations, making the output advice more acceptable and readily integrated into ongoing behavioral routines. We review the types of data informing personalised nutrition platforms and consider a hierarchy of the elements of assessment that provide the input for developing individualized dietary and lifestyle recommendations.
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