Preprint Article Version 1 This version is not peer-reviewed

Designing Optimal Breakfast for the United States Using Linear Programming and the NHANES 2011-2014 Database. A Study from the International Breakfast Research Initiative (IBRI)

Version 1 : Received: 8 May 2019 / Approved: 10 May 2019 / Online: 10 May 2019 (10:42:42 CEST)

A peer-reviewed article of this Preprint also exists.

Vieux, F.; Maillot, M.; D. Rehm, C.; Drewnowski, A. Designing Optimal Breakfast for the United States Using Linear Programming and the NHANES 2011–2014 Database: A Study from the International Breakfast Research Initiative (IBRI). Nutrients 2019, 11, 1374. Vieux, F.; Maillot, M.; D. Rehm, C.; Drewnowski, A. Designing Optimal Breakfast for the United States Using Linear Programming and the NHANES 2011–2014 Database: A Study from the International Breakfast Research Initiative (IBRI). Nutrients 2019, 11, 1374.

Journal reference: Nutrients 2019, 11, 1374
DOI: 10.3390/nu11061374

Abstract

The quality of dietary patterns can be optimized using a mathematical technique known as linear programming (LP).  LP methods have rarely been applied to individual meals. The present LP models optimized the breakfast meal for those participants in the nationally representative National Health and Nutrition Examination Survey 2011-2014 who ate breakfast (n=11,565).  The Nutrient Rich Food Index (NRF9.3) was a measure of diet quality.  Breakfasts in the bottom tertile of NRF9.3 scores (T1) were LP-modeled to meet nutrient requirements without deviating too much from current eating habits. Separate LP models were run for children and for adults.  The LP-modeled breakfasts resembled the existing ones in the top tertile of NRF9.3 scores (T3), but were more nutrient-rich.  Favoring fruit, cereals, and dairy, the LP-modeled breakfasts had less meat, added sugars and fats but more whole fruit and 100% juices, more whole grains, and more milk and yogurt.  LP modeling methods can build on existing dietary patterns to construct food-based dietary guidelines and identify individual meals and/or snacks that need improvement.

Subject Areas

breakfast; linear programming; NHANES; NRF9.3; nutrient density; food groups; nutrients; optimization

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