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. 2020 Jan 24;15(1):e0216516.
doi: 10.1371/journal.pone.0216516. eCollection 2020.

Best compromise nutritional menus for childhood obesity

Paul Bello  1 , Pedro Gallardo  1 , Lorena Pradenas  1 , Jacques A Ferland  2 , Victor Parada  3
Affiliations

Best compromise nutritional menus for childhood obesity

Paul Bello et al. PLoS One. .

Abstract

Childhood obesity is an undeniable reality that has rapidly increased in many countries. Obesity at an early age not only increases the risks of chronic diseases but also produces a problem for the whole healthcare system. One way to alleviate this problem is to provide each patient with an appropriate menu that is defined by a mathematical model. Existing mathematical models only partially address the objective and constraints of childhood obesity; therefore, the solutions provided are insufficient for health specialists to prepare nutritional menus for individual patients. This manuscript proposes a multiobjective mathematical programming model to aid in healthy nutritional menu planning that may prevent childhood obesity. This model provides a plan for combinations and amounts of food across different schedules and daily meals. This approach minimizes the major risk factors of childhood obesity (i.e., glycemic load and cholesterol intake). In addition, this approach considers the minimization of nutritional mismatch and total cost. The model is solved using a deterministic method and two metaheuristic methods. Test instances associated with children aged 4-18 years were created with the support of health professionals to complete this numerical study. The quality of the solutions generated using the three methods was similar, but the metaheuristic methods provided solutions in a shorter computational time. These results are submitted to statistical hypothesis tests to be validated. The numerical results indicate proper guidelines for personalized plans for individual children.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Representation of an individual in the decision space, planning for one day.
Fig 2
Fig 2. Example of crowding distance.
Each point is a nondominated solution.
Fig 3
Fig 3. Box plot of the factors for the Extent variable.
Fig 4
Fig 4. Box plot of the factors for the CPU Time variable.
See this image and copyright information in PMC

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