Advancing Cultural Competencies: Applying the Dietary Exchange List System to Jamaican Foods

Goldschmidt, Janice; Sankavaram, Kavitha; Udahogora, Margaret

doi:10.21767/1791-809x.1000563

Cited by 4 publications

(5 citation statements)

References 21 publications

(26 reference statements)

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“…Hoy en día, el sistema de porciones de intercambio de alimentos es una herramienta fundamental para los Nutricionistas tanto en el área asistencial como en el área de educación comunitaria 5,18 , así como también en la docencia. En este último ámbito, el sistema de porciones de intercambio es la forma predilecta de enseñanza en la formación de Nutricionistas en Chile, usando para ello la referencia "Porciones de intercambio y composición química de los alimentos de la pirámide alimentaria Chilena", publicada en 1999 y sin actualización a la fecha de esta revisión 20 .…”

Section: Resultsunclassified

“…Así se crearon listados con alimentos como cereales, verduras, frutas, carnes, lácteos, grasas y azúcares 15 . Sin embargo, es un hecho que la dieta de los distintos países y culturas varía según la disponibilidad de alimentos y las propias costumbres locales 6,18,22,33,55 . Es por esto que estas listas iniciales se fueron haciendo insuficientes en cuanto a la inclusión de alimentos, dificultando su uso universal para todas las sociedades.…”

Section: Listas De Alimentos Y Patrimonio Culturalunclassified

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Sistema de porciones de intercambio de alimentos en Chile y el mundo: Historia, usos y consideraciones

et al. 2020

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Section: Resultsunclassified

Section: Listas De Alimentos Y Patrimonio Culturalunclassified

Sistema de porciones de intercambio de alimentos en Chile y el mundo: Historia, usos y consideraciones

et al. 2020

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“…FELs must be updated to introduce new foods or adapt them to specific countries or populations ( 4 ) since one of the challenges of the FELs is the scarcity of regional food and the need for up-to-updating. Using up to date FELs with local/regional foods will allow the designed diet to gain greater acceptance with a better chance of successful implementation and avoid adherence-related issues due to foods being limited to the food exchange list ( 37 , 38 ). The model proposed in this study can classify and calculate the equivalent portion of a single or a list of foods.…”

Section: Discussionmentioning

confidence: 99%

“…This happens through algorithms, sets of rules a computer follows to reach a goal: prediction, classification, or clustering (35). A subfield of artificial intelligence, supervised machine learning algorithms can learn from training data to develop a function that can model the relation between input variables (e.g., nutrients) and an outputs variable (e.g., classification into groups, equivalent portion) (36).…”

Section: Discussionmentioning

confidence: 99%

Machine learning accurately predicts food exchange list and the exchangeable portion

Hernández-Hernández,

Perez-Lizaur,

Palacios-González

et al. 2023

Front. Nutr.

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IntroductionFood Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning algorithms could be a tool that facilitates this process and allows for updated FELs—the present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion.MethodsData mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.ResultsThe algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.DiscussionAccurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, machine learning is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.

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“…The University of Maryland at College Park conducted a study with 49 participants, 25 to 74 years of age to see whether the use of food for Jamaican immigrants could provide a link to their cultural heritage. Results of the study showed that food is very important to ones culture and serves as a bridge by helping immigrants find a place of familiarity while living in the United States (Goldschmidt, Sankavaram, & Udahogora, ). Food is also a symbol of pride for communities.…”

Section: Discussionmentioning

confidence: 99%

Health disparities: Using policies to rethink our strategies for eliminating the impact of food deserts by focusing on unhealthy dietary patterns

Wallace

2018

Journal of Public Affairs

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For years, we have been interested in understanding the relationship between dietary patterns and diseases, and most recently, we have put efforts toward analyzing the impact of food deserts as they relate to dietary patterns. Unhealthy eating has become an epidemic in low‐income neighborhoods that are considered to be food deserts due to the fact that people are not meeting their recommended daily intake of nutritionally dense foods. Adults should be consuming at least 20 to 35 g of fiber daily, however, many Americans only consume 12 to 17 g of fiber daily at best. Furthermore, as a society, we Americans consume way too much added sugar, saturated fat, and salt. Although there are a number of reasons that unhealthy dietary patterns exist in our society especially in low‐income communities, it is important that we pay particular attention to how food deserts have developed and how they are major contributors to the overall poor health of low‐income Americans. Therefore, the purpose of this is paper is to encourage its audience to rethink how we can implement policies to address the issue of unhealthy dietary patterns by reducing or eliminating food deserts. Specifically, we explore the effect of implementing evidence‐based policies such as nutrition initiatives, corner store initiatives, menu labeling, food assistance programs, and the punitive taxation of sugary beverages and unhealthy foods similar to the punitive taxes placed on tobacco.

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Advancing Cultural Competencies: Applying the Dietary Exchange List System to Jamaican Foods

Cited by 4 publications

References 21 publications

Sistema de porciones de intercambio de alimentos en Chile y el mundo: Historia, usos y consideraciones

Sistema de porciones de intercambio de alimentos en Chile y el mundo: Historia, usos y consideraciones

Machine learning accurately predicts food exchange list and the exchangeable portion

Health disparities: Using policies to rethink our strategies for eliminating the impact of food deserts by focusing on unhealthy dietary patterns

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