Innovative Nutrition Education: A Color-Coded Tool for Individuals with Low Literacy Level

Bawadi, Hiba; Al-Jayyousi, Ghadir Fakhri; Shabana, Hala; Boutefnouchet, Sana; Eljazzar, Sereen; Ismail, Shrooq

doi:10.3390/healthcare10020272

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…For instance, those sensitive to food intake and weight changes may want to keep track of the calories and amounts of carbohydrates, proteins, fats, and other nutrients ( 20 ). One approach used to provide that information in a simple and accessible way is by FELs ( 32 ). However, distinguishing and classifying food within a group and later calculating the equivalent portion can be challenging and resource-intensive since it is manual.…”

Section: Discussionmentioning

confidence: 99%

“…Ultimately, more is needed to have robust FELs generated with ML; it is also necessary that the information be transmitted in a didactic way to the patients. Bawadi et al points out that using a human-friendly food exchange list can be implemented for people with low literacy, as it is based on visual techniques ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

“…Finally, calculating the similarity coefficient, which is a much more flexible method compared with others (42), in the dimensionless space (43) results in the closeness in this space but not in the nutritional component space; working in this space has the advantage of eliminating the dependence on units, such as kcal, g, and mg, being Ultimately, more is needed to have robust FELs generated with ML; it is also necessary that the information be transmitted in a didactic way to the patients. Bawadi et al points out that using a human-friendly food exchange list can be implemented for people with low literacy, as it is based on visual techniques (32).…”

Section: Discussionmentioning

confidence: 99%

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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the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated.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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Machine learning accurately predicts food exchange list and the exchangeable portion

Hernández-Hernández,

Perez-Lizaur,

Palacios-González

et al. 2023

Front. Nutr.

View full text Add to dashboard Cite

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“…Many studies have confirmed that students’ perceptions and opinions are important references for the arrangement of educational programs [ 12 , 13 , 14 ], and collaboration between students and clinical faculties at internship hospitals can create effective educational programs [ 15 ]. Instructional media designed from the perspective of participants can help them follow instructions more effectively [ 16 ]. Owing to the COVID-19 pandemic prevention policy, many hospitals needed to offer a hybrid live/virtual pharmacy internship.…”

Section: Introductionmentioning

confidence: 99%

Cross-Sectional Analysis of Taiwanese Pharmacy Students’ Experiences and Perceptions of Transitioning from In-Hospital Internships to Distance Learning Due to COVID-19

et al. 2022

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The introductory pharmacy practice experiences (IPPE) in Taiwan, which are traditionally conducted in physical hospital settings, incorporated up to 30% distance learning from May 2021 due to Coronavirus Disease 2019 (COVID-19). A web-based cross-sectional survey was adopted to investigate pharmacy students’ experiences and perceptions of transitioning from in-hospital internships to distance learning due to COVID-19 in the pharmacy department of a university in Southern Taiwan. We analyzed the results to discover factors that significantly affected students’ perceptions of transitioning from in-hospital internships to distance learning. In total, 81 interns from the university’s pharmacy department responded to the questionnaire. Approximately half of the participants felt happy when they learned, before the internship began, that the internship would be partially replaced with distance learning. The overall satisfaction rate was 67.9%, and no significant differences was observed in students’ satisfaction between hospital size or distance-learning time. However, more students in the medical center felt they had insufficient time to finish assignments compared to those in the regional hospitals, and the students who had 11–15 days of distance learning felt that they interacted more smoothly with their peers compared to those who had other durations. Program designers should make distance internship courses more student-centered, with a focus on increasing interactions between students, teachers, and peers to compensate for the lack of physical presence.

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Exploring dietitians’ views on digital nutrition educational tools in Malaysia: a qualitative study

Manaf,

Rosli,

Noor

et al. 2024

Nutr Res Pract

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Innovative Nutrition Education: A Color-Coded Tool for Individuals with Low Literacy Level

Cited by 3 publications

References 16 publications

Machine learning accurately predicts food exchange list and the exchangeable portion

Machine learning accurately predicts food exchange list and the exchangeable portion

Cross-Sectional Analysis of Taiwanese Pharmacy Students’ Experiences and Perceptions of Transitioning from In-Hospital Internships to Distance Learning Due to COVID-19

Exploring dietitians’ views on digital nutrition educational tools in Malaysia: a qualitative study

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