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Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques
Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide informatio...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416398/ https://www.ncbi.nlm.nih.gov/pubmed/34484329 http://dx.doi.org/10.1155/2021/6624057 |
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author | Sefa-Yeboah, Sylvester M. Osei Annor, Kwabena Koomson, Valencia J. Saalia, Firibu K. Steiner-Asiedu, Matilda Mills, Godfrey A. |
author_facet | Sefa-Yeboah, Sylvester M. Osei Annor, Kwabena Koomson, Valencia J. Saalia, Firibu K. Steiner-Asiedu, Matilda Mills, Godfrey A. |
author_sort | Sefa-Yeboah, Sylvester M. |
collection | PubMed |
description | Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide information on the calorie contents of meals. Although knowledge of the calorie content is useful for meal planning, it is not sufficient as other factors, including health status (diabetes, hypertension, etc.) and level of physical activity, are essential in the decision process for obesity management. In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user's energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. The algorithm takes the users' input information on desired foods which are selected from a database and extracted records of users on cholesterol level, diabetes status, and level of physical activity, to predict possible meals required to meet the users need. The micro- and macronutrients of food content are used for the computation and prediction of the potential foods required to meet the daily calorie needs. The functionality and performance of the model were tested using a sample of 30 volunteers from the University of Ghana. Results revealed that the model was able to predict both glycemic and non-glycemic foods based on the condition of the user as well as the macro- and micronutrients requirements. Moreover, the system is able to adequately track the progress of the user's weight loss over time, daily nutritional needs, daily calorie intake, and predictions of meals that must be taken to avoid compromising their health. The proposed system can serve as a useful resource for individuals, dieticians, and other health management personnel for managing obesity, patients, and for training students in fields of dietetics and consumer science. |
format | Online Article Text |
id | pubmed-8416398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-84163982021-09-04 Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques Sefa-Yeboah, Sylvester M. Osei Annor, Kwabena Koomson, Valencia J. Saalia, Firibu K. Steiner-Asiedu, Matilda Mills, Godfrey A. Int J Telemed Appl Research Article Obesity is a major global health challenge and a risk factor for the leading causes of death, including heart disease, stroke, diabetes, and several types of cancer. Attempts to manage and regulate obesity have led to the implementation of various dietary regulatory initiatives to provide information on the calorie contents of meals. Although knowledge of the calorie content is useful for meal planning, it is not sufficient as other factors, including health status (diabetes, hypertension, etc.) and level of physical activity, are essential in the decision process for obesity management. In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user's energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. The algorithm takes the users' input information on desired foods which are selected from a database and extracted records of users on cholesterol level, diabetes status, and level of physical activity, to predict possible meals required to meet the users need. The micro- and macronutrients of food content are used for the computation and prediction of the potential foods required to meet the daily calorie needs. The functionality and performance of the model were tested using a sample of 30 volunteers from the University of Ghana. Results revealed that the model was able to predict both glycemic and non-glycemic foods based on the condition of the user as well as the macro- and micronutrients requirements. Moreover, the system is able to adequately track the progress of the user's weight loss over time, daily nutritional needs, daily calorie intake, and predictions of meals that must be taken to avoid compromising their health. The proposed system can serve as a useful resource for individuals, dieticians, and other health management personnel for managing obesity, patients, and for training students in fields of dietetics and consumer science. Hindawi 2021-08-27 /pmc/articles/PMC8416398/ /pubmed/34484329 http://dx.doi.org/10.1155/2021/6624057 Text en Copyright © 2021 Sylvester M. Sefa-Yeboah et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Sefa-Yeboah, Sylvester M. Osei Annor, Kwabena Koomson, Valencia J. Saalia, Firibu K. Steiner-Asiedu, Matilda Mills, Godfrey A. Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title | Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title_full | Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title_fullStr | Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title_full_unstemmed | Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title_short | Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques |
title_sort | development of a mobile application platform for self-management of obesity using artificial intelligence techniques |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8416398/ https://www.ncbi.nlm.nih.gov/pubmed/34484329 http://dx.doi.org/10.1155/2021/6624057 |
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