Healthy dietary habits: Persuasive technology model for dietary management

Kizito Eluemunor Anazia 1, *, Emmanuel Obiajulu Ojei 2, Erikefe Friday Eti 1 and Rebecca Okeoghene Idama 1

1 Department of Information Systems and Technology, Delta State University of Science and Technology, Ozoro, Delta State, Nigeria.
2 Department of Software Engineering, Delta State University of Science and Technology, Ozoro, Delta State, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 3455-3466.
Article DOI: 10.30574/ijsra.2024.13.2.2548
Publication history: 
Received on 14 November 2024; revised on 22 December 2024; accepted on 25 December 2024
 
Abstract: 
Unhealthy eating habits are a major public health issue, deeply affecting individual well-being. Their consequences go beyond immediate health problems, leading to long-term risks such as obesity, heart disease, and diabetes. Poor dietary choices significantly contribute to the prevalence of non-communicable diseases like cancer, obesity, and diabetes. The widespread use of technology presents a unique opportunity to enhance public health, particularly by encouraging healthier eating behaviors. By integrating behavioral science, innovative technology design, and user-centered strategies, persuasive technologies hold great potential to guide individuals toward adopting and maintaining healthier dietary habits over time. This study aimed to create a persuasive technology to promote healthy dietary habits by leveraging algorithms developed from Kaggle datasets. These algorithms analyzed individual dietary preferences, cultural influences, and lifestyle factors while incorporating mechanisms for providing timely feedback and monitoring user progress. A mixed-method approach was employed, combining qualitative and quantitative techniques for collection of data and analysis. The model was built using Xamarin's mobile authoring tools for the user interface and SQL Server for backend data management, following the Object-Oriented Methodology (OOM). The application achieved a performance matrix with accuracy rate of 91%, precision rate of 93%, and an F1 score rate of 87%.
 
Keywords: 
Healthy Dietary; Persuasive Technology; Kaggle Datasets; Fogg Behavior
 
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0