Mobile Health Innovation: An Android-Based Nutritional Grading App to Promote Healthier Food and Beverage Choices - Effendi, Pradita Maulidya; Rohman, Habib Fatkhul (Journal of Nutrition and Public Health, 2025)
Background: Unhealthy dietary patterns are one of the leading contributors to non-communicable diseases such as stroke, heart disease, and diabetes in Indonesia. The increasing availability of processed foods, combined with a lack of nutritional awareness, has created an urgent need for tools that support healthier consumption decisions. Aims: This study aims to develop a mobile health application that enables users to assess the nutritional quality of food and beverages through a grading system, thereby promoting healthier dietary choices. Methods: The application was developed using the waterfall software development model and implemented in Kotlin for the Android platform. It features a nutrition label scanning system powered by the device’s camera and applies Nutri-Grade rules to classify products into four categories (A–D) based on sugar and saturated fat content. The app’s architecture follows the Model-View-ViewModel (MVVM) pattern, and data communication is handled via Retrofit. Functional testing was conducted using black-box testing techniques. Result: The application successfully allows users to register, scan nutrition labels, and receive grading results instantly. It provides a user-friendly interface and accurate results according to Nutri-Grade guidelines. All features passed the expected output criteria during the black-box testing phase. Conclusion: This study demonstrates the feasibility and utility of integrating nutrition science into mobile application technology. The Android-based Nutri-Grade app serves as a practical tool to improve public dietary behavior, especially in urban and digital-native populations. It empowers users to make informed dietary choices in real-time and contributes to preventive health strategies through digital innovation. The app’s scalability and adaptability also open future pathways for integrating machine learning to enhance nutritional recognition and grading accuracy.