Calor Ease A smart calorie tracking app designed for your daily health goals.
DOI:
https://doi.org/10.29070/x1nwe940Keywords:
Calorie Tracker, Nutrition Management, Real-Time Data, Web Application, Macronutrient TrackingAbstract
CalorEase is a web application that tracks calories aimed at making it easy for individuals to monitor their daily nutrition consumption in a very efficient and convenient manner. CalorEase supports users in finding food products, entering consumed quantity, and having calculated results in terms of calories, proteins, carbohydrates, and fats. Real-time monitoring, user identification, and historical logs are among the features incorporated in CalorEase to provide a comprehensive and tailored nutrition management system. Developed on React.js, Node.js, and MongoDB, the application offers a seamless user interface as well as secure data management. Differing from most current platforms, which either appear cluttered or are paywalled, CalorEase is free of cost and is aimed at simplicity and ease of use. The objective of this project is to create a lean but effective utility for those seeking to enhance their food habits. This article describes the system design, implementation, and evaluation and compares it to other similar applications in the industry.
References
1. A. Burke, M. Styn, M. Sereika, W. Music, and L. Ewing, “Using mHealth technology to enhance self-monitoring for weight loss: A randomized trial,” Am. J. Prev. Med., vol. 46, no. 5, pp. 472–477, May 2014.
2. J. Wu, W. Xu, and Y. Zhang, “Development and evaluation of a mobile dietary intake tracking system,” IEEE Trans. Consum. Electron., vol. 59, no. 3, pp. 678–686, Aug. 2013.
3. Y. Wang and H. Wang, “Design of a personalized nutrition recommendation system based on health data,” Proc. IEEE Int. Conf. Big Data, pp. 3114–3119, Dec. 2018.
4. M. D. Brewer, “Nutrition tracking in digital health: An evaluation of mobile-based calorie tracking,” Health Informatics J., vol. 27, no. 2, pp. 156–167, Jun. 2021.
5. A. B. Patel, “Secure user authentication in mobile health applications,” Int. J. Inf. Secur., vol. 20, no. 3, pp. 317–326, Mar. 2021.
6. S. K. Panda and S. Singh, “Design and implementation of a diet tracking app using React and MongoDB,” Proc. Int. Conf. Comput. Appl., pp. 215–220, Nov. 2020.
7. C. Lee, T. Huang, and Y. Chen, “Usability of nutrition tracking apps: A comparative analysis,” J. Biomed. Inform., vol. 93, pp. 103157, Apr. 2019.
8. M. S. Rehman, A. Rauf, and M. Tariq, “Comparative study of mobile health apps for calorie tracking,” Comput. Methods Programs Biomed., vol. 183, pp. 105074, Feb. 2020.
9. F. Ngo, “Mobile interfaces and visual feedback in health apps,” Int. J. Hum.-Comput. Interact., vol. 36, no. 9, pp. 845–857, Sep. 2020.
10. H. Zhang, “Integration of food APIs for real-time nutrition data,” Proc. IEEE Int. Conf. Data Eng., pp. 1125–1129, Apr. 2022.
11. G. Marcu and L. Radu, “React-based user interface for dietary self-monitoring apps,” Proc. ACM SIGCHI, pp. 146–153, May 2018.
12. S. T. Garcia, “The use of MongoDB in scalable health tracking applications,” IEEE Softw., vol. 34, no. 6, pp. 45–50, Nov.–Dec. 2017.
13. R. Kumar and K. Joshi, “A framework for daily calorie intake prediction using machine learning,” J. Med. Syst., vol. 45, no. 2, pp. 24–32, Feb. 2021.
14. P. Sharma, “User satisfaction and behavioral engagement in calorie tracking mobile apps,” J. Healthc. Eng., vol. 2021, Article ID 6637284, 9 pages, 2021.
15. L. Xu, “Food recognition systems for dietary assessment using deep learning,” IEEE Access, vol. 8, pp. 100456–100466, 2020.
16. J. K. Brown and R. K. Smith, “System design and cloud architecture for health-related applications,” Proc. IEEE Cloud Comput. Conf., pp. 88–93, 2019.
17. M. J. Frost, “A comparative study of authentication techniques in eHealth systems,” Comput. Secur., vol. 81, pp. 74–85, Mar. 2019.
18. A. Roy, “Performance analysis of popular calorie tracker applications,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 9, pp. 58–65, Sep. 2020.
19. V. Khanna and P. Mehta, “Data-driven approaches to personalized diet planning,” J. Inf. Technol. Res., vol. 13, no. 3, pp. 21–34, Jul.–Sep. 2020.
20. M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
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