Dietify - AI Based Diet & Nutrition Consultation Application

Abstract

This paper provides a detailed review of Dietify, an AI-based application designed to offer personalized diet and nutrition consultations. Utilizing machine learning and predictive analytics, Dietify provides real-time dietary recommendations, continuously adapting meal plans based on user feedback and health objectives. This review examines Dietify’s functional architecture, methodology, advantages, and limitations. Furthermore, it situates Dietify within the broader field of AI-driven diet management applications and discusses future opportunities, including expanded integration with wearable health data, advanced predictive modeling, and increased personalization for chronic disease management. Dietify represents a significant advancement in personalized diet consultation, enhancing accessibility, individualization, and data-driven adaptability in health management.

Introduction

Dietify is a mobile health (mHealth) application that leverages artificial intelligence (AI) and machine learning (ML) to offer personalized dietary and fitness recommendations. With chronic lifestyle diseases like obesity, diabetes, and cardiovascular conditions on the rise, the app addresses the growing need for data-driven, adaptive nutrition guidance accessible to the general public.

Key Features:

1. Personalized Diet Planning:

   • Uses ML algorithms to tailor meal plans based on user input (age, weight, dietary preferences, allergies, health goals).

   • Recommendations are dynamic, updating in real time as users provide feedback or progress.

2. Real-Time Adaptation & Predictive Modeling:

   • Feedback loops allow continuous personalization of diets.

   • AI predicts potential health outcomes, guiding proactive health decisions.

3. User Profile & Goal Setting:

   • Collects demographic and physiological data (height, weight, activity level, etc.).

   • Calculates BMI to suggest health objectives like bulking, cutting, calorie/nutrient tracking.

4. Meal Planner & Nutrition Tracking:

   • Integrates a food library to help users log meals and understand nutritional values.

   • Offers goal-based meal suggestions, deficiency alerts, and daily/weekly nutrition summaries.

5. Community Engagement:

   • Users can share content, join forums, and participate in fitness challenges.

   • Encourages motivation and a supportive environment.

6. Explainable AI & Future Features:

   • Future updates plan to include evidence-based reasoning, AWS SageMaker analytics, and OpenCV-based posture detection for exercise form correction.

   • Enhances transparency and user trust.

System Architecture:

• Built with Flutter (frontend) and Django + SQLite (backend).

• Uses Gemini LLM for diet and exercise recommendation generation.

• Employs a hybrid AI model (collaborative filtering + neural networks) to personalize suggestions.

Methodology Breakdown:

• Step 1: User Profile Creation – Includes data collection and privacy compliance (GDPR).

• Step 2: BMI Calculation & Health Assessment – Helps inform diet/fitness goals.

• Step 3: Goal Selection – Options include bulk, cut, calorie or nutrient tracking.

• Step 4: Nutrition Tracking & Analytics – Users track intake and receive performance insights.

• Step 5: Meal Planner & Food Library – Ensures easy logging and informed dietary choices.

Literature Review & Impact:

• Cites studies confirming AI’s effectiveness in dietary adherence, chronic disease management, and preventive health.

• Compares Dietify to other mHealth tools (e.g., Foodwiser, AI tools in pediatric oncology).

• Emphasizes the potential of AI to improve long-term health outcomes through personalization and scalability.

Limitations & Future Direction:

• Challenges include ensuring data privacy, bias mitigation, and accessibility across diverse populations.

• Planned upgrades involve:

  • Integration with wearable devices.

  • Enhanced visual feedback via OpenCV.

  • Use of predictive fitness analytics for personalized progress visualization.

References

[1] Bélanger, V., Delorme, J., Napartuk, M., Bouchard, I., Meloche, C., Curnier, D., Sultan, S., Laverdière, C., Sinnett, D., Marcil, V. (2022). \"Early Nutritional Intervention to Promote Healthy Eating Habits in Pediatric Oncology: A Feasibility Study.\" Nutrients, 14(5), 1024. doi: 10.3390/nu14051024. [2] Tatte, A., Gehi, A., Sanghvi, A., Pandey, A., Khatri, S., Bandai, P. (2021). \"Foodwiser: Be Wise with What You Eat.\" International Journal for Research in Applied Science & Engineering Technology (IJRASET), 9(12). doi: 10.22214/ijraset.2021.39427. [3] Daley, B., Ni\'Man, M., Neves, M., Huda, M. B., Marsh, W., Fenton, N., Hitman, G., McLachlan, S. (2021). \"mHealth Apps for Gestational Diabetes Mellitus that Provide Clinical Decision Support or Artificial Intelligence: A Scoping Review.\" Diabetic Medicine, 39(1), e14735. doi: 10.1111/dme.14735. [4] Sefa-Yeboah, S., Osei Annor, K., Koomson, V., Saalia, F., Steiner-Asiedu, M., Mills, G. (2021). \"Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques.\" International Journal of Telemedicine and Applications, 2021, 6624057. doi: 10.1155/2021/6624057. [5] Hagger-Johnson, G., et al. (2021). \"Artificial Intelligence and Mobile Health for Disease Prevention and Wellness: A Review of the Current Landscape and Opportunities.\" Journal of Medical Internet Research, 23(8), e24158. doi: 10.2196/24158. [6] Greer, S., Denecke, K., Ruwaard, D., &Bellika, J. G. (2021). \"Mobile Applications in Diet and Nutrition: A Review and Analysis of the Current Landscape.\" Computers in Biology and Medicine, 129, 104136. doi: 10.1016/j.compbiomed.2020.104136. [7] Long, C., Yu, P., Wang, W., & Lu, Y. (2021). \"AI-Driven Nutritional Monitoring for Elderly Health Management.\" Computers in Human Behavior, 123, 106902. doi: 10.1016/j.chb.2021.106902. [8] Bandara, N., Ekanayake, M., &Perera, G. (2021). \"Integrating AI and Mobile Apps to Personalize Dietary Recommendations for Cardiovascular Patients.\" Journal of Personalized Medicine, 11(9), 812. doi: 10.3390/jpm11090812. [9] Perer, A., Patel, H., & Sanders, C. (2021). \"AI-Based Diet Recommendations in mHealth for Diabetes Management: A Meta-Analysis.\" Diabetes & Metabolism, 47(5), 101234. doi: 10.1016/j.diabet.2021.101234. [10] Valerio, J. D., & Barthel, E. (2021). \"AI and Machine Learning in Nutrition Management: Current Trends and Future Directions.\" Nutrition Research Reviews, 34(3), 335-355. doi: 10.1017/S095442242100012X. [11] Sharma, S., Shah, R., Gupta, R., & Prakash, M. (2021). \"Mobile Diet Tracking Apps for Weight Management: A Systematic Review.\" Nutrition Journal, 20(1), 35. doi: 10.1186/s12937-021-00684-8. [12] Nyenwe, E., Cleland, M., & Tomlinson, S. (2020). \"Artificial Intelligence in the Self-Management of Type 2 Diabetes.\" Digital Health, 6, 2055207619896467. doi: 10.1177/2055207619896467. [13] Alaa, A. M., & van der Schaar, M. (2020). \"Personalized Nutrition through Artificial Intelligence and Machine Learning.\" Current Developments in Nutrition, 4(3), 124-129. doi: 10.1093/cdn/nzaa023. [14] Hughes, A., Hibbert, L., & Robertson, S. (2020). \"Applications of AI in Pediatric Nutrition: A Literature Review.\" Pediatric Research, 87(5), 847-854. doi: 10.1038/s41390-019-0552-3. [15] Hu, X., Sun, Z., & Wang, L. (2020). \"AI-Based Dietary Applications for Healthy Eating and Nutrient Tracking.\" Computers in Human Behavior, 105, 106218. doi: 10.1016/j.chb.2019.106218. [16] Becker, W., Lee, H., & Paul, T. (2020). \"Machine Learning Applications in Precision Nutrition.\" Frontiers in Nutrition, 7, 107. doi: 10.3389/fnut.2020.00107. [17] Jovanovic, L., Marquez, D., & Carmody, C. (2020). \"AI-Powered mHealth Apps in Diet and Weight Management.\" International Journal of Environmental Research and Public Health, 17(15), 5295. doi: 10.3390/ijerph17155295. [18] Kaur, P., Gupta, R., &Bedi, P. (2020). \"Artificial Intelligence Techniques for Personalized Dietary Recommendations in Chronic Disease Management.\" Journal of Medical Systems, 44(9), 160. doi: 10.1007/s10916-020-01623-4. [19] Chen, J., Liang, Y., & Zou, X. (2019). \"AI and ML in Nutritional Interventions: Challenges and Opportunities.\" Nutrients, 11(11), 2734. doi: 10.3390/nu11112734. [20] Larsson, S., & Attwood, R. (2019). \"Emerging AI Tools for Personalized Nutrition in Aging Populations.\" Aging and Health Research, 19(4), 433-440. doi: 10.1016/j.ahr.2019.06 [21] A. D. Murumkar, A. Singh, B. R. Chachar, P. D. Bagade and G. Zaware, \"Artificial Intelligence (AI) based Nutrition Advisorusing an App,\" 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2023, pp. 586-590, doi: 10.1109/ICSCSS57650.2023.10169703. keywords: {Proteins;Schedules;Dairyproducts;Machinelearning;Artificialintelligence;Diseases;AI (Artificial Intelligence);Diet Plan;Counselor;BMI (Body Mass Index);Dietitian;AItechnology;DietEvaluation;Intelligent Health Management;Smart Diet Plan},Okaniwa F, Yoshida H Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled TrialJMIR Form Res 2022;6(6):e30630URL: https://formative.jmir.org/2022/6/e30630 DOI: 10.2196/30630 [22] Limketkai, B.N., Mauldin, K., Manitius, N. et al. The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition. Curr Surg Rep9, 20 (2021). https://doi.org/10.1007/s40137-021-00297-3 [23] Hoang YN, Chen Y, Ho DKN, et al. Consistency and Accuracy of Artificial Intelligence for Providing Nutritional Information. JAMA Netw Open. 2023;6(12):e2350367. doi:10.1001/jamanetworkopen.2023.50367 [24] Y. Mao and L. Zhang, \"Optimization of the Medical Service Consultation System Based on the Artificial Intelligence of the Internet of Things,\" in IEEE Access, vol. 9, pp. 98261-98274, 2021, doi: 10.1109/ACCESS.2021.3096188. [25] keywords: {Medical diagnostic imaging;Medicalservices;Artificialintelligence;Diseases;Monitoring;Convolutional neural networks;Intelligent medical treatment;consultation system optimization;convolutional neural network;the Internet of Things} [26] MélinaCôté and Benoît Lamarche. 2022. Artificial intelligence in nutrition research: perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism. 47(1): 1-8. https://doi.org/10.1139/apnm-2021-0448, [27] Knights, V.; Kolak, M.; Markovikj, G.; GajdošKljusuri?, J. Modeling and Optimization with Artificial Intelligence in Nutrition. Appl. Sci.2023, 13, 7835. https://doi.org/10.3390/app13137835 [28] Theodore Armand, T.P.; Nfor, K.A.; Kim, J.-I.; Kim, H.-C. Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients2024, 16, 1073. https://doi.org/10.3390/nu16071073 [29] Taiki Miyazawa, Yoichi Hiratsuka, Masako Toda, NozomuHatakeyama, Hitoshi Ozawa, Chizumi Abe, Ting-Yu Cheng, Yuji Matsushima, Yoshifumi Miyawaki, KinyaAshida, Jun Iimura, Tomohiro Tsuda, HirotoBushita, Kazuichi Tomonobu, Satoshi Ohta, Hsuan Chung, Yusuke Omae, Takayuki Yamamoto, Makoto Morinaga, Hiroshi Ochi, Hajime Nakada, Kazuhiro Otsuka, Teruo Miyazawa, Artificial intelligence in food science and nutrition: a narrative review, Nutrition Reviews, Volume 80, Issue 12, December 2022, Pages 2288–2300, https://doi.org/10.1093/nutrit/nuac033 [30] Theodore Armand TP, Nfor KA, Kim JI, Kim HC. Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients. 2024 Apr 6;16(7):1073. doi: 10.3390/nu16071073. PMID: 38613106; PMCID: PMC11013624. [31] Salinari, A.; Machì, M.; Armas Diaz, Y.; Cianciosi, D.; Qi, Z.; Yang, B.; FerreiroCotorruelo, M.S.; Villar, S.G.; Dzul Lopez, L.A.; Battino, M.; et al. The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases2023, 11, 97. https://doi.org/10.3390/diseases11030097 [32] Joshi, S., Bisht, B., Kumar, V. et al. Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare. Syst Microbiol and Biomanuf4, 86–101 (2024). https://doi.org/10.1007/s43393-023-00200-4 [33] Sosa-Holwerda, A.; Park, O.-H.; Albracht-Schulte, K.; Niraula, S.; Thompson, L.; Oldewage-Theron, W. The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients2024, 16, 2066. https://doi.org/10.3390/nu16132066

Copyright

Copyright © 2025 Aastha Bhatt, Devang Mestry, Vedant Sawant , Dr. Vaishali Wadhe. 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.