Sports nutrition as an example of effective implementation of innovative trends in nutrition: personalization and digitalization (literature review)

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The nutritional status in an athlete depends on the individual genetic characteristics of the body, the level of physical and psycho-emotional stress, and a balanced diet with the inclusion of specialized food products and dietary supplements. The development of big data analytics and artificial intelligence can contribute to the development of nutritional recommendations at the individual or stratified level.

The purpose of the review is to analyze and summarize research papers devoted to the possibilities of using digital technologies, deep machine learning techniques, and artificial intelligence in the field of sports nutrition to ensure a personalized approach to improving professional success. There were studied papers published in 2004–2024 in domestic and foreign electronic databases: Web of Science, Scopus, eLIBRARY.RU, Russian State Library, library collection of the Federal State Budgetary Scientific Institution “Federal Research Center of Nutrition and Biotechnology”.

The potential for AI-based technologies in sports nutrition is extremely diverse: dietary assessment, recognition and tracking of food diversity, predictive modelling of athletic performance and non-communicable diseases, and selection of personalized diets. To ensure sustainable growth in the coverage of digital products and technologies, further directions for their application in sports medicine should be aimed at improving the quality and standardization of data and reducing algorithmic bias.

Contribution of the authors:
Nikitjuk D.B. — research concept and design, editing, approval of the final version of the article;
Korosteleva M.M. — writing the text, compiling a list of references;
Tarmaeva I.Yu. — design, writing the text.
The co-authors approved the final version of the article and take responsibility for the integrity of all its parts.

Acknowledgment. The study had no sponsorship.

Conflict of interests. The authors declare no conflict of interest.

Received: October 14, 2024 / Accepted: December 11, 2024 / Published: February 28, 2025

作者简介

Dmitriy Nikitjuk

Federal Research Center of Nutrition, Biotechnology and Food Safety

Email: nikitjuk@ion.ru
ORCID iD: 0000-0002-4968-4517

Academician of the Russian Academy of Sciences, Director of the Federal Research Center of Nutrition and Biotechnology, Moscow, 109240, Russian Federation

e-mail: nikitjuk@ion.ru

Margarita Korosteleva

Federal Research Center of Nutrition, Biotechnology and Food Safety

Email: korostel@bk.ru
ORCID iD: 0000-0002-2279-648X

PhD (Medicine), senior researcher, Laboratories of Sports Anthropology and Nutrition, Federal Research Center of Nutrition and Biotechnology, Moscow, 109240, Russian Federation

e-mail: korostel@bk.ru

Inna Tarmaeva

Federal Research Center of Nutrition, Biotechnology and Food Safety

编辑信件的主要联系方式.
Email: tarmaeva@ion.ru
ORCID iD: 0000-0001-7791-1222

DSc (Medicine), Professor, Academic Secretary, Federal Research Center of Nutrition and Biotechnology, Moscow, 109240, Russian Federation

e-mail: tarmaeva@ion.ru

参考

  1. Nikityuk D.B., Kobelkova I.V. Sports nutrition as a model of maximum individualization and implementation of integrative medicine. Voprosy pitaniya. 2020; 89(4): 203–10. https://elibrary.ru/mavzkr (in Russian)
  2. Tutelyan V.A., Nikityuk D.B. Key challenges in the dietary intake structure and cutting-edge technologies for optimizing nutrition to protect the health of the Russian population. Voprosy pitaniya. 2024; 93(1): 6–21. https://doi.org/10.33029/0042-8833-2024-93-1-6-21 https://elibrary.ru/xcdqzj (in Russian)
  3. Kirk D., Catal C., Tekinerdogan B. Precision nutrition: A systematic literature review. Comput. Biol. Med. 2021; 133: 104365. https://doi.org/10.1016/j.compbiomed.2021.104365
  4. Bush C.L., Blumberg J.B., El-Sohemy A., Minich D.M., Ordovás J.M., Reed D.G., et al. Toward the definition of personalized nutrition: a proposal by the american nutrition association. J. Am. Coll. Nutr. 2020; 39(1): 5–15. https://doi.org/10.1080/07315724.2019.1685332
  5. An R., Wang X. Artificial intelligence applications to public health nutrition. Nutrients. 2023; 15(19): 4285. https://doi.org/10.3390/nu15194285
  6. Mehrabi Z., Delzeit R., Ignaciuk A., Levers C., Braich G., Bajaj K., et al. Research priorities for global food security under extreme events. One Earth. 2022; 5(7): 756–66. https://doi.org/10.1016/j.oneear.2022.06.008
  7. Kirk D., Kok E., Tufano M., Tekinerdogan B., Feskens E.J.M., Camps G. Machine learning in nutrition research. Adv. Nutr. 2022; 13(6): 2573–89. https://doi.org/10.1093/advances/nmac103
  8. Fu T., Liu H., Shi C., Zhao H., Liu F., Xia Y. Global hotspots and trends of nutritional supplements in sport and exercise from 2000 to 2024: a bibliometric analysis. J. Health Popul. Nutr. 2024; 43(1): 146. https://doi.org/10.1186/s41043-024-00638-9
  9. Adami P.E., Fitch K. The innovative role of Olympic sports and exercise in the promotion of health, gender equality and sustainability: past achievements and future challenges. J. Sports Med. Phys. Fit. 2021; 61(8): 1042–51. https://doi.org/10.23736/s0022-4707.21.12721-5
  10. Kelly B.S., Judge C., Bollard S.M., Clifford S.M., Healy G.M., Aziz A., et al. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur. Radiol. 2022; 32(11): 7998–8007. https://doi.org/10.1007/s00330-022-08784-6
  11. Waqas A., Bui M.M., Glassy E.F., El Naqa I., Borkowski P., Borkowski A.A., et al. Revolutionizing digital pathology with the power of generative artificial intelligence and foundation models. Lab. Invest. 2023; 103(11): 100255. https://doi.org/10.1016/j.labinv.2023.100255
  12. Sahu A., Mishra J., Kushwaha N. Artificial Intelligence (AI) in drugs and pharmaceuticals. Comb. Chem. High Throughput. Screen. 2022; 25(11): 1818–37. https://doi.org/10.2174/1386207325666211207153943
  13. Kim J., Lin S., Ferrara G., Hua J., Seto E. Identifying people based on machine learning classification of foods consumed in order to offer tailored healthier food options. In: Ahram T., Karwowski W., Vergnano A., Leali F., Taiar R., eds. Intelligent Human Systems Integration 2020. Advances in Intelligent Systems and Computing. Cham: Springer; 2020. https://doi.org/10.1007/978-3-030-39512-4_30
  14. Zellerbach K., Ruiz C. Machine learning to predict overeating from macronutrient composition. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). San Diego, CA; 2019. https://doi.org/10.1109/BIBM47256.2019.8983166
  15. 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. Nutrients. 2024; 16(7): 1073. https://doi.org/10.3390/nu16071073
  16. Salinari A., Machì M., Armas Diaz Y., Cianciosi D., Qi Z., Yang B., et al. The application of digital technologies and artificial intelligence in healthcare: an overview on nutrition assessment. Diseases. 2023; 11(3): 97. https://doi.org/10.3390/diseases11030097
  17. Li T., Wei W., Xing S., Min W., Zhang C., Jiang S. Deep learning-based near-infrared hyperspectral imaging for food nutrition estimation. Foods. 2023; 12(17): 3145. https://doi.org/10.3390/foods12173145
  18. Pouladzadeh P., Shirmohammadi S., Bakirov A., Bulut A., Yassine A. Cloud-based SVM for food categorization. Multimed. Tools Appl. 2015; 74: 5243–60. https://doi.org/10.1007/s11042-014-2116-x
  19. Mezgec S., Koroušić Seljak B. NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients. 2017; 9(7): 657. https://doi.org/10.3390/nu9070657
  20. Lo F.P.W., Sun Y., Qiu J., Lo B. A novel vision-based approach for dietary assessment using deep learning view synthesis. In: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN). Chicago, IL; 2019. https://doi.org/10.1109/BSN.2019.8771089
  21. Priyaa P.K., Sathyapriya S., Arockiam L. Nutrition monitoring and calorie estimation using internet of things (IoT). Int. J. Innov. Technol. Explor. Eng. 2019; 8(11): 2669–72. https://doi.org/10.35940/ijitee.K2072.0981119
  22. Kalantarian H., Alshurafa N., Le T., Sarrafzadeh M. Monitoring eating habits using a piezoelectric sensor-based necklace. Comput. Biol. Med. 2015; 58: 46–55. https://doi.org/10.1016/j.compbiomed.2015.01.005
  23. Kalantarian H., Sarrafzadeh M. Audio-based detection and evaluation of eating behavior using the smartwatch platform. Comput. Biol. Med. 2015; 65: 1–9. https://doi.org/10.1016/j.compbiomed.2015.07.013
  24. Tutelyan V.A., Tarmaeva I.Yu., Kade M.A., Nikityuk D.B. Medicine of the future: the role of artificial intelligence in optimizing nutrition for the health of the Russian population. Voprosy pitaniya. 2024; 93(4): 6–13. https://doi.org/10.33029/0042-8833-2024-93-4-6-13 https://elibrary.ru/kantys (in Russian)
  25. Singer P., Robinson E., Raphaeli O. The future of artificial intelligence in clinical nutrition. Curr. Opin. Clin. Nutr. Metab. Care. 2024; 27(2): 200–6. https://doi.org/10.1097/MCO.0000000000000977
  26. Porras E.M., Fajardo A.C., Medina R.P. Solving dietary planning problem using particle swarm optimization with genetic operators. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing. Da Lat; 2019.
  27. Zitouni H., Meshoul S., Mezioud C. New contextual collaborative filtering system with application to personalized healthy nutrition education. J. King Saud Univ. Comput. Inf. Sci. 2022; 34(4): 1124–37. https://doi.org/10.1016/j.jksuci.2020.04.012
  28. Taye M.M. Understanding of machine learning with deep learning: Architectures, workflow, applications and future directions. Computers. 2023; 12(5): 91. https://doi.org/10.3390/computers12050091
  29. Pray L., ed. Nutrigenomics and the Future of Nutrition: Proceedings of a Workshop. Washington, DC: National Academies Press; 2018.
  30. Panagoulias D.P., Sotiropoulos D.N., Tsihrintzis G.A. Nutritional biomarkers and machine learning for personalized nutrition applications and health optimization. Intell. Decis. Technol. 2021; 15(4): 645–53.
  31. Karakan T., Gundogdu A., Alagözlü H., Ekmen N., Ozgul S., Tunali V., et al. Artificial intelligence-based personalized diet: A pilot clinical study for irritable bowel syndrome. Gut. Microbes. 2022; 14(1): 2138672. https://doi.org/10.1080/19490976.2022.2138672
  32. Lagoumintzis G., Patrinos G.P. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Hum. Genomics. 2023; 17(1): 109. https://doi.org/10.1186/s40246-023-00561-w
  33. Sorokina E.Yu., Denisova N.N., Keshabyants E.E. Frequency of occurrence of genetic polymorphisms associated with sports success in elite athletes in team sports. Sportivnaya meditsina: nauka i praktika. 2021; 11(1): 5–10. https://doi.org/10.47529/2223-2524.2021.1.11 https://elibrary.ru/zkqqsn (in Russian)
  34. Sheveleva S.A., Kuvaeva I.B., Efimochkina N.R., Markova Yu.M., Prosyannikov M.Yu. Gut microbiome: from the reference of the norm to pathology. Voprosy pitaniya. 2020; 89(4): 35–51. https://elibrary.ru/savqcc (in Russian)
  35. Kobelkova I.V., Korosteleva M.M. Influence of basic nutrients on the composition of the intestinal microbiome and optimization of the athletes’ adaptive potential. Nauka i sport: sovremennye tendentsii. 2022; 10(2): 15–26. https://doi.org/10.36028/2308-8826-2022-10-2-15-26 https://elibrary.ru/dsmkuo (in Russian)
  36. Rivera-Pinto J., Egozcue J.J., Pawlowsky-Glahn V., Paredes R., Noguera-Julian M., Calle M.L. Balances: a new perspective for microbiome analysis. mSystems. 2018; 3(4): e00053–18. https://doi.org/10.1128/mSystems.00053-18
  37. Dora M., Kumar A., Mangla S.K., Pant A., Kamal M.M. Critical success factors influencing artificial intelligence adoption in food supply chains. Int. J. Prod. Res. 2022; 60(14): 4621–40. https://doi.org/10.1080/00207543.2021.1959665
  38. Vrinten J., Van Royen K., Pabian S., De Backer C., Matthys C. Development and validation of a short nutrition literacy scale for young adults. Front. Nutr. 2023; 10: 1008971. https://doi.org/10.3389/fnut.2023.1008971
  39. Woschank M., Rauch E., Zsifkovits H. A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability. 2020; 12(9): 3760. https://doi.org/10.3390/su12093760
  40. Pogozheva A.V., Smirnova E.A. To the health of the nation through multi-level educational programs for the population in the field of optimal nutrition. Voprosy pitaniya. 2020; 89(4): 262–72. https://elibrary.ru/mpogej (in Russian)
  41. Tutelyan V.A., Nikityuk D.B., Tarmaeva I.Yu. Formation of the all-Russian education system in the field of healthy food of the population. Gigiena i Sanitaria (Hygiene and Sanitation, Russian journal). 2023; 102(10): 1012–8. https://doi.org/10.47470/0016-9900-2023-102-10-1012-1018 https://elibrary.ru/bfloqs (in Russian)

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