Digital Governance of Nutrition and Food Security in the Arctic

Amid the accelerating digital transformation of public administration, the development of resilient mechanisms for ensuring food security and promoting health-preserving nutrition in the Arctic Zone of the Russian Federation is gaining particular relevance. This study aims to provide a scientific rationale for a digital nutrition management model based on the integration of intelligent technologies into the system of state social policy.
The objective of the research is to develop a scientifically grounded model for digital nutrition and food security management in the Arctic under intensifying climatic, logistical, and infrastructural constraints. In the context of increasing climate instability, nutritional and cognitive deficits, digital inequality, and limited access to medical care, there is a growing need to shift from traditional food assistance to a flexible system of adaptive and intelligent population nutrition governance.
The methodology combines structural-functional and comparative analysis, digital modeling, elements of behavioral diagnostics, microbiome-based approaches, and geospatial analytics. Scenario-based risk assessments, biosensor monitoring technologies, and tools for analyzing nutritional vulnerability are employed, taking into account demographic, climatic, and behavioral factors.
The results include the development of an original platform-based model for digital nutrition management, integrating telemedicine solutions, intelligent algorithms for dietary assessment and adjustment, digital dietary behavior traces, biosensors, wearable devices, and domestic digital products (e.g., «1C: Planned Nutrition», and the cloud-based «Scientific Nutrition Analysis Platform» (NIAP)). A system of indicators for early detection of alimentary risks in northern and Arctic municipalities is proposed, along with innovative mechanisms for personalized nutritional support, including digital diet twins and chrono-nutritional adaptation algorithms.
The conclusions affirm that digital nutrition in the Russian Arctic functions as a strategic resource for social sovereignty, adaptability, and sustainable development. Adaptive intelligent nutrition solutions enable the state to rapidly adjust social policy measures in response to regional challenges, prevent dietary deficiencies, and contribute to achieving the goals of national projects in demographics, healthcare, and digital transformation. 

1. Relevance of assessing the nutritional status in children with chronic heart failure / L. A. Gandaeva, T. E. Borovik, E. N. Basargina, et al. // Voprosy sovremennoi pediatrii (Current Pediatrics). 2015;14(6):699–705. https://doi.org/10.15690/vsp.v14i6.1479.

2. Borovikov V. P. Statistical Analysis and Data Processing in the SPSS Environment. St. Petersburg: Piter, 2021. 400 p. (In Russ.).

3. Vlasov A. N., Grishina E. O. Business analytics tools for big data visualization: a comparison of Power BI and Tableau // Economics and Management: Problems, Solutions [Ekonomika i upravlenie: problemy, resheniya]. 2021. N 4 (124). P. 98–104. (In Russ.).

4. Zubarev A. V., Osipov S. Yu. Plotly Dash in scientific and educational projects: a review of functionality // Modern Information Technologies and IT Education [Sovremennye informatsionnye tekhnologii i IT obrazovanie]. 2023. Vol. 19, N 1. P. 112–119. (In Russ.).

5. Ilyin E. P., Kukarenko N. N., Gribanov A. V. Human adaptation to extreme conditions and intestinal microbiota dysbiosis // Topical Issues of Medical Microbiology [Aktual’nye voprosy meditsinskoi mikrobiologii]. 2020. N 3. P. 12–19. (In Russ.).

6. Use of ArcGIS Pro for monitoring social infrastructure in the northern regions of the Russian Federation /ed. by P. P. Petrov // Regional Economics and Management [Regional’naya ekonomika i upravlenie]. 2021. N 1. P. 88–96. (In Russ.).

7. Kobelkova I. V., Seledkova Yu. A., Korosteleva M. M., Kade M. A., Solntseva T. N. Digital platforms for assessing athletes’ nutrition: comparison of 24-hour recall and food frequency methods using NIAP // Modern Issues of Biomedicine [Modern Issues of Biomedicine]. 2024. Vol. 8, N 4 (30). DOI: 10.24412/2588-0500-2024-8-4-28. (In Russ.).

8. Komarov S. A., Petrova I. Yu. Digital diagnostics of eating behavior: methodological approaches // Medical Informatics and Statistics [Meditsinskaya informatika i statistika]. 2023. N 2. P. 29–36. (In Russ.).

9. Komarov S. A., Khlebnikov E. A. Features of eating behavior among rotational shift workers in the Far North // Social Hygiene and Healthcare Organization [Sotsial’naya gigiena i organizatsiya zdravookhraneniya]. 2022. N 1. P. 18–25. (In Russ.).

10. Komarova E. S., Romanov A. D. Geoinformation dashboards in Plotly Dash and QGIS for spatial analysis of demographic risks // Geoinformation Systems [Geoinformatsionnye sistemy]. 2022. N 2. P. 60–68. (In Russ.).

11. Lavrova N. A., Kulikov A. Yu. Using Power BI for analysis of regional socio-economic indicators // Information Technologies and Computing Systems [Informatsionnye tekhnologii i vychislitel’nye sistemy]. 2021. N 3. P. 47–53. (In Russ.).

12. Makarova I. P., Shuvalov V. A. Tableau as a visualization tool in applied research: from data to decisions // Administrative Consulting [Upravlencheskoe konsul’tirovanie]. 2022. N 6. P. 71–77. (In Russ.).

13. Assessment and correction of nutritional status in chronic obstructive pulmonary disease // Pulmonology [Pul’monologiya]. 2022. N 4. P. 62–70. (In Russ.).

14. Portnov N. M., Egorova L. A. Application of mathematical-statistical methods to assess public health status // Bulletin of Scientific Research [Vestnik nauchnykh issledovanii]. 2020. N 4. P. 45–53. (In Russ.).

15. Rubin B. Agent-based modeling: methods and technologies for simulating human systems // Proceedings of the National Academy of Sciences. 2002. Vol. 99, Suppl. 3. P. 7257–7264. (In Russ.).

16. Savinov A. A., Makhrov A. N. Spatial analysis in regional policy: tools, cases, approaches // Regional Economy: Theory and Practice [Regional’naya ekonomika: teoriya i praktika]. 2022. N 5. P. 74–85. (In Russ.).

17. Sakharov A. A. Sustainable nutrition and the digitalization of northern territories // Problems of Territory Development [Problemy razvitiya territorii]. 2021. N 6 (120). P. 95–108. (In Russ.)

18. The QGIS system as a tool for geospatial analysis of northern regions // Geoinformation Technologies and Cartography [Geoinformatsionnye tekhnologii i kartografiya]. 2022. N 4. P. 45–53. (In Russ.).

19. Skobelkin O. K., Shlyakhto E. V. Problems of nutrition science in extreme climatic conditions // Problems of Nutrition [Voprosy pitaniya]. 2020. Vol. 89, N 6. P. 22–30. DOI: 10.24411/00428833-2020-10063. (In Russ.).

20. Tikhomirov A. V. Digital government and social adaptation: new challenges and approaches // Administrative Consulting [Upravlencheskoe konsul’tirovanie]. 2023. N 4 (172). P. 31–39. (In Russ.).

21. Chernyshov V. G., Lisovskaya N. B. Application of GIS technologies for planning medical care in remote regions // Information Technologies in Medicine [Informatsionnye tekhnologii v meditsine]. 2021. N 2. P. 45–52. (In Russ.).

22. Shchedrovitsky G. P. Comparative analysis as a method of cognition // Questions of Philosophy [Voprosy filosofii]. 1992. N 4. P. 31–42. (In Russ.).

23. Chen J., Lieffers J., Bauman A., Hanning R., Allman-Farinelli M. The use of smartphone health apps and other mobile health (mHealth) technologies in dietetic practice: a three country study // Journal of Human Nutrition and Dietetics. 2017. Vol. 30, N 4. P. 439–452. DOI: 10.1111/ jhn.12446.

24. Dobbie S., Schreckenberg K., Dyke J. G., Schaafsma M., Balbi S. Agent based modelling to assess community food security and sustainable livelihoods // Journal of Artificial Societies and Social Simulation. 2018. Vol. 21, N 1: Article 9 (P. 1–9). DOI: 10.18564/jasss.3639.

25. Ko I., Chang H. Interactive Visualization of Healthcare Data Using Tableau // Health Inform Res. 2017. Vol. 23, N 4. P. 349–354. DOI: 10.4258/hir.2017.23.4.349.

26. Kuznetsova T. Yu., Ivanov V. N. Digitalization of Nutrition and Health Services in the Arctic Regions of Russia // Journal of Arctic Social and Health Studies. 2022. Vol. 8, N 3. P. 15–28.

27. Kuznetsova T. Yu., Morozov A. P. Constructing a Regional Nutritional Adaptation Index: Case of Arctic Russia // Sustainability. 2023. Vol. 15, N 12. P. 10534. DOI: 10.3390/su151210534.

28. Kuznetsova T. Yu., Morozov A. P. Digital Twins of Nutritional Models for Arctic Populations // Journal of Arctic Healthcare and Technology. 2023. Vol. 5, N 2. P. 50–63.

29. Liang Y., Zheng D., Zhang W. Wearable Biosensors for Health Monitoring // Advanced Healthcare Materials. 2021. Vol. 10, N 4. P. 2100120.

30. Nilsson M., Sundqvist J., Nielsen H. Arctic Data Strategies: Systems for Sustainable Food Security // Journal of Arctic Policy Studies. 2022. Vol. 4 (2). P. 45–62.

31. Piwek L., Ellis D. A., Andrews S., Joinson A. The Rise of Consumer Health Wearables: Promises and Barriers // PLoS Medicine. 2016. Vol. 13, N 2. e1001953. DOI: 10.1371/journal.pmed.1001953.

32. Smith C., Johnson M. GIS for Public Health: Integrating Data for Smarter Resource Allocation // International Journal of Health Geographics. 2020. Vol. 19, Article 17. DOI: 10.1186/s12942020-00225-9.

33. Smith C., Johnson M. Wearable Devices and Digital Traces for Real-Time Nutritional Monitoring // Journal of Medical Internet Research. 2021. Vol. 23, N 9. e24589. DOI: 10.2196/24589.

34. Turnbaugh P. J., Hamady M., Yatsunenko T. et al. A core gut microbiome in obese and lean twins // Nature. 2009. Vol. 457. P. 480–484.

35. Van Boorn G., Hengeveld G., Vergeer J. Representing agent-based models to assess the resilience and efficiency of food supply chains // PLOS ONE. 2020. Vol. 15, N 11: e0242323. DOI: 10.1371/journal.pone.0242323.

36. Zhao L., Zhang C., Fang C. et al. Gut microbiota and personalized nutrition: Science and application // Nutrition Reviews. 2022. Vol. 80 (6). P. 1113–1128.