Patient-oriented systems for digital prevention of chronic non-communicable diseases

Introduction. The development of digitalization in healthcare based on digital information technologies has ensured the growth of patient-oriented systems for the prevention of chronic non-communicable diseases. In this aspect, patient-oriented systems act as a means of managing the health in each person and an innovative tool for digital disease prevention. With digital prevention of the disease we understand the field of digital health, focused on the use of information and communication technologies, namely digital devices and applications, to solve problems of preventive care for the population.

The purpose of the article is to systematize publications devoted to patient-oriented systems for the prevention of chronic non-communicable diseases and to present their multidimensional classification.

The selection of publications was carried out using search engines and information resources elibrary.ru, ScienceDirect, BMJ, MEDLINE/PubMed, Elsevier, Springer, MDPI, Sage Journals, JMIR for the period from 2013 to 2023. As a result of expert analysis, fifty four publications were included in the review. Definitions of the concepts “Digital Prevention”, “Patient-oriented Systems” are given, a multidimensional classification of reports in the field of patient-oriented systems is given: by main purpose (28%), by content (13%), by digital technologies used (39%), by type of tasks being solved in the field of disease prevention (41%). We also analyzed reports that used artificial intelligence technologies within patient-oriented systems (13%) and ready-to-use digital solutions (20%).

Conclusion. The results will contribute to further research, optimal implementation and effective use of digital technologies in the form of patient-oriented systems to improve the results of preventive measures for patients with chronic non-communicable diseases and the development of digital disease prevention.

Contribution of the authors:
Afanasieva T.V. — concept and design of the study, search and synthesis of publications, writing the text, compiling a list of references;
Zamashkin Iu.S. — text writing, processing and analysis of publications, editing.
All authors are responsible for the integrity of all parts of the manuscript and approval of the manuscript final version.

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

Funding. This research was performed within the framework of the state task in the field of scientific activity of the Ministry of Science and Higher Education of the Russian Federation, project “Models, methods, and algorithms of artificial intelligence in the problems of economics for the analysis and style transfer of multidimensional datasets, time series forecasting, and recommendation systems design”, grant no. FSSW-2023-0004.

Received: October 4, 2023/ Accepted: December 20, 2023 / Published: June 30, 2025

1. WHO. Global strategy on digital health 2020–2025; 2021. https://who.int/publications/i/item/9789240020924

2. Drapkina O.M., Kontsevaya A.V., Kalinina A.M., Avdeev S.M., Agaltsov M.V., Alexandrova L.M., et al. 2022 prevention of chronic non-communicable diseases in of the Russian Federation. National guidelines. Kardiovaskulyarnaya terapiya i profilaktika. 2022; 21(4): 5–232. https://doi.org/10.15829/1728-8800-2022-3235 https://elibrary.ru/dnbvat (in Russian)

3. PASSPORT of the Digital Transformation Strategy of the Healthcare industry until 2024 and for the planned period up to 2030; 2021. Available at: https://clck.ru/3M5CPf (in Russian)

4. Feigin V.L., Krishnamurthi R., Merkin A., Nair B., Kravchenko M., Jalili-Moghaddam S. Digital solutions for primary stroke and cardiovascular disease prevention: A mass individual and public health approach. The Lancet Regional Health – Western Pacific. 2022;29. https://doi.org/10.1016/j.lanwpc.2022.100511

5. EC. eHealth: Digital health and care. Available at: https://health.ec.europa.eu/ehealth-digital-health-and-care/overview_en

6. WHO Guideline: Recommendations on digital interventions for health system strengthening; 2019. Available at: https://who.int/publications/i/item/9789241550505

7. Project of Rospotrebnadzor of the Russian Federation «Healthy Nutrition»; 2019. https://здоровое-питание.рф (in Russian)

8. Robles-Bykbaev Y., Oyola-Flores C., Robles-Bykbaev V.E., López-Nores M., Ingavélez-Guerra P., Pazos-Arias J.J., et al. A bespoke social network for deaf women in Ecuador to access information on sexual and reproductive health. Int. J. Environ. Res. Public Health. 2019; 16(20): 3962. https://doi.org/10.3390/ijerph16203962

9. Aggarwal A., Chakradar M., Bhatia M.S., Kumar M., Stephan T., Gupta S.K., et al. COVID-19 risk prediction for diabetic patients using fuzzy inference system and machine learning approaches. J. Healthc. Eng. 2022; 2022: 4096950. https://doi.org/10.1155/2022/4096950

10. Leddy J., Green J.A., Yule C., Molecavage J., Coresh J., Chang A.R. Improving proteinuria screening with mailed smartphone urinalysis testing in previously unscreened patients with hypertension: a randomized controlled trial. BMC Nephrol. 2019; 20(1): 132. https://doi.org/10.1186/s12882-019-1324-z

11. Chen J., Li K., Rong H., Bilal K., Yang N., Li K. A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Inf. Sci. 2018; 435: 124–49. https://doi.org/10.1016/j.ins.2018.01.001

12. Nashif S., Raihan R., Islam R., Imam M.H. Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World J. Eng. Technol. 2018; 6(4): 854–73. https://doi.org/10.4236/wjet.2018.64057

13. Abbas A., Ali M., Khan M.U.S., Khan S.U. Personalized healthcare cloud services for disease risk assessment and wellness management using social media. Pervasive Mob. Comput. 2016; 28: 81–99.

14. Bykov A.V., Korenevskii N.A., Rodionova S.N., Tsymbal E.V. The method and fuzzy model of assessment of dynamics of development of critical ischemia of the lower extremities. Vestnik novykh meditsinskikh tekhnologiy. 2018; (4): 251–7. https://doi.org/10.24411/1609-2163-2018-16227 https://elibrary.ru/yrwkwl (in Russian)

15. Nasiri M., Minaei B., Kiani A. Dynamic recommendation: Disease prediction and prevention using recommender system. Int. J. Basic Sci. Med. 2016; 1(1): 13–7. https://doi.org/10.15171/ijbsm.2016.04

16. Cai Y., Yu F., Kumar M., Gladney R., Mostafa J. Health recommender systems development, usage, and evaluation from 2010 to 2022: A scoping review. Int. J. Environ. Res. Public Health. 2022; 19(22): 15115. https://doi.org/10.3390/ijerph192215115

17. Ferretto L.R., Bellei E.A., Biduski D., Bin L.C.P., Moro M.M., Cervi C.R., et al. A physical activity recommender system for patients with arterial hypertension. IEEE Access. 2020; 8: 61656–64. https://doi.org/10.1109/ACCESS.2020.2983564

18. Afanasieva T.V., Platov P.V. System model and architectural solution of a system of patient-oriented recommendations for risk management of cardiovascular events. Avtomatizatsiya protsessov upravleniya. 2023; (1): 15–24. https://doi.org/10.35752/1991-2927_2023_1_71_15 https://elibrary.ru/jtdymi (in Russian)

19. Granda Morales L.F., Valdiviezo-Diaz P., Reátegui R., Barba-Guaman L. Drug recommendation system for diabetes using a collaborative filtering and clustering approach: development and performance evaluation. J. Med. Internet Res. 2022; 24(7): e37233. https://doi.org/10.2196/37233

20. Chiang P.H., Wong M., Dey S. Using wearables and machine learning to enable personalized lifestyle recommendations to improve blood pressure. IEEE J. Transl. Eng. Health Med. 2021; 9: 2700513. https://doi.org/10.1109/jtehm.2021.3098173

21. Sookrah R., Dhowtal J.D., Nagowah S.D. A DASH diet recommendation system for hypertensive patients using machine learning. In: Proceedings of the 2019 7th International Conference on Information and Communication Technology (ICoICT). Kuala Lumpur; 2019. https://doi.org/10.1109/ICoICT.2019.8835323

22. Jung H., Chung K. Knowledge-based dietary nutrition recommendation for obese management. Inf. Technol. Manag. 2016; 17: 29–42. https://doi.org/10.1007/s10799-015-0218-4

23. Emerencia A., van der Krieke L., Sytema S., Petkov N., Aiello M. Generating personalized advice for schizophrenia patients. Artif. Intell. Med. 2013; 58(1): 23–36. https://doi.org/10.1016/j.artmed.2013.01.002

24. Gellert G.A., Orzechowski P.M., Price T., Kabat-Karabon A., Jaszczak J., Marcjasz N., et al. A multinational survey of patient utilization of and value conveyed through virtual symptom triage and healthcare referral. Front. Public Health. 2023; 10: 1047291. https://doi.org/10.3389/fpubh.2022.1047291

25. Valeeva E.R., Stepanova N.V., Abdullin D.D., Basyyrov A.M. Modern information technologies in forming a healthy lifestyle in population (software "VALEO LIFE"). Mediko-farmatsevticheskii zhurnal Pul’s. 2022; 24(2): 73–80. https://doi.org/10.26787/nydha-2686-6838-2022-24-2-73-80 https://elibrary.ru/cafgbs (in Russian)

26. De Santis K.K., Mergenthal L., Christianson L., Zeeb H. Digital technologies for health promotion and disease prevention in older people: protocol for a scoping review. JMIR Res. Protoc. 2022; 11(7): e37729. https://doi.org/10.2196/37729

27. Santos M.A.G., Munoz R., Olivares R., Filho P.P.R., Del Ser J., de Albuquerque V.H.C. Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook. Inf. Fusion. 2020;53:222–39. https://doi.org/10.1016/j.inffus.2019.06.004

28. Rachata N., Temdee P. Mobile-based self-monitoring for preventing patients with type 2 diabetes mellitus and hypertension from cardiovascular complication. Wireless Pers. Commun. 2021; 117: 151–75. https://doi.org/10.1007/s11277-020-07440-w

29. Clarke S., Jaimes L.G., Labrador M.A. MStress: A mobile recommender system for just-in-time interventions for stress. In: Proceedings of the 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC). Las Vegas; 2017: 1–5.

30. Gómez J., Oviedo B., Zhuma E. Patient monitoring system based on internet of things. Procedia Comput. Sci. 2016; 83: 90–7. https://doi.org/10.1016/j.procs.2016.04.103

31. Hors-Fraile S., Schneider F., Fernandez-Luque L., Luna-Perejon F., Civit A., Spachos D., et al. Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol. BMC Public Health. 2018; 18(1): 698. https://doi.org/10.1186/s12889-018-5612-5

32. Sana F., Isselbacher E.M., Singh J.P., Heist E.K., Pathik B., Armoundas A.A. Wearable devices for ambulatory cardiac monitoring: JACC state-of-the-art review. J. Am. Coll. Cardiol. 2020; 75(13): 1582–92. https://doi.org/10.1016/j.jacc.2020.01.046

33. Duncker D., Ding W.Y., Etheridge S., Noseworthy P.A., Veltmann C., Yao X., et al. Smart wearables for cardiac monitoring-real-world use beyond atrial fibrillation. Sensors (Basel). 2021; 21(7): 2539. https://doi.org/10.3390/s21072539

34. Son D.A., Turdalieva B.S., Aimbetova G.E. Application of modern information technologies to protect public health and prevent chronic non-communicable diseases. Nauka o zhizni i zdorov’e. 2019; (3): 82–7. https://doi.org/10.24411/2415-7414-2019-10042 https://elibrary.ru/szmhio (in Russian)

35. Tran T.N.T., Felfernig A., Trattner C., Holzinger A. Recommender systems in the healthcare domain: state-of-the-art and research issues. J. Intell. Inf. Syst. 2021; 57: 171–201. https://doi.org/10.1007/s10844-020-00633-6

36. Chaix B., Guillemassé A., Nectoux P., Delamon G., Brouard B. Vik: a chatbot to support patients with chronic diseases. Health. 2020; 12(07): 804–10. https://doi.org/10.4236/health.2020.127058

37. Kulikova M.S., Kalinina A.M., Kontsevaya A.V., Drapkina O.M. Remote control of weight loss using the Doctor PM mobile app: the views of patients and healthcare professionals. Profilakticheskaya meditsina. 2022; 25(10): 35‑43. https://doi.org/10.17116/profmed20222510135 https://elibrary.ru/oyksmy (in Russian)

38. Su D., Michaud T.L., Estabrooks P., Schwab R.J., Eiland L.A., Hansen G., et al. Diabetes management through remote patient monitoring: the importance of patient activation and engagement with the technology. Telemed. J.E. Health. 2019; 25(10): 952–9. https://doi.org/10.1089/tmj.2018.0205

39. Kotelnikova E.V., Senchikhin V.N., Lipchanskaya T.P. Possibilities of telemedical monitoring risk factors in patients with cardiovascular diseases: experience of using a patient-oriented model of remote rehabilitation care. Zdravookhranenie Rossiiskoi Federatsii. 2021; 65(6): 549–56. https://doi.org/10.47470/0044-197X-2021-65-6-549-556 https://elibrary.ru/nfzmia (in Russian)

40. Haleem A., Javaid M., Singh R.P., Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sens. Int. 2021; 2: 100117. doi: https://doi.org/10.1016/j.sintl.2021.100117

41. Foster C., Schinasi D., Kan K., Macy M., Wheeler D., Curfman A. Remote monitoring of patient- and family-generated health data in pediatrics. Pediatrics. 2022; 149(2): e2021054137. https://doi.org/10.1542/peds.2021-054137

42. Alian S., Li J., Pandey V.A A personalized recommendation system to support diabetes self-management for American Indians. IEEE Access. 2018; 6: 73041–51. https://doi.org/10.1109/ACCESS.2018.2882138

43. Huygens M.W., Swinkels I.C., de Jong J.D., Heijmans M.J., Friele R.D., van Schayck O.C., et al. Self-monitoring of health data by patients with a chronic disease: does disease controllability matter? BMC Fam. Pract. 2017; 18(1): 1–10. https://doi.org/10.1186/s12875-017-0615-3

44. Agapito G., Simeoni M., Calabrese B., Caré I., Lamprinoudi T., Guzzi P.H., et al. DIETOS: A dietary recommender system for chronic diseases monitoring and management. Comput. Methods Programs Biomed. 2018; 153: 93–104. https://doi.org/10.1016/j.cmpb.2017.10.014

45. Ornament. Personal Health Coach; 2022. Available at: https://ornament.health/

46. Vlasova A. Virtual assistants in medicine. Al’manakh «Iskusstvennyi intellekt». 2022; (11): 94–101. (in Russian)

47. Virtual assistant Medwhat; 2019. Available at: https://medwhat.com

48. SCORE Calculator. Available at: https://cmphmao.ru/node/234 (in Russian)

49. Willis V.C., Thomas Craig K.J., Jabbarpour Y., Scheufele E.L., Arriaga Y.E., Ajinkya M., et al. Digital health interventions to enhance prevention in primary care: scoping review. JMIR Med. Inform. 2022; 10(1): e33518. https://doi.org/10.2196/33518

50. Widmer R.J., Collins N.M., Collins C.S., West C.P., Lerman L.O., Lerman A. Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. Mayo Clin. Proc. 2015; 90(4): 469–80. https://doi.org/10.1016/j.mayocp.2014.12.026

51. Kobrinskii B.A. Intelligent recommender systems for medicine: particularity and limitations. Iskusstvennyi intellekt i prinyatie reshenii. 2022; (3): 51–62. https://doi.org/10.14357/20718594220304 https://elibrary.ru/hhpfqw (in Russian)

52. Chen Y., Perez-Cueto F.J.A., Giboreau A., Mavridis I., Hartwell H. The promotion of eating behaviour change through digital interventions. Int. J. Environ. Res. Public Health. 2020; 17(20): 7488. https://doi.org/10.3390/ijerph17207488/

53. Stark A.L., Geukes C., Dockweiler C. Digital health promotion and prevention in settings: scoping review. J. Med. Internet Res. 2022; 24(1): e21063. https://doi.org/10.2196/21063

54. Clephas P.R.D., Aydin D., Radhoe S.P., Brugts J.J. Recent advances in remote pulmonary artery pressure monitoring for patients with chronic heart failure: current evidence and future perspectives. Sensors (Basel). 2023; 23(3): 1364. https://doi.org/10.3390/s23031364