Journal of Biomedical Physics and Engineering

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Staff Members

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

A Systematic Review of Nutrition Recommendation Systems: With Focus on Technical Aspects

Document Type : Systematic Review

Authors

1 PhD Candidate, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

2 PhD, Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

3 MD, Professor, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran

4 PhD, Professor, Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran

5 PhD, Associate Professor, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran

6 MD, Associate Professor, Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran

7 PhD Candidate, Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran\

8 PhD student, Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran

Abstract

Background: Nutrition informatics has become a novel approach for registered dietitians to practice in this field and make a profit for health care. Recommendation systems considered as an effective technology into aid users to adjust their eating behavior and achieve the goal of healthier food and diet. The purpose of this study is to review nutrition recommendation systems (NRS) and their characteristics for the first time.
Material and Methods: The systematic review was conducted using a comprehensive selection of scientific databases as reference sources, allowing access to diverse publications in the field. The process of articles selection was based on the PRISMA strategy. We identified keywords from our initial research, MeSH database and expert’s opinion. Databases of PubMed, Web of Sciences, Scopus, Embase, and IEEE were searched. After evaluating, they obtained records from databases by two independent reviewers and inclusion and exclusion criteria were applied to each retrieved work to select those of interest. Finally, 25 studies were included.
Results: Hybrid recommender systems and knowledge-based recommender systems with 40% and 32%, respectively, were the mostly recommender types used in NRS. In NRS, rule-based and ontology techniques were used frequently. The frequented platform that applied in NRS was a mobile application with 28%.
Conclusion: If NRS was properly designed, implemented and finally evaluated, it could be used as an effective tool to improve nutrition and promote a healthy lifestyle. This study can help to inform specialists in the nutrition informatics domain, which was necessary to design and develop NRS.

Keywords

References
  1. Cederholm T, Barazzoni R, Austin P, Ballmer P, Biolo G, Bischoff SC, et al. ESPEN guidelines on definitions and terminology of clinical nutrition. Clin Nutr. 2017;36:49-64. doi: 10.1016/j.clnu.2016.09.004. PubMed PMID: 27642056.
  2. Organization WH. Diet, nutrition and the prevention of chronic diseases. World Health Organ Tech Rep Ser. 2003;916:1-149, backcover. PubMed PMID: 12768890.
  3. Organization WH. Diet, nutrition, and the prevention of chronic diseases. Report of a WHO Study Group. World Health Organ Tech Rep Ser. 1990;797:1-204. PubMed PMID: 2124402.
  4. Resilience B. The State Of Food Security And Nutrition In The World. Rome: Building resilience for peace and food security; 2017.
  5. Thompson B, Cohen MJ, Meerman J. World food insecurity and malnutrition: scope, trends, causes and consequences. The impact of climate change and bioenergy on nutrition: Springer; 2012. p. 21-41. doi: 10.1007/978-94-007-0110-6_3.
  6. North JC, Jordan KC, Metos J, Hurdle JF. Nutrition Informatics Applications in Clinical Practice: a Systematic Review. AMIA Annu Symp Proc. 2015;2015:963-72. PubMed PMID: 26958233; PubMed Central PMCID: PMCPMC4765562.
  7. Ayres EJ, Greer-Carney JL, Fatzinger McShane PE, Miller A, Turner P. Nutrition informatics competencies across all levels of practice: a national Delphi study. J Acad Nutr Diet. 2012;112:2042-53. doi: 10.1016/j.jand.2012.09.025. PubMed PMID: 23174690; PubMed Central PMCID: PMCPMC3652246.
  8. Bernstam EV, Smith JW, Johnson TR. What is biomedical informatics? J Biomed Inform. 2010;43:104-10. doi: 10.1016/j.jbi.2009.08.006. PubMed PMID: 19683067; PubMed Central PMCID: PMCPMC2814957.
  9. Trtovac D, Lee J. The Use of Technology in Identifying Hospital Malnutrition: Scoping Review. JMIR Med Inform. 2018;6:e4. doi: 10.2196/medinform.7601. PubMed PMID: 29351894; PubMed Central PMCID: PMCPMC5797288.
  10. Ayres EJ, Hoggle LB. 2011 nutrition informatics member survey. J Acad Nutr Diet. 2012;112:360-7. doi: 10.1016/j.jand.2012.01.003. PubMed PMID: 22709663; PubMed Central PMCID: PMCPMC3640305.
  11. Hoggle LB, Michael MA, Houston SM, Ayres EJ. Nutrition informatics. J Am Diet Assoc. 2006;106:134-9. doi: 10.1016/j.jada.2005.10.025. PubMed PMID: 16390678.
  12. Ayres EJ, Hoggle LB. Advancing practice: using nutrition information and technology to improve health—the nutrition informatics global challenge. Nutrition & Dietetics. 2012;69:195-7. doi: 10.1111/j.1747-0080.2012.01616.x.
  13. Kuo SE, Lai HS, Hsu JM, Yu YC, Zheng DZ, Hou TW. A clinical nutritional information system with personalized nutrition assessment. Comput Methods Programs Biomed. 2018;155:209-16. doi: 10.1016/j.cmpb.2017.10.029. PubMed PMID: 29512501.
  14. Buday R, Tapia R, Maze GR. Technology-driven dietary assessment: a software developer’s perspective. J Hum Nutr Diet. 2014;27 Suppl 1:10-7. doi: 10.1111/j.1365-277X.2012.01255.x. PubMed PMID: 22591224; PubMed Central PMCID: PMCPMC4365297.
  15. Kirk SF, Cade JE, Greenhalgh A. Dietitians and the internet: are dietitians embracing the new technology? J Hum Nutr Diet. 2001;14:477-84. doi: 10.1046/j.1365-277x.2001.00314.x. PubMed PMID: 11906590.
  16. Roberts S, Marshall AP, Gonzalez R, Chaboyer W. Technology to engage hospitalised patients in their nutrition care: a qualitative study of usability and patient perceptions of an electronic foodservice system. J Hum Nutr Diet. 2017;30:563-73. doi: 10.1111/jhn.12467. PubMed PMID: 28211190.
  17. Maunder K, Walton K, Williams P, Ferguson M, Beck E. eHealth readiness of dietitians. J Hum Nutr Diet. 2018;31:573-83. doi: 10.1111/jhn.12542. PubMed PMID: 29473238.
  18. Deshpande S, Basil MD, Basil DZ. Factors influencing healthy eating habits among college students: an application of the health belief model. Health Mark Q. 2009;26:145-64. doi: 10.1080/07359680802619834. PubMed PMID: 19408181.
  19. Schwartz C, Scholtens PA, Lalanne A, Weenen H, Nicklaus S. Development of healthy eating habits early in life. Review of recent evidence and selected guidelines. Appetite. 2011;57:796-807. doi: 10.1016/j.appet.2011.05.316. PubMed PMID: 21651929.
  20. Crovetto M, Valladares M, Espinoza V, Mena F, Onate G, Fernandez M, et al. Effect of healthy and unhealthy habits on obesity: a multicentric study. Nutrition. 2018;54:7-11. doi: 10.1016/j.nut.2018.02.003. PubMed PMID: 29677480.
  21. Tran TNT, Atas M, Felfernig A, Stettinger M. An overview of recommender systems in the healthy food domain. Journal of Intelligent Information Systems. 2018;50:501-26. doi: 10.1007/s10844-017-0469-0.
  22. Leipold N, Madenach M, Schäfer H, Lurz M, Terzimehic N, Groh G, et al., editors. Nutrilize a Personalized Nutrition Recommender System: an Enable Study. HealthRecSys@ RecSys; 2018.
  23. Norouzi S, Kamel Ghalibaf A, Sistani S, Banazadeh V, Keykhaei F, Zareishargh P, et al. A Mobile Application for Managing Diabetic Patients’ Nutrition: A Food Recommender System. Arch Iran Med. 2018;21:466-72. PubMed PMID: 30415555.
  24. Norouzi S, Nematy M, Zabolinezhad H, Sistani S, Etminani K. Food recommender systems for diabetic patients: a narrative review. Reviews in Clinical Medicine. 2016.
  25. Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. Recommender systems handbook: Springer; 2011. p. 1-35. doi: 10.1007/978-0-387-85820-3_1.
  26. Park DH, Kim HK, Choi IY, Kim JK. A literature review and classification of recommender systems research. Expert systems with applications. 2012;39:10059-72. doi: 10.1016/j.eswa.2012.02.038.
  27. Robillard M, Walker R, Zimmermann T. Recommendation systems for software engineering. IEEE software. 2009;27:80-6.
  28. Shani G, Gunawardana A. Evaluating recommendation systems. Recommender systems handbook: Springer; 2011. p. 257-97. doi: 10.1007/978-0-387-85820-3_8.
  29. Chen CH, Karvela M, Sohbati M, Shinawatra T, Toumazou C. PERSON-Personalized Expert Recommendation System for Optimized Nutrition. IEEE Trans Biomed Circuits Syst. 2018;12:151-60. doi: 10.1109/TBCAS.2017.2760504. PubMed PMID: 29377803.
  30. Chen R-C, Huang C-Y, Ting Y-H. A chronic disease diet recommendation system based on domain ontology and decision tree. Journal of Advanced Computational Intelligence and Intelligent Informatics. 2017;21:474-82. doi: 10.20965/jaciii.2017.p0474.
  31. Rehman F, Khalid O, Bilal K, Madani SA. Diet-Right: A Smart Food Recommendation System. KSII Transactions on Internet & Information Systems. 2017;11. doi: 10.3837/tiis.2017.06.006.
  32. Yang L, Hsieh CK, Yang H, Pollak JP, Dell N, Belongie S, et al. Yum-Me: A Personalized Nutrient-Based Meal Recommender System. ACM Trans Inf Syst. 2017;36. doi: 10.1145/3072614. PubMed PMID: 30464375; PubMed Central PMCID: PMCPMC6242282.
  33. Bianchini D, De Antonellis V, De Franceschi N, Melchiori M. PREFer: A prescription-based food recommender system. Computer Standards & Interfaces. 2017;54:64-75. doi: 10.1016/j.csi.2016.10.010.
  34. Raj Kumar B, Latha K. DFRS: Diet food recommendation system for diabetic patients based on ontology. Int J Appl Eng Res. 2015;10:2765-70.
  35. Espín V, Hurtado MV, Noguera M. Nutrition for Elder Care: a nutritional semantic recommender system for the elderly. Expert Systems. 2016;33:201-10. doi: 10.1111/exsy.12143.
  36. Oh Y, Choi A, Woo W. u-BabSang: a context-aware food recommendation system. the Journal of Supercomputing. 2010;54:61-81. doi: 10.1007/s11227-009-0314-5.
  37. Ivaşcu T, Diniş A, Cincar K, editors. A Disease-driven Nutrition Recommender System based on a Multi-agent Architecture. Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics; 2018: ACM. doi: 10.1145/3227609.3227685.
  38. Leipold N, Madenach M, Schäfer H, Lurz M, Terzimehic N, Groh G, et al., editors. Nutrilize a Personalized Nutrition Recommender System: an Enable Study. HealthRecSys@ RecSys; 2018.
  39. Ali SI, Amin MB, Kim S, Lee S, editors. A Hybrid Framework for a Comprehensive Physical Activity and Diet Recommendation System. International Conference on Smart Homes and Health Telematics; 2018: Springer.
  40. Xie J, Wang Q. A personalized diet and exercise recommender system for type 1 diabetes self-management: An in silico study. Smart Health. 2019:100069. doi: 10.1016/j.smhl.2019.100069 .
  41. Li C, Yang C, editors. The research and design of recommendation system for nutritional combo. 2016 2nd IEEE International Conference on Computer and Communications (ICCC); 2016: IEEE. doi: 10.1109/compcomm.2016.7924819.
  42. Zenun Franco R, editor Online Recommender System for Personalized Nutrition Advice. Proceedings of the Eleventh ACM Conference on Recommender Systems; 2017: ACM. doi: 10.1145/3109859.3109862.
  43. Bundasak S, editor A healthy food recommendation system by combining clustering technology with the weighted slope one predictor. 2017 International Electrical Engineering Congress (iEECON); 2017: IEEE. doi: 10.1109/ieecon.2017.8075820.
  44. Agapito G, Simeoni M, Calabrese B, Guzzi PH, Fuiano G, Cannataro M, editors. DIETOS: A Recommender System for Health Profiling and Diet Management in Chronic Diseases. HealthRecSys@ RecSys; 2017.
  45. Ge M, Ricci F, Massimo D, editors. Health-aware food recommender system. Proceedings of the 9th ACM Conference on Recommender Systems; 2015: ACM.
  46. Anggraini RNE, Rochimah S, Dalmi KD, editors. Mobile nutrition recommendation system for 0–2 year infant. 2014 The 1st International Conference on Information Technology, Computer, and Electrical Engineering; 2014: IEEE. doi: 10.1109/icitacee.2014.7065755.
  47. Agapito G, Calabrese B, Care I, Falcone D, Guzzi PH, Ielpo N, et al., editors. Profiling basic health information of tourists: towards a recommendation system for the adaptive delivery of medical certified nutrition contents. 2014 International Conference on High Performance Computing & Simulation (HPCS); 2014: IEEE. doi: 10.1109/hpcsim.2014.6903744.
  48. Donciu M, Ionita M, Dascalu M, Trausan-Matu S, editors. The Runner--Recommender System of Workout and Nutrition for Runners. 2011 13th international symposium on symbolic and numeric algorithms for scientific computing; 2011: IEEE. doi: 10.1109/synasc.2011.18.
  49. Ueta T, Iwakami M, Ito T, editors. A recipe recommendation system based on automatic nutrition information extraction. International Conference on Knowledge Science, Engineering and Management; 2011: Springer. doi: 10.1109/taai.2011.39.
  50. Husain W, Wei LJ, Cheng SL, Zakaria N, editors. Application of data mining techniques in a personalized diet recommendation system for cancer patients. 2011 IEEE Colloquium on Humanities, Science and Engineering; 2011: IEEE. doi: 10.1109/chuser.2011.6163724.
  51. Phanich M, Pholkul P, Phimoltares S, editors. Food recommendation system using clustering analysis for diabetic patients. 2010 International Conference on Information Science and Applications; 2010: IEEE. doi: 10.1109/icisa.2010.5480416.
  52. Kim J-H, Lee J-H, Park J-S, Lee Y-H, Rim K-W, editors. Design of diet recommendation system for healthcare service based on user information. 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology; 2009: IEEE. doi: 10.1109/iccit.2009.293.
  53. Valdez AC, Ziefle M, Verbert K, Felfernig A, Holzinger A. Recommender systems for health informatics: state-of-the-art and future perspectives. Machine Learning for Health Informatics: Springer; 2016. p. 391-414. doi: 10.1007/978-3-319-50478-0_20.
  54. Lam XN, Vu T, Le TD, Duong AD, editors. Addressing cold-start problem in recommendation systems. Proceedings of the 2nd international conference on Ubiquitous information management and communication; 2008: ACM.
  55. Kakihara M, editor Grasping a Global View of Smartphone Diffusion: An Analysis from a Global Smartphone Study. ICMB; 2014.
  56. Wiechmann W, Kwan D, Bokarius A, Toohey SL. There’s an App for That? Highlighting the Difficulty in Finding Clinically Relevant Smartphone Applications. West J Emerg Med. 2016;17:191-4. doi: 10.5811/westjem.2015.12.28781. PubMed PMID: 26973750; PubMed Central PMCID: PMCPMC4786244.
  57. Zhao J, Freeman B, Li M. Can Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review. J Med Internet Res. 2016;18:e287. doi: 10.2196/jmir.5692. PubMed PMID: 27806926; PubMed Central PMCID: PMCPMC5295827.
  58. Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17:e52. doi: 10.2196/jmir.3951. PubMed PMID: 25803266; PubMed Central PMCID: PMCPMC4376208.
Journal of Biomedical Physics and Engineering
Volume 9, Issue 6
36
December 2019
Pages 591-602
Files
Share
How to cite
Statistics