International Hellenic University, Greece
* Corresponding author
Ionian University Corfu, Greece

Article Main Content

Nutrition informatics is a rapidly growing interdisciplinary field of nutrition and dietetics sciences. As seen from large databases (as Pubmed, Weeb of Science etc.) in the field of informatics various applications have been developed that use nutritional data to provide tools that will help nutrition scientists, professionals and practitioners export useful conclusions and produce accurate and personalized guidelines concerning nutrition and diet. On the other hand, bibliometry uses quantitative methods for the analysis of written publications, such as books, papers in journals, conference proceedings, etc. In this paper we use bibliometric measurements and methods in order to examine the growing association and interconnection of informatics applications and nutrition.

References

  1. Guijt WJ, de Steenhuijsen Piters CB, Smaling EMA. Transforming food systems: governance for healthy, inclusive and sustainable food systems. 2021. (Wageningen Economic Research / report; No. 2021-093). Wageningen Economic Research. https://edepot.wur.nl/554386.
     Google Scholar
  2. American Health Information Management Association (AHIMA). AHIMA’s recommendations to ensure privacy and quality of personal health information on the Internet. [Internet]. 2000. Available from:
     Google Scholar
  3. http://www.ahima.org/infocenter/guidelines/tenets.html.
     Google Scholar
  4. List of freeware. 7 Best Free Nutrition Analysis Software. [Internet]. 2022. Available from: https://listoffreeware.com/best-free-nutrition-analysis-software/.
     Google Scholar
  5. Eat right Pro. Academy of nutrition and dietetics. Nutrition Informatics. [Internet]. Available from:
     Google Scholar
  6. https://www.eatrightpro.org/practice/practice-resources/nutrition-informatics.
     Google Scholar
  7. Chan L, Vasilevsky N, Thessen A, McMurry J, Haendel M. The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research. Database: The Journal of Biological Databases and Curation, 2021, baab003. https://doi.org/10.1093/database/baab003.
     Google Scholar
  8. Angarita-Zapata JS, Alonso-Vicario A, Masegosa AD, Legarda J. A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective. Sensors (Basel, Switzerland), 2021;21(20):6910. https://doi.org/10.3390/s21206910.
     Google Scholar
  9. Androulaki K, Dimitropoulakis P, Marak M, Markaki A, Fragkiadakis GA. [Internet]. (2014) Evaluation of two dietetics-software programs. Available from:
     Google Scholar
  10. https://www.academia.edu/31730211/Evaluation_of_two_Dietetics_software_programs
     Google Scholar
  11. Benedik E, Seljak B, Hribar M, Rogelj I, Bratani? B, Orel R, Fidler Mis N. Comparison of a web-based dietary assessment tool with software for the evaluation of dietary records. Slovenian Journal of Public Health, 2015;54:91–97.
     Google Scholar
  12. Gregori? M, Zdešar Kotnik K, Pigac I, Gabrijel?i? Blenkuš M. A web-based 24-h dietary recall could be a valid tool for the indicative assessment of dietary intake in older adults living in Slovenia. Nutrients, 2019;11(9):2234. https://doi.org/10.3390/nu11092234.
     Google Scholar
  13. Stumbo P. Considerations for selecting a dietary assessment system. Journal of food composition and analysis: an official publication of the United Nations University, International Network of Food Data Systems, 2008;21(Supplement 1), S13–S19. https://doi.org/10.1016/j.jfca.2007.07.011.
     Google Scholar
  14. Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, Albarede JL. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999 Feb;15(2):116–22. doi: 10.1016/s0899-9007(98)00171-3. PMID: 9990575.
     Google Scholar
  15. Zenun FR, Fallaize R, Lovegrove J, Hwang F. Popular nutrition-related mobile apps: a feature assessment. JMIR mhealth and uhealth. 2016;4. 10.2196/mhealth.5846.
     Google Scholar
  16. sourceforge.net. Nutrition Analysis Software. 2022. Available from: https://sourceforge.net/software/nutrition-analysis/.
     Google Scholar
  17. Central PubMed [Internet]. 2022. Available from: https://www.ncbi.nlm.nih.gov/pmc/.
     Google Scholar
  18. Aristovnik A, Ravšelj D, Umek L. A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape. Sustainability. 2020; 12(21):9132.
     Google Scholar
  19. https://doi.org/10.3390/su12219132
     Google Scholar
  20. Leeuwis C, Boogaard BK, Atta-Krah K. How food systems change (or not): governance implications for system transformation processes. Food security, 2021;13(4):761–780. https://doi.org/10.1007/s12571-021-01178-4.
     Google Scholar
  21. Captera. Food Service Management Software. [Internet]. 2022. Available from:
     Google Scholar
  22. https://www.capterra.com/food-service-management-software/Sdfsd.
     Google Scholar
  23. USDA – U.S. Department of Agriculture, Nutrition databases. Available from: https://www.usda.gov/.
     Google Scholar
  24. WHO – World Health Organization. Use of Nutrition Data in Decision Making: A Review Paper. [Internet]. 2020. Available from:
     Google Scholar
  25. https://www.who.int/publications/m/item/use-of-nutrition-data-in-decision-making-a-review-paper.
     Google Scholar
  26. Stefanidis V, Poulos M, Papavlasopoulos S. Bibliometrics associations between EEG entropies and connections between learning disabilities and the human brain activity. International Journal of Computers, 2018a;3:177–181.
     Google Scholar
  27. Stefanidis V, Poulos M, Papavlasopoulos S. Bibliometrics EEG metrics associations and connections between learning disabilities and the human brain activity, knowledge-based software engineering. Springer Smart Innovation, Systems and Technologies book series, 2018b.
     Google Scholar
  28. Stefanidis V, Anogiannakis G, Evangelou A, Poulos M. Learning difficulties prediction. International Journal of Biomedical Engineering and Technology, 2016;21(2):176–189.
     Google Scholar
  29. Stefanidis V, Anogiannakis G, Evangelou A, Poulos M. Stable EEG features, optimization, control, and applications in the information age. Springer Proceedings in Mathematics & Statistics, vol. 130, pp. 349–357, 2015.
     Google Scholar
  30. Stefanidis V, Papavlasopoulos S, Poulos M. Bibliometrics associations between EEG entropies and connections between learning disabilities and the human brain activity. 5th International Conference on Engineering and Technology Education (ETE '18), London, UK, October 26–28, 2018.
     Google Scholar
  31. Stefanidis V, Poulos M, Papavlasopoulos S. Bibliometrics EEG metrics associations and connections between learning disabilities and the human brain activity. 12th Joint Conference on Knowledge-Based Software Engineering (JCKBSE 2018), Corfu, Greece, August 27–30, 2018.
     Google Scholar
  32. Poulos ?, Stefanidis V, Anogianakis G, Evangelou A. synchronization of small set data on stable period. IEEE Proceedings, Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), pp. 263–267, 2016.
     Google Scholar
  33. Stefanidis V, Anogianakis G, Evangelou A, Poulos ?. Learning difficulties prediction using multichannel brain evoked potential data. IEEE Proceedings, Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), pp. 268–272, 2016.
     Google Scholar
  34. Poulos ?, Stefanidis V. Synchronization of small set data on stable period. 2nd International Conference Mathematics and Computers in Science and Industry, Malta, 2015.
     Google Scholar
  35. Stefanidis V, Poulos ?. Learning difficulties prediction. 2nd International Conference Mathematics and Computers in Science and Industry, Malta, 2015.
     Google Scholar
  36. Cohen J. Bioinformatics — An introduction for computer scientists. ACM Compu-Ting Surveys, 2004:36(ue 2,122–158, June).
     Google Scholar
  37. Derangula L. An Overview of nutrition informatics: a public health perspective. Acta Scientific Nutritional Health, 2021:16–17.
     Google Scholar
  38. Garner RM, Hirsch JA, Albuquerque FC, Fargen KM. Bibliometric indices: defining academic productivity and citation rates of researchers, departments and journals. Journal of Neuro Interventional Surgery, 2018;10(2):102–106. https://doi.org/10.1136/neurintsurg-2017-013265.
     Google Scholar
  39. Hoggle LB, Michael MA, Houston SM, Ayres EJ. Electronic health record: Where does nutrition fit in? Journal of the American Dietetic Association, 2006;106(10):1688–1695. https://doi.org/10.1016/j.jada.2006.07.031.
     Google Scholar
  40. Matthews G, Reinerman-Jones L, Abich J, Kustubayeva A. Metrics for individual differences in EEG response to cognitive workload: Optimizing per-formance prediction. Personality and Individual Differences, 2017;118:22–28.
     Google Scholar
  41. Rusnak S, Charney P. Position of the Academy of nutrition and dietetics: nutrition informatics. J. Acad. Nutr. Diet, 2019;119:1375–1382.
     Google Scholar
  42. Stapley BJ, Benoit G. Biobibliometrics: Information retrieval and visualization from co-occurrences of gene names in Medline abstracts. Pacific Symposium on Biocomputing, 2000:529–540. https://doi.org/10.1142/9789814447331_0050.
     Google Scholar
  43. Wikipedia.org. Introduction to general relativity [Internet]. 2021 [updated 2021 May 28; cited 2021 July 13]. [9 screens]. Available from:https://en.wikipedia.org/wiki/Introduction_to_general_relativity.
     Google Scholar
  44. Queensland University of Technology. Writing literature reviews. [Internet]. Available from:
     Google Scholar
  45. http://www.citewrite.qut.edu.au/write/litreviews.jsp.
     Google Scholar