Easy Nutrition: A Customized Dietary App to Highlight the Food Nutritional Value

  • Mayda Alrige
  • Samir Chatterjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)


Healthy Eating is a two-part system that should strike a balance between food quality and food quantity. In this study, we have designed, developed, and evaluated a nutrition app called, Easy Nutrition to highlight the nutritional value/quality of the food we eat. We introduced the novel concept of Nuval rather than old concepts such as calorie counting. In this context, Easy Nutrition presents the food nutrition in a simple, easy to understand manner. Easy Nutrition also tackles the cultural differences by suggesting recipes tailored to users’ food preferences. This paper delineates the build and evaluate phase of Easy Nutrition. Easy Nutrition has been evaluated from a sociotechnical perspective in for its of utility and quality. We conducted a cross-sectional study on Amazon Mechanical Turk platform to evaluate Easy Nutrition on a wide population. The results show that Easy Nutrition demonstrates a fairly high level of usability (SUS = 69.1), attractiveness (mean = 1.59), and hedonic and pragmatic quality.


Health app Nutrition mHealth Diet management Calories 


  1. 1.
    Eisenberg, D.M., Burgess, J.D.: Nutrition education in an era of global obesity and diabetes: thinking outside the box. Acad. Med. 90(7), 854–860 (2015)CrossRefGoogle Scholar
  2. 2.
    Ogden, C.L., Carroll, M.D., Fryar, C.D., Flegal, K.M.: Prevalence of obesity among adults and youth: United States, 2011–2014, November 2015. Accessed 13 Dec 2016
  3. 3.
    Frieden, T.R., Rothwell, C.: Health, United States (2015)Google Scholar
  4. 4.
    American Diabetes Association: 2451 Crystal Drive, Statistics about diabetes. American Diabetes Association. Accessed 12 Dec 2016
  5. 5.
    Arens-Volland, A.G., Spassova, L., Bohn, T.: Promising approaches of computer-supported dietary assessment and management-current research status and available applications. Int. J. Med. Inf. 84(12), 997–1008 (2015)CrossRefGoogle Scholar
  6. 6.
    Ma, Y., et al.: PDA-assisted low glycemic index dietary intervention for type II diabetes: a pilot study. Eur. J. Clin. Nutr. 60(10), 1235–1243 (2006)CrossRefGoogle Scholar
  7. 7.
    Theng, Y.-L., Lee, J.W.Y., Patinadan, P.V., Foo, S.S.B.: The use of videogames, gamification, and virtual environments in the self-management of diabetes: a systematic review of evidence. Games Health J. 4(5), 352–361 (2015)CrossRefGoogle Scholar
  8. 8.
    El-Gayar, O., Timsina, P., Nawar, N., Eid, W.: Mobile applications for diabetes self-management: status and potential. J. Diab. Sci. Technol. 7(1), 247–262 (2013)CrossRefGoogle Scholar
  9. 9.
    Arsand, E., et al.: Mobile health applications to assist patients with diabetes: lessons learned and design implications. J. Diab. Sci. Technol. 6(5), 1197–1206 (2012)CrossRefGoogle Scholar
  10. 10.
    Nuval SystemGoogle Scholar
  11. 11.
    ANDI Food Scores: Rating the Nutrient Density of Foods. Accessed 31 Oct 2016
  12. 12.
    Alrige, M., Chatterjee, S., Medina, E., Nuval, J.: Applying the concept of nutrient-profiling to promote healthy eating and raise individuals awareness of the nutritional quality of their food. In: proceeding of AMIA2017, Washington, DC (2017) Google Scholar
  13. 13.
    Epstein, L.H., Myers, M.D., Raynor, H.A., Saelens, B.E.: Treatment of pediatric obesity. Accessed 09 Dec 2016
  14. 14.
    Valocki, A.: Nutrient intake of obese children in a family-based behavioral weight control program. Int. J. Obes. 14(8), 667–677 (1990)Google Scholar
  15. 15.
    Epstein, L.H., Wing, R.R., Koeske, R., Ossip, D., Beck, S.: A comparison of lifestyle change and programmed aerobic exercise on weight and fitness changes in obese children. Behav. Ther. 13(5), 651–665 (1982)CrossRefGoogle Scholar
  16. 16.
    Epstein, L.H., Wing, R.R., Steranchak, L., Dickson, B., Michelson, J.: Comparison of family-based behavior modification and nutrition education for childhood obesity. J. Pediatr. Psychol. 5(1), 25–36 (1980)CrossRefGoogle Scholar
  17. 17.
    Epstein, L.H., et al.: Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol. 14(2), 109 (1995)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Epstein, L.H.: Child and parent weight loss in family-based behavior modification programsGoogle Scholar
  19. 19.
    Duffy, G., Spence, S.H.: The effectiveness of cognitive self-management as an adjunct to a behavioural intervention for childhood obesity: a research note - Google Search. J. Child Psychol. Psychiatry 34(6), 1043–1050 (1993)CrossRefGoogle Scholar
  20. 20.
    Hevner, A., Chatterjee, S.: Design science research in information systems. In: Hevner, A., Chatterjee, S. (eds.) Design Research in Information Systems, vol. 22, pp. 9–22. Springer, Boston (2010)CrossRefGoogle Scholar
  21. 21.
    Meth, H., Mueller, B., Maedche, A.: Designing a requirement mining system. J. Assoc. Inf. Syst. 16(9), 799 (2015)Google Scholar
  22. 22.
    Bader, A., Gougeon, R., Joseph, L., Da Costa, D., Dasgupta, K.: Nutritional education through internet-delivered menu plans among adults with type 2 diabetes mellitus: pilot study. JMIR Res. Protoc. 2(2), e41 (2013)CrossRefGoogle Scholar
  23. 23.
    Evert, A.B., et al.: Nutrition therapy recommendations for the management of adults with diabetes. Diab. Care. 37(Supplement_1), S120–S143 (2014)CrossRefGoogle Scholar
  24. 24.
    Walker, S.N., Sechrist, K.R., Pender, N.J.: The health-promoting lifestyle profile: development and psychometric characteristics. Nurs. Res. 36(2), 76 (1987)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Claremont Graduate UniversityClaremontUSA

Personalised recommendations