Advertisement

Healthy Hankerings: Motivating Adolescents to Combat Obesity with a Mobile Application

  • Farzana RahmanEmail author
  • Paul Henninger
  • David Kegley
  • Keegan Sullivan
  • James Yoo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10902)

Abstract

Obesity has become a major public health issue in most countries around the world. In addition, adolescent obesity is increasing in an alarming rate all over the world. Many attempts have been made to address this issue that ranges from doing exercise to following a diet plan to playing games. While the existence of the above works indicates the past and ongoing efforts to combat adolescent obesity, they are clearly not enough since it is still rising. Researchers have found that adolescent obesity controlling has a lot to do with combating unhealthy cravings that needs strong will power and motivation at such an age. Often, young people and their caregiver struggle to find healthy and nutritious recipes that would fulfill the craving of young people. In order to motivate unfit adolescents towards healthy eating, our research tries to provide them with alternative tasty yet healthier food options using a mobile application. In this paper, we present the design and development details of a mobile application, Healthy Hankerings, which has the potential to help deal with adolescent obesity by motivating its users to choose a healthier food option when they have craving for unhealthy or junk food. To find a healthy recipe, the application uses a recognition algorithm, a decision tree based learning algorithm that considers user’s meal intake history and current cravings. To evaluate our application, we also present the usability study of the prototype in this paper.

Keywords

Mobile application Obesity Healthcare Decision tree Yummly database 

References

  1. 1.
    Anwar, M., Hill, E., Skujins, J., Huynh, K., Doss, C.: A smartphone application for health-smart menu selection within a budget. In: Proceedings of ICSH, pp. 113–121 (2013)Google Scholar
  2. 2.
    Bandura, A., McClelland, D.C.: Social Learning Theory. Springer, Berlin (2013).  https://doi.org/10.1007/978-3-642-28753-4
  3. 3.
    Fogg, B.J.: A behavior model for persuasive design. In: Proceedings of Persuasive 2009, pp. 40:1–40:7, New York (2009)Google Scholar
  4. 4.
    Rabbi, M., Aung, M.H., Zhang, M., Choudhury, T.: Mybehavior: automatic personalized health feedback from user behaviors and preferences using smartphones. In: Proceedings of UbiComp 2015, pp. 707–718 (2015)Google Scholar
  5. 5.
    Rodgers, R.F., Pernal, W., Matsumoto, A., Shiyko, M., Intille, S., Franko, D.L.: Capitalizing on mobile technology to support healthy eating in ethnic minority college students. J. Am. Coll. Health 64(2), 125–132 (2016). PMID: 26630479CrossRefGoogle Scholar
  6. 6.
    National Heart Lung and Blood Institute (NHLBI) Obesity Education Initiative, The Practical Guide to the Identification, Evaluation and Treatment of Overweight and Obesity in Adults. NIH Publication Number 00-4084 (2000)Google Scholar
  7. 7.
    Puska, P., Nishida, C., Porter, D.: Obesity and overweight, world health organization (who) - global strategy on diet, physical activity and health fact sheets. Fact Sheet 2 (2003)Google Scholar
  8. 8.
  9. 9.
    Adler, N.E., Stewart, J.: Reducing obesity: motivating action while not blaming the victim. Milbank Q. 87(1), 49–70 (2009)CrossRefGoogle Scholar
  10. 10.
    Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Farzana Rahman
    • 1
    Email author
  • Paul Henninger
    • 2
  • David Kegley
    • 2
  • Keegan Sullivan
    • 2
  • James Yoo
    • 2
  1. 1.Florida International UniversityMiamiUSA
  2. 2.James Madison UniversityHarrisonburgUSA

Personalised recommendations