Start Date
11-12-2016 12:00 AM
Description
Despite a multitude of existing dietary guidelines, the rise of the number of people suffering from a diet-related disease occurs on a yearly basis. Studies show that the response to different diets varies individually, calling for more personalized measures available at any time in any context. Therefore, this paper proposes a research design based on a smartphone app, that delivers automated, personalized dietary recommendations, to encourage a healthier nutrition lifestyle. Founding on previous research in computer and nutritional science, we propose 6 different intervention factors: (1) type of dietary recommendations, (2) dietary assessment, (3) tracking of physical activity via smartphones or smart activity trackers, (4) feedback with visualization of personal nutritional data, (5) feedback with textual explanations behind recommendations, and (6) dietary recommendations including blood values. In an extensive 6-month field study, we plan to examine which of the factors influence a healthier behavior change and long-term app engagement most.
Recommended Citation
Terzimehić, Nađa; Leipold, Nadja; Schaefer, Hanna; Madenach, Mira; Böhm, Markus; Groh, Georg; Groh, Georg; and Gedrich, Kurt, "Can an Automated Personalized Nutrition Assistance System Successfully Change Nutrition Behavior? - Study Design" (2016). ICIS 2016 Proceedings. 17.
https://aisel.aisnet.org/icis2016/ISHealthcare/Presentations/17
Can an Automated Personalized Nutrition Assistance System Successfully Change Nutrition Behavior? - Study Design
Despite a multitude of existing dietary guidelines, the rise of the number of people suffering from a diet-related disease occurs on a yearly basis. Studies show that the response to different diets varies individually, calling for more personalized measures available at any time in any context. Therefore, this paper proposes a research design based on a smartphone app, that delivers automated, personalized dietary recommendations, to encourage a healthier nutrition lifestyle. Founding on previous research in computer and nutritional science, we propose 6 different intervention factors: (1) type of dietary recommendations, (2) dietary assessment, (3) tracking of physical activity via smartphones or smart activity trackers, (4) feedback with visualization of personal nutritional data, (5) feedback with textual explanations behind recommendations, and (6) dietary recommendations including blood values. In an extensive 6-month field study, we plan to examine which of the factors influence a healthier behavior change and long-term app engagement most.