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Title: Understanding and exploring people's food beliefs to design healthy eating applications
Author: Gao, Feng
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2014
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Wellness is a domain of growing interest in computing. Many interventions are designed to modify people’s behavior to make them healthy. But often, they ignore people’s beliefs and socioeconomic context so no sustained change are achieved. This thesis, therefore, focuses on rethinking how we can leverage people’s food beliefs to design healthy eating systems that can achieve sustained change. The thesis begins with a literature review on why healthy eating is complex and how it depends on various contexts including people’s food beliefs. Then we reflect upon current design notion of healthy eating applications and acknowledge the current designs can not address all the complex of healthy eating. Thus, we should explore the alternative design notion, which tries to leverage what people think about food. A study on diet-related topics and social interaction patterns on forums led us to the idea that crowd’s wisdom on food’s healthiness could help people to better understand food and potentially encourage them to change eating behavior. Thus, we designed and evaluated one early prototype that allows people to judge each other’s food’s healthiness by giving ratings and checking 5 predefined heuristics. The outcome of this prototype study confirmed that people like the idea of expressing own thoughts about food and like to have others’ thoughts to validate owns. We further explored exactly what kinds of food beliefs people hold via a survey study with real food pictures. And we found 8 categories of food beliefs and also validated that even experts can not reach consensus on food’s healthiness and thus there is clearly a gap between what we know from nutrition literature and how interventions are actually designed. To further leverage people’s food beliefs, we need to first capture it. We developed three simple UIs to capture such food beliefs, and evaluated the UIs from three different perspectives. We also found two patterns from the data we collect which can help us better visualize the data and identify group of people. In the end of the thesis, we discuss the thesis work and present the future works.
Supervisor: Schraefel, Monica Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: BF Psychology ; QA75 Electronic computers. Computer science ; RA Public aspects of medicine