Obesity has become a worldwide epidemic. Qatar, a rapidly developing country in the Middle East, has seen a sharp increase in the prevalence of obesity. The increase can be attributed to several reasons, including sedentary lifestyles imposed by a harsh climate and the introduction of Western fast food. Mobile technologies have been used and studied as a technology to support individuals' weight loss. The authors have developed a mobile application that implements three strategies drawn from proven theories of behavioral change. The application is localized to the cultural context of its proposed users. The objective of this paper is to present a method through which we adapted the messaging content of a weight loss application to the context of its users while retaining an effective degree of automation. The adaptation addressed body image, eating and physical exercise habits, and regional/cultural needs. The paper discusses how surveying potential users can be used to build a profile of a target population, find common patterns, and then develop a database of text messages. The text messages are automated and sent to the users at specific times of day, as suggested by the survey results.
This chapter introduces a context-appropriate mobile application for sustainable weight loss. Overweight and obesity are acknowledged to have become a worldwide health matter. Addressing weight loss and sustaining efforts remains in many ways a fragile undertaking. Strategies will vary by age group, gender, and social context. Moreover, the cultural, traditional ecosystem will impact weight loss strategies. In this chapter, the authors discuss contributions in the literature for technology-based weight loss support. They design a mobile application that leverages three strategies from proven behaviour change theories (increasing awareness of the aims of dieting, fostering motivation and self-efficacy, and impacting dieters’ attitudes). They adapt the application to the local context of a Middle Easterner’s society by conducting a usability testing experiment with potential users of the application. The authors also apply principles of localization to derive an appropriate application. Beyond the applied usage of the application, the chapter contributes to the currently scarce body of literature on Arabic-based mobile development.
Our aim is to develop a culturally aware robot capable of communicating with people from different ethnic and cultural backgrounds and performing competently in a multi-lingual, cross-cultural context. Our test bed is a female robot receptionist, named Hala, deployed at the reception area in Carnegie Mellon University in Qatar. Hala answers questions in Arabic and English about people, locations of offices, classrooms and other rooms in the building. She also provides information about the weather, Education City, and her personal life. Our first model, Hala 1.0, was a bilingual robot extending an American model whose personality and utterances conform to the American culture. Three years of interaction logs have shown that 89% of Hala 1.0's interactions were in English. We conjecture that this is due to the robot's poor ability to equally portray both Arabic and American cultures and to its limited Arabic content. In order for us to investigate cultural factors that bear on communication significantly, we developed Hala 2.0 which is also a bilingual robot designed to be an Arab-American robot with more Arabic features in appearance, expression and interaction. The robot's personality is constructed taking into account the socio-cultural context in which its interactions will take place. To achieve bilingualism we had to create symmetry between Arabic and English linguistic content. Since the robot's utterances were developed primarily in English we resorted to translating them into Arabic and adapting them to the constraints of our socio-cultural context. Since Arabic is a highly inflected language, we adopted the plural case in formulating the robot's replies so as to avoid gender bias. To improve query coverage, we added word synonyms, including context-related synonyms (exp:هل تحبين عملك؟/ هل يعجبك عملك؟ ) and different formulations for the same question (exp: do you sleep? / do you go to sleep? and هل تنامين؟/ أتنامين؟). Furthermore, based on three years of recorded query logs, we expanded the range of topics that the robot is knowledgeable about by adding 3000 question/answer sentences to increase the robot's capacity for engaging users. All content and utterances were developed to align with the robot's designed personal traits.
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