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Living with diabetes can be tough, especially for those newly diagnosed. How can we support people in making diabetic lifestyle changes necessary for their long-term health? Diabestie is a mobile app that incorporates AI and machine-learning technologies to discover diabetic-friendly dining and products, teach fundamental knowledge, and build healthy habits.
We conducted this project with the design thinking process, a non-linear, iterative approach used by teams to tackle complex problems and create innovative solutions.
We initiated the project by conducting an in-depth research to understand diabetes management, our target audience and their needs.
After researching potential competitors to analyze their strengths and weaknesses, we discovered that they provide diabetes-managing-focus features such as information storage, tracking glucose, carb, and insulin levels, as well as compatibility with wearable devices such as Dexcom.
We vividly heard life experiences from people who has three different types of diabetes, allowing us to gain a deeper insight into their lifestyles, challenges, beliefs, and needs.
We began by asking them questions to uncover the reasons behind their past goal failures and to identify essential user needs. We then organized the gathered data into overlapping categories using affinity mapping.
Based on the interview and affinity mapping, we created three personas to represent three types of diabetes - Type 1, Type 2, and Gestational.
We crafted journey maps to empathize with the daily challenges users might face, allowing us to pinpoint opportunities for potential solutions. Each persona has its own goal and scenario.
After numerous brainstorming sessions, we've outlined two key features based on Bailey and Yewon's needs that bring Diabestie one step closer to becoming a user-friendly tool for enhancing well-being.
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