A Novel Mobile App for Personalized Dietary Advice Leveraging Persuasive Technology, Computer Vision, and Cloud Computing: Development and Usability Study

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JMIR Formative Research


Background: The Australian Dietary Guidelines (ADG) translate the best available evidence in nutrition into food choice recommendations. However, adherence to the ADG is poor in Australia. Given that following a healthy diet can be a potentially cost-effective strategy for lowering the risk of chronic diseases, there is an urgent need to develop novel technologies for individuals to improve their adherence to the ADG. Objective: This study describes the development process and design of a prototype mobile app for personalized dietary advice based on the ADG for adults in Australia, with the aim of exploring the usability of the prototype. The goal of the prototype was to provide personalized, evidence-based support for self-managing food choices in real time. Methods: The guidelines of the design science paradigm were applied to guide the design, development, and evaluation of a progressive web app using Amazon Web Services Elastic Compute Cloud services via iterations. The food layer of the Nutrition Care Process, the strategies of cognitive behavioral theory, and the ADG were translated into prototype features guided by the Persuasive Systems Design model. A gain-framed approach was adopted to promote positive behavior changes. A cross-modal image-to-recipe retrieval model under an Apache 2.0 license was deployed for dietary assessment. A survey using the Mobile Application Rating Scale and semistructured in-depth interviews were conducted to explore the usability of the prototype through convenience sampling (N=15). Results: The prominent features of the prototype included the use of image-based dietary assessment, food choice tracking with immediate feedback leveraging gamification principles, personal goal setting for food choices, and the provision of recipe ideas and information on the ADG. The overall prototype quality score was “acceptable,” with a median of 3.46 (IQR 2.78-3.81) out of 5 points. The median score of the perceived impact of the prototype on healthy eating based on the ADG was 3.83 (IQR 2.75-4.08) out of 5 points. In-depth interviews identified the use of gamification for tracking food choices and innovation in the image-based dietary assessment as the main drivers of the positive user experience of using the prototype. Conclusions: A novel evidence-based prototype mobile app was successfully developed by leveraging a cross-disciplinary collaboration. A detailed description of the development process and design of the prototype enhances its transparency and provides detailed insights into its creation. This study provides a valuable example of the development of a novel, evidence-based app for personalized dietary advice on food choices using recent advancements in computer vision. A revised version of this prototype is currently under development.

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Funding Sponsor

Multiple Sclerosis Australia



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