A Unified Blockchain Schema for Chronic Diet Management

Dali Zhang, Usharani Hareesh Govindarajan, Yongmei Shi, Gagan Narang, Xiaojun Lu

Abstract: Data, information, and technology are improving healthcare aspects such as fitness tracking, patient-doctor communication, and access to health records, etc. Diet management approaches based on a functional estimation involve clinical and computer-supported methods that are utilized in the strategy of treatment. Clinical methods such as dietary records, 24-hour recall, and food frequency are common in practice and majorly rely on a calorie-conscious intake, which though with a cautionary proceeding works as expected, but inherently lags a focus on the processing levels of the ingredients such as the quantifiable content of natural or processed food. Calories consumed from natural sources share contrast to processed food. The exploration, hence, needs a hybrid approach that combines the computer-supported systems to build on a processed food proportion estimation methodology. Emerging augmented reality technologies seamlessly merge real-world environments with computer-supported perceptual information have the potential to solve the problem enabling user decision support through a unified schema of nutritional information. This research presents (i) a blueprint for a unified schema that merges clinical and computer-supported methods that underlie the viable standard for a host of multi-point chronic diet management applications, followed by (ii) a work-in-progress outline of the essential function blocks, resources, clinical considerations, and integrations that form a technology stack for the estimation of processed and non-processed food towards a clinical conscious diet intake.