Mobile health architecture for obesity management using sensory and social data

One of the principal causes of several chronic diseases (e.g., diabetes, high cholesterol, and hypertension) is the obesity epidemic in high and middle income countries. Obesity also leads to an increasingly negative effect on public health resources. Therefore, obesity and overweight have to be monitored to mitigate and prevent the potential risks generated from the threat of related diseases and from reducing productivity experienced by businesses. A mobile-health monitoring system includes sensing, transmitting, storing, processing, and analyzing intensive, continuous, and heterogeneous medical data. However, current approaches are standalone mobile applications, augmented mobile applications, or mobile health systems. These approaches only consider simple activities (assess, detect, or control obesity) and rely on a mobile phone to perform complex processing operations on the collected data. Such complex operations need (1) efficient data mining techniques, (2) more memory consumption and processing time, and (3) long life mobile battery. In this work, we develop a new comprehensive mobile architecture for tackling the challenging issues of obesity control, monitoring, and prevention. We introduce a set of business requirements considering stakeholders, sensor devices, and architecture requirements to meet our architecture's objectives. Our architecture system can also help individuals track food intake, lifestyle, calories intake, calories consumption, and exercise activities. We analyze the data collected from continuous monitoring using non-invasive sensors, in addition to the data collected from social communities created to propagate awareness and share appropriate information about the obesity problem and its solution. We develop data mining algorithms and sentiment analysis algorithms and generate intelligent suggestions, warnings, and recommendations to control and mitigate the risk of obesity and its related diseases. We develop schemes for reducing data and saving energy, which minimize the amount of network traffic within the community of sensors. Moreover, we totally implement our architecture system as a collection of Web services organized by the model–view–controller design pattern to write, retrieve, and access data to and from the cloud storage firebase. We finally evaluate the efficacy and scalability of the implemented system using a comprehensive cloud database including entered data, calculated data, sensory data, and social data of 50 underweight, overweight, normal, and obese volunteer subjects. The obtained results show our architecture's objectives are fulfilled.