In search of computer-aided social support in non-communicable diseases care

A computational model for adaptive recording of vital signs through context histories

Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

Applied Computing to Education on Noncommunicable Chronic Diseases: A Systematic Mapping Study

Background: The number of deaths from noncommunicable chronic diseases (NCDs) has increased worldwide. These deaths would be partly avoidable if prevention and follow-up measures were applied to reduce risk factors. Computing can help educate individuals, improving their knowledge about NCDs. This article presents a systematic mapping of studies that apply computing to education on NCDs. The results allow a general view of the literature and the identification of research opportunities. Materials and Methods: The methodology followed three steps: (1) definition of search databases from computer science and health sciences, (2) selection of keywords for search string composition, and (3) application of inclusion and exclusion criteria to filter the results. The survey occurred from January 2008 to April 2018. Results: The initial search resulted in 19,675 papers, of which 38 were selected after applying the filter criteria. The use of mobile computing stood out in 25 papers. The education modalities were self-management and educational content for diabetes (10 papers), asthma (1), cardiovascular disease (1) and chronic diseases (1), self-management of diabetes and games (1), educational content (15), games (6), personalized content (2), and virtual community (1). The percentage of papers on diabetes was 65%. Most solutions (55%) do not use data from individuals to provide information considering their health condition. In addition, 19 papers produced outcome measures by means of experiments. Conclusions: Mobile computing was the most used technology in the papers. In addition, self-management, educational content, and games were the most used mechanisms. A research opportunity consists of personalized assistance. In this sense, ubiquitous learning can provide a continuous and contextualized education.