Design and Development of a Mobile-Based Self-Care Application for Patients with Type 2 Diabetes.

INTRODUCTION: Mobile-based applications play a leading role in changes in life-style, improve medication adherence, and provide a unique opportunity to aid patients with type 2 diabetes mellitus (T2DM) elevate their healthcare level. Therefore, we aim to design and develop a mobile-based self-care application for patients with T2DM. METHODS: The present study was an applied and developmental study to design and develop a mobile-based self-care application for people living with T2DM conducted in 2020. The design and development of the T2DM self-care application were done in 2 main phases of determining the key features and capabilities, and design and development of the T2DM self-care mobile app. RESULTS: We identified the main model and a set of capabilities and features for the T2DM self-care application. By content analysis on 32 different applications and a previous study by the author, 18 features were extracted for the T2DM self-care mobile app. JAVA programming languages were used to design T2DM applications. Moreover, because of the cost-effectiveness, the Android operating system (AOS) was selected as a platform, and because of the widespread use of smartphones; these phones were chosen as the format of T2DM self-care application. CONCLUSIONS: In this study, we design and develop a mobile-based self-care application for patients with type 2 diabetes that shows potential in solving the shortcomings of mobile apps for diabetes care. By utilizing the T2DM self-care mobile app we are able to deploy a self-care application with a wide range of functionality such as text messaging, blood glucose monitoring, insulin dose suggestions, educational messaging, metabolic management, pedometer counts, and reporting. Future studies are needed to develop self-care applications for a different type of diabetes with different functions of diabetes care.

Identifying features of a mobile-based application for self-care of people living with T2DM.

INTRODUCTION: Diabetes self-care requires support to empower patients to improve self-monitoring and maintain the necessary self-care behaviors. We aimed to identify features of a mobile-based application as a technology-based device for self-care of people living with T2DM. METHOD: This study was conducted in two main phases in 2020. In the first phase, a literature review study was performed to identify the data elements and technical features of the T2DM self-care application. In the second phase, using the information obtained from the review of similar articles, a questionnaire was designed to validate identified requirements. The statistical population of the present study consisted of 22 endocrinologists and metabolic specialists. RESULTS: Identification of 55 data elements and technical features for mobile-based self-care application for people with T2DM, and according to the statistical population, 15data elements for demographic requirements, 16 data elements for clinical requirements, and 17 features for the technical capability of this app were selected. CONCLUSION: Blood sugar monitoring, exercise, nutrition, weight monitoring, and educational capabilities were the most highlighted technical features of the T2DM self-care application. Software designers can use these requirements to design a self-care app for people with type-2 diabetes that can help manage and improve patients' health status.

A Type-2 fuzzy data fusion approach for building reliable weighted protein interaction networks with application in protein complex detection.

Detecting the protein complexes is an important task in analyzing the protein interaction networks. Although many algorithms predict protein complexes in different ways, surveys on the interaction networks indicate that about 50% of detected interactions are false positives. Consequently, the accuracy of existing methods needs to be improved. In this paper we propose a novel algorithm to detect the protein complexes in 'noisy' protein interaction data. First, we integrate several biological data sources to determine the reliability of each interaction and determine more accurate weights for the interactions. A data fusion component is used for this step, based on the interval type-2 fuzzy voter that provides an efficient combination of the information sources. This fusion component detects the errors and diminishes their effect on the detection protein complexes. So in the first step, the reliability scores have been assigned for every interaction in the network. In the second step, we have proposed a general protein complex detection algorithm by exploiting and adopting the strong points of other algorithms and existing hypotheses regarding real complexes. Finally, the proposed method has been applied for the yeast interaction datasets for predicting the interactions. The results show that our framework has a better performance regarding precision and F-measure than the existing approaches.