Mobile health monitoring based studies for diabetes mellitus: a review

Abstract

Diabetes as a chronic disease is considered to be a serious problem not only for diabetic patients but also for caregivers, families and countries. Hazardously, as an example, 16% of the Middle East population died every year because of diabetes as it is reported by World Health Organization (WHO). Therefore, it is crucial to utilize the recent advances and technologies to find the best instrument for diabetes monitoring and management. Recently, mobile health (mHealth) technologies have a vital role in the healthcare industrial world. Undoubtedly, mHealth technologies are used to manage, track, monitor, diagnose, and prevent chronic diseases including, diabetes. Certainly, the main advantages of mHealth include a real-time and continuous monitoring with high reliability, accessibility, and availability. In addition to that, mHealth is considered to be a fast, accurate, simple, cheap, comfortable, and safe technology. Hence, the proposed study aims to review existing mHealth studies for managing, diagnosing, tracking, detecting, and predicting diabetic mellitus. Moreover, challenges and future trends of this emerging topic are also discussed.

Model of intention and actual use mobile learning in higher education institutions in Iraq

“…this is because using the ML requires adequate knowledge of the ML and its application. This finding is supported by prior literature such as [15], [31]. The moderating role of gender which showed that males are more involved in the complicated system than female and are affected by PE more than female while the female is more affected by SI indicated that there are differences in term of the users based on their gender.…”

“…The rate of adopting technology is affected by the desire of individuals to use and benefit from the technology [8]- [11]. Against this background, most of the studies focused on ML in developed and emerging economies while the use of this technology in developing countries is still under-researched [12]- [15].…”

Healthcare monitoring system for automatic database management using mobile application in IoT environment

“…Historically, medical diagnosis relied on doctors' expertise to diagnose and discover diseases. Recently, medical diagnostic devices began to spread in conjunction with the emergence of technology, where the semiconductor industry, programming, and artificial intelligence (AI) are playing important roles in diagnosing diseases such as pneumonia [2], cardiovascular diagnosis [3]- [8], breast cancer [9], [10], brain tumors, diabetes diseases [11]- [13], atherosclerosis diseases [14]. Moreover, AI algorithms and the internet of things (IoT) assist us to detect and predict diseases before they occur, such as, blood pressure [15], Parkinson's disease, or to enhance COVID-19 screening, and monitoring by cameras of people who do not wear masks to reduce the spread of pandemics [16], [17].…”

A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

“…Thus, high blood sugar occurs, which leads to many health disorders. Depending on World Health Organization (WHO) and American Diabetes Association (ADA), DM is classified into four types [2]- [4]: i) type-I-DM: or insulin-dependent diabetes mellitus (IDDM). The failure of the body to produce insulin due to the destruction of the pancreas generates this type.…”

“…it is caused by genetic and metabolic disorders. Diabetes is one of the main reasons for the increase in the number of deaths in the world, especially the Type-II-DM, which is the most common [4]. Many serious health disorders occur when neglected and not treated, such as heart attack, myocardial infarction, stroke, renal failure, blindness, neuropathy, gangrene, micro vascular damage and increased susceptibility to infection [5], [6].…”