Objectives: Despite the growing use of mobile health (mHealth), certain barriers seem to be hindering the use of mHealth applications in healthcare. This article presents a systematic review of the literature on barriers associated with mHealth reported by healthcare professionals. Methods: This systematic review was carried out to identify studies published from January 2015 to December 2019 by searching four electronic databases (PubMed/MEDLINE, Web of Science, Embase, and Google Scholar). Studies were included if they reported perceived barriers to the adoption of mHealth from healthcare providers’ perspectives. Content analysis and categorization of barriers were performed based on a focus group discussion that explored researchers’ knowledge and experiences. Results: Among the 273 papers retrieved through the search strategy, 18 works were selected and 18 barriers were identified. The relevant barriers were categorized into three main groups: technical, individual, and healthcare system. Security and privacy concerns from the category of technical barriers, knowledge and limited literacy from the category of individual barriers, and economic and financial factors from the category of healthcare system barriers were chosen as three of the most important challenges related to the adoption of mHealth described in the included publications. Conclusions mHealth adoption is a complex and multi-dimensional process that is widely implemented to increase access to healthcare services. However, it is influenced by various factors and barriers. Understanding the barriers to adoption of mHealth applications among providers, and engaging them in the adoption process will be important for the successful deployment of these applications.

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.
Artificial Intelligence ; Blood Glucose ; Blood Pressure ; Body Mass Index ; Classification ; Decision Making ; Delivery of Health Care ; Diabetes Mellitus ; Diabetes Mellitus, Type 2 ; Diagnosis ; Fasting ; Incidence ; Lipoproteins ; Machine Learning ; Mass Screening ; Methods ; Support Vector Machine ; Triglycerides