The Journal of Telemedicine3060-58301420241231Developing a Mobile-based Educational Game to Enhance Dietary Habits for Type 2 Diabetes Patients through Artificial Intelligence Algorithms1122engZahra KoohmarehDepartment of Health Information Technology, School of Allied Medical Science, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranMajid KarandishDiabetes Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.Ali MohammadHadianfardDepartment of Health Information Technology, School of Allied Medical Science, Jundishapur University of Medical Sciences, Ahvaz, Iran2024123020241230 This study aimed to design a mobile digital game to assist individuals with type 2 diabetes in better understanding food calories and the glycemic index, ultimately enhancing their management of the condition. The game was developed using fuzzy logic and initially tested in MATLAB 2018 before being converted to C# in Visual Studio and implemented in Unity. To develop the game, food calorie and glycemic index values were integrated into a fuzzy input system, utilizing a triangular membership function. The fuzzy output was translated into a numerical value through the centroid defuzzifier, employing the Mamdani fuzzy inference engine for determination. The outcome of this study was the development of a mobile game named "Diabetic Amoo," specifically designed for diabetic patients. Players advance through the first episode by correctly selecting appropriate food items, while the second stage focuses on educating them about low-sugar and low-calorie foods. Players receive ratings for their choices that range from "very bad" to "very good," with the goal of achieving a "very good" rating. By emphasizing patient education, such games can enhance motivation for self-care and improve adherence to diabetes diets and other health conditions.

The Journal of Telemedicine3060-58301420241231The Role of Telemedicine in the Early Detection and Management of Cardiovascular Diseases: A Systematic Review2333engZeynabNaseriStudent Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranSadegh SharafiMSc student in Medical Informatics, Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IranHosseinValizadeh LaktarashiMSc, Medical Informatics, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran2024121820241230 Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating early detection and effective management to reduce morbidity and mortality. Telemedicine has emerged as an innovative approach to address healthcare delivery challenges, particularly in underserved areas. This systematic review evaluates the role of telemedicine in the Early detection of cardiovascular diseases. A systematic search of PubMed, Cochrane Library, Embase, and Web of Science databases was conducted for studies published between 2014 and 2024. Eligible studies included randomized controlled trials, observational studies, and meta-analyses that evaluated telemedicine interventions in CVD detection or management. Study quality was assessed using the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale. Fifteen studies covered diverse telemedicine approaches such as remote monitoring, wearable devices, and mobile applications. Key findings included: Early Detection: Telemedicine improved diagnostic efficiency, with wearable devices identifying arrhythmias (sensitivity 95%) and telemonitoring accelerating hypertension diagnoses by 25%. Telemedicine is a valuable tool for the early detection and management of CVDs, significantly improving clinical outcomes. Despite its benefits, challenges such as the digital divide, privacy concerns, and provider training must be addressed. Future studies should explore the cost-effectiveness, scalability, and long-term outcomes of telemedicine in cardiovascular care.

The Journal of Telemedicine3060-58301420241231Development of an Internet of Things-based Smart Health Monitoring System for COVID-19110engShahlaFaramarziStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, IranAzitaYazdaniDepartment of Health Information Management, School of Health Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran.2024110420241120 The application of machine learning (ML) -based Internet of Things (IoT)- in healthcare during COVID-19 has proven effective in preventing disease spread. This paper proposed an IoT system to remotely detect and monitor suspected COVID-19 cases at home. To develop the system, IoT sensors, a mobile application, and ML algorithms were used for data analysis. The IoT node tracked health parameters, including body temperature, heart rate, blood oxygen saturation level, and blood pressure, as well as age and gender information, then used a user interface to display health status updates and sent them to a cloud server.  The decision-making module based on ML algorithms was placed on the server side. The system's efficiency was evaluated using accuracy, precision, recall, and F1 score metrics. Doctors could view and track users' health status through an interface, and Pearson's correlation coefficient was used to determine the correlation between sensor measurements and vital sign devices. The proposed system achieved 99.05% accuracy, 98.66% precision, 99.32% recall, and 98.99% F-measure using the random forest algorithm. The Pearson correlation coefficient also showed a strong correlation in evaluating the sensors. The system could aid in diagnosing and monitoring suspected COVID-19 cases.