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Background: Depression and anxiety are prevalent mental health issues addressed by online cognitive behavioral therapy (CBT) via mobile applications. This study introduces Sokoon, a gamified CBT app tailored for Arabic individuals, focusing on alleviating depression and anxiety symptoms (DASDs). Objectives: The objectives of this study were to: Evaluate the effectiveness of Sokoon in reducing symptoms of depression and anxiety. Assess the usability of the intervention through user engagement and adherence to CBT skills. Methods: A single-group pre-post design evaluated Sokoon's impact on adults with DASDs. In consultation with psychiatrists, Sokoon integrates evidence-based skills such as relaxation, gratitude, behavioral activation, and cognitive restructuring, represented by planets. Its design incorporates Hexad theory and gamification, supported by a dynamic difficulty adjustment algorithm. The study involves 30 participants aged 18-35 (86.7% female), specifically those with mild to moderate depression and anxiety. Results: Based on a sample of 30 participants, Sokoon, a smartphone-based intervention, significantly reduced symptoms of depression and anxiety (d = 2.7, d = 3.6, p < 0.001). Over a two-week trial, participants experienced a notable decrease in anxiety and depressive symptoms, indicating the effectiveness of the model. Sokoon shows potential as a valuable tool for addressing DASDs. Conclusion: Sokoon, the gamified CBT application, offers an innovative approach to increasing CBT skills adherence and engagement. By leveraging Hexad theory and gamification, Sokoon provides an enjoyable and engaging user experience while maintaining the effectiveness of traditional CBT techniques. The study findings suggest that Sokoon has a positive impact on reducing symptoms of depression and anxiety.
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Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.
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Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMs.