BackgroundDespite the plethora of evidence on mHealth interventions for patient education, there is a lack of information regarding their structures and delivery strategies.ObjectiveThis review aimed to investigate the structures and strategies of patient education programs delivered through smartphone apps for people with diverse conditions and illnesses. We also examined the aim of educational interventions in terms of health promotion, disease prevention, and illness management.MethodsWe searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, and PsycINFO for peer-reviewed papers that reported patient educational interventions using mobile apps and published from 2006 to 2016. We explored various determinants of educational interventions, including the content, mode of delivery, interactivity with health care providers, theoretical basis, duration, and follow-up. The reporting quality of studies was evaluated according to the mHealth evidence and reporting assessment criteria.ResultsIn this study, 15 papers met the inclusion criteria and were reviewed. The studies mainly focused on the use of mHealth educational interventions for chronic disease management, and the main format for delivering interventions was text. Of the 15 studies, 6 were randomized controlled trials (RCTs), which have shown statistically significant effects on patients’ health outcomes, including patients’ engagement level, hemoglobin A1c, weight loss, and depression. Although the results of RCTs were mostly positive, we were unable to identify any specific effective structure and strategy for mHealth educational interventions owing to the poor reporting quality and heterogeneity of the interventions.ConclusionsEvidence on mHealth interventions for patient education published in peer-reviewed journals demonstrates that current reporting on essential mHealth criteria is insufficient for assessing, understanding, and replicating mHealth interventions. There is a lack of theory or conceptual framework for the development of mHealth interventions for patient education. Therefore, further research is required to determine the optimal structure, strategies, and delivery methods of mHealth educational interventions.

ObjectivesSmartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients’ needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems.MethodsWe systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed.ResultsData mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients’ self-management.ConclusionsEmbedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

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BACKGROUND Despite the plethora of evidence on mHealth interventions for patient education, there is a lack of information regarding their structures and delivery strategies. OBJECTIVE This review aimed to investigate the structures and strategies of patient education programs delivered through smartphone apps for people with diverse conditions and illnesses. We also examined the aim of educational interventions in terms of health promotion, disease prevention, and illness management. METHODS We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, and PsycINFO for peer-reviewed papers that reported patient educational interventions using mobile apps and published from 2006 to 2016. We explored various determinants of educational interventions, including the content, mode of delivery, interactivity with health care providers, theoretical basis, duration, and follow-up. The reporting quality of studies was evaluated according to the mHealth evidence and reporting assessment criteria. RESULTS In this study, 15 papers met the inclusion criteria and were reviewed. The studies mainly focused on the use of mHealth educational interventions for chronic disease management, and the main format for delivering interventions was text. Of the 15 studies, 6 were randomized controlled trials (RCTs), which have shown statistically significant effects on patients’ health outcomes, including patients’ engagement level, hemoglobin A1c, weight loss, and depression. Although the results of RCTs were mostly positive, we were unable to identify any specific effective structure and strategy for mHealth educational interventions owing to the poor reporting quality and heterogeneity of the interventions. CONCLUSIONS Evidence on mHealth interventions for patient education published in peer-reviewed journals demonstrates that current reporting on essential mHealth criteria is insufficient for assessing, understanding, and replicating mHealth interventions. There is a lack of theory or conceptual framework for the development of mHealth interventions for patient education. Therefore, further research is required to determine the optimal structure, strategies, and delivery methods of mHealth educational interventions.

Background: A World Health Organization (WHO) February 2018 report recently has shown that the rate of deaths because of brain or central nervous system (CNS) cancer has the highest rate in the Asian continent. Timely and accurate diagnosis of brain tumor is crucial where small errors pose many risks to treatment. Classifying the types of tumors is an important factor in targeted treatment. Since tumor diagnosis is highly invasive, time-consuming, and costly, there is an urgent need for a precise tool to develop a non-invasive, cost-effective, and efficient tool for brain tumor description and grade estimation. Brain scans by using magnetic resonance imaging (MRI), computed tomography (CT), and other imaging techniques are fast and safe to detect tumors. Methods: In this paper, we used a standard dataset containing 3064 images from different skull views. The size and position of tumors at different angles make it difficult to detect the tumor in the specimens. This MRI dataset consisted of 3064 slices and 1047 coronal images. Coronal images were recorded from behind. Axial images taken from above included 990 images. Also, there were 1027 sagittal images extracted from the skull side. Images in this dataset belonged to 233 patients. The dataset consisted of 708 Meningioma, 1426 Glioma, and 930 Pituitary tumors; thus, we isolated images from different angles of sagittal, coronal, and axial images and then trained them in different categories by using Inception-V3 and Resent, which are deep learning classification methods to make this process more accurate and faster. Results: Finally, by adjusting the hyper-parameters of each of these methods with performing pre-processing and weighting combinations, we obtained an acceptable evaluation compared to previous methods.