Farahnaz Sadoughi

Mobile health applications provide a valuable tool for promoting oral hygiene self-care and preventing conditions, such as gingivitis and dental caries. This review aimed to systematically evaluate mobile applications designed for oral hygiene across all age groups, assessing their quality using the Mobile App Rating Scale. A systematic search was conducted in the Apple App Store and Google Play Store to identify mobile applications focused on oral hygiene. Two independent reviewers assessed the quality of the selected apps using the mobile app rating scale. Additionally, the relationships between mobile app rating scale scores, app-specific dimensions, Apple App Store user ratings, and the last update date were analyzed using Pearson and Spearman correlation coefficients. A total of 21 oral hygiene-related applications were included in the review. The overall mean mobile app rating scale score was 2.48 (SD = 0.77), with scores ranging from 1.2 to 3.9. The reviewed apps were generally of poor quality. The highest-rated mobile app rating scale dimension was information (mean = 2.59, SD = 0.62), followed by functionality, engagement, and aesthetics. Pearson correlation analysis revealed a strong association between the functionality and information dimensions of the mobile app rating scale with app-specific dimensions. The overall quality of mobile health applications for oral hygiene was suboptimal, with few apps meeting established quality and content standards. Many lacked access to evidence-based information, which is crucial for promoting effective oral hygiene practices. To enhance user trust and improve health outcomes, developers should adhere to evidence-based guidelines and best practices in app design and development.

Objective(s): Spinal muscular atrophy is a rare genetic disease of neuromuscular and it is considered the main cause of death of newborns, which affects spinal motor neurons. The variety of degrees of this disease and the lack of a complete and integrated information recording system hinder the quality of providing care, early ...

The COVID- 19 pandemic has significantly impacted global health, underscoring the crucial role of digital health solutions. The World Health Organization's Classification of Digital Interventions, Services, and Applications in Health (CDISAH) provides a framework for categorizing these technologies. This study aims to analyze the adoption and trends of digital health interventions during the COVID- 19 pandemic, mapping them to the CDISAH framework to identify the most and least utilized interventions and technologies. This overview-of-reviews study was conducted from 1 st January 2020 to 30 th December 2023, focusing on systematic reviews and meta-analyses retrieved from the Cochrane Database of Systematic Reviews, PubMed, Scopus, Web of Science, IEEE Xplore, and ProQuest. Additionally, gray literature was identified through searches on the Google Scholar platform and reviewing the citations and reference lists of the included studies. The findings were qualitatively mapped to the CDISAH framework. A total of 64 review articles were analyzed. A content analysis of the included studies identified 292 codes related to healthcare providers, 257 codes related to data services, 88 codes related to individuals, and 43 codes related to health management and support personnel. The results revealed that the most frequent interventions were associated with telemedicine and data management subcategories, while gaps were identified in areas such as individual-based data reporting during the pandemic, highlighting the need for individuals to take a more active role in managing their own health in preparation for future crises. This study identifies both the strengths and weaknesses of the current digital health landscape. It emphasizes the transformative impact of digital health technologies during the COVID- 19 pandemic and provides a roadmap for future improvements in digital health interventions. By providing a comprehensive overview of digital health during this period, the study underscores the importance of implementing robust digital health strategies within the healthcare system to address existing gaps, leverage strengths, and enhance preparedness and resilience in future public health crises.

Autism spectrum disorder is characterized by restricted and repetitive behaviors or interests, emotional difficulties, and challenges with social communication. Technological tools are crucial for individuals on the autism spectrum as they offer predictable environments free of social demands. Mobile smartphones are particularly suitable for individuals on the autism spectrum due to their attractive screens, visually stimulating displays, high portability, engaging music and game functions, and ease of use. This study investigated the current market situation for game-based mobile applications designed to teach socio-emotional skills to individuals on the autism spectrum. This study focused on identifying game-based mobile applications available on Android and iOS platforms, evaluating the extracted serious games using a serious games assessment tool, and assessing the apps with the Mobile Application Rating Scale (MARS). We also provided suggestions for future designs. 10 applications were identified on the Android and iOS. At first glance, it seems there are several game-based applications for teaching socio-emotional skills to individuals on the autism spectrum, but most of them contain advice and textual information and lack the main aspects of serious games. On average, the design of the selected applications based on the use of serious game design principles can be classified as good. Additionally, more than half of the apps (70%) received an acceptable MARS score. The findings reveal that few apps meet the predetermined criteria for quality, content, or performance, indicating a need for future designs to adhere to these principles.

Introduction /objective Osteoporosis often goes undetected until the first fracture, significantly increasing the risk of subsequent fractures. The Fracture Liaison Service (FLS) model aims to prevent secondary fractures and improve osteoporosis management. This study aims to provide a comprehensive overview of the current status of FLS implementation in Iran, applying the essential elements outlined in the FLS framework. Method This qualitative study used framework analysis to explore key components of the FLS framework from the perspectives of Iranian experts. Semi-structured, in-depth interviews were conducted over a 3-month period. Collaborative scenario synthesis was employed to accurately reflect the complexities of FLS operations. Results A total of 13 participants with an average practical experience of 3.2 years contributed to the study. The results covered the people involved, needed information, locations, timeframes, and operational details of FLS stages, as well as quality improvement. The study confirmed the FLS framework’s applicability in describing an implemented FLS and highlighted areas for improvement, such as coordinator training, role clarification, and data management issues. Conclusion Given the scarcity of comprehensive studies on FLS within Iran’s context and the recent growth and expansion of FLS initiatives in the country, this study offers insights into FLS implementation from the perspective of experts, serving as a guiding resource for future initiatives in this domain. Future research may benefit from developing tailored questionnaires based on the FLS framework to gather insights from a larger population for a better assessment. Key points • Iran lacks specialized training programs for FLS coordinators. Developing these programs is crucial, particularly for healthcare professionals from non-nursing specialties. • There is a lack of clarity regarding the timeline for each stage of the FLS process. A context-specific timeline is needed to optimize service implementation and workflow. • There are inaccuracies in coding patient information that pose significant challenges, potentially undermining hospital statistics and the quality of FLS data.

Summary This research conducts a comparative analysis and scoping review of 105 studies in the field of Fracture Liaison Service (FLS). The resulting two-dimensional framework represents a significant step toward FLS implementation. Purpose The primary goal is to review interventions in real world settings in order to provide the FLS framework that specifies the essential elements of its implementation and offers different perspectives on that. Method This study encompasses two phases: a comparative analysis of existing FLS models, including “Capture the Fracture,” “5IQ,” and “Ganda,” and a scoping review from 2012 to 2022 in PubMed, Web of Science, Scopus, ProQuest, and IEEE databases limited to publications in English. Results The resulting model of comparative analysis identifies patient identification, investigation, intervention and integration or continuity of care as the four main stages of FLS. Additionally, the elements of quality and information span across all stages. Following comparative analysis, the framework is designed to be used for content analysis of the included studies in the scoping review. The intersection of columns (Who, Where, When, What, How, Quality) with rows (Identification, Investigation, Intervention, and continuity of care) yields a set of questions, answered in tabular form based on the scoping review. Conclusion The framework offers potential benefits in facilitating the adoption of effective approaches for FLS implementation. It is recommended to undertake an in-depth review of each of these components in order to uncover novel and innovative approaches for improving their implementation.

Objectives: Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman–Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Method: Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)–Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. Result: The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. Conclusion: We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE–Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

Introduction: The Internet of Things (IoT) and Cloud computing are two recent technological advances whose potential have not been realized in a range of industries. These technologies have been used in healthcare systems to improve their performance. In this regard, the IoT generates a large amount of data; also, cloud computing is a viable option for data storage and complex computing. The purpose of this study is to identify and categorize various aspects of CIoT-based healthcare in terms of main application domains, sensors, wireless communication technologies, messaging protocols, cloud platforms, and artificial intelligence (AI) algorithms.Material and Methods: We conducted a literature systematic review and reported according to the PRISMA guideline. PubMed, IEEE, Scopus, and Web of Science were searched using the related keywords and their synonyms. Two independent authors reviewed the papers' eligibility according to the defined inclusion and exclusion criteria.Results: Of the 2,118 papers retrieved, 61 were eventually selected in the study. Results of the present study revealed that the majority of CIoT research works were applied to patient monitoring systems and cardiovascular patient monitoring systems using Amazon and IBM dominating Cloud platforms. In addition, the most widely used communication technologies for CIoT in healthcare are cellular networks (3G and 4G), Wi-Fi, and Bluetooth. Cardiovascular, environmental, and position sensors are also the most common types of sensors used in healthcare CIoT applications. Among the Cloud platform providers, Amazon and IBM have the highest utility in healthcare systems. The majority of the included studies used Cloud-based AI algorithms to diagnose, classify, and predict diseases.Conclusion: The integration of the Cloud into the IoT can support healthcare systems in terms of processing power, storage capacity, security, privacy, performance, reliability, and scalability. We suggest researchers conduct experimental studies to evaluate the effectiveness of the CIoT approach in healthcare applications.