Improving mobile fitness and weight loss apps to help Saudis overcoming obesity. Case study : Akser Waznk app

University of Technology Sydney. Faculty of Engineering and Information Technology.Obesity and its related illnesses are a major health problem around the world. Saudi Arabia has one of the highest national obesity rates globally; however, it is not easy to intervene to prevent obesity and overweightness due to Saudi Arabia’s cultural and social norms and linguistic barriers. In recent years, there has been an exponential growth in the usage of smartphones and apps in Saudi Arabia. These could be used as a cost-effective tool to facilitate the delivery of behaviour modification interventions for obese and overweight people. There are a variety of health and fitness apps that claim to offer lifestyle-modification tools. However, these do not identify the motivational features required to overcome obesity, consider the evidence-based practices for weight management, consider the social and cultural norms of Saudi society, or enhance the usability of apps by considering usability attributes. Therefore, this research aims at improving mobile fitness and weight loss apps usability guidelines and features to motivate obese users and especially Saudis to lose weight and then overcome obesity. Qualitative and quantitative studies were conducted with 26 obese Saudis who tested the level of usability of two Arabic fitness and weight-loss apps and then provided feedback and recommendations. The following usability attributes were tested: effectiveness, efficiency, satisfaction, memorability, errors, learnability and cognitive load. Qualitative studies were also conducted with seven health professionals (dietitians and physical activity professionals) to evaluate the tested apps regarding their nutrition and physical activity options. This was undertaken in collaboration with the Armed Forces hospitals Taif Region and King AbdulAziz Medical City Ministry of National Guard Health Affairs in Jeddah, Saudi Arabia. Based on the results, comprehensive usability guidelines for fitness and weight loss apps were established and an Arabic weight-loss app called Akser Waznk was developed to facilitate the adjustment of key nutritional and physical activities and behaviours of Saudi users. Akser Waznk app is an interactive, user-friendly app designed primarily for iPhones. It has several features intended to help users to monitor and track their food consumption and physical activities. The app provides personalised diet and weight loss advice. To validate the proposed app, its level of usability was tested and its nutrition and physical activity options evaluated by conducting qualitative and quantitative studies with the same 26 obese Saudi users and the same seven health professionals

The Role of Machine Learning in E-Learning Using the Web and AI-Enabled Mobile Applications

For over two decades, e-learning has been recognized as a flexible and faster method compared to the other established methods, especially in enhancing knowledge. Concurrently, the expansion of information technology applications, such as mobile applications and Artificial Intelligence (AI), has provided well-grounded foundations for e-learning to be more reachable. In particular, education can be seen as the most beneficial sector of advancements in e-learning. Machine learning is considered a form of personalized learning that could be used to give each student a specific personal experience through which students are directed to gain their own experience. Web and AI-enabled mobile applications can be recognized as one of the most broadly used platforms for e-learning where machine learning technology can be applied to measure many influences and predictions regarding the quality of e-learning, but we cannot ignore the complexities of use. This study shows the role of machine learning in the user’s ability to make use of the course and its contents to measure ease and clarity. Based on a former study shown previously, this paper attempts to pinpoint realities and complexities associated with web and AI-enabled mobile applications by evaluating user preferences. This paper forms the second phase using two user groups (21–30 years) where data were attained using a survey questionnaire to investigate the user preferences when using an application for e-learning. The analysis shows that the future of e-learning has greater potential in web-based applications, as they have more scope for development and improvements compared to mobile applications. The paper concludes with a conceptual framework that works as a machine that stimulates different information and uses e-learning applications that support artificial intelligence techniques. This research provides a solid underpinning for further research into the future of AI-enabled e-learning education and its implication with respect to cost, quality, and usability

A trustworthy, reliable and lightweight privacy and data integrity approach for the internet of things

Data integrity and authenticity are among the key challenges faced by the interacting devices of Internet of Things (IoT). The resource-constrained nature of sensor-embedded devices make it even more difficult to design lightweight security schemes for these networks. In view of limited resources of the IoT devices, this paper proposes a lightweight and trustworthy device-to-server mutual authentication scheme for edge-enabled IoT networks. Initially, a trusted authority (TA) generates and assigns identities (IDs) and mask them to servers and clients, also known as member devices, in an off-line phase. These IDs are utilized to prevent possible infiltration of the adversary device(s). Next, every device ensures the authenticity of requesting devices using a sophisticated challenge, which is encrypted using a 128-bits secret key, λi. Each device expects a reply from the intended destination device for resolving the encrypted challenge within the defined time-frame, i.e.,△T. Moreover, authenticity of the requesting device is verified through the stored IDs which are shared in the off-line phase. Simulation results have verified the exceptional performance of the proposed authentication scheme against field proven approaches in terms of computational and communication costs, respectively

Sensor-cloud architecture: a taxonomy of security issues in cloud-assisted sensor networks

© 2021 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://ieeexplore.ieee.org/document/9451213The orchestration of cloud computing with wireless sensor network (WSN), termed as sensor-cloud, has recently gained remarkable attention from both academia and industry. It enhances the processing and storage capabilities of the resources-constrained sensor networks in various applications such as healthcare, habitat monitoring, battlefield surveillance, disaster management, etc. The diverse nature of sensor network applications processing and storage limitations on the sensor networks, which can be overcome through integrating them with the cloud paradigm. Sensor-cloud offers numerous benefits such as flexibility, scalability, collaboration, automation, virtualization with enhanced processing and storage capabilities. However, these networks suffer from limited bandwidth, resource optimization, reliability, load balancing, latency, and security threats. Therefore, it is essential to secure the sensor-cloud architecture from various security attacks to preserve its integrity. The main components of the sensor-cloud architecture which can be attacked are: (i) the sensor nodes; (ii) the communication medium; and (iii) the remote cloud architecture. Although security issues of these components are extensively studied in the existing literature; however, a detailed analysis of various security attacks on the sensor-cloud architecture is still required. The main objective of this research is to present state-of-the-art literature in the context of security issues of the sensor-cloud architecture along with their preventive measures. Moreover, several taxonomies of the security attacks from the sensor-cloud’s architectural perspective and their innovative solutions are also provided.This work was supported by the Taif University, Taif, Saudi Arabia, through the Taif University Researchers Supporting Project under Grant TURSP-2020/126.Published versio

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Exploring Saudi Individuals&rsquo; Perspectives and Needs to Design a Hypertension Management Mobile Technology Solution: Qualitative Study

Hypertension is a chronic condition caused by a poor lifestyle that affects patients&rsquo; lives. Adherence to self-management programs increases hypertension self-monitoring, and allows greater prevention and disease management. Patient compliance with hypertension self-management is low in general; therefore, mobile health applications (mHealth-Apps) are becoming a daily necessity and provide opportunities to improve the prevention and treatment of chronic diseases, including hypertension. This research aims to explore Saudi individuals&rsquo; perspectives and needs regarding designing a hypertension management mobile app to be used by hypertension patients to better manage their illnesses. Semi-structured interviews were conducted with 21 Saudi participants to explore their perspectives and views about the needs and requirements in designing a hypertension mobile technology solution, as well as usability and culture in the Saudi context. The study used NVivo to analyze data and divided the themes into four main themes: the app&rsquo;s perceived health benefits, features and usability, suggestions for the app&rsquo;s content, and security and privacy. The results showed that there are many suggestions for improvements in mobile health apps that developers should take into consideration when designing apps. The mobile health apps should include physical activity tracking, related diet information, and reminders, which are promising, and could increase adherence to healthy lifestyles and consequently improve the self-management of hypertension patients. Mobile health apps provide opportunities to improve hypertension patients&rsquo; self-management and self-monitoring. However, this study asserts that mobile health apps should not share users&rsquo; data, and that adequate privacy disclosures should be implemented

Enabled Artificial Intelligence (AI) to Develop Sehhaty Wa Daghty App of Self-Management for Saudi Patients with Hypertension: A Qualitative Study

(1) Background: The prevalence of uncontrolled hypertension is rising all across the world, making it a concern for public health. The usage of mobile health applications has resulted in a number of positive outcomes for the management and control of hypertension. (2) Objective: The study’s primary goal is to explain the steps to create a hypertension application (app) that considers cultural and social standards in Saudi Arabia, motivational features, and the needs of male and female Saudi citizens. (3) Methods: This study reports the emerged features and content needed to be adapted or developed in health apps for hypertension patients during an interactive qualitative analysis focus group activity with (n = 5) experts from the Saudi Ministry of Health. A gap analysis was conducted to develop an app based on a deep understanding of user needs with a patient-centred approach. (4) Results: Based on the participant’s reviews in this study, the app was easy to use and can help Saudi patients to control their hypertension, the design was interactive, motivational features are user-friendly, and there is a need to consider other platforms such as Android and Blackberry in a future version. (5) Conclusions: Mobile health apps can help Saudis change their unhealthy lifestyles. Target users, usability, motivational features, and social and cultural standards must be considered to meet the app’s aim

Poor Compliance of Diabetic Patients with AI-Enabled E-Health Self-Care Management in Saudi Arabia

Still in its nascent stage, the Kingdom of Saudi Arabia’s self-care system lacks most features of a state-of-the-art e-health care system. With the Industrial Revolution 4.0 and the expanding use of artificial intelligence (AI), e-health initiatives in Saudi Arabia are increasing, which is compelling academics, clinicians, and policymakers to develop a better understanding of e-health trends, their efficacy, and their high impact areas. An increase in the number of diabetic patients in the Kingdom demands improvements to the current e-health care system, where the capability to manage diabetic patients is still in its infancy. In this survey, a total of 210 valid responses were obtained for analysis. SPSS version 27.0 was used for the quantitative analysis. The main technique used to address the aims of the data analysis was Spearman’s correlation analysis. This study indicated that the compliance rate with prescribed medication, blood glucose monitoring, and insulin injections from hospitals is increasing, with the highest rates found for Jeddah City. However, diet control and physical activity compliance levels were found to be poorly combined, predominantly due to the lower number of registered patients in the e-health care system. This non-compliance trends with selected variables (education and income) and highlights the dire need for improvement to the current health system by the inclusion of the latest technology, including big data, cloud computing, and the Internet of Things (IoT). Hence, this study suggests the implementation of government-regulated e-health care systems on mobile-based policies. The study revealed the experience of patients using e-health systems, which could be used to improve their efficacy and durability. More research needs to be conducted to address the deficiencies in the current e-health care system regarding diabetes care, and how it can be integrated into the healthcare system in general

Development Web-based Arabic Assessments for Deaf and Hard-of-Hearing Students

Arabic skills are the tools by which children are prepared for the educational procedures on which their life depends. Deaf and hard of hearing students (DHH), must be able to grasp the same Arabic terms as hearing students and their different meanings in a context of different sentences less than what they are supposed to be due to their inability. However, problems arise in the same Arabic word and their different meanings in a context for (DHH) students since the way of comprehending such words does not meet the needs and circumstances of (DHH) students. Therefore, researchers introduce web-based method for Arabic words and their meanings in a context prototype that can overcome those problems. Methodology: The study sample consists of 30 (DHH) students at Al Amal City of Palestine, Gaza Region (GR). Those participants that agreed to take part in this study were recruited using a purposeful sampling method. Additionally, to examine the survey information descriptively, the Statistical Packages for social Sciences (SPSS) version 24.0 was used. A sign language teaching movie is utilized in the prototype to standardize the process and verify that Arabic vocabulary and their implications are comprehended. The Evolutionary Process Model of Prototype technique was utilized to create this system. Finding: The findings of this study show that the prototype built is workable and has the ability to help DHHS differentiate between phrases that have the same letters but distinct meanings. The findings of this study are expected to contribute to a better understanding and application of Development of Web-based Arabic Assessments for (DHH) Students in developing countries, which will help to increase the use of Development of Web-based Arabic for (HDD) students in those countries. The empirical models of Web-based Arabic for (DHH) students are established as a proof of concept for the proposed model. The results of this study are predicted to have a significant impact to the information system practitioners and to the body of knowledge

Augmentation in Healthcare: Augmented Biosignal Using Deep Learning and Tensor Representation

In healthcare applications, deep learning is a highly valuable tool. It extracts features from raw data to save time and effort for health practitioners. A deep learning model is capable of learning and extracting the features from raw data by itself without any external intervention. On the other hand, shallow learning feature extraction techniques depend on user experience in selecting a powerful feature extraction algorithm. In this article, we proposed a multistage model that is based on the spectrogram of biosignal. The proposed model provides an appropriate representation of the input raw biosignal that boosts the accuracy of training and testing dataset. In the next stage, smaller datasets are augmented as larger data sets to enhance the accuracy of the classification for biosignal datasets. After that, the augmented dataset is represented in the TensorFlow that provides more services and functionalities, which give more flexibility. The proposed model was compared with different approaches. The results show that the proposed approach is better in terms of testing and training accuracy