Mobile Healthcare Applications Research Articles

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Mobile healthcare (m‐Health) based on artificial intelligence in healthcare 4.0

AbstractHealthcare 4.0 is about collecting huge amounts of data and getting it to work in applications, enabling healthcare management decisions well‐informed while providing for important gains in effectiveness and cost control. Diagnostics based on the digital footprint depend on wearable technology's ability to gather and extract essential patient data. Artificial intelligence (AI) technologies allow the analysis of real‐time observed data and continuously developing from data to understand the world surrounding them. To connect and access intelligent healthcare services, people, and devices at any time, a secure wireless mobile communication system is essential. This article suggests a mHealth‐based patient monitoring system (mHealth‐PMS) based on AI for healthcare 4.0. Mobile healthcare applications motorized by Convolutional Neural Network (CNN) have enabled people to triage their conditions and preemptive treatment decisions. Information collected has been analysed for substantiating cause, and alert and preventive messages have been immediately sent through the mobile application. The performance analysis has been executed, and the proposed mobile application‐based surveillance provided much‐enhanced reporting of information quickly on diseases, symptoms, factors, and more. The mHealth‐PMS strategy shows an accuracy ratio of 95.6%, monitoring ratio of 93.5%, data management ratio of 94.4%, data security ratio of 91.7%, data privacy ratio of 92.1%, prediction ratio of 95.3%, a cost‐effective ratio of 25.5% compared to the existing methods.

An Advanced Mobile Application Powered with Machine Learning to Provide Improved Healthcare Services

Healthcare mobile applications are widely accepted by the health care organizations and patients due to user friendliness, low cost, reliable etc. Mobile Applications powered by artificial intelligence for processing health data and analysis can save cost and time for the diagnosis and management of diseases and makes health care more effective and efficient. Across the world many are affected by diabetes and hypertension and most of them do not have enough information about their diseases to manage and control them. Selecting suitable food, booking appointments and managing blood pressure and diabetes are major issues in managing diabetes and blood pressure. So a mobile application is developed using Google flutter framework with firebase database. The application focuses on managing the appointments for all types of patients, and monitoring diabetes and blood pressure to control and manage their diseases. The application helps the patients to book their appointments based on the crowd level on the selected date and to make emergency calls. This also provides food recommendation for patients based on its nutrition score, fat, protein and carbohydrate. It will improve patient’s lifestyle by having an education source in their hands to avoid the complications of various diseases.

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

Paramedic Students' Experiences with Simulation-Based Learning

Technically oriented industries demand job-ready skill sets from employees upon the immediate completion of their post-secondary studies. To meet these needs, many post-secondary institutions have mandated the incorporation of simulation-based learning (SBL) into curriculum, across a wide array of disciplines (Fang, Tan, Thwin, Tan, & Koh, 2011). The purpose of this research was to explore the experiences of three recent graduates of a paramedic program that had engaged in an ambulance simulator used in curriculum at a western Canadian post-secondary institution. An investigation examined how the design and associated physical interactions within an industrial simulation, facilitated in this post-secondary institution, affected learning outcomes and emotion responses of the research participants. Interview data gathered revealed differing personal experiences grouped into four categories associated with learning in SBL: realism, facilitation, learning outcomes, and personal responses. For SBL to be compelling to the learner, it must be realistic, facilitated by properly trained staff, and aligned with clearly established and valid learning outcomes capable of inducing physical and emotions responses. The incorporation of an ambulance simulator to augment a program already rich in SBL was an effective training tool for use in this mobile-healthcare application.

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.

Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early. The main purpose of this work is to develop enhanced diabetes management applications which help in patient monitoring and technology-assisted decision-making. The proposed hybrid ensemble ML model predicts diabetes mellitus by combining both bagging and boosting methods. An online IoT-based application and offline questionnaire with 15 questions about health, family history, and lifestyle were used to recruit a total of 10221 people for the study. For both datasets, the experimental findings suggest that our proposed model outperforms state-of-the-art techniques.

Development of a Mobile-Healthcare Application for Safety and Prevention in Emergency Assistance at Marathon Events: A Case Study in CMU Marathon

The majority of marathon deaths are caused by sudden cardia arrest which occurs in approximately 1 in 100,000 runners around the world. In such cases, sudden resuscitation of first aid, including an early use of automated external defibrillator (AED) on runners by medical staff is immediately necessary. However, in some cases patient information of the runner is required to support the clinical decision-making of the medical staff, including the admission to hospital with certain symptoms. To make marathon events safer, we developed a mobile-healthcare emergency assistance application for safety and prevention in marathon events. Our project covers three phases: registration, data analytics and running phases. In the registration phase, we designed and developed a form containing the runner’s electronic personal health record filled in during the registration of the runner at the event. For the analytics and running phases, we analyze the data from the registration phase in order to prepare the capacity of resource and medical staff necessary to prevent and treat injuries and accidents including a sufficient number of AED devices. Finally, for the running phase, we developed a mobile application Medic and Track based on the qr code scanning in BIB for patient information that allows to locate the runner's emergency aid point and bring the patient to that rescue point or hospital. We also tested the application on a large number of runners participating in the CMU marathon 2020 with 9721 runners. The results of this study suggest that the purpose of our application can be generally accepted for the medical staff, and it also proved to be a suitable technology to prevent serious accidents in future marathon events.

A Personalized mHealth Monitoring System for Children and Adolescents with T1 Diabetes by Utilizing IoT Sensors and Assessing Physical Activities

The problem of diabetes mellitus is becoming alarming due to the increase in morbidity among children. Patients are undergoing vital insulin replacement therapy, the dose depends on the level of glucose in the blood. The glucose level prediction program, taking into account the impact of physical activity on the body, the use of mobile health capabilities will allow us to develop personalized tactics for a child patient and minimize the risks of a critical health condition. The target group of this study are children and adolescents with type 1 diabetes. This study provides an IoT based mHealth monitoring system, including sensors, medical bracelets, mobile devices with applications. The mobile healthcare application for personalized monitoring can implement the functions of more effectively targeting young users to support their own health and improve the quality of life. In addition to monitoring blood glucose levels, the effect of physical activity on the condition of patients is also taken into account. The use of the proposed method for calculating the probable change in the patient’s blood glucose level after the end of physical activity will allow the doctor to make individual recommendations for the diet before the start of physical activity and its intensity.

A Case Study of Mobile Health Applications: The OWASP Risk of Insufficient Cryptography

Mobile devices are being deployed rapidly for both private and professional reasons. One area of that has been growing is in releasing healthcare applications into the mobile marketplaces for health management. These applications help individuals track their own biorhythms and contain sensitive information. This case study examines the source code of mobile applications released to GitHub for the Risk of Insufficient Cryptography in the Top Ten Mobile Open Web Application Security Project risks. We first develop and justify a mobile OWASP Cryptographic knowledgegraph for detecting security weaknesses specific to mobile applications which can be extended to other domains involving cryptography. We then analyze the source code of 203 open source healthcare mobile applications and report on their usage of cryptography in the applications. Our findings show that none of the open source healthcare applications correctly applied cryptography in all elements of their applications. As humans adopt healthcare applications for managing their health routines, it is essential that they consider the privacy and security risks they are accepting when sharing their data. Furthermore, many open source applications and developers have certain environmental parameters which do not mandate adherence to regulations. In addition to creating new free tools for security risk identifications during software development such as standalone or compiler-embedded, the article suggests awareness and training modules for developers prior to marketplace software release.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications.

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks. The model is designed in such a way that it minimizes the delays and complexity while authenticating the networks in 5G networks. The attacks are mitigated using an evolutionary model when it is trained with the relevant attack datasets, and the model is validated to mitigate the attacks. The simulation is conducted to test the efficacy of the model, and the results of simulation show that the proposed method is effective in improving the handling and authentication and mitigation against various types of attacks in mobile health applications.

A Secure and Robust Architecture based on Mobile Healthcare Applications for Patient Monitoring Environments

The recent outbreak of COVID-19 pandemic realized the importance of patient monitoring environments, Mobile Healthcare Applications (MHA) plays very crucial role in the successful implementation of patient monitoring environments. Existing MHA’s in the realm of patient monitoring environments are prone to repackaging attacks; do not ensure security, application security and communication security. This paper proposes a secure and robust architecture for mobile healthcare applications in patient monitoring environments ensuring end to end security ensuring all the security properties by overcoming repackaging attacks which are very vital for success of mobile healthcare applications. We implemented our proposed protocol in Android Studio, Kotlin is designed to interoperate fully with Java. ECDH Key exchange algorithm is used for key exchange between MHA in patient’s smart phone and MHA in the hospital TPM. We created an EC key pairs (NIST P-256 aka secp256r1) at patient’s MHA and MHA of hospital TPM by using ECDH and we created a shared AES secret key. AES with GCM mode used for encryption and decryption of patient data.

LAMP: a monitoring framework for mHealth application research

The usage of mobile applications in healthcare has gained popularity in recent years. In 2018, at least, 10,000 apps related to mental health could be downloaded in the app stores. The popularity of healthcare apps, especially in the field of mental health, is based on in their simplicity in large-scale data collection scenarios used for the improvement of health-related services or research. For these apps, instruments to quantify the quality of an app and repositories for app quality ratings have emerged in recent years. What is rarely considered, however, is the degree of functional correctness of an app, which can have a serious impact on the data collection process and thus on data quality. The increasing restrictions of background services are a challenge for app developers, who need to implement recurring tasks reliably in the background, like the collection of longitudinal data based on questionnaires or sensor measurements. In this paper, we present a monitoring framework to investigate the degree of functional correctness regarding the background service implementation of apps based on notification events. With this framework, we want to enable the large-scale collection of app execution data in the wild to gain more insights into the execution of apps in different execution environments and configurations. The gained knowledge shall help to improve existing applications in the field of mental health and eventually to improve the degree of functional correctness of those apps.

Medical Image Compression Method Using Lightweight Multi-Layer Perceptron for Mobile Healthcare Applications

As video compression is one of the core technologies required to enable seamless medical data streaming in mobile healthcare applications, there is a need to develop powerful media codecs that can achieve minimum bitrates while maintaining high perceptual quality. Versatile Video Coding (VVC) is the latest video coding standard that can provide powerful coding performance with a similar visual quality compared to the previously developed method that is High Efficiency Video Coding (HEVC). In order to achieve this improved coding performance, VVC adopted various advanced coding tools, such as flexible Multi-type Tree (MTT) block structure which uses Binary Tree (BT) split and Ternary Tree (TT) split. However, VVC encoder requires heavy computational complexity due to the excessive Rate-distortion Optimization (RDO) processes used to determine the optimal MTT block mode. In this paper, we propose a fast MTT decision method with two Lightweight Neural Networks (LNNs) using Multi-layer Perceptron (MLP), which are applied to determine the early termination of the TT split within the encoding process. Experimental results show that the proposed method significantly reduced the encoding complexity up to 26% with unnoticeable coding loss compared to the VVC Test Model (VTM).

Effects of Mobile Healthcare Applications on the Lifestyle of Patients With Breast Cancer: A Protocol for a Randomized Clinical Trial.

Physical activity (PA) in patients with breast cancer is associated with improved quality of life (QoL); however, many breast cancer survivors do not meet the recommended PA level. This study aims to evaluate the effect of digital health interventions using mobile apps to promote PA and QoL in patients with postoperative breast cancer. This study will also identify effective digital intervention methods and perform an economic analysis. The main hypothesis is that the use of mobile healthcare apps will improve health-related quality of life (HRQOL), promote PA, and reduce healthcare costs. The Promotion of a better lifestyle (PA) with Precise and Practicable digital healthcare in postoperative CANCER patients through a Multi-Disciplinary Network (P4CancerMDnet) study is examined by a prospective 4-group randomized controlled trial with a concurrent cost-utility evaluation. Patients are randomly assigned to 3 different mobile app intervention groups or control groups in a 1:1:1:1 ratio. The intervention group is encouraged to use the assigned mobile app. The targeted outcomes are HRQOL, metabolic health markers, and quality-adjusted life-years. The outcomes will be measured at the 6- and 12-month follow-ups. This study will contribute towards a better lifestyle and HRQOL through digital healthcare for postoperative breast cancer patients. These findings are expected to provide evidence of the effectiveness of mobile apps for breast cancer survivors. Clinical Research Information Service Identifier: KCT0005447.

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

South Tangerang Health Office had the responsibility in giving outstanding healthcare services to its resident’s despite of its limitation due Covid-19 pandemic. Some programs were initiated to reduce maternal, babies and toddler mortality, and in the same time reduce the number of malnourished children. The integrated healthcare mobile application called Si Pandai Kemas TangSel had been launched and can be downloaded easily through smartphone. The study is expected to measure the effectiveness of Si Pandai Kemas TangSel using UTAUT approach by measuring the influence of performance expectancy, effort expectancy, facilitating condition and habit toward behavior intention in using Si Pandai Kemas TangSel mobile application. The study showed that the effort expectancy and habit can increase the intention to use Si Pandai Kemas TangSel application. Meanwhile, performance expectancy and facilitating conditions did not affect behavioral intention in using Si Pandai Kemas TangSel application.

Intrabases divergences in the mHealth era: a drug interaction investigation in an infectious-diseases hospital setting

Introduction: Information on potential drug interactions (PDI) are obtained from databases available on the web or through mobile healthcare applications (mHealth), and can prevent unfavorable clinical outcomes for patients. This study compared PDI information available in Micromedex® drug interaction checker, its web version and its mHealth app. Method: A cross-sectional study realized based on a retrospective review of drug prescriptions in a reference hospital in infectology in the Midwest Region of Brazil, 2018. We selected all prescriptions containing two or more drugs. Drugs were classified according to the first level of the Anatomical Therapeutic Chemical (ATC) classification, according to the route of administration and the number of drugs prescribed. PDIs were classified according to the severity system and four-level evidence classification system. Results: This study selected 72 patients, predominantly male, median age of 38 years, average length of stay of 15.8 days, and most diagnosed with HIV/AIDS. The most frequently prescribed anatomical groups according to ATC were digestive system and metabolism (22.1%) and general anti-infectives for systemic use (21.6%). The average number of drugs per prescription was 10.8 (SD±6.7). The Micromedex® mHealth app found 381 PDIs while its web version detected 502 PDIs, with an average of 5.3 and 7.0 and frequency of 61.1% and 72.2%, respectively. According to the severity classification in mHealth and web versions, the following stood out, respectively: 221 and 321 severe; 139 and 149 moderate. The majority (>65%) of identified PDIs had their documentation classified as reasonable. Conclusion: Digital tools although they aid decision-making, are not unanimous and consistent in detecting such interactions.

A decentralized agent‐based semantic service control and self‐adaptation in smart health mobile applications

AbstractIn this article, we propose a decentralized agent‐based autonomic semantic service adaptation controller and reconfiguration (ASSACR) to solve the problem of context‐aware service selection and deployment for distributed healthcare mobile applications, where functional and nonfunctional users' needs are expressed using domain‐independent ontology. The main objective of this approach is to provide optimal personalized services as fast as possible. Indeed, a new ontology for semantic description and parallel management of the contextual services is presented. At first, a new dominance operator‐based constraints violation degree is used to reduce the search space. Then, optimal personalized paths that meet user's needs, preferences and devices' availability are discovered and selected in a smaller search space more effectively and efficiently using dynamic services selection algorithm, based on new ASSACR multiagent strategies. To validate the performance and efficiency of the proposed approach, we compared the proposed approach with existing multiagent strategies of the diabetic follow‐up use cases. Experimental results show that the proposed approach effectively addressed both the semantic service representation and agent aspects in terms of performance and quality service selection.

A Public Key Authentication and Privacy Preserving Model for Securing Healthcare System

Over the last few years, the medical Internet of Things (IoMT) has experienced numerous security challenges related to the monitoring of patient healthcare data. In IoT centric healthcare framework, connected clinical devices are more prone to severe security menaces and attacks against other network devices. Current solutions can ensure the patient’s information during data transfer to some extent but cannot avoid some of the advanced cyber-attacks like data leakage, collision attacks, and data integrity. Thus, this paper proposes an efficient privacy-preserving and public-key authentication model for securing IoT-based healthcare systems. An efficient public-key authentication system with access policy-based confidentiality is used to preserve privacy. In this proposed model, the patients stay in their homes, and their health assistance will send the sensor data using a mobile healthcare application. First, the patient must be registered with the corresponding hospital using patient credentials on the hospital website or mobile application. After getting the unique login id and successful login, sensed data is encrypted using the proposed method and securely uploaded to the hospital cloud server by generating an access policy. Then, in the hospital, the corresponding doctor can download the secured patient data with high confidentiality using the same access policy generated while uploading the health data from the hospital cloud server. The results gained from the experiments demonstrate a superior performance of the proposed mechanism over the existing methodologies, and it is evidenced to be more secure and efficient.

Российские мобильные приложения для здоровья: систематический поиск в магазинах приложений

The purpose of the study was to analyze the popularity of mobile applications for health care in Russia. Material and methods. The app stores «App Store» of Apple and «Google Play Store» of Google were studied for comparative analysis. In case of The App Store we analyzed the categories «Medicine and Health and Fitness», in the Google Play Store – the categories «Medicine», «Motherhood and Childhood» and «Health and Fitness». The total amount of applications was 231. The reviewed applications were grouped into several groups and ranked in descending order of popularity. Results. The most popular applications by the number of downloads were applications from the group «Fitness and healthy lifestyle»: by individual positions – more than 1,2 billion. installations. On the second place was the group «Women's Health» with more than 285 million installations. The highest ratings of applications in the context of groups were received by services related to pregnancy control, evaluation of fetal and child development, as well as services for determining the symptoms of diseases. Conclusions. Thus, the most popular content for health care is the analysis of physical activity, regular diagnostics of the state of the body, monitoring of women's health indicators and monitoring the course of pregnancy, children's health.

Mobile Health, Disease Knowledge, and Self-Care Behavior in Chronic Kidney Disease: A Prospective Cohort Study

Mobile health (mHealth) management is an emerging strategy of care for patients with chronic diseases. However, the effect of mHealth management on clinical outcomes of patients with chronic kidney disease (CKD) has not been well-studied. The aim of this study was to investigate the additional influence of mHealth on disease knowledge and self-care behavior in CKD patients who had received traditional education. We designed and developed a new healthcare mobile application, called iCKD, which has several major features, including home-based physiological signal monitoring, disease health education, nutrition analysis, medication reminder, and alarms and a warning system. Trained nurses interviewed patients with CKD using structured questionnaires of disease knowledge and self-care behavior. After propensity score matching, we analyzed 107 patients who used iCKD and traditional education, and 107 who received traditional education. The patients who used iCKD had higher disease knowledge scores than those who received traditional education. In multivariate analysis, iCKD was significantly and positively associated with disease knowledge scores. Patients with high education levels could have greater disease knowledge through using mHealth. There was no significant difference in total scores of self-care behavior between the two groups. In conclusion, mHealth can significantly increase disease knowledge in patients with CKD.