Mobile Healthcare Applications Research Articles

Effectiveness of Home-Based Cardiac Rehabilitation with Optimized Exercise Prescriptions Using a Mobile Healthcare App in Patients with Acute Myocardial Infarction: A Randomized Controlled Trial.

Despite the effectiveness of cardiac rehabilitation (CR), the actual participation rate in CR is low. While home-based CR offers a viable alternative, it faces challenges in participation due to factors such as a lack of self-motivation and fear of exercising without supervision. Utilizing a mobile healthcare application (app) during counseling may be an effective strategy for patients. Therefore, the aim of this study was to assess whether 6 weeks of home-based CR with exercise readjustment using a mobile app is an effective therapy for patients with acute myocardial infarction (AMI). Post-AMI patients eligible for home-based CR were randomized into the intervention group (CR-Mobile) and the control group, which followed the usual home-based CR protocol (CR-Usual). Both groups participated in a 6-week home-based CR program, with exercise readjustment and encouragement carried out every 2 weeks. The CR-Mobile group was supervised using data recorded in the mobile app, while the CR-Usual group was supervised via phone consultations. The primary outcome measured was maximal oxygen consumption (VO2max). Within-group comparisons showed significant improvements in VO2max (PCR-Mobile = 0.011 vs. PCR-Usual = 0.020) and METs (PCR-Mobile = 0.011 vs. PCR-Usual = 0.011) for both groups. These findings suggest that a 6-week home-based CR program with exercise readjustment using a mobile app can potentially enhance exercise capacity as effectively as verbal supervision.

Reporting of asthma mobile health data for clinical practice: a qualitative study of pediatric provider perspectives

Objective Mobile health (mHealth), defined as the use of mobile phones or applications in healthcare, has been developed to enhance asthma care; yet implementation is inconsistent, and few studies have focused on provider perspectives on use in daily practice. The purpose of this study was to explore primary care pediatric provider perspectives regarding mHealth use in clinical practice for children with asthma. Methods A qualitative, descriptive approach was utilized to perform semi-structured interviews on asthma mHealth use with providers caring for children with asthma. Interview transcripts were coded by two independent investigators and any differences were reconciled. Interviews continued until thematic saturation was achieved. Results and Conclusions Seventeen pediatric providers were recruited and interviewed. Three themes identified included implementation benefits, implementation barriers, and reporting desires, with 11 subthemes. Many subthemes were consistent across providers (e.g. self-management benefits and electronic medical record integration), while others such as provider clinical burden and approach to integrating mHealth data reports into daily workflow demonstrated variability. Provider perspectives highlight the potential of mHealth applications in asthma self-management while offering challenges related to clinical burden and suggestions for reporting and workflow integration. These results provide valuable perspectives on mHealth use and reporting to ensure provider efficiency and technology-enhanced asthma care. This study investigates pediatric provider perspectives on asthma mobile health use and reporting in daily practice, a topic that has not sufficiently been explored within the literature. Results can guide best practices, encourage more consistent use, and maximize the benefits of asthma mHealth tools by providers.

Enhancing Mobile Acute Lymphoblastic Cancer Detection: Transfer Learning from YOLOv9 to TensorFlow for Real-Time Applications

The detection of acute lymphoblastic cancer (ALL) is critical for timely diagnosis and treatment. In this study, we propose a novel approach to enhance ALL detection using transfer learning techniques from YOLOv9 to TensorFlow, facilitating real-time application on mobile devices. Leveraging the robustness of YOLOv9 and the versatility of TensorFlow, we fine-tune the pre-trained model to optimize performance for ALL detection. The adapted model is then integrated into a mobile application, enabling users to perform real-time ALL detection using their smartphones. Our results demonstrate the efficacy of the proposed system in accurately identifying ALL cells, with a detection rate of 98% in one scenario and 100% in another. This research represents a significant step forward in leveraging advanced computer vision technologies for mobile healthcare applications, ultimately improving patient outcomes and healthcare accessibility.

A Review of Current Digital Mental Health Care Applications for Anxiety Symptoms and Future Prospects.

Since the impact of the coronavirus disease-2019 pandemic, the need for efficiency in medical services has become more urgent than ever. The digital treatment market is rapidly growing worldwide and digital therapeutics (DTx), a major part of the digital medical services, is also emerging as a new paradigm for treatment, with its industry growing rapidly as well. Increasing research is done on the effectiveness of mobile DTx in improving mental health conditions such as insomnia, panic, and depression. This review paper investigates 1) the functions and characteristics of mobile digital mental health care applications for the treatment of anxiety symptoms, 2) extracts common attributes of the applications, and 3) compares them with existing traditional treatment mechanisms. Among the 20,000 mental health management applications that have been developed so far, 8 applications that are relatively widely used were selected and reviewed. Check-in, self-help tips, quick relief, journal, courses for practice are common features of the digital mental health care applications for anxiety and are also widely used feature in the cognitive behavioral therapy. Based on this review, we have proposed the essential elements and directions for the development of a Korean digital mental health care applications for anxiety disorders.

DSEUNet: A lightweight UNet for dynamic space grouping enhancement for skin lesion segmentation

A prevalent cancer, skin cancer, requires precise segmentation for effective diagnosis and treatment. Despite the availability of numerous effective, lightweight neural network models, there remains a significant gap in their application in resource-limited settings, particularly in mobile healthcare. This study aims to bridge this gap by developing a model combining high efficiency and robust performance in such environments. This study introduces the Dynamic Spatial Group Enhanced Network (DSEUNet), significantly reducing the computational loads and parameter quantities while ensuring competitive segmentation accuracy. Our essential contribution is developing a model that excels in segmentation accuracy and addresses computational and parameter efficiency challenges in mobile healthcare applications. In extensive trials on the ISIC2017 and ISIC2018 datasets, DSEUNet not only surpasses traditional models like UNet in accuracy but does so with far fewer parameters and reduced computational demand. This research contributes to developing more accessible and efficient tools for medical image analysis, particularly in resource-constrained environments. As far as we know, this is the first model with 23.306 KB parameters and 9.497 M FLOPs.

Hotspots and Global Trends of Nursing Research on Mobile Applications in Nursing From 2012 to 2023: A Bibliometric Analysis.

The rapid integration of mobile applications in healthcare has prompted an evolutionary change in nursing domain. This study aimed to systematically analyze the basic publication characteristics, research priorities, emerging trends, and thematic evolutions concerning mobile applications in nursing, providing an overview of the field's developmental trajectory and future directions. This was a descriptive bibliometric study. Data were collected on July 5, 2023, from the Web of Science database and analyzed by using the Bibliometrix package in R software. The search strategy yielded 417 documents authored by 1969 researchers, cited 12 595 references, and featured 1213 author keywords, spanning from 2012 to 2023. Research on mobile applications in nursing exhibited several key trends: (1) substantial collaboration among authors; (2) significant growth in the number of publications; (3) self-management was the most prominent hot topic; and (4) an evolution of research themes from general topics to a more specific focus on people-centered and problem-centered research. The corpus of literature pertaining to research on mobile applications within the nursing domain is anticipated to expand continually. Future research and practice in the nursing field are expected to benefit significantly from multidisciplinary collaboration and advancements in emerging technologies, including artificial intelligence.

Mobile healthcare application on Android OS using cloud computing

Mobile healthcare application on Android OS using cloud computing

Using smart and connected health services to cope with pandemics: The interaction effects of event and coping appraisals

Pandemics like COVID-19 disrupt conventional healthcare services, pushing toward smart and connected health solutions including mobile healthcare applications. However, the dynamics of disruptive events influencing smart health service adoption are not well-understood. By incorporating the concept of event disruption into cognitive appraisal theory, this paper develops a research model on how individuals’ event appraisals (event disruption and event threat) interact with coping appraisals (response efficacy and self-efficacy) of smart and connected health services in driving their future usage of smart and connected health services as a coping method. We tested the model using a mixed-methods approach with quantitative and qualitative data. First, we conducted an online survey to collect quantitative data from individuals with experience in using smart and connected health services. The results showed that the moderating effect of the cognitive appraisals of an external event stimulated the usage of smart and connected health services. We then collected archival qualitative data via multiple social media platforms during the COVID-19 pandemic to cross-validate the results of the quantitative study. Implications for theory and practice are discussed.

Development and usability evaluation of a culturally adapted stroke prevention educational programme on WeChat apps.

The persistently high incidence of stroke in many nations is suggestive of an area for further improvement on existing strategies of primary stroke prevention. Although the era of digitalisation has led to the increasing use of mobile applications (apps) in healthcare, more studies are needed to determine the efficacy of apps in producing the desired health outcomes across different nations and cultures. To describe the development and evaluate the usability of a mobile app in delivering a culturally adapted stroke prevention educational programme for middle-aged adults in the Republic of China. The educational programme was developed in three phases. In Phase 1, the process involved analysing requirements and designing structured modules. Phase 2 concentrated on expert consultation and technical development to deliver the educational programme. Phase 3 included a usability trial and refinement of the educational program based on trial results. Educational content was derived from the Chinese Guidelines for the Prevention and Treatment of Stroke and the Dietary Guidelines for Residents. The WeChat platform was used to deliver the educational programme. Participants expressed satisfaction with the content, interface, and functions of the apps, indicating that the apps have good usability. The development process of the Educational Programme was designed to maximise the culturally appropriate, and impact of lifestyle changes and stroke prevention. An app-based educational programme that has demonstrated good usability is a vital factor prior to deploying it in an intervention to evaluate its effects on health outcomes.

MedConnect PLUS: Health Management System

Abstract: The utilization of mobile applications in healthcare represents a frontier of innovation with the potential, to significantly enhance patients' daily healthcare management. These applications have the capacity to improve the efficiency of healthcare delivery, ultimately leading to cost reduction, and play a pivotal role in disease prevention by influencing behavior change. The paper introduces a model for an interactive healthcare application designed to meet the collaborative needs of both doctors and patients. The envisioned mobile application seeks to optimize communication among various stakeholders in the healthcare sector, including doctors, pharmacists and patients, thereby enhancing overall healthcare service and delivery. Object Oriented Analysis is employed in the software development process. This proposed mobile application is geared towards facilitating effective communication by incorporating advanced technologies to streamline processes such as appointment scheduling, health record management, and patient-doctor interactions. Emphasis is placed on the application's commitment to data security and compliance with healthcare regulations, ensuring the protection of sensitive patient information within a secure environment. The user-friendly interfaces tailored for both patients and doctors highlight the application's intention to cultivate a more interconnected and accessible healthcare ecosystem.

Use of mobile applications in health care during the COVID-19 pandemic: an integrative review

Use of mobile applications in health care during the COVID-19 pandemic: an integrative review

Cloud Computing Assisted Mobile Healthcare Systems Using Distributed Data Analytic Model

Distributed cloud technologies enable mobile healthcare applications to support end users constantly. Data from electronic health records is made available through combination of user needs and heuristic mining. Because of inefficient data storage and reorganization, service and recommendation failures occur prematurely. A Distributed Data Analytics and Organization Model (DDAOM) is established in this article as a solution to the inefficiency of managing large amounts of data. Using this method, errors caused by performing several computations or storing large amounts of data in the medical field are minimized. Data organization and mining under predetermined schedules or factors provide information relevant to user services. One-to-many computations with varying input and output data allocations (for services) are executed in federated learning. The local input from several edges may be handled by allocating storage in a decentralized manner. The federated learning system uses the memory of past states to direct the allocation. Differentiating the states is necessary to allocate services and prevent mining in certain areas. With the help of realistic learning iterations, state management is maintained, guaranteeing the smooth deployment of services. Delays in storage and mining, uneven service provisioning, and service backlogs are used to evaluate the effectiveness of the suggested model.

Social Support, eHealth Literacy, and mHealth Use in Older Adults With Diabetes: Moderated Mediating Effect of the Perceived Importance of App Design.

Mobile healthcare has emerged as a prominent technological solution for self-management of health. However, the development and utilization of tailored mobile healthcare applications for older adults with diabetes mellitus remain limited. This study examined the relationship between social support and mobile healthcare use and further explored how this relationship varies with eHealth literacy and application design among older adults with diabetes mellitus. A descriptive cross-sectional trial was conducted with a structured self-report questionnaire, surveying 252 South Korean older adults with diabetes mellitus via offline and online modes. The mediating effect and moderated mediating effect were analyzed with the PROCESS macro of SPSS. eHealth literacy mediated the relationship between social support and mobile healthcare use. High levels of eHealth literacy and social support may increase mobile healthcare use among older adults with diabetes. Application design aesthetics facilitated mobile healthcare use. Future researchers, healthcare providers, and developers can contribute to the development of tailored mobile healthcare applications for older adults with diabetes mellitus by considering application design aspects such as font size, color, and menu configuration.

Development of Legal Regulation of the Use of Mobile Medical Technologies (mHealth) in the Law of International Integration Associations: European Union Experience

The paper examines the experience of legal regulation of the use of mobile medical technologies (MMT, mHealth) in the largest regional integration association — the European Union (EU). It is noted that digital healthcare is a multifaceted concept that includes various aspects of public health and the digital environment surrounding it. Digitalization of healthcare covers medical processes, including software to support the work of doctors (including telemedicine technologies), tools for managing a medical institution, remote interaction with patients and monitoring such interaction; big data analytics for drug development, clinical research management, patient population studies; additional patient-oriented tools and including applications related to the determination of physical condition and the course of treatment, as well as compliance with the medication regimen. The paper provides a detailed overview of the main EU documents in the relevant field, illustrates the impact of judicial practice on the development of regulation in the field of mobile medical technologies, and analyzes the mechanisms of selfregulation in this area using the example of specific court cases. It is emphasized that while self-regulation on the part of the applications store can set developers of mobile healthcare applications in the right direction by converting the provisions of Regulation 2016/679 (GDPR) into technical requirements for preliminary approval, compliance with privacy provisions is facilitated by increased awareness of both application users and developers, as well as brokers of medical data about the risks to the observance of fundamental human rights. from the side of mobile healthcare applications. In conclusion, the authors formulated the key problems and ways to improve EU regulatory framework in the field of mobile medical technologies, recommended the use of EU best practices in the development of MMT regulatory regulation in the Russian Federation and integration associations with its participation.

Automation in Mobile Testing: Techniques and Strategies for Faster, More Accurate Testing in Healthcare Applications

Amidst the fast changing healthcare industry, mobile apps are crucial in enhancing patient care, optimising operations, and enabling communication between patients and healthcare professionals. Nevertheless, it is essential to guarantee the quality and dependability of these apps by thorough testing in order to protect patient data and uphold the integrity of healthcare services. The conventional manual testing approaches often prove inadequate in fulfilling the requirements of contemporary mobile apps because of their time-consuming character and vulnerability to human fallibility. Therefore, the use of automation in mobile testing has become a crucial approach to improve the effectiveness and precision of the testing procedure. This work investigates the methodologies and approaches for integrating automation in mobile testing specifically in the healthcare application domain. Firstly, it addresses the distinct obstacles encountered while testing healthcare mobile applications, such as strict legal obligations, the need for thorough data protection, and the intricacy of healthcare processes. This study emphasises how automation tackles these issues by providing quicker execution, consistent outcomes, and the capacity to include comprehensive test scenarios that human testing may overlook.

Long-Term Efficacy of primary care combining a mobile healthcare application with human coaching

Abstract Background Chronic diseases have become a global burden, and digital self-healthcare using mobile devices has gained importance in managing these conditions. However, barriers to implementing mobile self-management healthcare applications in current primary care system, and previous research has focused on short-term effects. This study aims to evaluate the long-lasting effect of combining human coaching with mobile self-management healthcare applications for chronic disease patients in primary healthcare services. Methods The study recruited 178 patients with chronic diseases from participating primary care clinics. The intervention group (N = 128) received a 12-week mobile self-management healthcare application service combined with traditional primary care, while the control group (N = 50) received only traditional healthcare. The Noom application was used, with two weekly human coaching sessions by a nutritionist. A telephone survey on weight changes was conducted one year after the intervention. Results The intervention group showed a greater weight loss compared to the control group during the intervention period (Intervention: -1.29 ± 2.94 vs Control: -0.38 ± 1.72, P-value 0.002). Although there was no statistically significant difference in additional weight change between the two groups during the follow-up period, the difference in weight loss during the intervention period was maintained (Intervention: -2.16 ± 5.54 vs Control: -1.06 ± 3.24, P-value: 0.008). Additionally, among the intervention group continuously for 1 year using the health management application, the average weight loss was higher than the other groups. Conclusions Combining a mobile self-management healthcare application with human coaching has long-lasting effects and can be effective in primary healthcare services. This suggests that such an approach has the potential to be a treatment option in current primary healthcare systems, but further large-scale long-term studies are needed. Key messages • This study suggests that using mobile self-management healthcare apps with human coaching can be an effective long-term treatment option for chronic disease patients in primary healthcare services. • The combination of the two methods may provide better outcomes than traditional healthcare services alone.

A configurable hardware-efficient ECG classification inference engine based on CNN for mobile healthcare applications

—Electrocardiogram (ECG) processors for healthcare have been widely used, however most of them can only adapt to specific applications, lacking flexibility. For achieving scalable on-chip ECG classification, a flexible inference engine based on one-dimensional (1-D) convolutional neural network (CNN) is proposed. By utilizing the proposed computing strategy based on systolic arrays, filter level parallelism and output channel parallelism are achieved. The configurable processing units (PUs) and modifiable instruction registers allow this inference engine to support computing of 1-D convolutional layers or fully connected layers with different scales. Based on the proposed data buffers system with multi-level storage structure, the input feature values and weight values are reused, thereby reducing hardware overhead and memory access power. This design has been validated on FPGA using our proposed two arrhythmia classification models that represent low energy consumption and high accuracy requirements, achieving accuracy of 98.9% and 99.3%, respectively, with energy consumption of 2.05 μJ and 14.27 μJ per classification at 200 MHz. The hardware implementation results indicate that good configurability enables our design to adapt to different ECG classification scenarios, effectively improving the hardware universality in mobile healthcare applications.

Trends in Development of Applied Social and Communication Technol-Ogies in Medical Field: Mobile Applications and Programmes in Healthcare

The purpose of the article is to conduct a comprehensive analysis of the trends in the development of applied social and communication technologies in the medical sector, in particular, to trace dynamics of growth in the number of mobile applications and programmes in healthcare sector. The research methodology is formed by general scientific and special methods of studying the problem, the use of which allowed the authors to achieve the goal. The combination of the methods used helped to clarify the logical sequence and trends in the development of applied social and communication technologies in the modern information space of society. The basic principle was the principle of historicism, which implies consideration of any phenomenon in its self-development under the influence of various factors. The historical and systemic method is aimed to analyse society as a whole and the transformations within it caused by changes in the information and communication space. Quantitative methods are used to process the results of analytical research and to diagnose the phenomena under study. The scientific novelty of the work is to expand the understanding of the trends in the development of applied social and communication technologies in the medical field. The authors' study analysed modern domestic medical information systems, the possibilities of their interaction with eHealth, and identified priority technological areas in the medical sector, such as virtual reality, 3D printing, robotics, artificial intelligence, augmented reality, a global network of medical facilities, wearable devices, smart technologies, and precision (personalised) medicine. The authors state that in recent years, attention has increased to applications, mobile sensors and digital media in order to get help in maintaining one's health. Conclusions. The study has proved the positive dynamics in the growth of mobile applications and programmes in healthcare sector and determined the trends of the further development of information technologies in the medical sector. Key words: healthcare system, information technologies, medical information systems, mobile applications in healthcare.

Neural network models selection scheme for health mobile app development

Mobile healthcare application (mHealth app) assists the frontline health worker in providing necessary health services to the patient. Unfortunately, existing mHealth apps continue to have accuracy issues and limited number of disease detection systems. Thus, an intelligent disease diagnostics system may help medical staff as well as people in poor communities in rural areas. This study proposes a scheme for simultaneously selecting the best neural network models for intelligent disease detection systems on mobile devices. To find the best models for a given dataset, the proposed scheme employs neural network models capable of evolving altered neural network architectures. Eight neural network models are developed simultaneously and then implemented on the Android Studio platform. Mobile health applications use pre-trained neural network models to provide users with disease prediction results. The performance of the mobile application is measured against the existing available datasets. The trained neural network engines perform admirably, detecting 7 out of 8 diseases with high accuracy ranging from 86.00% to 100% and a low detection accuracy of 63%. The detection times vary from 0.01 to 0.057 seconds. The developed mHealth app may be used by health workers and patients to improve resource-poor community health services and patients' healthcare quality.

COMPASS App: A Patient-centered Physiological based Pain Assessment System

Chronic pain patients lack at-home pain assessment and management tools. The existing chronic-pain mobile applications are either solely relying on self-report pain levels or restricted to formal clinical settings. Our app, abbreviated from an NSF-funded project entitled Novel Computational Methods for Continuous Objective Multimodal Pain Assessment Sensing System (COMPASS), is a multi-dimensional pain app that collects physiological signals to predict objective pain levels and trace daily at-home activities by incorporating a daily check-in section. We conducted a usability test with 33 healthy participants under pain conditions. The results provided initial support for the validity of the signals in predicting internalizing pain levels among the participants. With further development and testing, we believe the COMPASS app system has the potential to be used by both patients and clinicians as an additional tool to better assess and manage pain, especially for mobile healthcare applications.