Search Results (304)

The COVID-19 pandemic catalyzed the rapid adoption of remote patient monitoring (RPM) technologies such as telemedicine and wearable devices (WDs), significantly transforming healthcare delivery. Telemedicine made virtual consultations possible, reducing in-person visits and infection risks, particularly for the management of chronic diseases. Wearable devices enabled the real-time continuous monitoring of health that assisted in condition prediction and management, such as for COVID-19. This narrative review addresses these transformations by uniquely synthesizing findings from 13 diverse studies (sourced from PubMed and Google Scholar, 2020–2024) to analyze the parallel evolution of telemedicine and WDs as interconnected RPM components. It highlights the pandemic’s dual impact, as follows: accelerating RPM innovation and adoption while simultaneously unmasking systemic challenges such as inequities in access and a need for robust integration approaches; while telemedicine usage soared during the pandemic, consumption post-pandemic, as indicated by the reviewed studies, suggests continued barriers to adoption among older adults. Likewise, wearable devices demonstrated significant potential in early disease detection and long-term health management, with promising applications extending beyond COVID-19, including long COVID conditions. Addressing the identified challenges is crucial for healthcare providers and systems to fully embrace these technologies and this would improve efficiency and patient outcomes. Full article

The incorporation of digital health into the medical curricula is becoming more important to better prepare doctors in the future. Digital health comprises a wide range of tools such as electronic health records, health information technology, telemedicine, telehealth, mobile health applications, wearable devices, artificial intelligence, and virtual reality. The present study aimed to explore the medical and biomedical students’ perspectives on the integration of digital health in medical curricula. A cross-sectional study was conducted on the medical and biomedical undergraduate students at the College of Medicine and Health Sciences at Sultan Qaboos University. Data was collected using a self-administered questionnaire. The response rate was 37%. The majority of respondents were in the MD (Doctor of Medicine) program (84.4%), while 29 students (15.6%) were from the BMS (Biomedical Sciences) program. A total of 55.38% agreed that they were familiar with the term ‘e-Health’. Additionally, 143 individuals (76.88%) reported being aware of the definition of e-Health. Specifically, 69 individuals (37.10%) utilize e-Health technologies every other week, 20 individuals (10.75%) reported using them daily, while 44 individuals (23.66%) indicated that they never used such technologies. Despite having several benefits, challenges exist in integrating digital health into the medical curriculum. There is a need to overcome the lack of infrastructure, existing educational materials, and digital health topics. In conclusion, embedding digital health into medical curricula is certainly beneficial for creating a digitally competent healthcare workforce that could help in better data storage, help in diagnosis, aid in patient consultation from a distance, and advise on medications, thereby leading to improved patient care which is a key public health priority. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation. Full article

Background/Objectives: Adherence to healthy behaviors after open-heart surgery is crucial for recovery and long-term health. Traditional patient education methods can be enhanced by using technology to improve engagement and self-care. This study aimed to develop and assess the feasibility of the “Term-Jai” smartphone application for promoting healthy heart behaviors in open-heart surgery patients. Methods: The “Term-Jai” psychological theory-based application was tested quantitatively and qualitatively over a 30-day period with 13 patients (age 44–78 years) following open-heart surgery between November 2023 and March 2024. Participant engagement, healthy behaviors, user experience, and usability were assessed using the System Usability Scale (SUS), satisfaction ratings, healthy behavior questionnaires, and semi-structured interviews. Results: The application was feasible, with 70% of participants remaining engaged during the intervention. The average SUS score was 80.2 ± 10.3, indicating good usability. Participants found the application’s information useful, clear, and easy to understand, showing improvements in health behaviors following application usage. The qualitative analysis highlighted the application’s intuitive design and potential for supporting cardiac rehabilitation. High satisfaction scores suggested its effectiveness despite some barriers to application usage around technical support and personalized exercise progression. Conclusions: The “Term-Jai” application is a promising tool for promoting healthy behaviors in patients following open-heart surgery. The application shows good usability and participant satisfaction, indicating its potential for broader implementation after further refinements. Full article

Stroke-induced hand dysfunction substantially impairs patients’ quality of life, creating an urgent need for portable, adaptive rehabilitation devices. This study introduces a smart rehabilitation glove actuated by shape-memory alloy (SMA) wires, leveraging their high power-to-weight ratio, controllable strain recovery, and reversible phase transformation to overcome the limitations of conventional motor-driven or pneumatic gloves. The glove incorporates SMA-based actuation units achieving 50 mm contraction (5% strain) within 7 s, enabling finger flexion to ~34° for personalized rehabilitation protocols. A mobile application provides wireless regulation of SMA actuation modes and facilitates real-time telemedicine consultations. The prototype demonstrates an ultra-lightweight, compact design enabled by SMA’s intrinsic properties, offering a promising solution for home-based post-stroke rehabilitation. This work establishes the transformative potential of SMAs in wearable biomedical technologies. Full article

Background: Asthma is the most common chronic respiratory disease in children and adolescents, associated with high morbidity and healthcare costs. Telemedicine has emerged as a strategy to improve access to care, adherence to treatment and symptom control. However, its effectiveness in the pediatric population has not been clearly studied. Objective: To assess the clinical effectiveness of telemedicine interventions in asthma control and health-related quality of life in asthmatic children and adolescents. Methodology: A systematic review and meta-analysis were performed following PRISMA-2020 guidelines, with previous registration in PROSPERO (CRD42025251000837). Sixteen randomized clinical trials (n = 2642) with patients aged 2–18 years were included. The interventions included videoconferencing, mobile applications, web systems, interactive voice response and mobile units in schools. The outcomes were measured using validated quality-of-life (PAQLQ) and asthma control (ACT/c-ACT) questionnaires. Results: Seven studies were incorporated into the PAQLQ meta-analysis, whose overall effect was non-significant (mean difference = 0.06; 95% CI: −0.06 to 0.18; I² = 5.7%). Five studies provided ACT/c-ACT data, showing a significant effect in favor of telemedicine (mean difference = 0.61; 95% CI: 0.32 to 0.90; I² = 73%). Complementary qualitative analysis revealed improvements in adherence, reduction in exacerbations, emergency department visits and use of rescue medication. Conclusions: Telemedicine improves the clinical control of pediatric asthma, although its impact on the quality of life is limited. Multicenter trials with long-term follow-up and cost-effectiveness evaluation are required. Full article

Scoliosis is one of the most common spinal deformities, which affects millions of people worldwide. Bracing and physiotherapy exercises represent the first-line, non-invasive approaches for managing scoliosis. In recent years, the use of wearable devices has spread as a novel approach to the treatment of scoliosis. However, their effectiveness in treatment planning and outcomes has not been thoroughly evaluated. This manuscript provides a scoping review of the classification and application of wearable devices and the role of artificial intelligence (AI) in interpreting the data collected by wearable devices and guiding the treatment. A systematic search was carried out on Scopus, Web of Science, PubMed, and EMBASE for studies published between January 2020 and February 2025. A total of 269 studies were screened, and 88 articles were reviewed in depth. Inclusion criteria encompassed articles focusing on wearable devices integrated into smart braces, rehabilitation systems for scoliosis management, AI and machine-learning (ML) applications in scoliosis treatment, virtual reality (VR), and telemedicine for scoliosis care. The literature shows that the use of wearable devices can enhance scoliosis treatment by improving the efficiency of braces and enabling remote monitoring in rehabilitation programs. However, more research is needed to evaluate user compliance, long-term effectiveness, and the need for personalized interventions. Future advancements in artificial intelligence, microsensor technology, and data analytics may enhance the efficacy of these devices, which can lead to more personalized and accessible scoliosis treatment. Full article

Background/Objectives: Despite the recent advances in glucose-lowering therapy, achieving diabetes control remains challenging. With the advancing progress of innovative digital health technologies, management of diabetes is taking advantage from telehealth and telemedicine, which allow for remote assistance, virtual visits, and monitoring of diabetes-related parameters, and facilitate the exchange of documents and reports to support clinical decisions. We aim to provide an overview of the impact of telehealth and digital technologies on the care of people with diabetes, from therapeutic management to the assessment of complications. Methods: A comprehensive literature search was conducted using PubMed to assess the impact of digital technologies and telemedicine on diabetes care. Results: From the comprehensive PubMed search, 86 peer-reviewed studies were selected based on relevance, clinical significance, and methodological quality. The selected literature addressed digital health tools such as continuous glucose monitoring, connected insulin pens, automatic insulin delivery systems, mobile applications, and telemedicine systems. These interventions were associated with improved glycemic control (e.g., reduced HbA1c, increased time in range), better adherence to therapy, enhanced patient engagement, and more efficient management of complications such as neuropathy, retinopathy, and cardiovascular risk. Conclusions: Telehealth may offer a fully patient-centered approach to disease management through a tailored individual management plan. This may lead to an improvement in adherence to proper therapy and lifestyle, resulting in a subsequent increase in the quality of life. Full article

Introduction: The rapid growth of mobile health (mHealth) applications has revolutionized healthcare delivery worldwide. These digital tools encompass a broad array of functionalities, including telemedicine, appointment scheduling, medication management, and health data tracking, all of which contribute to enhanced healthcare accessibility, increased patient engagement, and improved operational efficiency. However, despite their increasing prominence, the design, deployment, and use of mHealth applications continue to face several challenges, such as usability issues and overall sustained adoption. Objectives: This study aims to evaluate mHealth applications in Saudi Arabia, focusing on their design characteristics, usability features, and current feature gaps. Method: A total of 21 mHealth applications were selected and analyzed using a thematic analysis approach. The apps were selected based on usage popularity in the Saudi market and relevance to national digital health strategies. Data were drawn from publicly available app store information, official app documentation, and expert evaluations. Results: The findings reveal that while mHealth applications excel in areas such as telemedicine, appointment booking, and health education, there are notable gaps in features such as behavior modification, patient monitoring, and health management. Conclusions: This study contributes to the growing body of research on mHealth by offering grounded insights into the functional landscape of digital health tools in Saudi Arabia. It also outlines practical recommendations to enhance usability, feature diversity, and alignment with evolving healthcare needs in Saudi Arabia and beyond. Full article

Time-sensitive applications require the End-to-End (E2E) delay of wireless networks to be deterministic. For example, control signals in industrial automation, intelligent transportation, and telemedicine must be transmitted to their destinations within the millisecond range, with delay jitter controlled within the microsecond range. To formulate effective policies for maintaining E2E delay within a small deterministic range, it is essential to estimate the probability density function (PDF) of E2E delay. Data-driven methods based on mixture density networks have been employed to estimate the PDF of E2E delay in wireless networks. However, in WiFi networks, the estimation results produced by existing methods exhibit significant discrepancies and fluctuations when compared to actual measurements. Motivated by this, an improved estimation method is proposed, where the delay PDF is divided into three segments with different functional expressions that are coupled together. Moreover, the parameter estimation process is implemented in two stages. First, the two division thresholds for the three segments of the PDF are calculated based on the variation trend of E2E delay measurements. Second, the remaining parameters are obtained through training using an improved mixture density network. Experimental results indicate that the E2E delay PDF obtained by the proposed method exhibits a smaller gap compared to actual measurements than existing methods. Specifically, the mean absolute errors and average fluctuation amplitudes of tail probabilities at certain delay values decrease by at least one order of magnitude. Moreover, the multiple-segmentation feature of the proposed method enhances its robustness in situations where measurement data are affected by low levels of Gaussian noise. Full article

Postoperative pain management after hospital discharge remains one of the main clinical challenges. The use of digital and telemedicine tools offers new opportunities for the continuous monitoring of, and timely intervention in, patients discharged and followed at home. This scoping review, conducted according to the PRISMA-ScR checklist and the Joanna Briggs Institute methodology, analyzed 26 studies selected through a search of PubMed, Scopus, and Web of Science databases. Inclusion criteria comprised studies published between 2015 and 2025 that involved patients discharged home after surgery, that used digital or telemedicine tools for pain management, and that included active involvement of healthcare professionals and reported clinical outcomes. Studies show the use of a variety of digital tools, including mobile applications, web platforms, wearable sensors, automated messaging systems, and virtual reality technologies, alternating across settings for the assessment and management of pain at home, educational and therapeutic support, and to enhance communication between healthcare professionals and patients. Most reported outcomes focus on improved home-based pain control, a reduction in opioid consumption, and a high level of patient satisfaction. However, some challenges remain, particularly the low level of digital literacy among certain segments of the population. In conclusion, the implementation of telemedicine and digital technologies for managing postoperative pain at home proves to be a promising strategy. Nonetheless, it requires further scientific investigation and, from policymakers, significant investments in professional training and technological infrastructure to ensure an increasingly equitable and sustainable distribution of home healthcare services. Full article

Mobile edge computing was conceived to address the increasing computing demand generated by users at the communication network edge. It is expected to play a significant role in next-generation (5G, 6G, and beyond) communication systems as new applications such as augmented/extended reality, teleoperations, telemedicine, and gaming become prolific. As the networks become denser, more and more edge servers are expected to be deployed, and the question of task offloading becomes more complicated. In this study, we present a framework for task offloading in the presence of multiple edge servers that employ round-robin task scheduling. Most studies in the literature attempt to optimize the offloading process under the assumption that each user generates just a single task, or they generate one task every time slot in a discrete-time system where all the tasks are handled within a slot. Furthermore, first-come-first-served queueing models are typically used in studies where queueing is considered at all. The work presented is novel in that we assume continuous and stochastic task arrivals generated by multiple users and round-robin task scheduling at the edge servers. This setting is considerably more realistic with respect to the existing works, and we demonstrate through extensive simulations that round-robin task scheduling significantly reduces task delay. We also present a comparison of a number of server selection mechanisms. Full article

This study presents a novel approach to EEG signal classification in distributed environments using dynamic ensemble selection. In scenarios where data dispersion arises due to privacy constraints or decentralized data collection, traditional global modelling is impractical. We propose a framework where classifiers are trained locally on independent subsets of EEG data without requiring centralized access. A dynamic coalition-based ensemble strategy is employed to integrate the outputs of these local models, enabling adaptive and instance-specific decision-making. Coalitions are formed based on conflict analysis between model predictions, allowing either consensus (unified) or diversity (diverse) to guide the ensemble structure. Experiments were conducted on two benchmark datasets: an epilepsy EEG dataset comprising 150 segmented EEG time series from ten patients, and the BCI Competition IV Dataset 1, with continuous recordings from seven subjects performing motor imagery tasks, for which a total of 1400 segments were extracted. In the study, we also evaluated the non-distributed (centralized) approach to provide a comprehensive performance baseline. Additionally, we tested a convolutional neural network specifically designed for EEG data, ensuring our results are compared against advanced deep learning methods. Gradient Boosting combined with measurement-level fusion and unified coalitions consistently achieved the highest performance, with an F1-score, accuracy, and balanced accuracy of 0.987 (for nine local tables). The results demonstrate the effectiveness and scalability of dynamic coalition-based ensembles for EEG diagnosis in distributed settings, highlighting their potential in privacy-sensitive clinical and telemedicine applications. Full article

Smart telemedicine represents an innovative application of information and communication technology within the healthcare sector, encompassing healthcare delivery, disease management, public health surveillance, education, and research. The commercialization of 5G and the extensive adoption of the Internet of Things (IoT) enable smart telemedicine devices to mitigate geographical and transmission delays, hence enhancing the quality of treatment provided to individuals. Although intelligent medicine is significant, previous studies emphasize the implementation and adoption of systems or technologies with few studies conducted on the platform of smart telemedicine equipment. This study aims to address the research gap by forecasting future developments and delineating smart telemedicine device designs utilizing platform-based design. We introduce a hybrid multi-criteria model that delineates the components of the intelligent medical platform. A portable 12-lead electrocardiogram (ECG) system is used by a global telemedicine technology company to assess the viability of the suggested framework. The portable 12-lead ECG device integrates artificial intelligence (AI), cloud computing, and 6G technology. The results of this study provide a basis for product creation by other smart telemedicine companies, while the platform-based analytical methodology can be employed for future product design. Full article

Telemedicine has the potential to support healthy aging in older adults; however, many applications fail to consider their specific needs and preferences, resulting in a limited number of effective options. This study systematically reviews the existing literature on interface design elements, attempts to link the elements with a Technology Acceptance Model (TAM), evaluates their usability for the elderly population, and highlights the social sustainability of telemedicine systems. A comprehensive search was conducted across the Web of Science, Scopus, PubMed, and CINAHL databases to identify existing research pertaining to the factors influencing usability, evaluation methodologies, and recommendations for telemedicine interfaces specifically designed for older adults. A total of 41 studies were analyzed, yielding a comprehensive summary of research methodologies and interface design elements from four key dimensions: the functional framework, interaction logic, visual design, and user experience. Our analysis identified prevalent usability challenges and provided actionable recommendations. Although the majority of studies focused on existing applications and usability testing, there was a notable gap in research addressing the cognitive and emotional needs of older users. Furthermore, many studies fell short in offering a comprehensive and detailed examination of design elements relevant to this user group. Based on the existing literature, this paper summarizes interface design elements suitable for elderly users, offering insights into user experience and usability. This study provides guidance for the interface design and development of telemedicine for old adults, offers suggestions on interface usability for practitioners in the medical industry, and also provides a reference value for the government in formulating relevant medical policies and for entrepreneurs in making investments. Full article