Remote Patient Monitoring Research Articles

Enhancing Healthcare through Sensor-Enabled Digital Twins in Smart Environments: A Comprehensive Analysis.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

Predicted Multi-Chronic Disease by Supervised Machine Learning Algorithms: Performance and Evaluation

Current environmental conditions and human lifestyles have resulted in the emergence of numerous diseases. The medical field generates an enormous amount of new data each year for remote monitoring of patients. Due to increased data growth in the medical and healthcare industries, accurate medical data analysis has been advantageous to early patient care. However, physicians often face challenges in accurately diagnosing diseases in patients far from hospitals. Therefore, utilizing remote patient systems (telemedicine systems) due to the complexities associated with their chronic conditions. On the other hand, predicting illness is also a challenging task. Thus, data extracted from heterogeneous, fast-flowing, and reliable sources is crucial for decision-making and disease prediction. This research paper aims to utilize supervised Machine Learning (ML) techniques to predict chronic diseases such as heart and hypertension based on the patient’s features or symptoms by analyzing patient data collected by sensors and sources enabled by the Internet of Medical Things (IoMT). Supervised ML technology in Hadoop and Spark environments is employed to guarantee that this classification accurately identifies individuals with chronic illnesses. The methods are evaluated using 55,680 patient records to discover the proper match between the data set and the final disease-predicted result. The results demonstrate that the proposed procedure employing the Decision Tree (DT) algorithm is 94% accurate, and DT outperforms the other four ML algorithms. This includes the Support Vector Machine (SVM), a Naive Bayes (NB) model, Random Forest (RF), and Logistic Regression (LR) in terms of both performance and accuracy metrics (precision, recall, and F-score).

P121 Understanding barriers to digital inclusion from Urdu speakers’ perspective: remote monitoring of rheumatoid arthritis

Abstract Background/Aims Chronic musculoskeletal conditions are more prevalent in South Asian (SA) communities, particularly for women and older adults, who face existing barriers in accessing healthcare services. Remote patient monitoring (RPM) has the potential to enhance management of long-term conditions like rheumatoid arthritis, offering insights into disease activity and supporting shared decision-making. However, SA communities in the UK and elsewhere, are commonly underrepresented in the development and evaluation of digital health technologies where language and other barriers may limit adoption and use. This research aimed to explore perspectives of Urdu speaking members of South Asian communities on barriers for engaging with RPM using an app designed for people with rheumatoid arthritis (RA). The focus on Urdu speakers provides an example to ensure RPM benefits South Asian communities, and particularly where language may be a barrier. Methods The study included nine participants, primarily females with one male participant, aged 55-75, who were Urdu speakers and diagnosed with or suspected of having RA. They were recruited from community organisations in Greater Manchester and introduced to the RPM app for monitoring symptoms. Semi-structured interviews were conducted in Urdu to investigate barriers to digital inclusion. The interviews were transcribed and analysed in Urdu (by Urdu speaking members of the team) with selective translation into English. Thematic analysis was used to identify themes and subthemes related to digital exclusion and strategies for addressing these issues. Results Participants’ responses were themed into five key areas concerning digital inclusion and potential solutions: 1. Existing use of digital devices and resources ; most expressed limited use of digital technology in day to day living, 2. Concerns about privacy ; some expressing caution and lack of confidence in security, 3. Views about symptom tracking; many were positive about tracking symptoms to share with doctors, though some said they would not want the constant reminder of their condition, 4. Barriers to engagement with the app; women, older individuals, and those with limited tech exposure, emphasizing the need for translated versions and family support, 5.Social Support to engagement with technology; Suggestions included workshops to improve digital literacy, translated materials (including audio) and reminders, as well as family support. Conclusion The study revealed challenges faced by women, older individuals, and those with limited tech exposure when using the app, emphasizing the need for translated versions and family support. The study also highlighted a need to consider intersectional dimensions of inequality for tailoring solutions. Overall, the study emphasizes the significance of tailored support and accessibility measures for the diverse communities like SA Urdu community when implementing digital health solutions. Disclosure Y. Masood: None. S. Ali: None. H. Chadwick: None. K. Staniland: None. S. van der Veer: None. W. Dixon: Consultancies; WGD has received consultancy fees from Google unrelated to this work. C. Sanders: None.

P063 Beyond the clinical trials: real-world retention rate and use of digital interventions in patients with inflammatory arthritis

Abstract Background/Aims The use of digital tools to deliver healthcare interventions has increased rapidly. Inadequate engagement and retention are two major barriers to widespread and long-term adoption of electronic patient-reported outcomes (ePROs) for monitoring in clinical practice. Whilst retention rates are high in research studies, these are often short-term (<12months) and not representative of routine clinical practice. The reasons for variable engagement remain unclear. We present an analysis of engagement and retention of digital intervention in a routine rheumatology setting. The research aims to explore determinants of long-term sustainability of app-based intervention to collect ePROs in patients with inflammatory arthritis (IA). Methods This is a secondary analysis of real-world data obtained in clinical settings from IA patients who attended routine appointments in the rheumatology clinic at North Bristol NHS Trust between 2018 and 2022. Patients were invited to use a smartphone-based symptom diary (Living With) between appointments to track disease course by reporting RAPID3, HAQ-DI, or self-assessing joint pain/swelling. Statistical tests were performed using Stata16.1. Maximum elapsed days of the app use, number, and time between reports were explored as explanatory variables. Associations between demographic and disease parameters in different user subpopulations were explored by Poisson and tobit regression models. Results were stratified post-hoc by gender, age, diagnosis, and baseline disease activity. The research has ethical approval. Results 673 patients provided ≥1 report of any ePROs. 68% were female, mean age 53.7±13.9 years. 60% had rheumatoid arthritis; 24% - psoriatic arthritis and 17% - other IA. 613(91%) patients provided a RAPID3 measure of disease activity. Among them, 82(12%) participants reported only baseline RAPID3 and were classified as “lost to follow-up”. 531(79%) were identified as “follow-up” users. No difference between these subpopulations considering gender, age, IA type, and baseline arthritis activity was identified in the adjusted Poisson regression model. Retention rates reduced most in the first six months from a baseline of 91% to 38% at month 6 and subsequently to 24% at month 12, whereafter plateaued, highlighting a group of long-term users. Older patients (aged 60-69) were more likely to be long-term users compared to younger age (≤50) participants, demonstrating longer app engagement 379 [169; 645] vs 256 [90; 515] days, p < 0.006. Patients aged 60-69 and ≥70 provided more reports (18 [7; 46] and 25 [8; 62]) compared to younger users (8 [4;18], p < 0.0001). There was no association between the length or frequency of digital tool use and gender, baseline arthritis activity, and disability. Conclusion This analysis offers insights into what might be expected regarding engagement and the long-term sustainability of app-based interventions in routine clinical settings. Understanding the real-world performance and factors that could potentially improve user retention or dropout rates is critical to realizing the promise of digital interventions. Disclosure I. Biliavska: None. E. Lenguerrand: None. J. Tobias: None. P. Hamann: Consultancies; provided consultancy for Living With Ltd Software Company. Royalties; has limited royalty agreement with Living With Ltd Software Company for the development of the smartphone app LivingWith. Honoraria; have received honoraria from Gilead and AbbVie Pharmaceuticals for the production of training materials on remote monitoring for patients with arthritis..

Challenges and Facilitators in Implementing Remote Patient Monitoring Programs in Primary Care

BackgroundThe COVID-19 pandemic resulted in greater use of remote patient monitoring (RPM). However, the use of RPM has been modest compared to other forms of telehealth.ObjectiveTo identify and describe barriers to the implementation of RPM among primary care physicians (PCPs) that may be constraining its growth.DesignWe conducted 20 semi-structured interviews with PCPs across the USA who adopted RPM. Interview questions focused on implementation facilitators and barriers and RPM’s impact on quality. We conducted thematic analysis of semi-structured interviews using both inductive and deductive approaches. The analysis was informed by the NASSS (non-adoption and abandonment and challenges to scale-up, spread, and sustainability) framework.ParticipantsPCPs who practiced at least 10 h per week in an outpatient setting, served adults, and monitored blood pressure and/or blood glucose levels with automatic transmission of data with at least 3 patients.Key ResultsWhile PCPs generally agreed that RPM improved quality of care for their patients, many identified barriers to adoption and maintenance of RPM programs. Challenges included difficulties handling the influx of data and establishing a manageable workflow, along with digital and health literacy barriers. In addition to these barriers, many PCPs did not believe RPM was profitable.ConclusionsTo encourage ongoing growth of RPM, it will be necessary to address implementation barriers through changes in payment policy, training and education in digital and health literacy, improvements in staff roles and workflows, and new strategies to ensure equitable access.

A Multi-Faceted Digital Health Solution for Monitoring and Managing Diabetic Foot Ulcer Risk: A Case Series.

Diabetic foot ulcers (DFU) are a devastating complication of diabetes. There are numerous challenges with preventing diabetic foot complications and barriers to achieving the care processes suggested in established foot care guidelines. Multi-faceted digital health solutions, which combine multimodal sensing, patient-facing biofeedback, and remote patient monitoring (RPM), show promise in improving our ability to understand, prevent, and manage DFUs. Patients with a history of diabetic plantar foot ulcers were enrolled in a prospective cohort study and equipped with custom sensory insoles to track plantar pressure, plantar temperature, step count, and adherence data. Sensory insole data enabled patient-facing biofeedback to cue active plantar offloading in response to sustained high plantar pressures, and RPM assessments in response to data trends of concern in plantar pressure, plantar temperature, or sensory insole adherence. Three non-consecutive case participants that ultimately presented with pre-ulcerative lesions (a callus and/or erythematous area on the plantar surface of the foot) during the study were selected for this case series. Across three illustrative patients, continuous plantar pressure monitoring demonstrated promise for empowering both the patient and provider with information for data-driven management of pressure offloading treatments. Multi-faceted digital health solutions can naturally enable and reinforce the integrative foot care guidelines. Multi-modal sensing across multiple physiologic domains supports the monitoring of foot health at various stages along the DFU pathogenesis pathway. Furthermore, digital health solutions equipped with remote patient monitoring unlock new opportunities for personalizing treatments, providing periodic self-care reinforcement, and encouraging patient engagement-key tools for improving patient adherence to their diabetic foot care plan.

Remote active monitoring of patients after myocardial infarction: are there any advantages over standard outpatient monitoring?

Remote active monitoring of patients after myocardial infarction: are there any advantages over standard outpatient monitoring?

Feature engineering and deep learning-based approach for event detection in Medical Internet of Things (MIoT)

Medical Internet of Things (MIoT) is becoming increasingly mainstream as their applications continue to grow. These applications include remote healthcare monitoring, drug storage, alarm systems, and medical wearable devices. Remote patient monitoring, especially using wearable devices, has improved the health and protection of numerous people, while avoiding unnecessary hospital visits. Ideally, healthcare monitoring systems should accurately and timely detect if the patient is entering a critical condition, and alert the concerned doctor accordingly. However, in practice, there are two kinds of alarms raised by such systems, namely, events and anomalies. Events are non-erroneous data that do not conform to the expected values, indicating abnormality in the patient’s condition. On the other hand, anomalies are erroneous data that may result from faulty sensors, electromagnetic interference, malicious attacks, or hardware tampering. These anomalies lead to false alarms which affect the decision making process and may cause danger to the patient’s life. Traditionally, wearable MIoTs, measuring different vitals, frequently communicate with the patient’s phone, or a local processing unit (LPU), in order to analyze and process the collected data. However, the increase in processing capabilities of IoT devices has made on-device event detection possible. This adds an extra layer of defense while avoiding unnecessary communication with LPU. In this paper, a healthcare monitoring system is proposed which compromises of two layers - (1) an online lightweight approach, based on multivariate long-short term memory (LSTM) autoencoder, embedded in each MIoT, that is able to detect data abnormality and alarms the LPU, (2) a correlation technique, at the LPU, that differentiates between anomalies and events, and alerts the patient’s doctor only in case of an event. Since a single sensor reading does not provide enough clinical insight to detect the full length of an event, a feature engineering approach is implemented to extract interpretable statistical, dynamic and physiological features that provide the clinical insight needed. The proposed approach is simulated using Medical Information Mart for Intensive Care (MIMIC) dataset for various vital signs, such as heart rate, systolic and diastolic blood pressure. It is compared with four benchmarks, where the results show the robustness of the model in differentiating between anomalies and events, with event detection sensitivity and specificity above 93%.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

Implications of Advances in Remote Patient Monitoring for Nursing Care in Chronicity

Implications of Advances in Remote Patient Monitoring for Nursing Care in Chronicity

The impact of digital health technologies on pharmacy services and patient care

This comprehensive review delves into the profound impact of digital health technologies on the landscape of pharmacy services and patient care. The integration of electronic prescribing medication adherence apps, and tele-pharmacy services has revolutionized medication management in pharmacies. This amalgamation has not only streamlined processes but has also significantly minimized errors, leading to enhanced operational efficiency. Pharmacists, equipped with wearable devices, now engage in remote patient monitoring, allowing for personalized interventions and proactive healthcare strategies. The incorporation of genomic data, data analytics, and clinical decision support systems marks a transformative shift towards precision medicine within pharmacies. These technologies empower pharmacists to design tailored treatment plans based on individual genetic profiles, thereby optimizing therapeutic outcomes. As pharmacies increasingly embrace these digital tools, they metamorphose into dynamic healthcare hubs. Beyond traditional roles, pharmacies now offer virtual consultations and actively participate in patient education initiatives. This paradigm shift underscores a commitment to patient-centered care, exemplified by improved medication safety and a more interconnected healthcare ecosystem. The ongoing integration of digital health technologies holds promise for continued advancements in pharmacy services. This trajectory signifies a significant departure towards a more proactive, personalized, and technologically driven approach to patient care. The review underscores the transformative potential of digital health technologies in redefining the role of pharmacies and elevating the standards of patient-centered healthcare delivery.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients.

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.

The Impact of Telemedicine on Healthcare Delivery during and after the COVID-19 Pandemic: Opportunities, Innovations, Challenges, and Policy Implications

This narrative review evaluates the implementation purposes of telemedicine in medical service delivery in the context of the COVID-19 pandemic. Telemedicine covering all forms of e-Delivery of healthcare has undergone massive changes in technology, comprising patient-specific interfaces and seamless integration with Electronic Health records. With telemedicine, healthcare professionals can telecommute and have virtual meetings with patients, provide remote consultations, monitor patients' conditions and facilitate the diagnostic processes. The COVID-19 pandemic accelerated the adoption of telemedicine, leading to a surge in users and highlighting its potential to revolutionize healthcare delivery. However, challenges such as inequitable access, data protection concerns, and healthcare professional adaptation remains. In addition, this review has a policies segment, highlighting the need of comprehensive policies and government aids in order to maximize telemedicine benefits and address its issues. Keywords: Telemedicine, Healthcare Technology, COVID-19 Impact, Technological Advancements, Therapeutic Workflow, Remote Patient Monitoring and Healthcare Policy.

Beyond Audio-Video Telehealth: Perspective on the Current State and Future Directions of Digital Neurological Care in the United States

Background The COVID-19 pandemic transformed neurological care by both requiring digital health modalities to reach patients and profoundly lowering barriers to digital health adoption. This combination of factors has given rise to a distinctive, emerging care model in neurology characterized by new technologies, care arrangements, and uncertainties. As the pandemic transitions to an endemic, there is a need to characterize the current and future states of this unique period in neurology. Objective We sought to describe the current state of the pandemic- and postpandemic-related changes in neurological care and offer a view of the possible future directions of the field. Methods We reviewed several themes across the “new digital normal” in neurology, including trends in technology adoption, barriers to technology access, newly available telehealth services, unresolved questions, and an outlook on the future of digital neurology. Results In this new era of neurological care, we emphasize that synchronous audio-video telehealth remains the predominant form of digital interaction between neurologists and patients, mainly due to pandemic-related regulatory changes and the preexisting, steady adoption of video platforms in the prepandemic era. We also identify a persistent digital divide, with audio-only telehealth remaining a necessity for preserving care access. Asynchronous telehealth methods and services, including care coordination, interprofessional consultations, remote patient monitoring, and teletreatment are becoming increasingly important for neurological care. Finally, we identify several unanswered questions regarding the future of this “new normal,” including the lasting effects of emergency regulatory changes, the value proposition of telehealth, the future of telehealth reimbursement in neurology, as well as privacy considerations and trade-offs in asynchronous neurological care models. Conclusions The COVID-19 pandemic has ushered in an era of digital adoption and innovation in neurological care, characterized by novel care models, services, and technologies, as well as numerous unresolved questions regarding the future.

Exploring depressive symptom trajectories in COVID-19 patients with clinically mild condition in South Korea using remote patient monitoring: longitudinal data analysis.

During the height of the COVID-19 pandemic, the Korean government temporarily allowed full scale telehealth care for safety and usability. However, limited studies have evaluated the impact of telehealth by analyzing the physical and/or mental health data of patients with COVID-19 diagnosis collected through telehealth targeting Korean population. This study aimed to identify subgroup of depressive symptom trajectories in patients with clinically mild COVID-19 using collected longitudinal data from a telehealth-based contactless clinical trial. A total of 199 patients with COVID-19 were accrued for contactless clinical trial using telehealth from March 23 to July 20, 2022. Depressive symptoms were measured using the patient health questionnaire-9 on the start day of quarantine, on the final day of quarantine, and 1 month after release from quarantine. Additionally, acute COVID-19 symptoms were assessed every day during quarantine. This study used a latent class mixed model to differentiate subgroups of depressive symptom trajectories and a logistic regression model with Firth's correction to identify associations between acute COVID-19 symptoms and the subgroups. Two latent classes were identified: class 1 with declining linearity at a slow rate and class 2 with increasing linearity. Among COVID-19 symptoms, fever, chest pain, and brain fog 1 month after release from quarantine showed strong associations with class 2 (fever: OR, 19.43, 95% CI, 2.30-165.42; chest pain: OR, 6.55, 95% CI, 1.15-34.61; brain fog: OR, 7.03, 95% CI 2.57-20.95). Sleeping difficulty and gastrointestinal symptoms were also associated with class 2 (gastrointestinal symptoms: OR, 4.76, 95% CI, 1.71-14.21; sleeping difficulty: OR, 3.12, 95% CI, 1.71-14.21). These findings emphasize the need for the early detection of depressive symptoms in patients in the acute phase of COVID-19 using telemedicine. Active intervention, including digital therapeutics, may help patients with aggravated depressive symptoms.

Digital Health Interventions for Cancer Prevention Among Racial and Ethnic MinorityGroups in the United States: A Scoping Review.

The COVID-19 pandemic abruptly accelerated the use of digital health for cancer care. Previously, researchers identified a variety of digital health interventions for cancer prevention. The purpose of the present scoping review was to identify digital health interventions for cancer prevention designed for racial/ethnic minoritygroups. The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and was guided by the Arksey and O'Malley methodological framework. A search of PubMed, Ovid MEDLINE, and CINAHL for peer-reviewed research articles published from database inception to August 21, 2023, was conducted. Peer-reviewed studies published in English that employed digital health interventions for cancer prevention, that were conducted among racial/ethnic minoritygroups, and thatwere conducted in the United States were included. Also included were cancer prevention interventions for people who did not have cancer, people who did have cancer, and cancer survivors. Excluded were interventions that included non-Hispanic White individuals, interventions performed outside the United States, interventions that combined face-to-face methods with digital strategies, and interventions that did not clearly include digital health. Articles that focused on technologies for collecting and transmitting health data (e.g., remote patient monitoring) without an explicit tie-in to cancer prevention intervention outcomes were also excluded. Following screening, eight articles met the eligibility criteria. Six of the articles were published prior to the COVID-19 pandemic, and two were published during it. The digital health interventions for cancer prevention in racial/ethnic minoritygroups included screening (n = 5), emotional support and education (n = 1), human papillomavirus vaccination (n = 1), and education and treatment (n = 1). A consistently measured outcome was intervention efficacy. Four authors explicitly stated that theories or theoretical constructs were employed to guide intervention development. Also, no interventions were created using novel devices such as emerging technologies. We identified several notable gaps regarding digital health for cancer prevention among racial/ethnic minoritygroups. Addressing these gaps may help guide continued innovation in the use of digital health for cancer prevention among racial/ethnic minoritygroups.

Role of Smart Wearables in Remote Patient Monitoring for Diabetes

Diabetes is affecting people all over the world. Smart wearables are convenient alternatives to monitor diabetes and are rapidly adopted. Smart wearables enable remote patient monitoring which is a great boon for the elderly, wilderness care, and patients suffering from diabetic foot ulcers, and gestational diabetes. The objective of this review is to study the development, and trend in the use of smart wearables for diabetes treatment, and remote patient monitoring based on research papers available in the Scopus database. The present work examines the existing literature through network diagrams generated by the VOSviewer tool. Scopus is one of the largest databases used by researchers. Research papers from the year 1987 to 2023 were considered to observe the trend, and filters were applied to select the source type, “Journal”, language “En- glish”, and “Article, Reviews, and Conference papers” from the document type. The final number of documents was ascertained after removing irrelevant documents and employing VOSviewer software to create network diagrams. Results indicate that a significant increase in research happened after the year 2019. Network diagrams revealed that the United States published the highest number of research publications. The analysis of co-authorship with keywords is discussed. This review includes future directions for researchers based on the analysis of network diagrams. The distinctive feature of this study is the unique way of presenting the analysis of VOSviewer diagrams. Index Terms—Diabetes, Remote patient monitoring, Smart wearables, VOSviewer, Scopus

A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption

The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.

Approaching Mental Health Through a Preventive Data Analysis Platform

The rising prevalence of chronic diseases and the aging population globally are diminishing the overall quality of life, especially for those with demanding daily routines. As medical advancements extend lifespans, the proportion of individuals over 60 is set to double by 2050, necessitating societal shifts toward health-responsible citizenship. Despite longer lifespans, evidence suggests that older age often accompanies mental health challenges such as anxiety, depression, and substance misuse. Social isolation and loneliness further compound these issues, affecting both physical and mental well-being. Digital wellness empowers individuals to take charge of their health, promoting proactive care and literacy to foster health-conscious citizenship. This paper explores the intersection of mental health, aging populations, preventive wellness initiatives, and health literacy, emphasizing their significance within the Health 5.0 framework, especially for older adults. Traditionally, health regulators offer static workflows for adopting standard procedures in health and well-being, reflecting a reactive approach. However, the evolving landscape of wearable and mobile devices connecting to healthcare IT systems through secure online networks necessitates a shift. Technology now facilitates remote patient monitoring and telemedicine service subscriptions and empowers individuals to manage their health proactively. The BE4YOU project, conducted in Portugal, serves as a case study, facilitating individual maintenance of health and well-being through intelligent and dynamic workflows, which are defined based on analytical models considering each person’s risk profile. By facilitating data sharing, promoting healthy lifestyles, and enabling early detection of mental health issues, the project leverages technology to support personalized monitoring and enhance overall health outcomes. Through technology-driven empowerment, individuals and healthcare professionals are better equipped to assess risks and ensure ongoing wellness monitoring, underscoring the role of technology in fostering personal health and supporting healthcare effectiveness.