Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review
Abstract
:1. Introduction
2. Theoretical Information
2.1. Cardio–Cerebrovascular Diseases: Definition and Characteristics
2.2. Traditional Management of Cardio–Cerebrovascular Diseases
2.3. Home Monitoring Tools: Technologies and Applications
2.4. Advantages of Home Monitoring in Disease Management
2.5. Artificial Intelligence and the Internet of Things
3. Contribution and Evolution over Time
4. Methods
4.1. Search Chain
4.2. Tree of Science
4.3. Scientometric Analysis
5. Results
5.1. Country Analysis
5.2. Analysis by Journals
5.3. Analysis by Authors
6. Tree of Science Analysis
6.1. Root: The Evolution of IoT in Patient Care for Cardio–Cerebrovascular Diseases
6.2. Evolution of IoT Technology in Cardiovascular Patient Monitoring
6.3. Branch 1: Mobile Applications for the Prevention of Heart Disease Risk and Physical Health Management
6.4. Branch 2: Remote Cardiac Monitoring Devices for Clinical Tracking of Cardiovascular Patients
6.5. Branch 3: Deep Sleep-Based Technological Heart Monitoring for Stroke-Related Diseases
7. Discussion
7.1. Technological Challenges
7.2. Data Security
7.3. Post-COVID Impacts
7.4. Socioeconomic Factors
8. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Ref. | Year | Author | Description of Contribution |
---|---|---|---|
[28] | 2001 | Wilson | The article introduces the “Hospital Without Walls”, a healthcare model that allows patient care at home through remote monitoring of vital signs. This system aims to improve the quality and efficiency of healthcare, providing comfort and quality care in a familiar environment. The technical features and potential applications of this teleassistance approach are discussed. |
[29] | 2005 | Paradiso | The article describes a health monitoring system integrated into clothing through fabric sensors, allowing discreet and non-invasive tracking of vital signs and other health indicators, combining functionality with comfort for the user. |
[30] | 2006 | Villalba E | The article discusses the design and development of user interaction for a heart failure management system that uses wearable and mobile technologies to monitor vital signs daily. It highlights successful connectivity through the Internet and web services and notes significant improvements in user interaction and interface compared to previous versions, including a more attractive appearance and the introduction of avatars. |
[31] | 2007 | Zheng JW | The article describes a portable mobile health care system designed to continuously monitor vital signs over the long term in patients at high cardiovascular risk. Using a portable patient unit and a special shirt, the system monitors the electrocardiogram, respiration, and activity without causing discomfort. |
[32] | 2008 | Taccini N | The article discusses the Wealthy textile platform, equipped with piezoresistive sensors for monitoring pulmonary and cardiovascular diseases. It evaluates two types of textile sensors for plethysmography through lab tests and in vivo measurements. The platform enables remote monitoring of electrocardiograms and impedance pneumography using textile electrodes and differentiates breathing patterns with piezoresistive sensors on the abdomen and chest. Results were compared with standard BIOPAC® MP30 respiratory transducers under baseline conditions. |
[33] | 2009 | Villalba E | The article examines the iterative validation of user interaction in a heart failure management system that uses wearable and mobile technologies for daily vital sign monitoring. This continuous monitoring allows for constant evaluation of the disease’s status. The system’s results are crucial in motivating patients to adopt healthier lifestyles and facilitating the self-management of their chronic condition. |
Ref. | Year | Author | Description of Contribution |
---|---|---|---|
[27] | 2010 | Lin | This work presents a wireless telecardiology system with a lightweight ECG device and automatic alarm connected to a mobile platform that effectively detects atrial fibrillation, enabling rapid alerts for early interventions. |
[35] | 2011 | Zhao | The article discusses the Diabetic Telemedical Information System (DTMIS), which monitors health in real time using a sensor-equipped shirt, Bluetooth, and GSM and identifies abnormal data to provide suggestions to patients. |
[36] | 2012 | Lobodzinski | This review article details prominent wearable electrocardiogram (ECG) devices in both the recording and wireless transmission categories. Additionally, it provides a brief explanation of their potential uses in arrhythmia monitoring, ST-T segment variations, and treatment. |
[37] | 2013 | Lee | The authors present a method for predicting body mass index (BMI) using vocal features and machine learning. This approach can provide valuable medical information for the treatment and prognosis of diseases such as cardiovascular conditions, diabetes, and strokes without relying on traditional weight and height measurements. |
[38] | 2014 | Huang | The article introduces We Care, a telecardiology system for mobile networks that detects ECG with high accuracy and optimizes medical resources, which is useful for patients at cardiovascular risk. |
[39] | 2015 | Martin | This study on a mobile health intervention in cardiology shows that text messages improve short-term physical activity, providing preliminary data on its effectiveness and feasibility. |
[40] | 2016 | Mortazavi | The study compared the effectiveness of machine learning and traditional methods, such as logistic regression, in predicting heart failure readmissions, using data from the Tele-HF trial and exploring improvements in ML through hierarchical techniques. |
[41] | 2017 | Vegesna | The article reviews remote patient monitoring (RPM) using noninvasive digital technologies, highlighting the lack of evidence on health improvements and cost-benefit studies, and emphasizes the need for more research. |
[34] | 2018 | Yildirim | The article details a deep learning method that uses a 1D-CNN model to detect 17 types of cardiac arrhythmias in ECG signals, achieving 91.33% accuracy and rapid classification. |
Ref. | Year | Author | Description of Contribution |
---|---|---|---|
[43] | 2019 | Shan | The article reviews mobile health interventions for type 1 and type 2 diabetes, including apps, glucose meters, text messages, and virtual counseling. Of 13 interventions in trials, eight were effective, and five did not significantly reduce HbA1c. The importance of personalized content, social support, and gamification for patient engagement is highlighted. |
[42] | 2020 | Fan | The study introduces integrated textile triboelectric sensors (TATSA) embedded in clothing, which monitor pulse and breathing and assess cardiovascular diseases and sleep apnea. These sensors offer high sensitivity and stability, are aesthetically incorporated into garments, and transmit data to mobile devices, thus advancing wearable textile electronics for health. |
[44] | 2021 | Bayoumy | This review discusses wearable sensors in cardiology, highlighting challenges such as accuracy and privacy, and offers recommendations and an “ABCD” guide for their integration into clinical practice. |
[45] | 2022 | Yang | This review addresses POC microfluidic devices, their applications in diseases, and their potential integration into wearable devices and telemedicine, highlighting the need for standards. |
[46] | 2023 | Brunetti | This document from the Italian Society of Cardiology emphasizes the importance of ICT and artificial intelligence in cardiology, discusses machine learning for remote monitoring, and highlights its utility in selecting hospital admissions. |
[43] | 2019 | Shan | The article reviews mobile health interventions for type 1 and type 2 diabetes, including apps, glucose meters, text messages, and virtual counseling. Of 13 interventions in trials, eight were effective and five did not significantly reduce HbA1c. The importance of personalized content, social support, and gamification for patient engagement is highlighted. |
[42] | 2020 | Fan | The study introduces integrated textile triboelectric sensors (TATSA) embedded in clothing, which monitor pulse and breathing and assess cardiovascular diseases and sleep apnea. These sensors offer high sensitivity and stability, are aesthetically incorporated into garments, and transmit data to mobile devices, thus advancing wearable textile electronics for health. |
[44] | 2021 | Bayoumy | This review discusses wearable sensors in cardiology, highlighting challenges such as accuracy and privacy, and offers recommendations and an “ABCD” guide for their integration into clinical practice. |
[45] | 2022 | Yang | This review addresses POC microfluidic devices, their applications in diseases, and their potential integration into wearable devices and telemedicine, highlighting the need for standards. |
[46] | 2023 | Brunetti | This document from the Italian Society of Cardiology emphasizes the importance of ICT and artificial intelligence in cardiology, discusses machine learning for remote monitoring, and highlights its utility in selecting hospital admissions. |
Parameter | WoS | Scopus |
---|---|---|
Range | 2020–2023 | |
Date | 3 May 2024 | |
Type of Document | Article, Book, Book Chapter, Conference proceedings | |
Words | “telehealth” OR “homecare” OR “Telemedicine” OR “Teleconsultation” OR “Home health care” OR “In-home care” OR “Home care services” and (“machine learning” OR “iot” OR “wearable”) and (“cardiovascular” OR “cerebrovascular”) | |
Results | 95 | 366 |
Total (WoS + Scopus) | 462 |
Country | Production | Citation | Q1 | Q2 | Q3 | Q4 | ||
---|---|---|---|---|---|---|---|---|
USA | 106 | 25.79% | 3258 | 39.23% | 58 | 14 | 6 | 1 |
China | 46 | 11.19% | 1248 | 14.96% | 16 | 11 | 4 | 1 |
Italy | 33 | 8.03% | 940 | 11.32% | 8 | 7 | 3 | 1 |
United Kingdom | 21 | 5.11% | 370 | 4.46% | 14 | 2 | 2 | 0 |
India | 20 | 4.87% | 88 | 1.06% | 0 | 2 | 1 | 5 |
Germany | 16 | 3.89% | 84 | 1.01% | 5 | 3 | 4 | 0 |
Australia | 14 | 3.41% | 123 | 1.48% | 6 | 1 | 0 | 1 |
Korea | 14 | 3.41% | 204 | 2.46% | 5 | 3 | 1 | 0 |
Canada | 11 | 2.68% | 167 | 2.01% | 5 | 1 | 1 | 0 |
France | 9 | 2.19% | 78 | 0.94% | 2 | 2 | 0 | 0 |
Journal | Wos | Scopus | Total | Impact Factor | H Index | Quartile |
---|---|---|---|---|---|---|
International Journal of Environmental Research and Public Health | 2 | 10 | 10 | 0.83 | 167 | Q2 |
Jmir Mhealth and Uhealth | 3 | 7 | 8 | 1.51 | 84 | Q1 |
Journal of Medical Internet Research | 3 | 8 | 8 | 1.99 | 178 | Q1 |
Studies in Health Technology and Informatics | 0 | 8 | 8 | 0.29 | 64 | Q3 |
Sensors | 6 | 4 | 7 | 0.76 | 219 | Q1 |
Ieee Journal of Biomedical and Health Informatics | 0 | 6 | 6 | 1.67 | 146 | Q1 |
Telemedicine and E-Health | 5 | 6 | 6 | 1.24 | 87 | Q1 |
Cardiovascular Digital Health Journal | 2 | 5 | 5 | 0.61 | 6 | Q2 |
Journal of the American Heart Association | 0 | 5 | 5 | 2.08 | 118 | Q1 |
The Lancet Digital Health | 0 | 4 | 4 | 6.43 | 48 | Q1 |
No. | Researcher | Total Articles * | Scopus-Index | Affiliation |
---|---|---|---|---|
1 | Martin S | 9 | 53 | Johns Hopkins Medical Institutions, Baltimore, United States |
2 | Turakhia M | 6 | 51 | Stanford University School of Medicine, Stanford, United States |
3 | Kim J | 5 | 4 | Soonchunhyang University, Asan, South Korea |
4 | Lin C | 5 | 70 | University of Technology Sydney, Sydney, Australia |
5 | Mcconnell M | 5 | 47 | Stanford University School of Medicine, Stanford, United States |
6 | Chen C | 4 | 21 | Fudan University, Shanghai, China |
7 | Hindricks G | 4 | 99 | Herzzentrum Leipzig, Leipzig, Germany |
8 | Kim Y | 4 | 5 | Asan Medical Center, Seoul, South Korea |
9 | Lovell N | 4 | 58 | Unsw Sydney, Sydney, Australia |
10 | Marvel F | 4 | 14 | Johns Hopkins School of Medicine, Baltimore, United States |
Year | Reference | Description |
---|---|---|
2000 | [73] | Launch of basic heart rate monitors such as those from Polar, used mainly by athletes. These devices offered limited functionality, such as real-time heart rate monitoring. |
2002 | [74] | The introduction of the Bodybugg bracelet, one of the first devices to track physical activity and estimate calorie consumption. |
2004 | [75] | The first sports watches with integrated GPS, such as the Garmin Forerunner 201, began to offer route and distance tracking in addition to heart rate monitoring. |
Reference | Milestone | Description |
---|---|---|
[79] | First Advances in mHealth and Wearable Devices | The beginning of the integration of portable devices (wearables) in health monitoring. The first basic cardiac and physical activity monitoring devices. |
[80] | Increase in the use of mobile technologies (mHealth) in health promotion and prevention of cardiovascular diseases. | |
[81] | Development of initial frameworks to integrate wearable devices with health applications, facilitating the collection of vital data in real time. | |
[82] | Consolidation of mHealth and Tele-rehabilitation Platforms | Expansion of mobile applications for health monitoring and chronic disease management, including cardiac tele-rehabilitation. |
[83] | Development of cardiac tele-rehabilitation systems that allow patients to participate in rehabilitation programs at a distance. | |
[84] | Greater focus on designing specific content for mHealth platforms, including exercise programs and virtual counseling for cardiac rehabilitation. | |
[85] | IoT Integration and Health Outcomes Prediction | Massive implementation of smart sensors and advanced wearable devices, allowing continuous monitoring of vital parameters such as heart rate and blood pressure. |
[86] | Creation of predictive models based on data collected by remote monitoring devices, focusing on the identification of patients with a greater probability of improving their health. | |
[87] | Consolidation of remote monitoring systems in personalized care, with a focus on reducing unnecessary hospitalizations and improving the quality of life of patients with cardiovascular diseases. |
Reference | Milestone | Description |
---|---|---|
[91] | Health App and Machine Learning | Increasing use of apps to monitor cardiovascular health, such as heart rate and blood pressure. Personalization of exercise and diet plans. |
[92] | Improved monitoring accuracy through AI and sensors. | |
[93] | Advances in Telemedicine and Diagnostic Automation | AI and telemedicine improve diagnosis and accessibility in remote areas. |
[94] | AI optimizes the interpretation of cardiac images. | |
[95] | Impact of COVID and Continous Monitoring | Accelerating the use of remote monitoring and telemedicine to avoid hospital visits. |
[96] | Continuous Monitoring becomes the norm to ensure continuity of care. | |
[97] | Predictive AI and Weight Management | AI models are incorporated to identify the risks of cardiovascular diseases. |
[98] | Mobile applications support weight management, which is crucial for the prevention of heart disease. | |
[99] | Personalized Health and Global Expansion | Technology focused on personalizing care according to individual responses. |
[100] | Improved accessibility and availability worldwide | |
[101] | Proactive Prevention and Continous Innovation | Evolution toward applications that actively prevent heart disease through AI and wearable devices. |
[102] | Integration of new technologies such as AR and VR in cardiac health education and prevention. |
Reference | Milestone | Description |
---|---|---|
[106] | Expansion and consolidation of RCMD Technology | Early Detection and Continuous Monitoring: In recent years, RCMD technology has begun to consolidate, with a focus on the early detection of cardiac problems such as atrial fibrillation and other arrhythmias. The devices allow for continuous monitoring, capturing essential data that facilitates more accurate and timely diagnoses. |
[107] | Advances in Personalization: Personalized monitoring of patients with chronic cardiovascular diseases becomes a priority, with RCMDs providing real-time data on heart rate and blood pressure, improving clinical decision-making. | |
[108] | Innovations and Greater Accessibility. | Integration of New Technologies: During this period, more advanced and accessible devices are introduced, including wearable sensors and cloud-based monitoring platforms. These innovations enable continuous data collection, improving the ability of healthcare professionals to intervene in a timely manner. |
[109] | Reduction of Geographic Barriers: The implementation of these devices reduces the need for frequent visits to medical centers, offering patients greater autonomy and access to monitoring from anywhere. | |
[110] | Impact of COVID-19 and Mass Adoption | Pandemic Acceleration: The COVID-19 pandemic drives mass adoption of RCMDs, as health restrictions increase the need for remote monitoring. The devices become essential to avoid exposing patients to hospital environments, allowing continuity of care from home. |
[111] | Emergency Comprehensive Monitoring: RCMDs are used for more comprehensive monitoring, assessing not only cardiac health but also other vital indicators, reinforcing their role in overall health management. | |
[112] | Refinement and Advanced Personalization | Development of Predictive Algorithms: Advanced algorithms based on artificial intelligence (AI) are integrated into RCMDs to identify risk patterns in real time, offering preventive alerts and personalized recommendations to patients and doctors. |
[113] | Comprehensive and Personalized Monitoring: Devices now allow for a more personalized approach to care, with the ability to adjust monitoring and interventions based on each patient’s specific needs. | |
[114] | Advances in RCMD Tecnology | Continued Technology Innovations: RCMDs incorporate new technologies, such as augmented reality (AR) and virtual reality (VR), to improve heart health education and training. Additionally, more sophisticated sensors are being developed to capture data with greater precision. |
[45] | Global Expansion: These devices become more accessible globally, with efforts to reduce costs and improve availability in developing countries. | |
[115] | Proactive Prevention and Comprehensive Management | Focus on Proactive Prevention: RCMDs are evolving to not only monitor and diagnose but also actively prevent the onset of heart disease by integrating AI and data from wearable devices. |
[116] | Comprehensive Patient Management: The combination of technologies allows for more comprehensive and personalized management of cardiovascular health, marking a change toward more preventive and less reactive care. |
Reference | Milestone | Description |
---|---|---|
[120] | Consolidation and Expansion | During these years, the focus on deep sleep monitoring consolidated, primarily in the early detection of sleep disorders such as sleep apnea, which increases the risk of stroke. Continuous monitoring allowed for the detection of cardiac arrhythmias during deep sleep, providing crucial opportunities for early medical interventions. |
[121] | Innovation and Personalization | In this period, technology advanced with the integration of portable devices that allowed for more precise and personalized monitoring. In addition to heart rate variability, the health of the autonomic nervous system began to be analyzed more thoroughly, offering a more comprehensive view of cardiovascular risk. This personalized monitoring facilitated more targeted and effective interventions. |
[122] | Accelerated Digital Transformation | The impact of the COVID-19 pandemic in 2020 accelerated the adoption of remote monitoring technologies, including those focused on deep sleep. Telemedicine and digital monitoring allowed patients to continue their care from home, highlighting the importance of sleep in managing cardiovascular health. The ability to remotely track sleep and cardiac health became crucial for the continuity of care. |
[123] | Technological Refinement and Continuous Evaluation | During these years, technology continued to be refined with the use of advanced algorithms for more precise and continuous cardiovascular risk evaluation. Studies demonstrated the utility of portable devices for monitoring sleep and physical activity, providing critical data for physicians to adjust treatment plans and prevent stroke events. |
[124] | Continuous Innovations and Global Expansion | Technological innovation continued during 2022 and 2023, with advances in the integration of artificial intelligence and augmented reality to improve cardiovascular risk assessment during sleep. These devices became more globally accessible, helping to reduce healthcare disparities and improve the availability of monitoring tools in developing regions. |
[125] | Proactive Prevention and Comprehensive Management | In the final stretch of this period, the focus shifted toward proactive prevention and comprehensive management of cardiovascular health. Deep sleep-based monitoring devices are now used not only to detect existing problems but also to prevent the onset of cardiovascular diseases through the integration of AI and real-time data. This holistic approach has marked a shift toward more preventive and personalized care. |
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Restrepo-Parra, E.; Ariza-Colpas, P.P.; Torres-Bonilla, L.V.; Piñeres-Melo, M.A.; Urina-Triana, M.A.; Butt-Aziz, S. Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review. IoT 2024, 5, 524-559. https://doi.org/10.3390/iot5030024
Restrepo-Parra E, Ariza-Colpas PP, Torres-Bonilla LV, Piñeres-Melo MA, Urina-Triana MA, Butt-Aziz S. Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review. IoT. 2024; 5(3):524-559. https://doi.org/10.3390/iot5030024
Chicago/Turabian StyleRestrepo-Parra, Elisabeth, Paola Patricia Ariza-Colpas, Laura Valentina Torres-Bonilla, Marlon Alberto Piñeres-Melo, Miguel Alberto Urina-Triana, and Shariq Butt-Aziz. 2024. "Home Monitoring Tools to Support Tracking Patients with Cardio–Cerebrovascular Diseases: Scientometric Review" IoT 5, no. 3: 524-559. https://doi.org/10.3390/iot5030024