The Role of Wearables in Heart Failure

Using Artificial Intelligence to Predict Heart Failure Risk from Single-lead Electrocardiographic Signals: A Multinational Assessment

Importance

Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) can enable large-scale community-based risk assessment.

Objective

To evaluate an artificial intelligence (AI) algorithm to predict HF risk from noisy single-lead ECGs.

Design

Multicohort study.

Setting

Retrospective cohort of individuals with outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of UK Biobank (UKB) and Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

Participants

Individuals without HF at baseline.

Exposures

AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD).

Main Outcomes and Measures

Among individuals with ECGs, we isolated lead I ECGs and deployed a noise-adapted AI-ECG model trained to identify LVSD. We evaluated the association of the model probability with new-onset HF, defined as the first HF hospitalization. We compared the discrimination of AI-ECG against the pooled cohort equations to prevent HF (PCP-HF) score for new-onset HF using Harrel's C-statistic, integrated discrimination improvement (IDI), and net reclassification improvement (NRI).

Results

There were 194,340 YNHHS patients (age 56 years [IQR, 41-69], 112,082 women [58%]), 42,741 UKB participants (65 years [59-71], 21,795 women [52%]), and 13,454 ELSA-Brasil participants (56 years [41-69], 7,348 women [55%]) with baseline ECGs. A total of 3,929 developed HF in YNHHS over 4.5 years (2.6-6.6), 46 in UKB over 3.1 years (2.1-4.5), and 31 in ELSA-Brasil over 4.2 years (3.7-4.5). A positive AI-ECG screen was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability portended a 27-65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.725 in YNHHS, 0.792 in UKB, and 0.833 in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions in addition to PCP-HF resulted in improved Harrel's C-statistic (Δ=0.112-0.114), with an IDI of 0.078-0.238 and an NRI of 20.1%-48.8% for AI-ECG vs. PCP-HF.

Conclusions and Relevance

Across multinational cohorts, a noise-adapted AI model with lead I ECGs as the sole input defined HF risk, representing a scalable portable and wearable device-based HF risk-stratification strategy.

KEY POINTS

Question: Can single-lead electrocardiogram (ECG) tracings predict heart failure (HF) risk?Findings: We evaluated a noise-adapted artificial intelligence (AI) algorithm for single-lead ECGs as the sole input across multinational cohorts, spanning a diverse integrated US health system and large community-based cohorts in the UK and Brazil. A positive AI-ECG screen was associated with a 3- to 7-fold higher HF risk, independent of age, sex, and comorbidities. The AI model achieved incremental discrimination and improved reclassification for HF over the pooled cohort equations to prevent HF (PCP-HF).Meaning: A noise-adapted AI model for single-lead ECG predicted the risk of new-onset HF, representing a scalable HF risk-stratification strategy for portable and wearable devices.

Heart Failure Nurses within the Primary Care Setting

Cardiology services within primary care often focus on disease prevention, early identification of illness and prompt referral for diagnosis and specialist treatment. Due to advances in pharmaceuticals, implantable cardiac devices and surgical interventions, individuals with heart failure are living longer, which can place a significant strain on global healthcare resources. Heart failure nurses in a primary care setting offer a wealth of clinical knowledge and expertise across all phases of the heart failure trajectory and are able to support patients, family members and other community services, including general practitioners. This review examines the recently published evidence on the current and potential future practice of heart failure nurses within primary care.

Leveraging Emerging Technologies to Expand Accessibility and Improve Precision in Rehabilitation and Exercise for People with Disabilities

Physical rehabilitation and exercise training have emerged as promising solutions for improving health, restoring function, and preserving quality of life in populations that face disparate health challenges related to disability. Despite the immense potential for rehabilitation and exercise to help people with disabilities live longer, healthier, and more independent lives, people with disabilities can experience physical, psychosocial, environmental, and economic barriers that limit their ability to participate in rehabilitation, exercise, and other physical activities. Together, these barriers contribute to health inequities in people with disabilities, by disproportionately limiting their ability to participate in health-promoting physical activities, relative to people without disabilities. Therefore, there is great need for research and innovation focusing on the development of strategies to expand accessibility and promote participation in rehabilitation and exercise programs for people with disabilities. Here, we discuss how cutting-edge technologies related to telecommunications, wearables, virtual and augmented reality, artificial intelligence, and cloud computing are providing new opportunities to improve accessibility in rehabilitation and exercise for people with disabilities. In addition, we highlight new frontiers in digital health technology and emerging lines of scientific research that will shape the future of precision care strategies for people with disabilities.

Research Gaps in Pediatric Heart Failure: Defining the Gaps and Then Closing Them Over the Next Decade

Given the numerous opportunities and the wide knowledge gaps in pediatric heart failure, an international group of pediatric heart failure experts with diverse backgrounds were invited and tasked with identifying research gaps in each pediatric heart failure domain that scientists and funding agencies need to focus on over the next decade.

Bridging gaps and optimizing implementation of guideline-directed medical therapy for heart failure

Despite robust scientific evidence and strong guideline recommendations, there remain significant gaps in initiation and dose titration of guideline-directed medical therapy (GDMT) for heart failure (HF) among eligible patients. Reasons surrounding these gaps are multifactorial, and largely attributed to patient, healthcare professionals, and institutional challenges. Concurrently, HF remains a predominant cause of mortality and hospitalization, emphasizing the critical need for improved delivery of therapy to patients in routine clinical practice. To optimize GDMT, various implementation strategies have emerged in the recent decade such as in-hospital rapid initiation of GDMT, improving patient adherence, addressing clinical inertia, improving affordability, engagement in quality improvement registries, multidisciplinary clinics, and EHR-integrated interventions. This review highlights the current use and barriers to optimal utilization of GDMT, and proposes novel strategies aimed at improving GDMT in HF.

The Promise and Risks of mHealth in Heart Failure Care

Mobile health (mHealth) is an emerging approach to health care. It involves wearable, connected technologies that facilitate patient-symptom or physiological monitoring, support clinical feedback to patients and physicians, and promote patients' education and self-care. Evolving algorithms may involve artificial intelligence and can assist in data aggregation and health care teams' interpretations. Ultimately, the goal is not merely to collect data; rather, it is to increase actionability. mHealth technology holds particular promise for patients with heart failure, especially those with frequently changing clinical status. mHealth, ideally, can identify care opportunities, anticipate clinical courses and augment providers' capacity to implement, titrate and monitor interventions safely, including evidence-based therapies. Although there have been marked advancements in the past decade, uncertainties remain for mHealth, including questions regarding optimal indications and acceptable payment models. In regard to mHealth capability, a better understanding is needed of the incremental benefit of mHealth data over usual care, the accuracy of specific mHealth data points in making clinical care decisions, and the efficiency and precision of algorithms used to dictate actions. Importantly, emerging regulations in the wake of COVID-19, and now the end of the federal public health emergency, offer both opportunity and risks to the broader adoption of mHealth-enabled services. In this review, we explore the current state of mHealth in heart failure, with particular attention to the opportunities and challenges this technology creates for patients, health care providers and other stakeholders.

Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent.

Machine learning for the development of diagnostic models of decompensated heart failure or exacerbation of chronic obstructive pulmonary disease

Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are two chronic diseases with the greatest adverse impact on the general population, and early detection of their decompensation is an important objective. However, very few diagnostic models have achieved adequate diagnostic performance. The aim of this trial was to develop diagnostic models of decompensated heart failure or COPD exacerbation with machine learning techniques based on physiological parameters. A total of 135 patients hospitalized for decompensated heart failure and/or COPD exacerbation were recruited. Each patient underwent three evaluations: one in the decompensated phase (during hospital admission) and two more consecutively in the compensated phase (at home, 30 days after discharge). In each evaluation, heart rate (HR) and oxygen saturation (Ox) were recorded continuously (with a pulse oximeter) during a period of walking for 6 min, followed by a recovery period of 4 min. To develop the diagnostic models, predictive characteristics related to HR and Ox were initially selected through classification algorithms. Potential predictors included age, sex and baseline disease (heart failure or COPD). Next, diagnostic classification models (compensated vs. decompensated phase) were developed through different machine learning techniques. The diagnostic performance of the developed models was evaluated according to sensitivity (S), specificity (E) and accuracy (A). Data from 22 patients with decompensated heart failure, 25 with COPD exacerbation and 13 with both decompensated pathologies were included in the analyses. Of the 96 characteristics of HR and Ox initially evaluated, 19 were selected. Age, sex and baseline disease did not provide greater discriminative power to the models. The techniques with S and E values above 80% were the logistic regression (S: 80.83%; E: 86.25%; A: 83.61%) and support vector machine (S: 81.67%; E: 85%; A: 82.78%) techniques. The diagnostic models developed achieved good diagnostic performance for decompensated HF or COPD exacerbation. To our knowledge, this study is the first to report diagnostic models of decompensation potentially applicable to both COPD and HF patients. However, these results are preliminary and warrant further investigation to be confirmed.

Heart rate processing algorithms and exercise duration on reliability and validity decisions in biceps-worn Polar Verity Sense and OH1 wearables

Consumer wearable technology use is widespread and there is a need to validate measures obtained in uncontrolled settings. Because no standard exists for the treatment of heart rate data during exercise, the effect of different approaches on reliability (Coefficient of Variation [CV], Intraclass Correlation Coefficient [ICC]) and validity (Mean Absolute Percent Error [MAPE], Lin's Concordance Correlation Coefficient [CCC)] were determined in the Polar Verity Sense and OH1 during trail running. The Verity Sense met the reliability (CV < 5%, ICC > 0.7) and validity thresholds (MAPE < 5%, CCC > 0.9) in all cases. The OH1 met reliability thresholds in all cases except entire session average (ICC = 0.57). The OH1 met the validity MAPE threshold in all cases (3.3-4.1%), but not CCC (0.6-0.86). Despite various heart rate data processing methods, the approach may not affect reliability and validity interpretation provided adequate data points are obtained. It is also possible that a large volume of data will artificially inflate metrics.

Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) Framework to Identify Barriers and Facilitators for the Implementation of Digital Twins in Cardiovascular Medicine

A digital twin is a computer-based "virtual" representation of a complex system, updated using data from the "real" twin. Digital twins are established in product manufacturing, aviation, and infrastructure and are attracting significant attention in medicine. In medicine, digital twins hold great promise to improve prevention of cardiovascular diseases and enable personalised health care through a range of Internet of Things (IoT) devices which collect patient data in real-time. However, the promise of such new technology is often met with many technical, scientific, social, and ethical challenges that need to be overcome-if these challenges are not met, the technology is therefore less likely on balance to be adopted by stakeholders. The purpose of this work is to identify the facilitators and barriers to the implementation of digital twins in cardiovascular medicine. Using, the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a document analysis of policy reports, industry websites, online magazines, and academic publications on digital twins in cardiovascular medicine, identifying potential facilitators and barriers to adoption. Our results show key facilitating factors for implementation: preventing cardiovascular disease, in silico simulation and experimentation, and personalised care. Key barriers to implementation included: establishing real-time data exchange, perceived specialist skills required, high demand for patient data, and ethical risks related to privacy and surveillance. Furthermore, the lack of empirical research on the attributes of digital twins by different research groups, the characteristics and behaviour of adopters, and the nature and extent of social, regulatory, economic, and political contexts in the planning and development process of these technologies is perceived as a major hindering factor to future implementation.

Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020

Importance

Wearable devices may be able to improve cardiovascular health, but the current adoption of these devices could be skewed in ways that could exacerbate disparities.

Objective

To assess sociodemographic patterns of use of wearable devices among adults with or at risk for cardiovascular disease (CVD) in the US population in 2019 to 2020.

Design, Setting, and Participants

This population-based cross-sectional study included a nationally representative sample of the US adults from the Health Information National Trends Survey (HINTS). Data were analyzed from June 1 to November 15, 2022.

Exposures

Self-reported CVD (history of heart attack, angina, or congestive heart failure) and CVD risk factors (≥1 risk factor among hypertension, diabetes, obesity, or cigarette smoking).

Main Outcomes and Measures

Self-reported access to wearable devices, frequency of use, and willingness to share health data with clinicians (referred to as health care providers in the survey).

Results

Of the overall 9303 HINTS participants representing 247.3 million US adults (mean [SD] age, 48.8 [17.9] years; 51% [95% CI, 49%-53%] women), 933 (10.0%) representing 20.3 million US adults had CVD (mean [SD] age, 62.2 [17.0] years; 43% [95% CI, 37%-49%] women), and 5185 (55.7%) representing 134.9 million US adults were at risk for CVD (mean [SD] age, 51.4 [16.9] years; 43% [95% CI, 37%-49%] women). In nationally weighted assessments, an estimated 3.6 million US adults with CVD (18% [95% CI, 14%-23%]) and 34.5 million at risk for CVD (26% [95% CI, 24%-28%]) used wearable devices compared with an estimated 29% (95% CI, 27%-30%) of the overall US adult population. After accounting for differences in demographic characteristics, cardiovascular risk factor profile, and socioeconomic features, older age (odds ratio [OR], 0.35 [95% CI, 0.26-0.48]), lower educational attainment (OR, 0.35 [95% CI, 0.24-0.52]), and lower household income (OR, 0.42 [95% CI, 0.29-0.60]) were independently associated with lower use of wearable devices in US adults at risk for CVD. Among wearable device users, a smaller proportion of adults with CVD reported using wearable devices every day (38% [95% CI, 26%-50%]) compared with overall (49% [95% CI, 45%-53%]) and at-risk (48% [95% CI, 43%-53%]) populations. Among wearable device users, an estimated 83% (95% CI, 70%-92%) of US adults with CVD and 81% (95% CI, 76%-85%) at risk for CVD favored sharing wearable device data with their clinicians to improve care.

Conclusions and Relevance

Among individuals with or at risk for CVD, fewer than 1 in 4 use wearable devices, with only half of those reporting consistent daily use. As wearable devices emerge as tools that can improve cardiovascular health, the current use patterns could exacerbate disparities unless there are strategies to ensure equitable adoption.

Geriatric Care Management System Powered by the IoT and Computer Vision Techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients' data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient's position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff.

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

Telemedicine Technologies Selection for the Posthospital Patient Care Process after Total Hip Arthroplasty

For many years, the importance of using telematic technologies in medicine has been growing, especially in the period of the coronavirus pandemic, when direct contact and supervision of medical personnel over the patient is difficult. The existing possibilities of modern information and communication technologies (ICTs) are not fully used. The aim of the study is to identify the telemedicine technologies that can be used in future implementation projects of the posthospital patient care process after total hip arthroplasty (THA). The literature search is reported according to PRISMA 2020. The search strategy included databases and gray literature. In total, 28 articles (EMBASE, PubMed, PEDro) and 24 records from gray literature (Google Search and Technology presentations) were included in the research. This multi-source study analyzes the possibilities of using different technologies useful in the patient care process. The conducted research resulted in defining visual and wearable types of telemedicine technologies for the original posthospital patient care process after THA. As the needs of stakeholders in the posthospital patient care process after THA differ, the awareness of appropriate technologies selection, information flow, and its management importance are prerequisites for effective posthospital patient care with the use of telemedicine technologies.

Investigation of the time shift between wearable photoplethysmography sensors used for continuous heart rate monitoring

In this study, we investigated the effect of time shift in heartrate measurement by wearables, which might to be used in telehealth applications for patients suffering from heart failure. Six wearables commercially available on the market were tested in a 14-hour measurement. Each wearable was tested three times by five different test persons. A reference sensor was used to test the accuracy of the wearables. We found that different types of time shifts are common in the sensors we tested: time shifts of full days, time shifts of full hours (most probably due to incorrect or unspecified time zones) and time shifts in the range of seconds to minutes (most likely stemming from averaging, data transmission, etc.). We conclude that time shifts of all manufacturers need to be corrected prior comparison of a photoplethysmography signal with other signals. However, even after correction of the time shift, the reliability of the sensors seems to be too low for application in telehealth settings. Clinical relevance- This study shows that signals from state-of-the-art wearable photoplethysmography heart rate measurements show significant time shifts and marked differences even if time shifts were corrected. This limits their utility for clinical applications.

Digital Technologies to Support Better Outcome and Experience of Care in Patients with Heart Failure

PURPOSE OF REVIEW

In this article, we review a range of digital technologies for possible application in heart failure patients, with a focus on lessons learned. We also discuss a future model of heart failure management, as digital technologies continue to become part of standard care.

RECENT FINDINGS

Digital technologies are increasingly used by healthcare professionals and those living with heart failure to support more personalised and timely shared decision-making, earlier identification of problems, and an improved experience of care. The COVID-19 pandemic has accelerated the acceptability and implementation of a range of digital technologies, including remote monitoring and health tracking, mobile health (wearable technology and smartphone-based applications), and the use of machine learning to augment data interpretation and decision-making. Much has been learned over recent decades on the challenges and opportunities of technology development, including how best to evaluate the impact of digital health interventions on health and healthcare, the human factors involved in implementation and how best to integrate dataflows into the clinical pathway. Supporting patients with heart failure as well as healthcare professionals (both with a broad range of health and digital literacy skills) is crucial to success. Access to digital technologies and the internet remains a challenge for some patients. The aim should be to identify the right technology for the right patient at the right time, in a process of co-design and co-implementation with patients.

"Listen to Your Immune System When It's Calling for You": Monitoring Autoimmune Diseases Using the iShU App

The immune system plays a key role in protecting living beings against bacteria, viruses, and fungi, among other pathogens, which may be harmful and represent a threat to our own health. However, for reasons that are not fully understood, in some people this protective mechanism accidentally attacks the organs and tissues, thus causing inflammation and leads to the development of autoimmune diseases. Remote monitoring of human health involves the use of sensor network technology as a means of capturing patient data, and wearable devices, such as smartwatches, have lately been considered good collectors of biofeedback data, owing to their easy connectivity with a mHealth system. Moreover, the use of gamification may encourage the frequent usage of such devices and behavior changes to improve self-care for autoimmune diseases. This study reports on the use of wearable sensors for inflammation surveillance and autoimmune disease management based on a literature search and evaluation of an app prototype with fifteen stakeholders, in which eight participants were diagnosed with autoimmune or inflammatory diseases and four were healthcare professionals. Of these, six were experts in human–computer interaction to assess critical aspects of user experience. The developed prototype allows the monitoring of autoimmune diseases in pre-, during-, and post-inflammatory crises, meeting the personal needs of people with this health condition. The findings suggest that the proposed prototype—iShU—achieves its purpose and the overall experience may serve as a foundation for designing inflammation surveillance and autoimmune disease management monitoring solutions.

Wearable Devices for Physical Monitoring of Heart: A Review

Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.

Ambulatory Care in Adult Congenital Heart Disease-Time for Change?

BACKGROUND

The adult congenital heart disease (ACHD) population is growing in size and complexity. This study evaluates whether present ambulatory care adequately detects problems and considers costs.

METHODS

A UK single-centre study of clinic attendances amongst 100 ACHD patients (40.4 years, median ACHD AP class 2B) between 2014 and 2019 and the COVID-19 restrictions period (March 2020-July 2021).

RESULTS

Between 2014 and 2019, there were 575 appointments. Nonattendance was 10%; 15 patients recurrently nonattended. Eighty percent of appointments resulted in no decision other than continued review. Electrocardiograms and echocardiograms were frequent, but new findings were rare (5.1%, 4.0%). Decision-making was more common with the higher ACHD AP class and symptoms. Emergency admissions (n = 40) exceeded elective (n = 25), with over half following unremarkable clinic appointments. Distance travelled to the ACHD clinic was 14.9 km (1.6-265), resulting in 433-564 workdays lost. During COVID 19, there were 127 appointments (56% in-person, 41% telephone and 5% video). Decisions were made at 37% in-person and 19% virtual consultations. Nonattendance was 3.9%; there were eight emergency admissions.

CONCLUSION

The main purpose of the ACHD clinic is surveillance. Presently, the clinic does not sufficiently predict or prevent emergency hospital admissions and is costly to patient and provider. COVID-19 has enforced different methods for delivering care that require further evaluation.

An Experimental Feasibility Study Evaluating the Adequacy of a Sportswear-Type Wearable for Recording Exercise Intensity

Sportswear-type wearables with integrated inertial sensors and electrocardiogram (ECG) electrodes have been commercially developed. We evaluated the feasibility of using a sportswear-type wearable with integrated inertial sensors and electrocardiogram (ECG) electrodes for evaluating exercise intensity within a controlled laboratory setting. Six male college athletes were asked to wear a sportswear-type wearable while performing a treadmill test that reached up to 20 km/h. The magnitude of the filtered tri-axial acceleration signal, recorded by the inertial sensor, was used to calculate the acceleration index. The R-R intervals of the ECG were used to determine heart rate; the external validity of the heart rate was then evaluated according to oxygen uptake, which is the gold standard for physiological exercise intensity. Single regression analysis between treadmill speed and the acceleration index in each participant showed that the slope of the regression line was significantly greater than zero with a high coefficient of determination (walking, 0.95; jogging, 0.96; running, 0.90). Another single regression analysis between heart rate and oxygen uptake showed that the slope of the regression line was significantly greater than zero, with a high coefficient of determination (0.96). Together, these results indicate that the sportswear-type wearable evaluated in this study is a feasible technology for evaluating physical and physiological exercise intensity across a wide range of physical activities and sport performances.

mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review

The use of mHealth apps for the self-management of cardiovascular diseases (CVDs) is an increasing trend in patient-centered care. In this research, we conduct a scoping review of mHealth apps for CVD self-management within the period 2014 to 2021. Our review revolves around six main aspects of the current status of mHealth apps for CVD self-management: main CVDs managed, main app functionalities, disease stages managed, common approaches used for data extraction, analysis, management, common wearables used for CVD detection, monitoring and/or identification, and major challenges to overcome and future work remarks. Our review is based on Arksey and O’Malley’s methodological framework for conducting studies. Similarly, we adopted the PRISMA model for reporting systematic reviews and meta-analyses. Of the 442 works initially retrieved, the review comprised 38 primary studies. According to our results, the most common CVDs include arrhythmia (34%), heart failure (32%), and coronary heart disease (18%). Additionally, we found that the majority mHealth apps for CVD self-management can provide medical recommendations, medical appointments, reminders, and notifications for CVD monitoring. Main challenges in the use of mHealth apps for CVD self-management include overcoming patient reluctance to use the technology and achieving the interoperability of mHealth applications with other systems.

Wearable Cardioverter-Defibrillator Used as a Telemonitoring System in a Real-Life Heart Failure Unit Setting

BACKGROUND

In patients with reduced left ventricular ejection fraction (LVEF) who are at risk of sudden cardiac death, a wearable cardioverter-defibrillator (WCD) is recommended as a bridge to the recovery of LVEF or as a bridge to the implantation of a device. In addition to its function to detect and treat malignant arrhythmia, WCD can be used via an online platform as a telemonitoring system to supervise patients' physical activity, compliance, and heart rate.

METHODS

We retrospectively analyzed 173 patients with regard to compliance and heart rate after discharge.

RESULTS

Mean WCD wearing time was 59.75 ± 35.6 days; the daily wearing time was 21.19 ± 4.65 h. We found significant differences concerning the patients' compliance. Men showed less compliance than women, and younger patients showed less compliance than patients who were older. Furthermore, we analyzed the heart rate from discharge until the end of WCD prescription and found a significant decrease from discharge to 4, 8, or 12 weeks.

CONCLUSION

WCD can be used as a telemonitoring system to help the involved heart failure unit or physicians attend to and adjust the medical therapy. Furthermore, specific patient groups should be educated more intensively with respect to compliance.

ReBeatICG: Real-time Low-Complexity Beat-to-beat Impedance Cardiogram Delineation Algorithm

This work presents ReBeatICG, a real-time, low-complexity beat-to-beat impedance cardiography (ICG) delineation algorithm that allows hemodynamic parameters monitoring. The proposed procedure relies only on the ICG signal compared to most algorithms found in the literature that rely on synchronous electrocardiogram signal (ECG) recordings. ReBeatICG was designed with implementation on an ultra-low-power microcontroller (MCU) in mind. The detection accuracy of the developed algorithm is tested against points manually labeled by cardiologists. It achieves a detection Gmean accuracy of 94.9%, 98.6%, 90.3%, and 84.3% for the B, C, X, and O characteristic points, respectively. Furthermore, several hemodynamic parameters were calculated based on annotated characteristic points and compared with values generated from the cardiologists' annotations. ReBeatICG achieved mean error rates of 0.11 ms, 9.72 ms, 8.32 ms, and 3.97% for HR, LVET, IVRT, and relative C-point amplitude, respectively.

Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Background

Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized.

Objective

We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research.

Methods

We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types.

Results

The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded.

Conclusions

Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.

Education program for prevention of outdoor accidents in middle-high aged trekkers: Monitoring of change in blood pressure and heart rate during exercise

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Senior trekkers' accidents has increased markedly over the past 5 years.

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Educational program is effective to prevent extreme hemodynamics during exercise.

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Green exercise education promotes a healthy life-style in seniors.

This is an observational study to evaluate cardiovascular parameters during an educational trekking program. The number of alpine accidents involving elderly trekkers has been increasing in developed countries in recent years. Many middle-high aged trekkers have potential cardiovascular risks of which they are unaware. More than 77% of trekkers involved in alpine accidents in Japan were aged >40 years. The most common cardiovascular conditions were stroke or heart attack while trekking at altitude. An alpine club conducted an 8-month education program with participants aged >40 years in the setting of a mountain-side town. Blood pressure and heart rate during outdoor exercise were monitored, and any other adverse effects were recorded. As a result, the cardiovascular parameters evaluated during the first and final trek presented a physiological and similar behavior, however, lower heart rate values were registered at the highest point of the route in the final trek (p < 0.05). The trend of these parameters was similar in males and females, and there was little correlation between the cardiovascular parameters and age. In conclusion, the lower heart rate values may indicate the higher risk awareness of trekkers while self-pacing the physical activity outdoors, which may indicate the positive effect of the education program in increasing the safety of such unsupervised activities.

A Blockchain-Enabled Framework for mHealth Systems

Presently modern technology makes a significant contribution to the transition from traditional healthcare to smart healthcare systems. Mobile health (mHealth) uses advances in wearable sensors, telecommunications and the Internet of Things (IoT) to propose a new healthcare concept centered on the patient. Patients' real-time remote continuous health monitoring, remote diagnosis, treatment, and therapy is possible in an mHealth system. However, major limitations include the transparency, security, and privacy of health data. One possible solution to this is the use of blockchain technologies, which have found numerous applications in the healthcare domain mainly due to theirs features such as decentralization (no central authority is needed), immutability, traceability, and transparency. We propose an mHealth system that uses a private blockchain based on the Ethereum platform, where wearable sensors can communicate with a smart device (a smartphone or smart tablet) that uses a peer-to-peer hypermedia protocol, the InterPlanetary File System (IPFS), for the distributed storage of health-related data. Smart contracts are used to create data queries, to access patient data by healthcare providers, to record diagnostic, treatment, and therapy, and to send alerts to patients and medical professionals.

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.