Mobile Health Advances in Physical Activity, Fitness, and Atrial Fibrillation: Moving Hearts

Smartphone-based gamification intervention to increase physical activity participation among patients with coronary heart disease: A randomized controlled trial

INTRODUCTION

Despite proven benefits, patients with coronary heart disease (CHD) typically fail to participate in sufficient physical activity (PA). Effective interventions should be implemented to help patients maintain a healthy lifestyle and modify their present behavior. Gamification is the use of game design features (such as points, leaderboards, and progress bars) to improve motivation and engagement. It shows the potential for encouraging patients to engage in PA. However, empirical evidence on the efficacy of such interventions among patients with CHD is still emerging.

PURPOSE

The aim of the study is to explore whether a smartphone-based gamification intervention could increase PA participation and other physical and psychological outcomes in CHD patients.

METHODS

Participants with CHD were randomly assigned to three groups (control group, individual group, and team group). The individual and team groups received gamified behavior intervention based on behavioral economics. The team group combined gamified intervention with social interaction. The intervention lasted for 12 weeks, and the follow-up was12 weeks. The primary outcomes included the change in daily steps and the proportion of patient days that step goals were achieved. The secondary outcomes included competence, autonomy, relatedness, and autonomous motivation.

RESULTS

For the individual group, smartphone-based gamification intervention significantly increased PA among CHD patients over the 12-week period (step count difference 988; 95% CI 259-1717; p < 0.01) and had a good maintenance effect during the follow-up period (step count difference 819; 95% CI 24-1613; p < 0.01). There are also significant differences in competence, autonomous motivation, body mass index (BMI), and waist circumference in 12 weeks between the control group and individual group. For the team group, gamification intervention with collaboration didn't result in significant increases in PA. But patients in this group had a significant increase in competence, relatedness, and autonomous motivation.

CONCLUSION

A smartphone-based gamification intervention was proven to be an effective way to increase motivation and PA engagement, with a substantial maintenance impact (Chinese Clinical Trial Registry Identifier: ChiCTR2100044879).

Standardized assessment of evidence supporting the adoption of mobile health solutions: A Clinical Consensus Statement of the ESC Regulatory Affairs Committee: Developed in collaboration with the European Heart Rhythm Association (EHRA), the Association of Cardiovascular Nursing & Allied Professions (ACNAP) of the ESC, the Heart Failure Association (HFA) of the ESC, the ESC Young Community, the ESC Working Group on e-Cardiology, the ESC Council for Cardiology Practice, the ESC Council of Cardio-Oncology, the ESC Council on Hypertension, the ESC Patient Forum, the ESC Digital Health Committee, and the European Association of Preventive Cardiology (EAPC)

Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions' selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence. The new Medical Device Regulation is more stringent than its predecessor, yet its scope does not span all intended uses and several difficulties remain. The European Society of Cardiology Regulatory Affairs Committee set up a Task Force to explore existing assessment frameworks and clinical and cost-effectiveness evidence. This knowledge was used to propose criteria with which HCPs could evaluate mHealth solutions spanning diagnostic support, therapeutics, remote follow-up and education, specifically for cardiac rhythm management, heart failure and preventive cardiology. While curated national libraries of health apps may be helpful, their requirements and rigour in initial and follow-up assessments may vary significantly. The recently developed CEN-ISO/TS 82304-2 health app quality assessment framework has the potential to address this issue and to become a widely used and efficient tool to help drive decision-making internationally. The Task Force would like to stress the importance of co-development of solutions with relevant stakeholders, and maintenance of health information in apps to ensure these remain evidence-based and consistent with best practice. Several general and domain-specific criteria are advised to assist HCPs in their assessment of clinical evidence to provide informed advice to patients about mHealth utilization.

Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation

Atrial fibrillation (AF) is globally the most common arrhythmia associated with significant morbidity and mortality. It impairs the quality of the patient's life, imposing a remarkable burden on public health, and the healthcare budget. The detection of AF is important in the decision to initiate anticoagulation therapy to prevent thromboembolic events. Nonetheless, AF detection is still a major clinical challenge as AF is often paroxysmal and asymptomatic. AF screening recommendations include opportunistic or systematic screening in patients ≥65 years of age or in those individuals with other characteristics pointing to an increased risk of stroke. The popularities of well-being and taking personal responsibility for one's own health are reflected in the continuous development and growth of mobile health technologies. These novel mobile health technologies could provide a cost-effective solution for AF screening and an additional opportunity to detect AF, particularly its paroxysmal and asymptomatic forms.

Multidisciplinary Approach in Atrial Fibrillation: As Good as Gold

Atrial fibrillation (AF) represents the most common sustained arrhythmia necessitating dual focus: acute complication management and sustained longitudinal oversight to modulate disease progression and ensure comprehensive patient care over time. AF is a multifaceted disorder; due to such a great number of potential exacerbating conditions, a multidisciplinary team (MDT) should manage AF patients by cooperating with a cardiologist. Effective management of AF patients necessitates the implementation of a well-coordinated and tailored care pathway aimed at delivering optimized treatment through collaboration among various healthcare professionals. Management of AF should be carefully evaluated and mutually agreed upon in consultation with healthcare providers. It is crucial to recognize that treatment may evolve due to the emergence of new risk factors, symptoms, disease progression, and advancements in treatment modalities. In the context of multidisciplinary AF teams, a coordinated approach involves assembling a diverse team tailored to meet individual patients' unique needs based on local services' availability.

An Infrastructure Framework for Remote Patient Monitoring Interventions and Research

Remote patient monitoring (RPM) enables clinicians to maintain and adjust their patients' plan of care by using remotely gathered data, such as vital signs, to proactively make medical decisions about a patient's care. RPM interventions have been touted as a means to improve patient care and well-being while reducing costs and resource needs within the health care ecosystem. However, multiple interworking components must be successfully implemented for an RPM intervention to yield the desired outcomes, and the design and key driver of each component can vary depending on the medical context. This viewpoint and perspective paper presents a 4-component RPM infrastructure framework based on a synthesis of existing literature and practice related to RPM. Specifically, these components are identified and considered: (1) data collection, (2) data transmission and storage, (3) data analysis, and (4) information presentation. Interaction points to consider between components include transmission, interoperability, accessibility, workflow integration, and transparency. Within each of the 4 components, questions affecting research and practice emerge that can affect the outcomes of RPM interventions. This framework provides a holistic perspective of the technologies involved in RPM interventions and how these core elements interact to provide an appropriate infrastructure for deploying RPM in health systems. Further, it provides a common vocabulary to compare and contrast RPM solutions across health contexts and may stimulate new research and intervention opportunities.

Effectiveness of mobile health applications on clinical outcomes and health behaviors in patients with coronary heart disease: A systematic review and meta-analysis

Objective

Mobile health applications (apps) have gained significant popularity and widespread utilization among patients with coronary heart disease (CHD). The objective of this study is to evaluate the effects of mHealth apps on clinical outcomes and health behaviors in patients with CHD.

Methods

Databases were searched from inception until December 2023, including Cochrane Library, PubMed, EMBASE, Web of Science, CINAHL, China National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Service System (SinoMed), Wanfang Data, China Science and Technology Journal Database (VIP), for randomized controlled trials (RCTs) regarding the effectiveness of mHealth apps in patients with CHD. Two researchers conducted a comprehensive review of the literature, extracting relevant data and evaluating each study's methodological quality separately. The meta-analysis was performed utilizing Review Manager v5.4 software.

Results

A total of 34 RCTs were included, with 5,319 participants. The findings demonstrated that using mHealth apps could decrease the incidence of major adverse cardiac events (RR = 0.68, P = 0.03), readmission rate (RR = 0.56, P < 0.001), total cholesterol (WMD = -0.19, P = 0.03), total triglycerides (WMD = -0.24, P < 0.001), waist circumference (WMD = -1.92, P = 0.01), Self-Rating Anxiety Scale score (WMD = -6.70, P < 0.001), and Self-Rating Depression Scale score (WMD = -7.87, P < 0.001). They can also increase the LVEF (WMD = 6.50, P < 0.001), VO2 max (WMD = 1.89, P < 0.001), 6-min walk distance (6MWD) (WMD = 19.43, P = 0.004), Morisky Medication Adherence Scale-8 score (WMD = 0.96, P = 0.004), and medication adherence rate (RR = 1.24, P = 0.03). Nevertheless, there is no proof that mHealth apps can lower low-density lipoprote in cholesterol, blood pressure, BMI, or other indicator (P > 0.05).

Conclusion

Mobile health apps have the potential to lower the incidence of major adverse cardiac events (MACEs), readmission rates, and blood lipids in patients with CHD. They can also help enhance cardiac function, promote medication adherence, and alleviate symptoms of anxiety and depression. To further corroborate these results, larger-scale, multi-center RCTs with longer follow-up periods are needed.

Assessing cardiorespiratory fitness in clinical and community settings: Lessons and advancements in the 100th year anniversary of VO2max

Cardiorespiratory fitness (CRF) is a well-established biomarker that has applications to all adults across the health and disease spectrum. Despite overwhelming evidence supporting the prognostic utility of CRF, it remains vastly underutilized. CRF is optimally measured via cardiopulmonary exercise testing which may not be feasible to implement on a large scale. Therefore, it is prudent to develop ways to accurately estimate CRF that can be applied in clinical and community settings. As such, several prediction equations incorporating non-exercise information that is readily available from routine clinical encounters have been developed that provide an adequate reflection of CRF that could be implemented to raise awareness of the importance of CRF. Further, technological advances in smartphone apps and consumer-grade wearables have demonstrated promise to provide reasonable estimates of CRF that are widely available, which could enhance the utilization of CRF in both clinical and community settings.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study

Aims

Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity.

Methods and results

We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321.

Conclusion

Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

Anxiety, patient activation, and quality of life among stroke survivors prescribed smartwatches for atrial fibrillation monitoring

Background

The detection of atrial fibrillation (AF) in stroke survivors is critical to decreasing the risk of recurrent stroke. Smartwatches have emerged as a convenient and accurate means of AF diagnosis; however, the impact on critical patient-reported outcomes, including anxiety, engagement, and quality of life, remains ill defined.

Objectives

To examine the association between smartwatch prescription for AF detection and the patient-reported outcomes of anxiety, patient activation, and self-reported health.

Methods

We used data from the Pulsewatch trial, a 2-phase randomized controlled trial that included participants aged 50 years or older with a history of ischemic stroke. Participants were randomized to use either a proprietary smartphone-smartwatch app for 30 days of AF monitoring or no cardiac rhythm monitoring. Validated surveys were deployed before and after the 30-day study period to assess anxiety, patient activation, and self-rated physical and mental health. Logistic regression and generalized estimation equations were used to examine the association between smartwatch prescription for AF monitoring and changes in the patient-reported outcomes.

Results

A total of 110 participants (mean age 64 years, 41% female, 91% non-Hispanic White) were studied. Seventy percent of intervention participants were novice smartwatch users, as opposed to 84% of controls, and there was no significant difference in baseline rates of anxiety, activation, or self-rated health between the 2 groups. The incidence of new AF among smartwatch users was 6%. Participants who were prescribed smartwatches did not have a statistically significant change in anxiety, activation, or self-reported health as compared to those who were not prescribed smartwatches. The results held even after removing participants who received an AF alert on the watch.

Conclusion

The prescription of smartwatches to stroke survivors for AF monitoring does not adversely affect key patient-reported outcomes. Further research is needed to better inform the successful deployment of smartwatches in clinical practice.

Dynamic changes in nocturnal heart rate predict short-term cardiovascular events in patients using the wearable cardioverter-defibrillator: from the WEARIT-France cohort study

AIMS

While elevated resting heart rate measured at a single point of time has been associated with cardiovascular outcomes, utility of continuous monitoring of nocturnal heart rate (NHR) has never been evaluated. We hypothesized that dynamic NHR changes may predict, at short term, impending cardiovascular events in patients equipped with a wearable cardioverter-defibrillator (WCD).

METHODS AND RESULTS

The WEARIT-France prospective cohort study enrolled heart failure patients with WCD between 2014 and 2018. Night-time was defined as midnight to 7 a.m. NHR initial trajectories were classified into four categories based on mean NHR in the first week (High/Low) and NHR evolution over the second week (Up/Down) of WCD use. The primary endpoint was a composite of cardiovascular death and heart failure hospitalization. A total of 1013 [61 (interquartile range, IQR 53-68) years, 16% women, left ventricular ejection fraction 26% (IQR 22-30)] were included. During a median WCD wear duration of 68 (IQR 44-90) days, 58 patients (6%) experienced 69 events. After considering potential confounders, High-Up NHR trajectory was significantly associated with the primary endpoint compared to Low-Down [adjusted hazard ratio (HR) 6.08, 95% confidence interval (CI) 2.56-14.45, P < 0.001]. Additionally, a rise of >5 bpm in weekly average NHR from the preceding week was associated with 2.5 higher composite event risk (HR 2.51, 95% CI 1.22-5.18, P = 0.012) as well as total mortality (HR 11.21, 95% CI 3.55-35.37, P < 0.001) and cardiovascular hospitalization (HR 2.70, 95% CI 1.51-4.82, P < 0.001).

CONCLUSION

Dynamic monitoring of NHR may allow timely identification of impending cardiovascular events, with the potential for 'pre-emptive' action.

REGISTRATION NUMBER

Clinical Trials.gov Identifier: NCT03319160.

Wearable and Portable Devices for Acquisition of Cardiac Signals while Practicing Sport: A Scoping Review

Wearable and portable devices capable of acquiring cardiac signals are at the frontier of the sport industry. They are becoming increasingly popular for monitoring physiological parameters while practicing sport, given the advances in miniaturized technologies, powerful data, and signal processing applications. Data and signals acquired by these devices are increasingly used to monitor athletes' performances and thus to define risk indices for sport-related cardiac diseases, such as sudden cardiac death. This scoping review investigated commercial wearable and portable devices employed for cardiac signal monitoring during sport activity. A systematic search of the literature was conducted on PubMed, Scopus, and Web of Science. After study selection, a total of 35 studies were included in the review. The studies were categorized based on the application of wearable or portable devices in (1) validation studies, (2) clinical studies, and (3) development studies. The analysis revealed that standardized protocols for validating these technologies are necessary. Indeed, results obtained from the validation studies turned out to be heterogeneous and scarcely comparable, since the metrological characteristics reported were different. Moreover, the validation of several devices was carried out during different sport activities. Finally, results from clinical studies highlighted that wearable devices are crucial to improve athletes' performance and to prevent adverse cardiovascular events.

Application of smart devices in investigating the effects of air pollution on atrial fibrillation onset

Few studies have examined the link between short-term exposure to air pollutants and atrial fibrillation (AF) episodes. This study aims to examine the association of hourly criteria air pollutants with AF episodes. We employ a smart device-based photoplethysmography technology to screen AF from 2018 to 2021. Hourly concentrations of six criteria air pollutants are matched to the onset hour of AF for each participant. We adopt a time-stratified case-crossover design to capture the acute effects of air pollutants on AF episodes, using conditional logistic regression models. Subgroup analyses are conducted by age, gender, and season. A total of 11,906 episodes of AF are identified in 2976 participants from 288 Chinese cities. Generally, the strongest associations of air pollutants are present at lag 18-24 h, with positive and linear exposure-response relationships. For an interquartile range increase in inhalable particles, fine particles, nitrogen dioxide, and carbon monoxide, the odds ratio (OR) of AF is 1.19 [95% confidential interval (CI): 1.03, 1.37], 1.38 (95%CI: 1.14, 1.67), 1.60 (95%CI: 1.16, 2.20) and 1.48 (95%CI: 1.19, 1.84), respectively. The estimates are robust to the adjustment of co-pollutants, and they are larger in females, older people, and in cold seasons. There are insignificant associations for sulfur dioxide and ozone. This nationwide case-crossover study demonstrates robust evidence of significant associations between hourly exposure to air pollutants and the onset of AF episodes, which underscores the importance of ongoing efforts to further improve air quality as an effective target for AF prevention.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

Defining preventive cardiology: A clinical practice statement from the American Society for Preventive Cardiology

Remarkable transformations in science and healthcare have resulted in declines in mortality from cardiovascular disease over the past several decades, largely driven by progress in prevention and treatment of persons at risk. However, these trends are now beginning to stall, as our county faces increases in cardiovascular risk factors including overweight and obesity, type 2 diabetes mellitus, and metabolic syndrome. Furthermore, poor long-term adherence to a healthy lifestyle and lifesaving pharmacotherapy have exacerbated these trends, with recent data suggesting unprecedented increases in cardiovascular morbidity and mortality. A paradigm shift is needed to improve the cardiovascular health of our nation. Preventive cardiology, a growing subspecialty of cardiovascular medicine, is the practice of primordial, primary, and secondary prevention of all cardiovascular diseases. Preventive cardiologists and preventive cardiology specialists are well equipped with the knowledge and skill-set necessary to reduce deaths related to the growing burden of heart disease and its risk factors. Despite dedicated efforts, cardiovascular disease remains the leading killer of men and women in the United States. Although there is little debate regarding the importance of prevention, many healthcare professionals question the need for preventive cardiology as a distinct subspecialty. Additionally, the field's growth has been hampered by a lack of organization and standardization, and variability of training within programs across the country. The purpose of this document is to delineate the key attributes that define the field of preventive cardiology according to the American Society for Preventive Cardiology.

Opportunities and challenges in heart rhythm research: Rationale and development of an electrophysiology collaboratory

There are many challenges in the current landscape of electrophysiology (EP) clinical and translational research, including increasing costs and complexity, competing demands, regulatory requirements, and challenges with study implementation. This review seeks to broadly discuss the state of EP research, including challenges and opportunities. Included here are results from a Heart Rhythm Society (HRS) Research Committee member survey detailing HRS members' perspectives regarding both barriers to clinical and translational research and opportunities to address these challenges. We also provide stakeholder perspectives on barriers and opportunities for future EP research, including input from representatives of the U.S. Food and Drug Administration, industry, and research funding institutions that participated in a Research Collaboratory Summit convened by HRS. This review further summarizes the experiences of the heart failure and heart valve communities and how they have approached similar challenges in their own fields. We then explore potential solutions, including various models of research ecosystems designed to identify research challenges and to coordinate ways to address them in a collaborative fashion in order to optimize innovation, increase efficiency of evidence generation, and advance the development of new therapeutic products. The objectives of the proposed collaborative cardiac EP research community are to encourage and support scientific discourse, research efficiency, and evidence generation by exploring collaborative and equitable solutions in which stakeholders within the EP community can interact to address knowledge gaps, innovate, and advance new therapies.

Machine Learning ECG Classification Using Wavelet Scattering of Feature Extraction

The heart’s electrical activity is registered by an electrocardiogram (ECG), which consists of a wealth of pathological data on heart diseases such as arrhythmia. However, with increasing complexity and nonlinearity, direct observation of ECG signals and analysis is very tough. The highest accuracy of classification performance for machine learning approaches are 99.7 for neural network with wavelet scattering features extraction and 99.92 for SVM also with wavelet scattering features extraction. Through wavelet cascades with a neural network, the wavelet scattering transform can yield a translation invariant and deflection depictions of ECG signals. We suggested a new wavelet scattering transform-based method for automatically classifying three types of ECG heart diseases as follows: arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The bandwidth of the scaling function is used to critically downsample the wavelet scattering transform in time. As a result, each of the scattering paths has 16-time windows. Beat classification performance is classified by utilizing the MIT-BIH arrhythmia dataset. The suggested method is able to conduct high accuracy arrhythmia classification, with a 99.7% and 99.92% accuracy rate of the neural network (NN) and support vector machine (SVM), respectively, and will aid physicians in ECG explanation.

Analysis of Strategies to Increase User Retention of Fitness Mobile Apps during and after the COVID-19 Pandemic

The COVID-19 pandemic has changed the fitness-related field. More people started working out at home, and the use of fitness mobile apps that can measure the amount of exercise through a scientific method has increased compared to before the COVID-19 pandemic. This phenomenon is likely to continue even after the COVID-19 pandemic, and therefore this study aimed to investigate the importance of and satisfaction with a fitness app's functions according to consumers while using the fitness mobile app. Through this study, we intended to provide data for creating an environment where users can use fitness mobile apps consistently. A total of 420 questionnaires were distributed through Google Survey for about 3 months, from 13 September to 20 November 2020, and a total of 399 complete questionnaires were analyzed in this study. Regarding the data processing methods, frequency analysis, exploratory factor analysis, reliability analysis, descriptive statistical analysis, and IPA were used. The results are as follows. First, the first quadrant of the IPA matrix indicated the high importance of and satisfaction with the fitness mobile app, and included five attributes: cost-effectiveness, easy-to-understand information, ease of use and application, privacy protection, and compatibility with other devices. Second, the second quadrant of the matrix indicated relatively low satisfaction in association to high importance and included five attributes: accurate exercise information provision, design efficiency, daily exercise amount setting, convenient icons and interface, and provision of images and videos in appropriate proportions. Third, the third quadrant of the matrix, indicating low importance and low satisfaction, included five attributes: not sharing personal information, overall design composition and color, customer service, reliable security level, and providing information on goal achievement after exercising. Fourth, in the quadrant of the matrix, indicating low importance and high satisfaction, five attributes were included: exercise notification function, continuous service provision, step count and heart rate information, individual exercise recommendation, and individual body type analysis information.

Pragmatic randomized trial assessing the impact of digital health technology on quality of life in patients with heart failure: Design, rationale and implementation

BACKGROUND

Self-care and patient engagement are important elements of heart failure (HF) care, endorsed in the guidelines. Digital health tools may improve quality of life (QOL) in HF patients by promoting care, knowledge, and engagement. This manuscript describes the rationale and challenges of the design and implementation of a pragmatic randomized controlled trial to evaluate the efficacy of three digital health technologies in improving QOL for patients with HF.

HYPOTHESIS

We hypothesize that digital health interventions will improve QOL of HF patients through the early detection of warning signs of disease exacerbation, the opportunity of self-tracking symptoms, and the education provided, which enhances patient empowerment.

METHODS

Using a fully electronic enrollment and consent platform, the trial will randomize 200 patients across HF clinics in the Yale New Haven Health system to receive either usual care or one of three digital technologies designed to promote self-management and provide critical data to clinicians. The primary outcome is the change in QOL as assessed by the Kansas City Cardiomyopathy Questionnaire at 3 months.

RESULTS

First enrollment occurred in September 2021. Recruitment was anticipated to last 6-8 months and participants were followed for 6 months after randomization. Our recruitment efforts have highlighted the large digital divide in our population of interest.

CONCLUSION

Assessing clinical outcomes, patient usability, and ease of clinical integration of digital technologies will be beneficial in determining the feasibility of the integration of such technologies into the healthcare system.

Evaluation of a Population-Wide Mobile Health Physical Activity Program in 696 907 Adults in Singapore

Background

Evidence of scaled‐up physical activity interventions is scarce. This study evaluates the uptake, engagement, and effectiveness of one such intervention program.

Methods and Results

The program was open to individuals aged ≥17 years in Singapore. The main intervention components comprised device‐based daily physical activity recording paired with step count goals and financial rewards. According to the different reward opportunities, we divided the evaluation period (August 2017 to June 2018) into the baseline monitoring phase, the main challenge phase, and the maintenance phase. Uptake was assessed by the number of individuals registered, and engagement by the step recording duration after registration. The effectiveness was defined as changes in mean daily step count from baseline to the main challenge phase and the maintenance phase. A total of 696 907 participants registered, including more Singapore citizens (versus noncitizens), women, and younger (aged 17–39 years) individuals. The evaluation of engagement and effectiveness included 421 388 (60.5%) participants who provided plausible characteristic information and step count data. The median duration of engagement was 74 (IQR, 14–149) days. Compared with the baseline of 7509 (SD, 3467) steps, mean daily step count increased by 1579 (95% CI, 1564–1594) steps during the main challenge phase and 934 (95% CI, 916–952) steps during the maintenance phase. Greater engagement and activity increase were found in participants who are citizens, women, aged ≥40 years, non‐obese, and using separate wearables (versus smartphones).

Conclusions

Mobile health physical activity interventions can successfully reach a large population and be effective in increasing physical activity, despite declining program engagement over time.

An-m-Health Intervention Using Smartphone App to Improve Physical Activity in College Students: A Randomized Controlled Trial

Using m-Health apps can provide researchers and others with an effective way for improving physical activity (PA) and healthy lifestyle behaviors. The promotion of health should move from a model focused on the physical and biological basis of illness and towards a focus on the behavioral changes that support health. Therefore, the aims of the current study were to improve PA (step-counts) and body weight using a theory-based m-Health app. A 12-week randomized treatment trial was carried out at Texas A&M University, Texas, college station. College students (n = 130) were recruited. They were randomized in an equal ratio of 1:1 to intervention (m-Health app) (n = 65) and control (n = 65) conditions. The response rate was (87.6%). Both groups utilized a Smartphone app. The intervention group received PA goals of (10,000 steps/day), using an m-Health app. The control group was provided with information related to daily recommended PA levels. The primary change was daily step count between the baseline and follow-up. The secondary outcome was the body mass index (BMI). Descriptive statistics were used to summarize the baseline differences between the control and intervention groups. Independent sample t-test were used for comparison between the intervention and control groups. Post-intervention PAs were higher for the intervention group (mean = 54,896.) vs. control group (mean = 45,530.12; p < 0.05). The intervention group’s step-counts increased significantly (pre-mean = 40,320.38 steps per week; post-mean = 54,896.27 steps per week, p < 0.05). The body-weight changes were significant among the intervention group (p < 0.05). m-Health apps can increase PA and improve body weight, with goal setting and feedback as key intervention components. Future studies should personalize PA goals and feedback.

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology: Cardiovascular Round Table

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation

The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.

Atrial fibrillation future clinic. Novel platform to integrate smart device electrocardiogram into clinical practice

Background

Direct-to-consumer devices allow patients to record electrocardiograms (ECG) and detect atrial fibrillation (AF). Clinical adoption of these devices has been limited owing to the lack of efficient workflow.

Objective

To assess a new care model for following patients after AF ablation that uses a smartphone ECG coupled with a novel cloud-based platform.

Methods

This was a pilot study to describe AF detection, healthcare utilization, use of additional ECGs and cardiac monitors, and changes in anxiety after AF ablation. Patients presenting 3–4 months after early successful AF ablation were randomized into a control group with standard clinical follow-up or a self-monitoring group using smartphone ECG (Kardia Mobile, KM) coupled with a cloud-based platform (KardiaPro, KP) that alerted the physician when AF was detected and followed for 6 months

Results

A total of 100 patients were randomized: 51 to the KM/KP group and 48 to the control group (1 withdrew). AF was detected in 18 patients (18.2%), 11 (21.6%) in the KM/KP group and 7 (14.6%) in the control group (P = .42). AF detection occurred at a median of 68 and 91 days in the KM/KP and control groups, respectively (P = .93). These differences were not statistically significant. Healthcare utilization and changes in anxiety were similar between the groups. More patients required additional ECGs or cardiac monitors in the control group (27.1%) compared to the KM/KP group (5.9%) (P = .004).

Conclusions

Smartphone ECG with a cloud-based platform can be incorporated into the care of post–AF ablation patients without increasing anxiety and with less need for additional traditional monitors.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

The Effects of mHealth-Based Gamification Interventions on Participation in Physical Activity: Systematic Review

BACKGROUND

It is well known that regular physical exercise has associated benefits; yet, participation remains suboptimal. Mobile health (mHealth) has become an indispensable medium to deliver behavior change interventions, and there is a growing interest in the gamification apps in mHealth to promote physical activity (PA) participation. Gamification could use game design elements (such as points, leaderboards, and progress bars), and it has the potential to increase motivation for PA and engagement. However, mHealth-based gamification interventions are still emerging, and little is known about the application status and efficacy of such interventions.

OBJECTIVE

This systematic review aims to investigate gamification apps in mHealth for improving PA levels and simultaneously summarize the impact of gamification interventions on PA participation.

METHODS

We searched PubMed, Scopus, Web of Science, Embase, CINAHL (EBSCO host), and IEEE Xplore from inception to December 20, 2020. Original empirical research exploring the effects of gamification interventions on PA participation was included. The papers described at least one outcome regarding exercise or PA participation, which could be subjective self-report or objective indicator measurement. Of note, we excluded studies about serious games or full-fledged games.

RESULTS

Of 2944 studies identified from the database search, 50 (1.69%) were included, and the information was synthesized. The review revealed that gamification of PA had been applied to various population groups and broadly distributed among young people but less distributed among older adults and patients with a disease. Most of the studies (30/50, 60%) combined gamification with wearable devices to improve PA behavior change, and 50% (25/50) of the studies used theories or principles for designing gamified PA interventions. The most frequently used game elements were goal-setting, followed by progress bars, rewards, points, and feedback. This review demonstrated that gamification interventions could increase PA participation; however, the results were mixed, and modest changes were attained, which could be attributed to the heterogeneity across studies.

CONCLUSIONS

Overall, this study provides an overview of the existing empirical research in PA gamification interventions and provides evidence for the efficacy of gamification in enhancing PA participation. High-quality empirical studies are needed in the future to assess the efficacy of a combination of gamification and wearable activity devices to promote PA, and further exploration is needed to investigate the optimal implementation of these features of game elements and theories to enhance PA participation.

Mobile health-based gamification intervention to increase physical activity participation among patients with coronary heart disease: study protocol of a randomised controlled trial

INTRODUCTION

Despite proven benefits, physical activity participation remains low in patients with coronary heart disease (CHD). Scientific evidence suggests that mobile health (mHealth)-based gamification interventions could increase physical activity levels. However, several systematic reviews demonstrated that most gamification intervention designs do not appropriately leverage theories from health behaviour models, and empirical evidence on the efficacy of such interventions among patients with CHD is still emerging. This study embeds the principles of behavioural economics into a gamification intervention based on a smartphone app (WeChat applet) to explore whether a mHealth-based gamification intervention can improve participation in physical activity and other related physical and psychological outcomes in patients with CHD.

METHODS

We propose a single-blinded three-arm randomised controlled trial with 108 patients with CHD, who will be randomly divided into three groups (Control group: WeChat applet+step goal setting; Individual group: WeChat applet+step goal setting+gamification; Team group: WeChat applet+step goal setting+gamification+collaboration). The interventions will last for 12 weeks and follow-up for 12 weeks. All patients will receive only WeChat applet-based step goal setting in the follow-up period. The primary outcome is physical activity participation, which includes a change in daily steps and self-reported physical activity from the baseline to 12 and 24 weeks, and the proportion of patient-days that step goals achieved in 12 and 24 weeks. The secondary outcomes include biomedical and lifestyle-related risk factors, intrinsic motivation, enjoyment, competence, autonomy and relatedness, social support and mental health and patients' satisfaction, perceptions and intervention experience.

ETHICS AND DISSEMINATION

The Human Research Ethics Committee of the School of Nursing, Jilin University (HREC 2020122401) approved this. The results will be published in peer-reviewed journals and presented at conferences.

TRIAL REGISTRATION NUMBER

ChiCTR2100044879; Pre-results.

Mobile Health for Arrhythmia Diagnosis and Management

Palpitations are a common symptom managed by general practitioners and cardiologists; atrial fibrillation (AF) is the most common arrhythmia in adults. The recent commercial availability of smartphone-based devices and wearable technologies with arrhythmia detection capabilities has revolutionized the diagnosis and management of these common medical issues, as it has placed the power of arrhythmia detection into the hands of the patient. Numerous mobile health (mHealth) devices that can detect, record, and automatically interpret irregularities in heart rhythm and abrupt changes in heart rate using photoplethysmography (PPG)- and electrocardiogram-based technologies are now commercially available. As opposed to prescription-based external rhythm monitoring approaches, these devices are more inexpensive and allow for longer-term monitoring, thus increasing sensitivity for arrhythmia detection, particularly for patients with infrequent symptoms possibly due to cardiac arrhythmias. These devices can be used to correlate symptoms with cardiac arrhythmias, assess efficacy and toxicities of arrhythmia therapies, and screen the population for serious rhythm disturbances such as AF. Although several devices have received clearance for AF detection from the United States Food & Drug Administration, limitations include the need for ECG confirmation for arrhythmias detected by PPG alone, false positives, false negatives, charging requirements for the battery, and financial cost. In summary, the growth of commercially available devices for remote, patient-facing rhythm monitoring represents an exciting new opportunity in the care of patients with palpitations and known or suspected dysrhythmias. Physicians should be familiar with the evidence that underlies their added value to patient care and, importantly, their current limitations.

Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data

PURPOSE OF REVIEW

Artificial intelligence (AI) has changed virtually every aspect of modern life, and medicine is no exception. Pediatric cardiology is both a perceptual and a cognitive subspecialty that involves complex decision-making, so AI is a particularly attractive tool for this medical discipline. This review summarizes the foundational work and incremental progress made as AI applications have emerged in pediatric cardiology since 2020.

RECENT FINDINGS

AI-based algorithms can be useful for pediatric cardiology in many areas, including: (1) clinical examination and diagnosis, (2) image processing, (3) planning and management of cardiac interventions, (4) prognosis and risk stratification, (5) omics and precision medicine, and (6) fetal cardiology. Most AI initiatives showcased in medical journals seem to work well in silico, but progress toward implementation in actual clinical practice has been more limited. Several barriers to implementation are identified, some encountered throughout medicine generally, and others specific to pediatric cardiology.

SUMMARY

Despite barriers to acceptance in clinical practice, AI is already establishing a durable role in pediatric cardiology. Its potential remains great, but to fully realize its benefits, substantial investment to develop and refine AI for pediatric cardiology applications will be necessary to overcome the challenges of implementation.

A supervised machine learning semantic segmentation approach for detecting artifacts in plethysmography signals from wearables

Objective. Wearable devices equipped with plethysmography (PPG) sensors provided a low-cost, long-term solution to early diagnosis and continuous screening of heart conditions. However PPG signals collected from such devices often suffer from corruption caused by artifacts. The objective of this study is to develop an effective supervised algorithm to locate the regions of artifacts within PPG signals.Approach. We treat artifact detection as a 1D segmentation problem. We solve it via a novel combination of an active-contour-based loss and an adapted U-Net architecture. The proposed algorithm was trained on the PPG DaLiA training set, and further evaluated on the PPG DaLiA testing set, WESAD dataset and TROIKA dataset.Main results. We evaluated with the DICE score, a well-established metric for segmentation accuracy evaluation in the field of computer vision. The proposed method outperforms baseline methods on all three datasets by a large margin (≈7 percentage points above the next best method). On the PPG DaLiA testing set, WESAD dataset and TROIKA dataset, the proposed method achieved 0.8734 ± 0.0018, 0.9114 ± 0.0033 and 0.8050 ± 0.0116 respectively. The next best method only achieved 0.8068 ± 0.0014, 0.8446 ± 0.0013 and 0.7247 ± 0.0050.Significance. The proposed method is able to pinpoint exact locations of artifacts with high precision; in the past, we had only a binary classification of whether a PPG signal has good or poor quality. This more nuanced information will be critical to further inform the design of algorithms to detect cardiac arrhythmia.

Cancellations and Transfers Related to New-Onset Atrial Fibrillation: An Analysis of Survey and Patient Safety Reporting Data From Ambulatory Surgical Facilities

Atrial fibrillation (AF) is a cardiac arrhythmia characterized by an irregular rhythm and often rapid heart rate. People with AF can be symptomatic or asymptomatic and are at increased risk for stroke. In this study, we used two data sources—a survey and Pennsylvania Patient Safety Reporting System (PA-PSRS) reports—to examine new-onset AF in Pennsylvania ambulatory surgical facilities (ASFs). The survey was developed and conducted to learn more about new-onset AF– related cancellations and transfers in Pennsylvania ASFs and to update the Patient Safety Authority ASF Cancellation and Transfer Tracking Tool. The survey response rate was 53.1%, with 50.9% of respondents indicating new-onset AF–related cancellations in the last year. A five-year review of PA-PSRS data revealed an increase in the number of new-onset AF–related cancellation and transfer events that occurred in the last two years. In 70.9% of the reports, patients were 65 years of age and older. A paucity of research on this patient safety issue led us to identify areas for future research.

Impact of activity trackers on secondary prevention in patients with coronary artery disease: a systematic review and meta-analysis

AIMS

Coronary artery disease (CAD) is related to high rates of morbidity and mortality among cardiovascular diseases (CVDs). Activity trackers have been used in cardiac rehabilitation (CR) in the last years. However, their effectiveness to influence outcomes after CAD is debated. This review summarizes the latest data of impact of activity trackers on CVD risk and outcomes: peak oxygen consumption (VO2), major adverse cardiovascular events (MACE), quality of life (QoL), and low-density lipoprotein-cholesterol (LDL-C).

METHODS AND RESULTS

Articles from 1986 to 2020 in English were searched by electronic databases (PubMed, Cochrane Library, and Embase). Inclusion criteria were: randomized controlled trials of CAD secondary prevention using an activity tracker which include at least peak VO2, MACE, QoL, or LDL-C as outcomes. Meta-analysis was performed. After removing duplicates, 604 articles were included and the screening identified a total of 11 articles. Compared to control groups, intervention groups with activity trackers significantly increased peak VO2 [mean difference 1.54; 95% confidence interval (CI) (0.50-2.57); P = 0.004] and decreased MACE [risk ratio 0.51; 95% CI (0.31-0.86); P = 0.01]. Heterogeneity was low (I2 = 0%) for MACE and high (I2 = 51%) for peak VO2. Intervention with an activity tracker also has positive impact on QoL. There was no between-group difference in LDL-C.

CONCLUSION

CR using activity trackers has a positive and multi-faceted effect on peak VO2, MACE, and QoL in patients with CAD.

Efficacy of a centralized, blended electronic, and human intervention to improve direct oral anticoagulant adherence: Smartphones to improve rivaroxaban ADHEREnce in atrial fibrillation (SmartADHERE) a randomized clinical trial

BACKGROUND

Improving adherence to direct oral anticoagulants (DOAC) is challenging, and simple text messaging reminders have not been effective.

METHODS

SmartADHERE was a randomized trial that tested a personalized digital and human direct oral anticoagulant adherence intervention compared to usual care. Eligibility required age ≥ 18, newly-prescribed (≤90 days) rivaroxaban for atrial fibrillation (AF), 1 of 4 at-risk criteria for nonadherence, and a smartphone. The intervention consisted of combination of a medication management smartphone app, daily app-based reminders, adaptive text messaging, and phone-based counseling for severe nonadherence. The primary outcome was the proportion of days covered by rivaroxaban (PDC) at 6 months. There were 25 U.S. sites, all cardiology and electrophysiology outpatient practices, activated for a target sample size of 378, but the study was terminated by the sponsor prior to reaching target enrollment.

RESULTS

There were 139 participants (age 65±9.6 years, 30% female, median CHA₂DS₂-VASc score 3 with IQR 2 to 4, mean total medication burden 7.7±4.4). DOAC adherence was high in both arms with no difference in the primary outcome (PDC 0.86±0.25 intervention vs 0.88±0.25 control, p=0.62) or in secondary outcomes including PDC ≥ 0.80 and medication persistence. Per protocol analyses had similar results. Because of the high overall PDC, the likelihood to answer the primary hypothesis was only 51% even if target enrollment were achieved. There were no study-related adverse events.

CONCLUSIONS

The use of a centralized digital and human adherence intervention was feasible across multiple sites. Overall adherence was much higher than expected despite prescreening for at-risk individuals. SmartADHERE illustrates the challenges of trials of behavioral and technology interventions, where enrollment itself may lead to selection bias or treatment effects. Pragmatic study designs, such as cluster randomization or stepped-wedge implementation, should be considered to improve enrollment and generalizability.

Distinguishing atrial fibrillation from sinus rhythm using commercial pulse detection systems: The non-interventional BAYathlon study

Objective

Early diagnosis of atrial fibrillation (AFib) is a priority for stroke prevention. We sought to test four commercial pulse detection systems (CPDSs) for ability to distinguish AFib from normal sinus rhythm using a published algorithm (Zhou et al., PLoS One 2015;10:e0136544), compared with visual diagnosis by electrocardiogram inspection.

Methods

BAYathlon was a prospective, non-interventional, single-centre study. Adult cardiology patients with documented AFib or sinus rhythm who were due to have a routine 5-min electrocardiogram were randomized to undergo a parallel 5-min pulse assessment with a Polar V800, eMotion Faros 360, TomTom heart rate monitor, or Adidas miCoach Smart Run.

Results

144 patients (73 with AFib, 71 with sinus rhythm (based on electrocardiograms); median age: 73 years; 53.5% male) were analysed. Algorithm sensitivities (primary endpoint) and specificities for AFib when applied to CPDS recordings were 93.3% and 94.1% with the Polar V800, 90.0% and 84.2% with the eMotion Faros 360, and 0% and 100% with the other CPDSs (analysis period: 127 heart rate signals + 2 min). When applied to routine electrocardiograms, the algorithm correctly detected AFib in 71/73 patients. Different analysis periods (127 heart rate signals +1 or 3 min) only slightly changed the sensitivities with the Polar V800 and eMotion Faros 360 and had no effect on the sensitivities with the other CPDSs.

Conclusion

AFib screening using the applied algorithm is feasible with the Polar V800 and eMotion Faros 360 (which provide RR interval data) but not with the other CPDSs (which provide pre-processed heart rate time series).

ClinicalTrials.gov identifier: NCT02875106

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.

2021 ISHNE/HRS/EHRA/APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

Bending the Curve in Cardiovascular Disease Mortality: Bethesda + 40 and Beyond

More than 40 years after the 1978 Bethesda Conference on the Declining Mortality from Coronary Heart Disease provided the scientific community with a blueprint for systematic analysis to understand declining rates of coronary heart disease, there are indications the decline has ended or even reversed despite advances in our knowledge about the condition and treatment. Recent data show a more complex situation, with mortality rates for overall cardiovascular disease, including coronary heart disease and stroke, decelerating, whereas those for heart failure are increasing. To mark the 40th anniversary of the Bethesda Conference, the National Heart, Lung, and Blood Institute and the American Heart Association cosponsored the "Bending the Curve in Cardiovascular Disease Mortality: Bethesda + 40" symposium. The objective was to examine the immediate and long-term outcomes of the 1978 conference and understand the current environment. Symposium themes included trends and future projections in cardiovascular disease (in the United States and internationally), the evolving obesity and diabetes epidemics, and harnessing emerging and innovative opportunities to preserve and promote cardiovascular health and prevent cardiovascular disease. In addition, participant-led discussion explored the challenges and barriers in promoting cardiovascular health across the lifespan and established a potential framework for observational research and interventions that would begin in early childhood (or ideally in utero). This report summarizes the relevant research, policy, and practice opportunities discussed at the symposium.

Implementation of Online Hospitals and Factors Influencing the Adoption of Mobile Medical Services in China: Cross-Sectional Survey Study

Background

Online hospitals are part of an innovative model that allows China to explore telemedicine services based on national conditions with large populations, uneven distribution of medical resources, and lack of quality medical resources, especially among residents needing to be protected from COVID-19 infection.

Objective

In this study, we built a hypothesis model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) in order to analyze the factors that may influence patients’ willingness to use mobile medical services. This research was designed to assist in the development of mobile medical services. Residents who do not live in urban areas and cannot access medical assistance would greatly benefit from this research, as they could immediately go to the online hospital when needed.

Methods

A cross-sectional study based at the West China Hospital, Sichuan University, was conducted in July 2020. A total of 407 respondents, 18 to 59 years old, in Western China were recruited by convenience sampling. We also conducted an empirical test for the hypothesis model and applied structural equation modeling to estimate the significance of path coefficients so that we could better understand the influencing factors.

Results

Out of 407 respondents, 95 (23.3%) were aware of online hospitals, while 312 (76.7%) indicated that they have never heard of online hospitals before. Gender (P=.048) and education level (P=.04) affected people’s willingness to use online hospitals, and both of these factors promoted the use of online hospitals (odds ratio [OR] 2.844, 95% CI 1.010-8.003, and OR 2.187, 95% CI 1.031-4.636, respectively). According to structural equation modeling, the results of the path coefficient analysis indicated that performance expectancy, effort expectancy, and facilitating conditions have positive effects on patients’ willingness to use online hospitals.

Conclusions

The goal of our research was to determine the factors that influence patients’ awareness and willingness to use online hospitals. Currently, the public’s awareness and usage of online hospitals is low. In fact, effort expectancy was the most important factor that influenced the use of online hospitals; being female and having a high education also played positive roles toward the use of mobile medical services.

2021 ISHNE/HRS/EHRA/APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia Pacific Heart Rhythm Society describes the current status of mobile health ("mHealth") technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.

2021 ISHNE/HRS/EHRA/APHRS Expert Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society

This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society describes the current status of mobile health technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self-management are novel aspects of mobile health. The promises of predictive analytics but also operational challenges in embedding mobile health into routine clinical care are explored.

Mobile Health Technology to Improve Care for Patients With Atrial Fibrillation

BACKGROUND

Current management of patients with atrial fibrillation (AF) is limited by low detection of AF, non-adherence to guidelines, and lack of consideration of patients' preferences, thus highlighting the need for a more holistic and integrated approach to AF management.

OBJECTIVE

The objective of this study was to determine whether a mobile health (mHealth) technology-supported AF integrated management strategy would reduce AF-related adverse events, compared with usual care.

METHODS

This is a cluster randomized trial of patients with AF older than 18 years of age who were enrolled in 40 cities in China. Recruitment began on June 1, 2018 and follow-up ended on August 16, 2019. Patients with AF were randomized to receive usual care, or integrated care based on a mobile AF Application (mAFA) incorporating the ABC (Atrial Fibrillation Better Care) Pathway: A, Avoid stroke; B, Better symptom management; and C, Cardiovascular and other comorbidity risk reduction. The primary composite outcome was a composite of stroke/thromboembolism, all-cause death, and rehospitalization. Rehospitalization alone was a secondary outcome. Cardiovascular events were assessed using Cox proportional hazard modeling after adjusting for baseline risk.

RESULTS

There were 1,646 patients allocated to mAFA intervention (mean age, 67.0 years; 38.0% female) with mean follow-up of 262 days, whereas 1,678 patients were allocated to usual care (mean age, 70.0 years; 38.0% female) with mean follow-up of 291 days. Rates of the composite outcome of 'ischemic stroke/systemic thromboembolism, death, and rehospitalization' were lower with the mAFA intervention compared with usual care (1.9% vs. 6.0%; hazard ratio [HR]: 0.39; 95% confidence interval [CI]: 0.22 to 0.67; p < 0.001). Rates of rehospitalization were lower with the mAFA intervention (1.2% vs. 4.5%; HR: 0.32; 95% CI: 0.17 to 0.60; p < 0.001). Subgroup analyses by sex, age, AF type, risk score, and comorbidities demonstrated consistently lower HRs for the composite outcome for patients receiving the mAFA intervention compared with usual care (all p < 0.05).

CONCLUSIONS

An integrated care approach to holistic AF care, supported by mHealth technology, reduces the risks of rehospitalization and clinical adverse events. (Mobile Health [mHealth] technology integrating atrial fibrillation screening and ABC management approach trial; ChiCTR-OOC-17014138).

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.