The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review

A Morphology-Preserving Algorithm for Denoising of EMG-Contaminated ECG Signals

Goal: Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions: IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices.

A Database of Simultaneously Recorded ECG Signals With and Without EMG Noise

Goal: Noise on recorded electrocardiographic (ECG) signals may affect their clinical interpretation. Electromyographic (EMG) noise spectrally coincides with the QRS complex, which makes its removal particularly challenging. The problem of evaluating the noise-removal techniques has commonly been approached by algorithm testing on the contaminated ECG signals constructed ad hoc as an additive mixture of a noise-free ECG signal and noise. Consequently, there is an absence of a unique/standard database for testing and comparing different denoising methods. We present a SimEMG database recorded by a novel acquisition method that allows for direct recording of the genuine EMG-noise-free and -contaminated ECG signals. The database is available as open source.

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis

Atrial fibrillation (AF) is the most common arrhythmia with a high burden of morbidity including impaired quality of life and increased risk of thromboembolism. Early detection and management of AF could prevent thromboembolic events. Artificial intelligence (AI)--based methods in healthcare are developing quickly and can be proved as valuable for the detection of atrial fibrillation. In this metanalysis, we aim to review the diagnostic accuracy of AI-based methods for the diagnosis of atrial fibrillation. A predetermined search strategy was applied on four databases, the PubMed on 31 August 2022, the Google Scholar and Cochrane Library on 3 September 2022, and the Embase on 15 October 2022. The identified studies were screened by two independent investigators. Studies assessing the diagnostic accuracy of AI-based devices for the detection of AF in adults against a gold standard were selected. Qualitative and quantitative synthesis to calculate the pooled sensitivity and specificity was performed, and the QUADAS-2 tool was used for the risk of bias and applicability assessment. We screened 14,770 studies, from which 31 were eligible and included. All were diagnostic accuracy studies with case-control or cohort design. The main technologies used were: (a) photoplethysmography (PPG) with pooled sensitivity 95.1% and specificity 96.2%, and (b) single-lead ECG with pooled sensitivity 92.3% and specificity 96.2%. In the PPG group, 0% to 43.2% of the tracings could not be classified using the AI algorithm as AF or not, and in the single-lead ECG group, this figure fluctuated between 0% and 38%. Our analysis showed that AI-based methods for the diagnosis of atrial fibrillation have high sensitivity and specificity for the detection of AF. Further studies should examine whether utilization of these methods could improve clinical outcomes.

Changes in Autonomic Nervous System Activity in Children With Spina Bifida: A Case-Control Study

PURPOSE

We compared heart rate variability parameters between patients with spina bifida and a control group during urodynamic studies, with the goal of evaluating the autonomic nervous system dysfunction present in spina bifida.

METHODS

Continuous heart rate variability parameters were recorded during 3 successive periods (P0, the 2 minutes prior to the start of filling; P1, from the start of filling to the first desire to void; and P2, from P1 to the end of filling or the start of voiding). The control group consisted of children with vesicoureteral reflux who had undergone video-urodynamic studies. Our study included 11 patients with spina bifida and 9 control participants.

RESULTS

At baseline, patients with spina bifida exhibited lower values for the root mean square of successive differences in NN intervals, the percentage of successive R-R interval differences exceeding 50 msec relative to the total number of intervals, and high frequency (HF). In contrast, the low frequency (LF)/HF ratio was elevated in these patients (5.04 ± 4.75 vs. 0.67 ± 0.42, P = 0.014). During bladder filling, LF/HF values increased in the control group (P0, 0.67 ± 0.42; P1, 0.89 ± 0.34; P2, 1.21 ± 0.64; P = 0.018), while they declined in patients with spina bifida (P0, 5.04 ± 4.75; P1, 3.96 ± 4.35; P2, 3.26 ± 4.03; P < 0.001). The HF values were significantly elevated in children with spina bifida during bladder filling (P = 0.002). In the time domain, the standard deviations of all NN intervals were elevated only in the control group during bladder filling. Parasympathetic activity domains were reduced in the children with spina bifida at the initial assessment.

CONCLUSION

During the bladder filling phase, parasympathetic activity increased along with fixed sympathetic activity in the spina bifida group. In contrast, the control group exhibited a shift towards a sympathetic preponderance at the conclusion of bladder filling. These observations may be associated with the pathophysiology of neurogenic bladder in spina bifida.

Methods for Heart Rate Variability Biofeedback (HRVB): A Systematic Review and Guidelines

Heart Rate Variability Biofeedback (HRVB) has been widely used to improve cardiovascular health and well-being. HRVB is based on breathing at an individual's resonance frequency, which stimulates respiratory sinus arrhythmia (RSA) and the baroreflex. There is, however, no methodological consensus on how to apply HRVB, while details about the protocol used are often not well reported. Thus, the objectives of this systematic review are to describe the different HRVB protocols and detect methodological concerns. PsycINFO, CINALH, Medline and Web of Science were searched between 2000 and April 2021. Data extraction and quality assessment were based on PRISMA guidelines. A total of 143 studies were finally included from any scientific field and any type of sample. Three protocols for HRVB were found: (i) "Optimal RF" (n = 37), each participant breathes at their previously detected RF; (ii) "Individual RF" (n = 48), each participant follows a biofeedback device that shows the optimal breathing rate based on cardiovascular data in real time, and (iii) "Preset-pace RF" (n = 51), all participants breathe at the same rate rate, usually 6 breaths/minute. In addition, we found several methodological differences for applying HRVB in terms of number of weeks, duration of breathing or combination of laboratory and home sessions. Remarkably, almost 2/3 of the studies did not report enough information to replicate the HRVB protocol in terms of breathing duration, inhalation/exhalation ratio, breathing control or body position. Methodological guidelines and a checklist are proposed to enhance the methodological quality of future HRVB studies and increase the information reported.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Development of algorithm for work intensity evaluation using excess overwork index of construction workers with real-time heart rate measurement device

Background

The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance.

Methods

We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state.

Results

Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point.

Conclusion

The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

Changes in the Circadian Rhythm of High-Frequency Heart Rate Variability Associated With Depression

BACKGROUND

Heart rate variability (HRV) extracted from electrocardiogram measured for a short period during a resting state is clinically used as a bio-signal reflecting the emotional state. However, as interest in wearable devices increases, greater attention is being paid to HRV extracted from long-term electrocardiogram, which may contain additional clinical information. The purpose of this study was to examine the characteristics of HRV parameters extracted through long-term electrocardiogram and explore the differences between participants with and without depression and anxiety symptoms.

METHODS

Long-term electrocardiogram was acquired from 354 adults with no psychiatric history who underwent Holter monitoring. Evening and nighttime HRV and the ratio of nighttime-to-evening HRV were compared between 127 participants with depressive symptoms and 227 participants without depressive symptoms. Comparisons were also made between participants with and without anxiety symptoms.

RESULTS

Absolute values of HRV parameters did not differ between groups based on the presence of depressive or anxiety symptoms. Overall, HRV parameters increased at nighttime compared to evening. Participants with depressive symptoms showed a significantly higher nighttime-to-evening ratio of high-frequency HRV than participants without depressive symptoms. The nighttime-to-evening ratio of HRV parameters did not show a significant difference depending on the presence of anxiety symptoms.

CONCLUSION

HRV extracted through long-term electrocardiogram showed circadian rhythm. Depression may be associated with changes in the circadian rhythm of parasympathetic tone.

The Italian Society of Cardiology and Working Group on Telecardiology and Informatics 2023 updated position paper on telemedicine and artificial intelligence in cardiovascular disease

In 2015, the Italian Society of Cardiology and its Working Group on Telemedicine and Informatics issued a position paper on Telecardiology, resuming the most eminent evidence supporting the use of information and communication technology in principal areas of cardiovascular care, ranked by level of evidence. More than 5 years later and after the global shock inflicted by the SARS-CoV-2 pandemic, an update on the topic is warranted. Recent evidence and studies on principal areas of cardiovascular disease will be therefore reported and discussed, with particular focus on telemedicine for cardiovascular care in the COVID-19 context. Novel perspectives and opportunities disclosed by artificial intelligence and its applications in cardiovascular disease will also be discussed. Finally, modalities by which machine learning have realized remote patient monitoring and long-term care in recent years, mainly filtering critical clinical data requiring selective hospital admission, will be provided.

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.

Arrhythmias Beyond Atrial Fibrillation Detection Using Smartwatches: A Systematic Review

BACKGROUND

Over the past few years, smartwatches have become increasingly popular in the monitoring of arrhythmias. Although the detection of atrial fibrillation with smartwatches has been the subject of various articles, there is no comprehensive research on the detection of arrhythmias other than atrial fibrillation. In this study, we included individual cases from the literature to identify the characteristics of patients with smartwatch-detected arrhythmias other than atrial fibrillation.

METHODS

PubMed, Embase, and SCOPUS were searched for case reports, case series, or cohort studies that reported individual participant-level data, until January 6, 2022. The following search string was used for each databases: ('Smart Watch' OR 'Apple Watch' OR 'Samsung Gear') AND ('Supraventricular Tachycardia' OR 'Cardiac Arrhythmia' OR 'Ventricular Tachycardia' OR 'Atrioventricular Nodal Reentry Tachycardia' OR 'Atrioventricular Reentrant Tachycardia' OR 'Heart Block' OR 'Atrial Flutter' OR 'Ectopic Atrial Tachycardia' OR 'Bradyarrhythmia').

RESULTS

A total of 52 studies from PubMed, 20 studies from Embase, and 200 studies from SCOPUS were identified. After screening, 18 articles were included. A total of 22 patients were obtained from 14 case reports or case series. Four cohort studies evaluating various arrhythmias were included. Arrhythmias, including ventricular tachycardia, atrial fibrillation, atrial flutter, atrioventricular nodal reentry tachycardia, atrioventricular reentrant tachycardia, second- or third-degree atrioventricular block, and sinus bradycardia, were detected with smartwatches.

CONCLUSIONS

Cardiac arrhythmias other than atrial fibrillation are also commonly detected with smartwatches. Smartwatches have an important potential besides traditional methods in the detection of arrhythmias and clinical practice.

Quantitative Complexity Theory (QCT) in Integrative Analysis of Cardiovascular Hemodynamic Response to Posture Change

The explanation of physiological mechanisms involved in adaptation of the cardiovascular system to intrinsic and environmental demands is crucial for both basic science and clinical research. Computational algorithms integrating multivariable data that comprehensively depict complex mechanisms of cardiovascular reactivity are currently being intensively researched. Quantitative Complexity Theory (QCT) provides quantitative and holistic information on the state of multi-functional dynamic systems. The present paper aimed to describe the application of QCT in an integrative analysis of the cardiovascular hemodynamic response to posture change. Three subjects that underwent head-up tilt testing under beat-by-beat hemodynamic monitoring (impedance cardiography) were discussed in relation to the complexity trends calculated using QCT software. Complexity has been shown to be a sensitive marker of a cardiovascular hemodynamic response to orthostatic stress and vasodilator administration, and its increase has preceded changes in standard cardiovascular parameters. Complexity profiling has provided a detailed assessment of individual hemodynamic patterns of syncope. Different stimuli and complexity settings produce results of different clinical usability.

Prevalence and Prognostic Significance of Bradyarrhythmias in Patients Screened for Atrial Fibrillation vs Usual Care: Post Hoc Analysis of the LOOP Randomized Clinical Trial

Importance

There is increasing interest in heart rhythm monitoring and technologies to detect subclinical atrial fibrillation (AF), which may lead to incidental diagnosis of bradyarrhythmias.

Objective

To assess bradyarrhythmia prevalence and prognostic significance in persons screened for AF using implantable loop recorder (ILR) compared with unscreened persons.

Design, Setting, and Participants

This was a post hoc analysis of the Implantable Loop Recorder Detection of Atrial Fibrillation to Prevent Stroke (LOOP) randomized clinical trial, which took place in 4 sites in Denmark. Participants were 70 years or older without known AF but diagnosed with at least 1 of the following: hypertension, diabetes, heart failure, or prior stroke. Participants were recruited by letter invitation between January 31, 2014, and May 17, 2016. The median (IQR) follow-up period was 65 (59-70) months. Analysis took place between February and June 2022.

Interventions

ILR screening for AF with treatment of any bradyarrhythmia left to the discretion of the treating physician (ILR group) vs usual care (control group).

Main Outcomes and Measures

Adjudicated bradyarrhythmia episodes, pacemaker implantation, syncope, and sudden cardiovascular death.

Results

A total of 6004 participants were randomized (mean [SD] age, 75 [4.1] years; 2837 [47.3%] female; 5444 [90.7%] with hypertension; 1224 [20.4%] with prior syncope), 4503 to control and 1501 to ILR. Bradyarrhythmia was diagnosed in 172 participants (3.8%) in the control group vs 312 participants (20.8%) in the ILR group (hazard ratio [HR], 6.21 [95% CI, 5.15-7.48]; P < .001), and these were asymptomatic in 41 participants (23.8%) vs 249 participants (79.8%), respectively. The most common bradyarrhythmia was sinus node dysfunction followed by high-grade atrioventricular block. Risk factors for bradyarrhythmia included higher age, male sex, and prior syncope. A pacemaker was implanted in 132 participants (2.9%) vs 67 (4.5%) (HR, 1.53 [95% CI, 1.14-2.06]; P < .001), syncope occurred in 120 (2.7%) vs 33 (2.2%) (HR, 0.83 [95% CI, 0.56-1.22]; P = .34), and sudden cardiovascular death occurred in 49 (1.1%) vs 18 (1.2%) (HR, 1.11 [95% CI, 0.64-1.90]; P = .71) in the control and ILR groups, respectively. Bradyarrhythmias were associated with subsequent syncope, cardiovascular death, and all-cause death, with no interaction between bradyarrhythmia and randomization group.

Conclusions and Relevance

More than 1 in 5 persons older than 70 years with cardiovascular risk factors can be diagnosed with bradyarrhythmias when long-term continous monitoring for AF is applied. In this study, ILR screening led to a 6-fold increase in bradyarrhythmia diagnoses and a significant increase in pacemaker implantations compared with usual care but no change in the risk of syncope or sudden death.

One-year outcomes of catheter ablation for atrial fibrillation in young patients

BACKGROUND

Atrial fibrillation (AF) is relatively less frequent in younger patients (age < 50). Recently, studies have suggested that early restoration of sinus rhythm may lead to improved outcomes compared with rate control, however the efficacy of catheter ablation for AF in young is scarce.

METHODS

We included all hospitalized patients between 18 and 50 years with a diagnosis of AF from the Nationwide Readmission Database 2016-2017 from the Healthcare Cost and Utilization Project. Demographic and comorbidity data were collected and analyzed. Outcomes assessed included one-year AF readmission rates, all-cause readmission, ischemic stroke, and all-cause mortality. Subgroup analyses were performed for all demographic and comorbidity variables.

RESULTS

Overall, 52,598 patients (medium age 44, interquartile range 38-48, female 25.7%) were included in the study, including 2,146 (4.0%) who underwent catheter ablation for AF. Patients who underwent catheter ablation had a significantly lower rate of readmission for AF or any cause at one year (adjusted hazard ratios (HR) of 0.52 [95% confidence interval (CI): 0.43-0.63] and HR of 0.81 [95% CI: 0.72-0.89], respectively). There was no difference in 1-year readmission for stroke or all-cause mortality between the two groups. Subgroup analyses showed a consistent reduction in the risk of AF readmission among major demographic and comorbidity subgroups.

CONCLUSION

Catheter ablation in young patients with AF was associated with a reduction in 1-year AF related and all-cause readmissions. These data merit further prospective investigation for validation, through dedicated registries and multicenter collaborations to include young AF from diverse population.

Accuracy of continuous photoplethysmography-based 1 min mean heart rate assessment during atrial fibrillation

AIMS

Although mobile health tools using photoplethysmography (PPG) technology have been validated for the detection of atrial fibrillation (AF), their utility for heart rate assessment during AF remains unclear. Therefore, we aimed to evaluate the accuracy of continuous PPG-based 1 min mean heart rate assessment during AF.

METHODS AND RESULTS

Persistent AF patients were provided with Holter electrocardiography (ECG) (for ≥24 h) simultaneously with a PPG-equipped smartwatch. Both the PPG-based smartwatch and Holter ECG automatically and continuously monitored patients' heart rate/rhythm. ECG and PPG recordings were synchronized and divided into 1 min segments, from which a PPG-based and an ECG-based average heart rate estimation were extracted. In total, 47 661 simultaneous ECG and PPG 1 min heart rate segments were analysed in 50 patients (34% women, age 73 ± 8 years). The agreement between ECG-determined and PPG-determined 1 min mean heart rate was high [root mean squared error (RMSE): 4.7 bpm]. The 1 min mean heart rate estimated using PPG was accurate within ±10% in 93.7% of the corresponding ECG-derived 1 min mean heart rate segments. PPG-based 1 min mean heart rate estimation was more often accurate during night-time (97%) than day-time (91%, P < 0.001) and during low levels (96%) compared to high levels of motion (92%, P < 0.001). A neural network with a 10 min history of the recording did not further improve the PPG-based 1 min mean heart rate assessment [RMSE: 4.4 (95% confidence interval: 3.5-5.2 bpm)]. Only chronic heart failure was associated with a lower agreement between ECG-derived and PPG-derived 1 min mean heart rates (P = 0.040).

CONCLUSION

During persistent AF, continuous PPG-based 1 min mean heart rate assessment is feasible in 60% of the analysed period and shows high accuracy compared with Holter ECG for heart rates <110 bpm.

Factors affecting wearable ECG device adoption by general practitioners for atrial fibrillation screening: cross-sectional study

Introduction

Atrial fibrillation (AF) is a challenging cardiovascular disease worldwide. Wearable electrocardiograph devices (WEDs) have great potential to improve the detection rate of AF in primary care. However, the factors that influence general practitioners' (GPs) perception and acceptance of WEDs are not well understood. To identify factors that influence the intention of GPs to utilize WEDs in a clinical setting to screen patients for AF.

Method

The research hypotheses and questionnaire items were designed and developed based on the unified theory of acceptance and technology (UTAUT) framework. We used stratified sampling and obtained the data through an online survey. Structural equation modeling was used to analyze the collected data.Results: A total of 1,004 valid questionnaires from GPs across Sichuan province in China were collected. Three factors increased GPs' intention to utilize WEDs to screen patients for AF, including performance expectancy (β = 0.121, p = 0.004), social influence (β = 0.356, p < 0.001), and price perception (β = 0.587, p < 0.001). Perception risk (β = -0.059, p < 0.001) decreased usage intention, while effort expectancy (β = -0.079, p = 0.155) and facilitating conditions (β = -0.014, p = 0.868) did not affect usage intention. Gender (β = -0.022, p = 0.179), age (β = 0.006, p = 0.699), education level (β = -0.22, p = 0.184) and training (β = 0.007, p = 0.69) were not significantly correlated with usage intention, and these four factors had no moderating effect on the path coefficients.

Discussion

GPs' intention to utilize WEDs is affected by performance expectancy, price perception, perception risk and social influence. Researcher should improve the usability and perception of WEDs for screening and carry out studies to provide high-quality evidence for the security and efficacy of wearable devices.

Role of Heat Shock Proteins in Atrial Fibrillation: From Molecular Mechanisms to Diagnostic and Therapeutic Opportunities

Heat shock proteins (HSPs) are endogenous protective proteins and biomarkers of cell stress response, of which examples are HSP70, HSP60, HSP90, and small HSPs (HSPB). HSPs protect cells and organs, especially the cardiovascular system, against harmful and cytotoxic conditions. More recent attention has focused on the roles of HSPs in the irreversible remodeling of atrial fibrillation (AF), which is the most common arrhythmia in clinical practice and a significant contributor to mortality. In this review, we investigated the relationship between HSPs and atrial remodeling mechanisms in AF. PubMed was searched for studies using the terms "Heat Shock Proteins" and "Atrial Fibrillation" and their relevant abbreviations up to 10 July 2022. The results showed that HSPs have cytoprotective roles in atrial cardiomyocytes during AF by promoting reverse electrical and structural remodeling. Heat shock response (HSR) exhaustion, followed by low levels of HSPs, causes proteostasis derailment in cardiomyocytes, which is the basis of AF. Furthermore, potential implications of HSPs in the management of AF are discussed in detail. HSPs represent reliable biomarkers for predicting and staging AF. HSP inducers may serve as novel therapeutic modalities in postoperative AF. HSP induction, either by geranylgeranylacetone (GGA) or by other compounds presently in development, may therefore be an interesting new approach for upstream therapy for AF, a strategy that aims to prevent AF whilst minimizing the ventricular proarrhythmic risks of traditional anti-arrhythmic agents.

Agreement between two photoplethysmography-based wearable devices for monitoring heart rate during different physical activity situations: a new analysis methodology

Wearables are being increasingly used to monitor heart rate (HR). However, their usefulness for analyzing continuous HR in research or at clinical level is questionable. The aim of this study is to analyze the level of agreement between different wearables in the measurement of HR based on photoplethysmography, according to different body positions and physical activity levels, and compared to a gold-standard ECG. The proposed method measures agreement among several time scales since different wearables obtain HR at different sampling rates. Eighteen university students (10 men, 8 women; 22 ± 2.45 years old) participated in a laboratory study. Participants simultaneously wore an Apple Watch and a Polar Vantage watch. ECG was measured using a BIOPAC system. HR was recorded continuously and simultaneously by the three devices, for consecutive 5-min periods in 4 different situations: lying supine, sitting, standing and walking at 4 km/h on a treadmill. HR estimations were obtained with the maximum precision offered by the software of each device and compared by averaging in several time scales, since the wearables obtained HR at different sampling rates, although results are more detailed for 5 s and 30 s epochs. Bland–Altman (B-A) plots show that there is no noticeable difference between data from the ECG and any of the smartwatches while participants were lying down. In this position, the bias is low when averaging in both 5 s and 30 s. Differently, B-A plots show that there are differences when the situation involves some level of physical activity, especially for shorter epochs. That is, the discrepancy between devices and the ECG was greater when walking on the treadmill and during short time scales. The device showing the biggest discrepancy was the Polar Watch, and the one with the best results was the Apple Watch. We conclude that photoplethysmography-based wearable devices are suitable for monitoring HR averages at regular intervals, especially at rest, but their feasibility is debatable for a continuous analysis of HR for research or clinical purposes, especially when involving some level of physical activity. An important contribution of this work is a new methodology to synchronize and measure the agreement against a gold standard of two or more devices measuring HR at different and not necessarily even paces.

Prediction of atrial fibrillation using a home blood pressure monitor with a high-resolution system

Objective

The usefulness of screening for atrial fibrillation (AF) using several home blood pressure (BP) monitors has been reported. We evaluated the accuracy of a high-resolution system (HiRS) for AF prediction and its usefulness when installed in home BP monitors.

Methods

In patients with paroxysmal, persistent or permanent AF, ECG recording and BP measurements were performed simultaneously. The relationship between ECG rhythm diagnosis and pulse irregularity recognition, using a home BP monitor with HiRS, was investigated. The severity of a pulse disturbance during BP measurement was displayed as an irregular pulse rhythm symbol (IPRS) in three instances. The IPRS was not displayed if the pulse was regular, turned on if there was a weak variation in the pulse, and blinked if there was a strong variation in the pulse.

Results

One hundred and seven patients (44 paroxysmal AF, 63 persistent or permanent AF) were enrolled, and a total of 333 recordings were analysed. The rhythms recorded by each ECG were 73 sinus regular rhythms, 35 extrasystoles, 222 AFs and 3 atrial flutters. Sensitivity and specificity for the prediction of any arrhythmia by the IPRS display of the BP monitor were 95.8% (95% CI 92.6% to 97.6%) and 96.8% (95% CI 92.6% to 100%), respectively. In addition, sensitivity and specificity for the prediction of AF were 100% (95% CI 97.5% to 100%) and 74.8% (95% CI 65.6% to 82.5%), respectively. Sensitivity and specificity for the prediction of AF by the IPRS blinking display were 88.3% (95% CI 83.3% to 92.2%) and 94.6% (95% CI 88.6% to 98.0%%), respectively. IPRS exhibited lighting or blinking during AF occurrence; however, during sinus rhythm, IPRS was not displayed in 72 out of 73 recordings.

Conclusion

The IPRS device predicted AF with precision and may be particularly useful for predicting an arrhythmia attack in patients with paroxysmal AF.

Comprehensive metabolomic characterization of atrial fibrillation

Background

Using human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.

Objectives

By investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.

Methods

A cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.

Results

The metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8–90.2%.

Conclusions

Serum metabolomics is a powerful way to identify metabolic disturbances. Differences in small–molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.

Cardio-metabolic risk modeling and assessment through sensor-based measurements

OBJECTIVE

Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors.

METHODS

We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables.

RESULTS

Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%.

CONCLUSIONS

Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention.

Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study

Background

The detection of atrial fibrillation (AF) is a major clinical challenge as AF is often paroxysmal and asymptomatic. Novel mobile health (mHealth) technologies could provide a cost-effective and reliable solution for AF screening. However, many of these techniques have not been clinically validated.

Objective

The purpose of this study is to evaluate the feasibility and reliability of artificial intelligence (AI) arrhythmia analysis for AF detection with an mHealth patch device designed for personal well-being.

Methods

Patients (N=178) with an AF (n=79, 44%) or sinus rhythm (n=99, 56%) were recruited from the emergency care department. A single-lead, 24-hour, electrocardiogram-based heart rate variability (HRV) measurement was recorded with the mHealth patch device and analyzed with a novel AI arrhythmia analysis software. Simultaneously registered 3-lead electrocardiograms (Holter) served as the gold standard for the final rhythm diagnostics.

Results

Of the HRV data produced by the single-lead mHealth patch, 81.5% (3099/3802 hours) were interpretable, and the subject-based median for interpretable HRV data was 99% (25th percentile=77% and 75th percentile=100%). The AI arrhythmia detection algorithm detected AF correctly in all patients in the AF group and suggested the presence of AF in 5 patients in the control group, resulting in a subject-based AF detection accuracy of 97.2%, a sensitivity of 100%, and a specificity of 94.9%. The time-based AF detection accuracy, sensitivity, and specificity of the AI arrhythmia detection algorithm were 98.7%, 99.6%, and 98.0%, respectively.

Conclusions

The 24-hour HRV monitoring by the mHealth patch device enabled accurate automatic AF detection. Thus, the wearable mHealth patch device with AI arrhythmia analysis is a novel method for AF screening.

Trial Registration

ClinicalTrials.gov NCT03507335; https://clinicaltrials.gov/ct2/show/NCT03507335

An Overview of the Sensors for Heart Rate Monitoring Used in Extramural Applications

This work presents an overview of the main strategies that have been proposed for non-invasive monitoring of heart rate (HR) in extramural and home settings. We discuss three categories of sensing according to what physiological effect is used to measure the pulsatile activity of the heart, and we focus on an illustrative sensing modality for each of them. Therefore, electrocardiography, photoplethysmography, and mechanocardiography are presented as illustrative modalities to sense electrical activity, mechanical activity, and the peripheral effect of heart activity. In this paper, we describe the physical principles underlying the three categories and the characteristics of the different types of sensors that belong to each class, and we touch upon the most used software strategies that are currently adopted to effectively and reliably extract HR. In addition, we investigate the strengths and weaknesses of each category linked to the different applications in order to provide the reader with guidelines for selecting the most suitable solution according to the requirements and constraints of the application.

The Current State of Optical Sensors in Medical Wearables

Optical sensors play an increasingly important role in the development of medical diagnostic devices. They can be very widely used to measure the physiology of the human body. Optical methods include PPG, radiation, biochemical, and optical fiber sensors. Optical sensors offer excellent metrological properties, immunity to electromagnetic interference, electrical safety, simple miniaturization, the ability to capture volumes of nanometers, and non-invasive examination. In addition, they are cheap and resistant to water and corrosion. The use of optical sensors can bring better methods of continuous diagnostics in the comfort of the home and the development of telemedicine in the 21st century. This article offers a large overview of optical wearable methods and their modern use with an insight into the future years of technology in this field.

Supermarket/Hypermarket Opportunistic Screening for Atrial Fibrillation (SHOPS-AF): A Mixed Methods Feasibility Study Protocol

Aims: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a key risk factor for ischaemic stroke. Following AF detection, treatment with oral anticoagulation can significantly lower mortality and morbidity rates associated with this risk. The availability of several hand-held devices which can detect AF may enable trained health professionals to adopt AF screening approaches which do not interfere with people’s daily routines. This study aims to investigate the effectiveness of a hand-held device (the MyDiagnostick single-lead Electrocardiogram (ECG) sensor) in screening for AF when embedded into the handles of supermarket trolleys. Methods: A mixed methods two-phase approach will be taken. The quantitative first phase will involve the recruitment of 2000 participants from a convenience sample at four large supermarkets with pharmacies. Prospective participants will be asked to conduct their shopping using a trolley embedded with a MyDiagnostick sensor. If the device identifies a participant with AF, the in-store pharmacist will be dispatched to take a manual pulse measurement and a static control sensor reading and offer a cardiologist consultation referral. When the sensor does not detect AF, a researcher will confirm the reading with a manual pulse measurement. ECGs will be compiled, and the sensitivity, specificity and positive and negative predictive values will be determined. A qualitative second phase will consist of semi-structured interviews carried out with those pharmacists and store managers in-store during the running of the trial period. These will explore the perceptions of staff regarding the merits of embedding sensors in the handles of supermarket trolleys to detect AF. Conclusion: This feasibility study will inform a larger future definitive trial.

Use of a smartphone electrocardiogram to diagnose arrhythmias during exercise in athletes: a case series

Background

While athletes are generally very fit, intense exercise can increase the risk of atrial fibrillation. Moreover, other arrhythmias such as atrial flutter or supraventricular tachycardia can cause distressing, exercise-related symptoms. Given symptoms are infrequent and may occur during intense exertion, traditional monitoring devices are often impractical to use during exercise. Smartphone electrocardiograms (ECGs) such as the Alivecor Kardia device may be the portable and reliable tool required to help identify arrhythmias in this challenging population. This case series highlights the use of such devices in aiding the diagnosis of arrhythmias in the setting of exercise-related symptoms in athletes.

Case summary

The six cases in this series included one elite non-endurance athlete, two elite cricketers, one amateur middle-distance runner, and two semi-elite ultra-endurance runners, with an age range of 16-48 years. An accurate diagnosis of an arrhythmia was obtained in five cases (atrial fibrillation/flutter and supraventricular tachycardias) using the smartphone ECG, which helped guide definitive treatment. No arrhythmia was identified in the final case despite using the device during multiple symptomatic events.

Discussion

The smartphone ECG was able to accurately detect arrhythmias and provide a diagnosis in cases where traditional monitoring had not. The utility of detecting no arrhythmia during symptoms in one case was also highlighted, providing the athlete with the confidence to continue exercising. This reassurance and confidence across all cases is perhaps the most valuable aspect of this device, where clinicians and athletes can be more certain of reaching a diagnosis and undertaking appropriate management.

A framework for incorporating physical activity in treatment: Competencies, guidelines, and implications for family therapists

Physical activity can have a profound impact on the cognitive, emotional, and social aspects of our clients' lives, and can even boost and sustain therapy outcomes. And yet, family therapists may not be trained or educated in the importance of physical activity nor how to assess, educate, and collaborate with health professionals to incorporate physical activity in treatment. As the evidence grows on the connection between physical activity and biopsychosocial functioning and more family therapists enter medical and healthcare settings, a framework for physical activity competencies is needed. As such, the purpose of this conceptual and empirical review paper is to (a) review the important brain-boosting benefits of physical activity for mental, cognitive, and social health, (b) provide a framework for the domains for family therapists for incorporating exercise in assessment and treatment, and finally, (c) outline recommendations for practice, education and supervision, and research for family therapists.

Will Smartphone Applications Replace the Insertable Cardiac Monitor in the Detection of Atrial Fibrillation? The First Comparison in a Case Report of a Cryptogenic Stroke Patient

Background and Case

This case report exemplifies the clinical application of non-invasive photoplethysmography (PPG)-based rhythm monitoring in the awakening mobile health (mHealth) era to detect symptomatic and asymptomatic paroxysmal atrial fibrillation (AF) in a cryptogenic stroke patient. Despite extensive diagnostic workup, the etiology remains unknown in one out of three ischemic strokes (i.e., cryptogenic stroke). Prolonged cardiac monitoring can reveal asymptomatic atrial fibrillation in up to one-third of this population. This case report describes a cryptogenic stroke patient who received prolonged cardiac monitoring with an insertable cardiac monitor (ICM) as standard of care. In the context of a clinical study, the patient simultaneously monitored his heart rhythm with a PPG-based smartphone application. AF was detected simultaneously on both the ICM and smartphone application after three days of monitoring. Similar AF burden was detected during follow-up (five episodes, median duration of 28 and 34 h on ICM and mHealth, respectively, p = 0.5). The detection prompted the initiation of oral anticoagulation and AF catheter ablation procedure.

Conclusion

This is the first report of the cryptogenic stroke patient in whom PPG-based mHealth was able to detect occurrence and burden of the symptomatic and asymptomatic paroxysmal AF episodes with similar precision as ICM. It accentuates the potential role of PPG-based mHealth in prolonged cardiac rhythm monitoring in cryptogenic stroke patients.

Smartphone and new tools for Atrial Fibrillation diagnosis: evidence for clinical applicability

Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia in adults. AF increases the risk of heart failure, cardiac ischemic disease, dementia and Alzheimer's disease. Either clinical and subclinical AF increase the risk of stroke and worsen the patients' clinical outcome. The early diagnosis of AF episodes, even if asymptomatic or clinically silent, is of pivotal importance to ensure prompt and adequate thromboembolic risk prevention therapies. The development of technology is allowing new systematic mass screening possibilities, especially in patients with higher stroke risk. The mobile health devices available for AF detection are: smartphones, wristworn, earlobe sensors and handheld ECG. These devices showed a high accuracy in AF detection especially when a combined approach with single-Lead ECG and photoplethysmography algorithms is used. The use of wearable devices for AF screening is a feasible method but more head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness across different study populations.

Monitoring prolongation of QT interval in patients with multidrug-resistant tuberculosis and non-tuberculous mycobacterium using mobile health device AliveCor

Multidrug resistant tuberculosis and non-tuberculous mycobacterium infections present challenges due to complex treatment regimens. Extended treatment regimes expose patients to higher risks of toxic side-effects. A high drug toxicity profile necessitates closer monitoring. One of the more challenging issues is QTc prolongation with non-injectable regimens. This study investigates the portable AliveCor device to record and measure the QTc on a 6-lead ECG. An automated QTc readout from 12-Lead ECG for each patient (n = 13) and mean QTc value calculated from each patients' respective AliveCor tracing were compared. The general trend suggests AliveCor underestimates QTc - 92% cases calculated the AliveCor QTc as lower than their corresponding 12-Lead QTc readout. The use of AliveCor could potentially be translated into current clinical practice with caution of percentage variation either side. This could facilitate the use of AliveCor as a promising and convenient screening tool before further evaluation by a 12-Lead ECG is required.

NICE atrial fibrillation guideline snubs wearable technology: a missed opportunity?

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a growing public health epidemic. In the UK, over 1.3 million people have a diagnosis of AF and an estimated 400,000 remain undiagnosed. AF-related strokes account for a quarter of all strokes and, as AF episodes are often asymptomatic, are still often the first manifestation of AF. Early diagnosis and initiation of oral anticoagulation, where appropriate, may prevent some of these thromboembolic strokes. Public Health England is committed to decrease the incidence of AF-related strokes and has sponsored initiatives aimed at improving AF detection by promoting the uptake of wearable technologies. However, the National Institute for Health and Care Excellence (NICE) has not recommended wearable technology in their recent AF diagnosis and management guidelines (NG196). Diagnostic accuracy of single-lead electrocardiography (ECG) generated by the latest iteration of wearable devices is excellent and, in many cases, superior to general practitioner interpretation of the 12-lead ECG. High-quality ECG from wearable devices that unequivocally shows AF can expedite AF detection. Otherwise, there is a real risk of delaying AF diagnosis with the potential of devastating consequences for patients and their families.

Relationship Between Heart Rate Variability and Pulse Rate Variability Measures in Patients After Coronary Artery Bypass Graft Surgery

Background: Heart rate variability (HRV) and pulse rate variability (PRV) measures are two kinds of physiological indices that can be used to evaluate the autonomic nervous function of healthy subjects and patients with various kinds of illness.

Purpose: In this study, we compared the agreement and linear relationship between electrocardiographic signals (ECG)-derived HRV and photoplethysmographic signals (PPG)-derived right hand PRV (R-PRV) and left hand PRV (L-PRV) measures in 14 patients over 1 year after coronary artery bypass graft (CABG) surgery.

Method: The ECG and PPG signals of the patient were recorded simultaneously for 10 min in a supine position. The last 512 stationary RR intervals (RRI) and peak-to peak intervals (PPI) of pulse wave were derived for data analysis. Bland-Altman plot was used to assess the agreement among HRV and both hand PRV measures, while linear regression analysis was used to examine the relationship among corresponding measures of HRV, R-PRV, and L-PRV.

Result: The results revealed significant differences in total power (TP), very low-frequency power (VLF), low-frequency power (LF), high-frequency power (HF), and normalized VLF (VLFnorm) among HRV, R-PRV, and L-PRV. Bland-Altman plot analysis showed good agreements in almost all measures between R-PRV and L-PRV, except insufficient agreement was found in LF/HF. Insufficient agreements were found in root mean square successive difference (RMSSD), normalized HF (HFnorm), and LF/HF indices between HRV and L-PRV, and in VLFnorm, HFnorm, and LF/HF indices between HRV and R-PRV. Linear regression analysis showed that the HRV, R-PRV, and L-PRV measures were all highly correlated with one another (r = 0.94 ~ 1; p < 0.001).

Conclusion: Though PRV measures of either hand are not surrogates of HRV measures, they might still be used to evaluate the autonomic nervous functions of CABG patients due to the moderate to good agreements in most time-domain and frequency-domain HRV measures and the strong and positive correlations among HRV and both hands PRV measures in CABG patients.

Screening for Atrial Fibrillation in Sub-Saharan Africa: A Health Economic Evaluation to Assess the Feasibility in Nigeria

Background:

Cardiovascular disease reflects a major burden of non-communicable disease in Sub-Saharan Africa (SSA). Early detection and treatment of atrial fibrillation (AF), as a preventive measure against stroke, is currently not in the scope of the World Health Organization recommendation to reduce cardiovascular disease.

Objective:

We hypothesized that screening for AF would be an important approach to determine the true AF prevalence in the general population in African countries and to identify asymptomatic AF patients at risk for stroke to optimize prevention.

Methods:

A decision analytic model was developed to study the health-economic impact of AF screening in Nigeria over a life-time horizon. The patient population explored in the model was a population of newly detected AF cases that would be diagnosed with a one-time systematic screening for AF with a single lead ECG device in community health centres across Nigeria.

Conclusions:

The health gain per newly detected AF patient (N = 31,687) was 0.41 QALY at a cost of $5,205 per patient with 100% NOAC use, leading to an ICER of $12,587 per QALY gained. The intervention was cost-effective with a 99.9% warfarin use with an ICER of $1,363 per QALY gained. The total cost of a single screening session was $7.3 million for the total screened population in Nigeria or $1.60 per patient screened. Screening for AF to detect AF patients in need for stroke prevention can be a cost-effective intervention in the Sub-Saharan region, depending on type of anticoagulant used and drug costs.

Monitoring Physical Activity Intensity During Pregnancy

For apparently healthy pregnant women, regular physical activity is recommended. The American College of Obstetricians and Gynecologists (ACOG) created recommendations for physical activity and exercise during pregnancy in 1985. At that time, pregnant women were advised to not exceed a heart rate of 140 beats per minute with physical activity. The heart rate recommendation was subsequently removed with the recommendations published in 1994, 2002, and 2015. In 2020, the ACOG updated its recommendations on physical activity for pregnant and postpartum women. The recommendation included exercising at a "fairly light to somewhat hard" perceived intensity and at less than 60-80% of age-predicted maximum heart rate, usually not exceeding a heart rate of 140 beats per minute. Women often seek advice from healthcare providers on physical activity during pregnancy, yet providers report concern about giving appropriate physical activity guidance. This paper summarizes the key scientific literature on monitoring absolute and relative exercise intensity in relation to the current ACOG recommendations, providing background on intensity-related concepts used in the recommendation. This paper also provides practical guidance to assist healthcare providers in relaying this information to pregnant women.

Immersive Virtual Reality Influences Physiologic Responses to Submaximal Exercise: A Randomized, Crossover Trial

Objectives: This cross-sectional, randomly assigned study aimed to assess the influence of immersive virtual reality (VR) on exercise tolerance expressed as the duration of a submaximal exercise test (ET) on a cycle ergometer. Methods: The study enrolled 70 healthy volunteers aged 22-25years. Each participant performed an ET with and without VR. Time- and frequency-domain heart rate variability (HRV) parameters were analyzed for the first 3min (T1), the last 3min (T2), and the time at which the shorter of the two tests terminated (Tiso). In the time domain, a SD of R-R intervals (SDNN) and a root mean square of successive R-R interval differences (RMSSD) in milliseconds were computed. The following spectral components were considered: low frequency (LF), high frequency (HF), total power (TP), and LF/HF ratio. The study was registered in ClinicalTrials.gov (NCT04197024). Results: Compared to standard ET, tests in immersive VR lasted significantly longer (694 vs. 591s, p<0.00001) and were associated with lower HR response across the range of corresponding exercise levels, averaging 5-8 beats/min. In the multiple regression analysis, the ET duration was positively determined by male sex, immersion in VR, and negatively determined by HRT1 and RMSSDT1. Conclusion: Exercising in VR is associated with lower HR which allowed subjects to exercise for a longer time before reaching target heart rate (HR). In addition, the increase in exercise duration was found to be related to an adjustment in autonomic nervous activity at a given work rate favoring parasympathetic predominance.

Comparison of novel wearable cardiac monitors to yield accurate exercising ECG and heart rate data in horses

Exercise associated deaths (EADs) in horses are a problem for the equestrian industry. Sudden death (SD) is responsible for approximately 20% of EADs. The underlying cause of SD is suspected to be cardiovascular disease but often cannot be determined post-mortem. User-friendly cardiac monitors are needed for large scale investigations of arrhythmias associated with SD in horses. We hypothesised that novel wearable devices would provide exercising electrocardiograms (ECGs) of sufficient diagnostic quality for this purpose. Diagnostic quality of ECGs generated by two wearable devices (W2nd™ and Polar Equine™) were compared to simultaneous recordings with a telemetry unit (Televet™) in 5 Thoroughbreds completing 43 separate submaximal exercise tests on a high-speed treadmill. Maximal heart rate (HRmax) generated by mobile applications (HRmaxapp), HRmax after manual correction (HRmaxcorr), percentage of diagnostic ECGs (%diag) at the gallop, and overall quality assessed by visual analogue scale (VAS) were assessed by a blinded observer. HRmaxcorr did not differ significantly between groups. HRmaxapp was significantly lower for W2nd (166.8/min, 95% confidence interval (CI): 160.5-173.1/min) but did not differ significantly between Televet (178.8/min 95% CI: 165.8-191.1/min) and Polar (181.3/min, 95% CI: 174.5-188.1/min). HRmaxcorr was accurate and precise in all runs. HRmaxapp was within a priori limits of agreement in 16/23 W2nd and 18/19 Polar recordings. %diag was significantly lower (77.1%, 95% CI: 67.4-86.8) for W2nd than Polar (100%, 95% CI: 89.9-110.3). VAS was lower for W2nd (46.2, 95% CI: 35.5-57.0) than Polar (90.6, 25% CI: 79.4-101.9). In conclusion, wearable devices appear to be promising tools for investigation of equine exercising arrhythmias in large-scale studies.

Review of the 2020 ESC Guidelines for the Diagnosis and Management of Atrial Fibrillation-What Has Changed and How Does This Affect Daily Practice

The high prevalence of atrial fibrillation (AF) in the overall population and its association with substantial morbidity, increased mortality and health care cost has instigated significant basic and clinical research efforts over recent years. The publication of multiple new high-quality randomized multi-center trials in the area of AF management and the rapidly evolving technological progress in terms of diagnostic possibilities and catheter ablation in recent years demanded a revision of the previous ESC AF Guidelines from 2016. The 2020 guidelines provide up-to-date, evidence-based guidance for the management of AF. One of the most important innovations is the presentation of a new concept for structural characterization of AF (the “4S AF scheme”) replacing the traditional classification based on its temporal pattern alone (paroxysmal-persistent-permanent). The 4S-AF-scheme highlights the importance of systematic assessment of stroke risk, severity of symptoms, total AF burden and underlying substrate as the foundation for effective and individualized AF treatment for each and every patient. Further novelties relate to the presentation of an easy and intuitive management pathway (“ABC pathway”) and strengthening the recommendations for early rhythm control, in particular the role of first line catheter ablation in heart failure. Another core component of the guidelines is the focus on patient involvement to achieve optimal outcomes. Patient education, shared decision making and incorporation of patient values and patient reported outcome of treatment interventions as well as integrated care by a multidisciplinary team all have a central role in the proposed management pathway for AF.

Digital health in stroke medicine: what are the opportunities for stroke patients?

PURPOSE OF REVIEW

The European Stroke Organisation published a European Stroke Action Plan (SAP-E) for the years 2018-2030. The SAP-E addresses the entire chain of care from primary prevention through to life after stroke. Within this document digital health tools are suggested for their potential to facilitate greater access to stroke care. In this review, we searched for digital health solutions for every domain of the SAP-E.

RECENT FINDINGS

Currently available digital health solutions for the cerebrovascular disease have been designed to support professionals and patients in healthcare settings at all stages. Telemedicine in acute settings has notably increased the access to tissue plasminogen activator and thrombectomy whereas in poststroke settings it has improved access to rehabilitation. Moreover, numerous applications aim to monitor vital signs and prescribed treatment adherence.

SUMMARY

SAP-E with its seven domains covers the whole continuum of stroke care, where digital health solutions have been considered to provide utility at a low cost. These technologies are progressively being used in all phases of stroke care, allowing them to overcome geographical and organizational barriers. The commercially available applications may also be used by patients and their careers in various context to facilitate accessibility to health improvement strategies.

Inhibition of miR-214-3p Protects Endothelial Cells from ox-LDL-Induced Damage by Targeting GPX4

It is generally believed that excessive production of reactive oxygen species (ROS) during cardiovascular diseases impairs endothelial function. In this study, we aimed to investigate whether miR-214-3p is involved in the endothelial dysfunction induced by oxidized low-density lipoprotein (ox-LDL). In cultured vascular endothelial cells (VECs), the effects of miR-214-3p on endothelial injury induced by 100 mg/L ox-LDL were evaluated by knockdown of miR-214-3p. Western blotting was used to determine the expression of glutathione peroxidase 4 (GPX4) and endothelial nitric oxide synthase (eNOS) in VECs under different conditions. A luciferase reporter assay was used to identify GPX4 as the target of miR-214-3p. Our data showed that 100 mg/L ox-LDL significantly decreased the expression of GPX4 and eNOS, which was associated with increases in ROS levels and impairments of VEC viability and migration. Knockdown of miR-214-3p could partially reduce the increase in ROS, restore the decreased expression of GPX4 and eNOS, and thus rescue the impaired endothelial function caused by ox-LDL. Our data demonstrated that ox-LDL could induce upregulation of miR-214-3p and result in suppression of GPX4 in VECs. Downregulation of miR-214-3p could protect VECs from ROS-induced endothelial dysfunction by reversing its inhibitory effect on GPX4 expression.

New Technologies for Detection and Management of Atrial Fibrillation

Atrial fibrillation (AF) is a common and prevalent form of arrhythmia. It is associated with various morbidities with stroke being the major hazard. Since AF is often reported to be asymptomatic, many individuals remain unaware of their condition and may not receive the requisite treatment. Hence, screening for AF has gained substantial attention recently. Growing advancement in technology has paved way for numerous approaches for AF screening using medical-prescribed devices as well as consumer electronic devices. However, there still lies scope for large-scale randomized trials which would explore additional aspects associated with AF. This review very concisely summarizes AF, screening, present technology, current literature and clinical studies associated with it.

Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs

Automatic detection of abnormal heart rhythms, including atrial fibrillation (AF), using signals obtained from a single-lead wearable electrocardiogram (ECG) device, is useful for daily cardiac health monitoring. In this study, we propose a novel image-based deep learning framework to classify single-lead ECG recordings of short variable length into several different rhythms associated with arrhythmias. By transforming variable-length 1D ECG signals into fixed-size 2D time-morphology representations and feeding them to the beat-interval-texture convolutional neural network (BIT-CNN) model, we aimed to learn the comprehensible characteristics of beat shape and inter-beat patterns over time for arrhythmia classification. The proposed approach allows feature embedding vectors to provide interpretable time-morphology patterns focused at each step of the learning process. In addition, this method reduces the number of model parameters needed to be trained and aids visual interpretation, while maintaining similar performance to other CNN-based approaches to arrhythmia classification. For experiments, we used the PhysioNet/CinC Challenge 2017 dataset and achieved an overall F_{1_NAO} of 81.75% and F_{1_NAOP} of 76.87%, which are comparable to those of the state-of-the-art methods for variable-length ECGs.

Digital health solutions in the screening of subclinical atrial fibrillation

Atrial fibrillation (AF) will become one of the biggest challenges in cardiovascular medicine in the near future. Attempting an improvement in future patient care calls explicitly for the screening of subclinical AF. Digital health solutions implementing communication technologies for the collection and analysis of digitally assessable data will most likely serve this need. Several new rapidly developing methods were introduced in the past decade. Although the vast majority still require scientific validation, the body of evidence is growing and several randomized controlled trials are planned. This review aims to give an overview of current technologies with a specific focus on mobile health (mHealth) and appraise their value with regard to the available scientific data.

Analysis of Gender Differences in HRV of Patients with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Mobile-Health Technology

In a previous study using mobile-health technology (mHealth), we reported a robust association between chronic fatigue symptoms and heart rate variability (HRV) in female patients with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). This study explores HRV analysis as an objective, non-invasive and easy-to-apply marker of ME/CFS using mHealth technology, and evaluates differential gender effects on HRV and ME/CFS core symptoms. In our methodology, participants included 77 ME/CFS patients (32 men and 45 women) and 44 age-matched healthy controls (19 men and 25 women), all self-reporting subjective scores for fatigue, sleep quality, anxiety, and depression, and neurovegetative symptoms of autonomic dysfunction. The inter-beat cardiac intervals are continuously monitored/recorded over three 5-min periods, and HRV is analyzed using a custom-made application (iOS) on a mobile device connected via Bluetooth to a wearable cardiac chest band. Male ME/CFS patients show increased scores compared with control men in all symptoms and scores of fatigue, and autonomic dysfunction, as with women in the first study. No differences in any HRV parameter appear between male ME/CFS patients and controls, in contrast to our findings in women. However, we have found negative correlations of ME/CFS symptomatology with cardiac variability (SDNN, RMSSD, pNN50, LF) in men. We have also found a significant relationship between fatigue symptomatology and HRV parameters in ME/CFS patients, but not in healthy control men. Gender effects appear in HF, LF/HF, and HFnu HRV parameters. A MANOVA analysis shows differential gender effects depending on the experimental condition in autonomic dysfunction symptoms and HF and HFnu HRV parameters. A decreased HRV pattern in ME/CFS women compared to ME/CFS men may reflect a sex-related cardiac autonomic dysfunction in ME/CFS illness that could be used as a predictive marker of disease progression. In conclusion, we show that HRV analysis using mHealth technology is an objective, non-invasive tool that can be useful for clinical prediction of fatigue severity, especially in women with ME/CFS.

Remote Heart Rhythm Monitoring by Photoplethysmography-Based Smartphone Technology After Cardiac Surgery: Prospective Observational Study

BACKGROUND

Atrial fibrillation (AF) is the most common arrhythmia after cardiac surgery, yet the precise incidence and significance of arrhythmias after discharge home need to be better defined. Photoplethysmography (PPG)-based smartphone apps are promising tools to enable early detection and follow-up of arrhythmias.

OBJECTIVE

By using a PPG-based smartphone app, we aimed to gain more insight into the prevalence of AF and other rhythm-related complications upon discharge home after cardiac surgery and evaluate the implementation of this app into routine clinical care.

METHODS

In this prospective, single-center trial, patients recovering from cardiac surgery were asked to register their heart rhythm 3 times daily using a Food and Drug Administration-approved PPG-based app, for either 30 or 60 days after discharge home. Patients with permanent AF or a permanent pacemaker were excluded.

RESULTS

We included 24 patients (mean age 60.2 years, SD 12 years; 15/23, 65% male) who underwent coronary artery bypass grafting and/or valve surgery. During hospitalization, 39% (9/23) experienced postoperative AF. After discharge, the PPG app reported AF or atrial flutter in 5 patients. While the app notified flutter in 1 patient, this was a false positive, as electrocardiogram revealed a 2nd-degree, 2:1 atrioventricular block necessitating a permanent pacemaker. AF was confirmed in 4 patients (4/23, 17%) and interestingly, was associated with an underlying postoperative complication in 2 participants (pneumonia n=1, pericardial tamponade n=1). A significant increase in the proportion of measurements indicating sinus rhythm was observed when comparing the first to the second month of follow-up (P<.001). In the second month of follow-up, compliance was significantly lower with 2.2 (SD 0.7) measurements per day versus 3.0 (SD 0.8) measurements per day in the first month (P=.002). The majority of participants (17/23, 74%), as well as the surveyed primary care physicians, experienced positive value by using the app as they felt more involved in the postoperative rehabilitation.

CONCLUSIONS

Implementation of smartphone-based PPG technology enables detection of AF and other rhythm-related complications after cardiac surgery. An association between AF detection and an underlying complication was found in 2 patients. Therefore, smartphone-based PPG technology may supplement rehabilitation after cardiac surgery by acting as a sentinel for underlying complications, rhythm-related or otherwise.

Smartphone applications managing antithrombotic therapy: a scoping literature review (Preprint)

Background

Antithrombotic therapy is complex and requires informed decisions and high therapy adherence. Several mobile phone apps exist to either support physicians in the management of antithrombotic therapies or to educate and support patients. For the majority of these apps, both their medical evidence and their development background are unknown.

Objective

This review aims to investigate the available literature describing high-quality apps for managing antithrombotic therapy based on professional scientific information.

Methods

Keywords and Medical Subject Heading terms were used to search MEDLINE via PubMed and Ovid between December 2019 and January 2022. Inclusion criteria were the availability of full text and publications in the English language. Apps that solely focused on atrial fibrillation were excluded. Qualitative findings were thematically synthesized and reported narratively.

Results

Out of 149 identified records, 32 were classified as eligible. We identified four groups: (1) apps for patients supporting self-management of vitamin K antagonists, (2) apps for patients increasing therapy adherence, (3) educational apps for patients, and (4) apps for physicians in supporting guideline adherence.

Conclusions

Throughout the evaluated data, patients from all age groups receiving antithrombotic drugs expressed the desire for a digital tool that could support their therapy management. In addition, physicians using mobile guideline-based apps may have contributed to decreased adverse event rates among their patients. In general, digital apps encompassing both user-friendly designs and scientific backgrounds may enhance the safety of antithrombotic therapies. However, our evaluation did not identify any apps that addressed all antithrombotic drugs in combination with perioperative stratification strategies. Currently, strict regulations for smartphone apps seem to negatively affect the development of new apps. Therefore, new legal policies for medical digital apps are urgently needed.

A Heart Rate Monitoring App (FibriCheck) for Atrial Fibrillation in General Practice: Pilot Usability Study

Background

Atrial fibrillation (AF) is a major risk factor for stroke. The current opportunistic screening procedure consists of pulse palpation and an electrocardiogram when an irregular rhythm is found. Smartphone apps that measure heart rhythm could be useful in increasing the detection of AF in a primary care setting.

Objective

We conducted a pilot study with the smartphone app FibriCheck to assess whether the introduction of such an app is feasible.

Methods

Four general practices across Flanders provided patient data for the study. Inclusion criteria for participants were aged 65 or older and a CHARGE-AF score of at least 10%. We excluded patients with known AF or a pacemaker. Participants were asked to measure at least twice a day with FibriCheck (for at least 14 days). They were provided the 36-Item Short Form Survey (SF-36) questionnaire both before and after the study, as well as different surveys concerning their user experience and general perception of technology.

Results

There were 92 participants (36 women and 56 men). The study population was relatively homogenous concerning risk factors and medication use at baseline. During the study period, 5/86 (6%) participants were found to have AF (6 dropouts). The average study period was 23 days and the average number of measurements per day was 2.1. Patient compliance was variable, but high. On the whole, there were no appreciable changes in quality of life. The overall user experience and satisfaction were very high.

Conclusions

FibriCheck is a relatively easy-to-use smartphone app to complement AF screening in primary care. Its implementation in this setting is certainly achievable, and one can expect high rates of patient compliance. Based on these results, a planned cluster randomized trial will be going ahead.

Trial Registration

ClinicalTrials.gov NCT03509493; https://clinicaltrials.gov/ct2/show/NCT03509493