Dataset Information

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

ABSTRACT:

Background

Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.

Objective

This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research.

Methods

We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV).

Results

The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF.

Conclusions

A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.

SUBMITTER: Li KHC

PROVIDER: S-EPMC6396075 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Li Ka Hou Christien KHC White Francesca Anne FA Tipoe Timothy T Liu Tong T Wong Martin Cs MC Jesuthasan Aaron A Baranchuk Adrian A Tse Gary G Yan Bryan P BP

JMIR mHealth and uHealth 20190215 2

<h4>Background</h4>Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband.<h4>Objective</h4>This review seeks to explore the current state of mobile phone apps in cardiac rhythmol ...[more]

PMID: 30767904

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Project description:BackgroundAtrial fibrillation is the most common recurrent arrhythmia in clinical practice, with most clinical events occurring outside the hospital. Low detection and nonadherence to guidelines are the primary obstacles to atrial fibrillation management. Photoplethysmography is a novel technology developed for atrial fibrillation screening. However, there has been limited validation of photoplethysmography-based smart devices for the detection of atrial fibrillation and its underlying clinical factors impacting detection.ObjectiveThis study aimed to explore the feasibility of photoplethysmography-based smart devices for the detection of atrial fibrillation in real-world settings.MethodsSubjects aged ≥18 years (n=361) were recruited from September 14 to October 16, 2018, for screening of atrial fibrillation with active measurement, initiated by the users, using photoplethysmography-based smart wearable devices (ie, a smart band or smart watches). Of these, 200 subjects were also automatically and periodically monitored for 14 days with a smart band. The baseline diagnosis of "suspected" atrial fibrillation was confirmed by electrocardiogram and physical examination. The sensitivity and accuracy of photoplethysmography-based smart devices for monitoring atrial fibrillation were evaluated.ResultsA total of 2353 active measurement signals and 23,864 periodic measurement signals were recorded. Eleven subjects were confirmed to have persistent atrial fibrillation, and 20 were confirmed to have paroxysmal atrial fibrillation. Smart devices demonstrated >91% predictive ability for atrial fibrillation. The sensitivity and specificity of devices in detecting atrial fibrillation among active recording of the 361 subjects were 100% and about 99%, respectively. For subjects with persistent atrial fibrillation, 127 (97.0%) active measurements and 2240 (99.2%) periodic measurements were identified as atrial fibrillation by the algorithm. For subjects with paroxysmal atrial fibrillation, 36 (17%) active measurements and 717 (19.8%) periodic measurements were identified as atrial fibrillation by the algorithm. All persistent atrial fibrillation cases could be detected as "atrial fibrillation episodes" by the photoplethysmography algorithm on the first monitoring day, while 14 (70%) patients with paroxysmal atrial fibrillation demonstrated "atrial fibrillation episodes" within the first 6 days. The average time to detect paroxysmal atrial fibrillation was 2 days (interquartile range: 1.25-5.75) by active measurement and 1 day (interquartile range: 1.00-2.00) by periodic measurement (P=.10). The first detection time of atrial fibrillation burden of <50% per 24 hours was 4 days by active measurement and 2 days by periodic measurementThe first detection time of atrial fibrillation burden of >50% per 24 hours was 1 day for both active and periodic measurements (active measurement: P=.02, periodic measurement: P=.03).ConclusionsPhotoplethysmography-based smart devices demonstrated good atrial fibrillation predictive ability in both active and periodic measurements. However, atrial fibrillation type could impact detection, resulting in increased monitoring time.Trial registrationChinese Clinical Trial Registry of the International Clinical Trials Registry Platform of the World Health Organization ChiCTR-OOC-17014138; http://www.chictr.org.cn/showprojen.aspx?proj=24191.

Project description:BackgroundWith an explosive growth in mobile health, an estimated 500 million patients are potentially using mHealth apps for supporting health and self-care of chronic diseases. Therefore, this review focused on mHealth apps for use among patients with heart failure.ObjectiveThe aim of this integrative review was to identify and assess the functionalities of mHealth apps that provided usability and efficacy data and apps that are commercially available without supporting data, all of which are to support heart failure self-care management and thus impact heart failure outcomes.MethodsA search of published, peer-reviewed literature was conducted for studies of technology-based interventions that used mHealth apps specific for heart failure. The initial database search yielded 8597 citations. After filters for English language and heart failure, the final 487 abstracts was reviewed. After removing duplicates, a total of 18 articles that tested usability and efficacy of mobile apps for heart failure self-management were included for review. Google Play and Apple App Store were searched with specified criteria to identify mHealth apps for heart failure. A total of 26 commercially available apps specific for heart failure were identified and rated using the validated Mobile Application Rating Scale.ResultsThe review included studies with low-quality design and sample sizes ranging from 7 to 165 with a total sample size of 847 participants from all 18 studies. Nine studies assessed usability of the newly developed mobile health system. Six of the studies included are randomized controlled trials, and 4 studies are pilot randomized controlled trials with sample sizes of fewer than 40. There were inconsistencies in the self-care components tested, increasing bias. Thus, risk of bias was assessed using the Cochrane Collaboration's tool for risk of selection, performance, detection, attrition, and reporting biases. Most studies included in this review are underpowered and had high risk of bias across all categories. Three studies failed to provide enough information to allow for a complete assessment of bias, and thus had unknown or unclear risk of bias. This review on the commercially available apps demonstrated many incomplete apps, many apps with bugs, and several apps with low quality.ConclusionsThe heterogeneity of study design, sample size, intervention components, and outcomes measured precluded the performance of a systematic review or meta-analysis, thus introducing bias of this review. Although the heart failure-related outcomes reported in this review vary, they demonstrated trends toward making an impact and offer a potentially cost-effective solution with 24/7 access to symptom monitoring as a point of care solution, promoting patient engagement in their own home care.

Project description:AimsThe analysis of heart rate (HR) changes, such as the HR variability or HR turbulence, has been reported as a marker of cardiovascular events during sinus rhythm; however, those relationships during atrial fibrillation (AF) remain controversial, and those parameters are not commonly used in AF patients. We sought to investigate the relationship between a simple index focused on the HR and heart failure (HF) events in patients with permanent AF.Methods and resultsWe enrolled 198 patients with permanent AF and evaluated the HR range, defined as the maximum HR minus the minimum HR on 24-h Holter electrocardiogram recordings. The patients were divided into two groups, i.e., the larger (n = 101) and smaller (n = 97) HR range (HRR) groups, determined by the median value. The HF events were defined as hospitalizations for HF or urgent hospital visits due to exacerbations of one's HF status. The observation period of this study was set at 5 years from registration. The median age was 73 (68-77) years, and 29% were female. The median HRR was 84 (63-118) beats per minutes (bpm). During the observational period of 1825 days (median), HF events occurred in 37 (0.047 per patient-year) patients. In a log-rank test, the larger HRR group had more frequent HF events than the smaller HRR group (P = 0.0078). In the adjusted Cox proportional hazards model using the significantly different factors from the univariate analysis (Model 1) and factors and medications associated with HF (Model 2), the larger HRR group had a higher prevalence of HF events than the smaller HRR group for both models [Model 1, adjusted hazard ratio = 3.21, 95% confidence interval (CI) 1.593-6.708, P = 0.0009; Model 2, adjusted hazard ratio = 3.12, 95% CI 1.522-6.685, P = 0.002]. When analysed using the time-dependent Cox proportional hazards model, the HRR was associated with HF with a statistically significant difference in both the univariate and multivariate analyses [hazard ratio = 1.01, 95% CI 1.006-1.020, P = 0.0002; Model 1, adjusted hazard ratio = 1.02, 95% CI 1.011-1.027, P < 0.0001; Model 2, adjusted hazard ratio = 1.01, 95% CI 1.008-1.021, P = 0.0003). There was no significant difference in the chronotropic medications between the two groups.ConclusionsIn patients with permanent AF, a larger HRR was associated with HF events.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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