European Journal of Cardiovascular Medicine

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality globally, accounting for an estimated 17.9 million deaths annually, representing 32% of all global deaths [1]. Among these, cardiac arrhythmias—particularly atrial fibrillation (AF)—pose a significant public health burden due to their association with increased risk of stroke, heart failure, and sudden cardiac death [2]. Early and accurate detection of arrhythmias is critical in reducing adverse outcomes, optimizing therapy, and enhancing quality of life. However, the intermittent and asymptomatic nature of many arrhythmias presents diagnostic challenges with conventional modalities such as Holter monitors or in-hospital ECGs.

Recent advancements in mobile health (mHealth) and wearable technology have introduced novel tools capable of continuous, non-invasive cardiac rhythm monitoring. These wearable devices, including smartwatches, fitness trackers, and dedicated ECG patches, have revolutionized ambulatory cardiac surveillance, enabling real-time data acquisition, cloud-based analysis, and remote physician notification [3]. Leveraging photoplethysmography (PPG), single- or multi-lead ECG sensors, and AI-enhanced algorithms, these devices offer promise in detecting arrhythmic episodes outside clinical settings—where they often occur.

The Apple Heart Study by Perez et al. was one of the earliest large-scale validations of wearable arrhythmia detection. Conducted on over 400,000 participants using an Apple Watch, the study demonstrated that 84% of participants who received an irregular pulse notification were later confirmed to have atrial fibrillation on ECG patch monitoring [4]. This landmark study established the potential of wearable PPG-based devices as scalable, accessible tools for mass screening. Since then, a growing number of consumer-grade and clinical-grade wearables have entered the market with FDA clearance or CE marking for arrhythmia detection, including devices from Fitbit, Withings, AliveCor KardiaMobile, and Biobeat [5].

Despite the proliferation of such technologies, key concerns remain about their diagnostic accuracy, clinical utility, false-positive rates, and patient compliance. For instance, while single-lead wearable ECGs demonstrate high sensitivity (91–98%) for AF detection, specificity may vary based on movement artifacts, skin tone, and user engagement [6]. Moreover, many devices rely on user activation to capture events, which may be suboptimal for asymptomatic episodes. This has led to exploration of automated passive monitoring strategies, continuous patch-based ECGs, and algorithmic optimization through machine learning [7].

Furthermore, the integration of wearable device data into clinical decision-making workflows and electronic health records (EHRs) is still evolving. Studies evaluating the impact of such integration on patient outcomes, medication adjustments, anticoagulation decisions, or hospitalization rates are limited but growing. A randomized trial by Gladstone et al. (2021) demonstrated that a structured screening program using wearable ECG monitors significantly increased AF detection compared to usual care in older adults with hypertension [8].

Patient-managed care is another emerging paradigm fueled by wearable technologies. Empowered with real-time health insights, patients are increasingly participating in self-monitoring, lifestyle changes, and early medical consultation. Wearables have shown promise in improving patient adherence, especially among digitally literate populations. However, disparities in digital health literacy, cost barriers, and data privacy concerns must be addressed to ensure equitable adoption [9].

Given the rapid pace of innovation and heterogeneity in device design, algorithms, and validation protocols, a comprehensive synthesis of available evidence is needed to inform clinicians, regulators, and health policymakers. This systematic review aims to evaluate the efficacy of wearable cardiovascular monitoring devices in real-time arrhythmia detection and patient-managed care. Specifically, it assesses their diagnostic performance (sensitivity/specificity), usability, patient compliance, and impact on clinical outcomes.

Through analysis of peer-reviewed clinical studies from 2018 to 2025, this review will highlight strengths, limitations, and gaps in current evidence to guide future research and policy development. By synthesizing this information, the review hopes to support the integration of wearable devices into mainstream cardiology and preventive care frameworks [10].

Study Design

This study is a systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. It aimed to synthesize clinical evidence on the efficacy of wearable cardiovascular monitoring devices in detecting arrhythmias and enabling patient-managed care. No meta-analysis was conducted due to heterogeneity in study designs, devices, and outcome measures.

Eligibility Criteria

Studies were included based on the following inclusion criteria:

·         Published between January 2018 and April 2025.

·         Peer-reviewed articles indexed in PubMed.

·         Studies involving wearable devices with the capacity for real-time or near-real-time cardiac rhythm monitoring, specifically targeting arrhythmia detection (e.g., atrial fibrillation, tachyarrhythmias).

·         Populations including healthy subjects, at-risk individuals, or patients with known cardiac conditions.

·         Devices using ECG, PPG, or combined modalities.

·         Study types: randomized controlled trials (RCTs), prospective or retrospective observational studies, cohort studies, or validation studies.

Exclusion criteria:

·         Studies focused on implantable devices (e.g., ICDs, pacemakers).

·         Reviews, editorials, letters to editors, or non-original research.

·         Animal studies or those not involving human participants.

·         Studies with inaccessible full text or not available in English.

Search Strategy

A structured search was conducted using PubMed in April 2025. Multiple iterations of the search strategy were used, refining by date range and filters such as "Free Full Text" and "Clinical Trial." The final and most restrictive search string applied was:

("wearability"[All Fields] OR "wearable"[All Fields]) AND ("cardiovascular monitoring" OR "ECG" OR "PPG") AND ("arrhythmia detection" OR "atrial fibrillation") AND ("patient monitoring" OR "real-time")

Filters applied:

·         Publication date: Last 10 years.

·         Availability: Free full text.

·         Article types: Clinical trials, observational studies.

This yielded a total of 96 articles, which were screened further based on titles, abstracts, and full-text eligibility.

Study Selection Process

The initial search results were exported and screened manually in three stages:

1.       Title and abstract screening to identify studies mentioning wearable arrhythmia monitoring.

2.       Full-text review of shortlisted articles based on the inclusion/exclusion criteria.

3.       Final selection was based on relevance to the core research questions.

Duplicates, review articles, and studies focusing on implantable or non-cardiac monitoring devices were excluded. The final selection included 10 key studies which fulfilled all inclusion criteria and were exported for analysis in CSV format (csv-wearableca-set.csv).

Data Extraction

Data were extracted manually from full-text articles into a structured sheet. The following parameters were recorded:

·         PubMed ID (PMID)

·         Article title and citation

·         First author and year of publication

·         Country of study

·         Study design and sample size

·         Type of wearable device used

·         Monitoring modality (ECG, PPG)

·         Arrhythmia detected

·         Diagnostic accuracy (if available)

·         User compliance or usability metrics

·         Reported clinical outcomes (e.g., AF detection rate, intervention modification)

Data Synthesis

Due to substantial methodological heterogeneity among the selected studies in terms of device types, outcome metrics, and populations, a narrative synthesis approach was adopted. Outcomes are summarized descriptively across tables with focus on diagnostic performance, usability, and integration in patient care.

No quantitative meta-analysis was performed. However, where available, reported sensitivity, specificity, and predictive values were highlighted.

Risk of Bias Assessment

Risk of bias was not formally assessed using Cochrane or ROBINS-I tools due to the narrative nature of the synthesis and the diversity of included study designs. However, attention was given to the methodological transparency, sample size, device validation status (e.g., FDA approval), and declared conflicts of interest within each study.

1. Characteristics of Included Studies

The systematic review included 12 studies published between 2018 and 2025, encompassing diverse geographical locations such as the USA, Canada, Germany, Belgium, Norway, China, South Korea, and Poland. The sample sizes ranged from 60 (small feasibility studies) to 419,297 participants (Apple Heart Study). The majority of the studies were prospective observational or validation studies, with two notable randomized controlled trials. Fig 1

The devices used varied from commercial smartwatches (e.g., Apple Watch, Fitbit, Huawei) to clinical-grade patches, shirt-based monitors, and wearable cardioverter defibrillators. Most devices employed PPG and/or ECG modalities to detect atrial fibrillation, although several also captured ventricular arrhythmias, bradycardia, and premature atrial complexes. Follow-up durations were typically short to medium term, ranging from 7 to 117 days, depending on device type and study design. (Table 1)

2. Diagnostic Performance

Among the studies reporting diagnostic metrics, sensitivity values ranged from 85.0% to 95.2%, with the AT-Patch showing the highest sensitivity (95.2%) and Apple Watch demonstrating robust performance (84.0%). Specificity was highest for the AT-Patch (92.0%) and Gladstone's ECG patch (93.0%), confirming that ECG-based wearables outperform PPG-only systems in clinical accuracy.

Positive predictive values (PPVs) varied from 76.4% to 90.1%, with NPVs ranging from 71.0% to 96.4%, indicating a moderate-to-high level of diagnostic reliability across devices. Gold standard comparisons included 12-lead ECG, Holter monitors, and physician-adjudicated outcomes. Notably, some studies like Turakhia MP (2019) did not report performance metrics due to their design-phase nature. (Table 2)

3. Clinical Utility and Patient Outcomes

Wearable monitoring significantly impacted clinical care. AF detection rates varied between 0.52% (Perez) and 7.0% (Wasserlauf 2019), influenced by population risk profiles. Clinical interventions changed in 10.5% to 35.0% of patients following arrhythmia detection, particularly in Gladstone and Tscholl’s studies.

Hospitalization prevention was implied or explicitly reported in multiple studies, with some observing fewer emergency visits and admissions. Anticoagulation therapy initiation ranged from 7.5% to 21.0%, supporting the role of wearables in decision-making. Patient satisfaction was consistently high, with many studies reporting it as “very high” or “high,” especially in populations using Apple Watch or similar user-friendly interfaces. (Table 3)

4. Usability and Patient Engagement

Most devices demonstrated excellent wear compliance, with rates ranging from 85.0% to 93.5%. Data transmission success was similarly high, exceeding 90% in 9 out of 12 studies, confirming the feasibility of remote monitoring.

Devices like Apple Watch, Huawei Smartwatch, and AT-Patch had the lowest drop-out rates (2.1% to 3.6%), while wearable shirts and patches had slightly higher rates (up to 7.2%). The subjective ease of use was reported as “very easy” or “easy” in the majority of trials, emphasizing user acceptance and practicality in real-world use. (Table 4)

5. Technical and Regulatory Attributes

The review covered a spectrum of devices with varied technical sophistication. The Apple Watch Series 4, Wearable ECG patches, and LifeVest were all FDA-approved or CE-marked, whereas the ECG-Shirt remained at a prototype stage.

Most devices featured single-lead ECG or PPG + ECG hybrid systems, with battery life ranging from 1 to 14 days, depending on design. All included devices were Bluetooth/WiFi enabled, supporting seamless integration with cloud-based or mobile applications for real-time data monitoring.

Notably, clinical-grade devices like the Jewel IDE Patch and AT-Patch had superior diagnostic rigor, while consumer devices like Apple Watch offered broader population accessibility and scalability. (Table 5)

Table 1 - Summary of Included Studies

Table 2 - Diagnostic Performance of Wearable Devices

Table 3 - Clinical Utility and Patient Outcomes

Table 4 - Usability and Patient Engagement

Table 5 - Regulatory and Technical Attributes of Devices

The findings from this systematic review affirm the growing role of wearable cardiovascular monitoring devices in facilitating early detection of arrhythmias, particularly atrial fibrillation (AF), and enabling patient-managed care. Across the 12 included studies, evidence indicates that wearable technologies offer a reliable, user-friendly, and clinically impactful method of continuous cardiac rhythm surveillance.

One of the most influential contributions to this field remains the Apple Heart Study, which enrolled over 400,000 participants and demonstrated that wearable PPG monitoring combined with on-demand ECG could accurately identify AF with a positive predictive value of 84% [11]. Similarly, the SCREEN-AF trial by Gladstone et al. showed that using wearable ECG patches significantly improved AF detection in older hypertensive adults, resulting in appropriate clinical interventions including anticoagulation [12].

Device performance metrics varied across studies, but overall diagnostic sensitivity ranged from 84% to 95%, while specificity reached up to 93%, particularly in clinical-grade patches such as the AT-Patch [13]. Notably, consumer-grade devices, including the Apple Watch and Fitbit, also demonstrated robust accuracy, with newer models achieving diagnostic thresholds acceptable for clinical use [14]. This convergence of consumer and medical device functionality offers a unique opportunity to scale arrhythmia screening programs globally.

From a clinical impact perspective, detection of asymptomatic or paroxysmal AF through wearables led to meaningful treatment modifications in 10–35% of cases, including anticoagulant initiation and rhythm management decisions [15]. Importantly, devices like the LifeVest and Jewel IDE Patch also enabled detection of life-threatening arrhythmias such as ventricular tachyarrhythmias and supported timely cardioversion or ICD decisions [16].

Another critical aspect of wearable effectiveness lies in usability and patient adherence. Across studies, wear time compliance was consistently high (85–93.5%), with data transmission success exceeding 90% in most devices. Subjective ease-of-use ratings were particularly favorable for smartwatches, contributing to very low dropout rates (2–5%) [17]. This is a vital feature, especially in older adults and chronic disease populations where adherence can significantly affect monitoring outcomes.

However, disparities were noted in device validation status and technical robustness. While the Apple Watch, Zoll LifeVest, and AT-Patch hold FDA or CE approvals, others such as the shirt-based ECG system remain investigational or lack formal regulatory clearance [18]. This heterogeneity underscores the need for standardized evaluation frameworks, particularly as AI-enhanced algorithms become integrated into signal analysis.

Despite the advancements, certain limitations remain evident. Many studies featured relatively short follow-up periods (7–30 days), potentially missing episodic arrhythmias. Moreover, the Turakhia et al. study, while instrumental in study design guidance, did not report outcome data and thus was not included in comparative metrics [19]. Few trials assessed long-term clinical endpoints such as stroke or mortality, and only a subset evaluated healthcare resource utilization metrics like hospitalization reduction or ED avoidance.

Furthermore, cost-effectiveness, data privacy, and integration into electronic health records (EHRs) continue to pose challenges in widespread adoption. Only a few studies explicitly addressed how data from these devices influenced physician decision-making workflows or patient self-management strategies.

Nevertheless, this review highlights that wearable devices are not only feasible but increasingly essential tools in modern arrhythmia management. They offer scalable solutions for remote health monitoring, particularly relevant in post-pandemic digital health ecosystems. The positive feedback on user experience, coupled with real-world clinical benefits, suggests that wearables are poised to bridge critical gaps in cardiovascular screening and chronic disease surveillance.

This systematic review highlights the robust diagnostic accuracy and clinical utility of wearable cardiovascular monitoring devices for real-time arrhythmia detection and patient-led care. With high patient adherence, strong usability, and regulatory approval for several devices, wearables are rapidly integrating into mainstream cardiac monitoring strategies. While limitations such as short follow-up periods and heterogeneous validation standards persist, the overall evidence supports their value in early detection and timely intervention. As digital health continues to evolve, wearables hold great promise for improving cardiovascular outcomes through accessible, continuous, and proactive monitoring.

1.       World Health Organization. Cardiovascular diseases (CVDs). 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)

2.       Hindricks G, Potpara T, Dagres N, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation. Eur Heart J. 2021;42(5):373–498.

3.       Turakhia MP, Desai M, Hedlin H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J. 2019;207:66–75.

4.       Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381(20):1909–1917.

5.       Koshy AN, Sajeev JK, Nerlekar N, et al. Smart watches for heart rate assessment in atrial arrhythmias. Int J Cardiol. 2018;266:124–127.

6.       Bumgarner JM, Lambert CT, Hussein AA, et al. Smartwatch algorithm for automated detection of atrial fibrillation. J Am Coll Cardiol. 2018;71(21):2381–2388.

7.       Wasserlauf J, You C, Patel R, et al. Smartwatch performance for the detection of atrial fibrillation. Circ Arrhythm Electrophysiol. 2019;12(6):e006834.

8.       Gladstone DJ, Wachter R, Schmalstieg-Bahr K, et al. Screening for Atrial Fibrillation in the Older Population: A Randomized Clinical Trial. JAMA Cardiol. 2021;6(5):558–567.

9.       Gordon WJ, Landman A, Zhang H, et al. Beyond validation: getting health apps into clinical practice. NPJ Digit Med. 2020;3:14.

10.    Steinhubl SR, Waalen J, Edwards AM, et al. Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed AF: The mSToPS randomized clinical trial. JAMA. 2018;320(2):146–155.

11.    Perez MV, Mahaffey KW, Hedlin H, et al. Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation. N Engl J Med. 2019;381(20):1909–1917. doi:10.1056/NEJMoa1901183

12.    Gladstone DJ, Wachter R, Schmalstieg-Bahr K, et al. Screening for Atrial Fibrillation in the Older Population: A Randomized Clinical Trial. JAMA Cardiol. 2021;6(5):558–567. doi:10.1001/jamacardio.2021.0038

13.    Kwun JS, Choi YJ, Kim JH, et al. Diagnostic Performance of a Patch-Type Electrocardiographic Monitoring Device for the Detection of Atrial Fibrillation in High-Risk Hypertensive Patients. Korean Circ J. 2023;53(8):630–639. doi:10.4070/kcj.2022.0357

14.    Wouters F, Vandenberk B, Vranken J, et al. Comparison of smartwatches for heart rate and rhythm monitoring in patients with cardiac disease. JMIR Mhealth Uhealth. 2025;13:e41234.

15.    Müller M, Olsen AG, Anderssen S, et al. Wearable smartwatches for postoperative atrial fibrillation surveillance in valve surgery patients: A prospective observational study. Eur J Cardiothorac Surg. 2024;65(2):364–370.

16.    Tscholl V, Sütsch G, Osswald S, et al. Use of a Wearable Cardioverter Defibrillator in Myocarditis: Experience from a Prospective Cohort. ESC Heart Fail. 2021;8(2):1115–1122. doi:10.1002/ehf2.13179

17.    Wasserlauf J, You C, Patel R, et al. Smartwatch performance for the detection of atrial fibrillation. Circ Arrhythm Electrophysiol. 2019;12(6):e006834. doi:10.1161/CIRCEP.118.006834

18.    Balsam P, Borodzicz S, Grabowski M, et al. A Feasibility Study of Smart Wearable Devices for Cardiac Monitoring in Patients with Cardiovascular Disease. Kardiol Pol. 2018;76(10):970–976. doi:10.5603/KP.a2018.0141

19.    Turakhia MP, Desai M, Hedlin H, et al. Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. Am Heart J. 2019;207:66–75. doi:10.1016/j.ahj.2018.09.002

20.    Hummel J, Reuter H, Schwab JO, et al. Diagnostic and prognostic value of a novel wearable defibrillator system in patients with cardiac risk: The Jewel IDE Study. Heart Rhythm. 2024;21(1):24–31.

21.    Liu X, Zhang Y, Wang J, et al. Smartwatch-based monitoring of cardiac arrhythmias in post-TAVR patients: A prospective validation. J Geriatr Cardiol. 2022;19(6):426–433.

22.  Wasserlauf J, Orkaby AR, Patel R, et al. Real-world use of the Apple Watch for atrial fibrillation detection in diverse populations: A multicenter evaluation. Heart Rhythm O2. 2023;4(5):537–544.