Skip to main content
SpringerLink
Log in

Use of digital health applications for the detection of atrial fibrillation

Einsatz digitaler Gesundheitsanwendungen zur Erkennung von Vorhofflimmern

  • Schwerpunkt
  • Published:
Herzschrittmachertherapie + Elektrophysiologie Aims and scope Submit manuscript

Abstract

The advances in health care technologies over the last decade have led to improved capabilities in the use of digital health applications (DiHA) for the detection of atrial fibrillation (AFib). Thus, home-based remote heart rhythm monitoring is facilitated by smartphones or smartwatches alone or combined with external sensors. The available products differ in terms of type of application (wearable vs. handheld) and the technique used for rhythm detection (electrocardiography [ECG] vs. photoplethysmography [PPG]). While ECG-based algorithms often require additional sensors, PPG utilizes techniques integrated in smartphones or smartwatches. Algorithms based on artificial intelligence allow for the automated diagnosis of AFib, enabling high diagnostic accuracy for both ECG-based and PPG-based DiHA. Advantages for clinical use result from the widespread accessibility of rhythm monitoring, thereby permitting earlier diagnosis and higher AFib detection rates. DiHA are also useful for the follow-up of patients with known AFib by monitoring the success of therapeutic interventions to restore sinus rhythm, e.g. catheter ablation. Although some studies strongly suggest a potential benefit for the use of DiHA in the setting of AFib, the overall evidence for an improvement in hard, clinical endpoints and positive effects on clinical care is scarce. To enhance the acceptance of DiHA use in daily practice, more studies evaluating their clinical benefits for the detection of AFib are required. Moreover, most of the applications are still not reimbursable, although the German Digital Health Care Act (Digitale-Versorgung-Gesetz, DVG) made reimbursement possible in principle in 2019.

Zusammenfassung

Die technischen Fortschritte der letzten Jahre erleichtern uns mittlerweile die Diagnose von Vorhofflimmern (VHF) unter Einsatz digitaler Gesundheitsanwendungen (DiGA). Dadurch wird eine häusliche Herzrhythmusdiagnostik mit Smartphones oder Smartwatches allein oder in Kombination mit externen Sensoren möglich gemacht. Die verfügbaren Produkte unterscheiden sich in ihrer Art der Anwendung (Wearable vs. Handheld) und in der Technik zur Rhythmusdetektion (Elektrokardiogramm [EKG] vs. Photoplethysmographie [PPG]). Während EKG-basierte Algorithmen häufig externe Sensoren benötigen, bedient sich die PPG der in Smartphones oder Smartwatches integrierten Techniken. Auf künstlicher Intelligenz basierende Algorithmen erlauben die automatisierte Diagnose von VHF. Dies führt zu einer hohen diagnostischen Treffsicherheit EKG- und PPG-basierter DiGA. Der Vorteil in der klinischen Nutzung resultiert aus der großen Reichweite des Rhythmusmonitorings, wodurch frühere Diagnosestellungen und höhere Detektionsraten erzielt werden können. Darüber hinaus können DiGA für Patient*innen mit bekanntem VHF zur Überwachung des Therapieerfolgs nach Rhythmisierung von Nutzen sein, beispielsweise nach Katheterablation. Obwohl einige Studien stark auf einen Nutzen bei VHF hindeuten, besteht noch wenig Evidenz bezüglich der Verbesserung harter, klinischer Endpunkte und hinsichtlich positiver Versorgungseffekte durch DiGA. Um die Akzeptanz für die Implementierung in die tägliche Routine zu erhöhen, sind weitere Studien zur Evaluation des klinischen Nutzens in der VHF-Erkennung nötig. Außerdem sind die meisten Anwendungen bisher nicht erstattungsfähig, obwohl durch das Digitale-Versorgung-Gesetz seit 2019 die Erstattungsfähigkeit von DiGA prinzipiell gegeben ist.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Aljuaid M, Marashly Q, AlDanaf J et al (2020) Smartphone ECG monitoring system helps lower emergency room and clinic visits in post-atrial fibrillation ablation patients. Clin Med Insights Cardiol 14:1179546820901508. https://doi.org/10.1177/1179546820901508

    Article  PubMed  PubMed Central  Google Scholar 

  2. Andrade JG, Wells GA, Deyell MW et al (2020) Cryoablation or drug therapy for initial treatment of atrial fibrillation. N Engl J Med. https://doi.org/10.1056/NEJMoa2029980

    Article  PubMed  Google Scholar 

  3. Antich-Isern P, Caro-Barri J, Aparicio-Blanco J (2021) The combination of medical devices and medicinal products revisited from the new European legal framework. Int J Pharm 607:120992. https://doi.org/10.1016/j.ijpharm.2021.120992

    Article  CAS  PubMed  Google Scholar 

  4. Brasier N, Raichle CJ, Dörr M et al (2019) Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). Europace 21:41–47. https://doi.org/10.1093/europace/euy176

    Article  PubMed  Google Scholar 

  5. Bundesinstitut für Arzneimittel und Medizinprodukte (2022) DiGA-Verzeichnis. https://diga.bfarm.de/de/verzeichnis. Accessed 15 June 2022

  6. Bundesinstitut für Arzneimittel und Medizinprodukte (2022) Das Fast Track Verfahren für digitale Gesundheitsanwendungen (DiGA) nach § 139e SGB V

    Google Scholar 

  7. Bundestag (2019) Gesetz für eine bessere Versorgung durch Digitalisierung und Innovation (Digitale-Versorgung-Gesetz – DVG). Bundesgesetzblatt Teil I Nr. 49

    Google Scholar 

  8. Caillol T, Strik M, Ramirez FD et al (2021) Accuracy of a smartwatch-derived ECG for diagnosing bradyarrhythmias, tachyarrhythmias, and cardiac ischemia. Circ Arrhythm Electrophysiol 14:e9260. https://doi.org/10.1161/CIRCEP.120.009260

    Article  PubMed  Google Scholar 

  9. Ford C, Xie CX, Low A et al (2022) Comparison of 2 smart watch algorithms for detection of atrial fibrillation and the benefit of clinician interpretation: SMART WARS study. JACC Clin Electrophysiol. https://doi.org/10.1016/j.jacep.2022.02.013

    Article  PubMed  Google Scholar 

  10. Gawałko M, Duncker D, Manninger M et al (2021) The European TeleCheck-AF project on remote app-based management of atrial fibrillation during the COVID-19 pandemic: centre and patient experiences. Europace 23:1003–1015. https://doi.org/10.1093/europace/euab050

    Article  PubMed  PubMed Central  Google Scholar 

  11. Goldenthal IL, Sciacca RR, Riga T et al (2019) Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results. J Cardiovasc Electrophysiol 30:2220–2228. https://doi.org/10.1111/jce.14160

    Article  PubMed  PubMed Central  Google Scholar 

  12. Goldsack JC, Coravos A, Bakker JP et al (2020) Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for Biometric Monitoring Technologies (BioMeTs). Digit Med 3:1–15. https://doi.org/10.1038/s41746-020-0260-4

    Article  Google Scholar 

  13. Guo Y, Chen Y, Lane DA et al (2017) Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: mAF app trial. Am J Med 130:1388–1396.e6. https://doi.org/10.1016/j.amjmed.2017.07.003

    Article  PubMed  Google Scholar 

  14. Haverkamp W, Göing O, Anker M et al (2022) Vorhofflimmerndiagnostik mittels EKG-fähiger Smartwatches. Nervenarzt 93:175–178. https://doi.org/10.1007/s00115-021-01124-x

    Article  PubMed  Google Scholar 

  15. Hermans ANL, Gawalko M, Dohmen L et al (2021) Mobile health solutions for atrial fibrillation detection and management: a systematic review. Clin Res Cardiol. https://doi.org/10.1007/s00392-021-01941-9

    Article  PubMed  PubMed Central  Google Scholar 

  16. Hermans ANL, Gawalko M, Pluymaekers NAHA et al (2021) Long-term intermittent versus short continuous heart rhythm monitoring for the detection of atrial fibrillation recurrences after catheter ablation. Int J Cardiol 329:105–112. https://doi.org/10.1016/j.ijcard.2020.12.077

    Article  PubMed  Google Scholar 

  17. Hindricks G, Potpara T, Dagres N et al (2021) 2020 ESC guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Eur Heart J 42:373–498. https://doi.org/10.1093/eurheartj/ehaa612

    Article  PubMed  Google Scholar 

  18. Kleiman R, Darpo B, Brown R et al (2021) Comparison of electrocardiograms (ECG) waveforms and centralized ECG measurements between a simple 6‑lead mobile ECG device and a standard 12-lead ECG. Ann Noninvasive Electrocardiol 26:e12872. https://doi.org/10.1111/anec.12872

    Article  PubMed  PubMed Central  Google Scholar 

  19. Lowres N, Neubeck L, Salkeld G et al (2014) Feasibility and cost-effectiveness of stroke prevention through community screening for atrial fibrillation using iPhone ECG in pharmacies. The SEARCH-AF study. Thromb Haemost 111:1167–1176. https://doi.org/10.1160/TH14-03-0231

    Article  CAS  PubMed  Google Scholar 

  20. Perez MV, Mahaffey KW, Hedlin H et al (2019) Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med 381:1909–1917. https://doi.org/10.1056/NEJMoa1901183

    Article  PubMed  PubMed Central  Google Scholar 

  21. Pluymaekers NAHA, Hermans ANL, van der Velden RMJ et al (2020) Implementation of an on-demand app-based heart rate and rhythm monitoring infrastructure for the management of atrial fibrillation through teleconsultation: TeleCheck-AF. Europace. https://doi.org/10.1093/europace/euaa201

    Article  PubMed  PubMed Central  Google Scholar 

  22. Proesmans T, Mortelmans C, Van Haelst R et al (2019) Mobile phone-based use of the photoplethysmography technique to detect atrial fibrillation in primary care: diagnostic accuracy study of the FibriCheck app. JMIR Mhealth Uhealth 7:e12284. https://doi.org/10.2196/12284

    Article  PubMed  PubMed Central  Google Scholar 

  23. Rivezzi F, Vio R, Bilato C et al (2020) Screening of unknown atrial fibrillation through handheld device in the elderly. J Geriatr Cardiol 17:495–501. https://doi.org/10.11909/j.issn.1671-5411.2020.08.008

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sabar MI, Ara F, Henderson A et al (2019) A study to assess a novel automated electrocardiogram technology in screening for atrial fibrillation. Pacing Clin Electrophysiol 42:1383–1389. https://doi.org/10.1111/pace.13800

    Article  PubMed  Google Scholar 

  25. Stavrakis S, Stoner JA, Kardokus J et al (2017) Intermittent vs. Continuous Anticoagulation theRapy in patiEnts with Atrial Fibrillation (iCARE-AF): a randomized pilot study. J Interv Card Electrophysiol 48:51–60. https://doi.org/10.1007/s10840-016-0192-8

    Article  PubMed  Google Scholar 

  26. Svennberg E, Friberg L, Frykman V et al (2021) Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): a multicentre, parallel group, unmasked, randomised controlled trial. Lancet 398:1498–1506. https://doi.org/10.1016/S0140-6736(21)01637-8

    Article  PubMed  Google Scholar 

  27. Svennberg E, Tjong F, Goette A et al (2022) How to use digital devices to detect and manage arrhythmias: an EHRA practical guide. Europace. https://doi.org/10.1093/europace/euac038

    Article  PubMed  Google Scholar 

  28. Tieleman RG, Plantinga Y, Rinkes D et al (2014) Validation and clinical use of a novel diagnostic device for screening of atrial fibrillation. Europace 16:1291–1295. https://doi.org/10.1093/europace/euu057

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wineinger NE, Barrett PM, Zhang Y et al (2019) Identification of paroxysmal atrial fibrillation subtypes in over 13,000 individuals. Heart Rhythm 16:26–30. https://doi.org/10.1016/j.hrthm.2018.08.012

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Cardiology and Intensive Care Medicine, University hospital OWL of Bielefeld University, Campus Klinikum Bielefeld, Teutoburger Str. 50, 33604, Bielefeld, Germany

    Dennis Lawin,  Sophia Schulze Lammers,  Thorsten Lawrenz &  Christoph Stellbrink

  2. Department of Digital Medicine, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany

    Dennis Lawin &  Sebastian Kuhn

Authors
  1. Dennis Lawin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Kuhn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sophia Schulze Lammers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thorsten Lawrenz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Christoph Stellbrink
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dennis Lawin.

Ethics declarations

Conflict of interest

D. Lawin, S. Kuhn, S. Schulze Lammers, T. Lawrenz and C. Stellbrink declare that they have no competing interests.

No studies using humans or animals were performed for this review article. For the studies cited, the authors refer to the ethical guidelines stated in the articles.

Additional information

figure qr

Scan QR code & read article online

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lawin, D., Kuhn, S., Schulze Lammers, S. et al. Use of digital health applications for the detection of atrial fibrillation. Herzschr Elektrophys 33, 373–379 (2022). https://doi.org/10.1007/s00399-022-00888-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00399-022-00888-2

Keywords

Schlüsselwörter

Search

Navigation