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Review article

https://doi.org/10.15836/ccar2023.203

A Review of the Impact of Education on the Adoption of Smart Technologies for Atrial Fibrillation Detection

Adna Sijerčić orcid id orcid.org/0000-0003-3466-4481 ; International Burch University, Sarajevo, Bosnia and Herzegovina


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Abstract

SUMMARY
The main objective of this review was to investigate whether educational attainment has an impact on the occurrence of atrial fibrillation (AF) as well as the implementation of smart technology to detect this condition. Data on the relationship between education level and the occurrence of AF were collected, as well as data on smart devices for detecting AF. A lower level of education has been linked to an increased risk of AF. With this in mind, it is easy to explain the clear correlation between education level and AF, as well as the adoption of smart device detection and how it may improve illness prognosis. People with a higher level of education understand and embrace the notion of employing smart devices to detect and prevent AF; they also have decreased AF prevalence compared with those with a lower level of education.

Keywords

atrial fibrillation; smart device; detection; arrhythmia; education

Hrčak ID:

304003

URI

https://hrcak.srce.hr/304003

Publication date:

13.6.2023.

Article data in other languages: croatian

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Introduction

Atrial fibrillation (AF), a supraventricular tachyarrhythmia, is defined by erratic electrical activity of the atrium and resulting ineffectual atrial contractions. (1,2) Lower socioeconomic status (SES) has been linked to poorer physical health for hundreds of years. (3-5) In terms of SES, this review focuses on its impact on education and on the prevalence of AF. Furthermore, data indicating the clinical relevance of AF, smart device detection of AF, and a strong link between education and cardiovascular disease (CVD), particularly AF, is described herein, as is the influence of education on smart device detection of AF.

AF is the primary cause of mortality and morbidity in many Western industrialized countries. It occurs clinically as silent heart disease and stroke. (6) AF is one of the most common arrhythmias in adults. (7) This cardiac rhythm disorder is linked with worse quality of life, higher risk of heart failure, embolic events, and a 30% increased risk of death. (7,8) AF affects 1-2% of the population. (9) The incidence of this arrhythmia is projected to rise over the next decade as the population ages and risk factors increase. (9) It is estimated that AF will affect 14-17 million individuals in the European Union by 2030. (10)

Early identification and prevention are crucial factors that clearly affect health, followed by the appropriate use of therapy. The application of an electrocardiogram (ECG) is essential to confirm the diagnosis of AF. (11) New patient-operated devices such as wristbands, smartphones, and smart watches (followed by ECG verification) may also be used to detect AF, which is typically paroxysmal or quiet. Several ongoing studies are also examining the benefits of early AF identification and whether it improves disease outcomes. (12)

Worldwide, almost 20 million individuals use mobile health devices, and the number is growing every day. (13,14) According to research, 27% of people over 65 in Europe possessed a smartphone in 2014. (14) These smart devices and software provide strong prospects for faster diagnosis and monitoring of AF, which is a condition that most frequently affects the elderly and is on the rise. Consequently, AF can be identified earlier, enabling early introduction of medication and, consequently, a decrease in complications and total treatment costs.

This review investigates the influence of education on the use of smart technology for AF diagnosis and on AF occurrence in general, given that those with less education are more likely to acquire AF. With all of this in mind, it is easy to explain the clear correlation between education level and AF as well as smart device detection uptake and how it might influence disease outcomes.

A clinical perspective on atrial fibrillation

Considering that it is a highly prevalent dysrhythmia and the main cause of strokes, and is linked to considerable morbidity and death, AF poses a serious problem for healthcare systems throughout the world. (15,16)

Owing to the erratic electrical conduction via the AV node and ventricular response that define AF, the RR interval time series and ventricular response are unpredictable. (17,18) The RR intervals during AF episodes may be modeled probabilistically, and are therefore not entirely unexpected. (19) It has been demonstrated that RR intervals may be used to detect AF events, and numerous approaches have been suggested. (18) These approaches primarily depend on the extraction of characteristics from an RR interval time series that represent the unpredictable nature of the heartbeat. (18)

Despite perhaps appearing simple, applying this idea in clinical practice can be difficult at times. Irregular RR intervals are one of the essential characteristics of AF, and AV conduction must be preserved for them to be detected. If P waves appear in multiple leads at the same time and have the same morphology, AF is not a likely diagnosis. On the contrary, changing P morphology and variable intervals between the successive P waves indicate possible AF. (1,2)

What started as simple ectopic beats may, due to the multiple risk factors (such as behavioral patterns and comorbidities), progress to the formation of re-entry circuits that culminate in AF. (20) AF is characterized by extremely variable excitation waves found in both atria. The chaotic impulses disrupt the normal process of cardiac depolarization and repolarization, making it impossible for the normal, rhythmic contraction of the atrial walls to occur. As a result, the ventricles are arrhythmic and receive extremely erratic stimulation from the atria through the AV node. At a range of 60 to 130 beats per minute, very erratic chamber frequencies occur. Heart palpitations are experienced when the heart skips a beat and loses its synchronized action as a result of AF. (21,22)

Atrial fibrillation and stroke

Clinical occurrences and costs have significantly increased in correlation with the most commonly persistent cardiac arrhythmia, AF. (23) Between one and two percent of people suffer from this cardiac rhythm disorder. (24) Around 6 million people in Europe alone have been reported to experience this cardiac arrhythmia, and around 30 and 100 million people are thought to be impacted globally. Most of the time, those 65 years of age and above are the population group that is most afflicted by this condition. (25) Over the following ten years, it is anticipated that the prevalence of this cardiac arrhythmia will rise as the population ages and risk factors increase. (24) By 2030, around 15.5 million individuals in the European Union are expected to develop AF as a result. Every year, 120,000 to 155,000 preliminary diagnoses are anticipated. (26)

Ischemic stroke is a potentially lethal consequence of AF. (27,28) AF is thought to be a contributing factor in about 20% of strokes. (2) A clinical tool known as the CHA2DS2-VASc score was created to evaluate ischemic stroke risk in patients with AF and to direct the administration of oral anticoagulation (OAC) medication, which has been shown to reduce the risk of ischemic strokes. (2,27,29)

The treatment of thromboembolic stroke accounts for a sizable portion of the healthcare expenses related to AF, which total more than one percent of United Kingdom healthcare expenditures. (30) In the general population, AF is linked to four to five times increased risk of stroke, and it is thought to be the root cause of 15% of all strokes. Importantly, this percentage rises sharply with age. (20,21) When compared to patients with stroke but without AF, patients with ischemic stroke had a considerably greater prevalence of AF (nearly 25%), which was associated with lengthier hospital stays, more morbidity, increased rates of stroke recurrence, and higher fatality rates. (31,32) Notably, certain symptoms being absent (such as palpitations) did not reflect a decreased risk of thrombosis. Patients with paroxysmal or persistent AF were just as likely as those with permanent AF to suffer a stroke. (30)

Detection of atrial fibrillation using smart devices

Electrocardiogram or photoplethysmogram-based smart devices are often used in the healthcare industry. These may be in the form of a smartphone case, wristband, smartwatch, or even only as an additional algorithms and software that can transform current smartphone hardware into a tool for AF detection. (33,34) Of all these gadgets, a smartwatch is the one that is most frequently discussed in healthcare and the subject of smart device research for the detection of AF. (35)

In the late 1990s, spurred by the “quantified self” movement, smartwatches became popular as part of the category of wearable technologies and smart health systems. (36) The “quantified self” movement sought to focus on the patient in the process of providing healthcare. Wearable technology, such as smartwatches, which allow users to continually track their health information during normal activities or even sleep, was one of the instruments that would have made that possible. (36) Moreover, using these smart devices enables prolonged vital sign recording outside of the hospital setting. (37)

In general, smart devices may alter how health data is delivered by overcoming the everyday constraints faced by healthcare providers and using techniques that can detect occurrences that do not take place during in-person visits. (38,39) Cardiologists are particularly interested in the ability to continuously monitor heart rhythm, heart rate, and persistent non-invasive arrhythmias, thanks to the development of smart devices. (40,41) Additionally, the adoption of smartwatches and other smart gadgets for the diagnosis of AF will benefit from the arrival of 5G technology and the corresponding increased connection speed. (36)

The following are some restrictions on the smart devices used for the identification of AF: the battery life of smartwatches is limited, and they frequently need to be charged every day. As a result, less time is spent wearing the device and being monitored. It might also be difficult to detect brief and asymptomatic events of dysrhythmias, for instance, because the majority of existing methods for the detection of AF using a smartwatch require the patient’s active participation. (42,43) Due to the fact that smartwatches are still relatively new technologies, several legal concerns relating to data security still need to be overcome to secure the confidentiality of the recorded health information. (43,44)

Socioeconomic status and its influence on atrial fibrillation occurrence and identification using smart devices

It has been generally established that a higher risk of CVD is linked to poorer SES. (45) Several studies concluded that those with better SES had lower incidences of AF. (46,47) Nonetheless, higher income and education levels were linked to a lower risk of developing AF in young people, but the connection weakened with advancing age and was nearly nonexistent for the older demographic. (48)

Studying the socioeconomic factors of AF offers a chance to improve the health of patients with AF. The identification, assessment, treatment, and management of AF are heavily influenced by socioeconomic variables: ethnicity and racial background, financial means, area of residence and rurality, language proficiency, health literacy, and social support are all factors to consider. (49)

As was already noted, more frequent patient monitoring is advised, and continuous monitoring is the best method for preventing stroke and AF. (27) Smart devices are the perfect tool to tackle this issue. (35) Nonetheless, there is also compelling evidence of a digital gap across different ethnic and racial backgrounds, as well as intersections of gaps in age, income, occupation, education level, and SES. (50) The usage of smart devices and digital technologies in general are influenced by all of these aspects, and this of course applies to the use of smart devices for AF detection as well.

Impact of education on atrial fibrillation occurrence and identification using smart devices

Several studies found that education influenced the proportion of AF identified with a smart device, as well as that patients with higher education levels had a decreased incidence of AF, while patients with lower education levels had a higher incidence of AF.

Many studies demonstrated that cigarette smoking, dyslipidemia, and hypertension are all risk factors for CVD, and how this is related to education level. (51-53) According to the findings one study, the behaviors that impact risk factors for CVD are multifaceted and well-established, and they are frequently strengthened by traditions, culture, and continuous marketing. (51) Thus, the authors suggest that they are not likely to be considerably impacted by mainstream media alone. Additionally, face-to-face training and exhortation have a lengthy record of inadequacy, particularly when it comes to long-term changes in eating and smoking habits. (51) Furthermore, there is pessimism regarding the potential of public education to modify health behavior. All of this leads to the conclusion that overall education level is the foundation of understanding and being more receptive to embracing healthy lifestyle choices. (51,54)

Moreover, the incidence of CVD, such as AF, highly depends on regular therapeutic intake. Advances in pharmacological therapy have greatly improved the outcomes of patients with CVD in recent decades. (55,56) In people with AF, new OACs have lowered the risk of stroke. (55,57,58) In order for treatment to be effective, patients must take their medications on a regular basis. (59-62) The patients who take medications consistently differ from those who do not in terms of other risk factors for death. (63-66) Regular medication intake is also associated with education level, with those with a higher level of education being more likely to recognize the need for taking medicine regularly and doing so. (64-66)

Smartphones are on the verge of taking over our everyday tasks in both our personal and professional lives. (67-69) Furthermore, research has shown that those with a higher level of education are more inclined to use smart technology. (70,71) According to this study and others, smart technology solutions have been developed and tested to increase medication adherence and disease monitoring across a wide range of patient demographics. (72-75) These solutions are defined by the use of technology, mostly smartphones, tablets, and computers, to remotely monitor and train patients in order to increase their adherence and monitoring. (75,76) The low cost of these systems, as well as the use of existing technology and ease of use, are all benefits of employing them. (55,77,78)

Conclusion

Individuals with higher education levels and SES are less prone to AF and more likely to use smart devices. As a result, AF in these individuals would be more likely to be identified and receive prompt treatment, avoiding all the challenges brought on by silent AF. Higher levels of education are also associated with a reduced incidence of AF, probably due to being more informed, being knowledgeable, and healthy lifestyle acceptance. If more people are made aware of the advantages of utilizing smart devices for AF detection, more people will do so, increasing the possibility that AF will be detected early and preventing all of the fatalities and morbidities related to stroke.

LITERATURE

1 

Platonov PG, Corino VD. A clinical perspective on atrial fibrillation. Atrial Fibrillation from an Engineering Perspective. 2018:1-24. https://doi.org/10.1007/978-3-319-68515-1_1 https://doi.org/10.1007/978-3-319-68515-1_1

2 

Sörnmo L, editor. Atrial Fibrillation from an Engineering Perspective. Springer; 2018 May 15. https://doi.org/10.1007/978-3-319-68515-1 https://doi.org/10.1007/978-3-319-68515-1

3 

Kaplan GA, Keil JE. Socioeconomic factors and cardiovascular disease: a review of the literature. Circulation. 1993 October;88(4 Pt 1):1973–98. https://doi.org/10.1161/01.CIR.88.4.1973 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/8403348

4 

Winkleby MA, Jatulis DE, Frank E, Fortmann SP. Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease. Am J Public Health. 1992 June;82(6):816–20. https://doi.org/10.2105/AJPH.82.6.816 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/1585961

5 

Kilander L, Berglund L, Boberg M, Vessby B, Lithell H. Education, lifestyle factors and mortality from cardiovascular disease and cancer. A 25-year follow-up of Swedish 50-year-old men. Int J Epidemiol. 2001 October;30(5):1119–26. https://doi.org/10.1093/ije/30.5.1119 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/11689532

6 

Luepker RV, Murray DM, Jacobs DR Jr, Mittelmark MB, Bracht N, Carlaw R, et al. Community education for cardiovascular disease prevention: risk factor changes in the Minnesota Heart Health Program. Am J Public Health. 1994 September;84(9):1383–93. https://doi.org/10.2105/AJPH.84.9.1383 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/8092360

7 

Perlini S, Belluzzi F, Salinaro F, Musc F. Atrial Fibrillation and the Renin-Angiotensin-Aldosterone System. Atrial Fibrillation - Mechanisms and Treatment. 2013 Feb 27. IntechOpen. https://doi.org/10.5772/53917 https://doi.org/10.5772/53917

8 

Verdecchia P, Angeli F, Reboldi G. Hypertension and Atrial Fibrillation: Doubts and Certainties From Basic and Clinical Studies. Circ Res. 2018 January 19;122(2):352–68. https://doi.org/10.1161/CIRCRESAHA.117.311402 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29348255

9 

Mairesse GH, Moran P, Van Gelder IC, Elsner C, Rosenqvist M, Mant J, et al. ESC Scientific Document Group. Screening for atrial fibrillation: a European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLAECE). Europace. 2017 October 1;19(10):1589–623. https://doi.org/10.1093/europace/eux177 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29048522

10 

Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. Wytyczne ESC dotyczące leczenia migotania przedsionków w 2016 roku, opracowane we współpracy z EACTS [2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS]. Kardiol Pol. 2016;74(12):1359-1469. Polish. https://doi.org/10.5603/KP.2016.0172 https://doi.org/10.5603/KP.2016.0172

11 

Stergiou GS, Karpettas N, Protogerou A, Nasothimiou EG, Kyriakidis M. Diagnostic accuracy of a home blood pressure monitor to detect atrial fibrillation. J Hum Hypertens. 2009 October;23(10):654–8. https://doi.org/10.1038/jhh.2009.5 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/19279661

12 

Sörnmo L, Stridh M, Husser D, Bollmann A, Olsson SB. Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management. Philos Trans A Math Phys Eng Sci. 2009 Jan 28;367(1887):235-53. https://doi.org/10.1098/rsta.2008.0162 https://doi.org/10.1098/rsta.2008.0162

13 

Brasier N, Raichle CJ, Dörr M, Becke A, Nohturfft V, Weber S, et al. Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). Europace. 2019 January 1;21(1):41–7. https://doi.org/10.1093/europace/euy176 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30085018

14 

McMANUS DD, Chong JW, Soni A, Saczynski JS, Esa N, Napolitano C, et al. PULSE-SMART: Pulse-Based Arrhythmia Discrimination Using a Novel Smartphone Application. J Cardiovasc Electrophysiol. 2016 January;27(1):51–7. https://doi.org/10.1111/jce.12842 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/26391728

15 

Zulkifly H, Lip GYH, Lane DA. Epidemiology of atrial fibrillation. Int J Clin Pract. 2018 March;72(3):e13070. https://doi.org/10.1111/ijcp.13070 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29493854

16 

Waks JW, Josephson ME. Mechanisms of Atrial Fibrillation - Reentry, Rotors and Reality. Arrhythm Electrophysiol Rev. 2014 August;3(2):90–100. https://doi.org/10.15420/aer.2014.3.2.90 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/26835073

17 

Lian J, Wang L, Muessig D. A simple method to detect atrial fibrillation using RR intervals. Am J Cardiol. 2011 May 15;107(10):1494–7. https://doi.org/10.1016/j.amjcard.2011.01.028 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/21420064

18 

Oster J, Clifford GD. Impact of the presence of noise on RR interval-based atrial fibrillation detection. J Electrocardiol. 2015 November 1;48(6):947–51. https://doi.org/10.1016/j.jelectrocard.2015.08.013 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/26358629

19 

Corino VD, Sandberg F, Mainardi LT, Sornmo L. An atrioventricular node model for analysis of the ventricular response during atrial fibrillation. IEEE Trans Biomed Eng. 2011 August 25;58(12):3386–95. https://doi.org/10.1109/TBME.2011.2166262 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/21878405

20 

Czick ME, Shapter CL, Silverman DI. Atrial fibrillation: the science behind its defiance. Aging Dis. 2016 October 1;7(5):635–56. https://doi.org/10.14336/AD.2016.0211 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27699086

21 

McCabe PJ, Barton DL, DeVon HA. Older adults at risk for atrial fibrillation lack knowledge and confidence to seek treatment for signs and symptoms. SAGE Open Nurs. 2017 August;3:2377960817720324. https://doi.org/10.1177/2377960817720324 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30637335

22 

Wijesurendra RS, Casadei B. Mechanisms of atrial fibrillation. Heart. 2019 December 1;105(24):1860–7. https://doi.org/10.1136/heartjnl-2018-314267 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31444267

23 

Benezet-Mazuecos J, García-Talavera CS, Rubio JM. Smart devices for a smart detection of atrial fibrillation. J Thorac Dis. 2018 November;10 Suppl 33:S3824–7. https://doi.org/10.21037/jtd.2018.08.138 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30631488

24 

Mairesse GH, Moran P, Van Gelder IC, Elsner C, Rosenqvist M, Mant J, et al. ESC Scientific Document Group. Screening for atrial fibrillation: a European Heart Rhythm Association (EHRA) consensus document endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), and Sociedad Latinoamericana de Estimulación Cardíaca y Electrofisiología (SOLAECE). Europace. 2017 October 1;19(10):1589–623. https://doi.org/10.1093/europace/eux177 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29048522

25 

Jones NR, Taylor CJ, Hobbs FDR, Bowman L, Casadei B. Screening for atrial fibrillation: a call for evidence. Eur Heart J. 2020 March 7;41(10):1075–85. https://doi.org/10.1093/eurheartj/ehz834 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31811716

26 

Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur J Cardiothorac Surg. 2016 November;50(5):e1–88. https://doi.org/10.1093/ejcts/ezw313 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27663299

27 

Lisica L, Jurišić Z. Prevalence and Detection Methods for Atrial Fibrillation in Patients Hospitalized due to Ischemic Stroke and Its Impact on Clinical Patient Outcomes. Cardiol Croat. 2022;17(11-12):371–9. https://doi.org/10.15836/ccar2022.371

28 

Migdady I, Russman A, Buletko AB. Atrial Fibrillation and Ischemic Stroke: A Clinical Review. Semin Neurol. 2021 August;41(4):348–64. https://doi.org/10.1055/s-0041-1726332 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/33851396

29 

Reiffel JA. Atrial fibrillation and stroke: epidemiology. Am J Med. 2014 April;127(4):e15–6. https://doi.org/10.1016/j.amjmed.2013.06.002 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/24655742

30 

Lip GY, Lim HS. Atrial fibrillation and stroke prevention. Lancet Neurol. 2007 November;6(11):981–93. https://doi.org/10.1016/S1474-4422(07)70264-8 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/17945152

31 

Lakshminarayan K, Solid CA, Collins AJ, Anderson DC, Herzog CA. Atrial Fibrillation and Stroke in the General Medicare Population: a 10-year perspective (1992 to 2002). Stroke. 2006 August;37(8):1969–74. https://doi.org/10.1161/01.STR.0000230607.07928.17 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/16809573

32 

Marini C, De Santis F, Sacco S, Russo T, Olivieri L, Totaro R, et al. Contribution of atrial fibrillation to incidence and outcome of ischemic stroke: results from a population-based study. Stroke. 2005 June;36(6):1115–9. https://doi.org/10.1161/01.STR.0000166053.83476.4a PubMed: http://www.ncbi.nlm.nih.gov/pubmed/15879330

33 

Eckstein J, Mutke M. Smart mobile devices in health care-smart enough to detect atrial fibrillation? J Thorac Dis. 2018 September;10 Suppl 26:S3227–8. https://doi.org/10.21037/jtd.2018.08.21 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30370121

34 

Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, et al. MAFA II Investigators. Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation. J Am Coll Cardiol. 2019 November 12;74(19):2365–75. https://doi.org/10.1016/j.jacc.2019.08.019 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31487545

35 

Sijerčić A, Tahirović E. Photoplethysmography-Based Smart Devices for Detection of Atrial Fibrillation. Tex Heart Inst J. 2022 September 1;49(5):e217564. https://doi.org/10.14503/THIJ-21-7564 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/36301189

36 

Yetisen AK, Martinez-Hurtado JL, Ünal B, Khademhosseini A, Butt H. Wearables in Medicine. Adv Mater. 2018 June 11;30(33):e1706910. https://doi.org/10.1002/adma.201706910 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29893068

37 

Dias D, Paulo Silva Cunha J. Wearable Health Devices-Vital Sign Monitoring, Systems and Technologies. Sensors (Basel). 2018 July 25;18(8):2414. https://doi.org/10.3390/s18082414 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30044415

38 

Dobrev D, Potpara TS. Smart device-based detection of atrial fibrillation: Opportunities and challenges in the emerging world of digital health. Int J Cardiol. 2020 March 1;302:108–9. https://doi.org/10.1016/j.ijcard.2019.12.023 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31870783

39 

Reeder B, David A. Health at hand: A systematic review of smart watch uses for health and wellness. J Biomed Inform. 2016 October;63:269–76. https://doi.org/10.1016/j.jbi.2016.09.001 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27612974

40 

Carpenter A, Frontera A. Smart-watches: a potential challenger to the implantable loop recorder? Europace. 2016 June;18(6):791–3. https://doi.org/10.1093/europace/euv427 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/26847074

41 

Krivoshei L, Weber S, Burkard T, Maseli A, Brasier N, Kühne M, et al. Smart detection of atrial fibrillation†. Europace. 2017 May 1;19(5):753–7. https://doi.org/10.1093/europace/euw125 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27371660

42 

Dörr M, Nohturfft V, Brasier N, Bosshard E, Djurdjevic A, Gross S, et al. The WATCH AF Trial: SmartWATCHes for Detection of Atrial Fibrillation. JACC Clin Electrophysiol. 2019 February;5(2):199–208. https://doi.org/10.1016/j.jacep.2018.10.006 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30784691

43 

Sijerčić A, Tahirović E. Smart devices for detection of atrial fibrillation-literature review. Int. J. Innov. Sci. Res. Technol. 2020;5.https://bit.ly/2XQgsYV

44 

Ip JE. Wearable Devices for Cardiac Rhythm Diagnosis and Management. JAMA. 2019 January 29;321(4):337–8. https://doi.org/10.1001/jama.2018.20437 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30633301

45 

Kargoli F, Shulman E, Aagaard P, Briceno DF, Hoch E, Di Biase L, et al. Socioeconomic Status as a Predictor of Mortality in Patients Admitted With Atrial Fibrillation. Am J Cardiol. 2017 May 1;119(9):1378–81. https://doi.org/10.1016/j.amjcard.2017.01.041 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/28400027

46 

Mou L, Norby FL, Chen LY, O’Neal WT, Lewis TT, Loehr LR, et al. Lifetime Risk of Atrial Fibrillation by Race and Socioeconomic Status: ARIC Study (Atherosclerosis Risk in Communities). Circ Arrhythm Electrophysiol. 2018 July;11(7):e006350. https://doi.org/10.1161/CIRCEP.118.006350 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/30002066

47 

Wodschow K, Bihrmann K, Larsen ML, Gislason G, Ersbøll AK. Geographical variation and clustering are found in atrial fibrillation beyond socioeconomic differences: a Danish cohort study, 1987-2015. Int J Health Geogr. 2021 March 1;20(1):11. https://doi.org/10.1186/s12942-021-00264-2 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/33648527

48 

Lunde ED, Joensen AM, Lundbye-Christensen S, Fonager K, Paaske Johnsen S, Larsen ML, et al. Socioeconomic position and risk of atrial fibrillation: a nationwide Danish cohort study. J Epidemiol Community Health. 2020 January;74(1):7–13. https://doi.org/10.1136/jech-2019-212720 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31619458

49 

Essien UR, Kornej J, Johnson AE, Schulson LB, Benjamin EJ, Magnani JW. Social determinants of atrial fibrillation. Nat Rev Cardiol. 2021 November;18(11):763–73. https://doi.org/10.1038/s41569-021-00561-0 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/34079095

50 

Reddick CG, Enriquez R, Harris RJ, Sharma B. Determinants of broadband access and affordability: An analysis of a community survey on the digital divide. Cities. 2020 November;106:102904. https://doi.org/10.1016/j.cities.2020.102904 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/32921864

51 

Farquhar JW, Maccoby N, Wood PD, Alexander JK, Breitrose H, Brown BW Jr, et al. Community education for cardiovascular health. Lancet. 1977 June 4;1(8023):1192–5. https://doi.org/10.1016/S0140-6736(77)92727-1 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/68285

52 

Hajar R. Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views. 2017 July-September;18(3):109–14. https://doi.org/10.4103/HEARTVIEWS.HEARTVIEWS_106_17 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29184622

53 

Whelton SP, McEvoy JW, Shaw L, Psaty BM, Lima JAC, Budoff M, et al. Association of Normal Systolic Blood Pressure Level With Cardiovascular Disease in the Absence of Risk Factors. JAMA Cardiol. 2020 September 1;5(9):1011–8. https://doi.org/10.1001/jamacardio.2020.1731 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/32936272

54 

Li Y, Schoufour J, Wang DD, Dhana K, Pan A, Liu X, et al. Healthy lifestyle and life expectancy free of cancer, cardiovascular disease, and type 2 diabetes: prospective cohort study. BMJ. 2020 January 8;368:l6669. https://doi.org/10.1136/bmj.l6669 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31915124

55 

Treskes RW, Van der Velde ET, Schoones JW, Schalij MJ. Implementation of smart technology to improve medication adherence in patients with cardiovascular disease: is it effective? Expert Rev Med Devices. 2018 February;15(2):119–26. https://doi.org/10.1080/17434440.2018.1421456 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29271661

56 

Yang W, Chen X, Li Y, Guo S, Wang Z, Yu X. Advances in pharmacological activities of terpenoids. Natural Product Communications. 2020 Mar;15(3):1934578X20903555. https://doi.org/10.1177/1934578X20903555 https://doi.org/10.1177/1934578X20903555

57 

López-López JA, Sterne JAC, Thom HHZ, Higgins JPT, Hingorani AD, Okoli GN, et al. Oral anticoagulants for prevention of stroke in atrial fibrillation: systematic review, network meta-analysis, and cost effectiveness analysis. BMJ. 2017 November 28;359:j5058. https://doi.org/10.1136/bmj.j5058 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29183961

58 

Saraiva JFK. Stroke Prevention with Oral Anticoagulants: Summary of the Evidence and Efficacy Measures as an Aid to Treatment Choices. Cardiol Ther. 2018 June;7(1):15–24. https://doi.org/10.1007/s40119-018-0106-1 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29488150

59 

Perk J, De Backer G, Gohlke H, Graham I, Reiner Z, Verschuren M, et al. European Association for Cardiovascular Prevention & Rehabilitation (EACPR); ESC Committee for Practice Guidelines (CPG). European Guidelines on cardiovascular disease prevention in clinical practice (version 2012). The Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts). Eur Heart J. 2012 July;33(13):1635–701. https://doi.org/10.1093/eurheartj/ehs092 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/22555213

60 

Konieczyńska M, Bijak P, Malinowski KP, Undas A. Knowledge about atrial fibrillation and anticoagulation affects the risk of clinical outcomes. Thromb Res. 2022 May;213:105–12. https://doi.org/10.1016/j.thromres.2022.03.011 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/35320762

61 

Danchin N, Steg G, Mahé I, Hanon O, Jacoud F, Nolin M, et al. Comparative non-persistence in the first year of treatment with oral anticoagulants in patients with atrial fibrillation: A French comprehensive nationwide study. Arch Cardiovasc Dis. 2022 November;115(11):571–7. https://doi.org/10.1016/j.acvd.2022.06.006 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/36257903

62 

Pandya EY, Bajorek B. Factors Affecting Patients’ Perception On, and Adherence To, Anticoagulant Therapy: Anticipating the Role of Direct Oral Anticoagulants. Patient. 2017 April;10(2):163–85. https://doi.org/10.1007/s40271-016-0180-1 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27438598

63 

Simpson SH, Eurich DT, Majumdar SR, Padwal RS, Tsuyuki RT, Varney J, et al. A meta-analysis of the association between adherence to drug therapy and mortality. BMJ. 2006 July 1;333(7557):15. https://doi.org/10.1136/bmj.38875.675486.55 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/16790458

64 

Dunbar-Jacob J, Bohachick P, Mortimer MK, Sereika SM, Foley SM. Medication adherence in persons with cardiovascular disease. J Cardiovasc Nurs. 2003 July-August;18(3):209–18. https://doi.org/10.1097/00005082-200307000-00006 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/12837011

65 

Ferdinand KC, Yadav K, Nasser SA, Clayton-Jeter HD, Lewin J, Cryer DR, et al. Disparities in hypertension and cardiovascular disease in blacks: The critical role of medication adherence. J Clin Hypertens (Greenwich). 2017 October;19(10):1015–24. https://doi.org/10.1111/jch.13089 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/28856834

66 

Ferdinand KC, Senatore FF, Clayton-Jeter H, Cryer DR, Lewin JC, Nasser SA, et al. Improving Medication Adherence in Cardiometabolic Disease: Practical and Regulatory Implications. J Am Coll Cardiol. 2017 January 31;69(4):437–51. https://doi.org/10.1016/j.jacc.2016.11.034 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/28126162

67 

Babič F, Gašpar V. Mobile technologies education based on smart laboratory models. In2017 15th International Conference on Emerging eLearning Technologies and Applications (ICETA) 2017 Oct 26 (pp. 1-6). IEEE. https://doi.org/10.1109/ICETA.2017.8102464 https://doi.org/10.1109/ICETA.2017.8102464

68 

Sun J, Liu Y. Using Smart Bracelets to Assess Heart Rate Among Students During Physical Education Lessons: Feasibility, Reliability, and Validity Study. JMIR Mhealth Uhealth. 2020 August 5;8(8):e17699. https://doi.org/10.2196/17699 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/32663136

69 

Kim BY, Lee J. Smart Devices for Older Adults Managing Chronic Disease: A Scoping Review. JMIR Mhealth Uhealth. 2017 May 23;5(5):e69. https://doi.org/10.2196/mhealth.7141 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/28536089

70 

Özbek V, Alnıaçık Ü, Koc F, Akkılıç ME, Kaş E. The impact of personality on technology acceptance: A study on smart phone users. Procedia Soc Behav Sci. 2014 September 15;150:541–51. https://doi.org/10.1016/j.sbspro.2014.09.073

71 

Shin J, Park Y, Lee D. Who will be smart home users? An analysis of adoption and diffusion of smart homes. Technol Forecast Soc Change. 2018 September 1;134:246–53. https://doi.org/10.1016/j.techfore.2018.06.029

72 

Treskes RW, van der Velde ET, Barendse R, Bruining N. Mobile health in cardiology: a review of currently available medical apps and equipment for remote monitoring. Expert Rev Med Devices. 2016 September;13(9):823–30. https://doi.org/10.1080/17434440.2016.1218277 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/27477584

73 

Treskes RW, Van der Velde ET, Schoones JW, Schalij MJ. Implementation of smart technology to improve medication adherence in patients with cardiovascular disease: is it effective? Expert Rev Med Devices. 2018 February;15(2):119–26. https://doi.org/10.1080/17434440.2018.1421456 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/29271661

74 

Aldeer M, Javanmard M, Martin RP. A review of medication adherence monitoring technologies. Appl Syst Innov. 2018 May 6;1(2):14. https://doi.org/10.3390/asi1020014

75 

Arnet I, Rothen JP, Hersberger KE. Validation of a Novel Electronic Device for Medication Adherence Monitoring of Ambulatory Patients. Pharmacy (Basel). 2019 November 20;7(4):155. https://doi.org/10.3390/pharmacy7040155 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/31756904

76 

Checchi KD, Huybrechts KF, Avorn J, Kesselheim AS. Electronic medication packaging devices and medication adherence: a systematic review. JAMA. 2014 September 24;312(12):1237–47. https://doi.org/10.1001/jama.2014.10059 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/25247520

77 

Griffiths F, Lindenmeyer A, Powell J, Lowe P, Thorogood M. Why are health care interventions delivered over the internet? A systematic review of the published literature. J Med Internet Res. 2006 June 23;8(2):e10. https://doi.org/10.2196/jmir.8.2.e10 PubMed: http://www.ncbi.nlm.nih.gov/pubmed/16867965

78 

Ahmed G. Management of artificial intelligence enabled smart wearable devices for early diagnosis and continuous monitoring of CVDS. Int J Innov Technol Explor Eng. 2019;9(1):1211–5. https://doi.org/10.35940/ijitee.L3108.119119


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