Mobile Self-monitoring ECG Devices to Diagnose

Mobile Self-monitoring ECG Devices to Diagnose Arrhythmia (AR) that coincide with Palpitations: A Scoping Review Dr Hannah R. Marston1*, Dr Robin Hadley2, Dr Duncan Banks3 Ms Maria del Carmen Miranda Duro4

*1

Research Fellow, Health and Wellbeing Priority Research Area, Faculty of WELs, The Open University, Buckinghamshire, Milton Keynes, MK7 6AA 2 Research Consultant, Health and Wellbeing Priority Research Area, Faculty of WELs, The Open University, Buckinghamshire, Milton Keynes, MK7 6AA 3 Senior Lecturer, School of Life, Health & Chemical Sciences, Faculty of Science, Technology, Engineering & Mathematics, The Open University, UK 4 PhD Student. Faculty of Health Sciences, Department of Physiotherapy, Medicine and Biomedical Sciences, Oza Campus, University of A Coruña, A Coruña, 15006, Spain. *Correspondence Address Dr Hannah R. Marston, Research Fellow, Health and Wellbeing Priority Research Area, School of Health, Wellbeing & Social Care, Walton Drive, The Open University, Buckinghamshire, Milton Keynes, MK7 6AA [email protected] / [email protected]

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Abstract Background: The use and deployment of mobile devices across society is phenomenal. However, little is known about the use and deployment of mobile ECG monitoring of palpitations and arrhythmia. The popularity of the devices tied with the increase of individuals monitoring their health highlight the paucity of material on this recent trend. Objective: In this scoping literature review we identify the contemporary evidence which reports the use of mobile ECG monitoring to assess palpitations and AR across populations. Methods: The search was conducted across five electronic databases: Association for Computing Machinery (ACM), CINHAL, Google Scholar, PubMed, and Scopus, between February and March 2018. Results: A total of 981 records were identified and following the inclusion and exclusion criteria a total of nine records formed the final stage of the review. The results identified a total of six primary themes: purpose, environment, population, wearable devices, assessment, and study design. A further 24 secondary themes were identified across the primary themes. These included: detection, cost effectiveness, recruitment, type of setting, type of assessment, and commercial or purpose-built mobile device. Conclusions: This review highlights that further work is required to understand the impact of mobile ECG devices on how arrhythmias and palpitations are assessed and measured across all populations and ages of society. A positive trend revealed by this review demonstrates how mobile ECG devices can support primary care providers to deliver high-levels of care, at a low cost to the service provider. This has several benefits: alleviation of patient anxiety, lowering the risk of morbidity and mortality, while progressively impacting on national and international care pathway guidelines. Limitations of this work contain the paucity of knowledge and insight from primary care providers and lack of qualitative material. We argue that future studies consider qualitative and mixed-methods approaches studies to complement quantitative methodologies and to ensure all actors’ experiences are recorded. Keywords: Cardiology; Wearable Devices; Community Care; Primary Care; Technology; Clinical Care; Scoping Review

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Introduction The United Nations estimate the prevalence of patients presenting with AF will increase as a percentage of the global population [1]. Across the United States of America (USA), it is estimated between 2.7 and 6.1 million people experience AF, and by 2030 this is expected to increase to 12.1 million people [1]. In 2013, AF accounted for one per cent [1] of the United Kingdom’s (UK) National Health Service (NHS) budget. The purpose of this review was to explore the current trends in the use of wearable stand-alone devices capable of recording the electrical activity of the heart electrocardiogram (ECG/EKG) used for the detection of cardiac arrhythmias associated with palpitations. People describe palpitations as a feeling that their heart is pounding or fluttering or that the heart beat is irregular [3-4]. Such feelings can last from a few seconds to several minutes and are perceived by patients as something serious and a cause for concern [3-4]. There are many reasons for palpitations amongst these are: changes in emotional or psychological state, the use of hormones, prescribed and illegal drugs, excessive alcohol, smoking, strenuous exercise and excessive consumption of caffeinated drinks. In most cases, palpitations invariably raise a person’s anxiety leading to increased visits to their General Practitioner (GP) or hospital. Furthermore, palpitations are connected to greater morbidity, a higher risk of stroke, heart failure and an increase risk of mortality [5-9]. In the UK, The Arrhythmia Alliance [3] note four out of 100 people aged >65 years are affected by AF one of the most common types of AR. Patients’ present with varying symptoms including palpitations, shortness of breath or chest pains. However, some people may not display any symptoms, but will be detected through other signals [3]. Repeated visits to the GP lead to the phenomenon of the concerned healthy patient who may feel they are wasting health practitioners time and adding unnecessary costs on to the health service. However, their quality of life (QOL) is severely affected by this health complaint. If ARs are suspected the current recommended advice is to monitor a patient either in a hospital environment or to wear a 24-hour ECG device such as Holter monitor [3-4]. The recent development of substantially cheaper wearable technologies provides a challenging alternative to the traditional approach. These new devices are worth considering because they can reduce the time to diagnosis and be cost-effective, while enabling heart activity to be monitored over for a prolonged period. By contrast the traditional alternatives are uncomfortable to use and can only be worn for a very limited time, or in the case implanted loop recorders, require invasive surgery. Since 2010, wearable devices such as Fitbit devices, Jawbone UP, Garmin Vivofit and Misfit Shine have increasingly been used to monitor and analyze one’s daily activity through self-tracking users’ progress over time. Usually, goal oriented (gamification) tasks over a set time are agreed by the user (i.e. walk 10,000 steps per-day). The user can then review their progress, share their data with their friends, family and health practitioner (i.e. physician, nurse or consultant). Given the rise in ageing populations, reduction in healthcare services and additional strains on primary care, there is a need to explore alternative, accurate and cost-effective solutions to detect and diagnose AF.

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Contemporary evidence [9-13] provides an insight into the use of mobile ECG monitoring via wearable devices to measure heart rate and rhythm. Cheung and colleagues [9] discuss the current and ongoing developments of wearable devices which have entered the consumer market at a phenomenal rate, enabling physiological data, sleep patterns, Heart Rate (HR) and much more to be tracked. Cheung et al., [9] describe the various wearable devices which have the ability to track HR and AR. Nonetheless Cheung et al [9] noted that one of the limitations of wearable devices was their level of accuracy, that has only been evaluated on small sample sizes of patients presenting with unique symptoms. Furthermore, it is posited by Cheung et al., [9] the deployment of wearable devices within a clinical setting for detection or treatment of AF remains unanswered. Pevnick and colleagues [10] retrospective paper explores existing and future wearable devices, which have been designed specifically to measure activity, heart rate (HR) and heart rhythm. However, their paper is limited its examination of information. Moreover, this paper provides limited information and lacks critical insight into the deployment of mobile ECG monitoring in primary care settings. For example, it does not account for the perspective of health practitioners, physicians, and cardiologists. Likewise, their proposed frameworks and taxonomies lack clarity and theoretical grounding, resulting in a paucity of in-depth knowledge and experience of these devices. The remaining three studies [11-13] used the Zenicor ECG mobile device to explore AR. Two studies recruited participants aged 18> years [11], [13] while the third study focuses on AR on children aged between 5 and 17 years[12]. Hendrix and colleagues [11] conducted a prospective, observation, cross-sectional study within a clinical physiology department at a hospital. They recruited 108 participants who had been referred for ambiguous palpitations, or dizziness. All participants were given a 24-hour Holter ECG in addition to the Zenicor EKG handheld (for 30 seconds); readings were taken twice a day and when experiencing symptoms. In total, 95 patients (42 men and 53 women) were assessed with a mean age of 54.1 years completed the register. Results from the 24-hour Holter ECG ascertained two patients with AF and a third with atrioventricular (AV). The Zenicor EKG handheld AF in a further nine patients with three showing paroxysmal supraventricular tachycardia (PSVT), and one with AV-block-II. Hendrikx and colleagues concluded the use and deployment of the Zenicor EKG handheld to be more effective with patients than the 24hour Holter ECG in detecting AF and PSVT, specifically with patients experiencing ambiguous symptoms. The study conducted by Usadel and colleagues [12] conducted an investigation with patients aged 0-17 years, who have or do not have congenital heart defects, pacemaker/ICD or AF. A comparison with a lead-12 ECG was conducted with Zenicor EKG handheld. Recordings and transmission of data were completed successfully by the Zenicor EKG device, and data readings showed, thorough and consistent readings, while P wave detection was reported to be challenging, while 82 participants displayed heart rhythm disturbances. Detection of sensitivity via the Zenicor EKG handheld identified 92% diagnosed with supraventricular tachycardia, while abnormal ECGs were identified with 77% and 92% sensitivity and specificity respectively. Usadel and colleagues

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conclude the use of such a device with children is appropriate and suitable as a tool for diagnosis for detecting and excluding tachycardia in children. Moreover, Dahlqvist and colleagues [13] evaluated the Zenicor EKG handheld to ascertain whether AF and cardiac autonomic dysfunction is diagnosed in children with univentricular hearts? A total of 27 patients were recruited and used the Zenicor EKG handheld over a period of 14-days, while a manual AF analysis was conducted. Results from the study identified asymptomatic AF was discovered in one patient while HRV was also identified in some patients and Dahlqvist et al. [13] conclude the use the Zenicor EKG handheld device is a useful tool for detecting AF and cardiac autonomic dysfunction. In the UK the National Institute for Health Care Excellence (NICE) [14] provides advice, evidenced based resources, guidance, information, policy, practice, procedures, and quality standards for health, public health, and social care institutions and practitioners. However, to date there is no official National Health Service (NHS) information relating to the use of wearable technologies in the detection of AF. Rationale for This Review Wearable devices capable of measuring heart rate and rhythm have entered the consumer market at a phenomenal rate. Although this complex and rapidly changing field represents an attractive prospect for the diagnosis of heart and circulatory disease there are few reviews that look at mobile self-monitoring ECG devices designed to diagnose cardiac arrhythmia that coincide with cardiac event-related conditions such as palpitations. This review attempts to scan present day literature and conclude whether evidence obtained from such devices can support primary care providers to deliver highlevels of care, at a low cost to the service provider. Although the cause of palpitations is usually benign, they are occasionally a manifestation of potentially life-threatening arrhythmia. Under conditions where abnormal heart rhythms and cardiac symptoms are irregular and infrequent such mobile self-monitoring ECG devices could be used as event monitors during symptoms such as palpitations. Cheung et al [9] noted that one of the limitations of wearable devices was their evaluation using small sample sizes of patients presenting with unique symptoms. This review adds further evidence for the support of such devices in a community setting. Aim of this Review This scoping review is timely in that it provides an insight into an increasingly complex field that combines the precision demanded by the medical profession with wearable technology that has advanced rapidly with the development of miniaturized, and increasingly accurate, sensors. The aim of this review was to answer the question: what contemporary evidence reports the use of mobile ECG monitoring to assess palpitations that occur with AR across populations. 5

Methods Objectives This review was guided by a, five-stage framework [15], which includes: (a) establishing the research question, (b) identification of pertinent studies, (c) choice of studies (d) mapping the data (e) collating, summarizing and reporting the findings [16]. Search Strategy A systematic search of five electronic databases: Association for Computing Machinery (ACM); CINHAL; Google Scholar; PubMed, and Scopus. The material included was published from January 2010 to February 2018. The search was conducted between February and March 2018. Each database (Table 1) underwent individual search strategies and the limiters were ‘English’ and ‘humans’. Articles, their references (BibTeX format) and where possible the CSV files, were exported into Dropbox and Mendeley. An inclusion and exclusion criteria were developed and deployed with the complete criteria is given in . Table 1: Database searched, search terms used, reasons for adaptations Database ACM

CINHAL

Google scholar (wearable device)

Search term used (Arrhythmia Atrial Fibrillation ECG EKG Palpitations wearables) AND (-Algorithms -map -sensor -consumer -mathematical -statistical) AND keywords.author.keyword: (Arrhythmia Atrial Fibrillation ECG EKG Palpitations wearables -wavelet -brain -skin -posture -music -grasp -grip -sonic -speculative) AND record Abstract: (Arrhythmia Atrial Fibrillation ECG EKG Palpitations wearables) (TX ("Palpitations" OR "Arrhythmia" OR "Atrial Fibrillation")) AND (TX "Wearable ECG") AND (TX "Wearable EKG") OR (TX ("Wearable technologies" OR Wearable devices)) NOT (TX ("Catheter" OR "Surgery" OR "Ablation" OR "Catheter ablation" OR "Nursing Practice" OR "Gait")) NOT (TX "Students") ECG EKG Alive OR Cor OR Zoe OR Patch OR Scanadu OR Scout OR Perminova OR CoVa OR necklace OR Kardia OR ECG OR Necklace OR Cardio OR Analytics OR Heal OR Force OR Smart OR Cardio OR Beurer OR ME80 OR Beurer OR PM2 "wearable device" 6

Adaptions Manufacturers’/generi c names not recognized. NOT any: Algorithms map sensor consumer mathematical statistical. Keyword NOT: wavelet brain skin posture music grasp grip sonic Manufacturers’/generi c names not recognized. NOT "Catheter" OR “Surgery” OR "Ablation" OR "Catheter ablation" OR “Nursing Practice” NOT “Students” Excluded patents

Google scholar (wearable technology)

PubMed

PubMed MESH

Scopus

Scopus

ECG EKG Alive OR Cor OR Zoe OR Patch OR Scanadu OR Scout OR Perminova OR CoVa OR necklace OR Kardia OR ECG OR Necklace OR Cardio OR Analytics OR Heal OR Force OR Smart OR Cardio OR Beurer OR ME80 OR Beurer OR PM2 "wearable technology" Palpitations OR Arrhythmia OR Atrial Fibrillation And (ECG) AND (EKG) OR Wearable technologies OR Wearable devices)) AND (Alive Cor OR Zoe Patch OR Scanadu Scout OR Perminova CoVa necklace OR Kardia OR ECG Necklace OR Cardio Analytics OR Heal Force OR Smart Cardio OR Beurer ME80 OR Beurer PM25 OR Prince 180B OR Cardea SOLO OR Spyder Pro OR Spyder Personal OR MiCor A100)) NOT (sport AND algorithms)) Wearable devices OR Wearable technologies AND (ECG OR EKG) AND (Palpitations OR Arrhythmia OR Atrial Fibrillation) Palpitations OR Arrhythmia OR Atrial Fibrillation And {ECG} AND {EKG} OR Wearable* AND techonolo* OR device AND NOT algorithms Alive Cor" OR "Zoe Patch" OR "Scanadu Scout" OR "Perminova CoVa Necklace" OR "QardioCore" OR "Kardia" OR "ECG Necklace" OR "Cardio Analytics" OR "Heal Force" OR "Smart Cardio" Or "ChoiceMMed" OR "Beurer ME80" OR "Beurer PM25" OR "Zodore" OR "Prince 180B" OR "Cardea SOLO" OR "Spyder Pro" OR "Spyder Personal" OR "MiCor A100")

Excluded patents

AND NOT sport AND algorithms

Manufacturers’/generi c names not recognized. AND NOT sport AND algorithms Manufacturers’/generi c names not used Use wildcard * AND NOT algorithms Dropped: "Palpitations" OR "Arrhythmia" OR "Atrial Fibrillation" AND "ECG" OR "EKG"

Selection Criteria Studies were included if they met the inclusion criteria (). Titles of papers and abstracts were screened initially and where necessary, the full paper was vetted and the final decision determined by two authors (HM and DB). Both HM and DB reviewed all articles from each database separately and then collectively. Where additional discrepancies were highlighted, HM and DB reviewed and discussed the respective paper(s), before a final decision was made. Abstracts and full texts were retrieved to

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determine if they met the inclusion/exclusion criteria. Both authors jointly decided the final selection of papers for inclusion.

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Table 2: Criteria for study selection Inclusion Mobile apps (mApps) ECG/EKG Cardiogram Wearables Atrial Fibrillation (AF) Heart Human ECG Wearable Devices/Patches Mobile Health (mHealth) Security/Privacy Smart Fabric/textiles Papers published in Journals Commercial technologies Purpose built technologies Encryption Big Data Human Study designs: (RCT, Exploratory, Cohort, Prospective, Feasibility)

Exclusion Master’s and PhD thesis Conference proceedings Book Chapters Reports Reviews Pulse monitoring Theoretical papers Athletes Defibrillators Intensive care unit (ICU) or high dependency unit (HDU) WSBN Animals/non-human Co-morbidities (i.e. transplant patients) Newsletters Editorials PhD, MSc & BSc Thesis

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IDENTIFICATION

981 records identified via database searches

SCREENING

ELIGIBILITY

11 records irretrievable: 970 resports accessed

858 records assessed for eligibility: 800 excluded

112 duplicated papers removed

58 papers assessed: 49 papers excluded

INCLUDED

9 papers included in review

Exclusion criteria: Master’s or PhD thesis, Books or Book Chapters, Editorials, Newsletters, Conference proceedings, Reports, Reviews, Theoretical papers, Pulse monitoring, Athletes, Defibrillators, High dependency unit (HDU), Intensive care unit (ICU), WSBN, Co-morbidities (i.e. transplant patients), Animals/non-human

Figure 1: Diagram showing review process

Results

The initial database search yielded 981 records across five databases, 11 records were not available (six from CINHAL and five from Google Scholar). Following the removal of 112 duplicates, the first and second reviewers independently assessed 858 records for inclusion. Eight hundred records did not meet the inclusion criteria and the remaining 58 records were independently reviewed by HM and DB following the strategy outlined above. Nine records met the criteria for inclusion (). General characteristics of studies The initial search identified 981 abstracts published since 2012, of which 800 were excluded; 112 duplicates were removed, and the remaining 58 papers had full text assessment. A further 49 papers were then excluded (), resulting in nine papers that met the criteria for inclusion. Most of the final nine articles were published between 2015 and 2017 and the majority were located in the PubMed database (n=5). The sample size varied across all articles from 25,415 participants [17] to 22 participants [18]. The total sample includes 26,902 participants with a mean age of 2989±8411.47 for males and 49.25±21.85 for females constitute. Four studies reported the percentage or number of female participants while one study recruited only male participants [11-14]. Three 10

studies were performed in Europe [17, 18, 21], five studies were conducted in the USA [19, 22-25] and one study was conducted in Africa [21]. Study design varied, with three studies reporting an observation cohort study [17], [21], [23], two studies reported experimental study [20], [25], while one study [24] reported an experimental comparative study design; a further two studies [19-21] reported a prospective observation study design, and one study [17], reported an experimental RCT design. Themes Figure 2 displays the six primary themes and the 24 secondary themes which were identified through the review process across the nine selected papers. Across the majority of the secondary themes, a further breakdown has been provided that details the type of assessments used in the studies. Primary themes A total of six primary themes (purpose and objectives, environment, population, wearable devices, assessment, and study design) were identified through data analysis and are explained in the proceeding sections. Purpose and objectives theme comprised of five secondary themes: detection, validation & feasibility, comparison, cost effectiveness and study protocol. One study [21] does not directly report the purpose of the study, yet reading the paper in full, the purpose is to compare the AliveCor device with routine care. A total of five secondary themes were identified under this primary theme and included: detection, validation and feasibility, comparison, cost-effectiveness and study protocol. Four studies [17], [19], [22], [25] used detection of AF through a respective device, to validate and assess the feasibility of the ECG device (i.e. AliveCor, Zenicor) [20]. One study aimed to undertake a comparison of the energy expenditure (EE) [20] during a particular activity (i.e. resistance training) and compare a mobile ECG device against the traditional method of a 12-lead ECG [18], [23]. Cost effectiveness (CE) was also assessed in one study [17]. We have included a study protocol [25] that outlines a single center, prospective randomized control trial (RCT), deploying the AliveCor ECG device over a 30-day period. This study protocol reported three aims: (a) to document AF using real-time ECG capture; (b) to evaluate the impact on AF treatment and Quality-Adjusted Life Years (QALYs – a generic measure of disease burden); and (c) to evaluate the effectiveness of text messaging on AF knowledge and proactive self-management of multiple chronic conditions. Environment theme culminated in the type of environment in which the selected studies were undertaken. A total of four secondary themes were identified: multiple screening centers, university/laboratory, rural community/hospital and a Veteran Affairs center. One study [20] was undertaken in a rural community/hospital where cardiology resources were limited and in the primary hospital located in the study there was one 12-lead ECG tape available. Five studies were undertaken in university/laboratory settings [19], [21], [23], [25], [27] and one study was undertaken in a hospital setting [18]. Another study 11

[17] had multiple screening centers (n=6) as reported in their earlier publication [28]. One study [22] was conducted at the Veteran Affairs (VA) Palo Alto Health Care System. Population theme encompasses three secondary themes: recruitment, sample and sample size. Across all studies, the nature of recruitment varied from hospital clinics [21], to VAs, specific cardiology clinics/departments [20], [23], [25], or university students [23]. The sample also varied and included older adults [25], athletes [23], healthy adults [23] and those with existing comorbidities (i.e. coronary disease, heart failure) [23]. The size of samples ranged from 25,415 participants [18] to as few as 22 participants [18]. One study recruited participants aged between 18-35 years [19] while another study primarily recruited adults aged between 75-76 years [17]. One study recruited all male participant [22] while the majority of all studies recruited participants of both genders. Wearable devices theme encompassed three secondary themes: commercial, wearable patch and purpose-built. Two studies used the Zenicor ECG device [17], [18], one study [19] used multiple mobile ECG devices including the Apple Watch Series 2, Fitbit Blaze, Fitbit Charge 2, Polar H7, Polar A360, Garmin Vivo smart HR, TomTom Touch and the Bose Sound Sport Pulse (BSP) headphones. Five studies [20], [21], [23]-[25] used the AliveCor Kardia mobile device which is attached to an Apple iPhone. One study used a purpose built mApp, called PULSE-SMART to undertake participants ECG readings and that connected to an Apple iPhone 4S. Finally, one study [22] deployed the Zio wearable patch which sits against the chest skin. Assessment theme encapsulates 11 secondary themes: completion of assessment (i.e. health practitioner) [20]; self-assessment surveys – non validated (health – anxiety, perceived benefits from healthcare practitioners) [18], [21] qualitative data (i.e. patient diary of symptoms) [22]; AF scales for Assessment [24], Medication Assessment, Other Assessment [24], Quality of Life [25], Anxiety Scale [24]; Technology-based Assessment and ECG monitoring (Holter or Mobile device) [17], [18], [20-25] and patient health medical records (demographics, medical history, health behaviors) [22]. One study [24] deployed the majority of assessments (n=10), in conjunction with baseline and monthly (via electronic medical records system review) data throughout the six month study period. These included respectively, the Atrial Fibrillation Knowledge Scale (AFKS) [29], the Canadian Cardiovascular Society Severity in Atrial Fibrillation scale (CCS-SAF) [30], the Atrial Fibrillation Effect on Quality of Life (AFEQT) [31], the Control Attitudes Scale-Revised (CAS-R) [32], the Morisky 4-item Self-Report Measure of Medication-Taking Behavior (MMAS-4) [33], [34], the Self-Efficacy for Appropriate Medication Use Scale (SEAMS) [35], the Short Form Health Survey (SF-36 Quality of Life) [36], [37], European Questionnaire 5 Dimensions (EQ-5D) [38], [39], the Patient Health Questionnaire (PHQ-9) [40], and the State Trait Anxiety Inventory (STAI) [41]. Study design theme encompasses three secondary themes: study criteria, study type and duration. Across the studies (depending upon the study design), some studies required their patients/participants t provided additional information in conjunction to their respective mobile ECG reading. Readings were taken over various times in the 12

respective studies including: twice per week for 12-months, [21] or three times per week over a six-month period [24] a two-week period [17], [20], [22], 30-days [18], and a 2minute waveform reading [25]. Five studies noted their study design that a set criterion (i.e. inclusion/exclusion) was included. For example, [18] recruited 22-participants with a diagnosis of symptomatic paroxysmal AF, while [23] recruited 335-participants from Division I athletes, healthy young adults, and cardiology clinics. Study [24] recruited adults >18 years, who had a 30-day history of AF, were either male or female, able to use a smartphone, and participants who were able to read and receive text messages on the day of enrolment onto the study. Furthermore, study, [22] recruited participants from cardiology, echocardiography and stress-testing clinics with additional inclusion criteria of specific age and having a minimum of two risk factors [22]. The exclusion criteria included prior AF diagnoses, stroke, transient ischemic attacks, implantable pacemaker or defibrillator or with someone who experienced palpitations or syncope in the previous year [22]. One study [25] was aimed at testing a hypothesis using a prototype which measured waveforms via the iPhone 4S. Across all selected studies, each one reported a different type of study (i.e. RCT or comparative). None of the nine studies reported the same study design. Table 3: Summary of articles (N=9) included for this scoping review 1st Author Year Country Aronsso n [17] 2015 Sweden

Boudrea u [19] 2017 USA

Data base

Objectives

Partici pants

Pub Med

To estimate the costeffectiveness of 2 weeks of intermittent screening for asymptomatic atrial fibrillation (AF) in 75/76-yearold individuals To determine the validity of 8 monitors for Heart Rate (HR) compared with an ECG and seven monitors for Energy Expenditure (EE) compared

N=25, 415 Aged 75-76 years Femal e 55.9%

CIN HAL

N= 50 Aged1 8-35 yearsF emale = 56% female

Study Design

Observati onal Cohort study

Experime ntal comparati ve study

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Assess ment(s )

Technology

Main Findings

30 s recordi ngs twice daily, or when sympt oms of palpita tions for 2 weeks. Sessio n 1: Perfor med a graded exercis e test on a cycle ergom eter Sessio n 2:

Zenicor EKG device

With the use of a decision analytic simulation model, it has been shown that screening for asymptomatic AF in 75/76-yearold individuals is costeffective.

Apple Watch Series 2, Fitbit Blaze, Fitbit Charge 2, Polar H7, Polar A360, Garmin Vivosmart HR, TomTom Touch, and

This study revealed that both HR and EE differed among the eight wearable devices during both cycling and resistance exercise, and had varying levels of validity when compared with a six-lead ECG and

with a metabolic analyzer during graded cycling and resistance exercise.

Doliwa [18] 2012 Sweden

Scop us

To compare short intermittent heart rhythm recording with or without symptoms with continuous ECG recordings for 30-days, with

N=22 Aged 46-77 years Femal es 27%

Experime ntal study, randomize d controlled blinded trial.

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Perfor med a graded exercis e test of 4 differe nt strengt h trainin g exercis e on a resista nce exercis e machi ne Repeat ed 3days later in the laborat ory. Exclus ion: cardio vascul ar disease or muscul oskelet al injury in the last 6 month s. Record ings were taken twice daily; once in the mornin g and once in the

Bose SoundSport Pulse (BSP) headphones.

metabolic analyzer. It was also observed that HR measures from wearable devices were more accurate at rest and lower exercise intensities than at higher intensities. Among tested devices, HR accuracy, as reflected by intraclass correlation and MAPE values, was highest in the PH7, BSP, and AWS2. The PH7 and AWS2 also proved to provide more accurate caloric estimations than other devices. HR from wearable devices differed at different exercise intensities; EE estimates from wearable devices were inaccurate.

Zenicor EKG device.

AF episodes were diagnosed in 18 (82%) patients compared with 7 (32%) patients using continuous ECG, (p = 0.001. Short-term ECG registrations over extended periods’ time seem to be a more sensitive tool, compared

2 registrations of 10-seconds per day.

Evans [20] 2017 Kenya

Scop us

To examine the feasibility of using mobile ECG recording technology to detect AF.

N=50 Aged5 4.3 ±20.5 years. Femal es 66%

Prospectiv e observatio nal study

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evenin g for a 30-day period. Partici pants were asked to record when experi encing arrhyth mia sympt oms (record ed as sympt omatic ).. 2week duratio n In a rural comm unity Health practiti oners (physi cians, clinica l officer s, nurse) compl eted a selfassess ment of 4item scale relatin g to ICT access, knowl edge/i nterpre

with short continuous ECG recordings, for detection of AF episodes.

AliveCor Kardia Mobile ECG device

ECG tracings of 4 of the 50 patients who completed the study showed AF (8% AF yield), and none had been previously diagnosed with AF.. Using mobile ECG technology in screening for AF in lowresource settings is feasible, and can detect a significant proportion of AF cases that will otherwise go undiagnosed. Further study is needed to examine the costeffectiveness of this approach for detection of AF and its effect on reducing the risk of stroke in developing countries

Haberm an [23] 2015 USA

Goog le Scho lar

Compare the standard 12lead ECG to the smartphone ECG in healthy young adults, elite athletes, and cardiology clinic patients. Accuracy for determining baseline ECG intervals and rate and rhythm was assessed.

N=335 Aged3 5±20 years Femal e 51%

Experime ntal comparati ve study

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tation of results & percep tion of AF in the comm unity 30second lead I ECG wavef orms were obtain ed using an iPhone case or iPad. Standa rd 12lead ECGs were acquir ed immed iately after the smartp hone tracing was obtain ed. Deidentifi ed ECGs were interpr eted by autom ated algorit hms and adjudi cated

AliveCor device (30second ECG wireless reading). Patients trained over 1-2 minutes to take their own readings.

This study provides evidence that wireless ECG devices can be used on a large scale to detect rate, conduction intervals and AF. Incorporation of automated discrimination, with enhanced smartphone features with notification capability and decision support. Both smartphone and standard ECGs detected atrial rate and rhythm, AV block, and QRS delay with equal accuracy. Sensitivities ranged from 72% (QRS delay) to 94% (atrial fibrillation). Specificities were all above 94% for both modalities.

Hickey [24] 2016 USA

Pub Med

The primary aims of the iHEART study are to: (1) document AF using real-time ECG capture; (2) evaluate the impact on AF treatment and QALYs; and (3) evaluate the effectiveness of text messaging on AF knowledge and promoting proactive selfmanagement of multiple chronic conditions

N=300 Aged >18 years

Study protocol, observatio nal study. Single center prospectiv e

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by two boardcertifie d electro physio logists ECG readin g taken at baselin e. Compl ete all questio nnaires at baselin e and at 6 month sQuest ionnair es includi ng the Atrial Fibrill ation Knowl edge Scale,t he Canadi an Cardio vascul ar Societ y Severit y in Atrial Fibrill ation scale, the Atrial Fibrill ation Effect on Qualit y of

iPhone. AliveCor Mobile ECG Kardia app.

This will be the first study to investigate the utility of a mobile health intervention in a “real world” setting. We will evaluate the ability of the iHEART intervention to improve the detection and treatment of recurrent atrial fibrillation and assess the intervention's impact on improving clinical outcomes, quality of life, qualityadjusted life-years and diseasespecific knowledge.

Life ,the Contro l Attitud es ScaleRevise d , the Morisk y 4item SelfReport Measu re of Medic ationTaking Behavi or, the SelfEfficac y for Appro priate Medic ation Use Scale , the Short Form Health Survey , Europe an Questi onnair e5 Dimen sions , the Patient Health Questi onnair e , and the State Trait Anxiet y Invent ory .

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Halcox [21] 2017 UK

Pub Med

N=100 1, Aged= 72.6±5 .4 Females 53.34 %

Experime ntal study

Baseli ne charact eristics . Partici pant experi ence survey (compl eted at the end of the study). Questi ons includ ed anxiet y about their heart rhythm proble ms, more likely to visit their doctor, or prefer to switch to a study group (respo nses reporte d via a 10-pt visual analog scale). iECG patient s were asked about ease of use, restrict

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AliveCor Kardia device

Screening with twice-weekly single-lead iECG with remote interpretation in ambulatory patients ≥65 years of age at increased risk of stroke is significantly more likely to identify incident AF than RC over a 12month period. This approach is also highly acceptable to this group of patients, supporting further evaluation in an appropriately powered, eventdriven clinical trial.

ion of activiti es, anxiety , concer n about data securit y and a genera l satisfa ction with the device (via 5pt Likert scale).

McMan us [25] 2016 USA

Pub Med

To test whether an enhanced smartphone app for AF detection can discriminate

Experime ntal study

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Health econo mics were estimat ed from the UK NHS and person al social service s, using data from the study activit y and releva nt costs. Analys is of 219, 2min pulse recordi ngs

PULSESMART prototype App used via the iPhone 4S

The smartphonebased app demonstrated excellent sensitivity (0.970), specificity (0.935), and accuracy

between sinus rhythm (SR), AF, premature atrial contractions (PACs), and premature ventricular contractions (PVCs).

Turakhia 2015a [22] USA

Pub Med

To detect silent AF in asymptomatic in patients with known risk factors through the screening for AF using continuous ambulatory

N=75 Any female , Aged= 69±8.0 years.

Observati onal study, single center

21

Usabili ty questio nnaire to subgroup of n=65 app users. Exami ned the sensiti vity, specifi city, and predict ive accura cy of the app for AF, PAC, and PVC discri minati on from sinus rhythm using the 12lead EKG or 3lead teleme try as the gold standar d. Record s up to 14days of monito ring on a single vector. Partici pants

(0.951) for realtime identification of an irregular pulse during AF. The app also showed good accuracy for PAC (0.955) and PVC discrimination (0.960). The vast majority of surveyed app users (83%) reported that it was “useful” and “not complex” to use.

Zio wearable patch-based device

AF was detected in 4 subjects (5.3%; AF burden 28%}48%). Atrial tachycardia (AT) was present in 67% (≥4 beats), 44% (≥8 beats), and 6.7% (≥60 seconds) of subjects. The

ECG.

press the sympt omatic trigger on the device if sympt oms present ed Patient diary, detaili ng sympt oms. Baseli ne charact eristics: demographi cs, medica l history , ECG param eters, health behavi ors were abstrac t from patient medica l record by 2 trained investi gators

combined diagnostic yield of sustained AT/AF was 11%. In subjects without sustained AT/AF, 11 (16%) had ≥30 supraventricular ectopic complexes per hour Outpatient extended ECG screening for asymptomatic AF is feasible, with AF identified in 1 in 20 subjects and sustained AT/AF identified in 1 in 9 subjects, respectively. We also found a high prevalence of asymptomatic AT and frequent supraventricular ectopic complexes, which may be relevant to development of AF or stroke.

Discussion Principle Findings This review paper provides a contemporary insight into the growing field of mobile ECG monitoring and detecting AF that coincides with palpitations. Out of 981 abstracts a total of nine papers were selected for a comprehensive examination. A total of six primary 22

themes were identified and within each primary theme, a series of secondary themes were ascertained. Given the growing popularity of wearable devices, increase of ageing populations and the ability to provide cost-effective primary care, the evidence lends itself to physicians, cardiologists and organizations such as NICE [15] as a means of implementing mobile ECG monitoring into the pathways to care. The primary themes environment and population illustrate the varying research centers and laboratories, and although it could be argued these selected studies are community based, the participants are not reported to have been recruited through a physician, surgery, or from a hospital via a cardiologist. The theme of population highlighted the varying sample sizes and how in some instances, participants were recruited through cardiology clinics or departments. However, this was limited. Overall, the age range of participants illustrated a spread across population, ensuring those patients who presented with palpitations were involved in the studies. Wearable devices encompassed commercial, wearable and purpose-built technology as a means of detecting and measuring AR and AF during periods when palpitations are prevalent. Overall, four studies used the AliveCor Kardia device, accessible via the Apple iPhone, while two studies deployed the commercial ECG device Zenicor. To date, there has been a handful of studies published in academic journals [11]-[14], and accessible via the Zenicor website [42]. With the exception of [20] no other selected study provided an insight into the use of mobile ECG devices and monitoring from the perspective of health practitioners. The final nine selected papers provide insights into the varying rationales for monitoring palpitations and AR across societal populations. Studies have primarily aimed at exploring the cost effectiveness and energy expenditure of using this type of technology. We suggest that undertaking a community-based approach that included a physician(s) or consultant cardiologist would offer greater insights into the benefits of mobile ECG monitoring from the viewpoint of both health practitioner and providers. All nine studies used an assortment of assessments and measure, which formed 11 secondary themes. The assessment theme indicates the complexity of deploying mobile ECG devices in conjunction with additional health outcomes to ascertain patient’s levels of anxiety, quality of life, and detection of AF through self-reporting and/or clinical practitioners. Varying study designs were executed. Nonetheless, given the limited duration of assessment, the results showed a positive trend to detecting AF. While AF and AR may vary across patients, the studies did report patient response via the technology occurred at the time of the patient experiencing palpitations. Furthermore, the objectives of the studies also varied and ranged from validation, to feasibility, costeffectiveness and RCT. Drawing comparisons across the selected studies is problematic based upon the varying environments, assessments, populations and wearable devices. While, the cost effectiveness is a principal concern for primary care and healthcare strategists, preliminary evidence from this review (), coupled with findings from recent

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international studies [11]-[14] demonstrates the great potential of deploying wearable ECG devices across populations. Limitations One of the limitations of this scoping review is that each database requires its own set of limiters, consequently each search needed adapting. The database searches did not identify the published papers available via the Zenicor website [11]-[14], [29]. It is unclear why this was the case, given the search criteria for the databases and the inclusion criteria. However, given the papers were published in April and May 2018, this falls outside of the time period of when the databases were searched. Another paper is published in Swedish and as part of the inclusion criteria, we included papers that were published in English. Furthermore, the papers did not fit the inclusion criteria; weighted towards stroke, and/or there were sample issues. Although, [14] fits with the inclusion criteria, the authors decided not to include it into Table 3 given it did not appear in the search period. Moreover, [13] did not fit the inclusion criteria of this scoping review as participants in this respective study were children aged between 5 and 17 years old. One of the inclusion criteria for this study was samples to be aged 18> years and older. Future Research Based on the findings of this review, the authors propose several areas of furthering and expanding this research: 1. Clinicians and researches alike should explore the use of mobile ECG devices from the standpoint of health practitioners working in the delivery of primary and community care, similar to the work published by [20]. 2. Implementing and conducting qualitative data collection in future studies would provide a greater insight, understanding of the needs, apprehensions and expectations of practitioners in primary care, while also exploring the levels of engagement and expectations from patient’s and support networks. Taking into account the work published by [20] who illustrate the potential opportunities for mobile ECG monitoring from low, middle income countries (LMICs), this approach has the potential to offer substantial changes in developed and developing regions. 3. Future investigations should explore the adherence and adoption of mobile ECG devices, learning from previous health, gerontological and ICT studies [44]-[48]. Based on this previous research in different fields, technology has been deployed and evaluated for use by community dwelling adults living in different geographic locations. Understanding people’s motivations and behavior in relation to technology would significantly support future work in this field. In addition, the impact of technology efficacy by health practitioners on service delivery could be assessed. Moreover, further work needs to 4. Future studies should determine the exact cost-effectiveness of deploying mobile ECG devices providing evidence to health care strategists, governments and managers of the benefits to using this type of technology in the community. Which in turn will demonstrate the positive implications and change in pathways to care for the detection of AR and AF. 24

5. To ascertain how mobile ECG devices could impact on the delivery of primary care we suggest a large-scale feasibility study, encompassing variable populations (i.e. age range, ethnicity and socio economic) should be conducted to provide results to different actors (i.e. government, healthcare practitioners, healthcare strategists, researchers, patients and support networks). For example, the monitoring of patient anxiety, user engagement, quality of life and costeffectiveness. It is important that such studies include as full a range of actors as possible from primary care physicians, cardiologists, patients, lay people, patients’ support networks, health organizations (i.e. NICE), and government funding agencies. Conclusions This review demonstrates positive trends to using and deploying mobile ECG devices across different environmental settings and populations. With global populations set to increase [10] over the coming decades, the need to identify alternative solutions to facilitate and ensure primary care providers are able to deliver cost-effective healthcare is crucial, for both the service provider and the patient. Detecting and diagnosing AR/AF through the use of mobile ECG devices, would reduce the risk of morbidity and associated health implications such as stroke or mortality [5]-[7], [43]. Based on the evidence displayed in this review, the authors believe substantial work is warranted on both a national and international scale with a view to supporting primary care providers to deliver high-levels of care, at a low cost to the service provider. This, in turn will alleviate patient anxiety, risk of morbidity and mortality. In addition, it would positively impact on national and international guidelines concerning pathways to care. Acknowledgements Funding was sought via the Health and Wellbeing Priority Research Area (H&W PRA) at The Open University in January 2018. MD received funding through the Summon Grants program in conjunction with the company – INDITEX SA to undertake a 3-month stay at the Open University autumn 2018 forming part of her international doctoral program at the University of A Coruña. HM conceived, the paper, designed the methods section and undertook the paper selection for inclusion, analyzed the data and drafted the manuscript, DB conceived and contributed to the introduction, methods, and undertook the decisions of papers for inclusion selection, RH performed the search across the selected databases, and drafted the manuscript, MD analyzed the data. Conflicts of Interest None declared. Abbreviations ACM: Association of Computing Machinery 25

AF: Atrial Fibrillation AFEQT: Atrial Fibrillation Effect on Quality of Life AFKS: Atrial Fibrillation Knowledge Scale AR: Arrhythmia CAS-R: Control Attitudes Scale-Revised CCS-SAF: Canadian Cardiovascular Society Severity in Atrial Fibrillation scale CE: Cost Effectiveness ECG/EKG: Electrocardiogram EE: Energy Expenditure EQ-5D: European Questionnaire 5 Dimensions HR: Heart rate mApp: Mobile Apps NHS: National Health Service NICE: National Institute for Health Care Excellence PHQ-9: Patient Health Questionnaire QALY: Quality-Adjusted Life Years QOL: Quality of Life RCT: randomized controlled trial SEAMS: Self-Efficacy for Appropriate Medication Use Scale STAI: State Trait Anxiety Inventory UK: United Kingdom USA: United States of America VA: Veteran Affairs

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