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JMIR Cardio

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Published on 19.12.17 in Vol 1, No 2 (2017): Jul-Dec

Preprints (earlier versions) of this paper are available at http://preprints.jmir.org/preprint/8301, first published Jul 02, 2017.

This paper is in the following e-collection/theme issue:

    Original Paper

    Assessing the Use of Wrist-Worn Devices in Patients With Heart Failure: Feasibility Study

    1Ted Rogers Centre of Excellence in Heart Function, University Health Network, Toronto, ON, Canada

    2Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada

    Corresponding Author:

    Heather Joan Ross, MD, MHSc

    Ted Rogers Centre of Excellence in Heart Function

    University Health Network

    Toronto General Hospital

    190 Elizabeth St

    Toronto, ON, M5G 2C4

    Canada

    Phone: 1 416 340 3482

    Email:


    Related Article:

    This is a corrected version. See correction statement: http://cardio.jmir.org/2018/1/e10149/

    ABSTRACT

    Background: Exercise capacity and raised heart rate (HR) are important prognostic markers in patients with heart failure (HF). There has been significant interest in wrist-worn devices that track activity and HR.

    Objective: We aimed to assess the feasibility and accuracy of HR and activity tracking of the Fitbit and Apple Watch.

    Methods: We conducted a two-phase study assessing the accuracy of HR by Apple Watch and Fitbit in healthy participants. In Phase 1, 10 healthy individuals wore a Fitbit, an Apple Watch, and a GE SEER Light 5-electrode Holter monitor while exercising on a cycle ergometer with a 10-watt step ramp protocol from 0-100 watts. In Phase 2, 10 patients with HF and New York Heart Association (NYHA) Class II-III symptoms wore wrist devices for 14 days to capture overall step count/exercise levels.

    Results: Recorded HR by both wrist-worn devices had the best agreement with Holter readings at a workload of 60-100 watts when the rate of change of HR is less dynamic. Fitbit recorded a mean 8866 steps/day for NYHA II patients versus 4845 steps/day for NYHA III patients (P=.04). In contrast, Apple Watch recorded a mean 7027 steps/day for NYHA II patients and 4187 steps/day for NYHA III patients (P=.08).

    Conclusions: Both wrist-based devices are best suited for static HR rate measurements. In an outpatient setting, these devices may be adequate for average HR in patients with HF. When assessing exercise capacity, the Fitbit better differentiated patients with NYHA II versus NYHA III by the total number of steps recorded. This exploratory study indicates that these wrist-worn devices show promise in prognostication of HF in the continuous monitoring of outpatients.

    JMIR Cardio 2017;1(2):e8

    doi:10.2196/cardio.8301

    KEYWORDS



    Introduction

    Exercise capacity and raised heart rate (HR) are important prognostic markers in patients with heart failure (HF) [1,2]. In clinic, we rely on patients’ self-reported exercise capacity and classify their symptoms based on the New York Heart Association (NYHA) scale. Although widely used, this classification is subjective and poorly reproducible [2]. Furthermore, clinicians are exposed to only a snapshot of the patients’ HR in the ambulatory setting. There has been significant growth in wrist-worn fitness devices that track activity and HR [3]. These wearable devices use infrared and green light emitting diodes to track HR using the photoplethysmography (PPG) method [4]. The aim of this study was to validate the accuracy of HR monitoring using Fitbit and Apple Watch at rest and during structured cardiopulmonary exercise testing in healthy individuals and to then examine the relationship of physical activity in patients with HF.


    Methods

    We conducted a two-phase study assessing the accuracy of HR using Apple Watch and Fitbit wrist-based devices in healthy participants and then as continuous HR monitoring in patients with HF. In Phase 1, 10 healthy individuals wore a Fitbit, an Apple Watch, and a GE SEER Light 5-electrode Holter monitor while exercising on a cycle ergometer with a 10-watt step ramp protocol. During the first 60 seconds of the test, the workload was set to 0 watts and followed by increments of 10 watts with a maximum workload of 100 watts. In the recovery period, the workload was decreased to 10 watts. In Phase 1, two participants were excluded as data from one device could not be recorded. In Phase 2, 10 patients with HF with NYHA Class II-III symptoms wore both wrist devices for 14 days to capture overall step count/exercise levels. Two patients were excluded due to incomplete data recorded.

    For Phase 1, we calculated a single measures intraclass correlation (ICC) and a 95% confidence interval, specific to each exercise workload, between HR measured by Holter (gold standard) and HR measured by the Fitbit and Apple Watch devices. The mean ICC and 95% confidence interval for each device against the Holter as the gold standard was plotted against exercise workload.

    For Phase 2, we used a Kruskal-Wallis rank test to compare the mean number of steps, calculated by either device, between patients in NYHA Class II or III. We used STATA 13.1 and SPSS 22 statistical packages for our statistical analysis. The study protocol was approved by the University Health Network Research Ethics Board.


    Results

    Recorded HR by both devices was not significantly related with the Holter HR at rest (Fitbit ICC=.263, 95% CI 0.257-0.447, Apple Watch ICC=.218, 95% CI 0.010-0.408). However, with cycle ergometer workloads of 60-100 watts, both devices had stronger agreement with the Holter HR (Figure 1).

    Table 1 shows the baseline demographics of the patients included in Phase 2. Patients were predominantly male (5/8, 63%), with an average age of 58 years and ischemic cardiomyopathy (5/8, 63%). All patients were on guideline-directed medical therapy including a betablocker and either an angiotensin-converting enzyme (ACE) inhibitor or angiotensin-receptor blocker (ARB) when indicated. As shown in Figure 2, Fitbit recorded a mean 8866 steps/day for NYHA II patients versus 4845 steps/day for NYHA III patients (P=.04). In contrast, Apple Watch recorded a mean 7027 steps/day for NYHA II patients and 4187 steps/day for NYHA III patients (P=.08).

    Figure 1. Intraclass correlation curves (solid) with 95% confidence intervals (dotted) for workload comparing Fitbit to Holter and Apple Watch to Holter in healthy individuals.
    View this figure
    Table 1. Demographics and baseline data.
    View this table
    Figure 2. Total daily steps recorded (with 95% confidence interval) by Fitbit and Apple watch over 14 days for patients with New York Heart Association (NYHA) Class II and III symptoms .
    View this figure

    Discussion

    Principal Findings

    In this study, both PPG-based monitors had difficulty predicting HR when compared to a 5-lead electrode electrocardiogram (ECG) Holter as the gold standard in a cardiopulmonary study where HR is expected to change quickly over a 10-minute period. A recent study by Wang et al reported similar variability among four popular wrist-worn devices in relation to standard ECG limb leads and a Polar H7 chest strap monitor [5]. The HR underestimation was due to the inherent limitation of PPG that requires a longer settling time processing and averaging to eliminate optical and motion artifact. As a result, PPG monitors underestimated HR and showed poor correlation until the latter stages of the ramp study where HR would tend to saturate and the settling time was sufficient for the PPG HR estimation to compare favorably to the ECG gold standard. Although this indicates that PPG is less suited for dynamic HR measurements, in an outpatient monitoring context, PPG may be suitable for long-term static measurement of HR over long periods of time where settling time would not be an issue [6].

    Strengths and Limitations

    When assessing exercise capacity, the Fitbit better differentiated patients with NYHA II versus III by the total number of steps recorded. The limitations in this study include the small sample size; larger studies will be needed to confirm these findings. This is the first study in the literature that suggests the possibility of better classifying patients, quantitatively, and potentially remotely, rather than the current practice of determining class through self-reported symptoms with its inherent limitations. Reductions in daily step counts may herald the onset of progressive NYHA symptoms alerting physicians to assess patients in a timely manner.

    Conclusion

    This exploratory study indicates that wrist-worn devices show promise in prognostication of HF in the continuous monitoring of outpatients, but they require further validation.

    Conflicts of Interest

    None declared.

    References

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    2. Raphael C, Briscoe C, Davies J, Ian WZ, Manisty C, Sutton R, et al. Limitations of the New York Heart Association functional classification system and self-reported walking distances in chronic heart failure. Heart 2007 Apr;93(4):476-482 [FREE Full text] [CrossRef] [Medline]
    3. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA 2015 Feb 10;313(6):625-626. [CrossRef] [Medline]
    4. Allen J. Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 2007 Mar;28(3):R1-39. [CrossRef] [Medline]
    5. Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol 2017 Jan 01;2(1):104-106. [CrossRef] [Medline]
    6. Kroll R, Boyd JG, Maslove DM. Accuracy of a wrist-worn wearable device for monitoring heart rates in hospital inpatients: a prospective observational study. J Med Internet Res 2016 Sep 20;18(9):e253. [CrossRef] [Medline]


    Abbreviations

    ACE: angiotensin-converting enzyme
    ARB: angiotensin-receptor blocker
    ECG: electrocardiogram
    HF: heart failure
    HR: heart rate
    ICC: intraclass correlation
    NYHA: New York Heart Association
    PPG: photoplethysmography


    Edited by G Eysenbach; submitted 02.07.17; peer-reviewed by P Athilingam, JB Park; comments to author 01.10.17; revised version received 10.10.17; accepted 29.10.17; published 19.12.17

    ©Yasbanoo Moayedi, Raghad Abdulmajeed, Juan Duero Posada, Farid Foroutan, Ana Carolina Alba, Joseph Cafazzo, Heather Joan Ross. Originally published in JMIR Cardio (http://cardio.jmir.org), 19.12.2017.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on http://cardio.jmir.org, as well as this copyright and license information must be included.