Accuracy of wearable heart rate monitors in cardiac rehabilitation.
ABSTRACT: Background:To assess the accuracy of four wearable heart rate (HR) monitors in patients with established cardiovascular disease enrolled in phase II or III cardiac rehabilitation (CR). Methods:Eighty adult patients enrolled in phase II or III CR were monitored during a CR session that included exercise on a treadmill and/or stationary cycle. Participants underwent HR monitoring with standard ECG limb leads, an electrocardiographic (ECG) chest strap monitor (Polar H7), and two randomly assigned wrist-worn HR monitors (Apple Watch, Fitbit Blaze, Garmin Forerunner 235, TomTom Spark Cardio), one on each wrist. HR was recorded at rest and at 3, 5, and 7 minutes of steady-state exercise on the treadmill and stationary cycle. Results:Across all exercise conditions, the chest strap monitor (Polar H7) had the best agreement with ECG (rc=0.99) followed by the Apple Watch (rc=0.80), Fitbit Blaze (rc=0.78), TomTom Spark (rc=0.76) and Garmin Forerunner (rc=0.52). There was variability in accuracy under different exercise conditions. On the treadmill, only the Fitbit Blaze performed well (rc=0.76), while on the stationary cycle, Apple Watch (rc=0.89) and TomTom Spark (rc=0.85) were most accurate. Conclusions:In cardiac patients, the accuracy of wearable, optically based HR monitors varies, and none of those tested was as accurate as an electrode-containing chest monitor. This observation has implications for in-home CR, as electrode-containing chest monitors should be used when accurate HR measurement is imperative.
Project description:Endurance athletes, particularly competitive runners, are using wrist worn devices with the heart rate (HR) feature to guide their training. However, few studies have assessed the effectiveness of these at high levels of exertion. The purpose of this study was to measure the accuracy of the HR monitor feature in four watches at six different treadmill speeds. This prospective study recruited 50 healthy, athletic adults (68% male, mean age of 29, and mean BMI of 23 kg/m2). All subjects wore a three lead ECG and Polar H7 chest strap monitor and two different randomly assigned wrist worn HR monitors. These included the Apple Watch III, Fitbit Iconic, Garmin Vivosmart HR, and Tom Tom Spark 3. Once all devices were on, they were asked to run at the following speeds on a treadmill (in mph): 4, 5, 6, 7, 8, and 9 for two min. HR was assessed on all devices and agreement among measurements determined with Lin's concordance correlation coefficient (CCC) (rc). The Polar H7 chest strap had the greatest agreement with the ECG (rc=98). This was followed by the Apple Watch III (rc=96). The Fitbit Iconic, Garmin Vivosmart HR, and Tom Tom Spark 3 all had the same level of agreement (rc=89). The Polar H7 chest strap was the most accurate, and the Apple Watch was superior among watches. For endurance athletes and their coaches, a chest strap device or Apple Watch may be the best choice for guiding workouts and performance.
Project description:BACKGROUND:Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. OBJECTIVE:Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). METHODS:While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. RESULTS:While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9%-4.3%, 2.2%-6.7%, 2.9%-9.2%, and 4.1%-19.1%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5%-27.1%, 16.3%-28.0%, 15.9%-34.5%, and 8.0%-32.3%, respectively. CONCLUSIONS:The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested.
Project description:Wearable physical activity monitors are growing in popularity and provide the opportunity for large numbers of the public to self-monitor physical activity behaviours. The latest generation of these devices feature multiple sensors, ostensibly similar or even superior to advanced research instruments. However, little is known about the accuracy of their energy expenditure estimates. Here, we assessed their performance against criterion measurements in both controlled laboratory conditions (simulated activities of daily living and structured exercise) and over a 24 hour period in free-living conditions. Thirty men (n = 15) and women (n = 15) wore three multi-sensor consumer monitors (Microsoft Band, Apple Watch and Fitbit Charge HR), an accelerometry-only device as a comparison (Jawbone UP24) and validated research-grade multi-sensor devices (BodyMedia Core and individually calibrated Actiheart™). During discrete laboratory activities when compared against indirect calorimetry, the Apple Watch performed similarly to criterion measures. The Fitbit Charge HR was less consistent at measurement of discrete activities, but produced similar free-living estimates to the Apple Watch. Both these devices underestimated free-living energy expenditure (-394 kcal/d and -405 kcal/d, respectively; P<0.01). The multi-sensor Microsoft Band and accelerometry-only Jawbone UP24 devices underestimated most laboratory activities and substantially underestimated free-living expenditure (-1128 kcal/d and -998 kcal/d, respectively; P<0.01). None of the consumer devices were deemed equivalent to the reference method for daily energy expenditure. For all devices, there was a tendency for negative bias with greater daily energy expenditure. No consumer monitors performed as well as the research-grade devices although in some (but not all) cases, estimates were close to criterion measurements. Thus, whilst industry-led innovation has improved the accuracy of consumer monitors, these devices are not yet equivalent to the best research-grade devices or indeed equivalent to each other. We propose independent quality standards and/or accuracy ratings for consumer devices are required.
Project description:Background Wearable wrist-monitors offer an unobtrusive way to acquire heart rate data in an efficient manner. Previous work in this field has focused on studying healthy subjects during exercise but has yet to assess the efficacy of these devices in patients suffering from common cardiac arrhythmias such as atrial fibrillation. Objective The objective of this pilot study was to assess the accuracy of the Apple Watch heart rate monitor in fifty patients experiencing atrial fibrillation compared to telemetry. Results Results from this pilot clinical study demonstrated a correlation coefficient of 0.7 between all readings on the Apple Watch and telemetry. Furthermore, the Apple Watch assessed heart rate more accurately in patients who were in atrial fibrillation than in those that were not (rc = 0.86, patients in AF, vs. rc = 0.64, patients not in AF). Clinical Impact The presented data from this pilot study suggests that caution should be noted before using the Apple Watch 4 wearable wrist monitor to monitor heart rate in patients with cardiac arrhythmias such as atrial fibrillation.
Project description:BACKGROUND:Wrist-worn monitors claim to provide accurate measures of heart rate and energy expenditure. People wishing to lose weight use these devices to monitor energy balance, however the accuracy of these devices to measure such parameters has not been established. AIM:To determine the accuracy of four wrist-worn devices (Apple Watch, Fitbit Charge HR, Samsung Gear S and Mio Alpha) to measure heart rate and energy expenditure at rest and during exercise. METHODS:Twenty-two healthy volunteers (50% female; aged 24 ± 5.6 years) completed ~1-hr protocols involving supine and seated rest, walking and running on a treadmill and cycling on an ergometer. Data from the devices collected during the protocol were compared with reference methods: electrocardiography (heart rate) and indirect calorimetry (energy expenditure). RESULTS:None of the devices performed significantly better overall, however heart rate was consistently more accurate than energy expenditure across all four devices. Correlations between the devices and reference methods were moderate to strong for heart rate (0.67-0.95 [0.35 to 0.98]) and weak to strong for energy expenditure (0.16-0.86 [-0.25 to 0.95]). All devices underestimated both outcomes compared to reference methods. The percentage error for heart rate was small across the devices (range: 1-9%) but greater for energy expenditure (9-43%). Similarly, limits of agreement were considerably narrower for heart rate (ranging from -27.3 to 13.1 bpm) than energy expenditure (ranging from -266.7 to 65.7 kcals) across devices. CONCLUSION:These devices accurately measure heart rate. However, estimates of energy expenditure are poor and would have implications for people using these devices for weight loss.
Project description:A sedentary lifestyle and lack of physical activity are well-established risk factors for chronic disease and adverse health outcomes. Thus, there is enormous interest in measuring physical activity in biomedical research. Many consumer physical activity monitors, including Basis Health Tracker, BodyMedia Fit, DirectLife, Fitbit Flex, Fitbit One, Fitbit Zip, Garmin Vivofit, Jawbone UP, MisFit Shine, Nike FuelBand, Polar Loop, Withings Pulse O2, and others have accuracies similar to that of research-grade physical activity monitors for measuring steps. This review focuses on the unprecedented opportunities that consumer physical activity monitors offer for human physiology and pathophysiology research because of their ability to measure activity continuously under real-life conditions and because they are already widely used by consumers. We examine current and potential uses of consumer physical activity monitors as a measuring or monitoring device, or as an intervention in strategies to change behavior and predict health outcomes. The accuracy, reliability, reproducibility, and validity of consumer physical activity monitors are reviewed, as are limitations and challenges associated with using these devices in research. Other topics covered include how smartphone apps and platforms, such as the Apple ResearchKit, can be used in conjunction with consumer physical activity monitors for research. Lastly, the future of consumer physical activity monitors and related technology is considered: pattern recognition, integration of sleep monitors, and other biosensors in combination with new forms of information processing.
Project description:BACKGROUND:To assess whether commercial-grade activity monitors are appropriate for measuring step counts in older adults, it is essential to evaluate their measurement properties in this population. OBJECTIVE:This study aimed to evaluate test-retest reliability and criterion validity of step counting in older adults with self-reported intact and limited mobility from 6 commercial-grade activity monitors: Fitbit Charge, Fitbit One, Garmin vívofit 2, Jawbone UP2, Misfit Shine, and New-Lifestyles NL-1000. METHODS:For test-retest reliability, participants completed two 100-step overground walks at a usual pace while wearing all monitors. We tested the effects of the activity monitor and mobility status on the absolute difference in step count error (%) and computed the standard error of measurement (SEM) between repeat trials. To assess criterion validity, participants completed two 400-meter overground walks at a usual pace while wearing all monitors. The first walk was continuous; the second walk incorporated interruptions to mimic the conditions of daily walking. Criterion step counts were from the researcher tally count. We estimated the effects of the activity monitor, mobility status, and walk interruptions on step count error (%). We also generated Bland-Altman plots and conducted equivalence tests. RESULTS:A total of 36 individuals participated (n=20 intact mobility and n=16 limited mobility; 19/36, 53% female) with a mean age of 71.4 (SD 4.7) years and BMI of 29.4 (SD 5.9) kg/m2. Considering test-retest reliability, there was an effect of the activity monitor (P<.001). The Fitbit One (1.0%, 95% CI 0.6% to 1.3%), the New-Lifestyles NL-1000 (2.6%, 95% CI 1.3% to 3.9%), and the Garmin vívofit 2 (6.0%, 95 CI 3.2% to 8.8%) had the smallest mean absolute differences in step count errors. The SEM values ranged from 1.0% (Fitbit One) to 23.5% (Jawbone UP2). Regarding criterion validity, all monitors undercounted the steps. Step count error was affected by the activity monitor (P<.001) and walk interruptions (P=.02). Three monitors had small mean step count errors: Misfit Shine (-1.3%, 95% CI -19.5% to 16.8%), Fitbit One (-2.1%, 95% CI -6.1% to 2.0%), and New-Lifestyles NL-1000 (-4.3%, 95 CI -18.9% to 10.3%). Mean step count error was larger during interrupted walking than continuous walking (-5.5% vs -3.6%; P=.02). Bland-Altman plots illustrated nonsystematic bias and small limits of agreement for Fitbit One and Jawbone UP2. Mean step count error lay within an equivalence bound of ±5% for Fitbit One (P<.001) and Misfit Shine (P=.001). CONCLUSIONS:Test-retest reliability and criterion validity of step counting varied across 6 consumer-grade activity monitors worn by older adults with self-reported intact and limited mobility. Walk interruptions increased the step count error for all monitors, whereas mobility status did not affect the step count error. The hip-worn Fitbit One was the only monitor with high test-retest reliability and criterion validity.
Project description:Validation of heart rate responses in wearable technology devices is generally composed of laboratory-based protocols that are steady state in nature and as a result, high accuracy measures are returned. However, there is a need to understand device validity in applied settings that include varied intensities of exercise. The purpose was to determine concurrent heart rate validity during trail running. Twenty-one healthy participants volunteered (female n = 10, [mean (SD)]: age = 31  years, height = 173.0  cm, mass = 75.6  kg). Participants were outfitted with wearable technology devices (Garmin Fenix 5 wristwatch, Jabra Elite Sport earbuds, Motiv ring, Scosche Rhythm+ forearm band, Suunto Spartan Sport watch with accompanying chest strap) and completed a self-paced 3.22 km trail run while concurrently wearing a criterion heart rate strap (Polar H7 heart rate monitor). The trail runs were out-and-back with the first 1.61 km in an uphill direction, and the 1.61 return being downhill in nature. Validity was determined through three methods: Mean Absolute Percent Error (MAPE), Bland-Altman Limits of Agreement (LOA), and Lin's Concordance Coefficient (rC). Validity measures overall are as follows: Garmin Fenix 5 (MAPE = 13%, LOA = -32 to 162, rC = 0.32), Jabra Elite Sport (MAPE = 23%, LOA = -464 to 503, rC = 0.38), Motiv ring (MAPE = 16%, LOA = -52 to 96, rC = 0.29), Scosche Rhythm+ (MAPE = 6%, LOA = -114 to 120, rC = 0.79), Suunto Spartan Sport (MAPE = 2%, LOA = -62 to 61, rC = 0.96). All photoplethysmography-based (PPG) devices displayed poor heart rate agreement during variable intensity trail running. Until technological advances occur in PPG-based devices allowing for acceptable agreement, heart rate in outdoor environments should be obtained using an ECG-based chest strap that can be connected to a wristwatch or other comparable receiver.
Project description:Chronotropic incompetence (CI), an attenuated heart rate (HR) response to exercise, is common in patients with cardiovascular disease. The aim of this study was to assess changes in the chronotropic response (CR) during cardiopulmonary exercise testing (CPET) in patients undergoing cardiac rehabilitation and investigate the effects of β-blockers.Patients undergoing cardiac rehabilitation performed CPET. Failure to achieve 80% of the age-predicted maximal HR (APMHR) defined CI. Values of the metabolic chronotropic relationship (MCR) were calculated from the ratio of the HR reserve to metabolic reserve at 4 stages, warm-up (MCR-Wu), anaerobic threshold (MCR-AT), respiratory compensation (MCR-Rc), and peak point (MCR-Pk), using the Wilkoff model. In patients who showed an increase in MCR at ≥ 3 of the 4 exercise stages, CR was considered to have improved.Patients with high BNP levels (≥ 80 pg/ml) had a lower MCR at all stages compared with those with low BNP levels (< 80 pg/ml). Of the 80 patients, 47 showed an increase in both peak VO2 and AT, and of these 31 (66.0%) were taking β-blockers. Improvement in CR was observed in 30 of 47 patients with CI, and 70% of these were taking β-blockers. In patients not taking β-blockers, MCR-AT was lower than MCR-Rc, whereas in those taking β-blockers MCR-AT was higher than MCR-Rc.An attenuated HR response may occur during the early stages of exercise. The HR response according to the presence or absence of β-blockers is clearly identifiable by comparing MCR-AT and MCR-Rc using the Wilkoff model.
Project description:The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males, greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. In conclusion, most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).