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Improving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population.
The aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients.
EE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF).
A total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal;
Both activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms.
Improving physical fitness and physical activity (PA) levels are core components of cardiac rehabilitation (CR) and secondary prevention in patients with coronary artery disease (CAD) and chronic heart failure (CHF) [
Besides exercise training, enhancing daily PA and reducing sedentary behavior are also effective for the prevention of repetitive cardiac events and therefore highly recommended by the current guidelines [
Home-based exercise programs such as telerehabilitation, may be successful methods for improving PA and reducing sedentary behavior on long term [
Important prerequisites for activity trackers include high accuracy and responsiveness. Accuracy is defined as the closeness of agreement between the device measurement and the true value [
Kraal et al found that combining heart rate (HR) with accelerometer data provides a higher accuracy for measuring EE than using accelerometer or HR data alone in patients with CAD [
The aim of this study is to investigate the accuracy and responsiveness of 2 commercially available wrist-worn activity trackers to calculate EE in patients with CAD and CHF. This study may provide important information whether 2 modern activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), can be used for measuring EE to monitor PA levels in cardiac patients.
Patients (aged ≥18 years) were included based on their diagnosis to form 2 patient groups: patients with stable CAD with preserved left ventricular ejection fraction (LVEF) and patients with stable heart failure with reduced ejection fraction (HFrEF). These 2 patient categories were selected because HFrEF patients generally have lower activity levels compared with CAD patients with preserved LVEF. Household activities, for example, can be experienced as more intensive by HFrEF patients than by CAD patients with preserved LVEF. Also, HFrEF patients often suffer from chronotropic incompetence, yielding a difference in HR variation during activities. Therefore, both groups were analyzed separately. The participants were recruited via their cardiologist in the outpatient clinic of the Máxima Medical Center, the Netherlands, and were randomly selected from a list of patients who participated in previous studies and gave informed consent to be contacted for participation in future research projects. Patients were excluded if they suffered from hemodynamic significant valvular disease, permanent atrial fibrillation or peripheral vascular, neurological, or orthopedic conditions impairing exercise capacity. All patients provided written informed consent. The study was approved by the local medical ethical committee of the Máxima Medical Center and was conducted in accordance with the declaration of Helsinki.
Participants completed a laboratory protocol consisting of 14 low-to-moderate intensity activities, which was modified from a previous study with cardiac patients [
Activity protocol.
Activity type and activity | Duration in minutes | |
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Sitting | 5 |
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Standing | 2 |
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Typing | 3 |
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Table cleaning | 3 |
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(Un)loading the dishwasher | 3 |
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Vacuuming | 3 |
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Ascending | 1 |
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Descending | 1 |
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CADa 0 Watt; HFrEFb 0 Watt | 3 |
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CAD 40 Watt; HFrEF 25 Watt | 3 |
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CAD 70 Watt; HFrEF 50 Watt | 3 |
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CAD 4 km/h; HFrEF 3 km/h | 3 |
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CAD 5.5 km/h; HFrEF 4.5 km/h | 3 |
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CAD 4 km/h 5% slope; HFrEF 3 km/h 5% slope | 3 |
aCAD: coronary artery disease.
bHFrEF: heart failure with reduced ejection fraction.
Breath-by-breath oxygen uptake (VO2) and carbon dioxide production (VCO2) were measured during the entire length of the protocol using the Oxycon Mobile (OM; CareFusion). The OM is a light-weighted mobile device consisting of a facemask and a gas analyzer unit with battery attached to the patients back via a shoulder belt system. Real time data measured by the device were sent to a computer with corresponding software. Before the start of the protocol, automatic volume and gas calibration was performed and ambient conditions were checked. The OM has been validated before by comparing it with the golden standard, the Douglas Bag, and has been found reliable as a criterion measure [
The FC2 (Fitbit Inc) is a wrist-worn activity tracker consisting of a 3-axial accelerometer, an altimeter, and an optical HR tracker. EE calculation is based on a combination of basal metabolic rate (which is calculated by gender, age, height, and weight), activity counts during the activities, and, as claimed by the manufacturer on HR [
The MS (MIO Global) is a wrist-worn activity tracker consisting of a 2-axial accelerometer and an optical HR tracker. The activity tracker was worn on the nondominant wrist. Age, length, weight, gender, and wrist orientation were entered in the corresponding app for every patient. Afterwards, the activity tracker was synchronized with the Mio app via Bluetooth. The app contained the latest version of firmware. Information on algorithms used to calculate EE is not provided by the manufacturer. The manufacturer claims that both activity counts and HR are used for EE calculation when the workout mode is activated [
Raw data from the breathing analysis was exported and imported together with the values from the FC2 and MS in a custom-made MATLAB analysis program (R2018a [9.4.0.813654], Mathworks). The entire activity bouts were analyzed. First, the EE was calculated from breath-by-breath measurements using the Weir equation, as follows [
Then, outliers (eg, coughing) in the EE data were detected using a Hampel filter. Outliers were replaced if the value exceeded 3 standard deviations from the median of itself and 3 neighboring data points of that median value [
To achieve 80% power to detect an intraclass correlation coefficient (ICC) of 0.75 (excellent agreement) under the alternative hypothesis that the ICC is 0.35 (poor agreement), a sample size of 19 subjects per study group (ie, CAD and HFrEF) was calculated.
Descriptive statistics were used to describe the population regarding baseline clinical characteristics. Normality of data was assessed by visual inspection of histograms and by interpreting Skewness and Kurtosis [
A total of 38 patients were included and completed the protocol. The group was equally divided in CAD patients (n=19, age 61.4 years, SD 6.9 years) and patients with HFrEF (n=19, age 65.1 years, SD 6.6 years, LVEF 31.8%, SD 7.6%). In both groups, the majority of patients were using HR lowering medication (14 CAD patients [14/19, 74%] and 17 HFrEF patients [17/19, 89%]). Pulmonary diseases were present in 2 HFrEF patients (1 patient with mild bronchiectasis and 1 patient with COPD treated by the general practitioner). Additional patient characteristics are shown in
Patient characteristics.
Characteristics | CADa (N=19) | HFrEFb (N=19) | |||
Age (years), mean (SD) | 61.4 (6.9) | 65.1 (6.6) | |||
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Male | 14 (74) | 17 (89) | ||
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Female | 5 (26) | 2 (11) | ||
Height (cm), mean (SD) | 176 (6.8) | 177 (5.4) | |||
Weight (kg), mean (SD) | 84.3 (12.1) | 86.7 (13.7) | |||
BMIc (kg/m2), mean (SD) | 27.1 (3.1) | 27.7 (4.2) | |||
LVEFd (%), mean (SD) | 60.5 (4.5) | 31.8 (7.6) | |||
NYHAe classification I/II/III/Unknown, n (%) | —f | 6(32)/11(58)/1(5)/1(5) | |||
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Ischemic | — | 11 (58) | ||
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Nonischemic | — | 8 (42) | ||
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Beta-blocker | 12 (63) | 15 (79) | ||
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Calcium channel blocker (non-DHPg) | 2 (11) | 0 (0) | ||
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Amiodarone | 0 (0) | 4 (21) | ||
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Ivabradine | 0 (0) | 1 (5) |
aCAD: coronary artery disease.
bHFrEF: heart failure with reduced ejection fraction.
cBMI: body mass index.
dLVEF: left ventricular ejection fraction.
eNYHA: New York Heart Association.
fNot applicable.
gDHP: dihydropyridine.
The
Bland-Altman plots for total energy expenditure. The solid horizontal line corresponds to the mean difference, whereas the dashed horizontal lines correspond to limits of agreement. The dotted line is the line of equality. (a) Comparison of Oxycon Mobile with Fitbit Charge 2 for patients with CAD. (b) Comparison of Oxycon Mobile with Fitbit charge 2 for patients with HFrEF. (c) Comparison of Oxycon Mobile with Mio Slice for patients with CAD. (d) Comparison of Oxycon Mobile with Mio Slice for patients with HFrEF. CAD: coronary artery disease; EE: energy expenditure; FC2: Fitbit Charge 2; HFrEF: heart failure with reduced ejection fraction; MS: Mio Slice; OM: Oxycon Mobile.
The
FC2 was able to detect a difference between cycling at 0 versus 40 watts (mean difference 3.3 kcal,
FC2 was not able to detect changes at any walking speeds or cycling loads in the HFrEF group. MS was able to detect within-patient changes at cycling 0 versus 50 watts (mean difference 4.7 kcal,
Responsiveness of Fitbit Charge 2 and Mio Slice.
Group and activity | Oxycon Mobile, Mean difference (kcala) | Fitbit Charge 2, Mean difference (kcal) | Mio Slice, Mean difference (kcal) | |||||
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0 versus 40 watts | 2.7 | <.001 | 3.3 | .003 | 3.2 | <.001 |
0 versus 70 watts | 5.4 | <.001 | 2.6 | .11 | 5.1 | <.001 | ||
40 versus 70 watts | 2.7 | <.001 | 0.7 | .67 | 1.9 | .03 | ||
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4 km/h versus 4 km/h 5% slope | 2.5 | <.001 | 1.8 | .15 | 2.2 | .36 | |
4 km/h versus 5.5 km/h | 2.5 | <.001 | 2.6 | .15 | 3.8 | .31 | ||
4 km/h 5% slope versus 5.5 km/h | 0.1 | .71 | 4.4 | .01 | 1.6 | .42 | ||
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0 versus 25 watts | 1.2 | <.001 | 0.3 | .88 | 1.1 | .23 |
0 versus 50 watts | 3.0 | <.001 | 1.0 | .46 | 4.7 | .02 | ||
25 versus 50 watts | 1.9 | <.001 | 1.3 | .40 | 3.6 | .02 | ||
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3 km/h versus 3 km/h 5% slope | 1.1 | .002 | 1.9 | .16 | .8 | .66 | |
3 km/h versus 4.5 km/h | 1.5 | .001 | .3 | .89 | 2.2 | .16 | ||
3 km/h 5% slope versus 4.5 km/h | 0.4 | .27 | 2.2 | .08 | 3.0 | .03 |
akcal: kilocalories.
bCAD: coronary artery disease.
cHFrEF: heart failure with reduced ejection fraction.
This study is the first to evaluate the accuracy and responsiveness of wrist-worn activity trackers combining HR monitoring and accelerometry for EE calculation in patients with CAD with preserved LVEF and patients with HFrEF. Poor accuracy was observed for both devices in predicting EE, with MS performing worse than FC2. MS provides a higher responsiveness than FC2 with regard to the ability to detect changes in cycling load, but both devices performed poorly with respect to detecting within-patient differences in walking speed.
Both FC2 and MS significantly overestimated EE over the total activity protocol with a tendency of greater bias when EE increased. Other studies using wrist-worn Fitbit models that combine accelerometer data and heart data showed mixed results. The results from our study were in line with previous research from Bai et al which showed a whole-trial overestimation of EE (mean absolute percentage error 32.9%) of FC HR [
Possible causes for the limited accuracy of EE estimation by different wrist-worn devices include poor quality of HR and accelerometer assessment and inadequate algorithms to calculate EE (ie, not well tailored to the target population). Yet, the algorithms to calculate EE are usually not provided by the manufacturer. As patient characteristics such as length, weight, and exercise modality are fixed, EE estimates are most likely determined by the accuracy of the (accelerometer and HR) sensors and the reliability of the algorithms related to HR and activity counts. However, both the reliability of the algorithms and the accuracy of both sensors were not evaluated in this study. Concerning the accuracy of the HR sensor, Wallen et al concluded that Mio Alfa and FC HR slightly underestimate HR, within an expectable range (ICC Fitbit 0.78; ICC Mio 0.91) [
Another factor that may have influenced the accuracy of EE estimation is the location of the accelerometer. Waist placement is generally considered favorable as the sensor is close to the center of body mass and is able to detect whole body movement. A recent review evaluating the influence of body placement to accuracy of EE estimation concluded that wrist placement generally leads to overestimation and torso placement to underestimation of EE, with a greater mean error for devices placed at the wrist [
Nevertheless, our study clearly showed that EE estimates, using algorithms for commercially available wrist-worn devices, should be interpreted with caution in cardiac patients. Therefore, to improve the utility of these devices for this population, extraction of raw HR and accelerometer data are needed to be able to develop adequately tailored algorithms. However, most manufacturers of activity trackers do not provide this opportunity.
Whereas, MS was shown to be useful for detecting changes in cycling load, changes in walking speed and inclination were not detected. Furthermore, FC2 was not capable in detecting changes in both walking and cycling activities. Although the ability to detect changes in intensity within specific activities is an important feature of an activity tracker, previous research on responsiveness is scarce. Price et al concluded the hip-worn Fitbit One is able to detect gross changes in walking and running speed [
This study is the first to evaluate both the accuracy and responsiveness of wrist-worn activity trackers, which combine HR and accelerometer data in a cardiac population. The responsiveness of a device is a very important feature when implemented in practice, such as in cardiac telerehabilitation. The study is limited by not evaluating test-retest reliability. This would have given a more complete overview of the overall device validity. Moreover, patients were tested in a laboratory setting, so it is not sure whether these results can be extrapolated to free-living conditions. However, because we mimicked free-living conditions by creating a protocol consisting of daily life activities, we expect little differences with a free-living validation study. Another limitation is that HR boundaries were not personalized for each patient. As we did not assess the maximum HR for each individual patient, default settings of the activity trackers were used, which could have influenced the calculation of EE.
Both wrist-worn activity trackers demonstrated low accuracy in estimation of EE in patients with CAD and HFrEF. Importantly, both devices also showed poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Notwithstanding the fact that the use of activity trackers in cardiac patients might stimulate daily exercise behavior, these findings highlight the need for population-specific devices and algorithms.
Accuracy of energy expenditure measurement by Fitbit Charge 2 and Mio Slice, for participants with coronary artery disease.
Accuracy of energy expenditure measurement by Fitbit Charge 2 and Mio Slice, for participants with heart failure with reduced ejection fraction.
coronary artery disease
chronic heart failure
cardiac rehabilitation
energy expenditure
Fitbit Charge 2
heart failure with reduced ejection fraction
heart rate
intraclass correlation coefficient
limits of agreement
left ventricular ejection fraction
Mio Slice
Oxycon Mobile
physical activity
root mean square error
CH, JJK, and HMCK contributed to the conception and design of the study. CH and JJK contributed to the acquisition of data. CH, EMAvL, JJK, and MvH contributed to the analysis of data. All authors contributed to interpretation of data. CH drafted the manuscript. All authors critically revised the manuscript. All gave final approval and agreed to be accountable for all aspects of work ensuring integrity and accuracy.
None declared.