Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

Abstract

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

The use of commercial wrist-worn technology to track physiological outcomes in behavioral interventions

“…In contrast, other studies have found that activities, such as cycling, wherein the wrist is stationary and less susceptible to movement, result in more accurate HR readings [37 ▪ ,38]. Furthermore, accuracy may be dependent on device brand; findings from several studies suggest that Apple devices demonstrate the highest accuracy for HR assessment, even at high and maximal intensities, compared to devices from brands including Fitbit, Polar, Garmin, and Samsung [30,36,37 ▪ ,38].…”

“…A recent systematic review and meta-analysis reported significant differences in HR assessed by wrist-worn devices compared with criterion measures for both resistance training (mean difference of -7.26 beats per minute) and cycling (mean difference of -4.55 beats per minute), but not for treadmill or daily activities, citing restriction of blood flow to limbs or movement artifact as possible explanations for inaccuracies [40]. In contrast, other studies have found that activities, such as cycling, wherein the wrist is stationary and less susceptible to movement, result in more accurate HR readings [37 ▪ ,38]. Furthermore, accuracy may be dependent on device brand; findings from several studies suggest that Apple devices demonstrate the highest accuracy for HR assessment, even at high and maximal intensities, compared to devices from brands including Fitbit, Polar, Garmin, and Samsung [30,36,37 ▪ ,38].…”

Analyzing Mobility Patterns of Complex Chronic Patients Using Wearable Activity Trackers: A Machine Learning Approach

“…Wearables have the potential to provide continuous, objective, and non-invasive monitoring of a patient's physiological parameters. Moreover, the validity of the measures taken by the wearables have been studied regarding steps taken [4], heart rate [5] or sleep quality [6]. Combined, this type of technology can aid in the early detection and management of chronic diseases, improving the overall health outcomes of patients.…”

Deep Conv-LSTM Network for Arrhythmia Detection using ECG Data

“…Here, M epitomizes the count of memory cells, and N delineates the dimensionality of the input vector. A comprehensive elucidation of the LSTM's mathematical underpinnings, pivotal to its operational framework, is documented in [34], specifically referenced in Eq. ( 2) to (7).…”