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Below, we primarily reviewed the existing wearable HR literature that has used an ECG as the comparison method.

Therefore, many studies are comparing wearable HR accuracy with suboptimal comparison methods, which likely undermine findings. The current gold standard reference method for assessing HR is the ECG, which highlights the limitations of many studies that have utilized chest straps or pulse oximeters, which themselves contain a degree of error when compared with ECG.

Recently, there have been a variety of studies that have examined the accuracy of wearable PPG sensors as compared with ECG, polar chest straps, or pulse oximeters across various controlled laboratory conditions, including sitting, treadmill protocols for walking and running, cycling, weight training, and sleeping. As such, the goal of wearable HR accuracy validation studies is to assess that device measurements, such as those between wearables and a reference method (ie, electrocardiogram ECG), are not outside of clinically important limits of agreement (LoA), so that devices can supplement, replace, or even be used interchangeably. Although often used interchangeably, it is important to note that pulse rate and HR are 2 different physiological signals, with pulse rate representing the rate of change in blood pressure because of the ventricular ejection of blood, whereas HR represents the rate of heart contraction as indexed by heart electrical impulses. These devices contain a multitude of sensors, often including an optical sensor that uses photoplethysmography (PPG) that allows these devices to collect pulse rate or volumetric changes in blood profusion that act as a surrogate for heart rate (HR).

Wrist-worn smartwatches and fitness monitors or wearables have been widely adopted by consumers and are currently gaining increased attention by researchers for their potential contribution to digital measurement of health, especially in big data studies as these devices are scalable, unobtrusive, and potentially provide greater ecological validity (ie, the degree to which a research design matches naturalistic environments to generalize results to real-life settings), as compared with laboratory studies.