BACKGROUND: The utilization of commercially accessible wrist-worn fitness devices for tracking health metrics, such as energy expenditure and heart rate, can offer valuable data for epidemiological and physiological studies. However, despite the advancements in technology, significant errors may continue to arise within the metrics being collected. The purpose of this study was to determine if exercise intensity, modality, hand dominance, and skin tone impact the accuracy of heart rate (HR) and energy expenditure (EE) in wrist-worn devices utilizing photoplethysmography. METHODS: Twelve volunteers (12 male, age 24 + 5.9 years) completed a single-day laboratory trial comprising of two 15-minute progressive exercise protocols using a treadmill and a Monark 828e cycle ergometer. Two smart-watch brands, Apple Series 3 (AS3) and Fitbit Versa 2 (FV2) were selected by their commercial availability at the time of this study. Participants wore the same model at identical locations on their dominant (D) and non-dominant (ND) wrist, with the AS3 placed near the ulnar head, and the FV2 placed 2-3 finger widths above the ulnar head. Oxygen consumption and electrocardiography were assessed utilizing a metabolic cart (MGC Diagnostics Ultima CardiO2), which served as the gold standard to compare the values of HR and EE derived from the smartwatches. RESULTS: No significant differences were found for the impact of skin tone on HR or EE, as well as the comparison of HR across devices for a given intensity and modality. However, the accuracy for FV2 increased as treadmill exercise intensity increased. For EE, significant differences (p<0.01) were found between AS3 and FV2 for each modality, and for FV2 treadmill activities on both the D wrist (p<0.024) and ND wrist (p<0.006). AS3 accurately assessed treadmill HR and EE. Additionally, AS3 accurately assessed cycling HR but overestimated cycling EE (p>0.05). CONCLUSIONS: Exercise intensity and modality can elicit erroneous values in HR and EE. Individuals and clinicians should be aware of the strengths and limitations of devices that measure HR and EE, as errors ranged from upwards of 13% for HR, and 18% for EE. The accuracy of these devices should be considered in recreational and clinical settings to interpret the validity of collected health and fitness metrics.

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