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Updated: Sep 13, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Validation of the Fitbit Charge 5 for the Detection of Heart Rate and Electrodermal Activity
Katherine Ko1,2, Genevieve McArthur3,4, Carly Johnco1,2
1School of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.
Abstract:
Wearable devices are increasingly used to evaluate psychophysiological markers of anxiety for continuous health monitoring. Consumer-grade wearable devices, such as Fitbits, have the potential for widespread use and dissemination given their affordability and accessibility for both research and clinical settings. However, the validation of consumer-grade devices against research-grade devices is required. This study aimed to evaluate and compare the accuracy of the Fitbit Charge 5 against a research-grade wearable device, the Equivital EQ02, in measuring psychophysiological parameters of anxiety, specifically heart rate (HR) and electrodermal activity (EDA). Fifty-five undergraduate students (Mage = 19.4, SDage = 1.6, 46% female) wore both Fitbit and Equivital devices whilst completing social stressor and reading tasks. Statistical analyses demonstrated significant moderate correlations between the two devices for heart rate (HR) estimates (rs = 0.45-0.58) and low to moderate correlations for electrodermal activity (EDA) estimates (rs = 0.42-0.50). Intraclass correlations were moderate for both HR (ICCs = 0.53-0.72) and EDA (ICCs = 0.46-0.64) across conditions (ps < 0.05). Furthermore, Bland-Altman analyses revealed that the Fitbit showed a pattern of underestimation of HR (ranging from 24 to 32 bpm) and overestimation of EDA (ranging from -12.92 to 10.29 μS) compared to the Equivital. These findings highlight potential reliability concerns with the Fitbit Charge 5 in measuring physiological data. While the device may have some utility in assessing HR and EDA, it is crucial to approach the interpretation of data from consumer-grade wearable devices with caution due to potential accuracy limitations.
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