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Updated: Sep 14, 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
Daily validation using a metabolic simulator after regular calibration increases the quality of cardiopulmonary
Fan Xu1, Xing-Guo Sun1,2, Fang Liu1
1Department of Functional Testing Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, National Research Center of Clinical Medicine for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Gas exchange measurements, such as oxygen uptake ( ) and carbon dioxide output ( ), of cardiopulmonary exercise testing (CPET), are the key and gold standard for human cardiopulmonary functional evaluation. However, in terms of quality control, they are unstable and inaccurate. We used a metabolic simulator (MS) to detect measurement errors and enhance quality control.
Methods:
In the Fuwai CPET laboratory, we performed CPET after systems had: (I) passed all the steps of regular system calibrations for flow and the partial pressure of O2 and CO2; and (II) passed the MS validation of and at low, medium, and high metabolic rates (MRs) daily from 2014 to 2023 for eight different CPET carts/systems. The absolute percentage difference of the 1st validation of both and was calculated as follows: |[(measured - ideal) / ideal] × 100%|. A difference of <10% was set as the 1st validation pass standard to run the laboratory, while a difference of ≥10% was classified as a 1st validation failure. The absolute percentage difference of the 1st validation among the eight carts/systems was compared using the Kruskal-Wallis H test. The rate of the 1st validation failure, the number of validation days, and the median absolute percentage difference of the 1st validation among the different CPET carts/systems were clustered using the hierarchical clustering method.
Results:
In total, we completed 1,810 validation days for the eight CPET carts/systems, and found a 10,860 absolute percentage difference of the 1st validation of and . The number of validation days completed by each cart/system and the 1st validation failure rates were as follows: 8 (87.50%), 10 (90.00%), 54 (48.15%), 349 (43.27%), 20 (45.00%), 759 (21.21%), 525 (29.52%), and 85 (22.35%), respectively. The overall absolute percentage difference of the 1st validation of each cart/system was 7.32% (P25, P75: 3.67%, 13.82%), 9.12% (P25, P75: 3.33%, 30.4%), 6.82% (P25, P75: 4.31%, 9.06%), 5.40% (P25, P75: 2.60%, 8.26%), 4.90% (P25, P75: 2.21%, 9.68%), 4.32% (P25, P75: 2.17%, 6.78%), 5.62% (P25, P75: 2.96%, 8.19%), and 5.35% (P25, P75: 2.55%, 7.81%), respectively. The Kruskal-Wallis H test results revealed significant differences among the eight carts/systems (H=274.86, P<0.001), and the pairwise comparisons showed that cart/system F had the lowest absolute percentage difference of 4.32% (P25, P75: 2.17%, 6.78%). The hierarchical cluster classified carts/systems A and B as one cluster, carts/systems C, E, and H as another cluster, and carts/systems D, F, and G as yet another cluster.
Conclusions:
Using an MS can decrease measurement errors and variability for CPET. It can also improve the quality control of CPET.
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