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Updated: May 8, 2026

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
Published on: February 21, 2025
Accuracy of VO2 max Estimates From Apple Watch Series 10
Rory Lambe1,2, Moritz Schumann3, Lauren Donnelly1
1School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Objective:
To assess the validity of maximal oxygen uptake (VO2 max) estimates from Apple Watch Series 10 through comparison with indirect calorimetry, the criterion method.
Participants And Methods:
Participants completed a maximal exercise treadmill test using indirect calorimetry in accordance with the modified Åstrand protocol. They subsequently generated a VO2 max estimate with Apple Watch Series 10. The agreement between Apple Watch and indirect calorimetry was assessed using Bland-Altman analysis, mean absolute error, and mean absolute percentage error. Data were collected between October 2024 and January 2025.
Results:
Forty healthy adults (20 [50%] women) completed exercise testing. Apple Watch underestimated VO2 max overall, with wide limits of agreement indicating measurement variability (mean difference, -6.25 mL/kg per minute [95% CI, -8.28 to -4.23]; limits of agreement, -18.23 to 5.73). The mean absolute error was 6.79 mL/kg per minute (95% CI, 4.91-8.67), and the mean absolute percentage error was 13.2% (95% CI, 9.6-16.8). Based on reference standards from the Fitness Registry and the Importance of Exercise National Database, Apple Watch correctly classified the cardiorespiratory fitness percentile of 5 (14%) participants and classified a further 12 (34%) within 1 adjacent percentile band of their actual band.
Conclusion:
Apple Watch VO2 max estimates showed poor individual-level agreement with criterion measures, although our findings suggested utility for population-level assessment in research trials. Longitudinal validation of measurement reliability is needed to evaluate use as an alternative to conventional submaximal prediction methods.
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