Accuracy of VO2 max Estimates From Apple Watch Series 10

Objective: max) estimates from Apple Watch Series 10 through comparison with indirect calorimetry, the criterion method. Participants and Methods: 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: 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: 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.

Authors

Institutions

Publication Details

Journal
Mayo Clinic Proceedings Digital Health
Published
2026-04-07
DOI
https://doi.org/10.1016/j.mcpdig.2026.100357
Primary Topic
Transition Metal Oxide Nanomaterials
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accuracy of VO2 max Estimates From Apple Watch Series 10

Moritz Schumann, Saoirse Lally, Rory Lambe, Lauren Donnelly et al.
Mayo Clinic Proceedings Digital Health
Transition Metal Oxide Nanomaterials
article

Accuracy of VO2 max Estimates From Apple Watch Series 10

Moritz Schumann, Saoirse Lally, Rory Lambe, Lauren Donnelly, Sherene Hamilton, Seán O’Reilly, Thomas White, Cailbhe Doherty, Eve Rafter
article en

Abstract

Objective: max) estimates from Apple Watch Series 10 through comparison with indirect calorimetry, the criterion method. Participants and Methods: 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: 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: 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.

Mayo Clinic Proceedings Digital HealthVol. 4(2)
University College Dublin (IE), Dublin City University (IE), Technical University of Munich (DE)
Health Research Board, Research Ireland
Openalex Percentile: Top 36%
Transition Metal Oxide Nanomaterials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.