The accuracy of VO2 max, heart rate and energy expenditure measurements from Google Pixel Watch 3
Consumer wearables enable scalable physiological assessment and pre-symptomatic disease detection. However, validation of wearable data remains insufficient in scope and rigour, hindering integration into clinical and public health practice. Here, we evaluated the validity of VO 2 max, heart rate and energy expenditure from the Google Pixel Watch 3. Thirty-four participants completed a maximal exercise treadmill test using indirect calorimetry, while wearing a Polar H10 chest strap. Pixel Watch estimates of VO 2 max were generated in free-living conditions; heart rate and energy expenditure estimates were recorded during exercise testing. Bland-Altman analysis showed low bias but wide limits of agreement for VO 2 max estimates from the Pixel Watch 3 (bias −0.53 mL/kg/min; limits of agreement [LoA] −12.56 to 11.50; mean absolute percentage error [MAPE] 8.65%). Heart rate demonstrated strong agreement with the criterion overall, despite measurement variability (bias −2.39 bpm; LoA −24.42 to 19.65; MAPE 2.73%). The error for energy expenditure varied substantially between participants (bias −8.43 kcal; LoA −72.75 to 55.88; MAPE 15.02%). These results indicate that VO 2 max estimates lack sufficient individual-level validity for clinical use, although the bias suggests utility for population health monitoring. Heart rate showed acceptable error for exercise monitoring, whereas the large variation in error for energy expenditure limits its practical use. Our findings illustrate that wearable data require condition- and population-specific validation before incorporation into health care pathways.
Authors
- R. Lambe (ORCID: https://orcid.org/0009-0009-5866-361X)
- Sean O’Reilly
- Cailbhe Doherty (ORCID: https://orcid.org/0000-0002-5284-856X)
- Moritz Schumann (ORCID: https://orcid.org/0000-0001-9605-3489)
- Saoirse Lally (ORCID: https://orcid.org/0000-0002-4149-9894)
- Lauren Donnelly
- Sherene Hamilton
- Thomas White
Institutions
- University College Dublin (IE)
- Artificial Intelligence in Medicine (Canada) (CA)
- Technical University of Munich (DE)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-09-15
- DOI
- https://doi.org/10.1371/journal.pone.0356808
- Primary Topic
- Cardiovascular and exercise physiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00