This paper is in the following e-collection/theme issue:

Theme Issue 2020: Using Artificial Intelligence and Machine Learning to Improve E-health and M- health (1) Wearable Devices and Sensors (888) mHealth for Data Collection and Research (955) Fitness Trackers and Smart Pedometers/Accelerometers (561) Machine Learning (1465) Artificial Intelligence (1371) Innovations and Technology for Physical Activity Education (742)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23938, first published .
Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study

Authors of this article:

Ruairi O'Driscoll1 Author Orcid Image ;   Jake Turicchi1 Author Orcid Image ;   Mark Hopkins2 Author Orcid Image ;   Cristiana Duarte1 Author Orcid Image ;   Graham W Horgan3 Author Orcid Image ;   Graham Finlayson1 Author Orcid Image ;   R James Stubbs1 Author Orcid Image
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