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

O'Driscoll, R orcid.org/0000-0003-3995-0073, Turicchi, J orcid.org/0000-0003-1174-813X, Hopkins, M orcid.org/0000-0002-7655-0215 et al. (4 more authors) (2021) Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study. JMIR mHealth and uHealth, 9 (8). e23938. ISSN 2291-5222

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Ruairi O'Driscoll, Jake Turicchi, Mark Hopkins, Cristiana Duarte, Graham W Horgan, Graham Finlayson, R James Stubbs. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 04.08.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.
Keywords: bioenergetics; energy balance; accelerometers; machine learning; validation
Dates:
  • Accepted: 18 May 2021
  • Published (online): 4 August 2021
  • Published: 4 August 2021
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 12 Aug 2021 09:58
Last Modified: 12 Aug 2021 09:58
Status: Published
Publisher: JMIR Publications
Identification Number: https://doi.org/10.2196/23938
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