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 (678) mHealth for Data Collection and Research (703) Fitness Trackers and Smart Pedometers/Accelerometers (502) Machine Learning (1059) Artificial Intelligence (726) Innovations and Technology for Physical Activity Education (564)

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'Driscoll 1 Author Orcid Image ;   Jake Turicchi 1 Author Orcid Image ;   Mark Hopkins 2 Author Orcid Image ;   Cristiana Duarte 1 Author Orcid Image ;   Graham W Horgan 3 Author Orcid Image ;   Graham Finlayson 1 Author Orcid Image ;   R James Stubbs 1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Ruairi O'Driscoll   1 , MSc ;   Jake Turicchi   1 , BSc ;   Mark Hopkins   2 , PhD ;   Cristiana Duarte   1 , PhD ;   Graham W Horgan   3 , PhD ;   Graham Finlayson   1 , PhD ;   R James Stubbs   1 , PhD

1 Appetite Control and Energy Balance Group, School of Psychology, University of Leeds, Leeds, GB

2 School of Food Science and Nutrition, Faculty of Environment, University of Leeds, Leeds, GB

3 Biomathematics & Statistics Scotland, Aberdeen, GB

Corresponding Author:

  • Ruairi O'Driscoll, MSc
  • Appetite Control and Energy Balance Group
  • School of Psychology
  • University of Leeds
  • Woodhouse
  • Leeds
  • GB
  • Phone: 44 113 343 2846
  • Email: psrod@leeds.ac.uk