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 (825) mHealth for Data Collection and Research (917) Fitness Trackers and Smart Pedometers/Accelerometers (543) Machine Learning (1400) Artificial Intelligence (1214) Innovations and Technology for Physical Activity Education (703)

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
Article Authors Cited by (11) 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, United Kingdom

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

3 Biomathematics & Statistics Scotland, Aberdeen, United Kingdom

Corresponding Author:

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