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 (1401) Artificial Intelligence (1216) 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

Journals

  1. Dakin C, Beaulieu K, Hopkins M, Gibbons C, Finlayson G, Stubbs R. Do eating behavior traits predict energy intake and body mass index? A systematic review and meta‐analysis. Obesity Reviews 2023;24(1) View
  2. Perrett T, Masullo A, Damen D, Burghardt T, Craddock I, Mirmehdi M. Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning. JMIR Formative Research 2022;6(9):e33606 View
  3. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View
  4. Bilgi E, Karakus C. Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis. Journal of Nanoparticle Research 2023;25(8) View
  5. James Stubbs R, Horgan G, Robinson E, Hopkins M, Dakin C, Finlayson G. Diet composition and energy intake in humans. Philosophical Transactions of the Royal Society B: Biological Sciences 2023;378(1888) View
  6. Kim J, Choi J, Kim H, Lee T, Ha J, Lee S, Park J, Jeon G, Cho S. Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis. JMIR mHealth and uHealth 2023;11:e50663 View
  7. Rauf A, Ullah A, Rathi U, Ashfaq Z, Ullah H, Ashraf A, Kumar J, Faraz M, Akhtar W, Mehmoodi A, Malik J. Predicting stroke and mortality in mitral stenosis with atrial flutter: A machine learning approach. Annals of Noninvasive Electrocardiology 2023;28(5) View
  8. Vähä-Ypyä H, Husu P, Vasankari T, Sievänen H. Floating Epoch Length Improves the Accuracy of Accelerometry-Based Estimation of Coincident Oxygen Consumption. Sensors 2023;24(1):76 View
  9. Wright R, Parekh S, White R, Losey D. Safely and autonomously cutting meat with a collaborative robot arm. Scientific Reports 2024;14(1) View
  10. Chen D, Du Y, Liu Y, Hong J, Yin X, Zhu Z, Wang J, Zhang J, Chen J, Zhang B, Du L, Yang J, He X, Xu X. Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring. Scientific Reports 2024;14(1) View