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

Development and Evaluation of Research Methods, Instruments and Tools (439) Patient-Reported Outcome Measures (PROMs) (275) Research Instruments, Questionnaires, and Tools (545) mHealth for Data Collection and Research (917) Artificial Intelligence (1216) Machine Learning (1401)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43107, first published .
Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study

Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study

Authors of this article:

Kaushal Rao1 Author Orcid Image ;   William Speier1 Author Orcid Image ;   Yiwen Meng1 Author Orcid Image ;   Jinhan Wang1 Author Orcid Image ;   Nidhi Ramesh1 Author Orcid Image ;   Fenglong Xie2 Author Orcid Image ;   Yujie Su2 Author Orcid Image ;   W Benjamin Nowell3 Author Orcid Image ;   Jeffrey R Curtis2 Author Orcid Image ;   Corey Arnold1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Gandrup J, Selby D, Dixon W. Classifying Self-Reported Rheumatoid Arthritis Flares Using Daily Patient-Generated Data From a Smartphone App: Exploratory Analysis Applying Machine Learning Approaches. JMIR Formative Research 2024;8:e50679 View
  2. De Larochelambert Q, Hamri I, Chassard T, Meignié A, Storme F, Dupuit M, Diry A, Toussaint J, Louis P, Coulmy N, Antero J. Exploring the effect of the menstrual cycle or oral contraception on elite athletes’ training responses when workload is not objectively quantifiable: the MILS approach and findings from female Olympians. BMJ Open Sport & Exercise Medicine 2024;10(2):e001810 View