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

Development and Evaluation of Research Methods, Instruments and Tools (845) Research Instruments, Questionnaires, and Tools (800) mHealth for Data Collection and Research (1286) Machine Learning (2294) Patient-Reported Outcome Measures (PROMs) (396) Artificial Intelligence (2437)

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 (8) 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
  3. Talwar A, Turner S, Maw C, Quayle G, Watt T, Gohil S, Duckworth E, Ciurtin C. Sex bias consideration in healthcare machine-learning research: a systematic review in rheumatoid arthritis. BMJ Open 2025;15(3):e086117 View
  4. Storme F, Chassard T, Dupuit M, Delarochelambert Q, Brunet E, Sachet I, Toussaint J, Antero J. Impact of Menstrual Cycles or Combined Oral Contraception on Training Loads Assessed Using Latent Effort States in Female Elite Cyclists. Scandinavian Journal of Medicine & Science in Sports 2025;35(4) View
  5. Bilgin E. Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis. Therapeutic Advances in Musculoskeletal Disease 2025;17 View
  6. Milbourn E, Lai J, Robinson D, Ackland D, Lee P. Wearable Technology and Machine Learning for Prediction of Performance-Based and Patient-Reported Outcome Measures: A Systematic Review. Sensors 2026;26(4):1218 View
  7. Wolber J, Schuppert A, Mücke M, Sellin J. Predicting disease outcomes from remote monitoring using machine learning: a systematic review. BMC Medical Informatics and Decision Making 2026;26(1) View
  8. Huang Y, Bazzazzadehgan S, Maharjan S, Lin Y, Bentley J, Agarwal S, Yang Y. Predicting the risk of venous thromboembolism events in older adults with rheumatoid arthritis after initiating targeted disease-modifying antirheumatic drugs: a comparison of the random survival forest and regularized cox regression models. Current Medical Research and Opinion 2026:1 View