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

Development of Instruments and Surveys (104) Theme Issue: Human Factors of the COVID-19 Outbreak (74) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1542) Theme Issue: The Influence of the COVID-19 Pandemic on Rehabilitation (15) Protocols for Public Health Research and Surveillance (107) Occupational Therapy and Vocational Rehabilitation (50) Nursing in a Homecare Setting (24)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54993, first published .
Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Developing a Multisensor-Based Machine Learning Technology (Aidar Decompensation Index) for Real-Time Automated Detection of Post–COVID-19 Condition: Protocol for an Observational Study

Authors of this article:

Jenny Mathew1 Author Orcid Image ;   Jaclyn A Pagliaro2 Author Orcid Image ;   Sathyanarayanan Elumalai1 Author Orcid Image ;   Lauren K Wash2 Author Orcid Image ;   Ka Ly3 Author Orcid Image ;   Alison J Leibowitz2 Author Orcid Image ;   Varsha G Vimalananda2, 4 Author Orcid Image
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Journals

  1. Pagliaro J, Wash L, Ly K, Mathew J, Leibowitz A, Cabrera R, Wormwood J, Vimalananda V. Enrollment and Retention Outcomes from the Veterans Health Administration for a Remote Digital Health Study: Multisite Observational Study. JMIR Formative Research 2025;9:e68676 View