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

Mobile Health (mhealth) (3464) mHealth for Data Collection and Research (1196) Research Instruments, Questionnaires, and Tools (738) Sensors at Home and Domotics (81) mHealth for Telemedicine and Homecare (531) Wearable Devices and Sensors (1133) Digital Biomarkers and Digital Phenotyping (284)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63090, first published .
Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

Authors of this article:

Lito Kriara1 Author Orcid Image ;   Frank Dondelinger1 Author Orcid Image ;   Luca Capezzuto1 Author Orcid Image ;   Corrado Bernasconi1 Author Orcid Image ;   Florian Lipsmeier1 Author Orcid Image ;   Adriano Galati1 Author Orcid Image ;   Michael Lindemann1 Author Orcid Image
Article Authors Cited by (1) Tweetations (3) Metrics

Journals

  1. Sarathkumar E, Jayasree R. Machine learning-integrated lateral flow assays: Unlocking the future of intelligent point-of-care sensing. TrAC Trends in Analytical Chemistry 2025;193:118478 View