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

Mobile Health (mhealth) (3686) Research Instruments, Questionnaires, and Tools (793) mHealth for Data Collection and Research (1274) Wearable Devices and Sensors (1223) mHealth for Telemedicine and Homecare (569) Digital Biomarkers and Digital Phenotyping (329) Sensors at Home and Domotics (85)

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 (3) Tweetations (3) Metrics

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  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
  2. Płonka M, Klimas R, Stanev D, Angelini L, Napiórkowski N, González Chan G, Bunn L, Glazier P, Hosking R, Freeman J, Hobart J, Zanon M, Marsden J, Craveiro L, Rinderknecht M. Analytical and cross-sectional clinical validity of a smartphone-based U-Turn Test in multiple sclerosis. Multiple Sclerosis and Related Disorders 2026;110:107171 View
  3. Özel A, Yakşi E, Çevik Ç, Nazar M. Why your smartwatch may be misleading your doctor: a cross-sectional study on the impact of mobility aids on wearable accuracy in older adults. PeerJ 2026;14:e20690 View