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

Non-randomized Protocols and Methods (ehealth) (1140) New Methods (1897) mHealth for Data Collection and Research (1811) Methods and New Tools in Mental Health Research (965) Wearable Devices and Sensors (1686) Datasets (37) Development and Evaluation of Research Methods, Instruments and Tools (1489) Digital Biomarkers and Digital Phenotyping (485)

Published on in Vol 13 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53857, first published .
Open book with "Mental Health" and icons about discuss, solve, and love in a group therapy setting.

Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study

Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study

Authors of this article:

Robyn E Kilshaw1 Author Orcid Image ;   Abigail Boggins1 Author Orcid Image ;   Olivia Everett1 Author Orcid Image ;   Emma Butner1 Author Orcid Image ;   Feea R Leifker1 Author Orcid Image ;   Brian R W Baucom1 Author Orcid Image
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Szymkowiak J, Woźniak M, Reczka K, Zalewska Z. DIGITAL PHENOTYPING AS A NOVEL CLINICAL BIOMARKER: ASSESSING THE DIAGNOSTIC EFFICACY, ARTIFICIAL INTELLIGENCE INTEGRATION, AND SOCIO-ETHICAL IMPLICATIONS IN PSYCHIATRIC CARE. International Journal of Innovative Technologies in Social Science 2026;5(3(51)) View
  2. Jayasree K, Bharanidharan N. Multimodal Deep Learning Approach Using Laser Doppler Flowmetry, Fluorescence Spectroscopy, and Clinical Data for Multi‐Class Mental Health and Severity Classification. Engineering Reports 2026;8(11) View