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

Depression and Mood Disorders; Suicide Prevention (1407) Mobile Health (mhealth) (3005) Wearable Devices and Sensors (917) Digital Biomarkers and Digital Phenotyping (201) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1588) mHealth for Wellness, Behavior Change and Prevention (2949) Mobile Health in Psychiatry (444) mHealth for Data Collection and Research (994)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46895, first published .
Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study

Authors of this article:

Francesca Cormack1, 2, 3 Author Orcid Image ;   Maggie McCue4 Author Orcid Image ;   Caroline Skirrow1, 5 Author Orcid Image ;   Nathan Cashdollar1 Author Orcid Image ;   Nick Taptiklis1 Author Orcid Image ;   Tempest van Schaik6 Author Orcid Image ;   Ben Fehnert3, 6, 7 Author Orcid Image ;   James King3, 6, 7 Author Orcid Image ;   Lambros Chrones4 Author Orcid Image ;   Sara Sarkey4 Author Orcid Image ;   Jasmin Kroll1 Author Orcid Image ;   Jennifer H Barnett1, 2 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Sprint G, Schmitter-Edgecombe M, Cook D. Building a Human Digital Twin (HDTwin) Using Large Language Models for Cognitive Diagnosis: Algorithm Development and Validation. JMIR Formative Research 2024;8:e63866 View