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

Depression and Mood Disorders; Suicide Prevention (1749) Mobile Health (mhealth) (3614) Wearable Devices and Sensors (1200) Digital Biomarkers and Digital Phenotyping (317) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2242) mHealth for Wellness, Behavior Change and Prevention (3410) Mobile Health in Psychiatry (552) mHealth for Data Collection and Research (1245)

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 (9) 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
  2. Yang M, Ngai E, Hu X, Hu B, Liu J, Gelenbe E, Leung V. Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection. Proceedings of the IEEE 2024;112(12):1773 View
  3. Garzón-Partida A, Padilla-Gómez C, Martínez-Fernández D, García-Estrada J, Luquin S, Fernández-Quezada D. The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review. Frontiers in Digital Health 2025;7 View
  4. Brearly T, Elbich D, Dhinojwala M, Hakun J. Contextualized cognition: Clarifying associations between remote unsupervised performance and clinically relevant contextual factors. The Clinical Neuropsychologist 2025:1 View
  5. Liu B. Research on emotion recognition of art design based on fusion features and transfer learning. Journal of Computational Methods in Sciences and Engineering 2025 View
  6. Han H, Yu X, Li H, Sun N. The bidirectional relationship between depression and perceived social support among college freshmen: evidence from longitudinal and daily diary methods. Frontiers in Psychology 2025;16 View
  7. Malik S, Srivastava P. Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems. Current Psychiatry Reports 2026;28(1) View
  8. Ross-Adelman M, Aalbers G, Matcham F, Leightley D, Oetzmann C, Carr E, Siddi S, Haro J, Annas P, Dalby M, Narayan V, Hotopf M, Myin-Germeys I, Lamers F, Penninx B. Sleep, Steps, and Screens: Between- and within-person effects of digital markers of daily life behaviors on smartphone-based assessments of cognitive functioning in depression. Neuroscience Applied 2026;5:106985 View
  9. Kim K, Byun A, Castillo J, Youn Y, Torous J. Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders. Harvard Review of Psychiatry 2026;34(2):85 View