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

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