Kamath J, Leon Barriera R, Jain N, Keisari E, Wang B. Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives. World J Psychiatry 2022; 12(3): 393-409 [PMID: 35433319 DOI: 10.5498/wjp.v12.i3.393]
Corresponding Author of This Article
Jayesh Kamath, MD, PhD, Professor, Department of Psychiatry and Immunology, University of Connecticut School of Medicine, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06030, United States. jkamath@uchc.edu
Research Domain of This Article
Psychiatry
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Mar 19, 2022; 12(3): 393-409 Published online Mar 19, 2022. doi: 10.5498/wjp.v12.i3.393
Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives
Jayesh Kamath, Roberto Leon Barriera, Neha Jain, Efraim Keisari, Bing Wang
Jayesh Kamath, Department of Psychiatry and Immunology, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06030, United States
Jayesh Kamath, Roberto Leon Barriera, Neha Jain, Efraim Keisari, Department of Psychiatry, University of Connecticut School of Medicine, University of Connecticut Health Center, Farmington, CT 06032, United States
Bing Wang, Department of Computer Science and Engineering, University of Connecticut, Storrs, CT 06269, United States
Author contributions: Kamath J and Wang B are the primary authors of this manuscript and are Co-Principal Investigators of the studies described in the manuscript; Barriera RL wrote the active ecological momentary assessments sections; Jain N wrote the telepsychiatry sections; Keisari E wrote the privacy, legal, and ethical challenges section; all three contributed to other sections of the manuscript.
Conflict-of-interest statement: The authors declare no conflicts of interest regarding this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jayesh Kamath, MD, PhD, Professor, Department of Psychiatry and Immunology, University of Connecticut School of Medicine, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06030, United States. jkamath@uchc.edu
Received: June 30, 2021 Peer-review started: June 30, 2021 First decision: September 5, 2021 Revised: September 23, 2021 Accepted: February 12, 2022 Article in press: February 12, 2022 Published online: March 19, 2022
Core Tip
Core Tip: There are systematic/quantitative reviews and meta-analyses of digital phenotyping (DP) in depression available in literature. These reviews are primarily published by engineering groups and provide limited psychiatric perspective, especially clinical relevance and clinical integration. The current review presents an overview of digital phenotyping of depression diagnostics and assessment from both psychiatric and engineering perspective. The overview includes major advances in the field of DP of depression diagnostics, including active and passive ecological momentary assessment, DP using data from social media, and DP using data from electronic medical records. We briefly discuss investigations conducted by our group and present a model for clinical integration of DP informed by those investigations conducted by our group. Finally, we discuss benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics from an interdisciplinary perspective.
