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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
Processing time: 261 Days and 10.2 Hours
Abstract

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

Keywords: Digital phenotyping; Depression; Ecological momentary assessment; Telepsychiatry; Passive sensing; Smart phone

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.