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A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression
Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psy...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136319/ https://www.ncbi.nlm.nih.gov/pubmed/34025472 http://dx.doi.org/10.3389/fpsyt.2021.640741 |
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author | Sverdlov, Oleksandr Curcic, Jelena Hannesdottir, Kristin Gou, Liangke De Luca, Valeria Ambrosetti, Francesco Zhang, Bingsong Praestgaard, Jens Vallejo, Vanessa Dolman, Andrew Gomez-Mancilla, Baltazar Biliouris, Konstantinos Deurinck, Mark Cormack, Francesca Anderson, John J. Bott, Nicholas T. Peremen, Ziv Issachar, Gil Laufer, Offir Joachim, Dale Jagesar, Raj R. Jongs, Niels Kas, Martien J. Zhuparris, Ahnjili Zuiker, Rob Recourt, Kasper Zuilhof, Zoë Cha, Jang-Ho Jacobs, Gabriel E. |
author_facet | Sverdlov, Oleksandr Curcic, Jelena Hannesdottir, Kristin Gou, Liangke De Luca, Valeria Ambrosetti, Francesco Zhang, Bingsong Praestgaard, Jens Vallejo, Vanessa Dolman, Andrew Gomez-Mancilla, Baltazar Biliouris, Konstantinos Deurinck, Mark Cormack, Francesca Anderson, John J. Bott, Nicholas T. Peremen, Ziv Issachar, Gil Laufer, Offir Joachim, Dale Jagesar, Raj R. Jongs, Niels Kas, Martien J. Zhuparris, Ahnjili Zuiker, Rob Recourt, Kasper Zuilhof, Zoë Cha, Jang-Ho Jacobs, Gabriel E. |
author_sort | Sverdlov, Oleksandr |
collection | PubMed |
description | Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications. |
format | Online Article Text |
id | pubmed-8136319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81363192021-05-21 A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression Sverdlov, Oleksandr Curcic, Jelena Hannesdottir, Kristin Gou, Liangke De Luca, Valeria Ambrosetti, Francesco Zhang, Bingsong Praestgaard, Jens Vallejo, Vanessa Dolman, Andrew Gomez-Mancilla, Baltazar Biliouris, Konstantinos Deurinck, Mark Cormack, Francesca Anderson, John J. Bott, Nicholas T. Peremen, Ziv Issachar, Gil Laufer, Offir Joachim, Dale Jagesar, Raj R. Jongs, Niels Kas, Martien J. Zhuparris, Ahnjili Zuiker, Rob Recourt, Kasper Zuilhof, Zoë Cha, Jang-Ho Jacobs, Gabriel E. Front Psychiatry Psychiatry Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted. Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression. Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception. Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression. Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications. Frontiers Media S.A. 2021-05-06 /pmc/articles/PMC8136319/ /pubmed/34025472 http://dx.doi.org/10.3389/fpsyt.2021.640741 Text en Copyright © 2021 Sverdlov, Curcic, Hannesdottir, Gou, De Luca, Ambrosetti, Zhang, Praestgaard, Vallejo, Dolman, Gomez-Mancilla, Biliouris, Deurinck, Cormack, Anderson, Bott, Peremen, Issachar, Laufer, Joachim, Jagesar, Jongs, Kas, Zhuparris, Zuiker, Recourt, Zuilhof, Cha and Jacobs. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Sverdlov, Oleksandr Curcic, Jelena Hannesdottir, Kristin Gou, Liangke De Luca, Valeria Ambrosetti, Francesco Zhang, Bingsong Praestgaard, Jens Vallejo, Vanessa Dolman, Andrew Gomez-Mancilla, Baltazar Biliouris, Konstantinos Deurinck, Mark Cormack, Francesca Anderson, John J. Bott, Nicholas T. Peremen, Ziv Issachar, Gil Laufer, Offir Joachim, Dale Jagesar, Raj R. Jongs, Niels Kas, Martien J. Zhuparris, Ahnjili Zuiker, Rob Recourt, Kasper Zuilhof, Zoë Cha, Jang-Ho Jacobs, Gabriel E. A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title | A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title_full | A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title_fullStr | A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title_full_unstemmed | A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title_short | A Study of Novel Exploratory Tools, Digital Technologies, and Central Nervous System Biomarkers to Characterize Unipolar Depression |
title_sort | study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136319/ https://www.ncbi.nlm.nih.gov/pubmed/34025472 http://dx.doi.org/10.3389/fpsyt.2021.640741 |
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