A study of novel exploratory tools, digital technologies, and central nervous system biomarkers to characterize unipolar depression
Open accessAbstract
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.
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Sverdlov et al. found that selected digital biomarker features were able to predict individual MADRS total scores and be used as classifiers.
“Selected digital biomarker features (PHQ2 of Cambridge cognition; behavioral tracker features of BeHapp; neurophysiological features of Neurocart; EEG—resting state features of ElMindA Ltd. and EEG—BNA features of ElMindA Ltd.) were able to predict individual MADRS total scores, and use these models as classifiers”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [30]
Common efficacy scales such as HAM-D and MADRS are subjective and prone to bias.
“Indeed, in Sverdlov et al. (30), the authors points out that common efficacy scales like HAM-D and MADRS are subjective and prone to bias.”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [30]
Digital biomarkers have a potential role in predicting individual depression severity, once confirmed and replicated in larger population studies.
“digital biomarkers potential role in predicting individual depression severity may be a groundbreaking advancement, once confirmed and replicated in larger populations studies, providing clinicians with even more personalized assessment instruments (29, 30).”
From A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms (2023) · cites this paper as [30]
Remote measurement technologies can allow tracking of current depression status and identification of behavioral markers related to depression in a continuous and effortless manner for long-term passive monitoring.
“Remote measurement technologies can allow tracking of current depression status and identification of behavioral markers related to depression in a continuous and effortless manner for long-term passive monitoring.”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [135]
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- Authors
- Sverdlov, O., Curcic, J., Hannesdottir, K., Gou, L., De Luca, V., Ambrosetti, F., Zhang, B., Praestgaard, J., Vallejo, V., Dolman, A., Gomez-Mancilla, B., Biliouris, K., Deurinck, M., Cormack, F., Anderson, J. J., Bott, N. T., Peremen, Z., Issachar, G., Laufer, O., Joachim, D., Jagesar, R. R., Jongs, N., Kas, M. J., Zhuparris, A., Zuiker, R., Recourt, K., Zuilhof, Z., Cha, J.-H., & Jacobs, G. E.
- Affiliations
- Novartis Institutes for BioMedical Research (CH)
- Georgetown University (US)
- Cambridge Cognition (United Kingdom) (GB)
- Neurotrack Technologies (United States) (US)
- Stanford Medicine (US)
- ElMindA Ltd. (Israel) (IL)
- University of Groningen (NL)
- Centre for Human Drug Research (NL)
- Corresponding author
- Sverdlov, Oleksandr
- ORCID
- Venue
- Frontiers in Psychiatry
- Publisher
- Frontiers Media SA
- Published
- 2021-05-06
- Volume
- 12
- Pages
- 640741-640741
- ISSN
- 1664-0640 (ISSN-L)
- PMC
- PMC8136319
- OpenAlex
- W3158697864
- Cited by
- 34
- References
- 27
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 6 · 2023: 5 · 2022: 10 · 2021: 3
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- Leiden Repository (Leiden University) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- Novartis Institutes for BioMedical Research
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- major depressive disorder, dysthymia, digital biomarkers, digital endpoints, mobile application, cognitive assessment, social interaction monitoring, vocal biomarkers, eye tracking, EEG, brain network activity, depression diagnosis, digital health
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