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; et al. (Frontiers in Psychiatry, 2021)
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 assess feasibility of several novel digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and normal 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 subjects with unipolar depression (MDD and PDD/dysthymia) and 20 healthy volunteers 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 novel 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. 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.