Citation

Abbas A, Sauder C, Yadav V, Koesmahargyo V, Aghjayan A, Marecki S, Evans M, Galatzer-Levy IR. Front. Digit. Health 2021; 3: e610006.

Copyright

(Copyright © 2021, Frontiers Media)

DOI

10.3389/fdgth.2021.610006

PMID

34713091

PMCID

PMC8521884

Abstract

OBJECTIVES: Multiple machine learning-based visual and auditory digital markers have demonstrated associations between major depressive disorder (MDD) status and severity. The current study examines if such measurements can quantify response to antidepressant treatment (ADT) with selective serotonin reuptake inhibitors (SSRIs) and serotonin-norepinephrine uptake inhibitors (SNRIs).

METHODS: Visual and auditory markers were acquired through an automated smartphone task that measures facial, vocal, and head movement characteristics across 4 weeks of treatment (with time points at baseline, 2 weeks, and 4 weeks) on ADT (n = 18). MDD diagnosis was confirmed using the Mini-International Neuropsychiatric Interview (MINI), and the Montgomery-Åsberg Depression Rating Scale (MADRS) was collected concordantly to assess changes in MDD severity.

RESULTS: Patient responses to ADT demonstrated clinically and statistically significant changes in the MADRS [F ((2, 34)) = 51.62, p < 0.0001]. Additionally, patients demonstrated significant increases in multiple digital markers including facial expressivity, head movement, and amount of speech. Finally, patients demonstrated significantly decreased frequency of fear and anger facial expressions.

CONCLUSION: Digital markers associated with MDD demonstrate validity as measures of treatment response.

Language: en

Keywords

machine learning; antidepressant treatment; computer vision; digital biomarker; digital phenotyping; major depressive disorder; Montgomery-Åsberg Depression Rating Scale