Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

Abstract

Background Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful. Objective We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common. Methods We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma. Results A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes. Conclusions Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Asynchronous Technologies in Mental Health Care and Education

“…Digital phenotyping is the continuous monitoring of behavioral and mental states through user data tracking [ 66 ]. It provides objective measurements to improve disease classification, especially for trans-diagnostic symptoms.…”

“…Another study suggested that markers such as automated voice analysis, video behavior analysis, and physiological measures that include heart rate variability and electrodermal activity studies could aid in forming differential diagnoses for major depressive episodes. Although comparative studies are lacking, promising results were found in digital markers with physiological measures, such as heart rate variability, which showed significant differences in patients with bipolar depression compared to unipolar depression and more reactivity in patients with comorbid post-traumatic stress disorder in major depressive disorder when being reminded about a psychological trauma [ 66 ].…”

Task-State Skin Potential Abnormalities Can Distinguish Major Depressive Disorder and Bipolar Depression from Healthy Controls

“…This nding is notable for two reasons. First, it highlights that electrodermal activity abnormalities are core pathological features in the affective disorders 15,16,33 . Second, the SVM model we constructed proved to be suitable for discriminant analysis using this type of data, as the other models we employed in this study did not achieve the desired accuracy rates.…”

Digital Tools to Facilitate the Detection and Treatment of Bipolar Disorder: Key Developments and Future Directions

“…They achieved mixed results, yielding fair to excellent classification performances, with accuracy globally ranging from 60% to 97% [ 7 ]. A recent review assessing the application of digital tools for major depressive episodes described the following digital phenotype for BD: (1) speech alterations during a depressive episode, including decreased speech pause and reduced fundamental frequency, while these speech features were increased during a hypomanic episode; (2) irrespective of the mood state, heart rate variability was reduced, but the change in heart rate variability in the interepisodic phases remained unclear, and (3) an electrodermal hypoactivity in a depressive episode was reported, which increased when patients were euthymic [ 20 ].…”