Digital phenotype of mood disorders: A conceptual and critical review

Redwan Maatoug · Antoine Oudin · Vladimir Adrien · Bertrand Saudreau · Olivier Bonnot · Bruno Millet +3 more

43 citations0/0 files storedFrontiers in Psychiatry

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Mental Health Research Topics Emotion and Mood Recognition Digital Mental Health Interventions

Abstract

Background: Mood disorders are commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allows us to determine the digital signature of a pathology. This strategy assumes that behaviors are quantifiable from data extracted and analyzed through digital sensors, wearable devices, or smartphones. That concept could bring a shift in the diagnosis of mood disorders, introducing for the first time additional examinations on psychiatric routine care. Objective: The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of the digital phenotypes applied to mood disorders. Methods: We conducted a review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. Results: Out of 884 articles included for evaluation, 45 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, or body temperature). For depressive episodes, the main finding is a decrease in terms of functional and biological parameters [decrease in activities and walking, decrease in the number of calls and SMS messages, decrease in temperature and heart rate variability (HRV)], while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). Conclusion: The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.

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2018Depression detection from social network data using machine learning techniques 2017New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices 2018Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study 2022Research Domain Criteria (RDoC): Progress and Potential 2019Mood Prediction of Patients With Mood Disorders by Machine Learning Using Passive Digital Phenotypes Based on the Circadian Rhythm: Prospective Observational Cohort Study 2019Digital biomarkers of mood disorders and symptom change 2020Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors 2021Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

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