Previously submitted to: JMIR Mental Health (no longer under consideration since Jan 31, 2022)
Date Submitted: Dec 31, 2021
Open Peer Review Period: Dec 29, 2021 - Feb 23, 2022
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Digital Phenotype of Mood Disorders: a conceptual and critical review
ABSTRACT
Background:
Mood disorder is 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 allow one 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 for the diagnosis of mood disorder, introducing for the first time paraclinical testing 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 digital phenotypes applied to mood disorders.
Methods:
We conducted a selective review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with the relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence.
Results:
858 articles were included for evaluation, 43 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, body temperature). For depressive episodes, the main finding is the decrease in terms of functional and biological parameters (decrease in activities and walking, decrease in the number of calls and SMS, decrease in temperature and HRV) while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV).
Conclusions:
The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders
Citation
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