Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Jul 26:13:895860.
doi: 10.3389/fpsyt.2022.895860. eCollection 2022.

Digital phenotype of mood disorders: A conceptual and critical review

Redwan Maatoug  1   2 , Antoine Oudin  1   2 , Vladimir Adrien  2   3 , Bertrand Saudreau  2   4 , Olivier Bonnot  5   6 , Bruno Millet  1   2 , Florian Ferreri  2   3 , Stephane Mouchabac  2   3 , Alexis Bourla  2   3   7
Affiliations

Digital phenotype of mood disorders: A conceptual and critical review

Redwan Maatoug et al. Front Psychiatry. .

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.

Keywords: artificial intelligence; bipolar disorder; depressive disorder; digital phenotyping; machine learning; mood disorders.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The concept of digital phenotype for mood disorders as opposed to EMA.
FIGURE 2
FIGURE 2
PRISMA diagram.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Pasztor E. Dawkins, R. 1989. (original edition 1982; reprinted 1983, 1987, 1988, 1989): the extended phenotype. Oxford Paperbacks, Oxford University Press, Oxford, UK. 307 Pp. F6.95. J Evol Biol. (1991) 4:161–161. 10.1046/j.1420-9101.1991.4010161.x - DOI
    1. Jain SH, Powers BW, Hawkins JB, Brownstein JS. The digital phenotype. Nat Biotechnol. (2015) 33:462–3. 10.1038/nbt.3223 - DOI - PubMed
    1. Torous J, Onnela J-P, Keshavan M. New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Transl Psychiatry. (2017) 7:e1053–1053. 10.1038/tp.2017.25 - DOI - PMC - PubMed
    1. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre. L’Encéphale. (2018) 44:168–75. 10.1016/j.encep.2017.07.007 - DOI - PubMed
    1. Safa R, Bayat P, Moghtader L. Automatic detection of depression symptoms in twitter using multimodal analysis. J Supercomput. (2021):1–36. 10.1007/s11227-021-04040-8 - DOI - PMC - PubMed

Publication types

LinkOut - more resources