Digital phenotype of mood disorders: A conceptual and critical review.
artificial intelligence
bipolar disorder
depressive disorder
digital phenotyping
machine learning
mood disorders
Journal
Frontiers in psychiatry
ISSN: 1664-0640
Titre abrégé: Front Psychiatry
Pays: Switzerland
ID NLM: 101545006
Informations de publication
Date de publication:
2022
2022
Historique:
received:
14
03
2022
accepted:
07
07
2022
entrez:
12
8
2022
pubmed:
13
8
2022
medline:
13
8
2022
Statut:
epublish
Résumé
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. 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. 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. 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). The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
Sections du résumé
Background
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
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
UNASSIGNED
The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders.
Identifiants
pubmed: 35958638
doi: 10.3389/fpsyt.2022.895860
pmc: PMC9360315
doi:
Types de publication
Systematic Review
Langues
eng
Pagination
895860Informations de copyright
Copyright © 2022 Maatoug, Oudin, Adrien, Saudreau, Bonnot, Millet, Ferreri, Mouchabac and Bourla.
Déclaration de conflit d'intérêts
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.
Références
J Med Internet Res. 2018 Jul 20;20(7):e241
pubmed: 30030209
Gen Psychiatr. 2021 Feb 04;34(1):e100219
pubmed: 33644687
J Med Internet Res. 2020 Dec 1;22(12):e22634
pubmed: 33258783
JMIR Mhealth Uhealth. 2021 Jul 12;9(7):e26540
pubmed: 34255713
Health Inf Sci Syst. 2018 Aug 27;6(1):8
pubmed: 30186594
Int J Psychophysiol. 2019 Aug;142:57-65
pubmed: 31195066
J Affect Disord. 2020 Oct 1;275:136-144
pubmed: 32658816
J Psychiatr Res. 2017 Jan;84:169-173
pubmed: 27743529
JAMA Psychiatry. 2019 Feb 1;76(2):190-198
pubmed: 30540352
J Clin Med. 2021 Jul 08;10(14):
pubmed: 34300212
Comput Biol Med. 2019 Sep;112:103381
pubmed: 31404718
Front Neurosci. 2021 Jul 05;15:675624
pubmed: 34326716
Technol Health Care. 2019;27(S1):407-424
pubmed: 31045557
Transl Psychiatry. 2017 Mar 7;7(3):e1053
pubmed: 28267146
JMIR Mhealth Uhealth. 2021 Mar 8;9(3):e24365
pubmed: 33683207
JMIR Ment Health. 2021 Aug 10;8(8):e27589
pubmed: 34383685
Depress Anxiety. 2020 Jul;37(7):657-669
pubmed: 32383335
J Psychiatr Res. 2021 Sep;141:385-389
pubmed: 34329990
J Psychiatr Res. 2017 Dec;95:282-287
pubmed: 28926794
Sensors (Basel). 2020 Mar 04;20(5):
pubmed: 32143358
PLoS One. 2020 Aug 24;15(8):e0231995
pubmed: 32833958
J Nerv Ment Dis. 2019 Oct;207(10):893-896
pubmed: 31596769
J Med Internet Res. 2021 Sep 3;23(9):e22844
pubmed: 34477562
BMC Psychiatry. 2021 Jan 29;21(1):65
pubmed: 33514333
J Affect Disord. 2020 Apr 1;266:30-36
pubmed: 32056891
J Psychosom Res. 2021 Jun;145:110478
pubmed: 33820643
Encephale. 2018 Apr;44(2):168-175
pubmed: 29096909
Appl Psychophysiol Biofeedback. 2019 Sep;44(3):221-234
pubmed: 31041646
J Med Internet Res. 2019 Apr 17;21(4):e11029
pubmed: 30994461
Nat Biotechnol. 2015 May;33(5):462-3
pubmed: 25965751
Sensors (Basel). 2020 Jun 24;20(12):
pubmed: 32599801
Psychiatry Res. 2017 Dec;258:587-590
pubmed: 28844556
J Med Internet Res. 2021 Aug 13;23(8):e28918
pubmed: 34397386
J Psychiatr Res. 2019 Oct;117:38-44
pubmed: 31279242
Front Psychiatry. 2020 Dec 18;11:584711
pubmed: 33391050
Int J Psychophysiol. 2018 Sep;131:131-138
pubmed: 29117509
Front Psychiatry. 2019 Jan 17;9:735
pubmed: 30705641
Sci Rep. 2018 Nov 19;8(1):17030
pubmed: 30451895
Psychol Med. 2021 Jan 12;:1-7
pubmed: 33431104
JMIR Mhealth Uhealth. 2021 Oct 25;9(10):e24872
pubmed: 34694233
J Am Med Inform Assoc. 2020 Apr 1;27(4):522-530
pubmed: 31977041
Front Psychiatry. 2021 Apr 08;12:610457
pubmed: 33897487
Psychiatry Res. 2017 Oct;256:207-211
pubmed: 28646783
NPJ Digit Med. 2019 Feb 1;2:3
pubmed: 31304353
J Psychiatr Res. 2021 Apr;136:39-46
pubmed: 33549901
JMIR Mhealth Uhealth. 2020 Jan 3;8(1):e13244
pubmed: 31899454
Curr Dir Psychol Sci. 2022 Apr;31(2):107-114
pubmed: 35692384
Smart Health (Amst). 2020 Nov;18:
pubmed: 33043105
J Psychiatr Res. 2018 Apr;99:33-38
pubmed: 29407285
JMIR Mhealth Uhealth. 2021 Jul 30;9(7):e29840
pubmed: 34328441
J Supercomput. 2022;78(4):4709-4744
pubmed: 34518741
Brain Sci. 2021 Nov 01;11(11):
pubmed: 34827453
JMIR Mhealth Uhealth. 2020 Aug 4;8(8):e17281
pubmed: 32749997
Transl Psychiatry. 2021 Aug 2;11(1):415
pubmed: 34341338