Use of Mobile and Wearable Artificial Intelligence in Child and Adolescent Psychiatry: Scoping Review.

Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE

Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-

Autism Spectrum Disorder* ; Wearable Electronic Devices*; Adolescent ; Adolescent Psychiatry/instrumentation ; Artificial Intelligence ; Child Psychiatry/instrumentation ; Humans

Background: Mental health disorders are a leading cause of medical disabilities across an individual's lifespan. This burden is particularly substantial in children and adolescents because of challenges in diagnosis and the lack of precision medicine approaches. However, the widespread adoption of wearable devices (eg, smart watches) that are conducive for artificial intelligence applications to remotely diagnose and manage psychiatric disorders in children and adolescents is promising.
Objective: This study aims to conduct a scoping review to study, characterize, and identify areas of innovations with wearable devices that can augment current in-person physician assessments to individualize diagnosis and management of psychiatric disorders in child and adolescent psychiatry.
Methods: This scoping review used information from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search of several databases from 2011 to June 25, 2021, limited to the English language and excluding animal studies, was conducted. The databases included Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; Ovid Cochrane Database of Systematic Reviews; Web of Science; and Scopus.
Results: The initial search yielded 344 articles, from which 19 (5.5%) articles were left on the final source list for this scoping review. Articles were divided into three main groups as follows: studies with the main focus on autism spectrum disorder, attention-deficit/hyperactivity disorder, and internalizing disorders such as anxiety disorders. Most of the studies used either cardio-fitness chest straps with electrocardiogram sensors or wrist-worn biosensors, such as watches by Fitbit. Both allowed passive data collection of the physiological signals.
Conclusions: Our scoping review found a large heterogeneity of methods and findings in artificial intelligence studies in child psychiatry. Overall, the largest gap identified in this scoping review is the lack of randomized controlled trials, as most studies available were pilot studies and feasibility trials.
(©Victoria Welch, Tom Joshua Wy, Anna Ligezka, Leslie C Hassett, Paul E Croarkin, Arjun P Athreya, Magdalena Romanowicz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.03.2022.)

Psychol Med. 2019 Jul;49(9):1426-1448. (PMID: 30744717)
JAMA Psychiatry. 2017 Apr 01;74(4):370-378. (PMID: 28241180)
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4273-4277. (PMID: 33018940)
Lancet Psychiatry. 2016 Mar;3(3):243-50. (PMID: 26803397)
Ann Intern Med. 1991 Dec 1;115(11):843-8. (PMID: 1952470)
Ann Intern Med. 2018 Oct 2;169(7):467-473. (PMID: 30178033)
Acad Psychiatry. 2020 Aug;44(4):461-466. (PMID: 32424706)
Nat Med. 1998 Nov;4(11):1241-3. (PMID: 9809543)
Adv Exp Med Biol. 2019;1192:119-125. (PMID: 31705492)
Comput Biol Med. 2017 Jun 1;85:143-152. (PMID: 27080078)
J Child Psychol Psychiatry. 2006 Mar-Apr;47(3-4):372-94. (PMID: 16492264)
N Engl J Med. 1991 Dec 19;325(25):1767-73. (PMID: 1834940)
Lancet. 2015 Aug 22;386(9995):743-800. (PMID: 26063472)
JMIR Mhealth Uhealth. 2017 Jul 06;5(7):e92. (PMID: 28684384)
Sensors (Basel). 2019 Jul 03;19(13):. (PMID: 31277297)
Curr Psychiatry Rep. 2019 Nov 7;21(11):116. (PMID: 31701320)
Front Integr Neurosci. 2019 Nov 12;13:65. (PMID: 31798424)
Nat Med. 2019 Jan;25(1):16-18. (PMID: 30617332)
Neuropsychopharmacology. 2021 Jun;46(7):1272-1282. (PMID: 33452433)
PLoS One. 2019 Jan 16;14(1):e0210267. (PMID: 30650109)
Front Physiol. 2018 May 01;9:467. (PMID: 29765335)
IEEE Trans Biomed Eng. 2015 Mar;62(3):990-1000. (PMID: 25494497)
Sci Rep. 2020 Oct 29;10(1):18659. (PMID: 33122808)
J Child Psychol Psychiatry. 2016 Jun;57(6):706-16. (PMID: 26799153)
IEEE Trans Biomed Circuits Syst. 2011 Dec;5(6):546-54. (PMID: 23852552)
Sensors (Basel). 2021 Jan 17;21(2):. (PMID: 33477359)
Autism Res. 2019 Aug;12(8):1286-1296. (PMID: 31225952)
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:365-8. (PMID: 22254324)
Pharmacogenomics. 2019 Sep;20(14):983-988. (PMID: 31559920)
IEEE J Biomed Health Inform. 2021 Aug;25(8):3176-3184. (PMID: 33481724)
Front Neurosci. 2016 Jun 21;10:276. (PMID: 27445652)
Am J Med. 2018 Feb;131(2):129-133. (PMID: 29126825)
Bioinformatics. 2014 Jun 15;30(12):i121-9. (PMID: 24931975)
Digit Health. 2021 Apr 01;7:20552076211001215. (PMID: 33868703)
Clin Pharmacol Ther. 2019 Oct;106(4):855-865. (PMID: 31012492)

R01 MH113700 United States MH NIMH NIH HHS; R01 MH124655 United States MH NIMH NIH HHS

Keywords: artificial intelligence; child psychiatry; mobile computing; wearable technologies

Date Created: 20220314 Date Completed: 20220331 Latest Revision: 20220716

20240829

PMC8961347

10.2196/33560

35285812