Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.
Affect
Anxiety
/ diagnosis
Child
Child, Preschool
Depression
/ diagnosis
Female
Humans
Logistic Models
Male
Models, Statistical
Psychology, Child
Psychopathology
ROC Curve
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Supervised Machine Learning
/ statistics & numerical data
Wearable Electronic Devices
/ statistics & numerical data
Journal
PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081
Informations de publication
Date de publication:
2019
2019
Historique:
received:
25
05
2018
accepted:
18
12
2018
entrez:
17
1
2019
pubmed:
17
1
2019
medline:
12
10
2019
Statut:
epublish
Résumé
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause children to go unassessed-suffering in silence because they never exhibiting the disruptive behaviors that would lead to a referral for diagnostic assessment. If left untreated these disorders are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying children with internalizing disorders using an instrumented 90-second mood induction task. Participant motion during the task is monitored using a commercially available wearable sensor. We show that machine learning can be used to differentiate children with an internalizing diagnosis from controls with 81% accuracy (67% sensitivity, 88% specificity). We provide a detailed description of the modeling methodology used to arrive at these results and explore further the predictive ability of each temporal phase of the mood induction task. Kinematical measures most discriminative of internalizing diagnosis are analyzed in detail, showing affected children exhibit significantly more avoidance of ambiguous threat. Performance of the proposed approach is compared to clinical thresholds on parent-reported child symptoms which differentiate children with an internalizing diagnosis from controls with slightly lower accuracy (.68-.75 vs. .81), slightly higher specificity (.88-1.00 vs. .88), and lower sensitivity (.00-.42 vs. .67) than the proposed, instrumented method. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.
Identifiants
pubmed: 30650109
doi: 10.1371/journal.pone.0210267
pii: PONE-D-18-15797
pmc: PMC6334916
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e0210267Subventions
Organisme : NIMH NIH HHS
ID : R03 MH102648
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000433
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002240
Pays : United States
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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