Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors.
biomarkers
machine learning
mobile phone
social anxiety
social anxiety disorder
technology assessment, biomedical
Journal
Journal of medical Internet research
ISSN: 1438-8871
Titre abrégé: J Med Internet Res
Pays: Canada
ID NLM: 100959882
Informations de publication
Date de publication:
29 05 2020
29 05 2020
Historique:
received:
04
11
2019
accepted:
27
02
2020
revised:
26
02
2020
pubmed:
30
4
2020
medline:
20
11
2020
entrez:
30
4
2020
Statut:
epublish
Résumé
Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier. This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset. In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity. The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect. These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.
Sections du résumé
BACKGROUND
Social anxiety disorder is a highly prevalent and burdensome condition. Persons with social anxiety frequently avoid seeking physician support and rarely receive treatment. Social anxiety symptoms are frequently underreported and underrecognized, creating a barrier to the accurate assessment of these symptoms. Consequently, more research is needed to identify passive biomarkers of social anxiety symptom severity. Digital phenotyping, the use of passive sensor data to inform health care decisions, offers a possible method of addressing this assessment barrier.
OBJECTIVE
This study aims to determine whether passive sensor data acquired from smartphone data can accurately predict social anxiety symptom severity using a publicly available dataset.
METHODS
In this study, participants (n=59) completed self-report assessments of their social anxiety symptom severity, depressive symptom severity, positive affect, and negative affect. Next, participants installed an app, which passively collected data about their movement (accelerometers) and social contact (incoming and outgoing calls and texts) over 2 weeks. Afterward, these passive sensor data were used to form digital biomarkers, which were paired with machine learning models to predict participants' social anxiety symptom severity.
RESULTS
The results suggested that these passive sensor data could be utilized to accurately predict participants' social anxiety symptom severity (r=0.702 between predicted and observed symptom severity) and demonstrated discriminant validity between depression, negative affect, and positive affect.
CONCLUSIONS
These results suggest that smartphone sensor data may be utilized to accurately detect social anxiety symptom severity and discriminate social anxiety symptom severity from depressive symptoms, negative affect, and positive affect.
Identifiants
pubmed: 32348284
pii: v22i5e16875
doi: 10.2196/16875
pmc: PMC7293055
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e16875Subventions
Organisme : NIDA NIH HHS
ID : P30 DA029926
Pays : United States
Informations de copyright
©Nicholas C Jacobson, Berta Summers, Sabine Wilhelm. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.05.2020.
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