Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

Journal: Journal of affective disorders

PMID: 33401123

Abstract

BACKGROUND: Recent studies have demonstrated that passive smartphone and wearable sensor data collected throughout daily life can predict anxiety symptoms cross-sectionally. However, to date, no research has demonstrated the capacity for these digital biomarkers to predict long-term prognosis.

Authors

Nicholas C Jacobson

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH, United States.
Damien Lekkas

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States; Quantitative Biomedical Sciences Program, Dartmouth College, United States.
Raphael Huang

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States.
Natalie Thomas

Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States.

Keywords

Anxiety Disorders Deep Learning Humans Longitudinal Studies Panic Disorder Wearable Electronic Devices

External Resources

View on PubMed Access via DOI PubMed (33401123)

Deep learning paired with wearable passive sensing data predicts deterioration in anxiety disorder symptoms across 17-18 years.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals