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

Authors
  • Jacobson, Nicholas C1
  • Lekkas, Damien2
  • Huang, Raphael3
  • Thomas, Natalie3
  • 1 Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States; Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, United States; Department of Psychiatry, Geisel School of Medicine, Dartmouth College, United States; Quantitative Biomedical Sciences Program, Dartmouth College, United States. Electronic address: [email protected] , (United States)
  • 2 Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States; Quantitative Biomedical Sciences Program, Dartmouth College, United States. , (United States)
  • 3 Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, United States. , (United States)
Type
Published Article
Journal
Journal of affective disorders
Publication Date
Mar 01, 2021
Volume
282
Pages
104–111
Identifiers
DOI: 10.1016/j.jad.2020.12.086
PMID: 33401123
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. We utilized deep learning models based on wearable sensor technology to predict long-term (17-18-year) deterioration in generalized anxiety disorder and panic disorder symptoms from actigraphy data on daytime movement and nighttime sleeping patterns. As part of Midlife in the United States (MIDUS), a national longitudinal study of health and well-being, subjects (N = 265) (i) completed a phone-based interview that assessed generalized anxiety disorder and panic disorder symptoms at enrollment, (ii) participated in a one-week actigraphy study 9-14 years later, and (iii) completed a long-term follow-up, phone-based interview to quantify generalized anxiety disorder and panic disorder symptoms 17-18 years from initial enrollment. A deep auto-encoder paired with a multi-layered ensemble deep learning model was leveraged to predict whether participants experienced increased anxiety disorder symptoms across this 17-18 year period. Out-of-sample cross-validated results suggested that wearable movement data could significantly predict which individuals would experience symptom deterioration (AUC = 0.696, CI [0.598, 0.793], 84.6% sensitivity, 52.7% specificity, balanced accuracy = 68.7%). Passive wearable actigraphy data could be utilized to predict long-term deterioration of anxiety disorder symptoms. Future studies should examine whether these methods could be implemented to prevent deterioration of anxiety disorder symptoms. Copyright © 2020. Published by Elsevier B.V.

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