期刊
JOURNAL OF AFFECTIVE DISORDERS
卷 282, 期 -, 页码 104-111出版社
ELSEVIER
DOI: 10.1016/j.jad.2020.12.086
关键词
digital phenotyping; passive sensing; anxiety disorders; wearable movement; deep learning; artficial intelligence
资金
- John D. and Catherine T. MacArthur Foundation Research Network
- National Institute on Aging [U19-AG051426, P01-AG020166]
- National Institutes of Health National Center for Advancing Translational Sciences (NCATS) Clinical and Translational Science Award (CTSA) program [UL1TR001409, UL1TR001881, 1UL1RR025011]
- National Institute of Drug Abuse [NIDA-5 P30DA02992610]
- National Institute of Mental Health [R01-MH123482]
This study used deep learning models to predict long-term deterioration of anxiety disorder symptoms based on wearable sensor data, suggesting that wearable technology could be used to forecast the progression of anxiety disorders over time.
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. Methods: 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 autoencoder 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. Results: 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%). Conclusions: 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.
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