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

• Published in: Journal of affective disorders Vol. 282; pp. 104 - 111
• Main Authors: Jacobson, Nicholas C., Lekkas, Damien, Huang, Raphael, Thomas, Natalie
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2021
• Subjects: anxiety disorders; Anxiety Disorders - diagnosis; artficial intelligence; Deep Learning; digital phenotyping; Humans; Longitudinal Studies; Panic Disorder - diagnosis; passive sensing; Psychiatric/Mental Health; Wearable Electronic Devices; wearable movement; deep learning; artficial intelligence; passive sensing; anxiety disorders; wearable movement; digital phenotyping
• ISSN: 0165-0327; 1573-2517; 1573-2517
• DOI: 10.1016/j.jad.2020.12.086

•Passive, wearable sensor data has potential to predict deterioration in long term anxiety disorder symptoms.•The developed ensemble deep learning modelcould predict anxiety symptom deterioration across almost two decades.•This prediction pipelinemay help to narrow the longstanding wait between symptom deterioration and treatment initiation. 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.