Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life.

ABSTRACT:

Background

Generalized anxiety disorder (GAD) is a highly prevalent condition. Monitoring GAD symptoms requires substantial time, effort, and cost. The development of digital phenotypes of GAD may enable new scalable, timely, and inexpensive assessments of GAD symptoms.

Method

The current study used passive movement data collected within a large national cohort (N = 264) to assess GAD symptom severity.

Results

Using one week of movement data, machine learning models accurately predicted GAD symptoms across a continuum (r = 0.511) and accurately detected those individuals with elevated GAD symptoms (AUC = 0.892, 70.0% Sensitivity, 95.5% Specificity, Brier Score = 0.092). Those with a risk score at the 90^th percentile or above had 21 times the odds of having

SUBMITTER: Jacobson NC 

PROVIDER: S-EPMC9385727 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature