Digital phenotyping of depression during pregnancy using self-report data.

ABSTRACT:

Background

Depression is a common pregnancy complication yet is often under-detected and, subsequently, undertreated. Data collected through mobile health tools may be used to support the identification of depression symptoms in pregnancy.

Methods

An observational cohort study of 2062 pregnancies collected self-reports of patient history, mood, pregnancy-specific symptoms, and written language using a prenatal support app. These app inputs were used to model depression risk in subsequent 30- and 60-day periods throughout pregnancy. A selective inference lasso modeling approach examined the individual and additive value of each type of patient-reported app input.

Results

Depression models ranged in predictive power (AUC value of 0.64-0.83), depending on the type of inp

SUBMITTER: Allen K 

PROVIDER: S-EPMC11569620 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature