# Identification and Prediction of Peripartum Depression from Natural Language Collected in a Mobile Health App

> **NIH NIH R21** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $233,832

## Abstract

PROJECT SUMMARY
Background: Depression during pregnancy and the postpartum period affects up to 15% of US mothers, imposing costs on
mother, child, and society. Early detection can significantly reduce the incidence of depression, yet depressive symptoms
are often missed during prenatal visits, which tend to focus on maternal and fetal physical health, leaving less time for
maternal mental health. Even if mental health is addressed during prenatal care, women may not feel comfortable answering
questions that are perceived to be embarrassing or invasive. Failing to detect depression is even more likely during the
postpartum period due to infrequent physician visits once the baby has been born. Measurement in the form of daily journals,
which can be analyzed using natural language processing, can promote early and more frequent detection of depression
during pregnancy and the postpartum period.
Study Aims: 1) Model which dynamic features of language used over time best predict changes in depression status in the
pregnancy and postpartum periods, creating phenotypes of depression risk; 2) examine how the language patterns that
predict depression differ for African-American and Caucasian women; and 3) identify the relationship between the
characteristics of what depressed peripartum women say and their treatment-seeking behavior.
Innovation: The proposed research is innovative in its use of high frequency natural language measurements, captured in
daily journals using a smartphone app, combined with advances in natural language processing models, to assess the onset
and trajectory of depression during pregnancy and the postpartum period. This is the first prospective longitudinal study
using natural language collection for risk prediction in a clinical population and the first to: 1) characterize the critical topics
women discuss during the peripartum period over time using open-ended journals; 2) evaluate multiple facets of language
to gain a more comprehensive understanding of the relationship between language and depression; 3) use a longitudinal
design approach allowing for optimal modeling of language changes associated with depression onset.
Methodology and Expected Results: Monthly depression risk identified from the Edinburgh Postnatal Depression Scale.
will be collected through the MyHealthyPregnancy smartphone app, a mobile health application developed through close
collaboration between decision scientists, clinicians, statisticians, and local peripartum women. A daily journal embedded
in the MyHealthyPregnancy app will collect natural language text from the participants for 10 months (from their first
prenatal visit through two months postpartum). Using three distinct natural language processing algorithmic approaches,
this study will characterize how the natural language used by peripartum women in their daily journal entries is connected
to the onset and experience of peripartum depression, as measured through monthly-administered depre...

## Key facts

- **NIH application ID:** 9892136
- **Project number:** 1R21MH119450-01A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Tamar Krishnamurti
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $233,832
- **Award type:** 1
- **Project period:** 2020-02-19 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9892136

## Citation

> US National Institutes of Health, RePORTER application 9892136, Identification and Prediction of Peripartum Depression from Natural Language Collected in a Mobile Health App (1R21MH119450-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9892136. Licensed CC0.

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