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 depression scales. Group- based trajectory modeling will then classify women according to the patterns in their depression scores over time. Potential Impact: This work lays the foundation for developing and evaluating real-time interventions that could be deployed at scale to women who are using language that signals high depression risk.

Public Health Relevance

This research will examine how the topics (the people and events mentioned), sentiment (the positive, negative, and neutral affect), and other aspects of language expressed in daily journal entries correspond to diagnostic measures of depression and treatment-seeking in a peripartum clinical population. Psychometric and daily journal entry data will be gathered through an existing smartphone app, MyHealthyPregnancy, which monitors risk and delivers actionable information as part of routine prenatal care provided to the pregnant members of a large regional healthcare system.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Psychosocial Development, Risk and Prevention Study Section (PDRP)
Program Officer
Prabhakar, Janani
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Pittsburgh
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code