# MyHealthyPregnancy mobile health app: Combining behavioral science and machine learning for risk communication during the peripartum period

> **NIH ALLCDC R44** · NAIMA HEALTH, LLC · 2020 · $497,952

## Abstract

Project Summary/Abstract
Problem: Depression during pregnancy affects approximately 10% of women and is related to low
birthweight and preterm birth. Similarly, up to 9% of pregnant women experience intimate partner violence
(IPV) and abuse, with over 41% of assaults resulting in physical injury, and almost 30% requiring medical
treatment. When untreated, these risks cost health systems at least $50B/year. Mitigation has proven
difficult, where women are reluctant to disclose during clinical visits, and clinicians are unaware of
resources. There are no integrated health technologies that enable timely disclosure of risks during
pregnancy then aid in making decisions about risk mitigation. Fortunately, most women of reproductive
age own a smartphone, and users report comfort disclosing health information to smartphones under the
right conditions.
Naima Health’s Proposed Solution: Naima Health is developing a digital health platform that pairs our
MyHealthyPregnancy (MHP) smartphone application with an EPIC-integrated provider portal to (i)
identify risks early in pregnancy, (ii) communicate those risks to women and their providers, and (iii) assist
decision-making about risk mitigation. MHP identifies risk using ACOG-approved screenings, then helps
patients and providers make real-time decisions about mitigation. The proposed solution aligns with the
CDC’s priority of developing mobile app-based decision support systems for mental health and IPV
screening, assessment, and referral.
Proposed SBIR Work: In Phase I we developed expert and machine learning models to identify risks
during pregnancy, then characterized issues facing Spanish-speaking women. In Phase II we extend these
Phase I results using semi-structured interviews with patients and providers to understand site-specific
requirements for psychosocial risk screening and referral, then update the MHP platform to meet those
requirements (Aim 1). We then validate the updated platform’s performance using qualitative cognitive
testing with patients and providers to ensure the platform meets site-specific requirements (Aim 2). Finally,
we evaluate the platform’s acceptability and feasibility at two collaborating clinic sites, focusing on the rate
of depression and IPV detected through the platform compared to historical rates, and the prevalence of
risk mitigation actions measured through patient calls/click-throughs and provider referrals (Aim 3).

## Key facts

- **NIH application ID:** 10080792
- **Project number:** 2R44DP006417-02
- **Recipient organization:** NAIMA HEALTH, LLC
- **Principal Investigator:** Anabel F Castillo
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** ALLCDC
- **Fiscal year:** 2020
- **Award amount:** $497,952
- **Award type:** 2
- **Project period:** 2020-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10080792

## Citation

> US National Institutes of Health, RePORTER application 10080792, MyHealthyPregnancy mobile health app: Combining behavioral science and machine learning for risk communication during the peripartum period (2R44DP006417-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10080792. Licensed CC0.

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