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

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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).