Project Overview

Stress during pregnancy can have lasting impacts on maternal health, pregnancy outcomes, and child development across the lifespan. Next-day prenatal stress prediction algorithms have recently been developed, inferring personal moments of stress before they afflict the pregnant person and their child. However, for these machine learning (ML) algorithms, and the Just-in-Time Adaptive Interventions (JITAIs) leveraging them, to better align with pregnant peoples’ needs and goals, we must engage pregnant people in defining system requirements.

Process and Methods

Storyboards to discuss key ML and JITAI aspects with lay persons, Semi-structured interviews, 20 Design sessions (scenario, reflective activities, and prototype feedback) with pregnant people, Affinity Diagrams, Quantitative and qualitative survey analysis, Thematic analysis, prototyping, multi-lingual research (English and Spanish)

Deliverables

Design method to discuss ML with lay persons, ML-driven, high-fidelity prototype to test in-the-wild, academic posters and papers. This multi-year project is part of my PhD dissertation—more updates to come.

⚠️ Under construction, more details to come soon