The Dynamics of Pregnancy: Integrating Imaging, wearables, and modelling to uncover Maternal–Placental-Fetal Interactions

Year of award: 2026

Grantholders

  • Prof Josaphat Byamugisha

    Makerere University, Uganda

  • Prof Penny Gowland

    University of Nottingham, United Kingdom

  • Dr Magdalena Fiolna

    Nottingham University Hospitals NHS Trust, United Kingdom

  • Matthew Hubbard

    University of Nottingham, United Kingdom

  • Dr Reuben O'Dea

    University of Nottingham, United Kingdom

  • Prof Stephen Morgan

    University of Nottingham, United Kingdom

  • Prof Barrie Hayes-Gill

    University of Nottingham, United Kingdom

  • Dr Xin Chen

    University of Nottingham, United Kingdom

  • Prof Kate Walker

    University of Nottingham, United Kingdom

  • Dr SAM ALI

    Makerere University Hospital, Uganda

  • Dr Grazziela Figueredo

    University of Nottingham, United Kingdom

Project summary

During pregnancy, the fetus depends on an intimate physiological relationship with its mother, which is mediated by the placenta from the second trimester. Although long-term adaptations are well described, short-term responses are less examined. In particular, there are no comprehensive investigations into how fetal-placental–maternal communication varies with circumstance, or whether it can reveal decompensation (inability to respond physiologically) under stress. We will characterise these dynamic interactions across different settings (UK and Uganda) in healthy and compromised pregnancies. We hypothesise that impaired adaptive responses may contribute to unexplained stillbirths, particularly at late gestation. We will acquire dynamic physiological data using our novel Pregnancy Activity Monitor (PAM- recording fetal heart rate variability, fetal movement, placental and uterine contractions and placental oxygenation), MRI and ultrasound. We will develop a dynamic mathematical model of placental function to uncover the key drivers of placental dysfunction, identify new early-warning biomarkers and generate personalised estimates of placental efficiency. These data and estimates will underpin a normative model of pregnancy dynamics and a predictive model of pregnancy outcome. The findings will guide the design of a future clinical study evaluating the home-use of PAM to ultimately enable real-time, objective, early-warning of at-risk pregnancies, improving prenatal care worldwide.