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.