Ambulatory fetal activity monitoring predicts clinical outcome (2007–2010)

Abstract:
A small number of babies die unexpectedly while still in the womb: the numbers are much higher than those dying from Sudden Infant Death Syndrome (SIDS). Some of these babies slow their movements down in the days before death. It would be very helpful to be able to accurately monitor babies' movements in the womb so that we could help the few babies who need it, and so prevent poor outcomes. Mothers feel their babies moving, but it's often hard for them to pick up all the movements that do occur. The best way of measuring babies' movements is during an ultrasound. However, that's expensive and means that the pregnant mother needs to lie still for about half an hour to have this testing done. We are developing a way of recording babies' movements, which still lets the pregnant woman continue with her normal activities. We will do this using an AMBULATORY FETAL ACTIVITY MONITOR, which is an accelerometer, like an advanced pedometer. The ambulatory fetal activity monitor will measure the activity of the unborn baby during pregnancy, looking at the number of times s/he moves and how simple or complex the movements are. We expect that the unborn baby who is not getting enough nutrition during the pregnancy will have fewer movements than other unborn babies. This project involves checking that movements picked up by the ambulatory fetal activity monitor are the same as movements seen on an ultrasound. We will then monitor a large number of pregnant women with healthy and possibly unhealthy babies, to help identify the babies who need help. Once we have this information, we will be able to use it in the future to possibly prevent poor outcomes in those babies who do need help.
Grant type:
NHMRC Project Grant
Researchers:
  • Head of School
    School of Clinical Medicine
    Faculty of Medicine
  • Associate Dean (Research)
    Faculty of Engineering, Architecture and Information Technology
    Affiliated Professor
    Centre for Advanced Imaging
  • Professor
    School of Information Technology and Electrical Engineering
    Faculty of Engineering, Architecture and Information Technology
  • Honorary Professor
    Mater Research Institute-UQ
    Faculty of Medicine
    Honorary Professor
    Mater Clinical Unit
    Faculty of Medicine
Funded by:
National Health and Medical Research Council