The University of Leeds is offering an EPSRC funded PhD studentship working in a research team with members from the University, a digital health company and an NHS Fertility Unit. This project recognises the important relationship between lifestyle factors and fertility, and aims to investigate the feasibility of using artificial intelligence/machine learning based upon a digital app and precision exercise and lifestyle prescription to improve women’s fertility and the success of assisted conception (IVF).
Fertility rates in women have been falling, especially as more people leave pregnancy until later in life. The chances of becoming pregnant per menstrual cycle fall from 25% per cycle for women in their 20s and early 30s to 10% in their 40s. For those that then turn to the stress and cost of IVF, success rates vary between 30 and 40%. The limiting factors for this success are the health of both the female and the ova (egg) and optimising these factors in healthcare could have profound effects on IVF outcomes. These outcomes, measured as live births have been seen to be impacted by physical activity and nutrition.
Moderate levels and intensities of physical activity and nutritional interventions moderating glucose and lipid intake are associated with positive outcomes. However, higher volumes and intensities of physical activity and nutritional imbalance can rapidly reduce fertility. Thus, personalized advice and real-time sensing of physical activity and metabolic parameters (eg blood glucose, insulin etc) linked to clinical test results, offers the opportunity to transform fertility care. Enabling and empowering women undertaking fertility treatment to understand and dynamically alter their physical and nutritional health during their treatment pathway, and to take this control to their homes would offer enormous healthcare and patient benefit. The aim of this PhD is to develop and utilise a mobile technology system to enable a personalised and dynamic exercise and nutrition prescription for women undergoing IVF.
Working with a digital healthcare company we have now developed software for mobile devices that can be linked to fitness devices for real time sensing of physical activity. The novelty of this digital platform is that it houses a powerful automatic intelligence (AI) module that can be linked with clinical data (eg metabolic and hormonal measures, ultrasound measures). Thus, combining physical activity, physiological and lifestyle data enables the platform to learn and provide personalised feedback to the patient prior to their treatment for assisted conception.
This PhD would involve developing and using this application in patients undergoing fertility treatment to assess its feasibility and impact. The PhD will incorporate exercise prescription, the use of digital technology, metabolic and hormonal physiology and assessment of physiological, quality of life and fertility outcomes. The PhD will provide training in exercise science, intervention analysis, reproductive physiology and wearable technology.
Entry requirements
Candidates should hold, or expect to receive a first or upper second class degree in a relevant subject (e.g. Biomedical/Medical Science, Human Physiology, Sport and Exercise Science). A Master’s level qualification would also be advantageous.
English language requirements
The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent.