Using modelling approaches to reduce inequality in physical activity interventions. MRC GW4 BioMed DTP PhD studentship 2024/25 Entry, PhD in Mathematics & Statistics. at University of Exeter

Project title: 

Using modelling approaches to reduce inequality in physical activity interventions. MRC GW4 BioMed DTP PhD studentship 2024/25 Entry, PhD in Mathematics & Statistics.

The GW4 BioMed2 MRC DTP is offering up to 22 funded studentships across a range of biomedical disciplines, with a start date of October 2024.

These four-year studentships provide funding for fees and stipend at the rate set by the UK Research Councils, as well as other research training and support costs, and are available to UK and International students.

About the GW4 BioMed2 Doctoral Training Partnership

The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities, with opportunities to participate in interdisciplinary and ‘team science’. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 38 students over 2 cohorts in its second phase.

The 80 projects available for application, are aligned to the following themes;

Applications open on 4th September 2023 and close at 5.00pm on 1st November 2023.

Studentships will be 4 years full time. Part time study is also available.

Project Information

Research Theme: Population Health Sciences

Summary:

Physical activity promotes health & wellbeing. Policy makers seek interventions that increase population-level physical activity (e.g. traffic calming measures or cycle paths to promote active commuting). Many interventions however have differential impacts for different people. We propose combining advanced simulation & modelling approaches to provide estimates of effectiveness of various interventions & identify likely resulting inequalities.

Description:

**Background**

Physical activity is known to improve people’s health and wellbeing. Policy makers at all levels are interested in promoting behaviours that are good for public health. Interventions to increase physical activity are often complex and intervening in a real-world system can often have unintended consequences, including exacerbating inequalities. 

There is therefore a growing need for analysis methods that can properly capture the complexity of health and social systems and model interventions within them. Hybrid simulation is one such approach. In hybrid simulation, two (or more) different types of simulation models are combined in order to harness the benefits of each. Here we propose combining agent based modelling (to allow for the rich and complex nature of humans living within societies) with system dynamics modelling, an approach which attempts to explicitly model higher level effects and the interplay of system-level features.

The combination of these approaches would allow us to model complex physical activity interventions, such as traffic calming measures in a neighbourhood , and provide estimates of their effectiveness.