This PhD project will investigate how Physical Activity levels and patterns might change with changing climatic conditions (temperature, humidity, precipitation, water scarcity, natural disasters) at population level and the impact on health this might have. The student will work at different geographical scales and contexts (Global, EU, country, regional, and selected-town level) and combine, cross-reference and cross-validate micro and macro-level modelling. The project will use both trend analysis and probabilistic modelling. It will combine longitudinal PA and sports participation assessments (13 international cohort studies federated under the PROPASS consortium) and surveillance data (e.g. WHO data and Global Observatory for PA) with historical climate observation (NOAA and Copernicus) to analyse trends and model PA and sports participation in different climatic conditions. In addition, the student will utilise high temporal resolution physical behaviour data collected continuously with body-worn sensor over a full year and climate data to develop probabilistic PA behavioural models. The student will then use both trend and probabilistic models to forecast the change in PA level according to the different IPCC scenarios.

The project will suit a geographer, an environmental researcher, behavioural , data , health or sports scientist

This PhD project will investigate how Physical Activity levels and patterns might change with changing climatic conditions (temperature, humidity, precipitation, water scarcity, natural disasters) at population level and the impact on health this might have. The student will work at different geographical scales and contexts (Global, EU, country, regional, and selected-town level) and combine, cross-reference and cross-validate micro and macro-level modelling. The project will use both trend analysis and probabilistic modelling. It will combine longitudinal PA and sports participation assessments (13 international cohort studies federated under the PROPASS consortium) and surveillance data (e.g. WHO data and Global Observatory for PA) with historical climate observation (NOAA and Copernicus) to analyse trends and model PA and sports participation in different climatic conditions. In addition, the student will utilise high temporal resolution physical behaviour data collected continuously with body-worn sensor over a full year and climate data to develop probabilistic PA behavioural models. The student will then use both trend and probabilistic models to forecast the change in PA level according to the different IPCC scenarios.

The project will suit a geographer, an environmental researcher, behavioural , data , health or sports scientist