Approved research

Understanding the relationships between movement and long term conditions.

Lay summary

We will investigate the relations between actigraphic data (physical activity, physical inactivity, sleep, diurnal pattern) and a variety of long term health conditions. We are interested in potential covariates for the purposes of informing further study designs. Actigraphic covariates and confounders including pre-existing long term health conditions, weather and socioeconomic status, remain poorly characterised. This impairs useful interpretation of the data collected. In order to increase the power of actigraphic data for monitoring health conditions, we intend to analyse the relationship between a wide variety of health conditions and environmental factors, identify relevant interactions, and inform further studies. Actigraphic data has been shown to have utility in the study of multiple health conditions, which are currently posing significant public health burdens. Examples are diabetes, obesity, arthritis and cardiovascular diseases. Use of the technique is currently impaired by a lack of data to base study design decisions upon. By categorising the effects of various environmental and health related factors upon actigraphy, we hope to facilitate further research into these conditions with a view to development and refinement of new and existing healthcare interventions Biobank data will be used to categorise populations of interest and identify study and control subgroups. Where necessary, this will be combined with additional datasets (e.g. air pollution levels / weather). Actigraphic data will be then subjected to standard analytic techniques to determine how the chosen factors affect patient behaviour and actigraphic measurements. We will use these data to define day to day variations, variations within clinical populations, seasonal effects, and prospective changes to help inform future study design. Prospective data will be requested to enable study into longer term influences and changes associated with temporal or environmental factors. We require data (not samples) for the full cohort. Biobank demographic data will then be used to identify subgroups of interest and appropriately matched control groups within the full dataset. Prospective data will also be requested.