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This project will be supported with an MRC PHRU Studentship if there is a suitable candidate.


Observational studies conducted in high-income countries, including the UK Biobank, have suggested that self-reported and device-based methods appear to measure different constructs of physical activity. However, few studies have measured physical activity objectively on a large scale in low- and middle-income countries and how its associations with cardiometabolic risk factors and disease risks differ from those based on the self-reported physical activity. 

The China Kadoorie Biobank (CKB) is a prospective cohort study of >0.5 million adults recruited during 2004-2008 from 10 diverse regions of China, with extensive data collection of lifestyle information, physical measurements, biomarkers, genetic and other ‘omics data. As part of the third resurvey during 2019-2020, ~25,000 randomly selected individuals will have objective measures of physical activity, sedentary behavior and sleeping patterns collected using wrist-worn accelerometers. A range of cardiometabolic risk factors such as adiposity, blood pressure, blood glucose, blood lipids, inflammation markers, markers for kidney function, bone mineral density, and hand grip strength, along with ECG and cIMT will be assessed as well. This rich data source will allow us to perform detailed investigation on these accelerometer-derived exposures in relation to various cardiometabolic risk factors.


The general aims of this DPhil project will be to:

  1. Identify and characterise physical activity, sedentary behaviour and sleep profiles from complex time-series accelerometer data.
  2. Perform epidemiological investigations into the associations between these exposures with cardiometabolic markers, including ECG and cIMT. Potential confounding from and joint associations with other lifestyle factors (e.g. diet, smoking and alcohol consumption) will be explored.
  3. Perform genome-wide association analyses to identify genetic determinants of physical activity, sedentary behaviour and sleep which will facilitate future Mendelian Randomisation studies in order to assess the causal relevance of physical activity with chronic diseases.

This project will involve working within a multi-disciplinary team based at the Big Data Institute and the candidate will gain research experience in epidemiological and statistical methodology, statistical programming and presentation of findings.


There may be opportunities to participate in the fieldwork related to the conduct of the third re-survey or other CKB-related special surveys. By the end of their DPhil it is expected that the student will be able to plan, undertake and interpret statistical analysis of large-scale complex accelerometer data as well as classical epidemiological data, and to report the findings in a clear and concise manner. The student will have acquired transferable skills such as writing project proposals and gaining experience of presenting research findings at relevant meetings and national/international conferences. It is anticipated that the candidate will publish their results.


A candidate with a good first degree and a postgraduate qualification (ideally an MSc) in medical statistics, epidemiology, global health or other related subject and an interest in chronic disease epidemiology.