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Advanced analytics to investigate novel BP wearable technology to improve cardiovascular outcomes and falls

PGR-P-2026

Key facts

Type of research degree
PhD
Application deadline
Friday 31 January 2025
Country eligibility
UK only
Funding
Funded
Supervisors
Dr Oliver Todd
Additional supervisors
Dr Sam Relton, Dr Kate Best, Dr David Wong
Schools
School of Medicine
Research groups/institutes
Leeds Institute of Health Sciences
<h2 class="heading hide-accessible">Summary</h2>

This is a unique opportunity to work with the world’s largest research cohort of its kind harnessing detailed 24-hour blood pressure records and cardiovascular and falls outcomes on follow up for more than 26,000 people across three UK City regions.<br /> <br /> This PhD will include use of routine health and care assets (e.g. Connected Bradford, Combined Intelligence for Population Health Action (CIPHA) and DataLoch (NHS Lothian)) to investigate how wearable 24 hour blood pressure recording could help optimise management of hypertension in older adults at risk of falls.

<h2 class="heading hide-accessible">Full description</h2>

<p>There are multiple opportunities using these data sets for PhD study to develop and apply novel methodologies including: </p> <ul> <li>Use of prognosis research methods to investigate which summary measure of 24-hour Blood pressure profiles is predictive of falls, major adverse cardiovascular events, or all-cause mortality, over and above existing prediction tools. </li> <li>Use of machine learning techniques to investigate whether patterns of BP profile, may have superior predictive value over summary measures currently available.</li> <li>Use of propensity score matching to address confounding to undertake a causal inference study asking whether treatment change on basis of 24-hour BP recording changes outcomes.</li> <li>Use of individual patient data meta-analysis to combine data from three cohorts in a 2 stage IPD meta-analysis process. </li> </ul> <p>This PhD will form part of a larger programme of research funded by the NIHR which aims to improve the safety of BP treatment in older adults at risk of falls and make it easier for patients to be involved in decisions about their treatment. The databases used in this PhD will be the largest and most detailed of their kind worldwide and help us to better understand hypertension in old age. </p> <p>The candidate would be embedded in a vibrant group of data scientists and clinicians working on a range of projects harnessing routine health and care datasets. This would enable a range of training opportunities and apprenticeship learning opportunities as well embed the candidate in inter-disciplinary networks which may foster future opportunities for collaboration and career development. This work would be undertaken in collaboration with leading centres in hypertension research at the University of Oxford, falls research at Trinity College Dublin, and ageing research at Harvard University.</p> <p>We are seeking an exceptional candidate to become a future leader in health data science, providing access to training activities through the Centre for Data Science and Ageing. The successful candidate will join a vibrant cohort of PhD students, funded by the Health Data Research UK and the Dunhill Medical Trust Reimagine Ageing Doctoral Research Fellowship programmes, fostering interdisciplinary collaboration and networking across the UK.</p> <p>This PhD presents an opportunity for a motivated candidate from medical statistics/ data science/ mathematics background to use routine health care data to develop skills that include but are not limited to: </p> <ul> <li>data management and linkage </li> <li>writing code to clean, link and identify different continuous and categorical variables in routine data,  </li> <li>epidemiological analysis including survival analysis adjusting for competing risks and addressing missing data,  </li> <li>using advanced methods to deal with confounding such as propensity score matching,  </li> <li>undertaking causal inference using routine data, and  </li> <li>combining data for analysis from different data sets using individual patient data meta-analysis techniques.</li> </ul> <p>A number of different software will be available for cleaning, describing and analysing the data including Python, R, SQL and STATA.</p> <h5>Other Conditions</h5> <ul> <li> <p>Applicants must not have already been awarded or be currently studying for a doctoral degree.</p> </li> <li> <p>Award ideally to be taken up in October 2025 (although there may be flexibility for the right candidate)</p> </li> <li>Applicants must live within a reasonable distance of the University of Leeds whilst in receipt of this scholarship. </li> </ul>

<h2 class="heading">How to apply</h2>

<p>To apply for this scholarship opportunity applicants should complete an <a href="https://medicinehealth.leeds.ac.uk/faculty-graduate-school/doc/apply-2">online application form</a> and attach the following documentation to support their application. </p> <ul> <li>A full academic CV</li> <li>Degree certificate and transcripts of marks</li> <li>Evidence that you meet the University's minimum English language requirements (if applicable)</li> </ul> <p>To help us identify that you are applying for this scholarship project please ensure you provide the following information on your application form;</p> <ul> <li>Select PhD in Medicine as your programme of study</li> <li>Give the full project title and name the supervisors listed in this advert</li> <li>For source of funding please state you are applying for a LIHS PhD Scholarship</li> </ul> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants should normally have at least a first class or an upper second class British Bachelors Honours degree (or international equivalent) in a relevant subject area. Applicants for this opportunity should also have at least one of the following:<br /> - A Master's degree in a relevant subject area<br /> - Experience of statistical modelling using routine data<br /> - Domain knowledge of vital signs/blood pressure

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study in the School of Medicine is an IELTS of 6.5 overall with at least 6.0 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p>We are offering one full-time PhD Scholarship in the School of Medicine for one UK candidate, covering a maintenance grant and UK tuition fees for three years, subject to satisfactory progress. The award will be made for one year in the first instance and renewable for a further period of up to two years, subject to satisfactory progress.</p>

<h2 class="heading">Contact details</h2>

<p>For further information about the application process please contact the Admissions team<br /> e: <a href="mailto:fmhpgradmissions@leeds.ac.uk">fmhpgradmissions@leeds.ac.uk</a></p> <p>For informal enquiries regarding this project please contact Dr Oliver Todd: <a href="mailto:O.Todd@Leeds.ac.uk">O.Todd@Leeds.ac.uk</a></p>