Exploring how lifestyle factors influence digital biomarkers using wearable devices (Ref: SSEHS/AK26)

About the Project

Metabolic health and the risk of developing type 2 diabetes are influenced by lifestyle factors including diet, physical activity and sleep. Understanding how these behaviours affect blood glucose patterns in real-time have the potential to help people make healthier lifestyle choices. However, there is currently limited evidence on whether providing this information through wearable devices leads to better health outcomes in people without diabetes.

This PhD focuses on exploring trends between continuous glucose monitor (CGM) data, diet, lifestyle behaviours and health outcomes to identify patterns that could improve metabolic health in healthy adults or those at risk of diabetes. Understanding the types of information that might make the most impact on lifestyle outcomes could better inform how wearable technology can support people in reducing their diabetes risk and improving overall health.

This research will explore methods such as time series analysis (e.g., ARIMAX models) and machine learning (e.g., random forests, gradient boosting, LSTM models, or deep reinforcement learning for adaptive personalised recommendations) to build and explore relationships between glucose patterns and lifestyle factors. This could include developing personalised models that predict individual glucose responses, to understand which people benefit most and what drives lasting behaviour change versus acute effects.

Name of primary supervisor/CDT lead:

Dr Andrew Kingsnorth a.kingsnorth@lboro.ac.uk

Name of secondary supervisor:

Dr James Sanders

Entry requirements:

Applicants should have, or expect to achieve, a 2:1 Honours degree (or equivalent) in a subject related to exercise science, health data science or bioinformatics or other relevant subjects. A relevant Master’s degree and/or experience is desirable.

The studentship will appeal to students with a data science, health informatics, sports science or related background. Strong coding skills (Python, R, or MATLAB) are essential for managing and analysing large datasets from wearable devices. Experience with machine learning, health data analysis, or behaviour change research would be valuable. It is intended that a variety of qualitative and quantitative data collection techniques will be used and so experience in either would be beneficial.

Shortlisted candidates will be asked to complete a data analyses pre-interview task, which will be presented and discussed during the interview. Further details will be provided upon shortlisting.

English language requirements:

Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Funding information:

The studentship is for 3 years and provides a tax-free stipend of £20,780 per annum for the duration of the studentship plus tuition fees at the UK rate. Due to funding restrictions, this is only available to those eligible for UK fees

Bench fees required: No

Closing date of advert: 22nd May 2026

Start date: July 2026

Full-time/part-time availability: Full-time 3 years

Who is eligible to apply?: UK Only

How to apply

All applications should be made online. Under Campus, please select Loughborough and select Programme Sport, Exercise and Health Sciences. Please quote the advertised reference number SSEHS/AK26 in your application.

To avoid delays in processing your application, please ensure that you submit the minimum supporting documents. The following selection criteria will be used by academic schools to help them make a decision on your application.

Project search terms:

data analysis, health informatics, health data science, digital biomarkers, digital health

Email Address SSEHS:

ssehs.pgrapplications@mailbox.lboro.ac.uk