Continuous monitoring of vascular age

Long term vascular ageing presents a challenge to healthy ageing initiatives because its signs often go undetected. This project develops low-cost methods for the monitoring of ‘vascular age’ using wearables similar to fitness bracelets and smart watches, or by designing a prototype device using available components. Further testing with people of different ages with vascular disease, under different conditions, can then establish if low-cost devices can be used for continuous monitoring over longer periods.


Catalyst R1

Lead Organisation

University of Surrey


South East (England)


Managing Complaints of Ageing

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About the project

While healthy ageing is one of the most important challenges of our time, it is difficult to monitor ageing during normal life. Longterm vascular ageing is the results of many injurious events that occur during everyday life and currently go undetected. The ability to continuously measure ageing with low cost devices would open opportunities to test interventions that aim to slow ageing in a large number of individual people.

Progressive stiffening of the blood vessels is one of the most important features of human ageing. Importantly, arterial stiffness can be measured non-invasively as pulse wave velocity (PWV) and is viewed as the most important biomarker of vascular age, but it currently requires costly and bulky equipment and trained researchers to measure. However, personal devices like fitness bracelets and smart watches are now able to measure a variety of biological information including blood flow and electrical activity of the heart. This information could be used to calculate PWV with each heartbeat in real time and provide immediate information on the biological age of the blood vessels. More importantly, this approach would allow the identification of factors that accelerate and slow ageing in an individual person and monitor the success of ‘anti-ageing’ therapies in an individual person. So far, no technology is available that fulfils this task.

In the current project, we wish to develop low-cost methods to allow monitoring of individual ‘vascular age’ at scale during real life using wearables or design a prototype device with available components. In this first phase of the project, we will set up a developmental tool kit to integrate data from ECG (electrocardiogram, electrical activity of the heart) and PPG (photoplethysmography, blood flow in the skin) sensors, calculate PWV, and create useful visualization of integrated data. We will then test the accuracy, validity, and performance of PWV measurements in people at different age and with vascular disease under different conditions and compare the results with gold standard methods (tonometry) to measure PWV. Furthermore, we will design a prototype device build from low-cost standard sensor components (by Surrey Sensors Ltd.) and evaluate if existing low-cost devices can be used for continuous monitoring of PWV over time to help us decide on the focus of the next steps.

Reaching these objectives will form the basis of moving forward with a second application next year. In this second step, we will build a small number of prototype devices to and/or develop a software solution for using sensor data from existing fitness devices for continuous PWV measurements. We will then test the utility and applicability of continuous PWV in real life in people of different ages to establish normal values. Furthermore, we hope to validate the measurements by comparing results with invasively measured PWV during routine catheter procedures in the hospital. In a third step, we plan to perform a proof-of-concept study to demonstrate the abilities of continuous PWV measurements to detect acute responses of arterial stiffness to life-style interventions such as healthy diet and chronic changes in arterial stiffening over time (vascular ageing trajectory). In a future stage, we hope to be able to test the continuous PWV measurements in larger groups of people, potentially in collaboration with industry, NHS, and using sensor data from devices that people already own. Large data sets can then be analysed together with other individual health related data by machine learning to identify ageing patterns and effect of healthcare interventions on trajectories of vascular ageing.


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