Skip to main navigationSkip to main content
The University of Southampton
Institute for Life Sciences

Digital Health

Our researchers are examining and developing information and communication technologies to help address the health problems and challenges faced by patients.

Dr Adriane Chapman

With a rising population across the globe, many societies are struggling to meet healthcare demand. Digital health care interventions are key to tackling this crisis and help to enhance the efficiency, delivery and security of services to patients.

But with so many new digital technologies available and the immediate access to massive data sets how can we harness this information to ensure it makes a real difference to society? And how do we overcome the challenges of privacy and personal data protection?

Southampton scientists across medicine and electronics and computer science are combining machine learning, blockchain technology, genome sequencing and other computational methods to develop new digital health interventions to help healthcare professionals and patients to manage illnesses and promote health and wellbeing. This includes both hardware and software solutions including using Internet of Things smart devices, wearable devices and monitoring sensors.

Our teams are also using digital health technologies to analyse already available data sets to establish trends of behaviour and decision patterns with the aim of predicting future healthcare needs as well as examining the role data protection plays in this ever-expanding research field.

Key words

Cyber security, Data sets, Digital health, Healthcare, Interdisciplinary, Internet of Things, IoT Smart devices, Machine Learning, Patient consent

Please see a selection of postgraduate courses related to this subject area below. 


For the full range of undergraduate and postgraduate courses at the University of Southampton, please visit our courses webpages https://www.southampton.ac.uk/courses.page

MSc Demography

This programme covers contemporary demographic issues, demographic methods and approaches, as well as general social science research methods

MSc Gerontology

This programme focuses on evidence related to the wellbeing of older people, and policy knowledge relating to social policies aimed at preparing societies for ageing populations. 

MSc Public Health

We offer a challenging and rewarding masters programme in all aspects of public health with optional pathways specialising in nutrition, intelligence (working with information) and global health.

MSc in Statistics with Applications in Medicine

This one-year course provides sound Masters-level training in statistical methodology, with an emphasis on solving practical problems arising in the context of collecting and analysing medical data.

MSc Health Psychology

Health Psychology includes the study of psychological, social, emotional, and behavioural factors in physical illness, the improvement of the health care system, and formulation of health policy.

LLM Information Technology and Commerce

This programme focuses on the legal issues that arise when the worlds of IT and commerce meet and includes topics such as IP, e-commerce, data protection, and intermediary liability.

MSc Data Science

This programme focuses on operating high-performance computing clusters and cloud-based infrastructures, to devising and applying sophisticated Big Data analytics techniques.

MSc Computer Science

This programme includes modules from our more specialist programmes including artificial intelligence, cyber security, signal processing, software engineering, and web science and technology.

MSc Cyber Security

This programme offers a multi-disciplinary approach to this critically important topic, embracing not only the technical subjects, but also aspects of criminology, risk management, law and social sciences.

MSc Software Engineering

This programme includes computer vision, critical systems, cryptography, distributed computing systems, e-business, intelligent agents, model checking and multimedia.

MSc Artificial Intelligence

This programme includes intelligent agents, complexity science, computer vision, robotics and machine learning techniques.

Dr Mark Weal's Group
Dr Mark Weal's Group

The Listening Initiative

Smart devices are embedded within society and our individual lives on a daily basis. We rely on them for so much, but their presence can have a detrimental effect on our health. For example, the World Health Organisation estimates that 1.1 billion people worldwide are at risk of hearing loss caused by personal audio devices and entertainment.

The link between personal listening habits and the development of hearing loss and tinnitus is poorly understood. Southampton researchers have developed a new app that measures the ways in which people listen to music through their smartphone devices, how they interact with music apps and the duration and intensity of music listening, to explore listening habits and its relationship to hearing problems.

Participants are asked to download the app for four weeks and answer a questionnaire. The app anonymously tracks music information and volume on a pre-agreed list of apps and keeps track of when people are listening to audio on their smartphones and how they adjust the devices volume.

The information allows our teams to analyse behaviour patterns and their link to hearing-related conditions in order develop novel interventions to help mitigate against hearing loss. 

Contacts: Dr Mark Weal, Dr Mark Fletcher

Dr Adriane Chapman
Dr Adriane Chapman

Consent and security

With so much of our lives and personal data now online, privacy, consent and security become significant challenges. The digital economy is based on data sharing, but many people have limited knowledge about how their personal data is being used and stored and have little control over it. Moreover, while data management during web and app-based use is already a challenge, as the Internet of Things (IoT) scales up, the number of devices accessing and requiring personal data will go beyond what an individual can personally manage. Additionally, individuals change their preferences depending on the app or the website. Consent is an individual expression of each person’s stance on sharing, privacy and security concerns and therefore, a one consent fits all approach is inadequate.

There are several strands of research to facilitate safely and securely sharing health data, taking place across the University.

Members of the cyber security group are actively working on secure health data exchange by using healthcare frameworks based on international standards and next generation blockchain solutions. The new technique could, for the first time, realise fully decentralised software systems, ensuring high integrity guarantees and democratic control of personal medical data. The team is currently testing the strategy in hospitals in Europe.

Interdisciplinary researchers from across the University have come together for the first time to develop new approaches for managing privacy and consent preferences on many different levels. We are finding new ways of how to store and enforce individual’s personalised consent in an efficient manner, within data management system. 

Contacts: Prof Vladimiro Sassone, Dr Andrea Margheri, Dr Adriane Chapman and Dr George Konstantinidis

Using keystroke dynamics to detect Parkinson’s Disease

Our researchers are using machine learning to further our understanding of Parkinson’s Disease. There are around 145,000 people in the UK diagnosed with Parkinson’s disease and numbers are increasing. By analysing keystroke hold times from typing logs, they are able to classify people who have Parkinson's disease versus those that do not. The challenge is identifying how to represent the features to capture the variations in typing behaviour.

Recent work found that a simple feature designed to capture the dynamic variations between consecutive keystrokes was able to outperform previous work, only requiring a few hundred keystrokes for prediction. Newer work is considering how to use deep learning to model time series data obtained from keystrokes as these models have shown to be state of the art in other applications. 

Contact: Dr Kate Farrahi

List of related projects to
Related ProjectsStatusType
Share this research area Share this on Facebook Share this on Twitter Share this on Weibo
Privacy Settings