Over the next few months we’ll be catching up with each of the Hub’s funded project teams to learn more about their work and the people behind the research.
In this post, we spoke to Dr Lis Grey, a Research Fellow from the University of Bristol about her project Co-designing technological solutions to loneliness with at-risk populations.
Dr Lis Grey
About the author
I’m an applied health researcher and behavioural scientist. My research interests lie in health communication and behaviours, and experience of health care services. I am experienced in developing and evaluating health interventions and services.
Give us a very short intro to your project
Loneliness is a public health priority. Some groups of people are more at risk of loneliness. Technology has the potential to help them feel more socially connected. But these groups haven’t usually been involved in designing solutions.
We’re looking at how technology could be used to help address loneliness among three groups who are at greater risk of loneliness:
- Neurodivergent young people
- People with neurodegenerative disorders (like Parkinson’s or dementia)
- Carers of people with neurodegenerative disorders
By running a series of creative workshops with each group, we are co-designing solutions to their experiences of loneliness. By the end we will have several prototype solutions and recommendations for research and practice.
Can you tell us a bit about the team working on your project?
Our team came together at one of the LEAP sandpits (Care Outside of the Hospital) where we found a common interest in addressing loneliness. We’ve got a range of experience across applied health interventions and behavioural science research, software and games design, and community engagement.
The co-applicants are based across the University of Bristol (Lis Grey), University of Bath (David Ellis, Lukasz Piwek), Play Well for Life (Sarah Campbell) and The Care Forum (Ann-Marie Scott). We’re also lucky to have support from LEAP researchers in residence Matt Wragg and Emily Nielsen, and University of Bristol researchers Bradley Barker-Jones and Patrycja Nasiadka.
What progress have you made so far, do you have any findings that you can share with us?
We’re now testing low-fidelity prototypes with two of the groups (neurodivergent young people and carers). It has been interesting to hear about their experiences of loneliness – a common factor across both groups is a feeling of not being understood by others. But in other ways their experiences are quite different. This shows how important it is to design separate solutions with each group to meet their specific needs.
What would you say excites you the most about this project?
We have been using some creative methods in the workshops that were new to me in research, including modelling with Lego and PlayDoh, and working with an artist. The idea was that these activities could help participants explore a sensitive topic that can be hard to talk about.
Having to communicate through a different medium (like drawing or Lego) also seemed to help participants consider the topic from different perspectives. The activities had the added bonus of being fun – who doesn’t love playing with Lego?!
What are the next steps/future plans for your project?
We’re hoping to take one or two of the prototypes we create forward for further development and testing. In November we will be running an event for people in the tech industry as part of the Festival of Social Sciences. The event is going to be a ‘design jam’ – we will showcase our project and work together on ideas for further prototypes and projects.
Book a space at the event – How could technology support people who are experiencing loneliness?
Can you tell us of one recent publication in the world of Digital Health research that has interested you?
Another strand of my research is prognosis and care planning for people with Parkinson’s – there has been lots of interesting work recently using machine learning to both detect this disease and predict its progression.
For example: Kumar et al. Advanced comparative analysis of machine learning algorithms for early Parkinson’s disease detection using vocal biomarkers. DIGITAL HEALTH. 2025;11. doi:10.1177/20552076251342878
Machine learning is not my area of expertise but I’m very interested in how these tools will be used in practice.
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Photo courtesy of NIHR ArcWest