Sensor-rich physical activity recognition for blood glucose prediction in people with type 1 diabetes

Hybrid closed-loop artificial pancreas systems are the current state of the art of diabetes technology [1]. These systems reduce the burden placed on those with diabetes by performing some of the processing required in diabetes management. However, to reach the next goal of diabetes technology, the fully closed-loop system, there are still challenges and a significant one of these is around management during physical activity. The impact of physical activity on blood glucose levels is not fully understood, although it is thought to vary noticeably between individuals and activity type [2]. This project aims to explore the potential of using additional sensor data, such as heart rate or accelerometer readings, during physical activity to improve the prediction of future blood glucose.

This process will begin with a mixed method user study in which participants with type 1 diabetes will be recruited and questioned to understand:
- Which medical devices and strategies they use to manage their diabetes.
- How they currently manage physical activity.
- What additional sensing equipment they would find acceptable to use during activity.
This is to ensure that the algorithm developed is usable and useful to people and to gain an understanding of the nuances of blood glucose management that people with diabetes experience during physical activity. The link between physical activity and blood glucose levels will be explored through the user study, existing literature and available datasets. As part of this current glucose prediction algorithms will be investigated to inform the approach taken next.

The next stage will be then developing an algorithm that takes the additional data and predicts blood glucose in the future. Machine learning techniques will be utilised to build this algorithm, with consideration taken into how it can be personalised to individuals. The OhioT1DM Dataset appears to contain potentially useful data and may form a starting point for this algorithm [3]. However, building a dataset of blood glucose, insulin dosage and activity sensor data may be necessary for the project and will form a useful contribution to the wider research topic. This will be especially so if data can be collected of multiple people performing the same exercise to compare their blood glucose response.

Further stages of the project will be driven by areas of interest that arise during the earlier stages and directly by the users involved in this project.

The link between physical activity and blood glucose levels is not fully understood and the work here aims to contribute its understanding. There is also currently limited use of additional sensor data in closed-loop control algorithms, bringing novelty to the research. However, several papers do mention similar ideas, suggesting that there are other researchers currently looking into it. Additionally, having a user-driven approach to the design of closed-loop algorithms will produce novel HCI contributions.

References
[1] Weaver KW, Hirsch IB. The Hybrid Closed-Loop System: Evolution and Practical Applications. Diabetes Technol Ther. 2018;20(S2):S216-S223. doi:10.1089/dia.2018.0091
[2] Colberg SR, Hernandez MJ. The big blue test: effects of 14 minutes of physical activity on blood glucose levels. Diabetes Care. 2013;36(2):e21. doi:10.2337/dc12-1671
[3] Marling C, Bunescu R. The OhioT1DM Dataset for Blood Glucose Level Prediction: Update 2020. CEUR Workshop Proc. 2020;2675:71-74.

Impact on Health and Care
The CDT primarily addresses the most pressing needs of nations such as the UK - namely the growth of expenditure on long term health conditions. These conditions (e.g. diabetes, depression, arthritis) cost the NHS over £70Bn a year (~70% of its budget). As our populations continue to age these illnesses threaten the nation's health and its finances.

Digital technologies transforming our world - from transport to relationships, from entertainment to finance - and there is consensus that digital solutions will have a huge role to play in health and care. Through the CDT's emphasis on multidisciplinarity, teamwork, design and responsible innovation, it will produce future leaders positioned to seize that opportunity.

Impact on the Economy
The UK has Europe's 2nd largest medical technology industry and a hugely strong track record in health, technology and societal research. It is very well-placed to develop digital health and care solutions that meet the needs of society through the creation of new businesses.

Achieving economic impact is more than a matter of technology. The CDT has therefore been designed to ensure that its graduates are team players with deep understanding of health and social care systems, good design and the social context within which a new technology is introduced.

Many multinationals have been keen to engage the CDT (e.g. Microsoft, AstraZeneca, Lilly, Biogen, Arm, Huawei ) and part of the Director's role will be to position the UK as a destination for inwards investment in Digital Health. CDT partners collectively employ nearly 1,000,000 people worldwide and are easily in a position to create thousands of jobs in the UK.

The connection to CDT research will strongly benefit UK enterprises such as System C and Babylon, along with smaller companies such as Ayuda Heuristics and Evolyst.

Impact on the Public
When new technologies are proposed to collect and analyse highly personal health data, and are potentially involved in life or death decisions, it is vital that the public are given a voice. The team's experience is that listening to the public makes research better, however involving a full spectrum of the community in research also has benefits to those communities; it can be empowering, it can support the personal development of individuals within communities who may have little awareness of higher education and it can catalyse community groups to come together around key health and care issues.

Policy Makers
From the team's conversations with the senior leadership of the NHS, local leaders of health and social care transformation (see letters from NHS and Bristol City Council) and national reports, it is very apparent that digital solutions are seen as vital to the delivery of health and care. The research of the CDT can inform policy makers about the likely impact of new technology on future services.

Partner organisation Care & Repair will disseminate research findings around independent living and have a track record of translating academic research into changes in practice and policy.

Carers UK represent the role of informal carers, such as family members, in health and social care. They have a strong voice in policy development in the UK and are well-placed to disseminate the CDTs research to policy makers.

STEM Education
It has been shown that outreach for school age children around STEM topics can improve engagement in STEM topics at school. However female entry into STEM at University level remains dramatically lower than males; the reverse being true for health and life sciences. The CDT outreach leverages this fact to focus STEM outreach activities on digital health and care, which can encourage young women into computer science and impact on the next generation of women in higher education.

For academic impact see "Academic Beneficiaries" section.