Integrated tech to support ageing with diabetes
The University of Sheffield is developing a new tech platform to help monitor diabetes-related markers, including physical activity and cognitive abilities. The platform is a shift away from current unsustainably high demands on diabetes care in the health service. The platform demonstrates a new model, facilitating remote patient monitoring by clinicians and carers and prioritisation of those most in need, targeting the use of NHS resources more efficiently.
About the project
Latest data suggests that in the UK ~10% of the annual NHS £100 billion budget is spent on treating diabetes, equating to £192 million a week. Of this, nearly 80% is spent on treating irreversible, but preventable diabetes-related complications. Currently there are 3.8 million people diagnosed with diabetes, an estimated 900,000 are yet to be diagnosed, and in 2030 it is expected that more than 5 million people in the UK will have diabetes (as 80-85% of cases of type 2 diabetes is caused by obesity). Uncontrolled diabetes in older people leads to a range of problems.
Hypoglycaemia (low blood glucose) causes a slowing of cognition and may result in acute confusion, accidents and falls and an increased risk of developing dementia. Conversely, hyperglycaemia (high blood glucose) increases the risk of infections, dehydration, and in the longer-term can lead to a significantly higher loss of muscle quality and strength (sarcopenia) as well as irreversible damage to eyes, kidneys, and nerves supplying the feet. This places significant demands on NHS services including GP callouts, ambulance services, A&E attendances and lengthy hospital admissions. Increasing physical activity levels in people with diabetes would lead to better outcomes in terms of less diabetes-related complications, less depression, slower rates of cognitive decline, lower rates of cardiovascular disease and ultimately less healthcare resource use. However, some people with diabetes find it hard to exercise as the risk of hypoglycaemia is increased.
The complex relationship between diabetes, physical and cognitive decline, and ageing is not well understood (understudied) often leading to sub-optimal management of people with diabetes as they get older. This in turn results in higher risk of diabetes related complications and increased incidence of morbidity and disability in this population in later years. The aim of this proposal is to provide a single technology platform that will implement a data-driven approach to the analysis of this complex relationship via automated machine learning (ML)-driven analytics based on the real-time remote monitoring of the key diabetes markers (Blood Glucose, Insulin, Carbohydrates) and incorporating physical activity measures, as well as cognitive assessment scores. This integrated environment will provide decision support for optimal diabetes management and service planning and provision for healthcare, social and community care.
This will enable a shift from the current unsustainable, static and reactive management model, to a future-proof dynamic, intelligent proactive model that will impact in the following ways, for: – Patients: a personalisation of support to enable pro-active engagement and empowerment; improved quality of life; healthier ageing. – Clinicians / Carers: remote monitoring; decision support; prioritisation of those most in need; improved cognitive screening. – CCG and commissioners / wider NHS / Social service: optimised use of limited healthcare and social care resources; optimal pathways of care