Healthcare Wearables for Independent Living
Lead Research Organisation:
University of Oxford
Department Name: Engineering Science
Abstract
Around 1 in 4 people have multiple long-term conditions (MLTCs) rising to up to two-thirds in people over the age of 65 years. Treatment for this group is estimated to take up 70% of health care expenditure. Such people have poorer health, poorer quality of life, and a higher risk of dying. Key challenges for this group of people include maintaining their independence in their homes, avoiding developing further conditions that can threaten their health, and which would further impair their quality of life and minimising the high burden of healthcare for this group potentially made worse by uncoordinated health and social care. Our challenge is to improve outcomes through informed self-care and maintaining independence, while reducing healthcare costs. The current model for many MLTCs is for people to present to urgent care services when they can no longer cope at home. This reactive approach leads to frequent use of emergency hospital services when a severe episode occurs, shifting the focus of care to hospitals. Management then follows generic pathways within acute healthcare, in an attempt to stabilise the condition of the patient. Information-driven technologies will enable people to perform their own health management, which will change the model of care. Individuals will be able to manage their condition proactively. The integration of knowledge concerning individuals' co-morbidities (which are common in MLTCs) will allow personalised therapy, further maintaining independence, improving patient outcomes, and optimising the use of resources.
The proposed programme "Healthcare Wearables for Independent Living" (HW-IL) aims to develop, for the first time, a suite of predictive tools, based on regular wearable devices, to allow a step-change in the self-care of patients with MLTCs, and in the maintenance of their independence by avoiding deterioration. Patients and their carers will be guided, using such tools, to preventative management. For the first time, such tools will incorporate an integrated approach, exploiting patient-worn devices (at or near the patient), and healthcare data (from GPs and hospital information systems), working in real-time. All work will be ethically approved, and accord to the highest standards of patient confidentiality.
The proposed programme "Healthcare Wearables for Independent Living" (HW-IL) aims to develop, for the first time, a suite of predictive tools, based on regular wearable devices, to allow a step-change in the self-care of patients with MLTCs, and in the maintenance of their independence by avoiding deterioration. Patients and their carers will be guided, using such tools, to preventative management. For the first time, such tools will incorporate an integrated approach, exploiting patient-worn devices (at or near the patient), and healthcare data (from GPs and hospital information systems), working in real-time. All work will be ethically approved, and accord to the highest standards of patient confidentiality.
Publications
Lu HY
(2024)
Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes.
in IEEE reviews in biomedical engineering
Thakur A
(2023)
Self-Aware SGD: Reliable Incremental Adaptation Framework For Clinical AI Models.
in IEEE journal of biomedical and health informatics
Lu P
(2024)
Tetanus Severity Classification in Low-Middle Income Countries through ECG Wearable Sensors and a 1D-Vision Transformer
in BioMedInformatics
Lu L
(2023)
Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation.
in IEEE transactions on cybernetics
Taylor L
(2023)
Wearable Vital Signs Monitoring for Patients With Asthma: A Review
in IEEE Sensors Journal
Description | NIHR Biomedical Research Centres |
Amount | £4,700,000 (GBP) |
Funding ID | NIHR203311 |
Organisation | Oxford University Hospitals NHS Foundation Trust |
Sector | Academic/University |
Country | United Kingdom |
Start | 12/2022 |
End | 11/2027 |
Description | Research Chair |
Amount | £1,586,000 (GBP) |
Organisation | Royal Academy of Engineering |
Sector | Charity/Non Profit |
Country | United Kingdom |
Start | 04/2023 |
End | 04/2028 |
Description | Research Professorship |
Amount | £1,823,387 (GBP) |
Funding ID | NIHR302440 |
Organisation | National Institute for Health Research |
Sector | Public |
Country | United Kingdom |
Start | 12/2022 |
End | 11/2027 |
Description | Chinese University of Hong Kong |
Organisation | Chinese University of Hong Kong |
Country | Hong Kong |
Sector | Academic/University |
PI Contribution | This partnership involved the founding of the Centre for Cardiovascular Engineering, funded by £31m from the Innovation & Technology Commission of Hong Kong. |
Collaborator Contribution | Wearable sensing, AI for wearable sensor data, healthcare technologies. |
Impact | Patents, publications. |
Start Year | 2023 |
Description | Research collaboration with GlaxoSmithKline |
Organisation | GlaxoSmithKline (GSK) |
Country | Global |
Sector | Private |
PI Contribution | Jointly working on AI methods for improving healthcare and the development of medicines. |
Collaborator Contribution | Data, domain expertise, problem setting. |
Impact | Open-access publications, support for national awards. This is a multi-disciplinary collaboration between AI scientists, clinicians, medical statisticians, and medical scientists. |
Start Year | 2021 |
Description | Prestige Lecture |
Form Of Engagement Activity | A talk or presentation |
Part Of Official Scheme? | No |
Geographic Reach | International |
Primary Audience | Other audiences |
Results and Impact | Over 150 people attended the prestige lecture by Professor David Clifton on "AI for Global Health" at Hong Kong University, with invitees coming from around the south-east Asian region. |
Year(s) Of Engagement Activity | 2023 |