The Care-Full Study: A systems approach to older adults with multiple long term conditions’ home-based care: mapping, scoping, feasibility, and modelling of factors affecting outcomes for unpaid caregiving
Start date
July 2023End date
December 2024Overview
An estimated 1.5 million older people have needs beyond state care provision. Unpaid carers, comprising 1 in 5 UK adults, often meet this gap. Though rewarding, caring can also bring emotional, health and financial challenges. Unpaid carers of older people with multiple conditions carry out complex care work, but the current care system neither routinely or proactively identifies, involves, or supports unpaid carers, leading to poorer outcomes for both the unpaid carer and the person cared for.
The Care-Full study will employ participatory workshops and key stakeholder interviews, a literature review and repeat surveys and interviews with a longitudinal cohort of unpaid carers (and those they care for) across three research sites in England to meet the study aims and objective. Additionally, a team of three current or recently former unpaid carers, for an older person with multiple long-term conditions, will serve as peer researchers and support the data collection process and the involvement of unpaid carers in workstream 2 (see below).
By developing a workflow from stakeholder co-creation of systems maps to data collection and mathematically sophisticated models, we aim to create a paradigm that can be used throughout health and social care systems, wherever analysis of interactions among complex factors can enable support for decision-making that transcends an immediate situation, producing better outcomes for all stakeholders.
Aims and objectives
This project brings health science researchers and system engineers together with unpaid carers, older people living with frailty and multi-morbidities, care advocates and care providers, to translate real-world information into a mathematical model of the complex systems and networks of factors around unpaid care. The objective is for predictive modelling to support carers' decision-making, according to multiple objectives that consider their holistic well-being and that of the people they care for. The project aims are to:
- Understand the key events, trajectories, and outcomes for unpaid carers and the older people they care for.
- Identify what evidence and data sources can be collected for modelling this system of care.
- Explore how modelling methods drive interventions to support better outcomes for unpaid carers and older people living with multiple conditions.
Research Work Packages
Work Package 1
Developing understanding of the experiences and current context of unpaid caring and identification of the appropriate tools to measure impact of caregiving.
- Workstream 1: Participatory Systems Mapping (PSM) approach to produce visual map of current context of care.
- Workstream 2: Scoping exercise and feasibility testing to determine acceptability, relevance, and quality of different types of data collection instruments with unpaid carers over time.
Work Package 2
Building prototype systems models to identify links between the experiences and outcomes of unpaid carers and older people living with multi-morbidities to inform optimal service delivery.
Work Package 3
Building the research capacity, infrastructure, and knowledge base to create a national hub for unpaid caregiving research and innovation.
Funders
Team
Core (Surrey) study team
Co-Principal Investigator
Professor Caroline Nicholson
Professor of Palliative Care and Ageing
Biography
Caroline is a Clinical Academic Nurse and her research forwards understanding and care for older people living with complex needs. She is particularly interested in the transitions that occurs in the last phase of life. Caroline qualified as a Registered Nurse at St Bartholomew’s Hospital London. She worked as a specialist Palliative Care Nurse before undertaking a combined BSc (Hons) in Community Nursing DN/HV Certs at King’s College London. She went on to an MSc in Medical Anthropology at Brunel University London before completing her PhD at City University, London in 2009. She is a FHEA from the Institute of Education and holds a diploma in psycho-dynamic approaches to old age from the Tavistock and Portman NHS Foundation Trust, London
Caroline is a HEE/NIHR Senior Clinical Academic Lecturer, working between the School of Health Sciences at Surrey University and St Christopher’s Hospice, London. She is passionate in her belief that everyone should have access to the best care and support in the final years of their life. She has a long-held interest in the experiences and palliative care needs of older people and their families and is co-lead in End of life Care for the British Geriatrics Society.
Caroline studies the experiences and care of older people living with complex needs across care settings, to develop interventions which equally value quality of life with quantity of years in old age. She has a long-held interest in the experience of older people living with frailty, and their capabilities as well as their current and future vulnerabilities. Her work also includes the development of care services and a workforce that can recognize, facilitate and enhance the processes and outcomes of high-quality palliative and supportive care. Caroline is committed to building the next generation of clinical academics and is an NIHR Nurse Training Advocate . Research expertise includes participatory action research, narrative research, mixed method research and complex intervention development.
Co-Principal Investigator
Dr Sotiris Moschoyiannis
Reader in Complex Systems
Biography
I am a mathematician (Maths, University of Patras) doing research in computer science (PhD in Theoretical Computer Science, University of Surrey). My work on Learning and Control in complex networks focuses on when and where to intervene in a network in order to steer to a desirable outcome. I am keen on mathematical methods (control theory, approximations methods) combined with computational methods such as Reinforcement Learning (rule-based, deep).
This video clip provides a quick overview of my recent research activity.
I have led several UK and EU funded research projects. Current projects include:
- Virtual Clinical Trial Emulation with Generative AI Models, funded by MRC (MR/X005925/1), with University of Strathclyde and Professor Feng Dong (lead), and NHS Glasgow and Clyde and Professor Chris Sainsbury, and colleagues Professor Simon Skene and Professor Jo Armes at Surrey, on equipping generative AI models with causality and differential privacy and applying such models to speed up clinical trials
- Advanced Persistent Threats defence in 5G networks (APTd5G), funded by EPSRC UKI-FNI, with the 5G/6G Innovation Centre (Dr Mohammad Shojafar) and Amity University Mumbai, India and Professor S. A Abimannan, on identifying realistic APTs in 5G networks using Reinforcement Learning and defining APT protection requirements
- AI Evaluation award, funded by NHS England and NIHR, on evaluating the AI side of DERM which is a tool for skin cancer diagnosis developed by Skin Analytics for the NHS; with Professor Simon Skene and Unity Insights
- Vision-based positioning via diverse data sources, PhD project funded by Saab Group, on navigating an autonomous surface vessel based on on-board camera images without relying on GPS; PhD student Alastair Finlinson
Recent projects include:
- the Real-Time Flow (RTF) project, funded by EIT Digital IVZW - this is a collaboration with Amey UK (lead), Ferrovial, Ci3 (Spain) and Emu Analytics on modelling and predicting flows of passenger and train movements over transport networks
- the CoNTINuE (Capacity building in technology-driven innovation in healthcare) project, funded by GCRF, UKRI, which is an interdisciplinary project between Surrey Business School, Clinical and Experimental Medicine, and Computer Science
- AGELink (Automated GEneration of Linkages between delay events) which looks at minimising reactionary delay -- the knock-on effect on the rail network of a train being late. This is an EPSRC IAA project in collaboration with the Rail Delivery Group (RDG)
I am a member of the executive committee of the IEEE Technical Committee on Cloud Computing and the technical committee on IEEE Industrial Informatics.
On the Programme Committee for the annual conference on Complex Networks, the IEEE Service Oriented Computing and Applications (IEEE SOCA), and a co-chair on the RuleML+RR International Rule Challenge in 2019, 2020.
An Associate member of the Surrey Centre for Cyber Security (SCCS).
An IEEE Member (No. 41465193).
Dr Richard Green
Surrey Future Fellow
Biography
Richard was awarded a prestigious Surrey Future Fellowship in April 2023 to work with interdisciplinary colleagues from the Surrey Institute for People-Centred AI and across the university to develop a programme of research on the use of 'carebots' (chatbots and other artificial technologies) to support the health and wellbeing of older adults. Prior to this role, Richard was working as a Research Fellow, project managing The PALLUP Study - Improving home based palliative care for frail elders and his current fellowship continues and extends his research on the health and wellbeing of older adults in later life.
Richard completed a BSc in Criminology and Sociology at Royal Holloway university and then an MSc in Social Research Methods at the University of Surrey, before completing his PhD in Sociology in partnership with both universities on an ESRC studentship. His PhD explored men's experiences following treatment for prostate cancer in a qualitative interviewing study. Before joining the PALLUP study, Richard worked at the Office for National Statistics as a Senior Research Officer, working on facilitating research access to secure data for research that serves the public good.
Dr Haomiao Jin
Lecturer in Health Data Sciences
Biography
I am a health data scientist with a particular interest in the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance the collection and modelling of subjective health data. My recent research has focused on using AI to facilitate self-reported data collection and machine learning modelling of ageing-related data. My work delves into the self-reporting process, aiming to better understand and facilitate this process through novel digital technology. Additionally, my research seeks to better utilise data generated from the self-reporting process, including both the original responses and the paradata, to promote clinical and population health. I have contributed as a Co-Investigator to various research projects funded by the National Institutes of Health (NIH) in the United States, and the National Institute for Health and Care Research (NIHR) and the Engineering and Physical Sciences Research Council (EPSRC) in the United Kingdom. I have published over 40 papers. Please see my Google Scholar page.
Research themes
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