There is an urgent, unmet need for reliable, intelligent systems that can monitor patient condition in the home, and which can help patients manage long-term conditions. Delays in recognition of the changes in physiological state worsen outcomes and increase healthcare costs. The ASPIRE programme uses chronic obstructive pulmonary disorder (COPD) as an exemplar, which affects over 210 million people globally. This condition costs the National Health Service over £800 million each year, over half of which is spent treating patients in hospital, rather than caring for them in their homes. Intelligent monitoring systems are required to address the needs of patients with long-term conditions in their homes. However, no wearable systems have penetrated into clinical practice at scale, due to: (i) poor tolerance of existing wearable devices for monitoring; (ii) a lack of robustness in the estimates of the vital signs that wearable sensors produce; (iii) very limited battery life that requires batteries to be re-charged at a rate that prevents their use on a large scale; and (iv) limited subsequent use of the data for helping the patient understand and manage their condition. We propose to develop an "intelligent" home-based system, with smart algorithms embedded within lightweight healthcare sensors, to overcome these limitations. Our novel work will incorporate next-generation machine learning algorithms to combine information from healthcare sensors with information from GP and hospital visits. This will enable the system to learn "normal" health condition for individual patients, with knowledge of other conditions from which they may be suffering, and which can then make recommendations to the patient concerning self-management of their condition. This work will include close working with world-leading clinicians to ensure that the recommendations provided by the system are correct for the individual patient.

The Covid-19 pandemic has changed the face of healthcare delivery and has placed ever changing demands on nursing care. This qualitative study will use audio, visual diaries or written diaries kept over four weeks of a clinical placement and telephone or on-line interviews at the end of the placement, to gather data from second and third year student nurses in England, Wales, Scotland and Northern Ireland. Student nurses will describe their experiences of education both before and during the pandemic (including between the first and second waves) and their experience of on-line learning, commenting on what went well and what could have been done better. They will also identify any transferable skills they acquired and how these may be used in nursing to provide optimal care throughout the different phases of the pandemic and beyond.The study will investigate the psychological wellbeing of student nurses and the extent to which their experience within the Covid pandemic influences their identities as nurses and their intentions to pursue a career in nursing.