Imperial College Healthcare NHS Trust

It is widely assumed that physical activity affects weight loss outcomes for severely obese patients, but there is a scarcity of robust research on this subject. We propose to use smartphone sensors and advanced data mining techniques to conduct detailed investigations addressing this important question. The research participants will be obese people having bariatric (or weight-loss) surgery (e.g. gastric bypass), but our results will also benefit other people with weight problems and patients with other conditions where exercise is helpful. In England just over a quarter of adults were classified as obese in 2010. This group is more likely to suffer from a range of illnesses (e.g. type-2 diabetes) and to have a lower life expectancy. Surgery is recommended for those with severe and complex obesity that has not responded to other therapies, and is highly cost effective in achieving weight loss, overcoming associated illnesses and promoting longer term health. However long-term success is far from guaranteed, with up to 15% of gastric bypass and 50% of gastric band procedures being ultimately unsuccessful. Obese people often lead very sedentary lives, both before and after surgery. Research has shown that even small long-term increases in routine physical activity could be very significant for weight loss, so we are very interested in how we can motivate people to do that little bit more in their daily lives. Patients attending the Imperial Weight Centre (IWC) are reminded to exercise during their hospital visits, but what they ideally need is a personal trainer to encourage them every day. Recognising this, patients have asked us if there are any devices that can help, and so we began our research into how sensors and mobile phones can seamlessly track activity and deliver timely, personalised feedback and encouragement. IWC Patients have tried wristbands such as the Nike Fuelband - but despite initial enthusiasm the novelty soon wears off. These devices do not provide sufficiently detailed or meaningful information. Smartphone apps such as MyFitnessPal are also popular, but soon become tedious since users must log everything they eat or do: many trying them did not persevere for more than a few days. With the advent of new apps it is now possible to track physical activity effortlessly just by carrying around one's smartphone, using its inbuilt sensors. Data is processed in the "internet cloud" where it can be analysed by new software we are developing. These apps also produce a complete daily "storyline" detailing a user's travels, and the amount and type of activity at each location. Our pilot users have been delighted to be able to see their physical activity progress and said that they felt motivated to challenge themselves to do more each day. This project sets out to objectively monitor physical activites on a daily basis so that we can follow almost 1000 patients over protracted periods of time and throughout their weight loss journey. We will use advanced data mining tools to understand individual differences and responses to surgery in terms of physical activity and how these relate to weight loss and weight maintenance over time. We shall use our analysis and understanding of behaviour change methods to devise ways to encourage users to do better and thereby achieve longer and healthier lives. For example, individualised prompts could incorporate weather and location information to suggest suitable walks on fine days, support positive goal setting or inspire competition with other users. This project will pave the way for further behavioural studies, for example using emerging wearable-sensor technologies and should offer long-term benefits for obese people and others with many different types of health problems, where exercise helps - lifestyle recommendations and advice can be produced that will be more personalised and useful for individuals looking to optimise their health.

Motivation: The provision of healthcare is highly labour intensive, requiring a multi-disciplinary workforce with many years of training. Quality of care provided to patients depends crucially on both the availability and quality of individual staff and how they work together in teams. The NHS is the largest employer in England, employing more than a million people at a cost of more than £50 billion in 2016 (Department of Health, 2017). But NHS pay review bodies and the UK National Audit Office have noted that evidence on the efficient allocation of existing workers is scarce and long-term workforce planning is lacking (NHS Pay Review Body, 2017; Review Body on Doctor's and Dentist's Remuneration, 2017; National Audit Office, 2016). Such evidence and planning are important in order to contain costs and ensure that patients receive the highest possible quality of care. Aims and methodology: Our proposed research will use econometric techniques and rich administrative data to identify the causal effects of the way in which the health care workforce is organised on healthcare use and patient outcomes. Our first project explores the determinants of variation in the quality and productivity of the most senior doctors (NHS consultants). It is becoming clear that there is wide variation across doctors in their patient outcomes, even within the same hospital. Some of this may be due to patient allocation across doctors, for example, giving the most experienced harder to treat patients. But this does not appear to be the only driver. Our research will seek to quantify this variation and to determine what factors associated with the doctors are associated with this variation. We will use large scale data in order to separate out the effect of the individual doctor and the hospitals in which they work by exploiting movement in doctors across hospitals during their careers. Our second and third projects examine team production. Teams are the dominant form of organisation of staff in healthcare and it is therefore important to understand the causal effects of changes to teams. Project two examines explores the impacts of anticipated and unanticipated disruptions to nursing teams on patient care and costs. To do so, we will exploit new data sources that provide detailed data on staff rotas across all wards in 5 hospitals, which can be linked to treatments and patient outcomes. Project three will explore the relationship between doctor seniority, productivity and patient outcomes by analysing a series of strikes by junior doctors in 2016 and 2017. These strikes changed the mix of staff treating patients, leading to a temporarily higher proportion of senior staff (NHS consultants) working in these teams. To conduct our research we will exploit several data sources, including rich administrative data from the Hospital Episode Statistics and newly available, highly granular, data from one large London NHS hospital group. Applications, benefits and impact: Our ultimate aim is to allow policymakers to better understand the role of the workforce in variation in productivity, hospital utilisation and patient outcomes. Our findings will provide information and tools that help policymakers improve the efficiency of the existing workforce, raise the quality of patient care, and inform future workforce planning. We will maximise impact by producing a range of outputs that communicate the results to multiple audiences. We will submit a series of academic articles to top economic journals. We will also produce a number of press-released non-technical reports, which summarise the key findings directed at journalists, policymakers and other non-academic users. In particular, we will target national policymakers, including the Department of Health and Social Care and Health Education England, and health care providers, such as individual Acute Trusts. We will also engage with health care workers and their representatives.

The NHS is facing an unprecedented level of future pressure due to impending challenges driven by an ageing population, increase in long-term conditions, and rising costs and public expectations. In particular, rising health care demand, rising costs and flat real funding mean that the NHS could face an estimated £30 billion financial shortfall by 2021. If these challenges are not addressed there is a risk that many service providers may become financially unsustainable, and the safety and quality of patient care decline. In response Monitor (the regulator of NHS Foundation Trusts) working with NHS England, the NHS Trust Development Authority and the Local Government Association has instituted a new five year joint planning regime. The intention of this regime is to focus on the robustness of Foundation Trusts' strategies to deliver high quality patient care on a sustainable basis. Foundation Trusts will have to present five year financial projections, develop realistic transformational schemes and align their plans with those of other actors within the Local Health Economy (LHE). Planning on a five year basis and in conjunction with other healthcare providers is a new discipline and differs distinctly from the approach taken nationally to planning during the regime of Foundation Trusts (since 2004) when a more market oriented focus has dominated. Against this complex background, and as part of the new five year planning regime one of the areas that UK Trusts plan to review is elective care, which mainly involves planned surgery. This project aims to develop ideas for service innovation in the orthopaedic surgery domain using a design-engineering led approach. This approach enhances design thinking through the use of system thinking, human factors and engineering analysis. This research will develop and evaluate a patient-centric and system-wide solution for sustainable delivery of surgery services.

Flexible robots have light and compliant bodies that allow them to adapt well to unstructured environments. However, due to their deformability, controlling their movements accurately in the presence of disturbances is a challenging task. Additionally, the design and manufacturing of flexible robots is generally conducted without prior analysis of their dynamic performance and involves refinements through successive prototypes. These factors reduce the accuracy and effectiveness of flexible robots in real-world applications, including surgery, inspection and maintenance. Finally, bespoke design and control solutions confine advances in these areas to specific cases. This research aims to produce advanced control methods and design guidelines for different types of flexible robots in order to enhance their performance. To demonstrate the general validity of the proposed methods we have chosen two illustrative applications from our own track record: robot-assisted biopsy and robotic inspection. In the experimental part of the project, the control methods will be validated with two proof-of-concept prototypes from our recent work that are representative of each application. Successful completion of this research will contribute to enhancing the accuracy and effectiveness of flexible robots which is an essential prerequisite for their wider use. Potential applications of the research findings include minimally-invasive robotic surgery, robotic inspection and maintenance.

This research brings together a multidisciplinary collaborative team of engineers, clinicians and patients to deliver a user driven, patient centric, bespoke technology to treat chronic health-conditions. The proposal will develop an Adaptive, Real-time, Intelligent System (ARISES) that will run on a smart phone locally and collect data from multiple sources to deliver an intervention to the patient that allows self-management of chronic disease. The core of ARISES will use Case-based-reasoning (CBR), a consolidated artificial intelligence technique which can solve problems in much the same way as a human does, using historical data and scenarios as a reference to recommend a current solution which can treat the patient. CBR is also powerful in that it has the capability to be adaptive according to patient lifestyle and behavior and always provide the most optimum solutions for a given set of resources. ARISES will have the capability to collect data from wearable devices such as smart-watches, activity monitors, hear rate monitors and continuous glucose meters and using smart-algorithms will be able to extract meaningful information to provide to the CBR system. Underpinning this will be energy efficient algorithms which always make ARISES aware of what sensors are connected to the patients local area network, safety systems that minimise the risk of any possible undesired event related to the management of the disease, and a data security to make sure information is protected against non-authorised access. ARISES will provide a generic framework which can be used to treat many chronic diseases such as asthma, chronic obstructive airways disease, hypertension, heart failure, ischaemic heart disease, arrhythmias and chronic neurological conditions. Given the global incidence, as an exemplar chronic condition to demonstrate its use we have chosen diabetes which currently affects 3% of the world's population, and we will target improvement in glycaemic control which can reduce micro- and macrovascular complications associated with the disease. In this context the system will promote the self-management of diabetes by optimizing glucose control through insulin dosage recommendations, exercise and physical activity support, carbohydrate recommendations to prevent hypoglycaemia, and behavioral change through education.