NIHRDataInsights/HRCSData huggingface dataset:
Title: An investigation of how patients, public and stakeholders perceive and interpret information about anti-depressants in UK newspapers
Funder: Chief Scientist Office (Scotland)

Abstract: Objectives Anti-depressant drugs in depression have received much media attention. The objectives of this PhD fellowship are: to investigate how patients, public and stakeholders perceive and interpret information about anti-depressants as reported in UK newspapers; and to develop and apply computational techniques to examine the effects of language styles in media reporting. Design Computational linguistic literature analysis and qualitative study using focus groups and interviews Techniques/Approaches Two approaches will be applied to examine the interpretation and perception of newspaper coverage of anti-depressant drugs in depression treatment by public, patient and stakeholder groups. 1. Identify a suitable set of linguistic devices that mark human bias, including sentiment and emotion, and apply computational linguistic approaches such as sentiment analysis to automatically annotate articles. This will allow examination of the dissemination of information from research paper to newspaper article on a large scale. 2. The use of interviews and focus groups with patient, public and stakeholder groups to gain insight into beliefs and attitudes relating to a range of articles on antidepressant drugs in the treatment of depression. Measurement/Outcome This study will generate knowledge regarding both the methods of reporting, and public, patients' and stakeholders' interpretations of, health related media articles using the specific example of antidepressant drugs in the treatment of depression. The computational linguistic methods developed will be applicable for investigating reporting in other areas of health and in other types of news media. Findings will help formulate ways to improve health-related reporting that can subsequently be developed by working alongside the media.


Research activity codes: 7.1 INDIVIDUAL CARE NEEDS;8.3 POLICY, ETHICS AND RESEARCH GOVERNANCE
Health categories: MENTAL HEALTH
Title: The Benefit of Minocycline on Negative Symptoms in Schizophrenia: Extent and Mechanisms
Funder: Department of Health (England)

Abstract: AIMS: to use recently developed PsyGrid infrastructure for UK-wide clinical and imaging research in first episode psychosis to: i) determine whether negative symptoms can be lessened or prevented by minocycline treatment initiated early in the course of schizophrenia and ii) collect biomarker data to test hypotheses about how minocycline improves negative symptomsPRIMARY EFFICACY PREDICTION AND MECHANISTIC HYPOTHESES:1) Minocycline minimises later negative symptoms when administered during the acute phase of early psychosis2) Minocycline reduces or prevents the negative symptoms of schizophrenia by:a) reducing the loss of grey matter associated with early psychosis.b) interfering with inflammatory cytokine production.c) an action on glutamate systems to improve negative symptoms and cognitive function.DESIGN. Multicentre one year double-blind randomised placebo-controlled trial of minocycline versus placebo added to standard antipsychotic drug (APD) treatment for patients in an early episode of schizophrenia-related psychosis.INTERVENTION. Minocycline or matching placebo 300mg daily for 12 months.OUTCOMES. The primary clinical outcome is negative symdrome subscale score on the Positive and Negative Syndrome Scale (PANSS). The mechanistic biomarker variables are: 1) change in medial prefrontal grey matter volume over 12 months 2) circulating cytokine concentrations and 3) working memory performance and brain activation. These measures will be related to changes in PANSS negative symptoms at 2 and 12 months and to quality of life assessments.POPULATION AND SAMPLE SIZE. 170 patients with early psychosis recruited over 22 months from 6 established PsyGrid centres.STATISTICAL ANALYSIS. Group differences in outcomes and putative mediators will be evaluated using random effects models for longitudinal data (allowing for treatment centre and other baseline covariates). Tests of the mechanistic hypotheses (i.e. mediation) and their sensitivity of the results to possible hidden confounding will use instrumental variable methods from PI Dunn’s MRC Methodology Research Programme projects; see Emsley R Dunn G & White I (2009) modelling mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research published online on 16/07/09.EXPERTISE. The investigators are experienced in the recruitment of early psychosis patients and the confidential collection of clinical assessments and brain imaging through their involvement in PsyGrid and NeuroPsyGrid.


Research activity codes: 5.1 PHARMACEUTICALS;6.1 PHARMACEUTICALS
Health categories: MENTAL HEALTH
Title: Radically Open Dialectical Behaviour Therapy for Treatment-Resistant Depression: A Randomised Controlled Trial
Funder: Department of Health (England)

Abstract: Even with best treatment one third of patients with major depression do not achieve symptom relief and suicidal behaviour is more likely among those with chronic depression. A major cause of treatment-resistant depression (TRD) is co-morbid personality disorder (PD) present in 40-60% of these patients. The most common PDs reported in these patients are behaviourally over-controlled (obsessive-compulsive paranoid & avoidant). Depressed PD patients are 4 times more likely to have continuing or relapsing depression than those without PD; they respond poorly to psychotherapies and psychotropic drugs. Standard Dialectical Behaviour Therapy (DBT) has proved to be effective in treating borderline PD. Radically Open DBT (RO-DBT) a new treatment approach with strong roots in standard DBT has demonstrated promise for patients with difficult-to-treat depression and related overcontrolled disorders. Several studies have informed the development of RO-DBT for TRD in conjunction with a theoretical model which describes the maintenance of depressive symptoms and the barriers to successful treatment. This theory centers on the role of rigid coping styles lack of emotional expression and openness and limited psychological insight. These characteristics common in TRD patients limit opportunities for learning new skills exacerbate aloofness and the belief that the patient is unlovable and perpetuate constricted patterns of behaviour. Further the model predicts that negative experiences early in childhood may exacerbate a biological predisposition to heightened threat sensitivity and diminished reward sensitivity and in turn initiate this over-controlled coping style. We propose a 2-arm 2-stage 3-centre RCT of RO-DBT for participants with TRD. All participants will receive treatment as usual (TAU) mainly antidepressant medication and complete outcome assessments over 18 months. We shall use these assessments to compare combined TAU and RO-DBT lasting 29 weeks with TAU alone. We aim to estimate efficacy and cost-effectiveness of RO-DBT addressing the effects of both allocation to RO-DBT and receiving RO-DBT (exposure). We propose several innovative adaptations to our RCT to enable us to use cutting-edge statistical methods to estimate the causal effects of mediating variables including treatment-exposure therapeutic alliance and skill-learning. During stage 1 we shall recruit 26 participants to assess recruitment adherence and client satisfaction. In stage 2 we shall recruit participants at 3 sites randomising another 250. The primary outcome depressive symptoms will be measured at randomisation and 7 12 and 18 months later. TL (Southampton) one of the worlds leading researchers in RO-DBT heads our experienced trial team. ITR (Swansea) provides expertise in clinical trials and PC (Bristol) in causal analysis. Together TL ITR and PC have been CI or PI on more than 50 RCTs most in many centres.


Research activity codes: 6.6 PSYCHOLOGICAL AND BEHAVIOURAL
Health categories: MENTAL HEALTH
Title: Ketamine augmentation of electroconvulsive therapy to improve outcomes in depression
Funder: Department of Health (England)

Abstract: AIms To determine i) the size of clinical benefit of adjunctive ketamine given during electroconvulsive therapy (ECT) in preventing cognitive deficits and in speeding clinical response and ii) whether ketamine protects against ECT’s deleterious effects on frontal cortex function and its disruption of fronto-hippocampal connectivity using functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI). PRIMARY OUTCOME PREDICTIONS AND MECHANISTIC HYPOTHESES. 1) Ketamine will prevent ECT-induced cognitive impairment and speed up the clinical response to ECT;2) Ketamine will act by a) attenuating ECT-induced impairment of frontal cortex reactivity b) preventing ECT-induced disruption of fronto-hippocampal connectivity. DESIGN. A 3-year prospective randomised placebo-controlled trial of ketamine augmentation of standard ECT treatment in patients with severe depression. ECT treatment clinical and mechanistic outcomes will be assessed double-blind (ketamine administration will be by unblinded anaesthetist for safety reasons).INTERVENTION. Ketamine (0.5mg/kg) or saline given during anaesthesia for each standard ECT treatment.OUTCOMES. 1) verbal episodic memory autobiographical memory (AM) and verbal fluency (VF) after last ECT treatment (primary outcome); 2) reduction in MADRS score after 4 treatments; 3) frontal cortex activation to a VF task (increased activation ketamine>saline related to improved cognitive function) using fNIRS; 4) fronto-hippocampal connectivity (disruption ketamine < saline) using fMRI. Subsidiary outcomes: number of ECT treatments to remission persistence of AM impairment after 1 and 4 months glutamate increases with magnetic resonance spectroscopyPOPULATION AND SAMPLE SIZE. 160 patients with major depression due to have ECT recruited over 2 years in 6 NHS Trusts.ANALYSIS. Intention-to-treat analysis allowing for missing outcome data using variations on analysis of covariance adjusting for treatment site and important baseline covariates. Mediational mechanisms will be evaluated using recent approaches based on instrumental variable methods to allow for hidden confounding between putative mediator and clinical outcome.EXPERTISE. The investigators have expertise in clinical trials and experimental research including neuropsychological testing and brain imaging as well as clinical experience in delivering ECT treatment.


Research activity codes: 6.1 PHARMACEUTICALS
Health categories: MENTAL HEALTH
Title: Natural Speech Technology
Funder: Engineering and Physical Sciences Research Council

Abstract: Humans are highly adaptable, and speech is our natural medium for informal communication. When communicating, we continuously adjust to other people, to the situation, and to the environment, using previously acquired knowledge to make this adaptation seem almost instantaneous. Humans generalise, enabling efficient communication in unfamiliar situations and rapid adaptation to new speakers or listeners. Current speech technology works well for certain controlled tasks and domains, but is far from natural, a consequence of its limited ability to acquire knowledge about people or situations, to adapt, and to generalise. This accounts for the uneasy public reaction to speech-driven systems For example, text-to-speech synthesis can be as intelligible as human speech, but lacks expression and is not perceived as natural. Similarly, the accuracy of speech recognition systems can collapse if the acoustic environment or task domain changes, conditions which a human listener would handle easily. Research approaches to these problems have hitherto been piecemeal and as a result progress has been patchy. In contrast NST will focus on the integrated theoretical development of new joint models for speech recognition and synthesis. These models will allow us to incorporate knowledge about the speakers, the environment, the communication context and awareness of the task, and will learn and adapt from real world data in an online, unsupervised manner. This theoretical unification is already underway within the NST labs and, combined with our record of turning theory into practical state-of-the-art applications, will enable us to bring a naturalness to speech technology that is not currently attainable.The NST programme will yield technology which (1) approaches human adaptability to new communication situations, (2) is capable of personalised communication, and (3) takes account of speaker intention and expressiveness in speech recognition and synthesis. This is an ambitious vision. Its success will be measured in terms of how the theoretical development reshapes the field over the next decade, the takeup of the software systems that we shall develop, and through the impact of our exemplar interactive applications.We shall establish a strong User Group to maximise the impact of the project, with a members concerned with clinical applications, as well as more general speech technology. Members of the User Group include Toshiba, EADS Innovation Works, Cisco, Barnsley Hospital NHS Foundation Trust, and the Euan MacDonald Centre for MND Research. An important interaction with the User Group will be validating our systems on their data and tasks, discussed at an annual user workshop.


Research activity codes: 1.2 PSYCHOLOGICAL AND SOCIOECONOMIC PROCESSES
Health categories: MENTAL HEALTH
Title: Trajectories of Depression: Investigating the Correlation between Human Mobility Patterns and Mental Health Problems by means of Smartphones
Funder: Engineering and Physical Sciences Research Council

Abstract: Depression does not only affect the personal life of individuals and their families and social circles but it has also a strongly negative economic impact as shown in several reports. According to a recent study, workers in the United Kingdom suffer high levels of depression than those anywhere else in Europe. The survey found that 1 in 10 employees had taken time off at some point in their working lives because of depression problems Novel strategies for tackling the problem of depression and preventing suicides are needed. We believe that new emerging technologies, in particular mobile ones, together with the possibility of mining large amount of data in real-time can help to tackle this problem in new and more effective ways. Existing interview-based studies have shown that depression is significantly associated with a marked decline of physical activity. The goal of this project is to investigate how mobile phones can be used to collect and analyse mobility patterns of individuals in order to understand how mental health problems affect their daily routines and behaviour and how potential changes can be automatically detected. In particular, mobility patterns and levels of activity can be quantitatively measured by means of mobile phones, exploiting the GPS receiver and the accelerometers embedded in the devices. The data can be extremely helpful to understand the behaviour of a depressed person, and in particular, to detect potential changes in his or her behaviour, which might be linked to a worsening depressive state. By monitoring this information in real-time, health officers and charity workers might intervene by means of digital behaviour intervention delivered through mobile phones or by means of traditional methods such as by inviting the person for a meeting or by calling him or her by phone. In order to support these novel applications, it is necessary to build mathematical tools for analysing the mobility traces in real-time for the detection of gradual or sudden changes related to the emotional states of the individual. More specifically, we plan to devise analytical techniques for studying the relationships between human mobility patterns and emotional states. We plan to use existing datasets of human mobility and to collect data by means of a smartphone application distributed to people affected by depression. This can be considered as a sort of pilot study for a wider deployment of these technologies and it will provide a sound theoretical basis for further studies in this area. Finally, a key aspect of the proposed research work is the implementation of mechanisms for preserving the privacy of the individuals involved in the study.


Research activity codes: 7.1 INDIVIDUAL CARE NEEDS;2.3 PSYCHOLOGICAL, SOCIAL AND ECONOMIC FACTORS
Health categories: MENTAL HEALTH
Title: Clasp: Digital Tactile Anxiety Management for the Health Internet of Things
Funder: Engineering and Physical Sciences Research Council

Abstract: Although there is no register of people with autism in the UK, the National Autistic Society estimates that 700,000 people in the UK are on the autistic spectrum. Beyond this, however, almost all of us suffer from anxiety - in some form - during our lifetime. According to Anxiety UK, 1 in 6 people experience some form of 'neurotic health problem', of which the most common are anxiety and depression. Anxiety UK estimates that more than 1 in 10 people are likely to have a 'disabling anxiety disorder' at some stage in their life (http://www.anxietyuk.org.uk/about-anxiety/frequently-asked-questions/). And these figures do not include the many and varied minor forms of anxiety that all of us experience on a regular basis. The aim of this project is to co-design and co-produce, with a cohort of autism sufferers, their friends and family, a tactile digital anxiety management and peer-support tool to assist people with autism in understanding and managing their anxiety in social engagement. The prototype Clasp system will be developed to have three core design components: 1) a tactile digital anxiety object which communicates levels of anxiety to a connected smartphone, implemented using a squeezable Bluetooth-connected digital 'stress ball'; 2) a peer-support network communication facility via SMS and distributed Social Network Service (SNS) status updates; 3) an anxiety data aggregator and visualization for personal and community feedback. If use of the 'stress ball' reaches user-defined thresholds, a user-defined response will be triggered. The user can view their trigger history, which will show times and locations when triggers were reached. Hence, Clasp will allow those with autism to track their anxiety levels over time, to reflect on what made them anxious in different situations, to experiment with interventions that alleviate their anxiety, and to get support from their peers in situations of high anxiety. The specific features of Clasp will come about through an in-depth engagement with a wide variety of stakeholders, including those with autism. Clasp will be underpinned by a study into how anxiety in autistic adults is impacted by and impacts their social engagement and the role digital technology may play in managing anxiety. Clasp will evolve through a process that iteratively and collaboratively captures the requirements for an anxiety management and peer-support system, builds a prototype for such a system, and evaluates key design implications for future development of Clasp and similar digital tools. We will also investigate versions of Clasp targeted to non-autistic users, thus addressing anxiety issues in a broader range of users including those with mental/learning disabilities as well as people more generally.


Research activity codes: 7.1 INDIVIDUAL CARE NEEDS
Health categories: MENTAL HEALTH
Title: EPSRC - NIHR HTC Partnership Award: Social Computing And Mental Health Research Network
Funder: Engineering and Physical Sciences Research Council

Abstract: The current extent of ill mental health is estimated to cost the UK economy £105 billion per year in healthcare expenditure and lost revenue. Addressing mental health is therefore a clearly articulated priority for government. The broad use of ICT services, social media and smartphone applications has an increasingly important role to play in the delivery of clinical mental health services and in supporting societal mental health and wellbeing more generally. Indeed, for large sections of the population, and younger people in particular, the use of Social Computing technologies such as Facebook, Twitter and YouTube is already informally entwined with every facet of everyday life including health and wellbeing. The expectation by the healthcare community, and by commercial software developers, therefore is that digital mobile and social technology can provide compelling, scalable and convenient access to information, social and peer support, as well as therapy and shared self-monitoring tools and services. However, there are a numerous difficult challenges associated with the design, deployment, use and evaluation of such technology in mental health settings which must be solved using multidisciplinary, rigorous and scientific methods. The research field of Social Computing broadly focusses on the user-centred design, implementation, use, and evaluation of technologies that consider social context in an increasingly digitally-connected society. The UK's Social Computing research community therefore has a wealth of experience and expertise that is relevant to, but not typically directed at, mental health services and systems This network will address issues of support, coordination and collaboration in this context and bring together two communities: (i) engineering and physical sciences (EPS) researchers under the broad, and inclusive, banner of Social Computing covering sub-fields of Human Computer Interaction (HCI), ubiquitous and mobile computing, and web scienc (ii) clinical scientists, healthcare professionals, commercial partners, charities, user groups and patients who are concerned with mental health and wellbeing. The network will work closely with the NIHR MindTech Healthcare Technology Co-operative (HTC) centre to engage in a range of coordinated scientific, technological, management and clinical activities to realise its overarching aim of pump-priming future UK capacity to deliver internationally-leading research at the boundaries between social computing and mental healthcare.


Research activity codes: 8.5 RESOURCES AND INFRASTRUCTURE (HEALTH SERVICES)
Health categories: MENTAL HEALTH
Title: Advancing Consumer Protection Through Machine Learning: Reducing Harm in Gambling
Funder: Economic and Social Research Council

Abstract: The global gaming market is worth $500bn, with internet gaming growing at 16% YOY. As gambling continues to expand_x000D_ globally across digital channels there is growing concerns in increased problem gambling. Bet Buddy and City University_x000D_ have the opportunity to develop a solution to predict problem gambling behaviour, leveraging City University Machine_x000D_ Learning expertise together with Bet Buddy domain exprtise in problem gambling and software egnineering. This would_x000D_ enable Bet Buddy to apply machine learning techniques not yet deployd in area of growing market value and solving social_x000D_ problems Bet Buddy won its first major contract in 2013 with the Ontario Lottery and Gaming Corporation (OLG) in_x000D_ Canada, and has now signed re-seller agreements with the two of the world's biggest gaming software providers, making_x000D_ product exploitation achievable. This project will advance Bet Buddy's product offering by applying Artificial Intelligence (AI)_x000D_ techniques to enable Bet Buddy to offer its products to more clients globally, solve emerging problems in new forms of_x000D_ gambling (e.g. machine gambling), and to target new but related industries e.g. trading and social casino gaming.


Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: MENTAL HEALTH