We will work with young people to use digital technology to transform adolescent mental health and provide a safe, and supportive, digital environment to tackle the unmet need arising from mental health disorders in those aged 10-24 years old. We are facing a youth mental health crisis; in the UK, one in eight young people have a mental health disorder, and one in four young women aged 17-19 have significant depression or anxiety with half of those having self-harmed; non-suicidal self-harm has nearly tripled over the past 10 years, while suicide rates per 100,000 adolescents have almost doubled. However, less than a third of all young people with mental health disorders receive any treatment. Many mental health and wellbeing apps exist, but most have no evidence base and some could even be harmful. Meanwhile, few research-based digital interventions have been shown to have impact in the real world. The youth mental health crisis has coincided with huge changes in society with creation of the 'digital environment' where being online and using social media has become central to young people's lives. While social media can be a helpful place for accessing information, exchanging views and receiving support, it has also been linked with depression, suicide and self-harm. Yet not all young people are at risk of mental health problems with social media we don't yet understand why some young people are more vulnerable than others. The COVID-19 crisis has been associated with increased mental health problems and greater online activity in young people. While their need to access trusted support online is greater than ever, social media platforms are not designed to meet mental health needs of young people. Aims & objectives. We will work with young people in our Young Person Advisory Group to: 1. increase understanding of the relationship between digital risk, resilience and adolescent mental health. 2. develop and evaluate preventative and personalised digital interventions. We aim to: - identify risk and resilience factors related to troublesome online experiences and activities, to prevent or reduce the emergence of depression, anxiety, and self-harm in young people. - understand how individual differences affect digital engagement (e.g. with social media and games) and adolescent brain and psychosocial development. - build, adapt and pilot new a generation of personalised and adaptive digital interventions incorporating a mechanistic understanding of human support with a new digital platform for delivery and trials in adolescent mental health conditions. - develop and test a novel socially assistive robot to help regulate difficult emotions with a focus on adolescents who self-harm. - develop and test a new digital tool to help adolescents better manage impulsive and risky behaviour with a focus on reducing the risk of self-harm. Applications & benefits. This work will translate new knowledge into practical tools to support young people negotiate the digital world, develop resilience and protect their mental health. Our involvement of young people means that the outputs from the research will be suitable and meaningful. Young people will be actively involved shaping the research at all stages. Young people, their caregivers, teachers, clinicians and charities will benefit from a range of co-created apps and tools to manage youth mental health issues. Young people will benefit from research training offered as part of their involvement. Policy makers and academics will benefit from new understandings of risk and resilience in the digital world to support novel interventions and evidence-based policy. Our work will establish a new, ethical and responsible way of designing digital platforms and tools that supports young people's mental health. Our Mental Health & Digital Technology Policy Liaison Group and Partners Board will translate our research into a step-change in mental health outcomes.

The aim of the project is to develop an algorithmic model that calibrates a dynamic index for patient priority by addressing the shortcomings of the current allocation protocols of scarce medical resources. The total number of confirmed Covid-19 deaths in the UK has already passed the 45,000 mark. Such a horrific number of deaths is partly attributable to the shortage of PPE, medical staff, and ICU beds in the early stages of UK pandemic. For a second wave of Covid-19 likely in the winter when the healthcare system is most stretched, scientists have estimated that the UK could see about 120,000 new coronavirus deaths. To achieve the greatest good for the greatest number of patients, it is essential to have in place ethically and clinically sound policies on the allocation of scarce resources. Existing triage guidelines determine patient priority based on several attributes, including the illness severity and the near-term prognosis after discharge. They focus on individual patients but ignore the overall mixture of current patient profiles and the uncertainty in the number of patients who become critical ill over time. Previous research has shown that such frameworks could lead to preventative deaths and inefficient usage of scarce resources. We aim to address these limitations in this project via the development of an algorithmic model that calibrates a dynamic index (priority). Its performance is to be compared against the benchmarks via an empirical study using anonymised data of Covid-19 patients collected by Public Health England.

Respiratory protective equipment (RPE) is widely used to limit the transmission of viruses and bacteria, representing a critical means of controlling Covid-19. In particular, respirator masks of the FFP3/N95 types, originally designed to protect against airborne dust particles, have been widely used to protect healthcare workers (Fig 1). It is essential that these masks fit tightly against the face to make an airtight seal, checked by fit testing. However, these masks are typically designed for a white male workforce, providing a limited range of size and geometry. This can lead to overtightening to compensate for a poor fit, which is associated with soft tissue injuries, as well as an increased risk of infection. This multidisciplinary project will investigate the fit and biomechanics of RPE devices, to ensure provision of a safe interface with users. Computational modelling and MRI will be used to explore how the soft tissues of the face deform in contact with a mask. These models will be informed through experimental monitoring of mechanical and thermal loads during RPE application and the associated changes in local skin physiology. In addition, the project will utilise existing databases of face shapes to investigate the fit of masks on a more representative range of users. This will lead to the development of design templates for new masks (working with UK manufacturers), standard test methods (STMs) to evaluate the risk of facial injury (working with testing and standards organisations), and intelligent fitting software to ensure that users select the correct mask.