Depression is a massive, growing economic and societal problem; it is a leading cause of disability and suicides, costing the UK economy \>£56bn annually in lost productivity. There is an urgent need to relieve this burden on the economy and the NHS. The healthcare sector, however, needs better tools to tackle the problem of depression efficiently. Its heterogeneous clinical profile means that patients can have several unique combinations of depressive symptoms. Currently, identifying the right diagnosis and treatment in the UK may take many years, with some studies finding rates of untreated depression as high as 77%. There is an urgent need for a tool that can help clinicians objectively measure individual depression _symptoms and symptom clusters_ - just as physical illness ones (e.g. blood test markers). Artificial intelligence (AI) tools have been proposed to automatically detect depression (but not symptoms like mood, fatigue or anhedonia). They also face barriers to commercial implementation given their limited validity and generalisability. This is due to their focusing on small samples, a single time point and/or a single behaviour modality (e.g. voice, which reduces accuracy and sensitivity). **The aim of this project is to bridge this gap by developing an innovative healthcare solution in the form of a next generation, reliable AI-powered depression screening tool for clinicians ("I-DEPT").** To this end, we will collect several minutes' worth of gamified activities twice a week, over 3 months, from 550 depressed individuals and 550 controls via an online recruitment platform (Prolific; e.g. speech elicitation, working memory etc.). Ultimately, our end-to-end clinician support tool will save clinicians significant admin time and costs, doubling patient throughput and revenue, halving waiting times, all whilst improving clinical outcomes. The project will take us from proof-of-concept to a fully commercialisable product with strong, demonstrable return on investment.

Around 1 in 5 people are shift workers and a majority of them suffer from regular fatigue leading to decreased performance and serious physical and mental health risks, with huge costs for the UK economy and the NHS. **There is an urgent need to relieve this burden on the economy and the NHS, by timely detection of fatigue in shift workers in order to help mitigate it.** Early fatigue detection, however, has proven difficult. This is both due to a general lack of a consensus on a standard instrument as well as to the challenge of designing affordable, non-intrusive and easily acceptable tools. Machine learning offers a solution to automatically detect fatigue, however, this approach has limited generalisability. For example, machine learning models have either been developed using very small or unrepresentative samples, have used data from a single time point or have tried to detect fatigue based on a single behaviour type (e.g. looking at mouth opening in the face to detect yawning as a sign of sleepiness) instead of integrating multiple behaviour types (e.g. voice characteristics), which would allow for more accurate results. **The aim of this project is to leverage the power of machine learning models trained with data over time and using different behaviour types (speech and facial information) to develop a first-of-its-kind, reliable fatigue screening tool that is effective under conditions of limited time and intrusivity.** To this end, we will collect several minutes' worth of activities for two weeks from 1000 shift workers around their shift schedule via an online recruitment platform. Activities include short tasks that require individuals to produce speech and facial expressions, captured via participants' smart device microphone and camera (an approach we have previously successfully used and published as Fara et al., 2022). We will validate fatigue in the dataset by using a range of other tasks from the literature. Ultimately, the project will impact **shift workers and other safety-critical industries** via early fatigue detection, allowing timely interventions such as appropriate shift scheduling, rest breaks and naps. This will improve shift worker health, maximise productivity and **returns for the UK economy and indirectly relieve NHS demand**. Given that fatigue is a common mental ill-health symptom across many conditions, this project will set the stage for the **validation of our fatigue tracking tool for clinical purposes (e.g. depression)**.