Digital psychosis monitoring system trial launches
A groundbreaking smartphone app for remote digital data collection which aims to predict if an individual will relapse into psychosis is to be trialled across the UK in a 拢12.5 million study.
Led by University of 野狼社区 researchers, the system will be tested across six Higher Education Institutions and their partnering NHS Trusts in England, Wales and Scotland. The work is also being conducted in partnership with The McPin Foundation.
Called CONNECT, the project whose principle funder is the Wellcome Trust, aims to recruit up to 1100 people who experience psychosis. People will test the remote digital data collection system over 12 months.
Developed at The University of 野狼社区, the remote digital data collection system combines active and passive remote symptom, emotional, physical and contextual monitoring, along with regular clinical assessments.
The data from the project will be used to develop a relapse prediction algorithm and an adaptive sampling algorithm (for maximising engagement and information obtained from digital remote monitoring) using machine learning / AI methods.
Early Warning Signs, commonly reported to emerge in the days and weeks before a relapse, include anxiety, dysphoria, insomnia, and the beginnings of psychotic experiences.
There is an urgent need to be able to efficiently predict relapse to enable timely intervention and a personalised treatment response
However, signs are often missed or identified too late, and each patient鈥檚 Early Warning Signs are different, which up to now has made it difficult to design a system which can predict a relapse and open the door to time-sensitive, preventative treatment.
The remote digital data collection system designed by the research team sends regular prompts requesting the user complete a digital questionnaire at set times in the week, taking around 90 seconds to complete.
They system will also test whether data collected passively (via wearables and the Smartphone sensors), such as sleep disturbance, inactivity, social avoidance or sedentary behaviour, helps improve the predictive algorithm.
Machine learning methods will be used to detect complex high dimensional non-linear interactions to predict individual patient warning signs of relapse.
The Principal Investigator, Professor Sandra Bucci, said: 鈥淧sychosis is a common reason for contact with secondary care mental health services in the UK and a leading cause of disability worldwide.
Up to 80% of people who experience psychosis relapse within 5 years. Each relapse is associated with a higher risk of functional and clinical difficulties.
There is an urgent need to be able to efficiently predict relapse to enable timely intervention and a personalised treatment response.鈥�
John Ainsworth, Professor of Health Informatics at The University of 野狼社区, said: 鈥淭he system we will be developing at 野狼社区 provides real-time and in-context patient-generated symptom data, obtained through our remote digital data collection system technology鈥�.
Professor Bucci added: 鈥淭he system has the exciting potential of providing advanced warning of the need for support and intervention. It also has the potential to give mental health teams a clearer picture of the ebb and flow of an individual鈥檚 mental health trajectory.
Our remote digital data collection system could be a crucial advance in the care of people with psychosis.鈥�
Lynsey Bilsland, Head of Mental Health Translation at Wellcome, said: 鈥淭here have been huge strides recently into improving the outcomes for patients with psychosis but we still need further research into better detection of the early warning signs. Our support recognises the potential of the CONNECT system to enable early identification of patients at risk of relapse. The insight gained could be transformative to how we manage psychosis.鈥�