Title: Digital markers to predict psychosis relapse
Funder: Wellcome Trust

Abstract: Conventional methods of monitoring and assessing psychosis involve face-to-face assessments that rely on patients recalling their symptoms and emotional states over the period since their last assessment. Inaccurate or over-generalised recall can result in imprecise assessment and treatment planning. Furthermore, infrequent assessment can mean that early warning signs of relapse are missed, delaying identification, and increasing the risk, of relapse. Over the past decade, we have pioneered digital technology for active symptom monitoring in psychosis. ClinTouch comprises a clinically validated smartphone app that tracks symptoms daily. In CareLoop, we showed that this technology could be used for real-time monitoring and can be integrated into health-service systems. In EMPOWER, we expanded the functionality with a relapse risk prediction algorithm and a digitally-enhanced model of care and demonstrated that this is a valid model for reducing relapse, and time to relapse, in psychosis. However, our ability to predict relapse accurately is currently constrained by the limited availability of data in terms of breadth, depth and resolution. Our primary goal is to recruit a cohort of relapse-prone individuals sufficient in size (breadth), collect passively and actively sensed data (depth), with high sampling frequency (resolution). We will use a prospective observational cohort study design to derive and validate a personalised risk prediction algorithm to estimate the short-term risk of psychosis relapse. A second goal is to develop an adaptive sampling regime for maximising engagement and information obtained from digital remote monitoring; that is, asking the right questions, at the right frequency and time, for the right individuals.

Lay abstract: Conventional methods of monitoring and assessing psychosis involve face-to-face assessments that rely on patients recalling their symptoms and emotional states over the period since their last assessment. Inevitably, recall is inaccurate, resulting in imprecise assessment and treatment planning, and assessment is not frequent enough to detect early warning signs, thereby increasing the risk of relapse. Over the past decade, we have pioneered digital technology for active symptom monitoring in psychosis. ClinTouch comprises a clinically validated smartphone app that tracks symptoms daily. In CareLoop, we showed that this technology could be used for real-time monitoring and can be integrated into health-service systems. In EMPOWER, we expanded the functionality with a relapse risk prediction algorithm and a digitally-enhanced model of care and demonstrated that this is a valid model for reducing relapse, and time to relapse, in psychosis. However, our ability to predict relapse accurately is currently constrained by the limited availability of data in terms of breadth, depth and resolution. Our primary goal is to recruit a cohort of relapse-prone individuals sufficient in size (breadth), collect passively and actively sensed data (depth), with high sampling frequency (resolution). We will use a prospective observational cohort study design to derive and validate a personalised risk prediction algorithm to estimate the short-term risk of psychosis relapse. A second goal is to develop an adaptive sampling regime for maximising engagement and information obtained from digital remote monitoring; that is, asking the right questions, at the right frequency and time, for the right individuals.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.1 INDIVIDUAL CARE NEEDS;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: MENTAL HEALTH