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(MRC DTP) Methodology to develop prognostic models for symptom exacerbation in early psychosis through digital interventions


Project Description

Psychosis is a severe mental health problem characterised by unusual experiences such as hallucinations and persecutory beliefs; it is a major cause of distress, disability and personal and societal burden. Despite advancements in both pharmacological and psychological treatments, numerous barriers impact on the availability and uptake of these treatment approaches, with relatively modest outcomes. We have developed Actissist, a theory-informed digital psychological intervention (in the form of a Smartphone app) targeting mental health domains shown to predict relapse in early psychosis in an attempt to scale up access to psychological support.

The Actissist app has now collected longitudinal information on 200 early psychosis patients. The aim of this PhD will be to use these data to develop clinical prediction models (CPMs) that can predict : 1) app engagement and its influence on clinical outcomes; and 2) prediction of relapse in early psychosis patients. Additionally, one challenge of digital interventions is “app fatigue”, whereby the number of alerts generated lead to a reduction in engagement. Thus, the PhD will also explore developing models that can ensure alerts are only generated when absolutely necessary for an individual patient: so-called ‘interactive measurement’.

Hidden Markov Models will be used, which can represent an observed sequence of data (e.g. from Actissist) as dependent on an unobserved sequence of (latent) states, whereby patients can move between these states via state-specific probability distributions. Such models will allow us to cluster patients into (latent) subgroups, thereby generating clinically meaningful hypotheses. Moreover, the probability distributions will allow the prospective student to predict states for new (unseen) patients in the future. Such prediction could, for example, estimate the likely time that a patient might enter a clinically high-risk state (psychosis relapse), thereby enabling appropriate alert generation (interactive measurement) and early intervention, especially when combined with remote monitoring through Actissist.

The project will be supervised by: i) a clinical psychologist with extensive expertise in clinical trials and working with patients with severe mental health problems (Bucci); and ii) two biostatisticians, who bring the necessary expertise in statistical modelling, and who have a wealth of experience in working with real-world health data to develop CPMs (Sperrin and Martin).

The successful candidate will develop advanced research methods and skills in how digital health interventions are delivered in people with complex mental health needs. The successful candidate will develop interdisciplinary skills given the nature of the project. As such, this project would suit students with a strong methodological background in mathematics and (bio)statistics, with particular interest in applying such skills to a healthcare / medical setting.


Professor Sandra Bucci - https://www.research.manchester.ac.uk/portal/sandra.bucci.html
https://sites.manchester.ac.uk/actissist/
Dr Glen Martin - https://www.research.manchester.ac.uk/portal/glen.martin.html
Dr Matthew Sperrin - https://www.research.manchester.ac.uk/portal/matthew.sperrin.html

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the MRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the MRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

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