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Developing prognostic models 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. 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 (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 working with patients with severe mental health problems (Bucci); and ii) two biostatisticians, who bring the necessary expertise in statistical modelling (Sperrin and Martin). The successful candidate will develop advanced research methods and skills in the delivery of digital health interventions, as well as interdisciplinary skills. 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 setting.

Training/techniques to be provided:
The PhD will involve training and application of a range of advanced and innovative quantitative methods and statistical analyses. This is a predominantly quantitative PhD that aims to develop statistical skills (using statistical packages such as STATA and R) in the student and apply these to important health problems that utilises cohort data and a large clinical data set. The student will also learn qualitative methods to examine the usability of the prediction models that will be developed. The student will work at the interface of health informatics, biostatistics and medical research. In this way, the project is strongly interdisciplinary (Translational Medicine biostatistics, health informatics).

Entry Requirements:
Candidates are expected to hold (or be about to obtain) a minimum upper second-class honours degree (or equivalent) in a related area / subject such as mathematics, statistics or computer science, preferably supplemented with an appropriate masters degree. Candidates with experience in applying such skills to healthcare problems or with an interest in Psychology and Mental Health are encouraged to apply.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy 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.

References

Bucci, S., Barrowclough, C., Ainsworth, J., Machin, M., Morris, R., Berry, K., Emsley, R., Lewis, S., Edge, D., Buchan, I. and Haddock, G., 2018. Actissist: Proof-of-Concept Trial of a Theory-Driven Digital Intervention for Psychosis. Schizophrenia Bulletin, p.sby032. doi: 10.1093/schbul/sby032.

Bucci, S., Ainsworth, J., Barrowclough, C., Lewis, S., Haddock, G., Berry, K., Emsley, R., Edge D. & Machin, M. (2019). A Theory-Informed Digital Health Intervention in People with Severe Mental Health Problems. Studies in Health Technology and Informatics, 264:526-530.

Eisner, E., Drake, R.J., Berry, N., Barrowclough, C., Emsley, R., Machin, M. & Bucci, S. (2019). Development, usability and long-term acceptability of ExPRESS, a smartphone app to monitor basic symptoms and early signs of psychosis relapse. JMIR MHealth and UHealth.

Martin, G.P., Mamas, M.A., Peek, N., Buchan, I., Sperrin, M. Clinical Prediction in Defined Populations: a simulation study investigating when and how to aggregate existing models. BMC Med Res Methodol. (2017) DOI: 10.1186/s12874-016-0277-1.

Su, T-L., Jaki, T., Hickey, G. L., Buchan, I., Sperrin, M. A review of statistical updating methods for clinical prediction models. Statistical Methods in Medical Research. (2016) DOI: 10.1177/0962280215626466.

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