Identifying relapse risk in psychosis using 'basic symptoms' and smartphone technology

Student thesis: Phd

Abstract

Non-affective-psychosis tends to have a relapsing and remitting course. Relapses have profound adverse consequences for individuals and are costly to health services. Early signs interventions use timely prediction of relapse to prompt preventative action with the aim of averting, or lessening the impact of, relapse. The aim of this thesis was to investigate whether early signs interventions are efficacious and how they might be improved. To this end, existing studies testing early signs interventions were reviewed and four empirical studies were conducted. These studies examined the facilitators of, and barriers to, early signs interventions and tested whether adding 'basic symptoms' to conventional early signs and using smartphone technology to monitor these hypothesised predictors improves relapse prediction. Chapter 1 of the thesis outlines the importance of relapse prevention, provides background information about early signs, basic symptoms and symptom monitoring apps, and outlines the theoretical context of the thesis. Chapter 2 provides a detailed critical analysis of the methods used in the thesis and describes additional methods that were not covered in the five published PhD papers that are presented in Chapters 3 to 7. Findings from these papers are integrated with each other and with the wider literature in the discussion section of the thesis (Chapter 8), followed by an analysis of their theoretical, clinical and research implications. A systematic review of trials using early signs to target medication or psychosocial support (Chapter 3) showed that there is scope to further refine such interventions. Relapse rates were higher when targeted medication was used as an alternative to adequately dosed maintenance medication; although, not as high as when no medication was used. Targeted psychosocial interventions showed promise but there were insufficient studies of high enough quality to draw firm conclusions. To examine how early signs interventions might be improved, the thesis examined potential facilitators and barriers. Qualitative interviews with patients who had recently relapsed (Chapter 4) suggested that early signs monitoring may be easier for those who have the assistance of a family member or carer, an integrating recovery style, higher levels of insight and a good rapport with clinicians. Conversely, early signs monitoring may be more difficult for those with cognitive difficulties, low levels of literacy, a sealing over recovery style, a lack of insight, and high levels of residual psychotic symptoms. Some participants had encountered service-related barriers when attempting to seek help for an emerging relapse in the past; the capacity of a service to respond quickly to early signs is fundamental to a successful early signs intervention. With regard to relapse prediction, a systematised review of prospective cohort studies (Chapter 3) showed that conventional early signs have modest predictive validity and that studies using a wider variety of predictors and more frequent early signs assessments predicted relapse most accurately. Accordingly, the later PhD studies aimed to broaden the range of predictors (by adding basic symptoms) and to facilitate more frequent monitoring (using a smartphone app). The PhD findings supported the hypothesis that adding basic symptoms to conventional early signs improves relapse prediction. In retrospective interviews (Chapter 5), 74% of participants reported basic symptoms that began or increased in the three months before relapse. Prospective reports (Chapter 7), gathered using a smartphone app, showed that basic symptoms predicted increases in some psychotic symptoms items three weeks later, and that adding basic symptoms to early signs improved prediction in most cases. Prospective findings (Chapter 7) also indicated that using a smartphone app to assess early signs, basic symptoms and psychotic symptoms on a weekly basis was feasible and valid; qualitative intervi
Date of Award31 Dec 2019
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorRichard Drake (Supervisor), Richard Emsley (Supervisor) & Sandra Bucci (Supervisor)

Keywords

  • symptom monitoring
  • smartphone app
  • relapse prevention
  • early signs
  • early signs intervention
  • basic symptoms
  • digital mental health
  • psychosis
  • schizophrenia
  • relapse

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