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Personal profile


Having graduated with a joint honours degree in Psychology and Philosophy from the University of Oxford, I worked as a support worker on an acute psychiatric ward. This led to an interest in clinical research, particularly in the area of relapse prevention in schizophrenia-spectrum psychosis.

In 2009, I completed a Master’s degree at the University of Manchester. I then worked as a Research Associate at the University of Manchester, initially on a large randomised controlled trial (MIDAS) and then in a short-term position within the Clinical Psychology Department. In 2011, I began a Medical Research Council funded PhD entitled Identifying relapse risk in psychosis using 'basic symptoms' and smartphone technology, supervised by Prof Christine Barrowclough, Dr Richard Drake, Prof Richard Emsley and, more recently, Prof Sandra Bucci. I completed the PhD over eight years, including two periods of maternity leave, passing my viva in November 2019.

I then worked on a proof-of-concept study (DAWN-P; Digital Assessment of Wellbeing in New Parents), investigating the feasibility, acceptability and validity of using a smartphone app to screen for parental depression in the postnatal period. In a short-term position with Greater Manchester Mental Health NHS Trust, I collaborated with colleagues from the NHS and the University of Manchester to obtain NIHR funding (Research for Patient Benefit programme; RfPB) to continue work on the DAWN-P project. 

In May 2021, I began my current postdoctoral Research Associate role in Prof Sandra Bucci's research group. As part of this group, I conduct research examining digital symptom monitoring and other digital interventions for people with severe mental health problems.

The successful DAWN-P RfBP grant started in August 2022. I am delighted to be leading this feasibility RCT, collaborating with colleagues at the University of Manchester and three local NHS Trusts (GMMH, MFT, SHKT). The study will compare digital postnatal depression screening with usual NHS care for women and birthing people. It also incorporates a partner/father sub-study and qualitative interviews with Health Care Professionals. The plain English summary and further details are available here: https://fundingawards.nihr.ac.uk/award/NIHR203191  

Research interests

Relapse prediction in schizophrenia spectrum psychosis

My PhD research examined the value of 'basic symptoms' as predictors of psychosis relapse in the context of early signs interventions for relapse prevention, and the feasibility, acceptability and validity of using a smartphone app (ExPRESS) to assess these hypothesised relapse predictors. I began by publishing a review examining the predictive validity of early signs of relapse, the effect of early signs interventions on relapse outcomes and evidence in the literature to date regarding basic symptoms (Eisner et al, 2013). Subsequently I conducted a qualitative study exploring facilitators and barriers of early signs interventions (Eisner et al, 2014) and a retrospective study describing service users’ experiences of basic symptoms and conventional early signs prior to a recent relapse of psychosis (Eisner et al, 2018). In the final PhD study I examined the feasibility, acceptability and validity of using a smartphone application (‘app’) to assess early signs, basic symptoms, psychotic symptoms and relapse on a long term basis (Eisner et al, 2019a; Eisner et al, 2019b).


Early Signs Interventions

Early signs interventions work on the premise that timely prediction of relapses will allow preventative action to be taken, minimizing the chance of full relapse occurring (Birchwood, Spencer, & McGovern, 2000). The patient is assisted in identifying and monitoring early signs of relapse, and in developing concrete action plans for dealing with them. Early signs commonly reported to emerge in the weeks before a relapse include: anxiety, dysphoria, insomnia, poor concentration and attenuated psychotic symptoms (Birchwood et al., 1989). A variety of techniques may be included in the preventative action plan, such as short term increases in medication, intensive psychological support or a combination of relapse prevention techniques.


Early signs interventions show promise but could be further developed. My review of prospective studies (Eisner et al, 2013) indicated moderate predictive validity (median sensitivity 61%, median specificity 81%) of checklists of conventional early signs such as the Early Signs Scale (Birchwood et al., 1989). This could be improved by the addition of other hypothesised predictors such as basic symptoms (Eisner et al, 2013; Gumley et al, 2014).


Basic symptoms

Basic symptoms are subtle, sub-clinical disturbances in one’s experience of oneself and the world, which are predictive of first episode psychosis (Schultze-Lutter et al, 2007; Fusar-Poli et al, 2012). Typical basic symptoms include: changes in perceptions, such as increased vividness of colour vision; mild subjective cognitive problems; impaired tolerance to certain stressors. There is preliminary evidence from my PhD work (Eisner et al, 2018; Eisner et al, 2019a) and elsewhere (Bechdolf et al, 2002; Gaebel & Riesbeck, 2014) that increases in basic symptoms also precede relapse in those with established psychosis. 


Digital symptom monitoring

Frequent early signs monitoring (at least fortnightly; ideally more often) is crucial in the context of an early signs intervention but traditional monitoring methods (face-to-face, postal or text message assessments) are burdensome for both the participant and the researcher/clinician and the quality of information collected is limited. In recent years, there has been an explosion in smartphone ownership among both the general population and those with psychosis (Firth et al, 2016). Previous studies have shown that, at least in the short-term, symptom monitoring with smartphone apps is acceptable to those with psychosis (Palmier-Claus et al., 2012) and that it is less time consuming and more acceptable than monitoring with text message based systems (Ainsworth et al., 2013). Therefore, my PhD research investigated whether early signs monitoring using a smartphone app over a 6 month period would be feasible, acceptable and valid for those with psychosis (Eisner et al, 2019a; Eisner et al, 2019b).


Perinatal mental health

The mental health of new parents around the time of the birth of their baby has been identified as a key priority in the NHS Long Term plan (NHS England, 2019). Almost 20% of mothers develop postnatal depression during the three months after giving birth (Gavin et al, 2005). For fathers, the rate of depression peaks slightly later, with 7.7% of fathers developing depression in the first three months, rising to 26% in the 3-6 months postpartum (Paulson and Bazemore, 2010). Early detection and treatment can reduce the risk of severe postnatal depression developing (Davies et al, 2003).


At present, there is no nationally implemented method for monitoring parents’ mental health, although use of the Edinburgh Postnatal Depression Scale (EPDS) has been recommended (Davies et al, 2003; NICE, 2014). This is a widely-used paper-based questionnaire, typically administered by health visitors in the postnatal period. The EPDS shows moderate validity in both mothers (Eberhard-Gran et al, 2001; Gibson et al, 2005) and fathers (Edmondson et al, 2010). We contacted health visitors in Manchester to find out how they use EPDS in practice. Currently, health visitors only use the questionnaire if they feel there is a need during visits, and the questionnaire is not kept (only the overall score). Since so many new parents develop postnatal depression, more systematic and thorough screening is needed.


DAWN-P proof-of-concept study (Digital Assessment of Wellbeing in New Parents)

We developed an app version of the EPDS (ClinTouch DAWN-P) which takes less than 2 minutes to complete on a smartphone. In the DAWN-P proof-of-concept study, we examined the feasibility, acceptability, validity and safety of using the app to monitor perinatal mental health in women and their partners. For clarity: feasibility relates to whether people will actually use an app for this purpose; acceptability concerns to whether people like using an app for this purpose; validity examines whether the app measures what it is supposed to measure, by comparing it with current gold-standard assessments; safety evaluates whether there are any adverse effects of using the app.


DAWN-P feasibility RCT (Digital Assessment of Wellbeing in New Parents)

This NIHR-funded feasibility RCT began in August 2022 and addresses specific uncertainties in preparation for a full-scale RCT, which will test whether digital screening: identifies more true positives than usual practice; leads to better mental health 6 months postpartum; is safe, acceptable and cost-effective. The full-scale RCT design will be informed by the current proposal which aims to (i) refine the DAWN-P digital screening, in collaboration with key stakeholders; (ii) evaluate feasibility/acceptability of delivering a full-scale, single-blind RCT within NHS services; (iii) optimise RCT design, parameters and procedures. The design will be a single-blind pilot RCT of digital screening vs. standard practice in a randomised sample of n=80 antenatal mothers, recruited from two maternity services, with a partner/father (n=20) sub-study. Participants randomised to the experimental arm will use DAWN-P app daily from ≥36 weeks pregnancy until 8 weeks postpartum, in addition to usual care. Participants scoring above threshold on screening (digital or conventional) will have diagnosis confirmed at interview. This sample is sufficient to assess feasibility outcomes and estimate key parameters with adequate precision to inform definitive trial sample size. Qualitative interviews will be conducted with n=30 participating mothers and with n=30 health professionals. Using Framework Analysis, relevant overarching themes will be identified to explore any acceptability and implementation issues. We have involved parents, health visitors and Greater Manchester commissioners of mental health and maternity services in the study design and the dissemination plan. We will produce a range of outputs for mixed audiences: peer-reviewed papers, conference/seminar presentations, and participant newsletters/blogs/videos published on our website and social media.

Methodological knowledge

I have authored or co-authored peer-reviewed papers which included the following methods

  • Data collection using a smartphone app
  • Questionnaire design and psychometric evaluation
  • Systematic review, narrative review
  • A randomised controlled trial of a psychological intervention
  • Multiple regression, logistic regression
  • Multilevel models (mixed effects models)
  • Generalised estimating equation (GEE) models
  • Inter-rater reliability
  • Qualitative interviews
  • Thematic analysis
  • Framework analysis
  • Directed content analysis
  • Case note audit

I am also trained in the following clinical interviews:

  • Positive and Negative Syndrome Scale (PANSS)
  • Psychotic Symptoms Rating Scales (PSYRATS)
  • Schizophrenia Proneness Instrument, Adult Version (SPI-A)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being
  • SDG 16 - Peace, Justice and Strong Institutions

Education/Academic qualification

Doctor of Philosophy, Identifying relapse risk in psychosis using ‘basic symptoms’ and smartphone technology, The University of Manchester

1 Oct 20115 Nov 2019

Award Date: 8 Nov 2019

Master of Research, Research Methods in Psychology, The University of Manchester

1 Oct 20081 Oct 2009

Award Date: 19 Dec 2009

Bachelor of Arts, Psychology and Philosophy, Oxford University

1 Oct 20031 Jun 2006

Award Date: 30 Jul 2006

External positions

Research Associate, Greater Manchester Mental Health NHS Foundation Trust

24 Aug 202030 Apr 2021

Areas of expertise

  • BF Psychology
  • HA Statistics
  • RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry

Research Beacons, Institutes and Platforms

  • Digital Futures
  • Christabel Pankhurst Institute


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