Evidence map›Paper›PMID 31821154›Full record

ArticleJMIR research protocols2019

Understanding Implementation of a Digital Self-Monitoring Intervention for Relapse Prevention in Psychosis: Protocol for a Mixed Method Process Evaluation.

Stephanie Allan, Hamish Mcleod, Simon Bradstreet, Sara Beedie, Bethany Moir, John Gleeson, John Farhall, Emma Morton, Andrew Gumley

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Trial
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  3. Anosognosia in schizophrenia.CNS spectrums · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Stephanie AllanMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0002-1016-0708
Hamish McleodMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0002-4225-1815
Simon BradstreetMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0002-0339-1882
Sara BeedieMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0002-7847-2679
Bethany MoirMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0001-7824-5364
John GleesonSchool of Behavioural and Health Sciences, Australian Catholic University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-7969-492X
John FarhallDepartment of Psychology and Counselling, La Trobe University, Melbourne, Australia.ORCID https://orcid.org/0000-0003-2439-2830
Emma MortonSchool of Behavioural and Health Sciences, Australian Catholic University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-6179-1983
Andrew GumleyMental Health & Wellbeing, University of Glasgow, Glasgow, United Kingdom.ORCID https://orcid.org/0000-0002-8888-938X
University of Glasgow · GBAustralian Catholic University · AULa Trobe University · AU

Funding

Department of Health HTA/13/154/04
6 · The paper itself

Abstract

backgroundRelapse is common in people who experience psychosis and is associated with many negative consequences, both societal and personal. People who relapse often exhibit changes (early warning signs [EWS]) in the period before relapse. Successful identification of EWS offers an opportunity for relapse prevention. However, several known barriers impede the use of EWS monitoring approaches. Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) is a complex digital intervention that uses a mobile app to enhance the detection and management of self-reported changes in well-being. This is currently being tested in a pilot cluster randomized controlled trial. As digital interventions have not been widely used in relapse prevention, little is known about their implementation. Process evaluation studies run in parallel to clinical trials can provide valuable data on intervention feasibility.

objectiveThis study aims to transparently describe the protocol for the process evaluation element of the EMPOWER trial. We will focus on the development of a process evaluation framework sensitive to the worldview of service users, mental health staff, and carers; the aims of the process evaluation itself; the proposed studies to address these aims; and a plan for integration of results from separate process evaluation studies into one overall report.

methodsThe overall process evaluation will utilize mixed methods across 6 substudies. Among them, 4 will use qualitative methodologies, 1 will use a mixed methods approach, and 1 will use quantitative methodologies.

resultsThe results of all studies will be triangulated into an overall analysis and interpretation of key implementation lessons. EMPOWER was funded in 2016, recruitment finished in January 2018. Data analysis is currently under way and the first results are expected to be submitted for publication in December 2019.

conclusionsThe findings from this study will help identify implementation facilitators and barriers to EMPOWER. These insights will inform both upscaling decisions and optimization of a definitive trial.

trial registrationISRCTN Registry ISRCTN99559262; http://www.isrctn.com/ISRCTN99559262. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/15634.

Indexed as

implementation scienceschizophreniatelemedicine

Identifiers

PMID31821154
PMCPMC6930509
OpenAlexW2996334426

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.