ArticleJournal of medical Internet research2019
Developing a Hypothetical Implementation Framework of Expectations for Monitoring Early Signs of Psychosis Relapse Using a Mobile App: Qualitative Study.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
31 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- The value of qualitative research in randomized controlled trials involving drugs and medical devices: a systematic mapping review.International journal of technology assessment in health care · 2026Pooled it
- Barriers and Facilitators of User Engagement With Digital Mental Health Interventions for People With Psychosis or Bipolar Disorder: Systematic Review and Best-Fit Framework Synthesis.JMIR mental health · 2025Pooled it
- Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review.Journal of medical Internet research · 2021Pooled it
- "It's like having a friend in your pocket." the service user experience of the Actissist digital health intervention for early psychosis: a qualitative study.BMC psychiatry · 2025Trial
- The service user experience of SlowMo therapy: A co-produced thematic analysis of service users' subjective experience.Psychology and psychotherapy · 2022Trial
- Digital smartphone intervention to recognise and manage early warning signs in schizophrenia to prevent relapse: the EMPOWER feasibility cluster RCT.Health technology assessment (Winchester, England) · 2022Trial
- Process Evaluation of Digital Mental Health Interventions for Psychosis: Scoping Review With Framework Synthesis.JMIR human factors · 2026Article
- Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis.Schizophrenia bulletin · 2026Article
- Transdiagnostic App-Based Cognitive Bias Modification Intervention for Paranoia (Successful Treatment of Paranoia; STOP): Protocol for a Mixed Methods Process Evaluation Embedded in a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Exploration of Factors That Affect Engagement With the Experience Sampling Method and Service Users' Experience of This Within the AVATAR2 Trial: Mixed Methods Study.JMIR formative research · 2025Article
- Concepts for the Integration and Implementation of mHealth Apps for Patients With Mental Disorders: Scoping Review.Journal of medical Internet research · 2025Article
- Characteristics Associated With the Use of the Mindfulness Meditation App Headspace in a Large Public Health Deployment: Cross-Sectional Survey Study.JMIR formative research · 2025Article
- Implementation of an Online Mental Health Website for the Early Intervention in Psychosis Services, Developed for the Early Youth Engagement (EYE-2) Trial: A Cross-Sectional Survey Study of Clinical Barriers and Facilitators to Normalisation.Early intervention in psychiatry · 2025Article
- Designing smartphone-based cognitive assessments for schizophrenia: Perspectives from a multisite study.Schizophrenia research. Cognition · 2025Article
- Digital Assessment of Wellbeing in New Parents (DAWN-P): protocol of a randomised feasibility trial comparing digital screening for maternal postnatal depression with usual care screening.Pilot and feasibility studies · 2025Article
- How theories, models, and frameworks have been used to implement digital health interventions in services for people with severe mental health problems: a scoping review.BMC public health · 2025Article
- Acceptability and experience of a smartphone symptom monitoring app for people with psychosis in China (YouXin): a qualitative study.BMC psychiatry · 2024Article
- People with long-term conditions sharing personal health data via digital health technologies: A scoping review to inform design.PLOS digital health · 2023Article
- Accessibility and availability of smartphone apps for schizophrenia.Schizophrenia (Heidelberg, Germany) · 2022Article
- Automated Virtual Reality Cognitive Therapy (gameChange) in Inpatient Psychiatric Wards: Qualitative Study of Staff and Patient Views Using an Implementation Framework.JMIR formative research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundRelapse is a common experience for people diagnosed with psychosis, which is associated with increased service costs and profound personal and familial distress. EMPOWER (Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery) is a peer worker-supported digital intervention that aims to enable service users to self-monitor their mental health with the aim of encouraging self-management and the shared use of personal data to promote relapse prevention. Digital interventions have not been widely used in relapse prevention and, therefore, little is currently known about their likely implementation-both within trials and beyond.
objectiveSeeking the perspectives of all relevant stakeholder groups is recommended in developing theories about implementation because this can reveal important group differences in understandings and assumptions about whether and for whom the intervention is expected to work. However, the majority of intervention implementation research has been retrospective. This study aimed to discover and theoretically frame implementation expectations in advance of testing and synthesize these data into a framework.
methodsTo develop a hypothetical implementation framework, 149 mental health professionals, carers, and people diagnosed with psychosis participated in 25 focus groups in both Australia and the United Kingdom. An interview schedule informed by the normalization process theory was used to explore stakeholders' expectations about the implementation of the EMPOWER intervention. Data were analyzed using thematic analysis and then theoretically framed using the Medical Research Council guidelines for understanding the implementation of complex interventions.
resultsAll groups expected that EMPOWER could be successfully implemented if the intervention generated data that were meaningful to mental health staff, carers, and service users within their unique roles. However, there were key differences between staff, carers, and service users about what facilitators and barriers that stakeholders believe exist for intervention implementation in both the cluster randomized controlled trial stage and beyond. For example, service user expectations mostly clustered around subjective user experiences, whereas staff and carers spoke more about the impact upon staff interactions with service users.
conclusionsA hypothetical implementation framework synthesized from stakeholder implementation expectations provides an opportunity to compare actual implementation data gathered during an ongoing clinical trial, giving valuable insights into the accuracy of these stakeholders' previous expectations. This is among the first studies to assess and record implementation expectations for a newly developed digital intervention for psychosis in advance of testing in a clinical trial.
trial registrationISRCTN Registry ISRCTN99559262; http://www.isrctn.com/ISRCTN99559262.
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Registered trials
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.