SynthesisSchizophrenia bulletin2024
Adverse Events Reporting in Digital Interventions Evaluations for Psychosis: A Systematic Literature Search and Individual Level Content Analysis of Adverse Event Reports.
Synthesis in Schizophrenia bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled 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.
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
18 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Digital Therapeutics for People with Schizophrenia Spectrum Disorders: A Systematic Literature Review of Their Effect on Symptoms and Functioning.Schizophrenia bulletin · 2026Pooled it
- Healthcare professionals' perspectives and/or experiences of digital mental health tools in clinical practice: a systematic review and thematic synthesis.Frontiers in psychiatry · 2026Pooled it
- Methodological quality in randomised clinical trials of mental health apps: systematic review and longitudinal analysis.BMJ mental health · 2025Pooled 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
- A machine learning-based ecological momentary intervention for mental health promotion in youth: a micro-randomized trial.Translational psychiatry · 2026Trial
- "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
- Using Passive Sensing to Predict Psychosis Relapse: An In-Depth Qualitative Study Exploring Perspectives of People With Psychosis.Schizophrenia bulletin · 2026Article
- Immediate Effects and Experiences of a Digital Single-Session Behavioural Activation Based Intervention for Adolescents: A Single Arm Pre-post Programme Evaluation of Project ABC in the UK.Clinical child psychology and psychiatry · 2026Article
- Service Users' Views on Digital Remote Monitoring for Psychosis: Survey Study.JMIR human factors · 2026Article
- Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study.Journal of medical Internet research · 2026Article
- The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Mental Health Professionals' Technology Usage and Attitudes Toward Digital Health for Psychosis: Comparative Cross-Sectional Survey Study.JMIR mental health · 2025Article
- Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Healthcare professionals' views on implementing digital health tools in psychosis: a national survey in the UK.BMJ digital health & AI · 2025Article
- Development of a supportive mHealth device for persons with schizophrenia spectrum disorders (KisoLightFrontiers in psychology · 2025Article
- Systematic review and meta-analysis of adverse events in clinical trials of mental health apps.NPJ digital medicine · 2024Article
- Patients prefer easy adverse event reporting: Observational study within clinical trial.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
backgroundDigital health interventions (DHIs) have significant potential to upscale treatment access to people experiencing psychosis but raise questions around patient safety. Adverse event (AE) monitoring is used to identify, record, and manage safety issues in clinical trials, but little is known about the specific content and context contained within extant AE reports. This study aimed to assess current AE reporting in DHIs. STUDY
designA systematic literature search was conducted by the iCharts network (representing academic, clinical, and experts by experience) to identify trials of DHIs in psychosis. Authors were invited to share AE reports recorded in their trials. A content analysis was conducted on the shared reports. STUDY
resultsWe identified 593 AE reports from 18 DHI evaluations, yielding 19 codes. Only 29 AEs (4.9% of total) were preidentified by those who shared AEs as being related to the intervention or trial procedures. While overall results support the safety of DHIs, DHIs were linked to mood problems and psychosis exacerbation in a few cases. Additionally, 27% of studies did not report information on relatedness for all or at least some AEs; 9.6% of AE reports were coded as unclear because it could not be determined what had happened to participants.
conclusionsThe results support the safety of DHIs, but AEs must be routinely monitored and evaluated according to best practice. Individual-level analyses of AEs have merit to understand safety in this emerging field. Recommendations for best practice reporting in future studies are provided.
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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.