ArticleSchizophrenia bulletin2026
Using Passive Sensing to Predict Psychosis Relapse: An In-Depth Qualitative Study Exploring Perspectives of People With Psychosis.
Article in Schizophrenia bulletin, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Using AI to Detect Psychosis Relapse: Scoping Review.JMIR mental health · 2026Article
- Views of People With Psychosis About Algorithm-Based Relapse Prediction and Data Sharing: Qualitative Study.Journal of medical Internet research · 2026Article
- Digital Assessment of Metacognition Across the Psychosis Continuum: Measures, Validity, and Clinical Integration-A Scoping Review.Medicina (Kaunas, Lithuania) · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Artificial intelligence in digital mental health technology: Considerations for regulation.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
28 authors.
Funding
Abstract
backgroundRelapses result in negative consequences for individuals with psychosis and considerable health service costs. Digital remote monitoring (DRM) systems incorporating "passive sensing" (sensor data gathered via smartphones/wearables) may be a low-burden method for identifying relapses early, enabling prompt intervention and potentially averting the consequences of full relapse.
objectiveThis study examined detailed views from people with psychosis about using passive sensing in this context. STUDY
designQualitative interviews, analyzed using reflexive thematic analysis. Setting: Secondary care mental health services across the United Kingdom. An advisory group with relevant lived experience was involved throughout, from developing the topic guide to analysis. Participants: Clinician confirmed diagnosis of schizophrenia-spectrum psychosis (n = 58). STUDY
resultsFour overarching themes were developed. Theme 1 outlined participants' polarized feelings about passive sensing, highlighting specific challenges relating to privacy, especially regarding location data. Theme 2 examined participants' fears that clinicians might judge their movements or routines, creating a sense of pressure to modify their actions and undermining their autonomy. Theme 3 described potential solutions: offering users choice about what data are shared, when, and with whom. Theme 4 outlined specific benefits that participants valued, including intended functions of passive sensing within DRM (ease of use, early identification of relapse, and relevance of sleep monitoring) and novel uses.
conclusionsOur findings underline the importance of fully informed consent, choice, and autonomy. Given the potential privacy impacts, individuals are unlikely to engage with passive sensing unless they perceive clear personal benefits. Prospective DRM users need clear, accessible information about passive data collection and its relevant costs and benefits.
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What Socratic holds
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 Socratic graph.