ArticleTranslational psychiatry2021
Anomaly detection to predict relapse risk in schizophrenia.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 5 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
37 citing papers in PubMed, 5 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- Digital phenotyping for mental health conditions: a systematic review of implementation and application.Frontiers in digital health · 2026Pooled it
- Accuracy of machine learning methods in predicting prognosis of patients with psychotic spectrum disorders: a systematic review.BMJ open · 2025Pooled it
- Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review.JMIR mental health · 2025Pooled it
- Psychotic relapse prediction via biomarker monitoring: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2023Pooled it
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development Study.JMIR research protocols · 2026Article
- Using Passive Sensing to Predict Psychosis Relapse: An In-Depth Qualitative Study Exploring Perspectives of People With Psychosis.Schizophrenia bulletin · 2026Article
- Using AI to Detect Psychosis Relapse: Scoping Review.JMIR mental health · 2026Article
- Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.JMIR research protocols · 2026Article
- Money and mental health: a scoping review of financial variables, data sources, and analytical methods.Frontiers in public health · 2026Article
- Digital phenotyping for assessment and prediction of interoception, chronic stress, and self-regulation in adults: a scoping review.Frontiers in digital health · 2026Review
- The Use of Artificial Intelligence for Personalized Treatment in Psychiatry.Current psychiatry reports · 2025Review
- Smartwatch-Derived Digital Phenotypes Relate to Psychopathology Dimensions in Patients With Psychotic Spectrum Disorders: Longitudinal Observational Study.JMIR mental health · 2025Observational
- Diagnostic digital phenotyping in schizophrenia-spectrum disorders: a systematic review.NPJ digital medicine · 2025Article
- Smartphone Apps and Digital Tools for Managing Schizophrenia and Psychotic Disorders: A Clinical Review.Focus (American Psychiatric Publishing) · 2025Review
- Exploring the clinical utility of rhythmic digital markers for schizophrenia.PLOS digital health · 2025Article
- Smartphone-Based Self-Monitoring in First Episode Psychosis: Mixed-Methods Study of Barriers and Facilitators to Engagement.Journal of medical Internet research · 2025Article
- mindLAMPVis as a Co-Designed Clinician-Facing Data Visualization Portal to Integrate Clinical Observations From Digital Phenotyping in Schizophrenia: User-Centered Design Process and Pilot Implementation.JMIR formative research · 2025Article
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The integration of technology in clinical care is growing rapidly and has become especially relevant during the global COVID-19 pandemic. Smartphone-based digital phenotyping, or the use of integrated sensors to identify patterns in behavior and symptomatology, has shown potential in detecting subtle moment-to-moment changes. These changes, often referred to as anomalies, represent significant deviations from an individual's baseline, may be useful in informing the risk of relapse in serious mental illness. Our investigation of smartphone-based anomaly detection resulted in 89% sensitivity and 75% specificity for predicting relapse in schizophrenia. These results demonstrate the potential of longitudinal collection of real-time behavior and symptomatology via smartphones and the clinical utility of individualized analysis. Future studies are necessary to explore how specificity can be improved, just-in-time adaptive interventions utilized, and clinical integration achieved.
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What OpenQuestion 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 OpenQuestion graph.