ArticleSchizophrenia (Heidelberg, Germany)2023
Relapse prediction in schizophrenia with smartphone digital phenotyping during COVID-19: a prospective, three-site, two-country, longitudinal study.
Article in Schizophrenia (Heidelberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06043778 (Digital Implementation Support to Achieve Uptake and Integration of Task-Shared Care for Schizophrenia in Primary Care in India), which is not on this map. Cited by 49 papers, 6 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.
Digital Implementation Support to Achieve Uptake and Integration of Task-Shared Care for Schizophrenia in Primary Care in India
Who cites it
49 citing papers in PubMed, 6 syntheses or guidelines pooled it, 83 citations in OpenAlex.
- Digital phenotyping for mental health conditions: a systematic review of implementation and application.Frontiers in digital health · 2026Pooled it
- Geolocation-derived mobility indices and the association with clinical symptoms and functioning in severe mental illness: A multivariate meta-analysis.Journal of psychopathology and clinical science · 2025Pooled 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
- Assessing the Impact of Evidence-Based Mental Health Guidance During the COVID-19 Pandemic: Systematic Review and Qualitative Evaluation.JMIR mental health · 2023Pooled it
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- When Markets Shape AI Mental Health Self-Management Tools: Consequences for Serious Mental Illness.JMIR mental health · 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
- Beyond RCTs-Better Research Designs and Methods for Psychosocial Efficacy Studies in Mental Health.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2026Review
- Feasibility of smartphone-based digital phenotyping to measure visual function and mental health outcomes in patients with inherited retinal diseases.NPJ digital medicine · 2026Article
- The Changing Concept in the History of Schizophrenia.Brain sciences · 2026Review
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.NPP - digital psychiatry and neuroscience · 2026Article
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 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
- A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations.Frontiers in psychiatry · 2026Article
- Explainable artificial intelligence (XAI) in schizophrenia: enhancing transparency in clinical decision-making.Frontiers in systems biology · 2026Review
- Visualization of Experience Sampling Method Data in Mental Health: Qualitative Study of the Physicians' Perspective in Germany.Journal of medical Internet research · 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
18 authors at 5 institutions in 2 countries.
Funding
Abstract
Smartphone technology provides us with a more convenient and less intrusive method of detecting changes in behavior and symptoms that typically precede schizophrenia relapse. To take advantage of the aforementioned, this study examines the feasibility of predicting schizophrenia relapse by identifying statistically significant anomalies in patient data gathered through mindLAMP, an open-source smartphone app. Participants, recruited in Boston, MA in the United States, and Bangalore and Bhopal in India, were invited to use mindLAMP for up to a year. The passive data (geolocation, accelerometer, and screen state), active data (surveys), and data quality metrics collected by the app were then retroactively fed into a relapse prediction model that utilizes anomaly detection. Overall, anomalies were 2.12 times more frequent in the month preceding a relapse and 2.78 times more frequent in the month preceding and following a relapse compared to intervals without relapses. The anomaly detection model incorporating passive data proved a better predictor of relapse than a naive model utilizing only survey data. These results demonstrate that relapse prediction models utilizing patient data gathered by a smartphone app can warn the clinician and patient of a potential schizophrenia relapse.
Identifiers
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.