ArticleFrontiers in psychiatry2021
mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- Clinical psychopathology-based early relapse prediction model using speech and language in psychosis.Schizophrenia research. Cognition · 2026Review
- Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review.Journal of medical Internet research · 2024Article
- Patient Perspectives on AI-Driven Predictions of Schizophrenia Relapses: Understanding Concerns and Opportunities for Self-Care and Treatment.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024Article
- Digital Interventions for Relapse Prevention, Illness Self-Management, and Health Promotion In Schizophrenia: Recent Advances, Continued Challenges, and Future Opportunities.Current treatment options in psychiatry · 2023Article
- AIEgens-Doped Photonic Crystals for High Sensitivity Fluorescence Detection of Tumor Markers.Biosensors · 2023Article
- Accessibility and availability of smartphone apps for schizophrenia.Schizophrenia (Heidelberg, Germany) · 2022Article
- Development of the First Episode Digital Monitoring mHealth Intervention for People With Early Psychosis: Qualitative Interview Study With Clinicians.JMIR mental health · 2022Article
- Emotion Recognition in the Real-World: Passively Collecting and Estimating Emotions from Natural Speech Data of Individuals with Bipolar Disorder.IEEE transactions on affective computingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Theoretical views and a growing body of empirical evidence suggest that psychiatric relapses in schizophrenia-spectrum disorders (SSDs) have measurable warning signs. However, because they are time- and resource-intensive, existing assessment approaches are not well-suited to detect these warning signs in a timely, scalable fashion. Mobile technologies deploying frequent measurements-i.e., ecological momentary assessment-could be leveraged to detect increases in symptoms that may precede relapses. The present study examined EMA measurements with growth curve models in the 100 days preceding and following 27 relapses (among
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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.