Digital Health Around Clinical High Risk and First-Episode Psychosis
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CHILD AND ADOLESCENT DISORDERS (TD BENTON, SECTION EDITOR)
Digital Health Around Clinical High Risk and First-Episode Psychosis Philip Henson 1 & Hannah Wisniewski 1 & Charles Stromeyer IV 2 & John Torous 1
# Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Purpose of Review This review aims to examine relapse definitions and risk factors in psychosis as well as the role of technology in relapse predictions and risk modeling. Recent Findings There is currently no standard definition for relapse. Therefore, there is a need for data models that can account for the variety of factors involved in defining relapse. Smartphones have the ability to capture real-time, moment-to-moment assessment symptomology and behaviors via their variety of sensors and have high potential to be used to create prediction and risk modeling. Summary While there is still a need for further research on how technology can predict and model relapse, there are simple ways to begin incorporating technology for relapse prediction in clinical care. Keywords First-episode psychosis . Relapse . Technology . Smartphones
Introduction Prediction and prevention of relapse in psychosis is a global clinical priority and prominent target in recent psychiatric research. While the average rate of relapse among individuals with chronic schizophrenia varies, it may be as high as once every 2 years [1•]. Individuals with first-episode psychosis (FEP) may experience even greater relapse rates [2], and those at clinical high risk for psychosis (CHR-P) strongly benefit from early intervention to avoid conversion or relapse [3]. In addition, the financial burden of psychotic relapse is up to 5 times higher in relapsed patients than non-relapsed patients [4]. However, with over 50 definitions for relapse used in research demonstrating only 8.5% clinical relevancy [5], there is currently no consensus for defining relapse, and reported risk factors remain heterogeneous. Efforts to improve prediction by utilizing vast amounts of diverse patient data that might impact relapse risk have led to risk prediction models and scores. These risk scores are reported in This article is part of the Topical Collection on Child and Adolescent Disorders * John Torous [email protected] 1
Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA 02215, USA
2
Consumer Advisory Board, Massachusetts Mental Health Center, Boston, MA 02115, USA
different ways but can generally be represented as a single value between 0 and 100% indicating percent chance of relapse based on input data. Yet the clinical utility of these models remains limited given their moderately high sensitivity but lack of specificity, leading to a greater rate of false positives. Relapse prediction is especially critical in youth mental health. Apart from the immediate effects of personal suffering and higher costs of care, each episode of relapse during this early period is associated with poorer lifetime outcomes, worse chronicity of the illnes
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