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mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia
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 si...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202824/ https://www.ncbi.nlm.nih.gov/pubmed/34135781 http://dx.doi.org/10.3389/fpsyt.2021.642200 |
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author | Buck, Benjamin Hallgren, Kevin A. Campbell, Andrew T. Choudhury, Tanzeem Kane, John M. Ben-Zeev, Dror |
author_facet | Buck, Benjamin Hallgren, Kevin A. Campbell, Andrew T. Choudhury, Tanzeem Kane, John M. Ben-Zeev, Dror |
author_sort | Buck, Benjamin |
collection | PubMed |
description | 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 n = 20 individuals with SSDs) to identify (1) what symptoms changed in the periods gradually preceding, following, and right as relapses occur, (2) how large were these changes, and (3) on what time scale did they occur. Results demonstrated that, on average, participants reported elevations in negative mood (d = 0.34), anxiety (d =0.49), persecutory ideation (d =0.35), and hallucinations (d =0.34) on relapse days relative to their average during the study. These increases emerged gradually on average from significant and steady increases (d = 0.05 per week) in persecutory ideation and hallucinations over the 100-day period preceding relapse. This suggests that brief (i.e., 1–2 item) assessments of psychotic symptoms may detect meaningful signals that precede psychiatric relapses long before they occur. These assessments could increase opportunities for relapse prevention as remote measurement-based care management platforms develop. |
format | Online Article Text |
id | pubmed-8202824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82028242021-06-15 mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia Buck, Benjamin Hallgren, Kevin A. Campbell, Andrew T. Choudhury, Tanzeem Kane, John M. Ben-Zeev, Dror Front Psychiatry Psychiatry 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 n = 20 individuals with SSDs) to identify (1) what symptoms changed in the periods gradually preceding, following, and right as relapses occur, (2) how large were these changes, and (3) on what time scale did they occur. Results demonstrated that, on average, participants reported elevations in negative mood (d = 0.34), anxiety (d =0.49), persecutory ideation (d =0.35), and hallucinations (d =0.34) on relapse days relative to their average during the study. These increases emerged gradually on average from significant and steady increases (d = 0.05 per week) in persecutory ideation and hallucinations over the 100-day period preceding relapse. This suggests that brief (i.e., 1–2 item) assessments of psychotic symptoms may detect meaningful signals that precede psychiatric relapses long before they occur. These assessments could increase opportunities for relapse prevention as remote measurement-based care management platforms develop. Frontiers Media S.A. 2021-05-31 /pmc/articles/PMC8202824/ /pubmed/34135781 http://dx.doi.org/10.3389/fpsyt.2021.642200 Text en Copyright © 2021 Buck, Hallgren, Campbell, Choudhury, Kane and Ben-Zeev. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Buck, Benjamin Hallgren, Kevin A. Campbell, Andrew T. Choudhury, Tanzeem Kane, John M. Ben-Zeev, Dror mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title | mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title_full | mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title_fullStr | mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title_full_unstemmed | mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title_short | mHealth-Assisted Detection of Precursors to Relapse in Schizophrenia |
title_sort | mhealth-assisted detection of precursors to relapse in schizophrenia |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202824/ https://www.ncbi.nlm.nih.gov/pubmed/34135781 http://dx.doi.org/10.3389/fpsyt.2021.642200 |
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