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Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data
Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PT...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116643/ https://www.ncbi.nlm.nih.gov/pubmed/35584096 http://dx.doi.org/10.1371/journal.pone.0267749 |
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author | Sadeghi, Mahnoosh McDonald, Anthony D. Sasangohar, Farzan |
author_facet | Sadeghi, Mahnoosh McDonald, Anthony D. Sasangohar, Farzan |
author_sort | Sadeghi, Mahnoosh |
collection | PubMed |
description | Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments. |
format | Online Article Text |
id | pubmed-9116643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91166432022-05-19 Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data Sadeghi, Mahnoosh McDonald, Anthony D. Sasangohar, Farzan PLoS One Research Article Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments. Public Library of Science 2022-05-18 /pmc/articles/PMC9116643/ /pubmed/35584096 http://dx.doi.org/10.1371/journal.pone.0267749 Text en © 2022 Sadeghi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Sadeghi, Mahnoosh McDonald, Anthony D. Sasangohar, Farzan Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title | Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title_full | Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title_fullStr | Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title_full_unstemmed | Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title_short | Posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
title_sort | posttraumatic stress disorder hyperarousal event detection using smartwatch physiological and activity data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116643/ https://www.ncbi.nlm.nih.gov/pubmed/35584096 http://dx.doi.org/10.1371/journal.pone.0267749 |
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