This study was an open-label, single-group, treatment-development project aimed at developing and testing a method for applying virtual reality exposure therapy (VRET) to active duty service members diagnosed with combat post-traumatic stress disorder (PTSD). Forty-two service members with PTSD were enrolled, and 20 participants completed treatment. The PTSD Checklist-Military version, Patient Health Questionnaire-9 for depression, and the Beck Anxiety Inventory were used as outcome measures. Of those who completed post-treatment assessment, 75% had experienced at least a 50% reduction in PTSD symptoms and no longer met DSM-IV criteria for PTSD at post treatment. Average PSTD scores decreased by 50.4%, depression scores by 46.6%, and anxiety scores by 36%. Intention-to-treat analyses showed that statistically significant improvements in PTSD, depression, and anxiety occurred over the course of treatment and were maintained at follow up. There were no adverse events associated with VRET treatment. This study provides preliminary support for the use of VRET in combat-related PTSD. Further study will be needed to determine the wider utility of the method and to determine if it offers advantages over other established PTSD treatment modalities.

Download full-text PDF

Source
http://dx.doi.org/10.7205/milmed-d-11-00221DOI Listing

Publication Analysis

Top Keywords

service members
12
virtual reality
8
reality exposure
8
exposure therapy
8
post-traumatic stress
8
stress disorder
8
active duty
8
duty service
8
ptsd
8
treatment
5

Similar Publications

Context: Student-run health clinics (SRHC) are commonly utilized to provide clinical experiences to students in healthcare education programs as well as healthcare services to a target community. Recent reports on athletic training SRHCs (AT-SRHCs) with a client population of university students, employees and/or community members have reported positive patient outcomes and high patient satisfaction, however there is limited data about the treated conditions, services and value provided by AT-SRHC.

Objective: To track utilization of athletic training services at a free AT-SRHC.

View Article and Find Full Text PDF

Objective: Babies born between 27 and 31 weeks of gestation contribute substantially towards infant mortality and morbidity. In England, their care is delivered in maternity services colocated with highly specialised neonatal intensive care units (NICU) or less specialised local neonatal units (LNU). We investigated whether birth setting offered survival and/or morbidity advantages to inform National Health Service delivery.

View Article and Find Full Text PDF

Triticum mosaic virus (TriMV; Poacevirus tritici) is the founding member of the genus Poacevirus within the family Potyviridae. TriMV is one of the components of the wheat streak mosaic disease (WSMD) complex, an economically significant wheat disease in the Great Plains region of the USA. TriMV contains a single-stranded positive-sense RNA genome of 10,266 nts with an unusually long 5'-nontranslated region of 739 nts.

View Article and Find Full Text PDF

Automated 24-sector grid-map algorithm for prostate mpMRI improves precision and efficacy of prostate lesion location reporting.

Eur J Radiol

December 2024

Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany Berlin Institute of Health, Berlin, Germany. Electronic address:

Background: The Prostate Imaging-Reporting and Data System (PI-RADS) calls for reporting the prostate index lesion and the location within the transition (TZ) or peripheral zone (PZ) and location on a corresponding sector map. The aim of this study was to train a deep learning DL-based algorithm for automatic prostate sector mapping and to validate its' performance.

Methods: An automatic 24-sector grid-map (ASG) of the prostate was developed, based on an automatic zone-specific deep learning segmentation of the prostate.

View Article and Find Full Text PDF

Does syndromic surveillance assist public health practice in early detecting respiratory epidemics? Evidence from a wide Italian retrospective experience.

J Infect Public Health

December 2024

First Division of Infectious Diseases, ASST Fatebenefratelli Sacco, Luigi Sacco Hospital, Milan, Italy; Centre for Multidisciplinary Research in Health Science (MACH), University of Milan, Italy.

Background: Large-scale diagnostic testing has been proven ineffective for prompt monitoring of the spread of COVID-19. Electronic resources may facilitate enhanced early detection of epidemics. Here, we aimed to retrospectively explore whether examining trends in the use of emergency and healthcare services and the Google search engine is useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared with the currently used swab-based surveillance system.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!