Numerous studies have demonstrated that combat-exposed military veterans are at risk for numerous psychiatric disorders and rates of comorbid mental health and substance use disorders are high. Veterans wounded in combat are a particularly high-risk group of military veterans, however treatment services are often underutilized among this group and it is unclear whether an online treatment program that targets emotional and physical distress (including mental health symptoms and substance use disorders) would be appealing to Veterans wounded in combat. The goal of the current study was to conduct formative research on whether veterans wounded in combat would be interested in an online mindfulness-based treatment to help them cope with emotional and physical discomfort. We recruited Veterans from Combat Wounded Coalition ( = 163; 74.2% non-Hispanic White; 95.7% male) to complete an online survey of mental health and substance use disorder symptoms and willingness to participate in mindfulness treatment. The majority of participants reported significant mental health symptoms and indicated that they would be willing to participate in mindfulness treatment, either at the VA (54.0%) or online (59.5%). Those with problems in multiple health domains and lower self-compassion were significantly more likely to express interest in treatment and likely to represent a very high need group of veterans. The development of a mindfulness-based treatment for this group of individuals could be very helpful in reducing mental health symptoms and improving quality of life among wounded warriors.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6532979PMC
http://dx.doi.org/10.1007/s12671-018-1047-4DOI Listing

Publication Analysis

Top Keywords

mental health
24
participate mindfulness
12
mindfulness treatment
12
veterans wounded
12
wounded combat
12
health symptoms
12
willingness participate
8
treatment
8
veterans
8
military veterans
8

Similar Publications

The current study aims to determine how the interactions between practice (distributed/focused) and mental capacity (high/low) in the cloud-computing environment (CCE) affect the development of reproductive health skills and cognitive absorption. The study employed an experimental design, and it included a categorical variable for mental capacity (low/high) and an independent variable with two types of activities (distributed/focused). The research sample consisted of 240 students from the College of Science and College of Applied Medical Sciences at the University of Hail's.

View Article and Find Full Text PDF

The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was linked to significant neurological and psychiatric manifestations. This review examines the physiopathological mechanisms underlying these neuropsychiatric outcomes and discusses current management strategies. Primarily a respiratory disease, COVID-19 frequently leads to neurological issues, including cephalalgia and migraines, loss of sensory perception, cerebrovascular accidents, and neurological impairment such as encephalopathy.

View Article and Find Full Text PDF

The field of emotion recognition from physiological signals is a growing area of research with significant implications for both mental health monitoring and human-computer interaction. This study introduces a novel approach to detecting emotional states based on fractal analysis of electrodermal activity (EDA) signals. We employed detrended fluctuation analysis (DFA), Hurst exponent estimation, and wavelet entropy calculation to extract fractal features from EDA signals obtained from the CASE dataset, which contains physiological recordings and continuous emotion annotations from 30 participants.

View Article and Find Full Text PDF

Personalized Clustering for Emotion Recognition Improvement.

Sensors (Basel)

December 2024

Instituto de Estudios de Género, Universidad Carlos III de Madrid, Calle Madrid, 126, 28903 Getafe, Spain.

Emotion recognition through artificial intelligence and smart sensing of physical and physiological signals (affective computing) is achieving very interesting results in terms of accuracy, inference times, and user-independent models. In this sense, there are applications related to the safety and well-being of people (sexual assaults, gender-based violence, children and elderly abuse, mental health, etc.) that require even more improvements.

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!