Objective: To test the associations of physical and psychosocial factors with physical and mental health in individuals living with endometriosis (EM) by means of cross-sectional and longitudinal analyses.
Methods And Measures: Data were gathered via an online survey between February and August 2021. At survey date t1, sociodemographic, EM-related and psychosocial factors as well as physical and mental health of people with EM were assessed. At survey date t2 three months later, physical and mental health was reassessed. The sample consisted of = 723 (30.60 ± 6.31 years) and = 216 (30.56 ± 6.47 years) cis women with EM. Statistical analyses included bivariate and partial correlation analyses and hierarchical regression analyses.
Results: The participants' physical health was within the average range and their mental health was below-average at t1 and t2. Cross-sectional analyses revealed that worse health was associated with longer diagnostic delay, more surgeries, greater pelvic pain and lower sense of coherence, self-efficacy, sexual satisfaction and satisfaction with the gynecological treatment. In longitudinal analyses, pelvic pain and participants' satisfaction with the gynecological treatment remained significantly associated with health.
Conclusion: Treatment should address both pelvic pain and psychosocial factors to improve long-term physical and mental health in EM.
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http://dx.doi.org/10.1080/08870446.2024.2302486 | DOI Listing |
Neuromodulation
January 2025
Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.
Objectives: Biphasic sinusoidal repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation treatment that has been approved by the US Food and Drug Administration for treatment-resistant depression (TRD). Recent advances suggest that standard rTMS may be improved by altering the pulse shape; however, there is a paucity of research investigating pulse shape, owing primarily to the technologic limitations of currently available devices. This pilot study examined the feasibility, tolerability, and preliminary efficacy of biphasic and monophasic rectangular rTMS for TRD.
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View Article and Find Full Text PDFViruses
November 2024
Department of Toxicology, Drug Industry, Management and Legislation, Faculty of Pharmacy, "Victor Babeş" University of Medicine and Pharmacy, 2nd Eftimie Murgu Sq., 300041 Timişoara, Romania.
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 PDFSensors (Basel)
December 2024
Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
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 PDFSensors (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.
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