The aim of this study was to assess changes in mental health and wellbeing measures across a 50-day physical activity workplace program. The secondary aims assessed the relationship between demographic and pre-program physical activity self-reported variables, mental health, wellbeing and program engagement measures. The study utilized a naturalistic longitudinal design with a study population of 2903 people. Participants were engaged in the 10,000 step daily physical activity program for 50-days and measures of engagement were tracked. 1320 participants provided full pre/post-program data across a range of standardized mental health and wellbeing measures alongside demographic and program engagement measures. For individuals providing pre and post program data there was a significant reduction in anxiety (18.2%, p = .008), stress (13.0%, p = .014) and sleep related impairment (6.9%, p < .001) alongside a significant improvement in overall wellbeing (6.7%, p = .001). The data further showed no significant mental health differences were identified between individuals who recorded below versus equal to or above 10,000 steps. Regression analyses indicated numerous group and personal variables impacted mental health, wellbeing and program engagement. The study highlights improvements in a range of mental health and wellbeing scores occurred over the 50-day activity program for people who complete the program. Finally, the study identified a range of protective and risk factors for mental health benefits of these programs and level of engagement. Whilst there were similarities in the pre-program mental health and wellbeing scores of those who completed and those lost to follow-up, further research is required to better characterize and understand this group.
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http://dx.doi.org/10.1007/s12144-021-02525-6 | DOI Listing |
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.
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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.
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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.
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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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