Sports in Natural Environment, Sports in Urban Environment: An fMRI Study about Stress and Attention/Awareness.

This study aimed to explore, on one side, the differences between a group of athletes exercising outdoor (OG) and another group exercising indoor (IG) in stress and awareness, and, on the other side, between-group differences in the fMRI activations during the visualization of natural environment images versus urban images. In addition, we aimed to analyze the associations between the resulting task-related brain activations and stress and attention-awareness in each group separately. All the participants (N = 49; OG = 21, 11 females, mean age = 40, SD = 6.49; and IG = 25, 11 females, mean age = 40; 6.19) underwent an fMRI scan and completed the Perceived Stress Scale and the Mindful Attention Awareness Scale. Besides, we collected a sample of hair cortisol. Participants viewed three types of images: water nature, green nature and urban images. Two-sample t-test with corrected p=0.001 values were carried out. Further correlational analyses were performed to estimate the associations between task-related brain activations and our pyscho-emotional measures in each group. Fisher tests were used to explore for potential between-group differences in the correlational indexes. In OG, compared to IG, we found a higher activation of the middle occipital cortex and a cluster comprising the supplementary motor area (SMA), the premotor cortex and the pre-SMA while viewing green nature images versus urban images. In OG, more than in IG, the higher activation of the left SMA cluster negatively correlated with perceived stress, while in the IG, more than in OG, the higher premotor cortex activation was positively related to the total score on MAAS. No significant association was found with the hair cortisol levels. Exercising outdoor would relate to better psycho-emotional outcomes, also for athletes. On the other side, the exposition to green nature led to higher activation of brain areas related to motor planning, but also to emotion regulation and emotional response.

A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry.

Medical records generated during occupational health surveillance processes have large amounts of unexploited information that can help to reduce silica-related health risks and many occupational diseases. The methodology applied in this study consists in analyzing through machine learning techniques a database with 70,000 medical examinations from workers in the energy and construction industry in Spain. First, a general unsupervised Bayesian model is built and node force analysis is used to identify the factors with the greatest impact on the worker's health surveillance process. Second, a predictive Bayesian model is created and mutual information is employed to assess the more relevant factors affecting the medical capability of workers exposed to silica dust. The lung auscultation and the breathing exploration are the two factors that influence the most the medical capability of silica-exposed employees. Probabilistic inference shows a remarkable gender effect, where women present more resilience towards occupational diseases than men showing a higher proportion of normal results in certain key factors, such as body mass index (♀49.73%, ♂25.17%) or spirometry (♀53.73%, ♂48.91%). Finally, environmental conditions demonstrate to have a major influence on spatial variability of occupational diseases. The design of health prevention programs based on geographical variations can be crucial to the attainment of an ongoing and sustained healthier workforce with a reduction in the number of chronic workplace illnesses.