Influence of Physical Exercise on Sexual Activity: the Case of Practitioners of Physical Activities and Sports in the City of Douala

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Introduction: The contribution of physical activity on sexual activity seems to be controversial. The objective of this study was to assess the influence of physical activity on the sexual activity of practitioners of physical activity and sports in the city of Douala. Material and method: This was a cross-sectional and analytical study carried out from February to May 2014. The changes in sexual habits were determined by comparing the number of intercourse and sexual performance, before and after the beginning of the sport's practice. The Chi2 test and multivariate logistic regression were used for the associations. Results: Overall, 370 subjects were interviewed. The average age was 30.1 ± 7.73ans. An increase in sexual performance was observed in all participants.The rate of increase of the refractory period was on average higher among practitioners of sport (p = 0.03). Multi-varied logistic regression introduced a strong correlation between the age group [35-45] [OR = 0. 37; CI at95% of OR= 0.37-0.87; P = 0.002], the male sex [OR = 1, 84; CI at 95% of OR = 1.02-3.33; P = 0.04] with the improvement of sexual activity. Conclusion: Physical activity and sport improve sexual activity at any age. Physical activity and sport practice presents itself as a part of the solution to sexuality issues. Because of its positive effect on the prevention of chronic disease and its influence on some determinants of health, his practice should be widely encouraged.

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Brain imaging is becoming a powerful tool in the study of human cerebral functions related to close personal relationships. Outside of subcortical structures traditionally thought to be involved in reward-related systems, a wide range of neuroimaging studies in relationship science indicate a prominent role for different cortical networks and cognitive factors. Thus, the field needs a better anatomical/network/whole-brain model to help translate scientific knowledge from lab bench to clinical models and ultimately to the patients suffering from disorders associated with love and couple relationships. The aim of the present review is to provide a review across wide range of functional magnetic resonance imaging (fMRI) studies to critically identify the cortical networks associated with passionate love, and to compare and contrast it with other types of love (such as maternal love and unconditional love for persons with intellectual disabilities). Retrospective review of pertinent neuroimaging literature. Review of published literature on fMRI studies of love illustrating brain regions associated with different forms of love. Although all fMRI studies of love point to the subcortical dopaminergic reward-related brain systems (involving dopamine and oxytocin receptors) for motivating individuals in pair-bonding, the present meta-analysis newly demonstrated that different types of love involve distinct cerebral networks, including those for higher cognitive functions such as social cognition and bodily self-representation. These metaresults provide the first stages of a global neuroanatomical model of cortical networks involved in emotions related to different aspects of love. Developing this model in future studies should be helpful for advancing clinical approaches helpful in sexual medicine and couple therapy.
How do healthcare providers know if their patients are getting enough physical activity to promote good health and to reduce their risks of chronic diseases and injury? The first step is to identify the patient's current level of physical activity using questionnaires and/or motion sensors. Questionnaires assess activity levels by having patients answer a set of questions about the types and amounts of activity performed at some time in the past. Motion sensors assess physical activity by patients wearing a small monitoring device that records their body movement as it occurs. If a provider is interested in determining a patient's caloric energy expenditure, he/she can apply statistical regression models to the questionnaire and motion sensor data to estimate kilocalories. If more precise measures of energy expenditure are desired, a provider can use the isotopic doubly labelled water method to estimate kilocalories; however, this method is costly and is impractical in non-research clinical settings.
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