6 resultados para fitness assessments

em Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España


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[EN] BACKGROUND: To determine if there is an association between physical activity assessed by the short version of the International Physical Activity Questionnaire (IPAQ) and cardiorespiratory and muscular fitness. METHODOLOGY/PRINCIPAL FINDINGS: One hundred and eighty-two young males (age range: 20-55 years) completed the short form of the IPAQ to assess physical activity. Body composition (dual-energy X-Ray absorptiometry), muscular fitness (static and dynamic muscle force and power, vertical jump height, running speed [30 m sprint], anaerobic capacity [300 m running test]) and cardiorespiratory fitness (estimated VO(2)max: 20 m shuttle run test) were also determined in all subjects. Activity-related energy expenditure of moderate and vigorous intensity (EEPA(moderate) and EEPA(vigorous), respectively) was inversely associated with indices of adiposity (r = -0.21 to -0.37, P<0.05). Cardiorespiratory fitness (VO(2)max) was positively associated with LogEEPA(moderate) (r = 0.26, P<0.05) and LogEEPA(vigorous) (r = 0.27). However, no association between VO(2)max with LogEEPA(moderate), LogEPPA(vigorous) and LogEEPA(total) was observed after adjusting for the percentage of body fat. Multiple stepwise regression analysis to predict VO(2)max from LogEEPA(walking), LogEEPA(moderate), LogEEPA(vigorous), LogEEPA(total), age and percentage of body fat (%fat) showed that the %fat alone explained 62% of the variance in VO(2)max and that the age added another 10%, while the other variables did not add predictive value to the model [VO(2)max = 129.6-(25.1x Log %fat) - (34.0x Log age); SEE: 4.3 ml.kg(-1). min(-1); R(2) = 0.72 (P<0.05)]. No positive association between muscular fitness-related variables and physical activity was observed, even after adjusting for body fat or body fat and age. CONCLUSIONS/SIGNIFICANCE: Adiposity and age are the strongest predictors of VO(2)max in healthy men. The energy expended in moderate and vigorous physical activities is inversely associated with adiposity. Muscular fitness does not appear to be associated with physical activity as assessed by the IPAQ.

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[ES] The purpose of this study was to develop and validate the Observational System for the Proxemic Communication of the Fitness Instructor, as well as conduct a pilot application of the same. During the development and validation process of the new observation system it was employed five sequential phases. At the end of this process, it was established the validity and reliability of 5 coding dimensions and 23 categories of instructor proxemics behavior to create the final version of the observational system. This version of the observational system was applied in a pilot study conducted in a sample of 12 fitness instructors from four different group-exercise classes. The results indicated that instructor proxemics behaviour could be feasibly coded with the Observational System for the Proxemic Communication of the Fitness Instructor, having been held a comparative analysis about its intervention in the various activities, albeit with a small sample size.

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[EN]In this paper we present our concept of a virtual classroom for mathematics lectures in the study beginning phase. It describes the two main elements the course uses within our Learning Management System. The first element is the dynamic e-tests-method and the second interactive teaching videos. The tests are already in active use. Therefore, initial experience and feedback are described. The videos are in the prototype status. Therefore here only the evaluation approaches are described.

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[EN]The number of dietary exposure assessment studies focussing on children is very limited. Children are however a vulnerable group due to their higher food consumption level per kg body weight. Therefore, the EXPOCHI project aims [1] to create a relational network of individual food consumption databases in children, covering different geographical areas within Europe, and [2] to use these data to assess the usual intake of lead, chromium, selenium and food colours.