Intervenção mediacional e hábitos alimentares: um estudo de crianças com síndrome de Down

The inadequacy about eating habits have been established as a serious problem nowadays. It is a multifactorial and difficult to handle, given their different nuances and causes. A population particularly exposed to the bad eating habits arising harm are individuals with Down syndrome, both with regard to the aspects inherent to the individual's own condition, regarding eating misfits, making it the weight control a necessary measure for a proper development. Thus, this study aimed to develop and evaluate a proposal based nutrition education from the assumptions of mediated learning, with children three to four years with Down syndrome. The participants were five children, four girls and a boy. Also included his parents and / or guardians. The data collection procedure involved the use of eight playful workshops with children and nutritional evaluation of those five meetings with parents and three home visits with each participating family. We tried to build with these children and their families a nutritional education to contribute to their daily choices of eating. Using listening, observation and questionnaire, besides playful interventions, it was observed that the first meaning of the act of eating is built in the family and reinforced by their social life. Overall, our sample characteristics seem to agree with the literature. During the intervention, the children showed attention, but little understanding of the content. With mothers, the results were different, with reflections on the inadequate power both the type of food offered, and quantity and so this offer is performed, conducted along the interventions changes in your lifestyle, such as perception of influence they had on their children in the formation of their eating habits, as well as less frequent intake of soft drinks and sweets. Nutritional interventions and mediations conducted with the mothers is that they seem to be effective strategies to combat obesity. Face of what was discussed, we see the importance of implementing intervention measures in combating and preventing overweight or obese since childhood, particularly with children with Down syndrome. One should prevent childhood obesity with educational and informative measures from birth, with family and with each child, through the primary health care and schools.

Serious Game no tratamento de obesidade infantil: uma proposta de interação natural com monitoramento remoto

The number of overweight people has increased in the last few years. Factors such as attention to diet and changes in lifestyle are crucial in the prevention and control of obesity and diseases related to it. Experts believe that such actions are most effective when initiated during childhood, and that children raised in an environment that encourages physical activity ultimately become healthier adults. However, to arouse and maintain interest in such activities represent a major challenge, which are initially perceived as repetitive and boring, and, thus, soon abandoned. Computer games, traditionally seen as stimulants to a sedentary lifestyle are changing this perception using non-conventional controls that require constant movement of the player. Applications that combine the playfulness of such games to physical activity through devices, like Microsoft Kinect, might become interesting tools in this scenario, by using the familiarity of Natural User Interfaces along with the challenge and the fun of video games, in order to make attractive exercise routines for schoolchildren. The project carried out consists of an exergame composed of several activities designed and implemented with the participation of a Physical Educator, aimed at children between eight and ten years old, whose performance and progress can be remotely monitored by a professional via web interface. The application arising from this work was accompanied by tests with a group of graduating Physical Education students from the University of Rio Verde GO, and subsequently validated through questionnaires whose results are shown on this work.

Cidades saudáveis e qualidade de vida em Uberlândia (MG): aportes metodológicos para um processo em construção

The current study includes theoretical and methodological reflections on the quality of life in the city of Uberlândia, Minas Gerais. It started from the thought that the quality of life is multifactorial and is permanently under construction and the main objective of analyzing it as one of the componets of Healthy Cities's moviment. The theoretical research focused on the concepts of healthy cities, quality of life, health, sustainability, well-being, happiness, indexes and indicators. From the use of multiple search strategies, documentary and on field of quantitative and qualitative character, this research of exploratory descriptive nature can offers a contribution to the studies on the quality of life in cities. It is proposed that the studies startes to work with some concept, like some notions os life quality adequated for some paticular reality, whose notions can approach concepts already established as health. This step is important on the exploratory researches. The studies may include aspects of objective analysis, subjective or both. The objective dimension, which is most common approach, are traditionally considered variables and indicators related to: the urban infrastructure (health, education, leisure, security, mobility), dwelling (quantitative and qualitative dwlling deficit), the urban structure (density and mix uses), socioeconomic characteristics (age, income, education), urban infrastructure (sanitation, communication), governance (social mobilization and participation). To focus on the subjective dimension, most recent and unusual, it is proposed to consider the (dis)satisfaction, the personal assessment in relation to the objective aspects. In conclusion, being intrinsically related to the health, the quality of life also has a number of determinants, and the ideal of the reach of quality of life depends on the action of all citizens based on the recognition of networks and territories, in a interescalar perspective and intersectoral. Therefore, emphasis in given on the potential of tools, such as the observatories, to monitor and intervent in reality, aiming in a building process of healthy cities.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníese por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

Classificação de atividade eletromiográfia facial de indivíduos saudáveis e com hanseníase por meio de máquina de vetores de suporte

A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.