Maria Adele Milioli

Specifically designed physical exercise programs improve children's motor abilities

Physical activity in schools is declining in many countries and inactivity in childhood has become a recognized risk factor. Data from a program of professionally guided physical exercise in primary school children were collected before and after the academic year of training. Four thousand five hundred children (6–10 years) were enrolled, and conditional and coordinative motor abilities (speed, trunk flexibility, long jumping, somersault, Harre circuit test) were measured. Anthropometric measurements were focused on body mass index (BMI), weight and height. Females never showed a significant variation of BMI, whereas males in the first and fourth grades showed significant differences. On the contrary, when considering the motor abilities studied, all the comparisons were highly significant. At the end of training, both males and females did better than at the beginning, and males were constantly faster than females. Our data, generated on a large number of children, show that professionally guided programs of physical education in the primary school lead to significant progresses in the development of conditional and coordinative abilities, without altering BMI values, thus not interfering with the balanced progression of body weight and height.

Fuzzy Composite Indicators: An Application for Measuring Customer Satisfaction

Composite indicators should ideally measure multidimensional concepts which cannot be captured by a single variable. In this chapter, we suggest a method based on fuzzy set theory for the construction of a fuzzy synthetic index of a latent phenomenon (e.g., well-being, quality of life, etc.), using a set of manifest variables measured on different scales (quantitative, ordinal and binary). A few criteria for assigning values to the membership function are discussed, as well as criteria for defining the weights of the variables. For ordinal variables, we propose a fuzzy quantification method based on the sampling cumulative function and a weighting system which takes into account the relative frequency of each category. An application regarding the results of a survey on the users of a contact center is presented.

Anthropometric outcomes associated with a primary school-based health promotion programme in the Italian city of Parma

The City Council of Parma and allied partners launched a programme based on professionally guided physical exercise and nutritional education in all primary schools of the metropolitan area, with a total of 7,000 children involved, ranging from 6 to 10 years of age. One scientific end-point of the programme was the definition of the parameter(s) most associated with increased physical activity and potential modification of eating habits. To this purpose we studied in a cohort of 2,151 9-year-old children the associations among BMI, fat mass (FM), breakfast eating and the amount of physical activity, the most relevant variables that characterize lifestyle. There was a consistent significant inverse correlation between FM and physical activity and a significant correlation between FM and breakfast skipping. When sorted by BMI, an inverse significant correlation was found between FM and physical activity in boys, except in those underweight. In girls, a significant correlation was found in those of normal weight, but not in those overweight or obese. The number of sports practised was related to FM only in overweight and obese boys. Breakfast skipping was significantly correlated with FM only in underweight girls. Taken together, our data show that FM can be used to accurately evaluate physical activity and eating habits in children, and suggest that, in preventive health programmes, the fundamental parameter to pay attention to is the amount rather than the type of physical activity.

Comparing Fuzzy and Multidimensional Methods to Evaluate Well-Being in European Regions

We suggest a new criterion based on fuzzy sets theory in order to evaluate well-being in European regions at NUTS 2 level. With reference to the various domains of this vague and multidimensional concept, a subset of 16 variables available in Eurostat database is selected. After a fuzzy transformation, the variables are aggregated into a fuzzy synthetic indicator, considering different weighting criteria. For each region the fuzzy indicator value, in the range [0, 1], may be interpreted as a membership degree to the subset of the areas with the highest well-being. The results are compared with the ones obtained by principal component analysis (PCA) and k-means cluster analysis applied to the same dataset. Furthermore, the relationships of the fuzzy indicator with GDP per capita and with human development index (HDI) are highlighted. The advantages and the drawbacks of the suggested approach are discussed.

Variable Selection In Fuzzy Clustering

The aim of the present paper is to discuss methods for selecting a subset of initially observed variables in the context of fuzzy clustering. The suggested procedure is based on the optimization of an objective function which is differently specified according to the purpose of the selection. Measure of cluster validity, a generalization of Rand index and distance between dissimilarity matrices are then proposed as proper functions to optimize.