Distortion of self-image: risk factor for obesity in children and teenagers

Introduction: Self-image is important in the behaviour and lifestyle of children and adolescents. Analysing the self-image they have and the factors that might influence their distortion, can be used to prevent problems of obesity and anorexia. The main objective of present publication was to analyse the risk factors that may contribute to self-image distortion. Material and Methods: A descriptive survey study was conducted among 659 children and adolescents in two social classes (low and medium-high), measuring height and weight, calculating BMI percentile for age and gender. Body image and self-perception were registered. Results: The percentage of overweight-obesity is higher in scholars (41.8% boys, 28.7% girls) than in adolescents (30.1% and 22.2% respectively), with no difference between socioeconomic classes. The multinomial logistic regression analysis gives a risk of believing thinner higher (p=0.000) among boys OR=2.9(95%CI:1.43-3.37), school (p=0.000) OR=2.42(95%CI:1.56-3.76) and much lower (p=0.000) between normally nourished OR=0.08(95%CI:0.05-0.13), with no differences according to socioeconomic status. The risk of believing fatter is lower (p=0.000) between boys OR=0.28(95%CI:0.14-0.57), school(p=0.072) OR=0.54(95%CI:0.27-1.6), and much higher among underweight (p=0.000) OR=9x108(95% CI:4x108-19x108). Conclusions: Are risk factors of believing thinner: males, being in a group of schoolchildren and overweight-obesity. Conversely, are risk factors of believing fatter: females, teen and above all, be thin.

Social determinants of self-perceived discrimination in Spain

Objectives: To analyse the association between self-perceived discrimination and social determinants (social class, gender, country of origin) in Spain, and further to describe contextual factors which contribute to self-perceived discrimination. Methods: Cross-sectional design using data from the Spanish National Health Survey (2006). The dependent variable was self-perceived discrimination, and independent and stratifying variables were sociodemographic characteristics (e.g. sex, social class, country of origin, educational level). Logistic regression was used. Results: The prevalence of self-perceived discrimination was 4.2% for men and 6.3% for women. The likelihood of self-perceived discrimination was higher in people who originated from low-income countries: men, odds ratio (OR) 5.59 [95% confidence interval (CI) 4.55–6.87]; women, OR 4.06 (95% CI 3.42–4.83). Women were more likely to report self-perceived discrimination by their partner at home than men [OR 8.35 (95% CI 4.70–14.84)]. The likelihood of self-perceived discrimination when seeking work was higher among people who originated from low-income countries than their Spanish counterparts: men, OR 13.65 (95% CI 9.62–19.35); women, OR 10.64 (95% CI 8.31–13.62). In comparison with Spaniards, male white-collar workers who originated from low-income countries [OR 11.93 (95% CI 8.26–17.23)] and female blue-collar workers who originated from low-income countries (OR 1.6 (95% CI 1.08–2.39)] reported higher levels of self-perceived discrimination. Conclusions: Self-perceived discrimination is distributed unevenly in Spain and interacts with social inequalities. This particularly affects women and immigrants.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.