Indicators of obesity and the educational attainment of the Czech female population

The aim of this paper was to evaluate the relationship between selected indicators of obesity and the highest completed level of education in the adult female population of the Czech republic. As basic indicators of obesity, these following parameters were selected and measured via the bioimpedance method on the device InBody 720: BMI, WHR (waist-to-hip ratio), % body fat, % skeletal muscle mass, fitness score. The educational attainment of the tested women was inquired by a questionnaire. Based on the responses, we subsequently divided our sample into four categories according to the level of education: primary education, secondary education-apprenticeship, secondary education-graduate diploma, and university. The research was realized within the project CZ.1.07/2.3.00/20.0044. The measurements on the device InBody 720 and the accompanying survey were conducted between 2011-2013 in different regions of the Czech Republic. The sample consisted of 754 women divided into 3 age groups (18-39, 40-59, 60+ years). Based on the results (p<0.05), we can say that average values of the indicators obesity (BMI: 22.76, 24.88, 26.49 kg/m2, WHR: 0.85, 0.88, 0.90 and % body fat: 25.76, 30.23, 34.62%) increase with the increasing age of examined women. The average values of the fitness score (75.53, 73.86, 70.04 points) and % skeletal muscle mass (40.78, 38.15, 33.95%) decrease with increasing age. With regard to the educational attainment, women with secondary education-apprenticeship achieved the worst results. In contrast, the values in university educated women were the best in most indicators of obesity.

Indicators of success in the dietary management of overweight and obesity: weight, body fat loss and quality

Concepts: %WL: Percentage of weight loss; %FL: Percentage of fat loss. Objective: evaluate which unit of measurement for weight loss could determine the success or failure of dietary treatment for overweight and obesity. Method: 4,625 consultations carried out on 616 patients in the southeast of Spain from 2006 to 2012. All of the patients were over 25 years of age and suffered from overweight or obesity. The consultations were carried out every fortnight, using the Mediterranean or low-calorie diet. The patients were divided into four groups according to their %WL and %FL. Results: most of the sample consisted of: women; participants between 25-45 years of age; attended consultations for over a month and a half; obese. 80% of the patients obtained a %FL ≥ 5% (15.5 ± 12.8). The groups with a higher %FL obtained significant differences in weight loss (22.6 vs 11.2%, p = 0.000). The multinomial analysis shows significant differences between the groups with the highest %FL and the lowest %WL and %FL: sex (p = 0.006 vs p = 0.005), BMI (p = 0.010 vs p = 0.003) and attendance (p = 0.000 vs p = 0.000). Conclusion: the patients who lost < 5% of fat had higher initial parameters (percentage of weight and fat); most of the sample lost ≥ 5% of fat. This means that the method of personalised dietary treatment results in a high fat loss; fat is an indicator of the quality loss obtained. Recommendations: use the measurement of fat as a complementary unit of measurement to weight loss; establish a limit of 5% to evaluate such loss; and increase this type of research in any method of weight loss.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Global behavior of the Douglas–Rachford method for a nonconvex feasibility problem

In recent times the Douglas–Rachford algorithm has been observed empirically to solve a variety of nonconvex feasibility problems including those of a combinatorial nature. For many of these problems current theory is not sufficient to explain this observed success and is mainly concerned with questions of local convergence. In this paper we analyze global behavior of the method for finding a point in the intersection of a half-space and a potentially non-convex set which is assumed to satisfy a well-quasi-ordering property or a property weaker than compactness. In particular, the special case in which the second set is finite is covered by our framework and provides a prototypical setting for combinatorial optimization problems.