Life-style factors associated with overweight and obesity among Spanish adults

OBJECTIVES To assess the relationship between life styles and eating habits with the overweight and obesity prevalence in a Spanish adult population. METHODS A population-based, cross-sectional study conducted on 2640 subjects older than 15 years, in Cádiz (Spain). Surveys were conducted in subjects' homes to obtain life styles, eating habits, and anthropometric data. Logistic regression has been used to study the association between the life style variables and overweight and obesity. RESULTS Prevalence of overweight and obesity in Cadiz is 37% and 17%, respectively; higher in males and increases with age. BMI has an inverse relationship with educational level (PR = 2.3, 1.57-2.38). The highest levels of obesity are associated with daily alcohol consumption (PR = 1.39, 1.29-1.50), greater consumption of television,and sedentary pursuit (PR 1.5, 1.07-1.24). A lower prevalence of obesity is observed among those with active physical activity (10.9% vs 21.6%), with differences between sex. Following a slimming diet is more frequent in the obese and in women but dedicate more hours than men to passive activities. In men is greater the consumption of alcohol, high energy foods and snacks. Overweight and obesity is associated with the male sex (OR = 3.35 2.75-4.07), high consumption of alcohol (OR = 1.38 1.03-1.86) and watching television (OR = 1.52 1.11-2.07), and foods likes bread and cereals (OR = 1.47 1.13-1.91). Exercise activities is a protective factor (OR = 0.76 0.63-0.98). CONCLUSIONS Life styles factors associated with overweight and obesity present different patterns in men and women and is necessary to understand them to identify areas for behavioural intervention in overweight and obesity patients.

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.