Research has suggested that university students are at risk from certain unhealthy habits, such as poor diet or alcohol abuse. At the same time, anxiety levels appear to be higher among university students, which may lead to high levels of emotional eating. The aim of this study was to analyze the degree of adherence to the Mediterranean diet (AMD), emotional eating, alcohol intake, and anxiety among Spanish university students, and the interrelationship of these variables. A total of 252 university students filled out the Mediterranean Diet Quality Index (KIDMED) questionnaire for Mediterranean diet adherence, an Alcohol Use Disorders Identification Test, a State-Trait Anxiety Inventory and the Emotional Eater Questionnaire. We analyzed descriptive data, a t-test and analysis of variance (ANOVA) for differences, a Pearson correlation, and multiple regression tests. Results showed low levels of AMD among university students (15.5%) and considerable levels of emotional eating (29%) and anxiety (23.6%). However, levels of alcohol dependence were low (2.4%). State-anxiety was a predictor of the emotional eater score and its subscales, and sex also was predictive of subscale guilt and the total score. However, AMD was predicted only by trait-anxiety. These models accounted for between 1.9% and 19%. The results suggest the need for the implementation of educational programs to promote healthy habits among university students at risk.

Emotional eating (EE) patterns have been shown to play a relevant role in the development of overweight problems. However, there is a gap in research aimed at validating questionnaires to assess EE in specific populations. The aim of the study was to analyze factor structure and psychometric properties of Emotional Eater Questionnaire (EEQ) in Spanish universities. EEQ, state-anxiety subscale of STAI and a questionnaire about health habits were filled out by 295 students. Exploratory Factor Analysis (EFA) by using Unweight Least Squares (ULS) method was carried out. To determine factor numbers we used eigenvalues, parallel analysis, and goodness of fit statistics. Cronbach’s alpha and Spearman correlations were used to analyze reliability, convergent, and concurrent validity. The parallel analysis and goodness of fit statistics showed that unifactorial structure of seven items was the most appropriate what accounted for 57% of the variance. Internal consistency was good (α = 0.753), as well as convergent validity (r = 0.317; p < 0.001). Concurrent validity was significant for three of the five criteria (r = −0.224; p < 0.001 and r = −0.259; p < 0.001). The results suggest some differences in the structure of the psychometric assessment of EE in sub-clinical population in comparison with previous studies carried on with an overweight population, what could be relevant to obesity prevention.

Prostate cancer is the most common type of cancer among men and the one that causes the most deaths in the world. To start the diagnosis of prostate cancer, basically are used digital rectal examination (DRE) and prostate-specific antigen (PSA) levels. Currently, the biopsy is the only procedure able to confirm cancer, it has a high financial cost, and it is a very invasive procedure. In this research, a new method is suggested to aid in the screening of patients at risk of prostate cancer. The method was developed based on clinical variables (age, race, diabetes mellitus (DM), alcoholism, smoking, systemic arterial hypertension (SAH), DRE, and total PSA) obtained from the patient’s medical records. The method was tested using the algorithms of machine learning: Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Trees (DT), and Artificial Neural Networks (ANN), which predicted the samples between the presence or absence of prostate cancer. The method evaluation was made by performance metrics: accuracy, specificity, sensitivity, and AUROC (area under the receiver operating characteristic). The best performance found was through the Linear SVM model, resulting in an accuracy of 86.8%, sensitivity of 88.2%, specificity of 85.3%, and AUROC of 0.90.

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