The relationship between eating behavior and psychological distress among overweight and obese people: is there a role for mindfulness?

This research based on 3 indipendent studies, sought to explore the nature of the relationship between overweight/obesity, eating behaviors and psychological distress; the construct of Mindful eating trough the validation of the Italian adaptation of the Mindful Eating Questionnaire (MEQ); the role of mindfulnessand mindful eating as respectively potential mediator and moderator between overeating behavior (binge eating and emotional overeating) and negative outcomes (psychological distress, body dissatisfaction). All the samples were divided in normal weight, overweight and obese according to BMI categories. STUDY1: In a sample of 691 subjects (69.6% female, mean aged 39.26 years) was found that BMI was not associated with psychological distress, whereas binge eating increases the psychopathological level. BMI and male gender represent negative predictors of psychological distress, but certain types of overeating (i.e., NES/grazing, overeating during or out of meals, and guilt/restraint) result as positive predictors.. STUDY 2 : A sample of 1067 subjects (61.4% female, mean aged 34 years) was analized. The Italian MEQ resulted in a 26-item 4-factor model measuring Disinhibition, Awareness, Distraction, and Emotional response. Internal consistency and test-retest reliability were acceptable MEQ correlated positively with mindfulness (FMI) and it was associated with sociodemographic variables, BMI, meditation. type of exercise and diet. STUDY 3, based on a sample of 502 subjects (68.8% female, mean aged 39.42 years) showed that MEQ and FMI negatively correlated with BES, EOQ, SCL-90-R, and BIAQ. Obese people showed lower level of mindful eating and higher levels of binge eating, emotional overeating, and body dissatisfaction, compared to the other groups Mindfulness resulted to partially mediates the relationship between a) binge eating and psychological distress, b) emotional overeating and psychological distress, c) binge eating and mental well-being, d) emotional overeating and menal well-being. Mindful eating was a moderator only in the relationship between emotional overeating and body dissatisfaction.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.