Relationships between daily physical activity combinations and psychophysical health status of Italian breast cancer survivors.

In breast cancer survivors (BCS), the contemporaneous increase of sedentary time and reduction of physical activity (PA) requires early attention because it has negative consequences for their health. Aims of the study were to investigate: a) the correlations between PA, sedentarism, and health-related measures; b) the association between different patterns of daily activity and health-related outcomes. Two hundred and nineteen BCS (50.98 ± 6.28) were selected for this study. Psychological, anthropometric, endocrine, sleeping, and both daily sedentary time and PA variables were considered. Sedentarism and PA have opposite correlations with anthropometric variables, anxiety, depression, morning salivary cortisol, and sleeping characteristics. The first favors pathological values and the latter favors normal values. Regression tree analysis showed the impact of different daily sedentary time and PA combinations on the investigated variables and allowed the individualization of their optimal combination for health. Our results could be useful to healthcare providers and BCS.

Predictive functional ANOVA models for longitudinal analysis of mandibular shape changes.

In this paper, we introduce a Bayesian statistical model for the analysis of functional data observed at several time points. Examples of such data include the Michigan growth study where we wish to characterize the shape changes of human mandible profiles. The form of the mandible is often used by clinicians as an aid in predicting the mandibular growth. However, whereas many studies have demonstrated the changes in size that may occur during the period of pubertal growth spurt, shape changes have been less well investigated. Considering a group of subjects presenting normal occlusion, in this paper we thus describe a Bayesian functional ANOVA model that provides information about where and when the shape changes of the mandible occur during different stages of development. The model is developed by defining the notion of predictive process models for Gaussian process (GP) distributions used as priors over the random functional effects. We show that the predictive approach is computationally appealing and that it is useful to analyze multivariate functional data with unequally spaced observations that differ among subjects and times. Graphical posterior summaries show that our model is able to provide a biological interpretation of the morphometric findings and that they comprehensively describe the shape changes of the human mandible profiles. Compared with classical cephalometric analysis, this paper represents a significant methodological advance for the study of mandibular shape changes in two dimensions.

Simple Structure Detection Through Bayesian Exploratory Multidimensional IRT Models.

In modern validity theory, a major concern is the construct validity of a test, which is commonly assessed through confirmatory or exploratory factor analysis. In the framework of Bayesian exploratory Multidimensional Item Response Theory (MIRT) models, we discuss two methods aimed at investigating the underlying structure of a test, in order to verify if the latent model adheres to a chosen simple factorial structure. This purpose is achieved without imposing hard constraints on the discrimination parameter matrix to address the rotational indeterminacy. The first approach prescribes a 2-step procedure. The parameter estimates are obtained through an unconstrained MCMC sampler. The simple structure is, then, inspected with a post-processing step based on the Consensus Simple Target Rotation technique. In the second approach, both rotational invariance and simple structure retrieval are addressed within the MCMC sampling scheme, by introducing a sparsity-inducing prior on the discrimination parameters. Through simulation as well as real-world studies, we demonstrate that the proposed methods are able to correctly infer the underlying sparse structure and to retrieve interpretable solutions.

Quantile regression and Bayesian cluster detection to identify radon prone areas.

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.

Left ventricle relative wall thickness and plasma leptin levels: baseline relationships and effects of 4 months of walking training in healthy overweight postmenopausal women.

OBJECTIVE: Whether leptin has positive or negative influences on cardiac structure and function in healthy sedentary overweight postmenopausal women is unknown. We investigated the role of leptin in cardiac health and whether aerobic fitness enhancement influences any relationships revealed between leptin and echocardiographic measurements. METHODS: Thirty-nine sedentary postmenopausal women were enrolled after an initial screening. The women underwent blood sampling for hormone levels, anthropometric and echocardiographic measurements, dietary habits investigation, and fitness testing, both before and after 4 months of walking training. RESULTS: After the intervention, the women who had an adherence to training of 75% or higher showed significantly reduced percentage fat mass (P = 0.006) and plasma leptin levels (P < 0.001), whereas their maximum oxygen consumption increased significantly (P < 0.001). The women showed a significant reduction in left ventricle relative wall thickness (P = 0.039) and significant increases in both left ventricular end-diastolic diameter (P = 0.040) and left ventricular mass index (P = 0.043). At baseline, a negative correlation was seen between plasma leptin levels and left ventricle relative wall thickness (r = -0.553; P = 0.009). Further negative correlations were seen for the changes in left ventricle relative wall thickness with leptin levels (r = -0.456; P = 0.038) and with tricipital skinfold (r = -0.436; P = 0.05). CONCLUSIONS: In healthy overweight sedentary postmenopausal women with low fitness level, high plasma leptin levels seem to have a protective role against left ventricle relative wall thickness hypertrophy and to participate in its remodeling after 4 months of aerobic training.