Maternal feeding practices predict weight gain and obesogenic eating behaviours in young children : a prospective study

Background Maternal feeding practices have been proposed to play an important role in early child weight gain and obesogenic eating behariours. However, to date longitudinal investigations in young children exploring these relationships have been lacking. The aim of the present study was to explore prospective relationships between maternal feeding practices, child weight gain and obesogenic eating behaviours in 2-year-old children. The competing hypothesis that child eating behaviours predict changes in maternal feeding practices was also examined.

Methods A sample of 323 mother (mean age = 35 years, + 0.37) and child dyads (mean age = 2.03 years, + 0.37 at recruitment) were participants. Mothers completed a questionnaire assessing parental feeding practices and child eating behaviours at baseline and again one year later. Child BMI (predominantly objectively measured) was obtained at both time points.

Results Increases in child BMI z-scores over the follow-up period were predicted by maternal instrumental feeding practices. Furthermore, restriction, emotional feeding, encouragement to eat, weight-based restriction and fat restriction were associated prospectively with the development of obesogenic eating behaviours in children including emotional eating, tendency to overeat and food approach behaviours (such as enjoyment of food and good appetite). Maternal monitoring, however, predicted decreases in food approach eating behaviours. Partial support was also observed for child eating behaviours predicting maternal feeding practices.

Conclusions Maternal feeding practices play an important role in the development of weight gain and obesogenic eating behaviours in young children and are potential targets for effective prevention interventions aiming to decrease child obesity.

A simple-to-use calculator for determining the total solar heat gain of a glazing system

Architects and designers could readily use a quick and easy tool to determine the solar heat gains of their selected glazing systems for particular orientations, tilts and climate data. Speedy results under variable solar angles and degree of irradiance would be welcomed by most. Furthermore, a newly proposed program should utilise the outputs of existing glazing tools and their standard information, such as the use of U-values and Solar Heat Gain Coefficients (SHGC’s) as generated for numerous glazing configurations by the well-known program WINDOW 6.0 (LBNL, 2001). The results of this tool provide interior glass surface temperature and transmitted solar radiation which link into comfort analysis inputs required by the ASHRAE Thermal Comfort Tool –V2 (ASHRAE, 2011). This tool is a simple-to-use calculator providing the total solar heat gain of a glazing system exposed to various angles of solar incidence. Given basic climate (solar) data, as well as the orientation of the glazing under consideration the solar heat gain can be calculated. The calculation incorporates the Solar Heat Gain Coefficient function produced for the glazing system under various angles of solar incidence WINDOW 6.0 (LBNL, 2001). The significance of this work rests in providing an orientation-based heat transfer calculator through an easy-to-use tool (using Microsoft EXCEL) for user inputs of climate and Solar Heat Gain Coefficient (WINDOW-6) data. We address the factors to be considered such as solar position and the incident angles to the horizontal and the window surface, and the fact that the solar heat gain coefficient is a function of the angle of incidence. We also discuss the effect of the diffuse components of radiation from the sky and those from ground surface reflection, which require refinement of the calculation methods. The calculator is implemented in an Excel workbook allowing the user to input a dataset and immediately produce the resulting solar gain. We compare this calculated total solar heat gain with measurements from a test facility described elsewhere in this conference (Luther et.al., 2012).

Interventions designed to limit gestational weight gain: a systematic review of theory and meta-analysis of intervention components

Limiting gestational weight gain (GWG) to recommended levels is important to optimize health outcomes for mother and baby. Surprisingly, a recent review revealed that theory-based interventions to limit GWG were less effective than interventions that did not report a theory-base; however, strict criteria were used to identify theory-informed studies. We extended this review and others by systematically evaluating the theories of behaviour change informing GWG interventions using a generalized health psychology perspective, and meta-analysing behaviour change techniques reported in the interventions. Interventions designed to limit GWG were searched for using health, nursing and psychology databases. Papers reporting an underpinning theory were identified and the CALO-RE taxonomy was used to determine individual behaviour change techniques. Nineteen studies were identified for inclusion. Eight studies were informed by a behaviour change theory; six reported favourable effects on GWG. Overall, studies based on theory were as effective as non–theory-based studies at limiting GWG. Furthermore, the provision of information, motivational interviewing, behavioural self-monitoring and providing rewards contingent on successful behaviour appear to be key strategies when intervening in GWG. Combining these behaviour change techniques with dietary interventions may be most effective. Future research should focus on determining the exact combination of behaviour change techniques, or which underpinning theories, are most useful for limiting GWG.

Evaluation of feature selection algorithms for detection of depression from brain sMRI scans

Detection of depression from structural MRI (sMRI) scans is relatively new in the mental health diagnosis. Such detection requires processes including image acquisition and pre-processing, feature extraction and selection, and classification. Identification of a suitable feature selection (FS) algorithm will facilitate the enhancement of the detection accuracy by selection of important features. In the field of depression study, there are very limited works that evaluate feature selection algorithms for sMRI data. This paper investigates the performance of four algorithms for FS of volumetric attributes in sMRI scans. The algorithms are One Rule (OneR), Support Vector Machine (SVM), Information Gain (IG) and ReliefF. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. The result of the evaluation of the FS algorithms is discussed by using a number of analyses.

A new approach based on support vector machine for solving stochastic optimization

Making decision usually occurs in the state of being uncertain. These kinds of problems often expresses in a formula as optimization problems. It is desire for decision makers to find a solution for optimization problems. Typically, solving optimization problems in uncertain environment is difficult. This paper proposes a new hybrid intelligent algorithm to solve a kind of stochastic optimization i.e. dependent chance programming (DCP) model. In order to speed up the solution process, we used support vector machine regression (SVM regression) to approximate chance functions which is the probability of a sequence of uncertain event occurs based on the training data generated by the stochastic simulation. The proposed algorithm consists of three steps: (1) generate data to estimate the objective function, (2) utilize SVM regression to reveal a trend hidden in the data (3) apply genetic algorithm (GA) based on SVM regression to obtain an estimation for the chance function. Numerical example is presented to show the ability of algorithm in terms of time-consuming and precision.