Gestational weight gain information: seeking and sources among pregnant women

BACKGROUND: Promoting healthy gestational weight gain (GWG) is important for preventing obstetric and perinatal morbidity, along with obesity in both mother and child. Provision of GWG guidelines by health professionals predicts women meeting GWG guidelines. Research concerning women's GWG information sources is limited. This study assessed pregnant women's sources of GWG information and how, where and which women seek GWG information. METHODS: Consecutive women (n = 1032) received a mailed questionnaire after their first antenatal visit to a public maternity hospital in Melbourne, Australia. Recalled provision of GWG guidelines by doctors and midwives, recalled provided GWG goals, and the obtaining of GWG information and information sources were assessed. RESULTS: Participants (n = 368; 35.7 % response) averaged 32.5 years of age and 20.8 weeks gestation, with 33.7 % speaking a language other than English. One in ten women recalled receiving GWG guidelines from doctors or midwives, of which half were consistent with Institute of Medicine guidelines. More than half the women (55.4 %) had actively sought GWG information. Nulliparous (OR 7.07, 95 % CI = 3.91-12.81) and obese (OR 1.96, 95 % CI = 1.05-3.65) women were more likely to seek information. Underweight (OR 0.29, 95 % CI = 0.09-0.97) women and those working part time (OR 0.52, 95 % CI = 0.28-0.97) were less likely to seek information. Most frequently reported GWG sources included the internet (82.7 %), books (55.4 %) and friends (51.5 %). The single most important sources were identified as the internet (32.8 %), general practitioners (16.9 %) and books (14.9 %). CONCLUSION: More than half of women were seeking GWG guidance and were more likely to consult non-clinician sources. The small numbers given GWG targets, and the dominance of non-clinical information sources, reinforces that an important opportunity to provide evidence based advice and guidance in the antenatal care setting is currently being missed.

Energetic requirements for maintenance and premigratory body mass gain of waders wintering in Africa

The energy content of the deposited reserve tissue depended on the condition of the birds, since the energy required for body mass gain was low in lean birds and high in fat birds. Maintenance metabolism was relatively low compared to wader species wintering in temperate regions, suggested to be an adaptation towards reduced endogenous heat production, which may help in avoiding heat stress under tropical conditions. -from Authors

Habitat switching by Bewick's swans: Maximization of average long-term energy gain?

1. In a system where depletion drives a habitat shift, the hypothesis was tested that animals switch habitat as soon as the average daily net energy intake (or gain) drops below that attainable in the alternative habitat.

2. The study was performed in the Lauwersmeer area. Upon arrival during the autumn migration, Bewick's swans first feed on below-ground tubers of fennel pondweed on the lake, but subsequently switched to feeding on harvest remains in sugar beet fields.

3. The daily energy intake was estimated by multiplying the average time spent foraging per day with the instantaneous energy intake rate while foraging. In the case of pondweed feeding, the latter was estimated from the functional response and the depletion of tuber biomass. In the case of beet feeding, it was estimated from dropping production rate. Gross energy intake was converted to metabolizable energy intake using the assimilation as determined in digestion trials. The daily energy expenditure was estimated by the time-energy budget method. Energetic costs were determined using heart rate.

4. The daily gain of pondweed feeding at the median date of the habitat switch (i.e. when 50% of the swans had switched) was compared with that of beet feeding. The daily gain of beet feeding was calculated for two strategies depending on the night activity on the lake: additional pondweed feeding (mixed feeding) or sleeping (pure beet feeding).

5. The majority of the swans switched when the daily gain they could achieve by staying on the pondweed bed fell just below the average daily gain of pure beet feeders. However, mixed feeders would attain an average daily gain considerably above that of pondweed feeders. A sensitivity analysis showed that this result was robust.

6. We therefore reject the hypothesis that the habitat switch by swans can be explained by simple long-term energy rate maximization. State-dependency, predation risk, and protein requirements are put forward as explanations for the delay in habitat switch.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Bias and precision of measures of survival gain from right-censored data

In cost-effectiveness analyses of drugs or health technologies, estimates of life years saved or quality-adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close-out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain - difference in median, restricted mean and extended mean survival - when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost-effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade-offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable.

The role of parity in gestational weight gain and postpartum weight retention

OBJECTIVES: Parity, excessive gestational weight gain (GWG), and postpartum weight retention (PPWR) have been identified as risk factors for maternal obesity. The aim of this study was to explore whether GWG and PPWR at 6 and 12 months after birth differed for primiparous and multiparous Australian women. METHODS: One hundred thirty-eight Australian women provided weight measures in early to mid pregnancy (M = 16.7 weeks, SD = 2.3), late pregnancy (M = 37.7 weeks, SD = 2.4), 6 months postpartum (M = 6.1 months, SD = 1.4), and 12 months postpartum (M = 12.6 months, SD = 0.7). Height, parity, and demographic information were also collected. Prepregnancy body mass index (BMI), total GWG, incidence of excessive GWG, as well as change in BMI and BMI category from prepregnancy to 6 and 12 months postpartum were computed. Differences between primiparous and multiparous women were compared using analysis of covariance (controlling for age, prepregnancy BMI, and GWG) and χ(2) test of independence. RESULTS: Seventy women (50.7%) were primiparous and 68 women (49.3%) were multiparous. Primiparous women were more likely to retain weight at 12 months postpartum than multiparous women (p = .021; Cohen's d = .24). This difference was not reflected when analyzing change in BMI categories from prepregnancy to the postpartum. CONCLUSIONS: Evidence for the role of parity in PPWR is inconclusive. Future research should consider the temporal development of PPWR in primiparous and multiparous women, leading to tailored care in the postpartum period to help women return to a healthy prepregnancy weight.

Psychosocial factors and excessive gestational weight gain: the effect of parity in an Australian cohort

OBJECTIVES: psychosocial variables can be protective or risk factors for excessive gestational weight gain (GWG). Parity has also been associated with GWG; however, its effect on psychosocial risk factors for GWG is yet to be determined. The aim of this study was to investigate if, and how, psychosocial factors vary in their impact on the GWG of primiparous and multiparous women. DESIGN/PARTICIPANTS: pregnant women were recruited in 2011 via study advertisements placed in hospitals, online, in parenting magazines, and at baby and children's markets, resulting in a sample of 256 women (113 primiparous, 143 multiparous). Participants completed questionnaires at 16-18 weeks' gestation and their pregravid BMI was recorded. Final weight before delivery was measured and used to calculate GWG. FINDINGS: the findings revealed that primiparous women had significantly higher feelings of attractiveness (a facet of body attitude; p=0.01) than multiparous women. Hierarchical regressions revealed that in the overall sample, increased GWG was associated significantly with lower pre-pregnancy BMI (standardised coefficient β=-0.39, p<0.001), higher anxiety symptoms (β=0.25, p=0.004), and reduced self-efficacy to eat a healthy diet (β=-0.20, p=0.02). Although higher GWG was predicted significantly by decreased feelings of strength and fitness for primiparous women (β=-0.25, p=0.04) and higher anxiety was related significantly to greater GWG for multiparous women (β=0.43, p<0.001), statistical comparison of the model across the two groups suggested the magnitude of these effects did not differ across groups (p>0.05). CONCLUSIONS/IMPLICATIONS FOR PRACTICE: the findings suggest that psychosocial screening and interventions by healthcare professionals may help to identify women who are at risk of excessive GWG, and there may be specific psychosocial factors that are more relevant for each parity group.

The influence on population weight gain and obesity of the macronutrient composition and energy density of the food supply

Rates of overweight and obesity have increased dramatically in all regions of the world over the last few decades. Almost all of the world's population now has ubiquitous access to low-cost, but highly-processed, energy-dense, nutrient-poor food products. These changes in the food supply, rather than decreases in physical activity, are most likely the primary driver of population weight gain and obesity. To-date, the majority of prevention efforts focus on personalised approaches targeting individuals. Population-wide food supply interventions addressing sodium and trans fat reduction have proven highly effective and comparable efforts are now required to target obesity. The evidence suggests that strategies focusing upon reducing the energy density and portion size of foods will be more effective than those targeting specific macronutrients. Government leadership, clearly specified targets, accountability and transparency will be the key to achieving the food supply changes required to address the global obesity epidemic.

A path model of psychosocial and health behaviour change predictors of excessive gestational weight gain

This study aimed to evaluate a conceptual model of psychosocial, behaviour change, and behavioural predictors of excessive gestational weight gain (GWG). Background: Excessive GWG can place women and their babies at risk of poor health outcomes, including obesity. Models of psychosocial and behaviour change predictors of excessive GWG have not been extensively explored; understanding the mechanisms leading to excess GWG will provide crucial evidence towards the development of effective interventions. Method: Two hundred and eighty-eight pregnant women (≤18 weeks gestation) were recruited to a prospective study. Demographic, psychosocial, health behaviour change, and behavioural factors were assessed at 17 (Time 1, T1) and 33 weeks (Time 2, T2) gestation. Pre-pregnancy and final pregnancy weight were obtained and women were classified with/without excessive GWG. Logistic regressions refined the list of predictors of excessive GWG; variables with p < .1 were included in a path analysis. Results: Age, family income, T2 depression, T2 pregnancy-specific coping, T1 buttocks dissatisfaction, T2 GWG-specific self-efficacy, T1 dietary readiness, T1 dietary importance, and T1 vegetable intake predicted excessive GWG in the logistic regressions and were included in the path model. The baseline path model demonstrated poor fit. Once statistically and theoretically plausible paths were added, adequate model fit was achieved (χ² = 21.61(9), p < .05; RMSEA = .07; CFI = .93); this revised model explained 19.5% of the variance in excessive GWG. Women with high T1 buttocks dissatisfaction were more likely to exhibit low levels of dietary readiness. Women with low dietary readiness were more likely to have a lower vegetable intake, which predicted excessive GWG. Women with higher T2 depressive symptoms were more likely to report lower GWG self-efficacy and gain excessively. Conclusion: Future behavioural GWG trials should consider combining psychosocial and health behaviour change factors to optimise GWG.

Stable feature selection with support vector machines

The support vector machine (SVM) is a popular method for classification, well known for finding the maximum-margin hyperplane. Combining SVM with l1-norm penalty further enables it to simultaneously perform feature selection and margin maximization within a single framework. However, l1-norm SVM shows instability in selecting features in presence of correlated features. We propose a new method to increase the stability of l1-norm SVM by encouraging similarities between feature weights based on feature correlations, which is captured via a feature covariance matrix. Our proposed method can capture both positive and negative correlations between features. We formulate the model as a convex optimization problem and propose a solution based on alternating minimization. Using both synthetic and real-world datasets, we show that our model achieves better stability and classification accuracy compared to several state-of-the-art regularized classification methods.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.

Fast one-class support vector machine for novelty detection

Novelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.

Nutritional intervention to prevent weight gain in patients commenced on olanzapine: a randomized controlled trial

Objective: Olanzapine is the most commonly prescribed atypical antipsychotic medication in Australia. Research reports an average weight gain of between 4.5 and 7 kg in the 3 months following its commencement. Trying to minimize this weight gain in a population with an already high prevalence of obesity, mortality and morbidity is of clinical and social importance. This randomized controlled trial investigated the impact of individual nutrition education provided by a dietitian on weight gain in the 3 and 6 months following the commencement of olanzapine.


Method: Fifty-one individuals (29 females, 22 males) who had started on olanzapine in the previous 3 months (mean length of 27 days ± 20) were recruited through Peninsula Health Psychiatric Services and were randomly assigned to either the intervention (n = 29) or the control group (n = 22). Individuals in the intervention group received six 1 hour nutrition education sessions over a 3-month period. Weight, waist circumference, body mass index (BMI) and qualitative measures of exercise levels, quality of life, health and body image were collected at baseline at 3 and 6 months.


Results: After 3 months, the control group had gained significantly more weight than the treatment group (6.0 kg vs 2.0 kg, p ≤ 0.002). Weight gain of more than 7% of initial weight occurred in 64% of the control group compared to 13% of the treatment group. The control group's BMI increased significantly more than the treatment group's (2 kg/m2vs 0.7 kg/m2, p ≤ 0.03). The treatment group reported significantly greater improvements in moderate exercise levels, quality of life, health and body image compared to the controls. At 6 months, the control group continued to show significantly more weight gain since baseline than the treatment group (9.9 kg vs 2.0 kg, p ≤ 0.013) and consequently had significantly greater increases in BMI (3.2 kg/m2vs 0.8 kg/m2, p ≤ 0.017).