A school-based education programme to reduce salt intake in children and their families (School-EduSalt): protocol of a cluster randomised controlled trial

The current salt intake is very high for children as well as adults in China. A reduction in salt intake is one of the most cost-effective measures to curb the rapidly growing disease burden attributed to blood pressure and cardiovascular disease in the Chinese population. A lower salt diet starting from childhood has the potential to prevent the development of such conditions. The School-EduSalt (School-based Education Programme to Reduce Salt) study aims to determine whether an education programme targeted at school children can lower salt intake in children and their families.

The 6-PACK programme to decrease falls and fall-related injuries in acute hospitals : protocol for an economic evaluation alongside a cluster randomised controlled trial

Background Falls are a common hospital occurrence complicating the care of patients. From an economic perspective, the impact of in-hospital falls and related injuries is substantial. However, few studies have examined the economic implications of falls prevention interventions in an acute care setting. The 6-PACK programme is a targeted nurse delivered falls prevention programme designed specifically for acute hospital wards. It includes a risk assessment tool and six simple strategies that nurses apply to patients classified as high-risk by the tool.
Objective To examine the incremental cost-effectiveness of the 6-PACK programme for the prevention of falls and fall-related injuries, compared with usual care practice, from an acute hospital perspective.
Methods and design The 6-PACK project is a multicentre cluster randomised controlled trial (RCT) that includes 24 acute medical and surgical wards from six hospitals in Australia to investigate the efficacy of the 6-PACK programme. This economic evaluation will be conducted alongside the 6-PACK cluster RCT. Outcome and hospitalisation cost data will be prospectively collected on approximately 16 000 patients admitted to the participating wards during the 12-month trial period. The results of the economic evaluation will be expressed as ‘cost or saving per fall prevented’ and ‘cost or saving per fall-related injury prevented’ calculated from differences in mean costs and effects in the intervention and control groups, to generate an incremental cost-effectiveness ratio (ICER).
Discussion This economic evaluation will provide an opportunity to explore the cost-effectiveness of a targeted nurse delivered falls prevention programme for reducing in-hospital falls and fall-related injuries. This protocol provides a detailed statement of a planned economic evaluation conducted alongside a cluster RCT to investigate the efficacy of the 6-PACK programme to prevent falls and fall-related injuries.

Statistical approaches to testing the relationships of the built environment with resident-level physical activity behavior and health outcomes in cross-sectional studies with cluster sampling

To achieve valid conclusions, studies exploring associations of the built environment with residents' physical activity and health-related outcomes need to employ statistical approaches accounting for clustered data. This article discusses the following main statistical approaches: analysis of covariance, regression models with robust standard errors, generalized estimating equations, and multilevel generalized linear models. The choice of a statistical method depends on the characteristics of the study and research questions. While the first three approaches are employed to account for clustering in the data, multilevel models can also help unravel more substantive issues within a social ecological theoretical framework of health behavior.

A cluster randomised controlled trial of vascular risk factor management in general practice

Letter to the editor: Harris and colleagues report failure to obtain reduction in several important risk factor-based intermediate outcomes for vascular disease from their lifestyle intervention in the Health Improvement and Prevention Study (HIPS).1 Using intention-to-treat analysis, if only 117 of 384 participants completed at least two of six group sessions, a positive result could not be expected. We know that interventions for prevention of cardiovascular disease (CVD) and diabetes can be run successfully in Australian primary care, which raises questions about the design of Harris et al’s intervention.

Staff-focused interventions to increase referrals for depression in aged care facilities: a cluster randomized controlled trial

Objective: 

Neural network ensemble : evaluation of aggregation algorithms for electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.

A new energy efficient cluster-head and backup selection scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.

Learning aggregation weights from 3-tuple comparison sets

An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs. © 2013 IEEE.