Predicting behaviour to reduce stroke risk in at-risk populations : the role of beliefs

Aims: Changing behaviour to reduce stroke risk is a difficult prospect made particularly complex because of psychological factors. This study examined predictors of intentions and behaviours to reduce stroke risk in a sample of at-risk individuals, seeking to find how knowledge and health beliefs influenced both intention and actual behaviour to reduce stroke risk. Methods: A repeated measures design was used to assess behavioural intentions at time 1 (T1) and subsequent behaviour (T2). One hundred and twenty six respondents completed an online survey at T1, and behavioural follow-up data were collected from approximately 70 participants 1 month later. Predictors were stroke knowledge, demographic variables, and beliefs about stroke that were derived from an expanded health belief model. Dependent measures were: exercise and weight loss, and intention to engage in these behaviours to reduce stroke risk. Findings: Multiple hierarchical regression analyses showed that, for exercise and weight loss respectively, different health beliefs predicted intention to control stroke risk. The most important exercise-related health beliefs were benefits, susceptibility, and self-efficacy; for weight loss, the most important beliefs were barriers, and to a lesser degree, susceptibility and subjective norm. Conclusions: Health beliefs may play an important role in stroke prevention, particularly beliefs about susceptibility because these emerged for both behaviours. Stroke education and prevention programmes that selectively target the health beliefs relevant to specific behaviours may prove most efficacious.

Elicitator : an expert elicitation tool for regression in ecology

Expert elicitation is the process of retrieving and quantifying expert knowledge in a particular domain. Such information is of particular value when the empirical data is expensive, limited, or unreliable. This paper describes a new software tool, called Elicitator, which assists in quantifying expert knowledge in a form suitable for use as a prior model in Bayesian regression. Potential environmental domains for applying this elicitation tool include habitat modeling, assessing detectability or eradication, ecological condition assessments, risk analysis, and quantifying inputs to complex models of ecological processes. The tool has been developed to be user-friendly, extensible, and facilitate consistent and repeatable elicitation of expert knowledge across these various domains. We demonstrate its application to elicitation for logistic regression in a geographically based ecological context. The underlying statistical methodology is also novel, utilizing an indirect elicitation approach to target expert knowledge on a case-by-case basis. For several elicitation sites (or cases), experts are asked simply to quantify their estimated ecological response (e.g. probability of presence), and its range of plausible values, after inspecting (habitat) covariates via GIS.

Glycaemic load is associated with insulin resistance in older Australian women

Background: Diets with a high postprandial glycemic response may contribute to long-term development of insulin resistance and diabetes, however previous epidemiological studies are conflicting on whether glycemic index (GI) or glycemic load (GL) are dietary factors associated with the progression. Our objectives were to estimate GI and GL in a group of older women, and evaluate cross-sectional associations with insulin resistance. Subjects and Methods: Subjects were 329 Australian women aged 42-81 years participating in year three of the Longitudinal Assessment of Ageing in Women (LAW). Dietary intakes were assessed by diet history interviews and analysed using a customised GI database. Insulin resistance was defined as a homeostasis model assessment (HOMA) value of >3.99, based on fasting blood glucose and insulin concentrations. Results: GL was significantly higher in the 26 subjects who were classified as insulin resistant compared to subjects who were not (134±33 versus 114±24, P<0.001). In a logistic regression model, an increment of 15 GL units increased the odds of insulin resistance by 2.09 (95%CI 1.55, 2.80, P<0.001) independently of potential confounding variables. No significant associations were found when insulin resistance was assessed as a continuous variable. Conclusions: Results of this cross-sectional study support the concept that diets with a higher GL are associated with increased risk of insulin resistance. Further studies are required to investigate whether reducing glycemic intake, by either consuming lower GI foods and/or smaller serves of carbohydrate, can contribute to a reduction in development of insulin resistance and long-term risk of type 2 diabetes.

Comparison of three expert elicitation methods for logistic regression on predicting the presence of the threatened brush-tailed rock-wallaby petrogale penicillata

Numerous expert elicitation methods have been suggested for generalised linear models (GLMs). This paper compares three relatively new approaches to eliciting expert knowledge in a form suitable for Bayesian logistic regression. These methods were trialled on two experts in order to model the habitat suitability of the threatened Australian brush-tailed rock-wallaby (Petrogale penicillata). The first elicitation approach is a geographically assisted indirect predictive method with a geographic information system (GIS) interface. The second approach is a predictive indirect method which uses an interactive graphical tool. The third method uses a questionnaire to elicit expert knowledge directly about the impact of a habitat variable on the response. Two variables (slope and aspect) are used to examine prior and posterior distributions of the three methods. The results indicate that there are some similarities and dissimilarities between the expert informed priors of the two experts formulated from the different approaches. The choice of elicitation method depends on the statistical knowledge of the expert, their mapping skills, time constraints, accessibility to experts and funding available. This trial reveals that expert knowledge can be important when modelling rare event data, such as threatened species, because experts can provide additional information that may not be represented in the dataset. However care must be taken with the way in which this information is elicited and formulated.

What are students' understandings of how digital tools contribute to learning in design disciplines?

Building Information Modelling (BIM) is evolving in the Construction Industry as a successor to CAD. CAD is mostly a technical tool that conforms to existing industry practices, however BIM has the capacity to revolutionise industry practice. Rather than producing representations of design intent, BIM produces an exact Virtual Prototype of any building that in an ideal situation is centrally stored and freely exchanged between the project team, facilitating collaboration and allowing experimentation in design. Exposing design students to this technology through their formal studies allows them to engage with cutting edge industry practices and to help shape the industry upon their graduation. Since this technology is relatively new to the construction industry, there are no accepted models for how to “teach” BIM effectively at university level. Developing learning models to enable students to make the most out of their learning with BIM presents significant challenges to those teaching in the field of design. To date there are also no studies of students experiences of using this technology. This research reports on the introduction of Building Information Modeling (BIM) software into a second year Bachelor of Design course. This software has the potential to change industry standards through its ability to revolutionise the work practices of those involved in large scale design projects. Students’ understandings and experiences of using the software in order to complete design projects as part of their assessment are reported here. In depth semi-structured interviews with 6 students revealed that students had views that ranged from novice to sophisticate about the software. They had variations in understanding of how the software could be used to complete course requirements, to assist with the design process and in the workplace. They had engaged in limited exploration of the collaborative potential of the software as a design tool. Their understanding of the significance of BIM for the workplace was also variable. The results indicate that students are beginning to develop an appreciation for how BIM could aid or constrain the work of designers, but that this appreciation is highly varied and likely to be dependent on the students’ previous experiences of working in a design studio environment. Their range of understandings of the significance of the technology is a reflection of their level of development as designers (they are “novice” designers). The results also indicate that there is a need for subjects in later years of the course that allow students to specialise in the area of digital design and to develop more sophisticated views of the role of technology in the design process. There is also a need to capitalise on the collaborative potential inherent in the software in order to realise its capability to streamline some aspects of the design process. As students become more sophisticated designers we should explore their understanding of the role of technology as a design tool in more depth in order to make recommendations for improvements to teaching and learning practice related to BIM and other digital design tools.

Infrastructure sustainability : differential axial shortening of concrete structures

High density development has been seen as a contribution to sustainable development. However, a number of engineering issues play a crucial role in the sustainable construction of high rise buildings. Non linear deformation of concrete has an adverse impact on high-rise buildings with complex geometries, due to differential axial shortening. These adverse effects are caused by time dependent behaviour resulting in volume change known as ‘shrinkage’, ‘creep’ and ‘elastic’ deformation. These three phenomena govern the behaviour and performance of all concrete elements, during and after construction. Reinforcement content, variable concrete modulus, volume to surface area ratio of the elements, environmental conditions, and construction quality and sequence influence on the performance of concrete elements and differential axial shortening will occur in all structural systems. Its detrimental effects escalate with increasing height and non vertical load paths resulting from geometric complexity. The magnitude of these effects has a significant impact on building envelopes, building services, secondary systems, and lifetime serviceability and performance. Analytical and test procedures available to quantify the magnitude of these effects are limited to a very few parameters and are not adequately rigorous to capture the complexity of true time dependent material response. With this in mind, a research project has been undertaken to develop an accurate numerical procedure to quantify the differential axial shortening of structural elements. The procedure has been successfully applied to quantify the differential axial shortening of a high rise building, and the important capabilities available in the procedure have been discussed. A new practical concept, based on the variation of vibration characteristic of structure during and after construction and used to quantify the axial shortening and assess the performance of structure, is presented.

Latent variable models in statistical genetics

Understanding the complexities that are involved in the genetics of multifactorial diseases is still a monumental task. In addition to environmental factors that can influence the risk of disease, there is also a number of other complicating factors. Genetic variants associated with age of disease onset may be different from those variants associated with overall risk of disease, and variants may be located in positions that are not consistent with the traditional protein coding genetic paradigm. Latent Variable Models are well suited for the analysis of genetic data. A latent variable is one that we do not directly observe, but which is believed to exist or is included for computational or analytic convenience in a model. This thesis presents a mixture of methodological developments utilising latent variables, and results from case studies in genetic epidemiology and comparative genomics. Epidemiological studies have identified a number of environmental risk factors for appendicitis, but the disease aetiology of this oft thought useless vestige remains largely a mystery. The effects of smoking on other gastrointestinal disorders are well documented, and in light of this, the thesis investigates the association between smoking and appendicitis through the use of latent variables. By utilising data from a large Australian twin study questionnaire as both cohort and case-control, evidence is found for the association between tobacco smoking and appendicitis. Twin and family studies have also found evidence for the role of heredity in the risk of appendicitis. Results from previous studies are extended here to estimate the heritability of age-at-onset and account for the eect of smoking. This thesis presents a novel approach for performing a genome-wide variance components linkage analysis on transformed residuals from a Cox regression. This method finds evidence for a dierent subset of genes responsible for variation in age at onset than those associated with overall risk of appendicitis. Motivated by increasing evidence of functional activity in regions of the genome once thought of as evolutionary graveyards, this thesis develops a generalisation to the Bayesian multiple changepoint model on aligned DNA sequences for more than two species. This sensitive technique is applied to evaluating the distributions of evolutionary rates, with the finding that they are much more complex than previously apparent. We show strong evidence for at least 9 well-resolved evolutionary rate classes in an alignment of four Drosophila species and at least 7 classes in an alignment of four mammals, including human. A pattern of enrichment and depletion of genic regions in the profiled segments suggests they are functionally significant, and most likely consist of various functional classes. Furthermore, a method of incorporating alignment characteristics representative of function such as GC content and type of mutation into the segmentation model is developed within this thesis. Evidence of fine-structured segmental variation is presented.

Temperature and electric field dependent mobility in poly„3-hexylthiophene diodes

Current-voltage (I-V) curves of Poly(3-hexyl-thiophene) (P3HT) diodes have been collected to investigate the polymer hole-dominated charge transport. At room temperature and at low electric fields the I-V characteristic is purely Ohmic whereas at medium-high electric fields, experimental data shows that the hole transport is Trap Dominated - Space Charge Limited Current (TD-SCLC). In this regime, it is possible to extract the I-V characteristic of the P3HT/Al junction showing the ideal Schottky diode behaviour over five orders of magnitude. At high-applied electric fields, holes’ transport is found to be in the trap free SCLC regime. We have measured and modelled in this regime the holes’ mobility to evaluate its dependence from the electric field applied and the temperature of the device.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

A two-Dimensional finite volume method for variable density flow and solute transport through saturated-unsaturated media

Extensive groundwater withdrawal has resulted in a severe seawater intrusion problem in the Gooburrum aquifers at Bundaberg, Queensland, Australia. Better management strategies can be implemented by understanding the seawater intrusion processes in those aquifers. To study the seawater intrusion process in the region, a two-dimensional density-dependent, saturated and unsaturated flow and transport computational model is used. The model consists of a coupled system of two non-linear partial differential equations. The first equation describes the flow of a variable-density fluid, and the second equation describes the transport of dissolved salt. A two-dimensional control volume finite element model is developed for simulating the seawater intrusion into the heterogeneous aquifer system at Gooburrum. The simulation results provide a realistic mechanism by which to study the convoluted transport phenomena evolving in this complex heterogeneous coastal aquifer.

Alexithymia in alcohol dependent patients is partially mediated by alcohol expectancy

Background: Up to fifty percent of alcohol dependent individuals have alexithymia, a personality trait characterised by difficulties identifying and describing feelings, a lack of imagination and an externalised cognitive style. Although studies have examined alexithymia in relation to alcohol dependence, no research exists on mechanisms underlying this relationship. The present study examined the mediational effect of alcohol expectancies on alexithymia and alcohol dependence.----- ----- Methods: 230 outpatients completed the Toronto Alexithymia Scale (TAS-20), the Drinking Expectancy Questionnaire (DEQ) and the Alcohol Use Disorder Identification Test (AUDIT). Results: Regression analysis showed that alexithymia and alcohol dependence was, in two of three cases, partially mediated through alcohol expectancy.----- ----- Conclusions: Alcohol expectancies of assertion and affective change show promise as mediators of alcohol dependence in individuals with alexithymia.

Identifying relationship between skid resistance, road characteristics and crashes using probability-based risk approach

Road accidents are of great concerns for road and transport departments around world, which cause tremendous loss and dangers for public. Reducing accident rates and crash severity are imperative goals that governments, road and transport authorities, and researchers are aimed to achieve. In Australia, road crash trauma costs the nation A$ 15 billion annually. Five people are killed, and 550 are injured every day. Each fatality costs the taxpayer A$1.7 million. Serious injury cases can cost the taxpayer many times the cost of a fatality. Crashes are in general uncontrolled events and are dependent on a number of interrelated factors such as driver behaviour, traffic conditions, travel speed, road geometry and condition, and vehicle characteristics (e.g. tyre type pressure and condition, and suspension type and condition). Skid resistance is considered one of the most important surface characteristics as it has a direct impact on traffic safety. Attempts have been made worldwide to study the relationship between skid resistance and road crashes. Most of these studies used the statistical regression and correlation methods in analysing the relationships between skid resistance and road crashes. The outcomes from these studies provided mix results and not conclusive. The objective of this paper is to present a probability-based method of an ongoing study in identifying the relationship between skid resistance and road crashes. Historical skid resistance and crash data of a road network located in the tropical east coast of Queensland were analysed using the probability-based method. Analysis methodology and results of the relationships between skid resistance, road characteristics and crashes are presented.

Strategic management practices in the construction industry : a study of Indonesian enterprises

Since the establishment of the first national strategic development plan in the early 1970s, the construction industry has played an important role in terms of the economic, social and cultural development of Indonesia. The industry’s contribution to Indonesia’s gross domestic product (GDP) increased from 3.9% in 1973 to 7.7% in 2007. Business Monitoring International (2009) forecasts that Indonesia is home to one of the fastest-growing construction industries in Asia despite the average construction growth rate being expected to remain under 10% over the period 2006 – 2010. Similarly, Howlett and Powell (2006) place Indonesia as one of the 20 largest construction markets in 2010. Although the prospects for the Indonesian construction industry are now very promising, many local construction firms still face serious difficulties, such as poor performance and low competitiveness. There are two main reasons behind this problem: the environment that they face is not favourable; the other is the lack of strategic direction to improve competitiveness and performance. Furthermore, although strategic management has now become more widely used by many large construction firms in developed countries, practical examples and empirical studies related to the Indonesian construction industry remain scarce. In addition, research endeavours related to these topics in developing countries appear to be limited. This has potentially become one of the factors hampering efforts to guide Indonesian construction enterprises. This research aims to construct a conceptual model to enable Indonesian construction enterprises to develop a sound long-term corporate strategy that generates competitive advantage and superior performance. The conceptual model seeks to address the main prescription of a dynamic capabilities framework (Teece, Pisano & Shuen, 1997; Teece, 2007) within the context of the Indonesian construction industry. It is hypothesised that in a rapidly changing and varied environment, competitive success arises from the continuous development and reconfiguration of firm’s specific assets achieving competitive advantage is not only dependent on the exploitation of specific assets/capabilities, but on the exploitation of all of the assets and capabilities combinations in the dynamic capabilities framework. Thus, the model is refined through sequential statistical regression analyses of survey results with a sample size of 120 valid responses. The results of this study provide empirical evidence in support of the notion that a competitive advantage is achieved via the implementation of a dynamic capability framework as an important way for a construction enterprise to improve its organisational performance. The characteristics of asset-capability combinations were found to be significant determinants of the competitive advantage of the Indonesian construction enterprises, and that such advantage sequentially contributes to organisational performance. If a dynamic capabilities framework can work in the context of Indonesia, it suggests that the framework has potential applicability in other emerging and developing countries. This study also demonstrates the importance of the multi-stage nature of the model which provides a rich understanding of the dynamic process by which asset-capability should be exploited in combination by the construction firms operating in varying levels of hostility. Such findings are believed to be useful to both academics and practitioners, however, as this research represents a dynamic capabilities framework at the enterprise level, future studies should continue to explore and examine the framework in other levels of strategic management in construction as well as in other countries where different cultures or similar conditions prevails.

Design of experiments for bivariate binary responses modelled by Copula functions

Optimal design for generalized linear models has primarily focused on univariate data. Often experiments are performed that have multiple dependent responses described by regression type models, and it is of interest and of value to design the experiment for all these responses. This requires a multivariate distribution underlying a pre-chosen model for the data. Here, we consider the design of experiments for bivariate binary data which are dependent. We explore Copula functions which provide a rich and flexible class of structures to derive joint distributions for bivariate binary data. We present methods for deriving optimal experimental designs for dependent bivariate binary data using Copulas, and demonstrate that, by including the dependence between responses in the design process, more efficient parameter estimates are obtained than by the usual practice of simply designing for a single variable only. Further, we investigate the robustness of designs with respect to initial parameter estimates and Copula function, and also show the performance of compound criteria within this bivariate binary setting.