Predicting mothers’ decisions to introduce complementary feeding at 6 months : an investigation using an extended theory of planned behaviour

In Australia and other developed countries there is poor adherence to guidelines recommending the introduction of complementary feeding to infants at 6 months of age. We aimed to investigate, via adopting a theory of planned behaviour framework and incorporating additional normative and demographic influences, mothers’ complementary feeding intentions and behaviour. Participants were 375 primiparas who completed an initial questionnaire (infant age 13±3weeks) that assessed the theory of planned behaviour constructs of attitude, subjective norm, and perceived behavioural control, as well as group norm and additional maternal and infant variables of mothers’ age, education level, weight status perception, current maternal feeding practices, and infant birth weight. Approximately, 3 months after completion of the main questionnaire, mothers completed a follow-up questionnaire that assessed the age in months at which the infant was first introduced to solids. The theory of planned behaviour variables of attitude and subjective norm, along with group norm, predicted intentions, with intention, mothers’ age (older more likely), and weight status perception (overweight less likely) predicting behaviour. Overall, the results highlight the importance of attitudes, normative influences, and individual characteristics in complementary feeding decision-making which should be considered when designing interventions aimed at improving adherence to current maternal feeding guidelines.

The development of the extended adolescent injury checklist (E-AIC) : a measure for injury prevention program evaluation

The Extended Adolescent Injury Checklist (E-AIC), a self-report measure of injury based on the model of the Adolescent Injury Checklist (AIC), was developed for use in the evaluation of school-based interventions. The three stages of this development involved focus groups with adolescents and consultations with medical staff, pilot testing of the revised AIC in a high school context, and use of the finalised checklist in pre- and post-questionnaires to examine its utility. Results revealed that responses to the final version of the E-AIC were meaningful and remained consistent over time. The E-AIC appears to be a promising measure of adolescent injury that is simple, time-efficient and appropriate for use in the evaluation of school-based injury prevention programs.

Addressing sustainable living using a collaborative approach and multi-disciplinary techniques

In order to achieve meaningful reductions in individual ecological footprints, individuals must dramatically alter their day to day behaviours. Effective interventions will need to be evidence based and there is a necessity for the rapid transfer or communication of information from the point of research, into policy and practice. A number of health disciplines, including psychology and public health, share a common mission to promote health and well-being and it is becoming clear that the most practical pathway to achieving this mission is through interdisciplinary collaboration. This paper argues that an interdisciplinary collaborative approach will facilitate research that results in the rapid transfer of findings into policy and practice. The application of this approach is described in relation to the Green Living project which explored the psycho-social predictors of environmentally friendly behaviour. Following a qualitative pilot study, and in consultation with an expert panel comprising academics, industry professionals and government representatives, a self-administered mail survey was distributed to a random sample of 3000 residents of Brisbane and Moreton Bay (Queensland, Australia). The Green Living survey explored specific beliefs which included attitudes, norms, perceived control, intention and behaviour, as well as a number of other constructs such as environmental concern and altruism. This research has two beneficial outcomes. First, it will inform a practical model for predicting sustainable living behaviours and a number of local councils have already expressed an interest in making use of the results as part of their ongoing community engagement programs. Second, it provides an example of how a collaborative interdisciplinary project can provide a more comprehensive approach to research than can be accomplished by a single disciplinary project.

A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Understanding speeding in school zones in Malaysia and Australia using an extended Theory of Planned Behaviour : the potential role of mindfulness

Speeding in school zones is a problem in both Malaysia and Australia. While there are differences between the countries in terms of school zone treatments and more generally, these differences do not explain why people choose to speed in school zones. Because speeding is usually an intentional behaviour, the Theory of Planned Behaviour (TPB) has been used to understand speeding and develop interventions, however it has limitations which can be addressed by extending the model to incorporate other constructs. One promising construct is mindfulness, which can improve the explanatory value of the TPB by taking into account unintentional speeding attributable to a lack of focus on important elements of the driving environment. We explain what mindfulness is (and is not), how it can assist in providing a better understanding of speeding in school zones, and how it can contribute to the development of interventions. We then outline a program of research which has been commenced, investigating the contribution of mindfulness to an understanding of speed choice in school zones in two different settings (Australia and Malaysia) using the TPB.

Injuries across adolescence : an investigation using the Extended Adolescent Injury Checklist (E-AIC)

Injury is the leading cause of death among adolescents, and in many countries, accounts for more deaths than all other causes combined. Rates of death due to injury also increase dramatically across adolescence. The Australian Institute of Health and Welfare reported that, in 2005, there were 954 deaths of young Australians due to injury, which is a rate of 26 deaths per 100,000 young people. Of these deaths, 4% were adolescents aged 12-14, 17% were aged 15-17, and 80% were aged 18-24 years. Issues addressed: Injuries are the leading cause of death among adolescents. The current research examined a measure of adolescent injury in terms of whether it encompasses the diverse injury experiences of Australian adolescents, including high-risk and normative adolescents, and thus determine its utility as a tool for health promotion research. Grade 9 students from two Brisbane high schools (n=202, aged 13-14 years) and adolescents recruited from the Emergency Department waiting rooms of four Brisbane hospitals (n=98, aged 16-18 years) completed the Extended Adolescent Injury Checklist (E-AIC). The most common cause of injury among adolescents was a sports activity, followed by fights for all participants except schoolbased males, who experienced more bicycle injuries. Alcohol use was most frequently reported in association with interpersonal violence injuries. A broad variety of injuries, occurring in context of multiple risk as well as normative behaviours, were reported by adolescents in both school and ED settings, and were captured by the E-AIC. Findings suggest that the E-AIC is a useful measure that captures the injury experiences of adolescents in different contexts. The high occurrence of injuries that do not result in formal medical treatment also indicates scope for interventions to be based around lessons in first aid, while also incorporating injury prevention components.

Scaling acoustic data analysis through collaboration and automation

Monitoring and assessing environmental health is becoming increasingly important as human activity and climate change place greater pressure on global biodiversity. Acoustic sensors provide the ability to collect data passively, objectively and continuously across large areas for extended periods of time. While these factors make acoustic sensors attractive as autonomous data collectors, there are significant issues associated with large-scale data manipulation and analysis. We present our current research into techniques for analysing large volumes of acoustic data effectively and efficiently. We provide an overview of a novel online acoustic environmental workbench and discuss a number of approaches to scaling analysis of acoustic data; collaboration, manual, automatic and human-in-the loop analysis.

History-aware, real-time risk detection in business processes (Extended Version)

This paper proposes a novel approach for identifying risks in executable business processes and detecting them at run time. The approach considers risks in all phases of the business process management lifecycle, and is realized via a distributed, sensor-based architecture. At design-time, sensors are defined to specify risk conditions which when fulfilled, are a likely indicator of faults to occur. Both historical and current execution data can be used to compose such conditions. At run-time, each sensor independently notifies a sensor manager when a risk is detected. In turn, the sensor manager interacts with the monitoring component of a process automation suite to prompt the results to the user who may take remedial actions. The proposed architecture has been implemented in the YAWL system and its performance has been evaluated in practice.