A randomized controlled trial of a multiple health behavior change intervention delivered to colorectal cancer survivors: Effects on sedentary behavior

BACKGROUND Sedentary behavior may independently contribute to morbidity and mortality among survivors of colorectal cancer. In the current study, the authors assessed whether a telephone-delivered multiple health behavior change intervention had an effect on the sedentary behavior of recently diagnosed colorectal cancer survivors. METHODS A total of 410 participants were recruited through the Queensland Cancer Registry and randomized to the health coaching (intervention) or usual-care (control) group. Eleven health coaching sessions addressing multiple health behaviors, including sedentary behavior, were delivered over a period of 6 months. Data were collected at baseline (before randomization), at 6 months, and at 12 months via a telephone interview. RESULTS At 12 months, there was a significant decrease noted in the hours per day of sedentary time in both the health coaching (−1.21; 95% confidence interval [95% CI], −1.71 to −0.70) and usual-care groups (−0.55; 95% CI, −1.06 to −0.05), but the between-group difference was not found to be statistically significant (−0.65; 95% CI, −1.37 to 0.06 [P = .07]). In stratified subgroup analyses, the multiple health behavior change intervention was found to have a significant effect on total sedentary time (hours/day) at 12 months in survivors of colorectal cancer who were aged > 60 years (−0.90; 95% CI, −1.80 to −0.01 [P = .05]), male (−1.33; 95% CI, −2.44 to −0.21 [P = .02]), and nonobese (−1.10; 95% CI, −1.96 to −0.25; [P = .01]). CONCLUSIONS Incorporating simple messages about limiting sedentary behaviors into a multiple health behavior change intervention was found to have modest effects on sedentary behavior. A sedentary behavior-specific intervention strategy may be required to achieve substantial changes in sedentary behavior among colorectal cancer survivors

Individual difference predictors of change in career adaptability over time

Career adaptability is a psychosocial construct that reflects individuals' resources for managing career tasks and challenges. This study investigated the effects of demographic characteristics and three sets of individual difference variables (Big Five personality traits, core self-evaluations, and temporal focus) on changes over time in career adaptability and its dimensions (concern, control, curiosity, and confidence). Data came from 659 full-time employees in Australia who participated in two measurement waves six months apart. Results showed that age and future temporal focus predicted change in overall career adaptability. In addition, age, education, extraversion, neuroticism, openness to experience, core self-evaluations, and future temporal focus differentially predicted change over time in one or more of the four career adaptability dimensions. While the lagged effects found in this study were generally small, the findings suggest that certain individual difference characteristics predispose employees to experience change in career adaptability over time.

The impact of change on careers in the 21st century : what do individuals need to do

There is a growing literature (Arthur, Inkson, & Pringle, 1999; Collin & Young, 2000; Hall & Associates, 1996; Peiperl, Arthur,& Anand, 2002) about the changing workplace and the consequent changes to our understanding of the place of career in individuals’ lives (Richardson, 1996, 2000) - “Careers are becoming more varied and more difficult to manage for both individuals and organisations” (Arnold et al, 2005, p. 523). This chapter will present the background to the changes in the world of work and the changes which inevitably impact individuals’ careers. It will then focus on the close relationship between career development and lifelong learning and the importance of ongoing professional learning for individuals to maintain employability in a changing work world.

Building information modelling : an issue of adoption and change management

Building Information Modelling (BIM) is an IT enabled technology that allows storage, management, sharing, access, update and use of all the data relevant to a project through out the project life-cycle in the form of a data repository. BIM enables improved inter-disciplinary collaboration across distributed teams, intelligent documentation and information retrieval, greater consistency in building data, better conflict detection and enhanced facilities management. While the technology itself may not be new, and similar approaches have been in use in some other sectors like Aircraft and Automobile industry for well over a decade now, the AEC/FM (Architecture, Engineering and Construction/ Facilities Management) industry is still to catch up with them in its ability to exploit the benefits of the IT revolution. Though the potential benefits of the technology in terms of knowledge sharing, project management, project co-ordination and collaboration are near to obvious, the adoption rate has been rather lethargic, inspite of some well directed efforts and availability of supporting commercial tools. Since the technology itself has been well tested over the years in some other domains the plausible causes must be rooted well beyond the explanation of the ‘Bell Curve of innovation adoption’. This paper discusses the preliminary findings of an ongoing research project funded by the Cooperative Research Centre for Construction Innovation (CRC-CI) which aims to identify these gaps and come up with specifications and guidelines to enable greater adoption of the BIM approach in practice. A detailed literature review is conducted that looks at some of the similar research reported in the recent years. A desktop audit of some of the existing commercial tools that support BIM application has been conducted to identify the technological issues and concerns, and a workshop was organized with industry partners and various players in the AEC industry for needs analysis, expectations and feedback on the possible deterrents and inhibitions surrounding the BIM adoption.

Difference-based meta-analytic procedures for between-participant and/or within-participant designs : a tutorial review for sports and exercise scientists

The aim of this paper is to provide a contemporary summary of statistical and non-statistical meta-analytic procedures that have relevance to the type of experimental designs often used by sport scientists when examining differences/change in dependent measure(s) as a result of one or more independent manipulation(s). Using worked examples from studies on observational learning in the motor behaviour literature, we adopt a random effects model and give a detailed explanation of the statistical procedures for the three types of raw score difference-based analyses applicable to between-participant, within-participant, and mixed-participant designs. Major merits and concerns associated with these quantitative procedures are identified and agreed methods are reported for minimizing biased outcomes, such as those for dealing with multiple dependent measures from single studies, design variation across studies, different metrics (i.e. raw scores and difference scores), and variations in sample size. To complement the worked examples, we summarize the general considerations required when conducting and reporting a meta-analysis, including how to deal with publication bias, what information to present regarding the primary studies, and approaches for dealing with outliers. By bringing together these statistical and non-statistical meta-analytic procedures, we provide the tools required to clarify understanding of key concepts and principles.

Characterising anomalous events using change point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points.

Early detection : a strategy to reduce risk and severity?

Despite changes in surgical techniques, radiotherapy targeting and the apparent earlier detection of cancers, secondary lymphoedema is still a significant problem for about 20–30% of those who receive treatment for cancer, although the incidence and prevalence does seem to be falling. The figures above generally relate to detection of an enlarged limb or other area, but it seems that about 60% of all patients also suffer other problems with how the limb feels, what can or cannot be done with it and a range of social or psychological issues. Often these ‘subjective’ changes occur before the objective ones, such as a change in arm volume or circumference. For most of those treated for cancer lymphoedema does not develop immediately, and, while about 60–70% develop it in the first few years, some do not develop lymphoedema for up to 15 or 20 years. Those who will develop clinically manifest lymphoedema in the future are, for some time, in a latent or hidden phase of lymphoedema. There also seems to be some risk factors which are indicators for a higher likelihood of lymphoedema post treatment, including oedema at the surgical site, arm dominance, age, skin conditions, and body mass index (BMI).

The Innovation Management Program (IMP) : a holistic approach to managing change in knowledge based firms

Innovation Management (IM) in most knowledge based firms is used on an adhoc basis where senior managers use this term to leverage competitive edge without understanding its true meaning and how its robust application in organisation impacts organisational performance. There have been attempts in the manufacturing industry to harness the innovative potential of the business and apprehend its use as a point of difference to improve financial and non financial outcomes. However further work is required to innovatively extrapolate the lessons learnt to introduce incremental and/or radical innovation to knowledge based firms. An international structural engineering firm has been proactive in exploring and implementing this idea and has forged an alliance with the Queensland University of Technology to start the Innovation Management Program (IMP). The aim was to develop a permanent and sustainable program with which innovation can be woven through the fabric of the organisation. There was an intention to reinforce the firms’ vision and reinvigorate ideas and create new options that help in its realisation. This paper outlines the need for innovation in knowledge based firms and how this consulting engineering firm reacted to this exigency. The development of the Innovation Management Program, its different themes (and associated projects) and how they integrate to form a holistic model is also discussed. The model is designed around the need of providing professional qualification improvement opportunities for staff, setting-up organised, structured & easily accessible knowledge repositories to capture tacit and explicit knowledge and implement efficient project management strategies with a view to enhance client satisfaction. A Delphi type workshop is used to confirm the themes and projects. Some of the individual projects and their expected outcomes are also discussed. A questionnaire and interviews were used to collect data to select appropriate candidates responsible for leading these projects. Following an in-depth analysis of preliminary research results, some recommendations on the selection process will also be presented.

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Continued use of process modeling grammars : the impact of individual difference factors

Process modeling grammars are used by analysts to describe information systems domains in terms of the business operations an organization is conducting. While prior research has examined the factors that lead to continued usage behavior, little knowledge has been established as to what extent characteristics of the users of process modeling grammars inform usage behavior. In this study, a theoretical model is advanced that incorporates determinants of continued usage behavior as well as key antecedent individual difference factors of the grammar users, such as modeling experience, modeling background and perceived grammar familiarity. Findings from a global survey of 529 grammar users support the hypothesized relationships of the model. The study offers three central contributions. First, it provides a validated theoretical model of post-adoptive modeling grammar usage intentions. Second, it discusses the effects of individual difference factors of grammar users in the context of modeling grammar usage. Third, it provides implications for research and practice.

Abandoned object detection using multi-layer motion detection

Abandoned object detection (AOD) systems are required to run in high traffic situations, with high levels of occlusion. Systems rely on background segmentation techniques to locate abandoned objects, by detecting areas of motion that have stopped. This is often achieved by using a medium term motion detection routine to detect long term changes in the background. When AOD systems are integrated into person tracking system, this often results in two separate motion detectors being used to handle the different requirements. We propose a motion detection system that is capable of detecting medium term motion as well as regular motion. Multiple layers of medium term (static) motion can be detected and segmented. We demonstrate the performance of this motion detection system and as part of an abandoned object detection system.

Multi-spectral fusion for surveillance systems

Surveillance systems such as object tracking and abandoned object detection systems typically rely on a single modality of colour video for their input. These systems work well in controlled conditions but often fail when low lighting, shadowing, smoke, dust or unstable backgrounds are present, or when the objects of interest are a similar colour to the background. Thermal images are not affected by lighting changes or shadowing, and are not overtly affected by smoke, dust or unstable backgrounds. However, thermal images lack colour information which makes distinguishing between different people or objects of interest within the same scene difficult. ----- By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using either modality individually. We evaluate four approaches for fusing visual and thermal images for use in a person tracking system (two early fusion methods, one mid fusion and one late fusion method), in order to determine the most appropriate method for fusing multiple modalities. We also evaluate two of these approaches for use in abandoned object detection, and propose an abandoned object detection routine that utilises multiple modalities. To aid in the tracking and fusion of the modalities we propose a modified condensation filter that can dynamically change the particle count and features used according to the needs of the system. ----- We compare tracking and abandoned object detection performance for the proposed fusion schemes and the visual and thermal domains on their own. Testing is conducted using the OTCBVS database to evaluate object tracking, and data captured in-house to evaluate the abandoned object detection. Our results show that significant improvement can be achieved, and that a middle fusion scheme is most effective.

Improved simultaneous computation of motion detection and optical flow for object tracking

Object tracking systems require accurate segmentation of the objects from the background for effective tracking. Motion segmentation or optical flow can be used to segment incoming images. Whilst optical flow allows multiple moving targets to be separated based on their individual velocities, optical flow techniques are prone to errors caused by changing lighting and occlusions, both common in a surveillance environment. Motion segmentation techniques are more robust to fluctuating lighting and occlusions, but don't provide information on the direction of the motion. In this paper we propose a combined motion segmentation/optical flow algorithm for use in object tracking. The proposed algorithm uses the motion segmentation results to inform the optical flow calculations and ensure that optical flow is only calculated in regions of motion, and improve the performance of the optical flow around the edge of moving objects. Optical flow is calculated at pixel resolution and tracking of flow vectors is employed to improve performance and detect discontinuities, which can indicate the location of overlaps between objects. The algorithm is evaluated by attempting to extract a moving target within the flow images, given expected horizontal and vertical movement (i.e. the algorithms intended use for object tracking). Results show that the proposed algorithm outperforms other widely used optical flow techniques for this surveillance application.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.