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Rockhampton, Australia

Central Queensland University is an Australian dual sector university based in Queensland. Its main campus is in North Rockhampton, Queensland. However, it also has campuses in Rockhampton City, Bundaberg, Emerald, Gladstone City, Gladstone Marina, Mackay Ooralea, Mackay City and Noosa, as well as delivery sites in Cairns, Cannonvale, Townsville, Charters Towers, Yeppoon, Biloela, Geraldton, Karratha and Perth. On 31 October 2014, CQUniversity announced that it would open a full campus in the Townsville CBD in 2015. It has metropolitan campuses in Melbourne, Sydney, Adelaide and Brisbane. As of 2012 the metropolitan campuses hosted both international and domestic students. Wikipedia.


Hickie I.B.,University of Sydney | Rogers N.L.,University of Sydney | Rogers N.L.,Central Queensland University
The Lancet | Year: 2011

Major depression is one of the leading causes of premature death and disability. Although available drugs are effective, they also have substantial limitations. Recent advances in our understanding of the fundamental links between chronobiology and major mood disorders, as well as the development of new drugs that target the circadian system, have led to a renewed focus on this area. In this review, we summarise the associations between disrupted chronobiology and major depression and outline new antidepressant treatment strategies that target the circadian system. In particular, we highlight agomelatine, a melatonin-receptor agonist and selective serotonergic receptor subtype (ie, 5-HT 2C) antagonist that has chronobiotic, antidepressant, and anxiolytic effects. In the short-term, agomelatine has similar antidepressant efficacy to venlafaxine, fluoxetine, and sertraline and, in the longer term, fewer patients on agomelatine relapse (23·9) than do those receiving placebo (50·0). Patients with depression treated with agomelatine report improved sleep quality and reduced waking after sleep onset. As agomelatine does not raise serotonin levels, it has less potential for the common gastrointestinal, sexual, or metabolic side-effects that characterise many other antidepressant compounds. © 2011 Elsevier Ltd. Source


Li W.,Central Queensland University
IEEE Transactions on Software Engineering | Year: 2012

A major challenge of dynamic reconfiguration is Quality of Service (QoS) assurance, which is meant to reduce application disruption to the minimum for the system's transformation. However, this problem has not been well studied. This paper investigates the problem for component-based software systems from three points of view. First, the whole spectrum of QoS characteristics is defined. Second, the logical and physical requirements for QoS characteristics are analyzed and solutions to achieve them are proposed. Third, prior work is classified by QoS characteristics and then realized by abstract reconfiguration strategies. On this basis, quantitative evaluation of the QoS assurance abilities of existing work and our own approach is conducted through three steps. First, a proof-of-concept prototype called the reconfigurable component model is implemented to support the representation and testing of the reconfiguration strategies. Second, a reconfiguration benchmark is proposed to expose the whole spectrum of QoS problems. Third, each reconfiguration strategy is tested against the benchmark and the testing results are evaluated. The most important conclusion from our investigation is that the classified QoS characteristics can be fully achieved under some acceptable constraints. © 2012 IEEE. Source


Compared to females, males experience higher rates of chronic disease and mortality, yet few health promotion initiatives are specifically aimed at men. Therefore, the aim of the ManUp Study is to examine the effectiveness of an IT-based intervention to increase the physical activity and nutrition behaviour and literacy in middle-aged males (aged 35-54 years). The study design was a two-arm randomised controlled trial, having an IT-based (applying website and mobile phones) and a print-based intervention arm, to deliver intervention materials and to promote self-monitoring of physical activity and nutrition behaviours. Participants (n = 317) were randomised on a 2:1 ratio in favour of the IT-based intervention arm. Both intervention arms completed assessments at baseline, 3, and 9 months. All participants completed self-report assessments of physical activity, sitting time, nutrition behaviours, physical activity and nutrition literacy, perceived health status and socio-demographic characteristics. A randomly selected sub-sample in the IT-based (n = 61) and print-based (n = 30) intervention arms completed objective measures of height, weight, waist circumference, and physical activity as measured by accelerometer (Actigraph GT3X). The average age of participants in the IT-based and print-based intervention arm was 44.2 and 43.8 years respectively. The majority of participants were employed in professional occupations (IT-based 57.6%, Print-based 54.2%) and were overweight or obese (IT-based 90.8%, Print-based 87.3%). At baseline a lower proportion of participants in the IT-based (70.2%) group agreed that 30 minutes of physical activity each day is enough to improve health compared to the print-based (82.3%) group (p = .026). The IT-based group consumed a significantly lower number of serves of red meat in the previous week, compared to the print-based group (p = .017). No other significant between-group differences were observed at baseline. The ManUp Study will examine the effectiveness of an IT-based approach to improve physical activity and nutrition behaviour and literacy. Study outcomes will provide much needed information on the efficacy of this approach in middle aged males, which is important due to the large proportions of males at risk, and the potential reach of IT-based interventions. ACTRN12611000081910. Source


Gyasi-Agyei Y.,Central Queensland University
Water Resources Research | Year: 2011

A daily rainfall disaggregation model, which uses a copula to model the dependence structure between total depth, total duration of wet periods, and the maximum proportional depth of a wet period, is presented. The wet(1)-dry(0) binary sequence is modeled by the nonrandomized Bartlett-Lewis model with diurnal effect incorporated before superimposing the AR(1) depth process submodel. Unlike previous studies, the model is structured such that all wet day data available are considered in the analysis, without the need to discard any good quality daily data embedded in a month having some missing data. This increased the data size, thus improving the modeling process. Further, the daily data are classified according to the total duration of wet periods duration within the day. In this way a large proportion of the model parameters become seasonal invariant, the overriding factor being the total duration of wet periods. The potential of the developed model has been demonstrated by disaggregating both the data set used in developing the model parameters and also a 12 year continuous rainfall data set not used in the model parameterization. Gross rainfall statistics of several aggregation levels down to 6 min have been very well reproduced by the disaggregation model. The copula dependence structure and the variation of the depth process submodel parameters with the total duration of wet periods are also very well captured by the presented model. Copyright 2011 by the American Geophysical Union. Source


Vandelanotte C.,Central Queensland University
Journal of medical Internet research | Year: 2012

In randomized controlled trials, participants cannot choose their preferred intervention delivery mode and thus might refuse to participate or not engage fully if assigned to a nonpreferred group. This might underestimate the true effectiveness of behavior-change interventions. To examine whether receiving interventions either matched or mismatched with participants' preferred delivery mode would influence effectiveness of a Web-based physical activity intervention. Adults (n = 863), recruited via email, were randomly assigned to one of three intervention delivery modes (text based, video based, or combined) and received fully automated, Internet-delivered personal advice about physical activity. Personalized intervention content, based on the theory of planned behavior and stages of change concept, was identical across groups. Online, self-assessed questionnaires measuring physical activity were completed at baseline, 1 week, and 1 month. Physical activity advice acceptability and website usability were assessed at 1 week. Before randomization, participants were asked which delivery mode they preferred, to categorize them as matched or mismatched. Time spent on the website was measured throughout the intervention. We applied intention-to-treat, repeated-measures analyses of covariance to assess group differences. Attrition was high (575/863, 66.6%), though equal between groups (t(86) (3) =1.31, P =.19). At 1-month follow-up, 93 participants were categorized as matched and 195 as mismatched. They preferred text mode (493/803, 61.4%) over combined (216/803, 26.9%) and video modes (94/803, 11.7%). After the intervention, 20% (26/132) of matched-group participants and 34% (96/282) in the mismatched group changed their delivery mode preference. Time effects were significant for all physical activity outcomes (total physical activity: F(2,801) = 5.07, P = .009; number of activity sessions: F(2,801) = 7.52, P < .001; walking: F(2,801) = 8.32, P < .001; moderate physical activity: F(2,801) = 9.53, P < .001; and vigorous physical activity: F(2,801) = 6.04, P = .002), indicating that physical activity increased over time for both matched and mismatched groups. Matched-group participants improved physical activity outcomes slightly more than those in the mismatched group, but interaction effects were not significant. Physical activity advice acceptability (content scale: t(368) = .10, P = .92; layout scale: t(368) = 1.53, P = .12) and website usability (layout scale: t(426) = .05, P = .96; ease of use scale: t(426) = .21, P = .83) were generally high and did not differ between the matched and mismatched groups. The only significant difference (t(621) = 2.16, P = .03) was in relation to total time spent on the website: the mismatched group spent significantly more time on the website (14.4 minutes) than the matched group (12.1 minutes). Participants' preference regarding delivery mode may not significantly influence intervention outcomes. Consequently, allowing participants to choose their preferred delivery mode may not increase effectiveness of Web-based interventions. Source

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