The effect of physical activity on psychological distress, cortisol and obesity: results of the farming fit intervention program

Background:
Rural and regional Australians have a higher likelihood of mental illness throughout their lifetime than people living in major cities, although the underlying reasons are not yet well defined. Additionally, rural populations experience more lifestyle associated co-morbidities including obesity, diabetes and cardiovascular disease. Research conducted by the National Centre for Farmer Health between 2004 and 2009 revealed a positive correlation between obesity and psychological distress among the farming community. Chronic stress is known to overstimulate the regulation of the hypothalamic-pituitary-adrenal (HPA) axis and cortisol secretion which are associated with abdominal adiposity. Increasing physical activity may normalise cortisol secretion and thereby positively impact both physical and mental health. This paper assesses the effects of increasing physical activity on obesity, health behaviors and mental health in Victorian farming men and women.

Methods:
Farming Fit was a six month quasi-experimental (convenience sample) longitudinal design control-intervention study. Overweight or obese (BMI ?25?kg/m2) farm men (n?=?43) and women (n?=?29) were recruited with demographic, health behaviors, anthropometric, blood pressure and biochemistry data collected at baseline and at a six months. Salivary cortisol and depression anxiety stress scale results were collected at baseline, three and six months. The intervention group (n?=?37) received a personalized exercise program and regular phone coaching to promote physical activity.

Results:
The intervention group showed significant reductions in body weight and waist circumference. Results indicated that following the six month exercise program, the intervention group were 2.64???0.65?kg lighter (p?<?0.001), had reduced waist circumference by 2.01???0.86?cm (p?=?0.02) and BMI by 0.97???0.22?kg/m2 (p?<?0.001) relative to the control group.

Conclusion:
Increasing physical activity altered measures of obesity in farm men and women but did not affect mental health measures or cortisol secretion levels.

Improving assessment management using tools

This paper firstly explains the importance of assessment management, then introduces two assessment tools currently used in the School of Information Technology at Deakin University. A comparison of assignment marking was conducted after collecting test data from three sets of assignments. The importance of providing detailed marking guides and personalized comments is emphasized and future possible extension to the tools is also discussed at the end of this paper.

Privacy preserving for tagging recommender systems

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.

Digital systems for open access to formal and informal learning

 Today, Digital Systems and Services for Technology Supported Learning and Education are recognized as the key drivers to transform the way that individuals, groups and organizations “learn” and the way to “assess learning” in 21st Century. These transformations influence: Objectives - moving from acquiring new “knowledge” to developing new and relevant “competences”; Methods – moving from “classroom” based teaching to “context-aware” personalized learning; and Assessment – moving from “life-long” degrees and certifications to “on-demand” and “in-context” accreditation of qualifications. Within this context, promoting Open Access to Formal and Informal Learning, is currently a key issue in the public discourse and the global dialogue on Education, including Massive Open Online Courses (MOOCs) and Flipped School Classrooms.

A microfluidic system for testing the responses of head and neck squamous cell carcinoma tissue biopsies to treatment with chemotherapy drugs

Tumors are heterogeneous masses of cells characterized pathologically by their size and spread. Their chaotic biology makes treatment of malignancies hard to generalize. We present a robust and reproducible glass microfluidic system, for the maintenance and “interrogation” of head and neck squamous cell carcinoma (HNSCC) tumor biopsies, which enables continuous media perfusion and waste removal, recreating in vivo laminar flow and diffusion-driven conditions. Primary HNSCC or metastatic lymph samples were subsequently treated with 5-fluorouracil and cisplatin, alone and in combination, and were monitored for viability and apoptotic biomarker release ‘off-chip’ over 7 days. The concentration of lactate dehydrogenase was initially high but rapidly dropped to minimally detectable levels in all tumor samples; conversely, effluent concentration of WST-1 (cell proliferation) increased over 7 days: both factors demonstrating cell viability. Addition of cell lysis reagent resulted in increased cell death and reduction in cell proliferation. An apoptotic biomarker, cytochrome c, was analyzed and all the treated samples showed higher levels than the control, with the combination therapy showing the greatest effect. Hematoxylin- and Eosin-stained sections from the biopsy, before and after maintenance, demonstrated the preservation of tissue architecture. This device offers a novel method of studying the tumor environment, and offers a pre-clinical model for creating personalized treatment regimens.

Service and cloud computing supporting genomic analysis of the mammalian species

 This research focused on building Software as a Service clouds to support mammalian genomic applications such as personalized medicine. Outcomes of this research included a Software as a Service cloud framework, the Uncinus research cloud and novel genomic analysis software. Results have been published in high ranking peer-reviewed international journals.

Generating repudiable, memorizable and privacy preserving security questions using the Propp Theory of Narrative

 Security questions are often based on personal information that is limited in variety, available in the public record and very difficult to change if compromised. A personalized folktale shared only by the communicating parties provides memorizable basis for individualized security questions that can be readily replaced in the event of a security breach. We utilize the Propp theory of narrative to provide a basis of abstraction for story generation systems. We develop a proof-of-concept system based on placeholder replacement to demonstrate the generation of repudiate and memorizable questions and answers suitable for online security questions. A 3-component protocol is presented that demonstrates the use of this process to derive a shared secret key through privacy amplification. This combination of story generation and communication security provides the basis for improvements in current security question practice.

Biomarkers and clinical staging in psychiatry

Personalized medicine is rapidly becoming a reality in today's physical medicine. However, as yet this is largely an aspirational goal in psychiatry, despite significant advances in our understanding of the biochemical, genetic and neurobiological processes underlying major mental disorders. Preventive medicine relies on the availability of predictive tools; in psychiatry we still largely lack these. Furthermore, our current diagnostic systems, with their focus on well-established, largely chronic illness, do not support a pre-emptive, let alone a preventive, approach, since it is during the early stages of a disorder that interventions have the potential to offer the greatest benefit. Here, we present a clinical staging model for severe mental disorders and discuss examples of biological markers that have already undergone some systematic evaluation and that could be integrated into such a framework. The advantage of this model is that it explicitly considers the evolution of psychopathology during the development of a mental illness and emphasizes that progression of illness is by no means inevitable, but can be altered by providing appropriate interventions that target individual modifiable risk and protective factors. The specific goals of therapeutic intervention are therefore broadened to include the prevention of illness onset or progression, and to minimize the risk of harm associated with more complex treatment regimens. The staging model also facilitates the integration of new data on the biological, social and environmental factors that influence mental illness into our clinical and diagnostic infrastructure, which will provide a major step forward in the development of a truly pre-emptive psychiatry.

Managing learning evidence using eportfolio

This paper introduces ePortfolio and its use of managing learning evidence in an eLearning environment. The paper uses the outcomes of a recent project sponsored by Deakin University, Australia and examples from IT teaching practice based on CloudDeakin to explore how ePortfolios add value to personalized and reflective models of eLearning. ePortfolios are proved to be an effective tool for discussion, information collection and reflection, and new ways of thinking about learning evidence management.

Closed loop deep brain stimulation: an evolving technology

Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.

Scalable approximating SVD algorithm for recommender systems

With the rapid development of Internet, the amount of information on the Web grows explosively, people often feel puzzled and helpless in finding and getting the information they really need. For overcoming this problem, recommender systems such as singular value decomposition (SVD) method help users finding relevant information, products or services by providing personalized recommendations based on their profiles. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Thus, to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm which is called ApproSVD algorithm based on approximating SVD in this paper. The trick behind our algorithm is to sample some rows of a user-item matrix, rescale each row by an appropriate factor to form a relatively smaller matrix, and then reduce the dimensionality of the smaller matrix. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on MovieLens dataset and Flixster dataset, and show that our method has the best prediction quality. Furthermore, in order to show the superiority of the ApproSVD algorithm, we also conduct an empirical study to compare the prediction accuracy and running time between ApproSVD algorithm and incremental SVD algorithm on MovieLens dataset and Flixster dataset, and demonstrate that our proposed method has better performance overall.

SVD-based incremental approaches for recommender systems

Due to the serious information overload problem on the Internet, recommender systems have emerged as an important tool for recommending more useful information to users by providing personalized services for individual users. However, in the “big data“ era, recommender systems face significant challenges, such as how to process massive data efficiently and accurately. In this paper we propose an incremental algorithm based on singular value decomposition (SVD) with good scalability, which combines the Incremental SVD algorithm with the Approximating the Singular Value Decomposition (ApproSVD) algorithm, called the Incremental ApproSVD. Furthermore, strict error analysis demonstrates the effectiveness of the performance of our Incremental ApproSVD algorithm. We then present an empirical study to compare the prediction accuracy and running time between our Incremental ApproSVD algorithm and the Incremental SVD algorithm on the MovieLens dataset and Flixster dataset. The experimental results demonstrate that our proposed method outperforms its counterparts.

Deferentially private tagging recommendation based on topic model

Tagging recommender system allows Internet users to annotate resources with personalized tags and provides users the freedom to obtain recommendations. However, It is usually confronted with serious privacy concerns, because adversaries may re-identify a user and her/his sensitive tags with only a little background information. This paper proposes a privacy preserving tagging release algorithm, PriTop, which is designed to protect users under the notion of differential privacy. The proposed PriTop algorithm includes three privacy preserving operations: Private Topic Model Generation structures the uncontrolled tags, Private Weight Perturbation adds Laplace noise into the weights to hide the numbers of tags; while Private Tag Selection finally finds the most suitable replacement tags for the original tags. We present extensive experimental results on four real world datasets and results suggest the proposed PriTop algorithm can successfully retain the utility of the datasets while preserving privacy. © 2014 Springer International Publishing.

Web-based alcohol intervention for Māori university students: double-blind, multi-site randomized controlled trial

AIMS: Like many indigenous peoples, New Zealand Māori bear a heavy burden of alcohol-related harm relative to their non-indigenous compatriots, and disparities are greatest among young adults. We tested the effectiveness of web-based alcohol screening and brief intervention (e-SBI) for reducing hazardous drinking among Māori university students. DESIGN: Parallel, double-blind, multi-site, randomized controlled trial. SETTING: Seven of New Zealand's eight universities. PARTICIPANTS: In April 2010, we sent e-mail invitations to all 6697 17-24-year-old Māori students to complete a brief web questionnaire including the Alcohol Use Disorders Identification Test (AUDIT)-C, a screening tool for hazardous and harmful drinking. Those screening positive were computer randomized to: <10 minutes of web-based alcohol assessment and personalized feedback (intervention) or screening alone (control). MEASUREMENTS: We conducted a fully automated 5-month follow-up assessment with observers and participants blinded to study hypotheses, design and intervention delivery. Pre-determined primary outcomes were: (i) frequency of drinking, (ii) amount consumed per typical drinking occasion, (iii) overall volume of alcohol consumed and (iv) academic problems. FINDINGS: Of the participants, 1789 were hazardous or harmful drinkers (AUDIT-C ≥ 4) and were randomized: 850 to control, 939 to intervention. Follow-up assessments were completed by 682 controls (80%) and 733 intervention group members (78%). Relative to controls, participants receiving intervention drank less often [RR = 0.89; 95% confidence interval (CI): 0.82-0.97], less per drinking occasion (RR = 0.92; 95% CI: 0.84-1.00), less overall (RR = 0.78; 95% CI: 0.69-0.89) and had fewer academic problems (RR = 0.81; 95% CI: 0.69-0.95). CONCLUSIONS: Web-based screening and brief intervention reduced hazardous and harmful drinking among non-help-seeking Māori students in a large-scale pragmatic trial. The study has wider implications for behavioural intervention in the important but neglected area of indigenous health.

A telephone support program to reduce costs and hospital admissions for patients at risk of readmissions: lessons from an evaluation of a complex health intervention

This study aimed to evaluate the effectiveness of a telephone health coaching and support service provided to members of an Australian private health insurance fund-Telephonic Complex Care Program (TCCP)-on hospital use and associated costs. A case-control pre-post study design was employed using propensity score matching. Private health insurance members (n=273) who participated in TCCP between April and December 2012 (cases) were matched (1:1) to members who had not previously been enrolled in the program or any other disease management programs offered by the insurer (n=232). Eligible members were community dwelling, aged ≥65 years, and had 2 or more hospital admissions in the 12 months prior to program enrollment. Preprogram variables that estimated the propensity score included: participant demographics, diagnoses, and hospital use in the 12 months prior to program enrollment. TCCP participants received one-to-one telephone support, personalized care plan, and referral to community-based services. Control participants continued to access usual health care services. Primary outcomes were number of hospital admission claims and total benefits paid for all health care utilizations in the 12 months following program enrollment. Secondary outcomes included change in total benefits paid, hospital benefits paid, ancillary benefits paid, and total hospital bed days over the 12 months post enrollment. Compared with matched controls, TCCP did not appear to reduce health care utilization or benefits paid in the 12 months following program enrollment. However, program characteristics and implementation may have impacted its effectiveness. In addition, challenges related to evaluating complex health interventions such as TCCP are discussed.