Giving young Emirati women a voice : participatory action research on physical activity

International evidence on health promotion indicates the importance of regular physical activity for preventing and reducing the incidence of obesity and chronic diseases. This study investigated the relationship between physical activity and the social milieu of young Muslim women in the United Arab Emirates. This participatory action research project included semi-structured in-depth interviews and focus groups and yielded qualitative data. Set within a context of rapid social change, perceived barriers to daily exercise influenced participants’ physical activity levels and overall well-being. Results indicated a lack of physical exercise and strategies were proposed for implementation by college staff and students.

The role of the medical emergency team in end-of-life care : a multicenter, prospective, observational study

Objective: To investigate the role of medical emergency teams in end-of-life care planning.

Design: One month prospective audit of medical emergency team calls.

Setting: Seven university-affiliated hospitals in Australia, Canada, and Sweden.

Patients: Five hundred eighteen patients who received a medical emergency team call over 1 month.

Interventions: None.

Measurements and Main Results: There were 652 medical emergency team calls in 518 patients, with multiple calls in 99 (19.1%) patients. There were 161 (31.1%) patients with limitations of medical therapy during the study period. The limitation of medical therapy was instituted in 105 (20.3%) and 56 (10.8%) patients before and after the medical emergency team call, respectively. In 78 patients who died with a limitation of medical therapy in place, the last medical emergency team review was on the day of death in 29.5% of patients, and within 2 days in another 28.2%. Compared with patients who did not have a limitation of medical therapy, those with a limitation of medical therapy were older (80 vs. 66 yrs; p < .001), less likely to be male (44.1% vs. 55.7%; p .014), more likely to be medical admissions (70.8% vs. 51.3%; p < .001), and less likely to be admitted from home (74.5% vs. 92.2%, p < .001). In addition, those with a limitation of medical therapy were less likely to be discharged home (22.4% vs. 63.6%; p < .001) and more likely to die in hospital (48.4% vs. 12.3%; p < .001). There was a trend for increased likelihood of calls associated with limitations of medical therapy to occur out of hours (51.0% vs. 43.8%, p .089).

Conclusions: Issues around end-of-life care and limitations of medical therapy arose in approximately one-third of calls, suggesting a mismatch between patient needs for end-of-life care and resources at participating hospitals. These calls frequently occur in elderly medical patients and out of hours. Many such patients do not return home, and half die in hospital. There is a need for improved advanced care planning in our hospitals, and to confirm our findings in other organizations.

Magával ragadó interfészek Art Education tanítás és tanulás vegyes valóság virtuális és valós környezetben

How selected next generation technologies support collaborative participation between higher education students and educators within a virtual socially networked e-learning landscape and encourage the interaction of communities of learners in multiple modes, ranging from text and images accessed within the Deakin Studies Online learning management system to a constructed virtual world in which the user’s creative imagination transports them to the “other side” of their computer screens is discussed in this paper. These constructed environments enable multiple simultaneous participants to access graphically built 3D environments, interact with digital artifacts and various functional tools and represent themselves through avatars, to communicate with other participants and engage in collaborative art learning.

On the use of restricted dissimilarity and dissimilarity-like functions for defining penalty functions

In this work we study the relation between restricted dissimilarity functions-and, more generally, dissimilarity-like functions- and penalty functions and the possibility of building the latter using the former. Several results on convexity and quasiconvexity are also considered.

The timing of Rapid-Response Team activations: a multicentre international study

Background:
Most studies of Rapid-Response Teams (RRTs) assess their effect on outcomes of all hospitalised patients. Little information exists on RRT activation patterns or why RRT calls are needed. Triage error may necessitate RRT review of ward patients shortly after hospital admission. RRT diurnal activation rates may reflect the likely frequency of caregiver visits.

Objectives:
To study the timing of RRT calls in relation to time of day and day of week, and their frequency and outcomes in relation to days after hospital admission.

Methods:
We prospectively studied RRT calls over 1 month in seven hospitals during 2009, collecting data on patient age, sex, admitting unit, admission source, limitations of medical therapy (LOMTs), and admission and discharge dates. We assessed the timing of RRT calls in relation to hospital admission and circadian variation; and differences in characteristics and outcomes of calls occurring early (Days 0 and 1) versus late (after Day 7) after hospital admission.

Results:
There were 652 RRT calls for 518 patients. Calls were more likely on Mondays (P=0.018) and during work hours (P<0.0001) but less likely on weekends (P=0.003) or overnight (P<0.001). There were 177 early calls (27.1%) and 198 late calls (30.4%). Early calls involved younger patients (median ages, 67.5 years [early calls] v 73 years [late calls]; P= 0.01), fewer LOMTs (P=0.029), and lower in hospital mortality (12.8% [early calls] v 32.3% [late calls]; P<0.0001). The mortality difference remained in patients without LOMTs (5.6% [early calls] v 19.6% [late calls]; P=0.003).

Conclusions:
About one-quarter of RRT calls occurred shortly after hospital admission, and were more common when caregivers were around. Early calls may partially reflect suboptimal triage, though the associated mortality appeared low. Late calls may reflect suboptimal end-of-life care planning, and the associated mortality was high. There is a need to further assess the epidemiology of RRT calls at different phases of the hospital stay.

Birthweight and childhood cancer : preliminary findings from the International Childhood Cancer Cohort Consortium (I4C)

BACKGROUND: Evidence relating childhood cancer to high birthweight is derived primarily from registry and case-control studies. We aimed to investigate this association, exploring the potential modifying roles of age at diagnosis and maternal anthropometrics, using prospectively collected data from the International Childhood Cancer Cohort Consortium.

METHODS: We pooled data on infant and parental characteristics and cancer incidence from six geographically and temporally diverse member cohorts [the Avon Longitudinal Study of Parents and Children (UK), the Collaborative Perinatal Project (USA), the Danish National Birth Cohort (Denmark), the Jerusalem Perinatal Study (Israel), the Norwegian Mother and Child Cohort Study (Norway), and the Tasmanian Infant Health Survey (Australia)]. Birthweight metrics included a continuous measure, deciles, and categories (≥4.0 vs. <4.0 kilogram). Childhood cancer (377 cases diagnosed prior to age 15 years) risk was analysed by type (all sites, leukaemia, acute lymphoblastic leukaemia, and non-leukaemia) and age at diagnosis. We estimated hazard ratios (HR) and 95% confidence intervals (CI) from Cox proportional hazards models stratified by cohort.

RESULTS: A linear relationship was noted for each kilogram increment in birthweight adjusted for gender and gestational age for all cancers [HR = 1.26; 95% CI 1.02, 1.54]. Similar trends were observed for leukaemia. There were no significant interactions with maternal pre-pregnancy overweight or pregnancy weight gain. Birthweight ≥4.0 kg was associated with non-leukaemia cancer among children diagnosed at age ≥3 years [HR = 1.62; 95% CI 1.06, 2.46], but not at younger ages [HR = 0.7; 95% CI 0.45, 1.24, P for difference = 0.02].

CONCLUSION: Childhood cancer incidence rises with increasing birthweight. In older children, cancers other than leukaemia are particularly related to high birthweight. Maternal adiposity, currently widespread, was not demonstrated to substantially modify these associations. Common factors underlying foetal growth and carcinogenesis need to be further explored.

Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

Nonparametric discovery of online mental health-related communities

People are increasingly using social media, especially online communities, to discuss mental health issues and seek supports. Understanding topics, interaction, sentiment and clustering structures of these communities informs important aspects of mental health. It can potentially add knowledge to the underlying cognitive dynamics, mood swings patterns, shared interests, and interaction. There has been growing research interest in analyzing online mental health communities; however sentiment analysis of these communities has been largely under-explored. This study presents an analysis of online Live Journal communities with and without mental health-related conditions including depression and autism. Latent topics for mood tags, affective words, and generic words in the content of the posts made in these communities were learned using nonparametric topic modelling. These representations were then input into a nonparametric clustering to discover meta-groups among the communities. The best performance results can be achieved on clustering communities with latent mood-based representation for such communities. The study also found significant differences in usage latent topics for mood tags and affective features between online communities with and without affective disorders. The findings reveal useful insights into hyper-group detection of online mental health-related communities.

Exploiting feature relationships towards stable feature selection

Feature selection is an important step in building predictive models for most real-world problems. One of the popular methods in feature selection is Lasso. However, it shows instability in selecting features when dealing with correlated features. In this work, we propose a new method that aims to increase the stability of Lasso by encouraging similarities between features based on their relatedness, which is captured via a feature covariance matrix. Besides modeling positive feature correlations, our method can also identify negative correlations between features. We propose a convex formulation for our model along with an alternating optimization algorithm that can learn the weights of the features as well as the relationship between them. Using both synthetic and real-world data, we show that the proposed method is more stable than Lasso and many state-of-the-art shrinkage and feature selection methods. Also, its predictive performance is comparable to other methods.