Foot massage versus quiet presence on agitation and mood in people with dementia : a randomised controlled trial

Background There is increasing interest in using complementary and alternative treatments to manage behavioural and psychological symptoms of dementia such as agitation, aggression and depressed mood. Objective To compare the effect of foot massage (intervention) and quiet presence (control) on agitation and mood in people with dementia. Design A randomised controlled trial using a within-subjects, crossover design. Settings Five long-term care facilities in Brisbane, Australia. The primary outcome was the Cohen-Mansfield Agitation Inventory (CMAI) and the secondary outcome was the Observed Emotion Rating Scale (OERS). The screening and data collection research assistants, families, and care staff were blinded to participant allocation. Participants Participants of the study were 55 long-term care residents aged 74–103 years (mean age 86.5), with moderate to severe dementia and a history of agitated behaviour according to the Pittsburgh Agitation Scale. A computer-program randomised participants to 10-min foot massage (intervention) or quiet presence (control), every weekday for 3 weeks. Results A carry-over effect was identified in the data, and so the data was treated as a parallel groups RCT. The mean total CMAI increased in both groups (reflecting an increase in agitation) with this increase greater in the quiet presence group than the foot massage group (p=0.03). There was a trend towards a difference on OERS General Alertness, with a positive change in alertness for participants in the foot massage group (indicating reduced alertness) and a negative change for participants in the quiet presence group (indicating increased alertness) (F(1,51)=3.88, p=0.05, partial ή2=0.07). Conclusions The findings highlight the need for further research on the specific conditions under which massage might promote relaxation and improve mood for people with dementia. The unfamiliar research assistants and variations in usual activity may have contributed to the increase in agitation and this needs further research.

Foot massage and physiological stress in people with dementia : a randomized controlled trial

Background There is increasing interest in using complementary and alternative treatments to manage behavioural and psychological symptoms of dementia such as agitation, aggression and depressed mood. Objective To compare the effect of foot massage (intervention) and quiet presence (control) on agitation and mood in people with dementia. Design A randomised controlled trial using a within-subjects, crossover design. Settings Five long-term care facilities in Brisbane, Australia. The primary outcome was the Cohen-Mansfield Agitation Inventory (CMAI) and the secondary outcome was the Observed Emotion Rating Scale (OERS). The screening and data collection research assistants, families, and care staff were blinded to participant allocation. Participants Participants of the study were 55 long-term care residents aged 74–103 years (mean age 86.5), with moderate to severe dementia and a history of agitated behaviour according to the Pittsburgh Agitation Scale. A computer-program randomised participants to 10-min foot massage (intervention) or quiet presence (control), every weekday for 3 weeks. Results A carry-over effect was identified in the data, and so the data was treated as a parallel groups RCT. The mean total CMAI increased in both groups (reflecting an increase in agitation) with this increase greater in the quiet presence group than the foot massage group (p=0.03). There was a trend towards a difference on OERS General Alertness, with a positive change in alertness for participants in the foot massage group (indicating reduced alertness) and a negative change for participants in the quiet presence group (indicating increased alertness) (F(1,51)=3.88, p=0.05, partial ή2=0.07). Conclusions The findings highlight the need for further research on the specific conditions under which massage might promote relaxation and improve mood for people with dementia. The unfamiliar research assistants and variations in usual activity may have contributed to the increase in agitation and this needs further research.

Pollen image classification using the classifynder system: Algorithm comparison and a case study on New Zealand honey

We describe an investigation into how Massey University’s Pollen Classifynder can accelerate the understanding of pollen and its role in nature. The Classifynder is an imaging microscopy system that can locate, image and classify slide based pollen samples. Given the laboriousness of purely manual image acquisition and identification it is vital to exploit assistive technologies like the Classifynder to enable acquisition and analysis of pollen samples. It is also vital that we understand the strengths and limitations of automated systems so that they can be used (and improved) to compliment the strengths and weaknesses of human analysts to the greatest extent possible. This article reviews some of our experiences with the Classifynder system and our exploration of alternative classifier models to enhance both accuracy and interpretability. Our experiments in the pollen analysis problem domain have been based on samples from the Australian National University’s pollen reference collection (2,890 grains, 15 species) and images bundled with the Classifynder system (400 grains, 4 species). These samples have been represented using the Classifynder image feature set.We additionally work through a real world case study where we assess the ability of the system to determine the pollen make-up of samples of New Zealand honey. In addition to the Classifynder’s native neural network classifier, we have evaluated linear discriminant, support vector machine, decision tree and random forest classifiers on these data with encouraging results. Our hope is that our findings will help enhance the performance of future releases of the Classifynder and other systems for accelerating the acquisition and analysis of pollen samples.

SECA: SNP effect concordance analysis using genome-wide association summary results

The genomics era provides opportunities to assess the genetic overlap across phenotypes at the measured genotype level; however, current approaches require individual-level genome-wide association (GWA) single nucleotide polymorphism (SNP) genotype data in one or both of a pair of GWA samples. To facilitate the discovery of pleiotropic effects and examine genetic overlap across two phenotypes, I have developed a user-friendly web-based application called SECA to perform SNP effect concordance analysis using GWA summary results. The method is validated using publicly available summary data from the Psychiatric Genomics Consortium.

Research around practice partnerships: An example of building partnerships to address overweight and obesity in children

Prevention and management of childhood overweight and obesity is a health priority for governments and communities throughout the developed world. A conceptual model, Research around Practice in Childhood Obesity (RAPICO), has been developed to guide capacity building in a coordinated 'bench to fieldwork' initiative to address this public health problem. Translation of research findings into sustainable responses with optimal fit requires consideration of context-specific relevance, cost-effectiveness, feasibility and levels of available support. The RAPICO model uses program theory to describe a framework for progressing practitionercommunityresearch partnerships to address low, medium and high levels of risk for childhood overweight and obesity within community settings. A case study describing the development of a logic model to inform risk-linked responses to childhood overweight and obesity is presented for the Ipswich community in south-east Queensland.

Partial epilepsy syndrome in a Gypsy family linked to 5q31.3-q32

PURPOSE The restricted genetic diversity and homogeneous molecular basis of Mendelian disorders in isolated founder populations have rarely been explored in epilepsy research. Our long-term goal is to explore the genetic basis of epilepsies in one such population, the Gypsies. The aim of this report is the clinical and genetic characterization of a Gypsy family with a partial epilepsy syndrome. METHODS Clinical information was collected using semistructured interviews with affected subjects and informants. At least one interictal electroencephalography (EEG) recording was performed for each patient and previous data obtained from records. Neuroimaging included structural magnetic resonance imaging (MRI). Linkage and haplotype analysis was performed using the Illumina IVb Linkage Panel, supplemented with highly informative microsatellites in linked regions and Affymetrix SNP 5.0 array data. RESULTS We observed an early-onset partial epilepsy syndrome with seizure semiology strongly suggestive of temporal lobe epilepsy (TLE), with mild intellectual deficit co-occurring in a large proportion of the patients. Psychiatric morbidity was common in the extended pedigree but did not cosegregate with epilepsy. Linkage analysis definitively excluded previously reported loci, and identified a novel locus on 5q31.3-q32 with an logarithm of the odds (LOD) score of 3 corresponding to the expected maximum in this family. DISCUSSION The syndrome can be classified as familial temporal lobe epilepsy (FTLE) or possibly a new syndrome with mild intellectual deficit. The linked 5q region does not contain any ion channel-encoding genes and is thus likely to contribute new knowledge about epilepsy pathogenesis. Identification of the mutation in this family and in additional patients will define the full phenotypic spectrum.

An effective approach to verbose queries using a limited dependencies language model

Intuitively, any ‘bag of words’ approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies have been mixed or inconclusive. To improve the situation, this paper shows how the natural language properties of the target documents can be used to transform and enrich the term dependencies to more useful statistics. This is done in three steps. The term co-occurrence statistics of queries and documents are each represented by a Markov chain. The paper proves that such a chain is ergodic, and therefore its asymptotic behavior is unique, stationary, and independent of the initial state. Next, the stationary distribution is taken to model queries and documents, rather than their initial distributions. Finally, ranking is achieved following the customary language modeling paradigm. The main contribution of this paper is to argue why the asymptotic behavior of the document model is a better representation then just the document’s initial distribution. A secondary contribution is to investigate the practical application of this representation in case the queries become increasingly verbose. In the experiments (based on Lemur’s search engine substrate) the default query model was replaced by the stable distribution of the query. Just modeling the query this way already resulted in significant improvements over a standard language model baseline. The results were on a par or better than more sophisticated algorithms that use fine-tuned parameters or extensive training. Moreover, the more verbose the query, the more effective the approach seems to become.

Extending and validating the is-impact model in Saudi Arabia : accounting for computer network quality

Organizations from every industry sector seek to enhance their business performance and competitiveness through the deployment of contemporary information systems (IS), such as Enterprise Systems (ERP). Investments in ERP are complex and costly, attracting scrutiny and pressure to justify their cost. Thus, IS researchers highlight the need for systematic evaluation of information system success, or impact, which has resulted in the introduction of varied models for evaluating information systems. One of these systematic measurement approaches is the IS-Impact Model introduced by a team of researchers at Queensland University of technology (QUT) (Gable, Sedera, & Chan, 2008). The IS-Impact Model is conceptualized as a formative, multidimensional index that consists of four dimensions. Gable et al. (2008) define IS-Impact as "a measure at a point in time, of the stream of net benefits from the IS, to date and anticipated, as perceived by all key-user-groups" (p.381). The IT Evaluation Research Program (ITE-Program) at QUT has grown the IS-Impact Research Track with the central goal of conducting further studies to enhance and extend the IS-Impact Model. The overall goal of the IS-Impact research track at QUT is "to develop the most widely employed model for benchmarking information systems in organizations for the joint benefit of both research and practice" (Gable, 2009). In order to achieve that, the IS-Impact research track advocates programmatic research having the principles of tenacity, holism, and generalizability through extension research strategies. This study was conducted within the IS-Impact Research Track, to further generalize the IS-Impact Model by extending it to the Saudi Arabian context. According to Hofsted (2012), the national culture of Saudi Arabia is significantly different from the Australian national culture making the Saudi Arabian culture an interesting context for testing the external validity of the IS-Impact Model. The study re-visits the IS-Impact Model from the ground up. Rather than assume the existing instrument is valid in the new context, or simply assess its validity through quantitative data collection, the study takes a qualitative, inductive approach to re-assessing the necessity and completeness of existing dimensions and measures. This is done in two phases: Exploratory Phase and Confirmatory Phase. The exploratory phase addresses the first research question of the study "Is the IS-Impact Model complete and able to capture the impact of information systems in Saudi Arabian Organization?". The content analysis, used to analyze the Identification Survey data, indicated that 2 of the 37 measures of the IS-Impact Model are not applicable for the Saudi Arabian Context. Moreover, no new measures or dimensions were identified, evidencing the completeness and content validity of the IS-Impact Model. In addition, the Identification Survey data suggested several concepts related to IS-Impact, the most prominent of which was "Computer Network Quality" (CNQ). The literature supported the existence of a theoretical link between IS-Impact and CNQ (CNQ is viewed as an antecedent of IS-Impact). With the primary goal of validating the IS-Impact model within its extended nomological network, CNQ was introduced to the research model. The Confirmatory Phase addresses the second research question of the study "Is the Extended IS-Impact Model Valid as a Hierarchical Multidimensional Formative Measurement Model?". The objective of the Confirmatory Phase was to test the validity of IS-Impact Model and CNQ Model. To achieve that, IS-Impact, CNQ, and IS-Satisfaction were operationalized in a survey instrument, and then the research model was assessed by employing the Partial Least Squares (PLS) approach. The CNQ model was validated as a formative model. Similarly, the IS-Impact Model was validated as a hierarchical multidimensional formative construct. However, the analysis indicated that one of the IS-Impact Model indicators was insignificant and can be removed from the model. Thus, the resulting Extended IS-Impact Model consists of 4 dimensions and 34 measures. Finally, the structural model was also assessed against two aspects: explanatory and predictive power. The analysis revealed that the path coefficient between CNQ and IS-Impact is significant with t-value= (4.826) and relatively strong with â = (0.426) with CNQ explaining 18% of the variance in IS-Impact. These results supported the hypothesis that CNQ is antecedent of IS-Impact. The study demonstrates that the quality of Computer Network affects the quality of the Enterprise System (ERP) and consequently the impacts of the system. Therefore, practitioners should pay attention to the Computer Network quality. Similarly, the path coefficient between IS-Impact and IS-Satisfaction was significant t-value = (17.79) and strong â = (0.744), with IS-Impact alone explaining 55% of the variance in Satisfaction, consistent with results of the original IS-Impact study (Gable et al., 2008). The research contributions include: (a) supporting the completeness and validity of IS-Impact Model as a Hierarchical Multi-dimensional Formative Measurement Model in the Saudi Arabian context, (b) operationalizing Computer Network Quality as conceptualized in the ITU-T Recommendation E.800 (ITU-T, 1993), (c) validating CNQ as a formative measurement model and as an antecedent of IS Impact, and (d) conceptualizing and validating IS-Satisfaction as a reflective measurement model and as an immediate consequence of IS Impact. The CNQ model provides a framework to perceptually measure Computer Network Quality from multiple perspectives. The CNQ model features an easy-to-understand, easy-to-use, and economical survey instrument.

Application of a Bayesian Network complex system model to a successful community electricity demand reduction program

Utilities worldwide are focused on supplying peak electricity demand reliably and cost effectively, requiring a thorough understanding of all the factors influencing residential electricity use at peak times. An electricity demand reduction project based on comprehensive residential consumer engagement was established within an Australian community in 2008, and by 2011, peak demand had decreased to below pre-intervention levels. This paper applied field data discovered through qualitative in-depth interviews of 22 residential households at the community to a Bayesian Network complex system model to examine whether the system model could explain successful peak demand reduction in the case study location. The knowledge and understanding acquired through insights into the major influential factors and the potential impact of changes to these factors on peak demand would underpin demand reduction intervention strategies for a wider target group.

Medical semantic similarity with a neural language model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity. The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).