Patient Focused internet-based approaches to cardiovascular rehabilitation - a systematic review

Cardiac rehabilitation (CR) has been shown to improve health behaviours and risk factors and the evidence suggests that home CR is as effective as hospital-based CR. Telemedicine offers the potential for more patients to engage in CR. We reviewed the evidence for patient focused Internet-based approaches to cardiovascular rehabilitation. Searches were performed in PubMed, EMBASE, Scopus and the Cochrane Controlled Trials Register (CCTR). In total, 9 studies involving 830patients with heart disease that compared Internet-based cardiac rehabilitation to usual care were identified. The quality of trials was assessed using the Jadad scale. Outcome data were pooled under four subheadings: compliance; physical activity outcomes; clinical outcomes; psychosocial outcomes. Compliance rates were high but dropped over time in all studies. Physical activity measures were generally improved, as were clinical outcomes. Changes in psychosocial measures were positive, with two studies noting no change. No interventions noted a negative effect on outcomes. Despite the relatively small number of trials and the limited outcome measures, the results appeared to be positive with regard to patient outcomes and patient feedback. However, none had progressed to a clinical service

Study protocol: a randomised controlled trial investigating the effect of exercise training on peripheral blood gene expression in patients with stable angina

Background: Exercise training has been shown to reduce angina and promote collateral vessel development in patients with coronary artery disease. However, the mechanism whereby exercise exerts these beneficial effects is unclear. There has been increasing interest in the use of whole genome peripheral blood gene expression in a wide range of conditions to attempt to identify both novel mechanisms of disease and transcriptional biomarkers. This protocol describes a study in which we will assess the effect of a structured exercise programme on peripheral blood gene expression in patients with stable angina, and correlate this with changes in angina level, anxiety, depression, and exercise capacity. Methods/Design: Sixty patients with stable angina will be recruited and randomised 1: 1 to exercise training or conventional care. Patients randomised to exercise training will attend an exercise physiology laboratory up to three times weekly for supervised aerobic interval training sessions of one hour in total duration. Patients will undergo assessments of angina, anxiety, depression, and peripheral blood gene expression at baseline, after six and twelve weeks of training, and twelve weeks after formal exercise training ceases. Discussion: This study will provide comprehensive data on the effect of exercise training on peripheral blood gene expression in patients with angina. By correlating this with improvement in angina status we will identify candidate peripheral blood transcriptional markers predictive of improvements in angina level in response to exercise training

Efficient use of simultaneous multi-band observations for variable star analysis

The luminosity changes of most types of variable stars are correlated in the different wavelengths, and these correlations may be exploited for several purposes: for variability detection, for distinction of microvariability from noise, for period search or for classification. Principal component analysis is a simple and well-developed statistical tool to analyze correlated data. We will discuss its use on variable objects of Stripe 82 of the Sloan Digital Sky Survey, with the aim of identifying new RR Lyrae and SX Phoenicis-type candidates. The application is not straightforward because of different noise levels in the different bands, the presence of outliers that can be confused with real extreme observations, under- or overestimated errors and the dependence of errors on the magnitudes. These particularities require robust methods to be applied together with the principal component analysis. The results show that PCA is a valuable aid in variability analysis with multi-band data.Comment: 8 pages, 5 figures, Workshop on Astrostatistics and Data Mining in Astronomical Databases, May 29-June 4 2011, La Palm

Multivariate statistical process control based on principal component analysis: implementation of framework in R

The interest in multivariate statistical process control (MSPC) has increased as the industrial processes have become more complex. This paper presents an industrial process involving a plastic part in which, due to the number of correlated variables, the inversion of the covariance matrix becomes impossible, and the classical MSPC cannot be used to identify physical aspects that explain the causes of variation or to increase the knowledge about the process behaviour. In order to solve this problem, a Multivariate Statistical Process Control based on Principal Component Analysis (MSPC-PCA) approach was used and an R code was developed to implement it according some commercial software used for this purpose, namely the ProMV (c) 2016 from ProSensus, Inc. (www.prosensus.ca). Based on used dataset, it was possible to illustrate the principles of MSPC-PCA. This work intends to illustrate the implementation of MSPC-PCA in R step by step, to help the user community of R to be able to perform it.FCT - Fundação para a Ciência e a Tecnologia(UID/CEC/00319/2013

Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm

Over the past five decades, k-means has become the clustering algorithm of choice in many application domains primarily due to its simplicity, time/space efficiency, and invariance to the ordering of the data points. Unfortunately, the algorithm's sensitivity to the initial selection of the cluster centers remains to be its most serious drawback. Numerous initialization methods have been proposed to address this drawback. Many of these methods, however, have time complexity superlinear in the number of data points, which makes them impractical for large data sets. On the other hand, linear methods are often random and/or sensitive to the ordering of the data points. These methods are generally unreliable in that the quality of their results is unpredictable. Therefore, it is common practice to perform multiple runs of such methods and take the output of the run that produces the best results. Such a practice, however, greatly increases the computational requirements of the otherwise highly efficient k-means algorithm. In this chapter, we investigate the empirical performance of six linear, deterministic (non-random), and order-invariant k-means initialization methods on a large and diverse collection of data sets from the UCI Machine Learning Repository. The results demonstrate that two relatively unknown hierarchical initialization methods due to Su and Dy outperform the remaining four methods with respect to two objective effectiveness criteria. In addition, a recent method due to Erisoglu et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms (Springer, 2014). arXiv admin note: substantial text overlap with arXiv:1304.7465, arXiv:1209.196

Data-Driven Understanding of Smart Service Systems Through Text Mining

Smart service systems are everywhere, in homes and in the transportation, energy, and healthcare sectors. However, such systems have yet to be fully understood in the literature. Given the widespread applications of and research on smart service systems, we used text mining to develop a unified understanding of such systems in a data-driven way. Specifically, we used a combination of metrics and machine learning algorithms to preprocess and analyze text data related to smart service systems, including text from the scientific literature and news articles. By analyzing 5,378 scientific articles and 1,234 news articles, we identify important keywords, 16 research topics, 4 technology factors, and 13 application areas. We define ???smart service system??? based on the analytics results. Furthermore, we discuss the theoretical and methodological implications of our work, such as the 5Cs (connection, collection, computation, and communications for co-creation) of smart service systems and the text mining approach to understand service research topics. We believe this work, which aims to establish common ground for understanding these systems across multiple disciplinary perspectives, will encourage further research and development of modern service systems

Evaluation of a standard provision versus an autonomy promotive exercise referral programme: rationale and study design

Background The National Institute of Clinical Excellence in the UK has recommended that the effectiveness of ongoing exercise referral schemes to promote physical activity should be examined in research trials. Recent empirical evidence in health care and physical activity promotion contexts provides a foundation for testing the utility of a Self Determination Theory (SDT) -based exercise referral consultation. Methods/Design Design: An exploratory cluster randomised controlled trial comparing standard provision exercise on prescription with a Self Determination Theory-based (SDT) exercise on prescription intervention. Participants: 347 people referred to the Birmingham Exercise on Prescription scheme between November 2007 and July 2008. The 13 exercise on prescription sites in Birmingham were randomised to current practice (n=7) or to the SDT-based intervention (n=6). Outcomes measured at 3 and 6-months: Minutes of moderate or vigorous physical activity per week assessed using the 7-day Physical Activity Recall; physical health: blood pressure and weight; health status measured using the Dartmouth CO-OP charts; anxiety and depression measured by the Hospital Anxiety and Depression Scale and vitality measured by the subjective vitality score; motivation and processes of change: perceptions of autonomy support from the advisor, satisfaction of the needs for competence, autonomy, and relatedness via physical activity, and motivational regulations for exercise. Discussion This trial will determine whether an exercise referral programme based on Self Determination Theory increases physical activity and other health outcomes compared to a standard programme and will test the underlying SDT-based process model (perceived autonomy support, need satisfaction, motivation regulations, outcomes) via structural equation modelling. Trial registration The trial is registered as Current Controlled trials ISRCTN07682833

Components of acquisition-to-acquisition variance in continuous arterial spin labelling (CASL) imaging

<p>Abstract</p> <p>Background</p> <p>Images of perfusion estimates obtained with the continuous arterial spin labelling technique are characterized by variation between single acquisitions. Little is known about the spatial determinants of this variation during the acquisition process and their impact on voxel-by-voxel estimates of effects.</p> <p>Results</p> <p>We show here that the spatial patterns of covariance between voxels arising during the acquisition of these images uncover distinct mechanisms through which this variance arises: through variation in global perfusion levels; through the action of large vessels and other, less well characterized, large anatomical structures; and through the effect of noisy areas such as the edges of the brain.</p> <p>Conclusions</p> <p>Knowledge of these covariance patterns is important to experimenters for a correct interpretation of findings, especially for studies where relatively few acquisitions are made.</p

Structure-guided selection of specificity determining positions in the human kinome

Background: The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important