Using Pedometer Step-Count Goals to Promote Physical Activity in Cardiac Rehabilitation: A Feasibility Study of a Controlled Trial

Background: There is a need to improve the effectiveness of strategies to help cardiac rehabilitation patients achieve recommended levels of physical activity; the use of pedometers requires further research. We aimed to examine the feasibility of a randomised controlled trial, of an intervention using pedometer step-count goals, to promote physical activity for cardiac rehabilitation patients. Methods: We invited patients who completed a supervised cardiac rehabilitation programme to participate in this community-based study. Consenting participants wore a Yamax CW-701 pedometer for one week, blinded to stepcount readings, before being randomly allocated to groups. Intervention groups were told their step-counts; working with a clinical facilitator (nurse or physiotherapist) individually, they set daily step-count goals and reviewed these weekly. Baseline step-counts were hidden from controls, who were not given pedometers but received ongoing weekly facilitator support. After six weeks both groups wore ‘blinded’ pedometers for outcome assessment and participated in semi-structured interviews which explored their experiences of the study. Outcomes included rates of uptake, adherence and completion of measures, including step-counts, quality of life (EQ-5D) and stage of behaviour change. Results: Four programme groups were recruited; two received the intervention. Of 68 invitees, 45 participated (66%) (19 intervention; 26 control). Forty-two (93%) completed the outcomes. Baseline characteristics were comparable between groups. Mean steps/day increased more for intervention participants (2,742; 95%CI 1,169 to 4,315) than controls (-42; 95%CI -1,102 to 1,017) (p=0.004). The intervention and on-going clinical contact were welcomed; participants considered that step-counts, compared to time-related targets, encouraged them to become more active. Conclusion: These findings suggest that an intervention using individually tailored step-count goals may help increase and sustain physical activity following a cardiac rehabilitation programme. A definitive randomised controlled trial using blinded outcome measurements is feasible and of potential value in determining how best to translate physical activity advice into practice.

Effectiveness of a programme of exercise on physical function in survivors of critical illness following discharge from the ICU: study protocol for a randomised controlled trial (REVIVE)

Background: Following discharge home from the ICU, patients often suffer from reduced physical function, exercise capacity, health-related quality of life and social functioning. There is usually no support to address these longer term problems, and there has been limited research carried out into interventions which could improve patient outcomes. The aim of this study is to investigate the effectiveness and cost-effectiveness of a 6-week programme of exercise on physical function in patients discharged from hospital following critical illness compared to standard care.

Methods/Design: The study design is a multicentre prospective phase II, allocation-concealed, assessor-blinded, randomised controlled clinical trial. Participants randomised to the intervention group will complete three exercise sessions per week (two sessions of supervised exercise and one unsupervised session) for 6 weeks. Supervised sessions will take place in a hospital gymnasium or, if this is not possible, in the participants home and the unsupervised session will take place at home. Blinded outcome assessment will be conducted at baseline after hospital discharge, following the exercise intervention, and at 6 months following baseline assessment (or equivalent time points for the standard care group). The primary outcome measure is physical function as measured by the physical functioning subscale of the Short-Form-36 health survey following the exercise programme. Secondary outcomes are health-related quality of life, exercise capacity, anxiety and depression, self efficacy to exercise and healthcare resource use. In addition, semi-structured interviews will be conducted to explore participants’ perceptions of the exercise programme, and the feasibility (safety, practicality and acceptability) of providing the exercise programme will be assessed. A within-trial cost-utility analysis to assess the cost-effectiveness of the intervention compared to standard care will also be conducted.

Discussion: If the exercise programme is found to be effective, this study will improve outcomes that are meaningful to patients and their families. It will inform the design of a future multicentre phase III clinical trial of exercise following recovery from critical illness. It will provide useful information which will help the development of services for patients after critical illness.

Management in distributed systems: A semi-formal approach

The reverse engineering of a skeleton based programming environment and redesign to distribute management activities of the system and thereby remove a potential single point of failure is considered. The Ore notation is used to facilitate abstraction of the design and analysis of its properties. It is argued that Ore is particularly suited to this role as this type of management is essentially an orchestration activity. The Ore specification of the original version of the system is modified via a series of semi-formally justified derivation steps to obtain a specification of the decentralized management version which is then used as a basis for its implementation. Analysis of the two specifications allows qualitative prediction of the expected performance of the derived version with respect to the original, and this prediction is borne out in practice.

Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns. © 2014 Taylor & Francis.

Interpreting pedestrian behaviour by visualising and clustering movement data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Determinants of root-associated fungal communities within Asteraceae in a semi-arid grassland

Summary

1.While plant–fungal interactions are important determinants of plant community assembly and ecosystem functioning, the processes underlying fungal community composition are poorly understood.
2.Here, we studied for the first time the root-associated eumycotan communities in a set of co-occurring plant species of varying relatedness in a species-rich, semi-arid grassland in Germany. The study system provides an opportunity to evaluate the importance of host plants and gradients in soil type and landscape structure as drivers of fungal community structure on a relevant spatial scale. We used 454 pyrosequencing of the fungal internal transcribed spacer region to analyse root-associated eumycotan communities of 25 species within the Asteraceae, which were sampled at different locations within a soil type gradient. We partitioned the variance accounted for by three predictors (host plant phylogeny, spatial distribution and soil type) to quantify their relative roles in determining fungal community composition and used null model analyses to determine whether community composition was influenced by biotic interactions among the fungi.
3.We found a high fungal diversity (156 816 sequences clustered in 1100 operational taxonomic units (OTUs)). Most OTUs belonged to the phylum Ascomycota (35.8%); the most abundant phylotype best-matched Phialophora mustea. Basidiomycota were represented by 18.3%, with Sebacina as most abundant genus. The three predictors explained 30% of variation in the community structure of root-associated fungi, with host plant phylogeny being the most important variance component. Null model analysis suggested that many fungal taxa co-occurred less often than expected by chance, which demonstrates spatial segregation and indicates that negative interactions may prevail in the assembly of fungal communities.
4.Synthesis. The results show that the phylogenetic relationship of host plants is the most important predictor of root-associated fungal community assembly, indicating that fungal colonization of host plants might be facilitated by certain plant traits that may be shared among closely related plant species.

Uncovering the challenges of managing cachexia in advanced cancer: Preliminary results from semi-structured interviews with healthcare professionals in a regional cancer centre

Background: Cancer cachexia is a complex metabolic syndrome characterised by severe and progressive weight loss which is predominantly muscle mass. It is a devastating and distressing complication of advanced cancer with profound bio-psycho-social implications for patients and their families. At present there is no curative treatment for cachexiain advanced cancer therefore the most important healthcare response entails the minimisation of the psycho-social distress associated with it. However the literature suggests healthcare professionals’are missing opportunities to intervene and respond to the multi-dimensional needs of this population.

Objective:The objective of this study was to explore healthcare professionals’ response to cachexia in advanced cancer.

Methods: An interpretative qualitative approach was adopted in this study. A purposive sample of doctors, nurses, specialist nurses and dieticians were recruited from a regional cancer centre between November 2009 and November 2010. Data was collection was twofold: two multi-professional focus groups were conducted first to uncover the main themes and issues in cachexia management. This data then informed the interview schedule for the following 25 individual semi-structured interviews.

Results: Preliminary data analysis of the semi-structured interviews revealed distinct differences between disciplines in their perceptions of cancer cachexia which influenced their response to it in clinical practice. The commonality between disciplines, with the exception of palliative care, was a reliance on the biomedical approach to cancer cachexia management.

Discussion and Conclusions: Cancer cachexia is a complex and challenging syndrome which needs to be addressed from a holistic model of care to reflect the multi-dimensional needs of this patient group. The perspectives of those involved in care delivery is required in order to inform the development of interventions aimed at minimising the distress associated with this devastating syndrome.