An exploration of seniors’ motivation to use mobile brain-exercise software

This article reports on the development of an iPhone-based brain-exercise tool for seniors involving a series of focus groups (FGs) and field trials (FTs). Four FGs with 34 participants were conducted aimed at understanding the underlying motivational and de-motivational factors influencing seniors’ engagement with mobile brain-exercise software. As part of the FGs, participants had approximately 40 minutes hands-on experience with commercially available brain-exercise software. A content analysis was conducted on the data resulting in a ranking of 19 motivational factors, of which the top three were challenge, usefulness and familiarity and 15 de-motivational factors, of which the top-three were usability issues, poor communication and games that were too fast. Findings were used to inform the design of three prototype brain-exercise games for the iPhone contained within one overall application, named Brain jog. Subsequently, two FTs were conducted using Brain jog to investigate the part that time exposure has to play in shaping the factors influencing engagement. New factors arose with respect to the initial FGs including the motivational factor feedback and the de-motivational factor boring. The results of this research provide valuable guidelines for the design and evaluation of mobile brain-exercise software for seniors.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

The challenge to health professionals when carers resist truth telling at the end of life: a qualitative secondary analysis

Aims and objectives: To draw out the similar complexities faced by staff around
truth-telling in a children’s and adult population and to interrogate the dilemmas faced by staff when informal carers act to block truth-telling.

Background: Policy encourages normalisation of death, but carers may act to protect or prevent the patient from being told the truth. Little is known about the impact on staff.

Design: Secondary analysis of data using a supra-analysis design to identify commonality of experiences.

Methods: Secondary ‘supra-analysis’ was used to transcend the focus of two primary studies in the UK, which examined staff perspectives in a palliative children’s and a palliative adult setting, respectively. The analysis examined new theoretical questions relating to the commonality of issues independently derived in each primary study. Both primary studies used focus groups. Existing empirical data were analysed thematically and compared across the studies.

Results: Staff reported a hiding of the truth by carers and sustained use of activities aimed at prolonging life. Carers frequently ignored the advance of end of life, and divergence between staff and carer approaches to truth-telling challenged professionals. Not being truthful with patients had a deleterious effect on staff, causing anger and feelings of incompetence.

Conclusions: Both children’s and adult specialist palliative care staff found themselves caught in a dilemma, subject to policies that promoted openness in planning for death and informal carers who often prevented them from being truthful with patients about terminal prognosis. This dilemma had adverse psychological effects upon many staff.

Relevance to clinical practice: There remains a powerful death-denying culture in
many societies, and carers of dying patients may prevent staff from being truthful with their patients. The current situation is not ideal, and open discussion of this problem is the essential first step in finding a solution.

Individual Characteristics Associated with Mismatches between Self-Reported and Accelerometer-Measured Physical Activity

Background: Accurate assessment tools are required for the surveillance of physical activity (PA) levels and the assessment of the effect of interventions. In addition, increasing awareness of PA is often used as the first step in pragmatic behavioural interventions, as discrepancies between the amount of activity an individual perceives they do and the amount actually undertaken may act as a barrier to change. Previous research has demonstrated differences in the amount of activity individuals report doing, compared to their level of physical activity when measured with an accelerometer. Understanding the characteristics of those whose PA level is ranked differently when measured with either self-report or accelerometry is important as it may inform the choice of instrument for future research. The aim of this project was to determine which individual characteristics are associated with differences between self-reported and accelerometer measured physical activity.

Methods: Participant data from the 2009 wave of the Commuting and Health in Cambridge study were used. Quartiles of self-reported and accelerometer measured PA were derived by ranking each measure from lowest to highest. These quartiles were compared to determine whether individuals’ physical activity was ranked higher by either method. Multinomial logistic regression models were used to investigate the individual characteristics associated with different categories of mismatch.

Results: Data from 486 participants (70% female) were included in the analysis. In adjusted analyses, the physical activity of overweight or obese individuals was significantly more likely to be ranked higher by self-report than by accelerometer than that of normal-weight individuals (OR = 2.07, 95%CI = 1.28–3.34), particularly among women (OR = 3.97, 95%CI = 2.11–7.47).

Conclusions: There was a greater likelihood of mismatch between self-reported and accelerometer measured physical activity levels in overweight or obese adults. Future studies in overweight or obese adults should consider employing both methods of measurement.

Toward Unconstrained Ear Recognition From Two-Dimensional Images

Ear recognition, as a biometric, has several advantages. In particular, ears can be measured remotely and are also relatively static in size and structure for each individual. Unfortunately, at present, good recognition rates require controlled conditions. For commercial use, these systems need to be much more robust. In particular, ears have to be recognized from different angles ( poses), under different lighting conditions, and with different cameras. It must also be possible to distinguish ears from background clutter and identify them when partly occluded by hair, hats, or other objects. The purpose of this paper is to suggest how progress toward such robustness might be achieved through a technique that improves ear registration. The approach focuses on 2-D images, treating the ear as a planar surface that is registered to a gallery using a homography transform calculated from scale-invariant feature-transform feature matches. The feature matches reduce the gallery size and enable a precise ranking using a simple 2-D distance algorithm. Analysis on a range of data sets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees, and up to 18% occlusion. In addition, recognition remains accurate with masked ear images as small as 20 x 35 pixels.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.

Population Mobility Dynamics Estimated from Mobile Telephony Data

In the last decade, mobile phones and mobile devices using mobile cellular telecommunication network connections have become ubiquitous. In several developed countries, the penetration of such devices has surpassed 100 percent. They facilitate communication and access to large quantities of data without the requirement of a fixed location or connection. Assuming mobile phones usually are in close proximity with the user, their cellular activities and locations are indicative of the user's activities and movements. As such, those cellular devices may be considered as a large scale distributed human activity sensing platform. This paper uses mobile operator telephony data to visualize the regional flows of people across the Republic of Ireland. In addition, the use of modified Markov chains for the ranking of significant regions of interest to mobile subscribers is investigated. Methodology is then presented which demonstrates how the ranking of significant regions of interest may be used to estimate national population, results of which are found to have strong correlation with census data.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

Efficiency assessment of electricity power generation on Ireland’s Single Electricity Market by Data Envelopment Analysis

The efficiency of generation plants is an important measure for evaluating the operating performance. The objective of this paper is to evaluate electricity power generation by conducting an All-Island-Generator-Efficiency-Study (AIGES) for the Republic of Ireland and Northern Ireland by utilising a Data Envelopment Analysis (DEA) approach. An operational performance efficiency index is defined and pursued for the year 2008. The economic activities of electricity generation units/plants examined in this paper are characterized by numerous input and output indicators. Constant returns to scale (CRS) and variable returns to scale (VRS) type DEA models are employed in the analysis. Also a slacks based analysis indicates the level of inefficiency for each variable examined. The findings from this study provide a general ranking and evaluation but also facilitate various interesting efficiency comparisons between generators by fuel type.

An Energy Descriptor To Quantify Methane Selectivity in Fischer-Tropsch Synthesis:A Density Functional Theory Study

Selectivity is a fundamental issue in heterogeneous catalysis. In this study, the CH(4) selectivity in Fischer-Tropsch synthesis is chosen to be investigated: CH4 selectivity on Rh, Co, Ru, Fe, and Re surfaces is computed by first-principles methods. In conjunction with kinetic analyses, we are able to derive the effective barrier difference between methane formation and chain growth (Delta E(eff)) to quantify the CH(4) selectivity. By using this energy descriptor, the ranking of methane selectivity predicted from density functional theory (DFT) calculations is consistent with experimental work. Moreover, a linear correlation between Delta E(eff) and the chemisorption energy of C + 4H (Delta H) is found. This fundamental finding possesses the following significance: (i) it shows that the selectivity, which appears to have kinetic characteristics, is largely determined by thermodynamic properties; and (ii) it suggests that an increase of the binding strength of C + 4H will suppress methane selectivity.

Development of an app to support learning in A-level maths

This project involved producing an app for smart devices to enable modernised learning for A-level maths students. Research in a stakeholder school showed that 94% of pupils surveyed within the upper-secondary level owned a smartphone and most owned a tablet also, emphasising the opportunity for using apps to support learning. The app was developed using iBuildApp, an online app-creation programme which requires no programming. Past exam questions and solutions, notes and video tutorials were included and the topic was vectors, identified by teachers as problematic. Pupils generally found the app easy to use and wanted further development. The videos were popular despite this not ranking highly as a preferred method of revision previously. Teachers were happy for pupils to use the app to supplement their learning, both in the classroom and outside.

Core Outcomes in Ventilation Trials (COVenT): protocol for a core outcome set using a Delphi survey with a nested randomised trial and observational cohort study

Background
Among clinical trials of interventions that aim to modify time spent on mechanical ventilation for critically ill patients there is considerable inconsistency in chosen outcomes and how they are measured. The Core Outcomes in Ventilation Trials (COVenT) study aims to develop a set of core outcomes for use in future ventilation trials in mechanically ventilated adults and children.

Methods/design
We will use a mixed methods approach that incorporates a randomised trial nested within a Delphi study and a consensus meeting. Additionally, we will conduct an observational cohort study to evaluate uptake of the core outcome set in published studies at 5 and 10 years following core outcome set publication. The three-round online Delphi study will use a list of outcomes that have been reported previously in a review of ventilation trials. The Delphi panel will include a range of stakeholder groups including patient support groups. The panel will be randomised to one of three feedback methods to assess the impact of the feedback mechanism on subsequent ranking of outcomes. A final consensus meeting will be held with stakeholder representatives to review outcomes.

Discussion
The COVenT study aims to develop a core outcome set for ventilation trials in critical care, explore the best Delphi feedback mechanism for achieving consensus and determine if participation increases use of the core outcome set in the long term.

PAlliative Care in chronic Kidney diSease: the PACKS study—quality of life, decision making, costs and impact on carers in people managed without dialysis

BACKGROUND: The number of patients with advanced chronic kidney disease opting for conservative management rather than dialysis is unknown but likely to be growing as increasingly frail patients with advanced renal disease present to renal services. Conservative kidney management includes ongoing medical input and support from a multidisciplinary team. There is limited evidence concerning patient and carer experience of this choice. This study will explore quality of life, symptoms, cognition, frailty, performance decision making, costs and impact on carers in people with advanced chronic kidney disease managed without dialysis and is funded by the National Institute of Health Research in the UK.

METHODS: In this prospective, multicentre, longitudinal study, patients will be recruited in the UK, by renal research nurses, once they have made the decision not to embark on dialysis. Carers will be asked to 'opt-in' with consent from patients. The approach includes longitudinal quantitative surveys of quality of life, symptoms, decision making and costs for patients and quality of life and costs for carers, with questionnaires administered quarterly over 12 months. Additionally, the decision making process will be explored via qualitative interviews with renal physicians/clinical nurse specialists.

DISCUSSION: The study is designed to capture patient and carer profiles when conservative kidney management is implemented, and understand trajectories of care-receiving and care-giving with the aim of optimising palliative care for this population. It will explore the interactions that lead to clinical care decisions and the impact of these decisions on informal carers with the intention of improving clinical outcomes for patients and the experiences of care givers.

, Lexicographic Preferences in Discrete Choice Experiments: Consequences on Individual-Specific Willingness to Pay Estimates

In discrete choice experiments respondents are generally assumed to consider all of the attributes across each of the alternatives, and to choose their most preferred. However, results in this paper indicate that many respondents employ simplified lexicographic decision-making rules, whereby they have a ranking of the attributes, but their choice of an alternative is based solely on the level of their most important attribute(s). Not accounting for these simple decision-making heuristics introduces systemic errors and leads to biased point estimates, as they are a violation of the continuity axiom and a departure from the use of compensatory decision-making. In this paper the implications of lexicographic preferences are examined. In particular, using a mixed logit specification this paper investigates the sensitivity of individual-specific willingness to pay (WTP) estimates conditional on whether lexicographic decision-making rules are accounted for in the modelling of discrete choice responses. Empirical results are obtained from a discrete choice experiment that was carried out to address the value of a number of rural landscape attributes in Ireland