Ambivalence in digital health: co-designing an mHealth platform for HIV care

In reaction to polarised views on the benefits or drawbacks of digital health, the notion of ‘ambivalence’ has recently been proposed as a means to grasp the nuances and complexities at play when digital technologies are embedded within practices of care. This article responds to this proposal by demonstrating how ambivalence can work as a reflexive approach to evaluate the potential implications of digital health. We first outline current theoretical advances in sociology and organisation science and define ambivalence as a relational and multidimensional concept that can increase reflexivity within innovation processes. We then introduce our empirical case and highlight how we engaged with the HIV community to facilitate a co-design space where 97 patients (across five European clinical sites: Antwerp, Barcelona, Brighton, Lisbon, Zagreb) were encouraged to lay out their approaches, imaginations and anticipations towards a prospective mHealth platform for HIV care. Our analysis shows how patients navigated ambivalence within three dimensions of digital health: quantification, connectivity and instantaneity. We provide examples of how potential tensions arising through remote access to quantified data, new connections with care providers or instant health alerts were distinctly approached alongside embodied conditions (e.g. undetectable viral load) and embedded socio-material environments (such as stigma or unemployment). We conclude that ambivalence can counterbalance fatalistic and optimistic accounts of technology and can support social scientists in taking-up their critical role within the configuration of digital health interventions

Affective Facial Expression Processing via Simulation: A Probabilistic Model

Understanding the mental state of other people is an important skill for intelligent agents and robots to operate within social environments. However, the mental processes involved in `mind-reading' are complex. One explanation of such processes is Simulation Theory - it is supported by a large body of neuropsychological research. Yet, determining the best computational model or theory to use in simulation-style emotion detection, is far from being understood. In this work, we use Simulation Theory and neuroscience findings on Mirror-Neuron Systems as the basis for a novel computational model, as a way to handle affective facial expressions. The model is based on a probabilistic mapping of observations from multiple identities onto a single fixed identity (`internal transcoding of external stimuli'), and then onto a latent space (`phenomenological response'). Together with the proposed architecture we present some promising preliminary resultsComment: Annual International Conference on Biologically Inspired Cognitive Architectures - BICA 201

Lean Thinking: Theory, Application and Dissemination

This book was written and compiled by the University of Huddersfield to share the learnings and experiences of seven years of Knowledge Transfer Partnership (KTP) and Economic and Social Research Council (ESRC) funded projects with the National Health Service (NHS). The focus of these projects was the implementation of Lean thinking and optimising strategic decision making processes. Each of these projects led to major local improvements and this book explains how they were achieved and compiles the lessons learnt. The book is split into three chapters; Lean Thinking Theory, Lean Thinking Applied and Lean Thinking Dissemination

Between a rock and a hard place: Economic expansion and social responsibility in UK media discourses on the global alcohol industry.

CONTEXT: Transnational alcohol corporations (TACs) employ a range of strategies to achieve their business objectives, including attempts to frame perceptions of their activities in media debates. TACs aim to achieve a favourable regulatory environment by presenting themselves as socially responsible actors. However, the need to secure financial investment means they must also emphasise their potential for growth. This article investigates tensions between these objectives in coverage of the global alcohol industry in the UK print media. METHODS: This article examines coverage of the world's four largest TACs in five British daily newspapers and one industry publication between March 2012 and February 2013. 477 articles were identified for analysis through keyword searches of the LexisNexis database. Thematic coding of articles was conducted using Nvivo software. FINDINGS: Two conflicting framings of the alcohol industry emerge from our analysis. The first presents TACs as socially responsible actors; key partners to government in reducing alcohol-related harms. This is targeted at policy-makers and the public in an attempt to shape policy debates. The second framing highlights TACs' potential for economic growth by establishing new markets and identifying new customer bases. This is targeted at an audience of potential investors. CONCLUSIONS: A fundamental contradiction lies at the heart of these framings, reflecting the tensions that exist between TACs' political and financial strategies. Alcohol industry involvement in policy-making thus involves a fundamental conflict of interests. Consequently, the UK government should reassess the prominence it currently affords to the industry in the development and delivery of alcohol policy

A novel streamlined trauma response team training improves imaging efficiency for pediatric blunt abdominal trauma patients

Background/purpose The morbidity and mortality of children with traumatic injuries are directly related to the time to definitive management of their injuries. Imaging studies are used in the trauma evaluation to determine the injury type and severity. The goal of this project is to determine if a formal streamlined trauma response improves efficiency in pediatric blunt trauma by evaluating time to acquisition of imaging studies and definitive management. Methods This study is a chart review of patients < 18 years who presented to a pediatric trauma center following blunt trauma requiring trauma team activation. 413 records were reviewed to determine if training changed the efficiency of CT acquisition and 652 were evaluated for FAST efficiency. The metrics used for comparison were time from ED arrival to CT image, FAST, and disposition. Results Time from arrival to CT acquisition decreased from 37 (SD 23) to 28 (SD27) min (p < 0.05) after implementation. The proportion of FAST scans increased from 315 (63.5%) to 337 (80.8%) and the time to FAST decreased from 18 (SD15) to 8 (SD10) min (p < 0.05). The time to operating room (OR) decreased after implementation. Conclusion The implementation of a streamlined trauma team approach is associated with both decreased time to CT, FAST, OR, and an increased proportion of FAST scans in the pediatric trauma evaluation. This could result in the rapid identification of injuries, faster disposition from the ED, and potentially improve outcomes in bluntly injured children

Impacts of Simultaneous Multislice Acquisition on Sensitivity and Specificity in fMRI

Simultaneous multislice (SMS) imaging can be used to decrease the time between acquisition of fMRI volumes, which can increase sensitivity by facilitating the removal of higher-frequency artifacts and boosting effective sample size. The technique requires an additional processing step in which the slices are separated, or unaliased, to recover the whole brain volume. However, this may result in signal “leakage” between aliased locations, i.e., slice “leakage,” and lead to spurious activation (decreased specificity). SMS can also lead to noise amplification, which can reduce the benefits of decreased repetition time. In this study, we evaluate the original slice-GRAPPA (no leak block) reconstruction algorithmand acceleration factor (AF = 8) used in the fMRI data in the young adult Human Connectome Project (HCP). We also evaluate split slice-GRAPPA (leak block), which can reduce slice leakage. We use simulations to disentangle higher test statistics into true positives (sensitivity) and false positives (decreased specificity). Slice leakage was greatly decreased by split slice-GRAPPA. Noise amplification was decreased by using moderate acceleration factors (AF = 4). We examined slice leakage in unprocessed fMRI motor task data from the HCP. When data were smoothed, we found evidence of slice leakage in some, but not all, subjects. We also found evidence of SMS noise amplification in unprocessed task and processed resting-state HCP data

Theoretical predictions for how temperature affects the dynamics of interacting herbivores and plants

Concern about climate change has spurred experimental tests of how warming affects species' abundance and performance. As this body of research grows, interpretation and extrapolation to other species and systems have been limited by a lack of theory. To address the need for theory for how warming affects species interactions, we used consumer-prey models and the metabolic theory of ecology to develop quantitative predictions for how systematic differences between the temperature dependence of heterotrophic and autotrophic population growth lead to temperature-dependent herbivory. We found that herbivore and plant abundances change with temperature in proportion to the ratio of autotrophic to heterotrophic metabolic temperature dependences. This result is consistent across five different formulations of consumer-prey models and over varying resource supply rates. Two models predict that temperaturedependent herbivory causes primary producer abundance to be independent of temperature. This finding contradicts simpler extensions of metabolic theory to abundance that ignore trophic interactions, and is consistent with patterns in terrestrial ecosystems. When applied to experimental data, the model explained 77% and 66% of the variation in phytoplankton and zooplankton abundances, respectively. We suggest that metabolic theory provides a foundation for understanding the effects of temperature change on multitrophic ecological communities

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

This paper aims to solve a fundamental problem in intensity-based 2D/3D registration, which concerns the limited capture range and need for very good initialization of state-of-the-art image registration methods. We propose a regression approach that learns to predict rotation and translations of arbitrary 2D image slices from 3D volumes, with respect to a learned canonical atlas co-ordinate system. To this end, we utilize Convolutional Neural Networks (CNNs) to learn the highly complex regression function that maps 2D image slices into their correct position and orientation in 3D space. Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data. We extensively evaluate the effectiveness of our approach quantitatively on simulated MRI brain data with extreme random motion. We further demonstrate qualitative results on fetal MRI where our method is integrated into a full reconstruction and motion compensation pipeline. With our CNN regression approach we obtain an average prediction error of 7mm on simulated data, and convincing reconstruction quality of images of very young fetuses where previous methods fail. We further discuss applications to Computed Tomography and X-ray projections. Our approach is a general solution to the 2D/3D initialization problem. It is computationally efficient, with prediction times per slice of a few milliseconds, making it suitable for real-time scenarios.Comment: 8 pages, 4 figures, 6 pages supplemental material, currently under review for MICCAI 201