Nurses' recognition of domestic violence and abuse

Most literature and discourse on domestic violence and abuse (DVA) focuses on women but there is a need to be cognisant of the broader population experiencing DVA and the wide-ranging impacts that can affect anybody whatever their identity or background. Mental Health nurses are in a good position to help people who experience DVA but they need to be able to recognise it first. This paper reports on a review which aims to address the question: How can mental health nurses recognise domestic violence and abuse (DVA)? The databases CINAHL, Medline, PsychINFO and ASSIA were searched using key terms related to DVA and nursing and recognition. The term ‘nursing’ was used as the ‘mental health nursing’ search term found only two papers. Limits for the search were English language research only papers from 2002-2017. Fifteen papers were included in the review. Most of the located research focused on health care practitioners in multidisciplinary teams with nursing literature focused on adult health nurses rather than mental health nursing. The findings are presented in the categories: education, training and organisational support, and, screening, inquiry and the therapeutic relationship, with an additional category (given the original aim of the review) ‘mental health settings’. The experience of DVA has significant consequences for mental health yet we found only two research papers focused on mental health settings. We therefore discuss and extrapolate from reviewed literature the implications for practice in the context of mental health nursing

PRM4 – a review evaluating the validity of motion-based gaming platforms to measure clinical outcomes in clinical research

OBJECTIVES: Motion-based video game platforms provide the capability to track 3D body movements and may offer a versatile, easy to use and low-cost approach to measuring objective clinical outcomes. We reviewed published validation studies comparing clinical outcomes derived from video game platforms to gold-standard approaches. METHODS: We categorized studies in our review into three areas of application and summarized validation findings. We confined our review to studies using the Microsoft Kinect platform due to the volume of work in this area. RESULTS: Gait and balance: Five validation studies reported varied findings. One study in MS reported good correlation of most parameters with ClinROs; and a second study reported good validation of walk test parameters in Stroke patients. A treadmill test in healthy volunteers found Kinect underestimated joint flexion and over-estimated extension; and a further study was able to detect gait disturbances in MS during a speed-walking test compared to healthy volunteers although correlation to clinician assessment was modest (r=0.447). Kinect use during a battery of balance and dexterity tests in PD accurately measured the timing (ICCs: 0.940- 0.999) and gross spatial characteristics of clinically relevant movements, but spatial accuracy for smaller movements, such as toe tapping (ICC = 0.038), was poor. Upper extremity movement: Eight studies reported good validity in measurement of shoulder range of motion (r’s > 0.8, ICCs > 0.864). Spirometry: One study reported strong correlation of spirometry parameters (r>0.866) estimated using multiple sensors to generate a 3D image of the chest. CONCLUSIONS: Motion-based video gaming platforms offer potential for low-cost assessment of movement and mobility in large-scale clinical trials without reliance on specialist centers. Studies report good validity in some application areas. The ability to provide the level of accuracy needed in more rapid and finer movements requires more validation work

Enhancing the measurement of clinical outcomes using Microsoft Kinect

There is a growing body of applications leveraging Microsoft Kinect and the associated Windows Software Development Kit in health and wellness. In particular, this platform has been valuable in developing interactive solutions for rehabilitation including creating more engaging exercise regimens and ensuring that exercises are performed correctly for optimal outcomes. Clinical trials rely upon robust and validated methodologies to measure health status and to detect treatment-related changes over time to enable the efficacy and safety of new drug treatments to be assessed and measured. In many therapeutic areas, traditional outcome measures rely on subjective investigator and patient ratings. Subjective ratings are not always sensitive to detecting small improvements, are subject to inter- and intra-rater variability and limited in their ability to record detailed or subtle aspects of movement and mobility. For these reasons, objective measurements may provide greater sensitivity to detect treatment-related changes where they exist. In this review paper, we explore the use of the Kinect platform to develop low-cost approaches to objectively measure aspects of movement. We consider published applications that measure aspects of gait and balance, upper extremity movement, chest wall motion and facial analysis. In each case, we explore the utility of the approach for clinical trials, and the precision and accuracy of estimates derived from the Kinect output. We conclude that the use of games platforms such as Microsoft Kinect to measure clinical outcomes offer a versatile, easy to use and low-cost approach that may add significant value and utility to clinical drug development, in particular in replacing conventional subjective measures and providing richer information about movement than previously possible in large scale clinical trials, especially in the measurement of gross spatial movements. Regulatory acceptance of clinical outcomes collected in this way will be subject to comprehensive assessment of validity and clinical relevance, and this will require good quality peer-reviewed publications of scientific evidence

Meaningful change: defining the interpretability of changes in endpoints derived from interactive and mHealth technologies in healthcare and clinical research

Immersive, interactive and mHealth technologies are increasingly being used in clinical research, healthcare and rehabilitation solutions. Leveraging technology solutions to derive new and novel clinical outcome measures is important to the ongoing assessment of clinical interventions. While demonstrating statistically significant changes is an important element of intervention assessment, understanding whether changes detected reflect changes of a magnitude that are considered meaningful to patients is equally important. We describe methodologies used to determine meaningful change and recommend that these techniques are routinely included in the development and testing of clinical assessment and rehabilitation technology solutions

Performance of R-GMA for monitoring grid jobs for CMS data production

High energy physics experiments, such as the Compact Muon Solenoid (CMS) at the CERN laboratory in Geneva, have large-scale data processing requirements, with data accumulating at a rate of 1 Gbyte/s. This load comfortably exceeds any previous processing requirements and we believe it may be most efficiently satisfied through grid computing. Furthermore the production of large quantities of Monte Carlo simulated data provides an ideal test bed for grid technologies and will drive their development. One important challenge when using the grid for data analysis is the ability to monitor transparently the large number of jobs that are being executed simultaneously at multiple remote sites. R-GMA is a monitoring and information management service for distributed resources based on the grid monitoring architecture of the Global Grid Forum. We have previously developed a system allowing us to test its performance under a heavy load while using few real grid resources. We present the latest results on this system running on the LCG 2 grid test bed using the LCG 2.6.0 middleware release. For a sustained load equivalent to 7 generations of 1000 simultaneous jobs, R-GMA was able to transfer all published messages and store them in a database for 98% of the individual jobs. The failures experienced were at the remote sites, rather than at the archiver's MON box as had been expected

Proof-of-concept study: a mobile application to derive clinical outcome measures from expression and speech for mental health status evaluation

This proof-of-concept study aimed to assess the ability of a mobile application and cloud analytics software solution to extract facial expression information from participant selfie videos. This is one component of a solution aimed at extracting possible health outcome measures based on expression, voice acoustics and speech sentiment from video diary data provided by patients. Forty healthy volunteers viewed 21 validated images from the International Affective Picture System database through a mobile app which simultaneously captured video footage of their face using the selfie camera. Images were intended to be associated with the following emotional responses: anger, disgust, sadness, contempt, fear, surprise and happiness. Both valence and arousal scores estimated from the video footage associated with each image were adequate predictors of the IAPS image scores (p < 0.001 and p = 0.04 respectively). 12.2% of images were categorised as containing a positive expression response in line with the target expression; with happiness and sadness responses providing the greatest frequency of responders: 41.0% and 21.4% respectively. 71.2% of images were associated with no change in expression. This proof-of-concept study provides early encouraging findings that changes in facial expression can be detected when they exist. Combined with voice acoustical measures and speech sentiment analysis, this may lead to novel measures of health status in patients using a video diary in indications including depression, schizophrenia, autism spectrum disorder and PTSD amongst other conditions

Instability and `Sausage-String' Appearance in Blood Vessels during High Blood Pressure

A new Rayleigh-type instability is proposed to explain the `sausage-string' pattern of alternating constrictions and dilatations formed in blood vessels under influence of a vasoconstricting agent. Our theory involves the nonlinear elasticity characteristics of the vessel wall, and provides predictions for the conditions under which the cylindrical form of a blood vessel becomes unstable.Comment: 4 pages, 4 figures submitted to Physical Review Letter

Usability assessment of facial tracking for use in clinical outcomes

There is an ever-growing body of facial tracking assessment applications available within the health and wellness sector. One of the most prominent areas is the use of 3D cameras and processing technologies in the development of rehabilitation interventions and in the measurement of health outcomes. Recent advancements in facial tracking applications within mobile platforms and cloud computing analytics suggests that new clinical assessment and human computer assessment technologies have significant future potential for pervasive in-clinic and field-based health assessment solutions. This paper reviews the technical capabilities of three facial tracking platforms with a focus on the common issues relating to clinical measurement considerations required for patient-facing systems. Key factors are assessed in relation to 3D camera platforms, mobile applications and cloud computing applications including camera position, lighting and shadows, eye detection and common obstructions or features that affect tracking. Published examples of fcial tracking clinical and health wellness applications are presented in relation to human computer interaction interfaces. This paper aims to demonstrate the potential for future applications being developed relating to each of these technologies for clinic-based applications

Utilising the Intel RealSense camera for measuring health outcomes in clinical research

Applications utilising 3D Camera technologies for the measurement of health outcomes in the health and wellness sector continues to expand. The Intel® RealSense™ is one of the leading 3D depth sensing cameras currently available on the market and aligns itself for use in many applications, including robotics, automation, and medical systems. One of the most prominent areas is the production of interactive solutions for rehabilitation which includes gait analysis and facial tracking. Advancements in depth camera technology has resulted in a noticeable increase in the integration of these technologies into portable platforms, suggesting significant future potential for pervasive in-clinic and field based health assessment solutions. This paper reviews the Intel RealSense technology’s technical capabilities and discusses its application to clinical research and includes examples where the Intel RealSense camera range has been used for the measurement of health outcomes. This review supports the use of the technology to develop robust, objective movement and mobility-based endpoints to enable accurate tracking of the effects of treatment interventions in clinical trials